A 20-year-old woman had a 4-month history of painful red erosions around the mouth. She had no dysphagia or fatigue and no history of diarrhea, gluten intolerance, or diabetes mellitus. An antifungal-antibacterial ointment prescribed by her dentist had provided no relief.

• Hemoglobin 8.0 g/dL (reference range for females 12.3–15.3)
• Mean corpuscular volume 62 fL (80–100)
• Serum ferritin 1.3 ng/mL (15–200)
• Reticulocyte count 0.86% (0.5–1.5)
• White blood cell count 9.8 × 10⁹/L (4.5–11.0)
• Platelet count 450 × 10⁹/L (150–400).

Vitamin B₁₂ and folate levels were normal, and tests for antitissue transglutaminase and antinuclear antibodies were negative.

Based on these results, the diagnosis was angular cheilitis from iron deficiency anemia. Treatment with oral ferrous gluconate 300 mg twice daily cleared the cheilitis, and after 4 weeks of this treatment, the hemoglobin level increased to 9.8 g/dL, the serum ferritin increased to 7 ng/mL, and the reticulocyte count increased to 2.6%. She was advised to continue taking oral iron tablets for another 3 months until the hemoglobin level reached 12.0 g/dL.

During 2 years of follow-up, she had no recurrence of angular cheilitis, and her hemoglobin and serum ferritin levels remained normal. Ferrous gluconate was her only medication from the time of her diagnosis.

A BROAD DIFFERENTIAL DIAGNOSIS

Angular cheilitis (perlèche) is an inflammatory condition characterized by erosive inflammation at one or both angles of the mouth. It typically presents as erythema, scaling, fissuring, and ulceration. A wide variety of factors, including nutritional deficiencies, local and systemic factors, and drug side effects, may produce cheilitis.^1,2

Nutritional deficiencies account for 25% of all cases of angular cheilitis³ and include iron deficiency and deficiencies of the B vitamins riboflavin (B₂), niacin (B₃), pyridoxine (B₆), and cyanocobalamin (B₁₂).¹

Local causes include infection with Candida albicans or Staphylococcus aureus and allergic contact dermatitis. Common causes of allergic contact dermatitis include lipstick, toothpaste, mouthwash, cosmetics, sunscreen, fragrance, metals such as nickel, and dental appliances.¹

Systemic diseases associated with angular cheilitis include xerostomia, inflammatory bowel disease, Sjögren syndrome, glucagonoma, and human immunodeficiency virus.¹

Drugs that cause angular cheilitis include isotretinoin, sorafenib (antineoplastic kinase inhibitor), and ointments or creams such as neomycin sulfate–polymyxin B sulfate, bacitracin, idoxuridine, and steroids.^1,4

Conditions that mimic angular cheilitis include herpes simplex type 1 (herpes labialis) and actinic cheilitis. Herpes labialis, characterized by burning sensation, itching, or paresthesia, usually precedes a recurrence of vesicles that eventually ulcerate or form a crust and heal without a crust. Herpes labialis often recurs, affecting the vermilion border and lasting approximately 1 week.

Actinic cheilitis, a premalignant condition that commonly involves the lower lip with sparing of the corners of the mouth, is caused by excessive sun exposure. Patients often have persistent dryness and cracking of the lips.

In our patient, angular cheilitis was the main clinical manifestation of iron deficiency anemia, highlighting the importance of looking for iron deficiency in affected patients without a more obvious cause.

A 20-year-old woman had a 4-month history of painful red erosions around the mouth. She had no dysphagia or fatigue and no history of diarrhea, gluten intolerance, or diabetes mellitus. An antifungal-antibacterial ointment prescribed by her dentist had provided no relief.

• Hemoglobin 8.0 g/dL (reference range for females 12.3–15.3)
• Mean corpuscular volume 62 fL (80–100)
• Serum ferritin 1.3 ng/mL (15–200)
• Reticulocyte count 0.86% (0.5–1.5)
• White blood cell count 9.8 × 10⁹/L (4.5–11.0)
• Platelet count 450 × 10⁹/L (150–400).

Vitamin B₁₂ and folate levels were normal, and tests for antitissue transglutaminase and antinuclear antibodies were negative.

Based on these results, the diagnosis was angular cheilitis from iron deficiency anemia. Treatment with oral ferrous gluconate 300 mg twice daily cleared the cheilitis, and after 4 weeks of this treatment, the hemoglobin level increased to 9.8 g/dL, the serum ferritin increased to 7 ng/mL, and the reticulocyte count increased to 2.6%. She was advised to continue taking oral iron tablets for another 3 months until the hemoglobin level reached 12.0 g/dL.

During 2 years of follow-up, she had no recurrence of angular cheilitis, and her hemoglobin and serum ferritin levels remained normal. Ferrous gluconate was her only medication from the time of her diagnosis.

A BROAD DIFFERENTIAL DIAGNOSIS

Angular cheilitis (perlèche) is an inflammatory condition characterized by erosive inflammation at one or both angles of the mouth. It typically presents as erythema, scaling, fissuring, and ulceration. A wide variety of factors, including nutritional deficiencies, local and systemic factors, and drug side effects, may produce cheilitis.^1,2

Nutritional deficiencies account for 25% of all cases of angular cheilitis³ and include iron deficiency and deficiencies of the B vitamins riboflavin (B₂), niacin (B₃), pyridoxine (B₆), and cyanocobalamin (B₁₂).¹

Local causes include infection with Candida albicans or Staphylococcus aureus and allergic contact dermatitis. Common causes of allergic contact dermatitis include lipstick, toothpaste, mouthwash, cosmetics, sunscreen, fragrance, metals such as nickel, and dental appliances.¹

Systemic diseases associated with angular cheilitis include xerostomia, inflammatory bowel disease, Sjögren syndrome, glucagonoma, and human immunodeficiency virus.¹

Drugs that cause angular cheilitis include isotretinoin, sorafenib (antineoplastic kinase inhibitor), and ointments or creams such as neomycin sulfate–polymyxin B sulfate, bacitracin, idoxuridine, and steroids.^1,4

Conditions that mimic angular cheilitis include herpes simplex type 1 (herpes labialis) and actinic cheilitis. Herpes labialis, characterized by burning sensation, itching, or paresthesia, usually precedes a recurrence of vesicles that eventually ulcerate or form a crust and heal without a crust. Herpes labialis often recurs, affecting the vermilion border and lasting approximately 1 week.

Actinic cheilitis, a premalignant condition that commonly involves the lower lip with sparing of the corners of the mouth, is caused by excessive sun exposure. Patients often have persistent dryness and cracking of the lips.

In our patient, angular cheilitis was the main clinical manifestation of iron deficiency anemia, highlighting the importance of looking for iron deficiency in affected patients without a more obvious cause.

A 20-year-old woman had a 4-month history of painful red erosions around the mouth. She had no dysphagia or fatigue and no history of diarrhea, gluten intolerance, or diabetes mellitus. An antifungal-antibacterial ointment prescribed by her dentist had provided no relief.

• Hemoglobin 8.0 g/dL (reference range for females 12.3–15.3)
• Mean corpuscular volume 62 fL (80–100)
• Serum ferritin 1.3 ng/mL (15–200)
• Reticulocyte count 0.86% (0.5–1.5)
• White blood cell count 9.8 × 10⁹/L (4.5–11.0)
• Platelet count 450 × 10⁹/L (150–400).

Vitamin B₁₂ and folate levels were normal, and tests for antitissue transglutaminase and antinuclear antibodies were negative.

Based on these results, the diagnosis was angular cheilitis from iron deficiency anemia. Treatment with oral ferrous gluconate 300 mg twice daily cleared the cheilitis, and after 4 weeks of this treatment, the hemoglobin level increased to 9.8 g/dL, the serum ferritin increased to 7 ng/mL, and the reticulocyte count increased to 2.6%. She was advised to continue taking oral iron tablets for another 3 months until the hemoglobin level reached 12.0 g/dL.

During 2 years of follow-up, she had no recurrence of angular cheilitis, and her hemoglobin and serum ferritin levels remained normal. Ferrous gluconate was her only medication from the time of her diagnosis.

A BROAD DIFFERENTIAL DIAGNOSIS

Angular cheilitis (perlèche) is an inflammatory condition characterized by erosive inflammation at one or both angles of the mouth. It typically presents as erythema, scaling, fissuring, and ulceration. A wide variety of factors, including nutritional deficiencies, local and systemic factors, and drug side effects, may produce cheilitis.^1,2

Nutritional deficiencies account for 25% of all cases of angular cheilitis³ and include iron deficiency and deficiencies of the B vitamins riboflavin (B₂), niacin (B₃), pyridoxine (B₆), and cyanocobalamin (B₁₂).¹

Local causes include infection with Candida albicans or Staphylococcus aureus and allergic contact dermatitis. Common causes of allergic contact dermatitis include lipstick, toothpaste, mouthwash, cosmetics, sunscreen, fragrance, metals such as nickel, and dental appliances.¹

Systemic diseases associated with angular cheilitis include xerostomia, inflammatory bowel disease, Sjögren syndrome, glucagonoma, and human immunodeficiency virus.¹

Drugs that cause angular cheilitis include isotretinoin, sorafenib (antineoplastic kinase inhibitor), and ointments or creams such as neomycin sulfate–polymyxin B sulfate, bacitracin, idoxuridine, and steroids.^1,4

Conditions that mimic angular cheilitis include herpes simplex type 1 (herpes labialis) and actinic cheilitis. Herpes labialis, characterized by burning sensation, itching, or paresthesia, usually precedes a recurrence of vesicles that eventually ulcerate or form a crust and heal without a crust. Herpes labialis often recurs, affecting the vermilion border and lasting approximately 1 week.

Actinic cheilitis, a premalignant condition that commonly involves the lower lip with sparing of the corners of the mouth, is caused by excessive sun exposure. Patients often have persistent dryness and cracking of the lips.

In our patient, angular cheilitis was the main clinical manifestation of iron deficiency anemia, highlighting the importance of looking for iron deficiency in affected patients without a more obvious cause.

A 29-year-old nulliparous woman presents for a routine checkup. She has hypertension and type 2 diabetes mellitus. Her current medications are chlorpropamide 500 mg daily, metformin 500 mg twice a day, lisinopril 40 mg daily, simvastatin 40 mg daily, and aspirin 81 mg daily. Her body mass index is 37 kg/m² and her blood pressure is 130/80 mm Hg. Her hemoglobin A_1c level is 7.8% and her low-density lipoprotein cholesterol 90 mg/dL.

She is considering pregnancy. How would you counsel her?

DEFINING DIABETES IN PREGNANCY

Diabetes in pregnant women, both gestational and pregestational, is the most common medical complication associated with pregnancy.¹

• Gestational diabetes is defined as diabetes that is diagnosed during the second or third trimester of pregnancy and that is not clearly pregestational.²
• Pregestational diabetes exists before pregnancy and can be either type 1 or type 2.

Most cases of diabetes diagnosed during the first trimester reflect pregestational diabetes, as gestational diabetes occurs when insulin resistance increases in the later trimesters.

Type 1 diabetes involves autoimmune destruction of pancreatic islet cells, leading to insulin deficiency and the need for insulin therapy. Type 2 diabetes is characterized by insulin resistance rather than overall insulin deficiency. Type 2 diabetes tends to be associated with comorbidities such as obesity and hypertension, which are independent risk factors for adverse perinatal outcomes.^3,4

Gestational diabetes accounts for most cases of diabetes during pregnancy. Although both pregestational and gestational diabetes increase the risk of maternal and fetal complications, pregestational diabetes is associated with significantly greater risks.¹

IMPACT OF DIABETES ON THE MOTHER

Pregnancy increases the risk of maternal hypoglycemia, especially during the first trimester in patients with type 1 diabetes, as insulin sensitivity increases in early pregnancy.¹ Pregnant women with diabetes may also have an altered counterregulatory response and less hypoglycemic awareness.¹ Insulin resistance rises during the second and early third trimesters, increasing the risk of hyperglycemia in women with diabetes.¹

Glycemic control during pregnancy is usually easier to achieve in patients with type 2 diabetes than with type 1, but it may require much higher insulin doses.

Because pregnancy is inherently a ketogenic state, women with type 1 diabetes are at higher risk of diabetic ketoacidosis, particularly during the second and third trimesters.¹ There are reports of euglycemic diabetic ketoacidosis in pregnant women with either gestational or pregestational diabetes.⁵

Diabetes is associated with a risk of preeclampsia 4 times higher than in nondiabetic women.⁶ Other potential pregnancy-related complications include infections, polyhydram­nios, spontaneous abortion, and cesarean delivery.^1,7 The risk of pregnancy loss is similar in women with either type 1 or type 2 diabetes (2.6% and 3.7%, respectively), but the causes are different.⁸ Although preexisting diabetic complications such as retinopathy, nephropathy, and gastroparesis can be exacerbated during pregnancy,¹ only severe gastroparesis and advanced renal disease are considered relative contraindications to pregnancy.

IMPACT OF DIABETES ON THE FETUS

Fetal complications of maternal diabetes include embryopathy (fetal malformations) and fetopathy (overgrowth, ie, fetus large for gestational age, and increased risk of fetal death or distress). Maternal hyperglycemia is associated with diabetic embryopathy, resulting in major birth defects in 5% to 25% of pregnancies and spontaneous abortions in 15% to 20%.^9,10 There is a 2- to 6-fold increase in risk of congenital malformations.6

The most common diabetes-associated congenital malformations affect the cardiovascular system. Congenital heart disease includes tetralogy of Fallot, transposition of the great vessels, septal defects, and anomalous pulmonary venous return. Other relatively common defects involve the fetal central nervous system, spine, orofacial system, kidneys, urogenital system, gastrointestinal tract, and skeleton.¹¹

The risk of fetopathy is proportional to the degree of maternal hyperglycemia. Excess maternal glucose and fatty acid levels can lead to fetal hyperglycemia and overgrowth, which increases fetal oxygen requirements. Erythro­poietin levels rise, causing an increase in red cell mass, with subsequent hyperviscosity within the placenta and higher risk of fetal death.

Other complications include intrauterine growth restriction, prematurity, and preterm delivery. Fetal macrosomia (birth weight > 90th percentile or 4 kg, approximately 8 lb, 13 oz) occurs in 27% to 62% of children born to mothers with diabetes, a rate 10 times higher than in patients without diabetes. It contributes to shoulder dystocia (risk 2 to 4 times higher in diabetic pregnancies) and cesarean delivery.⁶ Infants born to mothers with diabetes also have higher risks of neonatal hypoglycemia, erythrocytosis, hyperbilirubinemia, hypocalcemia, respiratory distress, cardiomyopathy, and death, as well as for developing diabetes, obesity, and other adverse cardiometabolic outcomes later in life.¹¹

Nearly half of pregnancies in the general population are unplanned,¹⁵ so preconception diabetes assessment needs to be part of routine medical care for all reproductive-age women. Because most organogenesis occurs during the first 5 to 8 weeks after fertilization—potentially before a woman realizes she is pregnant—achieving optimal glycemic control before conception is necessary to improve pregnancy outcomes.¹

EVERY VISIT IS AN OPPORTUNITY

Every medical visit with a reproductive-age woman with diabetes is an opportunity for counseling about pregnancy. Topics that need to be discussed include the risks of unplanned pregnancy and of poor metabolic control, and the benefits of improved maternal and fetal outcomes with appropriate pregnancy planning and diabetes management.

Referral to a registered dietitian for individualized counseling about proper nutrition, particularly during pregnancy, has been associated with positive outcomes.¹⁶ Patients with diabetes and at high risk of pregnancy complications should be referred to a clinic that specializes in high-risk pregnancies.

Practitioners also should emphasize the importance of regular exercise and encourage patients to maintain or achieve a medically optimal weight before conception. Ideally, this would be a normal body mass index; however, this is not always possible.

In women who are planning pregnancy or are not on effective contraception, medications should be reviewed for potential teratogenicity. If needed, discuss alternative medications or switch to safer ones. However, these changes should not interrupt diabetes treatment.

In addition, ensure that the patient is up to date on age- and disease-appropriate preventive care (eg, immunizations, screening for sexually transmitted disease and malignancy). Counseling and intervention for use of tobacco, alcohol, and recreational drugs are also important. As with any preconception counseling, the patient (and her partner, if possible) should be advised to avoid travel to areas where Zika virus is endemic, and informed about the availability of expanded carrier genetic screening through her obstetric provider.

Finally, pregnant women with diabetes benefit from screening for diabetic complications including hypertension, retinopathy, cardiovascular disease, neuropathy, and nephropathy.

ASSESSING RISKS

Blood pressure

Chronic (preexisting) hypertension is defined as a systolic pressure 140 mm Hg or higher or a diastolic pressure 90 mm Hg or higher, or both, that antedates pregnancy or is present before the 20th week of pregnancy.³ Chronic hypertension has been reported in up to 5% of pregnant women and is associated with increased risk of preterm delivery, superimposed preeclampsia, low birth weight, and perinatal death.³

Reproductive-age women with diabetes and high blood pressure benefit from lifestyle and behavioral modifications.¹⁷ If drug therapy is needed, antihypertensive drugs that are safe for the fetus should be used. Treatment of mild or moderate hypertension during pregnancy reduces the risk of progression to severe hypertension but may not improve obstetric outcomes.

Diabetic retinopathy

Diabetic retinopathy can significantly worsen during pregnancy: the risk of progression is double that in the nonpregnant state.¹⁸ Women with diabetes who are contemplating pregnancy should have a comprehensive eye examination before conception, and any active proliferative retinopathy needs to be treated. These patients may require ophthalmologic monitoring and treatment during pregnancy. (Note: laser photocoagulation is not contraindicated during pregnancy.)

Cardiovascular disease

Cardiovascular physiology changes dramatically during pregnancy. Cardiovascular disease, especially when superimposed on diabetes, can increase the risk of maternal death. Thus, evaluation for cardiovascular risk factors as well as cardiovascular system integrity before conception is important. Listen for arterial bruits and murmurs, and assess peripheral pulses. Consideration should be given to obtaining a preconception resting electrocardiogram in women with diabetes who are over age 35 or who are suspected of having cardiovascular disease.¹⁶

Neurologic disorders

Peripheral neuropathy, the most common neurologic complication of diabetes, is associated with injury and infection.¹⁹

Autonomic neuropathy is associated with decreased cardiac responsiveness and orthostatic hypotension.¹⁹ Diabetic gastroparesis alone can precipitate serious complications during pregnancy, including extreme hypoglycemia and hyperglycemia, increased risk of diabetic ketoacidosis, weight loss, malnutrition, frequent hospitalizations, and increased requirement for parenteral nutrition.²⁰

Although diabetic neuropathy does not significantly worsen during pregnancy, women with preexisting gastroparesis should be counseled on the substantial risks associated with pregnancy. Screening for neuropathy should be part of all diabetic preconception examinations.

Renal complications

Pregnancy in women with diabetes and preexisting renal dysfunction increases their risk of accelerated progression of diabetic kidney disease.²¹ Preexisting renal dysfunction also increases the risk of pregnancy-related complications, such as stillbirth, intrauterine growth restriction, gestational hypertension, preeclampsia, and preterm delivery.^19,21,22 Further, the risk of pregnancy complications correlates directly with the severity of renal dysfunction.²²

Psychiatric disorders

Emotional wellness is essential for optimal diabetes management. It is important to recognize the emotional impact of diabetes in pregnant women and to conduct routine screening for depression, anxiety, stress, and eating disorders.¹⁶

Hemoglobin A_1c. The general consensus is to achieve the lowest hemoglobin A_1c level possible that does not increase the risk of hypoglycemia. The American Diabetes Association (ADA) recommends that, before attempting to conceive, women should lower their hemoglobin A_1c to below 6.5%.¹

Thyroid measures. Autoimmune thyroid disease is the most common autoimmune disorder associated with diabetes and has been reported in 35% to 40% of women with type 1 diabetes.²³ Recommendations are to check thyroid-stimulating hormone and thyroid peroxidase antibody levels before conception or early in pregnancy in all women with diabetes.^1,24 Overt hypothyroidism should be treated before conception, given that early fetal brain development depends on maternal thyroxine.

Renal function testing. Preconception assessment of renal function is important for counseling and risk stratification. This assessment should include serum creatinine level, estimated glomerular filtration rate, and urinary albumin excretion.²¹

Celiac screening. Because women with type 1 diabetes are more susceptible to autoimmune diseases, they should be screened for celiac disease before conception, with testing for  immunoglobulin A (IgA) and tissue transglutaminase antibodies, with or without IgA endomysial antibodies.^16,25,26 An estimated 6% of patients with type 1 diabetes have celiac disease vs 1% of the general population.²⁵ Celiac disease is 2 to 3 times more common in women, and asymptomatic people with type 1 diabetes are considered at increased risk for celiac disease.²⁶

The association between type 1 diabetes and celiac disease most likely relates to the overlap in human leukocyte antigens of the diseases. There is no established link between type 2 diabetes and celiac disease.²⁵

Undiagnosed celiac disease increases a woman’s risk of obstetric complications such as preterm birth, low birth weight, and stillbirth.²⁶ The most likely explanation for these adverse effects is nutrient malabsorption, which is characteristic of celiac disease. Adherence to a gluten-free diet before and during gestation may reduce the risk of preterm delivery by as much as 20%.²⁶

Vitamin B₁₂ level. Celiac disease interferes with the absorption of vitamin B₁₂-instrinsic factor in the ileum, which can lead to vitamin B₁₂ deficiency. Therefore, baseline vitamin B₁₂ levels should be checked before conception in women with celiac disease. Levels should also be checked in women taking metformin, which also decreases vitamin B₁₂ absorption. Of note, increased folate levels due to taking supplements can potentially mask vitamin B₁₂ deficiency.

MEDICATIONS TO REVIEW FOR PREGNANCY INTERACTIONS

Diabetic medications

Insulin is the first-line pharmacotherapy for pregnant patients with type 1, type 2, or gestational diabetes. Insulin does not cross the placenta to a measurable extent, and most insulin preparations have been classified as category B,¹ meaning no risks to the fetus have been found in humans.

Insulin dosing during pregnancy is not static. Beginning around mid-gestation, insulin requirements increase,^28,29 but after 32 weeks the need may decrease. These changes require practitioners to closely monitor blood glucose throughout pregnancy.

Both basal-bolus injections and continuous subcutaneous infusion are reasonable options during pregnancy.³⁰ However, the need for multiple and potentially painful insulin injections daily can lead to poor compliance. This inconvenience has led to studies using oral hypoglycemic medications instead of insulin for patients with gestational and type 2 diabetes.

Metformin is an oral biguanide that decreases hepatic gluconeogenesis and intestinal glucose absorption while peripherally increasing glucose utilization and uptake. Metformin does not pose a risk of hypoglycemia because its mechanism of action does not involve increased insulin production.⁷

Metformin does cross the placenta, resulting in umbilical cord blood levels higher than maternal levels. Nevertheless, studies support the efficacy and short-term safety of metformin use during a pregnancy complicated by gestational or type 2 diabetes.^7,31 Moreover, metformin has been associated with a lower risk of neonatal hypoglycemia and maternal weight gain than insulin.³² However, this agent should be used with caution, as long-term data are not yet available, and it may slightly increase the risk of premature delivery.

Glyburide is another oral hypoglycemic medication that has been used during pregnancy. This second-generation sulfonylurea enhances the release of insulin from the pancreas by binding beta islet cell ATP-calcium channel receptors. Compared with other sulfonylureas, glyburide has the lowest rate of maternal-to-fetal transfer, with umbilical cord plasma concentrations 70% of maternal levels.³³ Although some trials support the efficacy and short-term safety of glyburide treatment for gestational diabetes,³⁴ recent studies have associated glyburide use during pregnancy with a higher rate of neonatal hypoglycemia, neonatal respiratory distress, macrosomia, and neonatal intensive care unit admissions than insulin and metformin.^1,35

Patients treated with oral agents should be informed that these drugs cross the placenta, and that although no adverse effects on the fetus have been demonstrated, long-term safety data are lacking. In addition, oral agents are ineffective in type 1 diabetes and may be insufficient to overcome the insulin resistance in type 2 diabetes.

Antihypertensive drugs

All antihypertensive drugs cross the placenta, but several have an acceptable safety profile in pregnancy, including methyldopa, labeta­lol, clonidine, prazosin, and nifedipine. Hydralazine and labetalol are short-acting, come in intravenous formulations, and can be used for urgent blood pressure control during pregnancy. Diltiazem may be used for heart rate control during pregnancy, and it has been shown to lower blood pressure and proteinuria in pregnant patients with underlying renal disease.^36,37 The ADA recommends against chronic use of diuretics during pregnancy because of potential reductions in maternal plasma volume and uteroplacental perfusion.¹

Angiotensin-converting enzyme (ACE) inhibitors, angiotensin II receptor blockers (ARBs), and direct renin inhibitors are contraindicated during pregnancy because of the risk of fetal defects, particularly in the renal system.^21,38 Although there is evidence to question the association between first semester exposure and fetotoxicity,³⁹ we avoid these drugs during pregnancy and switch to a different agent in women planning pregnancy.

Other drugs

Statins are contraindicated in pregnancy because they interfere with the development of the fetal nervous system.²¹ Although preliminary data from a small study did not identify safety risks associated with pravastatin use after 12 weeks of gestation,⁴⁰ we recommend discontinuing statins in women attempting pregnancy.

Aspirin. The US Preventive Services Task Force41 recommends low-dose aspirin (81 mg/day) after 12 weeks of gestation for women with type 1 or type 2 diabetes, as well as those with renal disease or chronic hypertension, to prevent preeclampsia. Of note, higher doses need to be used with caution during pregnancy because fetal abnormalities have been reported, such as disruption of fetal vasculature (mesenteric vessels), gastroschisis, and small intestinal atresia.¹⁶

Folate supplementation (0.6–4 mg/day) is recommended in women with celiac disease to prevent neural tube defects in the offspring, and the US Preventive Services Task Force recommends 0.4 mg daily of folic acid supplementation for all women planning or capable of pregnancy.^42–44 Higher doses, ranging from 0.6 to 5 mg/day, have been proposed for patients with diabetes,¹³ and we recommend at least 1 mg for this group, based on data suggesting that higher doses further reduce the risk of neural tube defects.⁴³

Maternal diabetes, insulin therapy, and oral hypoglycemic agents are not contraindications to breastfeeding. The US Preventive Services Task Force recommends interventions by primary care physicians to promote and support breastfeeding.⁴⁵ Breastfeeding is encouraged based on various short- and long-term health benefits for both breastfed infants and breastfeeding mothers. Breastfeeding decreases a woman’s insulin requirements and increases the risk for hypoglycemia, especially in patients with insulin-dependent type 1 diabetes.¹

Additionally, insulin sensitivity increases immediately following delivery of the placenta.¹ Therefore, it is prudent to adjust insulin doses postpartum, especially while a patient is breastfeeding, or to suggest high-carbohydrate snacks before feeds.^9,29

Antihypertensive drugs considered safe to use during lactation include captopril, enalapril, quinapril, labetalol, propranolol, nifedipine, and hydralazine.^21,46 Methyldopa is not contraindicated, but it causes fatigue and worsened postpartum depression and should not be used as first-line therapy. Diuretics and ARBs are not recommended during lactation.²¹ Both metformin and glyburide enter breast milk in small enough amounts that they are not contraindicated during breastfeeding.¹⁶ The Lactmed database (www.toxnet.nlm.nih.gov) provides information about drugs and breastfeeding.

WHAT ABOUT CONTRACEPTIVES?

The ADA recommends contraception for women with diabetes because, just as in women without diabetes, the risks of unplanned pregnancy outweigh those of contraceptives.¹

We recommend low-dose combination estrogen-progestin oral contraceptives to normotensive women under age 35 with diabetes but without underlying microvascular disease. For women over age 35 or for those with microvascular disease, additional options include intrauterine devices or progestin implants. We prefer not to use injectable depot medroxyprogesterone acetate because of its side effects of insulin resistance and weight gain.⁴⁷

CASE DISCUSSION: NEXT STEPS

Our patient’s interest in family planning presents an opportunity for preconception counseling. We recommend a prenatal folic acid supplement, diet and regular exercise for weight loss, and screening tests including a comprehensive metabolic panel, hemoglobin A_1c, thyroid-stimulating hormone, and dilated eye examination. We make sure she is up to date on her indicated health maintenance (eg, immunizations, disease screening), and we review her medications for potential teratogens. She denies any recreational drug use. Also, she has no plans for long-distance travel.

Our counseling includes discussions of pregnancy risks associated with pregestational diabetes and suboptimal glycemic control. We encourage her to use effective contraception until she is “medically optimized” for pregnancy—ie, until her hemoglobin A_1c is lower than 6.5% and she has achieved a medically optimal weight. If feasible, a reduction of weight (7% or so) through lifestyle modification should be attempted, and if her hemoglobin A_1c remains elevated, adding insulin would be recommended.

Pregnant patients or patients contemplating pregnancy are usually motivated to modify their behavior, making this a good time to reinforce lifestyle modifications. Many patients benefit from individualized counseling by a registered dietitian to help achieve the recommended weight and glycemic control.

Our physical examination in this patient includes screening for micro- and macrovascular complications of diabetes, and the test results are negative. Patients with active proliferative retinopathy should be referred to an ophthalmologist for assessment and treatment.

We review her medications for potential teratogenic effects and stop her ACE inhibitor (lisinopril) and statin (simvastatin). We switch her from a first-generation sulfonylurea (chlorpropamide) to glyburide, a second-generation sulfonylurea. Second-generation sulfonylureas are considered more “fetus-friendly” because first-generation sulfonylureas cross the placenta more easily and can cause fetal hyperinsulinemia, leading to macrosomia and neonatal hypoglycemia.⁷

The management of diabetes during pregnancy leans toward insulin use, given the lack of information regarding long-term outcomes with oral agents. If insulin is needed, it is best to initiate it before the patient conceives, and then to stop other diabetes medications. We would not make any changes to her aspirin or metformin use.

Educating the patient and her family about prevention, recognition, and treatment of hypoglycemia is important to prevent and manage the increased risk of hypoglycemia with insulin therapy and in early pregnancy.¹ Consideration should be given to providing ketone strips as well as education on diabetic ketoacidosis prevention and detection.¹ If the patient conceives, begin prenatal care early to allow adequate planning for care of her disease and evaluation of the fetus. Because of the complexity of insulin management in pregnancy, the ADA recommends referral, if possible, to a center offering team-based care, including an obstetrician specialized in high-risk pregnancies, an endocrinologist, and a dietitian.¹

A 29-year-old nulliparous woman presents for a routine checkup. She has hypertension and type 2 diabetes mellitus. Her current medications are chlorpropamide 500 mg daily, metformin 500 mg twice a day, lisinopril 40 mg daily, simvastatin 40 mg daily, and aspirin 81 mg daily. Her body mass index is 37 kg/m² and her blood pressure is 130/80 mm Hg. Her hemoglobin A_1c level is 7.8% and her low-density lipoprotein cholesterol 90 mg/dL.

She is considering pregnancy. How would you counsel her?

DEFINING DIABETES IN PREGNANCY

Diabetes in pregnant women, both gestational and pregestational, is the most common medical complication associated with pregnancy.¹

• Gestational diabetes is defined as diabetes that is diagnosed during the second or third trimester of pregnancy and that is not clearly pregestational.²
• Pregestational diabetes exists before pregnancy and can be either type 1 or type 2.

Most cases of diabetes diagnosed during the first trimester reflect pregestational diabetes, as gestational diabetes occurs when insulin resistance increases in the later trimesters.

Type 1 diabetes involves autoimmune destruction of pancreatic islet cells, leading to insulin deficiency and the need for insulin therapy. Type 2 diabetes is characterized by insulin resistance rather than overall insulin deficiency. Type 2 diabetes tends to be associated with comorbidities such as obesity and hypertension, which are independent risk factors for adverse perinatal outcomes.^3,4

Gestational diabetes accounts for most cases of diabetes during pregnancy. Although both pregestational and gestational diabetes increase the risk of maternal and fetal complications, pregestational diabetes is associated with significantly greater risks.¹

IMPACT OF DIABETES ON THE MOTHER

Pregnancy increases the risk of maternal hypoglycemia, especially during the first trimester in patients with type 1 diabetes, as insulin sensitivity increases in early pregnancy.¹ Pregnant women with diabetes may also have an altered counterregulatory response and less hypoglycemic awareness.¹ Insulin resistance rises during the second and early third trimesters, increasing the risk of hyperglycemia in women with diabetes.¹

Glycemic control during pregnancy is usually easier to achieve in patients with type 2 diabetes than with type 1, but it may require much higher insulin doses.

Because pregnancy is inherently a ketogenic state, women with type 1 diabetes are at higher risk of diabetic ketoacidosis, particularly during the second and third trimesters.¹ There are reports of euglycemic diabetic ketoacidosis in pregnant women with either gestational or pregestational diabetes.⁵

Diabetes is associated with a risk of preeclampsia 4 times higher than in nondiabetic women.⁶ Other potential pregnancy-related complications include infections, polyhydram­nios, spontaneous abortion, and cesarean delivery.^1,7 The risk of pregnancy loss is similar in women with either type 1 or type 2 diabetes (2.6% and 3.7%, respectively), but the causes are different.⁸ Although preexisting diabetic complications such as retinopathy, nephropathy, and gastroparesis can be exacerbated during pregnancy,¹ only severe gastroparesis and advanced renal disease are considered relative contraindications to pregnancy.

IMPACT OF DIABETES ON THE FETUS

Fetal complications of maternal diabetes include embryopathy (fetal malformations) and fetopathy (overgrowth, ie, fetus large for gestational age, and increased risk of fetal death or distress). Maternal hyperglycemia is associated with diabetic embryopathy, resulting in major birth defects in 5% to 25% of pregnancies and spontaneous abortions in 15% to 20%.^9,10 There is a 2- to 6-fold increase in risk of congenital malformations.6

The most common diabetes-associated congenital malformations affect the cardiovascular system. Congenital heart disease includes tetralogy of Fallot, transposition of the great vessels, septal defects, and anomalous pulmonary venous return. Other relatively common defects involve the fetal central nervous system, spine, orofacial system, kidneys, urogenital system, gastrointestinal tract, and skeleton.¹¹

The risk of fetopathy is proportional to the degree of maternal hyperglycemia. Excess maternal glucose and fatty acid levels can lead to fetal hyperglycemia and overgrowth, which increases fetal oxygen requirements. Erythro­poietin levels rise, causing an increase in red cell mass, with subsequent hyperviscosity within the placenta and higher risk of fetal death.

Other complications include intrauterine growth restriction, prematurity, and preterm delivery. Fetal macrosomia (birth weight > 90th percentile or 4 kg, approximately 8 lb, 13 oz) occurs in 27% to 62% of children born to mothers with diabetes, a rate 10 times higher than in patients without diabetes. It contributes to shoulder dystocia (risk 2 to 4 times higher in diabetic pregnancies) and cesarean delivery.⁶ Infants born to mothers with diabetes also have higher risks of neonatal hypoglycemia, erythrocytosis, hyperbilirubinemia, hypocalcemia, respiratory distress, cardiomyopathy, and death, as well as for developing diabetes, obesity, and other adverse cardiometabolic outcomes later in life.¹¹

Nearly half of pregnancies in the general population are unplanned,¹⁵ so preconception diabetes assessment needs to be part of routine medical care for all reproductive-age women. Because most organogenesis occurs during the first 5 to 8 weeks after fertilization—potentially before a woman realizes she is pregnant—achieving optimal glycemic control before conception is necessary to improve pregnancy outcomes.¹

EVERY VISIT IS AN OPPORTUNITY

Every medical visit with a reproductive-age woman with diabetes is an opportunity for counseling about pregnancy. Topics that need to be discussed include the risks of unplanned pregnancy and of poor metabolic control, and the benefits of improved maternal and fetal outcomes with appropriate pregnancy planning and diabetes management.

Referral to a registered dietitian for individualized counseling about proper nutrition, particularly during pregnancy, has been associated with positive outcomes.¹⁶ Patients with diabetes and at high risk of pregnancy complications should be referred to a clinic that specializes in high-risk pregnancies.

Practitioners also should emphasize the importance of regular exercise and encourage patients to maintain or achieve a medically optimal weight before conception. Ideally, this would be a normal body mass index; however, this is not always possible.

In women who are planning pregnancy or are not on effective contraception, medications should be reviewed for potential teratogenicity. If needed, discuss alternative medications or switch to safer ones. However, these changes should not interrupt diabetes treatment.

In addition, ensure that the patient is up to date on age- and disease-appropriate preventive care (eg, immunizations, screening for sexually transmitted disease and malignancy). Counseling and intervention for use of tobacco, alcohol, and recreational drugs are also important. As with any preconception counseling, the patient (and her partner, if possible) should be advised to avoid travel to areas where Zika virus is endemic, and informed about the availability of expanded carrier genetic screening through her obstetric provider.

Finally, pregnant women with diabetes benefit from screening for diabetic complications including hypertension, retinopathy, cardiovascular disease, neuropathy, and nephropathy.

ASSESSING RISKS

Blood pressure

Chronic (preexisting) hypertension is defined as a systolic pressure 140 mm Hg or higher or a diastolic pressure 90 mm Hg or higher, or both, that antedates pregnancy or is present before the 20th week of pregnancy.³ Chronic hypertension has been reported in up to 5% of pregnant women and is associated with increased risk of preterm delivery, superimposed preeclampsia, low birth weight, and perinatal death.³

Reproductive-age women with diabetes and high blood pressure benefit from lifestyle and behavioral modifications.¹⁷ If drug therapy is needed, antihypertensive drugs that are safe for the fetus should be used. Treatment of mild or moderate hypertension during pregnancy reduces the risk of progression to severe hypertension but may not improve obstetric outcomes.

Diabetic retinopathy

Diabetic retinopathy can significantly worsen during pregnancy: the risk of progression is double that in the nonpregnant state.¹⁸ Women with diabetes who are contemplating pregnancy should have a comprehensive eye examination before conception, and any active proliferative retinopathy needs to be treated. These patients may require ophthalmologic monitoring and treatment during pregnancy. (Note: laser photocoagulation is not contraindicated during pregnancy.)

Cardiovascular disease

Cardiovascular physiology changes dramatically during pregnancy. Cardiovascular disease, especially when superimposed on diabetes, can increase the risk of maternal death. Thus, evaluation for cardiovascular risk factors as well as cardiovascular system integrity before conception is important. Listen for arterial bruits and murmurs, and assess peripheral pulses. Consideration should be given to obtaining a preconception resting electrocardiogram in women with diabetes who are over age 35 or who are suspected of having cardiovascular disease.¹⁶

Neurologic disorders

Peripheral neuropathy, the most common neurologic complication of diabetes, is associated with injury and infection.¹⁹

Autonomic neuropathy is associated with decreased cardiac responsiveness and orthostatic hypotension.¹⁹ Diabetic gastroparesis alone can precipitate serious complications during pregnancy, including extreme hypoglycemia and hyperglycemia, increased risk of diabetic ketoacidosis, weight loss, malnutrition, frequent hospitalizations, and increased requirement for parenteral nutrition.²⁰

Although diabetic neuropathy does not significantly worsen during pregnancy, women with preexisting gastroparesis should be counseled on the substantial risks associated with pregnancy. Screening for neuropathy should be part of all diabetic preconception examinations.

Renal complications

Pregnancy in women with diabetes and preexisting renal dysfunction increases their risk of accelerated progression of diabetic kidney disease.²¹ Preexisting renal dysfunction also increases the risk of pregnancy-related complications, such as stillbirth, intrauterine growth restriction, gestational hypertension, preeclampsia, and preterm delivery.^19,21,22 Further, the risk of pregnancy complications correlates directly with the severity of renal dysfunction.²²

Psychiatric disorders

Emotional wellness is essential for optimal diabetes management. It is important to recognize the emotional impact of diabetes in pregnant women and to conduct routine screening for depression, anxiety, stress, and eating disorders.¹⁶

Hemoglobin A_1c. The general consensus is to achieve the lowest hemoglobin A_1c level possible that does not increase the risk of hypoglycemia. The American Diabetes Association (ADA) recommends that, before attempting to conceive, women should lower their hemoglobin A_1c to below 6.5%.¹

Thyroid measures. Autoimmune thyroid disease is the most common autoimmune disorder associated with diabetes and has been reported in 35% to 40% of women with type 1 diabetes.²³ Recommendations are to check thyroid-stimulating hormone and thyroid peroxidase antibody levels before conception or early in pregnancy in all women with diabetes.^1,24 Overt hypothyroidism should be treated before conception, given that early fetal brain development depends on maternal thyroxine.

Renal function testing. Preconception assessment of renal function is important for counseling and risk stratification. This assessment should include serum creatinine level, estimated glomerular filtration rate, and urinary albumin excretion.²¹

Celiac screening. Because women with type 1 diabetes are more susceptible to autoimmune diseases, they should be screened for celiac disease before conception, with testing for  immunoglobulin A (IgA) and tissue transglutaminase antibodies, with or without IgA endomysial antibodies.^16,25,26 An estimated 6% of patients with type 1 diabetes have celiac disease vs 1% of the general population.²⁵ Celiac disease is 2 to 3 times more common in women, and asymptomatic people with type 1 diabetes are considered at increased risk for celiac disease.²⁶

The association between type 1 diabetes and celiac disease most likely relates to the overlap in human leukocyte antigens of the diseases. There is no established link between type 2 diabetes and celiac disease.²⁵

Undiagnosed celiac disease increases a woman’s risk of obstetric complications such as preterm birth, low birth weight, and stillbirth.²⁶ The most likely explanation for these adverse effects is nutrient malabsorption, which is characteristic of celiac disease. Adherence to a gluten-free diet before and during gestation may reduce the risk of preterm delivery by as much as 20%.²⁶

Vitamin B₁₂ level. Celiac disease interferes with the absorption of vitamin B₁₂-instrinsic factor in the ileum, which can lead to vitamin B₁₂ deficiency. Therefore, baseline vitamin B₁₂ levels should be checked before conception in women with celiac disease. Levels should also be checked in women taking metformin, which also decreases vitamin B₁₂ absorption. Of note, increased folate levels due to taking supplements can potentially mask vitamin B₁₂ deficiency.

MEDICATIONS TO REVIEW FOR PREGNANCY INTERACTIONS

Diabetic medications

Insulin is the first-line pharmacotherapy for pregnant patients with type 1, type 2, or gestational diabetes. Insulin does not cross the placenta to a measurable extent, and most insulin preparations have been classified as category B,¹ meaning no risks to the fetus have been found in humans.

Insulin dosing during pregnancy is not static. Beginning around mid-gestation, insulin requirements increase,^28,29 but after 32 weeks the need may decrease. These changes require practitioners to closely monitor blood glucose throughout pregnancy.

Both basal-bolus injections and continuous subcutaneous infusion are reasonable options during pregnancy.³⁰ However, the need for multiple and potentially painful insulin injections daily can lead to poor compliance. This inconvenience has led to studies using oral hypoglycemic medications instead of insulin for patients with gestational and type 2 diabetes.

Metformin is an oral biguanide that decreases hepatic gluconeogenesis and intestinal glucose absorption while peripherally increasing glucose utilization and uptake. Metformin does not pose a risk of hypoglycemia because its mechanism of action does not involve increased insulin production.⁷

Metformin does cross the placenta, resulting in umbilical cord blood levels higher than maternal levels. Nevertheless, studies support the efficacy and short-term safety of metformin use during a pregnancy complicated by gestational or type 2 diabetes.^7,31 Moreover, metformin has been associated with a lower risk of neonatal hypoglycemia and maternal weight gain than insulin.³² However, this agent should be used with caution, as long-term data are not yet available, and it may slightly increase the risk of premature delivery.

Glyburide is another oral hypoglycemic medication that has been used during pregnancy. This second-generation sulfonylurea enhances the release of insulin from the pancreas by binding beta islet cell ATP-calcium channel receptors. Compared with other sulfonylureas, glyburide has the lowest rate of maternal-to-fetal transfer, with umbilical cord plasma concentrations 70% of maternal levels.³³ Although some trials support the efficacy and short-term safety of glyburide treatment for gestational diabetes,³⁴ recent studies have associated glyburide use during pregnancy with a higher rate of neonatal hypoglycemia, neonatal respiratory distress, macrosomia, and neonatal intensive care unit admissions than insulin and metformin.^1,35

Patients treated with oral agents should be informed that these drugs cross the placenta, and that although no adverse effects on the fetus have been demonstrated, long-term safety data are lacking. In addition, oral agents are ineffective in type 1 diabetes and may be insufficient to overcome the insulin resistance in type 2 diabetes.

Antihypertensive drugs

All antihypertensive drugs cross the placenta, but several have an acceptable safety profile in pregnancy, including methyldopa, labeta­lol, clonidine, prazosin, and nifedipine. Hydralazine and labetalol are short-acting, come in intravenous formulations, and can be used for urgent blood pressure control during pregnancy. Diltiazem may be used for heart rate control during pregnancy, and it has been shown to lower blood pressure and proteinuria in pregnant patients with underlying renal disease.^36,37 The ADA recommends against chronic use of diuretics during pregnancy because of potential reductions in maternal plasma volume and uteroplacental perfusion.¹

Angiotensin-converting enzyme (ACE) inhibitors, angiotensin II receptor blockers (ARBs), and direct renin inhibitors are contraindicated during pregnancy because of the risk of fetal defects, particularly in the renal system.^21,38 Although there is evidence to question the association between first semester exposure and fetotoxicity,³⁹ we avoid these drugs during pregnancy and switch to a different agent in women planning pregnancy.

Other drugs

Statins are contraindicated in pregnancy because they interfere with the development of the fetal nervous system.²¹ Although preliminary data from a small study did not identify safety risks associated with pravastatin use after 12 weeks of gestation,⁴⁰ we recommend discontinuing statins in women attempting pregnancy.

Aspirin. The US Preventive Services Task Force41 recommends low-dose aspirin (81 mg/day) after 12 weeks of gestation for women with type 1 or type 2 diabetes, as well as those with renal disease or chronic hypertension, to prevent preeclampsia. Of note, higher doses need to be used with caution during pregnancy because fetal abnormalities have been reported, such as disruption of fetal vasculature (mesenteric vessels), gastroschisis, and small intestinal atresia.¹⁶

Folate supplementation (0.6–4 mg/day) is recommended in women with celiac disease to prevent neural tube defects in the offspring, and the US Preventive Services Task Force recommends 0.4 mg daily of folic acid supplementation for all women planning or capable of pregnancy.^42–44 Higher doses, ranging from 0.6 to 5 mg/day, have been proposed for patients with diabetes,¹³ and we recommend at least 1 mg for this group, based on data suggesting that higher doses further reduce the risk of neural tube defects.⁴³

Maternal diabetes, insulin therapy, and oral hypoglycemic agents are not contraindications to breastfeeding. The US Preventive Services Task Force recommends interventions by primary care physicians to promote and support breastfeeding.⁴⁵ Breastfeeding is encouraged based on various short- and long-term health benefits for both breastfed infants and breastfeeding mothers. Breastfeeding decreases a woman’s insulin requirements and increases the risk for hypoglycemia, especially in patients with insulin-dependent type 1 diabetes.¹

Additionally, insulin sensitivity increases immediately following delivery of the placenta.¹ Therefore, it is prudent to adjust insulin doses postpartum, especially while a patient is breastfeeding, or to suggest high-carbohydrate snacks before feeds.^9,29

Antihypertensive drugs considered safe to use during lactation include captopril, enalapril, quinapril, labetalol, propranolol, nifedipine, and hydralazine.^21,46 Methyldopa is not contraindicated, but it causes fatigue and worsened postpartum depression and should not be used as first-line therapy. Diuretics and ARBs are not recommended during lactation.²¹ Both metformin and glyburide enter breast milk in small enough amounts that they are not contraindicated during breastfeeding.¹⁶ The Lactmed database (www.toxnet.nlm.nih.gov) provides information about drugs and breastfeeding.

WHAT ABOUT CONTRACEPTIVES?

The ADA recommends contraception for women with diabetes because, just as in women without diabetes, the risks of unplanned pregnancy outweigh those of contraceptives.¹

We recommend low-dose combination estrogen-progestin oral contraceptives to normotensive women under age 35 with diabetes but without underlying microvascular disease. For women over age 35 or for those with microvascular disease, additional options include intrauterine devices or progestin implants. We prefer not to use injectable depot medroxyprogesterone acetate because of its side effects of insulin resistance and weight gain.⁴⁷

CASE DISCUSSION: NEXT STEPS

Our patient’s interest in family planning presents an opportunity for preconception counseling. We recommend a prenatal folic acid supplement, diet and regular exercise for weight loss, and screening tests including a comprehensive metabolic panel, hemoglobin A_1c, thyroid-stimulating hormone, and dilated eye examination. We make sure she is up to date on her indicated health maintenance (eg, immunizations, disease screening), and we review her medications for potential teratogens. She denies any recreational drug use. Also, she has no plans for long-distance travel.

Our counseling includes discussions of pregnancy risks associated with pregestational diabetes and suboptimal glycemic control. We encourage her to use effective contraception until she is “medically optimized” for pregnancy—ie, until her hemoglobin A_1c is lower than 6.5% and she has achieved a medically optimal weight. If feasible, a reduction of weight (7% or so) through lifestyle modification should be attempted, and if her hemoglobin A_1c remains elevated, adding insulin would be recommended.

Pregnant patients or patients contemplating pregnancy are usually motivated to modify their behavior, making this a good time to reinforce lifestyle modifications. Many patients benefit from individualized counseling by a registered dietitian to help achieve the recommended weight and glycemic control.

Our physical examination in this patient includes screening for micro- and macrovascular complications of diabetes, and the test results are negative. Patients with active proliferative retinopathy should be referred to an ophthalmologist for assessment and treatment.

We review her medications for potential teratogenic effects and stop her ACE inhibitor (lisinopril) and statin (simvastatin). We switch her from a first-generation sulfonylurea (chlorpropamide) to glyburide, a second-generation sulfonylurea. Second-generation sulfonylureas are considered more “fetus-friendly” because first-generation sulfonylureas cross the placenta more easily and can cause fetal hyperinsulinemia, leading to macrosomia and neonatal hypoglycemia.⁷

The management of diabetes during pregnancy leans toward insulin use, given the lack of information regarding long-term outcomes with oral agents. If insulin is needed, it is best to initiate it before the patient conceives, and then to stop other diabetes medications. We would not make any changes to her aspirin or metformin use.

Educating the patient and her family about prevention, recognition, and treatment of hypoglycemia is important to prevent and manage the increased risk of hypoglycemia with insulin therapy and in early pregnancy.¹ Consideration should be given to providing ketone strips as well as education on diabetic ketoacidosis prevention and detection.¹ If the patient conceives, begin prenatal care early to allow adequate planning for care of her disease and evaluation of the fetus. Because of the complexity of insulin management in pregnancy, the ADA recommends referral, if possible, to a center offering team-based care, including an obstetrician specialized in high-risk pregnancies, an endocrinologist, and a dietitian.¹

A 29-year-old nulliparous woman presents for a routine checkup. She has hypertension and type 2 diabetes mellitus. Her current medications are chlorpropamide 500 mg daily, metformin 500 mg twice a day, lisinopril 40 mg daily, simvastatin 40 mg daily, and aspirin 81 mg daily. Her body mass index is 37 kg/m² and her blood pressure is 130/80 mm Hg. Her hemoglobin A_1c level is 7.8% and her low-density lipoprotein cholesterol 90 mg/dL.

She is considering pregnancy. How would you counsel her?

DEFINING DIABETES IN PREGNANCY

Diabetes in pregnant women, both gestational and pregestational, is the most common medical complication associated with pregnancy.¹

• Gestational diabetes is defined as diabetes that is diagnosed during the second or third trimester of pregnancy and that is not clearly pregestational.²
• Pregestational diabetes exists before pregnancy and can be either type 1 or type 2.

Most cases of diabetes diagnosed during the first trimester reflect pregestational diabetes, as gestational diabetes occurs when insulin resistance increases in the later trimesters.

Type 1 diabetes involves autoimmune destruction of pancreatic islet cells, leading to insulin deficiency and the need for insulin therapy. Type 2 diabetes is characterized by insulin resistance rather than overall insulin deficiency. Type 2 diabetes tends to be associated with comorbidities such as obesity and hypertension, which are independent risk factors for adverse perinatal outcomes.^3,4

Gestational diabetes accounts for most cases of diabetes during pregnancy. Although both pregestational and gestational diabetes increase the risk of maternal and fetal complications, pregestational diabetes is associated with significantly greater risks.¹

IMPACT OF DIABETES ON THE MOTHER

Pregnancy increases the risk of maternal hypoglycemia, especially during the first trimester in patients with type 1 diabetes, as insulin sensitivity increases in early pregnancy.¹ Pregnant women with diabetes may also have an altered counterregulatory response and less hypoglycemic awareness.¹ Insulin resistance rises during the second and early third trimesters, increasing the risk of hyperglycemia in women with diabetes.¹

Glycemic control during pregnancy is usually easier to achieve in patients with type 2 diabetes than with type 1, but it may require much higher insulin doses.

Because pregnancy is inherently a ketogenic state, women with type 1 diabetes are at higher risk of diabetic ketoacidosis, particularly during the second and third trimesters.¹ There are reports of euglycemic diabetic ketoacidosis in pregnant women with either gestational or pregestational diabetes.⁵

Diabetes is associated with a risk of preeclampsia 4 times higher than in nondiabetic women.⁶ Other potential pregnancy-related complications include infections, polyhydram­nios, spontaneous abortion, and cesarean delivery.^1,7 The risk of pregnancy loss is similar in women with either type 1 or type 2 diabetes (2.6% and 3.7%, respectively), but the causes are different.⁸ Although preexisting diabetic complications such as retinopathy, nephropathy, and gastroparesis can be exacerbated during pregnancy,¹ only severe gastroparesis and advanced renal disease are considered relative contraindications to pregnancy.

IMPACT OF DIABETES ON THE FETUS

Fetal complications of maternal diabetes include embryopathy (fetal malformations) and fetopathy (overgrowth, ie, fetus large for gestational age, and increased risk of fetal death or distress). Maternal hyperglycemia is associated with diabetic embryopathy, resulting in major birth defects in 5% to 25% of pregnancies and spontaneous abortions in 15% to 20%.^9,10 There is a 2- to 6-fold increase in risk of congenital malformations.6

The most common diabetes-associated congenital malformations affect the cardiovascular system. Congenital heart disease includes tetralogy of Fallot, transposition of the great vessels, septal defects, and anomalous pulmonary venous return. Other relatively common defects involve the fetal central nervous system, spine, orofacial system, kidneys, urogenital system, gastrointestinal tract, and skeleton.¹¹

The risk of fetopathy is proportional to the degree of maternal hyperglycemia. Excess maternal glucose and fatty acid levels can lead to fetal hyperglycemia and overgrowth, which increases fetal oxygen requirements. Erythro­poietin levels rise, causing an increase in red cell mass, with subsequent hyperviscosity within the placenta and higher risk of fetal death.

Other complications include intrauterine growth restriction, prematurity, and preterm delivery. Fetal macrosomia (birth weight > 90th percentile or 4 kg, approximately 8 lb, 13 oz) occurs in 27% to 62% of children born to mothers with diabetes, a rate 10 times higher than in patients without diabetes. It contributes to shoulder dystocia (risk 2 to 4 times higher in diabetic pregnancies) and cesarean delivery.⁶ Infants born to mothers with diabetes also have higher risks of neonatal hypoglycemia, erythrocytosis, hyperbilirubinemia, hypocalcemia, respiratory distress, cardiomyopathy, and death, as well as for developing diabetes, obesity, and other adverse cardiometabolic outcomes later in life.¹¹

Nearly half of pregnancies in the general population are unplanned,¹⁵ so preconception diabetes assessment needs to be part of routine medical care for all reproductive-age women. Because most organogenesis occurs during the first 5 to 8 weeks after fertilization—potentially before a woman realizes she is pregnant—achieving optimal glycemic control before conception is necessary to improve pregnancy outcomes.¹

EVERY VISIT IS AN OPPORTUNITY

Every medical visit with a reproductive-age woman with diabetes is an opportunity for counseling about pregnancy. Topics that need to be discussed include the risks of unplanned pregnancy and of poor metabolic control, and the benefits of improved maternal and fetal outcomes with appropriate pregnancy planning and diabetes management.

Referral to a registered dietitian for individualized counseling about proper nutrition, particularly during pregnancy, has been associated with positive outcomes.¹⁶ Patients with diabetes and at high risk of pregnancy complications should be referred to a clinic that specializes in high-risk pregnancies.

Practitioners also should emphasize the importance of regular exercise and encourage patients to maintain or achieve a medically optimal weight before conception. Ideally, this would be a normal body mass index; however, this is not always possible.

In women who are planning pregnancy or are not on effective contraception, medications should be reviewed for potential teratogenicity. If needed, discuss alternative medications or switch to safer ones. However, these changes should not interrupt diabetes treatment.

In addition, ensure that the patient is up to date on age- and disease-appropriate preventive care (eg, immunizations, screening for sexually transmitted disease and malignancy). Counseling and intervention for use of tobacco, alcohol, and recreational drugs are also important. As with any preconception counseling, the patient (and her partner, if possible) should be advised to avoid travel to areas where Zika virus is endemic, and informed about the availability of expanded carrier genetic screening through her obstetric provider.

Finally, pregnant women with diabetes benefit from screening for diabetic complications including hypertension, retinopathy, cardiovascular disease, neuropathy, and nephropathy.

ASSESSING RISKS

Blood pressure

Chronic (preexisting) hypertension is defined as a systolic pressure 140 mm Hg or higher or a diastolic pressure 90 mm Hg or higher, or both, that antedates pregnancy or is present before the 20th week of pregnancy.³ Chronic hypertension has been reported in up to 5% of pregnant women and is associated with increased risk of preterm delivery, superimposed preeclampsia, low birth weight, and perinatal death.³

Reproductive-age women with diabetes and high blood pressure benefit from lifestyle and behavioral modifications.¹⁷ If drug therapy is needed, antihypertensive drugs that are safe for the fetus should be used. Treatment of mild or moderate hypertension during pregnancy reduces the risk of progression to severe hypertension but may not improve obstetric outcomes.

Diabetic retinopathy

Diabetic retinopathy can significantly worsen during pregnancy: the risk of progression is double that in the nonpregnant state.¹⁸ Women with diabetes who are contemplating pregnancy should have a comprehensive eye examination before conception, and any active proliferative retinopathy needs to be treated. These patients may require ophthalmologic monitoring and treatment during pregnancy. (Note: laser photocoagulation is not contraindicated during pregnancy.)

Cardiovascular disease

Cardiovascular physiology changes dramatically during pregnancy. Cardiovascular disease, especially when superimposed on diabetes, can increase the risk of maternal death. Thus, evaluation for cardiovascular risk factors as well as cardiovascular system integrity before conception is important. Listen for arterial bruits and murmurs, and assess peripheral pulses. Consideration should be given to obtaining a preconception resting electrocardiogram in women with diabetes who are over age 35 or who are suspected of having cardiovascular disease.¹⁶

Neurologic disorders

Peripheral neuropathy, the most common neurologic complication of diabetes, is associated with injury and infection.¹⁹

Autonomic neuropathy is associated with decreased cardiac responsiveness and orthostatic hypotension.¹⁹ Diabetic gastroparesis alone can precipitate serious complications during pregnancy, including extreme hypoglycemia and hyperglycemia, increased risk of diabetic ketoacidosis, weight loss, malnutrition, frequent hospitalizations, and increased requirement for parenteral nutrition.²⁰

Although diabetic neuropathy does not significantly worsen during pregnancy, women with preexisting gastroparesis should be counseled on the substantial risks associated with pregnancy. Screening for neuropathy should be part of all diabetic preconception examinations.

Renal complications

Pregnancy in women with diabetes and preexisting renal dysfunction increases their risk of accelerated progression of diabetic kidney disease.²¹ Preexisting renal dysfunction also increases the risk of pregnancy-related complications, such as stillbirth, intrauterine growth restriction, gestational hypertension, preeclampsia, and preterm delivery.^19,21,22 Further, the risk of pregnancy complications correlates directly with the severity of renal dysfunction.²²

Psychiatric disorders

Emotional wellness is essential for optimal diabetes management. It is important to recognize the emotional impact of diabetes in pregnant women and to conduct routine screening for depression, anxiety, stress, and eating disorders.¹⁶

Hemoglobin A_1c. The general consensus is to achieve the lowest hemoglobin A_1c level possible that does not increase the risk of hypoglycemia. The American Diabetes Association (ADA) recommends that, before attempting to conceive, women should lower their hemoglobin A_1c to below 6.5%.¹

Thyroid measures. Autoimmune thyroid disease is the most common autoimmune disorder associated with diabetes and has been reported in 35% to 40% of women with type 1 diabetes.²³ Recommendations are to check thyroid-stimulating hormone and thyroid peroxidase antibody levels before conception or early in pregnancy in all women with diabetes.^1,24 Overt hypothyroidism should be treated before conception, given that early fetal brain development depends on maternal thyroxine.

Renal function testing. Preconception assessment of renal function is important for counseling and risk stratification. This assessment should include serum creatinine level, estimated glomerular filtration rate, and urinary albumin excretion.²¹

Celiac screening. Because women with type 1 diabetes are more susceptible to autoimmune diseases, they should be screened for celiac disease before conception, with testing for  immunoglobulin A (IgA) and tissue transglutaminase antibodies, with or without IgA endomysial antibodies.^16,25,26 An estimated 6% of patients with type 1 diabetes have celiac disease vs 1% of the general population.²⁵ Celiac disease is 2 to 3 times more common in women, and asymptomatic people with type 1 diabetes are considered at increased risk for celiac disease.²⁶

The association between type 1 diabetes and celiac disease most likely relates to the overlap in human leukocyte antigens of the diseases. There is no established link between type 2 diabetes and celiac disease.²⁵

Undiagnosed celiac disease increases a woman’s risk of obstetric complications such as preterm birth, low birth weight, and stillbirth.²⁶ The most likely explanation for these adverse effects is nutrient malabsorption, which is characteristic of celiac disease. Adherence to a gluten-free diet before and during gestation may reduce the risk of preterm delivery by as much as 20%.²⁶

Vitamin B₁₂ level. Celiac disease interferes with the absorption of vitamin B₁₂-instrinsic factor in the ileum, which can lead to vitamin B₁₂ deficiency. Therefore, baseline vitamin B₁₂ levels should be checked before conception in women with celiac disease. Levels should also be checked in women taking metformin, which also decreases vitamin B₁₂ absorption. Of note, increased folate levels due to taking supplements can potentially mask vitamin B₁₂ deficiency.

MEDICATIONS TO REVIEW FOR PREGNANCY INTERACTIONS

Diabetic medications

Insulin is the first-line pharmacotherapy for pregnant patients with type 1, type 2, or gestational diabetes. Insulin does not cross the placenta to a measurable extent, and most insulin preparations have been classified as category B,¹ meaning no risks to the fetus have been found in humans.

Insulin dosing during pregnancy is not static. Beginning around mid-gestation, insulin requirements increase,^28,29 but after 32 weeks the need may decrease. These changes require practitioners to closely monitor blood glucose throughout pregnancy.

Both basal-bolus injections and continuous subcutaneous infusion are reasonable options during pregnancy.³⁰ However, the need for multiple and potentially painful insulin injections daily can lead to poor compliance. This inconvenience has led to studies using oral hypoglycemic medications instead of insulin for patients with gestational and type 2 diabetes.

Metformin is an oral biguanide that decreases hepatic gluconeogenesis and intestinal glucose absorption while peripherally increasing glucose utilization and uptake. Metformin does not pose a risk of hypoglycemia because its mechanism of action does not involve increased insulin production.⁷

Metformin does cross the placenta, resulting in umbilical cord blood levels higher than maternal levels. Nevertheless, studies support the efficacy and short-term safety of metformin use during a pregnancy complicated by gestational or type 2 diabetes.^7,31 Moreover, metformin has been associated with a lower risk of neonatal hypoglycemia and maternal weight gain than insulin.³² However, this agent should be used with caution, as long-term data are not yet available, and it may slightly increase the risk of premature delivery.

Glyburide is another oral hypoglycemic medication that has been used during pregnancy. This second-generation sulfonylurea enhances the release of insulin from the pancreas by binding beta islet cell ATP-calcium channel receptors. Compared with other sulfonylureas, glyburide has the lowest rate of maternal-to-fetal transfer, with umbilical cord plasma concentrations 70% of maternal levels.³³ Although some trials support the efficacy and short-term safety of glyburide treatment for gestational diabetes,³⁴ recent studies have associated glyburide use during pregnancy with a higher rate of neonatal hypoglycemia, neonatal respiratory distress, macrosomia, and neonatal intensive care unit admissions than insulin and metformin.^1,35

Patients treated with oral agents should be informed that these drugs cross the placenta, and that although no adverse effects on the fetus have been demonstrated, long-term safety data are lacking. In addition, oral agents are ineffective in type 1 diabetes and may be insufficient to overcome the insulin resistance in type 2 diabetes.

Antihypertensive drugs

All antihypertensive drugs cross the placenta, but several have an acceptable safety profile in pregnancy, including methyldopa, labeta­lol, clonidine, prazosin, and nifedipine. Hydralazine and labetalol are short-acting, come in intravenous formulations, and can be used for urgent blood pressure control during pregnancy. Diltiazem may be used for heart rate control during pregnancy, and it has been shown to lower blood pressure and proteinuria in pregnant patients with underlying renal disease.^36,37 The ADA recommends against chronic use of diuretics during pregnancy because of potential reductions in maternal plasma volume and uteroplacental perfusion.¹

Angiotensin-converting enzyme (ACE) inhibitors, angiotensin II receptor blockers (ARBs), and direct renin inhibitors are contraindicated during pregnancy because of the risk of fetal defects, particularly in the renal system.^21,38 Although there is evidence to question the association between first semester exposure and fetotoxicity,³⁹ we avoid these drugs during pregnancy and switch to a different agent in women planning pregnancy.

Other drugs

Statins are contraindicated in pregnancy because they interfere with the development of the fetal nervous system.²¹ Although preliminary data from a small study did not identify safety risks associated with pravastatin use after 12 weeks of gestation,⁴⁰ we recommend discontinuing statins in women attempting pregnancy.

Aspirin. The US Preventive Services Task Force41 recommends low-dose aspirin (81 mg/day) after 12 weeks of gestation for women with type 1 or type 2 diabetes, as well as those with renal disease or chronic hypertension, to prevent preeclampsia. Of note, higher doses need to be used with caution during pregnancy because fetal abnormalities have been reported, such as disruption of fetal vasculature (mesenteric vessels), gastroschisis, and small intestinal atresia.¹⁶

Folate supplementation (0.6–4 mg/day) is recommended in women with celiac disease to prevent neural tube defects in the offspring, and the US Preventive Services Task Force recommends 0.4 mg daily of folic acid supplementation for all women planning or capable of pregnancy.^42–44 Higher doses, ranging from 0.6 to 5 mg/day, have been proposed for patients with diabetes,¹³ and we recommend at least 1 mg for this group, based on data suggesting that higher doses further reduce the risk of neural tube defects.⁴³

Maternal diabetes, insulin therapy, and oral hypoglycemic agents are not contraindications to breastfeeding. The US Preventive Services Task Force recommends interventions by primary care physicians to promote and support breastfeeding.⁴⁵ Breastfeeding is encouraged based on various short- and long-term health benefits for both breastfed infants and breastfeeding mothers. Breastfeeding decreases a woman’s insulin requirements and increases the risk for hypoglycemia, especially in patients with insulin-dependent type 1 diabetes.¹

Additionally, insulin sensitivity increases immediately following delivery of the placenta.¹ Therefore, it is prudent to adjust insulin doses postpartum, especially while a patient is breastfeeding, or to suggest high-carbohydrate snacks before feeds.^9,29

Antihypertensive drugs considered safe to use during lactation include captopril, enalapril, quinapril, labetalol, propranolol, nifedipine, and hydralazine.^21,46 Methyldopa is not contraindicated, but it causes fatigue and worsened postpartum depression and should not be used as first-line therapy. Diuretics and ARBs are not recommended during lactation.²¹ Both metformin and glyburide enter breast milk in small enough amounts that they are not contraindicated during breastfeeding.¹⁶ The Lactmed database (www.toxnet.nlm.nih.gov) provides information about drugs and breastfeeding.

WHAT ABOUT CONTRACEPTIVES?

The ADA recommends contraception for women with diabetes because, just as in women without diabetes, the risks of unplanned pregnancy outweigh those of contraceptives.¹

We recommend low-dose combination estrogen-progestin oral contraceptives to normotensive women under age 35 with diabetes but without underlying microvascular disease. For women over age 35 or for those with microvascular disease, additional options include intrauterine devices or progestin implants. We prefer not to use injectable depot medroxyprogesterone acetate because of its side effects of insulin resistance and weight gain.⁴⁷

CASE DISCUSSION: NEXT STEPS

Our patient’s interest in family planning presents an opportunity for preconception counseling. We recommend a prenatal folic acid supplement, diet and regular exercise for weight loss, and screening tests including a comprehensive metabolic panel, hemoglobin A_1c, thyroid-stimulating hormone, and dilated eye examination. We make sure she is up to date on her indicated health maintenance (eg, immunizations, disease screening), and we review her medications for potential teratogens. She denies any recreational drug use. Also, she has no plans for long-distance travel.

Our counseling includes discussions of pregnancy risks associated with pregestational diabetes and suboptimal glycemic control. We encourage her to use effective contraception until she is “medically optimized” for pregnancy—ie, until her hemoglobin A_1c is lower than 6.5% and she has achieved a medically optimal weight. If feasible, a reduction of weight (7% or so) through lifestyle modification should be attempted, and if her hemoglobin A_1c remains elevated, adding insulin would be recommended.

Pregnant patients or patients contemplating pregnancy are usually motivated to modify their behavior, making this a good time to reinforce lifestyle modifications. Many patients benefit from individualized counseling by a registered dietitian to help achieve the recommended weight and glycemic control.

Our physical examination in this patient includes screening for micro- and macrovascular complications of diabetes, and the test results are negative. Patients with active proliferative retinopathy should be referred to an ophthalmologist for assessment and treatment.

We review her medications for potential teratogenic effects and stop her ACE inhibitor (lisinopril) and statin (simvastatin). We switch her from a first-generation sulfonylurea (chlorpropamide) to glyburide, a second-generation sulfonylurea. Second-generation sulfonylureas are considered more “fetus-friendly” because first-generation sulfonylureas cross the placenta more easily and can cause fetal hyperinsulinemia, leading to macrosomia and neonatal hypoglycemia.⁷

The management of diabetes during pregnancy leans toward insulin use, given the lack of information regarding long-term outcomes with oral agents. If insulin is needed, it is best to initiate it before the patient conceives, and then to stop other diabetes medications. We would not make any changes to her aspirin or metformin use.

Educating the patient and her family about prevention, recognition, and treatment of hypoglycemia is important to prevent and manage the increased risk of hypoglycemia with insulin therapy and in early pregnancy.¹ Consideration should be given to providing ketone strips as well as education on diabetic ketoacidosis prevention and detection.¹ If the patient conceives, begin prenatal care early to allow adequate planning for care of her disease and evaluation of the fetus. Because of the complexity of insulin management in pregnancy, the ADA recommends referral, if possible, to a center offering team-based care, including an obstetrician specialized in high-risk pregnancies, an endocrinologist, and a dietitian.¹

EXPERT COMMENTARY

GSM encompasses a constellation of symptoms involving the vulva, vagina, urethra, and bladder, and it can affect quality of life in more than half of women by 3 years past menopause.^1,2 Local estrogen creams, tablets, and rings are considered the gold standard treatment for GSM.³ The rising cost of many of these pharmacologic treatments has created headlines and concerns over price gouging for drugs used to treat female sexual dysfunction.⁴ Recent alternatives to local estrogens include vaginal moisturizers and lubricants, vaginal dehydroepiandrosterone (DHEA) suppositories, oral ospemifene, and vaginal laser therapy.

Laser treatment (with fractionated CO2, erbium, and hybrid lasers) activates heat shock proteins and tissue growth factors to stimulateneocollagenesis and neovascularization within the vaginal epithelium,but it is expensive and not covered by insurance because it is considered a cosmetic procedure.⁵Most evidence on laser therapy for GSM comes from prospective case series with small numbers and short-term follow-up with no comparison arms.^6,7 A recent trial by Cruz and colleagues, however, is notable because it is one of the first published studies that compared vaginal laser with vaginal estrogen alone and with a combination laser plus estrogen arm. We need level 1 comparative data from studies such as this to help us counsel the millions of US women with GSM.

Details of the study

In this single-site randomized, double-blind, placebo-controlled trial conducted in Brazil, postmenopausal women were assigned to 1 of 3 treatment groups (15 per group):

• CO2 laser (MonaLisa Touch, SmartXide 2 system; DEKA Laser; Florence, Italy): 2 treatments total, 1 month apart, plus placebo cream (laser arm)
• estriol cream (1 mg estriol 3 times per week for 20 weeks) plus sham laser (estriol arm)
• CO2 laser plus estriol cream 3 times per week (laser plus estriol combination arm).

The primary outcome included a change in visual analog scale (VAS) score for symptoms related to vulvovaginal atrophy (VVA), including dyspareunia, dryness, and burning (0–10 scale with 0 = no symptoms and 10 = most severe symptoms), and change in the objective Vaginal Health Index (VHI). Assessments were made at baseline and at 8 and 20 weeks. Participants were included if they were menopausal for at least 2 years and had at least 1 moderately bothersome VVA symptom (based on a VAS score of 4 or greater).

Secondary outcomes included the objective FSFI questionnaire evaluating desire, arousal, lubrication, orgasm, satisfaction, and pain. FSFI scores can range from 2 (severe dysfunction) to 36 (no dysfunction). A total FSFI score less than 26 was deemed equivalent to dysfunction. Cytologic smear evaluation using a vaginal maturation index was included in all 3 treatment arms. Sample size calculation of 45 patients (15 per arm) for this trial was based on a 3-point difference in the VHI.

The baseline characteristics for participants in each treatment arm were similar, except that participants in the vaginal estriol group were less symptomatic at baseline. This group had less burning at baseline based on the FSFI and less dyspareunia based on the VAS.

Laser treatment improved dryness, burning, and dyspareunia but caused more pain

All 3 treatment groups showed statistically significant improvement in vaginal dryness at 20 weeks, but only the laser-alone arm and the laser plus estriol arms showed improvement in dyspareunia and burning. The total FSFI scores improved significantly only in the laser plus estriol arm (TABLE). No difference in the vaginal maturation index was noted between groups; however, improved numbers of parabasal cells were found in participants in the laser treatment arms.

While participants in the laser treatment arms (alone and in combination with estriol) showed significant improvement in the VAS domains of dyspareunia and burning compared with those treated with estriol alone, there was a contradictory finding of more pain in both laser arms at 20 weeks compared with the estriol-alone group, based on the FSFI. The FSFI is a validated, objective quality-of-life questionnaire, and the finding of more pain with laser treatment is a concern.

Study strengths and weaknesses

This study is one of the first of its kind to compare laser therapy alone and in combination with local estriol to vaginal estriol alone for the treatment of GSM. The trial’s strength is in its design as a double-blind, placebo-controlled block randomized trial, which adds to the prospective cohort trials that generally show favorable outcomes for fractionated laser for the treatment of GSM.

The study’s weaknesses include its small sample size, single trial site, and short-term follow-up. Findings from this trial should be considered preliminary and not generalizable. Other weaknesses are the 3 of 45 participants lost to follow-up and the significant baseline differences among the women, with lower bothersome baseline VAS scores in the estriol arm.

Furthermore, this study was not powered for multiple comparisons, and conclusions favoring laser therapy cannot be overinflated. Lasers such as CO2 target the chromophore water, and indiscriminate use in severely dry vaginal epithelium may cause more pain or scarring. Longer-term follow-up is needed.

More research also is needed to develop guidelines related to pre-laser treatment to achieve optimal vaginal pH and ideal vaginal maturation, including, for example, vaginal priming with estrogen, DHEA, or other moisturizers.

This study also suggests the use of vaginal laser therapy as a drug delivery mechanism for combination therapy. Many vaginal estrogen treatments are expensive (despite prescription drug coverage), and laser treatments are very expensive (and not covered by insurance), so research to optimize outcomes and minimize patient expense is needed.

Share your thoughts! Send your Letter to the Editor to [email protected]. Please include your name and the city and state in which you practice.

EXPERT COMMENTARY

GSM encompasses a constellation of symptoms involving the vulva, vagina, urethra, and bladder, and it can affect quality of life in more than half of women by 3 years past menopause.^1,2 Local estrogen creams, tablets, and rings are considered the gold standard treatment for GSM.³ The rising cost of many of these pharmacologic treatments has created headlines and concerns over price gouging for drugs used to treat female sexual dysfunction.⁴ Recent alternatives to local estrogens include vaginal moisturizers and lubricants, vaginal dehydroepiandrosterone (DHEA) suppositories, oral ospemifene, and vaginal laser therapy.

Laser treatment (with fractionated CO2, erbium, and hybrid lasers) activates heat shock proteins and tissue growth factors to stimulateneocollagenesis and neovascularization within the vaginal epithelium,but it is expensive and not covered by insurance because it is considered a cosmetic procedure.⁵Most evidence on laser therapy for GSM comes from prospective case series with small numbers and short-term follow-up with no comparison arms.^6,7 A recent trial by Cruz and colleagues, however, is notable because it is one of the first published studies that compared vaginal laser with vaginal estrogen alone and with a combination laser plus estrogen arm. We need level 1 comparative data from studies such as this to help us counsel the millions of US women with GSM.

Details of the study

In this single-site randomized, double-blind, placebo-controlled trial conducted in Brazil, postmenopausal women were assigned to 1 of 3 treatment groups (15 per group):

• CO2 laser (MonaLisa Touch, SmartXide 2 system; DEKA Laser; Florence, Italy): 2 treatments total, 1 month apart, plus placebo cream (laser arm)
• estriol cream (1 mg estriol 3 times per week for 20 weeks) plus sham laser (estriol arm)
• CO2 laser plus estriol cream 3 times per week (laser plus estriol combination arm).

The primary outcome included a change in visual analog scale (VAS) score for symptoms related to vulvovaginal atrophy (VVA), including dyspareunia, dryness, and burning (0–10 scale with 0 = no symptoms and 10 = most severe symptoms), and change in the objective Vaginal Health Index (VHI). Assessments were made at baseline and at 8 and 20 weeks. Participants were included if they were menopausal for at least 2 years and had at least 1 moderately bothersome VVA symptom (based on a VAS score of 4 or greater).

Secondary outcomes included the objective FSFI questionnaire evaluating desire, arousal, lubrication, orgasm, satisfaction, and pain. FSFI scores can range from 2 (severe dysfunction) to 36 (no dysfunction). A total FSFI score less than 26 was deemed equivalent to dysfunction. Cytologic smear evaluation using a vaginal maturation index was included in all 3 treatment arms. Sample size calculation of 45 patients (15 per arm) for this trial was based on a 3-point difference in the VHI.

The baseline characteristics for participants in each treatment arm were similar, except that participants in the vaginal estriol group were less symptomatic at baseline. This group had less burning at baseline based on the FSFI and less dyspareunia based on the VAS.

Laser treatment improved dryness, burning, and dyspareunia but caused more pain

All 3 treatment groups showed statistically significant improvement in vaginal dryness at 20 weeks, but only the laser-alone arm and the laser plus estriol arms showed improvement in dyspareunia and burning. The total FSFI scores improved significantly only in the laser plus estriol arm (TABLE). No difference in the vaginal maturation index was noted between groups; however, improved numbers of parabasal cells were found in participants in the laser treatment arms.

While participants in the laser treatment arms (alone and in combination with estriol) showed significant improvement in the VAS domains of dyspareunia and burning compared with those treated with estriol alone, there was a contradictory finding of more pain in both laser arms at 20 weeks compared with the estriol-alone group, based on the FSFI. The FSFI is a validated, objective quality-of-life questionnaire, and the finding of more pain with laser treatment is a concern.

Study strengths and weaknesses

This study is one of the first of its kind to compare laser therapy alone and in combination with local estriol to vaginal estriol alone for the treatment of GSM. The trial’s strength is in its design as a double-blind, placebo-controlled block randomized trial, which adds to the prospective cohort trials that generally show favorable outcomes for fractionated laser for the treatment of GSM.

The study’s weaknesses include its small sample size, single trial site, and short-term follow-up. Findings from this trial should be considered preliminary and not generalizable. Other weaknesses are the 3 of 45 participants lost to follow-up and the significant baseline differences among the women, with lower bothersome baseline VAS scores in the estriol arm.

Furthermore, this study was not powered for multiple comparisons, and conclusions favoring laser therapy cannot be overinflated. Lasers such as CO2 target the chromophore water, and indiscriminate use in severely dry vaginal epithelium may cause more pain or scarring. Longer-term follow-up is needed.

More research also is needed to develop guidelines related to pre-laser treatment to achieve optimal vaginal pH and ideal vaginal maturation, including, for example, vaginal priming with estrogen, DHEA, or other moisturizers.

This study also suggests the use of vaginal laser therapy as a drug delivery mechanism for combination therapy. Many vaginal estrogen treatments are expensive (despite prescription drug coverage), and laser treatments are very expensive (and not covered by insurance), so research to optimize outcomes and minimize patient expense is needed.

Share your thoughts! Send your Letter to the Editor to [email protected]. Please include your name and the city and state in which you practice.

EXPERT COMMENTARY

GSM encompasses a constellation of symptoms involving the vulva, vagina, urethra, and bladder, and it can affect quality of life in more than half of women by 3 years past menopause.^1,2 Local estrogen creams, tablets, and rings are considered the gold standard treatment for GSM.³ The rising cost of many of these pharmacologic treatments has created headlines and concerns over price gouging for drugs used to treat female sexual dysfunction.⁴ Recent alternatives to local estrogens include vaginal moisturizers and lubricants, vaginal dehydroepiandrosterone (DHEA) suppositories, oral ospemifene, and vaginal laser therapy.

Laser treatment (with fractionated CO2, erbium, and hybrid lasers) activates heat shock proteins and tissue growth factors to stimulateneocollagenesis and neovascularization within the vaginal epithelium,but it is expensive and not covered by insurance because it is considered a cosmetic procedure.⁵Most evidence on laser therapy for GSM comes from prospective case series with small numbers and short-term follow-up with no comparison arms.^6,7 A recent trial by Cruz and colleagues, however, is notable because it is one of the first published studies that compared vaginal laser with vaginal estrogen alone and with a combination laser plus estrogen arm. We need level 1 comparative data from studies such as this to help us counsel the millions of US women with GSM.

Details of the study

In this single-site randomized, double-blind, placebo-controlled trial conducted in Brazil, postmenopausal women were assigned to 1 of 3 treatment groups (15 per group):

• CO2 laser (MonaLisa Touch, SmartXide 2 system; DEKA Laser; Florence, Italy): 2 treatments total, 1 month apart, plus placebo cream (laser arm)
• estriol cream (1 mg estriol 3 times per week for 20 weeks) plus sham laser (estriol arm)
• CO2 laser plus estriol cream 3 times per week (laser plus estriol combination arm).

The primary outcome included a change in visual analog scale (VAS) score for symptoms related to vulvovaginal atrophy (VVA), including dyspareunia, dryness, and burning (0–10 scale with 0 = no symptoms and 10 = most severe symptoms), and change in the objective Vaginal Health Index (VHI). Assessments were made at baseline and at 8 and 20 weeks. Participants were included if they were menopausal for at least 2 years and had at least 1 moderately bothersome VVA symptom (based on a VAS score of 4 or greater).

Secondary outcomes included the objective FSFI questionnaire evaluating desire, arousal, lubrication, orgasm, satisfaction, and pain. FSFI scores can range from 2 (severe dysfunction) to 36 (no dysfunction). A total FSFI score less than 26 was deemed equivalent to dysfunction. Cytologic smear evaluation using a vaginal maturation index was included in all 3 treatment arms. Sample size calculation of 45 patients (15 per arm) for this trial was based on a 3-point difference in the VHI.

The baseline characteristics for participants in each treatment arm were similar, except that participants in the vaginal estriol group were less symptomatic at baseline. This group had less burning at baseline based on the FSFI and less dyspareunia based on the VAS.

Laser treatment improved dryness, burning, and dyspareunia but caused more pain

All 3 treatment groups showed statistically significant improvement in vaginal dryness at 20 weeks, but only the laser-alone arm and the laser plus estriol arms showed improvement in dyspareunia and burning. The total FSFI scores improved significantly only in the laser plus estriol arm (TABLE). No difference in the vaginal maturation index was noted between groups; however, improved numbers of parabasal cells were found in participants in the laser treatment arms.

While participants in the laser treatment arms (alone and in combination with estriol) showed significant improvement in the VAS domains of dyspareunia and burning compared with those treated with estriol alone, there was a contradictory finding of more pain in both laser arms at 20 weeks compared with the estriol-alone group, based on the FSFI. The FSFI is a validated, objective quality-of-life questionnaire, and the finding of more pain with laser treatment is a concern.

Study strengths and weaknesses

This study is one of the first of its kind to compare laser therapy alone and in combination with local estriol to vaginal estriol alone for the treatment of GSM. The trial’s strength is in its design as a double-blind, placebo-controlled block randomized trial, which adds to the prospective cohort trials that generally show favorable outcomes for fractionated laser for the treatment of GSM.

The study’s weaknesses include its small sample size, single trial site, and short-term follow-up. Findings from this trial should be considered preliminary and not generalizable. Other weaknesses are the 3 of 45 participants lost to follow-up and the significant baseline differences among the women, with lower bothersome baseline VAS scores in the estriol arm.

Furthermore, this study was not powered for multiple comparisons, and conclusions favoring laser therapy cannot be overinflated. Lasers such as CO2 target the chromophore water, and indiscriminate use in severely dry vaginal epithelium may cause more pain or scarring. Longer-term follow-up is needed.

More research also is needed to develop guidelines related to pre-laser treatment to achieve optimal vaginal pH and ideal vaginal maturation, including, for example, vaginal priming with estrogen, DHEA, or other moisturizers.

This study also suggests the use of vaginal laser therapy as a drug delivery mechanism for combination therapy. Many vaginal estrogen treatments are expensive (despite prescription drug coverage), and laser treatments are very expensive (and not covered by insurance), so research to optimize outcomes and minimize patient expense is needed.

Share your thoughts! Send your Letter to the Editor to [email protected]. Please include your name and the city and state in which you practice.

Click here to access the August 2018 Digital Edition.

Table of Contents

• Access to Transplant Care and Services Within the Veterans Health Administration
• VA Boston Medical Forum: A Veteran With Fibromyalgia Presenting With Dyspnea
• Expanding the Scope of Telemedicine in Gastroenterology
• Acute Aortic Occlusion With Spinal Cord Infarction
• Cognitive Behavioral Therapy for Veterans With Tinnitus
• Am I My Brother’s/Sister’s Keeper?
• Louis Johnson: First Cold War Secretary of Defense

Click here to access the August 2018 Digital Edition.

Table of Contents

• Access to Transplant Care and Services Within the Veterans Health Administration
• VA Boston Medical Forum: A Veteran With Fibromyalgia Presenting With Dyspnea
• Expanding the Scope of Telemedicine in Gastroenterology
• Acute Aortic Occlusion With Spinal Cord Infarction
• Cognitive Behavioral Therapy for Veterans With Tinnitus
• Am I My Brother’s/Sister’s Keeper?
• Louis Johnson: First Cold War Secretary of Defense

Click here to access the August 2018 Digital Edition.

Table of Contents

• Access to Transplant Care and Services Within the Veterans Health Administration
• VA Boston Medical Forum: A Veteran With Fibromyalgia Presenting With Dyspnea
• Expanding the Scope of Telemedicine in Gastroenterology
• Acute Aortic Occlusion With Spinal Cord Infarction
• Cognitive Behavioral Therapy for Veterans With Tinnitus
• Am I My Brother’s/Sister’s Keeper?
• Louis Johnson: First Cold War Secretary of Defense

Cervical screening recommendations do not cover all circumstances

Starting cervical cancer screening at age 21 does not necessarily take into account the fact that we are seeing youngsters initiating sexual activity as young as age 9. We obviously see pregnancies early as well. Waiting to screen until age 21, therefore, may cause us to miss the development of high-grade lesions and cervical cancer. As you know, cases in the literature report instances of invasive cancer with first Pap test at age 21. Also, human papillomavirus (HPV) is spread by sexual activity, with the squamous columnar junction more susceptible to infection at a young age.

Recommendations regarding cervical cancer screening for older women also should take into account new sexual partners. Currently, both men and women are living longer and are remarrying or are sexually active with multiple partners. The fact that older women are desiring hormone replacement for vaginal lubrication and dyspareunia shows that they are sexually active even in their late 70s. I believe that the incidence of HPV infection to cervical, vaginal, and vulvar tissue will be increasing as a result.

In an age in which primary care physicians do not have time to perform Pap tests or vaginal, cervical, and vulvar exams because they are overwhelmed with keeping up with patients’ major medical issues is a misunderstanding regarding current recommendations for Pap test screening.

Elizabeth Reinoehl-McClaskey, DO
Onley, Virginia

Dr. Einstein responds

Sexual behavior can start early, but this does not lead to cancer. When we screen, we are looking for cancer, not HPV infection, which is quite common in women and men younger than age 21. Also, one might question whether current screening techniques pick up early-onset tumors. Regarding older women, sexual activity and the rate of older women getting cervical cancer should be considered in future guidelines.

Share your thoughts! Send your Letter to the Editor to [email protected]. Please include your name and the city and state in which you practice.

Cervical screening recommendations do not cover all circumstances

Starting cervical cancer screening at age 21 does not necessarily take into account the fact that we are seeing youngsters initiating sexual activity as young as age 9. We obviously see pregnancies early as well. Waiting to screen until age 21, therefore, may cause us to miss the development of high-grade lesions and cervical cancer. As you know, cases in the literature report instances of invasive cancer with first Pap test at age 21. Also, human papillomavirus (HPV) is spread by sexual activity, with the squamous columnar junction more susceptible to infection at a young age.

Recommendations regarding cervical cancer screening for older women also should take into account new sexual partners. Currently, both men and women are living longer and are remarrying or are sexually active with multiple partners. The fact that older women are desiring hormone replacement for vaginal lubrication and dyspareunia shows that they are sexually active even in their late 70s. I believe that the incidence of HPV infection to cervical, vaginal, and vulvar tissue will be increasing as a result.

In an age in which primary care physicians do not have time to perform Pap tests or vaginal, cervical, and vulvar exams because they are overwhelmed with keeping up with patients’ major medical issues is a misunderstanding regarding current recommendations for Pap test screening.

Elizabeth Reinoehl-McClaskey, DO
Onley, Virginia

Dr. Einstein responds

Sexual behavior can start early, but this does not lead to cancer. When we screen, we are looking for cancer, not HPV infection, which is quite common in women and men younger than age 21. Also, one might question whether current screening techniques pick up early-onset tumors. Regarding older women, sexual activity and the rate of older women getting cervical cancer should be considered in future guidelines.

Share your thoughts! Send your Letter to the Editor to [email protected]. Please include your name and the city and state in which you practice.

Cervical screening recommendations do not cover all circumstances

Starting cervical cancer screening at age 21 does not necessarily take into account the fact that we are seeing youngsters initiating sexual activity as young as age 9. We obviously see pregnancies early as well. Waiting to screen until age 21, therefore, may cause us to miss the development of high-grade lesions and cervical cancer. As you know, cases in the literature report instances of invasive cancer with first Pap test at age 21. Also, human papillomavirus (HPV) is spread by sexual activity, with the squamous columnar junction more susceptible to infection at a young age.

Recommendations regarding cervical cancer screening for older women also should take into account new sexual partners. Currently, both men and women are living longer and are remarrying or are sexually active with multiple partners. The fact that older women are desiring hormone replacement for vaginal lubrication and dyspareunia shows that they are sexually active even in their late 70s. I believe that the incidence of HPV infection to cervical, vaginal, and vulvar tissue will be increasing as a result.

In an age in which primary care physicians do not have time to perform Pap tests or vaginal, cervical, and vulvar exams because they are overwhelmed with keeping up with patients’ major medical issues is a misunderstanding regarding current recommendations for Pap test screening.

Elizabeth Reinoehl-McClaskey, DO
Onley, Virginia

Dr. Einstein responds

Sexual behavior can start early, but this does not lead to cancer. When we screen, we are looking for cancer, not HPV infection, which is quite common in women and men younger than age 21. Also, one might question whether current screening techniques pick up early-onset tumors. Regarding older women, sexual activity and the rate of older women getting cervical cancer should be considered in future guidelines.

Share your thoughts! Send your Letter to the Editor to [email protected]. Please include your name and the city and state in which you practice.

Peripherally inserted central catheters (PICCs) have evolved since their inception in the early 1970s and are used with increasing frequency for pediatric inpatients and outpatients.^1-3 Emerging literature, including a meta-analysis of international observational studies,⁴ reports PICC failure (complications necessitating premature removal) occurs in up to 30% of PICCs, most commonly due to infection, thrombosis, occlusion, and fracture.^4-7 Raffini et al.⁷ report the increasing incidence of pediatric PICC-related thrombosis increases morbidity and mortality⁸ and negatively impacts future vessel health and preservation.⁹

PICCs have progressed from relatively simple, silicone-based catheters with an external clamp to chemically engineered polyurethane with pressure-activated valves placed at the proximal or distal catheter hub with the intent to reduce occlusion.¹⁰ Further modernization of PICC material occurred with the incorporation of antithrombogenic (AT) material (Endexo^®). These PICCs are designed to contain a nonstick polymer, which is designed to reduce the adherence of blood components (platelets and clotting factors) and inhibit thrombus formation (and hence prevent deep vein thrombosis andocclusion, as well as inhibit microbial biofilm attachment [and subsequent infection]).¹¹

In addition to new materials, other aspects of this PICC design have been the addition of a pressure-activated safety valve (PASV^®) built into the proximal hub. Pressure-activated valve technology promises to prevent catheter occlusion by reducing blood reflux into the PICC; the valve opens with pressure during infusion and aspiration and remains closed with normal venous pressure, circumventing the need for clinicians to manually clamp the PICC and reducing human error and the potential for thrombosis, occlusion, and fracture development.¹² Hoffer et al.¹³ reported half as many occlusions of valved PICCs (3.3%) compared with nonvalved or clamped PICCs (7.1%); although not statistically significant (P = .10), perhaps due to the small sample, overall complications, including occlusion and infection, were significantly lessened with the valved PICC (35% vs 79%; P = .02). Comparatively, Pittiruti et al.¹⁴ conducted a trial of 2 types of valved PICCs with an open-ended, nonvalved PICC and found no reduction in PICC occlusion or catheter malfunction.

Today, PICC use is common for patients who require short-to-medium intravenous therapy. PICCs are increasingly recognized for their significant complications, including thrombosis and infection.¹⁵ Novel PICC technology, including the incorporation of AT material such as Endexo^® and PASV^®, may reduce complications; however, the clinical efficacy, cost-effectiveness, and acceptability of these innovations have not been tested through randomized trials in pediatric patients. In accordance with Medical Research Council guidelines¹⁶ for developing interventions, we pilot tested the feasibility of the BioFlo^® PICC, including intervention acceptability, compliance, recruitment, and initial estimates of effect, in anticipation of a subsequent full-scale efficacy randomized controlled trial. Our secondary aim was to compare the effectiveness of the BioFlo^® PICC with Endexo^® and PASV^® technology in reducing PICC complications and failure.

Design

We undertook a pilot randomized controlled trial comparing the standard polyurethane PICC (with external clamp) with the BioFlo^® PICC (with internal valve) in preventing catheter failure in pediatric patients. The study was prospectively registered with the Australian Clinical Trials Registry (ACTRN12615001290583), and the research protocol was published.¹⁷

Study Setting

The study commenced in March 2016 at the Lady Cilento Children’s Hospital in South Brisbane, Australia, a tertiary-level, specialist, pediatric teaching hospital in Queensland, Australia, providing full-spectrum health services to children and young people from birth to 18 years of age. Recruitment, including data collection, was completed in November 2016.

Sample

The target sample size was 110 participants, 50 participants per group plus 10% for potential attrition, as determined by standard pilot trial sample size recommendations.¹⁸ With ethics approval, the sample size was later increased to 150 participants in order to adequately pilot a microbiological substudy method (published separately).¹⁷ Participants were consecutively recruited if they met the inclusion criteria: PICC insertion, age <18 years, predicted hospital stay >24 hours, single-lumen PICC, and written informed consent by an English-speaking, legal parent or guardian. Patients were excluded if they had a current (<48 hours) blood stream infection (BSI), vessel size <2 mm, could not speak English without an interpreter, required a multilumen PICC, or were previously enrolled in the study.

Interventions

Participants were randomized to receive either of the following PICCs: (1) standard care: Cook™ polyurethane, turbo-ject, power-injectable PICC (Cook Medical, Bloomington, IN) or (2) comparison: BioFlo^® polyurethane with Endexo^® technology (AngioDynamics Inc, Queensbury, NY).

Outcomes

The primary outcome was feasibility of a full-efficacy trial established by composite analysis of the elements of eligibility (>70% of patients will be eligible), recruitment (>70% of patients will agree to enroll), retention and attrition (<15% of participants are lost to follow-up or withdraw from the study), protocol adherence (>80% of participants receive their allocated, randomly assigned study product), missing data (<10% of data are missed during data collection), parent and healthcare staff satisfaction, and PICC failure effect size estimates to allow sample size calculations.^18,19 PICC failure was defined as the following complications associated with PICC removal: (1) catheter-associated BSI,^8,20-22 (2) local site infection,²² (3) venous thrombosis,²³ (4) occlusion,^24,25 (5) PICC fracture, or (6) PICC dislodgement.^25,26 Parents (or caregivers) and healthcare staff were asked to rate their level of confidence with the study product and ease of PICC removal by using a 0 to 100 numeric rating scale (NRS) of increasing confidence and/or ease. These data were collected at the time of PICC removal. Operators were also asked to rate their levels of satisfaction with the insertion equipment and ease of PICC insertion immediately upon completion of the insertion procedure (both 0-100 NRS of increasing satisfaction and/or ease). Secondary outcomes included individual PICC complications (eg, occlusion) occurring at any time point during the PICC dwell (including at removal), adverse events, pain, redness at the insertion site, and overall PICC dwell.

Study Procedures

The research nurse (ReN) screened operating theater lists for patients, obtained written informed consent, and initiated the randomization. Randomization was computer generated, and web based via Griffith University (https://www151.griffith.edu.au/random) to ensure allocation concealment until study entry. Patients were randomly assigned in a 1:1 ratio with computer-generated and randomly varied block sizes of 2 and 4. Data were collected by the ReN on the day of insertion, at day 1 postinsertion, then every 2 to 3 days thereafter so that PICCs were checked at least twice per week until study completion. Participants were included in the trial until 12 weeks post-PICC insertion, study withdrawal or PICC removal (whichever came first), with an additional 48 hours follow-up for infection outcomes. Patient review was face to face during the inpatient stay, with discharged patients’ follow-up occurring via outpatient clinics, hospital-in-the-home service, or telephone.

Data collection was via Research Electronic Data Capture (http://project-redcap.org/). The ReN collected data on primary and secondary outcomes by using the predefined criteria. Demographic and clinical data were collected to assess the success of randomization, describe the participant group, and display characteristics known to increase the risk of PICC complication and thrombosis. A blinded radiologist and infectious disease specialist reviewed and diagnosed thrombosis of deep veins and catheter-associated BSI outcomes, respectively.

PICC Procedures

Extensive prestudy education for 2 months prior to trial commencement was provided to all clinicians involved with the insertion and care of PICCs, including the study products. PICCs were inserted in an operating theater environment by a qualified consultant pediatric anesthetist, a senior anesthetic registrar or fellow in an approved anesthetic training program, or pediatric vascular access nurse practitioner. Ultrasound guidance was used to assess a patient’s vasculature and puncture the vessel. The operator chose the PICC size on the basis of clinical judgment of vessel size and patient needs and then inserted the allocated PICC.²⁷ Preferred PICC tip location was the cavoatrial junction. All PICC tip positions were confirmed with a chest x-ray before use.

Postinsertion, PICCs were managed by local interdisciplinary clinicians in accordance with local practice guidelines.^27-31 PICC care and management includes the use of 2% chlorhexidine gluconate in 70% alcohol for site antisepsis and neutral displacement needleless connectors (TUTA Pulse; Medical Australia Limited, Lidcombe, New South Wales, Australia); normal saline was used to flush after medication administration, and if the device was not in use for 6 hours or longer, heparin is instilled with securement via bordered polyurethane dressing (Tegaderm 1616; 3M, St Paul, Minnesota) and a sutureless securement device (Statlock VPPCSP; Bard, Georgia).

Statistical Analyses

Data were exported to Stata 15³² for cleaning and analysis. Data cleaning of outlying figures and missing and implausible data was undertaken prior to analysis. Missing data were not imputed. The PICC was the unit of measurement, and all randomly assigned patients were analyzed on an intention-to-treat basis.³³ Descriptive statistics (frequencies and percentages) were used to ascertain the primary outcome of feasibility for the larger trial. Incidence rates (per 1,000 catheter days) and rate ratios, including 95% confidence intervals (CIs), were calculated. The comparability of groups at baseline was described across demographic, clinical, and device characteristics. Kaplan-Meier survival curves (with log-rank tests) were used to compare PICC failure between study groups over time. Associations between baseline characteristics and failure were described by calculating hazard ratios (HRs). Univariable Cox regression was performed only due to the relatively low number of outcomes. P values of <.05 were considered statistically significant.

Ethics

The Children’s Health Service District, Queensland (Human Research Ethics Committee/15/QRCH/164), and Griffith University (2016/077) Human Research Ethics Committees provided ethics and governance approval. Informed consent was obtained from parents or legal guardians, with children providing youth assent if they were 7 years or older, dependent upon cognitive ability.

Participant and PICC Characteristics

Feasibility Outcomes

PICC Failure and Complications

DISCUSSION

This is the first pilot feasibility trial of new PICC materials and valve design incorporated in the BioFlo^® PICC in the pediatric population. The trial incorporated best practice for randomized trials, including using a concurrent control group, centralized and concealed randomization, predetermined feasibility criteria, and a registered and published trial protocol.¹⁷ As in other studies,^15,24,34 PICC failure and complication prevalence was unacceptably high for this essential device. Standard care PICCs failed twice as often as the new BioFlo^® PICCs (22% vs 11%), which is a clinically important difference. As researchers in a pilot study, we did not expect to detect statistically significant differences; however, we found that overall complications during the dwell occurred significantly more with the standard care than BioFlo^® PICCs (P = .009).

BioFlo^® PICC material offers a major advancement in PICC material through the incorporation of AT technologies into catheter materials, such as PICCs. Endexo^® is a low molecular–weight, fluoro-oligomeric additive that self-locates to the top few nanometers of the material surface. When added to power-injectable polyurethane, the additive results in a strong but passive, nonstick, fluorinated surface in the base PICC material. This inhibits platelet adhesion, suppresses protein procoagulant conformation, and thereby reduces thrombus formation in medical devices. Additionally, Endexo^® is not a catheter coating; rather, it is incorporated within the polyurethane of the PICC, thereby ensuring these AT properties are present on the internal, external, and cut surfaces of the PICC. If this technology can reduce complication during treatment and reduce failure from infection, thrombosis, occlusion, fracture, and dislodgement, it will improve patient outcomes considerably and lower health system costs. Previous studies investigating valve technology in PICC design to reduce occlusion have been inconclusive.^12-14,35,36 Occlusion (both partial and complete) was less frequent in our study with the BioFlo^® group (n = 3; 4%) compared to the standard care group (n = 6; 8%). The results of this pilot study suggest that either the Endexo^® material or PASV^® technology has a positive association with occlusion reduction during PICC treatment.

Thrombosis was the primary failure type for the standard care PICCs, comprising one-third of failures. All but one patient with radiologically confirmed thrombosis required the removal of the PICC prior to completion of treatment. The decision to remove the PICC or retain and treat conservatively remained with the treating team. Raffini et al.⁷ found thrombosis to increase in patients with one or more coexisting chronic medical condition. Slightly more standard care than BioFlo^® patients were free of such comorbidities (25% vs 16%), yet standard care patients still had the higher number of thromboses (7% vs 3%). Morgenthaler and Rodriguez³⁷ reported vascular access-associated thrombosis in pediatrics to be less common than in adults but higher in medically complex children. Worryingly, Menendez et al.³⁸ reported pediatric thrombosis to be largely asymptomatic, so the true incidence in our study is likely higher because only radiologically confirmed thromboses were recorded.

Occlusion (partial or complete) was the predominant complication across the study, being associated with one-third of all failures. When occlusion complications during the dwell (some of which were resolved with treatment), in addition to those causing failure, were considered, this number was even greater. Occlusion complications are prevalent and costly. Smith et al.²⁴ reported that occlusion was the most common reason for PICC removal and the most likely complication to delay treatment. Both the BioFlo^® and standard care PICCs are pressure rated with good tensile strength; however, fracture occurred in 4% (n = 3) of standard care PICCs compared to no fractures in BioFlo^® PICCs. Although the numbers are small, it may suggest a superior tensile strength of the BioFlo^® material.

This study reinforces previously published results^24,38 that PICC tip position is important and can influence complications, such as occlusion and thrombosis. In addition, we found a significant association with failure when PICCs did not have a continuous infusion. These findings reinforce the need for optimal tip location at insertion and ongoing flushing and maintenance of PICCs not used for infusions.

Limitations of this study include the small sample size, which was not designed to detect statistical differences in the primary outcome between groups. Despite randomization, there were slight imbalances at baseline for inserter type and leukocyte count, although these were not significantly associated with PICC failure in the Cox regression (data not shown), and thus were unlikely to influence findings. Additionally, a difference of <10% was associated with PICC tip position, favoring the BioFlo^® group. PICC tip position outside the cavoatrial junction was positively associated with failure; therefore, the effect of tip positioning on outcomes is difficult to ascertain given the small sample size and feasibility nature of the study. Further study is warranted to further explore this effect. The population sampled was pediatric medical and surgical inpatients with a vessel size >2 mm attending the operating theater suite for PICC insertion, thereby limiting the study’s generalizability to adults and other populations, including neonates and those with PICCs inserted in the pediatric intensive care unit. The study could not be blinded because study products had to be visible to the clinical and research staff. However, it is unlikely that staff would intentionally sabotage PICCs to bias the study. Blinding was possible for the assessment of blood culture and ultrasound reports to diagnose infection and thrombosis. Strengths of this study included 100% protocol adherence, and no patients were lost to follow-up.

These results confirm that PICC failure is unacceptably high and suggest that the innovative BioFlo^® PICC material and design holds promise to improve PICC outcomes by reducing complications and overall PICC failure. Trials of this technology are feasible, safe, and acceptable to healthcare staff and parents. Further trials are required, including in other patient populations, to definitively identify clinical, cost-effective methods to prevent PICC failure and improve reliability during treatment.

Acknowledgments

The authors thank the children and parents of Lady Cilento Children’s Hospital for participating in this important research. A special thank you goes to the nurses within the Vascular Assessment and Management Service and to Karen Turner, Julieta Woosley, and Anna Dean for their efforts in data collecting and ensuring protocol adherence.

Disclosure

Griffith University has received unrestricted, investigator-initiated research or educational grants to support the research of T. K., A. J. U., and C. R. M. from product manufacturers 3M, Adhezion Inc, AngioDynamics, Bard Medical, Baxter, B. Braun Medical Inc, Becton Dickinson, CareFusion, Centurion Medical Products, Cook Medical, Entrotech, FloMedical, ICU Medical Inc, Medical Australia Limited, Medtronic, Smiths Medical, and Teleflex. Griffith University has received consultancy payments on behalf of C. R. M., A. J. U., and T. K. from manufacturers 3M, AngioDynamics, Bard Medical, B. Braun Medical Inc, Becton Dickinson, CareFusion, Mayo Healthcare Inc, ResQDevices, and Smiths Medical. AngioDynamics (the BioFlo^® PICC manufacturer) provided partial funds to undertake this research via an unrestricted donation to Griffith University (but not the study authors). Queensland Health provided in-kind support to fund the remainder of the trial. The funders had no role in the study design, collection, analysis, or interpretation of the data, writing of the report, or decision to submit the article for publication.

Peripherally inserted central catheters (PICCs) have evolved since their inception in the early 1970s and are used with increasing frequency for pediatric inpatients and outpatients.^1-3 Emerging literature, including a meta-analysis of international observational studies,⁴ reports PICC failure (complications necessitating premature removal) occurs in up to 30% of PICCs, most commonly due to infection, thrombosis, occlusion, and fracture.^4-7 Raffini et al.⁷ report the increasing incidence of pediatric PICC-related thrombosis increases morbidity and mortality⁸ and negatively impacts future vessel health and preservation.⁹

PICCs have progressed from relatively simple, silicone-based catheters with an external clamp to chemically engineered polyurethane with pressure-activated valves placed at the proximal or distal catheter hub with the intent to reduce occlusion.¹⁰ Further modernization of PICC material occurred with the incorporation of antithrombogenic (AT) material (Endexo^®). These PICCs are designed to contain a nonstick polymer, which is designed to reduce the adherence of blood components (platelets and clotting factors) and inhibit thrombus formation (and hence prevent deep vein thrombosis andocclusion, as well as inhibit microbial biofilm attachment [and subsequent infection]).¹¹

In addition to new materials, other aspects of this PICC design have been the addition of a pressure-activated safety valve (PASV^®) built into the proximal hub. Pressure-activated valve technology promises to prevent catheter occlusion by reducing blood reflux into the PICC; the valve opens with pressure during infusion and aspiration and remains closed with normal venous pressure, circumventing the need for clinicians to manually clamp the PICC and reducing human error and the potential for thrombosis, occlusion, and fracture development.¹² Hoffer et al.¹³ reported half as many occlusions of valved PICCs (3.3%) compared with nonvalved or clamped PICCs (7.1%); although not statistically significant (P = .10), perhaps due to the small sample, overall complications, including occlusion and infection, were significantly lessened with the valved PICC (35% vs 79%; P = .02). Comparatively, Pittiruti et al.¹⁴ conducted a trial of 2 types of valved PICCs with an open-ended, nonvalved PICC and found no reduction in PICC occlusion or catheter malfunction.

Today, PICC use is common for patients who require short-to-medium intravenous therapy. PICCs are increasingly recognized for their significant complications, including thrombosis and infection.¹⁵ Novel PICC technology, including the incorporation of AT material such as Endexo^® and PASV^®, may reduce complications; however, the clinical efficacy, cost-effectiveness, and acceptability of these innovations have not been tested through randomized trials in pediatric patients. In accordance with Medical Research Council guidelines¹⁶ for developing interventions, we pilot tested the feasibility of the BioFlo^® PICC, including intervention acceptability, compliance, recruitment, and initial estimates of effect, in anticipation of a subsequent full-scale efficacy randomized controlled trial. Our secondary aim was to compare the effectiveness of the BioFlo^® PICC with Endexo^® and PASV^® technology in reducing PICC complications and failure.

Design

We undertook a pilot randomized controlled trial comparing the standard polyurethane PICC (with external clamp) with the BioFlo^® PICC (with internal valve) in preventing catheter failure in pediatric patients. The study was prospectively registered with the Australian Clinical Trials Registry (ACTRN12615001290583), and the research protocol was published.¹⁷

Study Setting

The study commenced in March 2016 at the Lady Cilento Children’s Hospital in South Brisbane, Australia, a tertiary-level, specialist, pediatric teaching hospital in Queensland, Australia, providing full-spectrum health services to children and young people from birth to 18 years of age. Recruitment, including data collection, was completed in November 2016.

Sample

The target sample size was 110 participants, 50 participants per group plus 10% for potential attrition, as determined by standard pilot trial sample size recommendations.¹⁸ With ethics approval, the sample size was later increased to 150 participants in order to adequately pilot a microbiological substudy method (published separately).¹⁷ Participants were consecutively recruited if they met the inclusion criteria: PICC insertion, age <18 years, predicted hospital stay >24 hours, single-lumen PICC, and written informed consent by an English-speaking, legal parent or guardian. Patients were excluded if they had a current (<48 hours) blood stream infection (BSI), vessel size <2 mm, could not speak English without an interpreter, required a multilumen PICC, or were previously enrolled in the study.

Interventions

Participants were randomized to receive either of the following PICCs: (1) standard care: Cook™ polyurethane, turbo-ject, power-injectable PICC (Cook Medical, Bloomington, IN) or (2) comparison: BioFlo^® polyurethane with Endexo^® technology (AngioDynamics Inc, Queensbury, NY).

Outcomes

The primary outcome was feasibility of a full-efficacy trial established by composite analysis of the elements of eligibility (>70% of patients will be eligible), recruitment (>70% of patients will agree to enroll), retention and attrition (<15% of participants are lost to follow-up or withdraw from the study), protocol adherence (>80% of participants receive their allocated, randomly assigned study product), missing data (<10% of data are missed during data collection), parent and healthcare staff satisfaction, and PICC failure effect size estimates to allow sample size calculations.^18,19 PICC failure was defined as the following complications associated with PICC removal: (1) catheter-associated BSI,^8,20-22 (2) local site infection,²² (3) venous thrombosis,²³ (4) occlusion,^24,25 (5) PICC fracture, or (6) PICC dislodgement.^25,26 Parents (or caregivers) and healthcare staff were asked to rate their level of confidence with the study product and ease of PICC removal by using a 0 to 100 numeric rating scale (NRS) of increasing confidence and/or ease. These data were collected at the time of PICC removal. Operators were also asked to rate their levels of satisfaction with the insertion equipment and ease of PICC insertion immediately upon completion of the insertion procedure (both 0-100 NRS of increasing satisfaction and/or ease). Secondary outcomes included individual PICC complications (eg, occlusion) occurring at any time point during the PICC dwell (including at removal), adverse events, pain, redness at the insertion site, and overall PICC dwell.

Study Procedures

The research nurse (ReN) screened operating theater lists for patients, obtained written informed consent, and initiated the randomization. Randomization was computer generated, and web based via Griffith University (https://www151.griffith.edu.au/random) to ensure allocation concealment until study entry. Patients were randomly assigned in a 1:1 ratio with computer-generated and randomly varied block sizes of 2 and 4. Data were collected by the ReN on the day of insertion, at day 1 postinsertion, then every 2 to 3 days thereafter so that PICCs were checked at least twice per week until study completion. Participants were included in the trial until 12 weeks post-PICC insertion, study withdrawal or PICC removal (whichever came first), with an additional 48 hours follow-up for infection outcomes. Patient review was face to face during the inpatient stay, with discharged patients’ follow-up occurring via outpatient clinics, hospital-in-the-home service, or telephone.

Data collection was via Research Electronic Data Capture (http://project-redcap.org/). The ReN collected data on primary and secondary outcomes by using the predefined criteria. Demographic and clinical data were collected to assess the success of randomization, describe the participant group, and display characteristics known to increase the risk of PICC complication and thrombosis. A blinded radiologist and infectious disease specialist reviewed and diagnosed thrombosis of deep veins and catheter-associated BSI outcomes, respectively.

PICC Procedures

Extensive prestudy education for 2 months prior to trial commencement was provided to all clinicians involved with the insertion and care of PICCs, including the study products. PICCs were inserted in an operating theater environment by a qualified consultant pediatric anesthetist, a senior anesthetic registrar or fellow in an approved anesthetic training program, or pediatric vascular access nurse practitioner. Ultrasound guidance was used to assess a patient’s vasculature and puncture the vessel. The operator chose the PICC size on the basis of clinical judgment of vessel size and patient needs and then inserted the allocated PICC.²⁷ Preferred PICC tip location was the cavoatrial junction. All PICC tip positions were confirmed with a chest x-ray before use.

Postinsertion, PICCs were managed by local interdisciplinary clinicians in accordance with local practice guidelines.^27-31 PICC care and management includes the use of 2% chlorhexidine gluconate in 70% alcohol for site antisepsis and neutral displacement needleless connectors (TUTA Pulse; Medical Australia Limited, Lidcombe, New South Wales, Australia); normal saline was used to flush after medication administration, and if the device was not in use for 6 hours or longer, heparin is instilled with securement via bordered polyurethane dressing (Tegaderm 1616; 3M, St Paul, Minnesota) and a sutureless securement device (Statlock VPPCSP; Bard, Georgia).

Statistical Analyses

Data were exported to Stata 15³² for cleaning and analysis. Data cleaning of outlying figures and missing and implausible data was undertaken prior to analysis. Missing data were not imputed. The PICC was the unit of measurement, and all randomly assigned patients were analyzed on an intention-to-treat basis.³³ Descriptive statistics (frequencies and percentages) were used to ascertain the primary outcome of feasibility for the larger trial. Incidence rates (per 1,000 catheter days) and rate ratios, including 95% confidence intervals (CIs), were calculated. The comparability of groups at baseline was described across demographic, clinical, and device characteristics. Kaplan-Meier survival curves (with log-rank tests) were used to compare PICC failure between study groups over time. Associations between baseline characteristics and failure were described by calculating hazard ratios (HRs). Univariable Cox regression was performed only due to the relatively low number of outcomes. P values of <.05 were considered statistically significant.

Ethics

The Children’s Health Service District, Queensland (Human Research Ethics Committee/15/QRCH/164), and Griffith University (2016/077) Human Research Ethics Committees provided ethics and governance approval. Informed consent was obtained from parents or legal guardians, with children providing youth assent if they were 7 years or older, dependent upon cognitive ability.

Participant and PICC Characteristics

Feasibility Outcomes

PICC Failure and Complications

DISCUSSION

This is the first pilot feasibility trial of new PICC materials and valve design incorporated in the BioFlo^® PICC in the pediatric population. The trial incorporated best practice for randomized trials, including using a concurrent control group, centralized and concealed randomization, predetermined feasibility criteria, and a registered and published trial protocol.¹⁷ As in other studies,^15,24,34 PICC failure and complication prevalence was unacceptably high for this essential device. Standard care PICCs failed twice as often as the new BioFlo^® PICCs (22% vs 11%), which is a clinically important difference. As researchers in a pilot study, we did not expect to detect statistically significant differences; however, we found that overall complications during the dwell occurred significantly more with the standard care than BioFlo^® PICCs (P = .009).

BioFlo^® PICC material offers a major advancement in PICC material through the incorporation of AT technologies into catheter materials, such as PICCs. Endexo^® is a low molecular–weight, fluoro-oligomeric additive that self-locates to the top few nanometers of the material surface. When added to power-injectable polyurethane, the additive results in a strong but passive, nonstick, fluorinated surface in the base PICC material. This inhibits platelet adhesion, suppresses protein procoagulant conformation, and thereby reduces thrombus formation in medical devices. Additionally, Endexo^® is not a catheter coating; rather, it is incorporated within the polyurethane of the PICC, thereby ensuring these AT properties are present on the internal, external, and cut surfaces of the PICC. If this technology can reduce complication during treatment and reduce failure from infection, thrombosis, occlusion, fracture, and dislodgement, it will improve patient outcomes considerably and lower health system costs. Previous studies investigating valve technology in PICC design to reduce occlusion have been inconclusive.^12-14,35,36 Occlusion (both partial and complete) was less frequent in our study with the BioFlo^® group (n = 3; 4%) compared to the standard care group (n = 6; 8%). The results of this pilot study suggest that either the Endexo^® material or PASV^® technology has a positive association with occlusion reduction during PICC treatment.

Thrombosis was the primary failure type for the standard care PICCs, comprising one-third of failures. All but one patient with radiologically confirmed thrombosis required the removal of the PICC prior to completion of treatment. The decision to remove the PICC or retain and treat conservatively remained with the treating team. Raffini et al.⁷ found thrombosis to increase in patients with one or more coexisting chronic medical condition. Slightly more standard care than BioFlo^® patients were free of such comorbidities (25% vs 16%), yet standard care patients still had the higher number of thromboses (7% vs 3%). Morgenthaler and Rodriguez³⁷ reported vascular access-associated thrombosis in pediatrics to be less common than in adults but higher in medically complex children. Worryingly, Menendez et al.³⁸ reported pediatric thrombosis to be largely asymptomatic, so the true incidence in our study is likely higher because only radiologically confirmed thromboses were recorded.

Occlusion (partial or complete) was the predominant complication across the study, being associated with one-third of all failures. When occlusion complications during the dwell (some of which were resolved with treatment), in addition to those causing failure, were considered, this number was even greater. Occlusion complications are prevalent and costly. Smith et al.²⁴ reported that occlusion was the most common reason for PICC removal and the most likely complication to delay treatment. Both the BioFlo^® and standard care PICCs are pressure rated with good tensile strength; however, fracture occurred in 4% (n = 3) of standard care PICCs compared to no fractures in BioFlo^® PICCs. Although the numbers are small, it may suggest a superior tensile strength of the BioFlo^® material.

This study reinforces previously published results^24,38 that PICC tip position is important and can influence complications, such as occlusion and thrombosis. In addition, we found a significant association with failure when PICCs did not have a continuous infusion. These findings reinforce the need for optimal tip location at insertion and ongoing flushing and maintenance of PICCs not used for infusions.

Limitations of this study include the small sample size, which was not designed to detect statistical differences in the primary outcome between groups. Despite randomization, there were slight imbalances at baseline for inserter type and leukocyte count, although these were not significantly associated with PICC failure in the Cox regression (data not shown), and thus were unlikely to influence findings. Additionally, a difference of <10% was associated with PICC tip position, favoring the BioFlo^® group. PICC tip position outside the cavoatrial junction was positively associated with failure; therefore, the effect of tip positioning on outcomes is difficult to ascertain given the small sample size and feasibility nature of the study. Further study is warranted to further explore this effect. The population sampled was pediatric medical and surgical inpatients with a vessel size >2 mm attending the operating theater suite for PICC insertion, thereby limiting the study’s generalizability to adults and other populations, including neonates and those with PICCs inserted in the pediatric intensive care unit. The study could not be blinded because study products had to be visible to the clinical and research staff. However, it is unlikely that staff would intentionally sabotage PICCs to bias the study. Blinding was possible for the assessment of blood culture and ultrasound reports to diagnose infection and thrombosis. Strengths of this study included 100% protocol adherence, and no patients were lost to follow-up.

These results confirm that PICC failure is unacceptably high and suggest that the innovative BioFlo^® PICC material and design holds promise to improve PICC outcomes by reducing complications and overall PICC failure. Trials of this technology are feasible, safe, and acceptable to healthcare staff and parents. Further trials are required, including in other patient populations, to definitively identify clinical, cost-effective methods to prevent PICC failure and improve reliability during treatment.

Acknowledgments

The authors thank the children and parents of Lady Cilento Children’s Hospital for participating in this important research. A special thank you goes to the nurses within the Vascular Assessment and Management Service and to Karen Turner, Julieta Woosley, and Anna Dean for their efforts in data collecting and ensuring protocol adherence.

Disclosure

Griffith University has received unrestricted, investigator-initiated research or educational grants to support the research of T. K., A. J. U., and C. R. M. from product manufacturers 3M, Adhezion Inc, AngioDynamics, Bard Medical, Baxter, B. Braun Medical Inc, Becton Dickinson, CareFusion, Centurion Medical Products, Cook Medical, Entrotech, FloMedical, ICU Medical Inc, Medical Australia Limited, Medtronic, Smiths Medical, and Teleflex. Griffith University has received consultancy payments on behalf of C. R. M., A. J. U., and T. K. from manufacturers 3M, AngioDynamics, Bard Medical, B. Braun Medical Inc, Becton Dickinson, CareFusion, Mayo Healthcare Inc, ResQDevices, and Smiths Medical. AngioDynamics (the BioFlo^® PICC manufacturer) provided partial funds to undertake this research via an unrestricted donation to Griffith University (but not the study authors). Queensland Health provided in-kind support to fund the remainder of the trial. The funders had no role in the study design, collection, analysis, or interpretation of the data, writing of the report, or decision to submit the article for publication.

Peripherally inserted central catheters (PICCs) have evolved since their inception in the early 1970s and are used with increasing frequency for pediatric inpatients and outpatients.^1-3 Emerging literature, including a meta-analysis of international observational studies,⁴ reports PICC failure (complications necessitating premature removal) occurs in up to 30% of PICCs, most commonly due to infection, thrombosis, occlusion, and fracture.^4-7 Raffini et al.⁷ report the increasing incidence of pediatric PICC-related thrombosis increases morbidity and mortality⁸ and negatively impacts future vessel health and preservation.⁹

PICCs have progressed from relatively simple, silicone-based catheters with an external clamp to chemically engineered polyurethane with pressure-activated valves placed at the proximal or distal catheter hub with the intent to reduce occlusion.¹⁰ Further modernization of PICC material occurred with the incorporation of antithrombogenic (AT) material (Endexo^®). These PICCs are designed to contain a nonstick polymer, which is designed to reduce the adherence of blood components (platelets and clotting factors) and inhibit thrombus formation (and hence prevent deep vein thrombosis andocclusion, as well as inhibit microbial biofilm attachment [and subsequent infection]).¹¹

In addition to new materials, other aspects of this PICC design have been the addition of a pressure-activated safety valve (PASV^®) built into the proximal hub. Pressure-activated valve technology promises to prevent catheter occlusion by reducing blood reflux into the PICC; the valve opens with pressure during infusion and aspiration and remains closed with normal venous pressure, circumventing the need for clinicians to manually clamp the PICC and reducing human error and the potential for thrombosis, occlusion, and fracture development.¹² Hoffer et al.¹³ reported half as many occlusions of valved PICCs (3.3%) compared with nonvalved or clamped PICCs (7.1%); although not statistically significant (P = .10), perhaps due to the small sample, overall complications, including occlusion and infection, were significantly lessened with the valved PICC (35% vs 79%; P = .02). Comparatively, Pittiruti et al.¹⁴ conducted a trial of 2 types of valved PICCs with an open-ended, nonvalved PICC and found no reduction in PICC occlusion or catheter malfunction.

Today, PICC use is common for patients who require short-to-medium intravenous therapy. PICCs are increasingly recognized for their significant complications, including thrombosis and infection.¹⁵ Novel PICC technology, including the incorporation of AT material such as Endexo^® and PASV^®, may reduce complications; however, the clinical efficacy, cost-effectiveness, and acceptability of these innovations have not been tested through randomized trials in pediatric patients. In accordance with Medical Research Council guidelines¹⁶ for developing interventions, we pilot tested the feasibility of the BioFlo^® PICC, including intervention acceptability, compliance, recruitment, and initial estimates of effect, in anticipation of a subsequent full-scale efficacy randomized controlled trial. Our secondary aim was to compare the effectiveness of the BioFlo^® PICC with Endexo^® and PASV^® technology in reducing PICC complications and failure.

Design

We undertook a pilot randomized controlled trial comparing the standard polyurethane PICC (with external clamp) with the BioFlo^® PICC (with internal valve) in preventing catheter failure in pediatric patients. The study was prospectively registered with the Australian Clinical Trials Registry (ACTRN12615001290583), and the research protocol was published.¹⁷

Study Setting

The study commenced in March 2016 at the Lady Cilento Children’s Hospital in South Brisbane, Australia, a tertiary-level, specialist, pediatric teaching hospital in Queensland, Australia, providing full-spectrum health services to children and young people from birth to 18 years of age. Recruitment, including data collection, was completed in November 2016.

Sample

The target sample size was 110 participants, 50 participants per group plus 10% for potential attrition, as determined by standard pilot trial sample size recommendations.¹⁸ With ethics approval, the sample size was later increased to 150 participants in order to adequately pilot a microbiological substudy method (published separately).¹⁷ Participants were consecutively recruited if they met the inclusion criteria: PICC insertion, age <18 years, predicted hospital stay >24 hours, single-lumen PICC, and written informed consent by an English-speaking, legal parent or guardian. Patients were excluded if they had a current (<48 hours) blood stream infection (BSI), vessel size <2 mm, could not speak English without an interpreter, required a multilumen PICC, or were previously enrolled in the study.

Interventions

Participants were randomized to receive either of the following PICCs: (1) standard care: Cook™ polyurethane, turbo-ject, power-injectable PICC (Cook Medical, Bloomington, IN) or (2) comparison: BioFlo^® polyurethane with Endexo^® technology (AngioDynamics Inc, Queensbury, NY).

Outcomes

The primary outcome was feasibility of a full-efficacy trial established by composite analysis of the elements of eligibility (>70% of patients will be eligible), recruitment (>70% of patients will agree to enroll), retention and attrition (<15% of participants are lost to follow-up or withdraw from the study), protocol adherence (>80% of participants receive their allocated, randomly assigned study product), missing data (<10% of data are missed during data collection), parent and healthcare staff satisfaction, and PICC failure effect size estimates to allow sample size calculations.^18,19 PICC failure was defined as the following complications associated with PICC removal: (1) catheter-associated BSI,^8,20-22 (2) local site infection,²² (3) venous thrombosis,²³ (4) occlusion,^24,25 (5) PICC fracture, or (6) PICC dislodgement.^25,26 Parents (or caregivers) and healthcare staff were asked to rate their level of confidence with the study product and ease of PICC removal by using a 0 to 100 numeric rating scale (NRS) of increasing confidence and/or ease. These data were collected at the time of PICC removal. Operators were also asked to rate their levels of satisfaction with the insertion equipment and ease of PICC insertion immediately upon completion of the insertion procedure (both 0-100 NRS of increasing satisfaction and/or ease). Secondary outcomes included individual PICC complications (eg, occlusion) occurring at any time point during the PICC dwell (including at removal), adverse events, pain, redness at the insertion site, and overall PICC dwell.

Study Procedures

The research nurse (ReN) screened operating theater lists for patients, obtained written informed consent, and initiated the randomization. Randomization was computer generated, and web based via Griffith University (https://www151.griffith.edu.au/random) to ensure allocation concealment until study entry. Patients were randomly assigned in a 1:1 ratio with computer-generated and randomly varied block sizes of 2 and 4. Data were collected by the ReN on the day of insertion, at day 1 postinsertion, then every 2 to 3 days thereafter so that PICCs were checked at least twice per week until study completion. Participants were included in the trial until 12 weeks post-PICC insertion, study withdrawal or PICC removal (whichever came first), with an additional 48 hours follow-up for infection outcomes. Patient review was face to face during the inpatient stay, with discharged patients’ follow-up occurring via outpatient clinics, hospital-in-the-home service, or telephone.

Data collection was via Research Electronic Data Capture (http://project-redcap.org/). The ReN collected data on primary and secondary outcomes by using the predefined criteria. Demographic and clinical data were collected to assess the success of randomization, describe the participant group, and display characteristics known to increase the risk of PICC complication and thrombosis. A blinded radiologist and infectious disease specialist reviewed and diagnosed thrombosis of deep veins and catheter-associated BSI outcomes, respectively.

PICC Procedures

Extensive prestudy education for 2 months prior to trial commencement was provided to all clinicians involved with the insertion and care of PICCs, including the study products. PICCs were inserted in an operating theater environment by a qualified consultant pediatric anesthetist, a senior anesthetic registrar or fellow in an approved anesthetic training program, or pediatric vascular access nurse practitioner. Ultrasound guidance was used to assess a patient’s vasculature and puncture the vessel. The operator chose the PICC size on the basis of clinical judgment of vessel size and patient needs and then inserted the allocated PICC.²⁷ Preferred PICC tip location was the cavoatrial junction. All PICC tip positions were confirmed with a chest x-ray before use.

Postinsertion, PICCs were managed by local interdisciplinary clinicians in accordance with local practice guidelines.^27-31 PICC care and management includes the use of 2% chlorhexidine gluconate in 70% alcohol for site antisepsis and neutral displacement needleless connectors (TUTA Pulse; Medical Australia Limited, Lidcombe, New South Wales, Australia); normal saline was used to flush after medication administration, and if the device was not in use for 6 hours or longer, heparin is instilled with securement via bordered polyurethane dressing (Tegaderm 1616; 3M, St Paul, Minnesota) and a sutureless securement device (Statlock VPPCSP; Bard, Georgia).

Statistical Analyses

Data were exported to Stata 15³² for cleaning and analysis. Data cleaning of outlying figures and missing and implausible data was undertaken prior to analysis. Missing data were not imputed. The PICC was the unit of measurement, and all randomly assigned patients were analyzed on an intention-to-treat basis.³³ Descriptive statistics (frequencies and percentages) were used to ascertain the primary outcome of feasibility for the larger trial. Incidence rates (per 1,000 catheter days) and rate ratios, including 95% confidence intervals (CIs), were calculated. The comparability of groups at baseline was described across demographic, clinical, and device characteristics. Kaplan-Meier survival curves (with log-rank tests) were used to compare PICC failure between study groups over time. Associations between baseline characteristics and failure were described by calculating hazard ratios (HRs). Univariable Cox regression was performed only due to the relatively low number of outcomes. P values of <.05 were considered statistically significant.

Ethics

The Children’s Health Service District, Queensland (Human Research Ethics Committee/15/QRCH/164), and Griffith University (2016/077) Human Research Ethics Committees provided ethics and governance approval. Informed consent was obtained from parents or legal guardians, with children providing youth assent if they were 7 years or older, dependent upon cognitive ability.

Participant and PICC Characteristics

Feasibility Outcomes

PICC Failure and Complications

DISCUSSION

This is the first pilot feasibility trial of new PICC materials and valve design incorporated in the BioFlo^® PICC in the pediatric population. The trial incorporated best practice for randomized trials, including using a concurrent control group, centralized and concealed randomization, predetermined feasibility criteria, and a registered and published trial protocol.¹⁷ As in other studies,^15,24,34 PICC failure and complication prevalence was unacceptably high for this essential device. Standard care PICCs failed twice as often as the new BioFlo^® PICCs (22% vs 11%), which is a clinically important difference. As researchers in a pilot study, we did not expect to detect statistically significant differences; however, we found that overall complications during the dwell occurred significantly more with the standard care than BioFlo^® PICCs (P = .009).

BioFlo^® PICC material offers a major advancement in PICC material through the incorporation of AT technologies into catheter materials, such as PICCs. Endexo^® is a low molecular–weight, fluoro-oligomeric additive that self-locates to the top few nanometers of the material surface. When added to power-injectable polyurethane, the additive results in a strong but passive, nonstick, fluorinated surface in the base PICC material. This inhibits platelet adhesion, suppresses protein procoagulant conformation, and thereby reduces thrombus formation in medical devices. Additionally, Endexo^® is not a catheter coating; rather, it is incorporated within the polyurethane of the PICC, thereby ensuring these AT properties are present on the internal, external, and cut surfaces of the PICC. If this technology can reduce complication during treatment and reduce failure from infection, thrombosis, occlusion, fracture, and dislodgement, it will improve patient outcomes considerably and lower health system costs. Previous studies investigating valve technology in PICC design to reduce occlusion have been inconclusive.^12-14,35,36 Occlusion (both partial and complete) was less frequent in our study with the BioFlo^® group (n = 3; 4%) compared to the standard care group (n = 6; 8%). The results of this pilot study suggest that either the Endexo^® material or PASV^® technology has a positive association with occlusion reduction during PICC treatment.

Thrombosis was the primary failure type for the standard care PICCs, comprising one-third of failures. All but one patient with radiologically confirmed thrombosis required the removal of the PICC prior to completion of treatment. The decision to remove the PICC or retain and treat conservatively remained with the treating team. Raffini et al.⁷ found thrombosis to increase in patients with one or more coexisting chronic medical condition. Slightly more standard care than BioFlo^® patients were free of such comorbidities (25% vs 16%), yet standard care patients still had the higher number of thromboses (7% vs 3%). Morgenthaler and Rodriguez³⁷ reported vascular access-associated thrombosis in pediatrics to be less common than in adults but higher in medically complex children. Worryingly, Menendez et al.³⁸ reported pediatric thrombosis to be largely asymptomatic, so the true incidence in our study is likely higher because only radiologically confirmed thromboses were recorded.

Occlusion (partial or complete) was the predominant complication across the study, being associated with one-third of all failures. When occlusion complications during the dwell (some of which were resolved with treatment), in addition to those causing failure, were considered, this number was even greater. Occlusion complications are prevalent and costly. Smith et al.²⁴ reported that occlusion was the most common reason for PICC removal and the most likely complication to delay treatment. Both the BioFlo^® and standard care PICCs are pressure rated with good tensile strength; however, fracture occurred in 4% (n = 3) of standard care PICCs compared to no fractures in BioFlo^® PICCs. Although the numbers are small, it may suggest a superior tensile strength of the BioFlo^® material.

This study reinforces previously published results^24,38 that PICC tip position is important and can influence complications, such as occlusion and thrombosis. In addition, we found a significant association with failure when PICCs did not have a continuous infusion. These findings reinforce the need for optimal tip location at insertion and ongoing flushing and maintenance of PICCs not used for infusions.

Limitations of this study include the small sample size, which was not designed to detect statistical differences in the primary outcome between groups. Despite randomization, there were slight imbalances at baseline for inserter type and leukocyte count, although these were not significantly associated with PICC failure in the Cox regression (data not shown), and thus were unlikely to influence findings. Additionally, a difference of <10% was associated with PICC tip position, favoring the BioFlo^® group. PICC tip position outside the cavoatrial junction was positively associated with failure; therefore, the effect of tip positioning on outcomes is difficult to ascertain given the small sample size and feasibility nature of the study. Further study is warranted to further explore this effect. The population sampled was pediatric medical and surgical inpatients with a vessel size >2 mm attending the operating theater suite for PICC insertion, thereby limiting the study’s generalizability to adults and other populations, including neonates and those with PICCs inserted in the pediatric intensive care unit. The study could not be blinded because study products had to be visible to the clinical and research staff. However, it is unlikely that staff would intentionally sabotage PICCs to bias the study. Blinding was possible for the assessment of blood culture and ultrasound reports to diagnose infection and thrombosis. Strengths of this study included 100% protocol adherence, and no patients were lost to follow-up.

These results confirm that PICC failure is unacceptably high and suggest that the innovative BioFlo^® PICC material and design holds promise to improve PICC outcomes by reducing complications and overall PICC failure. Trials of this technology are feasible, safe, and acceptable to healthcare staff and parents. Further trials are required, including in other patient populations, to definitively identify clinical, cost-effective methods to prevent PICC failure and improve reliability during treatment.

Acknowledgments

The authors thank the children and parents of Lady Cilento Children’s Hospital for participating in this important research. A special thank you goes to the nurses within the Vascular Assessment and Management Service and to Karen Turner, Julieta Woosley, and Anna Dean for their efforts in data collecting and ensuring protocol adherence.

Disclosure

Griffith University has received unrestricted, investigator-initiated research or educational grants to support the research of T. K., A. J. U., and C. R. M. from product manufacturers 3M, Adhezion Inc, AngioDynamics, Bard Medical, Baxter, B. Braun Medical Inc, Becton Dickinson, CareFusion, Centurion Medical Products, Cook Medical, Entrotech, FloMedical, ICU Medical Inc, Medical Australia Limited, Medtronic, Smiths Medical, and Teleflex. Griffith University has received consultancy payments on behalf of C. R. M., A. J. U., and T. K. from manufacturers 3M, AngioDynamics, Bard Medical, B. Braun Medical Inc, Becton Dickinson, CareFusion, Mayo Healthcare Inc, ResQDevices, and Smiths Medical. AngioDynamics (the BioFlo^® PICC manufacturer) provided partial funds to undertake this research via an unrestricted donation to Griffith University (but not the study authors). Queensland Health provided in-kind support to fund the remainder of the trial. The funders had no role in the study design, collection, analysis, or interpretation of the data, writing of the report, or decision to submit the article for publication.

Testing for adrenal insufficiency (AI) is common in the hospital setting. The gold standard remains the insulin tolerance test (ITT), in which cortisol concentration is measured after the induction of hypoglycemia to <35 mg/dL.¹ Alternatively, metyrapone testing works by blocking cortisol synthesis. If pretest adrenocorticotropic hormone (ACTH) concentrations are low and ACTH concentrations do not rise after the administration of metyrapone, the patient is given a diagnosis of AI. Both assays pose some risk to patients with AI and are typically only performed as confirmatory tests. Morning random cortisol concentrations can be used to suggest AI if concentrations are <3 mcg/dL, but they often provide indeterminate results if concentrations are between 3 and 15 mcg/dL.² Thus, morning cortisol concentrations in isolation are not diagnostic of AI. For these reasons, most experts recommend a dynamic, high-dose cosyntropin stimulation testing (CST) with 250 mcg of intravenous cosyntropin to screen for AI. The test can be done any time of day.³ Historically, an incremental response to cosyntropin, or “delta,” was also required to indicate a normal response to stimulation.⁴ However, the baseline cortisol concentration is dependent on circadian rhythm and level of stress. For this reason, a delta, whether large or small, has been abandoned as a requisite for the diagnosis of AI.^5-7 A normal CST is widely accepted to be identified by any cortisol concentration >18 mcg/dL during the test (basal or poststimulation).⁸

The seminal studies by Lindholm, Kehlet, and coauthors^9-11 validated the CST against the gold standard ITT and utilized only 0- and 30-minute cortisol concentrations. A later study in patients with pituitary disease demonstrated that 30-minute concentrations had a stronger correlation with the ITT than 60-minute concentrations (false-negative rate: 10% vs 27%).¹² However, in that study, a higher threshold was used for the 60-minute concentration than for what was obtained at 30 minutes (25.4 vs 21.8 mcg/dL, respectively). Multiple studies have shown that the 60-minute concentration is higher than the 30-minute concentration after cosyntropin stimulation.^4,5,13 Subsequent, small studies of patients who were known to have AI have shown that 60-minute concentrations are as useful as 30-minute concentrations.^5,14,15 Because 30-minute cortisol concentrations are often lower than 60-minute concentrations, a single 30-minute result may lead to a falsely abnormal test.^16,17 As such, the use of a single 60-minute test may be more appropriate. Indeed, some authors have suggested that measuring only 30-minute concentrations may lead to overdiagnosis of AI by missing an appropriate response, serum cortisol >18 mcg/dL, at 60 minutes.^17-19 Peak cortisol concentrations after low-dose cosyntropin stimulation (1 mcg) are seen at 60 minutes, and low-dose stimulation has been shown to be more variable than in the high-dose test (250 mg).^19,20

There is a lack of consensus to guide clinicians as to when cortisol concentrations should be measured after stimulation, and standard references lack uniformity. Commonly accessed medical resources—such as UpToDate and Jameson’s Endocrinology—recommend basal, 30-minute, and 60-minute cortisol concentrations, while Williams Textbook of Endocrinology recommends basal and 30-minute concentrations, and the Washington Manual recommends only a single 30-minute concentration.^7,21,22 Goldman-Cecil Medicine⁸ recommends checking a cortisol concentration between 30 and 60 minutes and recommends the same 18 mcg/dL cutoff for any test obtained in this time period. As a result of these variable recommendations, all 3 time points are often obtained. Prominent review articles continue to recommend checking all 3 concentrations while presenting evidence of peak cortisol response at 60 minutes poststimulation.¹³

In this study, we retrospectively examined CSTs in hospitalized, adult patients both in the intensive care unit (ICU) and hospital ward and/or floor settings to evaluate for significant differences in 30- and 60-minute cortisol concentrations and compare the concordance of screening at each time point alone with traditional CST at all 3 time points. By using these results, we discuss the utility of obtaining 3 cortisol samples.

After receiving approval from the institutional review board, we retrospectively reviewed all standard, high-dose CSTs performed on adult inpatients at the Barnes-Jewish Hospital laboratory from January 1, 2012, to August 31, 2013. All patients received the same standard dose (250 mcg cosyntropin, a synthetic ACTH, at a concentration of 1 mcg/mL administered over 2 minutes) regardless of age or weight. We collected patient gender; age; time of baseline cortisol measurement; cortisol results at baseline, 30, and 60 minutes; and patient location (inpatient floor vs ICU status). Tests were included if results from all 3 time points (0, 30, 60 minute) were available.

Cortisol concentrations were assessed by the laboratory according to the manufacturer’s instructions by using the ADVIA Centaur Cortisol assay (Siemens Healthcare Diagnostics Inc, Tarrytown, NY), a competitive chemiluminescent immunoassay. For the traditional CST, a cortisol concentration ≥18 mcg/dL at any time point during the test was used to define normal (negative). Patients with a positive (no results >18 mcg/mL) CST were defined as “screen positives” for the purposes of this analysis. Patient location data were available that allowed for an ICU vs non-ICU comparison.

Statistical analyses were performed in SAS version 9.4 (SAS Institute Inc, Cary, North Carolina). Continuous variables were compared by using a 2-tailed Student t test. Percentiles and proportions were compared by using χ² tests or Fisher’s exact tests when appropriate. The concordance of screening at each time point compared with the traditional CST was calculated. Positive percent agreement (PPA) with the traditional CST in each subgroup (ICU and floor) and combined was also evaluated. A P value of .05 was used to determine significance.

Cortisol concentrations obtained at 30 minutes were significantly higher than baseline cortisol concentrations (baseline: 12.8 mcg/dL; 30 minutes: 23.9 mcg/dL; P < .001) for all patients. The average cortisol concentrations obtained at 60 minutes (27.4 mcg/dL) were significantly higher than those at baseline and 30 minutes (P < .001). This trend was seen in each subgroup of patients in the ICU and on the floor (Figure). The average baseline cortisol concentration was higher for ICU patients compared to floor patients (17.6 mcg/dL vs 10.9 mcg/dL, respectively).

By using the traditional CST, there were 26 (13.1%) positive tests for AI in ICU patients and 84 (16.7%) positive tests in floor patients (Table).

Our investigation of 702 CSTs, the largest retrospective analysis to date, finds that the 60-minute cortisol concentration is significantly higher than the 30-minute concentration in a standard, high-dose CST. Sixty-minute cortisol concentrations are more concordant with traditional CST results than the 30-minute concentrations in both critically ill ICU and noncritically ill floor patients. This suggests that a single 60-minute measurement is sufficient for AI screening. The use of only 30-minute concentrations would lead to a significant increase in false-positive screening tests and significantly lower PPA (98% vs 57%). With peak cortisol concentrations occurring at 60-minutes poststimulation, measuring both 30- and 60-minute poststimulation concentrations does not appear to be of significant clinical benefit. The cost-saving from reduced phlebotomy and laboratory expenses would be significant, especially in locations with limited staff or financial resources. Our findings are similar to other recent results by Chitale et al.,¹⁷ Mansoor et al.,¹⁶ and Zueger et al.¹⁸

Zueger et al.¹⁸ evaluated the results of high-dose CST in 73 patients and found 13.7% of patients with inadequate cortisol response (<18 mcg/dL) at 30 minutes had normal concentrations at 60 minutes (>18 mcg/dL). Their study did not identify a single case of normal cortisol concentration at 30 minutes that would have inappropriately screened positive for AI if cortisol concentrations were only checked at 60 minutes. Similarly, they suggested that the 30-minute test did not add any additional diagnostic value; however, no confirmatory testing was performed.

Higher cortisol concentrations at 60 minutes poststimulation may result in improved specificity for AI without reducing sensitivity, but it may also indicate that the cutoff value may need to be raised from 18 mcg/dL at 60 minutes to maintain an appropriate clinical sensitivity. Continued research should resolve this clinical question with gold-standard confirmatory testing. Furthermore, there is debate about an appropriate screening cortisol concentration threshold for critically ill patients. Researchers have compared concentrations of 25 mcg/dL to the traditional 18 mcg/dL to improve sensitivity for AI, but these studies do not involve comparisons to confirmatory testing and often result in reduced specificity.^23,24

In our study, only a small fraction of testing was performed in the early-morning hours, when basal cortisol results are of value. There may be indications to perform traditional CSTs with a basal concentration, such as for suspected secondary AI, but testing must be performed in the early morning for interpretable results per current recommendations. However, poststimulation cortisol concentrations may be interpreted regardless of the time of day at which the test was initiated.³

Our study is limited by its scope because it is a retrospective analysis. It is also limited by a lack of gold-standard, clinical confirmatory testing or analysis of other clinical data. Our method of testing and interpretation is considered the screening standard and is often used to plan treatment for AI without confirmatory testing, as ITT is not routinely available for hospitalized patients. The validation of the traditional CST to the ITT has been performed extensively, but a randomized trial comparing a single 60-minute concentration to the ITT may be useful. The exact timing of blood draws may have introduced error in the concentration measurements, and this is critical to screening accuracy. Total serum cortisol is 10% bound to albumin,²⁵ and medications such as steroids or opioids and medical conditions such as obesity or liver disease can affect cortisol concentrations.²⁶ Albumin and free cortisol concentrations that may be used to adjust for these variables were not available.

We recommend changes to the standard CST to exclude a basal cortisol concentration unless it is indicated for the evaluation of secondary AI or obtained at the appropriate early-morning hour. A single 60-minute poststimulation cortisol concentration may be an appropriate screening test for AI and demonstrates high concordance with the traditional CST. The use of a 30-minute poststimulation concentration alone may lead to a significantly higher number of false-positive results. Alternatively, the stimulated cortisol threshold used to define a normal test may need to be higher at 60 minutes to maintain the appropriate sensitivity. Further study and comparison with confirmatory testing are needed.

Disclosure

The authors have no relevant conflicts of interest to disclose.

Testing for adrenal insufficiency (AI) is common in the hospital setting. The gold standard remains the insulin tolerance test (ITT), in which cortisol concentration is measured after the induction of hypoglycemia to <35 mg/dL.¹ Alternatively, metyrapone testing works by blocking cortisol synthesis. If pretest adrenocorticotropic hormone (ACTH) concentrations are low and ACTH concentrations do not rise after the administration of metyrapone, the patient is given a diagnosis of AI. Both assays pose some risk to patients with AI and are typically only performed as confirmatory tests. Morning random cortisol concentrations can be used to suggest AI if concentrations are <3 mcg/dL, but they often provide indeterminate results if concentrations are between 3 and 15 mcg/dL.² Thus, morning cortisol concentrations in isolation are not diagnostic of AI. For these reasons, most experts recommend a dynamic, high-dose cosyntropin stimulation testing (CST) with 250 mcg of intravenous cosyntropin to screen for AI. The test can be done any time of day.³ Historically, an incremental response to cosyntropin, or “delta,” was also required to indicate a normal response to stimulation.⁴ However, the baseline cortisol concentration is dependent on circadian rhythm and level of stress. For this reason, a delta, whether large or small, has been abandoned as a requisite for the diagnosis of AI.^5-7 A normal CST is widely accepted to be identified by any cortisol concentration >18 mcg/dL during the test (basal or poststimulation).⁸

The seminal studies by Lindholm, Kehlet, and coauthors^9-11 validated the CST against the gold standard ITT and utilized only 0- and 30-minute cortisol concentrations. A later study in patients with pituitary disease demonstrated that 30-minute concentrations had a stronger correlation with the ITT than 60-minute concentrations (false-negative rate: 10% vs 27%).¹² However, in that study, a higher threshold was used for the 60-minute concentration than for what was obtained at 30 minutes (25.4 vs 21.8 mcg/dL, respectively). Multiple studies have shown that the 60-minute concentration is higher than the 30-minute concentration after cosyntropin stimulation.^4,5,13 Subsequent, small studies of patients who were known to have AI have shown that 60-minute concentrations are as useful as 30-minute concentrations.^5,14,15 Because 30-minute cortisol concentrations are often lower than 60-minute concentrations, a single 30-minute result may lead to a falsely abnormal test.^16,17 As such, the use of a single 60-minute test may be more appropriate. Indeed, some authors have suggested that measuring only 30-minute concentrations may lead to overdiagnosis of AI by missing an appropriate response, serum cortisol >18 mcg/dL, at 60 minutes.^17-19 Peak cortisol concentrations after low-dose cosyntropin stimulation (1 mcg) are seen at 60 minutes, and low-dose stimulation has been shown to be more variable than in the high-dose test (250 mg).^19,20

There is a lack of consensus to guide clinicians as to when cortisol concentrations should be measured after stimulation, and standard references lack uniformity. Commonly accessed medical resources—such as UpToDate and Jameson’s Endocrinology—recommend basal, 30-minute, and 60-minute cortisol concentrations, while Williams Textbook of Endocrinology recommends basal and 30-minute concentrations, and the Washington Manual recommends only a single 30-minute concentration.^7,21,22 Goldman-Cecil Medicine⁸ recommends checking a cortisol concentration between 30 and 60 minutes and recommends the same 18 mcg/dL cutoff for any test obtained in this time period. As a result of these variable recommendations, all 3 time points are often obtained. Prominent review articles continue to recommend checking all 3 concentrations while presenting evidence of peak cortisol response at 60 minutes poststimulation.¹³

In this study, we retrospectively examined CSTs in hospitalized, adult patients both in the intensive care unit (ICU) and hospital ward and/or floor settings to evaluate for significant differences in 30- and 60-minute cortisol concentrations and compare the concordance of screening at each time point alone with traditional CST at all 3 time points. By using these results, we discuss the utility of obtaining 3 cortisol samples.

After receiving approval from the institutional review board, we retrospectively reviewed all standard, high-dose CSTs performed on adult inpatients at the Barnes-Jewish Hospital laboratory from January 1, 2012, to August 31, 2013. All patients received the same standard dose (250 mcg cosyntropin, a synthetic ACTH, at a concentration of 1 mcg/mL administered over 2 minutes) regardless of age or weight. We collected patient gender; age; time of baseline cortisol measurement; cortisol results at baseline, 30, and 60 minutes; and patient location (inpatient floor vs ICU status). Tests were included if results from all 3 time points (0, 30, 60 minute) were available.

Cortisol concentrations were assessed by the laboratory according to the manufacturer’s instructions by using the ADVIA Centaur Cortisol assay (Siemens Healthcare Diagnostics Inc, Tarrytown, NY), a competitive chemiluminescent immunoassay. For the traditional CST, a cortisol concentration ≥18 mcg/dL at any time point during the test was used to define normal (negative). Patients with a positive (no results >18 mcg/mL) CST were defined as “screen positives” for the purposes of this analysis. Patient location data were available that allowed for an ICU vs non-ICU comparison.

Statistical analyses were performed in SAS version 9.4 (SAS Institute Inc, Cary, North Carolina). Continuous variables were compared by using a 2-tailed Student t test. Percentiles and proportions were compared by using χ² tests or Fisher’s exact tests when appropriate. The concordance of screening at each time point compared with the traditional CST was calculated. Positive percent agreement (PPA) with the traditional CST in each subgroup (ICU and floor) and combined was also evaluated. A P value of .05 was used to determine significance.

Cortisol concentrations obtained at 30 minutes were significantly higher than baseline cortisol concentrations (baseline: 12.8 mcg/dL; 30 minutes: 23.9 mcg/dL; P < .001) for all patients. The average cortisol concentrations obtained at 60 minutes (27.4 mcg/dL) were significantly higher than those at baseline and 30 minutes (P < .001). This trend was seen in each subgroup of patients in the ICU and on the floor (Figure). The average baseline cortisol concentration was higher for ICU patients compared to floor patients (17.6 mcg/dL vs 10.9 mcg/dL, respectively).

By using the traditional CST, there were 26 (13.1%) positive tests for AI in ICU patients and 84 (16.7%) positive tests in floor patients (Table).

Our investigation of 702 CSTs, the largest retrospective analysis to date, finds that the 60-minute cortisol concentration is significantly higher than the 30-minute concentration in a standard, high-dose CST. Sixty-minute cortisol concentrations are more concordant with traditional CST results than the 30-minute concentrations in both critically ill ICU and noncritically ill floor patients. This suggests that a single 60-minute measurement is sufficient for AI screening. The use of only 30-minute concentrations would lead to a significant increase in false-positive screening tests and significantly lower PPA (98% vs 57%). With peak cortisol concentrations occurring at 60-minutes poststimulation, measuring both 30- and 60-minute poststimulation concentrations does not appear to be of significant clinical benefit. The cost-saving from reduced phlebotomy and laboratory expenses would be significant, especially in locations with limited staff or financial resources. Our findings are similar to other recent results by Chitale et al.,¹⁷ Mansoor et al.,¹⁶ and Zueger et al.¹⁸

Zueger et al.¹⁸ evaluated the results of high-dose CST in 73 patients and found 13.7% of patients with inadequate cortisol response (<18 mcg/dL) at 30 minutes had normal concentrations at 60 minutes (>18 mcg/dL). Their study did not identify a single case of normal cortisol concentration at 30 minutes that would have inappropriately screened positive for AI if cortisol concentrations were only checked at 60 minutes. Similarly, they suggested that the 30-minute test did not add any additional diagnostic value; however, no confirmatory testing was performed.

Higher cortisol concentrations at 60 minutes poststimulation may result in improved specificity for AI without reducing sensitivity, but it may also indicate that the cutoff value may need to be raised from 18 mcg/dL at 60 minutes to maintain an appropriate clinical sensitivity. Continued research should resolve this clinical question with gold-standard confirmatory testing. Furthermore, there is debate about an appropriate screening cortisol concentration threshold for critically ill patients. Researchers have compared concentrations of 25 mcg/dL to the traditional 18 mcg/dL to improve sensitivity for AI, but these studies do not involve comparisons to confirmatory testing and often result in reduced specificity.^23,24

In our study, only a small fraction of testing was performed in the early-morning hours, when basal cortisol results are of value. There may be indications to perform traditional CSTs with a basal concentration, such as for suspected secondary AI, but testing must be performed in the early morning for interpretable results per current recommendations. However, poststimulation cortisol concentrations may be interpreted regardless of the time of day at which the test was initiated.³

Our study is limited by its scope because it is a retrospective analysis. It is also limited by a lack of gold-standard, clinical confirmatory testing or analysis of other clinical data. Our method of testing and interpretation is considered the screening standard and is often used to plan treatment for AI without confirmatory testing, as ITT is not routinely available for hospitalized patients. The validation of the traditional CST to the ITT has been performed extensively, but a randomized trial comparing a single 60-minute concentration to the ITT may be useful. The exact timing of blood draws may have introduced error in the concentration measurements, and this is critical to screening accuracy. Total serum cortisol is 10% bound to albumin,²⁵ and medications such as steroids or opioids and medical conditions such as obesity or liver disease can affect cortisol concentrations.²⁶ Albumin and free cortisol concentrations that may be used to adjust for these variables were not available.

We recommend changes to the standard CST to exclude a basal cortisol concentration unless it is indicated for the evaluation of secondary AI or obtained at the appropriate early-morning hour. A single 60-minute poststimulation cortisol concentration may be an appropriate screening test for AI and demonstrates high concordance with the traditional CST. The use of a 30-minute poststimulation concentration alone may lead to a significantly higher number of false-positive results. Alternatively, the stimulated cortisol threshold used to define a normal test may need to be higher at 60 minutes to maintain the appropriate sensitivity. Further study and comparison with confirmatory testing are needed.

Disclosure

The authors have no relevant conflicts of interest to disclose.

Testing for adrenal insufficiency (AI) is common in the hospital setting. The gold standard remains the insulin tolerance test (ITT), in which cortisol concentration is measured after the induction of hypoglycemia to <35 mg/dL.¹ Alternatively, metyrapone testing works by blocking cortisol synthesis. If pretest adrenocorticotropic hormone (ACTH) concentrations are low and ACTH concentrations do not rise after the administration of metyrapone, the patient is given a diagnosis of AI. Both assays pose some risk to patients with AI and are typically only performed as confirmatory tests. Morning random cortisol concentrations can be used to suggest AI if concentrations are <3 mcg/dL, but they often provide indeterminate results if concentrations are between 3 and 15 mcg/dL.² Thus, morning cortisol concentrations in isolation are not diagnostic of AI. For these reasons, most experts recommend a dynamic, high-dose cosyntropin stimulation testing (CST) with 250 mcg of intravenous cosyntropin to screen for AI. The test can be done any time of day.³ Historically, an incremental response to cosyntropin, or “delta,” was also required to indicate a normal response to stimulation.⁴ However, the baseline cortisol concentration is dependent on circadian rhythm and level of stress. For this reason, a delta, whether large or small, has been abandoned as a requisite for the diagnosis of AI.^5-7 A normal CST is widely accepted to be identified by any cortisol concentration >18 mcg/dL during the test (basal or poststimulation).⁸

The seminal studies by Lindholm, Kehlet, and coauthors^9-11 validated the CST against the gold standard ITT and utilized only 0- and 30-minute cortisol concentrations. A later study in patients with pituitary disease demonstrated that 30-minute concentrations had a stronger correlation with the ITT than 60-minute concentrations (false-negative rate: 10% vs 27%).¹² However, in that study, a higher threshold was used for the 60-minute concentration than for what was obtained at 30 minutes (25.4 vs 21.8 mcg/dL, respectively). Multiple studies have shown that the 60-minute concentration is higher than the 30-minute concentration after cosyntropin stimulation.^4,5,13 Subsequent, small studies of patients who were known to have AI have shown that 60-minute concentrations are as useful as 30-minute concentrations.^5,14,15 Because 30-minute cortisol concentrations are often lower than 60-minute concentrations, a single 30-minute result may lead to a falsely abnormal test.^16,17 As such, the use of a single 60-minute test may be more appropriate. Indeed, some authors have suggested that measuring only 30-minute concentrations may lead to overdiagnosis of AI by missing an appropriate response, serum cortisol >18 mcg/dL, at 60 minutes.^17-19 Peak cortisol concentrations after low-dose cosyntropin stimulation (1 mcg) are seen at 60 minutes, and low-dose stimulation has been shown to be more variable than in the high-dose test (250 mg).^19,20

There is a lack of consensus to guide clinicians as to when cortisol concentrations should be measured after stimulation, and standard references lack uniformity. Commonly accessed medical resources—such as UpToDate and Jameson’s Endocrinology—recommend basal, 30-minute, and 60-minute cortisol concentrations, while Williams Textbook of Endocrinology recommends basal and 30-minute concentrations, and the Washington Manual recommends only a single 30-minute concentration.^7,21,22 Goldman-Cecil Medicine⁸ recommends checking a cortisol concentration between 30 and 60 minutes and recommends the same 18 mcg/dL cutoff for any test obtained in this time period. As a result of these variable recommendations, all 3 time points are often obtained. Prominent review articles continue to recommend checking all 3 concentrations while presenting evidence of peak cortisol response at 60 minutes poststimulation.¹³

In this study, we retrospectively examined CSTs in hospitalized, adult patients both in the intensive care unit (ICU) and hospital ward and/or floor settings to evaluate for significant differences in 30- and 60-minute cortisol concentrations and compare the concordance of screening at each time point alone with traditional CST at all 3 time points. By using these results, we discuss the utility of obtaining 3 cortisol samples.

After receiving approval from the institutional review board, we retrospectively reviewed all standard, high-dose CSTs performed on adult inpatients at the Barnes-Jewish Hospital laboratory from January 1, 2012, to August 31, 2013. All patients received the same standard dose (250 mcg cosyntropin, a synthetic ACTH, at a concentration of 1 mcg/mL administered over 2 minutes) regardless of age or weight. We collected patient gender; age; time of baseline cortisol measurement; cortisol results at baseline, 30, and 60 minutes; and patient location (inpatient floor vs ICU status). Tests were included if results from all 3 time points (0, 30, 60 minute) were available.

Cortisol concentrations were assessed by the laboratory according to the manufacturer’s instructions by using the ADVIA Centaur Cortisol assay (Siemens Healthcare Diagnostics Inc, Tarrytown, NY), a competitive chemiluminescent immunoassay. For the traditional CST, a cortisol concentration ≥18 mcg/dL at any time point during the test was used to define normal (negative). Patients with a positive (no results >18 mcg/mL) CST were defined as “screen positives” for the purposes of this analysis. Patient location data were available that allowed for an ICU vs non-ICU comparison.

Statistical analyses were performed in SAS version 9.4 (SAS Institute Inc, Cary, North Carolina). Continuous variables were compared by using a 2-tailed Student t test. Percentiles and proportions were compared by using χ² tests or Fisher’s exact tests when appropriate. The concordance of screening at each time point compared with the traditional CST was calculated. Positive percent agreement (PPA) with the traditional CST in each subgroup (ICU and floor) and combined was also evaluated. A P value of .05 was used to determine significance.

Cortisol concentrations obtained at 30 minutes were significantly higher than baseline cortisol concentrations (baseline: 12.8 mcg/dL; 30 minutes: 23.9 mcg/dL; P < .001) for all patients. The average cortisol concentrations obtained at 60 minutes (27.4 mcg/dL) were significantly higher than those at baseline and 30 minutes (P < .001). This trend was seen in each subgroup of patients in the ICU and on the floor (Figure). The average baseline cortisol concentration was higher for ICU patients compared to floor patients (17.6 mcg/dL vs 10.9 mcg/dL, respectively).

By using the traditional CST, there were 26 (13.1%) positive tests for AI in ICU patients and 84 (16.7%) positive tests in floor patients (Table).

Our investigation of 702 CSTs, the largest retrospective analysis to date, finds that the 60-minute cortisol concentration is significantly higher than the 30-minute concentration in a standard, high-dose CST. Sixty-minute cortisol concentrations are more concordant with traditional CST results than the 30-minute concentrations in both critically ill ICU and noncritically ill floor patients. This suggests that a single 60-minute measurement is sufficient for AI screening. The use of only 30-minute concentrations would lead to a significant increase in false-positive screening tests and significantly lower PPA (98% vs 57%). With peak cortisol concentrations occurring at 60-minutes poststimulation, measuring both 30- and 60-minute poststimulation concentrations does not appear to be of significant clinical benefit. The cost-saving from reduced phlebotomy and laboratory expenses would be significant, especially in locations with limited staff or financial resources. Our findings are similar to other recent results by Chitale et al.,¹⁷ Mansoor et al.,¹⁶ and Zueger et al.¹⁸

Zueger et al.¹⁸ evaluated the results of high-dose CST in 73 patients and found 13.7% of patients with inadequate cortisol response (<18 mcg/dL) at 30 minutes had normal concentrations at 60 minutes (>18 mcg/dL). Their study did not identify a single case of normal cortisol concentration at 30 minutes that would have inappropriately screened positive for AI if cortisol concentrations were only checked at 60 minutes. Similarly, they suggested that the 30-minute test did not add any additional diagnostic value; however, no confirmatory testing was performed.

Higher cortisol concentrations at 60 minutes poststimulation may result in improved specificity for AI without reducing sensitivity, but it may also indicate that the cutoff value may need to be raised from 18 mcg/dL at 60 minutes to maintain an appropriate clinical sensitivity. Continued research should resolve this clinical question with gold-standard confirmatory testing. Furthermore, there is debate about an appropriate screening cortisol concentration threshold for critically ill patients. Researchers have compared concentrations of 25 mcg/dL to the traditional 18 mcg/dL to improve sensitivity for AI, but these studies do not involve comparisons to confirmatory testing and often result in reduced specificity.^23,24

In our study, only a small fraction of testing was performed in the early-morning hours, when basal cortisol results are of value. There may be indications to perform traditional CSTs with a basal concentration, such as for suspected secondary AI, but testing must be performed in the early morning for interpretable results per current recommendations. However, poststimulation cortisol concentrations may be interpreted regardless of the time of day at which the test was initiated.³

Our study is limited by its scope because it is a retrospective analysis. It is also limited by a lack of gold-standard, clinical confirmatory testing or analysis of other clinical data. Our method of testing and interpretation is considered the screening standard and is often used to plan treatment for AI without confirmatory testing, as ITT is not routinely available for hospitalized patients. The validation of the traditional CST to the ITT has been performed extensively, but a randomized trial comparing a single 60-minute concentration to the ITT may be useful. The exact timing of blood draws may have introduced error in the concentration measurements, and this is critical to screening accuracy. Total serum cortisol is 10% bound to albumin,²⁵ and medications such as steroids or opioids and medical conditions such as obesity or liver disease can affect cortisol concentrations.²⁶ Albumin and free cortisol concentrations that may be used to adjust for these variables were not available.

We recommend changes to the standard CST to exclude a basal cortisol concentration unless it is indicated for the evaluation of secondary AI or obtained at the appropriate early-morning hour. A single 60-minute poststimulation cortisol concentration may be an appropriate screening test for AI and demonstrates high concordance with the traditional CST. The use of a 30-minute poststimulation concentration alone may lead to a significantly higher number of false-positive results. Alternatively, the stimulated cortisol threshold used to define a normal test may need to be higher at 60 minutes to maintain the appropriate sensitivity. Further study and comparison with confirmatory testing are needed.

Disclosure

The authors have no relevant conflicts of interest to disclose.

Wasteful care may account for between 21% and 34% of the United States’ $3.2 trillion in annual healthcare expenditures, making it a prime target for cost-saving initiatives.^1,2 Telemetry is a target for value improvement strategies because telemetry is overutilized, rarely leads to a change in management, and has associated guidelines on appropriate use.^3-10 Telemetry use has been a focus of the Joint Commission’s National Patient Safety Goals since 2014, and it is also a focus of the Society of Hospital Medicine’s Choosing Wisely^® campaign.^11-13

Previous initiatives have evaluated how changes to telemetry orders or education and feedback affect telemetry use. Few studies have compared a system-wide electronic health record (EHR) approach to a multifaceted intervention. In seeking to address this gap, we adapted published guidelines from the American Heart Association (AHA) and incorporated them into our EHR ordering process.³ Simultaneously, we implemented a multifaceted quality improvement initiative and compared this combined program’s effectiveness to that of the EHR approach alone.

Study Design, Setting, and Population

We performed a 2-group observational pre- to postintervention study at University of Utah Health. Hospital encounters of patients 18 years and older who had at least 1 inpatient acute care, nonintensive care unit (ICU) room charge and an admission date between January 1, 2014, and July 31, 2016, were included. Patient encounters with missing encounter-level covariates, such as case mix index (CMI) or attending provider identification, were excluded. The Institutional Review Board classified this project as quality improvement and did not require review and oversight.

Intervention

On July 6, 2015, our Epic (Epic Systems Corporation, Madison, WI) EHR telemetry order was modified to discourage unnecessary telemetry monitoring. The new order required providers ordering telemetry to choose a clinical indication and select a duration for monitoring, after which the order would expire and require physician renewal or discontinuation. These were the only changes that occurred for nonhospitalist providers. The nonhospitalist group included all admitting providers who were not hospitalists. This group included neurology (6.98%); cardiology (8.13%); other medical specialties such as pulmonology, hematology, and oncology (21.30%); cardiothoracic surgery (3.72%); orthopedic surgery (14.84%); general surgery (11.11%); neurosurgery (11.07%); and other surgical specialties, including urology, transplant, vascular surgery, and plastics (16.68%).

Between January 2015 and June 2015, we implemented a multicomponent program among our hospitalist service. The hospitalist service is composed of 4 teams with internal medicine residents and 2 teams with advanced practice providers, all staffed by academic hospitalists. Our program was composed of 5 elements, all of which were made before the hospital-wide changes to electronic telemetry orders and maintained throughout the study period, as follows: (1) a single provider education session reviewing available evidence (eg, AHA guidelines, Choosing Wisely^® campaign), (2) removal of the telemetry order from hospitalist admission order set on March 23, 2015, (3) inclusion of telemetry discussion in the hospitalist group’s daily “Rounding Checklist,”¹⁴ (4) monthly feedback provided as part of hospitalist group meetings, and (5) a financial incentive, awarded to the division (no individual provider payment) if performance targets were met. See supplementary Appendix (“Implementation Manual”) for further details.

Data Source

We obtained data on patient age, gender, Medicare Severity-Diagnosis Related Group, Charlson comorbidity index (CCI), CMI, admitting unit, attending physician, admission and discharge dates, length of stay (LOS), 30-day readmission, bed charge (telemetry or nontelemetry), ICU stay, and inpatient mortality from the enterprise data warehouse. Telemetry days were determined through room billing charges, which are assigned based on the presence or absence of an active telemetry order at midnight. Code events came from a log kept by the hospital telephone operator, who is responsible for sending out all calls to the code team. Code event data were available starting July 19, 2014.

Measures

Our primary outcome was the percentage of hospital days that had telemetry charges for individual patients. All billed telemetry days on acute care floors were included regardless of admission status (inpatient vs observation), service, indication, or ordering provider. Secondary outcomes were inpatient mortality, escalation of care, code event rates, and appropriate telemetry utilization rates. Escalation of care was defined as transfer to an ICU after initially being admitted to an acute care floor. The code event rate was defined as the ratio of the number of code team activations to the number of patient days. Appropriate telemetry utilization rates were determined via chart review, as detailed below.

In order to evaluate changes in appropriateness of telemetry monitoring, 4 of the authors who are internal medicine physicians (KE, CC, JC, DG) performed chart reviews of 25 randomly selected patients in each group (hospitalist and nonhospitalist) before and after the intervention who received at least 1 day of telemetry monitoring. Each reviewer was provided a key based on AHA guidelines for monitoring indications and associated maximum allowable durations.³ Chart reviews were performed to determine the indication (if any) for monitoring, as well as the number of days that were indicated. The number of indicated days was compared to the number of telemetry days the patient received to determine the overall proportion of days that were indicated (“Telemetry appropriateness per visit”). Three reviewers (KE, AR, CC) also evaluated 100 patients on the hospitalist service after the intervention who did not receive any telemetry monitoring to evaluate whether patients with indications for telemetry monitoring were not receiving it after the intervention. For patients who had a possible indication, the indication was classified as Class I (“Cardiac monitoring is indicated in most, if not all, patients in this group”) or Class II (“Cardiac monitoring may be of benefit in some patients but is not considered essential for all patients”).³

Adjustment Variables

To account for differences in patient characteristics between hospitalist and nonhospitalist groups, we included age, gender, CMI, and CCI in statistical models. CCI was calculated according to the algorithm specified by Quan et al.¹⁵ using all patient diagnoses from previous visits and the index visit identified from the facility billing system.

Statistical Analysis

We computed descriptive statistics for study outcomes and visit characteristics for hospitalist and nonhospitalist visits for pre- and postintervention periods. Descriptive statistics were expressed as n (%) for categorical patient characteristics and outcome variables. For continuous patient characteristics, we expressed the variability of individual observations as the mean ± the standard deviation. For continuous outcomes, we expressed the precision of the mean estimates using standard error. Telemetry utilization per visit was weighted by the number of total acute care days per visit. Telemetry appropriateness per visit was weighted by the number of telemetry days per visit. Patients who did not receive any telemetry monitoring were included in the analysis and noted to have 0 telemetry days. All patients had at least 1 acute care day. Categorical variables were compared using χ² tests, and continuous variables were compared using t tests. Code event rates were compared using the binomial probability mid-p exact test for person-time data.¹⁶

We fitted generalized linear regression models using generalized estimating equations to evaluate the relative change in outcomes of interest in the postintervention period compared with the preintervention period after adjusting for study covariates. The models included study group (hospitalist and nonhospitalist), time period (pre- and postintervention), an interaction term between study group and time period, and study covariates (age, gender, CMI, and CCI). The models were defined using a binomial distributional assumption and logit link function for mortality, escalation of care, and whether patients had at least 1 telemetry day. A gamma distributional assumption and log link function were used for LOS, telemetry acute care days per visit, and total acute care days per visit. A negative binomial distributional assumption and log link function were used for telemetry utilization and telemetry appropriateness. We used the log of the acute care days as an offset for telemetry utilization and the log of the telemetry days per visit as an offset for telemetry appropriateness. An exchangeable working correlation matrix was used to account for physician-level clustering for all outcomes. Intervention effects, representing the difference in odds for categorical variables and in amount for continuous variables, were calculated as exponentiation of the beta parameters for the covariate minus 1.

P values <.05 were considered significant. We used SAS version 9.4 statistical software (SAS Institute Inc., Cary, NC) for data analysis.

The percent of patients who had any telemetry charges decreased from 36.2% to 15.9% (P < .001) in the hospitalist group and from 31.8% to 28.0% in the nonhospitalist group (P < .001; Table 1). Rates of code events did not change over time (P = .9).

In this study, implementing a change in the EHR telemetry order produced reductions in telemetry days. However, when combined with a multicomponent program including education, audit and feedback, financial incentives, and changes to remove telemetry orders from admission orders sets, an even more marked improvement was seen. Neither intervention reduced LOS, increased code event rates, or increased rates of escalation of care.

Prior studies have evaluated interventions to reduce unnecessary telemetry monitoring with varying degrees of success. The most successful EHR intervention to date, from Dressler et al.,¹⁸ achieved a 70% reduction in overall telemetry use by integrating the AHA guidelines into their EHR and incorporating nursing discontinuation guidelines to ensure that telemetry discontinuation was both safe and timely. Other studies using stewardship approaches and standardized protocols have been less successful.^19,20 One study utilizing a multidisciplinary approach but not including an EHR component showed modest improvements in telemetry.²¹

Although we are unable to differentiate the exact effect of each component of the intervention, we did note an immediate decrease in telemetry orders after removing the telemetry order from our admission order set, a trend that was magnified after the addition of broader EHR changes (Figure 1). Important additional contributors to our success seem to have been the standardization of rounds to include daily discussion of telemetry and the provision of routine feedback. We cannot discern whether other components of our program (such as the financial incentives) contributed more or less to our program, though the sum of these interventions produced an overall program that required substantial buy in and sustained focus from the hospitalist group. The importance of the hospitalist program is highlighted by the relatively large differences in improvement compared with the nonhospitalist group.

Our study has several limitations. First, the study was conducted at a single center, which may limit its generalizability. Second, the intervention was multifaceted, diminishing our ability to discern which aspects beyond the system-wide change in the telemetry order were most responsible for the observed effect among hospitalists. Third, we are unable to fully account for baseline differences in telemetry utilization between hospitalist and nonhospitalist groups. It is likely that different services utilize telemetry monitoring in different ways, and the hospitalist group may have been more aware of the existing guidelines for monitoring prior to the intervention. Furthermore, we had a limited sample size for the chart audits, which reduced the available statistical power for determining changes in the appropriateness of telemetry utilization. Additionally, because internal medicine residents rotate through various services, it is possible that the education they received on their hospitalist rotation as part of our intervention had a spillover effect in the nonhospitalist group. However, any effect should have decreased the difference between the groups. Lastly, although our postintervention time period was 1 year, we do not have data beyond that to monitor for sustainability of the results.

In this single-site study, combining EHR orders prompting physicians to choose a clinical indication and duration for monitoring with a broader program—including upstream changes in ordering as well as education, audit, and feedback—produced reductions in telemetry usage. Whether this reduction improves the appropriateness of telemetry utilization or reduces other effects of telemetry (eg, alert fatigue, calls for benign arrhythmias) cannot be discerned from our study. However, our results support the idea that multipronged approaches to telemetry use are most likely to produce improvements.

Acknowledgments

The authors thank Dr. Frank Thomas for his assistance with process engineering and Mr. Andrew Wood for his routine provision of data. The statistical analysis was supported by the University of Utah Study Design and Biostatistics Center, with funding in part from the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant 5UL1TR001067-05 (formerly 8UL1TR000105 and UL1RR025764).

Disclosure

The authors have no conflicts of interest to report.

Wasteful care may account for between 21% and 34% of the United States’ $3.2 trillion in annual healthcare expenditures, making it a prime target for cost-saving initiatives.^1,2 Telemetry is a target for value improvement strategies because telemetry is overutilized, rarely leads to a change in management, and has associated guidelines on appropriate use.^3-10 Telemetry use has been a focus of the Joint Commission’s National Patient Safety Goals since 2014, and it is also a focus of the Society of Hospital Medicine’s Choosing Wisely^® campaign.^11-13

Previous initiatives have evaluated how changes to telemetry orders or education and feedback affect telemetry use. Few studies have compared a system-wide electronic health record (EHR) approach to a multifaceted intervention. In seeking to address this gap, we adapted published guidelines from the American Heart Association (AHA) and incorporated them into our EHR ordering process.³ Simultaneously, we implemented a multifaceted quality improvement initiative and compared this combined program’s effectiveness to that of the EHR approach alone.

Study Design, Setting, and Population

We performed a 2-group observational pre- to postintervention study at University of Utah Health. Hospital encounters of patients 18 years and older who had at least 1 inpatient acute care, nonintensive care unit (ICU) room charge and an admission date between January 1, 2014, and July 31, 2016, were included. Patient encounters with missing encounter-level covariates, such as case mix index (CMI) or attending provider identification, were excluded. The Institutional Review Board classified this project as quality improvement and did not require review and oversight.

Intervention

On July 6, 2015, our Epic (Epic Systems Corporation, Madison, WI) EHR telemetry order was modified to discourage unnecessary telemetry monitoring. The new order required providers ordering telemetry to choose a clinical indication and select a duration for monitoring, after which the order would expire and require physician renewal or discontinuation. These were the only changes that occurred for nonhospitalist providers. The nonhospitalist group included all admitting providers who were not hospitalists. This group included neurology (6.98%); cardiology (8.13%); other medical specialties such as pulmonology, hematology, and oncology (21.30%); cardiothoracic surgery (3.72%); orthopedic surgery (14.84%); general surgery (11.11%); neurosurgery (11.07%); and other surgical specialties, including urology, transplant, vascular surgery, and plastics (16.68%).

Between January 2015 and June 2015, we implemented a multicomponent program among our hospitalist service. The hospitalist service is composed of 4 teams with internal medicine residents and 2 teams with advanced practice providers, all staffed by academic hospitalists. Our program was composed of 5 elements, all of which were made before the hospital-wide changes to electronic telemetry orders and maintained throughout the study period, as follows: (1) a single provider education session reviewing available evidence (eg, AHA guidelines, Choosing Wisely^® campaign), (2) removal of the telemetry order from hospitalist admission order set on March 23, 2015, (3) inclusion of telemetry discussion in the hospitalist group’s daily “Rounding Checklist,”¹⁴ (4) monthly feedback provided as part of hospitalist group meetings, and (5) a financial incentive, awarded to the division (no individual provider payment) if performance targets were met. See supplementary Appendix (“Implementation Manual”) for further details.

Data Source

We obtained data on patient age, gender, Medicare Severity-Diagnosis Related Group, Charlson comorbidity index (CCI), CMI, admitting unit, attending physician, admission and discharge dates, length of stay (LOS), 30-day readmission, bed charge (telemetry or nontelemetry), ICU stay, and inpatient mortality from the enterprise data warehouse. Telemetry days were determined through room billing charges, which are assigned based on the presence or absence of an active telemetry order at midnight. Code events came from a log kept by the hospital telephone operator, who is responsible for sending out all calls to the code team. Code event data were available starting July 19, 2014.

Measures

Our primary outcome was the percentage of hospital days that had telemetry charges for individual patients. All billed telemetry days on acute care floors were included regardless of admission status (inpatient vs observation), service, indication, or ordering provider. Secondary outcomes were inpatient mortality, escalation of care, code event rates, and appropriate telemetry utilization rates. Escalation of care was defined as transfer to an ICU after initially being admitted to an acute care floor. The code event rate was defined as the ratio of the number of code team activations to the number of patient days. Appropriate telemetry utilization rates were determined via chart review, as detailed below.

In order to evaluate changes in appropriateness of telemetry monitoring, 4 of the authors who are internal medicine physicians (KE, CC, JC, DG) performed chart reviews of 25 randomly selected patients in each group (hospitalist and nonhospitalist) before and after the intervention who received at least 1 day of telemetry monitoring. Each reviewer was provided a key based on AHA guidelines for monitoring indications and associated maximum allowable durations.³ Chart reviews were performed to determine the indication (if any) for monitoring, as well as the number of days that were indicated. The number of indicated days was compared to the number of telemetry days the patient received to determine the overall proportion of days that were indicated (“Telemetry appropriateness per visit”). Three reviewers (KE, AR, CC) also evaluated 100 patients on the hospitalist service after the intervention who did not receive any telemetry monitoring to evaluate whether patients with indications for telemetry monitoring were not receiving it after the intervention. For patients who had a possible indication, the indication was classified as Class I (“Cardiac monitoring is indicated in most, if not all, patients in this group”) or Class II (“Cardiac monitoring may be of benefit in some patients but is not considered essential for all patients”).³

Adjustment Variables

To account for differences in patient characteristics between hospitalist and nonhospitalist groups, we included age, gender, CMI, and CCI in statistical models. CCI was calculated according to the algorithm specified by Quan et al.¹⁵ using all patient diagnoses from previous visits and the index visit identified from the facility billing system.

Statistical Analysis

We computed descriptive statistics for study outcomes and visit characteristics for hospitalist and nonhospitalist visits for pre- and postintervention periods. Descriptive statistics were expressed as n (%) for categorical patient characteristics and outcome variables. For continuous patient characteristics, we expressed the variability of individual observations as the mean ± the standard deviation. For continuous outcomes, we expressed the precision of the mean estimates using standard error. Telemetry utilization per visit was weighted by the number of total acute care days per visit. Telemetry appropriateness per visit was weighted by the number of telemetry days per visit. Patients who did not receive any telemetry monitoring were included in the analysis and noted to have 0 telemetry days. All patients had at least 1 acute care day. Categorical variables were compared using χ² tests, and continuous variables were compared using t tests. Code event rates were compared using the binomial probability mid-p exact test for person-time data.¹⁶

We fitted generalized linear regression models using generalized estimating equations to evaluate the relative change in outcomes of interest in the postintervention period compared with the preintervention period after adjusting for study covariates. The models included study group (hospitalist and nonhospitalist), time period (pre- and postintervention), an interaction term between study group and time period, and study covariates (age, gender, CMI, and CCI). The models were defined using a binomial distributional assumption and logit link function for mortality, escalation of care, and whether patients had at least 1 telemetry day. A gamma distributional assumption and log link function were used for LOS, telemetry acute care days per visit, and total acute care days per visit. A negative binomial distributional assumption and log link function were used for telemetry utilization and telemetry appropriateness. We used the log of the acute care days as an offset for telemetry utilization and the log of the telemetry days per visit as an offset for telemetry appropriateness. An exchangeable working correlation matrix was used to account for physician-level clustering for all outcomes. Intervention effects, representing the difference in odds for categorical variables and in amount for continuous variables, were calculated as exponentiation of the beta parameters for the covariate minus 1.

P values <.05 were considered significant. We used SAS version 9.4 statistical software (SAS Institute Inc., Cary, NC) for data analysis.

The percent of patients who had any telemetry charges decreased from 36.2% to 15.9% (P < .001) in the hospitalist group and from 31.8% to 28.0% in the nonhospitalist group (P < .001; Table 1). Rates of code events did not change over time (P = .9).

In this study, implementing a change in the EHR telemetry order produced reductions in telemetry days. However, when combined with a multicomponent program including education, audit and feedback, financial incentives, and changes to remove telemetry orders from admission orders sets, an even more marked improvement was seen. Neither intervention reduced LOS, increased code event rates, or increased rates of escalation of care.

Prior studies have evaluated interventions to reduce unnecessary telemetry monitoring with varying degrees of success. The most successful EHR intervention to date, from Dressler et al.,¹⁸ achieved a 70% reduction in overall telemetry use by integrating the AHA guidelines into their EHR and incorporating nursing discontinuation guidelines to ensure that telemetry discontinuation was both safe and timely. Other studies using stewardship approaches and standardized protocols have been less successful.^19,20 One study utilizing a multidisciplinary approach but not including an EHR component showed modest improvements in telemetry.²¹

Although we are unable to differentiate the exact effect of each component of the intervention, we did note an immediate decrease in telemetry orders after removing the telemetry order from our admission order set, a trend that was magnified after the addition of broader EHR changes (Figure 1). Important additional contributors to our success seem to have been the standardization of rounds to include daily discussion of telemetry and the provision of routine feedback. We cannot discern whether other components of our program (such as the financial incentives) contributed more or less to our program, though the sum of these interventions produced an overall program that required substantial buy in and sustained focus from the hospitalist group. The importance of the hospitalist program is highlighted by the relatively large differences in improvement compared with the nonhospitalist group.

Our study has several limitations. First, the study was conducted at a single center, which may limit its generalizability. Second, the intervention was multifaceted, diminishing our ability to discern which aspects beyond the system-wide change in the telemetry order were most responsible for the observed effect among hospitalists. Third, we are unable to fully account for baseline differences in telemetry utilization between hospitalist and nonhospitalist groups. It is likely that different services utilize telemetry monitoring in different ways, and the hospitalist group may have been more aware of the existing guidelines for monitoring prior to the intervention. Furthermore, we had a limited sample size for the chart audits, which reduced the available statistical power for determining changes in the appropriateness of telemetry utilization. Additionally, because internal medicine residents rotate through various services, it is possible that the education they received on their hospitalist rotation as part of our intervention had a spillover effect in the nonhospitalist group. However, any effect should have decreased the difference between the groups. Lastly, although our postintervention time period was 1 year, we do not have data beyond that to monitor for sustainability of the results.

In this single-site study, combining EHR orders prompting physicians to choose a clinical indication and duration for monitoring with a broader program—including upstream changes in ordering as well as education, audit, and feedback—produced reductions in telemetry usage. Whether this reduction improves the appropriateness of telemetry utilization or reduces other effects of telemetry (eg, alert fatigue, calls for benign arrhythmias) cannot be discerned from our study. However, our results support the idea that multipronged approaches to telemetry use are most likely to produce improvements.

Acknowledgments

The authors thank Dr. Frank Thomas for his assistance with process engineering and Mr. Andrew Wood for his routine provision of data. The statistical analysis was supported by the University of Utah Study Design and Biostatistics Center, with funding in part from the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant 5UL1TR001067-05 (formerly 8UL1TR000105 and UL1RR025764).

Disclosure

The authors have no conflicts of interest to report.

Wasteful care may account for between 21% and 34% of the United States’ $3.2 trillion in annual healthcare expenditures, making it a prime target for cost-saving initiatives.^1,2 Telemetry is a target for value improvement strategies because telemetry is overutilized, rarely leads to a change in management, and has associated guidelines on appropriate use.^3-10 Telemetry use has been a focus of the Joint Commission’s National Patient Safety Goals since 2014, and it is also a focus of the Society of Hospital Medicine’s Choosing Wisely^® campaign.^11-13

Previous initiatives have evaluated how changes to telemetry orders or education and feedback affect telemetry use. Few studies have compared a system-wide electronic health record (EHR) approach to a multifaceted intervention. In seeking to address this gap, we adapted published guidelines from the American Heart Association (AHA) and incorporated them into our EHR ordering process.³ Simultaneously, we implemented a multifaceted quality improvement initiative and compared this combined program’s effectiveness to that of the EHR approach alone.

Study Design, Setting, and Population

We performed a 2-group observational pre- to postintervention study at University of Utah Health. Hospital encounters of patients 18 years and older who had at least 1 inpatient acute care, nonintensive care unit (ICU) room charge and an admission date between January 1, 2014, and July 31, 2016, were included. Patient encounters with missing encounter-level covariates, such as case mix index (CMI) or attending provider identification, were excluded. The Institutional Review Board classified this project as quality improvement and did not require review and oversight.

Intervention

On July 6, 2015, our Epic (Epic Systems Corporation, Madison, WI) EHR telemetry order was modified to discourage unnecessary telemetry monitoring. The new order required providers ordering telemetry to choose a clinical indication and select a duration for monitoring, after which the order would expire and require physician renewal or discontinuation. These were the only changes that occurred for nonhospitalist providers. The nonhospitalist group included all admitting providers who were not hospitalists. This group included neurology (6.98%); cardiology (8.13%); other medical specialties such as pulmonology, hematology, and oncology (21.30%); cardiothoracic surgery (3.72%); orthopedic surgery (14.84%); general surgery (11.11%); neurosurgery (11.07%); and other surgical specialties, including urology, transplant, vascular surgery, and plastics (16.68%).

Between January 2015 and June 2015, we implemented a multicomponent program among our hospitalist service. The hospitalist service is composed of 4 teams with internal medicine residents and 2 teams with advanced practice providers, all staffed by academic hospitalists. Our program was composed of 5 elements, all of which were made before the hospital-wide changes to electronic telemetry orders and maintained throughout the study period, as follows: (1) a single provider education session reviewing available evidence (eg, AHA guidelines, Choosing Wisely^® campaign), (2) removal of the telemetry order from hospitalist admission order set on March 23, 2015, (3) inclusion of telemetry discussion in the hospitalist group’s daily “Rounding Checklist,”¹⁴ (4) monthly feedback provided as part of hospitalist group meetings, and (5) a financial incentive, awarded to the division (no individual provider payment) if performance targets were met. See supplementary Appendix (“Implementation Manual”) for further details.

Data Source

We obtained data on patient age, gender, Medicare Severity-Diagnosis Related Group, Charlson comorbidity index (CCI), CMI, admitting unit, attending physician, admission and discharge dates, length of stay (LOS), 30-day readmission, bed charge (telemetry or nontelemetry), ICU stay, and inpatient mortality from the enterprise data warehouse. Telemetry days were determined through room billing charges, which are assigned based on the presence or absence of an active telemetry order at midnight. Code events came from a log kept by the hospital telephone operator, who is responsible for sending out all calls to the code team. Code event data were available starting July 19, 2014.

Measures

Our primary outcome was the percentage of hospital days that had telemetry charges for individual patients. All billed telemetry days on acute care floors were included regardless of admission status (inpatient vs observation), service, indication, or ordering provider. Secondary outcomes were inpatient mortality, escalation of care, code event rates, and appropriate telemetry utilization rates. Escalation of care was defined as transfer to an ICU after initially being admitted to an acute care floor. The code event rate was defined as the ratio of the number of code team activations to the number of patient days. Appropriate telemetry utilization rates were determined via chart review, as detailed below.

In order to evaluate changes in appropriateness of telemetry monitoring, 4 of the authors who are internal medicine physicians (KE, CC, JC, DG) performed chart reviews of 25 randomly selected patients in each group (hospitalist and nonhospitalist) before and after the intervention who received at least 1 day of telemetry monitoring. Each reviewer was provided a key based on AHA guidelines for monitoring indications and associated maximum allowable durations.³ Chart reviews were performed to determine the indication (if any) for monitoring, as well as the number of days that were indicated. The number of indicated days was compared to the number of telemetry days the patient received to determine the overall proportion of days that were indicated (“Telemetry appropriateness per visit”). Three reviewers (KE, AR, CC) also evaluated 100 patients on the hospitalist service after the intervention who did not receive any telemetry monitoring to evaluate whether patients with indications for telemetry monitoring were not receiving it after the intervention. For patients who had a possible indication, the indication was classified as Class I (“Cardiac monitoring is indicated in most, if not all, patients in this group”) or Class II (“Cardiac monitoring may be of benefit in some patients but is not considered essential for all patients”).³

Adjustment Variables

To account for differences in patient characteristics between hospitalist and nonhospitalist groups, we included age, gender, CMI, and CCI in statistical models. CCI was calculated according to the algorithm specified by Quan et al.¹⁵ using all patient diagnoses from previous visits and the index visit identified from the facility billing system.

Statistical Analysis

We computed descriptive statistics for study outcomes and visit characteristics for hospitalist and nonhospitalist visits for pre- and postintervention periods. Descriptive statistics were expressed as n (%) for categorical patient characteristics and outcome variables. For continuous patient characteristics, we expressed the variability of individual observations as the mean ± the standard deviation. For continuous outcomes, we expressed the precision of the mean estimates using standard error. Telemetry utilization per visit was weighted by the number of total acute care days per visit. Telemetry appropriateness per visit was weighted by the number of telemetry days per visit. Patients who did not receive any telemetry monitoring were included in the analysis and noted to have 0 telemetry days. All patients had at least 1 acute care day. Categorical variables were compared using χ² tests, and continuous variables were compared using t tests. Code event rates were compared using the binomial probability mid-p exact test for person-time data.¹⁶

We fitted generalized linear regression models using generalized estimating equations to evaluate the relative change in outcomes of interest in the postintervention period compared with the preintervention period after adjusting for study covariates. The models included study group (hospitalist and nonhospitalist), time period (pre- and postintervention), an interaction term between study group and time period, and study covariates (age, gender, CMI, and CCI). The models were defined using a binomial distributional assumption and logit link function for mortality, escalation of care, and whether patients had at least 1 telemetry day. A gamma distributional assumption and log link function were used for LOS, telemetry acute care days per visit, and total acute care days per visit. A negative binomial distributional assumption and log link function were used for telemetry utilization and telemetry appropriateness. We used the log of the acute care days as an offset for telemetry utilization and the log of the telemetry days per visit as an offset for telemetry appropriateness. An exchangeable working correlation matrix was used to account for physician-level clustering for all outcomes. Intervention effects, representing the difference in odds for categorical variables and in amount for continuous variables, were calculated as exponentiation of the beta parameters for the covariate minus 1.

P values <.05 were considered significant. We used SAS version 9.4 statistical software (SAS Institute Inc., Cary, NC) for data analysis.

The percent of patients who had any telemetry charges decreased from 36.2% to 15.9% (P < .001) in the hospitalist group and from 31.8% to 28.0% in the nonhospitalist group (P < .001; Table 1). Rates of code events did not change over time (P = .9).

In this study, implementing a change in the EHR telemetry order produced reductions in telemetry days. However, when combined with a multicomponent program including education, audit and feedback, financial incentives, and changes to remove telemetry orders from admission orders sets, an even more marked improvement was seen. Neither intervention reduced LOS, increased code event rates, or increased rates of escalation of care.

Prior studies have evaluated interventions to reduce unnecessary telemetry monitoring with varying degrees of success. The most successful EHR intervention to date, from Dressler et al.,¹⁸ achieved a 70% reduction in overall telemetry use by integrating the AHA guidelines into their EHR and incorporating nursing discontinuation guidelines to ensure that telemetry discontinuation was both safe and timely. Other studies using stewardship approaches and standardized protocols have been less successful.^19,20 One study utilizing a multidisciplinary approach but not including an EHR component showed modest improvements in telemetry.²¹

Although we are unable to differentiate the exact effect of each component of the intervention, we did note an immediate decrease in telemetry orders after removing the telemetry order from our admission order set, a trend that was magnified after the addition of broader EHR changes (Figure 1). Important additional contributors to our success seem to have been the standardization of rounds to include daily discussion of telemetry and the provision of routine feedback. We cannot discern whether other components of our program (such as the financial incentives) contributed more or less to our program, though the sum of these interventions produced an overall program that required substantial buy in and sustained focus from the hospitalist group. The importance of the hospitalist program is highlighted by the relatively large differences in improvement compared with the nonhospitalist group.

Our study has several limitations. First, the study was conducted at a single center, which may limit its generalizability. Second, the intervention was multifaceted, diminishing our ability to discern which aspects beyond the system-wide change in the telemetry order were most responsible for the observed effect among hospitalists. Third, we are unable to fully account for baseline differences in telemetry utilization between hospitalist and nonhospitalist groups. It is likely that different services utilize telemetry monitoring in different ways, and the hospitalist group may have been more aware of the existing guidelines for monitoring prior to the intervention. Furthermore, we had a limited sample size for the chart audits, which reduced the available statistical power for determining changes in the appropriateness of telemetry utilization. Additionally, because internal medicine residents rotate through various services, it is possible that the education they received on their hospitalist rotation as part of our intervention had a spillover effect in the nonhospitalist group. However, any effect should have decreased the difference between the groups. Lastly, although our postintervention time period was 1 year, we do not have data beyond that to monitor for sustainability of the results.

In this single-site study, combining EHR orders prompting physicians to choose a clinical indication and duration for monitoring with a broader program—including upstream changes in ordering as well as education, audit, and feedback—produced reductions in telemetry usage. Whether this reduction improves the appropriateness of telemetry utilization or reduces other effects of telemetry (eg, alert fatigue, calls for benign arrhythmias) cannot be discerned from our study. However, our results support the idea that multipronged approaches to telemetry use are most likely to produce improvements.

Acknowledgments

The authors thank Dr. Frank Thomas for his assistance with process engineering and Mr. Andrew Wood for his routine provision of data. The statistical analysis was supported by the University of Utah Study Design and Biostatistics Center, with funding in part from the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant 5UL1TR001067-05 (formerly 8UL1TR000105 and UL1RR025764).

Disclosure

The authors have no conflicts of interest to report.

Given the high cost of readmissions to the healthcare system, there has been a substantial push to reduce readmissions by policymakers.¹ Among these is the Hospital Readmissions Reduction Program (HRRP), in which hospitals with higher than expected readmission rates receive reduced payments from Medicare.² Recent evidence has suggested the success of such policy changes, with multiple reports demonstrating a decrease in 30-day readmission rates in the Medicare population starting in 2010.^3-8

Initiatives to reduce readmissions can also have an effect on total number of admissions.^9,10 Indeed, along with the recent reduction in readmission, there has been a reduction in all admissions among Medicare beneficiaries.^11,12 Some studies have found that as admissions have decreased, the burden of comorbidity has increased among hospitalized patients,^3,11 suggesting that hospitals may be increasingly filled with patients at high risk of readmission. However, whether readmission risk among hospitalized patients has changed remains unknown, and understanding changes in risk profile could help inform which patients to target with future interventions to reduce readmissions.

Hospital efforts to reduce readmissions may have differential effects on types of patients by risk. For instance, low-intensity, system-wide interventions such as standardized discharge instructions or medicine reconciliation may have a stronger effect on patients at relatively low risk of readmission who may have a few important drivers of readmission that are easily overcome. Alternatively, the impact of intensive care transitions management might be greatest for high-risk patients, who have the most need for postdischarge medications, follow-up, and self-care.

The purpose of this study was therefore twofold: (1) to observe changes in average monthly risk of readmission among hospitalized Medicare patients and (2) to examine changes in readmission rates for Medicare patients at various risk of readmission. We hypothesized that readmission risk in the Medicare population would increase in recent years, as overall number of admissions and readmissions have fallen.^7,11 Additionally, we hypothesized that standardized readmission rates would decline less in highest risk patients as compared with the lowest risk patients because transitional care interventions may not be able to mitigate the large burden of comorbidity and social issues present in many high-risk patients.^13,14

We performed a retrospective cohort study of hospitalizations to US nonfederal short-term acute care facilities by Medicare beneficiaries between January 2009 and June 2015. The design involved 4 steps. First, we estimated a predictive model for unplanned readmissions within 30 days of discharge. Second, we assigned each hospitalization a predicted risk of readmission based on the model. Third, we studied trends in mean predicted risk of readmission during the study period. Fourth, we examined trends in observed to expected (O/E) readmission for hospitalizations in the lowest, middle, and highest categories of predicted risk of readmission to determine whether reductions in readmissions were more substantial in certain risk groups than in others.

Data were obtained from the Centers for Medicare and Medicaid Services (CMS) Inpatient Standard Analytic File and the Medicare Enrollment Data Base. We included hospitalizations of fee-for-service Medicare beneficiaries age ≥65 with continuous enrollment in Part A Medicare fee-for-service for at least 1 year prior and 30 days after the hospitalization.¹⁵ Hospitalizations with a discharge disposition of death, transfer to another acute hospital, and left against medical advice (AMA) were excluded. We also excluded patients with enrollment in hospice care prior to hospitalization. We excluded hospitalizations in June 2012 because of an irregularity in data availability for that month.

Hospitalizations were categorized into 5 specialty cohorts according to service line. The 5 cohorts were those used for the CMS hospital-wide readmission measure and included surgery/gynecology, medicine, cardiovascular, cardiorespiratory, and neurology.¹⁵ Among the 3 clinical conditions tracked as part of HRRP, heart failure and pneumonia were a subset of the cardiorespiratory cohort, while acute myocardial infarction was a subset of the cardiovascular cohort. Our use of cohorts was threefold: first, the average risk of readmission differs substantially across these cohorts, so pooling them produces heterogeneous risk strata; second, risk variables perform differently in different cohorts, so one single model may not be as accurate for calculating risk; and, third, the use of disease cohorts makes our results comparable to the CMS model and similar to other readmission studies in Medicare.^7,8,15

For development of the risk model, the outcome was 30-day unplanned hospital readmission. Planned readmissions were excluded; these were defined by the CMS algorithm as readmissions in which a typically planned procedure occurred in a hospitalization with a nonacute principal diagnosis.¹⁶ Independent variables included age and comorbidities in the final hospital-wide readmission models for each of the 5 specialty cohorts.¹⁵ In order to produce the best possible individual risk prediction for each patient, we added additional independent variables that CMS avoids for hospital quality measurement purposes but that contribute to risk of readmission: sex, race, dual eligibility status, number of prior AMA discharges, intensive care unit stay during current hospitalization, coronary care unit stay during current hospitalization, and hospitalization in the prior 30, 90, and 180 days. We also included an indicator variable for hospitalizations with more than 9 discharge diagnosis codes on or after January 2011, the time at which Medicare allowed an increase of the number of International Classification of Diseases, 9th Revision-Clinical Modification diagnosis billing codes from 9 to 25.¹⁷ This indicator adjusts for the increased availability of comorbidity codes, which might otherwise inflate the predicted risk relative to hospitalizations prior to that date.

Based on the risk models, each hospitalization was assigned a predicted risk of readmission. For each specialty cohort, we pooled all hospitalizations across all study years and divided them into risk quintiles. We categorized hospitalizations as high risk if in the highest quintile, medium risk if in the middle 3 quintiles, and low risk if in the lowest quintile of predicted risk for all study hospitalizations in a given specialty cohort.

For our time trend analyses, we studied 2 outcomes: monthly mean predicted risk and monthly ratio of observed readmissions to expected readmissions for patients in the lowest, middle, and highest categories of predicted risk of readmission. We studied monthly predicted risk to determine whether the average readmission risk of patients was changing over time as admission and readmission rates were declining. We studied the ratio of O/E readmissions to determine whether the decline in overall readmissions was more substantial in particular risk strata; we used the ratio of O/E readmissions, which measures number of readmissions divided by number of readmissions predicted by the model, rather than crude observed readmissions, as O/E readmissions account for any changes in risk profiles over time within each risk stratum. Independent variables in our trend analyses were year—entered as a continuous variable—and indicators for postintroduction of the Affordable Care Act (ACA, March 2010) and for postintroduction of HRRP (October 2012); these time indicators were included because of prior studies demonstrating that the introduction of ACA was associated with a decrease from baseline in readmission rates, which leveled off after introduction of HRRP.⁷ We also included an indicator for calendar quarter to account for seasonal effects.

Statistical Analysis

We developed generalized estimating equation models to predict 30-day unplanned readmission for each of the 5 specialty cohorts. The 5 models were fit using all patients in each cohort for the included time period and were adjusted for clustering by hospital. We assessed discrimination by calculating area under the receiver operating characteristic curve (AUC) for the 5 models; the AUCs measured the models’ ability to distinguish patients who were readmitted versus those who were not.¹⁸ We also calculated AUCs for each year to examine model performance over time.

Using these models, we calculated predicted risk for each hospitalization and averaged these to obtain mean predicted risk for each specialty cohort for each month. To test for trends in mean risk, we estimated 5 time series models, one for each cohort, with the dependent variable of monthly mean predicted risk. For each cohort, we first estimated a series of 12 empty autoregressive models, each with a different autoregressive term (1, 2...12). For each model, we calculated χ² for the test that the autocorrelation was 0; based on a comparison of chi-squared values, we specified an autocorrelation of 1 month for all models. Accordingly, a 1-month lag was used to estimate one final model for each cohort. Independent variables included year and indicators for post-ACA and post-HRRP; these variables captured the effect of trends over time and the introduction of these policy changes, respectively.¹⁹

To determine whether changes in risk over time were a result of changes in particular risk groups, we categorized hospitalizations into risk strata based on quintiles of predicted risk for each specialty cohort for the entire study period. For each individual year, we calculated the proportion of hospitalizations in the highest, middle, and lowest readmission risk strata for each cohort.

We calculated the monthly ratio of O/E readmission for hospitalizations in the lowest 20%, middle 60%, and highest 20% of readmission risk by month; O/E reflects the excess or deficit observed events relative to the number predicted by the model. Using this monthly O/E as the dependent variable, we developed autoregressive time series models as above, again with a 1-month lag, for each of these 3 risk strata in each cohort. As before, independent variables were year as a continuous variable, indicator variables for post-ACA and post-HRRP, and a categorical variable for calendar quarter.

All analyses were done in SAS version 9.3 (SAS Institute Inc., Cary, NC) and Stata version 14.2 (StataCorp LLC, College Station, TX).

We included 47,288,961 hospitalizations in the study, of which 11,231,242 (23.8%) were in the surgery/gynecology cohort, 19,548,711 (41.3%) were in the medicine cohort, 5,433,125 (11.5%) were in the cardiovascular cohort, 8,179,691 (17.3%) were in the cardiorespiratory cohort, and 2,896,192 (6.1%) were in the neurology cohort. The readmission rate was 16.2% (n = 7,642,161) overall, with the highest rates observed in the cardiorespiratory (20.5%) and medicine (17.6%) cohorts and the lowest rates observed in the surgery/gynecology (11.8%) and neurology (13.8%) cohorts.

The final predictive models for each cohort ranged in number of parameters from 56 for the cardiorespiratory cohort to 264 for the surgery/gynecology cohort. The models had AUCs of 0.70, 0.65, 0.67, 0.65, and 0.63 for the surgery/gynecology, medicine, cardiovascular, cardiorespiratory, and neurology cohorts, respectively; AUCs remained fairly stable over time for all disease cohorts (Appendix Table 1).

A number of mechanisms may account for the across-the-board improvements in readmission reduction. Many hospitals have instituted system-wide interventions, including patient education, medicine reconciliation, and early postdischarge follow-up,²⁰ which may have reduced readmissions across all patient risk strata. Alternatively, hospitals may have implemented interventions that disproportionally benefited low-risk patients while simultaneously utilizing interventions that only benefited high-risk patients. For instance, increasing threshold for admission⁷ may have the greatest effect on low-risk patients who could be most easily managed at home, while many intensive transitional care interventions have been developed to target only high-risk patients.^21,22

With the introduction of HRRP, there have been a number of concerns about the readmission measure used to penalize hospitals for high readmission rates. One major concern has been that the readmission metric may be flawed in its ability to capture continued improvement related to readmission.²³ Some have suggested that with better population health management, admissions will decrease, patient risk of the remaining patients will increase, and hospitals will be increasingly filled with patients who have high likelihood of readmission. This potential for increased risk with HRRP was suggested by a recent study that found that comorbidities increased in hospitalized Medicare beneficiaries between 2010 and 2013.¹¹ Our results were mixed in supporting this potential phenomenon because we examined global risk of readmission and found that some of the cohorts had increased risk over time while others did not. Others have expressed concern that readmission measure does not account for socioeconomic status, which has been associated with readmission rates.^24-27 Although we did not directly examine socioeconomic status in our study, we found that hospitals have been able to reduce readmission across all levels of risk, which includes markers of socioeconomic status, including race and Medicaid eligibility status.

Although we hypothesized that readmission risk would increase as number of hospitalizations decreased over time, we found no increase in readmission risk among the cohorts with HRRP diagnoses that had the largest decrease in readmission rates.^7,8 Conversely, readmission risk did increase—with a concurrent increase in the proportion of high-risk hospitalizations—in the surgery/gynecology and neurology cohorts that were not subject to HRRP penalties. Nonetheless, rehospitalizations were reduced for all risk categories in these 2 cohorts. Notably, surgery/gynecology and neurology had the lowest readmission rates overall. These findings suggest that initiatives to prevent initial hospitalizations, such as increasing the threshold for postoperative admission, may have had a greater effect on low- versus high-risk patients in low-risk hospitalizations. However, once a patient is hospitalized, multidisciplinary strategies appear to be effective at reducing readmissions for all risk classes in these cohorts.

For the 3 cohorts in which we observed an increase in readmission risk among hospitalized patients, the risk appeared to increase in early 2011. This time was about 10 months after passage of ACA, the timing of which was previously associated with a drop in readmission rates,^7,8 but well before HRRP went into effect in October 2012. The increase in readmission risk coincided with an increase in the number of diagnostic codes that could be included on a hospital claim to Medicare.¹⁷ This increase in allowable codes allowed us to capture more diagnoses for some patients, potentially resulting in an increase in apparent predicted risk of readmissions. While we adjusted for this in our predictive models, we may not have fully accounted for differences in risk related to coding change. As a result, some of the observed differences in risk in our study may be attributable to coding differences. More broadly, studies demonstrating the success of HRRP have typically examined risk-adjusted rates of readmission.^3,7 It is possible that a small portion of the observed reduction in risk-adjusted readmission rates may be related to the increase in predicted risk of readmission observed in our study. Future assessment of trends in readmission during this period should consider accounting for change in the number of allowed billing codes.

Other limitations should be considered in the interpretation of this study. First, like many predictive models for readmission,¹⁴ ours had imperfect discrimination, which could affect our results. Second, our study was based on older Medicare patients, so findings may not be applicable to younger patients. Third, while we accounted for surrogates for socioeconomic status, including dual eligibility and race, our models lacked other socioeconomic and community factors that can influence readmission.^24-26 Nonetheless, 1 study suggested that easily measured socioeconomic factors may not have a strong influence on the readmission metric used by Medicare.²⁸ Fourth, while our study included over 47 million hospitalizations, our time trend analyses used calendar month as the primary independent variable. As our study included 77 months, we may not have had sufficient power to detect small changes in risk over time.

Medicare readmissions have declined steadily in recent years, presumably at least partly in response to policy changes including HRRP. We found that hospitals have been effective at reducing readmissions across a range of patient risk strata and clinical conditions. As a result, the overall risk of readmission for hospitalized patients has remained constant for some but not all conditions. Whether institutions can continue to reduce readmission rates for most types of patients remains to be seen.

Acknowledgments

This study was supported by the Agency for Healthcare Research and Quality (AHRQ) grant R01HS022882. Dr. Blecker was supported by the AHRQ grant K08HS23683. The authors would like to thank Shawn Hoke and Jane Padikkala for administrative support.

Disclosure

This study was supported by the Agency for Healthcare Research and Quality (AHRQ) grants R01HS022882 and K08HS23683. The authors have no conflicts to report.

Given the high cost of readmissions to the healthcare system, there has been a substantial push to reduce readmissions by policymakers.¹ Among these is the Hospital Readmissions Reduction Program (HRRP), in which hospitals with higher than expected readmission rates receive reduced payments from Medicare.² Recent evidence has suggested the success of such policy changes, with multiple reports demonstrating a decrease in 30-day readmission rates in the Medicare population starting in 2010.^3-8

Initiatives to reduce readmissions can also have an effect on total number of admissions.^9,10 Indeed, along with the recent reduction in readmission, there has been a reduction in all admissions among Medicare beneficiaries.^11,12 Some studies have found that as admissions have decreased, the burden of comorbidity has increased among hospitalized patients,^3,11 suggesting that hospitals may be increasingly filled with patients at high risk of readmission. However, whether readmission risk among hospitalized patients has changed remains unknown, and understanding changes in risk profile could help inform which patients to target with future interventions to reduce readmissions.

Hospital efforts to reduce readmissions may have differential effects on types of patients by risk. For instance, low-intensity, system-wide interventions such as standardized discharge instructions or medicine reconciliation may have a stronger effect on patients at relatively low risk of readmission who may have a few important drivers of readmission that are easily overcome. Alternatively, the impact of intensive care transitions management might be greatest for high-risk patients, who have the most need for postdischarge medications, follow-up, and self-care.

The purpose of this study was therefore twofold: (1) to observe changes in average monthly risk of readmission among hospitalized Medicare patients and (2) to examine changes in readmission rates for Medicare patients at various risk of readmission. We hypothesized that readmission risk in the Medicare population would increase in recent years, as overall number of admissions and readmissions have fallen.^7,11 Additionally, we hypothesized that standardized readmission rates would decline less in highest risk patients as compared with the lowest risk patients because transitional care interventions may not be able to mitigate the large burden of comorbidity and social issues present in many high-risk patients.^13,14

We performed a retrospective cohort study of hospitalizations to US nonfederal short-term acute care facilities by Medicare beneficiaries between January 2009 and June 2015. The design involved 4 steps. First, we estimated a predictive model for unplanned readmissions within 30 days of discharge. Second, we assigned each hospitalization a predicted risk of readmission based on the model. Third, we studied trends in mean predicted risk of readmission during the study period. Fourth, we examined trends in observed to expected (O/E) readmission for hospitalizations in the lowest, middle, and highest categories of predicted risk of readmission to determine whether reductions in readmissions were more substantial in certain risk groups than in others.

Data were obtained from the Centers for Medicare and Medicaid Services (CMS) Inpatient Standard Analytic File and the Medicare Enrollment Data Base. We included hospitalizations of fee-for-service Medicare beneficiaries age ≥65 with continuous enrollment in Part A Medicare fee-for-service for at least 1 year prior and 30 days after the hospitalization.¹⁵ Hospitalizations with a discharge disposition of death, transfer to another acute hospital, and left against medical advice (AMA) were excluded. We also excluded patients with enrollment in hospice care prior to hospitalization. We excluded hospitalizations in June 2012 because of an irregularity in data availability for that month.

Hospitalizations were categorized into 5 specialty cohorts according to service line. The 5 cohorts were those used for the CMS hospital-wide readmission measure and included surgery/gynecology, medicine, cardiovascular, cardiorespiratory, and neurology.¹⁵ Among the 3 clinical conditions tracked as part of HRRP, heart failure and pneumonia were a subset of the cardiorespiratory cohort, while acute myocardial infarction was a subset of the cardiovascular cohort. Our use of cohorts was threefold: first, the average risk of readmission differs substantially across these cohorts, so pooling them produces heterogeneous risk strata; second, risk variables perform differently in different cohorts, so one single model may not be as accurate for calculating risk; and, third, the use of disease cohorts makes our results comparable to the CMS model and similar to other readmission studies in Medicare.^7,8,15

For development of the risk model, the outcome was 30-day unplanned hospital readmission. Planned readmissions were excluded; these were defined by the CMS algorithm as readmissions in which a typically planned procedure occurred in a hospitalization with a nonacute principal diagnosis.¹⁶ Independent variables included age and comorbidities in the final hospital-wide readmission models for each of the 5 specialty cohorts.¹⁵ In order to produce the best possible individual risk prediction for each patient, we added additional independent variables that CMS avoids for hospital quality measurement purposes but that contribute to risk of readmission: sex, race, dual eligibility status, number of prior AMA discharges, intensive care unit stay during current hospitalization, coronary care unit stay during current hospitalization, and hospitalization in the prior 30, 90, and 180 days. We also included an indicator variable for hospitalizations with more than 9 discharge diagnosis codes on or after January 2011, the time at which Medicare allowed an increase of the number of International Classification of Diseases, 9th Revision-Clinical Modification diagnosis billing codes from 9 to 25.¹⁷ This indicator adjusts for the increased availability of comorbidity codes, which might otherwise inflate the predicted risk relative to hospitalizations prior to that date.

Based on the risk models, each hospitalization was assigned a predicted risk of readmission. For each specialty cohort, we pooled all hospitalizations across all study years and divided them into risk quintiles. We categorized hospitalizations as high risk if in the highest quintile, medium risk if in the middle 3 quintiles, and low risk if in the lowest quintile of predicted risk for all study hospitalizations in a given specialty cohort.

For our time trend analyses, we studied 2 outcomes: monthly mean predicted risk and monthly ratio of observed readmissions to expected readmissions for patients in the lowest, middle, and highest categories of predicted risk of readmission. We studied monthly predicted risk to determine whether the average readmission risk of patients was changing over time as admission and readmission rates were declining. We studied the ratio of O/E readmissions to determine whether the decline in overall readmissions was more substantial in particular risk strata; we used the ratio of O/E readmissions, which measures number of readmissions divided by number of readmissions predicted by the model, rather than crude observed readmissions, as O/E readmissions account for any changes in risk profiles over time within each risk stratum. Independent variables in our trend analyses were year—entered as a continuous variable—and indicators for postintroduction of the Affordable Care Act (ACA, March 2010) and for postintroduction of HRRP (October 2012); these time indicators were included because of prior studies demonstrating that the introduction of ACA was associated with a decrease from baseline in readmission rates, which leveled off after introduction of HRRP.⁷ We also included an indicator for calendar quarter to account for seasonal effects.

Statistical Analysis

We developed generalized estimating equation models to predict 30-day unplanned readmission for each of the 5 specialty cohorts. The 5 models were fit using all patients in each cohort for the included time period and were adjusted for clustering by hospital. We assessed discrimination by calculating area under the receiver operating characteristic curve (AUC) for the 5 models; the AUCs measured the models’ ability to distinguish patients who were readmitted versus those who were not.¹⁸ We also calculated AUCs for each year to examine model performance over time.

Using these models, we calculated predicted risk for each hospitalization and averaged these to obtain mean predicted risk for each specialty cohort for each month. To test for trends in mean risk, we estimated 5 time series models, one for each cohort, with the dependent variable of monthly mean predicted risk. For each cohort, we first estimated a series of 12 empty autoregressive models, each with a different autoregressive term (1, 2...12). For each model, we calculated χ² for the test that the autocorrelation was 0; based on a comparison of chi-squared values, we specified an autocorrelation of 1 month for all models. Accordingly, a 1-month lag was used to estimate one final model for each cohort. Independent variables included year and indicators for post-ACA and post-HRRP; these variables captured the effect of trends over time and the introduction of these policy changes, respectively.¹⁹

To determine whether changes in risk over time were a result of changes in particular risk groups, we categorized hospitalizations into risk strata based on quintiles of predicted risk for each specialty cohort for the entire study period. For each individual year, we calculated the proportion of hospitalizations in the highest, middle, and lowest readmission risk strata for each cohort.

We calculated the monthly ratio of O/E readmission for hospitalizations in the lowest 20%, middle 60%, and highest 20% of readmission risk by month; O/E reflects the excess or deficit observed events relative to the number predicted by the model. Using this monthly O/E as the dependent variable, we developed autoregressive time series models as above, again with a 1-month lag, for each of these 3 risk strata in each cohort. As before, independent variables were year as a continuous variable, indicator variables for post-ACA and post-HRRP, and a categorical variable for calendar quarter.

All analyses were done in SAS version 9.3 (SAS Institute Inc., Cary, NC) and Stata version 14.2 (StataCorp LLC, College Station, TX).

We included 47,288,961 hospitalizations in the study, of which 11,231,242 (23.8%) were in the surgery/gynecology cohort, 19,548,711 (41.3%) were in the medicine cohort, 5,433,125 (11.5%) were in the cardiovascular cohort, 8,179,691 (17.3%) were in the cardiorespiratory cohort, and 2,896,192 (6.1%) were in the neurology cohort. The readmission rate was 16.2% (n = 7,642,161) overall, with the highest rates observed in the cardiorespiratory (20.5%) and medicine (17.6%) cohorts and the lowest rates observed in the surgery/gynecology (11.8%) and neurology (13.8%) cohorts.

The final predictive models for each cohort ranged in number of parameters from 56 for the cardiorespiratory cohort to 264 for the surgery/gynecology cohort. The models had AUCs of 0.70, 0.65, 0.67, 0.65, and 0.63 for the surgery/gynecology, medicine, cardiovascular, cardiorespiratory, and neurology cohorts, respectively; AUCs remained fairly stable over time for all disease cohorts (Appendix Table 1).

A number of mechanisms may account for the across-the-board improvements in readmission reduction. Many hospitals have instituted system-wide interventions, including patient education, medicine reconciliation, and early postdischarge follow-up,²⁰ which may have reduced readmissions across all patient risk strata. Alternatively, hospitals may have implemented interventions that disproportionally benefited low-risk patients while simultaneously utilizing interventions that only benefited high-risk patients. For instance, increasing threshold for admission⁷ may have the greatest effect on low-risk patients who could be most easily managed at home, while many intensive transitional care interventions have been developed to target only high-risk patients.^21,22

With the introduction of HRRP, there have been a number of concerns about the readmission measure used to penalize hospitals for high readmission rates. One major concern has been that the readmission metric may be flawed in its ability to capture continued improvement related to readmission.²³ Some have suggested that with better population health management, admissions will decrease, patient risk of the remaining patients will increase, and hospitals will be increasingly filled with patients who have high likelihood of readmission. This potential for increased risk with HRRP was suggested by a recent study that found that comorbidities increased in hospitalized Medicare beneficiaries between 2010 and 2013.¹¹ Our results were mixed in supporting this potential phenomenon because we examined global risk of readmission and found that some of the cohorts had increased risk over time while others did not. Others have expressed concern that readmission measure does not account for socioeconomic status, which has been associated with readmission rates.^24-27 Although we did not directly examine socioeconomic status in our study, we found that hospitals have been able to reduce readmission across all levels of risk, which includes markers of socioeconomic status, including race and Medicaid eligibility status.

Although we hypothesized that readmission risk would increase as number of hospitalizations decreased over time, we found no increase in readmission risk among the cohorts with HRRP diagnoses that had the largest decrease in readmission rates.^7,8 Conversely, readmission risk did increase—with a concurrent increase in the proportion of high-risk hospitalizations—in the surgery/gynecology and neurology cohorts that were not subject to HRRP penalties. Nonetheless, rehospitalizations were reduced for all risk categories in these 2 cohorts. Notably, surgery/gynecology and neurology had the lowest readmission rates overall. These findings suggest that initiatives to prevent initial hospitalizations, such as increasing the threshold for postoperative admission, may have had a greater effect on low- versus high-risk patients in low-risk hospitalizations. However, once a patient is hospitalized, multidisciplinary strategies appear to be effective at reducing readmissions for all risk classes in these cohorts.

For the 3 cohorts in which we observed an increase in readmission risk among hospitalized patients, the risk appeared to increase in early 2011. This time was about 10 months after passage of ACA, the timing of which was previously associated with a drop in readmission rates,^7,8 but well before HRRP went into effect in October 2012. The increase in readmission risk coincided with an increase in the number of diagnostic codes that could be included on a hospital claim to Medicare.¹⁷ This increase in allowable codes allowed us to capture more diagnoses for some patients, potentially resulting in an increase in apparent predicted risk of readmissions. While we adjusted for this in our predictive models, we may not have fully accounted for differences in risk related to coding change. As a result, some of the observed differences in risk in our study may be attributable to coding differences. More broadly, studies demonstrating the success of HRRP have typically examined risk-adjusted rates of readmission.^3,7 It is possible that a small portion of the observed reduction in risk-adjusted readmission rates may be related to the increase in predicted risk of readmission observed in our study. Future assessment of trends in readmission during this period should consider accounting for change in the number of allowed billing codes.

Other limitations should be considered in the interpretation of this study. First, like many predictive models for readmission,¹⁴ ours had imperfect discrimination, which could affect our results. Second, our study was based on older Medicare patients, so findings may not be applicable to younger patients. Third, while we accounted for surrogates for socioeconomic status, including dual eligibility and race, our models lacked other socioeconomic and community factors that can influence readmission.^24-26 Nonetheless, 1 study suggested that easily measured socioeconomic factors may not have a strong influence on the readmission metric used by Medicare.²⁸ Fourth, while our study included over 47 million hospitalizations, our time trend analyses used calendar month as the primary independent variable. As our study included 77 months, we may not have had sufficient power to detect small changes in risk over time.

Medicare readmissions have declined steadily in recent years, presumably at least partly in response to policy changes including HRRP. We found that hospitals have been effective at reducing readmissions across a range of patient risk strata and clinical conditions. As a result, the overall risk of readmission for hospitalized patients has remained constant for some but not all conditions. Whether institutions can continue to reduce readmission rates for most types of patients remains to be seen.

Acknowledgments

This study was supported by the Agency for Healthcare Research and Quality (AHRQ) grant R01HS022882. Dr. Blecker was supported by the AHRQ grant K08HS23683. The authors would like to thank Shawn Hoke and Jane Padikkala for administrative support.

Disclosure

This study was supported by the Agency for Healthcare Research and Quality (AHRQ) grants R01HS022882 and K08HS23683. The authors have no conflicts to report.

Given the high cost of readmissions to the healthcare system, there has been a substantial push to reduce readmissions by policymakers.¹ Among these is the Hospital Readmissions Reduction Program (HRRP), in which hospitals with higher than expected readmission rates receive reduced payments from Medicare.² Recent evidence has suggested the success of such policy changes, with multiple reports demonstrating a decrease in 30-day readmission rates in the Medicare population starting in 2010.^3-8

Initiatives to reduce readmissions can also have an effect on total number of admissions.^9,10 Indeed, along with the recent reduction in readmission, there has been a reduction in all admissions among Medicare beneficiaries.^11,12 Some studies have found that as admissions have decreased, the burden of comorbidity has increased among hospitalized patients,^3,11 suggesting that hospitals may be increasingly filled with patients at high risk of readmission. However, whether readmission risk among hospitalized patients has changed remains unknown, and understanding changes in risk profile could help inform which patients to target with future interventions to reduce readmissions.

Hospital efforts to reduce readmissions may have differential effects on types of patients by risk. For instance, low-intensity, system-wide interventions such as standardized discharge instructions or medicine reconciliation may have a stronger effect on patients at relatively low risk of readmission who may have a few important drivers of readmission that are easily overcome. Alternatively, the impact of intensive care transitions management might be greatest for high-risk patients, who have the most need for postdischarge medications, follow-up, and self-care.

The purpose of this study was therefore twofold: (1) to observe changes in average monthly risk of readmission among hospitalized Medicare patients and (2) to examine changes in readmission rates for Medicare patients at various risk of readmission. We hypothesized that readmission risk in the Medicare population would increase in recent years, as overall number of admissions and readmissions have fallen.^7,11 Additionally, we hypothesized that standardized readmission rates would decline less in highest risk patients as compared with the lowest risk patients because transitional care interventions may not be able to mitigate the large burden of comorbidity and social issues present in many high-risk patients.^13,14

We performed a retrospective cohort study of hospitalizations to US nonfederal short-term acute care facilities by Medicare beneficiaries between January 2009 and June 2015. The design involved 4 steps. First, we estimated a predictive model for unplanned readmissions within 30 days of discharge. Second, we assigned each hospitalization a predicted risk of readmission based on the model. Third, we studied trends in mean predicted risk of readmission during the study period. Fourth, we examined trends in observed to expected (O/E) readmission for hospitalizations in the lowest, middle, and highest categories of predicted risk of readmission to determine whether reductions in readmissions were more substantial in certain risk groups than in others.

Data were obtained from the Centers for Medicare and Medicaid Services (CMS) Inpatient Standard Analytic File and the Medicare Enrollment Data Base. We included hospitalizations of fee-for-service Medicare beneficiaries age ≥65 with continuous enrollment in Part A Medicare fee-for-service for at least 1 year prior and 30 days after the hospitalization.¹⁵ Hospitalizations with a discharge disposition of death, transfer to another acute hospital, and left against medical advice (AMA) were excluded. We also excluded patients with enrollment in hospice care prior to hospitalization. We excluded hospitalizations in June 2012 because of an irregularity in data availability for that month.

Hospitalizations were categorized into 5 specialty cohorts according to service line. The 5 cohorts were those used for the CMS hospital-wide readmission measure and included surgery/gynecology, medicine, cardiovascular, cardiorespiratory, and neurology.¹⁵ Among the 3 clinical conditions tracked as part of HRRP, heart failure and pneumonia were a subset of the cardiorespiratory cohort, while acute myocardial infarction was a subset of the cardiovascular cohort. Our use of cohorts was threefold: first, the average risk of readmission differs substantially across these cohorts, so pooling them produces heterogeneous risk strata; second, risk variables perform differently in different cohorts, so one single model may not be as accurate for calculating risk; and, third, the use of disease cohorts makes our results comparable to the CMS model and similar to other readmission studies in Medicare.^7,8,15

For development of the risk model, the outcome was 30-day unplanned hospital readmission. Planned readmissions were excluded; these were defined by the CMS algorithm as readmissions in which a typically planned procedure occurred in a hospitalization with a nonacute principal diagnosis.¹⁶ Independent variables included age and comorbidities in the final hospital-wide readmission models for each of the 5 specialty cohorts.¹⁵ In order to produce the best possible individual risk prediction for each patient, we added additional independent variables that CMS avoids for hospital quality measurement purposes but that contribute to risk of readmission: sex, race, dual eligibility status, number of prior AMA discharges, intensive care unit stay during current hospitalization, coronary care unit stay during current hospitalization, and hospitalization in the prior 30, 90, and 180 days. We also included an indicator variable for hospitalizations with more than 9 discharge diagnosis codes on or after January 2011, the time at which Medicare allowed an increase of the number of International Classification of Diseases, 9th Revision-Clinical Modification diagnosis billing codes from 9 to 25.¹⁷ This indicator adjusts for the increased availability of comorbidity codes, which might otherwise inflate the predicted risk relative to hospitalizations prior to that date.

Based on the risk models, each hospitalization was assigned a predicted risk of readmission. For each specialty cohort, we pooled all hospitalizations across all study years and divided them into risk quintiles. We categorized hospitalizations as high risk if in the highest quintile, medium risk if in the middle 3 quintiles, and low risk if in the lowest quintile of predicted risk for all study hospitalizations in a given specialty cohort.

For our time trend analyses, we studied 2 outcomes: monthly mean predicted risk and monthly ratio of observed readmissions to expected readmissions for patients in the lowest, middle, and highest categories of predicted risk of readmission. We studied monthly predicted risk to determine whether the average readmission risk of patients was changing over time as admission and readmission rates were declining. We studied the ratio of O/E readmissions to determine whether the decline in overall readmissions was more substantial in particular risk strata; we used the ratio of O/E readmissions, which measures number of readmissions divided by number of readmissions predicted by the model, rather than crude observed readmissions, as O/E readmissions account for any changes in risk profiles over time within each risk stratum. Independent variables in our trend analyses were year—entered as a continuous variable—and indicators for postintroduction of the Affordable Care Act (ACA, March 2010) and for postintroduction of HRRP (October 2012); these time indicators were included because of prior studies demonstrating that the introduction of ACA was associated with a decrease from baseline in readmission rates, which leveled off after introduction of HRRP.⁷ We also included an indicator for calendar quarter to account for seasonal effects.

Statistical Analysis

We developed generalized estimating equation models to predict 30-day unplanned readmission for each of the 5 specialty cohorts. The 5 models were fit using all patients in each cohort for the included time period and were adjusted for clustering by hospital. We assessed discrimination by calculating area under the receiver operating characteristic curve (AUC) for the 5 models; the AUCs measured the models’ ability to distinguish patients who were readmitted versus those who were not.¹⁸ We also calculated AUCs for each year to examine model performance over time.

Using these models, we calculated predicted risk for each hospitalization and averaged these to obtain mean predicted risk for each specialty cohort for each month. To test for trends in mean risk, we estimated 5 time series models, one for each cohort, with the dependent variable of monthly mean predicted risk. For each cohort, we first estimated a series of 12 empty autoregressive models, each with a different autoregressive term (1, 2...12). For each model, we calculated χ² for the test that the autocorrelation was 0; based on a comparison of chi-squared values, we specified an autocorrelation of 1 month for all models. Accordingly, a 1-month lag was used to estimate one final model for each cohort. Independent variables included year and indicators for post-ACA and post-HRRP; these variables captured the effect of trends over time and the introduction of these policy changes, respectively.¹⁹

To determine whether changes in risk over time were a result of changes in particular risk groups, we categorized hospitalizations into risk strata based on quintiles of predicted risk for each specialty cohort for the entire study period. For each individual year, we calculated the proportion of hospitalizations in the highest, middle, and lowest readmission risk strata for each cohort.

We calculated the monthly ratio of O/E readmission for hospitalizations in the lowest 20%, middle 60%, and highest 20% of readmission risk by month; O/E reflects the excess or deficit observed events relative to the number predicted by the model. Using this monthly O/E as the dependent variable, we developed autoregressive time series models as above, again with a 1-month lag, for each of these 3 risk strata in each cohort. As before, independent variables were year as a continuous variable, indicator variables for post-ACA and post-HRRP, and a categorical variable for calendar quarter.

All analyses were done in SAS version 9.3 (SAS Institute Inc., Cary, NC) and Stata version 14.2 (StataCorp LLC, College Station, TX).

We included 47,288,961 hospitalizations in the study, of which 11,231,242 (23.8%) were in the surgery/gynecology cohort, 19,548,711 (41.3%) were in the medicine cohort, 5,433,125 (11.5%) were in the cardiovascular cohort, 8,179,691 (17.3%) were in the cardiorespiratory cohort, and 2,896,192 (6.1%) were in the neurology cohort. The readmission rate was 16.2% (n = 7,642,161) overall, with the highest rates observed in the cardiorespiratory (20.5%) and medicine (17.6%) cohorts and the lowest rates observed in the surgery/gynecology (11.8%) and neurology (13.8%) cohorts.

The final predictive models for each cohort ranged in number of parameters from 56 for the cardiorespiratory cohort to 264 for the surgery/gynecology cohort. The models had AUCs of 0.70, 0.65, 0.67, 0.65, and 0.63 for the surgery/gynecology, medicine, cardiovascular, cardiorespiratory, and neurology cohorts, respectively; AUCs remained fairly stable over time for all disease cohorts (Appendix Table 1).

A number of mechanisms may account for the across-the-board improvements in readmission reduction. Many hospitals have instituted system-wide interventions, including patient education, medicine reconciliation, and early postdischarge follow-up,²⁰ which may have reduced readmissions across all patient risk strata. Alternatively, hospitals may have implemented interventions that disproportionally benefited low-risk patients while simultaneously utilizing interventions that only benefited high-risk patients. For instance, increasing threshold for admission⁷ may have the greatest effect on low-risk patients who could be most easily managed at home, while many intensive transitional care interventions have been developed to target only high-risk patients.^21,22

With the introduction of HRRP, there have been a number of concerns about the readmission measure used to penalize hospitals for high readmission rates. One major concern has been that the readmission metric may be flawed in its ability to capture continued improvement related to readmission.²³ Some have suggested that with better population health management, admissions will decrease, patient risk of the remaining patients will increase, and hospitals will be increasingly filled with patients who have high likelihood of readmission. This potential for increased risk with HRRP was suggested by a recent study that found that comorbidities increased in hospitalized Medicare beneficiaries between 2010 and 2013.¹¹ Our results were mixed in supporting this potential phenomenon because we examined global risk of readmission and found that some of the cohorts had increased risk over time while others did not. Others have expressed concern that readmission measure does not account for socioeconomic status, which has been associated with readmission rates.^24-27 Although we did not directly examine socioeconomic status in our study, we found that hospitals have been able to reduce readmission across all levels of risk, which includes markers of socioeconomic status, including race and Medicaid eligibility status.

Although we hypothesized that readmission risk would increase as number of hospitalizations decreased over time, we found no increase in readmission risk among the cohorts with HRRP diagnoses that had the largest decrease in readmission rates.^7,8 Conversely, readmission risk did increase—with a concurrent increase in the proportion of high-risk hospitalizations—in the surgery/gynecology and neurology cohorts that were not subject to HRRP penalties. Nonetheless, rehospitalizations were reduced for all risk categories in these 2 cohorts. Notably, surgery/gynecology and neurology had the lowest readmission rates overall. These findings suggest that initiatives to prevent initial hospitalizations, such as increasing the threshold for postoperative admission, may have had a greater effect on low- versus high-risk patients in low-risk hospitalizations. However, once a patient is hospitalized, multidisciplinary strategies appear to be effective at reducing readmissions for all risk classes in these cohorts.

For the 3 cohorts in which we observed an increase in readmission risk among hospitalized patients, the risk appeared to increase in early 2011. This time was about 10 months after passage of ACA, the timing of which was previously associated with a drop in readmission rates,^7,8 but well before HRRP went into effect in October 2012. The increase in readmission risk coincided with an increase in the number of diagnostic codes that could be included on a hospital claim to Medicare.¹⁷ This increase in allowable codes allowed us to capture more diagnoses for some patients, potentially resulting in an increase in apparent predicted risk of readmissions. While we adjusted for this in our predictive models, we may not have fully accounted for differences in risk related to coding change. As a result, some of the observed differences in risk in our study may be attributable to coding differences. More broadly, studies demonstrating the success of HRRP have typically examined risk-adjusted rates of readmission.^3,7 It is possible that a small portion of the observed reduction in risk-adjusted readmission rates may be related to the increase in predicted risk of readmission observed in our study. Future assessment of trends in readmission during this period should consider accounting for change in the number of allowed billing codes.

Other limitations should be considered in the interpretation of this study. First, like many predictive models for readmission,¹⁴ ours had imperfect discrimination, which could affect our results. Second, our study was based on older Medicare patients, so findings may not be applicable to younger patients. Third, while we accounted for surrogates for socioeconomic status, including dual eligibility and race, our models lacked other socioeconomic and community factors that can influence readmission.^24-26 Nonetheless, 1 study suggested that easily measured socioeconomic factors may not have a strong influence on the readmission metric used by Medicare.²⁸ Fourth, while our study included over 47 million hospitalizations, our time trend analyses used calendar month as the primary independent variable. As our study included 77 months, we may not have had sufficient power to detect small changes in risk over time.

Medicare readmissions have declined steadily in recent years, presumably at least partly in response to policy changes including HRRP. We found that hospitals have been effective at reducing readmissions across a range of patient risk strata and clinical conditions. As a result, the overall risk of readmission for hospitalized patients has remained constant for some but not all conditions. Whether institutions can continue to reduce readmission rates for most types of patients remains to be seen.

Acknowledgments

This study was supported by the Agency for Healthcare Research and Quality (AHRQ) grant R01HS022882. Dr. Blecker was supported by the AHRQ grant K08HS23683. The authors would like to thank Shawn Hoke and Jane Padikkala for administrative support.

Disclosure

This study was supported by the Agency for Healthcare Research and Quality (AHRQ) grants R01HS022882 and K08HS23683. The authors have no conflicts to report.

Point-of-care ultrasound (POCUS) is a valuable tool to assist in the diagnosis and treatment of many common diseases.^1-11 Its use has increased in clinical settings over the years, primarily because of more portable, economical, high-quality devices and training availability.¹² POCUS improves procedural success and guides the diagnostic management of hospitalized patients.^2,9-12 Literature details the training of medical students,^13,14 residents,^15-21 and providers in emergency medicine²² and critical care,^23,24 as well as focused cardiac training with hospitalists.^25-27 However, no literature exists describing a comprehensive longitudinal training program for hospitalists or skills retention.

This document details the hospital medicine department’s ultrasound training program from Regions Hospital, part of HealthPartners in Saint Paul, Minnesota, a large tertiary care medical center. We describe the development and effectiveness of the Comprehensive Hospitalist Assessment and Mentorship with Portfolios (CHAMP) Ultrasound Program. This approach is intended to support the development of POCUS training programs at other organizations.

The aim of the program was to build a comprehensive bedside ultrasound training paradigm for hospitalists. The primary objective of the study was to assess the program’s effect on skills over time. Secondary objectives were confidence ratings in the use of ultrasound and with various patient care realms (volume management, quality of physical exam, and ability to narrow the differential diagnosis). We hypothesized there would be higher retention of ultrasound skills in those who completed portfolios and/or monthly scanning sessions as well as increased confidence through all secondary outcome measures (see below).

This was a retrospective descriptive report of hospitalists who entered the CHAMP Ultrasound Program. Study participants were providers from the 454-bed Regions Hospital in Saint Paul, Minnesota. The study was deemed exempt by the HealthPartners Institutional Review Board. Three discrete 3-day courses and two 1-day in-person courses held at the Regions Hospital Simulation Center (Saint Paul, Minnesota) were studied.

Program Description

In 2014, a working group was developed in the hospital medicine department to support the hospital-wide POCUS committee with a charter to provide standardized training for providers to complete credentialing.²⁸ The goal of the hospital medicine ultrasound program was to establish the use of ultrasound by credentialed hospitalists into well-defined applications integrated into the practice of hospital medicine. Two providers were selected to lead the efforts and completed additional training through the American College of Chest Physicians (CHEST) Certificate of Completion Program.²⁹ An overall director was designated with the responsibilities delineated in supplementary Appendix 1. This director provided leadership on group practice, protocols, and equipment, creating the organizational framework for success with the training program. The hospital medicine training program had a 3-day in-person component built off the CHEST Critical Care Ultrasonography Program.²⁴ The curriculum was adapted from the American College of Chest Physicians/Société de Réanimation de Langue Française Statement on Competence in Critical Care Ultrasonography.³⁰ See Table 1 for the components of the training program.

Online Modules

As a prerequisite to the 3-day introductory course, hospitalists were required to complete modules for precourse knowledge involving a set of focused-topic online reading and videos with quizzes (supplementary Appendix 3).

3-Day In-Person Course with Assessments

The 3-day course provided 6 hours of didactics, 8 hours of image interpretation, and 9 hours of hands-on instruction (supplementary Appendix 4). Hospitalists first attended a large group didactic, followed by divided groups in image interpretation and hands-on scanning.²⁴

Didactics were provided in a room with a 2-screen set up. Providers used 1 screen to present primary content and the other for simultaneously scanning a human model.

Image interpretation sessions were interactive smaller group learning forums in which participants reviewed high-yield images related to the care of hospital medicine patients and received feedback. Approximately 45 videos with normal and abnormal findings were reviewed during each session.

The hands-on scanning component was accomplished with human models and a faculty-to-participant ratio between 1:2 and 1:3. Human models for this course were paid community models. A variety of ultrasound machine platforms were provided for participants. Learning objectives were clearly delineated prior to each scanning session to ensure the coverage of required content.

Portfolios

Refresher Training: 1-Day In-Person Course with Assessments and Monthly Scanning Sessions (Optional)

Only hospitalists who completed the 3-day course were eligible to take the 1-day in-person refresher course (supplementary Appendix 5). The first half of the course incorporated scanning with live human models, while the second half of the course had scanning with hospitalized patients focusing on pathology (pleural effusion, hydronephrosis, reduced left ventricular function, etc.). The course was offered at 3, 6, and 12 months after the initial 3-day course.

Monthly scanning sessions occurred for 2 hours every third Friday and were also available prior to the 1-day refresher. The first 90 minutes had a hands-on scanning component with hospitalized patients with faculty supervision (1:2 ratio). The last 30 minutes had an image interpretation component.

Assessments

Knowledge and skills assessment were adapted from the CHEST model (supplementary Appendix 6).²⁴ Before and after the 3-day and 1-day in-person courses, the same hands-on skills assessment with a checklist was provided (supplementary Appendix 7). Before and after the 3-day course, a written knowledge assessment with case-based image interpretation was provided (supplementary Appendix 6). A final knowledge and skills assessment was given at either of the in-person courses to those who completed the required components of the training. Passing scores for the final knowledge assessment were established at 85% items correct by an expert panel by using the Angoff method.³¹ This same standard was applied to the final skills examination. Participants who do not pass the final assessments are provided opportunities for further training and allowed to reattempt the assessments. In this regard, there is a standard training outcome but variances in length of training time for each participant. Pre- and postcourse skills assessments used the same faculty, checklist, and ultrasound device. Raters received an orientation the day prior to each in-person course, reviewing common learner pitfalls, reviewing the checklist, and discussing specific examples.

Measurement

Participant demographic and clinical information was collected at the initial 3-day course for all participants, including age, gender, specialty, years of experience, and number and type of ultrasound procedures personally conducted or supervised in the past year. For skills assessment, a 20-item dichotomous checklist was developed and scored as done correctly or not done/done incorrectly. This same assessment was provided both before and after each of the 3-day and 1-day courses. A 20-question image-based knowledge assessment was also developed and administered both before and after the 3-day course only. The same 20-item checklist was used for the final skills examination. However, a new more detailed 50-question examination was written for the final examination after the portfolio of images was complete. Self-reported measures were confidence in the use of ultrasound, volume management, quality of physical exam, and ability to narrow the differential diagnosis. Confidence in ultrasound use, confidence in volume management, and quality of physical exam were assessed by using a questionnaire both before and after the 3-day course and 1-day course. Participants rated confidence and quality on a 5-point scale, 1 being least confident and 5 being most confident.

Statistical Analysis

Demographics of the included hospitalist population and pre and post 3-day assessments, including knowledge score, skills score, confidence in ultrasound use, confidence in volume management, and quality of physical exam, were summarized. Values for all assessment variables are presented as percentages. Confidence scores were reported as a percentage of the Likert scale (eg, 4/5 was reported as 80%). Skills and written examinations were expressed as percentages of items correct. Data were reported as median and interquartile range or means and standard deviation based on variable distributions. Differences between pre- and postvalues for 3-day course variables were assessed by using 2-sample paired Wilcoxon signed rank tests with a 95% confidence level.

For the subset of hospitalists who also completed the 1-day course, pre and post 1-day course assessments, including skills score, confidence in ultrasound use, confidence in volume management, and quality of physical exam, were summarized. Differences between pre- and postvalues for 1-day assessment variables were assessed by using 2-sample paired Wilcoxon signed rank tests with a 95% confidence level.

Demographics

3-Day In-Person Course

For the 53 hospitalists who completed skills-based assessments, performance increased significantly after the 3-day course. Knowledge scores also increased significantly from preassessment to postassessment. Self-reported confidence ratings for ultrasound use, confidence in volume management, and quality of physical exam all increased significantly from preassessment to postassessment (Table 2).

Refresher Training: 1-Day In-Person Course

Because the refresher training was encouraged but not required, only 25 of 53 hospitalists, 23 with complete data, completed the 1-day course. For the 23 hospitalists who completed skills-based assessments before and after the 1-day course, mean skills scores increased significantly (Table 2). Self-reported confidence ratings for ultrasound use, confidence in volume management, and quality of physical exam all increased significantly from preassessment to postassessment (Table 2).

Monthly Scanning Sessions and Portfolio Development

The skills retention from initial course to refresher course by portfolio completion and monthly scanning sessions is shown in Table 2. Multiple regression analysis showed that for every 10% increase in the percent of monthly sessions attended, the mean change in skills score was 3.7% (P = .017), and for every 10% increase in the percent of portfolio completed, the mean change in skills score was 2.5% (P = .04), showing that both monthly scanning session attendance and portfolio completion are significantly predictive of skills retention over time.

Final Assessments

Four providers met mastery at initial attempt. No providers to date have needed remediation. Many others are going through different stages of the process and are expected to attain mastery in a short period of time.

This is the first description of a successful longitudinal training program with assessments in POCUS for hospital medicine providers that shows an increase in skill retention with the use of a follow-up course and bedside scanning.

The CHAMP Ultrasound Program was developed to provide hospital medicine clinicians with a specialty focused in-house training pathway in POCUS and to assist in sustained skills acquisition by providing opportunities for regular feedback and practice. Practice with regular expert feedback is a critical aspect to develop and maintain skills in POCUS.^32,33 Arntfield³⁴ described the utility of remote supervision with feedback for ultrasound training in critical care, which demonstrated varying learning curves in the submission of portfolio images.^35,36 The CHAMP Ultrasound training program provided expert oversight, longitudinal supervision, and feedback for course participants. The educational method of mastery learning was employed by setting minimum standards and allowing learners to practice until they met that standard.^37-39

This unique program is made possible by the availability of expert-level faculty. Assessment scores improved with an initial 3-day course; however, they also decayed over time, most prominently with hospitalists that did not continue with POCUS scanning after their initial course. Ironically, those who performed more ultrasounds in the year prior to beginning the 3-day course had lower confidence ratings, likely explained by their awareness of their limitations and opportunities for improvement. The incorporation of refresher training to supplement the core 3-day course and portfolio development are key additions that differentiate this training program. These additions and the demonstration of successful training make this a durable pathway for other hospitalist programs. There are many workshops and short courses for medical students, residents, and practicing providers in POCUS.^40-43 However, without an opportunity for longitudinal supervision and feedback, there is a noted decrease in the skills for participants. The refresher training with its 2 components (1-day in-person course and monthly scanning sessions) provides evidence of the value of mentored training.

In the initial program development, refresher training was encouraged but optional. We intentionally tracked those that completed refresher training compared with those that did not. Based on the results showing significant skills retention among those attending some form of refresher training, the program is planned to change to make this a requirement. We recommend refresher training within 12 months of the initial introductory course. There were several hospitalists that were unable to accommodate taking a full-day refresher course and, therefore, monthly scanning sessions were provided as an alternative.

The main limitation of the study is that it was completed in a single hospital system with available training mentors in POCUS. This gave us the ability to perform longitudinal training but may make this less reproducible in other hospital systems. Another limitation is that our course participants did not complete the pre- and postknowledge assessments for the refresher training components of the program, though they did for the initial 3-day course. Our pre- and postassessments have not been externally shown to produce valid data, though they are based on the already validated CHEST ultrasound data.⁴⁴

Finally, our CHAMP Ultrasound Program required a significant time commitment by both faculty and learners. A relatively small percentage of hospitalists have completed the final assessments. The reasons are multifactorial, including program rigor, desire by certain hospitalists to know the basics but not pursue more expertise, and the challenges of developing a skillset that takes dedicated practice over time. We have aimed to address these barriers by providing additional hands-on scanning opportunities, giving timely feedback with portfolios, and obtaining more ultrasound machines. We expect more hospitalists to complete the final assessments in the coming year as evidenced by portfolio submissions to the shared online portal and many choosing to attend either the monthly scanning sessions and/or the 1-day course. We recognize that other institutions may need to adapt our program to suit their local environment.

A comprehensive longitudinal ultrasound training program including competency assessments significantly improved ultrasound acquisition skills with hospitalists. Those attending monthly scanning sessions and participating in the portfolio completion as well as a refresher course significantly retained and augmented their skills.

Acknowledgments

The authors would like to acknowledge Kelly Logue, Jason Robertson, MD, Jerome Siy, MD, Shauna Baer, and Jack Dressen for their support in the development and implementation of the POCUS program in hospital medicine.

Disclosure

The authors do not have any relevant financial disclosures to report.

Point-of-care ultrasound (POCUS) is a valuable tool to assist in the diagnosis and treatment of many common diseases.^1-11 Its use has increased in clinical settings over the years, primarily because of more portable, economical, high-quality devices and training availability.¹² POCUS improves procedural success and guides the diagnostic management of hospitalized patients.^2,9-12 Literature details the training of medical students,^13,14 residents,^15-21 and providers in emergency medicine²² and critical care,^23,24 as well as focused cardiac training with hospitalists.^25-27 However, no literature exists describing a comprehensive longitudinal training program for hospitalists or skills retention.

This document details the hospital medicine department’s ultrasound training program from Regions Hospital, part of HealthPartners in Saint Paul, Minnesota, a large tertiary care medical center. We describe the development and effectiveness of the Comprehensive Hospitalist Assessment and Mentorship with Portfolios (CHAMP) Ultrasound Program. This approach is intended to support the development of POCUS training programs at other organizations.

The aim of the program was to build a comprehensive bedside ultrasound training paradigm for hospitalists. The primary objective of the study was to assess the program’s effect on skills over time. Secondary objectives were confidence ratings in the use of ultrasound and with various patient care realms (volume management, quality of physical exam, and ability to narrow the differential diagnosis). We hypothesized there would be higher retention of ultrasound skills in those who completed portfolios and/or monthly scanning sessions as well as increased confidence through all secondary outcome measures (see below).

This was a retrospective descriptive report of hospitalists who entered the CHAMP Ultrasound Program. Study participants were providers from the 454-bed Regions Hospital in Saint Paul, Minnesota. The study was deemed exempt by the HealthPartners Institutional Review Board. Three discrete 3-day courses and two 1-day in-person courses held at the Regions Hospital Simulation Center (Saint Paul, Minnesota) were studied.

Program Description

In 2014, a working group was developed in the hospital medicine department to support the hospital-wide POCUS committee with a charter to provide standardized training for providers to complete credentialing.²⁸ The goal of the hospital medicine ultrasound program was to establish the use of ultrasound by credentialed hospitalists into well-defined applications integrated into the practice of hospital medicine. Two providers were selected to lead the efforts and completed additional training through the American College of Chest Physicians (CHEST) Certificate of Completion Program.²⁹ An overall director was designated with the responsibilities delineated in supplementary Appendix 1. This director provided leadership on group practice, protocols, and equipment, creating the organizational framework for success with the training program. The hospital medicine training program had a 3-day in-person component built off the CHEST Critical Care Ultrasonography Program.²⁴ The curriculum was adapted from the American College of Chest Physicians/Société de Réanimation de Langue Française Statement on Competence in Critical Care Ultrasonography.³⁰ See Table 1 for the components of the training program.

Online Modules

As a prerequisite to the 3-day introductory course, hospitalists were required to complete modules for precourse knowledge involving a set of focused-topic online reading and videos with quizzes (supplementary Appendix 3).

3-Day In-Person Course with Assessments

The 3-day course provided 6 hours of didactics, 8 hours of image interpretation, and 9 hours of hands-on instruction (supplementary Appendix 4). Hospitalists first attended a large group didactic, followed by divided groups in image interpretation and hands-on scanning.²⁴

Didactics were provided in a room with a 2-screen set up. Providers used 1 screen to present primary content and the other for simultaneously scanning a human model.

Image interpretation sessions were interactive smaller group learning forums in which participants reviewed high-yield images related to the care of hospital medicine patients and received feedback. Approximately 45 videos with normal and abnormal findings were reviewed during each session.

The hands-on scanning component was accomplished with human models and a faculty-to-participant ratio between 1:2 and 1:3. Human models for this course were paid community models. A variety of ultrasound machine platforms were provided for participants. Learning objectives were clearly delineated prior to each scanning session to ensure the coverage of required content.

Portfolios

Refresher Training: 1-Day In-Person Course with Assessments and Monthly Scanning Sessions (Optional)

Only hospitalists who completed the 3-day course were eligible to take the 1-day in-person refresher course (supplementary Appendix 5). The first half of the course incorporated scanning with live human models, while the second half of the course had scanning with hospitalized patients focusing on pathology (pleural effusion, hydronephrosis, reduced left ventricular function, etc.). The course was offered at 3, 6, and 12 months after the initial 3-day course.

Monthly scanning sessions occurred for 2 hours every third Friday and were also available prior to the 1-day refresher. The first 90 minutes had a hands-on scanning component with hospitalized patients with faculty supervision (1:2 ratio). The last 30 minutes had an image interpretation component.

Assessments

Knowledge and skills assessment were adapted from the CHEST model (supplementary Appendix 6).²⁴ Before and after the 3-day and 1-day in-person courses, the same hands-on skills assessment with a checklist was provided (supplementary Appendix 7). Before and after the 3-day course, a written knowledge assessment with case-based image interpretation was provided (supplementary Appendix 6). A final knowledge and skills assessment was given at either of the in-person courses to those who completed the required components of the training. Passing scores for the final knowledge assessment were established at 85% items correct by an expert panel by using the Angoff method.³¹ This same standard was applied to the final skills examination. Participants who do not pass the final assessments are provided opportunities for further training and allowed to reattempt the assessments. In this regard, there is a standard training outcome but variances in length of training time for each participant. Pre- and postcourse skills assessments used the same faculty, checklist, and ultrasound device. Raters received an orientation the day prior to each in-person course, reviewing common learner pitfalls, reviewing the checklist, and discussing specific examples.

Measurement

Participant demographic and clinical information was collected at the initial 3-day course for all participants, including age, gender, specialty, years of experience, and number and type of ultrasound procedures personally conducted or supervised in the past year. For skills assessment, a 20-item dichotomous checklist was developed and scored as done correctly or not done/done incorrectly. This same assessment was provided both before and after each of the 3-day and 1-day courses. A 20-question image-based knowledge assessment was also developed and administered both before and after the 3-day course only. The same 20-item checklist was used for the final skills examination. However, a new more detailed 50-question examination was written for the final examination after the portfolio of images was complete. Self-reported measures were confidence in the use of ultrasound, volume management, quality of physical exam, and ability to narrow the differential diagnosis. Confidence in ultrasound use, confidence in volume management, and quality of physical exam were assessed by using a questionnaire both before and after the 3-day course and 1-day course. Participants rated confidence and quality on a 5-point scale, 1 being least confident and 5 being most confident.

Statistical Analysis

Demographics of the included hospitalist population and pre and post 3-day assessments, including knowledge score, skills score, confidence in ultrasound use, confidence in volume management, and quality of physical exam, were summarized. Values for all assessment variables are presented as percentages. Confidence scores were reported as a percentage of the Likert scale (eg, 4/5 was reported as 80%). Skills and written examinations were expressed as percentages of items correct. Data were reported as median and interquartile range or means and standard deviation based on variable distributions. Differences between pre- and postvalues for 3-day course variables were assessed by using 2-sample paired Wilcoxon signed rank tests with a 95% confidence level.

For the subset of hospitalists who also completed the 1-day course, pre and post 1-day course assessments, including skills score, confidence in ultrasound use, confidence in volume management, and quality of physical exam, were summarized. Differences between pre- and postvalues for 1-day assessment variables were assessed by using 2-sample paired Wilcoxon signed rank tests with a 95% confidence level.

Demographics

3-Day In-Person Course

For the 53 hospitalists who completed skills-based assessments, performance increased significantly after the 3-day course. Knowledge scores also increased significantly from preassessment to postassessment. Self-reported confidence ratings for ultrasound use, confidence in volume management, and quality of physical exam all increased significantly from preassessment to postassessment (Table 2).

Refresher Training: 1-Day In-Person Course

Because the refresher training was encouraged but not required, only 25 of 53 hospitalists, 23 with complete data, completed the 1-day course. For the 23 hospitalists who completed skills-based assessments before and after the 1-day course, mean skills scores increased significantly (Table 2). Self-reported confidence ratings for ultrasound use, confidence in volume management, and quality of physical exam all increased significantly from preassessment to postassessment (Table 2).

Monthly Scanning Sessions and Portfolio Development

The skills retention from initial course to refresher course by portfolio completion and monthly scanning sessions is shown in Table 2. Multiple regression analysis showed that for every 10% increase in the percent of monthly sessions attended, the mean change in skills score was 3.7% (P = .017), and for every 10% increase in the percent of portfolio completed, the mean change in skills score was 2.5% (P = .04), showing that both monthly scanning session attendance and portfolio completion are significantly predictive of skills retention over time.

Final Assessments

Four providers met mastery at initial attempt. No providers to date have needed remediation. Many others are going through different stages of the process and are expected to attain mastery in a short period of time.

This is the first description of a successful longitudinal training program with assessments in POCUS for hospital medicine providers that shows an increase in skill retention with the use of a follow-up course and bedside scanning.

The CHAMP Ultrasound Program was developed to provide hospital medicine clinicians with a specialty focused in-house training pathway in POCUS and to assist in sustained skills acquisition by providing opportunities for regular feedback and practice. Practice with regular expert feedback is a critical aspect to develop and maintain skills in POCUS.^32,33 Arntfield³⁴ described the utility of remote supervision with feedback for ultrasound training in critical care, which demonstrated varying learning curves in the submission of portfolio images.^35,36 The CHAMP Ultrasound training program provided expert oversight, longitudinal supervision, and feedback for course participants. The educational method of mastery learning was employed by setting minimum standards and allowing learners to practice until they met that standard.^37-39

This unique program is made possible by the availability of expert-level faculty. Assessment scores improved with an initial 3-day course; however, they also decayed over time, most prominently with hospitalists that did not continue with POCUS scanning after their initial course. Ironically, those who performed more ultrasounds in the year prior to beginning the 3-day course had lower confidence ratings, likely explained by their awareness of their limitations and opportunities for improvement. The incorporation of refresher training to supplement the core 3-day course and portfolio development are key additions that differentiate this training program. These additions and the demonstration of successful training make this a durable pathway for other hospitalist programs. There are many workshops and short courses for medical students, residents, and practicing providers in POCUS.^40-43 However, without an opportunity for longitudinal supervision and feedback, there is a noted decrease in the skills for participants. The refresher training with its 2 components (1-day in-person course and monthly scanning sessions) provides evidence of the value of mentored training.

In the initial program development, refresher training was encouraged but optional. We intentionally tracked those that completed refresher training compared with those that did not. Based on the results showing significant skills retention among those attending some form of refresher training, the program is planned to change to make this a requirement. We recommend refresher training within 12 months of the initial introductory course. There were several hospitalists that were unable to accommodate taking a full-day refresher course and, therefore, monthly scanning sessions were provided as an alternative.

The main limitation of the study is that it was completed in a single hospital system with available training mentors in POCUS. This gave us the ability to perform longitudinal training but may make this less reproducible in other hospital systems. Another limitation is that our course participants did not complete the pre- and postknowledge assessments for the refresher training components of the program, though they did for the initial 3-day course. Our pre- and postassessments have not been externally shown to produce valid data, though they are based on the already validated CHEST ultrasound data.⁴⁴

Finally, our CHAMP Ultrasound Program required a significant time commitment by both faculty and learners. A relatively small percentage of hospitalists have completed the final assessments. The reasons are multifactorial, including program rigor, desire by certain hospitalists to know the basics but not pursue more expertise, and the challenges of developing a skillset that takes dedicated practice over time. We have aimed to address these barriers by providing additional hands-on scanning opportunities, giving timely feedback with portfolios, and obtaining more ultrasound machines. We expect more hospitalists to complete the final assessments in the coming year as evidenced by portfolio submissions to the shared online portal and many choosing to attend either the monthly scanning sessions and/or the 1-day course. We recognize that other institutions may need to adapt our program to suit their local environment.

A comprehensive longitudinal ultrasound training program including competency assessments significantly improved ultrasound acquisition skills with hospitalists. Those attending monthly scanning sessions and participating in the portfolio completion as well as a refresher course significantly retained and augmented their skills.

Acknowledgments

The authors would like to acknowledge Kelly Logue, Jason Robertson, MD, Jerome Siy, MD, Shauna Baer, and Jack Dressen for their support in the development and implementation of the POCUS program in hospital medicine.

Disclosure

The authors do not have any relevant financial disclosures to report.

Point-of-care ultrasound (POCUS) is a valuable tool to assist in the diagnosis and treatment of many common diseases.^1-11 Its use has increased in clinical settings over the years, primarily because of more portable, economical, high-quality devices and training availability.¹² POCUS improves procedural success and guides the diagnostic management of hospitalized patients.^2,9-12 Literature details the training of medical students,^13,14 residents,^15-21 and providers in emergency medicine²² and critical care,^23,24 as well as focused cardiac training with hospitalists.^25-27 However, no literature exists describing a comprehensive longitudinal training program for hospitalists or skills retention.

This document details the hospital medicine department’s ultrasound training program from Regions Hospital, part of HealthPartners in Saint Paul, Minnesota, a large tertiary care medical center. We describe the development and effectiveness of the Comprehensive Hospitalist Assessment and Mentorship with Portfolios (CHAMP) Ultrasound Program. This approach is intended to support the development of POCUS training programs at other organizations.

The aim of the program was to build a comprehensive bedside ultrasound training paradigm for hospitalists. The primary objective of the study was to assess the program’s effect on skills over time. Secondary objectives were confidence ratings in the use of ultrasound and with various patient care realms (volume management, quality of physical exam, and ability to narrow the differential diagnosis). We hypothesized there would be higher retention of ultrasound skills in those who completed portfolios and/or monthly scanning sessions as well as increased confidence through all secondary outcome measures (see below).

This was a retrospective descriptive report of hospitalists who entered the CHAMP Ultrasound Program. Study participants were providers from the 454-bed Regions Hospital in Saint Paul, Minnesota. The study was deemed exempt by the HealthPartners Institutional Review Board. Three discrete 3-day courses and two 1-day in-person courses held at the Regions Hospital Simulation Center (Saint Paul, Minnesota) were studied.

Program Description

In 2014, a working group was developed in the hospital medicine department to support the hospital-wide POCUS committee with a charter to provide standardized training for providers to complete credentialing.²⁸ The goal of the hospital medicine ultrasound program was to establish the use of ultrasound by credentialed hospitalists into well-defined applications integrated into the practice of hospital medicine. Two providers were selected to lead the efforts and completed additional training through the American College of Chest Physicians (CHEST) Certificate of Completion Program.²⁹ An overall director was designated with the responsibilities delineated in supplementary Appendix 1. This director provided leadership on group practice, protocols, and equipment, creating the organizational framework for success with the training program. The hospital medicine training program had a 3-day in-person component built off the CHEST Critical Care Ultrasonography Program.²⁴ The curriculum was adapted from the American College of Chest Physicians/Société de Réanimation de Langue Française Statement on Competence in Critical Care Ultrasonography.³⁰ See Table 1 for the components of the training program.

Online Modules

As a prerequisite to the 3-day introductory course, hospitalists were required to complete modules for precourse knowledge involving a set of focused-topic online reading and videos with quizzes (supplementary Appendix 3).

3-Day In-Person Course with Assessments

The 3-day course provided 6 hours of didactics, 8 hours of image interpretation, and 9 hours of hands-on instruction (supplementary Appendix 4). Hospitalists first attended a large group didactic, followed by divided groups in image interpretation and hands-on scanning.²⁴

Didactics were provided in a room with a 2-screen set up. Providers used 1 screen to present primary content and the other for simultaneously scanning a human model.

Image interpretation sessions were interactive smaller group learning forums in which participants reviewed high-yield images related to the care of hospital medicine patients and received feedback. Approximately 45 videos with normal and abnormal findings were reviewed during each session.

The hands-on scanning component was accomplished with human models and a faculty-to-participant ratio between 1:2 and 1:3. Human models for this course were paid community models. A variety of ultrasound machine platforms were provided for participants. Learning objectives were clearly delineated prior to each scanning session to ensure the coverage of required content.

Portfolios

Refresher Training: 1-Day In-Person Course with Assessments and Monthly Scanning Sessions (Optional)

Only hospitalists who completed the 3-day course were eligible to take the 1-day in-person refresher course (supplementary Appendix 5). The first half of the course incorporated scanning with live human models, while the second half of the course had scanning with hospitalized patients focusing on pathology (pleural effusion, hydronephrosis, reduced left ventricular function, etc.). The course was offered at 3, 6, and 12 months after the initial 3-day course.

Monthly scanning sessions occurred for 2 hours every third Friday and were also available prior to the 1-day refresher. The first 90 minutes had a hands-on scanning component with hospitalized patients with faculty supervision (1:2 ratio). The last 30 minutes had an image interpretation component.

Assessments

Knowledge and skills assessment were adapted from the CHEST model (supplementary Appendix 6).²⁴ Before and after the 3-day and 1-day in-person courses, the same hands-on skills assessment with a checklist was provided (supplementary Appendix 7). Before and after the 3-day course, a written knowledge assessment with case-based image interpretation was provided (supplementary Appendix 6). A final knowledge and skills assessment was given at either of the in-person courses to those who completed the required components of the training. Passing scores for the final knowledge assessment were established at 85% items correct by an expert panel by using the Angoff method.³¹ This same standard was applied to the final skills examination. Participants who do not pass the final assessments are provided opportunities for further training and allowed to reattempt the assessments. In this regard, there is a standard training outcome but variances in length of training time for each participant. Pre- and postcourse skills assessments used the same faculty, checklist, and ultrasound device. Raters received an orientation the day prior to each in-person course, reviewing common learner pitfalls, reviewing the checklist, and discussing specific examples.

Measurement

Participant demographic and clinical information was collected at the initial 3-day course for all participants, including age, gender, specialty, years of experience, and number and type of ultrasound procedures personally conducted or supervised in the past year. For skills assessment, a 20-item dichotomous checklist was developed and scored as done correctly or not done/done incorrectly. This same assessment was provided both before and after each of the 3-day and 1-day courses. A 20-question image-based knowledge assessment was also developed and administered both before and after the 3-day course only. The same 20-item checklist was used for the final skills examination. However, a new more detailed 50-question examination was written for the final examination after the portfolio of images was complete. Self-reported measures were confidence in the use of ultrasound, volume management, quality of physical exam, and ability to narrow the differential diagnosis. Confidence in ultrasound use, confidence in volume management, and quality of physical exam were assessed by using a questionnaire both before and after the 3-day course and 1-day course. Participants rated confidence and quality on a 5-point scale, 1 being least confident and 5 being most confident.

Statistical Analysis

Demographics of the included hospitalist population and pre and post 3-day assessments, including knowledge score, skills score, confidence in ultrasound use, confidence in volume management, and quality of physical exam, were summarized. Values for all assessment variables are presented as percentages. Confidence scores were reported as a percentage of the Likert scale (eg, 4/5 was reported as 80%). Skills and written examinations were expressed as percentages of items correct. Data were reported as median and interquartile range or means and standard deviation based on variable distributions. Differences between pre- and postvalues for 3-day course variables were assessed by using 2-sample paired Wilcoxon signed rank tests with a 95% confidence level.

For the subset of hospitalists who also completed the 1-day course, pre and post 1-day course assessments, including skills score, confidence in ultrasound use, confidence in volume management, and quality of physical exam, were summarized. Differences between pre- and postvalues for 1-day assessment variables were assessed by using 2-sample paired Wilcoxon signed rank tests with a 95% confidence level.

Demographics

3-Day In-Person Course

For the 53 hospitalists who completed skills-based assessments, performance increased significantly after the 3-day course. Knowledge scores also increased significantly from preassessment to postassessment. Self-reported confidence ratings for ultrasound use, confidence in volume management, and quality of physical exam all increased significantly from preassessment to postassessment (Table 2).

Refresher Training: 1-Day In-Person Course

Because the refresher training was encouraged but not required, only 25 of 53 hospitalists, 23 with complete data, completed the 1-day course. For the 23 hospitalists who completed skills-based assessments before and after the 1-day course, mean skills scores increased significantly (Table 2). Self-reported confidence ratings for ultrasound use, confidence in volume management, and quality of physical exam all increased significantly from preassessment to postassessment (Table 2).

Monthly Scanning Sessions and Portfolio Development

The skills retention from initial course to refresher course by portfolio completion and monthly scanning sessions is shown in Table 2. Multiple regression analysis showed that for every 10% increase in the percent of monthly sessions attended, the mean change in skills score was 3.7% (P = .017), and for every 10% increase in the percent of portfolio completed, the mean change in skills score was 2.5% (P = .04), showing that both monthly scanning session attendance and portfolio completion are significantly predictive of skills retention over time.

Final Assessments

Four providers met mastery at initial attempt. No providers to date have needed remediation. Many others are going through different stages of the process and are expected to attain mastery in a short period of time.

This is the first description of a successful longitudinal training program with assessments in POCUS for hospital medicine providers that shows an increase in skill retention with the use of a follow-up course and bedside scanning.

The CHAMP Ultrasound Program was developed to provide hospital medicine clinicians with a specialty focused in-house training pathway in POCUS and to assist in sustained skills acquisition by providing opportunities for regular feedback and practice. Practice with regular expert feedback is a critical aspect to develop and maintain skills in POCUS.^32,33 Arntfield³⁴ described the utility of remote supervision with feedback for ultrasound training in critical care, which demonstrated varying learning curves in the submission of portfolio images.^35,36 The CHAMP Ultrasound training program provided expert oversight, longitudinal supervision, and feedback for course participants. The educational method of mastery learning was employed by setting minimum standards and allowing learners to practice until they met that standard.^37-39

This unique program is made possible by the availability of expert-level faculty. Assessment scores improved with an initial 3-day course; however, they also decayed over time, most prominently with hospitalists that did not continue with POCUS scanning after their initial course. Ironically, those who performed more ultrasounds in the year prior to beginning the 3-day course had lower confidence ratings, likely explained by their awareness of their limitations and opportunities for improvement. The incorporation of refresher training to supplement the core 3-day course and portfolio development are key additions that differentiate this training program. These additions and the demonstration of successful training make this a durable pathway for other hospitalist programs. There are many workshops and short courses for medical students, residents, and practicing providers in POCUS.^40-43 However, without an opportunity for longitudinal supervision and feedback, there is a noted decrease in the skills for participants. The refresher training with its 2 components (1-day in-person course and monthly scanning sessions) provides evidence of the value of mentored training.

In the initial program development, refresher training was encouraged but optional. We intentionally tracked those that completed refresher training compared with those that did not. Based on the results showing significant skills retention among those attending some form of refresher training, the program is planned to change to make this a requirement. We recommend refresher training within 12 months of the initial introductory course. There were several hospitalists that were unable to accommodate taking a full-day refresher course and, therefore, monthly scanning sessions were provided as an alternative.

The main limitation of the study is that it was completed in a single hospital system with available training mentors in POCUS. This gave us the ability to perform longitudinal training but may make this less reproducible in other hospital systems. Another limitation is that our course participants did not complete the pre- and postknowledge assessments for the refresher training components of the program, though they did for the initial 3-day course. Our pre- and postassessments have not been externally shown to produce valid data, though they are based on the already validated CHEST ultrasound data.⁴⁴

Finally, our CHAMP Ultrasound Program required a significant time commitment by both faculty and learners. A relatively small percentage of hospitalists have completed the final assessments. The reasons are multifactorial, including program rigor, desire by certain hospitalists to know the basics but not pursue more expertise, and the challenges of developing a skillset that takes dedicated practice over time. We have aimed to address these barriers by providing additional hands-on scanning opportunities, giving timely feedback with portfolios, and obtaining more ultrasound machines. We expect more hospitalists to complete the final assessments in the coming year as evidenced by portfolio submissions to the shared online portal and many choosing to attend either the monthly scanning sessions and/or the 1-day course. We recognize that other institutions may need to adapt our program to suit their local environment.

A comprehensive longitudinal ultrasound training program including competency assessments significantly improved ultrasound acquisition skills with hospitalists. Those attending monthly scanning sessions and participating in the portfolio completion as well as a refresher course significantly retained and augmented their skills.

Acknowledgments

The authors would like to acknowledge Kelly Logue, Jason Robertson, MD, Jerome Siy, MD, Shauna Baer, and Jack Dressen for their support in the development and implementation of the POCUS program in hospital medicine.

Disclosure

The authors do not have any relevant financial disclosures to report.