Voluntary recall of eculizumab issued

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Voluntary recall of eculizumab issued

Vial of Soliris

Credit: Globovision

The manufacturer of the recently approved eculizumab (Soliris) issued a voluntary US recall on June 2 of a single affected lot of the drug, although it included 8 additional lots in the recall.

The manufacturer, Alexion Pharmaceuticals, found visible particulates in the 300 mg/30 mL concentrated solution for intravenous infusion, which could cause an immune reaction and blood clots in patients receiving the drug.

The single affected Soliris lot, distributed in the US only, is #1007A. Also included in the U.S. recall are lots 10002-1, 00006-1, 10003A, 10004A, 10005A, 10005AR, 10006A, and 10008A.

All lots in the recall were manufactured using the same process component.

Alexion believes it has identified the part of the process that has resulted in the visible particles in the solution and has changed the process component.

An earlier recall of eculizumab, also for particulate matter, occurred in December 2013.

Alexion does not anticipate any interruption to the patient supply of eculizumab.

Eculizumab recently received full US Food and Drug Administration (FDA) approval to treat adult and pediatric patients with atypical hemolytic uremic syndrome (aHUS). It had received accelerated approval for this indication in 2011.

Eculizumab is also FDA-approved to treat patients with paroxysmal nocturnal hemoglobinuria.

For more information on the recall, visit the company website.

Healthcare professionals and patients should report adverse events or side effects related to Soliris to the FDA's MedWatch Safety Information and Adverse Event Reporting Program.

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Vial of Soliris

Credit: Globovision

The manufacturer of the recently approved eculizumab (Soliris) issued a voluntary US recall on June 2 of a single affected lot of the drug, although it included 8 additional lots in the recall.

The manufacturer, Alexion Pharmaceuticals, found visible particulates in the 300 mg/30 mL concentrated solution for intravenous infusion, which could cause an immune reaction and blood clots in patients receiving the drug.

The single affected Soliris lot, distributed in the US only, is #1007A. Also included in the U.S. recall are lots 10002-1, 00006-1, 10003A, 10004A, 10005A, 10005AR, 10006A, and 10008A.

All lots in the recall were manufactured using the same process component.

Alexion believes it has identified the part of the process that has resulted in the visible particles in the solution and has changed the process component.

An earlier recall of eculizumab, also for particulate matter, occurred in December 2013.

Alexion does not anticipate any interruption to the patient supply of eculizumab.

Eculizumab recently received full US Food and Drug Administration (FDA) approval to treat adult and pediatric patients with atypical hemolytic uremic syndrome (aHUS). It had received accelerated approval for this indication in 2011.

Eculizumab is also FDA-approved to treat patients with paroxysmal nocturnal hemoglobinuria.

For more information on the recall, visit the company website.

Healthcare professionals and patients should report adverse events or side effects related to Soliris to the FDA's MedWatch Safety Information and Adverse Event Reporting Program.

Vial of Soliris

Credit: Globovision

The manufacturer of the recently approved eculizumab (Soliris) issued a voluntary US recall on June 2 of a single affected lot of the drug, although it included 8 additional lots in the recall.

The manufacturer, Alexion Pharmaceuticals, found visible particulates in the 300 mg/30 mL concentrated solution for intravenous infusion, which could cause an immune reaction and blood clots in patients receiving the drug.

The single affected Soliris lot, distributed in the US only, is #1007A. Also included in the U.S. recall are lots 10002-1, 00006-1, 10003A, 10004A, 10005A, 10005AR, 10006A, and 10008A.

All lots in the recall were manufactured using the same process component.

Alexion believes it has identified the part of the process that has resulted in the visible particles in the solution and has changed the process component.

An earlier recall of eculizumab, also for particulate matter, occurred in December 2013.

Alexion does not anticipate any interruption to the patient supply of eculizumab.

Eculizumab recently received full US Food and Drug Administration (FDA) approval to treat adult and pediatric patients with atypical hemolytic uremic syndrome (aHUS). It had received accelerated approval for this indication in 2011.

Eculizumab is also FDA-approved to treat patients with paroxysmal nocturnal hemoglobinuria.

For more information on the recall, visit the company website.

Healthcare professionals and patients should report adverse events or side effects related to Soliris to the FDA's MedWatch Safety Information and Adverse Event Reporting Program.

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Research reveals previously unidentified proteins

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Research reveals previously unidentified proteins

Newly synthesized proteins

Credit: Lu Wei

By cataloging more than 18,000 proteins, scientists have produced an “almost-complete” inventory of the human proteome.

They discovered protein fragments encoded by DNA outside of currently known genes and found that many known genes have become non-functional.

They also described an important function for messenger RNA (mRNA) and showed that protein profiles could predict drug sensitivity.

The team reported their findings in Nature.

They also made the information freely available in ProteomicsDB. This database includes information on the types, distribution, and abundance of proteins in various cells, tissues, and body fluids.

Through their protein cataloguing, the researchers showed that each mRNA determines the number of protein copies to be produced by a cell. And this “copying key” is specific to each protein.

“It appears that every mRNA molecule knows the unit amount for its protein so it knows whether to produce 10, 100, or 1000 copies,” said study author Bernhard Küster, PhD, of Technische Universitaet Muenchen in Germany.

“Since we now know this ratio for a large number of proteins, we can infer protein abundance from mRNA abundance in just about every tissue, and vice versa.”

The researchers were also surprised to discover hundreds of protein fragments that are encoded by DNA outside of currently known genes. The team believes these “new” proteins may possess novel biological properties and functions that could be exploited for therapeutic purposes.

On the other hand, Dr Küster and his colleagues have been unable to locate roughly 2000 proteins that should exist, according to the gene map. The team also found evidence suggesting that many known genes have become non-functional.

“We might be watching evolution in action here,” Dr Küster said. “The human organism deactivates superfluous genes and tests new gene prototypes at the same time.”

That being the case, the researchers noted that it might never be possible to determine exactly how many proteins there are in the human body.

Lastly, Dr Küster and his colleagues confirmed the findings of previous studies, which showed that specific protein patterns can predict the efficacy of a given drug.

The team evaluated 24 cancer drugs and found their effectiveness against 35 cancer cell lines was strongly correlated with their protein profiles.

“This edges us a little bit closer to the individualized treatment of patients,” Dr Küster said. “If we knew the protein profile of a tumor in detail, we might be able to administer drugs in a more targeted way.”

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Newly synthesized proteins

Credit: Lu Wei

By cataloging more than 18,000 proteins, scientists have produced an “almost-complete” inventory of the human proteome.

They discovered protein fragments encoded by DNA outside of currently known genes and found that many known genes have become non-functional.

They also described an important function for messenger RNA (mRNA) and showed that protein profiles could predict drug sensitivity.

The team reported their findings in Nature.

They also made the information freely available in ProteomicsDB. This database includes information on the types, distribution, and abundance of proteins in various cells, tissues, and body fluids.

Through their protein cataloguing, the researchers showed that each mRNA determines the number of protein copies to be produced by a cell. And this “copying key” is specific to each protein.

“It appears that every mRNA molecule knows the unit amount for its protein so it knows whether to produce 10, 100, or 1000 copies,” said study author Bernhard Küster, PhD, of Technische Universitaet Muenchen in Germany.

“Since we now know this ratio for a large number of proteins, we can infer protein abundance from mRNA abundance in just about every tissue, and vice versa.”

The researchers were also surprised to discover hundreds of protein fragments that are encoded by DNA outside of currently known genes. The team believes these “new” proteins may possess novel biological properties and functions that could be exploited for therapeutic purposes.

On the other hand, Dr Küster and his colleagues have been unable to locate roughly 2000 proteins that should exist, according to the gene map. The team also found evidence suggesting that many known genes have become non-functional.

“We might be watching evolution in action here,” Dr Küster said. “The human organism deactivates superfluous genes and tests new gene prototypes at the same time.”

That being the case, the researchers noted that it might never be possible to determine exactly how many proteins there are in the human body.

Lastly, Dr Küster and his colleagues confirmed the findings of previous studies, which showed that specific protein patterns can predict the efficacy of a given drug.

The team evaluated 24 cancer drugs and found their effectiveness against 35 cancer cell lines was strongly correlated with their protein profiles.

“This edges us a little bit closer to the individualized treatment of patients,” Dr Küster said. “If we knew the protein profile of a tumor in detail, we might be able to administer drugs in a more targeted way.”

Newly synthesized proteins

Credit: Lu Wei

By cataloging more than 18,000 proteins, scientists have produced an “almost-complete” inventory of the human proteome.

They discovered protein fragments encoded by DNA outside of currently known genes and found that many known genes have become non-functional.

They also described an important function for messenger RNA (mRNA) and showed that protein profiles could predict drug sensitivity.

The team reported their findings in Nature.

They also made the information freely available in ProteomicsDB. This database includes information on the types, distribution, and abundance of proteins in various cells, tissues, and body fluids.

Through their protein cataloguing, the researchers showed that each mRNA determines the number of protein copies to be produced by a cell. And this “copying key” is specific to each protein.

“It appears that every mRNA molecule knows the unit amount for its protein so it knows whether to produce 10, 100, or 1000 copies,” said study author Bernhard Küster, PhD, of Technische Universitaet Muenchen in Germany.

“Since we now know this ratio for a large number of proteins, we can infer protein abundance from mRNA abundance in just about every tissue, and vice versa.”

The researchers were also surprised to discover hundreds of protein fragments that are encoded by DNA outside of currently known genes. The team believes these “new” proteins may possess novel biological properties and functions that could be exploited for therapeutic purposes.

On the other hand, Dr Küster and his colleagues have been unable to locate roughly 2000 proteins that should exist, according to the gene map. The team also found evidence suggesting that many known genes have become non-functional.

“We might be watching evolution in action here,” Dr Küster said. “The human organism deactivates superfluous genes and tests new gene prototypes at the same time.”

That being the case, the researchers noted that it might never be possible to determine exactly how many proteins there are in the human body.

Lastly, Dr Küster and his colleagues confirmed the findings of previous studies, which showed that specific protein patterns can predict the efficacy of a given drug.

The team evaluated 24 cancer drugs and found their effectiveness against 35 cancer cell lines was strongly correlated with their protein profiles.

“This edges us a little bit closer to the individualized treatment of patients,” Dr Küster said. “If we knew the protein profile of a tumor in detail, we might be able to administer drugs in a more targeted way.”

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Race and Ethnicity in Bronchiolitis

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Racial/Ethnic differences in the presentation and management of severe bronchiolitis

Bronchiolitis is the leading cause of hospitalization for infants in the United States, costs more than $500 million annually, and has seen a 30% increase ($1.34 billion to $1.73 billion) in related hospital charges from 2000 to 2009.13 Almost all children <2 years old are infected with respiratory syncytial virus, the most common cause of bronchiolitis, with 40% developing clinically recognizable bronchiolitis and 2% becoming hospitalized with severe bronchiolitis.[4, 5] Current American Academy of Pediatrics (AAP) guidelines state that routine use of bronchodilators, corticosteroids, and chest x‐rays is not recommended, and supportive care is strongly encouraged.[6] However, a lack of consensus among clinicians persists regarding bronchiolitis management.[7, 8, 9, 10] Although minority children and those with a lower socioeconomic status (SES) in the United States are more likely to present with bronchiolitis to the emergency department (ED) and be subsequently admitted when compared to the general population,[11, 12, 13] to our knowledge, no study has yet examined if race/ethnicity is independently associated with differences in the presentation and management of severe bronchiolitis (ie, bronchiolitis causing hospitalization). Although a prior bronchiolitis‐related study reported that Hispanic children had a longer ED length of stay (LOS) than non‐Hispanic white (NHW) and non‐Hispanic black (NHB) children,[14] other studies concluded that race/ethnicity were not predictors of intensive care unit (ICU) admission or unscheduled healthcare visits post‐ED discharge.[15, 16]

Determining if race/ethnicity is independently associated with certain bronchiolitis management tendencies has implications from both a health disparities standpoint (ie, unequal care based on race/ethnicity) and from a clinical perspective (ie, the potential of certain practices, such as clinical pathways, to increase the likelihood of equitable treatment). To address this knowledge gap, we examined prospective data from a multicenter study designed to evaluate multiple factors related to bronchiolitis hospitalization.

METHODS

Study Design

We conducted a multicenter prospective cohort for 3 consecutive years (20072010) as part of the Multicenter Airway Research Collaboration (MARC), a division of the Emergency Medicine Network (EMNet) (www.emnet‐usa.org). Sixteen hospitals in 12 states (see Appendix) participated from November 1st until March 31st in each study year. At the beginning of each month, site investigators used a standardized protocol to enroll a target number of patients from the inpatient wards and ICU.

All patients were treated at the discretion of their physician. Inclusion criteria were hospital admission with physician diagnosis of bronchiolitis, age <2 years, and ability of the child's guardian (eg, parent) to give informed consent. Patients were enrolled within 18 hours of admission. Physician diagnosis of bronchiolitis followed the AAP definition of a child with an acute respiratory illness with some combination of rhinitis, cough, tachypnea, wheezing, crackles, and/or retractions.[6] The exclusion criteria were previous enrollment and if a patient was transferred to a participating site hospital>48 hours after the initial admission. All consent and data forms were translated into Spanish. The institutional review board at each participating site approved the study.

Data Collection

Site investigators used a standardized protocol to enroll 2207 patients admitted with bronchiolitis. Investigators conducted a structured interview that assessed patients' demographic characteristics, medical and environmental history, duration of symptoms, and details of the acute illness. Race/ethnicity was assigned by report of the child's guardian to standard US Census groups. For the purpose of this analysis, mutually exclusive race/ethnicity categories were determined: NHW, NHB, or Hispanic. Non‐Hispanic patients who identified as being both white and black were categorized as NHB. Patients were excluded from analysis if neither white or black race nor Hispanic ethnicity were reported (eg, if only Asian race was reported) because of small numbers (n=67), as were patients missing all race/ethnicity data (n=10). This resulted in a total of 2130 (97%) patients in our analytical dataset. SES was assessed with 2 variables: insurance status (public, private, none) and family income, estimated by matching patients' home ZIP codes and year of enrollment to ZIP code‐based median household annual incomes obtained from Esri Business Analyst Desktop (Esri, Redlands, CA).[17]

ED and daily clinical data, including laboratory tests (eg, complete blood count, basic metabolic panel, urine analysis, blood culture), respiratory rates, oxygen saturation, medical management, and disposition were obtained by medical chart review. Additionally, in an attempt to evaluate bronchiolitis severity at presentation, a modified respiratory distress severity score (RDSS) was calculated based on 4 assessments made during the preadmission visit (ie, ED or office visit before hospital admission): respiratory rate by age, presence of wheezing (yes or no), air entry (normal, mild difficulty, or moderate/severe), and retractions (none, mild, or moderate/severe).[18] Each component was assigned a score of 0, 1, or 2, with the exception of wheeze, which was assigned either a 0 (no wheeze) or a 2 (wheeze), and then summed for a possible total score of 0 to 8.

Last, a follow‐up telephone interview was conducted 1 week after hospital discharge for each enrolled patient. Interviews assessed acute relapse, recent symptoms, and provided additional end points for longitudinal analysis of specific symptoms. All data were manually reviewed at the EMNet Coordinating Center, and site investigators were queried about missing data and discrepancies identified.

Outcome Measures

The major outcomes of this analysis were: albuterol and corticosteroid (inhaled or systemic) use during preadmission visit and hospitalization, chest x‐rays performed at preadmission visit and hospitalization, need for intensive respiratory support (ie, receiving continuous positive airway pressure [CPAP], intubation, or ICU admission), hospital LOS 3 days, discharge on inhaled corticosteroids, and relapse of bronchiolitis requiring medical attention and a change of medication within 1 week of discharge.

Statistical Analysis

Stata 11.2 (StataCorp, College Station, TX) was used for all analyses. We examined unadjusted differences between racial/ethnic groups and clinical presentation, patient management, and outcomes using 2, Fisher exact, or Kruskal‐Wallis test, as appropriate, with results reported as proportions with 95% confidence interval (CI) or median with interquartile range (IQR). Imputed values, calculated with the Stata impute command, were used to calculate the RDSS when 1 of the 4 components was missing; patients missing more than 1 component were not assigned an RDSS value. Multivariable logistic regression was conducted to evaluate the adjusted association between race/ethnicity and the outcomes listed above. Besides race/ethnicity, all multivariable models included the demographic variables of age, sex, insurance, and median household income. Other factors were considered for inclusion if they were associated with the outcome in unadjusted analyses (P<0.20) or deemed clinically relevant. All models were adjusted for the possibility of clustering by site. Results are reported for the race/ethnicity factor as odds ratios with 95% CI.

RESULTS

Of the 2130 subjects included in this analysis, 818 (38%) were NHW, 511 (24%) were NHB, and 801 (38%) were Hispanic. The median age for children was 4.0 months (IQR, 1.88.5 months), and 60% were male. Most children were publicly insured (65%), 31% had private insurance, and approximately 4% had no insurance. The median household income defined by patient ZIP code was $51,810 (IQR, $39,916$66,272), and nearly all children (97%) had a primary care provider (PCP). Approximately 21% of all children had relevant comorbidities and 17% of children were enrolled from the ICU. Overall, the median LOS was 2 days (IQR, 14 days).

The unadjusted associations between race/ethnicity and other demographic and historical characteristics are shown in Table 1. NHB and Hispanic children were more likely to have public insurance and less likely to have relevant major comorbidities when compared to NHW children. With regard to care received the week before hospitalization, NHW children were more likely to have visited their PCP, taken corticosteroids and/or antibiotics, and were least likely to have visited an ED when compared to NHB and Hispanic children.

Demographic and Clinical Characteristics of Subjects Before the Preadmission Visit by Race/Ethnicity*
 White, Non‐Hispanic, n=818, %Black, Non‐Hispanic, n=511, %Hispanic, n=801, %P
  • NOTE: Abbreviations: ED, emergency department; ICU, intensive care unit; IQR, interquartile range. *Preadmission visit is the ED or office visit preceding hospital admission. Major relevant comorbid disorders included reactive airway disease or asthma, spastic di/quadriplegia, chronic lung disease, seizure disorder, immunodeficiency, congenital heart disease, gastroesophageal reflux, and other major medical disorders.

Demographic characteristics    
Age, months, median (IQR)3.2 (1.57.4)5.0 (1.99.2)4.4 (2.09.1)<0.001
Female39.740.940.40.91
Insurance   <0.001
Private56.717.013.1 
Medicaid32.870.977.5 
Other public6.87.64.2 
None3.74.65.3 
Median household income by ZIP code, US$, median (IQR)$60,406 ($48,086$75,077)$44,191 ($32,922$55,640)$50,394 ($39,242$62,148)<0.001
Has primary care provider98.597.395.40.001
History    
Gestational age at birth   0.002
<32 weeks4.59.26.6 
3235 weeks5.78.86.0 
3537 weeks13.310.09.7 
37 weeks76.071.476.9 
Missing0.40.60.7 
Weight when born   <0.001
<3 pounds3.36.95.8 
34.9 pounds6.811.96.2 
56.9 pounds33.439.733.2 
>7 pounds55.840.353.6 
Missing0.71.41.4 
Kept in an ICU, premature nursery, or any type of special‐care facility when born24.529.724.90.07
Breast fed62.349.165.5<0.001
Attends daycare20.725.213.3<0.001
Number of other children (<18 years old) living in home   <0.001
124.225.617.0 
242.729.228.3 
333.145.254.7 
Neither parent has asthma66.156.677.7<0.001
Maternal smoking during pregnancy21.821.36.0<0.001
Secondhand smoke exposure12.920.28.7<0.001
History of wheezing21.125.921.80.12
Ever intubated9.513.29.20.05
Major relevant comorbidities23.621.118.20.03
Received palivizumab (respiratory syncytial virus vaccine)8.712.78.90.04
Received influenza vaccine this year20.224.721.20.15
In past 12 months, admitted overnight to hospital for bronchiolitis/wheezing/reactive airway disease45.057.955.90.06
In past 12 months, admitted overnight to hospital for pneumonia16.114.925.00.049
Current illness (before index visit)    
Any primary care provider or clinic visits during past week75.044.158.3<0.001
Any ED visits during past week29.130.334.60.049
Over the past week used inhaled bronchodilator40.636.237.00.18
Over the past week used inhaled/nebulized corticosteroids8.78.17.70.76
Over the past week taken any steroid liquids or pills or shots for bronchiolitis12.811.78.30.012
Over the past week taken antibiotics21.917.017.90.045
Onset of difficulty breathing before admission   0.03
None2.02.22.4 
<24 hours28.827.225.1 
13 days41.141.645.9 
47 days22.119.121.2 
>7 days6.09.95.3 
Over the past 24 hours, the level of discomfort or distress felt by the child because of symptoms   <0.001
Mild15.521.318.5 
Moderate47.839.337.2 
Severe36.137.642.6 

The unadjusted associations between race/ethnicity and clinical characteristics at preadmission visit and hospital admission are shown in Table 2. RDSS values were calculated for 2130 children; 1,752 (82%) RDSS values contained all 4 components. Of those requiring imputed values, 234 (11%) were missing 1 component, and 139 (7%) were missing more than 1 component. Per RDSS scores, NHB children presented with a more severe case of bronchiolitis when compared to NHW and Hispanic children. During admission, minority children were more likely to receive nebulized albuterol and less likely to visit the ICU. NHB children received the least inpatient laboratory testing and were least likely to receive chest x‐rays during hospital admission among all groups.

Clinical Characteristics at Preadmission Visit and During Admission by Race/Ethnicity
 White, Non‐Hispanic, n=818, %Black, Non‐Hispanic, n=511, %Hispanic, n=801, %P
  • NOTE: Abbreviations: CPAP, continuous positive airway pressure; ICU, intensive care unit; IV, intravenous; RDSS, respiratory distress severity score.

Preadmission clinical findings and treatments    
Reason brought to the hospital    
Fever29.730.240.7<0.001
Fussy32.631.628.40.18
Ear Infection6.04.34.40.23
Not drinking well35.027.527.20.001
Cough54.155.561.30.009
Other reasons29.027.623.50.04
Apnea8.56.26.10.11
Respiratory rate (breaths/minute)   0.001
<4023.818.228.1 
404930.830.729.3 
505917.615.516.0 
6027.835.626.6 
Presence of cough83.088.087.00.045
Presence of wheezing63.069.063.00.42
Fever (temperature 100.4F)22.728.535.4<0.001
Retractions   0.002
None22.919.023.0 
Mild39.441.744.4 
Moderate/severe28.431.128.1 
Missing9.48.24.5 
Air entry on auscultation   0.01
Normal39.631.733.6 
Mild difficulty31.433.536.3 
Moderate difficulty11.014.314.1 
Severe difficulty2.02.32.6 
Missing16.018.213.4 
Oxygen saturation on room air <9012.29.811.80.37
Given nebulized albuterol53.065.063.0<0.001
Given nebulized epinephrine15.420.418.40.06
Given steroids, inhaled or systemic16.020.519.40.08
Given antibiotics25.522.627.80.12
Oral Intake   <0.001
Adequate41.350.740.8 
Inadequate43.532.547.7 
Missing15.216.811.5 
IV placed56.951.561.90.001
Any laboratory tests86.391.388.00.02
Chest x‐ray59.064.065.00.03
RDSS, tertiles   <0.001
1 (3.00)36.024.034.0 
2 (3.0115.00)30.033.034.0 
3 (>5)25.036.027.0 
Not calculated9.06.04.0 
Virology results    
Respiratory syncytial virus75.967.571.00.003
Human rhinovirus23.830.125.00.03
Human metapneumovirus6.16.88.40.20
Inpatient clinical findings and treatments    
Length of stay 3 days46.539.345.40.03
Ever in observation unit8.67.84.00.001
Ever in regular ward89.493.094.50.001
Ever in step‐down unit5.03.27.80.002
Ever in ICU20.315.015.90.02
Required CPAP or intubation7.74.68.80.02
Given nebulized albuterol37.648.046.7<0.001
Given nebulized epinephrine10.714.913.00.07
Given steroids, inhaled or systemic21.327.323.50.047
Given antibiotics38.934.338.60.19
Received IV fluids53.145.657.1<0.001
Any laboratory tests52.241.651.7<0.001
Chest x‐ray27.118.622.90.002

Discharge treatment and outcomes at 1‐week follow‐up are shown in Table 3. A total of 1771 patients (83%) were reached by telephone. No statistically significant differences between racial/ethnic groups were found regarding hospital discharge on corticosteroids and likelihood of bronchiolitis‐related relapse.

Discharge Treatment and Outcome Measures at 1‐Week Follow‐up by Race/Ethnicity
 White, Non‐Hispanic, n=818, %Black, Non‐Hispanic, n=511, %Hispanic, n=801, %P
Discharged on inhaled corticosteroids9.511.113.30.08
Discharged on oral corticosteroids9.812.48.50.11
Child's condition at 1‐week follow‐up compared to on discharge   0.001
Much worse/worse1.80.70.4 
About the same3.46.42.5 
Better38.239.134.2 
All better56.653.762.9 
Child's cough at 1‐week follow‐up compared to on discharge   0.10
Much worse/worse2.11.21.0 
About the same5.08.45.2 
Better29.431.228.6 
All better63.559.265.2 
Bronchiolitis relapse10.711.910.30.81

Given the large potential for confounding regarding our initial findings, we examined multivariable‐adjusted associations of race/ethnicity and bronchiolitis management (Table 4). Receiving albuterol during the preadmission visit and chest x‐rays during hospitalization remained significantly associated with race/ethnicity in adjusted analyses, as NHB children were most likely to receive albuterol during the preadmission visit but least likely to receive chest x‐rays during hospitalization. Several outcomes with statistically significant differences found during unadjusted analyses (eg, chest x‐rays at preadmission visit, albuterol during hospitalization, CPAP/emntubation use, ICU admission, and LOS) were not independently associated with race/ethnicity in multivariable models. By contrast, adjusted analyses revealed Hispanic children as significantly more likely to be discharged on inhaled corticosteroids when compared to NHW and NHB children; this association had borderline statistical significance (P=0.08) in the unadjusted analysis. Last, we observed no significant racial/ethnic differences with respect to corticosteroids given at preadmission visit or hospitalization as well as no differences regarding bronchiolitis‐related relapse in either unadjusted or adjusted analyses.

Multivariable Results of Clinical Decisions and Outcomes Among Children Admitted for Bronchiolitis by Race/Ethnicity
 White, Non‐HispanicBlack, Non‐HispanicHispanic
OR95%CIOR95%CIOR95%CI
  • NOTE: All models control for age, sex, median household income by ZIP code, and insurance status. Abbreviations: CI, confidence interval; CPAP, continuous positive airway pressure; ICU, intensive care unit; NICU, neonatal intensive care unit; OR, odds ratio; RDSS, respiratory distress severity score. *Also control for gestational age, parental asthma, past pneumonia or bronchiolitis admission, discomfort and dyspnea at home, chief complaint, virology. Also control for birth weight, medications and dyspnea before preadmission, virology. Values are considered statistically significant with P<0.05. Also control for birth weight, NICU, children at home, parental asthma, comorbidity, flu shot, medications at home, virology. Also control for gestational age, birth weight, prenatal smoking, palivizumab, past pneumonia admission, steroids before preadmission, preadmission oral intake, O2 saturation, RDSS, apnea, virology, antibiotics, labs. Also control for gestational age, wheezing history, flu shot, discomfort at home, chief complaint, preadmission medications, labs and virology, RDSS. #Also control for breast feeding, parental asthma, wheezing history, comorbidities, flu shot, past pneumonia admission, medications before preadmission, preadmission fever, O2 saturation, RDSS, apnea, virology, medications, and labs. **Also control for family history, birth weight, breast feeding, prenatal smoking, antibiotics and discomfort before preadmission, chief complaint, preadmission apnea, O2 saturation, RDSS, antibiotics, intravenous line, and labs. Also control for birth weight, prenatal smoking, past bronchiolitis admission, steroids before preadmission, preadmission O2 saturation, RDSS, apnea, intravenous line, and antibiotics. Also control for birth weight, NICU, other children at home, prenatal smoking, palivizumab, discomfort and dyspnea before preadmission, chief complaint, preadmission oral intake, O2 saturation, RDSS, virology, and epinephrine. Also control for prenatal smoking, wheezing history, palivizumab, medications before preadmission, chief complaint, oral intake, step down, ICU, inpatient steroids, and labs. Also control for parental asthma, intubation history, past bronchiolitis admission, virology, and length of stay.

Preadmission visit      
Chest x‐ray*1.00(Reference)1.06(0.831.36)1.09(0.741.60)
Albuterol use1.00(Reference)1.58(1.202.07)1.42(0.892.26)
Steroid use, inhaled or systemic1.00(Reference)1.05(0.721.54)1.11(0.751.65)
During hospitalization      
Chest x‐ray1.00(Reference)0.66(0.490.90)0.95(0.601.50)
Albuterol use1.00(Reference)1.21(0.821.79)1.23(0.632.38)
Steroid use, inhaled or systemic#1.00(Reference)1.13(0.721.80)1.19(0.791.79)
ICU care**1.00(Reference)0.74(0.421.29)0.87(0.631.21)
Required CPAP/emntubation1.00(Reference)0.72(0.361.41)1.84(0.933.64)
Length of stay >3 days1.00(Reference)0.77(0.581.03)1.05(0.761.47)
Discharge      
Discharged on inhaled steroids1.00(Reference)1.31(0.862.00)1.92(1.193.10)
Bronchiolitis relapse1.00(Reference)1.08(0.621.87)0.96(0.551.65)

DISCUSSION

It is unclear if management and treatment differences found in children with severe bronchiolitis are associated with race/ethnicity. We sought to determine if such differences exist by analyzing data from a prospective multicenter cohort study. Differences in management and treatment are discussed in the context of AAP guidelines, as they are widely used in clinical practice.

The RDSS was used to help assess severity of illness across race/ethnicity. NHB children had the highest RDSS score (ie, most severe bronchiolitis presentation) compared to NHW and Hispanic children. The reason for this difference in severity is unclear, but a potential explanation may be that minority communities lack access to care and as a result delay care and treatment for respiratory disease until care seems absolutely necessary.[19] Indeed, in our sample, minority children were less likely to visit their PCP and take corticosteroids the week before hospitalization when compared with NHW children. Our finding runs counter to a similar study by Boudreaux et al. that found no association between race/ethnicity and the clinical presentation of children with acute asthma during the preadmission setting.[20] The more severe bronchiolitis presentation among NHB children may have suggested that these children would require a longer hospital LOS (3 days). However, our multivariable analysis found no difference in LOS across racial/ethnic groups. This LOS finding is intriguing given previous studies suggesting that minorities, of diverse ages and with diverse diagnoses, were more likely to have a shorter LOS (as well as less likely to be admitted to the ICU with a similar diagnosis) when compared to nonminorities.[21, 22] Additionally, because our study sampled 16 sites, variation in clinical judgment and pediatric ICU protocol may have also played a role.[23]

Our findings also shed light on how differences in bronchiolitis management relate to AAP guidelines. According to the AAP, corticosteroid medications should not be used routinely in the management of bronchiolitis. Despite this recommendation, previous reports indicate that up to 60% of infants with severe bronchiolitis receive corticosteroid therapy.[24, 25] Our finding that Hispanic children with severe bronchiolitis were most likely to be discharged on inhaled corticosteroids is potentially concerning, as it exposes a subset of children to treatment that is not recommended. On the other hand, given the increased risk of future asthma in Hispanic communities, a higher use of inhaled corticosteroids might be seen as appropriate. Either way, our findings are inconsistent with related studies concluding that racial minority pediatric patients with asthma were less likely to receive inhaled corticosteroids.[26, 27, 28, 29] Similarly, NHB children were most likely to receive albuterol during the preadmission visit on multivariable analysis. Although a trial dose of albuterol may be common practice in treating severe bronchiolitis, AAP recommendations do not support its routine application. Increased albuterol during preadmission may have been related to an elevated bronchiolitis severity at presentation among NHB children (as indicated by the RDSS). Potential reasons for these 2 differences in treatment remain unclear. They may represent medical management efforts by discharging physicians to prescribe: (1) corticosteroids to racial/ethnic communities with a higher risk of childhood asthma; (2) albuterol to children presenting with a more severe case of bronchiolitis. These possibilities merit further study.

The AAP also recommends diagnosis of bronchiolitis on the basis of history and physical examination; laboratory and radiologic studies should not be routinely used for diagnostic purposes. Although it is possible for chest radiograph abnormalities to be consistent with bronchiolitis, there is little evidence that an abnormal finding is associated with disease severity.[30] The clinical value of diagnostic testing in children with bronchiolitis is not well supported by evidence, and limiting exposure to radiation should be a priority.[31, 32] Our analysis found that NHW and Hispanic children were more likely to receive chest x‐rays while hospitalized when compared with NHB children. Unnecessary and increased radiation exposure in children is potentially harmful and warrants intervention to minimize risk.

Establishing systematic clinical pathways in bronchiolitis management may address the practice variation found nationwide and across race/ethnicity in this study. Although clinical guidelines provide general recommendations, clinical pathways are defined treatment protocols aiming to standardize and optimize patient outcomes and clinical efficiency. The incorporation of clinical pathways into healthcare systems has increased recently as a result of their favorable association with medical complications, healthcare costs, and LOS.[33] With respect to bronchiolitis, implementation of clinical pathways has proven to reduce use of inappropriate therapies, decrease risk of bronchiolitis‐related hospital readmission, and help with discharge planning.[30, 34, 35]

Notwithstanding the differences found in this study, management of children with bronchiolitis was, in many respects, comparable across racial/ethnic groups. For example, our multivariable analysis found no significant differences across racial/ethnic groups with respect to chest x‐rays and corticosteroid use during the preadmission visit, administration of albuterol or corticosteroids during hospitalization, use of CPAP/emntubation, ICU admission, hospital LOS, or likelihood of a bronchiolitis‐related relapse. The general lack of race/ethnic differences is consistent with similar research on inpatient management of acute asthma.[36]

This study has potential limitations. The hospitals participating in the study are predominantly urban, academically affiliated hospitals. This may result in findings that are less generalizable to rural and community hospitals. Second, the race/ethnicity classification used does not take into consideration the diversity and complexity of defining race/ethnicity in the United States. Third, bronchiolitis is defined as a clinical diagnosis that can encapsulate multiple lower respiratory infection diagnoses. As a result, there may have been variability in clinical and institutional practice. An additional limitation was utilizing RDSS to assess bronchiolitis severity. Although there is currently no validated, universally accepted score to assess bronchiolitis severity, several scores are available in the literature with varying performance. Last, the ZIP code‐based median household incomes used to assess SES are higher than federal data in similar geographic locations, potentially resulting in findings that are less generalizable.

CONCLUSION

This multicenter prospective cohort study found several differences in bronchiolitis presentation and management among children stratified by race/ethnicity in 16 geographically dispersed sites after controlling for multiple factors including SES. Our analysis showed that, when compared to NHW children, NHB children were more likely to be given albuterol during the preadmission visit and less likely to receive chest x‐rays as inpatients; Hispanic children were more likely to be discharged on inhaled corticosteroids. These differences are concerning for 2 reasons: (1) based on current evidence, race/ethnicity should not affect care in children with severe bronchiolitis; and (2) the observed differences in diagnostic testing and treatment are not recommended by the evidence‐based AAP guidelines. It is also important to note that these differences do not demonstrate that a specific race/ethnicity received better or worse clinical care. The goal of this analysis was not to determine the effectiveness of certain management tendencies in children with severe bronchiolitis, but rather to examine differences in the presentation and management of children from different racial/ethnic groups. The causes for the observed findings require further study. In the meantime, we suggest increasing the number of hospitals that incorporate clinical care pathways for severe bronchiolitis to control variation in practice and limit the impact that race/ethnicity may have in the provision of services.

Acknowledgements

The authors thank the MARC‐30 investigators for their ongoing dedication to bronchiolitis research.

Disclosures: This study was supported by the grant U01 AI‐67693 (Camargo) from the National Institutes of Health (Bethesda, MD). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Allergy and Infectious Diseases or the National Institutes of Health. The authors have no financial relationships relevant to this article or conflicts of interest to disclose. Mr. Santiago conceptualized the analysis, interpreted the data, drafted the initial manuscript, and approved the final manuscript as submitted. Dr. Mansbach conceptualized and designed the initial study, coordinated data collection at 1 of the sites, critically reviewed the manuscript, and approved the final manuscript as submitted. Dr. Chou was responsible for analysis and interpretation of data, critically reviewed the manuscript, and approved the final manuscript as submitted. Dr. Delgado coordinated data collection at 1 of the sites, critically reviewed the manuscript, and approved the final manuscript as submitted. Dr. Piedra conceptualized and designed the initial study, coordinated virology testing, critically reviewed the manuscript, and approved the final manuscript as submitted. Ms. Sullivan coordinated data collection at all sites, critically reviewed the manuscript, and approved the final manuscript as submitted. Ms. Espinola was responsible for data management, analysis and interpretation of data, drafting of the initial manuscript, and approved the final manuscript as submitted. Dr. Camargo conceptualized and designed the initial study, assisted with data analysis and interpretation of data, critically reviewed the manuscript, and approved the final manuscript as submitted.

APPENDIX

Principal Investigators at the 16 Participating Sites in MARC‐30
Besh Barcega, MDLoma Linda University Children's Hospital, Loma Linda, CA
John Cheng, MD and Carlos Delgado, MDChildren's Healthcare of Atlanta at Egleston, Atlanta, GA
Dorothy Damore, MD and Nikhil Shah, MDNew York Presbyterian Hospital, New York, NY
Haitham Haddad, MDRainbow Babies & Children's Hospital, Cleveland, OH
Paul Hain, MD and Mark Riederer, MDMonroe Carell Jr. Children's Hospital at Vanderbilt, Nashville, TN
Frank LoVecchio, DOMaricopa Medical Center, Phoenix, AZ
Charles Macias, MD, MPHTexas Children's Hospital, Houston, TX
Jonathan Mansbach, MD, MPHBoston Children's Hospital, Boston, MA
Eugene Mowad, MDAkron Children's Hospital, Akron, OH
Brian Pate, MDChildren's Mercy Hospital & Clinics, Kansas City, MO
M. Jason Sanders, MDChildren's Memorial Hermann Hospital, Houston, TX
Alan Schroeder, MDSanta Clara Valley Medical Center, San Jose, CA
Michelle Stevenson, MD, MSKosair Children's Hospital, Louisville, KY
Erin Stucky Fisher, MDRady Children's Hospital, San Diego, CA
Stephen Teach, MD, MPHChildren's National Medical Center, Washington, DC
Lisa Zaoutis, MDChildren's Hospital of Philadelphia, Philadelphia, PA

 

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References
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Bronchiolitis is the leading cause of hospitalization for infants in the United States, costs more than $500 million annually, and has seen a 30% increase ($1.34 billion to $1.73 billion) in related hospital charges from 2000 to 2009.13 Almost all children <2 years old are infected with respiratory syncytial virus, the most common cause of bronchiolitis, with 40% developing clinically recognizable bronchiolitis and 2% becoming hospitalized with severe bronchiolitis.[4, 5] Current American Academy of Pediatrics (AAP) guidelines state that routine use of bronchodilators, corticosteroids, and chest x‐rays is not recommended, and supportive care is strongly encouraged.[6] However, a lack of consensus among clinicians persists regarding bronchiolitis management.[7, 8, 9, 10] Although minority children and those with a lower socioeconomic status (SES) in the United States are more likely to present with bronchiolitis to the emergency department (ED) and be subsequently admitted when compared to the general population,[11, 12, 13] to our knowledge, no study has yet examined if race/ethnicity is independently associated with differences in the presentation and management of severe bronchiolitis (ie, bronchiolitis causing hospitalization). Although a prior bronchiolitis‐related study reported that Hispanic children had a longer ED length of stay (LOS) than non‐Hispanic white (NHW) and non‐Hispanic black (NHB) children,[14] other studies concluded that race/ethnicity were not predictors of intensive care unit (ICU) admission or unscheduled healthcare visits post‐ED discharge.[15, 16]

Determining if race/ethnicity is independently associated with certain bronchiolitis management tendencies has implications from both a health disparities standpoint (ie, unequal care based on race/ethnicity) and from a clinical perspective (ie, the potential of certain practices, such as clinical pathways, to increase the likelihood of equitable treatment). To address this knowledge gap, we examined prospective data from a multicenter study designed to evaluate multiple factors related to bronchiolitis hospitalization.

METHODS

Study Design

We conducted a multicenter prospective cohort for 3 consecutive years (20072010) as part of the Multicenter Airway Research Collaboration (MARC), a division of the Emergency Medicine Network (EMNet) (www.emnet‐usa.org). Sixteen hospitals in 12 states (see Appendix) participated from November 1st until March 31st in each study year. At the beginning of each month, site investigators used a standardized protocol to enroll a target number of patients from the inpatient wards and ICU.

All patients were treated at the discretion of their physician. Inclusion criteria were hospital admission with physician diagnosis of bronchiolitis, age <2 years, and ability of the child's guardian (eg, parent) to give informed consent. Patients were enrolled within 18 hours of admission. Physician diagnosis of bronchiolitis followed the AAP definition of a child with an acute respiratory illness with some combination of rhinitis, cough, tachypnea, wheezing, crackles, and/or retractions.[6] The exclusion criteria were previous enrollment and if a patient was transferred to a participating site hospital>48 hours after the initial admission. All consent and data forms were translated into Spanish. The institutional review board at each participating site approved the study.

Data Collection

Site investigators used a standardized protocol to enroll 2207 patients admitted with bronchiolitis. Investigators conducted a structured interview that assessed patients' demographic characteristics, medical and environmental history, duration of symptoms, and details of the acute illness. Race/ethnicity was assigned by report of the child's guardian to standard US Census groups. For the purpose of this analysis, mutually exclusive race/ethnicity categories were determined: NHW, NHB, or Hispanic. Non‐Hispanic patients who identified as being both white and black were categorized as NHB. Patients were excluded from analysis if neither white or black race nor Hispanic ethnicity were reported (eg, if only Asian race was reported) because of small numbers (n=67), as were patients missing all race/ethnicity data (n=10). This resulted in a total of 2130 (97%) patients in our analytical dataset. SES was assessed with 2 variables: insurance status (public, private, none) and family income, estimated by matching patients' home ZIP codes and year of enrollment to ZIP code‐based median household annual incomes obtained from Esri Business Analyst Desktop (Esri, Redlands, CA).[17]

ED and daily clinical data, including laboratory tests (eg, complete blood count, basic metabolic panel, urine analysis, blood culture), respiratory rates, oxygen saturation, medical management, and disposition were obtained by medical chart review. Additionally, in an attempt to evaluate bronchiolitis severity at presentation, a modified respiratory distress severity score (RDSS) was calculated based on 4 assessments made during the preadmission visit (ie, ED or office visit before hospital admission): respiratory rate by age, presence of wheezing (yes or no), air entry (normal, mild difficulty, or moderate/severe), and retractions (none, mild, or moderate/severe).[18] Each component was assigned a score of 0, 1, or 2, with the exception of wheeze, which was assigned either a 0 (no wheeze) or a 2 (wheeze), and then summed for a possible total score of 0 to 8.

Last, a follow‐up telephone interview was conducted 1 week after hospital discharge for each enrolled patient. Interviews assessed acute relapse, recent symptoms, and provided additional end points for longitudinal analysis of specific symptoms. All data were manually reviewed at the EMNet Coordinating Center, and site investigators were queried about missing data and discrepancies identified.

Outcome Measures

The major outcomes of this analysis were: albuterol and corticosteroid (inhaled or systemic) use during preadmission visit and hospitalization, chest x‐rays performed at preadmission visit and hospitalization, need for intensive respiratory support (ie, receiving continuous positive airway pressure [CPAP], intubation, or ICU admission), hospital LOS 3 days, discharge on inhaled corticosteroids, and relapse of bronchiolitis requiring medical attention and a change of medication within 1 week of discharge.

Statistical Analysis

Stata 11.2 (StataCorp, College Station, TX) was used for all analyses. We examined unadjusted differences between racial/ethnic groups and clinical presentation, patient management, and outcomes using 2, Fisher exact, or Kruskal‐Wallis test, as appropriate, with results reported as proportions with 95% confidence interval (CI) or median with interquartile range (IQR). Imputed values, calculated with the Stata impute command, were used to calculate the RDSS when 1 of the 4 components was missing; patients missing more than 1 component were not assigned an RDSS value. Multivariable logistic regression was conducted to evaluate the adjusted association between race/ethnicity and the outcomes listed above. Besides race/ethnicity, all multivariable models included the demographic variables of age, sex, insurance, and median household income. Other factors were considered for inclusion if they were associated with the outcome in unadjusted analyses (P<0.20) or deemed clinically relevant. All models were adjusted for the possibility of clustering by site. Results are reported for the race/ethnicity factor as odds ratios with 95% CI.

RESULTS

Of the 2130 subjects included in this analysis, 818 (38%) were NHW, 511 (24%) were NHB, and 801 (38%) were Hispanic. The median age for children was 4.0 months (IQR, 1.88.5 months), and 60% were male. Most children were publicly insured (65%), 31% had private insurance, and approximately 4% had no insurance. The median household income defined by patient ZIP code was $51,810 (IQR, $39,916$66,272), and nearly all children (97%) had a primary care provider (PCP). Approximately 21% of all children had relevant comorbidities and 17% of children were enrolled from the ICU. Overall, the median LOS was 2 days (IQR, 14 days).

The unadjusted associations between race/ethnicity and other demographic and historical characteristics are shown in Table 1. NHB and Hispanic children were more likely to have public insurance and less likely to have relevant major comorbidities when compared to NHW children. With regard to care received the week before hospitalization, NHW children were more likely to have visited their PCP, taken corticosteroids and/or antibiotics, and were least likely to have visited an ED when compared to NHB and Hispanic children.

Demographic and Clinical Characteristics of Subjects Before the Preadmission Visit by Race/Ethnicity*
 White, Non‐Hispanic, n=818, %Black, Non‐Hispanic, n=511, %Hispanic, n=801, %P
  • NOTE: Abbreviations: ED, emergency department; ICU, intensive care unit; IQR, interquartile range. *Preadmission visit is the ED or office visit preceding hospital admission. Major relevant comorbid disorders included reactive airway disease or asthma, spastic di/quadriplegia, chronic lung disease, seizure disorder, immunodeficiency, congenital heart disease, gastroesophageal reflux, and other major medical disorders.

Demographic characteristics    
Age, months, median (IQR)3.2 (1.57.4)5.0 (1.99.2)4.4 (2.09.1)<0.001
Female39.740.940.40.91
Insurance   <0.001
Private56.717.013.1 
Medicaid32.870.977.5 
Other public6.87.64.2 
None3.74.65.3 
Median household income by ZIP code, US$, median (IQR)$60,406 ($48,086$75,077)$44,191 ($32,922$55,640)$50,394 ($39,242$62,148)<0.001
Has primary care provider98.597.395.40.001
History    
Gestational age at birth   0.002
<32 weeks4.59.26.6 
3235 weeks5.78.86.0 
3537 weeks13.310.09.7 
37 weeks76.071.476.9 
Missing0.40.60.7 
Weight when born   <0.001
<3 pounds3.36.95.8 
34.9 pounds6.811.96.2 
56.9 pounds33.439.733.2 
>7 pounds55.840.353.6 
Missing0.71.41.4 
Kept in an ICU, premature nursery, or any type of special‐care facility when born24.529.724.90.07
Breast fed62.349.165.5<0.001
Attends daycare20.725.213.3<0.001
Number of other children (<18 years old) living in home   <0.001
124.225.617.0 
242.729.228.3 
333.145.254.7 
Neither parent has asthma66.156.677.7<0.001
Maternal smoking during pregnancy21.821.36.0<0.001
Secondhand smoke exposure12.920.28.7<0.001
History of wheezing21.125.921.80.12
Ever intubated9.513.29.20.05
Major relevant comorbidities23.621.118.20.03
Received palivizumab (respiratory syncytial virus vaccine)8.712.78.90.04
Received influenza vaccine this year20.224.721.20.15
In past 12 months, admitted overnight to hospital for bronchiolitis/wheezing/reactive airway disease45.057.955.90.06
In past 12 months, admitted overnight to hospital for pneumonia16.114.925.00.049
Current illness (before index visit)    
Any primary care provider or clinic visits during past week75.044.158.3<0.001
Any ED visits during past week29.130.334.60.049
Over the past week used inhaled bronchodilator40.636.237.00.18
Over the past week used inhaled/nebulized corticosteroids8.78.17.70.76
Over the past week taken any steroid liquids or pills or shots for bronchiolitis12.811.78.30.012
Over the past week taken antibiotics21.917.017.90.045
Onset of difficulty breathing before admission   0.03
None2.02.22.4 
<24 hours28.827.225.1 
13 days41.141.645.9 
47 days22.119.121.2 
>7 days6.09.95.3 
Over the past 24 hours, the level of discomfort or distress felt by the child because of symptoms   <0.001
Mild15.521.318.5 
Moderate47.839.337.2 
Severe36.137.642.6 

The unadjusted associations between race/ethnicity and clinical characteristics at preadmission visit and hospital admission are shown in Table 2. RDSS values were calculated for 2130 children; 1,752 (82%) RDSS values contained all 4 components. Of those requiring imputed values, 234 (11%) were missing 1 component, and 139 (7%) were missing more than 1 component. Per RDSS scores, NHB children presented with a more severe case of bronchiolitis when compared to NHW and Hispanic children. During admission, minority children were more likely to receive nebulized albuterol and less likely to visit the ICU. NHB children received the least inpatient laboratory testing and were least likely to receive chest x‐rays during hospital admission among all groups.

Clinical Characteristics at Preadmission Visit and During Admission by Race/Ethnicity
 White, Non‐Hispanic, n=818, %Black, Non‐Hispanic, n=511, %Hispanic, n=801, %P
  • NOTE: Abbreviations: CPAP, continuous positive airway pressure; ICU, intensive care unit; IV, intravenous; RDSS, respiratory distress severity score.

Preadmission clinical findings and treatments    
Reason brought to the hospital    
Fever29.730.240.7<0.001
Fussy32.631.628.40.18
Ear Infection6.04.34.40.23
Not drinking well35.027.527.20.001
Cough54.155.561.30.009
Other reasons29.027.623.50.04
Apnea8.56.26.10.11
Respiratory rate (breaths/minute)   0.001
<4023.818.228.1 
404930.830.729.3 
505917.615.516.0 
6027.835.626.6 
Presence of cough83.088.087.00.045
Presence of wheezing63.069.063.00.42
Fever (temperature 100.4F)22.728.535.4<0.001
Retractions   0.002
None22.919.023.0 
Mild39.441.744.4 
Moderate/severe28.431.128.1 
Missing9.48.24.5 
Air entry on auscultation   0.01
Normal39.631.733.6 
Mild difficulty31.433.536.3 
Moderate difficulty11.014.314.1 
Severe difficulty2.02.32.6 
Missing16.018.213.4 
Oxygen saturation on room air <9012.29.811.80.37
Given nebulized albuterol53.065.063.0<0.001
Given nebulized epinephrine15.420.418.40.06
Given steroids, inhaled or systemic16.020.519.40.08
Given antibiotics25.522.627.80.12
Oral Intake   <0.001
Adequate41.350.740.8 
Inadequate43.532.547.7 
Missing15.216.811.5 
IV placed56.951.561.90.001
Any laboratory tests86.391.388.00.02
Chest x‐ray59.064.065.00.03
RDSS, tertiles   <0.001
1 (3.00)36.024.034.0 
2 (3.0115.00)30.033.034.0 
3 (>5)25.036.027.0 
Not calculated9.06.04.0 
Virology results    
Respiratory syncytial virus75.967.571.00.003
Human rhinovirus23.830.125.00.03
Human metapneumovirus6.16.88.40.20
Inpatient clinical findings and treatments    
Length of stay 3 days46.539.345.40.03
Ever in observation unit8.67.84.00.001
Ever in regular ward89.493.094.50.001
Ever in step‐down unit5.03.27.80.002
Ever in ICU20.315.015.90.02
Required CPAP or intubation7.74.68.80.02
Given nebulized albuterol37.648.046.7<0.001
Given nebulized epinephrine10.714.913.00.07
Given steroids, inhaled or systemic21.327.323.50.047
Given antibiotics38.934.338.60.19
Received IV fluids53.145.657.1<0.001
Any laboratory tests52.241.651.7<0.001
Chest x‐ray27.118.622.90.002

Discharge treatment and outcomes at 1‐week follow‐up are shown in Table 3. A total of 1771 patients (83%) were reached by telephone. No statistically significant differences between racial/ethnic groups were found regarding hospital discharge on corticosteroids and likelihood of bronchiolitis‐related relapse.

Discharge Treatment and Outcome Measures at 1‐Week Follow‐up by Race/Ethnicity
 White, Non‐Hispanic, n=818, %Black, Non‐Hispanic, n=511, %Hispanic, n=801, %P
Discharged on inhaled corticosteroids9.511.113.30.08
Discharged on oral corticosteroids9.812.48.50.11
Child's condition at 1‐week follow‐up compared to on discharge   0.001
Much worse/worse1.80.70.4 
About the same3.46.42.5 
Better38.239.134.2 
All better56.653.762.9 
Child's cough at 1‐week follow‐up compared to on discharge   0.10
Much worse/worse2.11.21.0 
About the same5.08.45.2 
Better29.431.228.6 
All better63.559.265.2 
Bronchiolitis relapse10.711.910.30.81

Given the large potential for confounding regarding our initial findings, we examined multivariable‐adjusted associations of race/ethnicity and bronchiolitis management (Table 4). Receiving albuterol during the preadmission visit and chest x‐rays during hospitalization remained significantly associated with race/ethnicity in adjusted analyses, as NHB children were most likely to receive albuterol during the preadmission visit but least likely to receive chest x‐rays during hospitalization. Several outcomes with statistically significant differences found during unadjusted analyses (eg, chest x‐rays at preadmission visit, albuterol during hospitalization, CPAP/emntubation use, ICU admission, and LOS) were not independently associated with race/ethnicity in multivariable models. By contrast, adjusted analyses revealed Hispanic children as significantly more likely to be discharged on inhaled corticosteroids when compared to NHW and NHB children; this association had borderline statistical significance (P=0.08) in the unadjusted analysis. Last, we observed no significant racial/ethnic differences with respect to corticosteroids given at preadmission visit or hospitalization as well as no differences regarding bronchiolitis‐related relapse in either unadjusted or adjusted analyses.

Multivariable Results of Clinical Decisions and Outcomes Among Children Admitted for Bronchiolitis by Race/Ethnicity
 White, Non‐HispanicBlack, Non‐HispanicHispanic
OR95%CIOR95%CIOR95%CI
  • NOTE: All models control for age, sex, median household income by ZIP code, and insurance status. Abbreviations: CI, confidence interval; CPAP, continuous positive airway pressure; ICU, intensive care unit; NICU, neonatal intensive care unit; OR, odds ratio; RDSS, respiratory distress severity score. *Also control for gestational age, parental asthma, past pneumonia or bronchiolitis admission, discomfort and dyspnea at home, chief complaint, virology. Also control for birth weight, medications and dyspnea before preadmission, virology. Values are considered statistically significant with P<0.05. Also control for birth weight, NICU, children at home, parental asthma, comorbidity, flu shot, medications at home, virology. Also control for gestational age, birth weight, prenatal smoking, palivizumab, past pneumonia admission, steroids before preadmission, preadmission oral intake, O2 saturation, RDSS, apnea, virology, antibiotics, labs. Also control for gestational age, wheezing history, flu shot, discomfort at home, chief complaint, preadmission medications, labs and virology, RDSS. #Also control for breast feeding, parental asthma, wheezing history, comorbidities, flu shot, past pneumonia admission, medications before preadmission, preadmission fever, O2 saturation, RDSS, apnea, virology, medications, and labs. **Also control for family history, birth weight, breast feeding, prenatal smoking, antibiotics and discomfort before preadmission, chief complaint, preadmission apnea, O2 saturation, RDSS, antibiotics, intravenous line, and labs. Also control for birth weight, prenatal smoking, past bronchiolitis admission, steroids before preadmission, preadmission O2 saturation, RDSS, apnea, intravenous line, and antibiotics. Also control for birth weight, NICU, other children at home, prenatal smoking, palivizumab, discomfort and dyspnea before preadmission, chief complaint, preadmission oral intake, O2 saturation, RDSS, virology, and epinephrine. Also control for prenatal smoking, wheezing history, palivizumab, medications before preadmission, chief complaint, oral intake, step down, ICU, inpatient steroids, and labs. Also control for parental asthma, intubation history, past bronchiolitis admission, virology, and length of stay.

Preadmission visit      
Chest x‐ray*1.00(Reference)1.06(0.831.36)1.09(0.741.60)
Albuterol use1.00(Reference)1.58(1.202.07)1.42(0.892.26)
Steroid use, inhaled or systemic1.00(Reference)1.05(0.721.54)1.11(0.751.65)
During hospitalization      
Chest x‐ray1.00(Reference)0.66(0.490.90)0.95(0.601.50)
Albuterol use1.00(Reference)1.21(0.821.79)1.23(0.632.38)
Steroid use, inhaled or systemic#1.00(Reference)1.13(0.721.80)1.19(0.791.79)
ICU care**1.00(Reference)0.74(0.421.29)0.87(0.631.21)
Required CPAP/emntubation1.00(Reference)0.72(0.361.41)1.84(0.933.64)
Length of stay >3 days1.00(Reference)0.77(0.581.03)1.05(0.761.47)
Discharge      
Discharged on inhaled steroids1.00(Reference)1.31(0.862.00)1.92(1.193.10)
Bronchiolitis relapse1.00(Reference)1.08(0.621.87)0.96(0.551.65)

DISCUSSION

It is unclear if management and treatment differences found in children with severe bronchiolitis are associated with race/ethnicity. We sought to determine if such differences exist by analyzing data from a prospective multicenter cohort study. Differences in management and treatment are discussed in the context of AAP guidelines, as they are widely used in clinical practice.

The RDSS was used to help assess severity of illness across race/ethnicity. NHB children had the highest RDSS score (ie, most severe bronchiolitis presentation) compared to NHW and Hispanic children. The reason for this difference in severity is unclear, but a potential explanation may be that minority communities lack access to care and as a result delay care and treatment for respiratory disease until care seems absolutely necessary.[19] Indeed, in our sample, minority children were less likely to visit their PCP and take corticosteroids the week before hospitalization when compared with NHW children. Our finding runs counter to a similar study by Boudreaux et al. that found no association between race/ethnicity and the clinical presentation of children with acute asthma during the preadmission setting.[20] The more severe bronchiolitis presentation among NHB children may have suggested that these children would require a longer hospital LOS (3 days). However, our multivariable analysis found no difference in LOS across racial/ethnic groups. This LOS finding is intriguing given previous studies suggesting that minorities, of diverse ages and with diverse diagnoses, were more likely to have a shorter LOS (as well as less likely to be admitted to the ICU with a similar diagnosis) when compared to nonminorities.[21, 22] Additionally, because our study sampled 16 sites, variation in clinical judgment and pediatric ICU protocol may have also played a role.[23]

Our findings also shed light on how differences in bronchiolitis management relate to AAP guidelines. According to the AAP, corticosteroid medications should not be used routinely in the management of bronchiolitis. Despite this recommendation, previous reports indicate that up to 60% of infants with severe bronchiolitis receive corticosteroid therapy.[24, 25] Our finding that Hispanic children with severe bronchiolitis were most likely to be discharged on inhaled corticosteroids is potentially concerning, as it exposes a subset of children to treatment that is not recommended. On the other hand, given the increased risk of future asthma in Hispanic communities, a higher use of inhaled corticosteroids might be seen as appropriate. Either way, our findings are inconsistent with related studies concluding that racial minority pediatric patients with asthma were less likely to receive inhaled corticosteroids.[26, 27, 28, 29] Similarly, NHB children were most likely to receive albuterol during the preadmission visit on multivariable analysis. Although a trial dose of albuterol may be common practice in treating severe bronchiolitis, AAP recommendations do not support its routine application. Increased albuterol during preadmission may have been related to an elevated bronchiolitis severity at presentation among NHB children (as indicated by the RDSS). Potential reasons for these 2 differences in treatment remain unclear. They may represent medical management efforts by discharging physicians to prescribe: (1) corticosteroids to racial/ethnic communities with a higher risk of childhood asthma; (2) albuterol to children presenting with a more severe case of bronchiolitis. These possibilities merit further study.

The AAP also recommends diagnosis of bronchiolitis on the basis of history and physical examination; laboratory and radiologic studies should not be routinely used for diagnostic purposes. Although it is possible for chest radiograph abnormalities to be consistent with bronchiolitis, there is little evidence that an abnormal finding is associated with disease severity.[30] The clinical value of diagnostic testing in children with bronchiolitis is not well supported by evidence, and limiting exposure to radiation should be a priority.[31, 32] Our analysis found that NHW and Hispanic children were more likely to receive chest x‐rays while hospitalized when compared with NHB children. Unnecessary and increased radiation exposure in children is potentially harmful and warrants intervention to minimize risk.

Establishing systematic clinical pathways in bronchiolitis management may address the practice variation found nationwide and across race/ethnicity in this study. Although clinical guidelines provide general recommendations, clinical pathways are defined treatment protocols aiming to standardize and optimize patient outcomes and clinical efficiency. The incorporation of clinical pathways into healthcare systems has increased recently as a result of their favorable association with medical complications, healthcare costs, and LOS.[33] With respect to bronchiolitis, implementation of clinical pathways has proven to reduce use of inappropriate therapies, decrease risk of bronchiolitis‐related hospital readmission, and help with discharge planning.[30, 34, 35]

Notwithstanding the differences found in this study, management of children with bronchiolitis was, in many respects, comparable across racial/ethnic groups. For example, our multivariable analysis found no significant differences across racial/ethnic groups with respect to chest x‐rays and corticosteroid use during the preadmission visit, administration of albuterol or corticosteroids during hospitalization, use of CPAP/emntubation, ICU admission, hospital LOS, or likelihood of a bronchiolitis‐related relapse. The general lack of race/ethnic differences is consistent with similar research on inpatient management of acute asthma.[36]

This study has potential limitations. The hospitals participating in the study are predominantly urban, academically affiliated hospitals. This may result in findings that are less generalizable to rural and community hospitals. Second, the race/ethnicity classification used does not take into consideration the diversity and complexity of defining race/ethnicity in the United States. Third, bronchiolitis is defined as a clinical diagnosis that can encapsulate multiple lower respiratory infection diagnoses. As a result, there may have been variability in clinical and institutional practice. An additional limitation was utilizing RDSS to assess bronchiolitis severity. Although there is currently no validated, universally accepted score to assess bronchiolitis severity, several scores are available in the literature with varying performance. Last, the ZIP code‐based median household incomes used to assess SES are higher than federal data in similar geographic locations, potentially resulting in findings that are less generalizable.

CONCLUSION

This multicenter prospective cohort study found several differences in bronchiolitis presentation and management among children stratified by race/ethnicity in 16 geographically dispersed sites after controlling for multiple factors including SES. Our analysis showed that, when compared to NHW children, NHB children were more likely to be given albuterol during the preadmission visit and less likely to receive chest x‐rays as inpatients; Hispanic children were more likely to be discharged on inhaled corticosteroids. These differences are concerning for 2 reasons: (1) based on current evidence, race/ethnicity should not affect care in children with severe bronchiolitis; and (2) the observed differences in diagnostic testing and treatment are not recommended by the evidence‐based AAP guidelines. It is also important to note that these differences do not demonstrate that a specific race/ethnicity received better or worse clinical care. The goal of this analysis was not to determine the effectiveness of certain management tendencies in children with severe bronchiolitis, but rather to examine differences in the presentation and management of children from different racial/ethnic groups. The causes for the observed findings require further study. In the meantime, we suggest increasing the number of hospitals that incorporate clinical care pathways for severe bronchiolitis to control variation in practice and limit the impact that race/ethnicity may have in the provision of services.

Acknowledgements

The authors thank the MARC‐30 investigators for their ongoing dedication to bronchiolitis research.

Disclosures: This study was supported by the grant U01 AI‐67693 (Camargo) from the National Institutes of Health (Bethesda, MD). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Allergy and Infectious Diseases or the National Institutes of Health. The authors have no financial relationships relevant to this article or conflicts of interest to disclose. Mr. Santiago conceptualized the analysis, interpreted the data, drafted the initial manuscript, and approved the final manuscript as submitted. Dr. Mansbach conceptualized and designed the initial study, coordinated data collection at 1 of the sites, critically reviewed the manuscript, and approved the final manuscript as submitted. Dr. Chou was responsible for analysis and interpretation of data, critically reviewed the manuscript, and approved the final manuscript as submitted. Dr. Delgado coordinated data collection at 1 of the sites, critically reviewed the manuscript, and approved the final manuscript as submitted. Dr. Piedra conceptualized and designed the initial study, coordinated virology testing, critically reviewed the manuscript, and approved the final manuscript as submitted. Ms. Sullivan coordinated data collection at all sites, critically reviewed the manuscript, and approved the final manuscript as submitted. Ms. Espinola was responsible for data management, analysis and interpretation of data, drafting of the initial manuscript, and approved the final manuscript as submitted. Dr. Camargo conceptualized and designed the initial study, assisted with data analysis and interpretation of data, critically reviewed the manuscript, and approved the final manuscript as submitted.

APPENDIX

Principal Investigators at the 16 Participating Sites in MARC‐30
Besh Barcega, MDLoma Linda University Children's Hospital, Loma Linda, CA
John Cheng, MD and Carlos Delgado, MDChildren's Healthcare of Atlanta at Egleston, Atlanta, GA
Dorothy Damore, MD and Nikhil Shah, MDNew York Presbyterian Hospital, New York, NY
Haitham Haddad, MDRainbow Babies & Children's Hospital, Cleveland, OH
Paul Hain, MD and Mark Riederer, MDMonroe Carell Jr. Children's Hospital at Vanderbilt, Nashville, TN
Frank LoVecchio, DOMaricopa Medical Center, Phoenix, AZ
Charles Macias, MD, MPHTexas Children's Hospital, Houston, TX
Jonathan Mansbach, MD, MPHBoston Children's Hospital, Boston, MA
Eugene Mowad, MDAkron Children's Hospital, Akron, OH
Brian Pate, MDChildren's Mercy Hospital & Clinics, Kansas City, MO
M. Jason Sanders, MDChildren's Memorial Hermann Hospital, Houston, TX
Alan Schroeder, MDSanta Clara Valley Medical Center, San Jose, CA
Michelle Stevenson, MD, MSKosair Children's Hospital, Louisville, KY
Erin Stucky Fisher, MDRady Children's Hospital, San Diego, CA
Stephen Teach, MD, MPHChildren's National Medical Center, Washington, DC
Lisa Zaoutis, MDChildren's Hospital of Philadelphia, Philadelphia, PA

 

Bronchiolitis is the leading cause of hospitalization for infants in the United States, costs more than $500 million annually, and has seen a 30% increase ($1.34 billion to $1.73 billion) in related hospital charges from 2000 to 2009.13 Almost all children <2 years old are infected with respiratory syncytial virus, the most common cause of bronchiolitis, with 40% developing clinically recognizable bronchiolitis and 2% becoming hospitalized with severe bronchiolitis.[4, 5] Current American Academy of Pediatrics (AAP) guidelines state that routine use of bronchodilators, corticosteroids, and chest x‐rays is not recommended, and supportive care is strongly encouraged.[6] However, a lack of consensus among clinicians persists regarding bronchiolitis management.[7, 8, 9, 10] Although minority children and those with a lower socioeconomic status (SES) in the United States are more likely to present with bronchiolitis to the emergency department (ED) and be subsequently admitted when compared to the general population,[11, 12, 13] to our knowledge, no study has yet examined if race/ethnicity is independently associated with differences in the presentation and management of severe bronchiolitis (ie, bronchiolitis causing hospitalization). Although a prior bronchiolitis‐related study reported that Hispanic children had a longer ED length of stay (LOS) than non‐Hispanic white (NHW) and non‐Hispanic black (NHB) children,[14] other studies concluded that race/ethnicity were not predictors of intensive care unit (ICU) admission or unscheduled healthcare visits post‐ED discharge.[15, 16]

Determining if race/ethnicity is independently associated with certain bronchiolitis management tendencies has implications from both a health disparities standpoint (ie, unequal care based on race/ethnicity) and from a clinical perspective (ie, the potential of certain practices, such as clinical pathways, to increase the likelihood of equitable treatment). To address this knowledge gap, we examined prospective data from a multicenter study designed to evaluate multiple factors related to bronchiolitis hospitalization.

METHODS

Study Design

We conducted a multicenter prospective cohort for 3 consecutive years (20072010) as part of the Multicenter Airway Research Collaboration (MARC), a division of the Emergency Medicine Network (EMNet) (www.emnet‐usa.org). Sixteen hospitals in 12 states (see Appendix) participated from November 1st until March 31st in each study year. At the beginning of each month, site investigators used a standardized protocol to enroll a target number of patients from the inpatient wards and ICU.

All patients were treated at the discretion of their physician. Inclusion criteria were hospital admission with physician diagnosis of bronchiolitis, age <2 years, and ability of the child's guardian (eg, parent) to give informed consent. Patients were enrolled within 18 hours of admission. Physician diagnosis of bronchiolitis followed the AAP definition of a child with an acute respiratory illness with some combination of rhinitis, cough, tachypnea, wheezing, crackles, and/or retractions.[6] The exclusion criteria were previous enrollment and if a patient was transferred to a participating site hospital>48 hours after the initial admission. All consent and data forms were translated into Spanish. The institutional review board at each participating site approved the study.

Data Collection

Site investigators used a standardized protocol to enroll 2207 patients admitted with bronchiolitis. Investigators conducted a structured interview that assessed patients' demographic characteristics, medical and environmental history, duration of symptoms, and details of the acute illness. Race/ethnicity was assigned by report of the child's guardian to standard US Census groups. For the purpose of this analysis, mutually exclusive race/ethnicity categories were determined: NHW, NHB, or Hispanic. Non‐Hispanic patients who identified as being both white and black were categorized as NHB. Patients were excluded from analysis if neither white or black race nor Hispanic ethnicity were reported (eg, if only Asian race was reported) because of small numbers (n=67), as were patients missing all race/ethnicity data (n=10). This resulted in a total of 2130 (97%) patients in our analytical dataset. SES was assessed with 2 variables: insurance status (public, private, none) and family income, estimated by matching patients' home ZIP codes and year of enrollment to ZIP code‐based median household annual incomes obtained from Esri Business Analyst Desktop (Esri, Redlands, CA).[17]

ED and daily clinical data, including laboratory tests (eg, complete blood count, basic metabolic panel, urine analysis, blood culture), respiratory rates, oxygen saturation, medical management, and disposition were obtained by medical chart review. Additionally, in an attempt to evaluate bronchiolitis severity at presentation, a modified respiratory distress severity score (RDSS) was calculated based on 4 assessments made during the preadmission visit (ie, ED or office visit before hospital admission): respiratory rate by age, presence of wheezing (yes or no), air entry (normal, mild difficulty, or moderate/severe), and retractions (none, mild, or moderate/severe).[18] Each component was assigned a score of 0, 1, or 2, with the exception of wheeze, which was assigned either a 0 (no wheeze) or a 2 (wheeze), and then summed for a possible total score of 0 to 8.

Last, a follow‐up telephone interview was conducted 1 week after hospital discharge for each enrolled patient. Interviews assessed acute relapse, recent symptoms, and provided additional end points for longitudinal analysis of specific symptoms. All data were manually reviewed at the EMNet Coordinating Center, and site investigators were queried about missing data and discrepancies identified.

Outcome Measures

The major outcomes of this analysis were: albuterol and corticosteroid (inhaled or systemic) use during preadmission visit and hospitalization, chest x‐rays performed at preadmission visit and hospitalization, need for intensive respiratory support (ie, receiving continuous positive airway pressure [CPAP], intubation, or ICU admission), hospital LOS 3 days, discharge on inhaled corticosteroids, and relapse of bronchiolitis requiring medical attention and a change of medication within 1 week of discharge.

Statistical Analysis

Stata 11.2 (StataCorp, College Station, TX) was used for all analyses. We examined unadjusted differences between racial/ethnic groups and clinical presentation, patient management, and outcomes using 2, Fisher exact, or Kruskal‐Wallis test, as appropriate, with results reported as proportions with 95% confidence interval (CI) or median with interquartile range (IQR). Imputed values, calculated with the Stata impute command, were used to calculate the RDSS when 1 of the 4 components was missing; patients missing more than 1 component were not assigned an RDSS value. Multivariable logistic regression was conducted to evaluate the adjusted association between race/ethnicity and the outcomes listed above. Besides race/ethnicity, all multivariable models included the demographic variables of age, sex, insurance, and median household income. Other factors were considered for inclusion if they were associated with the outcome in unadjusted analyses (P<0.20) or deemed clinically relevant. All models were adjusted for the possibility of clustering by site. Results are reported for the race/ethnicity factor as odds ratios with 95% CI.

RESULTS

Of the 2130 subjects included in this analysis, 818 (38%) were NHW, 511 (24%) were NHB, and 801 (38%) were Hispanic. The median age for children was 4.0 months (IQR, 1.88.5 months), and 60% were male. Most children were publicly insured (65%), 31% had private insurance, and approximately 4% had no insurance. The median household income defined by patient ZIP code was $51,810 (IQR, $39,916$66,272), and nearly all children (97%) had a primary care provider (PCP). Approximately 21% of all children had relevant comorbidities and 17% of children were enrolled from the ICU. Overall, the median LOS was 2 days (IQR, 14 days).

The unadjusted associations between race/ethnicity and other demographic and historical characteristics are shown in Table 1. NHB and Hispanic children were more likely to have public insurance and less likely to have relevant major comorbidities when compared to NHW children. With regard to care received the week before hospitalization, NHW children were more likely to have visited their PCP, taken corticosteroids and/or antibiotics, and were least likely to have visited an ED when compared to NHB and Hispanic children.

Demographic and Clinical Characteristics of Subjects Before the Preadmission Visit by Race/Ethnicity*
 White, Non‐Hispanic, n=818, %Black, Non‐Hispanic, n=511, %Hispanic, n=801, %P
  • NOTE: Abbreviations: ED, emergency department; ICU, intensive care unit; IQR, interquartile range. *Preadmission visit is the ED or office visit preceding hospital admission. Major relevant comorbid disorders included reactive airway disease or asthma, spastic di/quadriplegia, chronic lung disease, seizure disorder, immunodeficiency, congenital heart disease, gastroesophageal reflux, and other major medical disorders.

Demographic characteristics    
Age, months, median (IQR)3.2 (1.57.4)5.0 (1.99.2)4.4 (2.09.1)<0.001
Female39.740.940.40.91
Insurance   <0.001
Private56.717.013.1 
Medicaid32.870.977.5 
Other public6.87.64.2 
None3.74.65.3 
Median household income by ZIP code, US$, median (IQR)$60,406 ($48,086$75,077)$44,191 ($32,922$55,640)$50,394 ($39,242$62,148)<0.001
Has primary care provider98.597.395.40.001
History    
Gestational age at birth   0.002
<32 weeks4.59.26.6 
3235 weeks5.78.86.0 
3537 weeks13.310.09.7 
37 weeks76.071.476.9 
Missing0.40.60.7 
Weight when born   <0.001
<3 pounds3.36.95.8 
34.9 pounds6.811.96.2 
56.9 pounds33.439.733.2 
>7 pounds55.840.353.6 
Missing0.71.41.4 
Kept in an ICU, premature nursery, or any type of special‐care facility when born24.529.724.90.07
Breast fed62.349.165.5<0.001
Attends daycare20.725.213.3<0.001
Number of other children (<18 years old) living in home   <0.001
124.225.617.0 
242.729.228.3 
333.145.254.7 
Neither parent has asthma66.156.677.7<0.001
Maternal smoking during pregnancy21.821.36.0<0.001
Secondhand smoke exposure12.920.28.7<0.001
History of wheezing21.125.921.80.12
Ever intubated9.513.29.20.05
Major relevant comorbidities23.621.118.20.03
Received palivizumab (respiratory syncytial virus vaccine)8.712.78.90.04
Received influenza vaccine this year20.224.721.20.15
In past 12 months, admitted overnight to hospital for bronchiolitis/wheezing/reactive airway disease45.057.955.90.06
In past 12 months, admitted overnight to hospital for pneumonia16.114.925.00.049
Current illness (before index visit)    
Any primary care provider or clinic visits during past week75.044.158.3<0.001
Any ED visits during past week29.130.334.60.049
Over the past week used inhaled bronchodilator40.636.237.00.18
Over the past week used inhaled/nebulized corticosteroids8.78.17.70.76
Over the past week taken any steroid liquids or pills or shots for bronchiolitis12.811.78.30.012
Over the past week taken antibiotics21.917.017.90.045
Onset of difficulty breathing before admission   0.03
None2.02.22.4 
<24 hours28.827.225.1 
13 days41.141.645.9 
47 days22.119.121.2 
>7 days6.09.95.3 
Over the past 24 hours, the level of discomfort or distress felt by the child because of symptoms   <0.001
Mild15.521.318.5 
Moderate47.839.337.2 
Severe36.137.642.6 

The unadjusted associations between race/ethnicity and clinical characteristics at preadmission visit and hospital admission are shown in Table 2. RDSS values were calculated for 2130 children; 1,752 (82%) RDSS values contained all 4 components. Of those requiring imputed values, 234 (11%) were missing 1 component, and 139 (7%) were missing more than 1 component. Per RDSS scores, NHB children presented with a more severe case of bronchiolitis when compared to NHW and Hispanic children. During admission, minority children were more likely to receive nebulized albuterol and less likely to visit the ICU. NHB children received the least inpatient laboratory testing and were least likely to receive chest x‐rays during hospital admission among all groups.

Clinical Characteristics at Preadmission Visit and During Admission by Race/Ethnicity
 White, Non‐Hispanic, n=818, %Black, Non‐Hispanic, n=511, %Hispanic, n=801, %P
  • NOTE: Abbreviations: CPAP, continuous positive airway pressure; ICU, intensive care unit; IV, intravenous; RDSS, respiratory distress severity score.

Preadmission clinical findings and treatments    
Reason brought to the hospital    
Fever29.730.240.7<0.001
Fussy32.631.628.40.18
Ear Infection6.04.34.40.23
Not drinking well35.027.527.20.001
Cough54.155.561.30.009
Other reasons29.027.623.50.04
Apnea8.56.26.10.11
Respiratory rate (breaths/minute)   0.001
<4023.818.228.1 
404930.830.729.3 
505917.615.516.0 
6027.835.626.6 
Presence of cough83.088.087.00.045
Presence of wheezing63.069.063.00.42
Fever (temperature 100.4F)22.728.535.4<0.001
Retractions   0.002
None22.919.023.0 
Mild39.441.744.4 
Moderate/severe28.431.128.1 
Missing9.48.24.5 
Air entry on auscultation   0.01
Normal39.631.733.6 
Mild difficulty31.433.536.3 
Moderate difficulty11.014.314.1 
Severe difficulty2.02.32.6 
Missing16.018.213.4 
Oxygen saturation on room air <9012.29.811.80.37
Given nebulized albuterol53.065.063.0<0.001
Given nebulized epinephrine15.420.418.40.06
Given steroids, inhaled or systemic16.020.519.40.08
Given antibiotics25.522.627.80.12
Oral Intake   <0.001
Adequate41.350.740.8 
Inadequate43.532.547.7 
Missing15.216.811.5 
IV placed56.951.561.90.001
Any laboratory tests86.391.388.00.02
Chest x‐ray59.064.065.00.03
RDSS, tertiles   <0.001
1 (3.00)36.024.034.0 
2 (3.0115.00)30.033.034.0 
3 (>5)25.036.027.0 
Not calculated9.06.04.0 
Virology results    
Respiratory syncytial virus75.967.571.00.003
Human rhinovirus23.830.125.00.03
Human metapneumovirus6.16.88.40.20
Inpatient clinical findings and treatments    
Length of stay 3 days46.539.345.40.03
Ever in observation unit8.67.84.00.001
Ever in regular ward89.493.094.50.001
Ever in step‐down unit5.03.27.80.002
Ever in ICU20.315.015.90.02
Required CPAP or intubation7.74.68.80.02
Given nebulized albuterol37.648.046.7<0.001
Given nebulized epinephrine10.714.913.00.07
Given steroids, inhaled or systemic21.327.323.50.047
Given antibiotics38.934.338.60.19
Received IV fluids53.145.657.1<0.001
Any laboratory tests52.241.651.7<0.001
Chest x‐ray27.118.622.90.002

Discharge treatment and outcomes at 1‐week follow‐up are shown in Table 3. A total of 1771 patients (83%) were reached by telephone. No statistically significant differences between racial/ethnic groups were found regarding hospital discharge on corticosteroids and likelihood of bronchiolitis‐related relapse.

Discharge Treatment and Outcome Measures at 1‐Week Follow‐up by Race/Ethnicity
 White, Non‐Hispanic, n=818, %Black, Non‐Hispanic, n=511, %Hispanic, n=801, %P
Discharged on inhaled corticosteroids9.511.113.30.08
Discharged on oral corticosteroids9.812.48.50.11
Child's condition at 1‐week follow‐up compared to on discharge   0.001
Much worse/worse1.80.70.4 
About the same3.46.42.5 
Better38.239.134.2 
All better56.653.762.9 
Child's cough at 1‐week follow‐up compared to on discharge   0.10
Much worse/worse2.11.21.0 
About the same5.08.45.2 
Better29.431.228.6 
All better63.559.265.2 
Bronchiolitis relapse10.711.910.30.81

Given the large potential for confounding regarding our initial findings, we examined multivariable‐adjusted associations of race/ethnicity and bronchiolitis management (Table 4). Receiving albuterol during the preadmission visit and chest x‐rays during hospitalization remained significantly associated with race/ethnicity in adjusted analyses, as NHB children were most likely to receive albuterol during the preadmission visit but least likely to receive chest x‐rays during hospitalization. Several outcomes with statistically significant differences found during unadjusted analyses (eg, chest x‐rays at preadmission visit, albuterol during hospitalization, CPAP/emntubation use, ICU admission, and LOS) were not independently associated with race/ethnicity in multivariable models. By contrast, adjusted analyses revealed Hispanic children as significantly more likely to be discharged on inhaled corticosteroids when compared to NHW and NHB children; this association had borderline statistical significance (P=0.08) in the unadjusted analysis. Last, we observed no significant racial/ethnic differences with respect to corticosteroids given at preadmission visit or hospitalization as well as no differences regarding bronchiolitis‐related relapse in either unadjusted or adjusted analyses.

Multivariable Results of Clinical Decisions and Outcomes Among Children Admitted for Bronchiolitis by Race/Ethnicity
 White, Non‐HispanicBlack, Non‐HispanicHispanic
OR95%CIOR95%CIOR95%CI
  • NOTE: All models control for age, sex, median household income by ZIP code, and insurance status. Abbreviations: CI, confidence interval; CPAP, continuous positive airway pressure; ICU, intensive care unit; NICU, neonatal intensive care unit; OR, odds ratio; RDSS, respiratory distress severity score. *Also control for gestational age, parental asthma, past pneumonia or bronchiolitis admission, discomfort and dyspnea at home, chief complaint, virology. Also control for birth weight, medications and dyspnea before preadmission, virology. Values are considered statistically significant with P<0.05. Also control for birth weight, NICU, children at home, parental asthma, comorbidity, flu shot, medications at home, virology. Also control for gestational age, birth weight, prenatal smoking, palivizumab, past pneumonia admission, steroids before preadmission, preadmission oral intake, O2 saturation, RDSS, apnea, virology, antibiotics, labs. Also control for gestational age, wheezing history, flu shot, discomfort at home, chief complaint, preadmission medications, labs and virology, RDSS. #Also control for breast feeding, parental asthma, wheezing history, comorbidities, flu shot, past pneumonia admission, medications before preadmission, preadmission fever, O2 saturation, RDSS, apnea, virology, medications, and labs. **Also control for family history, birth weight, breast feeding, prenatal smoking, antibiotics and discomfort before preadmission, chief complaint, preadmission apnea, O2 saturation, RDSS, antibiotics, intravenous line, and labs. Also control for birth weight, prenatal smoking, past bronchiolitis admission, steroids before preadmission, preadmission O2 saturation, RDSS, apnea, intravenous line, and antibiotics. Also control for birth weight, NICU, other children at home, prenatal smoking, palivizumab, discomfort and dyspnea before preadmission, chief complaint, preadmission oral intake, O2 saturation, RDSS, virology, and epinephrine. Also control for prenatal smoking, wheezing history, palivizumab, medications before preadmission, chief complaint, oral intake, step down, ICU, inpatient steroids, and labs. Also control for parental asthma, intubation history, past bronchiolitis admission, virology, and length of stay.

Preadmission visit      
Chest x‐ray*1.00(Reference)1.06(0.831.36)1.09(0.741.60)
Albuterol use1.00(Reference)1.58(1.202.07)1.42(0.892.26)
Steroid use, inhaled or systemic1.00(Reference)1.05(0.721.54)1.11(0.751.65)
During hospitalization      
Chest x‐ray1.00(Reference)0.66(0.490.90)0.95(0.601.50)
Albuterol use1.00(Reference)1.21(0.821.79)1.23(0.632.38)
Steroid use, inhaled or systemic#1.00(Reference)1.13(0.721.80)1.19(0.791.79)
ICU care**1.00(Reference)0.74(0.421.29)0.87(0.631.21)
Required CPAP/emntubation1.00(Reference)0.72(0.361.41)1.84(0.933.64)
Length of stay >3 days1.00(Reference)0.77(0.581.03)1.05(0.761.47)
Discharge      
Discharged on inhaled steroids1.00(Reference)1.31(0.862.00)1.92(1.193.10)
Bronchiolitis relapse1.00(Reference)1.08(0.621.87)0.96(0.551.65)

DISCUSSION

It is unclear if management and treatment differences found in children with severe bronchiolitis are associated with race/ethnicity. We sought to determine if such differences exist by analyzing data from a prospective multicenter cohort study. Differences in management and treatment are discussed in the context of AAP guidelines, as they are widely used in clinical practice.

The RDSS was used to help assess severity of illness across race/ethnicity. NHB children had the highest RDSS score (ie, most severe bronchiolitis presentation) compared to NHW and Hispanic children. The reason for this difference in severity is unclear, but a potential explanation may be that minority communities lack access to care and as a result delay care and treatment for respiratory disease until care seems absolutely necessary.[19] Indeed, in our sample, minority children were less likely to visit their PCP and take corticosteroids the week before hospitalization when compared with NHW children. Our finding runs counter to a similar study by Boudreaux et al. that found no association between race/ethnicity and the clinical presentation of children with acute asthma during the preadmission setting.[20] The more severe bronchiolitis presentation among NHB children may have suggested that these children would require a longer hospital LOS (3 days). However, our multivariable analysis found no difference in LOS across racial/ethnic groups. This LOS finding is intriguing given previous studies suggesting that minorities, of diverse ages and with diverse diagnoses, were more likely to have a shorter LOS (as well as less likely to be admitted to the ICU with a similar diagnosis) when compared to nonminorities.[21, 22] Additionally, because our study sampled 16 sites, variation in clinical judgment and pediatric ICU protocol may have also played a role.[23]

Our findings also shed light on how differences in bronchiolitis management relate to AAP guidelines. According to the AAP, corticosteroid medications should not be used routinely in the management of bronchiolitis. Despite this recommendation, previous reports indicate that up to 60% of infants with severe bronchiolitis receive corticosteroid therapy.[24, 25] Our finding that Hispanic children with severe bronchiolitis were most likely to be discharged on inhaled corticosteroids is potentially concerning, as it exposes a subset of children to treatment that is not recommended. On the other hand, given the increased risk of future asthma in Hispanic communities, a higher use of inhaled corticosteroids might be seen as appropriate. Either way, our findings are inconsistent with related studies concluding that racial minority pediatric patients with asthma were less likely to receive inhaled corticosteroids.[26, 27, 28, 29] Similarly, NHB children were most likely to receive albuterol during the preadmission visit on multivariable analysis. Although a trial dose of albuterol may be common practice in treating severe bronchiolitis, AAP recommendations do not support its routine application. Increased albuterol during preadmission may have been related to an elevated bronchiolitis severity at presentation among NHB children (as indicated by the RDSS). Potential reasons for these 2 differences in treatment remain unclear. They may represent medical management efforts by discharging physicians to prescribe: (1) corticosteroids to racial/ethnic communities with a higher risk of childhood asthma; (2) albuterol to children presenting with a more severe case of bronchiolitis. These possibilities merit further study.

The AAP also recommends diagnosis of bronchiolitis on the basis of history and physical examination; laboratory and radiologic studies should not be routinely used for diagnostic purposes. Although it is possible for chest radiograph abnormalities to be consistent with bronchiolitis, there is little evidence that an abnormal finding is associated with disease severity.[30] The clinical value of diagnostic testing in children with bronchiolitis is not well supported by evidence, and limiting exposure to radiation should be a priority.[31, 32] Our analysis found that NHW and Hispanic children were more likely to receive chest x‐rays while hospitalized when compared with NHB children. Unnecessary and increased radiation exposure in children is potentially harmful and warrants intervention to minimize risk.

Establishing systematic clinical pathways in bronchiolitis management may address the practice variation found nationwide and across race/ethnicity in this study. Although clinical guidelines provide general recommendations, clinical pathways are defined treatment protocols aiming to standardize and optimize patient outcomes and clinical efficiency. The incorporation of clinical pathways into healthcare systems has increased recently as a result of their favorable association with medical complications, healthcare costs, and LOS.[33] With respect to bronchiolitis, implementation of clinical pathways has proven to reduce use of inappropriate therapies, decrease risk of bronchiolitis‐related hospital readmission, and help with discharge planning.[30, 34, 35]

Notwithstanding the differences found in this study, management of children with bronchiolitis was, in many respects, comparable across racial/ethnic groups. For example, our multivariable analysis found no significant differences across racial/ethnic groups with respect to chest x‐rays and corticosteroid use during the preadmission visit, administration of albuterol or corticosteroids during hospitalization, use of CPAP/emntubation, ICU admission, hospital LOS, or likelihood of a bronchiolitis‐related relapse. The general lack of race/ethnic differences is consistent with similar research on inpatient management of acute asthma.[36]

This study has potential limitations. The hospitals participating in the study are predominantly urban, academically affiliated hospitals. This may result in findings that are less generalizable to rural and community hospitals. Second, the race/ethnicity classification used does not take into consideration the diversity and complexity of defining race/ethnicity in the United States. Third, bronchiolitis is defined as a clinical diagnosis that can encapsulate multiple lower respiratory infection diagnoses. As a result, there may have been variability in clinical and institutional practice. An additional limitation was utilizing RDSS to assess bronchiolitis severity. Although there is currently no validated, universally accepted score to assess bronchiolitis severity, several scores are available in the literature with varying performance. Last, the ZIP code‐based median household incomes used to assess SES are higher than federal data in similar geographic locations, potentially resulting in findings that are less generalizable.

CONCLUSION

This multicenter prospective cohort study found several differences in bronchiolitis presentation and management among children stratified by race/ethnicity in 16 geographically dispersed sites after controlling for multiple factors including SES. Our analysis showed that, when compared to NHW children, NHB children were more likely to be given albuterol during the preadmission visit and less likely to receive chest x‐rays as inpatients; Hispanic children were more likely to be discharged on inhaled corticosteroids. These differences are concerning for 2 reasons: (1) based on current evidence, race/ethnicity should not affect care in children with severe bronchiolitis; and (2) the observed differences in diagnostic testing and treatment are not recommended by the evidence‐based AAP guidelines. It is also important to note that these differences do not demonstrate that a specific race/ethnicity received better or worse clinical care. The goal of this analysis was not to determine the effectiveness of certain management tendencies in children with severe bronchiolitis, but rather to examine differences in the presentation and management of children from different racial/ethnic groups. The causes for the observed findings require further study. In the meantime, we suggest increasing the number of hospitals that incorporate clinical care pathways for severe bronchiolitis to control variation in practice and limit the impact that race/ethnicity may have in the provision of services.

Acknowledgements

The authors thank the MARC‐30 investigators for their ongoing dedication to bronchiolitis research.

Disclosures: This study was supported by the grant U01 AI‐67693 (Camargo) from the National Institutes of Health (Bethesda, MD). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Allergy and Infectious Diseases or the National Institutes of Health. The authors have no financial relationships relevant to this article or conflicts of interest to disclose. Mr. Santiago conceptualized the analysis, interpreted the data, drafted the initial manuscript, and approved the final manuscript as submitted. Dr. Mansbach conceptualized and designed the initial study, coordinated data collection at 1 of the sites, critically reviewed the manuscript, and approved the final manuscript as submitted. Dr. Chou was responsible for analysis and interpretation of data, critically reviewed the manuscript, and approved the final manuscript as submitted. Dr. Delgado coordinated data collection at 1 of the sites, critically reviewed the manuscript, and approved the final manuscript as submitted. Dr. Piedra conceptualized and designed the initial study, coordinated virology testing, critically reviewed the manuscript, and approved the final manuscript as submitted. Ms. Sullivan coordinated data collection at all sites, critically reviewed the manuscript, and approved the final manuscript as submitted. Ms. Espinola was responsible for data management, analysis and interpretation of data, drafting of the initial manuscript, and approved the final manuscript as submitted. Dr. Camargo conceptualized and designed the initial study, assisted with data analysis and interpretation of data, critically reviewed the manuscript, and approved the final manuscript as submitted.

APPENDIX

Principal Investigators at the 16 Participating Sites in MARC‐30
Besh Barcega, MDLoma Linda University Children's Hospital, Loma Linda, CA
John Cheng, MD and Carlos Delgado, MDChildren's Healthcare of Atlanta at Egleston, Atlanta, GA
Dorothy Damore, MD and Nikhil Shah, MDNew York Presbyterian Hospital, New York, NY
Haitham Haddad, MDRainbow Babies & Children's Hospital, Cleveland, OH
Paul Hain, MD and Mark Riederer, MDMonroe Carell Jr. Children's Hospital at Vanderbilt, Nashville, TN
Frank LoVecchio, DOMaricopa Medical Center, Phoenix, AZ
Charles Macias, MD, MPHTexas Children's Hospital, Houston, TX
Jonathan Mansbach, MD, MPHBoston Children's Hospital, Boston, MA
Eugene Mowad, MDAkron Children's Hospital, Akron, OH
Brian Pate, MDChildren's Mercy Hospital & Clinics, Kansas City, MO
M. Jason Sanders, MDChildren's Memorial Hermann Hospital, Houston, TX
Alan Schroeder, MDSanta Clara Valley Medical Center, San Jose, CA
Michelle Stevenson, MD, MSKosair Children's Hospital, Louisville, KY
Erin Stucky Fisher, MDRady Children's Hospital, San Diego, CA
Stephen Teach, MD, MPHChildren's National Medical Center, Washington, DC
Lisa Zaoutis, MDChildren's Hospital of Philadelphia, Philadelphia, PA

 

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References
  1. Pelletier AJ, Mansbach JM, Camargo CA. Direct medical costs of bronchiolitis‐related hospitalizations in the United States. Pediatrics. 2006;118(6):24182423.
  2. Hasegawa K, Tsugawa Y, Brown DF, Mansbach JM, Camargo CA. Trends in bronchiolitis hospitalizations in the United States, 2000–2009. Pediatrics. 2013;132(1):2836.
  3. Yorita KL, Holman RC, Sejvar JJ, Steiner CA, Schonberger LB. Infectious disease hospitalizations among infants in the United States. Pediatrics. 2008;121(2):244252.
  4. Ruuskanen O, Ogra PL. Respiratory syncytial virus. Curr Probl Pediatr. 1993;23(2):5079.
  5. Boyce TG, Mellen BG, Mitchel EF, Wright PF, Griffin MR. Rates of hospitalization for respiratory syncytial virus infection among children in Medicaid. J Pediatr. 2000;137(6):865870.
  6. American Academy of Pediatrics Subcommittee on Diagnosis and Management of Bronchiolitis. Diagnosis and management of bronchiolitis. Pediatrics. 2006;118(4):17741793.
  7. Mansbach JM, Pelletier AJ, Camargo CA. US outpatient office visits for bronchiolitis, 1993–2004. Ambul Pediatr. 2007;7(4):304307.
  8. Wang EE, Law BJ, Stephens D, et al. Study of interobserver reliability in clinical assessment of RSV lower respiratory illness: a pediatric investigators collaborative network for infections in Canada (PICNIC) study. Pediatr Pulmonol. 1996;22(1):2327.
  9. Wilson DF, Horn SD, Hendley JO, Smout R, Gassaway J. Effect of practice variation on resource utilization in infants hospitalized for viral lower respiratory illness. Pediatrics. 2001;108(4):851855.
  10. Mansbach JM, Edmond JA, Camargo CA. Bronchiolitis in US emergency departments 1992 to 2000: epidemiology and practice variation. Pediatr Emerg Care. 2005;21(4):242247.
  11. Leader S, Kohlhase K. Recent trends in severe respiratory syncytial virus (RSV) among US infants, 1997 to 2000. J Pediatr. 2003; 143(5 suppl):S127S132.
  12. Glezen WP, Paredes A, Allison JE, Taber LH, Frank AL. Risk of respiratory syncytial virus infection for infants from low‐income families in relationship to age, sex, ethnic group, and maternal antibody level. J Pediatr. 1981;98(5):708715.
  13. Jansson L, Nilsson P, Olsson M. Socioeconomic environmental factors and hospitalization for acute bronchiolitis during infancy. Acta Paediatr. 2002;91(3):335338.
  14. Mansbach JM, Clark S, Barcega BR, Haddad H, Camargo CA. Factors associated with longer emergency department length of stay for children with bronchiolitis: a prospective multicenter study. Pediatr Emerg Care. 2009;25(10):636641.
  15. Damore D, Mansbach JM, Clark S, Ramundo M, Camargo CA. Prospective multicenter bronchiolitis study: predicting intensive care unit admissions. Acad Emerg Med. 2008;15(10):887894.
  16. Norwood A, Mansbach JM, Clark S, Waseem M, Camargo CA. Prospective multicenter study of bronchiolitis: predictors of an unscheduled visit after discharge from the emergency department. Acad Emerg Med. 2010;17(4):376382.
  17. Esri. Demographic, consumer, and business data. Available at: http://www.esri.com/data/esri_data/demographic‐overview/demographic. Accessed July 25, 2013.
  18. Bajaj L, Turner CG, Bothner J. A randomized trial of home oxygen therapy from the emergency department for acute bronchiolitis. Pediatrics. 2006;117(3):633640.
  19. Rand CS, Butz AM, Huss K, Eggleston P, Thompson L, Malveaux FJ. Adherence to therapy and access to care: the relationship to excess asthma morbidity in African‐American children. Pediatr Asthma Aller. 1994;8(3):179184.
  20. Boudreaux ED, Emond SD, Clark S, Camargo CA. Race/ethnicity and asthma among children presenting to the emergency department: differences in disease severity and management. Pediatrics. 2003;111(5 pt 1):e615e621.
  21. Yergan J, Flood AB, LoGerfo JP, Diehr P. Relationship between patient race and the intensity of hospital services. Med Care. 1987;25(7):592603.
  22. Williams JF, Zimmerman JE, Wagner DP, Hawkins M, Knaus WA. African‐American and white patients admitted to the intensive care unit: is there a difference in therapy and outcome? Crit Care Med. 1995;23(4):626636.
  23. Roberts JS, Bratton SL, Brogan TV. Acute severe asthma: differences in therapies and outcomes among pediatric intensive care units. Crit Care Med. 2002;30(3):581585.
  24. Behrendt CE, Decker MD, Burch DJ, Watson PH. International variation in the management of infants hospitalized with respiratory syncytial virus. International RSV Study Group. Eur J Pediatr. 1998;157(3):215220.
  25. Wilson DF, Horn SD, Hendley JO, Smout R, Gassaway J. Effect of practice variation on resource utilization in infants for viral lower respiratory illness. Pediatrics. 2001;108:851855.
  26. Ortega AN, Gergen PJ, Paltiel AD, Bauchner H, Belanger KD, Leaderer BP. Impact of site of care, race, and Hispanic ethnicity on medication use for childhood asthma. Pediatrics. 2002;109(1):E1.
  27. Lieu TA, Lozano P, Finkelstein JA, et al. Racial/ethnic variation in asthma status and management practices among children in managed Medicaid. Pediatrics. 2002;109(5):857865.
  28. Celano M, Geller RJ, Phillips KM, Ziman R. Treatment adherence among low‐income children with asthma. J Pediatr Psychol. 1998;23(6):345349.
  29. American Academy of Pediatrics Steering Committee on Quality Improvement and Management. Classifying recommendations for clinical practice guidelines. Pediatrics. 2004;114(3):874877.
  30. Swingler GH, Hussey GD, Zwarenstein M. Randomised controlled trial of clinical outcome after chest radiograph in ambulatory acute lower‐respiratory infection in children. Lancet. 1998;351(9100):404408.
  31. Kleinerman RA. Cancer risks following diagnostic and therapeutic radiation exposure in children. Pediatr Radiol. 2006;36(suppl 2):121125.
  32. Rotter T, Kinsman L, James E, et al. Clinical pathways: effects on professional practice, patient outcomes, length of stay and hospital costs. Cochrane Database Syst Rev. 2010;(3):CD006632.
  33. Wilson SD, Dahl BB, Wells RD. An evidence‐based clinical pathway for bronchiolitis safely reduces antibiotic overuse. Am J Med Qual. 2002;17(5):195199.
  34. Cheney J, Barber S, Altamirano L, et al. A clinical pathway for bronchiolitis is effective in reducing readmission rates. J Pediatr. 2005;147(5):622626.
  35. Ralston S, Garber M, Narang S, et al. Decreasing unnecessary utilization in acute bronchiolitis care: results from the value in inpatient pediatrics network. J Hosp Med. 2013;8(1):2530.
  36. Chandra D, Clark S, Camargo CA. Race/Ethnicity differences in the inpatient management of acute asthma in the United States. Chest. 2009;135(6):15271534.
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Address for correspondence and reprint requests: Carlos A. Camargo, MD, Department of Emergency Medicine, Massachusetts General Hospital, 326 Cambridge Street, Suite 410, Boston, MA 02114; Telephone: 617–726‐5276; Fax: 617‐724‐4050; E‐mail: [email protected]
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It has been 2 months since we implemented an electronic health record. I’ve made peace with it, but it is oh so far from perfect. Around the office, tempers are flared and people are grumpy. There are threats to quit by staff and doctors alike. Chaotic accurately describes what the first few weeks were like.

For a week, I had a greatly thinned schedule, but was pretty much back up to my usual 15-minute follow-up schedule by week 3. This meant, frankly, that if I did not want my patients to be waiting for hours beyond their appointment time, shortcuts had to be made. And 2 months in, the trend continues. I am definitely not able to dutifully fill out each and every blank that meaningful use requires me to fill out. There is just not enough time to document every follow-up patient’s family history and surgical history all over again. If you thought pointing-and-clicking were going to make life much easier for you, think again.

The issue is that the electronic record, rather than being a blank space, is essentially pages and pages of blanks to be filled out. So instead of freely typing as the patient jumps from, say, the history of their present illness to their family history to their systems review back to their present illness – because as we all know, our patients do not tell their stories in a nice linear fashion – I find myself having to interrupt the patient as I find the right blank on the right page.

Another sore point for me, and of course this issue predates the EHR, is that I don’t like having to reduce my patients to codes. I understand that codes are useful, but they can also be limiting and, frankly, idiotic. For example, Medicare in Rhode Island will no longer reimburse for zoledronic acid for patients coded as 733.00 (Osteoporosis NOS), but will cover if a patient is coded as 733.01 (postmenopausal/senile osteoporosis). I would like to know who came up with that rule and how they came up with it.

Codes also fall short of really capturing what a patient looks like. One lupus patient can look quite different from the next. Some of my patients are extremely sick and have complicated histories that have taken many months to piece together, and those four digits just do not capture that complexity and absolutely do not do justice to the patient. Patients do not fit neatly into boxes, so why must we be forced to make them fit?

It is not all bad. There are definitely a number of things that I appreciate about having an EHR.

I like being able to access patient records from home. It makes call a lot easier when you have the ability to look up a patient, know what meds they’re on, and document what you did for them over the weekend.

I like being able to hand the patient a document at the end of the visit outlining what we talked about. Although composing this definitely slows me down, it is helpful for some of the more forgetful patients in our panel who cannot be expected to remember their instructions for tapering their prednisone or that methotrexate is taken weekly, not daily.

I like that our system has a homunculus and will automatically calculate the Clinical Disease Activity Index for me and track down the patient’s progress. The system is too new for me to use this functionality, but I look forward to trying it out.

I like being able to electronically prescribe meds. The med list can get cluttered with old medications that the patient is not taking anymore (how often do patients change NSAIDs or go from gabapentin to cyclobenzaprine to amitriptyline?), and it can get quite confusing, but I’m sure in the long run it will make life much easier. When I fill out the blasted prior authorization forms, I will now be able to check on a single screen what generic drugs the patient has failed.

I must admit that the forthcoming Medicare penalty for not having an EHR was a big motivator for us to get on board. In the end, though, it shouldn’t be about avoiding penalties. It should be about providing better-quality care. And once most doctors are on board and Medicare has access to measurable data, I fervently hope that the data shows us that all of this pain was worth it.

Dr. Chan practices rheumatology in Pawtucket, R.I.

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It has been 2 months since we implemented an electronic health record. I’ve made peace with it, but it is oh so far from perfect. Around the office, tempers are flared and people are grumpy. There are threats to quit by staff and doctors alike. Chaotic accurately describes what the first few weeks were like.

For a week, I had a greatly thinned schedule, but was pretty much back up to my usual 15-minute follow-up schedule by week 3. This meant, frankly, that if I did not want my patients to be waiting for hours beyond their appointment time, shortcuts had to be made. And 2 months in, the trend continues. I am definitely not able to dutifully fill out each and every blank that meaningful use requires me to fill out. There is just not enough time to document every follow-up patient’s family history and surgical history all over again. If you thought pointing-and-clicking were going to make life much easier for you, think again.

The issue is that the electronic record, rather than being a blank space, is essentially pages and pages of blanks to be filled out. So instead of freely typing as the patient jumps from, say, the history of their present illness to their family history to their systems review back to their present illness – because as we all know, our patients do not tell their stories in a nice linear fashion – I find myself having to interrupt the patient as I find the right blank on the right page.

Another sore point for me, and of course this issue predates the EHR, is that I don’t like having to reduce my patients to codes. I understand that codes are useful, but they can also be limiting and, frankly, idiotic. For example, Medicare in Rhode Island will no longer reimburse for zoledronic acid for patients coded as 733.00 (Osteoporosis NOS), but will cover if a patient is coded as 733.01 (postmenopausal/senile osteoporosis). I would like to know who came up with that rule and how they came up with it.

Codes also fall short of really capturing what a patient looks like. One lupus patient can look quite different from the next. Some of my patients are extremely sick and have complicated histories that have taken many months to piece together, and those four digits just do not capture that complexity and absolutely do not do justice to the patient. Patients do not fit neatly into boxes, so why must we be forced to make them fit?

It is not all bad. There are definitely a number of things that I appreciate about having an EHR.

I like being able to access patient records from home. It makes call a lot easier when you have the ability to look up a patient, know what meds they’re on, and document what you did for them over the weekend.

I like being able to hand the patient a document at the end of the visit outlining what we talked about. Although composing this definitely slows me down, it is helpful for some of the more forgetful patients in our panel who cannot be expected to remember their instructions for tapering their prednisone or that methotrexate is taken weekly, not daily.

I like that our system has a homunculus and will automatically calculate the Clinical Disease Activity Index for me and track down the patient’s progress. The system is too new for me to use this functionality, but I look forward to trying it out.

I like being able to electronically prescribe meds. The med list can get cluttered with old medications that the patient is not taking anymore (how often do patients change NSAIDs or go from gabapentin to cyclobenzaprine to amitriptyline?), and it can get quite confusing, but I’m sure in the long run it will make life much easier. When I fill out the blasted prior authorization forms, I will now be able to check on a single screen what generic drugs the patient has failed.

I must admit that the forthcoming Medicare penalty for not having an EHR was a big motivator for us to get on board. In the end, though, it shouldn’t be about avoiding penalties. It should be about providing better-quality care. And once most doctors are on board and Medicare has access to measurable data, I fervently hope that the data shows us that all of this pain was worth it.

Dr. Chan practices rheumatology in Pawtucket, R.I.

It has been 2 months since we implemented an electronic health record. I’ve made peace with it, but it is oh so far from perfect. Around the office, tempers are flared and people are grumpy. There are threats to quit by staff and doctors alike. Chaotic accurately describes what the first few weeks were like.

For a week, I had a greatly thinned schedule, but was pretty much back up to my usual 15-minute follow-up schedule by week 3. This meant, frankly, that if I did not want my patients to be waiting for hours beyond their appointment time, shortcuts had to be made. And 2 months in, the trend continues. I am definitely not able to dutifully fill out each and every blank that meaningful use requires me to fill out. There is just not enough time to document every follow-up patient’s family history and surgical history all over again. If you thought pointing-and-clicking were going to make life much easier for you, think again.

The issue is that the electronic record, rather than being a blank space, is essentially pages and pages of blanks to be filled out. So instead of freely typing as the patient jumps from, say, the history of their present illness to their family history to their systems review back to their present illness – because as we all know, our patients do not tell their stories in a nice linear fashion – I find myself having to interrupt the patient as I find the right blank on the right page.

Another sore point for me, and of course this issue predates the EHR, is that I don’t like having to reduce my patients to codes. I understand that codes are useful, but they can also be limiting and, frankly, idiotic. For example, Medicare in Rhode Island will no longer reimburse for zoledronic acid for patients coded as 733.00 (Osteoporosis NOS), but will cover if a patient is coded as 733.01 (postmenopausal/senile osteoporosis). I would like to know who came up with that rule and how they came up with it.

Codes also fall short of really capturing what a patient looks like. One lupus patient can look quite different from the next. Some of my patients are extremely sick and have complicated histories that have taken many months to piece together, and those four digits just do not capture that complexity and absolutely do not do justice to the patient. Patients do not fit neatly into boxes, so why must we be forced to make them fit?

It is not all bad. There are definitely a number of things that I appreciate about having an EHR.

I like being able to access patient records from home. It makes call a lot easier when you have the ability to look up a patient, know what meds they’re on, and document what you did for them over the weekend.

I like being able to hand the patient a document at the end of the visit outlining what we talked about. Although composing this definitely slows me down, it is helpful for some of the more forgetful patients in our panel who cannot be expected to remember their instructions for tapering their prednisone or that methotrexate is taken weekly, not daily.

I like that our system has a homunculus and will automatically calculate the Clinical Disease Activity Index for me and track down the patient’s progress. The system is too new for me to use this functionality, but I look forward to trying it out.

I like being able to electronically prescribe meds. The med list can get cluttered with old medications that the patient is not taking anymore (how often do patients change NSAIDs or go from gabapentin to cyclobenzaprine to amitriptyline?), and it can get quite confusing, but I’m sure in the long run it will make life much easier. When I fill out the blasted prior authorization forms, I will now be able to check on a single screen what generic drugs the patient has failed.

I must admit that the forthcoming Medicare penalty for not having an EHR was a big motivator for us to get on board. In the end, though, it shouldn’t be about avoiding penalties. It should be about providing better-quality care. And once most doctors are on board and Medicare has access to measurable data, I fervently hope that the data shows us that all of this pain was worth it.

Dr. Chan practices rheumatology in Pawtucket, R.I.

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‘Nanodaisies’ deliver drug cocktail to leukemia cells

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‘Nanodaisies’ deliver drug cocktail to leukemia cells

Drug release in a cancer cell

Credit: PNAS

Biomedical engineers have reported that daisy-shaped, nanoscale structures can deliver a cocktail of drugs directly to cancer cells.

The “nanodaisies” effectively delivered a 2-drug combination in a range of cell lines, including the leukemia cell line HL-60.

The drug-delivery vehicles also proved effective in a mouse model of lung cancer.

Zhen Gu, PhD, of North Carolina State University and the University of North Carolina at Chapel Hill, and his colleagues detailed these results in Biomaterials.

“We found that this technique was much better than conventional drug-delivery techniques at inhibiting the growth of lung cancer tumors in mice,” Dr Gu said.

“And based on in vitro tests in 9 different cell lines, the technique is also promising for use against leukemia, breast, prostate, liver, ovarian, and brain cancers.”

To make the “nanodaisies,” the researchers begin with a solution that contains a polymer called polyethylene glycol (PEG). The PEG forms long strands that have much shorter strands branching off to either side.

The researchers directly link the anticancer drug camptothecin (CPT) onto the shorter strands and introduce the anticancer drug doxorubicin (Dox) into the solution.

PEG is hydrophilic, but CPT and Dox are hydrophobic. As a result, the CPT and Dox cluster together in the solution, wrapping the PEG around themselves. This results in a daisy-shaped drug cocktail, only 50 nanometers in diameter, that can (in theory) be injected into a cancer patient.

Once injected, the nanodaisies float through the bloodstream until they are absorbed by cancer cells. In fact, one of the reasons the researchers chose to use PEG is because it has chemical properties that prolong the life of the drugs in the bloodstream. Once in a cancer cell, the drugs are released.

“Both drugs attack the cell’s nucleus but via different mechanisms,” said study author Wanyi Tai, PhD, who was previously a researcher in Dr Gu’s lab but is now at the University of Washington in Seattle.

“Combined, the drugs are more effective than either drug is by itself,” Dr Gu added. “We are very optimistic about this technique and are hoping to begin preclinical testing in the near future.”

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Drug release in a cancer cell

Credit: PNAS

Biomedical engineers have reported that daisy-shaped, nanoscale structures can deliver a cocktail of drugs directly to cancer cells.

The “nanodaisies” effectively delivered a 2-drug combination in a range of cell lines, including the leukemia cell line HL-60.

The drug-delivery vehicles also proved effective in a mouse model of lung cancer.

Zhen Gu, PhD, of North Carolina State University and the University of North Carolina at Chapel Hill, and his colleagues detailed these results in Biomaterials.

“We found that this technique was much better than conventional drug-delivery techniques at inhibiting the growth of lung cancer tumors in mice,” Dr Gu said.

“And based on in vitro tests in 9 different cell lines, the technique is also promising for use against leukemia, breast, prostate, liver, ovarian, and brain cancers.”

To make the “nanodaisies,” the researchers begin with a solution that contains a polymer called polyethylene glycol (PEG). The PEG forms long strands that have much shorter strands branching off to either side.

The researchers directly link the anticancer drug camptothecin (CPT) onto the shorter strands and introduce the anticancer drug doxorubicin (Dox) into the solution.

PEG is hydrophilic, but CPT and Dox are hydrophobic. As a result, the CPT and Dox cluster together in the solution, wrapping the PEG around themselves. This results in a daisy-shaped drug cocktail, only 50 nanometers in diameter, that can (in theory) be injected into a cancer patient.

Once injected, the nanodaisies float through the bloodstream until they are absorbed by cancer cells. In fact, one of the reasons the researchers chose to use PEG is because it has chemical properties that prolong the life of the drugs in the bloodstream. Once in a cancer cell, the drugs are released.

“Both drugs attack the cell’s nucleus but via different mechanisms,” said study author Wanyi Tai, PhD, who was previously a researcher in Dr Gu’s lab but is now at the University of Washington in Seattle.

“Combined, the drugs are more effective than either drug is by itself,” Dr Gu added. “We are very optimistic about this technique and are hoping to begin preclinical testing in the near future.”

Drug release in a cancer cell

Credit: PNAS

Biomedical engineers have reported that daisy-shaped, nanoscale structures can deliver a cocktail of drugs directly to cancer cells.

The “nanodaisies” effectively delivered a 2-drug combination in a range of cell lines, including the leukemia cell line HL-60.

The drug-delivery vehicles also proved effective in a mouse model of lung cancer.

Zhen Gu, PhD, of North Carolina State University and the University of North Carolina at Chapel Hill, and his colleagues detailed these results in Biomaterials.

“We found that this technique was much better than conventional drug-delivery techniques at inhibiting the growth of lung cancer tumors in mice,” Dr Gu said.

“And based on in vitro tests in 9 different cell lines, the technique is also promising for use against leukemia, breast, prostate, liver, ovarian, and brain cancers.”

To make the “nanodaisies,” the researchers begin with a solution that contains a polymer called polyethylene glycol (PEG). The PEG forms long strands that have much shorter strands branching off to either side.

The researchers directly link the anticancer drug camptothecin (CPT) onto the shorter strands and introduce the anticancer drug doxorubicin (Dox) into the solution.

PEG is hydrophilic, but CPT and Dox are hydrophobic. As a result, the CPT and Dox cluster together in the solution, wrapping the PEG around themselves. This results in a daisy-shaped drug cocktail, only 50 nanometers in diameter, that can (in theory) be injected into a cancer patient.

Once injected, the nanodaisies float through the bloodstream until they are absorbed by cancer cells. In fact, one of the reasons the researchers chose to use PEG is because it has chemical properties that prolong the life of the drugs in the bloodstream. Once in a cancer cell, the drugs are released.

“Both drugs attack the cell’s nucleus but via different mechanisms,” said study author Wanyi Tai, PhD, who was previously a researcher in Dr Gu’s lab but is now at the University of Washington in Seattle.

“Combined, the drugs are more effective than either drug is by itself,” Dr Gu added. “We are very optimistic about this technique and are hoping to begin preclinical testing in the near future.”

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PICC Placement and Related Complications

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Screening for novel risk factors related to peripherally inserted central catheter‐associated complications

Peripherally inserted central venous catheters (PICCs) are used for a variety of indications, including administration of long‐term intravenous (IV) antibiotics, home IV medications, chemotherapy, and parenteral nutrition.[1, 2, 3] Additionally, PICCs have also been recognized as an alternative to large‐bore central venous catheters such as subclavian or internal jugular central venous catheters. PICCs have been associated with fewer bloodstream infections in patients with cancer than tunneled catheters.[4] Compared to central venous catheters, they demonstrate reduced complication rates,[5] decreased cost,[6] and increased safety for longer durations of use.[1, 2, 3, 7, 8, 9]

Despite the numerous benefits of PICCs, Prandoni et al. estimate an all‐cause complication rate of 12% to 17% with the use of PICCs.[10] Associated complications include infection,[11] pain, bleeding, and mechanical dysfunction, all of which contribute to patient discomfort and additional healthcare costs.[12] Bloodstream infections, for example, had previously been thought to occur at a substantially lower rate in PICCs than central venous catheters.[13] However, a recent systematic review suggests the rate of PICC‐associated bloodstream infections in the inpatient setting is actually comparable to that of central venous catheters.[14] Perhaps the most serious PICC‐associated complication is catheter‐related venous thrombosis. A recent systematic review and meta‐analysis found evidence to suggest the rate of catheter‐related venous thrombosis was highest in patients with cancer or critical illness15; additionally, rates of thrombosis associated with PICCs were higher than those associated with subclavian or internal jugular central venous catheters.[15, 16] Fletcher et al. showed an 8.1% incidence of symptomatic PICC‐related upper extremity deep vein thrombosis (DVT) in the neurosurgical intensive care unit, with 15% of patients subsequently developing a pulmonary embolism.[17] A recent prospective, randomized controlled trial by Itkin et al. similarly demonstrated symptomatic DVT rates of approximately 4%.[18] However, in this study, when PICCs were routinely screened for thrombosis (with or without associated symptoms), approximately 72% demonstrated thrombosis,[18] suggesting that many PICC‐associated thromboses may be clinically undetected. This may have far‐reaching clinical significance, as pulmonary embolism complicates upper extremity DVT in 9% of cases and can result in a mortality rate as high as 25%.[10, 19]

Some strategies to reduce the rate of catheter‐related complications include identification of characteristics that put patients at risk. Many potential risk factors have been investigated, including catheter size,[12, 20, 21, 22, 23, 24] choice of vein,[24] location of catheter tip,[25] and history of malignancy or prior DVT.[12] However, to date, no definitive consensus has been reached. Special attention has been paid to the investigation of underlying risk factors and treatment for catheter‐related DVT, given its significant morbidity and mortality. Results have been equivocal, though, and in some instances, complicated by a diagnosis of underlying malignancy.[26, 27, 28]

As PICCs become more widely utilized, assessments of factors that place patients at greater risk of PICC‐related complications are needed.[21] The purpose of this study was to establish the incidence of complications associated with PICCs placed in the inpatient setting and examine risk factors predisposing patients to these complications.

MATERIALS AND METHODS

Study Design

A case control analysis of adult inpatients who underwent PICC placement between January 2009 and January 2010 was conducted at Scott & White Healthcare (now Baylor Scott & White Healthcare) to determine the incidence and risk factors for PICC‐associated complications.

Study Site

The study took place at Scott & White Memorial Hospital in Temple, Texas, a 636‐bed multispecialty teaching hospital and level 1 trauma center. It is part of a healthcare system that includes 12 hospitals and more than 60 regional clinics, all of which share an electronic medical record to enable full integration.

Human Subjects Approval

This study received approval from the institutional review board at Scott & White Healthcare.

PICC Placement Technique

Inpatient PICC placement was performed by the PICC consult service. The consult service was comprised of 3 separate provider teams: (1) internal medicine, including select hospitalists and internal medicine residents; (2) radiology, including interventional radiologists and radiology residents; and (3) nursing, including registered nurses with advanced training in PICC placement. Following placement of a consult, the PICC consult service assessed the patient, obtained consent, and subsequently placed the catheter. Members of the PICC consult service followed a system‐wide protocol wherein target veins were identified by ultrasound prior to attempting catheter placement, and actual placement of the PICC was ultrasound guided. Images obtained during the procedure were permanently documented in the medical record. At the time of this study, no formal protocol existed wherein target veins were mapped for caliber. Operators relied on their professional judgment to determine if vein caliber appeared sufficient to accommodate catheter placement.

All PICCs were placed using industry standard sterile precautions. A universally accepted modified Seldinger technique was used to obtain venous access.[29] A guidewire was then positioned in the desired vessel to facilitate proper venous placement of the catheter. During the course of the study period, catheters used were either single‐ (4 Fr) or double lumen (5 Fr).

Catheters were placed at the bedside by hospitalists or registered nurse teams; the location of the catheter tip at the cavoatrial junction was confirmed by chest radiography. Catheter insertions by radiologists were performed in the interventional radiology suite, and confirmation of location of the catheter tip was obtained with fluoroscopy.

PICC Maintenance

Following placement, nurses managed the PICC site according to nursing policy. Per policy, the site was assessed each shift. Documentation of assessment was recorded in nursing notes. Routine dressing changes were performed every 7 days, and as needed, to maintain a sterile site. Date and time of dressing changes were documented in nursing notes and on the PICC dressing. Catheter hubs and injection ports were disinfected with an antiseptic preparation for 15 seconds and allowed to air dry for 30 seconds prior to accessing the catheter. Catheters were flushed with 10 mL of normal saline before and after use. Any abnormality noted during PICC assessment was relayed to the primary provider. If the catheter did not flush readily or demonstrate appropriate blood return, nursing staff obtained an order for alteplase to be administered in an effort to salvage the line. PICCs were discontinued at the discretion of the healthcare provider.

Participants

Records of all patients 18 years of age and older who underwent PICC placement between January 2009 and January 2010 were reviewed (N=1444) for study inclusion. There were no exclusion criteria.

Data Collection

Patients who experienced complications were identified by electronic medical record review. One‐to‐one matching was performed for age and gender‐matched controls randomly selected from inpatients who underwent PICC placement during the same time period without complications. A total of 170 cases with PICC‐related complications were identified. One hundred seventy exact age‐ and gender‐matched controls, who based upon documentation available in the electronic medical record did not experience complications, were then randomly selected. Prior to data collection, the research team reviewed and discussed the data collection form and agreed upon a standardized protocol for data collection. Data collection was completed by authors J.M. and J.H. on the standardized data collection form. Although a formal analysis of inter‐rater agreement was not performed, J.M. and J.H. discussed any items where questions arose and arrived at a consensus decision regarding completion of the data point.

End points of the chart review were completion of medical therapy for which the PICC was indicated (eg, IV antibiotics or total parenteral nutrition [TPN]) or documentation of a complication that led to 1 of the following: discontinuation of the PICC or adjustment of either catheter placement or medical therapy. All complications were identified via International Classification of Diseases, 9th Revision codes and systematic chart review.

Complications resulting in discontinuation of the PICC, adjustment of catheter placement, or change in medical therapy were identified by review of nursing or physician documentation, and were categorized as follows: mechanical complications (defined as loss of the ability of the catheter to flush or draw properly, inadvertent catheter dislodgement, or retained portion of the catheter following catheter removal), catheter‐associated bloodstream infection (development of a positive blood culture attributable to the central catheter with no other clearly identifiable source of bacteremia present), cellulitis (defined as cellulitis in the extremity where the catheter was placed), bleeding from the site of catheter, fever (for which no other cause could be identified), and catheter‐associated thrombosis (identified by Doppler ultrasonography in patients exhibiting symptoms such as pain, swelling, redness, or warmth in the extremity in which the PICC was placed).[30]

Demographic data were collected, including insurance status, age, ethnicity, and gender. Clinical data included body mass index (BMI), presence of malnutrition (defined by a serum albumin of less than 3 g/dL),[31] previous or active cancer, previous DVT, use of anticoagulants (eg, warfarin, heparin, or low‐molecular‐weight heparin) or antiplatelet agent (eg, aspirin or clopidogrel) at the time of placement, and indication for PICC placement. A patient's history of previous or active cancer and previous DVT were identified by clinical documentation. Indications for PICC placement included: treating infectious processes (ie, infusion of antimicrobials), providing TPN, chemotherapy administration, and IV access. Catheter‐specific data were also collected and included venous access obtained (cephalic, basilic, brachial), catheter size (single lumen [4 Fr] or double lumen [5 Fr]), type of complication, and time to complication. The procedure note accompanying PICC placement was reviewed for data regarding time of day inserted (with after hours defined as documentation of placement occurring after 5 pm), and procedure operator to identify type of team (internal medicine, radiology, nursing) responsible for placement.

Data Analysis

Demographic characteristics and potential risk factors for patients in both the case and control groups of the study were summarized using descriptive statistics: mean ( standard deviation [SD]) for continuous variables and frequency (percent) for categorical variables. Univariate and multivariable conditional logistic regression analyses of variables that were potential risk factors of PICC‐related complications were utilized. A stepwise selection method was used for multivariable conditional logistic regression models. Alpha=0.2 was used for the significance to enter the model, and =0.05 was used for significance level to remain in the model. Attribution of PICC‐related complications was evaluated in terms of odds ratios (OR) and 95% confidence interval (CI). A P value of <0.05 indicated statistical significance. No prospective power analysis was performed. However, for a retrospective power analysis for 1:1 matching with 170 cases and 170 matched controls, assuming 20% of controls were affected and an of 0.05, one would achieve 80% power to detect an odds ratio of 2. SAS 9.2 (SAS Institute Inc., Cary, NC) was used for data analysis.

RESULTS

In 2009, 1444 PICCs were placed, and 170 cases in which patients experienced complications associated with PICC placement were identified, resulting in a complication rate of 11.77% (95% CI: 10.11%‐13.44%). The most common complications experienced by our patient population included catheter‐associated thrombosis (3%, n = 46), mechanical complications (4%, n=67), inadvertent catheter dislodgement (2%, n=36), mechanical dysfunction (2%, n=30), retained portion of the catheter following catheter removal (<1%, n=1), catheter‐associated bloodstream infections (2%, n=24), and cellulitis at the catheter insertion site (1%, n=15). Other documented complications included unexplained fever and bleeding (Table 1).

Type of Complication
ComplicationN (%)
  • NOTE: N=1,444. Mechanical dysfunction (N=30), retained portion of the catheter (N=1 [0%]). Sum of the % in the columns were not exactly 100% for some cases due to rounding. *Inadvertent catheter dislodgement (N=36).

Thrombosis46 (3)
Infection24 (2)
Cellulitis15 (1)
Mechanical complications*67 (4)
Unexplained fever15 (1)
Bleeding3 (0)
No complication1,274 (88)

The mean age of the total cohort (N=340), comprised of case (N=170) and control (N=170) groups, was 58 years (SD 17), and 55% (n=94) were females. There were no significant differences in complications between groups based on ethnicity (P=0.66). In the case group, 46% (n=78) of PICCs were placed by the radiology team, 41% (n=69) were placed by the internal medicine team, and 14% (n=23) were placed by nursing. In the control group, 44% (n=74) of PICCs were placed by radiology, 36% (n=62) by internal medicine, and 20% (n=34) by nursing. Based on univariate conditional analysis, provider team was not significantly associated with complications (P=0.29).

Predictors of All‐Cause Complications

Based upon univariate conditional logistic regression analyses of complications related to PICC placement (N=340), the following variables demonstrated a statistically significant increased risk for complications: malnutrition (OR: 1.88 [95% CI: 1.023.44], P=0.04) and after‐hours placement (OR: 8.67 [95% CI: 2.62‐28.63], P=0.0004) (Table 2). Anticoagulation was associated with a decreased risk of complications (OR: 0.27 [95% CI: 0.16‐0.45], P=0.04). Based upon multivariable logistic regression analysis, after‐hours placement (OR: 9.52 [95% CI: 2.68‐33.78], P=0.0005) and BMI >30 (OR: 1.98 [95% CI: 1.09‐3.61], P=0.02) were significantly associated with an increased risk of PICC‐associated complications. Conversely, anticoagulation/antiplatelet use was associated with a decreased risk of complications (OR: 0.24 [95% CI: 0.14‐0.43], P<0.0001).

Any Complication: Descriptive Statistics and Conditional Logistic Regression Analysis
VariableCase, N (%)Control, N (%)UnivariateMultivariable
OR (95% CI)P ValueAOR (95% CI)P Value
  • NOTE: N=170 in each group. Sum of the % in the columns was not exactly 100% for some cases due to rounding. Abbreviations: AOR, adjusted odds ratio; BMI, body mass index; CI, confidence interval; DVT, deep vein thrombosis; IV, intravenous; OR, odds ratio; PICC, peripherally inserted central venous catheter; SD, standard deviation.

  • Overall significance of the factor.

  • Frequency missing=1.

  • Frequency missing=2.

  • Osteomyelitis, abscess, cellulitis, pyelonephritis, meningitis.

Age, y, meanSD58175817    
BMI, meanSD29.29.527.97.91.02 (0.991.05)0.12  
30108 (64)116 (68%)1.00 1.00 
>3062 (36)54 (32%)1.29 (0.792.11)0.321.98 (1.093.61)0.02
Length of stay, d, meanSD182214161.01 (1.001.03)0.06  
Length of stay group, d   0.11a  
<741 (24)52 (31)1.00   
729101 (59)103 (61)1.19 (0.721.98)0.49  
3028 (16)15 (9)2.21 (1.074.58)0.03  
Gender      
Female94 (55)94 (55)    
Male76 (45)76 (45)    
Ethnicity   0.66a  
Caucasian131 (77)125 (74)1.00   
African American26 (15)28 (16)0.88 (0.481.60)0.67  
Hispanic/Asian13 (8)17 (10)0.70 (0.311.58)0.38  
Provider team   0.29a  
Radiology78 (46)74 (44)1.00   
Internal medicine69 (41)62 (36)1.05 (0.681.64)0.82  
Nursing23 (14)34 (20)0.65 (0.351.19)0.16  
Insuranceb   0.22a  
Private insurance46 (27)42 (25)1.00   
Uninsured17 (10)24 (14)0.73 (0.351.55)0.41  
Medicare57 (34)62 (37)0.73 (0.381.40)0.34  
Medicaid39 (23)25 (15)1.51 (0.743.06)0.26  
Tricare/Veterans Administration11 (6)16 (9)0.59 (0.241.45)0.25  
History of DVT27 (16)26 (15)1.05 (0.581.91)0.88  
Malnutritionb149 (88)134 (79)1.88 (1.023.44)0.04  
Cancer25 (15)36 (21)0.58 (0.311.09)0.09  
Fluoroscopy129 (76)139 (82)0.71 (0.421.19)0.19  
Anticoagulation use50 (29)100 (59)0.27 (0.160.45)<0.00010.24 (0.140.43)<0.0001
Multilumenc99 (58)111 (66)0.70 (0.441.11)0.13  
Veinb   0.39a  
Basilic98 (58)86 (51)1.00   
Cephalic11 (6)8 (5)1.37 (0.483.89)0.55  
Brachial61 (36)74 (44)0.70 (0.451.09)0.12  
Internal mammary0 (0)1 (1)<0.001 (<0.001>999)0.99  
Time of dayb      
Morning/afternoon144 (85)166 (98)1.00 1.00 
After hours26 (15)3 (2)8.67 (2.6228.63)0.00049.52 (2.6833.78)0.0005
Indication for PICC   0.02a  
Infection88 (52)71 (42)1.00   
Pneumonia21 (12)14 (8)1.07 (0.502.29)0.87  
Chemotherapy5 (3)2 (1)1.84 (0.349.93)0.48  
IV access36 (21)66 (39)0.44 (0.250.75)0.003  
Total parenteral nutrition20 (12)17 (10)0.96 (0.442.14)0.93  

Predictors of Nonmechanical Complications

To study risk factors related to nonmechanical complications, a secondary analysis (N=206) was performed in which all patients who experienced mechanical complications (N=67) and matched controls (N=67) were excluded. Based upon multivariable logistic regression analysis, after‐hours placement (OR: 6.93 [95% CI: 1.35‐35.56], P=0.02) and malnutrition (OR: 2.83 [95% CI: 1.037.81], P=0.04) were significantly associated with increased risk of nonmechanical complications. The use of anticoagulation/antiplatelet agents was associated with decreased risk of nonmechanical complications (OR: 0.17 [95% CI: 0.07‐0.40], P<0.0001). Variables not significantly associated with nonmechanical complications included BMI>30, previous history of DVT, history of cancer, catheter size, and venous access choice (Table 3).

Complications Other Than Mechanical: Descriptive Statistics and Conditional Logistic Regression Analysis
VariableCase, N (%)Control, N (%)UnivariateMultivariable
OR (95% CI)P ValueAOR (95% CI)P Value
  • NOTE: N=103 in each group. Sum of the % in the columns were not exactly 100% for some cases due to rounding. Abbreviations: AOR, adjusted odds ratio; BMI, body mass index; CI, confidence interval; DVT, deep vein thrombosis; IV, intravenous; OR, odds ratio; PICC, peripherally inserted central venous catheter; SD, standard deviation.

  • Overall significance of the factor.

  • Frequency missing=1.

  • Frequency missing=2.

  • Osteomyelitis, abscess, cellulitis, pyelonephritis, meningitis.

Age, y, meanSD58165816    
BMI, meanSD29.79.828.57.91.03 (0.991.07)0.22  
3064 (62)68 (66)1.00   
>3039 (38)35 (34)1.27 (0.642.49)0.49  
Length of stay, d, meanSD202614181.02 (1.001.03)0.08  
Length of stay group, d   0.03a  
<722 (21)28 (27)1.00   
72960 (58)68 (66)0.95 (0.491.82)0.87  
3021 (20)7 (7)3.24 (1.238.54)0.02  
Gender      
Female63 (61)63 (61)    
Male40 (39)40 (39)    
Ethnicity   0.95a  
Caucasian75 (73)75 (73)1.00   
African American19 (18)18 (17)1.06 (0.512.21)0.87  
Hispanic/Asian9 (9)10 (10)0.88 (0.322.44)0.81  
Provider team   0.81a  
Radiology43 (42)44 (43)1.00   
Internal medicine45 (44)41 (40)1.11 (0.621.96)0.73  
Nursing15 (15)18 (17)0.86 (0.391.90)0.71  
Insuranceb   0.22a  
Private insurance29 (28)27 (26)1.00   
Uninsured13 (13)12 (12)1.18 (0.433.26)0.74  
Medicare32 (31)40 (39)0.52 (0.211.29)0.16  
Medicaid21 (20)12 (12)1.81 (0.694.74)0.23  
Tricare/Veterans Administration8 (8)11 (11)0.58 (0.191.79)0.34  
History of DVT15 (15)15 (15)1.00 (0.462.16)1.00  
Malnutritionb93 (90)79 (77)2.86 (1.216.76)0.022.83 (1.037.81)0.04
Cancer17 (17)22 (21)0.67 (0.301.48)0.32  
Fluoroscopy78 (76)85 (83)0.65 (0.321.31)0.23  
Anticoagulation use29 (28)60 (58)0.21 (0.100.44)<0.00010.17 (0.070.40)<0.0001
Multilumenc64 (62)67 (66)0.83 (0.461.51)0.55  
Veinb   0.32a  
Basilic54 (52)49 (48)1.00   
Cephalic8 (8)3 (3)2.45 (0.649.32)0.19  
Brachial41 (40)49 (48)0.72 (0.421.24)0.24  
Internal mammary0 (0)1 (1)<0.001 (<0.001>999)0.99  
Time of dayb      
Morning/afternoon87 (84)100 (98)1.00 1.00 
After hours16 (16)2 (2)8.00 (1.8434.79)0.0066.93 (1.3535.56)0.02
Indication for PICC   0.13  
Infectiond52 (50)45 (44)1.00   
Pneumonia14 (14)7 (7)1.46 (0.514.18)0.48  
Chemotherapy5 (5)0 (0)>999 (<0.001>999)0.99  
IV access22 (21)43 (42)0.48 (0.240.96)0.04  
Total parenteral nutrition10 (10)8 (8)1.08 (0.323.62)0.90  

Predictors of Thrombotic Complications

Of 1444 patients who underwent PICC placement, 3% (n=46) were subsequently diagnosed with a catheter‐associated thrombosis, representing 27% of all observed complications. In an attempt to better identify factors predisposing patients to thrombotic complications, an additional subgroup analysis (N=92) was performed on those patients who experienced catheter‐associated thrombosis (N=46) and matched controls (N=46). Variables examined in the analysis included BMI, length of stay (LOS), history of DVT, history of cancer, utilization of anticoagulation/antiplatelet agents, malnutrition, and catheter size.

Based on conditional univariate analyses, the following variables were significantly associated with increased risk of catheter‐associated thrombosis: LOS (as a continuous variable) (OR: 1.04 [95% CI: 1.001.09], P=0.05), malnutrition (OR: 4 [95% CI: 1.1314.18], P=0.03), and after‐hours placement (OR: 8.00 [95% CI: 1.0063.96], P=0.05) (Table 4). Use of anticoagulation/antiplatelet agents (OR: 0.29 [95% CI: 0.11‐0.80], P=0.02) was associated with decreased risk of thrombosis. History of previous DVT and history of cancer were nonsignificant. In the multivariable logistic regression model, malnutrition (OR: 10.16 [95% CI: 1.76‐58.71], P=0.01) remained associated with increased risk of catheter‐associated thrombosis, whereas use of anticoagulation/antiplatelet agents (OR: 0.11 [95% CI: 0.02‐0.51], P=0.005) was associated with decreased risk of catheter‐associated thrombosis (Table 4).

Cathether‐Associated Thrombosis: Descriptive Statistics and Conditional Logistic Regression Analysis
VariableCase, N (%)Control, N (%)UnivariateMultivariable
OR (95% CI)P ValueAOR (95% CI)P Value
  • NOTE: N=46 in each group. Sum of the % in the columns were not exactly 100% for some cases due to rounding. Abbreviations: AOR, adjusted odds ratio; BMI, body mass index; CI, confidence interval; DVT, deep vein thrombosis; IV, intravenous; OR, odds ratio; PICC, peripherally inserted central venous catheter; SD, standard deviation.

  • Overall significance of the factor.

  • Frequency missing=1.

  • Frequency missing=2.

  • Osteomyelitis, abscess, cellulitis, pyelonephritis, meningitis.

Age, y, meanSD58185818    
BMI, meanSD27.77.127.77.81.00 (0.931.08)0.98  
3034 (74)33 (72)    
>3012 (26)13 (28)0.83 (0.252.73)0.76  
Length of stay, d, meanSD17121191.04 (1.001.09)0.05  
Length of stay group, d   0.15  
<78 (17)14 (30)1.00   
72929 (63)30 (65)1.13 (0.413.07)0.82  
309 (20)2 (4)4.65 (0.9822.13)0.05  
Gender      
Female26 (57)26 (57)    
Male20 (43)20 (43)    
Ethnicity   0.44a  
Caucasian31 (67)36 (78)1.00   
African American11 (24)6 (13)2.02 (0.695.93)0.20  
Hispanic/Asian4 (9)4 (9)1.12 (0.225.68)0.89  
Provider team   0.26a  
Radiology23 (50)19 (41)1.00   
Internal medicine20 (43)18 (39)1.00 (0.432.31)1.00  
Nursing3(7)9 (20)0.33 (0.091.27)0.11  
Insuranceb   0.38a  
Private insurance13 (28)11 (24)1.00   
Uninsured8 (17)4 (9)2.01 (0.3810.58)0.41  
Medicare14 (30)21 (47)0.39 (0.101.47)0.16  
Medicaid8 (17)7 (16)1.23 (0.285.36)0.78  
Tricare/Veterans Administration3 (7)2 (4)1.01 (0.128.27)1.00  
History of DVT7 (15)8 (17)0.88 (0.322.41)0.80  
Malnutritionb43 (93)33 (73)4.00 (1.1314.18)0.0310.16 (1.7658.71)0.01
Cancer10 (22)13 (28)0.67 (0.241.87)0.44  
Fluoroscopy33 (72)39 (85)0.46 (0.161.31)0.14  
Anticoagulation use16 (35)28 (61)0.29 (0.110.80)0.020.11 (0.020.51)0.005
Multilumenc22 (48)28 (62)0.53 (0.231.26)0.15  
Veinb   0.93a  
Basilic24 (52)21 (47)1.00   
Cephalic1 (2)1 (2)0.86 (0.0514.39)0.92  
Brachial21 (46)22 (49)0.75 (0.311.79)0.51  
Internal mammary0 (0)1 (2)<0.001 (<0.001>999)0.99  
Time of dayb      
Morning/afternoon38 (83)44 (98)1.00   
After hours8 (17)1 (2)8.00 (1.0063.96)0.05  
Indication for PICC   0.80a  
Infectiond20 (43)17 (37)1.00   
Pneumonia5 (11)6 (13)0.60 (0.142.56)0.49  
Chemotherapy3 (7)0 (0)>999 (<0.001>999)0.99  
IV access14 (30)20 (43)0.58 (0.231.44)0.24  
Total parenteral nutrition4 (9)3 (7)1.22 (0.197.70)0.83  

DISCUSSION

The goal of this study was to identify factors related to PICC placement that place the general population of patients at risk. The type and rate of complications associated with PICCs in this study were similar to those previously reported in the literature including catheter‐related infection and thrombosis.[10, 32] Two unique risk factors, not well recognized previously,[10, 27, 28, 33] were observed in this study: malnutrition and after‐hours placement. Malnutrition, defined as serum albumin <3 g/dL was associated with an increase in PICC‐related complications (such as catheter‐associated bloodstream infections and cellulitis) and catheter‐related thrombosis. Malnutrition itself has long been associated with a decreased resistance to infection[34]; in addition, low serum albumin may also be a marker of the presence of other severe comorbidities, which may contribute to increased risk of thrombosis. It has been noted in previous studies that critical illness increases risk of thrombosis.[15] Despite an exhaustive search of the literature, we have been unable to find additional studies examining the extent to which malnutrition may impact PICC‐associated complications.

After‐hours placement was also associated with increased nonmechanical complications, as well as catheter‐related thrombosis. In an effort to improve both patient and consulting provider satisfaction and provide more expedient service, PICCs were often placed after hours (between 5 pm and 8 am) by both interventional radiology (n=14) and internal medicine (n=15) teams.

LOS has been associated with PICC placement complications in other studies.[12] In both primary and secondary analyses, hospital stays >30 days were associated with a higher risk of complications than hospitalizations <7 days. In light of the clinical significance of catheter‐related thrombosis, a subgroup analysis of patients with an LOS >30 days was conducted. The conditional univariate regression analysis showed an increased risk with greater LOS, malnutrition, and after‐hours placement. Use of anticoagulant or antiplatelet agents were associated with decreased risk of thrombosis (Table 4). The association between LOS and PICC‐related thrombosis is consistent with findings from Evans et al. involving 1728 patients in a similar center.[12] In these circumstances, increased LOS may be a surrogate marker for increased severity of illness, in that those patients who are more ill require lengthier hospitalizations. In a systematic review and meta‐analysis, Chopra et al. observed that increased severity of illness correlated with higher rates of catheter‐associated thrombosis, which is supportive of these findings.[15]

In the multivariate logistic regression analysis, BMI >30 was associated with a statistically significant increased risk for PICC‐associated complications after adjusting for anticoagulation and time of placement (Table 2). In the secondary analysis, where patients with mechanical complications were removed, BMI >30 was no longer associated with an increased risk for PICC‐associated complications (Table 3). This suggests that patients with a BMI >30 had an increased risk of mechanical complications, but were not necessarily at increased risk of developing other complications, such as catheter‐related thrombosis, infection, or bleeding. This finding is congruent with studies by Evans et al.,[12] who found no association between BMI and catheter‐associated thrombosis. Our association between BMI and complications is unique; to date, there are few additional studies that examine the extent to which BMI impacts the rate and type of complications associated with PICCs. At this time, the mechanism of the association between mechanical complications (such as inadvertent catheter removal or mechanical malfunction) and BMI is uncertain and warrants further investigation.

Use of Anticoagulant Agents

Anticoagulant (ie, any agent used for DVT prophylaxis or therapeutic anticoagulation) or antiplatelet agent use at the time of PICC placement and during the patient's hospitalization was associated with a decreased risk of thrombosis in our analysis. However, it should be noted that no specific anticoagulant agent was studied, and that antiplatelet agents were included in this analysis, unlike that of Evans et al.[12] Although current literature in oncologic populations, as well as the evidence‐based clinical practice guidelines, recommend against routine use of venous thromboprophylaxis in patients with central venous catheters,[33, 35, 36, 37] we believe this deserves further study, particularly in light of conflicting data in this area.[38, 39] Evans et al.[12] noted that although use of anticoagulants initially appeared to be associated with greater incidence of upper extremity venous thrombosis, when previous diagnosis of DVT was removed from the analysis the association was no longer significant.

In our analyses, no associations between catheter size, choice of venous access, history of previous deep venous thrombosis, or history of malignancy and risk for complications were found. Our findings differed from previous studies, where a relationship between increasing catheter bore size and site of access have been associated with increased PICC‐related thrombosis or other complications.[12, 20, 40, 41] There were also no significant differences in risk for complications between provider teams (eg, internal medicine, radiology, nursing) for PICCs placed during the morning or afternoon, which is consistent with findings by Funk et al.[1] Yet, after‐hours placement of PICCs was associated with greater complications than daytime placement. Although the exploration of factors associated with after‐hours placement was beyond the scope of this study, the findings from this study caused the authors, primarily comprised of members of the internal medicine inpatient medicine division, to reexamine the division's protocol on PICC placement. A consensus decision was made to discontinue after‐hours placement of PICCs by internal medicine teams in an effort to promote patient safety until further data could be collected. As a result, internal medicine teams no longer place PICCs after regular working hours at our institution.

Limitations

Limitations include the categorization of antiplatelet and anticoagulant agents together. We did not distinguish between high‐ and low‐dose aspirin, nor did we distinguish between therapeutic dosing of heparin and low‐molecular‐weight heparin versus DVT prophylaxis dosing. Additionally, for patients who were on warfarin or heparin drip, we did not evaluate for therapeutic range of international normalized ratio or partial thromboplastin time, as this was beyond the present scope of this study. In addition, malnutrition defined by albumin alone may have been somewhat narrow, as conditions aside from malnutrition can impact albumin levels. In future evaluations, this relationship may be clarified by including other determinants of clinical malnutrition including BMI <18 or the measurement of prealbumin. For determination of after‐hours placement of PICCs, we relied upon time of procedure dictation, assuming that all dictations immediately followed catheter placement. If there was a lapse in time between catheter placement and dictation, the category may have been recorded in error. Another limitation of after‐hours categorization was that we were unable to determine whether the PICC was placed on a weekend or holiday.

CONCLUSIONS AND FUTURE DIRECTIONS

Our results suggest that more stringent screening of patients undergoing PICC placement may reduce the risk of complications, with special attention to characteristics such as BMI >30, increased LOS, and protein‐calorie malnutrition (albumin <3). Furthermore, placement of PICC lines in emergent or after‐hours settings should be carefully considered and weighed against relative risks of central venous catheter placement. Further examination of the role anticoagulant and antiplatelet agents may have in the prevention of catheter‐related thrombosis should be undertaken. We hope that the identification of these risk factors will decrease the rate of complications and ultimately enhance patient safety and satisfaction.

Acknowledgments

The authors sincerely thank Glen Cryer, Publications Manager, Baylor Scott & White Health, for his assistance with this article.

Disclosures: Nothing to report.

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  37. Carrier M, Tay J, Fergusson D, Wells PS. Thromboprophylaxis for catheter‐related thrombosis in patients with cancer: a systematic review of the randomized, controlled trials. J Thromb Haemost. 2007;5:25522554.
  38. Boraks P, Seale J, Price J, et al. Prevention of central venous catheter associated thrombosis using minidose warfarin in patients with haematological malignancies. Br J Haematol. 1998;10:483486.
  39. Bern HM, Lokich JJ, Wallach SR, et al. Very low doses of warfarin can prevent thrombosis in central venous catheters. A randomized prospective trial. Ann Intern Med. 1990;112:423428.
  40. Loewenthal MR, Dobson PM, Starkey RE, Dagg SA, Petersen A, Boyle MJ. The peripherally inserted central catheter (PICC): a prospective study of its natural history after cubital fossa insertion. Anesth Intensive Care. 2002;30:2124.
  41. Smith JP. Thrombotic complications in intravenous access. J Intraven Nurs. 1998;21:96100.
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Journal of Hospital Medicine - 9(8)
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Peripherally inserted central venous catheters (PICCs) are used for a variety of indications, including administration of long‐term intravenous (IV) antibiotics, home IV medications, chemotherapy, and parenteral nutrition.[1, 2, 3] Additionally, PICCs have also been recognized as an alternative to large‐bore central venous catheters such as subclavian or internal jugular central venous catheters. PICCs have been associated with fewer bloodstream infections in patients with cancer than tunneled catheters.[4] Compared to central venous catheters, they demonstrate reduced complication rates,[5] decreased cost,[6] and increased safety for longer durations of use.[1, 2, 3, 7, 8, 9]

Despite the numerous benefits of PICCs, Prandoni et al. estimate an all‐cause complication rate of 12% to 17% with the use of PICCs.[10] Associated complications include infection,[11] pain, bleeding, and mechanical dysfunction, all of which contribute to patient discomfort and additional healthcare costs.[12] Bloodstream infections, for example, had previously been thought to occur at a substantially lower rate in PICCs than central venous catheters.[13] However, a recent systematic review suggests the rate of PICC‐associated bloodstream infections in the inpatient setting is actually comparable to that of central venous catheters.[14] Perhaps the most serious PICC‐associated complication is catheter‐related venous thrombosis. A recent systematic review and meta‐analysis found evidence to suggest the rate of catheter‐related venous thrombosis was highest in patients with cancer or critical illness15; additionally, rates of thrombosis associated with PICCs were higher than those associated with subclavian or internal jugular central venous catheters.[15, 16] Fletcher et al. showed an 8.1% incidence of symptomatic PICC‐related upper extremity deep vein thrombosis (DVT) in the neurosurgical intensive care unit, with 15% of patients subsequently developing a pulmonary embolism.[17] A recent prospective, randomized controlled trial by Itkin et al. similarly demonstrated symptomatic DVT rates of approximately 4%.[18] However, in this study, when PICCs were routinely screened for thrombosis (with or without associated symptoms), approximately 72% demonstrated thrombosis,[18] suggesting that many PICC‐associated thromboses may be clinically undetected. This may have far‐reaching clinical significance, as pulmonary embolism complicates upper extremity DVT in 9% of cases and can result in a mortality rate as high as 25%.[10, 19]

Some strategies to reduce the rate of catheter‐related complications include identification of characteristics that put patients at risk. Many potential risk factors have been investigated, including catheter size,[12, 20, 21, 22, 23, 24] choice of vein,[24] location of catheter tip,[25] and history of malignancy or prior DVT.[12] However, to date, no definitive consensus has been reached. Special attention has been paid to the investigation of underlying risk factors and treatment for catheter‐related DVT, given its significant morbidity and mortality. Results have been equivocal, though, and in some instances, complicated by a diagnosis of underlying malignancy.[26, 27, 28]

As PICCs become more widely utilized, assessments of factors that place patients at greater risk of PICC‐related complications are needed.[21] The purpose of this study was to establish the incidence of complications associated with PICCs placed in the inpatient setting and examine risk factors predisposing patients to these complications.

MATERIALS AND METHODS

Study Design

A case control analysis of adult inpatients who underwent PICC placement between January 2009 and January 2010 was conducted at Scott & White Healthcare (now Baylor Scott & White Healthcare) to determine the incidence and risk factors for PICC‐associated complications.

Study Site

The study took place at Scott & White Memorial Hospital in Temple, Texas, a 636‐bed multispecialty teaching hospital and level 1 trauma center. It is part of a healthcare system that includes 12 hospitals and more than 60 regional clinics, all of which share an electronic medical record to enable full integration.

Human Subjects Approval

This study received approval from the institutional review board at Scott & White Healthcare.

PICC Placement Technique

Inpatient PICC placement was performed by the PICC consult service. The consult service was comprised of 3 separate provider teams: (1) internal medicine, including select hospitalists and internal medicine residents; (2) radiology, including interventional radiologists and radiology residents; and (3) nursing, including registered nurses with advanced training in PICC placement. Following placement of a consult, the PICC consult service assessed the patient, obtained consent, and subsequently placed the catheter. Members of the PICC consult service followed a system‐wide protocol wherein target veins were identified by ultrasound prior to attempting catheter placement, and actual placement of the PICC was ultrasound guided. Images obtained during the procedure were permanently documented in the medical record. At the time of this study, no formal protocol existed wherein target veins were mapped for caliber. Operators relied on their professional judgment to determine if vein caliber appeared sufficient to accommodate catheter placement.

All PICCs were placed using industry standard sterile precautions. A universally accepted modified Seldinger technique was used to obtain venous access.[29] A guidewire was then positioned in the desired vessel to facilitate proper venous placement of the catheter. During the course of the study period, catheters used were either single‐ (4 Fr) or double lumen (5 Fr).

Catheters were placed at the bedside by hospitalists or registered nurse teams; the location of the catheter tip at the cavoatrial junction was confirmed by chest radiography. Catheter insertions by radiologists were performed in the interventional radiology suite, and confirmation of location of the catheter tip was obtained with fluoroscopy.

PICC Maintenance

Following placement, nurses managed the PICC site according to nursing policy. Per policy, the site was assessed each shift. Documentation of assessment was recorded in nursing notes. Routine dressing changes were performed every 7 days, and as needed, to maintain a sterile site. Date and time of dressing changes were documented in nursing notes and on the PICC dressing. Catheter hubs and injection ports were disinfected with an antiseptic preparation for 15 seconds and allowed to air dry for 30 seconds prior to accessing the catheter. Catheters were flushed with 10 mL of normal saline before and after use. Any abnormality noted during PICC assessment was relayed to the primary provider. If the catheter did not flush readily or demonstrate appropriate blood return, nursing staff obtained an order for alteplase to be administered in an effort to salvage the line. PICCs were discontinued at the discretion of the healthcare provider.

Participants

Records of all patients 18 years of age and older who underwent PICC placement between January 2009 and January 2010 were reviewed (N=1444) for study inclusion. There were no exclusion criteria.

Data Collection

Patients who experienced complications were identified by electronic medical record review. One‐to‐one matching was performed for age and gender‐matched controls randomly selected from inpatients who underwent PICC placement during the same time period without complications. A total of 170 cases with PICC‐related complications were identified. One hundred seventy exact age‐ and gender‐matched controls, who based upon documentation available in the electronic medical record did not experience complications, were then randomly selected. Prior to data collection, the research team reviewed and discussed the data collection form and agreed upon a standardized protocol for data collection. Data collection was completed by authors J.M. and J.H. on the standardized data collection form. Although a formal analysis of inter‐rater agreement was not performed, J.M. and J.H. discussed any items where questions arose and arrived at a consensus decision regarding completion of the data point.

End points of the chart review were completion of medical therapy for which the PICC was indicated (eg, IV antibiotics or total parenteral nutrition [TPN]) or documentation of a complication that led to 1 of the following: discontinuation of the PICC or adjustment of either catheter placement or medical therapy. All complications were identified via International Classification of Diseases, 9th Revision codes and systematic chart review.

Complications resulting in discontinuation of the PICC, adjustment of catheter placement, or change in medical therapy were identified by review of nursing or physician documentation, and were categorized as follows: mechanical complications (defined as loss of the ability of the catheter to flush or draw properly, inadvertent catheter dislodgement, or retained portion of the catheter following catheter removal), catheter‐associated bloodstream infection (development of a positive blood culture attributable to the central catheter with no other clearly identifiable source of bacteremia present), cellulitis (defined as cellulitis in the extremity where the catheter was placed), bleeding from the site of catheter, fever (for which no other cause could be identified), and catheter‐associated thrombosis (identified by Doppler ultrasonography in patients exhibiting symptoms such as pain, swelling, redness, or warmth in the extremity in which the PICC was placed).[30]

Demographic data were collected, including insurance status, age, ethnicity, and gender. Clinical data included body mass index (BMI), presence of malnutrition (defined by a serum albumin of less than 3 g/dL),[31] previous or active cancer, previous DVT, use of anticoagulants (eg, warfarin, heparin, or low‐molecular‐weight heparin) or antiplatelet agent (eg, aspirin or clopidogrel) at the time of placement, and indication for PICC placement. A patient's history of previous or active cancer and previous DVT were identified by clinical documentation. Indications for PICC placement included: treating infectious processes (ie, infusion of antimicrobials), providing TPN, chemotherapy administration, and IV access. Catheter‐specific data were also collected and included venous access obtained (cephalic, basilic, brachial), catheter size (single lumen [4 Fr] or double lumen [5 Fr]), type of complication, and time to complication. The procedure note accompanying PICC placement was reviewed for data regarding time of day inserted (with after hours defined as documentation of placement occurring after 5 pm), and procedure operator to identify type of team (internal medicine, radiology, nursing) responsible for placement.

Data Analysis

Demographic characteristics and potential risk factors for patients in both the case and control groups of the study were summarized using descriptive statistics: mean ( standard deviation [SD]) for continuous variables and frequency (percent) for categorical variables. Univariate and multivariable conditional logistic regression analyses of variables that were potential risk factors of PICC‐related complications were utilized. A stepwise selection method was used for multivariable conditional logistic regression models. Alpha=0.2 was used for the significance to enter the model, and =0.05 was used for significance level to remain in the model. Attribution of PICC‐related complications was evaluated in terms of odds ratios (OR) and 95% confidence interval (CI). A P value of <0.05 indicated statistical significance. No prospective power analysis was performed. However, for a retrospective power analysis for 1:1 matching with 170 cases and 170 matched controls, assuming 20% of controls were affected and an of 0.05, one would achieve 80% power to detect an odds ratio of 2. SAS 9.2 (SAS Institute Inc., Cary, NC) was used for data analysis.

RESULTS

In 2009, 1444 PICCs were placed, and 170 cases in which patients experienced complications associated with PICC placement were identified, resulting in a complication rate of 11.77% (95% CI: 10.11%‐13.44%). The most common complications experienced by our patient population included catheter‐associated thrombosis (3%, n = 46), mechanical complications (4%, n=67), inadvertent catheter dislodgement (2%, n=36), mechanical dysfunction (2%, n=30), retained portion of the catheter following catheter removal (<1%, n=1), catheter‐associated bloodstream infections (2%, n=24), and cellulitis at the catheter insertion site (1%, n=15). Other documented complications included unexplained fever and bleeding (Table 1).

Type of Complication
ComplicationN (%)
  • NOTE: N=1,444. Mechanical dysfunction (N=30), retained portion of the catheter (N=1 [0%]). Sum of the % in the columns were not exactly 100% for some cases due to rounding. *Inadvertent catheter dislodgement (N=36).

Thrombosis46 (3)
Infection24 (2)
Cellulitis15 (1)
Mechanical complications*67 (4)
Unexplained fever15 (1)
Bleeding3 (0)
No complication1,274 (88)

The mean age of the total cohort (N=340), comprised of case (N=170) and control (N=170) groups, was 58 years (SD 17), and 55% (n=94) were females. There were no significant differences in complications between groups based on ethnicity (P=0.66). In the case group, 46% (n=78) of PICCs were placed by the radiology team, 41% (n=69) were placed by the internal medicine team, and 14% (n=23) were placed by nursing. In the control group, 44% (n=74) of PICCs were placed by radiology, 36% (n=62) by internal medicine, and 20% (n=34) by nursing. Based on univariate conditional analysis, provider team was not significantly associated with complications (P=0.29).

Predictors of All‐Cause Complications

Based upon univariate conditional logistic regression analyses of complications related to PICC placement (N=340), the following variables demonstrated a statistically significant increased risk for complications: malnutrition (OR: 1.88 [95% CI: 1.023.44], P=0.04) and after‐hours placement (OR: 8.67 [95% CI: 2.62‐28.63], P=0.0004) (Table 2). Anticoagulation was associated with a decreased risk of complications (OR: 0.27 [95% CI: 0.16‐0.45], P=0.04). Based upon multivariable logistic regression analysis, after‐hours placement (OR: 9.52 [95% CI: 2.68‐33.78], P=0.0005) and BMI >30 (OR: 1.98 [95% CI: 1.09‐3.61], P=0.02) were significantly associated with an increased risk of PICC‐associated complications. Conversely, anticoagulation/antiplatelet use was associated with a decreased risk of complications (OR: 0.24 [95% CI: 0.14‐0.43], P<0.0001).

Any Complication: Descriptive Statistics and Conditional Logistic Regression Analysis
VariableCase, N (%)Control, N (%)UnivariateMultivariable
OR (95% CI)P ValueAOR (95% CI)P Value
  • NOTE: N=170 in each group. Sum of the % in the columns was not exactly 100% for some cases due to rounding. Abbreviations: AOR, adjusted odds ratio; BMI, body mass index; CI, confidence interval; DVT, deep vein thrombosis; IV, intravenous; OR, odds ratio; PICC, peripherally inserted central venous catheter; SD, standard deviation.

  • Overall significance of the factor.

  • Frequency missing=1.

  • Frequency missing=2.

  • Osteomyelitis, abscess, cellulitis, pyelonephritis, meningitis.

Age, y, meanSD58175817    
BMI, meanSD29.29.527.97.91.02 (0.991.05)0.12  
30108 (64)116 (68%)1.00 1.00 
>3062 (36)54 (32%)1.29 (0.792.11)0.321.98 (1.093.61)0.02
Length of stay, d, meanSD182214161.01 (1.001.03)0.06  
Length of stay group, d   0.11a  
<741 (24)52 (31)1.00   
729101 (59)103 (61)1.19 (0.721.98)0.49  
3028 (16)15 (9)2.21 (1.074.58)0.03  
Gender      
Female94 (55)94 (55)    
Male76 (45)76 (45)    
Ethnicity   0.66a  
Caucasian131 (77)125 (74)1.00   
African American26 (15)28 (16)0.88 (0.481.60)0.67  
Hispanic/Asian13 (8)17 (10)0.70 (0.311.58)0.38  
Provider team   0.29a  
Radiology78 (46)74 (44)1.00   
Internal medicine69 (41)62 (36)1.05 (0.681.64)0.82  
Nursing23 (14)34 (20)0.65 (0.351.19)0.16  
Insuranceb   0.22a  
Private insurance46 (27)42 (25)1.00   
Uninsured17 (10)24 (14)0.73 (0.351.55)0.41  
Medicare57 (34)62 (37)0.73 (0.381.40)0.34  
Medicaid39 (23)25 (15)1.51 (0.743.06)0.26  
Tricare/Veterans Administration11 (6)16 (9)0.59 (0.241.45)0.25  
History of DVT27 (16)26 (15)1.05 (0.581.91)0.88  
Malnutritionb149 (88)134 (79)1.88 (1.023.44)0.04  
Cancer25 (15)36 (21)0.58 (0.311.09)0.09  
Fluoroscopy129 (76)139 (82)0.71 (0.421.19)0.19  
Anticoagulation use50 (29)100 (59)0.27 (0.160.45)<0.00010.24 (0.140.43)<0.0001
Multilumenc99 (58)111 (66)0.70 (0.441.11)0.13  
Veinb   0.39a  
Basilic98 (58)86 (51)1.00   
Cephalic11 (6)8 (5)1.37 (0.483.89)0.55  
Brachial61 (36)74 (44)0.70 (0.451.09)0.12  
Internal mammary0 (0)1 (1)<0.001 (<0.001>999)0.99  
Time of dayb      
Morning/afternoon144 (85)166 (98)1.00 1.00 
After hours26 (15)3 (2)8.67 (2.6228.63)0.00049.52 (2.6833.78)0.0005
Indication for PICC   0.02a  
Infection88 (52)71 (42)1.00   
Pneumonia21 (12)14 (8)1.07 (0.502.29)0.87  
Chemotherapy5 (3)2 (1)1.84 (0.349.93)0.48  
IV access36 (21)66 (39)0.44 (0.250.75)0.003  
Total parenteral nutrition20 (12)17 (10)0.96 (0.442.14)0.93  

Predictors of Nonmechanical Complications

To study risk factors related to nonmechanical complications, a secondary analysis (N=206) was performed in which all patients who experienced mechanical complications (N=67) and matched controls (N=67) were excluded. Based upon multivariable logistic regression analysis, after‐hours placement (OR: 6.93 [95% CI: 1.35‐35.56], P=0.02) and malnutrition (OR: 2.83 [95% CI: 1.037.81], P=0.04) were significantly associated with increased risk of nonmechanical complications. The use of anticoagulation/antiplatelet agents was associated with decreased risk of nonmechanical complications (OR: 0.17 [95% CI: 0.07‐0.40], P<0.0001). Variables not significantly associated with nonmechanical complications included BMI>30, previous history of DVT, history of cancer, catheter size, and venous access choice (Table 3).

Complications Other Than Mechanical: Descriptive Statistics and Conditional Logistic Regression Analysis
VariableCase, N (%)Control, N (%)UnivariateMultivariable
OR (95% CI)P ValueAOR (95% CI)P Value
  • NOTE: N=103 in each group. Sum of the % in the columns were not exactly 100% for some cases due to rounding. Abbreviations: AOR, adjusted odds ratio; BMI, body mass index; CI, confidence interval; DVT, deep vein thrombosis; IV, intravenous; OR, odds ratio; PICC, peripherally inserted central venous catheter; SD, standard deviation.

  • Overall significance of the factor.

  • Frequency missing=1.

  • Frequency missing=2.

  • Osteomyelitis, abscess, cellulitis, pyelonephritis, meningitis.

Age, y, meanSD58165816    
BMI, meanSD29.79.828.57.91.03 (0.991.07)0.22  
3064 (62)68 (66)1.00   
>3039 (38)35 (34)1.27 (0.642.49)0.49  
Length of stay, d, meanSD202614181.02 (1.001.03)0.08  
Length of stay group, d   0.03a  
<722 (21)28 (27)1.00   
72960 (58)68 (66)0.95 (0.491.82)0.87  
3021 (20)7 (7)3.24 (1.238.54)0.02  
Gender      
Female63 (61)63 (61)    
Male40 (39)40 (39)    
Ethnicity   0.95a  
Caucasian75 (73)75 (73)1.00   
African American19 (18)18 (17)1.06 (0.512.21)0.87  
Hispanic/Asian9 (9)10 (10)0.88 (0.322.44)0.81  
Provider team   0.81a  
Radiology43 (42)44 (43)1.00   
Internal medicine45 (44)41 (40)1.11 (0.621.96)0.73  
Nursing15 (15)18 (17)0.86 (0.391.90)0.71  
Insuranceb   0.22a  
Private insurance29 (28)27 (26)1.00   
Uninsured13 (13)12 (12)1.18 (0.433.26)0.74  
Medicare32 (31)40 (39)0.52 (0.211.29)0.16  
Medicaid21 (20)12 (12)1.81 (0.694.74)0.23  
Tricare/Veterans Administration8 (8)11 (11)0.58 (0.191.79)0.34  
History of DVT15 (15)15 (15)1.00 (0.462.16)1.00  
Malnutritionb93 (90)79 (77)2.86 (1.216.76)0.022.83 (1.037.81)0.04
Cancer17 (17)22 (21)0.67 (0.301.48)0.32  
Fluoroscopy78 (76)85 (83)0.65 (0.321.31)0.23  
Anticoagulation use29 (28)60 (58)0.21 (0.100.44)<0.00010.17 (0.070.40)<0.0001
Multilumenc64 (62)67 (66)0.83 (0.461.51)0.55  
Veinb   0.32a  
Basilic54 (52)49 (48)1.00   
Cephalic8 (8)3 (3)2.45 (0.649.32)0.19  
Brachial41 (40)49 (48)0.72 (0.421.24)0.24  
Internal mammary0 (0)1 (1)<0.001 (<0.001>999)0.99  
Time of dayb      
Morning/afternoon87 (84)100 (98)1.00 1.00 
After hours16 (16)2 (2)8.00 (1.8434.79)0.0066.93 (1.3535.56)0.02
Indication for PICC   0.13  
Infectiond52 (50)45 (44)1.00   
Pneumonia14 (14)7 (7)1.46 (0.514.18)0.48  
Chemotherapy5 (5)0 (0)>999 (<0.001>999)0.99  
IV access22 (21)43 (42)0.48 (0.240.96)0.04  
Total parenteral nutrition10 (10)8 (8)1.08 (0.323.62)0.90  

Predictors of Thrombotic Complications

Of 1444 patients who underwent PICC placement, 3% (n=46) were subsequently diagnosed with a catheter‐associated thrombosis, representing 27% of all observed complications. In an attempt to better identify factors predisposing patients to thrombotic complications, an additional subgroup analysis (N=92) was performed on those patients who experienced catheter‐associated thrombosis (N=46) and matched controls (N=46). Variables examined in the analysis included BMI, length of stay (LOS), history of DVT, history of cancer, utilization of anticoagulation/antiplatelet agents, malnutrition, and catheter size.

Based on conditional univariate analyses, the following variables were significantly associated with increased risk of catheter‐associated thrombosis: LOS (as a continuous variable) (OR: 1.04 [95% CI: 1.001.09], P=0.05), malnutrition (OR: 4 [95% CI: 1.1314.18], P=0.03), and after‐hours placement (OR: 8.00 [95% CI: 1.0063.96], P=0.05) (Table 4). Use of anticoagulation/antiplatelet agents (OR: 0.29 [95% CI: 0.11‐0.80], P=0.02) was associated with decreased risk of thrombosis. History of previous DVT and history of cancer were nonsignificant. In the multivariable logistic regression model, malnutrition (OR: 10.16 [95% CI: 1.76‐58.71], P=0.01) remained associated with increased risk of catheter‐associated thrombosis, whereas use of anticoagulation/antiplatelet agents (OR: 0.11 [95% CI: 0.02‐0.51], P=0.005) was associated with decreased risk of catheter‐associated thrombosis (Table 4).

Cathether‐Associated Thrombosis: Descriptive Statistics and Conditional Logistic Regression Analysis
VariableCase, N (%)Control, N (%)UnivariateMultivariable
OR (95% CI)P ValueAOR (95% CI)P Value
  • NOTE: N=46 in each group. Sum of the % in the columns were not exactly 100% for some cases due to rounding. Abbreviations: AOR, adjusted odds ratio; BMI, body mass index; CI, confidence interval; DVT, deep vein thrombosis; IV, intravenous; OR, odds ratio; PICC, peripherally inserted central venous catheter; SD, standard deviation.

  • Overall significance of the factor.

  • Frequency missing=1.

  • Frequency missing=2.

  • Osteomyelitis, abscess, cellulitis, pyelonephritis, meningitis.

Age, y, meanSD58185818    
BMI, meanSD27.77.127.77.81.00 (0.931.08)0.98  
3034 (74)33 (72)    
>3012 (26)13 (28)0.83 (0.252.73)0.76  
Length of stay, d, meanSD17121191.04 (1.001.09)0.05  
Length of stay group, d   0.15  
<78 (17)14 (30)1.00   
72929 (63)30 (65)1.13 (0.413.07)0.82  
309 (20)2 (4)4.65 (0.9822.13)0.05  
Gender      
Female26 (57)26 (57)    
Male20 (43)20 (43)    
Ethnicity   0.44a  
Caucasian31 (67)36 (78)1.00   
African American11 (24)6 (13)2.02 (0.695.93)0.20  
Hispanic/Asian4 (9)4 (9)1.12 (0.225.68)0.89  
Provider team   0.26a  
Radiology23 (50)19 (41)1.00   
Internal medicine20 (43)18 (39)1.00 (0.432.31)1.00  
Nursing3(7)9 (20)0.33 (0.091.27)0.11  
Insuranceb   0.38a  
Private insurance13 (28)11 (24)1.00   
Uninsured8 (17)4 (9)2.01 (0.3810.58)0.41  
Medicare14 (30)21 (47)0.39 (0.101.47)0.16  
Medicaid8 (17)7 (16)1.23 (0.285.36)0.78  
Tricare/Veterans Administration3 (7)2 (4)1.01 (0.128.27)1.00  
History of DVT7 (15)8 (17)0.88 (0.322.41)0.80  
Malnutritionb43 (93)33 (73)4.00 (1.1314.18)0.0310.16 (1.7658.71)0.01
Cancer10 (22)13 (28)0.67 (0.241.87)0.44  
Fluoroscopy33 (72)39 (85)0.46 (0.161.31)0.14  
Anticoagulation use16 (35)28 (61)0.29 (0.110.80)0.020.11 (0.020.51)0.005
Multilumenc22 (48)28 (62)0.53 (0.231.26)0.15  
Veinb   0.93a  
Basilic24 (52)21 (47)1.00   
Cephalic1 (2)1 (2)0.86 (0.0514.39)0.92  
Brachial21 (46)22 (49)0.75 (0.311.79)0.51  
Internal mammary0 (0)1 (2)<0.001 (<0.001>999)0.99  
Time of dayb      
Morning/afternoon38 (83)44 (98)1.00   
After hours8 (17)1 (2)8.00 (1.0063.96)0.05  
Indication for PICC   0.80a  
Infectiond20 (43)17 (37)1.00   
Pneumonia5 (11)6 (13)0.60 (0.142.56)0.49  
Chemotherapy3 (7)0 (0)>999 (<0.001>999)0.99  
IV access14 (30)20 (43)0.58 (0.231.44)0.24  
Total parenteral nutrition4 (9)3 (7)1.22 (0.197.70)0.83  

DISCUSSION

The goal of this study was to identify factors related to PICC placement that place the general population of patients at risk. The type and rate of complications associated with PICCs in this study were similar to those previously reported in the literature including catheter‐related infection and thrombosis.[10, 32] Two unique risk factors, not well recognized previously,[10, 27, 28, 33] were observed in this study: malnutrition and after‐hours placement. Malnutrition, defined as serum albumin <3 g/dL was associated with an increase in PICC‐related complications (such as catheter‐associated bloodstream infections and cellulitis) and catheter‐related thrombosis. Malnutrition itself has long been associated with a decreased resistance to infection[34]; in addition, low serum albumin may also be a marker of the presence of other severe comorbidities, which may contribute to increased risk of thrombosis. It has been noted in previous studies that critical illness increases risk of thrombosis.[15] Despite an exhaustive search of the literature, we have been unable to find additional studies examining the extent to which malnutrition may impact PICC‐associated complications.

After‐hours placement was also associated with increased nonmechanical complications, as well as catheter‐related thrombosis. In an effort to improve both patient and consulting provider satisfaction and provide more expedient service, PICCs were often placed after hours (between 5 pm and 8 am) by both interventional radiology (n=14) and internal medicine (n=15) teams.

LOS has been associated with PICC placement complications in other studies.[12] In both primary and secondary analyses, hospital stays >30 days were associated with a higher risk of complications than hospitalizations <7 days. In light of the clinical significance of catheter‐related thrombosis, a subgroup analysis of patients with an LOS >30 days was conducted. The conditional univariate regression analysis showed an increased risk with greater LOS, malnutrition, and after‐hours placement. Use of anticoagulant or antiplatelet agents were associated with decreased risk of thrombosis (Table 4). The association between LOS and PICC‐related thrombosis is consistent with findings from Evans et al. involving 1728 patients in a similar center.[12] In these circumstances, increased LOS may be a surrogate marker for increased severity of illness, in that those patients who are more ill require lengthier hospitalizations. In a systematic review and meta‐analysis, Chopra et al. observed that increased severity of illness correlated with higher rates of catheter‐associated thrombosis, which is supportive of these findings.[15]

In the multivariate logistic regression analysis, BMI >30 was associated with a statistically significant increased risk for PICC‐associated complications after adjusting for anticoagulation and time of placement (Table 2). In the secondary analysis, where patients with mechanical complications were removed, BMI >30 was no longer associated with an increased risk for PICC‐associated complications (Table 3). This suggests that patients with a BMI >30 had an increased risk of mechanical complications, but were not necessarily at increased risk of developing other complications, such as catheter‐related thrombosis, infection, or bleeding. This finding is congruent with studies by Evans et al.,[12] who found no association between BMI and catheter‐associated thrombosis. Our association between BMI and complications is unique; to date, there are few additional studies that examine the extent to which BMI impacts the rate and type of complications associated with PICCs. At this time, the mechanism of the association between mechanical complications (such as inadvertent catheter removal or mechanical malfunction) and BMI is uncertain and warrants further investigation.

Use of Anticoagulant Agents

Anticoagulant (ie, any agent used for DVT prophylaxis or therapeutic anticoagulation) or antiplatelet agent use at the time of PICC placement and during the patient's hospitalization was associated with a decreased risk of thrombosis in our analysis. However, it should be noted that no specific anticoagulant agent was studied, and that antiplatelet agents were included in this analysis, unlike that of Evans et al.[12] Although current literature in oncologic populations, as well as the evidence‐based clinical practice guidelines, recommend against routine use of venous thromboprophylaxis in patients with central venous catheters,[33, 35, 36, 37] we believe this deserves further study, particularly in light of conflicting data in this area.[38, 39] Evans et al.[12] noted that although use of anticoagulants initially appeared to be associated with greater incidence of upper extremity venous thrombosis, when previous diagnosis of DVT was removed from the analysis the association was no longer significant.

In our analyses, no associations between catheter size, choice of venous access, history of previous deep venous thrombosis, or history of malignancy and risk for complications were found. Our findings differed from previous studies, where a relationship between increasing catheter bore size and site of access have been associated with increased PICC‐related thrombosis or other complications.[12, 20, 40, 41] There were also no significant differences in risk for complications between provider teams (eg, internal medicine, radiology, nursing) for PICCs placed during the morning or afternoon, which is consistent with findings by Funk et al.[1] Yet, after‐hours placement of PICCs was associated with greater complications than daytime placement. Although the exploration of factors associated with after‐hours placement was beyond the scope of this study, the findings from this study caused the authors, primarily comprised of members of the internal medicine inpatient medicine division, to reexamine the division's protocol on PICC placement. A consensus decision was made to discontinue after‐hours placement of PICCs by internal medicine teams in an effort to promote patient safety until further data could be collected. As a result, internal medicine teams no longer place PICCs after regular working hours at our institution.

Limitations

Limitations include the categorization of antiplatelet and anticoagulant agents together. We did not distinguish between high‐ and low‐dose aspirin, nor did we distinguish between therapeutic dosing of heparin and low‐molecular‐weight heparin versus DVT prophylaxis dosing. Additionally, for patients who were on warfarin or heparin drip, we did not evaluate for therapeutic range of international normalized ratio or partial thromboplastin time, as this was beyond the present scope of this study. In addition, malnutrition defined by albumin alone may have been somewhat narrow, as conditions aside from malnutrition can impact albumin levels. In future evaluations, this relationship may be clarified by including other determinants of clinical malnutrition including BMI <18 or the measurement of prealbumin. For determination of after‐hours placement of PICCs, we relied upon time of procedure dictation, assuming that all dictations immediately followed catheter placement. If there was a lapse in time between catheter placement and dictation, the category may have been recorded in error. Another limitation of after‐hours categorization was that we were unable to determine whether the PICC was placed on a weekend or holiday.

CONCLUSIONS AND FUTURE DIRECTIONS

Our results suggest that more stringent screening of patients undergoing PICC placement may reduce the risk of complications, with special attention to characteristics such as BMI >30, increased LOS, and protein‐calorie malnutrition (albumin <3). Furthermore, placement of PICC lines in emergent or after‐hours settings should be carefully considered and weighed against relative risks of central venous catheter placement. Further examination of the role anticoagulant and antiplatelet agents may have in the prevention of catheter‐related thrombosis should be undertaken. We hope that the identification of these risk factors will decrease the rate of complications and ultimately enhance patient safety and satisfaction.

Acknowledgments

The authors sincerely thank Glen Cryer, Publications Manager, Baylor Scott & White Health, for his assistance with this article.

Disclosures: Nothing to report.

Peripherally inserted central venous catheters (PICCs) are used for a variety of indications, including administration of long‐term intravenous (IV) antibiotics, home IV medications, chemotherapy, and parenteral nutrition.[1, 2, 3] Additionally, PICCs have also been recognized as an alternative to large‐bore central venous catheters such as subclavian or internal jugular central venous catheters. PICCs have been associated with fewer bloodstream infections in patients with cancer than tunneled catheters.[4] Compared to central venous catheters, they demonstrate reduced complication rates,[5] decreased cost,[6] and increased safety for longer durations of use.[1, 2, 3, 7, 8, 9]

Despite the numerous benefits of PICCs, Prandoni et al. estimate an all‐cause complication rate of 12% to 17% with the use of PICCs.[10] Associated complications include infection,[11] pain, bleeding, and mechanical dysfunction, all of which contribute to patient discomfort and additional healthcare costs.[12] Bloodstream infections, for example, had previously been thought to occur at a substantially lower rate in PICCs than central venous catheters.[13] However, a recent systematic review suggests the rate of PICC‐associated bloodstream infections in the inpatient setting is actually comparable to that of central venous catheters.[14] Perhaps the most serious PICC‐associated complication is catheter‐related venous thrombosis. A recent systematic review and meta‐analysis found evidence to suggest the rate of catheter‐related venous thrombosis was highest in patients with cancer or critical illness15; additionally, rates of thrombosis associated with PICCs were higher than those associated with subclavian or internal jugular central venous catheters.[15, 16] Fletcher et al. showed an 8.1% incidence of symptomatic PICC‐related upper extremity deep vein thrombosis (DVT) in the neurosurgical intensive care unit, with 15% of patients subsequently developing a pulmonary embolism.[17] A recent prospective, randomized controlled trial by Itkin et al. similarly demonstrated symptomatic DVT rates of approximately 4%.[18] However, in this study, when PICCs were routinely screened for thrombosis (with or without associated symptoms), approximately 72% demonstrated thrombosis,[18] suggesting that many PICC‐associated thromboses may be clinically undetected. This may have far‐reaching clinical significance, as pulmonary embolism complicates upper extremity DVT in 9% of cases and can result in a mortality rate as high as 25%.[10, 19]

Some strategies to reduce the rate of catheter‐related complications include identification of characteristics that put patients at risk. Many potential risk factors have been investigated, including catheter size,[12, 20, 21, 22, 23, 24] choice of vein,[24] location of catheter tip,[25] and history of malignancy or prior DVT.[12] However, to date, no definitive consensus has been reached. Special attention has been paid to the investigation of underlying risk factors and treatment for catheter‐related DVT, given its significant morbidity and mortality. Results have been equivocal, though, and in some instances, complicated by a diagnosis of underlying malignancy.[26, 27, 28]

As PICCs become more widely utilized, assessments of factors that place patients at greater risk of PICC‐related complications are needed.[21] The purpose of this study was to establish the incidence of complications associated with PICCs placed in the inpatient setting and examine risk factors predisposing patients to these complications.

MATERIALS AND METHODS

Study Design

A case control analysis of adult inpatients who underwent PICC placement between January 2009 and January 2010 was conducted at Scott & White Healthcare (now Baylor Scott & White Healthcare) to determine the incidence and risk factors for PICC‐associated complications.

Study Site

The study took place at Scott & White Memorial Hospital in Temple, Texas, a 636‐bed multispecialty teaching hospital and level 1 trauma center. It is part of a healthcare system that includes 12 hospitals and more than 60 regional clinics, all of which share an electronic medical record to enable full integration.

Human Subjects Approval

This study received approval from the institutional review board at Scott & White Healthcare.

PICC Placement Technique

Inpatient PICC placement was performed by the PICC consult service. The consult service was comprised of 3 separate provider teams: (1) internal medicine, including select hospitalists and internal medicine residents; (2) radiology, including interventional radiologists and radiology residents; and (3) nursing, including registered nurses with advanced training in PICC placement. Following placement of a consult, the PICC consult service assessed the patient, obtained consent, and subsequently placed the catheter. Members of the PICC consult service followed a system‐wide protocol wherein target veins were identified by ultrasound prior to attempting catheter placement, and actual placement of the PICC was ultrasound guided. Images obtained during the procedure were permanently documented in the medical record. At the time of this study, no formal protocol existed wherein target veins were mapped for caliber. Operators relied on their professional judgment to determine if vein caliber appeared sufficient to accommodate catheter placement.

All PICCs were placed using industry standard sterile precautions. A universally accepted modified Seldinger technique was used to obtain venous access.[29] A guidewire was then positioned in the desired vessel to facilitate proper venous placement of the catheter. During the course of the study period, catheters used were either single‐ (4 Fr) or double lumen (5 Fr).

Catheters were placed at the bedside by hospitalists or registered nurse teams; the location of the catheter tip at the cavoatrial junction was confirmed by chest radiography. Catheter insertions by radiologists were performed in the interventional radiology suite, and confirmation of location of the catheter tip was obtained with fluoroscopy.

PICC Maintenance

Following placement, nurses managed the PICC site according to nursing policy. Per policy, the site was assessed each shift. Documentation of assessment was recorded in nursing notes. Routine dressing changes were performed every 7 days, and as needed, to maintain a sterile site. Date and time of dressing changes were documented in nursing notes and on the PICC dressing. Catheter hubs and injection ports were disinfected with an antiseptic preparation for 15 seconds and allowed to air dry for 30 seconds prior to accessing the catheter. Catheters were flushed with 10 mL of normal saline before and after use. Any abnormality noted during PICC assessment was relayed to the primary provider. If the catheter did not flush readily or demonstrate appropriate blood return, nursing staff obtained an order for alteplase to be administered in an effort to salvage the line. PICCs were discontinued at the discretion of the healthcare provider.

Participants

Records of all patients 18 years of age and older who underwent PICC placement between January 2009 and January 2010 were reviewed (N=1444) for study inclusion. There were no exclusion criteria.

Data Collection

Patients who experienced complications were identified by electronic medical record review. One‐to‐one matching was performed for age and gender‐matched controls randomly selected from inpatients who underwent PICC placement during the same time period without complications. A total of 170 cases with PICC‐related complications were identified. One hundred seventy exact age‐ and gender‐matched controls, who based upon documentation available in the electronic medical record did not experience complications, were then randomly selected. Prior to data collection, the research team reviewed and discussed the data collection form and agreed upon a standardized protocol for data collection. Data collection was completed by authors J.M. and J.H. on the standardized data collection form. Although a formal analysis of inter‐rater agreement was not performed, J.M. and J.H. discussed any items where questions arose and arrived at a consensus decision regarding completion of the data point.

End points of the chart review were completion of medical therapy for which the PICC was indicated (eg, IV antibiotics or total parenteral nutrition [TPN]) or documentation of a complication that led to 1 of the following: discontinuation of the PICC or adjustment of either catheter placement or medical therapy. All complications were identified via International Classification of Diseases, 9th Revision codes and systematic chart review.

Complications resulting in discontinuation of the PICC, adjustment of catheter placement, or change in medical therapy were identified by review of nursing or physician documentation, and were categorized as follows: mechanical complications (defined as loss of the ability of the catheter to flush or draw properly, inadvertent catheter dislodgement, or retained portion of the catheter following catheter removal), catheter‐associated bloodstream infection (development of a positive blood culture attributable to the central catheter with no other clearly identifiable source of bacteremia present), cellulitis (defined as cellulitis in the extremity where the catheter was placed), bleeding from the site of catheter, fever (for which no other cause could be identified), and catheter‐associated thrombosis (identified by Doppler ultrasonography in patients exhibiting symptoms such as pain, swelling, redness, or warmth in the extremity in which the PICC was placed).[30]

Demographic data were collected, including insurance status, age, ethnicity, and gender. Clinical data included body mass index (BMI), presence of malnutrition (defined by a serum albumin of less than 3 g/dL),[31] previous or active cancer, previous DVT, use of anticoagulants (eg, warfarin, heparin, or low‐molecular‐weight heparin) or antiplatelet agent (eg, aspirin or clopidogrel) at the time of placement, and indication for PICC placement. A patient's history of previous or active cancer and previous DVT were identified by clinical documentation. Indications for PICC placement included: treating infectious processes (ie, infusion of antimicrobials), providing TPN, chemotherapy administration, and IV access. Catheter‐specific data were also collected and included venous access obtained (cephalic, basilic, brachial), catheter size (single lumen [4 Fr] or double lumen [5 Fr]), type of complication, and time to complication. The procedure note accompanying PICC placement was reviewed for data regarding time of day inserted (with after hours defined as documentation of placement occurring after 5 pm), and procedure operator to identify type of team (internal medicine, radiology, nursing) responsible for placement.

Data Analysis

Demographic characteristics and potential risk factors for patients in both the case and control groups of the study were summarized using descriptive statistics: mean ( standard deviation [SD]) for continuous variables and frequency (percent) for categorical variables. Univariate and multivariable conditional logistic regression analyses of variables that were potential risk factors of PICC‐related complications were utilized. A stepwise selection method was used for multivariable conditional logistic regression models. Alpha=0.2 was used for the significance to enter the model, and =0.05 was used for significance level to remain in the model. Attribution of PICC‐related complications was evaluated in terms of odds ratios (OR) and 95% confidence interval (CI). A P value of <0.05 indicated statistical significance. No prospective power analysis was performed. However, for a retrospective power analysis for 1:1 matching with 170 cases and 170 matched controls, assuming 20% of controls were affected and an of 0.05, one would achieve 80% power to detect an odds ratio of 2. SAS 9.2 (SAS Institute Inc., Cary, NC) was used for data analysis.

RESULTS

In 2009, 1444 PICCs were placed, and 170 cases in which patients experienced complications associated with PICC placement were identified, resulting in a complication rate of 11.77% (95% CI: 10.11%‐13.44%). The most common complications experienced by our patient population included catheter‐associated thrombosis (3%, n = 46), mechanical complications (4%, n=67), inadvertent catheter dislodgement (2%, n=36), mechanical dysfunction (2%, n=30), retained portion of the catheter following catheter removal (<1%, n=1), catheter‐associated bloodstream infections (2%, n=24), and cellulitis at the catheter insertion site (1%, n=15). Other documented complications included unexplained fever and bleeding (Table 1).

Type of Complication
ComplicationN (%)
  • NOTE: N=1,444. Mechanical dysfunction (N=30), retained portion of the catheter (N=1 [0%]). Sum of the % in the columns were not exactly 100% for some cases due to rounding. *Inadvertent catheter dislodgement (N=36).

Thrombosis46 (3)
Infection24 (2)
Cellulitis15 (1)
Mechanical complications*67 (4)
Unexplained fever15 (1)
Bleeding3 (0)
No complication1,274 (88)

The mean age of the total cohort (N=340), comprised of case (N=170) and control (N=170) groups, was 58 years (SD 17), and 55% (n=94) were females. There were no significant differences in complications between groups based on ethnicity (P=0.66). In the case group, 46% (n=78) of PICCs were placed by the radiology team, 41% (n=69) were placed by the internal medicine team, and 14% (n=23) were placed by nursing. In the control group, 44% (n=74) of PICCs were placed by radiology, 36% (n=62) by internal medicine, and 20% (n=34) by nursing. Based on univariate conditional analysis, provider team was not significantly associated with complications (P=0.29).

Predictors of All‐Cause Complications

Based upon univariate conditional logistic regression analyses of complications related to PICC placement (N=340), the following variables demonstrated a statistically significant increased risk for complications: malnutrition (OR: 1.88 [95% CI: 1.023.44], P=0.04) and after‐hours placement (OR: 8.67 [95% CI: 2.62‐28.63], P=0.0004) (Table 2). Anticoagulation was associated with a decreased risk of complications (OR: 0.27 [95% CI: 0.16‐0.45], P=0.04). Based upon multivariable logistic regression analysis, after‐hours placement (OR: 9.52 [95% CI: 2.68‐33.78], P=0.0005) and BMI >30 (OR: 1.98 [95% CI: 1.09‐3.61], P=0.02) were significantly associated with an increased risk of PICC‐associated complications. Conversely, anticoagulation/antiplatelet use was associated with a decreased risk of complications (OR: 0.24 [95% CI: 0.14‐0.43], P<0.0001).

Any Complication: Descriptive Statistics and Conditional Logistic Regression Analysis
VariableCase, N (%)Control, N (%)UnivariateMultivariable
OR (95% CI)P ValueAOR (95% CI)P Value
  • NOTE: N=170 in each group. Sum of the % in the columns was not exactly 100% for some cases due to rounding. Abbreviations: AOR, adjusted odds ratio; BMI, body mass index; CI, confidence interval; DVT, deep vein thrombosis; IV, intravenous; OR, odds ratio; PICC, peripherally inserted central venous catheter; SD, standard deviation.

  • Overall significance of the factor.

  • Frequency missing=1.

  • Frequency missing=2.

  • Osteomyelitis, abscess, cellulitis, pyelonephritis, meningitis.

Age, y, meanSD58175817    
BMI, meanSD29.29.527.97.91.02 (0.991.05)0.12  
30108 (64)116 (68%)1.00 1.00 
>3062 (36)54 (32%)1.29 (0.792.11)0.321.98 (1.093.61)0.02
Length of stay, d, meanSD182214161.01 (1.001.03)0.06  
Length of stay group, d   0.11a  
<741 (24)52 (31)1.00   
729101 (59)103 (61)1.19 (0.721.98)0.49  
3028 (16)15 (9)2.21 (1.074.58)0.03  
Gender      
Female94 (55)94 (55)    
Male76 (45)76 (45)    
Ethnicity   0.66a  
Caucasian131 (77)125 (74)1.00   
African American26 (15)28 (16)0.88 (0.481.60)0.67  
Hispanic/Asian13 (8)17 (10)0.70 (0.311.58)0.38  
Provider team   0.29a  
Radiology78 (46)74 (44)1.00   
Internal medicine69 (41)62 (36)1.05 (0.681.64)0.82  
Nursing23 (14)34 (20)0.65 (0.351.19)0.16  
Insuranceb   0.22a  
Private insurance46 (27)42 (25)1.00   
Uninsured17 (10)24 (14)0.73 (0.351.55)0.41  
Medicare57 (34)62 (37)0.73 (0.381.40)0.34  
Medicaid39 (23)25 (15)1.51 (0.743.06)0.26  
Tricare/Veterans Administration11 (6)16 (9)0.59 (0.241.45)0.25  
History of DVT27 (16)26 (15)1.05 (0.581.91)0.88  
Malnutritionb149 (88)134 (79)1.88 (1.023.44)0.04  
Cancer25 (15)36 (21)0.58 (0.311.09)0.09  
Fluoroscopy129 (76)139 (82)0.71 (0.421.19)0.19  
Anticoagulation use50 (29)100 (59)0.27 (0.160.45)<0.00010.24 (0.140.43)<0.0001
Multilumenc99 (58)111 (66)0.70 (0.441.11)0.13  
Veinb   0.39a  
Basilic98 (58)86 (51)1.00   
Cephalic11 (6)8 (5)1.37 (0.483.89)0.55  
Brachial61 (36)74 (44)0.70 (0.451.09)0.12  
Internal mammary0 (0)1 (1)<0.001 (<0.001>999)0.99  
Time of dayb      
Morning/afternoon144 (85)166 (98)1.00 1.00 
After hours26 (15)3 (2)8.67 (2.6228.63)0.00049.52 (2.6833.78)0.0005
Indication for PICC   0.02a  
Infection88 (52)71 (42)1.00   
Pneumonia21 (12)14 (8)1.07 (0.502.29)0.87  
Chemotherapy5 (3)2 (1)1.84 (0.349.93)0.48  
IV access36 (21)66 (39)0.44 (0.250.75)0.003  
Total parenteral nutrition20 (12)17 (10)0.96 (0.442.14)0.93  

Predictors of Nonmechanical Complications

To study risk factors related to nonmechanical complications, a secondary analysis (N=206) was performed in which all patients who experienced mechanical complications (N=67) and matched controls (N=67) were excluded. Based upon multivariable logistic regression analysis, after‐hours placement (OR: 6.93 [95% CI: 1.35‐35.56], P=0.02) and malnutrition (OR: 2.83 [95% CI: 1.037.81], P=0.04) were significantly associated with increased risk of nonmechanical complications. The use of anticoagulation/antiplatelet agents was associated with decreased risk of nonmechanical complications (OR: 0.17 [95% CI: 0.07‐0.40], P<0.0001). Variables not significantly associated with nonmechanical complications included BMI>30, previous history of DVT, history of cancer, catheter size, and venous access choice (Table 3).

Complications Other Than Mechanical: Descriptive Statistics and Conditional Logistic Regression Analysis
VariableCase, N (%)Control, N (%)UnivariateMultivariable
OR (95% CI)P ValueAOR (95% CI)P Value
  • NOTE: N=103 in each group. Sum of the % in the columns were not exactly 100% for some cases due to rounding. Abbreviations: AOR, adjusted odds ratio; BMI, body mass index; CI, confidence interval; DVT, deep vein thrombosis; IV, intravenous; OR, odds ratio; PICC, peripherally inserted central venous catheter; SD, standard deviation.

  • Overall significance of the factor.

  • Frequency missing=1.

  • Frequency missing=2.

  • Osteomyelitis, abscess, cellulitis, pyelonephritis, meningitis.

Age, y, meanSD58165816    
BMI, meanSD29.79.828.57.91.03 (0.991.07)0.22  
3064 (62)68 (66)1.00   
>3039 (38)35 (34)1.27 (0.642.49)0.49  
Length of stay, d, meanSD202614181.02 (1.001.03)0.08  
Length of stay group, d   0.03a  
<722 (21)28 (27)1.00   
72960 (58)68 (66)0.95 (0.491.82)0.87  
3021 (20)7 (7)3.24 (1.238.54)0.02  
Gender      
Female63 (61)63 (61)    
Male40 (39)40 (39)    
Ethnicity   0.95a  
Caucasian75 (73)75 (73)1.00   
African American19 (18)18 (17)1.06 (0.512.21)0.87  
Hispanic/Asian9 (9)10 (10)0.88 (0.322.44)0.81  
Provider team   0.81a  
Radiology43 (42)44 (43)1.00   
Internal medicine45 (44)41 (40)1.11 (0.621.96)0.73  
Nursing15 (15)18 (17)0.86 (0.391.90)0.71  
Insuranceb   0.22a  
Private insurance29 (28)27 (26)1.00   
Uninsured13 (13)12 (12)1.18 (0.433.26)0.74  
Medicare32 (31)40 (39)0.52 (0.211.29)0.16  
Medicaid21 (20)12 (12)1.81 (0.694.74)0.23  
Tricare/Veterans Administration8 (8)11 (11)0.58 (0.191.79)0.34  
History of DVT15 (15)15 (15)1.00 (0.462.16)1.00  
Malnutritionb93 (90)79 (77)2.86 (1.216.76)0.022.83 (1.037.81)0.04
Cancer17 (17)22 (21)0.67 (0.301.48)0.32  
Fluoroscopy78 (76)85 (83)0.65 (0.321.31)0.23  
Anticoagulation use29 (28)60 (58)0.21 (0.100.44)<0.00010.17 (0.070.40)<0.0001
Multilumenc64 (62)67 (66)0.83 (0.461.51)0.55  
Veinb   0.32a  
Basilic54 (52)49 (48)1.00   
Cephalic8 (8)3 (3)2.45 (0.649.32)0.19  
Brachial41 (40)49 (48)0.72 (0.421.24)0.24  
Internal mammary0 (0)1 (1)<0.001 (<0.001>999)0.99  
Time of dayb      
Morning/afternoon87 (84)100 (98)1.00 1.00 
After hours16 (16)2 (2)8.00 (1.8434.79)0.0066.93 (1.3535.56)0.02
Indication for PICC   0.13  
Infectiond52 (50)45 (44)1.00   
Pneumonia14 (14)7 (7)1.46 (0.514.18)0.48  
Chemotherapy5 (5)0 (0)>999 (<0.001>999)0.99  
IV access22 (21)43 (42)0.48 (0.240.96)0.04  
Total parenteral nutrition10 (10)8 (8)1.08 (0.323.62)0.90  

Predictors of Thrombotic Complications

Of 1444 patients who underwent PICC placement, 3% (n=46) were subsequently diagnosed with a catheter‐associated thrombosis, representing 27% of all observed complications. In an attempt to better identify factors predisposing patients to thrombotic complications, an additional subgroup analysis (N=92) was performed on those patients who experienced catheter‐associated thrombosis (N=46) and matched controls (N=46). Variables examined in the analysis included BMI, length of stay (LOS), history of DVT, history of cancer, utilization of anticoagulation/antiplatelet agents, malnutrition, and catheter size.

Based on conditional univariate analyses, the following variables were significantly associated with increased risk of catheter‐associated thrombosis: LOS (as a continuous variable) (OR: 1.04 [95% CI: 1.001.09], P=0.05), malnutrition (OR: 4 [95% CI: 1.1314.18], P=0.03), and after‐hours placement (OR: 8.00 [95% CI: 1.0063.96], P=0.05) (Table 4). Use of anticoagulation/antiplatelet agents (OR: 0.29 [95% CI: 0.11‐0.80], P=0.02) was associated with decreased risk of thrombosis. History of previous DVT and history of cancer were nonsignificant. In the multivariable logistic regression model, malnutrition (OR: 10.16 [95% CI: 1.76‐58.71], P=0.01) remained associated with increased risk of catheter‐associated thrombosis, whereas use of anticoagulation/antiplatelet agents (OR: 0.11 [95% CI: 0.02‐0.51], P=0.005) was associated with decreased risk of catheter‐associated thrombosis (Table 4).

Cathether‐Associated Thrombosis: Descriptive Statistics and Conditional Logistic Regression Analysis
VariableCase, N (%)Control, N (%)UnivariateMultivariable
OR (95% CI)P ValueAOR (95% CI)P Value
  • NOTE: N=46 in each group. Sum of the % in the columns were not exactly 100% for some cases due to rounding. Abbreviations: AOR, adjusted odds ratio; BMI, body mass index; CI, confidence interval; DVT, deep vein thrombosis; IV, intravenous; OR, odds ratio; PICC, peripherally inserted central venous catheter; SD, standard deviation.

  • Overall significance of the factor.

  • Frequency missing=1.

  • Frequency missing=2.

  • Osteomyelitis, abscess, cellulitis, pyelonephritis, meningitis.

Age, y, meanSD58185818    
BMI, meanSD27.77.127.77.81.00 (0.931.08)0.98  
3034 (74)33 (72)    
>3012 (26)13 (28)0.83 (0.252.73)0.76  
Length of stay, d, meanSD17121191.04 (1.001.09)0.05  
Length of stay group, d   0.15  
<78 (17)14 (30)1.00   
72929 (63)30 (65)1.13 (0.413.07)0.82  
309 (20)2 (4)4.65 (0.9822.13)0.05  
Gender      
Female26 (57)26 (57)    
Male20 (43)20 (43)    
Ethnicity   0.44a  
Caucasian31 (67)36 (78)1.00   
African American11 (24)6 (13)2.02 (0.695.93)0.20  
Hispanic/Asian4 (9)4 (9)1.12 (0.225.68)0.89  
Provider team   0.26a  
Radiology23 (50)19 (41)1.00   
Internal medicine20 (43)18 (39)1.00 (0.432.31)1.00  
Nursing3(7)9 (20)0.33 (0.091.27)0.11  
Insuranceb   0.38a  
Private insurance13 (28)11 (24)1.00   
Uninsured8 (17)4 (9)2.01 (0.3810.58)0.41  
Medicare14 (30)21 (47)0.39 (0.101.47)0.16  
Medicaid8 (17)7 (16)1.23 (0.285.36)0.78  
Tricare/Veterans Administration3 (7)2 (4)1.01 (0.128.27)1.00  
History of DVT7 (15)8 (17)0.88 (0.322.41)0.80  
Malnutritionb43 (93)33 (73)4.00 (1.1314.18)0.0310.16 (1.7658.71)0.01
Cancer10 (22)13 (28)0.67 (0.241.87)0.44  
Fluoroscopy33 (72)39 (85)0.46 (0.161.31)0.14  
Anticoagulation use16 (35)28 (61)0.29 (0.110.80)0.020.11 (0.020.51)0.005
Multilumenc22 (48)28 (62)0.53 (0.231.26)0.15  
Veinb   0.93a  
Basilic24 (52)21 (47)1.00   
Cephalic1 (2)1 (2)0.86 (0.0514.39)0.92  
Brachial21 (46)22 (49)0.75 (0.311.79)0.51  
Internal mammary0 (0)1 (2)<0.001 (<0.001>999)0.99  
Time of dayb      
Morning/afternoon38 (83)44 (98)1.00   
After hours8 (17)1 (2)8.00 (1.0063.96)0.05  
Indication for PICC   0.80a  
Infectiond20 (43)17 (37)1.00   
Pneumonia5 (11)6 (13)0.60 (0.142.56)0.49  
Chemotherapy3 (7)0 (0)>999 (<0.001>999)0.99  
IV access14 (30)20 (43)0.58 (0.231.44)0.24  
Total parenteral nutrition4 (9)3 (7)1.22 (0.197.70)0.83  

DISCUSSION

The goal of this study was to identify factors related to PICC placement that place the general population of patients at risk. The type and rate of complications associated with PICCs in this study were similar to those previously reported in the literature including catheter‐related infection and thrombosis.[10, 32] Two unique risk factors, not well recognized previously,[10, 27, 28, 33] were observed in this study: malnutrition and after‐hours placement. Malnutrition, defined as serum albumin <3 g/dL was associated with an increase in PICC‐related complications (such as catheter‐associated bloodstream infections and cellulitis) and catheter‐related thrombosis. Malnutrition itself has long been associated with a decreased resistance to infection[34]; in addition, low serum albumin may also be a marker of the presence of other severe comorbidities, which may contribute to increased risk of thrombosis. It has been noted in previous studies that critical illness increases risk of thrombosis.[15] Despite an exhaustive search of the literature, we have been unable to find additional studies examining the extent to which malnutrition may impact PICC‐associated complications.

After‐hours placement was also associated with increased nonmechanical complications, as well as catheter‐related thrombosis. In an effort to improve both patient and consulting provider satisfaction and provide more expedient service, PICCs were often placed after hours (between 5 pm and 8 am) by both interventional radiology (n=14) and internal medicine (n=15) teams.

LOS has been associated with PICC placement complications in other studies.[12] In both primary and secondary analyses, hospital stays >30 days were associated with a higher risk of complications than hospitalizations <7 days. In light of the clinical significance of catheter‐related thrombosis, a subgroup analysis of patients with an LOS >30 days was conducted. The conditional univariate regression analysis showed an increased risk with greater LOS, malnutrition, and after‐hours placement. Use of anticoagulant or antiplatelet agents were associated with decreased risk of thrombosis (Table 4). The association between LOS and PICC‐related thrombosis is consistent with findings from Evans et al. involving 1728 patients in a similar center.[12] In these circumstances, increased LOS may be a surrogate marker for increased severity of illness, in that those patients who are more ill require lengthier hospitalizations. In a systematic review and meta‐analysis, Chopra et al. observed that increased severity of illness correlated with higher rates of catheter‐associated thrombosis, which is supportive of these findings.[15]

In the multivariate logistic regression analysis, BMI >30 was associated with a statistically significant increased risk for PICC‐associated complications after adjusting for anticoagulation and time of placement (Table 2). In the secondary analysis, where patients with mechanical complications were removed, BMI >30 was no longer associated with an increased risk for PICC‐associated complications (Table 3). This suggests that patients with a BMI >30 had an increased risk of mechanical complications, but were not necessarily at increased risk of developing other complications, such as catheter‐related thrombosis, infection, or bleeding. This finding is congruent with studies by Evans et al.,[12] who found no association between BMI and catheter‐associated thrombosis. Our association between BMI and complications is unique; to date, there are few additional studies that examine the extent to which BMI impacts the rate and type of complications associated with PICCs. At this time, the mechanism of the association between mechanical complications (such as inadvertent catheter removal or mechanical malfunction) and BMI is uncertain and warrants further investigation.

Use of Anticoagulant Agents

Anticoagulant (ie, any agent used for DVT prophylaxis or therapeutic anticoagulation) or antiplatelet agent use at the time of PICC placement and during the patient's hospitalization was associated with a decreased risk of thrombosis in our analysis. However, it should be noted that no specific anticoagulant agent was studied, and that antiplatelet agents were included in this analysis, unlike that of Evans et al.[12] Although current literature in oncologic populations, as well as the evidence‐based clinical practice guidelines, recommend against routine use of venous thromboprophylaxis in patients with central venous catheters,[33, 35, 36, 37] we believe this deserves further study, particularly in light of conflicting data in this area.[38, 39] Evans et al.[12] noted that although use of anticoagulants initially appeared to be associated with greater incidence of upper extremity venous thrombosis, when previous diagnosis of DVT was removed from the analysis the association was no longer significant.

In our analyses, no associations between catheter size, choice of venous access, history of previous deep venous thrombosis, or history of malignancy and risk for complications were found. Our findings differed from previous studies, where a relationship between increasing catheter bore size and site of access have been associated with increased PICC‐related thrombosis or other complications.[12, 20, 40, 41] There were also no significant differences in risk for complications between provider teams (eg, internal medicine, radiology, nursing) for PICCs placed during the morning or afternoon, which is consistent with findings by Funk et al.[1] Yet, after‐hours placement of PICCs was associated with greater complications than daytime placement. Although the exploration of factors associated with after‐hours placement was beyond the scope of this study, the findings from this study caused the authors, primarily comprised of members of the internal medicine inpatient medicine division, to reexamine the division's protocol on PICC placement. A consensus decision was made to discontinue after‐hours placement of PICCs by internal medicine teams in an effort to promote patient safety until further data could be collected. As a result, internal medicine teams no longer place PICCs after regular working hours at our institution.

Limitations

Limitations include the categorization of antiplatelet and anticoagulant agents together. We did not distinguish between high‐ and low‐dose aspirin, nor did we distinguish between therapeutic dosing of heparin and low‐molecular‐weight heparin versus DVT prophylaxis dosing. Additionally, for patients who were on warfarin or heparin drip, we did not evaluate for therapeutic range of international normalized ratio or partial thromboplastin time, as this was beyond the present scope of this study. In addition, malnutrition defined by albumin alone may have been somewhat narrow, as conditions aside from malnutrition can impact albumin levels. In future evaluations, this relationship may be clarified by including other determinants of clinical malnutrition including BMI <18 or the measurement of prealbumin. For determination of after‐hours placement of PICCs, we relied upon time of procedure dictation, assuming that all dictations immediately followed catheter placement. If there was a lapse in time between catheter placement and dictation, the category may have been recorded in error. Another limitation of after‐hours categorization was that we were unable to determine whether the PICC was placed on a weekend or holiday.

CONCLUSIONS AND FUTURE DIRECTIONS

Our results suggest that more stringent screening of patients undergoing PICC placement may reduce the risk of complications, with special attention to characteristics such as BMI >30, increased LOS, and protein‐calorie malnutrition (albumin <3). Furthermore, placement of PICC lines in emergent or after‐hours settings should be carefully considered and weighed against relative risks of central venous catheter placement. Further examination of the role anticoagulant and antiplatelet agents may have in the prevention of catheter‐related thrombosis should be undertaken. We hope that the identification of these risk factors will decrease the rate of complications and ultimately enhance patient safety and satisfaction.

Acknowledgments

The authors sincerely thank Glen Cryer, Publications Manager, Baylor Scott & White Health, for his assistance with this article.

Disclosures: Nothing to report.

References
  1. Funk D, Gray J, Plourde PJ. Two‐year trends of peripherally inserted central catheter‐line complications at a tertiary‐care hospital: role of nursing expertise. Infect Control Hosp Epidemiol. 2001;22:377379.
  2. Ng PK, Ault MJ, Maldonado LS. Peripherally inserted central catheters in the intensive care unit. J Intensive Care Med. 1996;11:4954.
  3. Lam S, Scannell R, Roessler D, Smith MA. Peripherally inserted central catheters in an acute‐care hospital. Arch Intern Med. 1994;154:18331837.
  4. Mollee P, Jones M, Stackelroth J, et al. Catheter‐associated bloodstream infection incidence and risk factors in adults with cancer: a prospective cohort study. J Hosp Infect. 2011;78:2630.
  5. Loughran SC, Borzatta M. Peripherally inserted central catheters: a report of 2506 catheter days. JPEN J Parenter Enteral Nutr. 1995;19:133136.
  6. Ng PK, Ault MJ, Ellrodt AG, Maldonado L. Peripherally inserted central catheters in general medicine. Mayo Clin Proc. 1997;72:225233.
  7. Neuman ML, Murphy BD, Rosen MP. Bedside placement of peripherally inserted central catheters: a cost‐effectiveness analysis. Radiology. 1998;206:423428.
  8. Moser KM, Fedullo PF, LittleJohn JK, Crawford R. Frequent asymptomatic pulmonary embolism in patients with deep venous thrombosis. JAMA. 1994;271:223225.
  9. Miller KD, Dietrick CL. Experience with PICC at a university medical center. J Intraven Nurs. 1997;20:141147.
  10. Prandoni P, Polistena P, Bernardi E, et al. Upper‐extremity deep vein thrombosis. Risk factors, diagnosis, and complications. Arch Intern Med. 1997;157:5762.
  11. Butler PJ, Sood S, Mojibian H, Tal MG. Previous PICC placement may be associated with catheter‐related infections in hemodialysis patients. Cardiovasc Intervent Radiol. 2011;34:120123.
  12. Evans RS, Sharp JH, Linford LH, et al. Risk of symptomatic DVT associated with peripherally inserted central catheters. Chest. 2010;138:803810.
  13. Butterfield S. Be picky about PICCs. ACP Hospitalist, American College of Physicians website. Available at: http://www.acphospitalist.org/archives/2013/09/coverstory.htm. Accessed January 4, 2014.
  14. Chopra V, O'Horo JC, Rogers MA, Maki DG, Safdar N. The risk of bloodstream infection associated with peripherally inserted central catheters compared with central venous catheters in adults: a systematic review and meta‐analysis. Infect Control Hosp Epidemiol. 2013;34:908918.
  15. Chopra V, Anand S, Hickner A, et al. Risk of venous thromboembolism associated with peripherally inserted central catheters: a systematic review and meta‐analysis. Lancet. 2013;382:311325.
  16. Kahn S, Lim W, Dunn AS, et al. Prevention of VTE in nonsurgical patients: antithrombotic therapy and prevention of thrombosis, 9th ed: American College of Chest Physicians evidence‐based practice guidelines. Chest. 2012;141(2 suppl):e195Se226S.
  17. Fletcher JJ, Stetler W, Wilson TJ. The clinical significance of peripherally inserted central venous catheter‐related deep vein thrombosis. Neurocrit Care. 2011;15:454460.
  18. Itkin M, Mondshein JI, Stavropoulos WS, Shlanski‐Goldberg RD, Soulen MA, Trerotola SO. Peripherally inserted central catheter thrombosis‐reverse tapered versus nontapered catheters: a randomized controlled study. J Vasc Interv Radiol. 2014;25:8591.
  19. Kerr TM, Lutter KS, Moeller DM, et al. Upper extremity venous thrombosis diagnosed by duplex scanning. Am J Surg. 1990;160:202206.
  20. Evans RS, Sharp JH, Linford LH, et al. Reduction of peripherally inserted central catheter‐associated DVT. Chest. 2013;143:627633.
  21. Goldhaber SZ. Preventing DVT in peripherally inserted central catheters. Chest. 2013;143:589590.
  22. Abdullah BJ, Mohammad N, Sangkar JV, et al. Incidence of upper limb venous thrombosis associated with peripherally inserted central catheters (PICC). Br J Radiol. 2005;78:596600.
  23. Evans RS, Linford LH, Sharp JH, White G, Lloyd JF, Weaver LK. Computer identification of symptomatic deep venous thrombosis associated with peripherally inserted central catheters. AMIA Annu Symp Proc. 2007:226230.
  24. Grove JR, Pevec WC. Venous thrombosis related to peripherally inserted central catheters. J Vasc Interv Radiol. 2000;11:837884.
  25. Cadman A, Lawrance JA, Fitzsimmons L, Spencer‐Shaw A, Swindell R. To clot or not to clot? That is the question in central venous catheters. Clin Radiol. 2004;59:349355.
  26. Paauw JD, Borders H, Ingalls N, et al. The Incidence of PICC line–associated thrombosis with and without the use of prophylactic anticoagulants. JPEN J Parenter Enteral Nutr. 2008;32:443447.
  27. Tian G, Zhu Y, Qi L, Guo F, Xu H. Efficacy of multifaceted interventions in reducing complications of peripherally inserted central catheter in adult oncology patients. Support Care Cancer. 2010;18:12931298.
  28. King MM, Rasnake MS, Rodriquez RG, Riley NJ, Stamm JA. Peripherally inserted central venous catheter‐associated thrombosis: retrospective analysis of clinical risk factors in adult patients. South Med J. 2006;99:10731077.
  29. Goodwin ML. The Seldinger method for PICC insertion. J Intraven Nurs. 1989;12:238243.
  30. Allen AW, Megargell JL, Brown DB, et al. Venous thrombosis associated with the placement of peripherally inserted central catheters. J Vasc Interv Radiol. 2000;11:13091314.
  31. Morley JE. Protein‐energy undernutrition. Merk Manual of Diagnosis and Therapy. 18th ed. Available at: http://www.merckmanuals.com/professional/nutritional_disorders/undernutrition/protein‐energy_undernutrition.html. Accessed May 26, 2013.
  32. Lobo BL, Vaidean G, Broyles J, Reaves AB, Shorr RI. Risk of venous thromboembolism in hospitalized patients with peripherally inserted central catheters. J Hosp Med. 2009;4:417422.
  33. Marnejon T, Angelo D, Abu Abdou A, Gemmel D. Risk factors for upper extremity venous thrombosis associated with peripherally inserted central venous catheters. J Vasc Access. 2012;13:231238.
  34. Scrimshaw NS. Historical concepts of interactions, synergism and antagonism between nutrition and infection. J Nutr. 2003;133:316S321S.
  35. Debourdeau P, Kassab Chahmi D, Gal G, et al. 2008 SOR guidelines for the prevention and treatment of thrombosis associated with central venous catheters in patients with cancer: report from the working group. Ann Oncol. 2009;20:14591471.
  36. Chaukiyal P, Nautiyal A, Radhakrishnan S, Singh S, Navaneethan SD. Thromboprophylaxis in cancer patients with central venous catheters. A systematic review and meta‐analysis. Thromb Haemost. 2008;99:3843.
  37. Carrier M, Tay J, Fergusson D, Wells PS. Thromboprophylaxis for catheter‐related thrombosis in patients with cancer: a systematic review of the randomized, controlled trials. J Thromb Haemost. 2007;5:25522554.
  38. Boraks P, Seale J, Price J, et al. Prevention of central venous catheter associated thrombosis using minidose warfarin in patients with haematological malignancies. Br J Haematol. 1998;10:483486.
  39. Bern HM, Lokich JJ, Wallach SR, et al. Very low doses of warfarin can prevent thrombosis in central venous catheters. A randomized prospective trial. Ann Intern Med. 1990;112:423428.
  40. Loewenthal MR, Dobson PM, Starkey RE, Dagg SA, Petersen A, Boyle MJ. The peripherally inserted central catheter (PICC): a prospective study of its natural history after cubital fossa insertion. Anesth Intensive Care. 2002;30:2124.
  41. Smith JP. Thrombotic complications in intravenous access. J Intraven Nurs. 1998;21:96100.
References
  1. Funk D, Gray J, Plourde PJ. Two‐year trends of peripherally inserted central catheter‐line complications at a tertiary‐care hospital: role of nursing expertise. Infect Control Hosp Epidemiol. 2001;22:377379.
  2. Ng PK, Ault MJ, Maldonado LS. Peripherally inserted central catheters in the intensive care unit. J Intensive Care Med. 1996;11:4954.
  3. Lam S, Scannell R, Roessler D, Smith MA. Peripherally inserted central catheters in an acute‐care hospital. Arch Intern Med. 1994;154:18331837.
  4. Mollee P, Jones M, Stackelroth J, et al. Catheter‐associated bloodstream infection incidence and risk factors in adults with cancer: a prospective cohort study. J Hosp Infect. 2011;78:2630.
  5. Loughran SC, Borzatta M. Peripherally inserted central catheters: a report of 2506 catheter days. JPEN J Parenter Enteral Nutr. 1995;19:133136.
  6. Ng PK, Ault MJ, Ellrodt AG, Maldonado L. Peripherally inserted central catheters in general medicine. Mayo Clin Proc. 1997;72:225233.
  7. Neuman ML, Murphy BD, Rosen MP. Bedside placement of peripherally inserted central catheters: a cost‐effectiveness analysis. Radiology. 1998;206:423428.
  8. Moser KM, Fedullo PF, LittleJohn JK, Crawford R. Frequent asymptomatic pulmonary embolism in patients with deep venous thrombosis. JAMA. 1994;271:223225.
  9. Miller KD, Dietrick CL. Experience with PICC at a university medical center. J Intraven Nurs. 1997;20:141147.
  10. Prandoni P, Polistena P, Bernardi E, et al. Upper‐extremity deep vein thrombosis. Risk factors, diagnosis, and complications. Arch Intern Med. 1997;157:5762.
  11. Butler PJ, Sood S, Mojibian H, Tal MG. Previous PICC placement may be associated with catheter‐related infections in hemodialysis patients. Cardiovasc Intervent Radiol. 2011;34:120123.
  12. Evans RS, Sharp JH, Linford LH, et al. Risk of symptomatic DVT associated with peripherally inserted central catheters. Chest. 2010;138:803810.
  13. Butterfield S. Be picky about PICCs. ACP Hospitalist, American College of Physicians website. Available at: http://www.acphospitalist.org/archives/2013/09/coverstory.htm. Accessed January 4, 2014.
  14. Chopra V, O'Horo JC, Rogers MA, Maki DG, Safdar N. The risk of bloodstream infection associated with peripherally inserted central catheters compared with central venous catheters in adults: a systematic review and meta‐analysis. Infect Control Hosp Epidemiol. 2013;34:908918.
  15. Chopra V, Anand S, Hickner A, et al. Risk of venous thromboembolism associated with peripherally inserted central catheters: a systematic review and meta‐analysis. Lancet. 2013;382:311325.
  16. Kahn S, Lim W, Dunn AS, et al. Prevention of VTE in nonsurgical patients: antithrombotic therapy and prevention of thrombosis, 9th ed: American College of Chest Physicians evidence‐based practice guidelines. Chest. 2012;141(2 suppl):e195Se226S.
  17. Fletcher JJ, Stetler W, Wilson TJ. The clinical significance of peripherally inserted central venous catheter‐related deep vein thrombosis. Neurocrit Care. 2011;15:454460.
  18. Itkin M, Mondshein JI, Stavropoulos WS, Shlanski‐Goldberg RD, Soulen MA, Trerotola SO. Peripherally inserted central catheter thrombosis‐reverse tapered versus nontapered catheters: a randomized controlled study. J Vasc Interv Radiol. 2014;25:8591.
  19. Kerr TM, Lutter KS, Moeller DM, et al. Upper extremity venous thrombosis diagnosed by duplex scanning. Am J Surg. 1990;160:202206.
  20. Evans RS, Sharp JH, Linford LH, et al. Reduction of peripherally inserted central catheter‐associated DVT. Chest. 2013;143:627633.
  21. Goldhaber SZ. Preventing DVT in peripherally inserted central catheters. Chest. 2013;143:589590.
  22. Abdullah BJ, Mohammad N, Sangkar JV, et al. Incidence of upper limb venous thrombosis associated with peripherally inserted central catheters (PICC). Br J Radiol. 2005;78:596600.
  23. Evans RS, Linford LH, Sharp JH, White G, Lloyd JF, Weaver LK. Computer identification of symptomatic deep venous thrombosis associated with peripherally inserted central catheters. AMIA Annu Symp Proc. 2007:226230.
  24. Grove JR, Pevec WC. Venous thrombosis related to peripherally inserted central catheters. J Vasc Interv Radiol. 2000;11:837884.
  25. Cadman A, Lawrance JA, Fitzsimmons L, Spencer‐Shaw A, Swindell R. To clot or not to clot? That is the question in central venous catheters. Clin Radiol. 2004;59:349355.
  26. Paauw JD, Borders H, Ingalls N, et al. The Incidence of PICC line–associated thrombosis with and without the use of prophylactic anticoagulants. JPEN J Parenter Enteral Nutr. 2008;32:443447.
  27. Tian G, Zhu Y, Qi L, Guo F, Xu H. Efficacy of multifaceted interventions in reducing complications of peripherally inserted central catheter in adult oncology patients. Support Care Cancer. 2010;18:12931298.
  28. King MM, Rasnake MS, Rodriquez RG, Riley NJ, Stamm JA. Peripherally inserted central venous catheter‐associated thrombosis: retrospective analysis of clinical risk factors in adult patients. South Med J. 2006;99:10731077.
  29. Goodwin ML. The Seldinger method for PICC insertion. J Intraven Nurs. 1989;12:238243.
  30. Allen AW, Megargell JL, Brown DB, et al. Venous thrombosis associated with the placement of peripherally inserted central catheters. J Vasc Interv Radiol. 2000;11:13091314.
  31. Morley JE. Protein‐energy undernutrition. Merk Manual of Diagnosis and Therapy. 18th ed. Available at: http://www.merckmanuals.com/professional/nutritional_disorders/undernutrition/protein‐energy_undernutrition.html. Accessed May 26, 2013.
  32. Lobo BL, Vaidean G, Broyles J, Reaves AB, Shorr RI. Risk of venous thromboembolism in hospitalized patients with peripherally inserted central catheters. J Hosp Med. 2009;4:417422.
  33. Marnejon T, Angelo D, Abu Abdou A, Gemmel D. Risk factors for upper extremity venous thrombosis associated with peripherally inserted central venous catheters. J Vasc Access. 2012;13:231238.
  34. Scrimshaw NS. Historical concepts of interactions, synergism and antagonism between nutrition and infection. J Nutr. 2003;133:316S321S.
  35. Debourdeau P, Kassab Chahmi D, Gal G, et al. 2008 SOR guidelines for the prevention and treatment of thrombosis associated with central venous catheters in patients with cancer: report from the working group. Ann Oncol. 2009;20:14591471.
  36. Chaukiyal P, Nautiyal A, Radhakrishnan S, Singh S, Navaneethan SD. Thromboprophylaxis in cancer patients with central venous catheters. A systematic review and meta‐analysis. Thromb Haemost. 2008;99:3843.
  37. Carrier M, Tay J, Fergusson D, Wells PS. Thromboprophylaxis for catheter‐related thrombosis in patients with cancer: a systematic review of the randomized, controlled trials. J Thromb Haemost. 2007;5:25522554.
  38. Boraks P, Seale J, Price J, et al. Prevention of central venous catheter associated thrombosis using minidose warfarin in patients with haematological malignancies. Br J Haematol. 1998;10:483486.
  39. Bern HM, Lokich JJ, Wallach SR, et al. Very low doses of warfarin can prevent thrombosis in central venous catheters. A randomized prospective trial. Ann Intern Med. 1990;112:423428.
  40. Loewenthal MR, Dobson PM, Starkey RE, Dagg SA, Petersen A, Boyle MJ. The peripherally inserted central catheter (PICC): a prospective study of its natural history after cubital fossa insertion. Anesth Intensive Care. 2002;30:2124.
  41. Smith JP. Thrombotic complications in intravenous access. J Intraven Nurs. 1998;21:96100.
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Screening for novel risk factors related to peripherally inserted central catheter‐associated complications
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Address for correspondence and reprint requests: Jennifer Moran, MD, Department of Medicine, Baylor Scott Telephone: 254‐724‐0454; Fax: 254‐724‐0983; E‐mail: [email protected]
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Study elucidates enzyme’s role in cell survival

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Study elucidates enzyme’s role in cell survival

Apoptosis in cancer cells

Credit: Egelberg

Investigators say they have solved the mystery of an enzyme’s role in cell survival, thereby offering clues as to how the immune system fights infection and pointing to possible strategies for treating cancers.

The enzyme, receptor-interacting protein kinase 1 (RIPK1), is known to play a pivotal role in survival after birth.

But the new research, published in Cell, reveals that RIPK1 inhibits the pathways that control apoptosis and necroptosis.

By removing different components of each pathway in different combinations, the investigators demonstrated that, after birth, RIPK1 helps cells maintain a balanced response to signals that promote either pathway.

“We are learning that, in disease, this balancing act can be perturbed to produce damage and cell death,” said study author Douglas Green, PhD, of the St Jude Children’s Research Hospital in Memphis, Tennessee.

The results resolve long-standing questions about RIPK1’s role in cell survival and provide clues about how the immune system might use these pathways to contain infections.

The findings have also prompted the researchers to launch an investigation into whether RIPK1 could be harnessed to kill cancer cells or provide insight into tumor development.

“This study fundamentally changes the way we think about RIPK1, a molecule that we care about because it is required for life,” Dr Green said. “The results helped us identify new pathways involved in regulating programmed cell death and suggest that we might be able to develop cancer therapies that target these pathways or engage them in other ways to advance treatment of a range of diseases.”

The report builds on previous research from Dr Green’s lab regarding regulation of the pathways that control apoptosis and necroptosis. The investigators knew that apoptosis is driven by the enzyme caspase-8, which forms a complex with FADD and other proteins.

And necroptosis involves a pathway orchestrated by the enzyme receptor-interacting protein kinase 3 (RIPK3). Before birth, RIPK1 works through RIPK3 to trigger cell death by necroptosis, but, until now, the enzyme’s primary role after birth was uncertain.

So the investigators bred mice lacking different combinations of genes for RIPK1, RIPK3, caspase-8, FADD and other components of both the apoptotic and necroptotic pathways.

Mice lacking RIPK1 died. Mice missing 2 genes—RIPK1 plus RIPK3 or RIPK1 plus caspase-8 or FADD—also died soon after birth.

However, mice survived and developed normally when the investigators removed 3 genes—RIPK1, RIPK3, and either caspase-8 or FADD.

“The fact that the mice survived was totally unexpected and made us rethink how these pathways work,” Dr Green said.

The results also demonstrated that other pathways must exist in cells to maintain a balanced response to signals pushing for cell death via apoptosis or necroptosis.

Evidence in this study, for example, suggested one possible new pathway that triggered necroptosis using interferon and other elements of the immune response to infections.

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Topics

Apoptosis in cancer cells

Credit: Egelberg

Investigators say they have solved the mystery of an enzyme’s role in cell survival, thereby offering clues as to how the immune system fights infection and pointing to possible strategies for treating cancers.

The enzyme, receptor-interacting protein kinase 1 (RIPK1), is known to play a pivotal role in survival after birth.

But the new research, published in Cell, reveals that RIPK1 inhibits the pathways that control apoptosis and necroptosis.

By removing different components of each pathway in different combinations, the investigators demonstrated that, after birth, RIPK1 helps cells maintain a balanced response to signals that promote either pathway.

“We are learning that, in disease, this balancing act can be perturbed to produce damage and cell death,” said study author Douglas Green, PhD, of the St Jude Children’s Research Hospital in Memphis, Tennessee.

The results resolve long-standing questions about RIPK1’s role in cell survival and provide clues about how the immune system might use these pathways to contain infections.

The findings have also prompted the researchers to launch an investigation into whether RIPK1 could be harnessed to kill cancer cells or provide insight into tumor development.

“This study fundamentally changes the way we think about RIPK1, a molecule that we care about because it is required for life,” Dr Green said. “The results helped us identify new pathways involved in regulating programmed cell death and suggest that we might be able to develop cancer therapies that target these pathways or engage them in other ways to advance treatment of a range of diseases.”

The report builds on previous research from Dr Green’s lab regarding regulation of the pathways that control apoptosis and necroptosis. The investigators knew that apoptosis is driven by the enzyme caspase-8, which forms a complex with FADD and other proteins.

And necroptosis involves a pathway orchestrated by the enzyme receptor-interacting protein kinase 3 (RIPK3). Before birth, RIPK1 works through RIPK3 to trigger cell death by necroptosis, but, until now, the enzyme’s primary role after birth was uncertain.

So the investigators bred mice lacking different combinations of genes for RIPK1, RIPK3, caspase-8, FADD and other components of both the apoptotic and necroptotic pathways.

Mice lacking RIPK1 died. Mice missing 2 genes—RIPK1 plus RIPK3 or RIPK1 plus caspase-8 or FADD—also died soon after birth.

However, mice survived and developed normally when the investigators removed 3 genes—RIPK1, RIPK3, and either caspase-8 or FADD.

“The fact that the mice survived was totally unexpected and made us rethink how these pathways work,” Dr Green said.

The results also demonstrated that other pathways must exist in cells to maintain a balanced response to signals pushing for cell death via apoptosis or necroptosis.

Evidence in this study, for example, suggested one possible new pathway that triggered necroptosis using interferon and other elements of the immune response to infections.

Apoptosis in cancer cells

Credit: Egelberg

Investigators say they have solved the mystery of an enzyme’s role in cell survival, thereby offering clues as to how the immune system fights infection and pointing to possible strategies for treating cancers.

The enzyme, receptor-interacting protein kinase 1 (RIPK1), is known to play a pivotal role in survival after birth.

But the new research, published in Cell, reveals that RIPK1 inhibits the pathways that control apoptosis and necroptosis.

By removing different components of each pathway in different combinations, the investigators demonstrated that, after birth, RIPK1 helps cells maintain a balanced response to signals that promote either pathway.

“We are learning that, in disease, this balancing act can be perturbed to produce damage and cell death,” said study author Douglas Green, PhD, of the St Jude Children’s Research Hospital in Memphis, Tennessee.

The results resolve long-standing questions about RIPK1’s role in cell survival and provide clues about how the immune system might use these pathways to contain infections.

The findings have also prompted the researchers to launch an investigation into whether RIPK1 could be harnessed to kill cancer cells or provide insight into tumor development.

“This study fundamentally changes the way we think about RIPK1, a molecule that we care about because it is required for life,” Dr Green said. “The results helped us identify new pathways involved in regulating programmed cell death and suggest that we might be able to develop cancer therapies that target these pathways or engage them in other ways to advance treatment of a range of diseases.”

The report builds on previous research from Dr Green’s lab regarding regulation of the pathways that control apoptosis and necroptosis. The investigators knew that apoptosis is driven by the enzyme caspase-8, which forms a complex with FADD and other proteins.

And necroptosis involves a pathway orchestrated by the enzyme receptor-interacting protein kinase 3 (RIPK3). Before birth, RIPK1 works through RIPK3 to trigger cell death by necroptosis, but, until now, the enzyme’s primary role after birth was uncertain.

So the investigators bred mice lacking different combinations of genes for RIPK1, RIPK3, caspase-8, FADD and other components of both the apoptotic and necroptotic pathways.

Mice lacking RIPK1 died. Mice missing 2 genes—RIPK1 plus RIPK3 or RIPK1 plus caspase-8 or FADD—also died soon after birth.

However, mice survived and developed normally when the investigators removed 3 genes—RIPK1, RIPK3, and either caspase-8 or FADD.

“The fact that the mice survived was totally unexpected and made us rethink how these pathways work,” Dr Green said.

The results also demonstrated that other pathways must exist in cells to maintain a balanced response to signals pushing for cell death via apoptosis or necroptosis.

Evidence in this study, for example, suggested one possible new pathway that triggered necroptosis using interferon and other elements of the immune response to infections.

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Method reveals new targets of p53

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Tumor cells producing p53

Credit: A.T. Tikhonenko

A novel sequencing technique has allowed researchers to identify direct targets of p53, providing new insight into this gene’s anticancer activity.

The research, published in eLife, revealed nearly 200 genes that were directly regulated by p53, and many of these had never been identified before.

The study’s authors said this work lays the foundation for investigations into which of these genes are necessary for p53’s cancer-killing effects and how cancer cells evade these genes.

The researchers noted that all cancers must deal with p53’s antitumor effects. Generally, there are 2 ways they do this: by mutating p53 directly or by producing the protein MDM2, which inhibits p53 function. With the current study, the team explored the second strategy.

“MDM2 inhibitors, which are through phase 1 human trials, effectively activate p53 but manage to kill only about 1 in 20 tumors,” said study author Joaquín Espinosa, PhD, of the University of Colorado in Boulder.

“The question is why. What else is happening in these cancer cells that allow them to evade p53?”

According to the researchers, the answer is in the downstream effects of p53. The gene sets in motion a cascade of events that lead to cancer cell destruction. But it has been unclear exactly which other genes are directly activated by p53.

To identify genetic targets of p53, Dr Espinosa and his colleagues used a technique called Global Run-On Sequencing (GRO-Seq). Unlike other methods, GRO-Seq measures new RNA being created, not overall RNA levels.

“Many teams around the world have been getting cancer cells, treating them with MDM2 inhibitors, and waiting hours and hours to see what genes turn on, and then, only imprecisely,” Dr Espinosa said. “GRO-Seq lets us do it in minutes, and the discoveries are massive.”

The technique generates a large quantity of data because it requires counting tens of thousands of RNA molecules before and after p53 activation. So this research required designing algorithms to sort through the data, as well as a computational biologist driving a supercomputer.

But the researchers were able to pinpoint new genes directly regulated by p53. And they believe this could aid the future development of cancer-fighting strategies.

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Tumor cells producing p53

Credit: A.T. Tikhonenko

A novel sequencing technique has allowed researchers to identify direct targets of p53, providing new insight into this gene’s anticancer activity.

The research, published in eLife, revealed nearly 200 genes that were directly regulated by p53, and many of these had never been identified before.

The study’s authors said this work lays the foundation for investigations into which of these genes are necessary for p53’s cancer-killing effects and how cancer cells evade these genes.

The researchers noted that all cancers must deal with p53’s antitumor effects. Generally, there are 2 ways they do this: by mutating p53 directly or by producing the protein MDM2, which inhibits p53 function. With the current study, the team explored the second strategy.

“MDM2 inhibitors, which are through phase 1 human trials, effectively activate p53 but manage to kill only about 1 in 20 tumors,” said study author Joaquín Espinosa, PhD, of the University of Colorado in Boulder.

“The question is why. What else is happening in these cancer cells that allow them to evade p53?”

According to the researchers, the answer is in the downstream effects of p53. The gene sets in motion a cascade of events that lead to cancer cell destruction. But it has been unclear exactly which other genes are directly activated by p53.

To identify genetic targets of p53, Dr Espinosa and his colleagues used a technique called Global Run-On Sequencing (GRO-Seq). Unlike other methods, GRO-Seq measures new RNA being created, not overall RNA levels.

“Many teams around the world have been getting cancer cells, treating them with MDM2 inhibitors, and waiting hours and hours to see what genes turn on, and then, only imprecisely,” Dr Espinosa said. “GRO-Seq lets us do it in minutes, and the discoveries are massive.”

The technique generates a large quantity of data because it requires counting tens of thousands of RNA molecules before and after p53 activation. So this research required designing algorithms to sort through the data, as well as a computational biologist driving a supercomputer.

But the researchers were able to pinpoint new genes directly regulated by p53. And they believe this could aid the future development of cancer-fighting strategies.

Tumor cells producing p53

Credit: A.T. Tikhonenko

A novel sequencing technique has allowed researchers to identify direct targets of p53, providing new insight into this gene’s anticancer activity.

The research, published in eLife, revealed nearly 200 genes that were directly regulated by p53, and many of these had never been identified before.

The study’s authors said this work lays the foundation for investigations into which of these genes are necessary for p53’s cancer-killing effects and how cancer cells evade these genes.

The researchers noted that all cancers must deal with p53’s antitumor effects. Generally, there are 2 ways they do this: by mutating p53 directly or by producing the protein MDM2, which inhibits p53 function. With the current study, the team explored the second strategy.

“MDM2 inhibitors, which are through phase 1 human trials, effectively activate p53 but manage to kill only about 1 in 20 tumors,” said study author Joaquín Espinosa, PhD, of the University of Colorado in Boulder.

“The question is why. What else is happening in these cancer cells that allow them to evade p53?”

According to the researchers, the answer is in the downstream effects of p53. The gene sets in motion a cascade of events that lead to cancer cell destruction. But it has been unclear exactly which other genes are directly activated by p53.

To identify genetic targets of p53, Dr Espinosa and his colleagues used a technique called Global Run-On Sequencing (GRO-Seq). Unlike other methods, GRO-Seq measures new RNA being created, not overall RNA levels.

“Many teams around the world have been getting cancer cells, treating them with MDM2 inhibitors, and waiting hours and hours to see what genes turn on, and then, only imprecisely,” Dr Espinosa said. “GRO-Seq lets us do it in minutes, and the discoveries are massive.”

The technique generates a large quantity of data because it requires counting tens of thousands of RNA molecules before and after p53 activation. So this research required designing algorithms to sort through the data, as well as a computational biologist driving a supercomputer.

But the researchers were able to pinpoint new genes directly regulated by p53. And they believe this could aid the future development of cancer-fighting strategies.

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Long-term follow-up shows 22% survival rate for advanced GIST

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Long-term follow-up shows 22% survival rate for advanced GIST

CHICAGO – More than 10 years on, nearly one-fourth of patients with gastrointestinal stromal tumors treated initially with imatinib are still alive, according to results from a collaborative trial reported at the annual meeting of the American Society of Clinical Oncology.

"A significant fraction of patients can survive for more than 10 years with imatinib [Gleevec] as their initial therapy for advanced GIST; and for almost half as their only systemic therapy for advanced GIST, understanding the pathobiology of these exceptional outcomes will be important to understanding the disease better," said Dr. George Demetri, director of the Center for Sarcoma and Bone Oncology, Dana-Farber Cancer Institute, Boston.

Dr. George D. Demetri

In the 14 years that have ensued since the first patient with GIST received imatinib and had an "extraordinary" response, new drugs in the tyrosine kinase inhibitor (TKI) class have become available for patients whose disease has progressed on the TKI imatinib. The evolution in the treatment of GIST emphasizes the fact that overall survival as a trial endpoint is really "a composite endpoint of on-study and poststudy interventions," Dr. Demetri said at meeting.

In the phase III Southwest Oncology Group (SWOG) Intergroup S0033 trial, initiated in 2000, 746 patients with metastatic or unresectable GIST were randomly assigned to receive daily imatinib at a dose of either 400 mg or 800 mg (400 mg twice daily). Patients in the 400-mg qd group had the option of crossing over to the 800-mg dose at the time of disease progression, and 130 patients chose to do so.

The trial was sparked by the discovery by Japanese investigators in 1998 of gain-of-function mutations in the gene encoding for KIT kinase.

As the SWOG S0033 investigators reported in 2008, median progression-free survival at a median follow-up of 4.5 years was 18 months for patients on the 400-mg dose and 20 months for those receiving 800 mg. The median overall survival was 55 months for patients in the 400-mg arm and 51 months for those in the 800-mg arm.

"A question we ask ourselves is, what accounts for this 33-month survival median difference after objective disease progression? It seems like a long time, which is why we wanted to know what happened to these patients after progression," Dr. Demetri said.

The investigators looked at survival by mutational status. Data on the GIST genotype were available on 395 patients, 282 of whom (71%) had KIT exon 11 mutations, 32 of whom (8%) had KIT exon 9 mutations, and 14 (4%) had other KIT or PDGFRA (platelet-derived growth factor receptor–alpha) mutations. Another 67 patients (17%) had no detectable KIT or PDGFRA mutations.

An analysis of data on these patients at 4.5 months’ median follow-up showed that patients with KIT exon 11 mutations had significantly better overall survival than patients with either wild-type (no mutation) KIT (P = .0011) or exon 9 mutations (P = .049).

Updated data with follow-up out to 10 years shows that patients with the KIT exon 9 mutation had significantly worse overall survival than patients with either exon 11 mutations (P = .0001) or no mutations (P = .047). There was no significant difference between patients with exon 11 mutations and no KIT or PDGFRA mutations.

Other factors significantly associated with overall survival in multivariate analysis included age by decade, male sex, performance status, maximum tumor diameter, and serum albumin (3.5 g/dL or less vs. more than 3.5 g/dL).

An analysis of on-study and postprogression therapies among 137 of 180 long-term survivors (8 years and longer) showed that 67 of the 137 (49%) had taken imatinib continuously as the only long-term therapy.

The remaining 70 patients (51%) had some additional therapy, including systemic therapies such as sunitinib (Sutent; 30% of the 137 patients), sorafenib (Nexavar; 12%), and other agents (31%).

In addition, 41 patients (30%) had metastasectomy or other type of surgery, 10 (7%) had radiofrequency ablation of tumors, and 6 (4%) had radiation therapy.

"We know that the landscape of therapeutic options for GIST has evolved greatly since this early large-scale study. We have new TKI therapies like sunitinib, sorafenib, and regorafenib [Stivarga], which has been approved for patients following progression of first-line imatinib, and we now accept the fact that multidisciplinary management of GIST, with resection of limited sites of oligoclonal resistant disease, is a standard option, with continuation of TKI therapy to control residual, unresectable disease," he said.

"Nonetheless , what the survival curves show us is that new options for management of KIT exon 9 mutant and other resistant genotypes are still needed," he concluded.

 

 

Dr. Jon Trent, director of the bone and soft-tissue program at the University of Miami Sylvester Cancer Center, the invited discussant, commented that "molecular subtyping should be required for all GIST patients."

"Most of all, I think we really need a tool other than the current version of RECIST [Response Evaluation Criteria in Solid Tumors], in order to really identify early makers of response and early markers of progression in our GIST patients," he said.

RECIST criteria often fail to provide useful information about early responses to therapy in GIST, he said.

The study was supported by the National Cancer Institute. Dr. Demetri disclosed serving as a consultant or adviser to ARIAD, Bayer, Novartis, and Pfizer; receiving research funding from Bayer, Novartis, and Pfizer; and receiving other remuneration from Novartis. Dr. Trent disclosed consulting/advising for Ariad, Bayer/Onyx, Novartis, and Pfizer, and receiving honoraria from Pfizer.

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CHICAGO – More than 10 years on, nearly one-fourth of patients with gastrointestinal stromal tumors treated initially with imatinib are still alive, according to results from a collaborative trial reported at the annual meeting of the American Society of Clinical Oncology.

"A significant fraction of patients can survive for more than 10 years with imatinib [Gleevec] as their initial therapy for advanced GIST; and for almost half as their only systemic therapy for advanced GIST, understanding the pathobiology of these exceptional outcomes will be important to understanding the disease better," said Dr. George Demetri, director of the Center for Sarcoma and Bone Oncology, Dana-Farber Cancer Institute, Boston.

Dr. George D. Demetri

In the 14 years that have ensued since the first patient with GIST received imatinib and had an "extraordinary" response, new drugs in the tyrosine kinase inhibitor (TKI) class have become available for patients whose disease has progressed on the TKI imatinib. The evolution in the treatment of GIST emphasizes the fact that overall survival as a trial endpoint is really "a composite endpoint of on-study and poststudy interventions," Dr. Demetri said at meeting.

In the phase III Southwest Oncology Group (SWOG) Intergroup S0033 trial, initiated in 2000, 746 patients with metastatic or unresectable GIST were randomly assigned to receive daily imatinib at a dose of either 400 mg or 800 mg (400 mg twice daily). Patients in the 400-mg qd group had the option of crossing over to the 800-mg dose at the time of disease progression, and 130 patients chose to do so.

The trial was sparked by the discovery by Japanese investigators in 1998 of gain-of-function mutations in the gene encoding for KIT kinase.

As the SWOG S0033 investigators reported in 2008, median progression-free survival at a median follow-up of 4.5 years was 18 months for patients on the 400-mg dose and 20 months for those receiving 800 mg. The median overall survival was 55 months for patients in the 400-mg arm and 51 months for those in the 800-mg arm.

"A question we ask ourselves is, what accounts for this 33-month survival median difference after objective disease progression? It seems like a long time, which is why we wanted to know what happened to these patients after progression," Dr. Demetri said.

The investigators looked at survival by mutational status. Data on the GIST genotype were available on 395 patients, 282 of whom (71%) had KIT exon 11 mutations, 32 of whom (8%) had KIT exon 9 mutations, and 14 (4%) had other KIT or PDGFRA (platelet-derived growth factor receptor–alpha) mutations. Another 67 patients (17%) had no detectable KIT or PDGFRA mutations.

An analysis of data on these patients at 4.5 months’ median follow-up showed that patients with KIT exon 11 mutations had significantly better overall survival than patients with either wild-type (no mutation) KIT (P = .0011) or exon 9 mutations (P = .049).

Updated data with follow-up out to 10 years shows that patients with the KIT exon 9 mutation had significantly worse overall survival than patients with either exon 11 mutations (P = .0001) or no mutations (P = .047). There was no significant difference between patients with exon 11 mutations and no KIT or PDGFRA mutations.

Other factors significantly associated with overall survival in multivariate analysis included age by decade, male sex, performance status, maximum tumor diameter, and serum albumin (3.5 g/dL or less vs. more than 3.5 g/dL).

An analysis of on-study and postprogression therapies among 137 of 180 long-term survivors (8 years and longer) showed that 67 of the 137 (49%) had taken imatinib continuously as the only long-term therapy.

The remaining 70 patients (51%) had some additional therapy, including systemic therapies such as sunitinib (Sutent; 30% of the 137 patients), sorafenib (Nexavar; 12%), and other agents (31%).

In addition, 41 patients (30%) had metastasectomy or other type of surgery, 10 (7%) had radiofrequency ablation of tumors, and 6 (4%) had radiation therapy.

"We know that the landscape of therapeutic options for GIST has evolved greatly since this early large-scale study. We have new TKI therapies like sunitinib, sorafenib, and regorafenib [Stivarga], which has been approved for patients following progression of first-line imatinib, and we now accept the fact that multidisciplinary management of GIST, with resection of limited sites of oligoclonal resistant disease, is a standard option, with continuation of TKI therapy to control residual, unresectable disease," he said.

"Nonetheless , what the survival curves show us is that new options for management of KIT exon 9 mutant and other resistant genotypes are still needed," he concluded.

 

 

Dr. Jon Trent, director of the bone and soft-tissue program at the University of Miami Sylvester Cancer Center, the invited discussant, commented that "molecular subtyping should be required for all GIST patients."

"Most of all, I think we really need a tool other than the current version of RECIST [Response Evaluation Criteria in Solid Tumors], in order to really identify early makers of response and early markers of progression in our GIST patients," he said.

RECIST criteria often fail to provide useful information about early responses to therapy in GIST, he said.

The study was supported by the National Cancer Institute. Dr. Demetri disclosed serving as a consultant or adviser to ARIAD, Bayer, Novartis, and Pfizer; receiving research funding from Bayer, Novartis, and Pfizer; and receiving other remuneration from Novartis. Dr. Trent disclosed consulting/advising for Ariad, Bayer/Onyx, Novartis, and Pfizer, and receiving honoraria from Pfizer.

CHICAGO – More than 10 years on, nearly one-fourth of patients with gastrointestinal stromal tumors treated initially with imatinib are still alive, according to results from a collaborative trial reported at the annual meeting of the American Society of Clinical Oncology.

"A significant fraction of patients can survive for more than 10 years with imatinib [Gleevec] as their initial therapy for advanced GIST; and for almost half as their only systemic therapy for advanced GIST, understanding the pathobiology of these exceptional outcomes will be important to understanding the disease better," said Dr. George Demetri, director of the Center for Sarcoma and Bone Oncology, Dana-Farber Cancer Institute, Boston.

Dr. George D. Demetri

In the 14 years that have ensued since the first patient with GIST received imatinib and had an "extraordinary" response, new drugs in the tyrosine kinase inhibitor (TKI) class have become available for patients whose disease has progressed on the TKI imatinib. The evolution in the treatment of GIST emphasizes the fact that overall survival as a trial endpoint is really "a composite endpoint of on-study and poststudy interventions," Dr. Demetri said at meeting.

In the phase III Southwest Oncology Group (SWOG) Intergroup S0033 trial, initiated in 2000, 746 patients with metastatic or unresectable GIST were randomly assigned to receive daily imatinib at a dose of either 400 mg or 800 mg (400 mg twice daily). Patients in the 400-mg qd group had the option of crossing over to the 800-mg dose at the time of disease progression, and 130 patients chose to do so.

The trial was sparked by the discovery by Japanese investigators in 1998 of gain-of-function mutations in the gene encoding for KIT kinase.

As the SWOG S0033 investigators reported in 2008, median progression-free survival at a median follow-up of 4.5 years was 18 months for patients on the 400-mg dose and 20 months for those receiving 800 mg. The median overall survival was 55 months for patients in the 400-mg arm and 51 months for those in the 800-mg arm.

"A question we ask ourselves is, what accounts for this 33-month survival median difference after objective disease progression? It seems like a long time, which is why we wanted to know what happened to these patients after progression," Dr. Demetri said.

The investigators looked at survival by mutational status. Data on the GIST genotype were available on 395 patients, 282 of whom (71%) had KIT exon 11 mutations, 32 of whom (8%) had KIT exon 9 mutations, and 14 (4%) had other KIT or PDGFRA (platelet-derived growth factor receptor–alpha) mutations. Another 67 patients (17%) had no detectable KIT or PDGFRA mutations.

An analysis of data on these patients at 4.5 months’ median follow-up showed that patients with KIT exon 11 mutations had significantly better overall survival than patients with either wild-type (no mutation) KIT (P = .0011) or exon 9 mutations (P = .049).

Updated data with follow-up out to 10 years shows that patients with the KIT exon 9 mutation had significantly worse overall survival than patients with either exon 11 mutations (P = .0001) or no mutations (P = .047). There was no significant difference between patients with exon 11 mutations and no KIT or PDGFRA mutations.

Other factors significantly associated with overall survival in multivariate analysis included age by decade, male sex, performance status, maximum tumor diameter, and serum albumin (3.5 g/dL or less vs. more than 3.5 g/dL).

An analysis of on-study and postprogression therapies among 137 of 180 long-term survivors (8 years and longer) showed that 67 of the 137 (49%) had taken imatinib continuously as the only long-term therapy.

The remaining 70 patients (51%) had some additional therapy, including systemic therapies such as sunitinib (Sutent; 30% of the 137 patients), sorafenib (Nexavar; 12%), and other agents (31%).

In addition, 41 patients (30%) had metastasectomy or other type of surgery, 10 (7%) had radiofrequency ablation of tumors, and 6 (4%) had radiation therapy.

"We know that the landscape of therapeutic options for GIST has evolved greatly since this early large-scale study. We have new TKI therapies like sunitinib, sorafenib, and regorafenib [Stivarga], which has been approved for patients following progression of first-line imatinib, and we now accept the fact that multidisciplinary management of GIST, with resection of limited sites of oligoclonal resistant disease, is a standard option, with continuation of TKI therapy to control residual, unresectable disease," he said.

"Nonetheless , what the survival curves show us is that new options for management of KIT exon 9 mutant and other resistant genotypes are still needed," he concluded.

 

 

Dr. Jon Trent, director of the bone and soft-tissue program at the University of Miami Sylvester Cancer Center, the invited discussant, commented that "molecular subtyping should be required for all GIST patients."

"Most of all, I think we really need a tool other than the current version of RECIST [Response Evaluation Criteria in Solid Tumors], in order to really identify early makers of response and early markers of progression in our GIST patients," he said.

RECIST criteria often fail to provide useful information about early responses to therapy in GIST, he said.

The study was supported by the National Cancer Institute. Dr. Demetri disclosed serving as a consultant or adviser to ARIAD, Bayer, Novartis, and Pfizer; receiving research funding from Bayer, Novartis, and Pfizer; and receiving other remuneration from Novartis. Dr. Trent disclosed consulting/advising for Ariad, Bayer/Onyx, Novartis, and Pfizer, and receiving honoraria from Pfizer.

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gastrointestinal stromal tumors, imatinib, American Society of Clinical Oncology, Gleevec, initial therapy, advanced GIST, Dr. George Demetri, Center for Sarcoma and Bone Oncology, Dana-Farber Cancer Institute, Boston, tyrosine kinase inhibitor, TKI class, Southwest Oncology Group, SWOG Intergroup S0033 trial, metastatic or unresectable GIST, KIT or PDGFRA
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gastrointestinal stromal tumors, imatinib, American Society of Clinical Oncology, Gleevec, initial therapy, advanced GIST, Dr. George Demetri, Center for Sarcoma and Bone Oncology, Dana-Farber Cancer Institute, Boston, tyrosine kinase inhibitor, TKI class, Southwest Oncology Group, SWOG Intergroup S0033 trial, metastatic or unresectable GIST, KIT or PDGFRA
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Major finding: Ten-year overall survival for patients with metastatic or unresectable gastrointestinal tumors (GIST) treated with imatinib was 22%.

Data source: Long-term follow-up of data on 746 patients enrolled in the randomized phase III SWOG Intergroup S0033 trial.

Disclosures: The study was supported by the National Cancer Institute. Dr. Demetri disclosed serving as a consultant or adviser to ARIAD, Bayer, Novartis, and Pfizer; receiving research funding from Bayer, Novartis, and Pfizer; and receiving other remuneration from Novartis. Dr. Trent disclosed consulting/advising for Ariad, Bayer/Onyx, Novartis, and Pfizer, and receiving honoraria from Pfizer.

My patient got arrested! What do I do?

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My patient got arrested! What do I do?

Nonforensic psychiatrists in private practice rarely expect to be dealing with patients involved in the correctional system, but unexpected things happen even with the most carefully chosen patients. I’m writing this column to offer guidance to clinicians facing this situation for the first time, based upon the most common questions I get asked.

The most common situation I hear about is that a patient has missed an appointment, and the clinician hears from a family member that the patient has been arrested. The conscientious doctor wants to make sure that his seriously mentally ill client doesn’t experience an interruption in treatment, and that an appointment will be ready after release. The first challenge is to locate the patient.

Dr. Annette Hanson

In small communities, or when a family member was present at the time of arrest, it’s relatively easy to figure out which detention center or jail the patient was taken to. If the patient was arrested in a large urban area, or even out of state, this can be more of a challenge. Fortunately, many states and even now some local county or city jurisdictions have inmate locator web pages. The website will provide search capabilities to identify anyone currently in custody, and will generally provide a unique booking or inmate number that should be used in any facility communication, along with a date of birth and the address of the facility. Be aware that a large jail with high turnover may not have real-time data capability, meaning that new arrests may not show up on the website for 24 hours.

For psychiatrists who spend a lot of time tracking down their patients in custody, there is even an iPhone- and Android-compatible app called MobilePatrol, which provides a convenient interface to many inmate locator databases nationwide. MobilePatrol does not provide information about charges or date of birth, so it’s mainly useful if the patient can be identified by age and has a unique name.

The next step is to ensure that the patient has been identified as needing psychiatric care within the facility. Almost all jails and prisons now have routine multilevel screens to identify arrestees with chronic medical or mental health needs, and to assess suicide risk at intake. This is required by any jail or prison accredited by the National Commission on Correctional Health Care. Nevertheless, some patients are reluctant to self-identify out of fear they might be inappropriately or precipitously thrown into a suicide observation cell.

When it comes to transmitting information to a correctional facility, don’t rely on custody staff. They aren’t clinicians, they change with every shift, and they won’t know what questions to ask about the patient. This includes the warden’s office. The best thing to do is call the psychology department to transmit the patient’s name, date of birth, jail or prison number, and any pertinent clinical information. Don’t rely on an administrative assistant or nonmedical therapist to do this for you – I can’t tell you the number of times I’ve gotten a message that "...John Doe is in your jail and he needs to be seen..." with absolutely no information about medication names, dosage, and frequency, or even a diagnosis! An initial phone call will ensure that the patient is found within the facility and scheduled to see the institutional psychiatrist.

Follow the phone call up with a letter. This will ensure that the clinical information is still available on the day the psychiatrist comes in, and for the next institutional physician if the patient is transferred to another facility. The letter should summarize pertinent symptoms, violence or suicide risk factors, and previous medication trials. The past med trial information is particularly important for correctional psychiatrists, given that many jails and prisons require "fail-first" prescribing policies. Outside documentation that supports a current nonformulary medication regimen can be crucial to ensuring a smooth transition of care. But please, resist the temptation to reprimand the correctional psychiatrist in advance for making a medication change – there are many valid clinical reasons for a correctional psychiatrist to alter a treatment regimen upon arrest that have nothing to do with formulary issues.

Finally, encourage the patient’s family members to maintain contact with their incarcerated loved one if that relationship is healthy and supportive. No one knows a patient better than those in his own household, and a family member can be particularly sensitive to early signs of relapse sometimes through nothing more than a patient’s tone of voice during a phone call. Give the primary caregivers contact information for the institutional psychology department and encourage them to call if they observe anything concerning during a visit or court appearance. Court dates are particularly stressful times and may serve as a crisis point for a suicidal inmate. Having an extra pair of eyes on the scene could be lifesaving.

 

 

Once the patient has been identified and referred, and treatment started, your job is done until release. For misdemeanor offenders in local detention, this could take place within days or a few weeks, or even the day of arrest if the patient is able to make bail. Following the steps I’ve recommended to ensure continuity of care will help your patient return to you in at least as good a condition as when he came in.

Dr. Hanson is a forensic psychiatrist and coauthor of "Shrink Rap: Three Psychiatrists Explain Their Work" (Baltimore: The Johns Hopkins University Press, 2011). The opinions expressed are those of the author only, and do not represent those of any of Dr. Hanson’s employers or consultees, including the Maryland Department of Health and Mental Hygiene or the Maryland Division of Correction.

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Nonforensic psychiatrists in private practice rarely expect to be dealing with patients involved in the correctional system, but unexpected things happen even with the most carefully chosen patients. I’m writing this column to offer guidance to clinicians facing this situation for the first time, based upon the most common questions I get asked.

The most common situation I hear about is that a patient has missed an appointment, and the clinician hears from a family member that the patient has been arrested. The conscientious doctor wants to make sure that his seriously mentally ill client doesn’t experience an interruption in treatment, and that an appointment will be ready after release. The first challenge is to locate the patient.

Dr. Annette Hanson

In small communities, or when a family member was present at the time of arrest, it’s relatively easy to figure out which detention center or jail the patient was taken to. If the patient was arrested in a large urban area, or even out of state, this can be more of a challenge. Fortunately, many states and even now some local county or city jurisdictions have inmate locator web pages. The website will provide search capabilities to identify anyone currently in custody, and will generally provide a unique booking or inmate number that should be used in any facility communication, along with a date of birth and the address of the facility. Be aware that a large jail with high turnover may not have real-time data capability, meaning that new arrests may not show up on the website for 24 hours.

For psychiatrists who spend a lot of time tracking down their patients in custody, there is even an iPhone- and Android-compatible app called MobilePatrol, which provides a convenient interface to many inmate locator databases nationwide. MobilePatrol does not provide information about charges or date of birth, so it’s mainly useful if the patient can be identified by age and has a unique name.

The next step is to ensure that the patient has been identified as needing psychiatric care within the facility. Almost all jails and prisons now have routine multilevel screens to identify arrestees with chronic medical or mental health needs, and to assess suicide risk at intake. This is required by any jail or prison accredited by the National Commission on Correctional Health Care. Nevertheless, some patients are reluctant to self-identify out of fear they might be inappropriately or precipitously thrown into a suicide observation cell.

When it comes to transmitting information to a correctional facility, don’t rely on custody staff. They aren’t clinicians, they change with every shift, and they won’t know what questions to ask about the patient. This includes the warden’s office. The best thing to do is call the psychology department to transmit the patient’s name, date of birth, jail or prison number, and any pertinent clinical information. Don’t rely on an administrative assistant or nonmedical therapist to do this for you – I can’t tell you the number of times I’ve gotten a message that "...John Doe is in your jail and he needs to be seen..." with absolutely no information about medication names, dosage, and frequency, or even a diagnosis! An initial phone call will ensure that the patient is found within the facility and scheduled to see the institutional psychiatrist.

Follow the phone call up with a letter. This will ensure that the clinical information is still available on the day the psychiatrist comes in, and for the next institutional physician if the patient is transferred to another facility. The letter should summarize pertinent symptoms, violence or suicide risk factors, and previous medication trials. The past med trial information is particularly important for correctional psychiatrists, given that many jails and prisons require "fail-first" prescribing policies. Outside documentation that supports a current nonformulary medication regimen can be crucial to ensuring a smooth transition of care. But please, resist the temptation to reprimand the correctional psychiatrist in advance for making a medication change – there are many valid clinical reasons for a correctional psychiatrist to alter a treatment regimen upon arrest that have nothing to do with formulary issues.

Finally, encourage the patient’s family members to maintain contact with their incarcerated loved one if that relationship is healthy and supportive. No one knows a patient better than those in his own household, and a family member can be particularly sensitive to early signs of relapse sometimes through nothing more than a patient’s tone of voice during a phone call. Give the primary caregivers contact information for the institutional psychology department and encourage them to call if they observe anything concerning during a visit or court appearance. Court dates are particularly stressful times and may serve as a crisis point for a suicidal inmate. Having an extra pair of eyes on the scene could be lifesaving.

 

 

Once the patient has been identified and referred, and treatment started, your job is done until release. For misdemeanor offenders in local detention, this could take place within days or a few weeks, or even the day of arrest if the patient is able to make bail. Following the steps I’ve recommended to ensure continuity of care will help your patient return to you in at least as good a condition as when he came in.

Dr. Hanson is a forensic psychiatrist and coauthor of "Shrink Rap: Three Psychiatrists Explain Their Work" (Baltimore: The Johns Hopkins University Press, 2011). The opinions expressed are those of the author only, and do not represent those of any of Dr. Hanson’s employers or consultees, including the Maryland Department of Health and Mental Hygiene or the Maryland Division of Correction.

Nonforensic psychiatrists in private practice rarely expect to be dealing with patients involved in the correctional system, but unexpected things happen even with the most carefully chosen patients. I’m writing this column to offer guidance to clinicians facing this situation for the first time, based upon the most common questions I get asked.

The most common situation I hear about is that a patient has missed an appointment, and the clinician hears from a family member that the patient has been arrested. The conscientious doctor wants to make sure that his seriously mentally ill client doesn’t experience an interruption in treatment, and that an appointment will be ready after release. The first challenge is to locate the patient.

Dr. Annette Hanson

In small communities, or when a family member was present at the time of arrest, it’s relatively easy to figure out which detention center or jail the patient was taken to. If the patient was arrested in a large urban area, or even out of state, this can be more of a challenge. Fortunately, many states and even now some local county or city jurisdictions have inmate locator web pages. The website will provide search capabilities to identify anyone currently in custody, and will generally provide a unique booking or inmate number that should be used in any facility communication, along with a date of birth and the address of the facility. Be aware that a large jail with high turnover may not have real-time data capability, meaning that new arrests may not show up on the website for 24 hours.

For psychiatrists who spend a lot of time tracking down their patients in custody, there is even an iPhone- and Android-compatible app called MobilePatrol, which provides a convenient interface to many inmate locator databases nationwide. MobilePatrol does not provide information about charges or date of birth, so it’s mainly useful if the patient can be identified by age and has a unique name.

The next step is to ensure that the patient has been identified as needing psychiatric care within the facility. Almost all jails and prisons now have routine multilevel screens to identify arrestees with chronic medical or mental health needs, and to assess suicide risk at intake. This is required by any jail or prison accredited by the National Commission on Correctional Health Care. Nevertheless, some patients are reluctant to self-identify out of fear they might be inappropriately or precipitously thrown into a suicide observation cell.

When it comes to transmitting information to a correctional facility, don’t rely on custody staff. They aren’t clinicians, they change with every shift, and they won’t know what questions to ask about the patient. This includes the warden’s office. The best thing to do is call the psychology department to transmit the patient’s name, date of birth, jail or prison number, and any pertinent clinical information. Don’t rely on an administrative assistant or nonmedical therapist to do this for you – I can’t tell you the number of times I’ve gotten a message that "...John Doe is in your jail and he needs to be seen..." with absolutely no information about medication names, dosage, and frequency, or even a diagnosis! An initial phone call will ensure that the patient is found within the facility and scheduled to see the institutional psychiatrist.

Follow the phone call up with a letter. This will ensure that the clinical information is still available on the day the psychiatrist comes in, and for the next institutional physician if the patient is transferred to another facility. The letter should summarize pertinent symptoms, violence or suicide risk factors, and previous medication trials. The past med trial information is particularly important for correctional psychiatrists, given that many jails and prisons require "fail-first" prescribing policies. Outside documentation that supports a current nonformulary medication regimen can be crucial to ensuring a smooth transition of care. But please, resist the temptation to reprimand the correctional psychiatrist in advance for making a medication change – there are many valid clinical reasons for a correctional psychiatrist to alter a treatment regimen upon arrest that have nothing to do with formulary issues.

Finally, encourage the patient’s family members to maintain contact with their incarcerated loved one if that relationship is healthy and supportive. No one knows a patient better than those in his own household, and a family member can be particularly sensitive to early signs of relapse sometimes through nothing more than a patient’s tone of voice during a phone call. Give the primary caregivers contact information for the institutional psychology department and encourage them to call if they observe anything concerning during a visit or court appearance. Court dates are particularly stressful times and may serve as a crisis point for a suicidal inmate. Having an extra pair of eyes on the scene could be lifesaving.

 

 

Once the patient has been identified and referred, and treatment started, your job is done until release. For misdemeanor offenders in local detention, this could take place within days or a few weeks, or even the day of arrest if the patient is able to make bail. Following the steps I’ve recommended to ensure continuity of care will help your patient return to you in at least as good a condition as when he came in.

Dr. Hanson is a forensic psychiatrist and coauthor of "Shrink Rap: Three Psychiatrists Explain Their Work" (Baltimore: The Johns Hopkins University Press, 2011). The opinions expressed are those of the author only, and do not represent those of any of Dr. Hanson’s employers or consultees, including the Maryland Department of Health and Mental Hygiene or the Maryland Division of Correction.

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