A 51‐year‐old man presented to the emergency department after 1 day of progressive dyspnea and increasing confusion.

Acute dyspnea most commonly stems from a cardiac or pulmonary disorder such as heart failure, acute coronary syndrome, pneumonia, pulmonary embolism, or exacerbations of asthma or chronic obstructive pulmonary disease. Less frequent cardiopulmonary considerations include pericardial or pleural effusion, pneumothorax, aspiration, and upper airway obstruction. Dyspnea might also be the initial manifestation of profound anemia or metabolic acidosis.

The presence of confusion suggests either a severe presentation of any of the aforementioned possibilities (with confusion resulting from hypoxia, hypercapnia, or hypotension); a multiorgan illness such as sepsis, malignancy, thromboembolic disease, vasculitis, thyroid dysfunction, or toxic ingestion; or a metabolic derangement related to the underlying cause of dyspnea (for example, hypercalcemia or hyponatremia associated with lung cancer).

Twelve hours prior to presentation, he started to have visual hallucinations. He denied fever, chills, cough, chest discomfort, palpitations, weight gain, headache, neck pain, or weakness.

Visual hallucinations could result from a toxic‐metabolic encephalopathy, such as drug overdose or withdrawal, liver or kidney failure, or hypoxia. A structural brain abnormality may also manifest with visual hallucination. Acute onset at age 51 and the absence of auditory hallucinations argue against a neurodegenerative illness and a primary psychiatric disturbance, respectively.

Episodic hallucinations would support the possibility of seizures, monocular hallucinations would point to a retinal or ocular problem, and a description of yellow‐green hue would suggest a side effect of digoxin.

His past medical history was remarkable for diet‐controlled type 2 diabetes mellitus, hypertension, hyperlipidemia, and chronic low back pain. His medications included metoprolol tartrate 25 mg twice daily, omeprazole 40 mg daily, baclofen 15 mg twice daily, oxycodone 30 mg 3 times daily, and hydrocodone 10 mg/acetaminophen 325 mg, 2 tablets 3 times daily as needed for back pain. He was a smoker with a 30 pack‐year history. He had a history of alcohol and cocaine use, but denied any recent substance use. He had no known history of obstructive pulmonary disease.

The patient takes 3 medications well known to cause confusion and hallucinations (oxycodone, hydrocodone, and baclofen), especially when they accumulate due to excessive ingestion or impaired clearance. Although these medications may suppress ventilatory drive, dyspnea would not be a common presenting complaint. He has risk factors for ischemic heart disease and cardiomyopathy, and his smoking history raises the possibility of malignancy.

On exam, the patient's temperature was 94.4C, heart rate 128 beats per minute, respiration rate 28 breaths per minute, blood pressure 155/63 mm Hg, and oxygen saturation 100% while breathing ambient air. The patient was cachectic and appeared in moderate respiratory distress. His pupils were equal and reactive to light, and extraocular movements were intact. He did not have scleral icterus, or cervical or clavicular lymphadenopathy. His oropharynx was negative for erythema, edema, or exudate. His cardiovascular exam revealed a regular tachycardia without rubs or diastolic gallops. There was a 2/6 systolic murmur heard best at left sternal border, without radiation. He did not have jugular venous distention. His pulmonary exam was notable for tachypnea but with normal vesicular breath sounds throughout. He did not have stridor, wheezing, rhonchi, or rales. His abdomen had normal bowel tones and was soft without tenderness, distention, or organomegaly. His extremities were warm, revealed normal pulses, and no edema was present. His joints were cool to palpation, without effusion. On neurologic exam, he was oriented to person and place and able to answer yes/no questions, but unable to provide detailed history. His speech was fluent. His motor exam was without focal deficits. His skin was without any notable lesions.

The constellation of findings does not point to a specific toxidrome. The finding of warm extremities in a hypothermic patient suggests heat loss due to inappropriate peripheral vasodilation. In the absence of vasodilators or features of aortic insufficiency, sepsis becomes a leading consideration. Infection could result in hypothermia and altered sensorium, and accompanying lactic acidosis could trigger tachypnea.

Shortly after admission, he became more somnolent and developed progressive respiratory distress, requiring intubation. Arterial blood gas revealed a pH of 6.93, PaCO₂ 20 mm Hg, PaO₂ 127 mm Hg, and HCO₃ 5 mEq/L. Other laboratory results included a lactate of 4.1 mmol/L, blood urea nitrogen 49 mg/dL, creatinine 2.3 mg/dL (0.8 at 1 month prior), sodium level of 143 mmol/L, chloride of 106 mmol/L, and bicarbonate level of 5 mg/dL. His aspartate aminotransferase was 34 IU/L, alanine transaminase was 28 IU/L, total bilirubin was 0.6 mg/dL, International Normalized Ratio was 1.3. A complete blood count revealed a white blood cell count of 23,000/L, hemoglobin of 10.6 g/dL, and platelet count of 454,000/L. A urinalysis was unremarkable. Cultures of blood, urine, and sputum were collected. Head computed tomography was negative.

This patient has a combined anion gap and nongap metabolic acidosis, as well as respiratory alkalosis. Although his acute kidney failure could produce these 2 types of metabolic acidosis, the modest elevation of the serum creatinine is not commensurate with such profound acidosis. Similarly, sepsis without hypotension or more striking elevation in lactate levels would not account for the entirety of the acidosis. Severe diabetic ketoacidosis can result in profound metabolic acidosis, and marked hyperglycemia or hyperosmolarity could result in somnolence; however, his diabetes has been controlled without medication and there is no obvious precipitant for an episode of ketoacidosis.

Remaining causes of anion gap acidosis include ingestion of methanol, ethylene glycol, ethanol, or salicylates. A careful history of ingestions and medications from witnesses including any prehospital personnel might suggest a source of intoxication. Absent this information, the hypothermia favors an ingestion of an alcohol over salicylates, and the lack of urine crystals and the presence of prominent visual hallucinations would point more toward methanol poisoning than ethylene glycol. A serum osmolarity measurement would allow determination of the osmolar gap, which would be elevated in the setting of methanol or ethylene glycol poisoning. If he were this ill from ethanol, I would have expected to see evidence of hepatotoxicity.

I would administer sodium bicarbonate to reverse the acidosis and to promote renal clearance of salicylates, methanol, ethylene glycol, and their metabolites. Orogastric decontamination with activated charcoal should be considered. If the osmolar gap is elevated, I would also administer intravenous fomepizole to attempt to reverse methanol or ethylene glycol poisoning. I would not delay treatment while waiting for these serum levels to return.

Initial serologic toxicology performed in the emergency department revealed negative ethanol, salicylates, and ketones. His osmolar gap was 13 mOsm/kg. His acetaminophen level was 69 g/mL (normal 120 g/mL). A creatinine phosphokinase was 84 IU/L and myoglobin was 93 ng/mL. His subsequent serum toxicology screen was negative for methanol, ethylene glycol, isopropranol, and hippuric acid. Urine toxicology was positive for opiates, but negative for amphetamine, benzodiazepine, cannabinoid, and cocaine.

Serum and urine ketone assays typically involve the nitroprusside reaction and detect acetoacetate, but not ‐hydroxybutyrate, and can lead to negative test results early in diabetic or alcoholic ketoacidosis. However, the normal ethanol level argues against alcoholic ketoacidosis. Rare causes of elevated anion gap acidosis include toluene toxicity, acetaminophen poisoning, and ingestion of other alcohols. Toluene is metabolized to hippuric acid, and acetaminophen toxicity and associated glutathione depletion can lead to 5‐oxoproline accumulation, producing an anion gap. Patients who abuse alcohol are at risk for acetaminophen toxicity even at doses considered normal. However, this degree of encephalopathy would be unusual for acetaminophen toxicity unless liver failure had developed or unless there was another ingestion that might alter sensorium. Furthermore, the elevated osmolar gap is not a feature of acetaminophen poisoning. I would monitor liver enzyme tests and consider a serum ammonia level, but would not attribute the entire picture to acetaminophen.

The combination of elevated anion gap with an elevated osmolar gap narrows the diagnostic possibilities. Ingestion of several alcohols (ethanol, methanol, ethylene glycol, diethylene glycol) or toluene could produce these abnormalities. Of note, the osmolar gap is typically most markedly elevated early in methanol and ethylene glycol ingestions, and then as the parent compound is metabolized, the osmolar gap closes and the accumulation of metabolites produces the anion gap. Hallucinations are more common with methanol and toluene, and renal failure is more typical of ethylene glycol or toluene. The lack of oxalate crystalluria does not exclude ethylene glycol poisoning. Unfortunately, urine testing for oxalate crystals or fluorescein examination are neither sensitive nor specific enough to diagnosis ethylene glycol toxicity reliably. In most hospitals, assays used for serum testing for alcohols are insensitive, and require confirmation with gas chromatography performed at a specialty lab.

Additional history might reveal the likely culprit or culprits. Inhalant abuse including huffing would point to toluene or organic acid exposure. Solvent ingestion (eg, antifreeze, brake fluid) would suggest methanol or ethylene glycol. Absent this history, I remain suspicious for poisoning with methanol or ethylene glycol and would consider empiric treatment after urgent consultation with a medical toxicologist. A careful ophthalmologic exam might demonstrate characteristic features of methanol poisoning. Serum samples should be sent to a regional lab for analysis for alcohols and organic acids.

He was admitted to the intensive care unit, and empiric antibiotics started. He was empirically started on N‐acetylcysteine and sodium bicarbonate drips. However, his acidemia persisted and he required hemodialysis, which was initiated 12 hours after initial presentation. His acidemia and mental status quickly improved after hemodialysis. He was extubated on hospital day 2 and no longer required hemodialysis.

The differential diagnosis at this point consists of 3 main possibilities: ingestion of methanol, ethylene glycol, or inhalant abuse such as from toluene. The normal hippuric acid level points away from toluene, whereas serum levels can be misleading in the alcohol poisonings. Other discriminating features to consider include exposure history and unique clinical aspects. In this patient, an exposure history is lacking, but 4 clinical features stand out: visual hallucinations, acute kidney injury, mild lactic acidosis, and rapid improvement with hemodialysis. Both ethylene glycol and methanol toxicity may produce a mild lactic acidosis by increasing hepatic metabolism of pyruvate to lactate, and both are rapidly cleared by dialysis. Although it is tempting to place methanol at the top of the list of possibilities due to the report of visual hallucinations, the subjective visual complaints without objective exam corollaries (loss of visual acuity, abnormal pupillary reflexes, or optic disc hyperemia) are nonspecific and might be provoked by alcohol or an inhalant. Furthermore, the acute renal failure is much more typical of ethylene glycol, and thus I would consider ethylene glycol as being the more likely of the ingestions. Coingestion of multiple alcohols is a possibility, but it would be statistically less likely. Confirmation of ethylene glycol poisoning would consist of further insight into his exposures and measurement of levels using gas chromatography.

A urine sample from his emergency department presentation was sent to an outside lab for organic acid levels. Based on high clinical suspicion for 5‐oxoprolinemia (pyroglutamic acidemia) the patient was counseled to avoid any acetaminophen. His primary care provider was informed of this and acetaminophen was added as an adverse drug reaction. The patient left against medical advice soon after extubation. Following discharge, his 5‐oxoproline (pyroglutamic acid) level returned markedly elevated at greater than 10,000 mmol/mol creatinine (200 times the upper limit of normal).

Elevations in 5‐oxoproline levels in this patient most likely stem from glutathione depletion related to chronic acetaminophen use. Alcohol use and malnutrition may have heightened this patient's susceptibility. Despite the common occurrence of acetaminophen use in alcohol abusers or the malnourished, the rarity of severe 5‐oxoproline toxicity suggests unknown factors may be present in predisposed individuals, or under‐recognition. Although acetaminophen‐induced hepatotoxicity may occur along with 5‐oxoprolinemia, this does not always occur.

Several features led me away from this syndrome. First, its rarity lowered my pretest probability. Second, the lack of exposure history and details about the serum assays, specifically whether the measurements were confirmed by gas chromatography, reduced my confidence in eliminating more common ingestions. Third, several aspects proved to be less useful discriminating features: the mild elevation in osmolar gap, renal failure, and hallucinations, which in retrospect proved to be nonspecific.

The patient admitted that he had a longstanding use of acetaminophen in addition to using his girlfriend's acetaminophen‐hydrocodone. He had significant weight loss of over 50 pounds over the previous year, which he attributed to poor appetite. On further chart review, he had been admitted 3 times with a similar clinical presentation and recovered quickly with intensive and supportive care, with no etiology found at those times. He had 2 subsequent hospital admissions for altered mental status and respiratory failure, and his final hospitalization resulted in cardiac arrest and death.

DISCUSSION

5‐Oxoprolinemia is a rare, but potentially lethal cause of severe anion gap metabolic acidosis.[1, 2] The mechanism is thought to be impairment of glutathione metabolism, in the context of other predisposing factors. This can be a congenital error of metabolism, or can be acquired and exacerbated by acetaminophen use. Ingestion of acetaminophen leads to glutathione depletion, which in turn may precipitate accumulation of pyroglutamic acid and subsequent anion gap metabolic acidosis (Figure 1). Additional risk factors that may predispose patients to this condition include malnutrition, renal insufficiency, concurrent infection, and female gender.[1, 2, 3]

Figure 1

The diagnosis of 5‐oxoprolinemia is made via urine or serum organic acid analysis, testing routinely performed in pediatric populations when screening for congenital metabolic disorders. The pathophysiology suggests that obtaining a urine sample early in presentation, when acidosis is greatest, would lead to the highest 5‐oxoproline levels and best chance for diagnosis. Case patients have had normal levels prior to and in convalescent phases after the acute episode.[4] Given the long turnaround time for lab testing, presumptive diagnosis and treatment may be necessary.

Treatment of 5‐oxoprolinemia is primarily supportive, aimed at the metabolic acidosis. Fluid resuscitation and bicarbonate therapy are reasonable temporizing measures. Hemodialysis can clear 5‐oxoproline and may be indicated in severe acidosis.[5] Furthermore, the proposed pathophysiology suggests that administration of N‐acetylcysteine (NAC) may help to address the underlying process, but there are no trials to support a specific dosing regimen. However, given the fulminant presentation and common competing concern for acetaminophen toxicity, it is reasonable to initiate NAC aimed at treatment for possible acetaminophen overdose. Prevention of recurrence includes avoidance of acetaminophen, and counseling the patient to avoid acetaminophen in prescription combination medications and over‐the‐counter preparations.

Recent regulatory changes regarding acetaminophen/opioid combinations may reduce the incidence of 5‐oxoprolinemia. The US Food and Drug Administration has taken action to reduce adverse effects from acetaminophen exposure by limiting the amount of acetaminophen in opioid combination pills from 500 mg to a maximum of 325 mg per pill. This is aimed at preventing hepatotoxicity from ingestion of higher‐than‐recommended doses. However, clinicians should remember that 5‐oxoprolinemia can result from ingestion of acetaminophen at therapeutic levels.

Given its rare incidence, low clinical suspicion, and transient nature of confirmatory testing, it is likely this remains an underdiagnosed syndrome. In the case discussed, subsequent chart review demonstrated 5 previous admissions in multiple hospitals for severe transient anion gap acidosis. The likelihood that 5‐oxoprolinemia was missed in each of these cases supports a lack of awareness of this syndrome. In this patient, the discussant appropriately identified the possibility of 5‐oxoproline toxicity, but felt ethylene glycol ingestion was more likely. As this case underscores, a cornerstone in the management of suspected ingestions is empiric treatment for the most likely etiologies. Here, treatment for acetaminophen overdose and for methanol or ethylene glycol were warranted, and fortunately also addressed the rarer possibility of 5‐oxoproline toxicity.

The mnemonic MUDPILES is commonly used to identify possible causes of life‐threatening anion gap metabolic acidosis, as such heuristics have benefits in rapidly generating a differential diagnosis to guide initial evaluation. Given the fact that the traditional letter P (paraldehyde) in MUDPILES is no longer clinically utilized, some authors have suggested replacing this with pyroglutamic acid (a synonym of 5‐oxoproline). Such a change may help providers who have ruled out other causes of a high anion gap metabolic acidosis, facilitating diagnosis of this life‐threatening syndrome. In any case, clinicians must be mindful that simple memory aids may mislead clinicians, and a complete differential diagnosis may require more than a mnemonic.

TEACHING POINTS

1. Acetaminophen use, even at therapeutic levels, can lead to 5‐oxoprolinemia, a potentially lethal anion gap metabolic acidosis.
2. 5‐oxoprolinemia is likely related to glutathione depletion, worsened by acetaminophen, malnutrition, renal insufficiency, female gender, and infection. This implies theoretical benefit from administration of NAC for glutathione repletion.
3. Mnemonics can be useful, but have limitations by way of oversimplification. This case suggests that changing the letter P in MUDPILES from paraldehyde to pyroglutamic acid could reduce underdiagnosis.

Disclosure: Nothing to report.

A 51‐year‐old man presented to the emergency department after 1 day of progressive dyspnea and increasing confusion.

Acute dyspnea most commonly stems from a cardiac or pulmonary disorder such as heart failure, acute coronary syndrome, pneumonia, pulmonary embolism, or exacerbations of asthma or chronic obstructive pulmonary disease. Less frequent cardiopulmonary considerations include pericardial or pleural effusion, pneumothorax, aspiration, and upper airway obstruction. Dyspnea might also be the initial manifestation of profound anemia or metabolic acidosis.

The presence of confusion suggests either a severe presentation of any of the aforementioned possibilities (with confusion resulting from hypoxia, hypercapnia, or hypotension); a multiorgan illness such as sepsis, malignancy, thromboembolic disease, vasculitis, thyroid dysfunction, or toxic ingestion; or a metabolic derangement related to the underlying cause of dyspnea (for example, hypercalcemia or hyponatremia associated with lung cancer).

Twelve hours prior to presentation, he started to have visual hallucinations. He denied fever, chills, cough, chest discomfort, palpitations, weight gain, headache, neck pain, or weakness.

Visual hallucinations could result from a toxic‐metabolic encephalopathy, such as drug overdose or withdrawal, liver or kidney failure, or hypoxia. A structural brain abnormality may also manifest with visual hallucination. Acute onset at age 51 and the absence of auditory hallucinations argue against a neurodegenerative illness and a primary psychiatric disturbance, respectively.

Episodic hallucinations would support the possibility of seizures, monocular hallucinations would point to a retinal or ocular problem, and a description of yellow‐green hue would suggest a side effect of digoxin.

His past medical history was remarkable for diet‐controlled type 2 diabetes mellitus, hypertension, hyperlipidemia, and chronic low back pain. His medications included metoprolol tartrate 25 mg twice daily, omeprazole 40 mg daily, baclofen 15 mg twice daily, oxycodone 30 mg 3 times daily, and hydrocodone 10 mg/acetaminophen 325 mg, 2 tablets 3 times daily as needed for back pain. He was a smoker with a 30 pack‐year history. He had a history of alcohol and cocaine use, but denied any recent substance use. He had no known history of obstructive pulmonary disease.

The patient takes 3 medications well known to cause confusion and hallucinations (oxycodone, hydrocodone, and baclofen), especially when they accumulate due to excessive ingestion or impaired clearance. Although these medications may suppress ventilatory drive, dyspnea would not be a common presenting complaint. He has risk factors for ischemic heart disease and cardiomyopathy, and his smoking history raises the possibility of malignancy.

On exam, the patient's temperature was 94.4C, heart rate 128 beats per minute, respiration rate 28 breaths per minute, blood pressure 155/63 mm Hg, and oxygen saturation 100% while breathing ambient air. The patient was cachectic and appeared in moderate respiratory distress. His pupils were equal and reactive to light, and extraocular movements were intact. He did not have scleral icterus, or cervical or clavicular lymphadenopathy. His oropharynx was negative for erythema, edema, or exudate. His cardiovascular exam revealed a regular tachycardia without rubs or diastolic gallops. There was a 2/6 systolic murmur heard best at left sternal border, without radiation. He did not have jugular venous distention. His pulmonary exam was notable for tachypnea but with normal vesicular breath sounds throughout. He did not have stridor, wheezing, rhonchi, or rales. His abdomen had normal bowel tones and was soft without tenderness, distention, or organomegaly. His extremities were warm, revealed normal pulses, and no edema was present. His joints were cool to palpation, without effusion. On neurologic exam, he was oriented to person and place and able to answer yes/no questions, but unable to provide detailed history. His speech was fluent. His motor exam was without focal deficits. His skin was without any notable lesions.

The constellation of findings does not point to a specific toxidrome. The finding of warm extremities in a hypothermic patient suggests heat loss due to inappropriate peripheral vasodilation. In the absence of vasodilators or features of aortic insufficiency, sepsis becomes a leading consideration. Infection could result in hypothermia and altered sensorium, and accompanying lactic acidosis could trigger tachypnea.

Shortly after admission, he became more somnolent and developed progressive respiratory distress, requiring intubation. Arterial blood gas revealed a pH of 6.93, PaCO₂ 20 mm Hg, PaO₂ 127 mm Hg, and HCO₃ 5 mEq/L. Other laboratory results included a lactate of 4.1 mmol/L, blood urea nitrogen 49 mg/dL, creatinine 2.3 mg/dL (0.8 at 1 month prior), sodium level of 143 mmol/L, chloride of 106 mmol/L, and bicarbonate level of 5 mg/dL. His aspartate aminotransferase was 34 IU/L, alanine transaminase was 28 IU/L, total bilirubin was 0.6 mg/dL, International Normalized Ratio was 1.3. A complete blood count revealed a white blood cell count of 23,000/L, hemoglobin of 10.6 g/dL, and platelet count of 454,000/L. A urinalysis was unremarkable. Cultures of blood, urine, and sputum were collected. Head computed tomography was negative.

This patient has a combined anion gap and nongap metabolic acidosis, as well as respiratory alkalosis. Although his acute kidney failure could produce these 2 types of metabolic acidosis, the modest elevation of the serum creatinine is not commensurate with such profound acidosis. Similarly, sepsis without hypotension or more striking elevation in lactate levels would not account for the entirety of the acidosis. Severe diabetic ketoacidosis can result in profound metabolic acidosis, and marked hyperglycemia or hyperosmolarity could result in somnolence; however, his diabetes has been controlled without medication and there is no obvious precipitant for an episode of ketoacidosis.

Remaining causes of anion gap acidosis include ingestion of methanol, ethylene glycol, ethanol, or salicylates. A careful history of ingestions and medications from witnesses including any prehospital personnel might suggest a source of intoxication. Absent this information, the hypothermia favors an ingestion of an alcohol over salicylates, and the lack of urine crystals and the presence of prominent visual hallucinations would point more toward methanol poisoning than ethylene glycol. A serum osmolarity measurement would allow determination of the osmolar gap, which would be elevated in the setting of methanol or ethylene glycol poisoning. If he were this ill from ethanol, I would have expected to see evidence of hepatotoxicity.

I would administer sodium bicarbonate to reverse the acidosis and to promote renal clearance of salicylates, methanol, ethylene glycol, and their metabolites. Orogastric decontamination with activated charcoal should be considered. If the osmolar gap is elevated, I would also administer intravenous fomepizole to attempt to reverse methanol or ethylene glycol poisoning. I would not delay treatment while waiting for these serum levels to return.

Initial serologic toxicology performed in the emergency department revealed negative ethanol, salicylates, and ketones. His osmolar gap was 13 mOsm/kg. His acetaminophen level was 69 g/mL (normal 120 g/mL). A creatinine phosphokinase was 84 IU/L and myoglobin was 93 ng/mL. His subsequent serum toxicology screen was negative for methanol, ethylene glycol, isopropranol, and hippuric acid. Urine toxicology was positive for opiates, but negative for amphetamine, benzodiazepine, cannabinoid, and cocaine.

Serum and urine ketone assays typically involve the nitroprusside reaction and detect acetoacetate, but not ‐hydroxybutyrate, and can lead to negative test results early in diabetic or alcoholic ketoacidosis. However, the normal ethanol level argues against alcoholic ketoacidosis. Rare causes of elevated anion gap acidosis include toluene toxicity, acetaminophen poisoning, and ingestion of other alcohols. Toluene is metabolized to hippuric acid, and acetaminophen toxicity and associated glutathione depletion can lead to 5‐oxoproline accumulation, producing an anion gap. Patients who abuse alcohol are at risk for acetaminophen toxicity even at doses considered normal. However, this degree of encephalopathy would be unusual for acetaminophen toxicity unless liver failure had developed or unless there was another ingestion that might alter sensorium. Furthermore, the elevated osmolar gap is not a feature of acetaminophen poisoning. I would monitor liver enzyme tests and consider a serum ammonia level, but would not attribute the entire picture to acetaminophen.

The combination of elevated anion gap with an elevated osmolar gap narrows the diagnostic possibilities. Ingestion of several alcohols (ethanol, methanol, ethylene glycol, diethylene glycol) or toluene could produce these abnormalities. Of note, the osmolar gap is typically most markedly elevated early in methanol and ethylene glycol ingestions, and then as the parent compound is metabolized, the osmolar gap closes and the accumulation of metabolites produces the anion gap. Hallucinations are more common with methanol and toluene, and renal failure is more typical of ethylene glycol or toluene. The lack of oxalate crystalluria does not exclude ethylene glycol poisoning. Unfortunately, urine testing for oxalate crystals or fluorescein examination are neither sensitive nor specific enough to diagnosis ethylene glycol toxicity reliably. In most hospitals, assays used for serum testing for alcohols are insensitive, and require confirmation with gas chromatography performed at a specialty lab.

Additional history might reveal the likely culprit or culprits. Inhalant abuse including huffing would point to toluene or organic acid exposure. Solvent ingestion (eg, antifreeze, brake fluid) would suggest methanol or ethylene glycol. Absent this history, I remain suspicious for poisoning with methanol or ethylene glycol and would consider empiric treatment after urgent consultation with a medical toxicologist. A careful ophthalmologic exam might demonstrate characteristic features of methanol poisoning. Serum samples should be sent to a regional lab for analysis for alcohols and organic acids.

He was admitted to the intensive care unit, and empiric antibiotics started. He was empirically started on N‐acetylcysteine and sodium bicarbonate drips. However, his acidemia persisted and he required hemodialysis, which was initiated 12 hours after initial presentation. His acidemia and mental status quickly improved after hemodialysis. He was extubated on hospital day 2 and no longer required hemodialysis.

The differential diagnosis at this point consists of 3 main possibilities: ingestion of methanol, ethylene glycol, or inhalant abuse such as from toluene. The normal hippuric acid level points away from toluene, whereas serum levels can be misleading in the alcohol poisonings. Other discriminating features to consider include exposure history and unique clinical aspects. In this patient, an exposure history is lacking, but 4 clinical features stand out: visual hallucinations, acute kidney injury, mild lactic acidosis, and rapid improvement with hemodialysis. Both ethylene glycol and methanol toxicity may produce a mild lactic acidosis by increasing hepatic metabolism of pyruvate to lactate, and both are rapidly cleared by dialysis. Although it is tempting to place methanol at the top of the list of possibilities due to the report of visual hallucinations, the subjective visual complaints without objective exam corollaries (loss of visual acuity, abnormal pupillary reflexes, or optic disc hyperemia) are nonspecific and might be provoked by alcohol or an inhalant. Furthermore, the acute renal failure is much more typical of ethylene glycol, and thus I would consider ethylene glycol as being the more likely of the ingestions. Coingestion of multiple alcohols is a possibility, but it would be statistically less likely. Confirmation of ethylene glycol poisoning would consist of further insight into his exposures and measurement of levels using gas chromatography.

A urine sample from his emergency department presentation was sent to an outside lab for organic acid levels. Based on high clinical suspicion for 5‐oxoprolinemia (pyroglutamic acidemia) the patient was counseled to avoid any acetaminophen. His primary care provider was informed of this and acetaminophen was added as an adverse drug reaction. The patient left against medical advice soon after extubation. Following discharge, his 5‐oxoproline (pyroglutamic acid) level returned markedly elevated at greater than 10,000 mmol/mol creatinine (200 times the upper limit of normal).

Elevations in 5‐oxoproline levels in this patient most likely stem from glutathione depletion related to chronic acetaminophen use. Alcohol use and malnutrition may have heightened this patient's susceptibility. Despite the common occurrence of acetaminophen use in alcohol abusers or the malnourished, the rarity of severe 5‐oxoproline toxicity suggests unknown factors may be present in predisposed individuals, or under‐recognition. Although acetaminophen‐induced hepatotoxicity may occur along with 5‐oxoprolinemia, this does not always occur.

Several features led me away from this syndrome. First, its rarity lowered my pretest probability. Second, the lack of exposure history and details about the serum assays, specifically whether the measurements were confirmed by gas chromatography, reduced my confidence in eliminating more common ingestions. Third, several aspects proved to be less useful discriminating features: the mild elevation in osmolar gap, renal failure, and hallucinations, which in retrospect proved to be nonspecific.

The patient admitted that he had a longstanding use of acetaminophen in addition to using his girlfriend's acetaminophen‐hydrocodone. He had significant weight loss of over 50 pounds over the previous year, which he attributed to poor appetite. On further chart review, he had been admitted 3 times with a similar clinical presentation and recovered quickly with intensive and supportive care, with no etiology found at those times. He had 2 subsequent hospital admissions for altered mental status and respiratory failure, and his final hospitalization resulted in cardiac arrest and death.

DISCUSSION

5‐Oxoprolinemia is a rare, but potentially lethal cause of severe anion gap metabolic acidosis.[1, 2] The mechanism is thought to be impairment of glutathione metabolism, in the context of other predisposing factors. This can be a congenital error of metabolism, or can be acquired and exacerbated by acetaminophen use. Ingestion of acetaminophen leads to glutathione depletion, which in turn may precipitate accumulation of pyroglutamic acid and subsequent anion gap metabolic acidosis (Figure 1). Additional risk factors that may predispose patients to this condition include malnutrition, renal insufficiency, concurrent infection, and female gender.[1, 2, 3]

Figure 1

The diagnosis of 5‐oxoprolinemia is made via urine or serum organic acid analysis, testing routinely performed in pediatric populations when screening for congenital metabolic disorders. The pathophysiology suggests that obtaining a urine sample early in presentation, when acidosis is greatest, would lead to the highest 5‐oxoproline levels and best chance for diagnosis. Case patients have had normal levels prior to and in convalescent phases after the acute episode.[4] Given the long turnaround time for lab testing, presumptive diagnosis and treatment may be necessary.

Treatment of 5‐oxoprolinemia is primarily supportive, aimed at the metabolic acidosis. Fluid resuscitation and bicarbonate therapy are reasonable temporizing measures. Hemodialysis can clear 5‐oxoproline and may be indicated in severe acidosis.[5] Furthermore, the proposed pathophysiology suggests that administration of N‐acetylcysteine (NAC) may help to address the underlying process, but there are no trials to support a specific dosing regimen. However, given the fulminant presentation and common competing concern for acetaminophen toxicity, it is reasonable to initiate NAC aimed at treatment for possible acetaminophen overdose. Prevention of recurrence includes avoidance of acetaminophen, and counseling the patient to avoid acetaminophen in prescription combination medications and over‐the‐counter preparations.

Recent regulatory changes regarding acetaminophen/opioid combinations may reduce the incidence of 5‐oxoprolinemia. The US Food and Drug Administration has taken action to reduce adverse effects from acetaminophen exposure by limiting the amount of acetaminophen in opioid combination pills from 500 mg to a maximum of 325 mg per pill. This is aimed at preventing hepatotoxicity from ingestion of higher‐than‐recommended doses. However, clinicians should remember that 5‐oxoprolinemia can result from ingestion of acetaminophen at therapeutic levels.

Given its rare incidence, low clinical suspicion, and transient nature of confirmatory testing, it is likely this remains an underdiagnosed syndrome. In the case discussed, subsequent chart review demonstrated 5 previous admissions in multiple hospitals for severe transient anion gap acidosis. The likelihood that 5‐oxoprolinemia was missed in each of these cases supports a lack of awareness of this syndrome. In this patient, the discussant appropriately identified the possibility of 5‐oxoproline toxicity, but felt ethylene glycol ingestion was more likely. As this case underscores, a cornerstone in the management of suspected ingestions is empiric treatment for the most likely etiologies. Here, treatment for acetaminophen overdose and for methanol or ethylene glycol were warranted, and fortunately also addressed the rarer possibility of 5‐oxoproline toxicity.

The mnemonic MUDPILES is commonly used to identify possible causes of life‐threatening anion gap metabolic acidosis, as such heuristics have benefits in rapidly generating a differential diagnosis to guide initial evaluation. Given the fact that the traditional letter P (paraldehyde) in MUDPILES is no longer clinically utilized, some authors have suggested replacing this with pyroglutamic acid (a synonym of 5‐oxoproline). Such a change may help providers who have ruled out other causes of a high anion gap metabolic acidosis, facilitating diagnosis of this life‐threatening syndrome. In any case, clinicians must be mindful that simple memory aids may mislead clinicians, and a complete differential diagnosis may require more than a mnemonic.

TEACHING POINTS

1. Acetaminophen use, even at therapeutic levels, can lead to 5‐oxoprolinemia, a potentially lethal anion gap metabolic acidosis.
2. 5‐oxoprolinemia is likely related to glutathione depletion, worsened by acetaminophen, malnutrition, renal insufficiency, female gender, and infection. This implies theoretical benefit from administration of NAC for glutathione repletion.
3. Mnemonics can be useful, but have limitations by way of oversimplification. This case suggests that changing the letter P in MUDPILES from paraldehyde to pyroglutamic acid could reduce underdiagnosis.

Disclosure: Nothing to report.

A 51‐year‐old man presented to the emergency department after 1 day of progressive dyspnea and increasing confusion.

Acute dyspnea most commonly stems from a cardiac or pulmonary disorder such as heart failure, acute coronary syndrome, pneumonia, pulmonary embolism, or exacerbations of asthma or chronic obstructive pulmonary disease. Less frequent cardiopulmonary considerations include pericardial or pleural effusion, pneumothorax, aspiration, and upper airway obstruction. Dyspnea might also be the initial manifestation of profound anemia or metabolic acidosis.

The presence of confusion suggests either a severe presentation of any of the aforementioned possibilities (with confusion resulting from hypoxia, hypercapnia, or hypotension); a multiorgan illness such as sepsis, malignancy, thromboembolic disease, vasculitis, thyroid dysfunction, or toxic ingestion; or a metabolic derangement related to the underlying cause of dyspnea (for example, hypercalcemia or hyponatremia associated with lung cancer).

Twelve hours prior to presentation, he started to have visual hallucinations. He denied fever, chills, cough, chest discomfort, palpitations, weight gain, headache, neck pain, or weakness.

Visual hallucinations could result from a toxic‐metabolic encephalopathy, such as drug overdose or withdrawal, liver or kidney failure, or hypoxia. A structural brain abnormality may also manifest with visual hallucination. Acute onset at age 51 and the absence of auditory hallucinations argue against a neurodegenerative illness and a primary psychiatric disturbance, respectively.

Episodic hallucinations would support the possibility of seizures, monocular hallucinations would point to a retinal or ocular problem, and a description of yellow‐green hue would suggest a side effect of digoxin.

His past medical history was remarkable for diet‐controlled type 2 diabetes mellitus, hypertension, hyperlipidemia, and chronic low back pain. His medications included metoprolol tartrate 25 mg twice daily, omeprazole 40 mg daily, baclofen 15 mg twice daily, oxycodone 30 mg 3 times daily, and hydrocodone 10 mg/acetaminophen 325 mg, 2 tablets 3 times daily as needed for back pain. He was a smoker with a 30 pack‐year history. He had a history of alcohol and cocaine use, but denied any recent substance use. He had no known history of obstructive pulmonary disease.

The patient takes 3 medications well known to cause confusion and hallucinations (oxycodone, hydrocodone, and baclofen), especially when they accumulate due to excessive ingestion or impaired clearance. Although these medications may suppress ventilatory drive, dyspnea would not be a common presenting complaint. He has risk factors for ischemic heart disease and cardiomyopathy, and his smoking history raises the possibility of malignancy.

On exam, the patient's temperature was 94.4C, heart rate 128 beats per minute, respiration rate 28 breaths per minute, blood pressure 155/63 mm Hg, and oxygen saturation 100% while breathing ambient air. The patient was cachectic and appeared in moderate respiratory distress. His pupils were equal and reactive to light, and extraocular movements were intact. He did not have scleral icterus, or cervical or clavicular lymphadenopathy. His oropharynx was negative for erythema, edema, or exudate. His cardiovascular exam revealed a regular tachycardia without rubs or diastolic gallops. There was a 2/6 systolic murmur heard best at left sternal border, without radiation. He did not have jugular venous distention. His pulmonary exam was notable for tachypnea but with normal vesicular breath sounds throughout. He did not have stridor, wheezing, rhonchi, or rales. His abdomen had normal bowel tones and was soft without tenderness, distention, or organomegaly. His extremities were warm, revealed normal pulses, and no edema was present. His joints were cool to palpation, without effusion. On neurologic exam, he was oriented to person and place and able to answer yes/no questions, but unable to provide detailed history. His speech was fluent. His motor exam was without focal deficits. His skin was without any notable lesions.

The constellation of findings does not point to a specific toxidrome. The finding of warm extremities in a hypothermic patient suggests heat loss due to inappropriate peripheral vasodilation. In the absence of vasodilators or features of aortic insufficiency, sepsis becomes a leading consideration. Infection could result in hypothermia and altered sensorium, and accompanying lactic acidosis could trigger tachypnea.

Shortly after admission, he became more somnolent and developed progressive respiratory distress, requiring intubation. Arterial blood gas revealed a pH of 6.93, PaCO₂ 20 mm Hg, PaO₂ 127 mm Hg, and HCO₃ 5 mEq/L. Other laboratory results included a lactate of 4.1 mmol/L, blood urea nitrogen 49 mg/dL, creatinine 2.3 mg/dL (0.8 at 1 month prior), sodium level of 143 mmol/L, chloride of 106 mmol/L, and bicarbonate level of 5 mg/dL. His aspartate aminotransferase was 34 IU/L, alanine transaminase was 28 IU/L, total bilirubin was 0.6 mg/dL, International Normalized Ratio was 1.3. A complete blood count revealed a white blood cell count of 23,000/L, hemoglobin of 10.6 g/dL, and platelet count of 454,000/L. A urinalysis was unremarkable. Cultures of blood, urine, and sputum were collected. Head computed tomography was negative.

This patient has a combined anion gap and nongap metabolic acidosis, as well as respiratory alkalosis. Although his acute kidney failure could produce these 2 types of metabolic acidosis, the modest elevation of the serum creatinine is not commensurate with such profound acidosis. Similarly, sepsis without hypotension or more striking elevation in lactate levels would not account for the entirety of the acidosis. Severe diabetic ketoacidosis can result in profound metabolic acidosis, and marked hyperglycemia or hyperosmolarity could result in somnolence; however, his diabetes has been controlled without medication and there is no obvious precipitant for an episode of ketoacidosis.

Remaining causes of anion gap acidosis include ingestion of methanol, ethylene glycol, ethanol, or salicylates. A careful history of ingestions and medications from witnesses including any prehospital personnel might suggest a source of intoxication. Absent this information, the hypothermia favors an ingestion of an alcohol over salicylates, and the lack of urine crystals and the presence of prominent visual hallucinations would point more toward methanol poisoning than ethylene glycol. A serum osmolarity measurement would allow determination of the osmolar gap, which would be elevated in the setting of methanol or ethylene glycol poisoning. If he were this ill from ethanol, I would have expected to see evidence of hepatotoxicity.

I would administer sodium bicarbonate to reverse the acidosis and to promote renal clearance of salicylates, methanol, ethylene glycol, and their metabolites. Orogastric decontamination with activated charcoal should be considered. If the osmolar gap is elevated, I would also administer intravenous fomepizole to attempt to reverse methanol or ethylene glycol poisoning. I would not delay treatment while waiting for these serum levels to return.

Initial serologic toxicology performed in the emergency department revealed negative ethanol, salicylates, and ketones. His osmolar gap was 13 mOsm/kg. His acetaminophen level was 69 g/mL (normal 120 g/mL). A creatinine phosphokinase was 84 IU/L and myoglobin was 93 ng/mL. His subsequent serum toxicology screen was negative for methanol, ethylene glycol, isopropranol, and hippuric acid. Urine toxicology was positive for opiates, but negative for amphetamine, benzodiazepine, cannabinoid, and cocaine.

Serum and urine ketone assays typically involve the nitroprusside reaction and detect acetoacetate, but not ‐hydroxybutyrate, and can lead to negative test results early in diabetic or alcoholic ketoacidosis. However, the normal ethanol level argues against alcoholic ketoacidosis. Rare causes of elevated anion gap acidosis include toluene toxicity, acetaminophen poisoning, and ingestion of other alcohols. Toluene is metabolized to hippuric acid, and acetaminophen toxicity and associated glutathione depletion can lead to 5‐oxoproline accumulation, producing an anion gap. Patients who abuse alcohol are at risk for acetaminophen toxicity even at doses considered normal. However, this degree of encephalopathy would be unusual for acetaminophen toxicity unless liver failure had developed or unless there was another ingestion that might alter sensorium. Furthermore, the elevated osmolar gap is not a feature of acetaminophen poisoning. I would monitor liver enzyme tests and consider a serum ammonia level, but would not attribute the entire picture to acetaminophen.

The combination of elevated anion gap with an elevated osmolar gap narrows the diagnostic possibilities. Ingestion of several alcohols (ethanol, methanol, ethylene glycol, diethylene glycol) or toluene could produce these abnormalities. Of note, the osmolar gap is typically most markedly elevated early in methanol and ethylene glycol ingestions, and then as the parent compound is metabolized, the osmolar gap closes and the accumulation of metabolites produces the anion gap. Hallucinations are more common with methanol and toluene, and renal failure is more typical of ethylene glycol or toluene. The lack of oxalate crystalluria does not exclude ethylene glycol poisoning. Unfortunately, urine testing for oxalate crystals or fluorescein examination are neither sensitive nor specific enough to diagnosis ethylene glycol toxicity reliably. In most hospitals, assays used for serum testing for alcohols are insensitive, and require confirmation with gas chromatography performed at a specialty lab.

Additional history might reveal the likely culprit or culprits. Inhalant abuse including huffing would point to toluene or organic acid exposure. Solvent ingestion (eg, antifreeze, brake fluid) would suggest methanol or ethylene glycol. Absent this history, I remain suspicious for poisoning with methanol or ethylene glycol and would consider empiric treatment after urgent consultation with a medical toxicologist. A careful ophthalmologic exam might demonstrate characteristic features of methanol poisoning. Serum samples should be sent to a regional lab for analysis for alcohols and organic acids.

He was admitted to the intensive care unit, and empiric antibiotics started. He was empirically started on N‐acetylcysteine and sodium bicarbonate drips. However, his acidemia persisted and he required hemodialysis, which was initiated 12 hours after initial presentation. His acidemia and mental status quickly improved after hemodialysis. He was extubated on hospital day 2 and no longer required hemodialysis.

The differential diagnosis at this point consists of 3 main possibilities: ingestion of methanol, ethylene glycol, or inhalant abuse such as from toluene. The normal hippuric acid level points away from toluene, whereas serum levels can be misleading in the alcohol poisonings. Other discriminating features to consider include exposure history and unique clinical aspects. In this patient, an exposure history is lacking, but 4 clinical features stand out: visual hallucinations, acute kidney injury, mild lactic acidosis, and rapid improvement with hemodialysis. Both ethylene glycol and methanol toxicity may produce a mild lactic acidosis by increasing hepatic metabolism of pyruvate to lactate, and both are rapidly cleared by dialysis. Although it is tempting to place methanol at the top of the list of possibilities due to the report of visual hallucinations, the subjective visual complaints without objective exam corollaries (loss of visual acuity, abnormal pupillary reflexes, or optic disc hyperemia) are nonspecific and might be provoked by alcohol or an inhalant. Furthermore, the acute renal failure is much more typical of ethylene glycol, and thus I would consider ethylene glycol as being the more likely of the ingestions. Coingestion of multiple alcohols is a possibility, but it would be statistically less likely. Confirmation of ethylene glycol poisoning would consist of further insight into his exposures and measurement of levels using gas chromatography.

A urine sample from his emergency department presentation was sent to an outside lab for organic acid levels. Based on high clinical suspicion for 5‐oxoprolinemia (pyroglutamic acidemia) the patient was counseled to avoid any acetaminophen. His primary care provider was informed of this and acetaminophen was added as an adverse drug reaction. The patient left against medical advice soon after extubation. Following discharge, his 5‐oxoproline (pyroglutamic acid) level returned markedly elevated at greater than 10,000 mmol/mol creatinine (200 times the upper limit of normal).

Elevations in 5‐oxoproline levels in this patient most likely stem from glutathione depletion related to chronic acetaminophen use. Alcohol use and malnutrition may have heightened this patient's susceptibility. Despite the common occurrence of acetaminophen use in alcohol abusers or the malnourished, the rarity of severe 5‐oxoproline toxicity suggests unknown factors may be present in predisposed individuals, or under‐recognition. Although acetaminophen‐induced hepatotoxicity may occur along with 5‐oxoprolinemia, this does not always occur.

Several features led me away from this syndrome. First, its rarity lowered my pretest probability. Second, the lack of exposure history and details about the serum assays, specifically whether the measurements were confirmed by gas chromatography, reduced my confidence in eliminating more common ingestions. Third, several aspects proved to be less useful discriminating features: the mild elevation in osmolar gap, renal failure, and hallucinations, which in retrospect proved to be nonspecific.

The patient admitted that he had a longstanding use of acetaminophen in addition to using his girlfriend's acetaminophen‐hydrocodone. He had significant weight loss of over 50 pounds over the previous year, which he attributed to poor appetite. On further chart review, he had been admitted 3 times with a similar clinical presentation and recovered quickly with intensive and supportive care, with no etiology found at those times. He had 2 subsequent hospital admissions for altered mental status and respiratory failure, and his final hospitalization resulted in cardiac arrest and death.

DISCUSSION

5‐Oxoprolinemia is a rare, but potentially lethal cause of severe anion gap metabolic acidosis.[1, 2] The mechanism is thought to be impairment of glutathione metabolism, in the context of other predisposing factors. This can be a congenital error of metabolism, or can be acquired and exacerbated by acetaminophen use. Ingestion of acetaminophen leads to glutathione depletion, which in turn may precipitate accumulation of pyroglutamic acid and subsequent anion gap metabolic acidosis (Figure 1). Additional risk factors that may predispose patients to this condition include malnutrition, renal insufficiency, concurrent infection, and female gender.[1, 2, 3]

Figure 1

The diagnosis of 5‐oxoprolinemia is made via urine or serum organic acid analysis, testing routinely performed in pediatric populations when screening for congenital metabolic disorders. The pathophysiology suggests that obtaining a urine sample early in presentation, when acidosis is greatest, would lead to the highest 5‐oxoproline levels and best chance for diagnosis. Case patients have had normal levels prior to and in convalescent phases after the acute episode.[4] Given the long turnaround time for lab testing, presumptive diagnosis and treatment may be necessary.

Treatment of 5‐oxoprolinemia is primarily supportive, aimed at the metabolic acidosis. Fluid resuscitation and bicarbonate therapy are reasonable temporizing measures. Hemodialysis can clear 5‐oxoproline and may be indicated in severe acidosis.[5] Furthermore, the proposed pathophysiology suggests that administration of N‐acetylcysteine (NAC) may help to address the underlying process, but there are no trials to support a specific dosing regimen. However, given the fulminant presentation and common competing concern for acetaminophen toxicity, it is reasonable to initiate NAC aimed at treatment for possible acetaminophen overdose. Prevention of recurrence includes avoidance of acetaminophen, and counseling the patient to avoid acetaminophen in prescription combination medications and over‐the‐counter preparations.

Recent regulatory changes regarding acetaminophen/opioid combinations may reduce the incidence of 5‐oxoprolinemia. The US Food and Drug Administration has taken action to reduce adverse effects from acetaminophen exposure by limiting the amount of acetaminophen in opioid combination pills from 500 mg to a maximum of 325 mg per pill. This is aimed at preventing hepatotoxicity from ingestion of higher‐than‐recommended doses. However, clinicians should remember that 5‐oxoprolinemia can result from ingestion of acetaminophen at therapeutic levels.

Given its rare incidence, low clinical suspicion, and transient nature of confirmatory testing, it is likely this remains an underdiagnosed syndrome. In the case discussed, subsequent chart review demonstrated 5 previous admissions in multiple hospitals for severe transient anion gap acidosis. The likelihood that 5‐oxoprolinemia was missed in each of these cases supports a lack of awareness of this syndrome. In this patient, the discussant appropriately identified the possibility of 5‐oxoproline toxicity, but felt ethylene glycol ingestion was more likely. As this case underscores, a cornerstone in the management of suspected ingestions is empiric treatment for the most likely etiologies. Here, treatment for acetaminophen overdose and for methanol or ethylene glycol were warranted, and fortunately also addressed the rarer possibility of 5‐oxoproline toxicity.

The mnemonic MUDPILES is commonly used to identify possible causes of life‐threatening anion gap metabolic acidosis, as such heuristics have benefits in rapidly generating a differential diagnosis to guide initial evaluation. Given the fact that the traditional letter P (paraldehyde) in MUDPILES is no longer clinically utilized, some authors have suggested replacing this with pyroglutamic acid (a synonym of 5‐oxoproline). Such a change may help providers who have ruled out other causes of a high anion gap metabolic acidosis, facilitating diagnosis of this life‐threatening syndrome. In any case, clinicians must be mindful that simple memory aids may mislead clinicians, and a complete differential diagnosis may require more than a mnemonic.

TEACHING POINTS

1. Acetaminophen use, even at therapeutic levels, can lead to 5‐oxoprolinemia, a potentially lethal anion gap metabolic acidosis.
2. 5‐oxoprolinemia is likely related to glutathione depletion, worsened by acetaminophen, malnutrition, renal insufficiency, female gender, and infection. This implies theoretical benefit from administration of NAC for glutathione repletion.
3. Mnemonics can be useful, but have limitations by way of oversimplification. This case suggests that changing the letter P in MUDPILES from paraldehyde to pyroglutamic acid could reduce underdiagnosis.

Disclosure: Nothing to report.

Hospital medicine (HM), which is the fastest growing medical specialty in the United States, includes more than 40,000 healthcare providers.[1] Hospitalists include practitioners from a variety of medical specialties, including internal medicine and pediatrics, and professional backgrounds such as physicians, nurse practitioners. and physician assistants.[2, 3] Originally defined as specialists of inpatient medicine, hospitalists must diagnose and manage a wide variety of clinical conditions, coordinate transitions of care, provide perioperative management to surgical patients, and contribute to quality improvement and hospital administration.[4, 5]

With the evolution of the HM, the need for effective continuing medical education (CME) has become increasingly important. Courses make up the largest percentage of CME activity types,[6] which also include regularly scheduled lecture series, internet materials, and journal‐related CME. Successful CME courses require educational content that matches the learning needs of its participants.[7] In 2006, the Society for Hospital Medicine (SHM) developed core competencies in HM to guide educators in identifying professional practice gaps for useful CME.[8] However, knowing a population's characteristics and learning needs is a key first step to recognizing a practice gap.[9] Understanding these components is important to ensuring that competencies in the field of HM remain relevant to address existing practice gaps.[10] Currently, little is known about the demographic characteristics of participants in HM CME.

Research on the characteristics of effective clinical teachers in medicine has revealed the importance of establishing a positive learning climate, asking questions, diagnosing learners needs, giving feedback, utilizing established teaching frameworks, and developing a personalized philosophy of teaching.[11] Within CME, research has generally demonstrated that courses lead to improvements in lower level outcomes,[12] such as satisfaction and learning, yet higher level outcomes such as behavior change and impacts on patients are inconsistent.[13, 14, 15] Additionally, we have shown that participant reflection on CME is enhanced by presenters who have prior teaching experience and higher teaching effectiveness scores, by the use of audience participation and by incorporating relevant content.[16, 17] Despite the existence of research on CME in general, we are not aware of prior studies regarding characteristics of effective CME in the field of HM.

To better understand and improve the quality of HM CME, we sought to describe the characteristics of participants at a large, national HM CME course, and to identify associations between characteristics of presentations and CME teaching effectiveness (CMETE) scores using a previously validated instrument.

METHODS

RESULTS

There were 32 presentations during the MCP conference in 2014. A total of 277 (75.2%) out of 368 participants completed the survey. This yielded 7947 CMETE evaluations for analysis, with an average of 28.7 per person (median: 31, interquartile range: 2732, range: 632).

Demographic characteristics of course participants are listed in Table 1. Participants (number, %), described themselves as hospitalists (181, 70.4%), ABIM certified with HM focus (48, 18.8%), physicians with MD or MBBS degrees (181, 70.4%), nurse practitioners or physician assistants (52; 20.2%), and in practice 20 years (73, 28%). The majority of participants (148, 58.3%) worked in private practice, whereas only 63 (24.8%) worked in academic settings.

CMETE scores (mean [SD]) were significantly associated with the use of ARS (4.64 [0.16]) vs no ARS (4.49 [0.16]; P=0.01, Table 2, Figure 1), longer presentations (30 minutes: 4.67 [0.13] vs <30 minutes: 4.51 [0.18]; P=0.02), and larger number of slides (50: 4.66 [0.17] vs <50: 4.55 [0.17]; P=0.04). There were no significant associations between CMETE scores and use of clinical cases, defined goals, or summary slides.

Figure 1

The magnitude of score differences observed in this study are substantial when considered in terms of Cohen's effect sizes. For number of slides, the effect size is 0.65, for audience response the effect size is 0.94, and for presentation length the effect size is approximately 1. According to Cohen, effect sizes of 0.5 to 0.8 are moderate, and effect sizes >0.8 are large. Consequently, the effect sizes of our observed differences are moderate to large.[20, 21]

DISCUSSION

To our knowledge, this is the first study to measure associations between validated teaching effectiveness scores and characteristics of presentations in HM CME. We found that the use of ARS and longer presentations were associated with significantly higher CMETE scores. Our findings have implications for HM CME course directors and presenters as they attempt to develop methods to improve the quality of CME.

CME participants in our study crossed a wide range of ages and experience, which is consistent with national surveys of hospitalists.[22, 23] Interestingly, however, nearly 1 in 3 participants trained in a specialty other than internal medicine. Additionally, the professional degrees of participants were diverse, with 20% of participants having trained as nurse practitioners or physician assistants. These findings are at odds with an early national survey of inpatient practitioners,[22] but consistent with recent literature that the diversity of training backgrounds among hospitalists is increasing as the field of HM evolves.[24] Hospital medicine CME providers will need to be cognizant of these demographic changes as they work to identify practice gaps and apply appropriate educational methods.

The use of an ARS allows for increased participation and engagement among lecture attendees, which in turn promotes active learning.[25, 26, 27] The association of higher teaching scores with the use of ARS is consistent with previous research in other CME settings such as clinical round tables and medical grand rounds.[17, 28] As it pertains to HM specifically, our findings also build upon a recent study by Sehgal et al., which reported on the novel use of bedside CME to enhance interactive learning and discussion among hospitalists, and which was viewed favorably by course participants.[29]

The reasons why longer presentations in our study were linked to higher CMETE scores are not entirely clear, as previous CME research has failed to demonstrate this relationship.[18] One possibility is that course participants prefer learning from presentations that provide granular, content‐rich information. Another possibility may be that characteristics of effective presenters who gave longer presentations and that were not measured in this study, such as presenter experience and expertise, were responsible for the observed increase in CMETE scores. Yet another possibility is that effective presentations were longer due to the use of ARS, which was also associated with better CMETE scores. This explanation may be plausible because the ARS requires additional slides and provides opportunities for audience interaction, which may lengthen the duration of any given presentation.

This study has several limitations. This was a single CME conference sponsored by a large academic medical center, which may limit generalizability, especially to smaller conferences in community settings. However, the audience was large and diverse in terms of participants experiences, practice settings, professional backgrounds, and geographic locations. Furthermore, the demographic characteristics of hospitalists at our course appear very similar to a recently reported national cross‐section of hospitalist groups.[30] Second, this is a cross‐sectional study without a comparison group. Nonetheless, a systematic review showed that most published education research studies involved single‐group designs without comparison groups.[31] Last, the outcomes of the study include attitudes and objectively measured presenter behaviors such as the use of ARS, but not patient‐related outcomes. Nonetheless, evidence indicates that the majority of medical education research does not present outcomes beyond knowledge,[31] and it has been noted that behavior‐related outcomes strike the ideal balance between feasibility and rigor.[32, 33] Finally, the instrument used in this study to measure teaching effectiveness is supported by prior validity evidence.[16]

In summary, we found that hospital medicine CME presentations, which are longer and use audience responses, are associated with greater teaching effectiveness ratings by CME course participants. These findings build upon previous CME research and suggest that CME course directors and presenters should strive to incorporate opportunities that promote audience engagement and participation. Additionally, this study adds to the existing validity of evidence for the CMETE assessment tool. We believe that future research should explore potential associations between teacher effectiveness and patient‐related outcomes, and determine whether course content that reflects the SHM core competencies improves CME teaching effectiveness scores.

Hospital medicine (HM), which is the fastest growing medical specialty in the United States, includes more than 40,000 healthcare providers.[1] Hospitalists include practitioners from a variety of medical specialties, including internal medicine and pediatrics, and professional backgrounds such as physicians, nurse practitioners. and physician assistants.[2, 3] Originally defined as specialists of inpatient medicine, hospitalists must diagnose and manage a wide variety of clinical conditions, coordinate transitions of care, provide perioperative management to surgical patients, and contribute to quality improvement and hospital administration.[4, 5]

With the evolution of the HM, the need for effective continuing medical education (CME) has become increasingly important. Courses make up the largest percentage of CME activity types,[6] which also include regularly scheduled lecture series, internet materials, and journal‐related CME. Successful CME courses require educational content that matches the learning needs of its participants.[7] In 2006, the Society for Hospital Medicine (SHM) developed core competencies in HM to guide educators in identifying professional practice gaps for useful CME.[8] However, knowing a population's characteristics and learning needs is a key first step to recognizing a practice gap.[9] Understanding these components is important to ensuring that competencies in the field of HM remain relevant to address existing practice gaps.[10] Currently, little is known about the demographic characteristics of participants in HM CME.

Research on the characteristics of effective clinical teachers in medicine has revealed the importance of establishing a positive learning climate, asking questions, diagnosing learners needs, giving feedback, utilizing established teaching frameworks, and developing a personalized philosophy of teaching.[11] Within CME, research has generally demonstrated that courses lead to improvements in lower level outcomes,[12] such as satisfaction and learning, yet higher level outcomes such as behavior change and impacts on patients are inconsistent.[13, 14, 15] Additionally, we have shown that participant reflection on CME is enhanced by presenters who have prior teaching experience and higher teaching effectiveness scores, by the use of audience participation and by incorporating relevant content.[16, 17] Despite the existence of research on CME in general, we are not aware of prior studies regarding characteristics of effective CME in the field of HM.

To better understand and improve the quality of HM CME, we sought to describe the characteristics of participants at a large, national HM CME course, and to identify associations between characteristics of presentations and CME teaching effectiveness (CMETE) scores using a previously validated instrument.

METHODS

RESULTS

There were 32 presentations during the MCP conference in 2014. A total of 277 (75.2%) out of 368 participants completed the survey. This yielded 7947 CMETE evaluations for analysis, with an average of 28.7 per person (median: 31, interquartile range: 2732, range: 632).

Demographic characteristics of course participants are listed in Table 1. Participants (number, %), described themselves as hospitalists (181, 70.4%), ABIM certified with HM focus (48, 18.8%), physicians with MD or MBBS degrees (181, 70.4%), nurse practitioners or physician assistants (52; 20.2%), and in practice 20 years (73, 28%). The majority of participants (148, 58.3%) worked in private practice, whereas only 63 (24.8%) worked in academic settings.

CMETE scores (mean [SD]) were significantly associated with the use of ARS (4.64 [0.16]) vs no ARS (4.49 [0.16]; P=0.01, Table 2, Figure 1), longer presentations (30 minutes: 4.67 [0.13] vs <30 minutes: 4.51 [0.18]; P=0.02), and larger number of slides (50: 4.66 [0.17] vs <50: 4.55 [0.17]; P=0.04). There were no significant associations between CMETE scores and use of clinical cases, defined goals, or summary slides.

Figure 1

The magnitude of score differences observed in this study are substantial when considered in terms of Cohen's effect sizes. For number of slides, the effect size is 0.65, for audience response the effect size is 0.94, and for presentation length the effect size is approximately 1. According to Cohen, effect sizes of 0.5 to 0.8 are moderate, and effect sizes >0.8 are large. Consequently, the effect sizes of our observed differences are moderate to large.[20, 21]

DISCUSSION

To our knowledge, this is the first study to measure associations between validated teaching effectiveness scores and characteristics of presentations in HM CME. We found that the use of ARS and longer presentations were associated with significantly higher CMETE scores. Our findings have implications for HM CME course directors and presenters as they attempt to develop methods to improve the quality of CME.

CME participants in our study crossed a wide range of ages and experience, which is consistent with national surveys of hospitalists.[22, 23] Interestingly, however, nearly 1 in 3 participants trained in a specialty other than internal medicine. Additionally, the professional degrees of participants were diverse, with 20% of participants having trained as nurse practitioners or physician assistants. These findings are at odds with an early national survey of inpatient practitioners,[22] but consistent with recent literature that the diversity of training backgrounds among hospitalists is increasing as the field of HM evolves.[24] Hospital medicine CME providers will need to be cognizant of these demographic changes as they work to identify practice gaps and apply appropriate educational methods.

The use of an ARS allows for increased participation and engagement among lecture attendees, which in turn promotes active learning.[25, 26, 27] The association of higher teaching scores with the use of ARS is consistent with previous research in other CME settings such as clinical round tables and medical grand rounds.[17, 28] As it pertains to HM specifically, our findings also build upon a recent study by Sehgal et al., which reported on the novel use of bedside CME to enhance interactive learning and discussion among hospitalists, and which was viewed favorably by course participants.[29]

The reasons why longer presentations in our study were linked to higher CMETE scores are not entirely clear, as previous CME research has failed to demonstrate this relationship.[18] One possibility is that course participants prefer learning from presentations that provide granular, content‐rich information. Another possibility may be that characteristics of effective presenters who gave longer presentations and that were not measured in this study, such as presenter experience and expertise, were responsible for the observed increase in CMETE scores. Yet another possibility is that effective presentations were longer due to the use of ARS, which was also associated with better CMETE scores. This explanation may be plausible because the ARS requires additional slides and provides opportunities for audience interaction, which may lengthen the duration of any given presentation.

This study has several limitations. This was a single CME conference sponsored by a large academic medical center, which may limit generalizability, especially to smaller conferences in community settings. However, the audience was large and diverse in terms of participants experiences, practice settings, professional backgrounds, and geographic locations. Furthermore, the demographic characteristics of hospitalists at our course appear very similar to a recently reported national cross‐section of hospitalist groups.[30] Second, this is a cross‐sectional study without a comparison group. Nonetheless, a systematic review showed that most published education research studies involved single‐group designs without comparison groups.[31] Last, the outcomes of the study include attitudes and objectively measured presenter behaviors such as the use of ARS, but not patient‐related outcomes. Nonetheless, evidence indicates that the majority of medical education research does not present outcomes beyond knowledge,[31] and it has been noted that behavior‐related outcomes strike the ideal balance between feasibility and rigor.[32, 33] Finally, the instrument used in this study to measure teaching effectiveness is supported by prior validity evidence.[16]

In summary, we found that hospital medicine CME presentations, which are longer and use audience responses, are associated with greater teaching effectiveness ratings by CME course participants. These findings build upon previous CME research and suggest that CME course directors and presenters should strive to incorporate opportunities that promote audience engagement and participation. Additionally, this study adds to the existing validity of evidence for the CMETE assessment tool. We believe that future research should explore potential associations between teacher effectiveness and patient‐related outcomes, and determine whether course content that reflects the SHM core competencies improves CME teaching effectiveness scores.

Hospital medicine (HM), which is the fastest growing medical specialty in the United States, includes more than 40,000 healthcare providers.[1] Hospitalists include practitioners from a variety of medical specialties, including internal medicine and pediatrics, and professional backgrounds such as physicians, nurse practitioners. and physician assistants.[2, 3] Originally defined as specialists of inpatient medicine, hospitalists must diagnose and manage a wide variety of clinical conditions, coordinate transitions of care, provide perioperative management to surgical patients, and contribute to quality improvement and hospital administration.[4, 5]

With the evolution of the HM, the need for effective continuing medical education (CME) has become increasingly important. Courses make up the largest percentage of CME activity types,[6] which also include regularly scheduled lecture series, internet materials, and journal‐related CME. Successful CME courses require educational content that matches the learning needs of its participants.[7] In 2006, the Society for Hospital Medicine (SHM) developed core competencies in HM to guide educators in identifying professional practice gaps for useful CME.[8] However, knowing a population's characteristics and learning needs is a key first step to recognizing a practice gap.[9] Understanding these components is important to ensuring that competencies in the field of HM remain relevant to address existing practice gaps.[10] Currently, little is known about the demographic characteristics of participants in HM CME.

Research on the characteristics of effective clinical teachers in medicine has revealed the importance of establishing a positive learning climate, asking questions, diagnosing learners needs, giving feedback, utilizing established teaching frameworks, and developing a personalized philosophy of teaching.[11] Within CME, research has generally demonstrated that courses lead to improvements in lower level outcomes,[12] such as satisfaction and learning, yet higher level outcomes such as behavior change and impacts on patients are inconsistent.[13, 14, 15] Additionally, we have shown that participant reflection on CME is enhanced by presenters who have prior teaching experience and higher teaching effectiveness scores, by the use of audience participation and by incorporating relevant content.[16, 17] Despite the existence of research on CME in general, we are not aware of prior studies regarding characteristics of effective CME in the field of HM.

To better understand and improve the quality of HM CME, we sought to describe the characteristics of participants at a large, national HM CME course, and to identify associations between characteristics of presentations and CME teaching effectiveness (CMETE) scores using a previously validated instrument.

METHODS

RESULTS

There were 32 presentations during the MCP conference in 2014. A total of 277 (75.2%) out of 368 participants completed the survey. This yielded 7947 CMETE evaluations for analysis, with an average of 28.7 per person (median: 31, interquartile range: 2732, range: 632).

Demographic characteristics of course participants are listed in Table 1. Participants (number, %), described themselves as hospitalists (181, 70.4%), ABIM certified with HM focus (48, 18.8%), physicians with MD or MBBS degrees (181, 70.4%), nurse practitioners or physician assistants (52; 20.2%), and in practice 20 years (73, 28%). The majority of participants (148, 58.3%) worked in private practice, whereas only 63 (24.8%) worked in academic settings.

CMETE scores (mean [SD]) were significantly associated with the use of ARS (4.64 [0.16]) vs no ARS (4.49 [0.16]; P=0.01, Table 2, Figure 1), longer presentations (30 minutes: 4.67 [0.13] vs <30 minutes: 4.51 [0.18]; P=0.02), and larger number of slides (50: 4.66 [0.17] vs <50: 4.55 [0.17]; P=0.04). There were no significant associations between CMETE scores and use of clinical cases, defined goals, or summary slides.

Figure 1

The magnitude of score differences observed in this study are substantial when considered in terms of Cohen's effect sizes. For number of slides, the effect size is 0.65, for audience response the effect size is 0.94, and for presentation length the effect size is approximately 1. According to Cohen, effect sizes of 0.5 to 0.8 are moderate, and effect sizes >0.8 are large. Consequently, the effect sizes of our observed differences are moderate to large.[20, 21]

DISCUSSION

To our knowledge, this is the first study to measure associations between validated teaching effectiveness scores and characteristics of presentations in HM CME. We found that the use of ARS and longer presentations were associated with significantly higher CMETE scores. Our findings have implications for HM CME course directors and presenters as they attempt to develop methods to improve the quality of CME.

CME participants in our study crossed a wide range of ages and experience, which is consistent with national surveys of hospitalists.[22, 23] Interestingly, however, nearly 1 in 3 participants trained in a specialty other than internal medicine. Additionally, the professional degrees of participants were diverse, with 20% of participants having trained as nurse practitioners or physician assistants. These findings are at odds with an early national survey of inpatient practitioners,[22] but consistent with recent literature that the diversity of training backgrounds among hospitalists is increasing as the field of HM evolves.[24] Hospital medicine CME providers will need to be cognizant of these demographic changes as they work to identify practice gaps and apply appropriate educational methods.

The use of an ARS allows for increased participation and engagement among lecture attendees, which in turn promotes active learning.[25, 26, 27] The association of higher teaching scores with the use of ARS is consistent with previous research in other CME settings such as clinical round tables and medical grand rounds.[17, 28] As it pertains to HM specifically, our findings also build upon a recent study by Sehgal et al., which reported on the novel use of bedside CME to enhance interactive learning and discussion among hospitalists, and which was viewed favorably by course participants.[29]

The reasons why longer presentations in our study were linked to higher CMETE scores are not entirely clear, as previous CME research has failed to demonstrate this relationship.[18] One possibility is that course participants prefer learning from presentations that provide granular, content‐rich information. Another possibility may be that characteristics of effective presenters who gave longer presentations and that were not measured in this study, such as presenter experience and expertise, were responsible for the observed increase in CMETE scores. Yet another possibility is that effective presentations were longer due to the use of ARS, which was also associated with better CMETE scores. This explanation may be plausible because the ARS requires additional slides and provides opportunities for audience interaction, which may lengthen the duration of any given presentation.

This study has several limitations. This was a single CME conference sponsored by a large academic medical center, which may limit generalizability, especially to smaller conferences in community settings. However, the audience was large and diverse in terms of participants experiences, practice settings, professional backgrounds, and geographic locations. Furthermore, the demographic characteristics of hospitalists at our course appear very similar to a recently reported national cross‐section of hospitalist groups.[30] Second, this is a cross‐sectional study without a comparison group. Nonetheless, a systematic review showed that most published education research studies involved single‐group designs without comparison groups.[31] Last, the outcomes of the study include attitudes and objectively measured presenter behaviors such as the use of ARS, but not patient‐related outcomes. Nonetheless, evidence indicates that the majority of medical education research does not present outcomes beyond knowledge,[31] and it has been noted that behavior‐related outcomes strike the ideal balance between feasibility and rigor.[32, 33] Finally, the instrument used in this study to measure teaching effectiveness is supported by prior validity evidence.[16]

In summary, we found that hospital medicine CME presentations, which are longer and use audience responses, are associated with greater teaching effectiveness ratings by CME course participants. These findings build upon previous CME research and suggest that CME course directors and presenters should strive to incorporate opportunities that promote audience engagement and participation. Additionally, this study adds to the existing validity of evidence for the CMETE assessment tool. We believe that future research should explore potential associations between teacher effectiveness and patient‐related outcomes, and determine whether course content that reflects the SHM core competencies improves CME teaching effectiveness scores.

The national healthcare improvement paradigm is shifting toward a more comprehensive, value‐focused, and patient‐centered approach. Reducing hospital readmissions has become a focal point as a policy strategy to improve care quality while reducing cost. Section 3025 of the Affordable Care Act mandated the Centers for Medicare and Medicaid Services to make progressive reductions in Medicare payments to hospitals that have higher than expected readmission rates for 3 conditions (heart failure, acute myocardial infarction, and pneumonia), and expanding to include chronic obstructive pulmonary disease and total hip and knee arthroplasty in 2015.[1] In response, hospitals and systems are developing and implementing programs that coordinate care beyond hospital walls to reduce readmissions and healthcare costs.[2, 3] However, patients are readmitted for a variety of reasons, and programs that address the needs of some may not address the distinct needs of others. Understanding the characteristics of patients with frequent readmissions will permit the well‐informed creation of solutions specific to this population to reduce cost, free resources, and provide better care.

Although a solid body of literature already exists that describes the characteristics of patients who frequently visit the emergency department (ED),[4, 5, 6, 7, 8, 9, 10, 11, 12] it is not clear to what extent these characteristics also apply to patients with frequent hospital admissions. Frequent ED visitors have been found to be largely insured (85%) although with over‐representation of public insurance, and to be heavy users of the healthcare system overall.[6] A high disease burden associated with multiple chronic conditions has been found to predict frequent ED use.[4, 9, 11, 12] Some characteristics may vary by location; for example, alcohol abuse and psychiatric morbidity have been found to be associated with frequent ED use in New York and San Francisco, but it is not clear to what extent they are a factor in less urban areas.[4, 6, 12]

Several previous studies have investigated the characteristics of frequently admitted patients at single sites.[13, 14, 15, 16] Nguyen et al. (2013) studied patients with the highest costs and the most admissions at a large academic medical center in San Francisco.[13] High admit patients were defined as those responsible for the top decile of admissions, and were grouped into equal‐sized high‐ and low‐cost cohorts. The high‐admission/high‐cost group represented 5% of all patients, 25% of all costs, and 16% of all admissions. These patients were hospitalized primarily for medical conditions (78%) and had a high 30‐day readmission rate (47%). The high‐admission/low‐cost group accounted for 5% of all patients, 12% of all admissions, and 7% of all costs. These patients were also predominantly admitted for medical conditions (87%), with the most common admitting diagnoses representing respiratory, gastrointestinal, and cardiovascular conditions.[13]

Hwa (2012) conducted an analysis of 29 patients admitted 6 or more times in 1 year to an inpatient medical service in San Francisco.[14] These patients represented just 1% of all patients, but 13% of readmissions. Fifty‐five percent of these patients had a psychiatric diagnosis, and 52% had chronic pain. Ninety percent had a primary care physician in the hospital system, 100% were insured either privately or publicly, and 93% had housing, although for 17% housing was described as marginal.[14]

In a third study, Boonyasai et al. (2012) identified 76 patients with 82 readmissions at a Baltimore, Maryland, hospital and classified them as isolated (1 readmission per 6‐month period) or serial (more than 1 readmission per 6‐month period) readmissions.[15] Patients with serial readmissions accounted for 70% of the total. Isolated readmissions were most likely to be related to suboptimal quality of care and care coordination, whereas serial readmissions were more likely to result from disease progression, psychiatric illness, and substance abuse.[15]

All of these studies were conducted at single‐site academic medical centers serving inner city populations. We undertook this study to identify patient and hospital‐level characteristics of frequently admitted patients in a broad sample of 101 US academic medical centers to determine whether previously reported findings are generalizable, and to identify characteristics of frequently admitted patients that can inform interventions designed to meet the needs of this relatively small but resource‐intensive group of patients.

METHODS

All data were obtained from the University HealthSystem Consortium (UHC) (Chicago, IL) Clinical Data Base/Resource Manager (CDB), a large administrative database to which UHC principal members submit comprehensive administrative data files. UHC's principal members include approximately 120 US academic medical centers delivering tertiary and quaternary care, with an average of 647 acute care beds. The CDB includes primary and secondary diagnoses using International Classification of Diseases, Ninth Revision (ICD‐9)[17] codes.

The data of 101 academic medical centers with complete datasets for the study period (October 1, 2011, to September 30, 2012) were included in this analysis. Frequently admitted patients were defined as patients admitted 5 or more times to the same facility in a 12‐month period; all admissions were included, even those more than 30 days apart. This definition was established based on a naturally occurring break in the frequency distribution (Figure 1) and our intention to focus on the unique characteristics of patients at the far right of the distribution. We excluded obstetric (MDC 14, ICD‐9)[17] admissions and pediatric (<18 years of age at index admission) patients, as well as admissions with principal diagnoses for chemotherapy (ICD‐9 diagnosis codes v5811v5812), dialysis (ICD‐9 diagnosis codes v560v568), and rehabilitation (ICD‐9 diagnosis codes v570v579), which are typically planned. The Agency for Healthcare Research and Quality (AHRQ) comorbidity software was used to identify comorbid conditions,[18, 19] and a score based on the Elixhauser comorbidity measures was calculated using a modified acuity point system.[20] For comparisons based on safety net status, we used a definition of payer mix being 25% Medicaid or uninsured.

Figure 1

Our analyses included patient demographics, admission source and discharge status, clinical diagnoses, procedures, and comorbidities, cost, and length of stay. Patients defined as frequently admitted were compared in aggregate to all other hospitalized patients (all other admissions).

To evaluate associations, we used [2] tests for categorical variables and t tests for continuous variables. When comparing the non‐normally distributed comorbidities of the control group to the normally distributed comorbidities of the frequently admitted patients, we performed a Kruskal‐Wallis test on the medians.

RESULTS

During a 1‐year period (October 1, 2011, to September 30, 2012), 1,758,027 patients were admitted 2,388,124 times at 101 academic medical centers. Of these, 28,291 patients had 5 or more admissions during this period, resulting in 180,185 admissions. These frequently admitted patients represented 1.6% of all patients (Figure 1) and 7.6% of all inpatient admissions. By comparison, nonfrequently admitted patients were admitted once (79%), twice (14%), 3 times (4%), or 4 times (2%).

Among hospitals, the volume and impact of frequently admitted patients varied widely. The frequently admitted patient population ranged from 64 patients (0.7% of all patients) to 785 patients (3.5%), with an average of 280 patients (1.6%). To look for differences that might explain this range, we compared hospitals in the top and bottom deciles with respect to geographic region and to safety net status, but found no significant or meaningful differences. The average number of admissions per patient was 6.4, with a range of 5 to 76. Days per patient ranged from 5 to 434 days, with an average of 42. The average patient‐day percentage (frequently admitted patient days/total patient days) was 8.4%, and ranged from 3.2% to 15.4%.

Frequently admitted patients were more likely to be younger than all other patients (71.9% under the age of 65 years, as compared with 65.3% of all other patients (P<0.001)). They were also more likely to have either Medicaid or no healthcare insurance (27.6% compared with 21.6%, P<0.001), although nearly three‐quarters had either private insurance or Medicare coverage.

Eighty‐four percent of frequently admitted patient admissions were to medical services (vs 58% of all other patients (P<0.001)). The admission status for these patients was much less likely to be elective (9.1% of frequently admitted patient admissions vs 26.6% of all other patients' admissions [P<0.001]). Frequently admitted patients were more likely to be discharged to a skilled nursing facility (9.3% vs 8.4%, [P<0.001]) or with home health services (19.7% vs 13.4% [P<0.001]).

The 10 most common primary diagnoses for patient admissions are shown in Table 1. No single primary diagnosis accounted for a large share of the admissions of these patients; the most common diagnosis, sickle cell disease with crisis, accounted for only about 4% of admissions. The 10 most common diagnoses accounted for <20% of all admissions. The remainder of the diagnoses was spread over more than 3000 diagnosis codes; only about 300 codes had more than 100 admissions each.

Secondary diagnoses were mainly chronic conditions, including hypertension, hyperlipidemia, esophageal reflux, and diabetes mellitus type 2 (Table 1.) Combined, congestive heart failure and diabetes mellitus accounted for 43.3% of the secondary diagnoses of admissions of frequently admitted patients, but for only 28.6% of other patients. Acute kidney failure was more common in frequently admitted patients (13.8% vs 9.4% [P<0.001]). Psychiatric disorders accounted for <1% of primary diagnoses for both frequently admitted patients and all other patients. As a secondary diagnosis, depressive disorder appeared in the top 10 for both groups, although more commonly for frequently admitted patients (15.4% vs 10.3% [P<0.001]).

The most commonly performed principal procedures are also shown in Table 1. These include hemodialysis (6.32%) and packed cell transfusion (4.49%), nonoperating room procedures associated with chronic medical conditions.

Comorbidities were compared using the AHRQ comorbidity software.[18, 19] Comorbid conditions were counted once per patient, regardless of the number of admissions in which the condition was coded. Frequently admitted patients have a significantly higher mean number of comorbidities: 7.1 compared to 2.5 for all other patients (P<0.001; Figure 2). In an additional analysis using the Elixhauser comorbidity measures to determine acuity scores, the mean scores were 13.1 for frequently admitted patients and 3.17 for all others (P<0.001). The most common comorbidities were hypertension (74%), fluid and electrolyte disorders (73%), and deficiency anemias (66%). The only behavioral health comorbidity that affected more than a quarter of frequently admitted patients was depression (40% as compared to 13% for all others).

Figure 2

Additionally, frequently admitted patients were significantly more likely to have comorbidities of psychosis (18% vs 5% [P<0.001]), alcohol abuse (16% vs 7% [P<0.001]), and drug abuse (20% vs 7% [P<0.001]). Among hospitals, these comorbidities ranged widely: psychosis (3% 48%); alcohol abuse (3%46%); and drug abuse (3%58%). Hospitals with the highest rates (top decile) of frequently admitted patients with alcohol and drug abuse comorbidities were more likely to be safety net hospitals than those in the lowest decile (P<0.05 for each independently), but no such difference was found regarding rates of patients with psychosis.

Although the frequently admitted patient population accounted for only 1.6% of patients, they accounted for an average of 8.4% of all bed days and 7.1% of direct cost. The average cost per day was $1746, compared to $2144 for all other patients (Table 2).

DISCUSSION

An extensive analysis of the characteristics of frequently admitted patients at 101 US academic medical centers, from October 1, 2011 to September 30, 2012, revealed that these patients comprised 1.6% of all patients, but accounted for 8% of all admissions and 7% of direct costs. Relative to all other hospitalized patients, frequently admitted patients were likely to be younger, of lower socioeconomic status, in poorer health, and more often affected by mental health or substance abuse conditions that may mediate their health behaviors. However, the prevalence of patients with psychiatric or behavior conditions varied widely among hospitals, and hospitals with the highest rates of patients with substance abuse comorbidities were more likely to be safety net hospitals. Frequently admitted patients' diagnoses and procedures suggest that their admissions were related to complex chronic diseases; more than three‐quarters were admitted to medicine services, and their average length of stay was nearly 7 days. No single primary diagnosis accounted for a predominant share of their admissions; the most common diagnosis, sickle cell disease with crisis, accounted for only about 4%. The cost of their care was lower than that of other patients, reflecting the preponderance of their admissions to medicine service lines.

In many ways, frequently admitted patients seem similar to frequent ED visitors. Their visits were driven by a high disease burden associated with multiple chronic conditions, and they were heavy users of the healthcare system overall.[4, 6] The majority of both groups were insured, although there was over‐representation of public insurance.[6] As with frequent ED users, some frequently admitted patients are affected by psychiatric morbidity and substance abuse.[4, 12]

Our results in some ways confirmed, and in some ways differed from, findings of prior studies of patients with frequent hospital admissions. Although each study performed to date has defined the population differently, comparison of findings is useful. Our population was similar to the high‐admission groups identified by Nguyen et al. (patients responsible for the top decile of admissions).[13] These patients were also predominantly admitted for medical conditions, with common admitting diagnoses representing respiratory, gastrointestinal, and cardiovascular conditions. However, the median length of stay (3 days for the high‐admission/low‐cost group and 5 days for the high‐admission/high‐cost group) was lower than that of our population (5.5 days).

Hwa, who studied 29 patients admitted 6 or more times in 1 year to an inpatient medical service in San Francisco,[14] found that 55% of frequently admitted patients had a psychiatric diagnosis, higher than our patient population. Our findings are similar to those of Boonyasai et al.[15] whose serial readmitters had admissions resulting from disease progression, psychiatric illness, and substance abuse.

Our more nationally representative analysis documented a wide range of patient volumes and clinical characteristics, including psychiatric and substance abuse comorbidities, across study hospitals. It demonstrates that different approachesand resourcesare needed to meet the needs of these varied groups of patients. Each hospital must identify, evaluate, and understand its own population of frequently admitted patients to create well‐informed solutions to prevent repeat hospitalization for these patients.

Our ability to create a distinctive picture of the population of frequently admitted patients in US academic medical centers is based on access to an expansive dataset that captures complete diagnostic and demographic information on the universe of patients admitted to our member hospitals. The availability of clinical and administrative data for the entire population of patients permits both an accurate description of patient characteristics and a standardized comparison of groups. All data conform to accepted formats and definitions; their validity is universally recognized by contributing database participants.

CONCLUSION

The valuable information reported from analysis of nearly 2 million patients in the UHC Clinical Data Base/Resource Manager can be used to better understand the characteristics of frequently admitted patients. This important cohort of individuals has complex care needs that often result in hospitalization, but may be amenable to solutions that allow patients to remain in their communities. By understanding the demographic, social, and medical characteristics of these patients, hospitals can develop and implement solutions that address the needs of this small group of patients who consume a highly disproportionate share of healthcare resources.

The national healthcare improvement paradigm is shifting toward a more comprehensive, value‐focused, and patient‐centered approach. Reducing hospital readmissions has become a focal point as a policy strategy to improve care quality while reducing cost. Section 3025 of the Affordable Care Act mandated the Centers for Medicare and Medicaid Services to make progressive reductions in Medicare payments to hospitals that have higher than expected readmission rates for 3 conditions (heart failure, acute myocardial infarction, and pneumonia), and expanding to include chronic obstructive pulmonary disease and total hip and knee arthroplasty in 2015.[1] In response, hospitals and systems are developing and implementing programs that coordinate care beyond hospital walls to reduce readmissions and healthcare costs.[2, 3] However, patients are readmitted for a variety of reasons, and programs that address the needs of some may not address the distinct needs of others. Understanding the characteristics of patients with frequent readmissions will permit the well‐informed creation of solutions specific to this population to reduce cost, free resources, and provide better care.

Although a solid body of literature already exists that describes the characteristics of patients who frequently visit the emergency department (ED),[4, 5, 6, 7, 8, 9, 10, 11, 12] it is not clear to what extent these characteristics also apply to patients with frequent hospital admissions. Frequent ED visitors have been found to be largely insured (85%) although with over‐representation of public insurance, and to be heavy users of the healthcare system overall.[6] A high disease burden associated with multiple chronic conditions has been found to predict frequent ED use.[4, 9, 11, 12] Some characteristics may vary by location; for example, alcohol abuse and psychiatric morbidity have been found to be associated with frequent ED use in New York and San Francisco, but it is not clear to what extent they are a factor in less urban areas.[4, 6, 12]

Several previous studies have investigated the characteristics of frequently admitted patients at single sites.[13, 14, 15, 16] Nguyen et al. (2013) studied patients with the highest costs and the most admissions at a large academic medical center in San Francisco.[13] High admit patients were defined as those responsible for the top decile of admissions, and were grouped into equal‐sized high‐ and low‐cost cohorts. The high‐admission/high‐cost group represented 5% of all patients, 25% of all costs, and 16% of all admissions. These patients were hospitalized primarily for medical conditions (78%) and had a high 30‐day readmission rate (47%). The high‐admission/low‐cost group accounted for 5% of all patients, 12% of all admissions, and 7% of all costs. These patients were also predominantly admitted for medical conditions (87%), with the most common admitting diagnoses representing respiratory, gastrointestinal, and cardiovascular conditions.[13]

Hwa (2012) conducted an analysis of 29 patients admitted 6 or more times in 1 year to an inpatient medical service in San Francisco.[14] These patients represented just 1% of all patients, but 13% of readmissions. Fifty‐five percent of these patients had a psychiatric diagnosis, and 52% had chronic pain. Ninety percent had a primary care physician in the hospital system, 100% were insured either privately or publicly, and 93% had housing, although for 17% housing was described as marginal.[14]

In a third study, Boonyasai et al. (2012) identified 76 patients with 82 readmissions at a Baltimore, Maryland, hospital and classified them as isolated (1 readmission per 6‐month period) or serial (more than 1 readmission per 6‐month period) readmissions.[15] Patients with serial readmissions accounted for 70% of the total. Isolated readmissions were most likely to be related to suboptimal quality of care and care coordination, whereas serial readmissions were more likely to result from disease progression, psychiatric illness, and substance abuse.[15]

All of these studies were conducted at single‐site academic medical centers serving inner city populations. We undertook this study to identify patient and hospital‐level characteristics of frequently admitted patients in a broad sample of 101 US academic medical centers to determine whether previously reported findings are generalizable, and to identify characteristics of frequently admitted patients that can inform interventions designed to meet the needs of this relatively small but resource‐intensive group of patients.

METHODS

All data were obtained from the University HealthSystem Consortium (UHC) (Chicago, IL) Clinical Data Base/Resource Manager (CDB), a large administrative database to which UHC principal members submit comprehensive administrative data files. UHC's principal members include approximately 120 US academic medical centers delivering tertiary and quaternary care, with an average of 647 acute care beds. The CDB includes primary and secondary diagnoses using International Classification of Diseases, Ninth Revision (ICD‐9)[17] codes.

The data of 101 academic medical centers with complete datasets for the study period (October 1, 2011, to September 30, 2012) were included in this analysis. Frequently admitted patients were defined as patients admitted 5 or more times to the same facility in a 12‐month period; all admissions were included, even those more than 30 days apart. This definition was established based on a naturally occurring break in the frequency distribution (Figure 1) and our intention to focus on the unique characteristics of patients at the far right of the distribution. We excluded obstetric (MDC 14, ICD‐9)[17] admissions and pediatric (<18 years of age at index admission) patients, as well as admissions with principal diagnoses for chemotherapy (ICD‐9 diagnosis codes v5811v5812), dialysis (ICD‐9 diagnosis codes v560v568), and rehabilitation (ICD‐9 diagnosis codes v570v579), which are typically planned. The Agency for Healthcare Research and Quality (AHRQ) comorbidity software was used to identify comorbid conditions,[18, 19] and a score based on the Elixhauser comorbidity measures was calculated using a modified acuity point system.[20] For comparisons based on safety net status, we used a definition of payer mix being 25% Medicaid or uninsured.

Figure 1

Our analyses included patient demographics, admission source and discharge status, clinical diagnoses, procedures, and comorbidities, cost, and length of stay. Patients defined as frequently admitted were compared in aggregate to all other hospitalized patients (all other admissions).

To evaluate associations, we used [2] tests for categorical variables and t tests for continuous variables. When comparing the non‐normally distributed comorbidities of the control group to the normally distributed comorbidities of the frequently admitted patients, we performed a Kruskal‐Wallis test on the medians.

RESULTS

During a 1‐year period (October 1, 2011, to September 30, 2012), 1,758,027 patients were admitted 2,388,124 times at 101 academic medical centers. Of these, 28,291 patients had 5 or more admissions during this period, resulting in 180,185 admissions. These frequently admitted patients represented 1.6% of all patients (Figure 1) and 7.6% of all inpatient admissions. By comparison, nonfrequently admitted patients were admitted once (79%), twice (14%), 3 times (4%), or 4 times (2%).

Among hospitals, the volume and impact of frequently admitted patients varied widely. The frequently admitted patient population ranged from 64 patients (0.7% of all patients) to 785 patients (3.5%), with an average of 280 patients (1.6%). To look for differences that might explain this range, we compared hospitals in the top and bottom deciles with respect to geographic region and to safety net status, but found no significant or meaningful differences. The average number of admissions per patient was 6.4, with a range of 5 to 76. Days per patient ranged from 5 to 434 days, with an average of 42. The average patient‐day percentage (frequently admitted patient days/total patient days) was 8.4%, and ranged from 3.2% to 15.4%.

Frequently admitted patients were more likely to be younger than all other patients (71.9% under the age of 65 years, as compared with 65.3% of all other patients (P<0.001)). They were also more likely to have either Medicaid or no healthcare insurance (27.6% compared with 21.6%, P<0.001), although nearly three‐quarters had either private insurance or Medicare coverage.

Eighty‐four percent of frequently admitted patient admissions were to medical services (vs 58% of all other patients (P<0.001)). The admission status for these patients was much less likely to be elective (9.1% of frequently admitted patient admissions vs 26.6% of all other patients' admissions [P<0.001]). Frequently admitted patients were more likely to be discharged to a skilled nursing facility (9.3% vs 8.4%, [P<0.001]) or with home health services (19.7% vs 13.4% [P<0.001]).

The 10 most common primary diagnoses for patient admissions are shown in Table 1. No single primary diagnosis accounted for a large share of the admissions of these patients; the most common diagnosis, sickle cell disease with crisis, accounted for only about 4% of admissions. The 10 most common diagnoses accounted for <20% of all admissions. The remainder of the diagnoses was spread over more than 3000 diagnosis codes; only about 300 codes had more than 100 admissions each.

Secondary diagnoses were mainly chronic conditions, including hypertension, hyperlipidemia, esophageal reflux, and diabetes mellitus type 2 (Table 1.) Combined, congestive heart failure and diabetes mellitus accounted for 43.3% of the secondary diagnoses of admissions of frequently admitted patients, but for only 28.6% of other patients. Acute kidney failure was more common in frequently admitted patients (13.8% vs 9.4% [P<0.001]). Psychiatric disorders accounted for <1% of primary diagnoses for both frequently admitted patients and all other patients. As a secondary diagnosis, depressive disorder appeared in the top 10 for both groups, although more commonly for frequently admitted patients (15.4% vs 10.3% [P<0.001]).

The most commonly performed principal procedures are also shown in Table 1. These include hemodialysis (6.32%) and packed cell transfusion (4.49%), nonoperating room procedures associated with chronic medical conditions.

Comorbidities were compared using the AHRQ comorbidity software.[18, 19] Comorbid conditions were counted once per patient, regardless of the number of admissions in which the condition was coded. Frequently admitted patients have a significantly higher mean number of comorbidities: 7.1 compared to 2.5 for all other patients (P<0.001; Figure 2). In an additional analysis using the Elixhauser comorbidity measures to determine acuity scores, the mean scores were 13.1 for frequently admitted patients and 3.17 for all others (P<0.001). The most common comorbidities were hypertension (74%), fluid and electrolyte disorders (73%), and deficiency anemias (66%). The only behavioral health comorbidity that affected more than a quarter of frequently admitted patients was depression (40% as compared to 13% for all others).

Figure 2

Additionally, frequently admitted patients were significantly more likely to have comorbidities of psychosis (18% vs 5% [P<0.001]), alcohol abuse (16% vs 7% [P<0.001]), and drug abuse (20% vs 7% [P<0.001]). Among hospitals, these comorbidities ranged widely: psychosis (3% 48%); alcohol abuse (3%46%); and drug abuse (3%58%). Hospitals with the highest rates (top decile) of frequently admitted patients with alcohol and drug abuse comorbidities were more likely to be safety net hospitals than those in the lowest decile (P<0.05 for each independently), but no such difference was found regarding rates of patients with psychosis.

Although the frequently admitted patient population accounted for only 1.6% of patients, they accounted for an average of 8.4% of all bed days and 7.1% of direct cost. The average cost per day was $1746, compared to $2144 for all other patients (Table 2).

DISCUSSION

An extensive analysis of the characteristics of frequently admitted patients at 101 US academic medical centers, from October 1, 2011 to September 30, 2012, revealed that these patients comprised 1.6% of all patients, but accounted for 8% of all admissions and 7% of direct costs. Relative to all other hospitalized patients, frequently admitted patients were likely to be younger, of lower socioeconomic status, in poorer health, and more often affected by mental health or substance abuse conditions that may mediate their health behaviors. However, the prevalence of patients with psychiatric or behavior conditions varied widely among hospitals, and hospitals with the highest rates of patients with substance abuse comorbidities were more likely to be safety net hospitals. Frequently admitted patients' diagnoses and procedures suggest that their admissions were related to complex chronic diseases; more than three‐quarters were admitted to medicine services, and their average length of stay was nearly 7 days. No single primary diagnosis accounted for a predominant share of their admissions; the most common diagnosis, sickle cell disease with crisis, accounted for only about 4%. The cost of their care was lower than that of other patients, reflecting the preponderance of their admissions to medicine service lines.

In many ways, frequently admitted patients seem similar to frequent ED visitors. Their visits were driven by a high disease burden associated with multiple chronic conditions, and they were heavy users of the healthcare system overall.[4, 6] The majority of both groups were insured, although there was over‐representation of public insurance.[6] As with frequent ED users, some frequently admitted patients are affected by psychiatric morbidity and substance abuse.[4, 12]

Our results in some ways confirmed, and in some ways differed from, findings of prior studies of patients with frequent hospital admissions. Although each study performed to date has defined the population differently, comparison of findings is useful. Our population was similar to the high‐admission groups identified by Nguyen et al. (patients responsible for the top decile of admissions).[13] These patients were also predominantly admitted for medical conditions, with common admitting diagnoses representing respiratory, gastrointestinal, and cardiovascular conditions. However, the median length of stay (3 days for the high‐admission/low‐cost group and 5 days for the high‐admission/high‐cost group) was lower than that of our population (5.5 days).

Hwa, who studied 29 patients admitted 6 or more times in 1 year to an inpatient medical service in San Francisco,[14] found that 55% of frequently admitted patients had a psychiatric diagnosis, higher than our patient population. Our findings are similar to those of Boonyasai et al.[15] whose serial readmitters had admissions resulting from disease progression, psychiatric illness, and substance abuse.

Our more nationally representative analysis documented a wide range of patient volumes and clinical characteristics, including psychiatric and substance abuse comorbidities, across study hospitals. It demonstrates that different approachesand resourcesare needed to meet the needs of these varied groups of patients. Each hospital must identify, evaluate, and understand its own population of frequently admitted patients to create well‐informed solutions to prevent repeat hospitalization for these patients.

Our ability to create a distinctive picture of the population of frequently admitted patients in US academic medical centers is based on access to an expansive dataset that captures complete diagnostic and demographic information on the universe of patients admitted to our member hospitals. The availability of clinical and administrative data for the entire population of patients permits both an accurate description of patient characteristics and a standardized comparison of groups. All data conform to accepted formats and definitions; their validity is universally recognized by contributing database participants.

CONCLUSION

The valuable information reported from analysis of nearly 2 million patients in the UHC Clinical Data Base/Resource Manager can be used to better understand the characteristics of frequently admitted patients. This important cohort of individuals has complex care needs that often result in hospitalization, but may be amenable to solutions that allow patients to remain in their communities. By understanding the demographic, social, and medical characteristics of these patients, hospitals can develop and implement solutions that address the needs of this small group of patients who consume a highly disproportionate share of healthcare resources.

The national healthcare improvement paradigm is shifting toward a more comprehensive, value‐focused, and patient‐centered approach. Reducing hospital readmissions has become a focal point as a policy strategy to improve care quality while reducing cost. Section 3025 of the Affordable Care Act mandated the Centers for Medicare and Medicaid Services to make progressive reductions in Medicare payments to hospitals that have higher than expected readmission rates for 3 conditions (heart failure, acute myocardial infarction, and pneumonia), and expanding to include chronic obstructive pulmonary disease and total hip and knee arthroplasty in 2015.[1] In response, hospitals and systems are developing and implementing programs that coordinate care beyond hospital walls to reduce readmissions and healthcare costs.[2, 3] However, patients are readmitted for a variety of reasons, and programs that address the needs of some may not address the distinct needs of others. Understanding the characteristics of patients with frequent readmissions will permit the well‐informed creation of solutions specific to this population to reduce cost, free resources, and provide better care.

Although a solid body of literature already exists that describes the characteristics of patients who frequently visit the emergency department (ED),[4, 5, 6, 7, 8, 9, 10, 11, 12] it is not clear to what extent these characteristics also apply to patients with frequent hospital admissions. Frequent ED visitors have been found to be largely insured (85%) although with over‐representation of public insurance, and to be heavy users of the healthcare system overall.[6] A high disease burden associated with multiple chronic conditions has been found to predict frequent ED use.[4, 9, 11, 12] Some characteristics may vary by location; for example, alcohol abuse and psychiatric morbidity have been found to be associated with frequent ED use in New York and San Francisco, but it is not clear to what extent they are a factor in less urban areas.[4, 6, 12]

Several previous studies have investigated the characteristics of frequently admitted patients at single sites.[13, 14, 15, 16] Nguyen et al. (2013) studied patients with the highest costs and the most admissions at a large academic medical center in San Francisco.[13] High admit patients were defined as those responsible for the top decile of admissions, and were grouped into equal‐sized high‐ and low‐cost cohorts. The high‐admission/high‐cost group represented 5% of all patients, 25% of all costs, and 16% of all admissions. These patients were hospitalized primarily for medical conditions (78%) and had a high 30‐day readmission rate (47%). The high‐admission/low‐cost group accounted for 5% of all patients, 12% of all admissions, and 7% of all costs. These patients were also predominantly admitted for medical conditions (87%), with the most common admitting diagnoses representing respiratory, gastrointestinal, and cardiovascular conditions.[13]

Hwa (2012) conducted an analysis of 29 patients admitted 6 or more times in 1 year to an inpatient medical service in San Francisco.[14] These patients represented just 1% of all patients, but 13% of readmissions. Fifty‐five percent of these patients had a psychiatric diagnosis, and 52% had chronic pain. Ninety percent had a primary care physician in the hospital system, 100% were insured either privately or publicly, and 93% had housing, although for 17% housing was described as marginal.[14]

In a third study, Boonyasai et al. (2012) identified 76 patients with 82 readmissions at a Baltimore, Maryland, hospital and classified them as isolated (1 readmission per 6‐month period) or serial (more than 1 readmission per 6‐month period) readmissions.[15] Patients with serial readmissions accounted for 70% of the total. Isolated readmissions were most likely to be related to suboptimal quality of care and care coordination, whereas serial readmissions were more likely to result from disease progression, psychiatric illness, and substance abuse.[15]

All of these studies were conducted at single‐site academic medical centers serving inner city populations. We undertook this study to identify patient and hospital‐level characteristics of frequently admitted patients in a broad sample of 101 US academic medical centers to determine whether previously reported findings are generalizable, and to identify characteristics of frequently admitted patients that can inform interventions designed to meet the needs of this relatively small but resource‐intensive group of patients.

METHODS

All data were obtained from the University HealthSystem Consortium (UHC) (Chicago, IL) Clinical Data Base/Resource Manager (CDB), a large administrative database to which UHC principal members submit comprehensive administrative data files. UHC's principal members include approximately 120 US academic medical centers delivering tertiary and quaternary care, with an average of 647 acute care beds. The CDB includes primary and secondary diagnoses using International Classification of Diseases, Ninth Revision (ICD‐9)[17] codes.

The data of 101 academic medical centers with complete datasets for the study period (October 1, 2011, to September 30, 2012) were included in this analysis. Frequently admitted patients were defined as patients admitted 5 or more times to the same facility in a 12‐month period; all admissions were included, even those more than 30 days apart. This definition was established based on a naturally occurring break in the frequency distribution (Figure 1) and our intention to focus on the unique characteristics of patients at the far right of the distribution. We excluded obstetric (MDC 14, ICD‐9)[17] admissions and pediatric (<18 years of age at index admission) patients, as well as admissions with principal diagnoses for chemotherapy (ICD‐9 diagnosis codes v5811v5812), dialysis (ICD‐9 diagnosis codes v560v568), and rehabilitation (ICD‐9 diagnosis codes v570v579), which are typically planned. The Agency for Healthcare Research and Quality (AHRQ) comorbidity software was used to identify comorbid conditions,[18, 19] and a score based on the Elixhauser comorbidity measures was calculated using a modified acuity point system.[20] For comparisons based on safety net status, we used a definition of payer mix being 25% Medicaid or uninsured.

Figure 1

Our analyses included patient demographics, admission source and discharge status, clinical diagnoses, procedures, and comorbidities, cost, and length of stay. Patients defined as frequently admitted were compared in aggregate to all other hospitalized patients (all other admissions).

To evaluate associations, we used [2] tests for categorical variables and t tests for continuous variables. When comparing the non‐normally distributed comorbidities of the control group to the normally distributed comorbidities of the frequently admitted patients, we performed a Kruskal‐Wallis test on the medians.

RESULTS

During a 1‐year period (October 1, 2011, to September 30, 2012), 1,758,027 patients were admitted 2,388,124 times at 101 academic medical centers. Of these, 28,291 patients had 5 or more admissions during this period, resulting in 180,185 admissions. These frequently admitted patients represented 1.6% of all patients (Figure 1) and 7.6% of all inpatient admissions. By comparison, nonfrequently admitted patients were admitted once (79%), twice (14%), 3 times (4%), or 4 times (2%).

Among hospitals, the volume and impact of frequently admitted patients varied widely. The frequently admitted patient population ranged from 64 patients (0.7% of all patients) to 785 patients (3.5%), with an average of 280 patients (1.6%). To look for differences that might explain this range, we compared hospitals in the top and bottom deciles with respect to geographic region and to safety net status, but found no significant or meaningful differences. The average number of admissions per patient was 6.4, with a range of 5 to 76. Days per patient ranged from 5 to 434 days, with an average of 42. The average patient‐day percentage (frequently admitted patient days/total patient days) was 8.4%, and ranged from 3.2% to 15.4%.

Frequently admitted patients were more likely to be younger than all other patients (71.9% under the age of 65 years, as compared with 65.3% of all other patients (P<0.001)). They were also more likely to have either Medicaid or no healthcare insurance (27.6% compared with 21.6%, P<0.001), although nearly three‐quarters had either private insurance or Medicare coverage.

Eighty‐four percent of frequently admitted patient admissions were to medical services (vs 58% of all other patients (P<0.001)). The admission status for these patients was much less likely to be elective (9.1% of frequently admitted patient admissions vs 26.6% of all other patients' admissions [P<0.001]). Frequently admitted patients were more likely to be discharged to a skilled nursing facility (9.3% vs 8.4%, [P<0.001]) or with home health services (19.7% vs 13.4% [P<0.001]).

The 10 most common primary diagnoses for patient admissions are shown in Table 1. No single primary diagnosis accounted for a large share of the admissions of these patients; the most common diagnosis, sickle cell disease with crisis, accounted for only about 4% of admissions. The 10 most common diagnoses accounted for <20% of all admissions. The remainder of the diagnoses was spread over more than 3000 diagnosis codes; only about 300 codes had more than 100 admissions each.

Secondary diagnoses were mainly chronic conditions, including hypertension, hyperlipidemia, esophageal reflux, and diabetes mellitus type 2 (Table 1.) Combined, congestive heart failure and diabetes mellitus accounted for 43.3% of the secondary diagnoses of admissions of frequently admitted patients, but for only 28.6% of other patients. Acute kidney failure was more common in frequently admitted patients (13.8% vs 9.4% [P<0.001]). Psychiatric disorders accounted for <1% of primary diagnoses for both frequently admitted patients and all other patients. As a secondary diagnosis, depressive disorder appeared in the top 10 for both groups, although more commonly for frequently admitted patients (15.4% vs 10.3% [P<0.001]).

The most commonly performed principal procedures are also shown in Table 1. These include hemodialysis (6.32%) and packed cell transfusion (4.49%), nonoperating room procedures associated with chronic medical conditions.

Comorbidities were compared using the AHRQ comorbidity software.[18, 19] Comorbid conditions were counted once per patient, regardless of the number of admissions in which the condition was coded. Frequently admitted patients have a significantly higher mean number of comorbidities: 7.1 compared to 2.5 for all other patients (P<0.001; Figure 2). In an additional analysis using the Elixhauser comorbidity measures to determine acuity scores, the mean scores were 13.1 for frequently admitted patients and 3.17 for all others (P<0.001). The most common comorbidities were hypertension (74%), fluid and electrolyte disorders (73%), and deficiency anemias (66%). The only behavioral health comorbidity that affected more than a quarter of frequently admitted patients was depression (40% as compared to 13% for all others).

Figure 2

Additionally, frequently admitted patients were significantly more likely to have comorbidities of psychosis (18% vs 5% [P<0.001]), alcohol abuse (16% vs 7% [P<0.001]), and drug abuse (20% vs 7% [P<0.001]). Among hospitals, these comorbidities ranged widely: psychosis (3% 48%); alcohol abuse (3%46%); and drug abuse (3%58%). Hospitals with the highest rates (top decile) of frequently admitted patients with alcohol and drug abuse comorbidities were more likely to be safety net hospitals than those in the lowest decile (P<0.05 for each independently), but no such difference was found regarding rates of patients with psychosis.

Although the frequently admitted patient population accounted for only 1.6% of patients, they accounted for an average of 8.4% of all bed days and 7.1% of direct cost. The average cost per day was $1746, compared to $2144 for all other patients (Table 2).

DISCUSSION

An extensive analysis of the characteristics of frequently admitted patients at 101 US academic medical centers, from October 1, 2011 to September 30, 2012, revealed that these patients comprised 1.6% of all patients, but accounted for 8% of all admissions and 7% of direct costs. Relative to all other hospitalized patients, frequently admitted patients were likely to be younger, of lower socioeconomic status, in poorer health, and more often affected by mental health or substance abuse conditions that may mediate their health behaviors. However, the prevalence of patients with psychiatric or behavior conditions varied widely among hospitals, and hospitals with the highest rates of patients with substance abuse comorbidities were more likely to be safety net hospitals. Frequently admitted patients' diagnoses and procedures suggest that their admissions were related to complex chronic diseases; more than three‐quarters were admitted to medicine services, and their average length of stay was nearly 7 days. No single primary diagnosis accounted for a predominant share of their admissions; the most common diagnosis, sickle cell disease with crisis, accounted for only about 4%. The cost of their care was lower than that of other patients, reflecting the preponderance of their admissions to medicine service lines.

In many ways, frequently admitted patients seem similar to frequent ED visitors. Their visits were driven by a high disease burden associated with multiple chronic conditions, and they were heavy users of the healthcare system overall.[4, 6] The majority of both groups were insured, although there was over‐representation of public insurance.[6] As with frequent ED users, some frequently admitted patients are affected by psychiatric morbidity and substance abuse.[4, 12]

Our results in some ways confirmed, and in some ways differed from, findings of prior studies of patients with frequent hospital admissions. Although each study performed to date has defined the population differently, comparison of findings is useful. Our population was similar to the high‐admission groups identified by Nguyen et al. (patients responsible for the top decile of admissions).[13] These patients were also predominantly admitted for medical conditions, with common admitting diagnoses representing respiratory, gastrointestinal, and cardiovascular conditions. However, the median length of stay (3 days for the high‐admission/low‐cost group and 5 days for the high‐admission/high‐cost group) was lower than that of our population (5.5 days).

Hwa, who studied 29 patients admitted 6 or more times in 1 year to an inpatient medical service in San Francisco,[14] found that 55% of frequently admitted patients had a psychiatric diagnosis, higher than our patient population. Our findings are similar to those of Boonyasai et al.[15] whose serial readmitters had admissions resulting from disease progression, psychiatric illness, and substance abuse.

Our more nationally representative analysis documented a wide range of patient volumes and clinical characteristics, including psychiatric and substance abuse comorbidities, across study hospitals. It demonstrates that different approachesand resourcesare needed to meet the needs of these varied groups of patients. Each hospital must identify, evaluate, and understand its own population of frequently admitted patients to create well‐informed solutions to prevent repeat hospitalization for these patients.

Our ability to create a distinctive picture of the population of frequently admitted patients in US academic medical centers is based on access to an expansive dataset that captures complete diagnostic and demographic information on the universe of patients admitted to our member hospitals. The availability of clinical and administrative data for the entire population of patients permits both an accurate description of patient characteristics and a standardized comparison of groups. All data conform to accepted formats and definitions; their validity is universally recognized by contributing database participants.

CONCLUSION

The valuable information reported from analysis of nearly 2 million patients in the UHC Clinical Data Base/Resource Manager can be used to better understand the characteristics of frequently admitted patients. This important cohort of individuals has complex care needs that often result in hospitalization, but may be amenable to solutions that allow patients to remain in their communities. By understanding the demographic, social, and medical characteristics of these patients, hospitals can develop and implement solutions that address the needs of this small group of patients who consume a highly disproportionate share of healthcare resources.

Chronic kidney disease (CKD) affects over 13% of the US population and is associated with increased morbidity, mortality, and healthcare costs.[1] However, only 10% of individuals with CKD are aware of their diagnoses.[2] Even in those with stage 5 CKD, only 60% of individuals are aware of their CKD.[3] To our knowledge, no work has examined CKD awareness in a hospitalized patient population.

Patient awareness of their CKD diagnosis is important because progression of kidney disease can be slowed by patient self‐management of diabetes and hypertension.[4] Patient awareness of CKD may also increase acceptance of preend‐stage renal disease (ESRD) patient education and nephrology referral, which have been shown to delay CKD progression and improve clinical status at dialysis initiation.[5] However, only 60% of patients with advanced CKD have visited a nephrologist in the past year or have seen a nephrologist prior to dialysis initiation.[1]

The hospital is an important site for patient education and linkage to outpatient care for patients with CKD.[6] The hospital serves high‐risk patients who may not be well connected to outpatient care or who have lesswell‐controlled disease.[6, 7] Thus, hospitalization represents an opportunity to identify existing CKD and to use a multidisciplinary approach to preventative care, patient education, and patient‐provider planning for renal replacement therapy needs. In our cross‐sectional study in an urban, minority‐serving hospital, we sought to determine what patient factors were associated with hospitalized patients correctly self‐identifying as having CKD.

METHODS

RESULTS

DISCUSSION

Although prior work has examined CKD awareness in the general population and in high‐risk cohorts,[2, 3, 13] this is the first study examining CKD awareness in an urban, underserved hospitalized population. We found that overall patients' CKD awareness was low (32%), but increased as high as 63% for CKD stage 5, even after controlling for patient demographic, clinical characteristics, and healthcare use. Our overall rate of CKD awareness was higher than prior studies overall and at lower CKD stages.[2, 3, 13] Our work is consistent with prior literature that shows increasing CKD awareness with advancing CKD stage.[2, 3, 13]

Older patients (>80 years) had lower awareness of CKD. Older patients are more likely to have a near normal creatinine, despite a markedly reduced eGFR, so their CKD may go unnoticed.[14] Even with appropriate recognition, providers may also feel like their CKD is unlikely to progress to ESRD, given its stability and/or their competing risk of death.[15] Finally, older hospitalized patients may also be less likely to report a CKD diagnosis due to difficulty in recall due to denial, dementia, or delirium.

One limitation is that our case‐finding for CKD was physician ICD‐9 coding, which is highly specific but not sensitive.[9] The majority of patients with physician‐identified CKD were CKD unspecified, perhaps due to poor coding, physician underdocumentation, or physician under‐recognition of CKD stage. Although only 27% of the CKD unspecified group correctly self‐identified as having CKD, over 75% were found to be CKD stage 3 or higher, which should trigger additional monitoring or care based on guidelines.[10] In addition, despite statistical significance, we may not be able to make meaningful inferences about our small other group (nonwhite, non‐African American). Our sample was from 1 hospitalan urban, academic, tertiary care center with a large proportion of African American patientswhich may limit generalizability. The multivariable model will need to be tested in other populations for reproducibility.

Our study significantly contributes to the literature by examining patient awareness of CKD in a high‐risk, urban, hospitalized minority population. Other study strengths include use of basic demographic information, as well as survey and laboratory data for a richer examination of the associations between patient factors and CKD awareness.

CONCLUSION

Hospitalized patients with CKD have a low CKD awareness. Patient awareness of their CKD is increased with physician documentation of CKD severity. Patient awareness of their CKD must be coupled with provider awareness and CKD documentation to link patients to multidisciplinary CKD education and care to slow CKD progression and reduce associated cardiovascular and metabolic complications. Further work is needed across hospitals to determineand improveCKD awareness among both patients and providers.

Chronic kidney disease (CKD) affects over 13% of the US population and is associated with increased morbidity, mortality, and healthcare costs.[1] However, only 10% of individuals with CKD are aware of their diagnoses.[2] Even in those with stage 5 CKD, only 60% of individuals are aware of their CKD.[3] To our knowledge, no work has examined CKD awareness in a hospitalized patient population.

Patient awareness of their CKD diagnosis is important because progression of kidney disease can be slowed by patient self‐management of diabetes and hypertension.[4] Patient awareness of CKD may also increase acceptance of preend‐stage renal disease (ESRD) patient education and nephrology referral, which have been shown to delay CKD progression and improve clinical status at dialysis initiation.[5] However, only 60% of patients with advanced CKD have visited a nephrologist in the past year or have seen a nephrologist prior to dialysis initiation.[1]

The hospital is an important site for patient education and linkage to outpatient care for patients with CKD.[6] The hospital serves high‐risk patients who may not be well connected to outpatient care or who have lesswell‐controlled disease.[6, 7] Thus, hospitalization represents an opportunity to identify existing CKD and to use a multidisciplinary approach to preventative care, patient education, and patient‐provider planning for renal replacement therapy needs. In our cross‐sectional study in an urban, minority‐serving hospital, we sought to determine what patient factors were associated with hospitalized patients correctly self‐identifying as having CKD.

METHODS

RESULTS