Using Electronic Health Records to Characterize Prescription Patterns: Focus on Antidepressants in Nonpsychiatric Outpatient Settings

Objective To characterize nonpsychiatric prescription patterns of antidepressants according to drug labels and evidence assessments (on-label, evidence-based, and off-label) using structured outpatient electronic health record (EHR) data. Methods A retrospective analysis was conducted using deidentified EHR data from an outpatient practice at a New York City-based academic medical center. Structured “medication–diagnosis” pairs for antidepressants from 35 325 patients between January 2010 and December 2015 were compared to the latest drug product labels and evidence assessments. Results Of 140 929 antidepressant prescriptions prescribed by primary care providers (PCPs) and nonpsychiatry specialists, 69% were characterized as “on-label/evidence-based uses.” Depression diagnoses were associated with 67 233 (48%) prescriptions in this study, while pain diagnoses were slightly less common (35%). Manual chart review of “off-label use” prescriptions revealed that on-label/evidence-based diagnoses of depression (39%), anxiety (25%), insomnia (13%), mood disorders (7%), and neuropathic pain (5%) were frequently cited as prescription indication despite lacking ICD-9/10 documentation. Conclusions The results indicate that antidepressants may be prescribed for off-label uses, by PCPs and nonpsychiatry specialists, less frequently than believed. This study also points to the fact that there are a number of off-label uses that are efficacious and widely accepted by expert clinical opinion but have not been included in drug compendia. Despite the fact that diagnosis codes in the outpatient setting are notoriously inaccurate, our approach demonstrates that the correct codes are often documented in a patient’s recent diagnosis history. Examining both structured and unstructured data will help to further validate findings. Routinely collected clinical data in EHRs can serve as an important resource for future studies in investigating prescribing behaviors in outpatient clinics

Using Electronic Health Records to Characterize Prescription Patterns: Focus on Antidepressants in Nonpsychiatric Outpatient Settings

Objective To characterize nonpsychiatric prescription patterns of antidepressants according to drug labels and evidence assessments (on-label, evidence-based, and off-label) using structured outpatient electronic health record (EHR) data. Methods A retrospective analysis was conducted using deidentified EHR data from an outpatient practice at a New York City-based academic medical center. Structured “medication–diagnosis” pairs for antidepressants from 35 325 patients between January 2010 and December 2015 were compared to the latest drug product labels and evidence assessments. Results Of 140 929 antidepressant prescriptions prescribed by primary care providers (PCPs) and nonpsychiatry specialists, 69% were characterized as “on-label/evidence-based uses.” Depression diagnoses were associated with 67 233 (48%) prescriptions in this study, while pain diagnoses were slightly less common (35%). Manual chart review of “off-label use” prescriptions revealed that on-label/evidence-based diagnoses of depression (39%), anxiety (25%), insomnia (13%), mood disorders (7%), and neuropathic pain (5%) were frequently cited as prescription indication despite lacking ICD-9/10 documentation. Conclusions The results indicate that antidepressants may be prescribed for off-label uses, by PCPs and nonpsychiatry specialists, less frequently than believed. This study also points to the fact that there are a number of off-label uses that are efficacious and widely accepted by expert clinical opinion but have not been included in drug compendia. Despite the fact that diagnosis codes in the outpatient setting are notoriously inaccurate, our approach demonstrates that the correct codes are often documented in a patient’s recent diagnosis history. Examining both structured and unstructured data will help to further validate findings. Routinely collected clinical data in EHRs can serve as an important resource for future studies in investigating prescribing behaviors in outpatient clinics

Association of restless legs syndrome and mortality in end-stage renal disease: an analysis of the United States Renal Data System (USRDS)

Abstract Background Objective of the study is to assess prevalence and survival among end stage renal disease patients with restless legs syndrome (RLS) within a national database (USRDS). Methods A case-control, retrospective analysis was performed. Differences in characteristics between the groups, RLS and those with no sleep disorder (NSD), were determined using χ2 tests. Cox proportional hazard regression was used to assess survival between those with RLS and propensity score matched controls. Results Cases of restless legs syndrome were defined as patients that had received an ICD-9 code of 333.94 at any point during their treatment (n = 372). RLS group demonstrated a significantly higher proportion of patients with major depressive disorder, dysthymic disorder, anxiety, depression, minor depressive disorder, and psychological disorder. The difference between the survival was not statistically significant in those without sleep disorder as compared to those with RLS (HR =1.16±0.14, p = 0.3). Conclusions True prevalence of RLS in dialysis patients can only be estimated if knowledge gap for care providers in diagnosis of RLS is addressed. RLS patients also have increased incidence of certain psychological disorders which needs to be addressed

Prognosis of Acute Kidney Injury and Hepatorenal Syndrome in Patients with Cirrhosis: A Prospective Cohort Study

Background/Aims. Acute kidney injury is a common problem for patients with cirrhosis and is associated with poor survival. We aimed to examine the association between type of acute kidney injury and 90-day mortality. Methods. Prospective cohort study at a major US liver transplant center. A nephrologist’s review of the urinary sediment was used in conjunction with the 2007 Ascites Club Criteria to stratify acute kidney injury into four groups: prerenal azotemia, hepatorenal syndrome, acute tubular necrosis, or other. Results. 120 participants with cirrhosis and acute kidney injury were analyzed. Ninety-day mortality was 14/40 (35%) with prerenal azotemia, 20/35 (57%) with hepatorenal syndrome, 21/36 (58%) with acute tubular necrosis, and 1/9 (11%) with other (p=0.04 overall). Mortality was the same in hepatorenal syndrome compared to acute tubular necrosis (p=0.99). Mortality was lower in prerenal azotemia compared to hepatorenal syndrome (p=0.05) and acute tubular necrosis (p=0.04). Ten participants (22%) were reclassified from hepatorenal syndrome to acute tubular necrosis because of granular casts on urinary sediment. Conclusions. Hepatorenal syndrome and acute tubular necrosis result in similar 90-day mortality. Review of urinary sediment may add important diagnostic information to this population. Multicenter studies are needed to validate these findings and better guide management

A Plasma Long‐Chain Acylcarnitine Predicts Cardiovascular Mortality in Incident Dialysis Patients

BackgroundThe marked excess in cardiovascular mortality that results from uremia remains poorly understood.Methods and resultsIn 2 independent, nested case-control studies, we applied liquid chromatography-mass spectrometry-based metabolite profiling to plasma obtained from participants of a large cohort of incident hemodialysis patients. First, 100 individuals who died of a cardiovascular cause within 1 year of initiating hemodialysis (cases) were randomly selected along with 100 individuals who survived for at least 1 year (controls), matched for age, sex, and race. Four highly intercorrelated long-chain acylcarnitines achieved the significance threshold adjusted for multiple testing (P<0.0003). Oleoylcarnitine, the long-chain acylcarnitine with the strongest association with cardiovascular mortality in unadjusted analysis, remained associated with 1-year cardiovascular death after multivariable adjustment (odds ratio per SD 2.3 [95% confidence interval, 1.4 to 3.8]; P=0.001). The association between oleoylcarnitine and 1-year cardiovascular death was then replicated in an independent sample (n=300, odds ratio per SD 1.4 [95% confidence interval, 1.1 to 1.9]; P=0.008). Addition of oleoylcarnitine to clinical variables improved cardiovascular risk prediction using net reclassification (NRI, 0.38 [95% confidence interval, 0.20 to 0.56]; P<0.0001). In physiologic profiling studies, we demonstrate that the fold change in plasma acylcarnitine levels from the aorta to renal vein and from pre- to posthemodialysis samples exclude renal or dialytic clearance of long-chain acylcarnitines as confounders in our analysis.ConclusionsOur data highlight clinically meaningful alterations in acylcarnitine homeostasis at the time of dialysis initiation, which may represent an early marker, effector, or both of uremic cardiovascular risk

A Plasma Long‐Chain Acylcarnitine Predicts Cardiovascular Mortality in Incident Dialysis Patients

BACKGROUND: The marked excess in cardiovascular mortality that results from uremia remains poorly understood. METHODS AND RESULTS: In 2 independent, nested case‐control studies, we applied liquid chromatography‐mass spectrometry‐based metabolite profiling to plasma obtained from participants of a large cohort of incident hemodialysis patients. First, 100 individuals who died of a cardiovascular cause within 1 year of initiating hemodialysis (cases) were randomly selected along with 100 individuals who survived for at least 1 year (controls), matched for age, sex, and race. Four highly intercorrelated long‐chain acylcarnitines achieved the significance threshold adjusted for multiple testing (P<0.0003). Oleoylcarnitine, the long‐chain acylcarnitine with the strongest association with cardiovascular mortality in unadjusted analysis, remained associated with 1‐year cardiovascular death after multivariable adjustment (odds ratio per SD 2.3 [95% confidence interval, 1.4 to 3.8]; P=0.001). The association between oleoylcarnitine and 1‐year cardiovascular death was then replicated in an independent sample (n=300, odds ratio per SD 1.4 [95% confidence interval, 1.1 to 1.9]; P=0.008). Addition of oleoylcarnitine to clinical variables improved cardiovascular risk prediction using net reclassification (NRI, 0.38 [95% confidence interval, 0.20 to 0.56]; P<0.0001). In physiologic profiling studies, we demonstrate that the fold change in plasma acylcarnitine levels from the aorta to renal vein and from pre‐ to post hemodialysis samples exclude renal or dialytic clearance of long‐chain acylcarnitines as confounders in our analysis. CONCLUSIONS: Our data highlight clinically meaningful alterations in acylcarnitine homeostasis at the time of dialysis initiation, which may represent an early marker, effector, or both of uremic cardiovascular risk