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

Senior Thesis Proposal

Senior Thesis Proposa

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

Trimethoprim-Sulfamethoxazole (Bactrim) Dose Optimization in Pneumocystis jirovecii Pneumonia (PCP) Management: A Systematic Review

(1) Background: Pneumocystis jirovecii pneumonia (PCP) has a substantial impact on the morbidity and mortality of patients, especially those with autoimmune disorders, thus requiring optimal dosing strategies of Trimethoprim–Sulfamethoxazole (TMP-SMX). Therefore, to ensure the safety of TMP-SMX, there is a high demand to review current evidence in PCP patients with a focus on dose optimization strategies; (2) Methods: Various databases were searched from January 2000 to December 2021 for articles in English, focusing on the dose optimization of TMP-SMX. The data were collected in a specific form with predefined inclusion and exclusion criteria. The quality of each article was evaluated using a Newcastle–Ottawa Scale (NOS) for retrospective studies, Joanna Briggs Institute (JBI) critical checklist for case reports, and Cochrane bias tool for randomized clinical trials (RCTs); (3) Results: Thirteen studies met the inclusion criteria for final analysis. Of the 13 selected studies, nine were retrospective cohort studies, two case reports, and two randomized controlled trials (RCT). Most of the studies compared the high-dose with low-dose TMP-SMX therapy for PCP. We have found that a low dose of TMP-SMX provides satisfactory outcomes while reducing the mortality rate and PCP-associated adverse events. This strategy reduces the economic burden of illness and enhances patients’ compliance to daily regimen plan; (4) Conclusions: The large-scale RCTs and cohort studies are required to improve dosing strategies to prevent initial occurrence of PCP or to prevent recurrence of PCP in immune compromised patients