Precision Phenotyping for Curating Research Cohorts of Patients with Post-Acute Sequelae of COVID-19 (PASC) as a Diagnosis of Exclusion.

Scalable identification of patients with the post-acute sequelae of COVID-19 (PASC) is challenging due to a lack of reproducible precision phenotyping algorithms and the suboptimal accuracy, demographic biases, and underestimation of the PASC diagnosis code (ICD-10 U09.9). In a retrospective case-control study, we developed a precision phenotyping algorithm for identifying research cohorts of PASC patients, defined as a diagnosis of exclusion. We used longitudinal electronic health records (EHR) data from over 295 thousand patients from 14 hospitals and 20 community health centers in Massachusetts. The algorithm employs an attention mechanism to exclude sequelae that prior conditions can explain. We performed independent chart reviews to tune and validate our precision phenotyping algorithm. Our PASC phenotyping algorithm improves precision and prevalence estimation and reduces bias in identifying Long COVID patients compared to the U09.9 diagnosis code. Our algorithm identified a PASC research cohort of over 24 thousand patients (compared to about 6 thousand when using the U09.9 diagnosis code), with a 79.9 percent precision (compared to 77.8 percent from the U09.9 diagnosis code). Our estimated prevalence of PASC was 22.8 percent, which is close to the national estimates for the region. We also provide an in-depth analysis outlining the clinical attributes, encompassing identified lingering effects by organ, comorbidity profiles, and temporal differences in the risk of PASC. The PASC phenotyping method presented in this study boasts superior precision, accurately gauges the prevalence of PASC without underestimating it, and exhibits less bias in pinpointing Long COVID patients. The PASC cohort derived from our algorithm will serve as a springboard for delving into Long COVID's genetic, metabolomic, and clinical intricacies, surmounting the constraints of recent PASC cohort studies, which were hampered by their limited size and available outcome data.

Human Immunodeficiency Virus (HIV) Quality Indicators Are Similar Across HIV Care Delivery Models.

BACKGROUND: There are limited data on human immunodeficiency virus (HIV) quality indicators according to model of HIV care delivery. Comparing HIV quality indicators by HIV care model could help inform best practices because patients achieving higher levels of quality indicators may have a mortality benefit. METHODS: Using the Partners HIV Cohort, we categorized 1565 patients into 3 HIV care models: infectious disease provider only (ID), generalist only (generalist), or infectious disease provider and generalist (ID plus generalist). We examined 12 HIV quality indicators used by 5 major medical and quality associations and grouped them into 4 domains: process, screening, immunization, and HIV management. We used generalized estimating equations to account for most common provider and multivariable analyses adjusted for prespecified covariates to compare composite rates of HIV quality indicator completion. RESULTS: We found significant differences between HIV care models, with the ID plus generalists group achieving significantly higher quality measures than the ID group in HIV management (94.4% vs 91.7%, P = .03) and higher quality measures than generalists in immunization (87.8% vs 80.6%, P = .03) in multivariable adjusted analyses. All models achieved rates that equaled or surpassed previously reported quality indicator rates. The absolute differences between groups were small and ranged from 2% to 7%. CONCLUSIONS: Our results suggest that multiple HIV care models are effective with respect to HIV quality metrics. Factors to consider when determining HIV care model include healthcare setting, feasibility, and physician and patient preference.

Aspirin Use for Primary and Secondary Prevention in Human Immunodeficiency Virus (HIV)-Infected and HIV-Uninfected Patients.

BACKGROUND: Human immunodeficiency virus (HIV) infection is associated with increased risk of myocardial infarction (MI). The use of aspirin for primary and secondary MI prevention in HIV infection has not been extensively studied. METHODS: We performed a cross-sectional study of 4037 patients infected with HIV and 36 338 demographics-matched control patients in the Partners HealthCare System HIV cohort. We developed an algorithm to ascertain rates of nonepisodic acetylsalicylic acid (ASA) use using medication and electronic health record free text data. We assessed rates of ASA use among HIV-infected and HIV-uninfected (negative) patients with and without coronary heart disease (CHD). RESULTS: Rates of ASA use were lower among HIV-infected compared with HIV-uninfected patients (12.4% vs 15.3%, P < .001), with a relatively greater difference among patients with ≥2 CHD risk factors (22.1% vs 42.4%, P < .001). This finding was present among men and among patients in the 30-39 and 40-49 age groups. Among patients with prevalent CHD using ASA for secondary prevention, rates of ASA use were also lower among HIV-infected patients compared with HIV-uninfected patients (51.6% vs 65.4%, P < .001). CONCLUSIONS: Rates of ASA use were lower among HIV-infected patients compared with controls, with a greater relative difference among those with elevated CHD risk and those with known CHD. Further studies are needed to investigate the optimal strategies for ASA use among patients infected with HIV.