High-throughput target trial emulation for Alzheimer's disease drug repurposing with real-world data.

Target trial emulation is the process of mimicking target randomized trials using real-world data, where effective confounding control for unbiased treatment effect estimation remains a main challenge. Although various approaches have been proposed for this challenge, a systematic evaluation is still lacking. Here we emulated trials for thousands of medications from two large-scale real-world data warehouses, covering over 10 years of clinical records for over 170 million patients, aiming to identify new indications of approved drugs for Alzheimer's disease. We assessed different propensity score models under the inverse probability of treatment weighting framework and suggested a model selection strategy for improved baseline covariate balancing. We also found that the deep learning-based propensity score model did not necessarily outperform logistic regression-based methods in covariate balancing. Finally, we highlighted five top-ranked drugs (pantoprazole, gabapentin, atorvastatin, fluticasone, and omeprazole) originally intended for other indications with potential benefits for Alzheimer's patients.

Interatrial Block Association With Adverse Cardiovascular Outcomes in Patients Without a History of Atrial Fibrillation.

BACKGROUND: Interatrial block (IAB) is associated with thromboembolism and atrial arrhythmias. However, prior studies included small patient cohorts so it remains unclear whether IAB predicts adverse outcomes particularly in context of atrial fibrillation (AF)/atrial flutter (AFL). OBJECTIVES: This study sought to determine whether IAB portends increased stroke risk in a large cohort in the presence or absence of AFAF/AFL. METHODS: We performed a 5-center retrospective analysis of 4,837,989 electrocardiograms (ECGs) from 1,228,291 patients. IAB was defined as P-wave duration ≥120 ms in leads II, III, or aVF. Measurements were extracted as .XML files. After excluding patients with prior AF/AFL, 1,825,958 ECGs from 458,994 patients remained. Outcomes were analyzed using restricted mean survival time analysis and restricted mean time lost. RESULTS: There were 86,317 patients with IAB and 355,032 patients without IAB. IAB prevalence in the cohort was 19.6% and was most common in Black (26.1%), White (20.9%), and Hispanic (18.5%) patients and least prevalent in Native Americans (9.2%). IAB was independently associated with increased stroke probability (restricted mean time lost ratio coefficient [RMTLRC]: 1.43; 95% CI: 1.35-1.51; tau = 1,895), mortality (RMTLRC: 1.14; 95% CI: 1.07-1.21; tau = 1,924), heart failure (RMTLRC: 1.94; 95% CI: 1.83-2.04; tau = 1,921), systemic thromboembolism (RMTLRC: 1.62; 95% CI: 1.53-1.71; tau = 1,897), and incident AF/AFL (RMTLRC: 1.16; 95% CI: 1.10-1.22; tau = 1,888). IAB was not associated with stroke in patients with pre-existing AF/AFL. CONCLUSIONS: IAB is independently associated with stroke in patients with no history of AF/AFL even after adjustment for incident AF/AFL and CHA2DS2-VASc score. Patients are at increased risk of stroke even when AF/AFL is not identified.

Multi-Dimensional Laboratory Test Score as a Proxy for Health.

The standard of care for a physician to review laboratory tests results is to weigh each individual laboratory test result and compare it to against a standard reference range. Such a method of scanning can lead to missing high-level information. Different methods have tried to overcome a part of the problem by creating new types of reference values. This research proposes looking at test scores in a higher dimension space. And using machine learning approach, determine whether a subject has abnormal tests result that, according to current practice, would be defined as valid - and thus indicating a possible disease or illness. To determine health status, we look both at a disease-specific level and disease-independent level, while looking at several different outcomes.

Deep phenotyping of Alzheimer's disease leveraging electronic medical records identifies sex-specific clinical associations.

Alzheimer's Disease (AD) is a neurodegenerative disorder that is still not fully understood. Sex modifies AD vulnerability, but the reasons for this are largely unknown. We utilize two independent electronic medical record (EMR) systems across 44,288 patients to perform deep clinical phenotyping and network analysis to gain insight into clinical characteristics and sex-specific clinical associations in AD. Embeddings and network representation of patient diagnoses demonstrate greater comorbidity interactions in AD in comparison to matched controls. Enrichment analysis identifies multiple known and new diagnostic, medication, and lab result associations across the whole cohort and in a sex-stratified analysis. With this data-driven method of phenotyping, we can represent AD complexity and generate hypotheses of clinical factors that can be followed-up for further diagnostic and predictive analyses, mechanistic understanding, or drug repurposing and therapeutic approaches.

Development and validation of techniques for phenotyping ST-elevation myocardial infarction encounters from electronic health records.

OBJECTIVES: Classifying hospital admissions into various acute myocardial infarction phenotypes in electronic health records (EHRs) is a challenging task with strong research implications that remains unsolved. To our knowledge, this study is the first study to design and validate phenotyping algorithms using cardiac catheterizations to identify not only patients with a ST-elevation myocardial infarction (STEMI), but the specific encounter when it occurred. MATERIALS AND METHODS: We design and validate multi-modal algorithms to phenotype STEMI on a multicenter EHR containing 5.1 million patients and 115 million patient encounters by using discharge summaries, diagnosis codes, electrocardiography readings, and the presence of cardiac catheterizations on the encounter. RESULTS: We demonstrate that robustly phenotyping STEMIs by selecting discharge summaries containing "STEM" has the potential to capture the most number of STEMIs (positive predictive value [PPV] = 0.36, N = 2110), but that addition of a STEMI-related International Classification of Disease (ICD) code and cardiac catheterizations to these summaries yields the highest precision (PPV = 0.94, N = 952). DISCUSSION AND CONCLUSION: In this study, we demonstrate that the incorporation of percutaneous coronary intervention increases the PPV for detecting STEMI-related patient encounters from the EHR.