Electronic health record data for assessing risk of hospitalization for COVID-19: Methodological considerations applied to multiple sclerosis.

Introduction: During the COVID-19 pandemic, electronic health record (EHR) data has been used to investigate disease severity and risk factors for severe COVID-19 in people with multiple sclerosis (pwMS). Methodological challenges including sampling bias, and residual confounding should be considered when conducting EHR-based studies. We aimed to address these limitations related to the use of EHR data in order to identify risk factors, including the use of disease modifying therapies (DMTs), associated with hospitalization for COVID-19 amongst pwMS.

Natural history and burden of Huntington's disease in the UK: A population-based cohort study.

Background: Huntington's disease (HD) is a rare neurodegenerative disease that presents with progressive psychological, cognitive and motor impairment. These diverse symptoms place a high burden on the patient, families and the healthcare systems they rely on. This study aimed to describe the epidemiology and clinical burden in individuals with HD compared with controls from the general population.

Huntington's Disease in Israel: A Population-Based Study Using 20 Years of Routinely-Collected Healthcare Data.

Background: Huntington's disease (HD) is a rare, genetic, neurodegenerative disease. Obtaining population-level data on epidemiology and disease management is challenging.

Objective: To investigate the epidemiology, clinical manifestations, treatment, and healthcare utilization of patients with HD in Israel.

Machine Learning to Predict Tamoxifen Nonadherence Among US Commercially Insured Patients With Metastatic Breast Cancer.

Purpose: Adherence to tamoxifen citrate among women diagnosed with metastatic breast cancer can improve survival and minimize recurrence. This study aimed to use real-world data and machine learning (ML) methods to classify tamoxifen nonadherence.

Methods: A cohort of women diagnosed with metastatic breast cancer from 2012 to 2017 were identified from IBM MarketScan Commercial Claims and Encounters and Medicare claims databases.

Digital Tumor Board Solutions Have Significant Impact on Case Preparation.

Purpose: Multidisciplinary tumor boards (TBs) are the gold standard for decision-making in cancer care. Variability in preparation, conduction, and impact is widely reported. The benefit of digital technologies to support TBs is unknown.