Identification of cardiac wall motion abnormalities in diverse populations by deep learning of the electrocardiogram.

Cardiac wall motion abnormalities (WMA) are strong predictors of mortality, but current screening methods using Q waves from electrocardiograms (ECGs) have limited accuracy and vary across racial and ethnic groups. This study aimed to identify novel ECG features using deep learning to enhance WMA detection, referencing echocardiography as the gold standard. We collected ECG and echocardiogram data from 35,210 patients in California and labeled WMA using unstructured language parsing of echocardiographic reports.

Genomic analysis of comprehensive next generation sequencing data to explore the criteria for MET amplification as an actionable biomarker in NSCLC.

Introduction: MET amplification (METamp) can be a de novo or acquired resistance driver; however, the definition of METamp that best captures patients who may respond to targeted therapy remains debated. We explored the genomic landscape of METamp NSCLC including degree of amplification, co-drivers, amplicon size, and outcomes to MET inhibitors.

Methods: Hybrid-capture NGS-based genomic profiling from 88,547 tissue and 12,428 liquid NSCLC samples were queried for METamp (copy number (CN) ≥ ploidy + 4, or amplification ratio (AmpRatio; [CN/sample ploidy] ≥ 3).

Comparing two-sample log-linear exposure estimation with Bayesian model-informed precision dosing of tobramycin in adult patients with cystic fibrosis.

Tobramycin dosing in patients with cystic fibrosis (CF) is challenged by its high pharmacokinetic (PK) variability and narrow therapeutic window. Doses are typically individualized using two-sample log-linear regression (LLR) to quantify the area under the concentration-time curve (AUC). Bayesian model-informed precision dosing (MIPD) may allow dose individualization with fewer samples; however, the relative performance of these methods is unknown.

The European Drug-Drug Interaction (EuroDDI) Study Protocol: A Cross-Country Comparison of Drug-Drug Interaction Prevalence in the Older Community-Dwelling Population.

Background: Drug-drug interactions (DDIs), highly prevalent amongst the elderly, can lead to avoidable medication-related harm. Cardiovascular and central nervous system (CNS) drugs are commonly implicated. To date, there is no consensus on how to measure DDIs, making comparisons across countries challenging.