Can machine learning improve patient selection for cardiac resynchronization therapy?

Rationale: Multiple clinical trials support the effectiveness of cardiac resynchronization therapy (CRT); however, optimal patient selection remains challenging due to substantial treatment heterogeneity among patients who meet the clinical practice guidelines.

Objective: To apply machine learning to create an algorithm that predicts CRT outcome using electronic health record (EHR) data avaible before the procedure.

Methods And Results: We applied machine learning and natural language processing to the EHR of 990 patients who received CRT at two academic hospitals between 2004-2015.

In-hospital mortality in older patients after ventricular assist device implantation: A national cohort study.

Objectives: To assess baseline patient characteristics and identify factors associated with in-hospital mortality after ventricular assist device (VAD) placement.

Methods: Cross-sectional study using the National Inpatient Sample database from January 2010 to December 2014. Analyses were performed with sample weights provided by the National Inpatient Sample, which are reported ± the standard error of the mean.

The role of selective serotonin reuptake inhibitors in preventing relapse of major depressive disorder.

The objective of this review was to evaluate the efficacy of selective serotonin reuptake inhibitors (SSRIs) and SSRIs compared with other treatment modalities in preventing relapse after an episode of major depressive disorder (MDD). An Ovid MEDLINE and PsycINFO search (from 1987 to August 2017) was conducted using the following terms: selective serotonin reuptake inhibitors, antidepressants, depression, prevention, prophylaxis, relapse and MDD. Using predefined criteria, two authors independently selected and reached consensus on the included studies.