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. Sixteen articles met the criteria: 10 compared the relapse rate of selective SSRIs with placebo or other SSRIs; one discussed the effectiveness of SSRIs plus psychotherapy, two compared SSRI tricyclic antidepressants (TCAs), two were mainly composed of TCAs plus psychotherapy, and one compared SSRIs and serotonin norepinephrine reuptake inhibitors (SNRIs). According to the included studies, the relapse risk in adults was lower when SSRIs were combined with psychotherapy. Results comparing SSRIs and SNRIs were inconclusive. TCAs may be equally as effective as SSRIs. Atypical antidepressants (mirtazapine and St John's Wort) had no significant difference in efficacy and remission rates compared with SSRIs. Escitalopram appeared to fare better in efficacy than other SSRIs, owing to a higher prophylactic efficacy and lower side effects; however, according to the current data, this difference was not significant. To conclude, this review provides evidence that continuing SSRIs for 1 year reduces risk of MDD and relapse. Furthermore, the combination of SSRIs and cognitive behavioural therapy may effectively reduce relapse. Escitalopram appeared to yield better results and fewer side effects than did other SSRIs or SNRIs. The effectiveness in reducing relapse of SSRIs was similar to that of TCAs and atypical antidepressants.

RationaleMultiple 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.ObjectiveTo apply machine learning to create an algorithm that predicts CRT outcome using electronic health record (EHR) data avaible before the procedure.Methods and resultsWe applied machine learning and natural language processing to the EHR of 990 patients who received CRT at two academic hospitals between 2004–2015. The primary outcome was reduced CRT benefit, defined as <0% improvement in left ventricular ejection fraction (LVEF) 6–18 months post-procedure or death by 18 months. Data regarding demographics, laboratory values, medications, clinical characteristics, and past health services utilization were extracted from the EHR available before the CRT procedure. Bigrams (i.e., two-word sequences) were also extracted from the clinical notes using natural language processing. Patients accrued on average 75 clinical notes (SD, 29) before the procedure including data not captured anywhere else in the EHR. A machine learning model was built using 80% of the patient sample (training and validation dataset), and tested on a held-out 20% patient sample (test dataset). Among 990 patients receiving CRT the mean age was 71.6 (SD, 11.8), 78.1% were male, 87.2% non-Hispanic white, and the mean baseline LVEF was 24.8% (SD, 7.69). Out of 990 patients, 403 (40.7%) were identified as having a reduced benefit from the CRT device (<0% LVEF improvement in 25.2%, death by 18 months in 15.6%). The final model identified 26% of these patients at a positive predictive value of 79% (model performance: Fβ (β = 0.1): 77%; recall 0.26; precision 0.79; accuracy 0.65).ConclusionsA machine learning model that leveraged readily available EHR data and clinical notes identified a subset of CRT patients who may not benefit from CRT before the procedure.

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 AE the standard error of the mean.Results: Weighted samples yielded 15,021 AE 1111 patients who received a VAD. The mean age at time of implantation was 56.6 years. Most recipients were white (59.9%) and male (75.0%). Among older patients, in-hospital mortality increased from 17.2% to 48.2% when 1 or more high-risk interventions (cardiac surgery, prolonged mechanical ventilation, hemodialysis, or extracorporeal membrane oxygenation) preceded VAD placement (P < .001). In comparison, in-hospital mortality in younger patients increased from 11.1% to 29.4% when 1 or more of these same procedures preceded VAD placement. The mortality difference associated with these procedures was 19% greater in older patients compared with younger patients (95% confidence interval [CI], 9%-28%). In-hospital mortality among VAD recipients was associated with age older than 65 years (odds ratio [