Effect of adding hydrochlorothiazide to usual treatment of patients with acute decompensated heart failure: a randomized clinical trial.

Acute decompensated heart failure (ADHF) is the leading cause of hospitalization in patients aged 65 years or older, and most of them present with congestion. The use of hydrochlorothiazide (HCTZ) may increase the response to loop diuretics. To evaluate the effect of adding HCTZ to furosemide on congestion and symptoms in patients with ADHF. This randomized clinical trial compared HCTZ 50 mg versus placebo for 3 days in patients with ADHF and signs of congestion. The primary outcome of the study was daily weight reduction. Secondary outcomes were change in creatinine, need for vasoactive drugs, change in natriuretic peptides, congestion score, dyspnea, thirst, and length of stay. Fifty-one patients were randomized-26 to the HCTZ group and 25 to the placebo group. There was an increment of 0.73 kg/day towards additional weight reduction in the HCTZ group (HCTZ: - 1.78 ± 1.08 kg/day vs placebo: - 1.05 ± 1.51 kg/day; p = 0.062). In post hoc analysis, the HCTZ group demonstrated significant weight reduction for every 40 mg of intravenous furosemide (HCTZ: - 0.74 ± 0.47 kg/40 mg vs placebo: - 0.33 ± 0.80 kg/40 mg; p = 0.032). There was a trend to increase in creatinine in the HCTZ group (HCTZ: 0.50 ± 0.37 vs placebo: 0.27 ± 0.40; p = 0.05) but no significant difference in onset of acute renal failure (HCTZ: 58% vs placebo: 41%; p = 0.38). No differences were found in the remaining outcomes. Adding hydrochlorothiazide to usual treatment of patients with acute decompensated heart failure did not cause significant difference in daily body weight reduction compared to placebo. In analysis adjusted to the dose of intravenous furosemide, adding HCTZ 50 mg to furosemide resulted in a significant synergistic effect on weight loss.Trial registration: The Brazilian Clinical Trials Registry (ReBEC), a publically accessible primary register that participates in the World Health Organization International Clinical Trial Registry Platform; number RBR-5qkn8h. Registered in 23/07/2019 (retrospectively), http://www.ensaiosclinicos.gov.br/rg/RBR-5qkn8h/ .

The impact of machine learning techniques in the study of bipolar disorder: A systematic review.

Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. We found 757 abstracts and included 51 studies in our review. Most of the included studies used multiple levels of biological data to distinguish the diagnosis of bipolar disorder from other psychiatric disorders or healthy controls. We also found studies that assessed the prediction of clinical outcomes and studies using unsupervised machine learning to build more consistent clinical phenotypes of bipolar disorder. We concluded that given the clinical heterogeneity of samples of patients with BD, machine learning techniques may provide clinicians and researchers with important insights in fields such as diagnosis, personalized treatment and prognosis orientation.