Background Coronavirus disease 2019 (COVID-19) is a growing pandemic that confers augmented risk for right ventricular (RV) dysfunction and dilation; the prognostic utility of adverse RV remodeling in COVID-19 patients is uncertain. Objectives The purpose of this study was to test whether adverse RV remodeling (dysfunction/dilation) predicts COVID-19 prognosis independent of clinical and biomarker risk stratification. Methods Consecutive COVID-19 inpatients undergoing clinical transthoracic echocardiography at 3 New York City hospitals were studied; images were analyzed by a central core laboratory blinded to clinical and biomarker data. Results In total, 510 patients (age 64 ± 14 years, 66% men) were studied; RV dilation and dysfunction were present in 35% and 15%, respectively. RV dysfunction increased stepwise in relation to RV chamber size (p = 0.007). During inpatient follow-up (median 20 days), 77% of patients had a study-related endpoint (death 32%, discharge 45%). RV dysfunction (hazard ratio [HR]: 2.57; 95% confidence interval [CI]: 1.49 to 4.43; p = 0.001) and dilation (HR: 1.43; 95% CI: 1.05 to 1.96; p = 0.02) each independently conferred mortality risk. Patients without adverse RV remodeling were more likely to survive to hospital discharge (HR: 1.39; 95% CI: 1.01 to 1.90; p = 0.041). RV indices provided additional risk stratification beyond biomarker strata; risk for death was greatest among patients with adverse RV remodeling and positive biomarkers and was lesser among patients with isolated biomarker elevations (p ≤ 0.001). In multivariate analysis, adverse RV remodeling conferred a >2-fold increase in mortality risk, which remained significant (p < 0.01) when controlling for age and biomarker elevations; the predictive value of adverse RV remodeling was similar irrespective of whether analyses were performed using troponin, D-dimer, or ferritin. Conclusions Adverse RV remodeling predicts mortality in COVID-19 independent of standard clinical and biomarker-based assessment.

Background Although metabolic surgery was originally performed to treat hypercholesterolemia, the effects of contemporary bariatric surgery on serum lipids have not been systematically characterized. Methods and Results MEDLINE, EMBASE and Cochrane databases were searched for studies with ≥20 obese adults undergoing bariatric surgery [Roux-en-Y Gastric Bypass (RYGBP), Adjustable Gastric Banding, Bilio-Pancreatic Diversion (BPD), or Sleeve Gastrectomy]. The primary outcome was change in lipids from baseline to one-year after surgery. The search yielded 178 studies with 25,189 subjects (pre-operative BMI 45.5±4.8kg/m2) and 47,779 patient-years of follow-up. In patients undergoing any bariatric surgery, compared to baseline, there were significant reductions in total cholesterol (TC; -28.5mg/dL), low density lipoprotein cholesterol (LDL-C; -22.0mg/dL), triglycerides (-61.6mg/dL) and a significant increase in high density lipoprotein cholesterol (6.9mg/dL) at one year (P<0.00001 for all). The magnitude of this change was significantly greater than that seen in non-surgical control patients (eg LDL-C; -22.0mg/dL vs -4.3mg/dL). When assessed separately, the magnitude of changes varied greatly by surgical type (Pinteraction<0.00001; eg LDL-C: BPD -42.5mg/dL, RYGBP -24.7mg/dL, Adjustable Gastric Banding -8.8mg/dL, Sleeve Gastrectomy -7.9mg/dL). In the cases of Adjustable Gastric Banding (TC and LDL-C) and Sleeve Gastrectomy (LDL-C), the response at one year following surgery was not significantly different from non-surgical control patients. Conclusions Contemporary bariatric surgical techniques produce significant improvements in serum lipids, but changes vary widely, likely due to anatomic alterations unique to each procedure. These differences may be relevant in deciding the most appropriate technique for a given patient.

Natural language processing (NLP) is a set of automated methods to organise and evaluate the information contained in unstructured clinical notes, which are a rich source of real-world data from clinical care that may be used to improve outcomes and understanding of disease in cardiology. The purpose of this systematic review is to provide an understanding of NLP, review how it has been used to date within cardiology and illustrate the opportunities that this approach provides for both research and clinical care. We systematically searched six scholarly databases (ACM Digital Library, Arxiv, Embase, IEEE Explore, PubMed and Scopus) for studies published in 2015–2020 describing the development or application of NLP methods for clinical text focused on cardiac disease. Studies not published in English, lacking a description of NLP methods, non-cardiac focused and duplicates were excluded. Two independent reviewers extracted general study information, clinical details and NLP details and appraised quality using a checklist of quality indicators for NLP studies. We identified 37 studies developing and applying NLP in heart failure, imaging, coronary artery disease, electrophysiology, general cardiology and valvular heart disease. Most studies used NLP to identify patients with a specific diagnosis and extract disease severity using rule-based NLP methods. Some used NLP algorithms to predict clinical outcomes. A major limitation is the inability to aggregate findings across studies due to vastly different NLP methods, evaluation and reporting. This review reveals numerous opportunities for future NLP work in cardiology with more diverse patient samples, cardiac diseases, datasets, methods and applications.

The present body of evidence suggests that in patients with CAD, intensive systolic BP control to ≤ 135 mm Hg and possibly to ≤ 130 mm Hg is associated with a modest reduction in stroke and heart failure but at the expense of hypotension. Lower was better, although not consistently so for myocardial infarction, stroke, heart failure and perhaps angina. Further trials are needed to prove these findings.