Early and long‐term outcomes of decompensated heart failure patients in a tertiary‐care centre in India

Abstract Aim Long‐term outcome data of acute decompensated heart failure (HF) are scarce from India. The aim of the study was to collect in‐hospital and long‐term outcome data of HF patients admitted during 2001–2010 in a tertiary‐care centre in South India. Methods and results Consecutive patients admitted with first episode of decompensated HF were part of the registry. Data regarding diagnosis, risk factors, treatment, early (in‐hospital), and late (5 and 10year) mortality outcomes were captured. During this period, 1502 patients were admitted with first episode of decompensated HF [37.7% of women, mean age of 51.1 (SD = 14.3) years]. Common causes were ischaemic heart disease (36.2%), rheumatic heart disease (34.3%), and cardiomyopathies (9.9%). HF with reduced ejection fraction (HFrEF) was present in 26.9% of patients, and 33.8% had atrial arrhythmias. Diabetes, hypertension, and renal dysfunction were prevalent in 27.4%, 28.6%, and 37.4%, respectively. Median duration of hospitalization was 6 days (interquartile range: 3–10), and 247 patients (16.4%) died during index admission. The total time at risk was 6248 person years, and 1051 patients died during the study period with a median survival time of 3.7 years. Overall mortality rate was 16.8 per 100 person years (95% CI: 15.8–17.9 per 100 person years). Older age [hazard ratio (HR) = 1.08, 95% CI: 1.02–1.14, P = 0.007], anaemia (HR = 1.34, 95% CI: 1.08–1.65, P = 0.007), renal dysfunction (HR = 1.38, 95% CI: 1.20–1.59, P < 0.001), HFpEF (HR = 0.61, 95% CI: 0.52–0.73, P < 0.001 against HFrEF), and the use of guideline‐directed therapies (GDT; beta blockers: HR = 0.57, 95% CI: 0.49–0.66, P < 0.0001; and angiotensin converting enzyme inhibitor/angiotensin receptor blocker: HR = 0.59, 95% CI: 0.51–0.69, P < 0.001) were important predictors of mortality. Patients with HF and mid‐range EF also benefited from GDT. Conclusion In our cohort, ischaemic and rheumatic heart diseases were the leading contributors for HF. Anaemia, renal dysfunction, poor ejection fraction, and suboptimal prescriptions of GDT were the main predictors of long‐term mortality. Both patients with HFrEF and mid‐range EF benefited from GDT

Building a best-in-class automated de-identification tool for electronic health records through ensemble learning

Summary: The presence of personally identifiable information (PII) in natural language portions of electronic health records (EHRs) constrains their broad reuse. Despite continuous improvements in automated detection of PII, residual identifiers require manual validation and correction. Here, we describe an automated de-identification system that employs an ensemble architecture, incorporating attention-based deep-learning models and rule-based methods, supported by heuristics for detecting PII in EHR data. Detected identifiers are then transformed into plausible, though fictional, surrogates to further obfuscate any leaked identifier. Our approach outperforms existing tools, with a recall of 0.992 and precision of 0.979 on the i2b2 2014 dataset and a recall of 0.994 and precision of 0.967 on a dataset of 10,000 notes from the Mayo Clinic. The de-identification system presented here enables the generation of de-identified patient data at the scale required for modern machine-learning applications to help accelerate medical discoveries. The bigger picture: Clinical notes in electronic health records convey rich historical information regarding disease and treatment progression. However, this unstructured text often contains personally identifiable information such as names, phone numbers, or residential addresses of patients, thereby limiting its dissemination for research purposes. The removal of patient identifiers, through the process of de-identification, enables sharing of clinical data while preserving patient privacy. Here, we present a best-in-class approach to de-identification, which automatically detects identifiers and substitutes them with fabricated ones. Our approach enables de-identification of patient data at the scale required to harness the unstructured, context-rich information in electronic health records to aid in medical research and advancement