A new scoring system for predicting short-term outcomes in Chinese patients with critically-ill acute decompensated heart failure

• Published in: BMC cardiovascular disorders 2021-05, Vol.21 (1), p.228-228, Article 228
• Main Authors: Mo, Ran, Yu, Li-Tian, Tan, Hui-Qiong, Wang, Yang, Yang, Yan-Min, Liang, Yan, Zhu, Jun
• Format: Article
• Language: English
• Subjects: Acute decompensated heart failure; Adult; Age; Aged; APACHE; Bilirubin; Blood pressure; Body mass index; Cardiac arrhythmia; Cardiovascular disease; China; Congestive heart failure; Critical Illness; Critically ill; Databases, Factual; Decision Support Techniques; Ejection fraction; Emergency medical care; Epidermal growth factor receptors; Female; Function tests (Medicine); Glomerular filtration rate; Health Status; Health Status Indicators; Heart attacks; Heart failure; Heart Failure - diagnosis; Heart Failure - mortality; Heart Failure - physiopathology; Heart Failure - therapy; Heart rate; Hematocrit; Hemoglobin; Hospital Mortality; Hospital patients; Hospitals; Humans; Intensive care; Male; Medical examination; Medical records; Methods; Middle Aged; Mortality; Patient outcomes; Pleural effusion; Population; Potassium; Predictive Value of Tests; Prognosis; Pulmonary arteries; Reproducibility of Results; Retrospective Studies; Risk Assessment; Risk Factors; Risk groups; Scoring system; Software; Statistical analysis
• ISSN: 1471-2261; 1471-2261
• DOI: 10.1186/s12872-021-02041-2

Acute decompensated heart failure (ADHF) contributes millions of emergency department (ED) visits and it is associated with high in-hospital mortality. The aim of this study was to develop and validate a multiparametric score for critically-ill ADHF patients. In this single-center, retrospective study, a total of 1268 ADHF patients in China were enrolled and divided into derivation (n = 1014) and validation (n = 254) cohorts. The primary endpoint was any in-hospital death, cardiac arrest or utilization of mechanical support devices. Logistic regression model was preformed to identify risk factors and build the new scoring system. The assigning point of each parameter was determined according to its β coefficient. The discrimination was validated internally using C statistic and calibration was evaluated by the Hosmer-Lemeshow goodness-of-fit test. We constructed a predictive score based on six significant risk factors [systolic blood pressure (SBP), white blood cell (WBC) count, hematocrit (HCT), total bilirubin (TBIL), estimated glomerular filtration rate (eGFR) and NT-proBNP]. This new model was computed as (1 × SBP  9.2 × 10 /L) + (1 × HCT ≤ 0.407) + (2 × TBIL > 34.2 μmol/L) + (2 × eGFR