A New Risk Score to Predict In-Hospital Mortality in Elderly Patients With Acute Heart Failure: On Behalf of the Journey HF-TR Study Investigators


Gok G., Karadag M., SİNAN Ü. Y., ZOGHİ M.

ANGIOLOGY, cilt.71, sa.10, ss.948-954, 2020 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 71 Sayı: 10
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1177/0003319720941758
  • Dergi Adı: ANGIOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, CINAHL, EMBASE, MEDLINE
  • Sayfa Sayıları: ss.948-954
  • İstanbul Üniversitesi Adresli: Hayır

Özet

We aimed to predict in-hospital mortality of elderly patients with heart failure (HF) by using a risk score model which could be easily applied in routine clinical practice without using an electronic calculator. The study population (n = 1034) recruited from the Journey HF-TR (Patient Journey in Hospital with Heart Failure in Turkish Population) study was divided into a derivation and a validation cohort. The parameters related to in-hospital mortality were first analyzed by univariate analysis, then the variables found to be significant in that analysis were entered into a stepwise multivariate logistic regression (LR) analysis. Patients were classified as low, intermediate, and high risk. A risk score obtained by taking into account the regression coefficients of the significant variables as a result of the LR analysis was tested in the validation cohort using receiver operating characteristic curve analysis. In total, 6 independent variables (age, blood urea nitrogen, previous history of hemodialysis/hemofiltration, inotropic agent use, and length of intensive care stay) associated with in-hospital mortality were included in the analysis. The risk score had a good discrimination in both the derivation and validation cohorts. A new validated risk score to determine the risk of in-hospital mortality of elderly hospitalized patients with HF was developed by including 6 independent predictors.