Open AccessDecision tree model for predicting in‐hospital cardiac arrest among patients admitted with acute coronary syndrome
Author(s) -
Li Hong,
Wu Ting Ting,
Yang Dong Liang,
Guo Yang Song,
Liu Pei Chang,
Chen Yuan,
Xiao Li Ping
Publication year - 2019
Publication title -
clinical cardiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.263
H-Index - 72
eISSN - 1932-8737
pISSN - 0160-9289
DOI - 10.1002/clc.23255
Subject(s) - medicine , acute coronary syndrome , receiver operating characteristic , early warning score , killip class , decision tree , emergency medicine , vital signs , diabetes mellitus , troponin , cardiology , myocardial infarction , surgery , percutaneous coronary intervention , machine learning , computer science , endocrinology
Background In‐hospital cardiac arrest (IHCA) may be preventable, with patients often showing signs of physiological deterioration before an event. Our objective was to develop and validate a simple clinical prediction model to identify the IHCA risk among cardiac arrest (CA) patients hospitalized with acute coronary syndrome (ACS). Hypothesis A predicting model could help to identify the risk of IHCA among patients admitted with ACS. Methods We conducted a case‐control study and analyzed 21 337 adult ACS patients, of whom 164 had experienced CA. Vital signs, demographic, and laboratory data were extracted from the electronic health record. Decision tree analysis was applied with 10‐fold cross‐validation to predict the risk of IHCA. Results The decision tree analysis detected seven explanatory variables, and the variables' importance is as follows: VitalPAC Early Warning Score (ViEWS), fatal arrhythmia, Killip class, cardiac troponin I, blood urea nitrogen, age, and diabetes. The development decision tree model demonstrated a sensitivity of 0.762, a specificity of 0.882, and an area under the receiver operating characteristic curve (AUC) of 0.844 (95% CI, 0.805 to 0.849). A 10‐fold cross‐validated risk estimate was 0.198, while the optimism‐corrected AUC was 0.823 (95% CI, 0.786 to 0.860). Conclusions We have developed and internally validated a good discrimination decision tree model to predict the risk of IHCA. This simple prediction model may provide healthcare workers with a practical bedside tool and could positively impact decision‐making with regard to deteriorating patients with ACS.