The ED-PLANN Score: A Simple Risk Stratification Tool for Out-of-Hospital Cardiac Arrests Derived from Emergency Departments in Korea

Abstract

Early risk stratification of out-of-hospital cardiac arrest (OHCA) patients with insufficient information in emergency departments (ED) is difficult but critical in improving intensive care resource allocation. This study aimed to develop a simple risk stratification score using initial information in the ED. Adult patients who had OHCA with medical etiology from 2016 to 2020 were enrolled from the Korean Cardiac Arrest Research Consortium (KoCARC) database. To develop a scoring system, a backward logistic regression analysis was conducted. The developed scoring system was validated in both external dataset and internal bootstrap resampling. A total of 8240 patients were analyzed, including 4712 in the development cohort and 3528 in the external validation cohort. An ED-PLANN score (range 0–5) was developed incorporating 1 point for each: P for serum pH ≤ 7.1, L for serum lactate ≥ 10 mmol/L, A for age ≥ 70 years old, N for non-shockable rhythm, and N for no-prehospital return of spontaneous circulation. The area under the receiver operating characteristics curve (AUROC) for favorable neurological outcome was 0.93 (95% CI, 0.92–0.94) in the development cohort, 0.94 (95% CI, 0.92–0.95) in the validation cohort. Hosmer–Lemeshow goodness-of-fit tests also indicated good agreement. The ED-PLANN score is a practical and easily applicable clinical scoring system for predicting favorable neurological outcomes of OHCA patients.

A model study for the classification of high-risk groups for cardiac arrest in general ward patients using simulation techniques

“…[ 19 ] Hyouk Jae Lim et al (2022) utilized the Korean Cardiac Arrest Research Consortium database to calculate pH, lactate, age, shockable rhythm, prehospital return of spontaneous circulation (ROSC) factors for the calculation of the ED-PLANN score for screening cardiac arrest. [ 20 ] In addition, with the development of artificial intelligence technology, studies on models or systems for high-risk patient selection have been conducted using various deep learning methods. Yunseob Shin et al (2022) proposed a pDEWS model to screen for cardiac arrest using age and 20 consecutive vital signs, namely respiratory rate, heart rate, systolic blood pressure, diastolic blood pressure, and body temperature.…”

Bayesian network predicted variables for good neurological outcomes in patients with out-of-hospital cardiac arrest

“…Fifteen variables were used based on previous studies [11,[25][26][27][28][29][30][31][32]: cause of cardiac arrest, age, sex, presence of bystander CPR, presence of witnesses/no flow time, initial emergency medical services (EMS) rhythm, presence of epinephrine administration, low flow time, motor…”