Decision-tree model for predicting outcomes after out-of-hospital cardiac arrest in the emergency department

被引:42
作者
Goto, Yoshikazu [1 ]
Maeda, Tetsuo [1 ]
Goto, Yumiko [2 ]
机构
[1] Kanazawa Univ Hosp, Sect Emergency Med, Kanazawa, Ishikawa 9208641, Japan
[2] Yawata Med Ctr, Dept Cardiol, Komatsu 9238551, Japan
关键词
cardiac arrest; cardiopulmonary resuscitation; emergency department; outcome; prediction model; INTERNATIONAL LIAISON COMMITTEE; EUROPEAN RESUSCITATION COUNCIL; AMERICAN-HEART-ASSOCIATION; CARDIOPULMONARY-RESUSCITATION; COMATOSE SURVIVORS; STROKE FOUNDATION; CARE; STATEMENT; CONSENSUS; CANADA;
D O I
10.1186/cc12812
中图分类号
R4 [临床医学];
学科分类号
1002 ; 100602 ;
摘要
Introduction: Estimation of outcomes in patients after out-of-hospital cardiac arrest (OHCA) soon after arrival at the hospital may help clinicians guide in-hospital strategies, particularly in the emergency department. This study aimed to develop a simple and generally applicable bedside model for predicting outcomes after cardiac arrest. Methods: We analyzed data for 390,226 adult patients who had undergone OHCA, from a prospectively recorded nationwide Utstein-style Japanese database for 2005 through 2009. The primary end point was survival with favorable neurologic outcome (cerebral performance category (CPC) scale, categories 1 to 2 [CPC 1 to 2]) at 1 month. The secondary end point was survival at 1 month. We developed a decision-tree prediction model by using data from a 4-year period (2005 through 2008, n = 307,896), with validation by using external data from 2009 (n = 82,330). Results: Recursive partitioning analysis of the development cohort for 10 predictors indicated that the best single predictor for survival and CPC 1 to 2 was shockable initial rhythm. The next predictors for patients with shockable initial rhythm were age (<70 years) followed by witnessed arrest and age (>= 70 years) followed by arrest witnessed by emergency medical services (EMS) personnel. For patients with unshockable initial rhythm, the next best predictor was witnessed arrest. A simple decision-tree prediction mode permitted stratification into four prediction groups: good, moderately good, poor, and absolutely poor. This model identified patient groups with a range from 1.2% to 30.2% for survival and from 0.3% to 23.2% for CPC 1 to 2 probabilities. Similar results were observed when this model was applied to the validation cohort. Conclusions: On the basis of a decision-tree prediction model using four prehospital variables (shockable initial rhythm, age, witnessed arrest, and witnessed by EMS personnel), OHCA patients can be readily stratified into the four groups (good, moderately good, poor, and absolutely poor) that help predict both survival at 1 month and survival with favorable neurologic outcome at 1 month. This simple prediction model may provide clinicians with a practical bedside tool for the OHCA patient's stratification in the emergency department.
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页数:9
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