• Lindsay E Kuo, Kristina D Simmons, Daniel N Holena, Giorgos Karakousis, and Rachel R Kelz.
• Department of Surgery, University of Pennsylvania, Philadelphia, PA. Electronic address: Lindsay.Kuo@uphs.upenn.edu.
• J Crit Care. 2015 Apr 1;30(2):310-4.

IntroductionSurgeons struggle to counsel families on the role of surgery and likelihood of survival in the moribund patient. We sought to develop a risk prediction model for postoperative inpatient death for the moribund surgical candidate.Materials And MethodsUsing 2007-2012 American College of Surgeons National Surgical Quality Improvement Program data, we identified American Society of Anesthesiologists class 5 (moribund) patients. The sample was randomly divided into development and validation cohorts. In the development cohort, preoperative patient characteristics were evaluated. The primary outcome measure was in-hospital mortality. Factors significant in univariate analysis were entered into a multivariable model; points were assigned based on β coefficients. A scoring system was generated to predict inpatient mortality. Models were developed separately for operations performed within and after 24 hours of admission, and tested on the validation cohort.ResultsA total of 3120 patients were included. In-hospital mortality was 50.6%. In multivariable analysis, patient characteristics associated with in-hospital mortality were age, functional status, recent dialysis, recent myocardial infarction, ventilator dependence, body mass index, and procedure type. The scoring system generated from this model accurately predicted in-hospital mortality for patients undergoing surgery within and after 24 hours.ConclusionA simple risk prediction model using readily available preoperative patient characteristics accurately predicts postoperative mortality in the moribund surgical patient. This scoring system can assist in decision making.Copyright © 2014 Elsevier Inc. All rights reserved.

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