INTRODUCTION: Surgeons 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 METHODS: Using 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. RESULTS: A 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. CONCLUSION: A 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.
INTRODUCTION: Surgeons 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 METHODS: Using 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. RESULTS: A 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. CONCLUSION: A 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.