title: A risk prediction model for mortality in the moribund general surgical patient.
abstract: BACKGROUND
  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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Surgery, University of Pennsylvania, Philadelphia, PA. Electronic address: Lindsay.Kuo@uphs.upenn.edu.
authors.forename: Lindsay E
authors.initials: LE
authors.lastname: Kuo
authors.affiliation: Department of Surgery, University of Pennsylvania, Philadelphia, PA.
authors.forename: Kristina D
authors.initials: KD
authors.lastname: Simmons
authors.affiliation: Department of Surgery, University of Pennsylvania, Philadelphia, PA.
authors.forename: Daniel N
authors.initials: DN
authors.lastname: Holena
authors.affiliation: Department of Surgery, University of Pennsylvania, Philadelphia, PA.
authors.forename: Giorgos
authors.initials: G
authors.lastname: Karakousis
authors.affiliation: Department of Surgery, University of Pennsylvania, Philadelphia, PA.
authors.forename: Rachel R
authors.initials: RR
authors.lastname: Kelz
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D016638:Critical Illness; D003661:Decision Support Techniques; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D008297:Male; D008875:Middle Aged; D011183:Postoperative Complications; D058996:Quality Improvement; D012189:Retrospective Studies; D012306:Risk; D018570:Risk Assessment; D012307:Risk Factors; D013514:Surgical Procedures, Operative; D055815:Young Adult
keywords: Moribund; Mortality; Risk prediction; Surgery