title: Can routine preoperative data predict adverse outcomes in the elderly? Development and validation of a simple risk model incorporating a chart-derived frailty score.
abstract: BACKGROUND
  Frailty has emerged as an important predictor of operative risk among elderly surgical patients. However, the complexity of prospective frailty scores has limited their widespread use. Our goal was to develop two frailty-based surgical risk models using only routine preoperative data. Our hypothesis was that these models could easily integrate into an electronic medical record to predict 30-day morbidity and mortality.
  
  
  METHODS
  American College of Surgeons NSQIP Participant Use Data Files from 2005 to 2010 were reviewed, and patients 65 years and older who underwent elective lower gastrointestinal surgery were identified. Two multivariate logistic regression models were constructed and internally cross-validated. The first included simple functional data, a comorbidity index based on the Charlson Comorbidity Index, demographics, BMI, and laboratory data (ie, albumin <3.4 g/dL, hematocrit <35%, and creatinine >2 mg/dL). The second model contained only parameters that can directly autopopulate from an electronic medical record (ie, demographics, laboratory data, BMI, and American Society of Anesthesiologists score). To assess diagnostic accuracy, receiver operating characteristic curves were constructed.
  
  
  RESULTS
  There were 76,106 patients who met criteria for inclusion. Thirty-day mortality was seen in 2,853 patients or 3.7% of the study population and 18,436 patients (24.2%) experienced a major complication. The c-statistic of the first expanded model was 0.813 for mortality and 0.629 for morbidity. The second simplified model had a c-statistic of 0.795 for mortality and 0.621 for morbidity. Both models were well calibrated per the Hosmer-Lemeshow test.
  
  
  CONCLUSIONS
  Our work demonstrates that routine preoperative data can approximate frailty and predict geriatric-specific surgical risk. The models' predicative powers were comparable with that of established prospective frailty scores. Our calculator could be used as a low-cost simple screen for high-risk individuals who might require additional evaluation or specialized services.
authors.affiliation: Department of Anesthesiology, Icahn School of Medicine at Mount Sinai, New York, NY.
authors.forename: Levana G
authors.initials: LG
authors.lastname: Amrock
authors.affiliation: Department of Anesthesiology and Critical Care, The University of Pennsylvania, Philadelphia, PA.
authors.forename: Mark D
authors.initials: MD
authors.lastname: Neuman
authors.affiliation: Department of Health Evidence and Policy, Icahn School of Medicine at Mount Sinai, New York, NY.
authors.forename: Hung-Mo
authors.initials: HM
authors.lastname: Lin
authors.affiliation: Department of Anesthesiology, Icahn School of Medicine at Mount Sinai, New York, NY; Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY; Department of Geriatrics and Palliative Care, Icahn School of Medicine at Mount Sinai, New York, NY. Electronic address: stacie.deiner@mountsinai.org.
authors.forename: Stacie
authors.initials: S
authors.lastname: Deiner
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D057286:Electronic Health Records / Q000706:statistics & numerical data; D005260:Female; D016330:Frail Elderly / Q000706:statistics & numerical data*; D015577:Geriatric Assessment / Q000379:methods*; D017052:Hospital Mortality / Q000639:trends; D006801:Humans; D008297:Male; D009017:Morbidity / Q000639:trends; D018579:Patient Selection; D011183:Postoperative Complications / Q000453:epidemiology* / Q000517:prevention & control; D057234:Preoperative Period; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D013514:Surgical Procedures, Operative / Q000009:adverse effects*; D015996:Survival Rate / Q000639:trends; D014481:United States