title: Surgical Risk Preoperative Assessment System (SURPAS): I. Parsimonious, Clinically Meaningful Groups of Postoperative Complications by Factor Analysis.
abstract: To use factor analysis to cluster the 18 American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) perioperative complications into a reproducible, smaller number of clinically meaningful groups of postoperative complications, facilitating and streamlining future study and application in live clinical settings.
  
  
  
  The ACS NSQIP collects and reports on eighteen 30-day postoperative complications (excluding mortality), which are variably grouped in published analyses using ACS NSQIP data. This hinders comparison between studies of this widely used quality improvement dataset.
  
  
  
  Factor analysis was used to develop a series of complication clusters, which were then analyzed to identify a parsimonious, clinically meaningful grouping, using 2,275,240 surgical cases in the ACS NSQIP Participant Use File (PUF), 2005 to 2012. The main outcome measures are reproducible, data-driven, clinically meaningful clusters of complications derived from factor solutions.
  
  
  
  Factor analysis solutions for 5 to 9 latent factors were examined for their percent of total variance, parsimony, and clinical interpretability. Applying the first 2 of these criteria, we identified the 7-factor solution, which included clusters of pulmonary, infectious, wound disruption, cardiac/transfusion, venous thromboembolic, renal, and neurological complications, as the best solution for parsimony and clinical meaningfulness. Applying the last (clinical interpretability), we combined the wound disruption with the infectious clusters resulting in 6 clusters for future clinical applications.
  
  
  
  Factor analysis of ACS NSQIP postoperative complication data provides 6 clinically meaningful complication clusters in lieu of 18 postoperative morbidities, which will facilitate comparisons and clinical implementation of studies of postoperative morbidities.
authors.affiliation: *Surgical Outcomes and Applied Research Program, University of Colorado School of Medicine, Aurora†Department of Surgery, University of Colorado School of Medicine, Aurora‡Adult and Child Center for Health Outcomes Research and Delivery Science, University of Colorado School of Medicine, Aurora§Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora¶Division of Cardiology, Department of Medicine, University of Colorado School of Medicine, Aurora.
authors.forename: Robert A
authors.initials: RA
authors.lastname: Meguid
authors.forename: Michael R
authors.initials: MR
authors.lastname: Bronsert
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Juarez-Colunga
authors.forename: Karl E
authors.initials: KE
authors.lastname: Hammermeister
authors.forename: William G
authors.initials: WG
authors.lastname: Henderson
mesh_terms: D005163:Factor Analysis, Statistical; D006302:Health Services Research; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000379:methods; D011183:Postoperative Complications / Q000453:epidemiology*; D058996:Quality Improvement; D019984:Quality Indicators, Health Care; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D014481:United States / Q000453:epidemiology