title: Rapid identification of slow healing wounds.
abstract: Chronic nonhealing wounds have a prevalence of 2% in the United States, and cost an estimated $50 billion annually. Accurate stratification of wounds for risk of slow healing may help guide treatment and referral decisions. We have applied modern machine learning methods and feature engineering to develop a predictive model for delayed wound healing that uses information collected during routine care in outpatient wound care centers. Patient and wound data was collected at 68 outpatient wound care centers operated by Healogics Inc. in 26 states between 2009 and 2013. The dataset included basic demographic information on 59,953 patients, as well as both quantitative and categorical information on 180,696 wounds. Wounds were split into training and test sets by randomly assigning patients to training and test sets. Wounds were considered delayed with respect to healing time if they took more than 15 weeks to heal after presentation at a wound care center. Eleven percent of wounds in this dataset met this criterion. Prognostic models were developed on training data available in the first week of care to predict delayed healing wounds. A held out subset of the training set was used for model selection, and the final model was evaluated on the test set to evaluate discriminative power and calibration. The model achieved an area under the curve of 0.842 (95% confidence interval 0.834-0.847) for the delayed healing outcome and a Brier reliability score of 0.00018. Early, accurate prediction of delayed healing wounds can improve patient care by allowing clinicians to increase the aggressiveness of intervention in patients most at risk.
authors.affiliation: Program in Biomedical Informatics, Stanford University, Stanford, California.
authors.forename: Kenneth
authors.initials: K
authors.lastname: Jung
authors.affiliation: Healogics Inc., Jacksonville, Florida.
authors.forename: Scott
authors.initials: S
authors.lastname: Covington
authors.affiliation: Departments of Surgery, Comprehensive Wound Center, Davis Heart and Lung Research Institute, Centers for Regenerative Medicine and Cell-based Therapies, The Ohio State University Medical Center, Columbus, Ohio.
authors.forename: Chandan K
authors.initials: CK
authors.lastname: Sen
authors.affiliation: Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, California.
authors.forename: Michael
authors.initials: M
authors.lastname: Januszyk
authors.affiliation: Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, Florida.
authors.forename: Robert S
authors.initials: RS
authors.lastname: Kirsner
authors.affiliation: Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, California.
authors.forename: Geoffrey C
authors.initials: GC
authors.lastname: Gurtner
authors.affiliation: Center for Biomedical Informatics Research, Stanford University, Stanford, California.
authors.forename: Nigam H
authors.initials: NH
authors.lastname: Shah
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D002908:Chronic Disease; D019468:Disease Management; D042241:Early Diagnosis; D061345:Early Medical Intervention; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D016015:Logistic Models; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D011379:Prognosis; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D013997:Time Factors; D014945:Wound Healing; D014947:Wounds and Injuries / Q000175:diagnosis* / Q000628:therapy; D055815:Young Adult
keywords: data mining; predictive model; prognostic model; statistics; wound healing