title: A model to predict limb salvage in severe combat-related open calcaneus fractures.
abstract: BACKGROUND
  Open calcaneus fractures can be limb threatening and almost universally result in some measure of long-term disability. A major goal of initial management in patients with these injuries is setting appropriate expectations and discussing the likelihood of limb salvage, yet there are few tools that assist in predicting the outcome of this difficult fracture pattern.
  
  
  OBJECTIVE
  We developed two decision support tools, an artificial neural network and a logistic regression model, based on presenting data from severe combat-related open calcaneus fractures. We then determined which model more accurately estimated the likelihood of amputation and which was better suited for clinical use.
  
  
  METHODS
  Injury-specific data were collected from wounded active-duty service members who sustained combat-related open calcaneus fractures between 2003 and 2012. One-hundred fifty-five open calcaneus fractures met inclusion criteria. Median followup was 3.5 years (interquartile range: 1.5, 5.1 years), and amputation rate was 44%. We developed an artificial neural network designed to estimate the likelihood of amputation, using information available on presentation. For comparison, a conventional logistic regression model was developed with variables identified on univariate analysis. We determined which model more accurately estimated the likelihood of amputation using receiver operating characteristic analysis. Decision curve analysis was then performed to determine each model's clinical utility.
  
  
  RESULTS
  An artificial neural network that contained eight presenting features resulted in smaller error. The eight features that contributed to the most predictive model were American Society of Anesthesiologist grade, plantar sensation, fracture treatment before arrival, Gustilo-Anderson fracture type, Sanders fracture classification, vascular injury, male sex, and dismounted blast mechanism. The artificial neural network was 30% more accurate, with an area under the curve of 0.8 (compared to 0.65 for logistic regression). Decision curve analysis indicated the artificial neural network resulted in higher benefit across the broadest range of threshold probabilities compared to the logistic regression model and is perhaps better suited for clinical use.
  
  
  CONCLUSIONS
  This report demonstrates an artificial neural network was capable of accurately estimating the likelihood of amputation. Furthermore, decision curve analysis suggested the artificial neural network is better suited for clinical use than logistic regression. Once properly validated, this may provide a tool for surgeons and patients faced with combat-related open calcaneus fractures in which decisions between limb salvage and amputation remain difficult.
authors.affiliation: Regenerative Medicine Department, Naval Medical Research Center, 503 Robert Grant Avenue, Silver Spring, MD, 20910, USA.
authors.forename: Adam J
authors.initials: AJ
authors.lastname: Bevevino
authors.forename: Jonathan F
authors.initials: JF
authors.lastname: Dickens
authors.forename: Benjamin K
authors.initials: BK
authors.lastname: Potter
authors.forename: Theodora
authors.initials: T
authors.lastname: Dworak
authors.forename: Wade
authors.initials: W
authors.lastname: Gordon
authors.forename: Jonathan A
authors.initials: JA
authors.lastname: Forsberg
mesh_terms: D000328:Adult; D000671:Amputation, Surgical; D002111:Calcaneus / Q000293:injuries / Q000601:surgery*; D003661:Decision Support Techniques; D005260:Female; D018409:Foot Injuries / Q000175:diagnosis / Q000601:surgery*; D005592:Fracture Fixation / Q000009:adverse effects / Q000379:methods*; D017102:Fracture Healing; D050723:Fractures, Bone / Q000175:diagnosis / Q000601:surgery*; D006801:Humans; D016013:Likelihood Functions; D023821:Limb Salvage; D016015:Logistic Models; D008297:Male; D008887:Military Medicine; D008889:Military Personnel; D016571:Neural Networks, Computer; D018579:Patient Selection; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors; D012720:Severity of Illness Index; D013997:Time Factors; D016896:Treatment Outcome; D014481:United States; D055815:Young Adult