title: An inception cohort analysis to predict nonunion in tibia and 17 other fracture locations.
abstract: BACKGROUND
  The epidemiology of fracture nonunion has been characterized so it is potentially possible to predict nonunion using patient-related risk factors. However, prediction models are currently too cumbersome to be useful. We test a hypothesis that nonunion can be predicted with ≤10 variables, retaining the predictive accuracy of a full model with 42 variables.
  
  
  METHODS
  We sought to predict nonunion with prospectively-acquired inception cohort data for 18 different bones, using the smallest possible number of variables that did not substantially decrease prediction accuracy. An American nationwide claims database of ∼90.1 million participants was used, which included medical and drug expenses for 2011-2012. Continuous enrollment was required for 12 months after fracture, to allow sufficient time to capture a nonunion diagnosis. Health claims were evaluated for 309,330 fractures. A training dataset used a random subset of 2/3 of these fractures, while the remaining fractures formed a validation dataset. Multivariate logistic regression and stepwise logistic regression were used to identify variables predictive of nonunion. P value and the Akaike Information Criterion (AIC) were used to select variables for reduced models. Area-under-the-curve (AUC) was calculated to characterize the success of prediction.
  
  
  RESULTS
  Nonunion rate in 18 fracture locations averaged 4.93%. Algorithms to predict nonunion in 18 locations in the full-model validation set had average AUC=0.680 (±0.034). In the reduced models, average validation set AUC=0.680 (±0.033) and the average number of risk factors required for prediction was 7.6. There was agreement across training set, validation set, and reduced set; in tibia, reduced model validation AUC=0.703, while the full-model validation AUC=0.709. Certain risk factors were important for predicting nonunion in ≥10 bones, including open fracture, multiple fracture, osteoarthritis, surgical treatment, and use of certain medications, including anticoagulants, anticonvulsants, or analgesics.
  
  
  CONCLUSIONS
  Nonunion can be predicted in 18 fracture locations using parsimonious models with <10 patient demography-related risk factors. The model reduction approach used results in simplified models that have nearly the same AUC as the full model. Reduced algorithms can predict nonunion because risk factors important in the full models remain important in the reduced models. This prognostic inception cohort study provides Level I evidence.
authors.affiliation: Dept. of Orthopaedic Surgery, Louisiana State University Medical Center, New Orleans, LA, United States. Electronic address: rzura@lsuhsc.edu.
authors.forename: Robert
authors.initials: R
authors.lastname: Zura
authors.affiliation: Dept. of Orthopaedic Surgery, Saint Louis University School of Medicine, St. Louis, MO, United States. Electronic address: watsonjt@slu.edu.
authors.forename: J Tracy
authors.initials: JT
authors.lastname: Watson
authors.affiliation: Dept. of Orthopaedic Surgery, NYU Langone Medical Center, New York, NY, United States. Electronic address: Thomas.Einhorn@nyumc.org.
authors.forename: Thomas
authors.initials: T
authors.lastname: Einhorn
authors.affiliation: Dept. of Orthopaedic Surgery, Hospital of the University of Pennsylvania, Philadelphia, PA, United States. Electronic address: Samir.Mehta@uphs.upenn.edu.
authors.forename: Samir
authors.initials: S
authors.lastname: Mehta
authors.affiliation: Dept. of Orthopaedic Surgery, Duke University, Durham, NC, United States. Electronic address: gregory.della.rocca@duke.edu.
authors.forename: Gregory J
authors.initials: GJ
authors.lastname: Della Rocca
authors.affiliation: Dept. of Statistics, North Carolina State University, Raleigh, NC, United States. Electronic address: zxiong@ncsu.edu.
authors.forename: Ze
authors.initials: Z
authors.lastname: Xiong
authors.affiliation: Dept. of Statistics, North Carolina State University, Raleigh, NC, United States. Electronic address: zwang28@ncsu.edu.
authors.forename: Zhe
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Clinical Affairs, Bioventus LLC, Durham, NC, United States. Electronic address: John.Jones@bioventusglobal.com.
authors.forename: John
authors.initials: J
authors.lastname: Jones
authors.affiliation: Dept. of Orthopaedic Surgery, Louisiana State University Medical Center, New Orleans, LA, United States. Electronic address: Grant.Steen@bioventusglobal.com.
authors.forename: R Grant
authors.initials: RG
authors.lastname: Steen
mesh_terms: D000328:Adult; D000465:Algorithms; D019540:Area Under Curve; D015331:Cohort Studies; D015897:Comorbidity; D005260:Female; D005592:Fracture Fixation; D017102:Fracture Healing / Q000502:physiology*; D005599:Fractures, Ununited / Q000000981:diagnostic imaging* / Q000453:epidemiology / Q000503:physiopathology; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011336:Probability; D018570:Risk Assessment; D012307:Risk Factors; D014481:United States; D055815:Young Adult
keywords: Ankle; Clavicle; Femur; Humerus; Metatarsal; Radius; Scaphoid; Tarsal; Ulna