Robert Zura1, Mary Jo Braid-Forbes2, Kyle Jeray3, Samir Mehta4, Thomas A Einhorn5, J Tracy Watson6, Gregory J Della Rocca7, Kevin Forbes8, R Grant Steen9. 1. Dept. of Orthopaedic Surgery, Louisiana State University, New Orleans, LA, USA. Electronic address: rzura@lsuhsc.edu. 2. Braid-Forbes Health Research, Silver Spring, MD, USA. Electronic address: mjbraidforbes@braidforbes.com. 3. Dept. of Orthopedic Surgery, University of South Carolina, Greenville, SC, USA. Electronic address: KJeray@ghs.org. 4. Dept. of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: Samir.Mehta@uphs.upenn.edu. 5. Dept. of Orthopaedic Surgery, NYU Langone Medical Center, New York, NY, USA. Electronic address: Thomas.Einhorn@nyumc.org. 6. Dept. of Orthopaedic Surgery, Saint Louis University School of Medicine, St. Louis, MO, USA. Electronic address: watsonjt@slu.edu. 7. Dept. of Orthopaedic Surgery, University of Missouri, Columbia, MO, USA. Electronic address: dellaroccag@health.missouri.edu. 8. School of Business and Economics, The Catholic University of America, Washington, DC, USA. Electronic address: forbes@cua.edu. 9. Medical Affairs, Bioventus LLC, 4721 Emperor Blvd., Suite 100, Durham, NC 27703, USA. Electronic address: Grant.Steen@bioventusglobal.com.
Abstract
BACKGROUND: Fracture nonunion risk is related to severity of injury and type of treatment, yet fracture healing is not fully explained by these factors alone. We hypothesize that patient demographic factors assessable by the clinician at fracture presentation can predict nonunion. METHODS: A prospective cohort study design was used to examine ~2.5 million Medicare patients nationwide. Patients making fracture claims in the 5% Medicare Standard Analytic Files in 2011 were analyzed; continuous enrollment for 12months after fracture was required to capture the ICD-9-CM nonunion diagnosis code (733.82) or any procedure codes for nonunion repair. A stepwise regression analysis was used which dropped variables from analysis if they did not contribute sufficient explanatory power. In-sample predictive accuracy was assessed using a receiver operating characteristic (ROC) curve approach, and an out-of-sample comparison was drawn from the 2012 Medicare 5% SAF files. RESULTS: Overall, 47,437 Medicare patients had 56,492 fractures and 2.5% of fractures were nonunion. Patients with healed fracture (age 75.0±12.7SD) were older (p<0.0001) than patients with nonunion (age 69.2±13.4SD). The death rate among all Medicare beneficiaries was 4.8% per year, but fracture patients had an age- and sex-adjusted death rate of 11.0% (p<0.0001). Patients with fracture in 14 of 18 bones were significantly more likely to die within one year of fracture (p<0.0001). Stepwise regression yielded a predictive nonunion model with 26 significant explanatory variables (all, p≤0.003). Strength of this model was assessed using an area under the curve (AUC) calculation, and out-of-sample AUC=0.710. CONCLUSIONS: A logistic model predicted nonunion with reasonable accuracy (AUC=0.725). Within the Medicare population, nonunion patients were younger than patients who healed normally. Fracture was associated with increased risk of death within 1year of fracture (p<0.0001) in 14 different bones, confirming that geriatric fracture is a major public health issue. Comorbidities associated with increased risk of nonunion include past or current smoking, alcoholism, obesity or morbid obesity, osteoarthritis, rheumatoid arthritis, type II diabetes, and/or open fracture (all, multivariate p<0.001). Nonunion prediction requires knowledge of 26 patient variables but predictive accuracy is currently comparable to the Framingham cardiovascular risk prediction.
BACKGROUND:Fracture nonunion risk is related to severity of injury and type of treatment, yet fracture healing is not fully explained by these factors alone. We hypothesize that patient demographic factors assessable by the clinician at fracture presentation can predict nonunion. METHODS: A prospective cohort study design was used to examine ~2.5 million Medicare patients nationwide. Patients making fracture claims in the 5% Medicare Standard Analytic Files in 2011 were analyzed; continuous enrollment for 12months after fracture was required to capture the ICD-9-CM nonunion diagnosis code (733.82) or any procedure codes for nonunion repair. A stepwise regression analysis was used which dropped variables from analysis if they did not contribute sufficient explanatory power. In-sample predictive accuracy was assessed using a receiver operating characteristic (ROC) curve approach, and an out-of-sample comparison was drawn from the 2012 Medicare 5% SAF files. RESULTS: Overall, 47,437 Medicare patients had 56,492 fractures and 2.5% of fractures were nonunion. Patients with healed fracture (age 75.0±12.7SD) were older (p<0.0001) than patients with nonunion (age 69.2±13.4SD). The death rate among all Medicare beneficiaries was 4.8% per year, but fracturepatients had an age- and sex-adjusted death rate of 11.0% (p<0.0001). Patients with fracture in 14 of 18 bones were significantly more likely to die within one year of fracture (p<0.0001). Stepwise regression yielded a predictive nonunion model with 26 significant explanatory variables (all, p≤0.003). Strength of this model was assessed using an area under the curve (AUC) calculation, and out-of-sample AUC=0.710. CONCLUSIONS: A logistic model predicted nonunion with reasonable accuracy (AUC=0.725). Within the Medicare population, nonunion patients were younger than patients who healed normally. Fracture was associated with increased risk of death within 1year of fracture (p<0.0001) in 14 different bones, confirming that geriatric fracture is a major public health issue. Comorbidities associated with increased risk of nonunion include past or current smoking, alcoholism, obesity or morbid obesity, osteoarthritis, rheumatoid arthritis, type II diabetes, and/or open fracture (all, multivariate p<0.001). Nonunion prediction requires knowledge of 26 patient variables but predictive accuracy is currently comparable to the Framingham cardiovascular risk prediction.
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