Kevin O'Halloran1, Max Coale2, Timothy Costales2, Timothy Zerhusen2, Renan C Castillo3, Jason W Nascone2, Robert V O'Toole4. 1. Regions Hospital/University of Minnesota, St Paul, MN, USA. 2. R Adams Cowley Shock Trauma Center, Department of Orthopaedics, University of Maryland School of Medicine, 2200 South Greene Street, SuiteT3R62, Baltimore, MD, 21201, USA. 3. Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. 4. R Adams Cowley Shock Trauma Center, Department of Orthopaedics, University of Maryland School of Medicine, 2200 South Greene Street, SuiteT3R62, Baltimore, MD, 21201, USA. rvo3@yahoo.com.
Abstract
BACKGROUND: Accurate prediction of tibial nonunions has eluded researchers. Reliably predicting tibial nonunions at the time of fixation could change management strategies and stimulate further research. QUESTIONS/PURPOSES: We asked (1) whether data from medical records, fracture characteristics, and radiographs obtained at the time of fixation would identify features predictive of tibial fracture nonunion; and (2) whether this information could be used to create a model to assess the chance of nonunion at the time of intramedullary (IM) nail fixation of the tibia. METHODS: We retrospectively reviewed all tibial shaft fractures treated at our center from 2007 to 2014. We conducted a literature review and collected data on 35 factors theorized to contribute to delayed bone healing. Patients were followed to fracture healing or surgery for nonunion. Patients with planned prophylactic nonunion surgery were excluded because their nonunions were anticipated and our focus was on unanticipated nonunions. Our cohort consisted of 382 patients treated with IM nails for tibial shaft fractures (nonunion, 56; healed, 326). Bivariate and multivariate regression techniques and stepwise modeling approaches examined the relationship between variables available at definitive fixation. Factors were included in our model if they were identified as having a modest to large effect size (odds ratio > 2) at the p < 0.05 level. RESULTS: A multiple variable logistic regression model was developed, including seven factors (p < 0.05; odds ratio > 2.0). With these factors, we created the Nonunion Risk Determination (NURD) score. The NURD score assigns 5 points for flaps, 4 points for compartment syndrome, 3 points for chronic condition(s), 2 points for open fractures, 1 point for male gender, and 1 point per grade of American Society of Anesthesiologists Physical Status and percent cortical contact. One point each is subtracted for spiral fractures and for low-energy injuries, which were found to be predictive of union. A NURD score of 0 to 5 had a 2% chance of nonunion; 6 to 8, 22%; 9 to 11, 42%; and > 12, 61%. CONCLUSIONS: The proposed nonunion prediction model (NURDS) seems to have potential to allow clinicians to better determine which patients have a higher risk of nonunion. Future work should be directed at prospectively validating and enhancing this model. LEVEL OF EVIDENCE: Level III, diagnostic study.
BACKGROUND: Accurate prediction of tibial nonunions has eluded researchers. Reliably predicting tibial nonunions at the time of fixation could change management strategies and stimulate further research. QUESTIONS/PURPOSES: We asked (1) whether data from medical records, fracture characteristics, and radiographs obtained at the time of fixation would identify features predictive of tibial fracture nonunion; and (2) whether this information could be used to create a model to assess the chance of nonunion at the time of intramedullary (IM) nail fixation of the tibia. METHODS: We retrospectively reviewed all tibial shaft fractures treated at our center from 2007 to 2014. We conducted a literature review and collected data on 35 factors theorized to contribute to delayed bone healing. Patients were followed to fracture healing or surgery for nonunion. Patients with planned prophylactic nonunion surgery were excluded because their nonunions were anticipated and our focus was on unanticipated nonunions. Our cohort consisted of 382 patients treated with IM nails for tibial shaft fractures (nonunion, 56; healed, 326). Bivariate and multivariate regression techniques and stepwise modeling approaches examined the relationship between variables available at definitive fixation. Factors were included in our model if they were identified as having a modest to large effect size (odds ratio > 2) at the p < 0.05 level. RESULTS: A multiple variable logistic regression model was developed, including seven factors (p < 0.05; odds ratio > 2.0). With these factors, we created the Nonunion Risk Determination (NURD) score. The NURD score assigns 5 points for flaps, 4 points for compartment syndrome, 3 points for chronic condition(s), 2 points for open fractures, 1 point for male gender, and 1 point per grade of American Society of Anesthesiologists Physical Status and percent cortical contact. One point each is subtracted for spiral fractures and for low-energy injuries, which were found to be predictive of union. A NURD score of 0 to 5 had a 2% chance of nonunion; 6 to 8, 22%; 9 to 11, 42%; and > 12, 61%. CONCLUSIONS: The proposed nonunion prediction model (NURDS) seems to have potential to allow clinicians to better determine which patients have a higher risk of nonunion. Future work should be directed at prospectively validating and enhancing this model. LEVEL OF EVIDENCE: Level III, diagnostic study.
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