Predicting tibia shaft nonunions at initial fixation: An external validation of the Nonunion Risk Determination (NURD) score in the SPRINT trial data. Issue 10 (October 2020)

• Record Type: Journal Article
• Authors: O'Hara, Nathan N.; Slobogean, Gerard P.; O'Halloran, Kevin; Castillo, Renan; Sprague, Sheila; Bhandari, Mohit; O'Toole, Robert V.
• Is Part Of: Injury. Volume 51:Issue 10(2020)
• Page Start: 2302
• Page End: 2308
• Publication Date: 2020-10
• Subjects: Tibia -- Fracture -- Nonunion -- Prediction -- Prognostic; Wounds and injuries -- Surgery -- Periodicals; Accidents -- Periodicals; Wounds and Injuries -- surgery -- Periodicals; Lésions et blessures -- Chirurgie -- Périodiques; Electronic journals; Electronic journals; 617.1
• DOI: 10.1016/j.injury.2020.06.050
• Languages: English
• ISSNs: 0020-1383

Abstract

Highlights: This study tested the external validity of the non-union risk determination (NURD) score in data from the study to prospectively evaluate reamed intramedullary nails in patient with tibial fractures (SPRINT) trial. The NURD score lacks generalizability as it underperforms with respect to discrimination in the SPRINT trial data. After adjusting for case-mix differences, the performance of the NURD score is comparable between the two datasets, suggesting robust reproducibility. Abstract: Background: Predictive models are common in orthopedic research; however, most models are not validated in an external population. The Nonunion Risk Determination (NURD) score was developed using a single-center cohort of 382 patients to reliably predict tibia shaft nonunions at the time of initial intramedullary nail fixation. The purpose of this study was to externally validate the NURD score using data from the SPRINT Trial. Methods: The SPRINT trial was a multicenter study comparing reamed versus unreamed intramedullary nails in tibial shaft fracture patients. We assessed the prognostic performance of the NURD score in the SPRINT trial data with comparisons of the c-statistics, calibration plots, and a comparison of predicted probabilities at cut-points defined in the study to derive the NURD score. In addition, we compared the odds ratios of the NURD score components between the derivation (NURD) and external validation (SPRINT) data. Results: The NURD score demonstrated significantly worse discrimination in the SPRINT data than was observed in the original data (c-statistic: 0.61 vs. 0.85, p <0.01). The NURD score was well-calibrated in the derivation and SPRINT data. The SPRINT data had less heterogeneity, as determined by the standard deviation of the linear predictors (NURD: 1.4 vs. SPRINT: 0.4). Once we adjusted for case-mix differences, the NURD score had similarly strong discrimination in the SPRINT data (c-statistic: 0.81 vs. 0.85, p = 0.17). Discussion: Based on our external validation, the NURD score lacks generalizability as it underperforms with respect to discrimination in the SPRINT trial data. However, after adjusting for case-mix differences, the performance of the NURD score is comparable between the two datasets, suggesting robust reproducibility.