Will My Tibial Fracture Heal? Predicting Nonunion at the Time of Definitive Fixation Based on Commonly Available Variables

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.

Circular external fixation and cemented PMMA spacers for the treatment of complex tibial fractures and infected nonunions with segmental bone loss

“…As with all these complex disease patterns, the study cohort was heterogeneous, making it difficult to control possible confounding factors. Westgeet et al 41 Journal of Orthopaedic Surgery 25 (2) et al 42 showed that AO classification (odd ratio (OR) 1.86), gunshot wounds (GSW) (OR 1.98), fracture location and pattern (OR 0.46-1.36), open fractures (OR 3.9), smoking (OR 1.8), EtOH use (OR 1.6), HIV (OR 4), NSAID use (OR 1.5), diabetes (OR 2.2), compartment syndrome (OR 4), vascular injury (OR 2), muscle flap (9.6), and deep infection (OR 12.75) were variables that potentially influences union. However, it can be assumed that all factors with the exception of muscle flap soft-tissue cover (100% in the tibial fracture group, 58% in the infected nonunion group) and deep infection (0% in the tibial fracture group and 100% in the infected nonunion group) were equally distributed within the two groups and did not influence the results greatly.…”

Contrast-Enhanced Ultrasound (CEUS) Identifies Perfusion Differences Between Tibial Fracture Unions and Non-Unions

“…This limits its use to fractures with a cortical displacement. However, a lack of cortical continuity on radiographs after operative fixation is presumed to be a risk factor for the development of a non-union [9,11,13]. In these cases, CEUS could support accurate determination of the consolidation status towards an early detection of failed fracture healing.…”

“…Tibial non-unions mainly affect patients at the age of 30-44 years [14]. The potential long-term disability can result in manifold socioeconomic problems [10,11]. Thus, early diagnosis and correct classification are of utmost importance in order to optimize treatment and to minimize subsequent disability [12,13].…”

Virtual structural analysis of tibial fracture healing from low-dose clinical CT scans

“…The RUST approach has demonstrable intra-and inter-observer reliability (Leow et al, 2016;Whelan et al, 2010), has been adapted for use in metaphyseal fractures (Litrenta et al, 2015), and has become a commonly used tool in the design of randomized controlled trials (Castillo et al, 2017). The advantage of RUST scoring is that it was designed to capture the development of external callus at the fracture site and has demonstrated utility as a tool to differentiate between united and nonunited fractures (RUST > 10 for union) in clinical studies (Bishop et al, 2012;O'Halloran et al, 2016;Van Houten et al, 2014). However, RUST scores are not objective and interpretation of the scoring criteria can be ambiguous, leading to known difficulties in inter-rater agreement (Leow et al, 2016).…”