Any Cortical Bridging Predicts Healing of Tibial Shaft Fractures

Lack, William D.; Starman, James S.; Seymour, Rachel B.; Bosse, Michael J.; Karunakar, Madhav A.; Sims, Stephen H.; Kellam, James F

doi:10.2106/jbjs.m.00385

Cited by 41 publications

(30 citation statements)

References 24 publications

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“…Lack et al [29] established a 4-month postoperative nonunion prediction model based on cortical bridging of one cortex. Although innovative, this model is not useful at time zero.…”

Section: Discussionmentioning

confidence: 99%

“…Pro [32] NSAID use at admission Pro [13,27] Con [17,35] NSAID use at discharge Pro [13,27] Con [17,35] Opioid use at admission Pro [12] Opioid use at discharge Pro [12] Human immunodeficiency virus Pro [24] Con [20] Hepatitis C None Diabetes Pro [13,21,27] ASA score Pro [16] Smoking status Pro [13,21,31,36] Con [29,35] Alcohol use Pro [10,13,21] AO classification Pro [30] Con [2,22] ISS score Pro [30] TRISS score Pro [30] ICU days None * Pro articles support a correlation between nonunion and the variable; Con articles show that the variable has no correlation with nonunion; GSW = gunshot wound; NSAID = nonsteroidal anti-inflammatory drug; ASA = American Society of Anesthesiologists; AO = Arbeitsgemeinschaft für Osteosynthesefragen; ISS = Injury Severity Score; TRISS = Trauma and Injury Severity Score; ICU = intensive care unit.…”

Section: Compartment Syndromementioning

confidence: 99%

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

O’Halloran

Coale

Costales

et al. 2016

Clinical Orthopaedics &Amp; Related Research

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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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“…Lack et al [29] established a 4-month postoperative nonunion prediction model based on cortical bridging of one cortex. Although innovative, this model is not useful at time zero.…”

Section: Discussionmentioning

confidence: 99%

Section: Compartment Syndromementioning

confidence: 99%

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

O’Halloran

Coale

Costales

et al. 2016

Clinical Orthopaedics &Amp; Related Research

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“…14 A 2014 study concluded that any cortical bridging within 4 months predicted final healing with an accuracy of 99%. 15 Union can be defined radiologically as the presence of bridging callus at three of four cortices on AP and lateral views. 16 A scoring scale was developed to evaluate healing in tibial fractures by Whelan et al, called the Radiographic Union Scale for Tibial Fractures score.…”

Section: Tibial Shaft Fracturesmentioning

confidence: 99%

Radiologic Evaluation of Lower Leg, Ankle, and Foot Fracture Fixation Hardware

Mar

Schilling

Lomasney

et al. 2019

Semin Musculoskelet Radiol

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Radiologists should be familiar with basic principles of fracture fixation and the normal imaging appearances of implant constructs and their complications. The surgeon's selection of external fixation, intramedullary nail fixation, open reduction and internal fixation, or some combination depends on patient factors, fracture configuration, injury to the soft tissue envelope, and surgeon experience. Complications including loss of fixation with resultant malalignment, nonunion, infection, and posttraumatic osteoarthritis present additional challenges for the surgeon as well as the radiologist. This article reviews the rationale behind fracture fixation in fractures of the lower leg, ankle, and foot. Examples of postoperative complications are also reviewed.

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“…The presence of any radiographic bridging callus by four months postoperatively was highly sensitive and specific in predicting the final healing outcome. 34 Given the delayed mineralization of callus during fracture healing and the limitations of plain radiographic assessments, 3D imaging may offer an even earlier and more accurate prediction of the final healing outcome as has been suggested for modalities such as ultrasound. 32 Additionally, FE analysis offers the potential to demonstrate how imaging findings and patient symptoms correspond with increasing mechanical stability at the fracture site.…”

Section: Barriers To In Silico Assessment Of Clinical Fracture Healingmentioning

confidence: 99%

Computational techniques for the assessment of fracture repair

Anderson

Thomas

Marin

et al. 2014

Injury

Self Cite

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The combination of high-resolution three-dimensional medical imaging, increased computing power, and modern computational methods provide unprecedented capabilities for assessing the repair and healing of fractured bone. Fracture healing is a natural process that restores the mechanical integrity of bone and is greatly influenced by the prevailing mechanical environment. Mechanobiological theories have been proposed to provide greater insight into the relationships between mechanics (stress and strain) and biology. Computational approaches for modeling these relationships have evolved from simple tools to analyse fracture healing at a single point in time to current models that capture complex biological events such as angiogenesis, stochasticity in cellular activities, and cell-phenotype specific activities. The predictive capacity of these models has been established using corroborating physical experiments. For clinical application, mechanobiological models accounting for patient-to-patient variability hold the potential to predict fracture healing and thereby help clinicians to customize treatment. Advanced imaging tools permit patient-specific geometries to be used in such models. Refining the models to study the strain fields within a fracture gap and adapting the models for case-specific simulation may provide more accurate examination of the relationship between strain and fracture healing in actual patients. Medical imaging systems have significantly advanced the capability for less invasive visualization of injured musculoskeletal tissues, but all too often the consideration of these rich datasets has stopped at the level of subjective observation. Computational image analysis methods have not yet been applied to study fracture healing, but two comparable challenges which have been addressed in this general area are the evaluation of fracture severity and of fracture-associated soft tissue injury. CT-based methodologies developed to assess and quantify these factors are described and results presented to show the potential of these analysis methods.

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Any Cortical Bridging Predicts Healing of Tibial Shaft Fractures

Abstract: Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.

Cited by 41 publications

References 24 publications

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

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

Radiologic Evaluation of Lower Leg, Ankle, and Foot Fracture Fixation Hardware

Computational techniques for the assessment of fracture repair

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