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. 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. 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. 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.

Certain common medications are associated with an elevated risk of fracture and recent data suggests that medications can also increase nonunion risk. Medication use is a modifiable nonunion risk factor, but it is unknown whether risk accrues solely to chronic medication use or whether there is also risk inherent to acute use. Multivariate logistic regression was used in an inception cohort to calculate odds ratios (OR) for fracture nonunion associated with medication use, in context with other risk factors demonstrated to influence nonunion. Patient-level health claims for medical and drug expenses were compiled from a payer database. Patients were included if they had a fracture coded in 2011, with continuous enrollment for 1 month prior to and 12 months after fracture. The database contained demographic descriptors, treatment procedures per CPT codes, co-morbidities per ICD-9 codes, and prescriptions per National Drug Codes. Chronic medication use was defined as ≥30 days of prescription prior to fracture with ≥1 day afterward; acute use was any other prescription. Most non-analgesic medications were safe in acute or chronic use, but risk of nonunion was elevated for a wide range of analgesics. Overall, 45,085 fractures (14.6% of fractures) affected patients using chronic opioids. Nonunion OR was elevated for acute and chronic use of Schedule 2 opioids including acetaminophen/oxycodone, hydromorphone, oxycodone, and acetaminophen/hydrocodone bitartrate, as well as Schedule 3-5 opioids including tramadol (all, p < 0.0001). The highest ORs were associated with chronic administration of Schedule 2 opioids. Most medications do not increase nonunion risk, but acute and chronic use of NSAIDs or opioids was associated with impaired fracture healing. There is particular risk in prescribing opioid analgesics for fracture, though literature suggests that roughly half of opioid-naïve patients receive such a prescription. Patients evaluated in this study were not a random sample of Americans; they may approximate a random sample of the Emergency Department population in the United States. Thus, trauma patients may represent a population enriched for nonunion risk factors. Opioids impair recovery from injury; if they also predispose to injury, the ongoing opioid epidemic could presage an increase in nonunion prevalence.

The epidemiology of fracture nonunion has been characterized so it is potentially possible to predict nonunion using patient-related risk factors. However, prediction models are currently too cumbersome to be useful. We test a hypothesis that nonunion can be predicted with ≤10 variables, retaining the predictive accuracy of a full model with 42 variables. We sought to predict nonunion with prospectively-acquired inception cohort data for 18 different bones, using the smallest possible number of variables that did not substantially decrease prediction accuracy. An American nationwide claims database of ∼90.1 million participants was used, which included medical and drug expenses for 2011-2012. Continuous enrollment was required for 12 months after fracture, to allow sufficient time to capture a nonunion diagnosis. Health claims were evaluated for 309,330 fractures. A training dataset used a random subset of 2/3 of these fractures, while the remaining fractures formed a validation dataset. Multivariate logistic regression and stepwise logistic regression were used to identify variables predictive of nonunion. P value and the Akaike Information Criterion (AIC) were used to select variables for reduced models. Area-under-the-curve (AUC) was calculated to characterize the success of prediction. Nonunion rate in 18 fracture locations averaged 4.93%. Algorithms to predict nonunion in 18 locations in the full-model validation set had average AUC=0.680 (±0.034). In the reduced models, average validation set AUC=0.680 (±0.033) and the average number of risk factors required for prediction was 7.6. There was agreement across training set, validation set, and reduced set; in tibia, reduced model validation AUC=0.703, while the full-model validation AUC=0.709. Certain risk factors were important for predicting nonunion in ≥10 bones, including open fracture, multiple fracture, osteoarthritis, surgical treatment, and use of certain medications, including anticoagulants, anticonvulsants, or analgesics. Nonunion can be predicted in 18 fracture locations using parsimonious models with <10 patient demography-related risk factors. The model reduction approach used results in simplified models that have nearly the same AUC as the full model. Reduced algorithms can predict nonunion because risk factors important in the full models remain important in the reduced models. This prognostic inception cohort study provides Level I evidence.

Background/Objectives: Bone nonunion remains a clinical challenge in orthopedic surgery with significant impacts on mental and physical wellbeing for patients. There are several previously established risk factors of nonunion that are connected to nutrition, but this has yet to be substantially explored. This review seeks to assess all studies that present associations between nutrition and nonunion to understand the potential for clinical relevance in nonunion prevention. Methods: Case–control and cohort studies comparing nonunion risk based on nutritional factors were gathered through PubMed in July 2024. Data were extracted with dual verification through Covidence and assessed for bias using the Newcastle–Ottawa Scale. Results: A total of 21 studies were included in this literature review. Vitamin D deficiency was a significant risk factor of nonunion in six studies and not significant in six other studies. Albumin was significant in three of the five studies addressing this lab value. Iron deficiency anemia was significant in a study assessing its impact on nonunion. Calcium was not significant in the one study mentioned. ICD-10-coded malnutrition was significant in one of the two studies. Sarcopenia, nutritional care plans, and dietitian-diagnosed malnutrition were statistically significant clinical indicators for predicting nonunion, but food insecurity was insignificant. Conclusions: Vitamin D, calcium, albumin, iron deficiency anemia, sarcopenia, and clinically diagnosed malnutrition have all been associated with an increased risk of nonunion in observational studies and should be considered when preventing nonunion development.

Background: Heterotopic ossification (HO) is a frequent complication following hip surgery. Using data from the Hip Fracture Evaluation with Alternatives of Total Hip Arthroplasty versus Hemiarthroplasty (HEALTH) trial, we aimed to (1) determine the prevalence of HO following total hip arthroplasty (THA) for femoral neck fracture in patients ≥50 years of age, (2) identify whether HO is associated with an increased risk of revision surgery within 24 months after the fracture, and (3) determine the impact of HO on functional outcomes. Methods: We performed a multivariable Cox regression analysis using revision surgery as the dependent variable and HO as the independent variable. We compared Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores between participants with and those without HO at 24 months. Results: Of 1,441 participants in the study, 287 (19.9%) developed HO within 24 months. HO was not associated with subsequent revision surgery. Grade-III HO was associated with statistically significant and clinically relevant deterioration in the total WOMAC score, which was mainly related to the function component of the score, compared with grade I or II. Conclusions: The impact of grade-III HO on the functional outcomes and quality of life after THA for hip fracture is clinically important, and HO prophylaxis for selected high-risk patients may be appropriate. Level of Evidence: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Background: We evaluated antimicrobial resistance (AMR) patterns following local antibiotic use in a large cohort of patients with fractures from the PREP-IT (A Program of Randomized Trials to Evaluate Preoperative Antiseptic Skin Solutions in Orthopaedic Trauma) study. We hypothesized that, among patients with extremity fractures who developed fracture-related infection (FRI), there would be no difference in AMR rates between those who had or had not received local antibiotic therapy with surgical fixation. Methods: This was a secondary analysis of all patients in the PREP-IT trial who developed FRI. Patient demographics, injury and fracture characteristics, and the primary outcome of the presence of an antimicrobial-resistant FRI were evaluated on the basis of whether the patient had or had not received local antibiotics in the operating room prior to, or at, definitive fixation. Results: A total of 555 FRIs in 546 patients (mean age, 50 years; 39% female; and 82% White) were included. A total of 268 fractures (264 patients) received local antibiotics. The Injury Severity Score and the proportion of open fractures were higher among patients and fractures that received local antibiotics, respectively. There were more Gustilo-Anderson type-IIIB or IIIC fractures in the local antibiotic group, but the rate did not differ significantly from that in the group with no local antibiotics (20% versus 14%; p = 0.14). Other baseline and fracture characteristics were similar between the groups, with the exception of age (lower in the group with local antibiotics). When examining FRIs with gram-positive organisms, we found that 3 (1.7%) of the FRIs in fractures that had been treated with local vancomycin had organisms resistant to vancomycin compared with 2 (0.9%) of the FRIs in fractures for which local vancomycin had not been used (p = 0.67). When examining FRIs with gram-negative organisms, the number of FRIs with aminoglycoside-resistant organisms was 8 (11.6%) among fractures that received local aminoglycosides and 10 (6.2%) among fractures that did not receive local aminoglycosides (p = 0.26). Conclusions: Among extremity fractures that developed FRI, we were unable to detect differences in the rates of AMR between fractures treated with or without local antibiotic prophylactic strategies in our analysis of a randomized trial of various skin preparation solutions for extremity trauma surgery. These findings provide cautious reassurance regarding the safety of local antibiotics but underscore the need for further prospective analysis. Level of Evidence: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.