Thomas Einhorn

Epidemiology of Fracture Nonunion in 18 Human Bones.

ImportancenFailure of bone fracture healing occurs in 5% to 10% of all patients. Nonunion risk is associated with the severity of injury and with the surgical treatment technique, yet progression to nonunion is not fully explained by these risk factors.nnnObjectivenTo test a hypothesis that fracture characteristics and patient-related risk factors assessable by the clinician at patient presentation can indicate the probability of fracture nonunion.nnnDesign, Setting, and ParticipantsnAn inception cohort study in a large payer database of patients with fracture in the United States was conducted using patient-level health claims for medical and drug expenses compiled for approximately 90.1 million patients in calendar year 2011. The final database collated demographic descriptors, treatment procedures as per Current Procedural Terminology codes; comorbidities as per International Classification of Diseases, Ninth Revision codes; and drug prescriptions as per National Drug Code Directory codes. Logistic regression was used to calculate odds ratios (ORs) for variables associated with nonunion. Data analysis was performed from January 1, 2011, to December 31, 2012.nnnExposuresnContinuous enrollment in the database was required for 12 months after fracture to allow sufficient time to capture a nonunion diagnosis.nnnResultsnThe final analysis of 309u202f330 fractures in 18 bones included 178 952 women (57.9%); mean (SD) age was 44.48 (13.68) years. The nonunion rate was 4.9%. Elevated nonunion risk was associated with severe fracture (eg, open fracture, multiple fractures), high body mass index, smoking, and alcoholism. Women experienced more fractures, but men were more prone to nonunion. The nonunion rate also varied with fracture location: scaphoid, tibiau2009plusu2009fibula, and femur were most likely to be nonunion. The ORs for nonunion fractures were significantly increased for risk factors, including number of fractures (OR, 2.65; 95% CI, 2.34-2.99), use of nonsteroidal anti-inflammatory drugs plus opioids (OR, 1.84; 95% CI, 1.73-1.95), operative treatment (OR, 1.78; 95% CI, 1.69-1.86), open fracture (OR, 1.66; 95% CI, 1.55-1.77), anticoagulant use (OR, 1.58; 95% CI, 1.51-1.66), osteoarthritis with rheumatoid arthritis (OR, 1.58; 95% CI, 1.38-1.82), anticonvulsant use with benzodiazepines (OR, 1.49; 95% CI, 1.36-1.62), opioid use (OR, 1.43; 95% CI, 1.34-1.52), diabetes (OR, 1.40; 95% CI, 1.21-1.61), high-energy injury (OR, 1.38; 95% CI, 1.27-1.49), anticonvulsant use (OR, 1.37; 95% CI, 1.31-1.43), osteoporosis (OR, 1.24; 95% CI, 1.14-1.34), male gender (OR, 1.21; 95% CI, 1.16-1.25), insulin use (OR, 1.21; 95% CI, 1.10-1.31), smoking (OR, 1.20; 95% CI, 1.14-1.26), benzodiazepine use (OR, 1.20; 95% CI, 1.10-1.31), obesity (OR, 1.19; 95% CI, 1.12-1.25), antibiotic use (OR, 1.17; 95% CI, 1.13-1.21), osteoporosis medication use (OR, 1.17; 95% CI, 1.08-1.26), vitamin D deficiency (OR, 1.14; 95% CI, 1.05-1.22), diuretic use (OR, 1.13; 95% CI, 1.07-1.18), and renal insufficiency (OR, 1.11; 95% CI, 1.04-1.17) (multivariate Pu2009<u2009.001 for all).nnnConclusions and RelevancenThe probability of fracture nonunion can be based on patient-specific risk factors at presentation. Risk of nonunion is a function of fracture severity, fracture location, disease comorbidity, and medication use.

Progranulin suppresses titanium particle induced inflammatory osteolysis by targeting TNFα signaling

Aseptic loosening is a major complication of prosthetic joint surgery, characterized by chronic inflammation, pain, and osteolysis surrounding the bone-implant interface. Progranulin (PGRN) is known to have anti-inflammatory action by binding to Tumor Necrosis Factor (TNF) receptors and antagonizing TNFα. Here we report that titanium particles significantly induced PGRN expression in RAW264.7 cells and also in a mouse air-pouch model of inflammation. PGRN-deficiency enhanced, whereas administration of recombinant PGRN effectively inhibited, titanium particle-induced inflammation in an air pouch model. In addition, PGRN also significantly inhibited titanium particle-induced osteoclastogenesis and calvarial osteolysis in vitro, ex vivo and in vivo. Mechanistic studies demonstrated that the inhibition of PGRN on titanium particle induced-inflammation is primarily via neutralizing the titanium particle-activated TNFα/NF-κB signaling pathway and this is evidenced by the suppression of particle-induced IκB phosphorylation, NF-κB p65 nuclear translocation, and activity of the NF-κB-specific reporter gene. Collectively, these findings not only demonstrate that PGRN plays an important role in inhibiting titanium particle-induced inflammation, but also provide a potential therapeutic agent for the prevention of wear debris-induced inflammation and osteolysis.

Bone fracture nonunion rate decreases with increasing age: A prospective inception cohort study

BACKGROUNDnFracture 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.nnnMETHODSnA 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.nnnRESULTSnOverall, 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.nnnCONCLUSIONSnA 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.

Re-evaluation of low intensity pulsed ultrasound in treatment of tibial fractures (TRUST): randomized clinical trial

Objective To determine whether low intensity pulsed ultrasound (LIPUS), compared with sham treatment, accelerates functional recovery and radiographic healing in patients with operatively managed tibial fractures. Design A concealed, randomized, blinded, sham controlled clinical trial with a parallel group design of 501 patients, enrolled between October 2008 and September 2012, and followed for one year. Setting 43 North American academic trauma centers. Participants Skeletally mature men or women with an open or closed tibial fracture amenable to intramedullary nail fixation. Exclusions comprised pilon fractures, tibial shaft fractures that extended into the joint and required reduction, pathological fractures, bilateral tibial fractures, segmental fractures, spiral fractures >7.5 cm in length, concomitant injuries that were likely to impair function for at least as long as the patient’s tibial fracture, and tibial fractures that showed <25% cortical contact and >1 cm gap after surgical fixation. 3105 consecutive patients who underwent intramedullary nailing for tibial fracture were assessed, 599 were eligible and 501 provided informed consent and were enrolled. Interventions Patients were allocated centrally to self administer daily LIPUS (n=250) or use a sham device (n=251) until their tibial fracture showed radiographic healing or until one year after intramedullary fixation. Main outcome measures Primary registry specified outcome was time to radiographic healing within one year of fixation; secondary outcome was rate of non-union. Additional protocol specified outcomes included short form-36 (SF-36) physical component summary (PCS) scores, return to work, return to household activities, return to ≥80% of function before injury, return to leisure activities, time to full weight bearing, scores on the health utilities index (mark 3), and adverse events related to the device. Results SF-36 PCS data were acquired from 481/501 (96%) patients, for whom we had 2303/2886 (80%) observations, and radiographic healing data were acquired from 482/501 (96%) patients, of whom 82 were censored. Results showed no impact on SF-36 PCS scores between LIPUS and control groups (mean difference 0.55, 95% confidence interval −0.75 to 1.84; P=0.41) or for the interaction between time and treatment (P=0.30); minimal important difference is 3-5 points) or in other functional measures. There was also no difference in time to radiographic healing (hazard ratio 1.07, 95% confidence interval 0.86 to 1.34; P=0.55). There were no differences in safety outcomes between treatment groups. Patient compliance was moderate; 73% of patients administered ≥50% of all recommended treatments. Conclusions Postoperative use of LIPUS after tibial fracture fixation does not accelerate radiographic healing and fails to improve functional recovery. Study registration ClinicalTrialGov Identifier: NCT00667849

Efficacy of Electrical Stimulators for Bone Healing: A Meta-Analysis of Randomized Sham-Controlled Trials.

Electrical stimulation is a common adjunct used to promote bone healing; its efficacy, however, remains uncertain. We conducted a meta-analysis of randomized sham-controlled trials to establish the efficacy of electrical stimulation for bone healing. We identified all trials randomizing patients to electrical or sham stimulation for bone healing. Outcomes were pain relief, functional improvement, and radiographic nonunion. Two reviewers assessed eligibility and risk of bias, performed data extraction, and rated the quality of the evidence. Fifteen trials met our inclusion criteria. Moderate quality evidence from 4 trials found that stimulation produced a significant improvement in pain (mean difference (MD) on 100-millimeter visual analogue scaleu2009=u2009−7.7u2009mm; 95% CI −13.92 to −1.43; pu2009=u20090.02). Two trials found no difference in functional outcome (MDu2009=u2009−0.88; 95% CI −6.63 to 4.87; pu2009=u20090.76). Moderate quality evidence from 15 trials found that stimulation reduced radiographic nonunion rates by 35% (95% CI 19% to 47%; number needed to treatu2009=u20097; pu2009<u20090.01). Patients treated with electrical stimulation as an adjunct for bone healing have less pain and are at reduced risk for radiographic nonunion; functional outcome data are limited and requires increased focus in future trials.

The Role of Patient Education in Arthritis Management: The Utility of Technology

Technologies continue to shape the path of medical treatment. Orthopedic surgeons benefit from becoming more aware of how twenty-first century information technology (IT) can benefit patients. The percentage of orthopedic patients utilizing IT resources is increasing, and new IT tools are becoming utilized. These include disease-specific applications. This article highlights the opportunity for developing IT tools applicable to the growing population of patients with osteoarthritis (OA), and presents a potential solution that can facilitate the way OA education and treatment are delivered, and thereby maximize efficiency for the health care system, the physician, and the patient.

An inception cohort analysis to predict nonunion in tibia and 17 other fracture locations.

INTRODUCTIONnThe 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.nnnMETHODSnWe 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.nnnRESULTSnNonunion 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.nnnDISCUSSIONnNonunion 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.