Surveillance of atypical femoral fractures in a nationwide fracture register.

Background and purpose - To continuously assess the incidence of atypical femoral fractures (AFFs) in the population is important, to allow the evaluation of the risks and benefits associated with osteoporosis treatment. Therefore, we investigated the possibility to use the Swedish Fracture Register (SFR) as a surveillance tool for AFFs in the population and to explore means of improvement. Patients and methods - All AFF registrations in the SFR from January 1, 2015 to December 31, 2018 were enrolled in the study. For these patients, radiographs were obtained and combined with radiographs from 176 patients with normal femoral fractures, to form the study cohort. All images were reviewed and classified into AFFs or normal femur fractures by 2 experts in the field (gold-standard classification) and 1 orthopedic resident educated on the specific radiographic features of AFF (educated-user classification). Furthermore, we estimated the incidence rate of AFFs in the population captured by the register through comparison with a previous cohort and calculated the positive predictive value (PPV) and, where possible, the inter-observer agreement (Cohen's kappa) between the different classifications. Results - Of the 178 available patients with AFF in the SFR, 104 patients were classified as AFF using the goldstandard classification, and 89 using the educated-user classification. The PPV increased from 0.58 in the SFR classification to 0.93 in the educated-user classification. The interobserver agreement between the gold-standard classification and the educated-user classification was 0.81. Interpretation - With a positive predictive value of 0.58 the Swedish Fracture Register outperforms radiology reports and reports to the Swedish Medical Products Agency on adverse drug reactions as a diagnostic tool to identify atypical femoral fractures.

Deep neural networks with promising diagnostic accuracy for the classification of atypical femoral fractures.

Background and purpose - A correct diagnosis is essential for the appropriate treatment of patients with atypical femoral fractures (AFFs). The diagnostic accuracy of radiographs with standard radiology reports is very poor. We derived a diagnostic algorithm that uses deep neural networks to enable clinicians to discriminate AFFs from normal femur fractures (NFFs) on conventional radiographs.Patients and methods - We entered 433 radiographs from 149 patients with complete AFF and 549 radiographs from 224 patients with NFF into a convolutional neural network (CNN) that acts as a core classifier in an automated pathway and a manual intervention pathway (manual improvement of image orientation). We tested several deep neural network structures (i.e., VGG19, InceptionV3, and ResNet) to identify the network with the highest diagnostic accuracy for distinguishing AFF from NFF. We applied a transfer learning technique and used 5-fold cross-validation and class activation mapping to evaluate the diagnostic accuracy.Results - In the automated pathway, ResNet50 had the highest diagnostic accuracy, with a mean of 91% (SD 1.3), as compared with 83% (SD 1.6) for VGG19, and 89% (SD 2.5) for InceptionV3. The corresponding accuracy levels for the intervention pathway were 94% (SD 2.0), 92% (2.7), and 93% (3.7), respectively. With regards to sensitivity and specificity, ResNet outperformed the other networks with a mean AUC (area under the curve) value of 0.94 (SD 0.01) and surpassed the accuracy of clinical diagnostics.Interpretation - Artificial intelligence systems show excellent diagnostic accuracies for the rare fracture type of AFF in an experimental setting.

Reduced Risk of Reoperation Using Intramedullary Nailing with Femoral Neck Protection in Low-Energy Femoral Shaft Fractures.

BACKGROUND: In Sweden, approximately 1 in 4 women who are ≥50 years of age will sustain a hip fracture. Patients treated for a femoral shaft fracture are likely to have an even higher risk. We hypothesized that intramedullary nails protecting the femoral neck reduce the risk of subsequent hip fracture and allow the patient to avoid a challenging reoperation. METHODS: Between 2008 and 2010, 5,475 fractures of the femoral shaft, in patients who were ≥55 years of age, were registered in a national registry in Sweden. Of these patients, 897 fulfilled the inclusion criteria. We used radiographs and register data to identify the reasons for and the types of reoperation that occurred between the index surgical procedure and December 31, 2014. The categories of implants were determined through a review of radiographs as intramedullary nails with and without femoral neck protection. Reoperations related to peri-implant fractures (including hip fractures) were analyzed as a subgroup of all major reoperations. Multivariable-adjusted, cause-specific hazard ratios (HRs) were calculated to compare the risk of reoperation between cases with nails with and without femoral neck protection. RESULTS: Among the 897 patients, a total of 82 reoperations were performed. In 640 patients who were treated with intramedullary nails with femoral neck protection, there were 7 peri-implant fractures (no hip fractures) and 27 major reoperations. Among the 257 patients who were treated with intramedullary nails without femoral neck protection, 14 peri-implant hip fractures and 24 major reoperations were identified. Patients who received nails with femoral neck protection had a lower hazard for any peri-implant fracture (multivariable-adjusted cause-specific HR, 0.19 [95% confidence interval (CI), 0.07 to 0.5]) and major reoperation (multivariable-adjusted cause-specific HR, 0.51 [95% CI, 0.28 to 0.92]). CONCLUSIONS: Intramedullary nails with femoral neck protection in the treatment of low-energy femoral shaft fractures prevent secondary hip fractures and decrease the overall risk of reoperation for 4 to 6 years postoperatively. LEVEL OF EVIDENCE: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.