Prevention of Unplanned Surgery Due to Delayed Diagnosis of Occult Ipsilateral Femoral Neck Fractures Associated with Femoral Shaft Fractures: A Study of the CT Capsular Sign with Lipohemarthrosis

MRI characteristics of radiographically occult femoral neck fractures in trauma patients with ipsilateral femoral shaft fractures

PURPOSE

Ipsilateral femoral neck fractures can be seen alongside femoral shaft fractures in high-velocity trauma patients. These neck fractures are often occult on radiographs and CT, and can have a significant impact on patient outcomes if not treated promptly. Limited protocol pelvic MRI has been used to increase sensitivity for these occult fractures. Detailed characterization of these fractures on MRI is lacking.

METHODS

427 consecutive trauma patients presenting to our emergency department who had known femoral diaphyseal fractures but no ipsilateral femoral neck fracture on radiographs or CT were included in this study. These patients were scanned using a limited protocol MRI with coronal T1 and coronal STIR sequences. Presence of an ipsilateral femoral neck fracture and imaging characteristics of the fracture were obtained.

RESULTS

31 radiographically occult ipsilateral femoral neck fractures were found, representing 7% of all cases. All neck fractures were incomplete. All fractures originated along the lateral cortex of the femoral neck and extended medially towards the junction of the medial femoral neck and the lesser trochanter. 58% (18/31) were vertical in orientation. 61% (19/31) did not demonstrate any appreciate edema on STIR images.

CONCLUSION

Implementation of limited protocol MRI protocol increases sensitivity for detection of femoral neck fractures in the setting of ipsilateral femoral shaft fractures not seen on radiograph or CT imaging. We describe the characteristic MR imaging features of these fractures.

Artificial intelligence for detection of effusion and lipo-hemarthrosis in X-rays and CT of the knee

BACKGROUND

Traumatic knee injuries are challenging to diagnose accurately through radiography and to a lesser extent, through CT, with fractures sometimes overlooked. Ancillary signs like joint effusion or lipo-hemarthrosis are indicative of fractures, suggesting the need for further imaging. Artificial Intelligence (AI) can automate image analysis, improving diagnostic accuracy and help prioritizing clinically important X-ray or CT studies.

OBJECTIVE

To develop and evaluate an AI algorithm for detecting effusion of any kind in knee X-rays and selected CT images and distinguishing between simple effusion and lipo-hemarthrosis indicative of intra-articular fractures.

METHODS

This retrospective study analyzed post traumatic knee imaging from January 2016 to February 2023, categorizing images into lipo-hemarthrosis, simple effusion, or normal. It utilized the FishNet-150 algorithm for image classification, with class activation maps highlighting decision-influential regions. The AI's diagnostic accuracy was validated against a gold standard, based on the evaluations made by a radiologist with at least four years of experience.

RESULTS

Analysis included CT images from 515 patients and X-rays from 637 post traumatic patients, identifying lipo-hemarthrosis, simple effusion, and normal findings. The AI showed an AUC of 0.81 for detecting any effusion, 0.78 for simple effusion, and 0.83 for lipo-hemarthrosis in X-rays; and 0.89, 0.89, and 0.91, respectively, in CTs.

CONCLUSION

The AI algorithm effectively detects knee effusion and differentiates between simple effusion and lipo-hemarthrosis in post-traumatic patients for both X-rays and selected CT images further studies are needed to validate these results.

Characteristics of Femoral Shaft Fractures That Predict Ipsilateral Femoral Neck Fractures

INTRODUCTION

This study aims to characterize radiographic features and fracture characteristics in femoral shaft fractures with associated femoral neck fractures, with the goal of establishing predictive indicators for the presence of ipsilateral femoral neck fractures (IFNFs).

METHODS

A retrospective cohort was collected from the electronic medical record of three level I trauma centers over a 5-year period (2017 to 2022) by current procedural terminology (CPT) codes. Current CPT codes for combined femoral shaft and IFNFs were identified to generate our study group. CPT codes for isolated femur fractures were identified to generate a control group.

RESULTS

One hundred forty patients comprised our IFNF cohort, and 280 comprised the control cohort. On univariate, there were significant differences in mechanism of injury (P < 0.001), Orthopedic Trauma Association (OTA)/Arbeitsgemeinshaft fur Osteosynthesefragen (AO) classification (P = 0.002), and fracture location (P < 0.001) between cohorts. On multivariate, motor vehicle crashes were more commonly associated with IFNFs compared with other mechanism of injuries. OTA/AO 32A fractures were more commonly associated with IFNFs when compared with OTA/AO 32B fractures (adjusted odds ratio = 0.36, P < 0.001). Fractures through the isthmus were significantly more commonly associated with IFNFs than fractures more proximal (adjusted odds ratio = 2.52, P = 0.011).

DISCUSSION

Detecting IFNFs in femoral shaft fractures is challenging. Motor vehicle crashes and motorcycle collisions, OTA/AO type 32A fractures, and isthmus fractures are predictive of IFNFs.

Capsular distension in high-energy femoral shaft fractures is associated with occult femoral neck fracture: a preliminary retrospective comparative study

OBJECTIVES

To determine the association between hip capsular distension, the computed tomography (CT) capsular sign, and lipohemarthrosis as they relate to occult femoral neck fracture (FNF) in the setting of ipsilateral femoral shaft fracture (FSF).

DESIGN

Retrospective comparative study.

SETTING

Level 1 trauma center.

PATIENTS/PARTICIPANTS

Two hundred and forty-two patients with high-energy FSF and no evidence of FNF on preoperative radiographs and pelvis CT. All patients were stabilized with non-reconstruction style nails.

INTERVENTION

Pelvis CT scans were examined for hip capsular distension irrespective of the other side, differing side-to-side measurements of capsular distension (i.e., the CT capsular sign), and lipohemarthrosis.

MAIN OUTCOME MEASUREMENTS

FNF was observed for on postoperative radiographs. Relative risk (RR), number needed to treat (NNT), sensitivity (SN), and specificity (SP) were determined.

RESULTS

Fifty-eight patients (24.0%) had capsular distension. Forty-two patients (17.4%) had differing capsular measurements (i.e., the CT capsular sign), and 16 (6.6%) had symmetrical distension from bilateral hip effusions. Eight patients (3.3%) had lipohemarthrosis. Four FNFs (1.7%) were identified. Three patients had capsular distension, 2 had CT capsular signs, and 1 had lipohemarthrosis. The last patient had no CT abnormalities. Only capsular distension (RR = 10, CI = 1.001-90, P = 0.049; SN = 75%, SP = 77%; NNT = 22) and lipohemarthrosis (RR = 23, CI = 1.6-335, P = 0.022; SN = 50%, SP = 96%; NNT = 8) were associated with occult FNF.

CONCLUSIONS

Capsular distension is associated with FNF irrespective of the contralateral hip. Preemptive stabilization using a reconstruction nail could be considered in the setting of capsular distension or lipohemarthrosis to prevent displacement of an occult FNF.

LEVEL OF EVIDENCE

Diagnostic Level III.

Extra-capsular floating fat at the extremity: a review

Extra-capsular floating fat may present in a variety of non-articular locations at the extremity. Manifestation of floating fat or fat-fluid level(s) outside of a joint is a marker of trauma or infection. Recognizing radiologic sign(s) of extra-capsular floating fat can aid radiologists to provide an informed differential diagnosis and facilitate clinical care. This review discusses the etiology, mechanisms, and imaging appearance of extracapsular floating fat in specific anatomic and non-anatomic locations at the extremity.

Does Garden type I incomplete femoral neck fracture really exist in older adults? To evaluate the stability and consistency of Garden classification

Background

Accurate classification of femoral neck fracture (FNF) is crucial for treatment plan and therapeutic outcomes. Garden classification is commonly used in the clinic, but its stability and consistency remain controversial. The aim of this study was to evaluate the stability and consistency of Garden classification based on X and CT images, and to analyze whether it is valid for Garden I in the elderly.

Methods

X-ray and CT images from 886 elderly patients with FNF were collected, four orthopaedic surgeons and four radiologists evaluated these images independently, and determined the fracture type based on Garden classification. Three months later, The exercise was repeated and the results were compared based on 4 types Garden classification (I, II, III and IV) and 3 types Garden classification (I + II, III and IV). Kappa was used to measure inter- and intraobserver agreement. The patients with Garden I incomplete FNF confirmed by 8 observers together based on images combined with medical history were compared with the intraoperative results.

Results

Four types Garden classification, there was little consistency inter- and intraobservers (Kappa from 0.18 to 0.43) based on X-ray images, while professors consistency (0.56 to 0.76) was higher than residents (0.28 to 0.35) based on CT. 3 types Garden classification showed almost perfect agreement inter- and intraobservers, which ranged from 0.76 to 0.90. Totally 52 patients were diagnosed as Garden I, 38 of whom underwent arthroplasty. All surgical cases showed complete fracture during operation.

Conclusions

There was low consistency and repeatability in 4 types Garden classification (I, II, III and IV), while 3 types Garden classification (I + II, III and IV) had high consistency among observers. In the elderly, all undisplaced femoral neck fracture may be Garden II, no Garden I.

Prediction Models for Prognosis of Femoral Neck–Fracture Patients 6 Months after Total Hip Arthroplasty

Purpose

To establish prediction models for 6-month prognosis in femoral neck–fracture patients receiving total hip arthroplasty (THA).

Patients and Methods

In total, 182 computed tomography image pairs from 85 patients were collected and divided into a training set (n=127) and testing set (n=55). Least absolute shrinkage–selection operator regression was used for selecting optimal predictors. A random-forest algorithm was used to establish the prediction models, which were evaluated for accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC).

Results

The best model in this study was constructed based on demographic data, preoperative laboratory indicators, and three preoperative radiomic features. In the random-forest model, activated partial thromboplastin time, a preoperative radiomic feature (maximum diameter), and fibrinogen were important variables correlating with patient outcomes. The AUC, sensitivity, specificity, PPV, NPV, and accuracy in the training set were 0.986 (95% CI 0.971–1), 0.925 (95% CI 0.862–0.988), 0.983 (95% CI 0.951–1.016), 0.984 (95% CI 0.953–1.014), 0.922 (95% CI 0.856–0.988), and 0.953 (95% CI 0.916–0.990), respectively. The AUC, sensitivity, specificity, PPV, NPV, and accuracy in the testing set were 0.949 (95% CI 0.885–1), 0.767 (95% CI 0.615–0.918), 1 (95% CI 1–1), 1 (95% CI 1–1), 0.781 (95% CI 0.638–0.924), and 0.873 (95% CI 0.785–0.961), respectively.

Conclusion

The model based on demographic, preoperative clinical, and preoperative radiomic data showed the best predictive ability for 6-month prognosis in the femoral neck–fracture patients receiving THA.