Determining the Causal Effect of Statins on Reducing the Incidence of Venous Thromboembolism after Ankle Fractures

Category: Ankle; Other Introduction/Purpose: It can be challenging to decide when to give preventative medication for venous thromboembolism (VTE). It is challenging to identify VTE in its early phases, and surgeons disagree on when prophylaxis should be given and when to suspect VTE. Additionally, patients who are not at a high risk of VTE are advised not to take prophylaxis due to the potential risk of bleeding adverse events and the evidence does not fully support providing prophylaxis in isolated foot and ankle procedures (BAE). The effectiveness of prevention, particularly in isolated foot and ankle fractures, is debatable, and research into how patients react to statins is necessary. By utilizing causal inference techniques, this research simulates a randomized control trial (RCT) from observational data. Methods: Out of a total of 1,175 patients, 238 had confirmed VTE 180 days after the incidence of ankle fracture (Case group, n=238). The inclusion criteria were 1- ankle fracture diagnosed by a physician and confirmed radiologically via X-ray or CT scan; 2- Age of 18 years or older; 3- Symptomatic VTE confirmed by a clinician and through radiologic (Duplex ultrasound, CT angiography, and/or angiography). To infer causal effects, we took the three following steps: first, the causal diagram must be created. Second, the set of variables necessary for causal inference must be identified. Third, the average treatment effect should be estimated for different treatment regimens. Results: Table 1 shows the incidence of VTE among patients who had VTE chemoprophylaxis. A non-significant 2.9% increase in VTE incidence was found, 95% CI [-1%, 7%]. The results state that, if a patient is already taking Statins, the incidence of VTE is reduced by 1%, 95% CI [-4.2%, 2.8%] as compared to patients who are not taking the drug which is not statistically significant. This indicates that there is no significant difference between administering VTE prophylaxis or not, in patients that are already consuming Statins. On the contrary, for patients in the sample that are not already taking Statins, the VTE incidence increases by 6%, 95% CI [1.6%, 10%] compared to no treatment administration which is significant since the confidence interval does not contain 0. Conclusion: Our findings provide more evidence that VTE prophylaxis may not, on average, be successful in lowering the incidence of VTE after ankle fracture, particularly in individuals who do not take Statins. To put it another way, doctors may decide not to give extra prophylaxis to avoid VTE in isolated ankle fractures if a patient is on Statins. Here, we established the causal inference methodology that can help us reproduce the results of an expensive RCT

Automated AI Detection Tool for Ankle Fractures Using X-Rays and Smart Devices

Category: Ankle; Trauma Introduction/Purpose: The use of artificial intelligence (AI) is particularly salient to visually oriented medical professions, especially orthopedics. The most prominent use of AI in orthopedics comes in the form of medical imaging examinations. AI has a huge potential to help doctors make diagnoses by acting as a second pair of eyes. Some results suggested a very high level of agreement between AI models’ and the clinician’s assessments of radiographs. Furthermore, it was also suggested that the sensitivity and specificity of emergency medicine physicians for detecting some pathologies are significantly improved when aided by an AI tool. In view of these observations, our study aimed to create an AI-based ankle fracture detection tool that can be used on smart devices for X-ray interpretation. Methods: We examined 2,193 patients’ charts from 2 academic and 1 community hospital in Boston. We retrieved the anteroposterior (AP), oblique, and lateral ankle X-rays of each patient. Patients with ankle fractures and adults older than 18 years met our inclusion criteria. We excluded patients younger than 18 years old and those with any artifact, such as a cast, screws, or other artifacts in their X-rays. The study comprised 352 healthy controls and a total of 579 ankle fracture patients. Other than the digital images obtained from Electronic patient records (EPR), we used two different smart devices, a cellphone, and a tablet, to capture images from the monitor screen. Using Machine Learning models, we developed a fracture detection model using all three types of imaging and named it the “combination model”. We subsequently tested the combination model on digital X-rays, smart devices, and on both datasets together (Table 1). Results: We extracted the X-rays of a total of 931 patients in this study. Following the development and testing of our AI models, we noticed that all performed well with AUCs and accuracies above 0.85 and 0.86, respectively (Table1). The best performance was found when the combination model was tested on images taken from the camera of our smart devices, with an AUC of 0.88, a sensitivity of 0.86, and an accuracy of 0.89. This performance was closely followed by that of our model tested on a mix of both smart devices and original digital images with an AUC of 0.88, a sensitivity of 0.86, and an accuracy of 0.88. Conclusion: Our AI-based tool showed promising performance in the detection of ankle fractures using smart devices and images obtained from the monitor screen. We were able to reach an accuracy of diagnosis on smart device-captured images that were comparable with the original digital X-rays. The outcome of this study can be used to help providers who lack sufficient experience in detecting fractures. It can also be used for educational purposes for trainees in this field