Nissar Shaikh

Fat embolism syndrome (FES) is a rare clinical entity. Fat embolization in patients with trauma and long-bone fractures occurs in a majority of patients, and only a few patients develop the triad of skin petechia, respiratory distress, and neurological disturbances, which are termed FES. The exact incidence is not known, as there are no definitive criteria. All English-language publications about FES were searched and reviewed using Google Scholar, PubMed, various databases, conference proceedings, and abstracts, which were included in this study. FES is particularly underdiagnosed and underreported in body contour plastic surgery patients. The following two main theories of FES pathophysiology are proposed: mechanical and biochemical. There are various risk factors for FES, mainly in young patients and bilateral femoral fractures. The diagnosis of FES was traditionally a diagnosis of exclusion. Recently, the combination of clinical parameters and imaging studies has increased the accuracy of FES diagnosis. Point-of-care ultrasound can show fat particles floating from the inferior vena cava to the right heart. MRI is more specific in diagnosing FES with brain involvement. A high index of suspicion, combined with early imaging studies and the application of clinical criteria, must be used to diagnose FES early and accurately. Pharmacological treatments, ranging from ethanol to aspirin, are used without any definitive recommendations. A recent meta-analysis showed the beneficial effect of steroids for FES. The early surgical intervention with open reduction and internal fixation appears to be a better choice, as conservative management and intramedullary nailing increase the risk of systemic fat embolization. The various types of bone reamers were not preventive of FES. The use of reamer irrigation and aspiration systems was found to decrease systemic fat embolization. With all the recent developments in acute and critical care, namely hydration, ventilation, and other resuscitative measures, the outcomes have improved. Blindness due to ophthalmic fat embolism syndrome is rarely reported. The mortality ranges from 5% to 15% in cases of FES.

Accurately predicting the severity of subarachnoid hemorrhage (SAH) is critical for informing clinical decisions and improving patient outcomes. This study addresses the challenges of imbalanced data in SAH severity classification by employing the Modified Rankin Scale (MRS) within a three-stage classification framework. We utilize a three-stage approach to effectively categorize SAH severity. In the first stage, we performed binary classification, grouping SAH severity into “Good Outcome” (class 0), which includes MRS levels 0, 1, 2, and 3, and “Poor Outcome” (class 1), encompassing levels 4, 5, and 6. Feature selection was done using a Random Forest algorithm to identify the top 20 features for the SAH severity prediction. We evaluated thirteen machine learning models at each stage, selecting the top-performing classifiers to optimize results. The dataset comprised 535 samples across seven MRS severity levels and was validated using 5-fold cross-validation and diverse subgroups to ensure robust model performance across various scenarios. Binary classification in the first stage achieved approximately 90% accuracy with Extra Trees. In the second stage, targeting the “Good Outcome” group, the Random Forest model reached 88% accuracy, while in the third stage, it achieved 86% accuracy for the “Poor Outcome” group. By increasing accuracy across unbalanced classes and emphasizing its potential for practical use, the multi-stage technique presents a promising solution for predicting the severity of SAH. Future research will concentrate on additional tuning to improve the model’s efficacy in actual healthcare environments.