Diagnostic significance of fat globules in blood in fulminant-type fat embolism syndrome

Establishment of machine learning-based tool for early detection of pulmonary embolism

BACKGROUND AND OBJECTIVES

Pulmonary embolism (PE) is a complex disease with high mortality and morbidity rate, leading to increasing society burden. However, current diagnosis is solely based on symptoms and laboratory data despite its complex pathology, which easily leads to misdiagnosis and missed diagnosis by inexperienced doctors. Especially, CT pulmonary angiography, the gold standard method, is not widely available. In this study, we aim to establish a rapid and accurate screening model for pulmonary embolism using machine learning technology. Importantly, data required for disease prediction are easily accessed, including routine laboratory data and medical record information of patients.

METHODS

We extracted features from patients' routine laboratory results and medical records, including blood routine, biochemical group, blood coagulation routine and other test results, as well as symptoms and medical history information. Samples with a feature loss rate greater than 0.8 were deleted from the original database. Data from 4723 cases were retained, 231 of which were positive for pulmonary embolism. 50 features were retained through the positive and negative statistical hypothesis testing which was used to build the predictive model. In order to avoid identification as majority-class samples caused by the imbalance of sample proportion, we used the method of Synthetic Minority Oversampling Technique (SMOTE) to increase the amount of information on minority samples. Five typical machine learning algorithms were used to model the screening of pulmonary embolism, including Support Vector Machines, Logistic Regression, Random Forest, XGBoost, and Back Propagation Neural Networks. To evaluate model performance, sensitivity, specificity and AUC curve were analyzed as the main evaluation indicators. Furthermore, a baseline model was established using the characteristics of the pulmonary embolism guidelines as a comparison model.

RESULTS

We found that XGBoost showed better performance compared to other models, with the highest sensitivity and specificity (0.99 and 0.99, respectively). Moreover, it showed significant improvement in performance compared to the baseline model (sensitivity and specificity were 0.76 and 0.76 respectively). More important, our model showed low missed diagnosis rate (0.46) and high AUC value (0.992). Finally, the calculation time of our model is only about 0.05 s to obtain the possibility of pulmonary embolism.

CONCLUSIONS

In this study, five machine learning classification models were established to assess the likelihood of patients suffering from pulmonary embolism, and the XGBoost model most significantly improved the precision, sensitivity, and AUC for pulmonary embolism screening. Collectively, we have established an AI-based model to accurately predict pulmonary embolism at early stage.

Multimodal Retinal Imaging of Intravascular Lipid in Severe/Extreme Hypertriglyceridemia

Retinal intravascular lipid aggregates were detected in the left eye in a patient with uncontrolled diabetes and very high blood triglycerides. The patient suffered visual loss in the left eye due to retinal ischemia. Optical coherence tomography, fluorescein angiography, infrared fundus photography, and autofluorescence studies of the retina demonstrated unique findings. Slowed choroidal and retinal flow was detected on fluorescein angiography, and a prominent middle layer membrane sign was present on OCT. Intravascular lipid reflectivity was manifest on retinal infrared and autofluorescence imaging. Eventually, insulin and statin therapy proved effective in reversing the vascular lesions, although retinal atrophy finally ensued. This report of a very rare clinical condition provides unique findings on multimodal retinal imaging and confirms the need for prompt insulin and statin therapy in severe/extreme hypertriglyceridemia and dysregulated diabetes. One similar case was reported in the past when multimodal imaging studies of the retina were not available. The lesions described herein should be differentiated from the more frequently encountered lipemia retinalis as they may confer worse prognosis.