Background Most of the mortality resulting from COVID-19 has been associated with severe disease. Effective treatment of severe cases remains a challenge due to the lack of early detection of the infection. Objective This study aimed to develop an effective prediction model for COVID-19 severity by combining radiological outcome with clinical biochemical indexes. Methods A total of 46 patients with COVID-19 (10 severe, 36 nonsevere) were examined. To build the prediction model, a set of 27 severe and 151 nonsevere clinical laboratory records and computerized tomography (CT) records were collected from these patients. We managed to extract specific features from the patients’ CT images by using a recently published convolutional neural network. We also trained a machine learning model combining these features with clinical laboratory results. Results We present a prediction model combining patients’ radiological outcomes with their clinical biochemical indexes to identify severe COVID-19 cases. The prediction model yielded a cross-validated area under the receiver operating characteristic (AUROC) score of 0.93 and an F1 score of 0.89, which showed a 6% and 15% improvement, respectively, compared to the models based on laboratory test features only. In addition, we developed a statistical model for forecasting COVID-19 severity based on the results of patients’ laboratory tests performed before they were classified as severe cases; this model yielded an AUROC score of 0.81. Conclusions To our knowledge, this is the first report predicting the clinical progression of COVID-19, as well as forecasting severity, based on a combined analysis using laboratory tests and CT images.
BACKGROUND Most of mortality of COVID-19 were from severe patients. Effective treatment of these severe cases remains a challenge due to a lack of early detection. OBJECTIVE Herein, we presented a prediction model, combining the radiological outcome with the clinical biochemical indexes, to identify the severe cases. METHODS A total set of 27 severe and 151 non-severe clinical and CT (computerized tomography) records from 46 COVID-19 patients (10 severe, 36 non-severe) was collected for building the model. Using a recent published CNN (convolutional neural network), we managed to extract features from CT images. A machine learning model which combines these features with clinical laboratory results also was trained. RESULTS Herein, we presented a prediction model, combining the radiological outcome with the clinical biochemical indexes, to identify the severe cases. The prediction model yields a cross-validated AUROC score of 0.93 and F1 score of 0.89, which improved 6% and 15%, respectively, from the models with laboratory tests features only. In addition, we also developed a statistical model for forecasting severity based on patients’ laboratory tests results before turning severe cases, with an AUROC score of 0.81. CONCLUSIONS To our knowledge, this is the first report to predict COVID-19 patient’s severity progression, as well as severity forecasting, through a combination analysis of laboratory tests and CT images.
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