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  • Title: Development of a novel machine learning model based on laboratory and imaging indices to predict acute cardiac injury in cancer patients with COVID-19 infection: a retrospective observational study.
    Author: Wan G, Wu X, Zhang X, Sun H, Yu X.
    Journal: J Cancer Res Clin Oncol; 2023 Dec; 149(19):17039-17050. PubMed ID: 37747525.
    Abstract:
    PURPOSE: Due to the increased risk of acute cardiac injury (ACI) and poor prognosis in cancer patients with COVID-19 infection, our aim was to develop a novel and interpretable model for predicting ACI occurrence in cancer patients with COVID-19 infection. METHODS: This retrospective observational study screened 740 cancer patients with COVID-19 infection from December 2022 to April 2023. The least absolute shrinkage and selection operator (LASSO) regression was used for the preliminary screening of the indices. To enhance the model accuracy, we introduced an alpha index to further screen and rank the indices based on their significance. Random forest (RF) was used to construct the prediction model. The Shapley Additive Explanation (SHAP) and Local Interpretable Model-Agnostic Explanation (LIME) methods were utilized to explain the model. RESULTS: According to the inclusion criteria, 201 cancer patients with COVID-19, including 36 variables indices, were included in the analysis. The top eight indices (albumin, lactate dehydrogenase, cystatin C, neutrophil count, creatine kinase isoenzyme, red blood cell distribution width, D-dimer and chest computed tomography) for predicting the occurrence of ACI in cancer patients with COVID-19 infection were included in the RF model. The model achieved an area under curve (AUC) of 0.940, an accuracy of 0.866, a sensitivity of 0.750 and a specificity of 0.900. The calibration curve and decision curve analysis showed good calibration and clinical practicability. SHAP results demonstrated that albumin was the most important index for predicting the occurrence of ACI. LIME results showed that the model could predict the probability of ACI in each cancer patient infected with COVID-19 individually. CONCLUSION: We developed a novel machine-learning model that demonstrates high explainability and accuracy in predicting the occurrence of ACI in cancer patients with COVID-19 infection, using laboratory and imaging indices.
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