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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Data availability
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
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Acknowledgements
We thank all the researchers who contributed to this study.
Funding
This work was supported by Jilin Province Health Technology Innovation Project of China (2018J025).
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GW and XY contributed to the study concepts and the study design, collected data, analyzed data, created tables, created figures, and drafted a paper. XW and XZ collected data, verified data, and drafted a paper. XZ and HS analyzed data and edited the paper. GW and XZ edited the paper. XY and XW supervised data, verified data, and edited the paper. All the Authors approved the manuscript submission.
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This study was approved by the Jilin Cancer Hospital Institutional Review Board for retrospective analysis (No. 202307‐06‐01). This article is a retrospective study. Therefore, the Institutional waived the requirement to obtain distinct written informed consent from the patients.
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Wan, G., Wu, X., Zhang, X. et al. 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. J Cancer Res Clin Oncol 149, 17039–17050 (2023). https://doi.org/10.1007/s00432-023-05417-3
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DOI: https://doi.org/10.1007/s00432-023-05417-3