Journal
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
Volume 165, Issue -, Pages -Publisher
ELSEVIER IRELAND LTD
DOI: 10.1016/j.ijmedinf.2022.104835
Keywords
COVID-19; Machine learning; Prognosis; Blood biomarkers; Artificial intelligence; Imbalance
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Despite an extensive network of primary care availability, Brazil has suffered profoundly during the COVID-19 pandemic, experiencing the greatest sanitary collapse in its history. This study proposes to predict the risk of COVID-19 death through machine learning, using blood biomarkers data from patients admitted to two large hospitals in Brazil. The results suggest that routine laboratory variables could be useful to identify COVID-19 patients under higher risk of death using machine learning.
Background:: Despite an extensive network of primary care availability, Brazil has suffered profoundly during the COVID-19 pandemic, experiencing the greatest sanitary collapse in its history. Thus, it is important to understand phenotype risk factors for SARS-CoV-2 infection severity in the Brazilian population in order to provide novel insights into the pathogenesis of the disease. Objective:: This study proposes to predict the risk of COVID-19 death through machine learning, using blood biomarkers data from patients admitted to two large hospitals in Brazil. Methods:: We retrospectively collected blood biomarkers data in a 24-h time window from 6,979 patients with COVID-19 confirmed by positive RT-PCR admitted to two large hospitals in Brazil, of whom 291 (4.2%) died and 6,688 (95.8%) were discharged. We then developed a large-scale exploration of risk models to predict the probability of COVID-19 severity, finally choosing the best performing model regarding the average AUROC. To improve generalizability, for each model five different testing scenarios were conducted, including two external validations. Results:: We developed a machine learning-based panel composed of parameters extracted from the complete blood count (lymphocytes, MCV, platelets and RDW), in addition to C-Reactive Protein, which yielded an average AUROC of 0.91 +/- 0.01 to predict death by COVID-19 confirmed by positive RT-PCR within a 24-h window. Conclusion:: Our study suggests that routine laboratory variables could be useful to identify COVID-19 patients under higher risk of death using machine learning. Further studies are needed for validating the model in other populations and contexts, since the natural history of SARS-CoV-2 infection and its consequences on the hematopoietic system and other organs is still quite recent.
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