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Colegio de Medicina Interna de México.

2024, Number 6

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Med Int Mex 2024; 40 (6)

Inflammatory biomarkers as predictor of the outcome in COVID-19 patients: A comparative study based on machine learning classification algorithms

Jiménez JX, Barragán HIJ, Jiménez JS, Vuelvas OCR, Cortés ÁNY

Language: Spanish
References: 39
Page: 346-355
PDF size: 250.54 Kb.


ABSTRACT

Objective: To assess through of machine learning classification algorithms the predictive value of inflammation biomarkers for fatal outcomes in patients with different severity by COVID-19.
Materials and Method: A retrospective, observational study evaluated clinical records of Mexican patients from March 2021 to January 2022 using a systematic sampling method. Demographic and clinical values including D-dimer, procalcitonin, ferritin and fibrinogen of each patient were used as predictors.
Results: There were included 191 patients. Analysis of different machine learning algorithms showed that the kernel support vector machine algorithm showed the better performance achieving 0.80 accuracy, 0.06 standard deviation and 0.71 sensitivity. Additionally, D-dimer (OR: 1.0032 [1.0130, 1.7230], p ‹ 0.05; ROC: 0.580), ferritin (OR: 1.023 [1.019, 1.843], p ‹ 0.05; ROC: 0.885), and ferritin/procalcitonin ratio (OR: 1.324 [1.012, 1.478], p ‹ 0.05; ROC: 0.859) were potential predictors of progression and fatal events due to COVID-19.
Conclusions: The machine learning classification algorithms could be useful to the prediction of the severity and fatal events in infectious outbreaks. In this study, it was shown that D-dimer is a good predictor of severity and fatal outcomes due to COVID-19.


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