Heliyon
Volume 9, Issue 3, March 2023, e14015
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Research article
Effect of ferritin, INR, and D-dimer immunological parameters levels as predictors of COVID-19 mortality: A strong prediction with the decision trees

https://doi.org/10.1016/j.heliyon.2023.e14015Get rights and content
Under a Creative Commons license
open access

Highlights

  • All immunological parameters were significantly higher in the non-surviving COVID-19 patients.

  • Our interpretable decision tree was built with only ferritin, INR and D-dimer.

  • Our decision tree predicted survivors (99.7%) and deaths (92.7%) highly accurately.

  • The range of ferritin (389.0 μg/L -395.0 μg/L) was important in COVID-19 mortality.

  • The value of D-dimer (<1239.140 μg/L) was remarkable in COVID-19 mortality.

Abstract

Background and objective

A hyperinflammatory environment is thought to be the distinctive characteristic of COVID-19 infection and an important mediator of morbidity. This study aimed to determine the effect of other immunological parameter levels, especially ferritin, as a predictor of COVID-19 mortality via decision-trees analysis.

Material and method

This is a retrospective study evaluating a total of 2568 patients who died (n = 232) and recovered (n = 2336) from COVID-19 in August and December 2021. Immunological laboratory data were compared between two groups that died and recovered from patients with COVID-19. In addition, decision trees from machine learning models were used to evaluate the performance of immunological parameters in the mortality of the COVID-19 disease.

Results

Non-surviving from COVID-19 had 1.75 times higher ferritin, 10.7 times higher CRP, 2.4 times higher D-dimer, 1.14 times higher international-normalized-ratio (INR), 1.1 times higher Fibrinogen, 22.9 times higher procalcitonin, 3.35 times higher troponin, 2.77 mm/h times higher erythrocyte-sedimentation-rate (ESR), 1.13sec times longer prothrombin time (PT) when compared surviving patients. In addition, our interpretable decision tree, which was constructed with only the cut-off values of ferritin, INR, and D-dimer, correctly predicted 99.7% of surviving patients and 92.7% of non-surviving patients.

Conclusions

This study perfectly predicted the mortality of COVID-19 with our interpretable decision tree constructed with INR and D-dimer, especially ferritin. For this reason, we think that it may be important to include ferritin, INR, and D-dimer parameters and their cut-off values in the scoring systems to be planned for COVID-19 mortality.

Keywords

COVID-19
Mortality risk biomarkers
Coagulation tests
Immunological tests
Ferritin
CHAID decision Trees
Machine learning
Artificial intelligence

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