Predictive model of hyperinflammation in patients with COVID-19

Authors

Keywords:

COVID-19, electrical bioimpedance, biomarkers, hyperinflammation

Abstract

Introduction: Most severe cases of COVID-19 are associated with hyperinflammation. The scales developed in the context of the recent pandemic have not focused on determining its onset, but rather primarily on prognosis. Early prediction of this disorder is crucial for implementing measures to minimize complications from the disease.

Objective: Design a model for the early prediction of hyperinflammation in patients with COVID‑19.

Methods: An observational, analytical, cohort study was conducted from July 1 to October 31, 2021, at the “Dr. Joaquín Castillo Duany” Military Hospital in Santiago de Cuba; 472 patients were studied. A multivariate analysis was performed using binary logistic regression.

Results: Male patients predominated (72.0%); 27.1% of infected patients took > 3 days to reach the hospital, and the majority were discharged alive (75.0%). In the multivariate analysis, the arterial oxygen pressure/fractional inspired oxygen ratio, phase angle, lactodehydrogenase, taking more than 3 days to reach the hospital, and age ≥ 70 years were significantly related to hyperinflammation. The constructed model showed an area under the curve of 0.930; (95% CI: 0.896–0.954), the Hosmer-Lemeshow test had a value of 0.150 and the Nagelkerke R2 was 0.713.

Conclusions: The predictive model designed from clinical, humoral, hemogasometric and bioelectrical elements showed good fit and discriminatory power.

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References

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Published

2025-09-30

How to Cite

1.
Ferrer Castro JE, del Río Caballero G, Amaro Guerra I, Benítez Sánchez E, Rodríguez González Z. Predictive model of hyperinflammation in patients with COVID-19. Rev. cuba. med. mil [Internet]. 2025 Sep. 30 [cited 2025 Oct. 3];54(4):e025076744. Available from: https://revmedmilitar.sld.cu/index.php/mil/article/view/76744

Issue

Vol. 54 No. 4 (2025): October - December (In Progress)

Section

Research Article

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Copyright (c) 2025 Jacno Erik Ferrer Castro, Germán del Río Caballero, Irlán Amaro Guerra, Edgar Benítez Sánchez, Zahilyn Rodríguez González

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