tetano
Editor, Senior Moderator
Hum Immunol
. 2025 Jun 16;86(4):111337.
doi: 10.1016/j.humimm.2025.111337. Online ahead of print. Precision Medicine and Machine Learning to predict critical disease and death due to Coronavirus disease 2019 (COVID-19)
Walton Luiz Del Tedesco Júnior[SUP] 1 [/SUP], Tiago Danelli[SUP] 2 [/SUP], Zuleica Naomi Tano[SUP] 3 [/SUP], Pedro Luis Candido de Souza Cassela[SUP] 4 [/SUP], Guilherme Lerner Trigo[SUP] 5 [/SUP], Kauê de Morais Cardoso[SUP] 6 [/SUP], Livia Padovam Loni[SUP] 7 [/SUP], Tainah Mendes Ahrens[SUP] 8 [/SUP], Beatriz Rabello Espinosa[SUP] 9 [/SUP], Alvaro Jungblut Fernandes[SUP] 10 [/SUP], Elaine Regina Delicato de Almeida[SUP] 11 [/SUP], Marcell Alysson Batisti Lozovoy[SUP] 12 [/SUP], Edna Maria Vissoci Reiche[SUP] 13 [/SUP], Michael Maes[SUP] 14 [/SUP], Andréa Name Colado Simão[SUP] 15 [/SUP]
Affiliations
Introduction: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes Coronavirus Disease 2019 (COVID-19) and induces activation of inflammatory pathways, including the inflammasome.
Objective: The aim was to construct Machine Learning (ML) models to predict critical disease and death in patients with COVID-19.
Methods: A total of 528 individuals with SARS-CoV-2 infection were included, comprising 308 with critical and 220 with non-critical COVID-19. The ML models included imaging, demographic, inflammatory biomarkers, NLRP3 (rs10754558 and rs10157379) and IL18 (rs360717 and rs187238) inflammasome variants.
Results: Individuals with critical COVID-19 were older, higher male/female ratio, body mass index (BMI), rate of type 2 diabetes mellitus (T2DM), hypertension, inflammatory biomarkers, need of orotracheal intubation, intensive care unit admission, incidence of death, and sickness symptom complex (SSC) scores and lower peripheral oxygen saturation (SpO[SUB]2[/SUB]) compared to those with non-critical disease. We found that 49.5 % of the variance in the severity of critical COVID-19 was explained by SpO[SUB]2[/SUB] and SSC (negatively associated), chest computed tomography alterations (CCTA), inflammatory biomarkers, severe acute respiratory syndrome (SARS), BMI, T2DM, and age (positively associated). In this model, the NLRP3/IL18 variants showed indirect effects on critical COVID-19 that were mediated by inflammatory biomarkers, SARS, and SSC. Neural network models yielded a prediction of critical disease and death due to COVID-19 with an area under the receiving operating characteristic curve of 0.930 and 0.927, respectively.
Conclusion: These ML methods increase the accuracy of predicting severity, critical illness, and mortality caused by COVID-19 and show that the genetic variants contribute to the predictive power of the ML models.
Keywords: COVID-19; Inflammasome; Machine Learning; SARS-CoV-2; Sickness behavior.
. 2025 Jun 16;86(4):111337.
doi: 10.1016/j.humimm.2025.111337. Online ahead of print. Precision Medicine and Machine Learning to predict critical disease and death due to Coronavirus disease 2019 (COVID-19)
Walton Luiz Del Tedesco Júnior[SUP] 1 [/SUP], Tiago Danelli[SUP] 2 [/SUP], Zuleica Naomi Tano[SUP] 3 [/SUP], Pedro Luis Candido de Souza Cassela[SUP] 4 [/SUP], Guilherme Lerner Trigo[SUP] 5 [/SUP], Kauê de Morais Cardoso[SUP] 6 [/SUP], Livia Padovam Loni[SUP] 7 [/SUP], Tainah Mendes Ahrens[SUP] 8 [/SUP], Beatriz Rabello Espinosa[SUP] 9 [/SUP], Alvaro Jungblut Fernandes[SUP] 10 [/SUP], Elaine Regina Delicato de Almeida[SUP] 11 [/SUP], Marcell Alysson Batisti Lozovoy[SUP] 12 [/SUP], Edna Maria Vissoci Reiche[SUP] 13 [/SUP], Michael Maes[SUP] 14 [/SUP], Andréa Name Colado Simão[SUP] 15 [/SUP]
Affiliations
- PMID: 40527075
- DOI: 10.1016/j.humimm.2025.111337
Introduction: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes Coronavirus Disease 2019 (COVID-19) and induces activation of inflammatory pathways, including the inflammasome.
Objective: The aim was to construct Machine Learning (ML) models to predict critical disease and death in patients with COVID-19.
Methods: A total of 528 individuals with SARS-CoV-2 infection were included, comprising 308 with critical and 220 with non-critical COVID-19. The ML models included imaging, demographic, inflammatory biomarkers, NLRP3 (rs10754558 and rs10157379) and IL18 (rs360717 and rs187238) inflammasome variants.
Results: Individuals with critical COVID-19 were older, higher male/female ratio, body mass index (BMI), rate of type 2 diabetes mellitus (T2DM), hypertension, inflammatory biomarkers, need of orotracheal intubation, intensive care unit admission, incidence of death, and sickness symptom complex (SSC) scores and lower peripheral oxygen saturation (SpO[SUB]2[/SUB]) compared to those with non-critical disease. We found that 49.5 % of the variance in the severity of critical COVID-19 was explained by SpO[SUB]2[/SUB] and SSC (negatively associated), chest computed tomography alterations (CCTA), inflammatory biomarkers, severe acute respiratory syndrome (SARS), BMI, T2DM, and age (positively associated). In this model, the NLRP3/IL18 variants showed indirect effects on critical COVID-19 that were mediated by inflammatory biomarkers, SARS, and SSC. Neural network models yielded a prediction of critical disease and death due to COVID-19 with an area under the receiving operating characteristic curve of 0.930 and 0.927, respectively.
Conclusion: These ML methods increase the accuracy of predicting severity, critical illness, and mortality caused by COVID-19 and show that the genetic variants contribute to the predictive power of the ML models.
Keywords: COVID-19; Inflammasome; Machine Learning; SARS-CoV-2; Sickness behavior.