tetano
Editor, Senior Moderator
Front Immunol
. 2024 Nov 1:15:1445618.
doi: 10.3389/fimmu.2024.1445618. eCollection 2024. Prediction of the risk of mortality in older patients with coronavirus disease 2019 using blood markers and machine learning
Linchao Zhu[SUP] 1 [/SUP], Yimin Yao[SUP] 1 [/SUP]
Affiliations
Introduction: The mortality rate among older people infected with severe acute respiratory syndrome coronavirus 2 is alarmingly high. This study aimed to explore the predictive value of a novel model for assessing the risk of death in this vulnerable cohort.
Methods: We enrolled 199 older patients with coronavirus disease 2019 (COVID-19) from Zhejiang Provincial Hospital of Chinese Medicine (Hubin) between 16 December 2022 and 17 January 2023. Additionally, 90 patients from two other centers (Qiantang and Xixi) formed an external independent testing cohort. Univariate and multivariate analyses were used to identify the risk factors for mortality. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to select variables associated with COVID-19 mortality. Nine machine-learning algorithms were used to predict mortality risk in older patients, and their performance was assessed using receiver operating characteristic curves, area under the curve (AUC), calibration curve analysis, and decision curve analysis.
Results: Neutrophil-monocyte ratio, neutrophil-lymphocyte ratio, C- reactive protein, interleukin 6, and D-dimer were considered to be relevant factors associated with the death risk of COVID-19-related death by LASSO regression. The Gaussian naive Bayes model was the best-performing model. In the validation cohort, the model had an AUC of 0.901, whereas in the testing cohort, the model had an AUC of 0.952. The calibration curve showed a good correlation between the actual and predicted probabilities, and the decision curve indicated a strong clinical benefit. Furthermore, the model had an AUC of 0.873 in an external independent testing cohort.
Discussion: In this study, a predictive machine-learning model was developed with an online prediction tool designed to assist clinicians in evaluating mortality risk factors and devising targeted and effective treatments for older patients with COVID-19, potentially reducing the mortality rates.
Keywords: COVID-19; Gaussian naïve Bayes; blood markers; machine learning; mortality; older patients.
. 2024 Nov 1:15:1445618.
doi: 10.3389/fimmu.2024.1445618. eCollection 2024. Prediction of the risk of mortality in older patients with coronavirus disease 2019 using blood markers and machine learning
Linchao Zhu[SUP] 1 [/SUP], Yimin Yao[SUP] 1 [/SUP]
Affiliations
- PMID: 39555074
- PMCID: PMC11563789
- DOI: 10.3389/fimmu.2024.1445618
Introduction: The mortality rate among older people infected with severe acute respiratory syndrome coronavirus 2 is alarmingly high. This study aimed to explore the predictive value of a novel model for assessing the risk of death in this vulnerable cohort.
Methods: We enrolled 199 older patients with coronavirus disease 2019 (COVID-19) from Zhejiang Provincial Hospital of Chinese Medicine (Hubin) between 16 December 2022 and 17 January 2023. Additionally, 90 patients from two other centers (Qiantang and Xixi) formed an external independent testing cohort. Univariate and multivariate analyses were used to identify the risk factors for mortality. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to select variables associated with COVID-19 mortality. Nine machine-learning algorithms were used to predict mortality risk in older patients, and their performance was assessed using receiver operating characteristic curves, area under the curve (AUC), calibration curve analysis, and decision curve analysis.
Results: Neutrophil-monocyte ratio, neutrophil-lymphocyte ratio, C- reactive protein, interleukin 6, and D-dimer were considered to be relevant factors associated with the death risk of COVID-19-related death by LASSO regression. The Gaussian naive Bayes model was the best-performing model. In the validation cohort, the model had an AUC of 0.901, whereas in the testing cohort, the model had an AUC of 0.952. The calibration curve showed a good correlation between the actual and predicted probabilities, and the decision curve indicated a strong clinical benefit. Furthermore, the model had an AUC of 0.873 in an external independent testing cohort.
Discussion: In this study, a predictive machine-learning model was developed with an online prediction tool designed to assist clinicians in evaluating mortality risk factors and devising targeted and effective treatments for older patients with COVID-19, potentially reducing the mortality rates.
Keywords: COVID-19; Gaussian naïve Bayes; blood markers; machine learning; mortality; older patients.