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Early Prediction of Septic Shock in Severe COVID-19 Patients: Development and Validation of a Nomogram Model
Yinbing Jin1, Junbao Ma1, Wenhan Zhou1
1Emergency Department, The Yangzhou Clinical Medical College of Xuzhou Medical University, Xuzhou, Jiangsu, People's Republic of China.
Purpose:
Septic shock is a severe complication in critically ill patients with COVID-19, often associated with poor prognosis. Predictive factors for septic shock remain undetermined. Our objective was to develop an early predictive model for septic shock in severe COVID-19 patients to assist emergency and critical care physicians in resource allocation and medical decision-making.
Patients And Methods:
The training cohort was sourced from the cases admitted to Northern Jiangsu People's Hospital between December 2022 and February 2023, while the validation cohort was retrieved from the MIMIC-IV dataset. The Least Absolute Shrinkage and Selection Operator (LASSO) analysis was used to screen for predictors. A multivariate logistic regression was employed to build the predictive model, which was then represented as a nomogram. The performance of the nomogram was evaluated using the Receiver Operating Characteristic (ROC) curve, calibration plot, and Decision Curve Analysis (DCA). External validation was conducted by assessing the model's performance in the validation cohort.
Results:
A collective of 274 patients and 75 patients were respectively enrolled as the training cohort and the validation cohort in this study. The predictors included in the nomogram were albumin, mean arterial pressure, lactate, and the Sequential Organ Failure Assessment (SOFA) score. The area under the ROC curve (AUC) for the modeling set was 0.800 (95% CI 0.741-0.858), and for the validation set, it was 0.775 (95% CI 0.651-0.899). Additionally, the calibration curve indicated a correlation between predicted and observed outcomes, and DCA highlighted the clinical utility of the nomogram.
Conclusion:
We developed and validated a diagnostic nomogram model for septic shock in critically ill COVID-19 patients, incorporating four parameters: SOFA score, albumin, mean arterial pressure, and lactate. This model demonstrates significant potential in predicting septic shock among critically ill COVID-19 patients.
