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. 2024 Feb 20;12(1):8.
doi: 10.1186/s40560-024-00721-7.

Development and validation of a nomogram to predict the risk of sepsis-associated encephalopathy for septic patients in PICU: a multicenter retrospective cohort study

Guan Wang #  1 , Xinzhu Jiang #  2 , Yanan Fu  3 , Yan Gao  2 , Qin Jiang  4 , Enyu Guo  5 , Haoyang Huang  6 , Xinjie Liu  7
Affiliations

Development and validation of a nomogram to predict the risk of sepsis-associated encephalopathy for septic patients in PICU: a multicenter retrospective cohort study

Guan Wang et al. J Intensive Care. .

Abstract

Background: Patients with sepsis-associated encephalopathy (SAE) have higher mortality rates and longer ICU stays. Predictors of SAE are yet to be identified. We aimed to establish an effective and simple-to-use nomogram for the individual prediction of SAE in patients with sepsis admitted to pediatric intensive care unit (PICU) in order to prevent early onset of SAE.

Methods: In this retrospective multicenter study, we screened 790 patients with sepsis admitted to the PICU of three hospitals in Shandong, China. Least absolute shrinkage and selection operator regression was used for variable selection and regularization in the training cohort. The selected variables were used to construct a nomogram to predict the risk of SAE in patients with sepsis in the PICU. The nomogram performance was assessed using discrimination and calibration.

Results: From January 2017 to May 2022, 613 patients with sepsis from three centers were eligible for inclusion in the final study. The training cohort consisted of 251 patients, and the two independent validation cohorts consisted of 193 and 169 patients. Overall, 237 (38.7%) patients developed SAE. The morbidity of SAE in patients with sepsis is associated with the respiratory rate, blood urea nitrogen, activated partial thromboplastin time, arterial partial pressure of carbon dioxide, and pediatric critical illness score. We generated a nomogram for the early identification of SAE in the training cohort (area under curve [AUC] 0.82, 95% confidence interval [CI] 0.76-0.88, sensitivity 65.6%, specificity 88.8%) and validation cohort (validation cohort 1: AUC 0.80, 95% CI 0.74-0.86, sensitivity 75.0%, specificity 74.3%; validation cohort 2: AUC 0.81, 95% CI 0.73-0.88, sensitivity 69.1%, specificity 83.3%). Calibration plots for the nomogram showed excellent agreement between SAE probabilities of the observed and predicted values. Decision curve analysis indicated that the nomogram conferred a high net clinical benefit.

Conclusions: The novel nomogram and online calculator showed performance in predicting the morbidity of SAE in patients with sepsis admitted to the PICU, thereby potentially assisting clinicians in the early detection and intervention of SAE.

Keywords: Nomogram; PICU; Prediction; Sepsis-associated encephalopathy.

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Conflict of interest statement

The authors declare that this study was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

Figures

Fig. 1
Fig. 1
Flowchart of study participants in the training and validation cohorts. SAE: sepsis-associated encephalopathy
Fig. 2
Fig. 2
Feature selection using the least absolute shrinkage and selection operator (LASSO) regression in the training cohort. A LASSO coefficient profiles of the 34 features. B Identification of the optimal penalization coefficient (λ) in the LASSO model was performed via tenfold cross-validation
Fig. 3
Fig. 3
Construction of SAE prediction nomogram in the training cohort. The points of all features were added to obtain the total points, and a vertical line was drawn on the total point to obtain the corresponding ‘predicted value’, which indicates the risk of SAE. PaCO2: arterial partial pressure of carbon dioxide; PCIS: pediatric critical illness score; RR: respiratory rate; BUN: blood urea nitrogen; APTT: activated partial thromboplastin time.
Fig. 4
Fig. 4
The receiver operating characteristic (ROC) curves of the nomogram in the training cohort (A), validation cohort 1 (B), and validation cohort 2 (C)
Fig. 5
Fig. 5
The calibration curves of the nomogram in the training cohort (A), validation cohort 1 (B), and validation cohort 2 (C)
Fig. 6
Fig. 6
Decision curve analysis (DCA) of the nomogram of SAE. DCA compares the net benefits of three scenarios in predicting the risk of SAE: A perfect prediction model (grey line), screen none (horizontal solid black line), and screen based on the nomogram (ride line). The DCA curves were depicted in the training cohort (A), validation cohort 1 (B), and validation cohort 2 (C)
Fig. 7
Fig. 7
An Internet browser-based online calculator for the SAE prediction nomogram (website: https://johny2020.shinyapps.io/Dyn_SAE/)
See this image and copyright information in PMC

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