Construction and evaluation of a neonatal septic shock prediction model based on multimodal data

This study aimed to develop and validate a predictive model using multimodal data for early identification of septic shock in neonates with systemic inflammatory response syndrome (SIRS) or sepsis. This retrospective cohort study included neonates diagnosed with SIRS, sepsis, or septic shock at Fuzhou First General Hospital between January 2021 and December 2023. Univariate and multivariate logistic regression analyses were performed to identify independent predictors, and receiver operating characteristic curves were used to evaluate the model's predictive performance. Multivariate analysis identified 7 independent predictors of septic shock, namely, the age of neonatal (OR = 2.293, 95% CI = 2.129-3.482), critical illness score (OR = 1.835, 95% CI = 1.582-2.354), cerebral oxygen saturation (ScO2) (OR = 0.289, 95% CI = 0.281-0.359), pulsatility index of right middle cerebral artery (OR = 0.837, 95% CI = 0.828-1.022), peak velocity (PSV) of left renal hilum (OR = 0.952, 95% CI = 0.868-1.157), procalcitonin (OR = 1.875, 95% CI = 1.725-2.061), and lactate (OR = 9.654, 95% CI = 8.612-10.572) were independent influencing factors for the occurrence of septic shock. Based on the result of the regression analysis, we constructed a model for predicting septic shock, namely, multimodal model = (-0.378 × neonatal age) - (0.145 × critical illness score) - (0.366 × ScO2) + (0.416 × pulsatility index of right middle cerebral artery) + (0.825 × PSV of left renal hilum) + (12.288 × procalcitonin) + (5.167 × lactate) + 1.804. And results of receiver operating characteristic curve showed that the area under curve of the multimodal model for predicting septic shock in the SIRS + septic shock population, sepsis + septic shock population, and SIRS + sepsis + septic shock population is 0.862, 0.746, and 0.820, respectively. A multimodal prediction model incorporating clinical, hemodynamic, and biochemical parameters demonstrated robust performance in early identification of neonatal septic shock. Further validation through multicenter studies is warranted.

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Publication Details

Journal
Medicine
Published
2025-06-06
DOI
https://doi.org/10.1097/md.0000000000042746
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00

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article

Construction and evaluation of a neonatal septic shock prediction model based on multimodal data

Jinlan Chen, Zhen Li, Xiaoning Yan, Fan Liu et al.
Medicine
Sepsis Diagnosis and Treatment
article

Construction and evaluation of a neonatal septic shock prediction model based on multimodal data

Jinlan Chen, Zhen Li, Xiaoning Yan, Fan Liu, Zheng Yan, Qinglan Huang
article en

Abstract

This study aimed to develop and validate a predictive model using multimodal data for early identification of septic shock in neonates with systemic inflammatory response syndrome (SIRS) or sepsis. This retrospective cohort study included neonates diagnosed with SIRS, sepsis, or septic shock at Fuzhou First General Hospital between January 2021 and December 2023. Univariate and multivariate logistic regression analyses were performed to identify independent predictors, and receiver operating characteristic curves were used to evaluate the model's predictive performance. Multivariate analysis identified 7 independent predictors of septic shock, namely, the age of neonatal (OR = 2.293, 95% CI = 2.129-3.482), critical illness score (OR = 1.835, 95% CI = 1.582-2.354), cerebral oxygen saturation (ScO2) (OR = 0.289, 95% CI = 0.281-0.359), pulsatility index of right middle cerebral artery (OR = 0.837, 95% CI = 0.828-1.022), peak velocity (PSV) of left renal hilum (OR = 0.952, 95% CI = 0.868-1.157), procalcitonin (OR = 1.875, 95% CI = 1.725-2.061), and lactate (OR = 9.654, 95% CI = 8.612-10.572) were independent influencing factors for the occurrence of septic shock. Based on the result of the regression analysis, we constructed a model for predicting septic shock, namely, multimodal model = (-0.378 × neonatal age) - (0.145 × critical illness score) - (0.366 × ScO2) + (0.416 × pulsatility index of right middle cerebral artery) + (0.825 × PSV of left renal hilum) + (12.288 × procalcitonin) + (5.167 × lactate) + 1.804. And results of receiver operating characteristic curve showed that the area under curve of the multimodal model for predicting septic shock in the SIRS + septic shock population, sepsis + septic shock population, and SIRS + sepsis + septic shock population is 0.862, 0.746, and 0.820, respectively. A multimodal prediction model incorporating clinical, hemodynamic, and biochemical parameters demonstrated robust performance in early identification of neonatal septic shock. Further validation through multicenter studies is warranted.

MedicineVol. 104(23)
Fujian Medical University (CN), First Affiliated Hospital of Fujian Medical University (CN)
Natural Science Foundation of Fujian Province
Good health and well-being
Openalex Percentile: Top 81%
Sepsis Diagnosis and Treatment
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