Development and validation of a predictive model for invasive ventilation risk within 48 hours of admission in patients with early sepsis-associated acute kidney injury

TL;DR

This study develops a model to predict which patients with early sepsis-related kidney injury will need invasive ventilation within 48 hours, helping doctors act quickly to improve outcomes.

Contribution

A novel nomogram model was developed and validated for predicting invasive ventilation risk in early sepsis-associated acute kidney injury patients.

Findings

The nomogram achieved high AUC values (0.857 in training, 0.850 in internal validation, and 0.791 in external validation).

Lactate, pro-BNP, albumin, peripheral oxygen saturation, and pulmonary infection were identified as key risk factors.

Machine learning models did not significantly outperform the nomogram in validation.

Abstract

To identify patients with early sepsis-associated acute kidney injury (SA-AKI) at high risk of requiring invasive ventilation within 48 h of admission, facilitating timely interventions to improve prognosis.

This retrospective study included patients with early SA-AKI admitted to Dongyang People’s Hospital between January 2011 and October 2024 and Yiwu Tianxiang Dongfang Hospital between January 2016 and December 2024. Variables included age, blood parameters, and vital signs at admission. Patients were divided into training and validation cohorts. Independent risk factors were identified in the training cohort, and a nomogram was developed. The discriminatory ability was assessed using the area under the receiver operating characteristic curves (AUC). Calibration was assessed using GiViTI calibration plots, while clinical utility was evaluated via decision curve analysis (DCA). Validation was performed in the internal and external validation groups. Additional models based on Sequential Organ Failure Assessment (SOFA) and National Early Warning Score (NEWS) scores, machine learning models including Support Vector Machine (SVM), C5.0, Extreme Gradient Boosting (XGBoost), and an ensemble model were compared with the nomogram on the discrimination power using DeLong’s test.

The key independent risk factors for invasive ventilation in patients with early SA-AKI included lactate, pro-BNP, albumin, peripheral oxygen saturation, and pulmonary infection. The nomogram demonstrated an AUC of 0.857 in the training cohort (Hosmer-Lemeshow P = 0.533), 0.850 in the inner-validation cohort (Hosmer-Lemeshow P = 0.826) and 0.791 in the external validation cohort (Hosmer-Lemeshow P = 0.901). DCA curves indicated robust clinical utility. The SOFA score model exhibited weaker discrimination powers (training AUC: 0.621; validation AUC: 0.676; P < 0.05), as did the NEWS score model (training AUC: 0.676; validation AUC: 0.614; P < 0.05). Machine learning models (SVM, C5.0, XGBoost, and ensemble methods) did not significantly outperform the nomogram in the validation cohort (P > 0.05), with respective AUCs of 0.741, 0.792, 0.842, and 0.820.

The nomogram developed in this study is capable of accurately predicting the risk of invasive ventilation in SA-AKI patients within 48 h of admission, offering a valuable tool for early clinical decision-making.

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