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Development and validation of a predictive model for continuous renal replacement therapy in sepsis patients using
Binglin Song1, Ping Liu2,3, Chun Liu3
1Clinical Medical College of North Sichuan Medical College, Nanchong, 637000, China.
Scientific Reports
|July 2, 2025
Summary
This study developed a dynamic nomogram to predict continuous renal replacement therapy (CRRT) needs in intensive care unit (ICU) septic patients. The model, using 10 clinical factors, demonstrated strong predictive performance and clinical utility.
Area of Science:
- Critical Care Medicine
- Nephrology
- Medical Informatics
Background:
- Sepsis is a leading cause of acute kidney injury (AKI) requiring continuous renal replacement therapy (CRRT) in intensive care units (ICUs).
- Accurate prediction of CRRT need in septic patients is crucial for timely intervention and improved outcomes.
- Existing predictive models often lack dynamic capabilities and real-time applicability.
Purpose of the Study:
- To develop and validate a dynamic nomogram for predicting the requirement of CRRT in septic patients admitted to the ICU.
- To identify key clinical risk factors associated with CRRT initiation in this patient population.
- To provide a user-friendly tool for clinicians to assess individual patient risk.
Main Methods:
- Retrospective cohort study using data from the MIMIC-IV 3.0 database (N=7361).
- LASSO regression and binary logistic regression were employed to identify predictive factors and build the model.
- A dynamic nomogram was created using R language and the DynNom package for visualization and prediction.
- Model performance was evaluated using Area Under the Receiver Operating Characteristic Curve (AUC), Harrell's C-index, calibration curves, and Decision Curve Analysis (DCA).
Main Results:
- The final model incorporated 10 clinical features: respiratory rate, oxygen saturation, INR, APTT, creatinine, lactate, pH, body weight, renal disease, and severe liver disease.
- The dynamic nomogram demonstrated robust predictive performance with a C-index of 0.871.
- AUC values were 0.87 (training set) and 0.86 (validation set), with good calibration and significant clinical utility confirmed by DCA.
- A web-based dynamic nomogram is accessible for clinical use.
Conclusions:
- A validated dynamic nomogram effectively predicts CRRT requirement in ICU septic patients.
- The nomogram integrates readily available clinical data, offering a practical tool for risk stratification.
- This tool can aid clinicians in making informed decisions regarding CRRT initiation for septic patients.
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