DEVELOPMENT AND VALIDATION OF A PREDICTION MODEL FOR SEPTIC SHOCK-ASSOCIATED ACUTE KIDNEY INJURY: A MULTICENTER STUDY...

Area of Science:

• Nephrology
• Critical Care Medicine
• Medical Informatics

Background:

• Septic shock-associated acute kidney injury (SS-AKI) is a critical condition with significant mortality.
• Early identification of patients at risk for SS-AKI is crucial for effective management.
• Predictive models can aid clinicians in assessing SS-AKI risk.

Purpose of the Study:

• To identify risk factors for acute kidney injury (AKI) in patients with septic shock.
• To develop and validate a predictive nomogram for SS-AKI occurrence.
• To improve clinical decision-making for SS-AKI prevention and management.

Main Methods:

• A retrospective study of 507 septic shock patients, divided into training (n=355) and validation (n=152) sets.
• Binary logistic regression analysis to identify significant risk factors for AKI.
• Nomogram construction and evaluation using ROC analysis, calibration curves, and decision curve analysis.

Main Results:

• 174 (34.3%) patients developed AKI.
• Key predictive factors included chronic kidney disease, diuretic use, deresuscitation, mechanical ventilation, source control failure, restrictive fluid resuscitation, and SOFA scores.
• The nomogram demonstrated excellent predictive performance with AUCs of 0.788 (training) and 0.770 (validation).

Conclusions:

• The developed nomogram is a valuable tool for assessing SS-AKI risk in clinical practice.
• Early risk assessment facilitates timely interventions to potentially improve patient outcomes.
• The nomogram shows strong discriminatory ability and clinical utility in both training and validation cohorts.