Systematic Review and Meta-Analysis of Predictive Models for Acute Kidney Injury Linked to Sepsis
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By
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Muze Huang
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Shiyuan Wu
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Zi-Han Shen
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Sarena Jiayao Zhang
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Yaw-syan Fu
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Jingyi Wu
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September 14, 2026
Clinical Scorecard: Systematic Review and Meta-Analysis of Predictive Models for Acute Kidney Injury Linked to Sepsis
At a Glance
| Category | Detail |
|---|
| Condition | Sepsis-associated acute kidney injury (SA-AKI) |
| Key Mechanisms | Inflammation, microcirculatory failure, mitochondrial impairment, and apoptosis |
| Target Population | Patients with sepsis in intensive care units |
| Care Setting | Critical care and intensive care units |
Key Highlights
- Pooled C-statistic for predictive models was 0.817, indicating moderate-to-good discrimination for SA-AKI.
- Substantial heterogeneity was observed among studies (I2 = 92.8%).
- Models developed in Asian regions showed a higher pooled C-statistic compared to those from North America.
- Studies with low risk of bias demonstrated better predictive performance than those with high risk of bias.
Guideline-Based Recommendations
Diagnosis
- Utilize established KDIGO guidelines for diagnostic criteria of AKI.
Management
- Implement early screening and renoprotective interventions for high-risk patients.
Monitoring & Follow-up
- Regularly assess predictive model performance and update based on new data.
Risks
- Consider the potential for persistent renal impairment or progression to chronic kidney disease.
Patient & Prescribing Data
Patients with sepsis at risk of acute kidney injury
Advanced multidimensional risk prediction approaches are recommended for timely risk stratification.
Clinical Best Practices
- Incorporate electronic health records and machine learning algorithms in predictive modeling.
- Focus on developing robust, population-specific predictive models.
- Conduct multicenter external validation to enhance generalizability.
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