
31/07/2025
📣 🅜🅔🅣🅐 Published Today ‒ July 31, 2025 (Philippines)
This meta‑analysis systematically reviews prediction models for sepsis‑associated encephalopathy, evaluating accuracy and methodological quality in adult sepsis patients.
TITLE:
DIAGNOSTIC MODELS FOR SEPSIS‑ASSOCIATED ENCEPHALOPATHY: A COMPREHENSIVE SYSTEMATIC REVIEW AND META‑ANALYSIS
🅼 Link: https://doi.org/10.3389/fneur.2025.1645397
🅼 Key Findings:
🔬 Evaluated 10 studies with 55,244 sepsis patients; SAE incidence ranged from 15 % to 62.4%.
🔬 Identified 29 prediction models, including 10 top models using logistic regression or machine learning.
🔬 Pooled AUC for five logistic regression models was 0.85 (95 % CI 0.77–0.93), indicating moderate-to‑good discrimination.
🔬 Between‑study heterogeneity was high (I² = 91.8 %).
🔬 PROBAST assessment revealed high risk of bias across all models.
🔬 Common methodological flaws: lack of external validation, inconsistent SAE definitions, poor reporting.
🔬 Recommend adherence to TRIPOD guidelines and prospective validation with standardized endpoints.
🔬 Emphasize need for improved interpretability and generalizability to enable clinical uptake.
🅼 Clinical Relevance:
Current SAE prediction models yield decent AUCs (~0.85) but suffer from bias and lack real-world validation. Improving rigor and validation is vital before using them to guide sepsis patient care.
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