Association between platelet-albumin-bilirubin score and 30-day in-hospital mortality in patients with sepsis: evidence from a large database and machine learning modeling.
A simple blood test score from routine labs predicted sepsis survival risk, offering doctors a practical tool to identify high-risk patients earlier.
This MIMIC-IV-based study evaluated the platelet-albumin-bilirubin (PALBI) score as a sepsis mortality predictor, using machine learning to develop and validate a prognostic model for 30-day in-hospital mortality. The study contributes to the evidence base for CBC-accessible composite biomarkers in critical care risk stratification, with potential for near-term clinical application given the routine availability of the component laboratory tests.
What the study was
- Study design
- Retrospective database study + ML model development and validation
- Population
- Sepsis patients in MIMIC-IV database (pediatric ICU, Chongqing Medical University Children's Hospital)
- Category
- Diagnostics
- Maturity
- Exploratory
- Journal
- BMC Infectious Diseases
Why it surfaced
MIMIC-IV database + ML validation provides reasonable evidence level for a CBC-component composite score in sepsis ICU; sepsis mortality is a high-impact endpoint; scores derived from routine labs (platelets, albumin, bilirubin) are inherently implementable if validated.
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