A machine learning model for in-hospital mortality prediction in patients with sepsis-related myocardial injury.
An eight-variable prediction model moderately identifies which sepsis patients face highest death risk, aiding clinical decision-making.
This PubMed-indexed study examined sepsis-related myocardial injury (SRMI) is associated with high mortality, but the transportability of prediction models across heterogeneous cohorts remains uncertain. The abstract reports in two external cohorts, an 8-variable random forest model showed moderate discrimination for mortality prediction in SRMI.
What the study was
- Study design
- Diagnostic/Validation Study
- Category
- Diagnostics
- Maturity
- Validated
- Journal
- PLOS digital health
Why it surfaced
Conservative triage based on the PubMed abstract: diagnostic/validation study with direct watchlist relevance; score reflects metadata and abstract-level evidence only.
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