A55-50 A Biomarker-based AI Tool That Predicts Corticosteroid Treatment Benefit or Harm in Pneumonia
Abstract
Community-acquired pneumonia (CAP) is a leading cause of infectious mortality. Corticosteroids have mixed efficacy in CAP. A precision medicine approach that identifies patients most likely to benefit from or be harmed by steroids could optimize treatment, leading to improved outcomes in CAP.
To develop and validate an artificial intelligence (AI) algorithm, cortiCAP, which integrates clinical features and plasma biomarkers to identify patients with predicted clinical benefit or harm from corticosteroid treatment in CAP.
Secondary analysis of a prospective observational, multicenter cohort study. CortiCAP is a multivariable algorithm that uses patient characteristics at hospital admission for CAP to predict benefit or harm from acute treatment with corticosteroids. This study describes the derivation and validation of cortiCAP, created using supervised machine learning techniques and validated using logistic regression and propensity-matched analyses based on known steroids receipt and mortality.
Seven hospitals in the United States.
Included were patients aged 18+ presenting to the hospital February 2018-September 2023 with a blood culture order within 6 hours and an ICD-9 or ICD-10 discharge diagnosis of CAP. Exclusions included COVID-19, asthma, COPD, late or subtherapeutic corticosteroid exposure, and missing >30% of data. Of 1,025 eligible patients, 656 were assigned to the derivation cohort and 369 to the validation cohort.
Receipt of systemic corticosteroids…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 1,025 participants or records.
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