Quantifying immune dysregulation in pneumonia and sepsis with a parsimonious machine-learning model: a multicohort analysis across care settings and reanalysis of a hydrocortisone randomised controlled trial.

BACKGROUND: Sepsis is a dysregulated host response to infection resulting in life-threatening organ failure. Although immune dysregulation is central to the sepsis definition, immunomodulation trials enrol participants based on clinical severity, not the extent of dysregulation, which could contribute to treatment heterogeneity. A pragmatic way to quantify immune dysregulation could improve prognostication, help to evaluate treatment responses, and identify individuals most likely to benefit from immunomodulation. We aimed to construct a parsimonious machine-learning tool that defines and quantifies immune dysregulation, thereby supporting biologically informed immunomodulation. METHODS: In this multicohort analysis and reanalysis of a randomised controlled trial, the primary objective was to derive and validate a categorical and continuous immune dysregulation score that is independent of clinical presentation or outcome. We measured 35 plasma biomarkers reflecting key host response domains in individuals with community-acquired pneumonia (CAP) across different care settings (emergency department, general ward, and intensive care unit) and disease severities using data from three independent cohorts. We applied unsupervised trajectory inference analysis to identify an immune dysregulation gradient captured as discrete immune dysregulation stages (Dysregulated Immune Profile [DIP]) and a continuous score (cDIP; 0-1). We developed two parsimonious machine-learning models to predict the DIP stages and cDIP scores based on 35 biomarkers, and validated their ability to capture immune dysregulation and predict clinical outcomes in five independent cohorts. On the basis of our hypothesis that only individuals with severe immune dysregulation benefit from immunomodulation, we carried out a post-hoc analysis of a randomised trial evaluating hydrocortisone in severe CAP (CAPE COD trial, NCT02517489), assessing treatment effects across DIP stages and the cDIP continuum, and how hydrocortisone influenced dysregulation trajectories over time. FINDINGS: We organised 398 participants with CAP along a continuum of immune dysregulation from mild to severe on the basis of 35 plasma biomarkers, yielding three dysregulation stages (DIP1-3) and a continuous score (cDIP). Clinical severity proved to be an inadequate proxy for immune dysregulation. A three-biomarker machine-learning framework (procalcitonin, soluble TREM-1, and IL-6) accurately predicted the degree of dysregulation derived from 35 biomarkers (DIP stage accuracy 91·2%; cDIP root mean square error 0·056). Although the framework was not designed for outcome prediction, increased immune dysregulation-reflected in DIP and cDIP-was associated with a gradual rise in mortality (cDIP odds ratio [OR] 1·26 [95% CI 1·13-1·40] per 10% increase, p<0·0001) and secondary infections OR 1·50 [1·22-1·93] per 10% increase, p=0·0005, independent of clinical severity. The three-biomarker tool was validated in five external cohorts of varying infections, severities, and care settings n=1191. Reanalysis of the CAPE COD trial showed that hydrocortisone conferred a survival benefit only in participants classified as severely dysregulated by our model 30-day mortality: DIP3 OR 0·25 [0·05-0·85], p=0·042; cDIP ≥0·63 OR 0·21 [0·10-0·72], p=0·011, accompanied by faster immune recovery (time × treatment interaction, p<0·0001). No such effect modification was observed when stratifying participants by clinical severity. INTERPRETATION: We have provided a publicly available three-biomarker framework to determine the extent of host response dysregulation with potential value for precision-guided immunomodulatory therapy. FUNDING: EU Horizon 2020.