Identification and validation of robust hospital-acquired pneumonia subphenotypes associated with all-cause mortality: a multi-cohort derivation and validation

Abstract

Purpose: Despite optimal antimicrobial therapy, the treatment failure rate of hospital-acquired pneumonia (HAP) routinely reaches 40% in critically ill patients. Subphenotypes have been identified within sepsis and acute respiratory distress syndrome with important predictive and possibly therapeutic implications. We derived prognosis subphenotypes for HAP and explored whether they were associated with biological markers and response to treatment.

Methods: We separately analysed data from four cohorts of critically ill patients in France (PNEUMOCARE, n = 511, ATLANREA, n = 401), Netherlands (MARS, n = 1351) and Europe-South America (ENIRRI, n = 900) to investigate HAP heterogeneity using unsupervised clustering based on clinical and routine biological variables available at HAP diagnosis. Then, we developed a machine learning-based workflow to create a simplified classification model using discovery data sets. This model was validated by applying it to an independent replication data set from an international randomized clinical trial comparing linezolid and tedizolid for the treatment of HAP (VITAL, n = 726 patients). The primary outcome was the association of subphenotypes with 28-day all-cause mortality. Secondary analyses included subphenotype associations with treatment failure at test-of-cure, respiratory microbiome and cytokine profiles in the ATLANREA subgroup, and treatment response in the VITAL trial.

Results: We tested twelve metrics and determined that a two-cluster model best fits all cohorts. HAP subphenotype 2 had greater disease severity, lower body temperature, and worse PaO2/FiO2 ratio than subphenotype 1 patients. Although the prevalence of subphenotype 2 ranged from 26.9 to 66.9% across the four derivation cohorts, the rates of 28-day mortality and treatment failure at test-of-cure were consistently higher to subphenotype 1 (p < 0.01 for all comparisons). Subphenotype 2 was associated with greater respiratory microbiome dysbiosis and higher levels of proinflammatory cytokines in the ATLANREA cohort, as well as with statistically significant tedizolid effect modification in the VITAL trial (Relative Risk of treatment failure with tedizolid = 1.52; 95% CI 1.12-2.06 in subphenotype 1 vs. = 0.98; 95% CI 0.7-1.38 in subphenotype 2).

Conclusions: We identified two robust clinical subphenotypes by extensively analyzing HAP data sets. Their associations with respiratory microbiome composition, systemic inflammation, and treatment efficacy in independent data sets highlight their potential for prognostic value and predictive enrichment in future clinical trials aimed at personalized therapies.

Keywords: Antimicrobial therapy; Hospital-acquired pneumonia; Lung microbiome; Machine learning; Mortality; Subphenotype.