Self-organising map clustering identifies high-risk clusters of post-acute mortality in a prospective multicentre study of community-acquired pneumonia.

Background: Community-acquired pneumonia (CAP) is a leading cause of morbidity and mortality. While tools predicting short-term prognosis exist, there is urgent need for the early identification of patients requiring close follow-up monitoring for post-acute mortality. We therefore conducted cluster analysis of baseline clinical data to investigate predictors of post-acute mortality in CAP. Methods: We analysed 7840 participants from the German CAPNETZ cohort, using self-organising map (SOM)-clustering and survival analyses. Random survival forest (RSF) models were used to identify key predictors of mortality, which were then analysed using time-dependent area under the curve and Cox proportional hazard regression models. Results: SOM-clustering based on 10 predictors identified 879 (12%, in four clusters) patients with high risk for post-acute (30-180 days) mortality. Across the cohort, age and urea were the most important predictors of post-acute mortality, while in the high-risk cohort, body mass index emerged as the strongest predictor, as identified by RSF modelling. In one high-risk cluster, there was an association with elevated platelet counts (HR: 1.13, 95% CI 1.03-1.21, p=0.01; increments of 40 platelets·nL Conclusion: Using 10 clinical predictors for post-acute mortality in CAP, predictive SOM-clustering revealed several high-risk subgroups, with heterogeneous biomarkers, suggestive of differences in the underlying pathophysiology (thrombocytes, urea, CRP). Adapting medical therapy to these high-risk subgroups may reduce post-acute mortality following CAP.