Multimorbidity patterns and outcomes among adults (non-maternity) with community-onset sepsis in Ireland: a retrospective national analysis of acute hospital data, 2020-2025.

BACKGROUND

Community-onset sepsis accounts for most sepsis cases and frequently occurs in individuals with underlying comorbidity. While national analyses in Ireland have described its epidemiology, impact of multimorbidity and how comorbidities co-exist to form patterns remain poorly characterised.

OBJECTIVES

To identify multimorbidity patterns among adult (non-maternity) episodes with community-onset sepsis and examine their association with critical care admission and in-hospital mortality.

METHODS

We conducted a secondary analysis of Hospital In-Patient Enquiry data from all acute public hospitals in Ireland from January 2020 to December 2025. Community-onset sepsis was defined using the present-on-admission flag. The sample comprised adult episodes aged ≥ 16 years excluding maternal admissions from conception to 6 weeks postpartum. K-means clustering identified patterns of co-occurring diseases. Multivariable logistic and negative binomial regression examined associations between patterns and outcomes.

RESULTS

Of 80,628 sepsis admissions, 58,101 (72.0%) were community-onset. Of these, 53,812 (93.0%) were adult (non-maternity); of these, 35,316 (65.6%) had at least one Charlson comorbidity. Five data-derived multimorbidity patterns were identified: metabolic-multimorbid (31%), oncological-dominant (29%), renal-metabolic (15%), progressive metabolic (13%) and cardiopulmonary (12%). Outcomes varied across patterns, with differences in critical care admission and in-hospital mortality. Cardiopulmonary pattern had the highest odds of in-hospital mortality (adjusted-OR 2.01, 95% CI 1.86-2.17).

CONCLUSIONS

Charlson comorbidity was recorded in approximately two-thirds of adult community-onset sepsis episodes and formed data-derived patterns associated with differences in critical care admission and in-hospital mortality. Multimorbidity clustering may support future risk stratification research and targeted clinical vigilance for episodes at highest risk of adverse outcomes.