Objectives:

To estimate the incidence of healthcare-associated infections (HAIs) in Italian long-term care facilities (LTCFs) and to evaluate whether an artificial intelligence (AI) approach, through unsupervised machine learning (ML), could stratify residents into clinically distinct groups with differing susceptibility to HAIs.

Design:

Prospective cohort study with 12-month follow-up.

Setting:

24 LTCFs in Italy, participating in the European Centre for Disease Prevention and Control 12-month longitudinal study on HAIs in LTCFs, 2022–2023.

Participants:

395 residents enrolled across the participating LTCFs.

Methods:

Incidence measures of HAIs (rate and ratio) were estimated, using generalized estimating equations. A hierarchical cluster analysis based on residents’ clinical and demographic characteristics was implemented as an unsupervised ML approach.

Results:

Overall, 75 HAIs per 100 residents (95% CI, 70.3–78.3) and 0.23 HAIs per 1,000 resident-days (95% CI, 0.11–0.76) were estimated. Respiratory tract infections (29.5%, 95% CI 24.2–31.1), COVID-19 (26.3%, 95% CI 22.1–28.4), and urinary tract infections (15%, 95% CI 11.0–35.4) were the most frequent. Clustering identified two reproducible resident groups: Group 1 (39%), more independent and cognitively preserved, with fewer comorbidities and lower infection incidence; and Group 2 (61%), more dependent and clinically complex, with higher incidence of HAIs. Cluster stability was high (mean ARI = 0.83).

Conclusions:

This study confirms the high burden of HAIs in Italian LTCFs and provides exploratory evidence that AI-based clustering can identify reproducible HAI susceptibility profiles in a setting where such approaches have been scarcely applied.