Elderly Patients Surgical Site Infection Phenotypes Identification

This study utilizes Latent Class Analysis (LCA) to identify phenotypes of Surgical Site Infection (SSI) in elderly patients following non-cardiac surgery. By analyzing data from two large cohorts, the research establishes a predictive model that uncovers independent risk factors for SSI, including age, hyperlipidemia, and surgical characteristics. The model, with AUCs ranging from 0.753 to 0.791 across cohorts, offers a calibrated prediction of SSI risk. Furthermore, LCA delineates four distinct SSI subphenotypes, highlighting a critical subgroup with a higher infection rate. This subgroup presents a complex interplay of risk factors, indicating the need for tailored preventive strategies. The study's findings contribute to a nuanced understanding of SSI in elderly surgical patients and pave the way for more targeted infection control measures.