BACKGROUND: This study aimed to compare the clinical characteristics and prognosis of immunocompromised and non-immunocompromised patients with sepsis and to develop a novel mortality prediction model for these patients. METHODS: This prospective cohort study was conducted between February and October 2023 in the emergency department (ED) of Peking Union Medical College Hospital and included 145 sepsis patients (67 with immunocompromised state and 78 without). The primary outcome was 90-day all-cause mortality. Kaplan-Meier survival analysis and Cox proportional hazards regression were employed to assess the relationship between immune status and mortality risk, identify independent risk factors, and develop a predictive nomogram. RESULTS: The 90-day mortality rate in immunocompromised patients was significantly higher than that in the control group (55.2% vs. 34.6%, p = 0.013). Multivariate Cox proportional hazards regression analysis identified immunocompromised state as an independent risk factor for mortality (HR = 2.980, 95% CI: 1.679-5.288; p < 0.001). In the subgroup analysis, autoimmune diseases (HR = 3.926, 95% CI: 1.763-8.744), poorly controlled diabetes (HR = 4.432, 95% CI: 1.638-11.992), and malignant neoplasms (HR = 3.195, 95% CI: 1.203-8.486) were significantly associated with an increased mortality risk. After statistical adjustment, seven factors were significantly associated with sepsis mortality (p < 0.05), including respiratory tract infection, urinary tract infection, intensive care unit (ICU) admission, the Sequential Organ Failure Assessment (SOFA) score, immunocompromised state, septic shock, and urgent surgery. The nomogram model based on these factors demonstrated strong predictive ability and accuracy, with a C-index of 0.808 (p < 0.001). CONCLUSION: Immunocompromised state is an independent risk factor for mortality in sepsis patients, and different types of immunosuppression exert varying impacts on prognosis. The nomogram model developed on the basis of these findings offers valuable guidance for individualized risk assessment in clinical practice.
Hu et al. (2026) studied this question.