Abstract: Objective To explore the latent classes of symptoms in patients with post-sepsis syndrome, and to analyze the factors affecting the different classes. Methods Convenience sampling method was used to select sepsis patients discharged from a tertiary Grade A general hospital in Fujian Province from January 2021 to July 2021. Questionnaire survey was conducted three months after discharge through outpatient department, telephone and wechat. The latent classes of symptoms were analyzed，and the influencing factors were identified by univariate analysis and multinomial Logistic regression. Results Among 347 discharged patients with sepsis, 216 cases (62.25% ) developed post-sepsis syndrome, and the incidence of cognitive impairment 67.12% ，anxiety 61.57% , depression 51.78%，and sleep disorder 78.70% three months after discharge. The incidence of disability of daily living was 56.48%. The symptoms of post-sepsis syndrome patients were “high symptom group”（25.93%），“cognitive daily living impairment group” （32.87% ） and “sleep disorder group” （41.20% ）. The odds of patients having a previous history and using more analgesics were greater in the “high symptom group”, the odds of having a higher APACHEⅡ score in the “high symptom group” and “cognitive-daily living impairment group” were greater, and the odds of having a higher level of social support in the “high symptom group” and “cognitive-daily living impairment group” were lower. The higher the use of hormone medication, the lower the risk of being classified as “sleep disorder group”. Conclusion The symptoms of patients with post-sepsis syndrome can be divided into three latent classes：“high symptom group” “cognitive-daily living impairment group” and “sleep disorder group”. There is heterogeneity in the latent classes of symptoms of patients with post-sepsis syndrome concerning previous history,analgesic drug use, hormone drug use, APACHEⅡscore and social support. The latent classes of symptoms can be predicted.

Key words: Post-sepsis Syndrome, Latent Class Analysis, Symptom Experience Model, Critical Care Nursing