OBJECTIVE: To investigate the incidence of sepsis in Xinjiang Uygur Autonomous Region and the compliance with sepsis diagnosis and treatment guidelines in intensive care unit (ICU) at different levels of hospitals, and to identify the risk factors associated with poor prognosis in patients with sepsis in this region. METHODS: A prospective cross-sectional survey was conducted in ICU of Xinjiang Uygur Autonomous Region Critical Care Medicine Alliance. The survey period was from 10:00 on January 31, 2024, to 09:59 on February 1, 2024. The patients diagnosed with sepsis admitted to the ICU during the study period were included in the analysis. Data on patient demographics, physiology, microbiology, and treatment protocols were collected, with follow-up until the 28th day after ICU admission or death. Baseline characteristics and treatment information of septic patients across different hospital levels were compared, as well as clinical data of septic patients with different 28-day outcomes. Multivariate Cox proportional hazards model was used to identify risk factors for 28-day death in septic patients. RESULTS: A total of 77 units of Xinjiang Uygur Autonomous Region Critical Care Medicine Alliance from 14 prefectures/cities in Xinjiang participated in the survey. On the survey day, 727 patients were admitted to ICU, of whom 179 (24.6%) were diagnosed with sepsis, and 64 (35.8%) died within 28 days, 115 (64.2%) survived. Among the participating institutions, 33 were tertiary hospitals (42.9%), managing 97 septic cases (54.2%), and 44 were secondary hospitals (57.1%), managing 82 septic cases (45.8%). The lactic acid monitoring rate and continuous renal replacement therapy (CRRT) rate for septic patients in tertiary hospitals were significantly higher than those in secondary hospitals [lactic acid monitoring rate: 92.8% (90/97) vs. 82.9% (68/82), CRRT rate: 17.5% (17/97) vs. 3.7% (3/82), both P < 0.05]. No statistically significant differences were observed between tertiary and secondary hospitals in length of ICU stay or 28-day mortality [length of ICU stay (days): 11.0 (16.0) vs. 10.0 (22.0), 28-day mortality: 35.1% (34/97) vs. 36.6% (30/82), both P > 0.05]. Compared with survivors, non-survivors had higher acute physiology and chronic health evaluation II (APACHE II) score, sequential organ failure assessment (SOFA) score, Charlson comorbidity index (CCI) score and lower Glasgow coma scale (GCS) score. Significant differences were noted in vital signs [heart rate, blood pressure, body temperature, pulse oxygen saturation (SpO₂)], laboratory markers [red blood cell count (RBC), white blood cell count (WBC), lymphocyte ratio (LYM%), blood urea nitrogen (BUN), total protein (TP), albumin (Alb), pH value, base excess (BE)], and monitoring, diagnosis and treatment information (invasive blood pressure monitoring, mechanical ventilation, CRRT, usage of norepinephrine). Multivariate Cox proportional hazards model indicated that body temperature [hazard ratio (HR) = 1.416, 95% confidence interval (95%CI) was 1.022-1.961, P = 0.037] and WBC (HR = 1.040, 95%CI was 1.010-1.071, P = 0.009) were independent risk factors for 28-day death in patients with sepsis. CONCLUSIONS Sepsis in Xinjiang Uygur Autonomous Region is characterized by a high mortality. In this region, tertiary hospitals demonstrate better compliance with bundled treatment strategies such as lactic acid monitoring and the usage of CRRT compared to secondary hospitals, yet they do not show significant advantages in clinical outcomes. Body temperature and WBC are independent risk factors for 28-day death in patients with sepsis in this region. However, clinicians should still consider the actual situation of patients, along with more optimal early warning indicators and comprehensive system assessments, to identify and prevent risk factors for adverse outcomes in patients.
Humans ; Sepsis/diagnosis* ; Cross-Sectional Studies ; Prospective Studies ; Risk Factors ; Intensive Care Units ; Prognosis ; China/epidemiology* ; Male ; Female ; Middle Aged ; Aged ; Proportional Hazards Models ; Incidence