Objective:To assess the predictive value of acute liver failure (ALF) for sepsis-free survival (SFS) in burn patients and to identify associated risk factors.Methods:A retrospective cohort study was conducted on burn patients meeting inclusion criteria from the 2014 Kunshan aluminum dust explosion disaster (August 2, 2014 - April 13, 2015). Eligible patients were stratified into ALF and non-ALF groups based on the development of ALF. Demographic characteristics, total burn surface area, organ dysfunction, time to sepsis onset, and clinical outcomes were collected and compared between groups. Kaplan-Meier survival analysis and multivariate Cox regression were performed to assess the impact of ALF on SFS. A nomogram model was constructed for individualized risk prediction.Results:Among 185 enrolled patients (ALF group:21, non-ALF group:164), ALF incidence was 11.35%. The ALF group demonstrated higher mortality (85.71% vs. 34.15%, P<0.001) and SFS failure rates (100.00% vs. 61.59%, P<0.001) compared to non-ALF patients. Multivariate Cox analysis identified ALF as an independent sepsis predictor ( HR=1.68, 95% CI: 1.00-2.80, P<0.05). Time-dependent ROC analysis showed AUCs of 0.626, 0.714, 0.703, and 0.706 for SFS prediction at 2, 4, 8, and 12 weeks respectively. The nomogram model demonstrated that ALF combined with other parameters effectively predicted sepsis risk within 2-12 weeks post-injury. ALF development showed significant associations with concurrent organ dysfunction including acute kidney injury, acute heart failure, and acute respiratory distress syndrome (all P<0.001). A higher proportion of ALF patients received hemodialysis ( P<0.001) and pre-hospital central venous catheterization ( P=0.017). Conclusions:ALF independently predicts SFS failure and correlates strongly with poor prognosis in burn patients. Early ALF recognition and targeted interventions may facilitate sepsis risk stratification and precision prevention strategies.

Objective To establish a predictive model for spinal canal labor analgesia-related in-trapartum fever and validate its predictive efficacy.Methods A total of 2 276 parturients who received labor analgesia from January to December 2021 were selected as the training set,aged≥18 years,BMI 18.5-40.0 kg/m2,ASA physical status Ⅰ or Ⅱ.The patients were divided into fever group and non-fever group according to the occurrence of intrapartum fever(body temperature≥38.0 ℃).The independent risk factors of intrapartum fever were screened by multivariate logistic regression,and the predictive model was established.A total of 568 parturients who received labor analgesia in the same hospital from January to March 2022 were selected as the verification.The inclusion criteria were the same as the training set,and the model was externally verified by R language.Results There were 197 parturients(8.7％)in the train-ing set and 46 parturients(8.1％)in the validation set experienced intrapartum fever.The multivariate lo-gistic regression analysis showed that primiparity,a high neutrophil count,anemia,and a heavier estimated fetal weight were risk factors of intrapartum fever,while a large body surface area and large cervical dilata-tion degree before labor analgesia were protective factors against intrapartum fever.According to the predic-tors,the predictive model for spinal canal labor analgesia-related maternal fever was established.The area under the receiver operating characteristic(ROC)curve(AUC)was 0.698(95％CI 0.660-0.732),the sensitivity and specificity was 83.2％and 47.9％,respectively.Using R language for the external validation,the AUC of the predictive model was 0.703(95％CI 0.634-0.772),the sensibility and specificity was 65.2％and 71.3％,respectively.The results showed that the prediction model for spinal canal labor analgesia-related intrapartum fever is effective.Conclusion Primiparity,a high neutrophil count,anemia,and a heavier estimated fetal weight were risk factors of intrapartum fever,while a large body surface area and large cervical dilatation degree before labor analgesia were protective factors.The predictive model established based on these indicators can effectively predict the occurrence of intrapartum fever before labor analgesia.

Objective:To develop a predictive model for intrapartum fever related to chorioamnionitis in parturients undergoing epidural labor analgesia.Methods:This was a retrospective study. The parturients with intrapartum fever (axillary temperature ≥38 ℃) who received epidural labor analgesia from January 2020 to December 2022 in Sichuan Maternal and Child Health Hospital were selected as model group, and parturients with intrapartum fever who received epidural labor analgesia from January to October 2023 in Sichuan Maternal and Child Health Hospital were selected as validation group. The parturients in model group were divided into histological chorioamnionitis stage ≥Ⅱ group (HCA≥Ⅱ group) and histological chorioamnionitis stage <Ⅱ group (HCA<Ⅱ group) according to the results of placental histopathological examination. Logistic regression analysis was used to screen the independent risk factors for intrapartum fever related to chorioamnionitis in parturients, and then a nomogram model was established. The discrimination of the model was verified by the area under the the receiver operating characteristic curve. The consistency of the model was verified by the calibration curve, and the clinical effectiveness of the model was determined by the decision curve. The validation dataset was used to further evaluate the model.Results:A total of 308 parturients were finally included in model group and 99 parturients in validation group. Multivariate logistic regression analysis showed that the gestational age, meconium-stained amniotic fluid, c-reactive protein concentration and maximum body temperature were independent risk factors for intrapartum fever related to chorioamnionitis in parturients undergoing epidural labor analgesia ( P<0.05). Based on this, a nomogram risk prediction model was developed. The area under the curve (95% confidence interval) was 0.844 (0.744-0.944) in model group and 0.812 (0.674-0.950) in validation group. The calibration curve showed that the prediction probability of the model had good consistency with the actual probability of diagnosis. The decision curve showed that the threshold probability of the prediction model in model group and validation group was 10%-98% and 10%-78%, respectively. Conclusions:A nomogram prediction model for intrapartum fever related to chorioamnionitis is successfully established based on the gestational age, c-reactive protein concentration, meconium-stained amniotic fluid and maximum body temperature in parturients undergoing epidural labor analgesia. The model has good predictive performance and clinical value.