Objective:To develop and validate a predictive model of 28-day mortality in sepsis based on lactate dehydrogenase-to-albumin ratio (LAR).Methods:Sepsis patients diagnosed in the department of intensive care medicine of the First Affiliated Hospital of Soochow University from August 1, 2017 to September 1, 2022 were retrospective selected. Clinical data, laboratory indicators, disease severity scores [acute physiology and chronic health evaluation Ⅱ(APACHEⅡ), sequential organ failure assessment (SOFA)] were collected. Patients were divided into death group and survival group according to whether they died at 28 days, and the difference between the two groups was compared. The dataset was randomly divided into training set and validation set according to 7∶3. Lasso regression method was used to screen the risk factors affecting the 28-day death of sepsis patients, and incorporating multivariate Logistic regression analysis (stepwise regression) were included, a prediction model was constructed based on the independent risk factors obtained, and a nomogram was drawn. The nomogram prediction model was established. Receiver operator characteristic curve (ROC curve) was drawn to analyze and evaluate the predictive efficacy of the model. Hosmer-Lemeshow test, calibration curve and decision curve analysis (DCA) were used to evaluate the accuracy and clinical practicability of the model, respectively.Results:A total of 394 patients with sepsis were included, with 248 survivors and 146 non-survivors at 28 days. Compared with the survival group, the age, proportion of chronic obstructive pneumonia, respiratory rate, lactic acid, red blood cell distribution width, prothrombin time, activated partial thromboplastin time, alanine aminotransferase, aspartate aminotransferase, blood urea nitrogen, creatinine, blood potassium, blood phosphorus, LAR, SOFA score, and APACHEⅡ score in the death group were significantly increased, while oxygenation index, monocyte count, platelet count, fibrinogen, total cholesterol, triglycerides, high-density lipoprotein, low-density lipoprotein, and blood calcium were significantly reduced. In the training set, LAR, age, oxygenation index, blood urea nitrogen, lactic acid, total cholesterol, fibrinogen, blood potassium and blood phosphorus were screened by Lasso regression. Multivariate Logistic regression analysis finally included LAR [odds ratio ( OR) = 1.029, 95% confidence interval (95% CI) was 1.014-1.047, P < 0.001], age ( OR = 1.023, 95% CI was 1.005-1.043, P = 0.012), lactic acid ( OR = 1.089, 95% CI was 1.003-1.186, P = 0.043), oxygenation index ( OR = 0.996, 95% CI was 0.993-0.998, P = 0.002), total cholesterol ( OR = 0.662, 95% CI was 0.496-0.865, P = 0.003) and blood potassium ( OR = 1.852, 95% CI was 1.169-2.996, P = 0.010). A total of 6 predictor variables were used to establish a prediction model. ROC curve showed that the area under the curve (AUC) of the model in the training set and validation set were 0.773 (95% CI was 0.715-0.831) and 0.793 (95% CI was 0.703-0.884), which was better than APACHEⅡ score (AUC were 0.699 and 0.745) and SOFA score (AUC were 0.644 and 0.650), and the cut-off values were 0.421 and 0.309, the sensitivity were 62.4% and 82.2%, and the specificity were 82.2% and 68.9%, respectively. The results of Hosmer-Lemeshow test and calibration curve showed that the predicted results of the model were in good agreement with the actual clinical observation results, and the DCA showed that the model had good clinical application value. Conclusion:The prediction model based on LAR has a good predictive value for 28-day mortality in patients with sepsis and can guide clinical decision-making.

The COVID-19 epidemic has spread to the whole world for three years and has had a serious impact on human life, health and economic activities. China's epidemic prevention and control has gone through the following stages: emergency unconventional stage, emergency normalization stage, and the transitional stage from the emergency normalization to the "Category B infectious disease treated as Category B" normalization, and achieved a major and decisive victory. The designated hospitals for prevention and control of COVID-19 epidemic in Tianjin has successfully completed its tasks in all stages of epidemic prevention and control, and has accumulated valuable experience. This article summarizes the experience of constructing a hospital infection prevention and control system during the "Category B infectious disease treated as Category A" period in designated hospital. The experience is summarized as the "Cluster" hospital infection prevention and control system, namely "three rings" outside, middle and inside, "three districts" of green, orange and red, "three things" before, during and after the event, "two-day pre-purification" and "two-director system", and "one zone" management. In emergency situations, we adopt a simplified version of the cluster hospital infection prevention and control system. In emergency situations, a simplified version of the "Cluster" hospital infection prevention and control system can be adopted. This system has the following characteristics: firstly, the system emphasizes the characteristics of "cluster" and the overall management of key measures to avoid any shortcomings. The second, it emphasizes the transformation of infection control concepts to maximize the safety of medical services through infection control. The third, it emphasizes the optimization of the process. The prevention and control measures should be comprehensive and focused, while also preventing excessive use. The measures emphasize the use of the least resources to achieve the best infection control effect. The fourth, it emphasizes the quality control work of infection control, pays attention to the importance of the process, and advocates the concept of "system slimming, process fattening". Fifthly, it emphasizes that the future development depends on artificial intelligence, in order to improve the quality and efficiency of prevention and control to the greatest extent. Sixth, hospitals need to strengthen continuous training and retraining. We utilize diverse training methods, including artificial intelligence, to ensure that infection control policies and procedures are simple. We have established an evaluation and feedback mechanism to ensure that medical personnel are in an emergency state at all times.