[Establishment and validation of a sepsis 28-day mortality prediction model based on the lactate dehydrogenase-to-albumin ratio in patients with sepsis].

To develop and validate a predictive model of 28-day mortality in sepsis based on lactate dehydrogenase-to-albumin ratio (LAR). 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 (APACHE II), 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. 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 APACHE II 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 APACHE II 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. 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.