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Introduction This study aimed to explore the association of serum lactate with clinical outcomes in elderly patients with sepsis based on data from the MIMIC-IV database. Methods All elderly patients with sepsis (age ≥ 65 years) were included. Different models were constructed for exploring the relationships between lactate and 28-day mortality. A two-segment linear regression model was performed to verify the threshold effects of lactate on clinical outcomes and smooth curve fitting was performed. Results A total of 4199 elderly patients with sepsis were included. The 28-day mortality was 32.22% ( n = 1395). After adjustment for all potential cofounders, for each 1 mmol/l increment in lactate, the odds ratio (OR) of 28-day mortality was 1.23 (95% CI 1.18–1.28, P < 0.0001). Smooth fitting curves indicated a non-linear positive relationship between lactate and 28-day mortality. The turning point of lactate level was 5.7 mmol/l: at ≤ 5.7 mmol/l, with each 1 mmol/l increment in lactate, the risk of 28-day mortality increased significantly (OR 1.32, 95% CI 1.25–1.38, P < 0.0001); the significantly positive relationship was still present at lactate > 5.7 mmol/l (OR 1.10, 95% CI 1.04–1.18, P = 0.0019). The area under the ROC curve (AUC) of lactate was 0.618 (95% CI 0.599–0.635) and the cutoff value of lactate was 2.4 mmol/l with a sensitivity of 0.483 and a specificity of 0.687. Conclusion In elderly patients with sepsis, a non-linear positive relationship was discovered between serum lactate and 28-day mortality. Physicians should be alert to lactate assessment at admission and pay more attention to those patients with higher levels of lactate. Supplementary Information The online version contains supplementary material available at 10.1007/s40121-022-00736-3.

Objective Early identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis. Methods This study was a retrospective design. Sepsis patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were enrolled. A predictive model for predicting 30-day morality in sepsis was performed based on the ANN approach. Results A total of 2874 patients with sepsis were included and 30-day mortality was 29.8%. The study population was categorized into the training set (n = 1698) and validation set (n = 1176) based on the ratio of 6:4. 11 variables which showed significant differences between survivor group and nonsurvivor group in training set were selected for constructing the ANN model. In training set, the predictive performance based on the area under the receiver-operating characteristic curve (AUC) were 0.873 for ANN model, 0.720 for logistic regression, 0.629 for APACHEII score and 0.619 for SOFA score. In validation set, the AUCs of ANN, logistic regression, APAHCEII score, and SOFA score were 0.811, 0.752, 0.607, and 0.628, respectively. Conclusion An ANN model for predicting 30-day mortality in sepsis was performed. Our predictive model can be beneficial for early detection of patients with higher risk of poor prognosis.