[Correlation between serum lactate levels and disseminated intravascular coagulation in patients with heat stroke]

To investigate the relationship between serum lactate level and disseminated intravascular coagulation (DIC) in patients with heat stroke, and to construct and validate a lactate-based predictive model for the risk of DIC occurrence.

A multicenter retrospective cohort study was conducted. Clinical data of patients with heat stroke admitted to Changzhou NO.2 People's Hospital and Nantong First People's Hospital from July 2015 to October 2025 were collected, including general demographic information, underlying diseases, disease type, neurological symptoms, admission vital signs, treatment measures, length of hospital stay, and routine blood tests, liver and kidney function, and coagulation parameters within 24 hours of admission. Patients were divided into DIC and non-DIC groups according to whether DIC occurred during emergency stay and/or hospitalization. Least absolute shrinkage and selection operator (LASSO) regression was used to screen important variables associated with DIC occurrence. Logistic regression and restricted cubic spline (RCS) analysis were used to examine the relationship between lactate level and DIC. Receiver operator characteristic curve (ROC curve) was plotted to assess the predictive performance of lactate level for DIC. Mediation analysis was performed to explore the mediating role of lactate level between body temperature and DIC. Subgroup analyses were conducted to evaluate the strength of the association between lactate and DIC across different subgroups, with results presented in a forest plot. A nomogram prediction model for DIC risk based on lactate was constructed; its predictive performance was evaluated by ROC curve, clinical decision analysis (DCA) was used to evaluate the clinical net benefit rate, and calibration curve was used to assess model accuracy. Leave-group cross-validation (LGOCV) was used for internal validation, with the area under the curve (AUC) used as the evaluation metric.

A total of 305 patients with heat stroke were finally enrolled, including 69 in the DIC group and 236 in the non-DIC group. LASSO regression showed that after model simplification, lactate, body temperature, white blood cell count (WBC), platelet count (PLT), and D-dimer were important variables associated with DIC occurrence in heat stroke patients. Logistic regression analysis demonstrated that lactate level was an independently associated factor for DIC, and the association remained significant after adjustment for confounders [odds ratio (OR)=1.815, 95% confidence interval (95%CI) was 1.494-2.203, P<0.001]. RCS analysis revealed that elevated lactate levels were significantly and positively associated with the risk of DIC in patients with heatstroke (overall P<0.001), exhibiting a linear dose-response relationship (nonlinearity P=0.064). ROC curve showed that lactate level had good predictive performance for DIC [AUC=0.807 (95%CI was 0.752-0.862)], with an optimal cutoff value of 3.65 mmol/L, sensitivity of 76.8%, and specificity of 73.7%. Mediation analysis indicated lactate mediated the relationship between body temperature and DIC, with a mediation proportion of 16.29% (P<0.001). Subgroup analysis forest plot revealed significant independent positive associations between lactate level and DIC across various subgroups stratified by disease type, sex, age, body temperature, tracheal intubation, and vasoactive drug use (all P<0.05), with no significant interactions among subgroups (all interaction P>0.05). The lactate-based prediction model showed excellent predictive performance [AUC=0.901 (95%CI was 0.860-0.941)], with sensitivity of 85.6% and specificity of 81.6%. DCA showed that the model provided a higher net clinical benefit than lactate level alone. Calibration curve demonstrated that the predicted probabilities closely matched actual observed probabilities. LGOCV validation confirmed that the model maintained excellent predictive performance [AUC=0.887 (95%CI was 0.868-0.907)], with sensitivity of 92.9% and specificity of 76.5%.

Elevated serum lactate level is significantly associated with increased risk of DIC in patients with heat stroke. The lactate-based prediction model can effectively predict the risk of DIC occurrence in these patients.