A nomogram based on InLDH and InNLR for predicting disseminated intravascular coagulation in patients with heat stroke - Lulu Wan; Gan Lin; Jiale Yang; Anwei Liu; Xuezhi Shi; Jinhu Li; Lian Xie; Ronglin Chen; Huasheng Tong (Therapeutic Advances in Hematology)
Background: Heat stroke (HS), a potentially fatal heat-related illness, is often accompanied by disseminated intravascular coagulation (DIC) early, resulting in a poorer prognosis. Unfortunately, diagnosis by current DIC scores is often too late to identify DIC. This study aims to investigate the predictors and predictive model of DIC in HS to identify DIC early. Methods: This retrospective study analyzed clinical data of patients with HS in a tertiary hospital from January 1, 2008 to December 31, 2020. Univariate and multivariate logistic regression analyses were employed to identify the risk factors for DIC in HS. The predictive models based on these risk factors were constructed and externally validated, and their predictive efficacy was evaluated using receiver operating characteristic curves. Results: A total of 219 HS patients, including 49 with DIC, were included. The independent risk factors for DIC were identified as follows: neutrophil percentage (Neu%), lymphocyte count, lymphocyte percentage (Lym%), creatine kinase-MB (CKMB), lactate dehydrogenase (LDH), aspartate aminotransferase (AST), neutrophil–lymphocyte ratio (NLR), monocyte–lymphocyte ratio (MLR), and rhabdomyolysis (RM). After logarithmization, the final predictive model based on the logarithm of lactate dehydrogenase (InLDH; odds ratio (OR) = 9.266, 95% confidence interval (95%CI; 4.379–19.607), p < 0.0001) and the logarithm of neutrophil–lymphocyte ratio (InNLR; OR = 3.393, 95%CI (1.834–6.277), p < 0.0001) was constructed with the largest area under the curve (0.928). A nomogram incorporating InLDH and InNLR was developed and showed excellent discrimination and calibration capabilities. Conclusion: Nine independent risk factors were identified for the occurrence of DIC in HS patients. The predictive model based on InLDH and InNLR can effectively predict the incidence of DIC. A nomogram based on InLDH and InNLR was developed to facilitate early identification and timely treatment of DIC in HS patients.