Some patients with heatstroke also experience intracerebral hemorrhage (ICH). However, clinical case reports of heatstroke-induced ICH are rare. The risk factors for cerebral hemorrhage after heatstroke remain unknown. The present study evaluated the clinical characteristics and risk factors of patients with heatstroke-related ICH. In this retrospective observational study, we collected data on all ICHs after heatstroke occurred between 2012 and 2022. The characteristics of patients with heatstroke-induced ICH were described. The risk factors for cerebral hemorrhage after heatstroke were examined using logistic regression analysis. In total, 177 patients were included in this study, and 11 patients with ICH secondary to heatstroke were identified. Variables with P values of <.05 in univariate models, comparing the cerebral hemorrhage and control groups, included heatstroke cause, temperature, heart rate, respiratory rate, vasopressor use, mechanical ventilation use, Acute Physiology and Chronic Health Evaluation II, total bilirubin, creatinine, platelet count, prothrombin time, procalcitonin, creatine kinase, disseminated intravascular coagulation (DIC) occurrence, and DIC score. Multivariate logistic regression showed that heatstroke patients with higher DIC scores (odds ratio, 18.402, 95% confidence interval, 1.384–244.763, P = .027) and higher creatine kinase levels (odds ratio, 1.021, 95% confidence interval, 1.002–1.041, P = .033) were at a higher risk of developing ICH. The death rate was higher in the cerebral hemorrhage group than in the control group (P = .042). Heatstroke-related cerebral hemorrhage may be associated with elevated creatinine levels and DIC severity (International Society on Thrombosis and Hemostasis score) after heatstroke, and heatstroke with cerebral hemorrhage may accelerate death.

Introduction: Early prediction of multiple organ dysfunction syndrome (MODS) secondary to severe heat stroke (SHS) is crucial for improving patient outcomes. This study aims to develop and validate a risk prediction model for those patients based on immediate assessment indicators on ICU admission.Methods: Two hundred eighty-four cases with SHS in our hospital between July 2009 and April 2024 were retrospectively reviewed, and categorized into non-MODS and MODS groups. Logistic regression analyses were performed to identify risk factors for MODS, and then to construct a risk prediction model, which was visualized by a nomogram. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow (HL) test, calibration curve, and decision curve analysis (DCA). Finally, the AUCs of the prediction model was compared with other scoring systems.Results: Acute gastrointestinal injury (AGI), heart rate (HR) >100 bpm, a decreased Glasgow Coma Scale (GCS) score, and elevated total bilirubin (TBil) within the first 24 h of ICU admission are identified as independent risk factors for the development of MODS in SHS patients. The model demonstrated good discriminative ability, and the AUC was 0.910 (95% CI: 0.856–0.965). Applying the predictive model to the internal validation dataset demonstrated good discrimination with an AUC of 0.933 (95% CI: 0.880–0.985) and good fit and calibration. The DCA of this model showed a superior clinical net benefit.Discussion: The risk prediction model based on AGI, HR, GCS, and TBil shows robust predictive performance and clinical utility, which could serve as a reference for assessing and screening the risk of MODS in SHS patients.

Objective: Heat stroke can progress rapidly, and the risk of in-hospital mortality increases once multiple organ dysfunction develops. Early risk stratification is therefore clinically important, yet comparative evidence across commonly used severity scores in heat stroke remains limited. Methods: We conducted a multicenter retrospective cohort study of patients admitted with a first diagnosis of heat stroke to two tertiary hospitals in China between 2013 and 2023. The recalibrated SOFA 2.0 score (SOFA2), original SOFA, Modified Early Warning Score, National Early Warning Score, and Heat Stroke Severity Score were calculated using the first available data within 24 h of admission. In-hospital death was the primary outcome, with discharge alive treated as a competing event. Cumulative incidence functions and Fine–Gray models were used to assess risk gradients, and unsupervised clustering based on early clinical and laboratory features was applied to identify clinical subtypes. Results: Among 292 patients (mean age 29.8 ± 14.9 years), 24 (8.2%) died during hospitalization. The cumulative incidence of in-hospital death increased stepwise across SOFA2 quartiles (Gray test, P < 0.001), whereas separation across original SOFA quartiles was less distinct. Higher SOFA2 scores were associated with an increased risk of mortality risk, with spline analyses indicating a generally monotonic risk increase. Two major clinical subtypes were identified; in the higher-risk subtype identified by data-driven clustering, SOFA2 showed numerically consistent discrimination and stable net benefit trends; however, these subtype-specific findings should be interpreted cautiously. Conclusions: SOFA2 may provide an early, continuous representation of in-hospital mortality risk in patients with heat stroke, although external validation is required.

Heatstroke (HS) is a life-threatening injury requiring neurocritical care which could lead to central nervous system dysfunction and severe multiple organ failure syndrome. The cell–cell adhesion and cell permeability are two key factors for characterizing HS. To investigate the process of HS, a biochip-based electrical model was proposed and applied to HS. During the process, the value of TEER is associated with cell permeability and C_I which represents cell–cell adhesion decreases that are consistent with the reduction in cell–cell adhesion and cell permeability characterized by proteins (occludin, VE-Cadherin and ZO-1) and RNA level. The results imply that the model can be used to monitor the biological process and other biomedical applications.