A NOMOGRAM FOR PREDICTING PATIENTS WITH SEVERE HEATSTROKE

Prediction of in-hospital mortality in patients with exertional heatstroke: a 13-year retrospective study

Hyperactivity of coagulation is common in exertional heatstroke (EHS). Disseminated intravascular coagulation (DIC) is the most severe form of coagulation dysfunction and associated with poor outcome. DIC, temperature and Glasgow coma scale score were identified as independent risk factors for in-hospital mortality by multivariate logistic regression analysis, and we developed a nomogram for predicting in-hospital mortality in a 13-year EHS patient cohort. The nomogram was assessed by calibration curves and bootstrap with 1,000 resamples. The receiver operating characteristic curve was constructed, and the area under the curve (AUC) was compared. Two hundred and ten patients were included. The in-hospital mortality was 9.0%, and the incidence of DIC was 17.6%. The AUC of the nomogram was 0.897 (95% CI 0.848-0.935, p < .0001) and was non-inferior to SOFA and APACHE II scores but superior to SIRS score, which were widely-used score systems of disease severity. The nomogram contributed to the adverse outcome prediction of EHS.

Lactate combined with SOFA score for improving the predictive efficacy of SOFA score in patients with severe heatstroke

BACKGROUND

The relationship between lactate levels and multiple organ dysfunction in patients with severe heatstroke remains unclear. In this study, we aimed to elucidate the clinical significance of lactate in severe heatstroke prognosis and assess whether incorporating lactate in the SOFA score improves its predictive efficacy.

METHODS

This study was a multicenter retrospective cohort investigation included 275 patients. Logistic regression analysis was performed to examine the relationship between lactate levels and patient outcomes and complications, including acute kidney injury (AKI), disseminated intravascular coagulation (DIC), and myocardial injury. Further, receiver operating characteristic (ROC) curves and clinical decision curve analysis (DCA) were used to evaluate the predictive power of lactate and SOFA scores in severe heatstroke-associated death. Lastly, the Kaplan-Meier survival curve was employed to differentiate the survival rates among the various patient groups.

RESULTS

After adjusting for confounding factors, lactate was demonstrated as an independent risk factor for death (OR = 1.353, 95% CI [1.170, 1.569]), AKI (OR = 1.158, 95% CI [1.007, 1.332]), DIC (OR = 1.426, 95% CI [1.225, 1.659]), and myocardial injury (OR = 2.039, 95% CI [1.553, 2.679]). The area under the curve (AUC) of lactate for predicting death from severe heatstroke was 0.7540, with a cutoff of 3.35. The Kaplan-Meier survival curve analysis showed that patients with elevated lactate levels had higher mortality rates. Additionally, the ROC curves demonstrated that combining lactate with the SOFA score provided better predictive efficacy than the SOFA score alone in patients with severe heatstroke (AUC: 0.9025 vs. 0.8773, DeLong test, P < 0.001). Finally, the DCA curve revealed a higher net clinical benefit rate for lactate combined with the SOFA score.

CONCLUSIONS

Lactate is an independent risk factor for severe heatstroke-related death as well as a risk factor for AKI, DIC, and myocardial injury associated with severe heatstroke. Thus, combining lactate with the SOFA score can significantly improve its predictive efficacy in patients with severe heatstroke.

Molecular Investigation and Preliminary Validation of Candidate Genes Associated with Neurological Damage in Heat Stroke

Heat stroke (HS) is a severe medical condition characterized by a systemic inflammatory response that may precipitate multi-organ dysfunction, with a particular predilection for inducing profound central nervous system impairments. We aim to employ bioinformatics techniques for the retrieval and analysis of genes associated with heat stroke-induced neurological damage. We performed a comprehensive analysis of the GSE64778 dataset from the Sequence Read Archive, resulting in the identification of 1178 significantly differentially expressed genes (DEGs). We retrieved 2914 genes associated with heat stroke from the GeneCards database and 2377 genes associated with heat stroke from the Comparative Toxicogenomics Database (CTD). The intersection of the top 300 DEGs in the GSE64778 dataset intersected with the search results of GeneCards and CTD, yielding 25 final candidates for DEGs associated with heat stroke. Gene Ontology functional annotation results indicated that the target genes were mainly involved in apoptosis, stress response, and negative regulation of cellular processes and function in processes such as protein dimerization and protein binding. The Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed a predominant enrichment of candidate target genes within the PI3K-AKT signaling pathway. Subsequent protein-protein interaction network analysis highlighted HSP90aa1 as a central gene, indicating its pivotal role by possessing the highest number of edges among the genes enriched in the PI3K-AKT signaling pathway. Quantitative reverse transcription-polymerase chain reaction analysis performed on blood samples from patients validated the expression of Hsp90aa1 in individuals exhibiting early neurological damage in HS, consistent with the findings from the mRNA bioinformatics analysis. Additionally, the bioinformatics analysis of the upstream microRNAs (miRNAs) regulating HSP90aa1 and the target miRNAs associated with candidate long non-coding RNAs (lncRNAs) identified three lncRNAs, eight miRNAs, and one mRNA in the regulatory network. The DIANA Tools database and algorithms were employed for pathway enrichment and correlation analysis, revealing a significant association between LOC102547734 and MIR-206-3p, with the latter being identified as a target binding site Moreover, the analysis unveiled a correlation between MIR-206-3p and HSP90aa1, implicating the latter as a potential target binding site within the regulatory network.

Clinical relevance of neutrophil/lymphocyte ratio combined with APACHEII for prognosis of severe heatstroke

We evaluated clinical implication of neutrophil-lymphocyte ratio (NLR) for severe heatstroke and predictive value of combined acute physiology and chronic health evaluation (APACHEII) score for prognosis of severe heatstroke. Retrospectively, we studied 185 individuals that have been admitted at emergency department for severe heatstroke. On the basis of their prognosis, we sorted the patients into two categories, namely non-survival (n = 43) and survival groups (n = 142). The primary outcome was 30-day mortality. A considerably higher NLR was observed among the non-survivors compared to survivors (P < 0.05). After correction for confounders, statistical analysis using multi-variable Cox regression indicated NLR as an independent risk factor for patient death (HR = 1.167, 95%CI = 1.110-1.226, P < 0.001). Through receiver-operating characteristics (ROC) curve, we estimated area-under the curve (AUC) of NLR to be 0.7720 (95% CI [0.6953, 0.8488]). Also, transformation of NLR into a profile type analysis showed that the marker remained a risk factor for death, which showed trend variation (P for trend <0.001). Subgroup forest plot analysis showed robustness in the predictive ability of NLR after exclusion of confounders. Besides, we demonstrated through Kaplan-Meier (KM) survival analysis curve that high risk NLR mortality substantially exceeded low risk NLR. The combined prediction of NLR and APACHEII achieved higher efficacy than NLR and APACHEII alone (AUC = 0.880, 95% CI [0.8280, 0.9290]). Additionally, Delong test indicated that the combined prediction demonstrated a significantly greater ROC than NLR and APACHEII alone, while DCA showed a considerably higher clinical net benefit rate. Increased NLR is a high risk factor and has predictive value for death in individuals with severe heatstroke. Suggestively, combination of NLR and APACHEII have greater predictive value.

Construction of a Nomogram Prediction Model for Prognosis in Patients with Large Artery Occlusion-Acute Ischemic Stroke

BACKGROUND

Patients with large artery occlusion-acute ischemic stroke (LAO-AIS) can experience adverse outcomes, such as brain herniation due to complications. This study aimed to construct a nomogram prediction model for prognosis in patients with LAO-AIS in order to maximize the benefits for clinical patients.

METHODS

Retrospective analysis of 243 patients with LAO-AIS from January 2019 to January 2022 with medical history data and blood examination at admission. Univariate and multivariate analyses were conducted through binary logistic regression equation analysis, and a nomogram prediction model was constructed.

RESULTS

Results of this study showed that hyperlipidemia (odds ratio [OR] = 2.849, 95% confidence interval [CI] = 1.100-7.375, P = 0.031), right cerebral infarction (OR = 2.144, 95% CI = 1.106-4.156, P = 0.024), D-Dimer>500 ng/mL (OR = 2.891, 95% CI = 1.398-5.980, P = 0.004), and neutrophil-lymphocyte ratio >7.8 (OR = 2.149, 95% CI = 1.093-4.225, P = 0.027) were independent risk factors for poor early prognosis in patients with LAO-AIS. In addition, hypertension (OR = 1.947, 95% CI = 1.114-3.405, P = 0.019), hyperlipidemia (OR = 2.594, 95% CI = 1.281-5.252, P = 0.008), smoking (OR = 2.414, 95% CI = 1.368-4.261, P = 0.002), D-dimer>500 ng/mL (OR = 3.170, 95% CI = 1.533-6.553, P = 0.002), and neutrophil-lymphocyte ratio >7.8 (OR = 2.144, 95% CI = 1.231-3.735, P = 0.007) were independent risk factors for poor long-term prognosis. The early prognosis nomogram receiver operating characteristic curve area under the curve value was 0.688 for the training set and 0.805 for the validation set, which was highly differentiated. The mean error was 0.025 for the training set calibration curve and 0.016 for the validation set calibration curve. Both the training and validation set decision curve analyses indicated that the clinical benefit of the nomogram was significant. The long-term prognosis nomogram receiver operating characteristic curve area under the curve values was 0.697 for the training set and 0.735 for the validation set, showing high differentiation. The mean error was 0.041 for the training set calibration curve and 0.021 for the validation set calibration curve. Both of the training and validation set decision curve analyses demonstrated a substantial clinical benefit of the nomogram.

CONCLUSIONS

The nomogram prediction model based on admission history data and blood examination are easy-to-use tools that provide an accurate individualized prediction for patients with LAO-AIS and can assist in early clinical decisions and in obtaining an early prognosis.

Nomogram for predicting disseminated intravascular coagulation in heatstroke patients: A 10 years retrospective study

Background

Disseminated intravascular coagulation (DIC) can lead to multiple organ failure and death in patients with heatstroke. This study aimed to identify independent risk factors of DIC and construct a predictive model for clinical application.

Methods

This retrospective study included 87 patients with heatstroke who were treated in the intensive care unit of our hospital from May 2012 to October 2022. Patients were divided into those with DIC (n = 23) or without DIC (n = 64). Clinical and hematological factors associated with DIC were identified using a random forest model, least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE). Overlapping factors were used to develop a nomogram model, which was diagnostically validated. Survival at 30 days after admission was compared between patients with or without DIC using Kaplan-Meier analysis.

Results

Random forest, LASSO, and SVM-RFE identified a low maximum amplitude, decreased albumin level, high creatinine level, increased total bilirubin, and aspartate transaminase (AST) level as risk factors for DIC. Principal component analysis confirmed that these independent variables differentiated between patients who experienced DIC or not, so they were used to construct a nomogram. The nomogram showed good predictive power, with an area under the receiver operating characteristic curve of 0.976 (95% CI 0.948–1.000) and 0.971 (95% CI, 0.914–0.989) in the internal validation. Decision curve analysis indicated clinical utility for the nomogram. DIC was associated with significantly lower 30 days survival for heatstroke patients.

Conclusion

A nomogram incorporating coagulation-related risk factors can predict DIC in patients with heatstroke and may be useful in clinical decision-making.