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A prehospital risk assessment tool predicts clinical outcomes in hospitalized patients with heat-related illness: a Japanese nationwide prospective observational study
We previously developed a risk assessment tool to predict outcomes after heat-related illness (J-ERATO score), which consists of six binary prehospital vital signs. We aimed to evaluate the ability of the score to predict clinical outcomes for hospitalized patients with heat-related illnesses. In a...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9867691/ https://www.ncbi.nlm.nih.gov/pubmed/36681704 http://dx.doi.org/10.1038/s41598-023-28498-z |
Sumario: | We previously developed a risk assessment tool to predict outcomes after heat-related illness (J-ERATO score), which consists of six binary prehospital vital signs. We aimed to evaluate the ability of the score to predict clinical outcomes for hospitalized patients with heat-related illnesses. In a nationwide, prospective, observational study, adult patients hospitalized for heat-related illnesses were registered. A binary logistic regression model and receiver operating characteristic (ROC) curve analysis were used to assess the relationship between the J-ERATO and survival at hospital discharge as a primary outcome. Among eligible patients, 1244 (93.0%) survived to hospital discharge. Multivariable logistic regression analysis revealed that the J-ERATO was an independent predictor for survival to discharge (adjusted odds ratio [OR] 0.47; 95% confidence interval [CI] 0.37–0.59) and occurrence of disseminated intravascular coagulation (DIC) on day 1 (adjusted OR 2.07; 95% CI 1.73–2.49). ROC analyses revealed an optimal J-ERATO cut-off of 5 for prediction of mortality at discharge (area under the curve [AUC] 0.742; 95% CI 0.691–0.787) and DIC development on day 1 (AUC 0.723; 95% CI 0.684–0.758). The J-ERATO obtained before transportation could be helpful in predicting the severity and mortality of hospitalized patients with heat-related illnesses. |
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