Heatstroke is a life-threatening event that affects people worldwide. Currently, there are no established tools to predict the outcomes of heatstroke. Although the Sequential Organ Failure Assessment (SOFA) score is a promising tool for judging the severity of critically ill patients. Therefore, in this study, we investigated whether the SOFA score could predict the outcome of patients hospitalized with severe heatstroke, including the classical and exertional types, by using data from a Japanese nationwide multicenter observational registry. We performed retrospective subanalyses of the Japanese Association for Acute Medicine heatstroke registry, 2019. Adults with a SOFA score ≥ 1 hospitalized for heatstroke were analyzed. We analyzed data for 225 patients. Univariate and multivariable analyses showed a significant difference in the SOFA score between non-survivors and survivors in classical and exertional heatstroke cases. The area under the receiver operating characteristic curve were 0.863 (classical) and 0.979 (exertional). The sensitivity and specificity of SOFA scores were 50.0% and 97.5% (classical), 66.7% and 97.5% (exertional), respectively, at a cutoff of 12.5, and 35.0% and 98.8% (classical), 33.3% and 100.0% (exertional), respectively, at a cutoff of 13.5. This study revealed that the SOFA score may predict mortality in patients with heatstroke and might be useful for assessing prognosis.
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http://dx.doi.org/10.1038/s41598-022-20878-1 | DOI Listing |
Clin Nutr ESPEN
January 2025
Department of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, 400042, China; Chongqing Municipal Health Commission Key Laboratory of Intelligent Clinical Nutrition and Transformation, Chongqing, 400042, China. Electronic address:
Background: Acute pancreatitis (AP) is a common acute abdominal condition that can lead to severe complications. Malnutrition significantly impacts the prognosis of patients with AP, so effective tools are needed to identify those at high nutritional risk. This study validated the ability of the modified NUTRIC score to predict all-cause mortality and identify nutritional risk in patients with acute pancreatitis in the ICU.
View Article and Find Full Text PDFEur J Med Res
January 2025
Department of Thoracic Medicine, Chang Gung Memorial Hospital, Linkou Branch, No. 5, Fu-Shing St., GuiShan, Taoyuan, Taiwan.
Background: This study compared the ventilatory variables and computed tomography (CT) features of patients with coronavirus disease 2019 (COVID-19) versus those of patients with pulmonary non-COVID-19-related acute respiratory distress syndrome (ARDS) during the early phase of ARDS.
Methods: This prospective, observational cohort study of ARDS patients in Taiwan was performed between February 2017 and June 2018 as well as between October 2020 and January 2024. Analysis was performed on clinical characteristics, including consecutive ventilatory variables during the first week after ARDS diagnosis.
Res Social Adm Pharm
January 2025
Laboratory of Teaching and Research in Social Pharmacy (LEPFS), Department of Pharmacy, Federal University of Sergipe, Cidade Universitária "Prof. José Aloísio Campos", Jardim Rosa Elze, São Cristóvão, SE, CEP: 49100-000, Brazil. Electronic address:
Background: The identification and reduction of drug-related problems (DRPs) through DRP-oriented medical records during the hospitalization of critically impatients can optimize health indicators, such as length of hospital stay.
Objective: To determine the effect of medical records focused on drug-related problems on the duration of stay for patients in intensive care units.
Method: A randomized controlled clinical trial was conducted with patients assigned to intervention or the usual care groups involving clinical pharmacists.
J Am Med Inform Assoc
January 2025
Department of Computer Science, Duke University, Durham, NC 27708, United States.
Objective: Prediction of mortality in intensive care unit (ICU) patients typically relies on black box models (that are unacceptable for use in hospitals) or hand-tuned interpretable models (that might lead to the loss in performance). We aim to bridge the gap between these 2 categories by building on modern interpretable machine learning (ML) techniques to design interpretable mortality risk scores that are as accurate as black boxes.
Material And Methods: We developed a new algorithm, GroupFasterRisk, which has several important benefits: it uses both hard and soft direct sparsity regularization, it incorporates group sparsity to allow more cohesive models, it allows for monotonicity constraint to include domain knowledge, and it produces many equally good models, which allows domain experts to choose among them.
J Intensive Med
January 2025
Medical Intensive Care Unit, APHP Saint-Louis University Hospital, Paris, France.
Background: Cancer patients who are exposed to sepsis and had previous chemotherapy may have increased severity. Among chemotherapeutic agents, anthracyclines have been associated with cardiac toxicity. Like other chemotherapeutic agents, they may cause endothelial toxicity.
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