Dataset Information

A prehospital risk assessment tool predicts clinical outcomes in hospitalized patients with heat-related illness: a Japanese nationwide prospective observational study.

ABSTRACT: 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.

SUBMITTER: Takegawa R

PROVIDER: S-EPMC9867691 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A prehospital risk assessment tool predicts clinical outcomes in hospitalized patients with heat-related illness: a Japanese nationwide prospective observational study.

Takegawa Ryosuke R Kanda Jun J Yaguchi Arino A Yokobori Shoji S Hayashida Kei K

Scientific reports 20230121 1

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) cur ...[more]

PMID: 36681704

Similar Datasets

Project description:IntroductionExtreme heat is a significant cause of morbidity and mortality, and the incidence of acute heat illness (AHI) will likely increase secondary to anthropogenic climate change. Prompt diagnosis and treatment of AHI are critical; however, relevant diagnostic and surveillance tools have received little attention. In this exploratory cross-sectional and diagnostic accuracy study, we evaluated three tools for use in the prehospital setting: 1) case definitions; 2) portable loggers to measure on-scene heat exposure; and 3) prevalence data for potential AHI risk factors.MethodsWe enrolled 480 patients who presented to emergency medical services with chief complaints consistent with AHI in Ahmedabad, India, from April-June 2016 in a cross-sectional study. We evaluated AHI case definition test characteristics in reference to trained prehospital provider impressions, compared on-scene heat index measured by portable loggers to weather station measurements, and identified AHI behavioral and environmental risk factors using logistic regression.ResultsThe case definition for heat exhaustion was 23.8% (12.1-39.5%) sensitive and 93.6% (90.9-95.7%) specific. The positive and negative predictive values were 33.5% (20.8-49.0%) and 90.1% (88.5-91.5%), respectively. Mean scene heat index was 6.7°C higher than the mean station heat index (P < 0.001), and station data systematically underestimated heat exposure, particularly for AHI cases. Heat exhaustion cases were associated with on-scene heat index ≥ 49°C (odds ratio [OR] 2.66 [1.13-6.25], P = 0.025) and a history of recent exertion (OR 3.66 [1.30-10.29], P = 0.014), while on-scene air conditioning was protective (OR 0.29 [0.10-0.85], P = 0.024).ConclusionSystematic collection of prehospital data including recent activity history and presence of air conditioning can facilitate early AHI detection, timely intervention, and surveillance. Scene temperature data can be reliably collected and improve heat exposure and AHI risk assessment. Such data may be important elements of surveillance, clinical practice, and climate change adaptation.

Project description:BackgroundDiagnostic uncertainty in patients with dyspnea is associated with worse outcomes. We hypothesized that prehospital point-of-care ultrasound (POCUS) can improve diagnostic accuracy.MethodsProspective observational study of adult patients suffering dyspnea. Prehospital critical care physicians registered a suspected diagnosis based on clinical examination alone, performed POCUS of the heart and lungs, and finally registered suspected diagnoses based on their clinical examination supplemented with POCUS. Pre- and post-POCUS diagnoses were compared to endpoint committee adjudicated diagnoses. The primary outcome was improved sensitivity for diagnosing acute heart failure. Secondary outcomes included other diagnostic accuracy measures in relation to acute heart failure and other causes of dyspnea.ResultsIn total, 214 patients were included. The diagnosis of acute heart failure was suspected in 64/214 (30%) of patients before POCUS and 64/214 (30%) patients after POCUS, but POCUS led to reclassification in 53/214 (25%) patients. The endpoint committee adjudicated the diagnosis of acute heart failure in 87/214 (41%) patients. The sensitivity for the diagnosis of acute heart failure was 58% (95% CI 46%-69%) before POCUS compared to 65% (95% CI 53%-75%) after POCUS (p = 0.12). ROC AUC for the diagnosis acute heart failure was 0.72 (95% CI 0.66-0.78) before POCUS compared to 0.79 (0.73-0.84) after POCUS (p < 0.001). ROC AUC for the diagnosis acute exacerbation (AE) of chronic obstructive pulmonary disease (COPD) or asthma was 0.87 (0.82-0.91) before POCUS and 0.93 (0.88-0.97) after POCUS (p < 0.001). A POCUS finding of any of severely reduced left ventricular function, bilateral B-lines or bilateral pleural effusion demonstrated the highest sensitivity for acute heart failure at 88% (95% CI 79%-94%), whereas the combination of all of these three findings yielded the highest specificity at 99% (95% CI 95%-100%).ConclusionSupplementary prehospital POCUS leads to an improvement of diagnostic accuracy of both heart failure and AE-COPD/-asthma overall described by ROC AUC, but the increase in sensitivity for the diagnoses of acute heart failure did not reach statistical significance. Tailored use of POCUS findings optimizes diagnostic accuracy for rule-out and rule-in of acute heart failure.Trial registrationRegistered in Clinical Trials, 05.04.2019 (identifier: NCT03905460) https://clinicaltrials.gov/ct2/show/study/NCT03905460?term=NCT03905460&cond=Dyspnea&cntry=DK&draw=2&rank=1 .

Project description:BackgroundCommunity acquired pneumonia (CAP) is a high incidence disease resulting in about 260,000 hospital admissions per year in Germany, more than myocardial infarction or stroke. Worldwide, CAP is the most frequent infectious disease with high lethality ranging from 1.2 % in those 20-29 years old to over 10 % in patients older than 70 years, even in industrial nations. CAP poses numerous medical challenges, which the PROGRESS (Pneumonia Research Network on Genetic Resistance and Susceptibility for the Evolution of Severe Sepsis) network aims to tackle: Operationalization of disease severity throughout the course of disease, outcome prediction for hospitalized patients and prediction of transitions from uncomplicated CAP to severe CAP, and finally, to CAP with sepsis and organ failure as a life-threatening condition. It is a major aim of PROGRESS to understand and predict patient heterogeneity regarding outcome in the hospital and to develop novel treatment concepts.MethodsPROGRESS was designed as a clinical, observational, multi-center study of patients with CAP requiring hospitalization. More than 1600 patients selected for low burden of co-morbidities have been enrolled, aiming at a total of 3000. Course of disease, along with therapy, was closely monitored by daily assessments and long-term follow-up. Daily blood samples allow in depth molecular-genetic characterization of patients. We established a well-organized workflow for sample logistics and a comprehensive data management system to collect and manage data from more than 50 study centers in Germany and Austria. Samples are stored in a central biobank and clinical data are stored in a central data base which also integrates all data from molecular assessments.DiscussionWith the PROGRESS study, we established a comprehensive data base of high quality clinical and molecular data allowing investigation of pressing research questions regarding CAP. In-depth molecular characterization will contribute to the discovery of disease mechanisms and establishment of diagnostic and predictive biomarkers. A strength of PROGRESS is the focus on younger patients with low burden of co-morbidities, allowing a more direct look at host biology with less confounding. As a resulting limitation, insights from PROGRESS will require validation in representative patient cohorts to assess clinical utility.Trial registrationThe PROGRESS study was retrospectively registered on May 24(th), 2016 with ClinicalTrials.gov: NCT02782013.

Dataset Information

A prehospital risk assessment tool predicts clinical outcomes in hospitalized patients with heat-related illness: a Japanese nationwide prospective observational study.

Publications

A prehospital risk assessment tool predicts clinical outcomes in hospitalized patients with heat-related illness: a Japanese nationwide prospective observational study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets