Role of HEART score in evaluating clinical outcomes among emergency department patients with different ethnicities.
Ontology highlight
ABSTRACT: Objective
We aimed to examine the role of the HEART (history, EKG, age, risk factors, and troponin) score in the evaluation of six clinical outcomes among three groups of patients in the emergency department (ED).Methods
We performed a retrospective observational study among three ED patient groups including White, Black, and Hispanic patients. ED providers used the HEART score to assess the need for patient hospital admission and for emergent cardiac imaging tests (CITs). HEART scores were measured using classification accuracy rates. Performance accuracies were measured in terms of HEART score in relation to four clinical outcomes (positive findings of CITs, ED returns, hospital readmissions, and 30-day major adverse cardiac events [MACE]).Results
A high classification accuracy rate (87%) was found for use of the HEART score to determine hospital admission. HEART scores showed moderate accuracy (area under the receiver operating characteristic curve 0.66-0.78) in predicting results of emergent CITs, 30-day hospital readmissions, and 30-day MACE outcomes.Conclusions
Providers adhered to use of the HEART score to determine hospital admission. The HEART score may be associated with emergent CIT findings, 30-day hospital readmissions, and 30-day MACE outcomes, with no differences among White, Black, and Hispanic patient populations.
SUBMITTER: Meyering SH
PROVIDER: S-EPMC8113935 | biostudies-literature | 2021 Apr
REPOSITORIES: biostudies-literature
Publications
Role of HEART score in evaluating clinical outcomes among emergency department patients with different ethnicities.
Meyering Stefan H SH Schrader Chet D CD Kumar Darren D Zhou Yuan Y Alanis Naomi N Shaikh Sajid S Cheeti Radhika R Smiley Rebecca R Iloma Chukwuagozie C Wang Hao H
The Journal of international medical research 20210401 4
<h4>Objective</h4>We aimed to examine the role of the HEART (history, EKG, age, risk factors, and troponin) score in the evaluation of six clinical outcomes among three groups of patients in the emergency department (ED).<h4>Methods</h4>We performed a retrospective observational study among three ED patient groups including White, Black, and Hispanic patients. ED providers used the HEART score to assess the need for patient hospital admission and for emergent cardiac imaging tests (CITs). HEART ...[more]
Similar Datasets
Project description:BackgroundChoosing the most effective chemotherapeutic agent with safest side effect profile is a common challenge in cancer treatment. Although there are standardized chemotherapy protocols in place, protocol changes made after extensive clinical trials demonstrate significant improvement in the efficacy and tolerability of certain drugs. The pharmacokinetics, pharmacodynamics, and tolerance of anti-cancer medications are all highly individualized. A driving force behind these differences lies within a person's genetic makeup.Recent findingsPharmacogenomics, the study of how an individual's genes impact the processing and action of a drug, can optimize drug responsiveness and reduce toxicities by creating a customized medication regimen. However, these differences are rarely considered in the initial determination of standardized chemotherapeutic protocols and treatment algorithms. Because pharmacoethnicity is influenced by both genetic and nongenetic variables, clinical data highlighting disparities in the frequency of polymorphisms between different ethnicities is steadily growing. Recent data suggests that ethnic variations in the expression of allelic variants may result in different pharmacokinetic properties of the anti-cancer medication. In this article, the clinical outcomes of various chemotherapy classes in patients of different ethnicities were reviewed.ConclusionGenetic and nongenetic variables contribute to the interindividual variability in response to chemotherapeutic drugs. Considering pharmacoethnicity in the initial determination of standard chemotherapeutic protocols and treatment algorithms can lead to better clinical outcomes of patients of different ethnicities.
| S-EPMC10440845 | biostudies-literature
Project description:Study objectiveThe HEART score is a risk stratification aid that may safely reduce chest pain admissions for emergency department patients. However, differences in interpretation of subjective components potentially alters the performance of the score. We compared agreement between HEART scores determined during clinical practice with research-generated scores and estimated their accuracy in predicting 30-day major adverse cardiac events.MethodsWe prospectively enrolled adult ED patients with symptoms concerning for acute coronary syndrome at a single tertiary center. ED clinicians submitted their clinical HEART scores during the patient encounter. Researchers then independently interviewed patients to generate a research HEART score. Patients were followed by phone and chart review for major adverse cardiac events. Weighted kappa; unweighted Cohen's kappa; prevalence-adjusted, bias-adjusted kappa (PABAK); and test probabilities were calculated.ResultsFrom November 2016 to June 2019, 336 patients were enrolled, 261 (77.7%) were admitted, and 30 (8.9%) had major adverse cardiac events. Dichotomized HEART score agreement was 78% (kappa 0.48, 95% confidence interval [CI] 0.37 to 0.58; PABAK 0.57, 95% CI 0.48 to 0.65) with the lowest agreement in the history (72%; WK 0.14, 95% CI 0.06 to 0.22) and electrocardiogram (85%; WK 0.4, 95% CI 0.3 to 0.49) components. Compared with researchers, clinicians had 100% sensitivity (95% CI 88.4% to 100%) (versus 86.7%, 95% CI 69.3% to 96.2%) and 27.8% specificity (95% CI 22.8% to 33.2%) (versus 34.6%, 95% CI 29.3% to 40.3%) for major adverse cardiac events. Four participants with low research HEART scores had major adverse cardiac events.ConclusionED clinicians had only moderate agreement with research HEART scores. Combined with uncertainties regarding accuracy in predicting major adverse cardiac events, we urge caution in the widespread use of the HEART score as the sole determinant of ED disposition.
| S-EPMC8324528 | biostudies-literature
Project description:IntroductionThe emergency department (ED) is an inherently high-risk setting. Risk scores can help practitioners understand the risk of ED patients for developing poor outcomes after discharge. Our objective was to develop two risk scores that predict either general inpatient admission or death/intensive care unit (ICU) admission within seven days of ED discharge.MethodsWe conducted a retrospective cohort study of patients age > 65 years using clinical data from a regional, integrated health system for years 2009-2010 to create risk scores to predict two outcomes, a general inpatient admission or death/ICU admission. We used logistic regression to predict the two outcomes based on age, body mass index, vital signs, Charlson comorbidity index (CCI), ED length of stay (LOS), and prior inpatient admission.ResultsOf 104,025 ED visit discharges, 4,638 (4.5%) experienced a general inpatient admission and 531 (0.5%) death or ICU admission within seven days of discharge. Risk factors with the greatest point value for either outcome were high CCI score and a prolonged ED LOS. The C-statistic was 0.68 and 0.76 for the two models.ConclusionRisk scores were successfully created for both outcomes from an integrated health system, inpatient admission or death/ICU admission. Patients who accrued the highest number of points and greatest risk present to the ED with a high number of comorbidities and require prolonged ED evaluations.
| S-EPMC6123082 | biostudies-literature
Project description:Study objectiveThe use of the HEART score to risk stratify patients for short-term major adverse cardiac events in the emergency department (ED) setting is well established. Although discharge to home for low-risk HEART score patients is widely accepted as safe practice, there are limited outcomes data on moderate-risk HEART score patients discharged to home. We investigated the safety of discharging moderate-risk HEART score patients to home from the ED with established early cardiology follow-up.MethodsWe performed a retrospective cohort analysis of patients presenting to the ED with chest pain from April 2020 through December 2020. Patients were evaluated in the ED and underwent serial conventional troponin testing and electrocardiogram (ECG). Clinicians calculated a HEART score and employed shared decision-making with moderate-risk patients (score 4-6), offering hospital admission versus discharge home with a formalized process for rapid cardiology follow-up (within 2 business days). We assessed the frequency of acute myocardial infarction or death at 30 days and before cardiology follow-up.ResultsDuring our study period, 2939 patient encounters were screened for chest pain. Of these, 333 of 547 eligible moderate-risk HEART score patients were referred for rapid follow-up. The median time to follow-up appointment was 2.9 business days (interquartile range 1.3, 6.5), and 264 (79%) of patients kept their follow-up appointment. One patient (0.3%) suffered death within 30 days, before cardiology follow-up. There were no myocardial infarctions.ConclusionsThese results suggest that moderate-risk HEART score patients may be considered for discharge from the ED with rapid cardiology follow-up. Formalizing processes to facilitate these early evaluations may represent a viable alternative to hospital admission, without diminishing patient outcomes.
| S-EPMC10492236 | biostudies-literature
Project description:The U.S. Global Climate Change Research Program has identified climate change as a growing public health threat. We investigated the potential effects of changes in ambient daily maximum temperature on hyperthermia and cardiovascular emergency department (ED) visits using records for patients age 64 and younger from a private insurance database for the May-September period for 2005-2012. We found a strong positive relationship between daily maximum temperatures and ED visits for hyperthermia but not for cardiovascular conditions. Using the fitted relationship from 136 metropolitan areas, we calculated the number and rate of hyperthermia ED visits for climates representative of year 1995 (baseline period), as well as years 2050 and 2090 (future periods), for two climate change scenarios based on outcomes from five global climate models. Without considering potential adaptation or population growth and movement, we calculate that climate change alone will result in an additional 21,000-28,000 hyperthermia ED visits for May to September, with associated treatment costs between $6 million and $52 million (2015 U.S. dollars) by 2050; this increases to approximately 28,000-65,000 additional hyperthermia ED visits with treatment costs between $9 million and $118 million (2015 U.S. dollars) by 2090. The range in projected additional hyperthermia visits reflects the difference between alternative climate scenarios, and the additional range in valuation reflects different assumptions about per-case valuation.
| S-EPMC7007124 | biostudies-literature
Project description:ObjectivePatient crowding and boarding in the emergency department (ED) is associated with adverse outcomes and has become increasingly problematic in recent years. We investigated the impact of an ED patient flow countermeasure using an early warning score.MethodsWe conducted a cross-sectional analysis of observational data from patients who presented to the ED of a Level 1 Trauma Center in Pennsylvania. We implemented a modified version of the Modified Early Warning Score (MEWS), called mMEWS, to address patient flow. Patients aged ≥18 years old admitted to the adult hospital medicine service were included in the study. We compared the pre-mMEWS (February 19, 2017-February 18, 2019) to the post-mMEWS implementation period (February 19, 2019-June 30, 2020). During the intervention, low MEWS (0-1) scoring admissions went directly to the inpatient floor with expedited orders, the remainder waited in the ED until the hospital medicine admitting team evaluated the patient and then placed orders. We investigated the association between mMEWS, ED length of stay (LOS), and 24-hour rapid response team (24 hour-RRT) activation. RRT activation rates were used as a measure of adverse outcome for the new process and are a network team response for admitted patients who are rapidly decompensating. The association between mMEWS and the outcomes of ED length of stay in minutes and 24 hour-RRT activation was assessed using linear and logistic regression adjusting for a priori selected confounders, respectively.ResultsOf the total 43,892 patients admitted, 19,962 (45.5%) were in the pre-mMEWS and 23,930 (54.5%) in the post-mMEWS implementation period. The median post-mMEWS ED LOS was shorter than the pre-mMEWS (376 vs 415 minutes; P < 0.01). After accounting for potential confounders, there was a 4.57% decrease in the ED LOS after implementing mMEWS (95% confidence interval [CI], 4.20-4.94; P < 0.01). The proportion of 24 hour-RRT did not differ significantly when comparing pre- and post-mMEWS (33.5% vs 34.4%; P = 0.83).ConclusionThe use of a modified MEWS enhanced admission process to the hospital medicine service, even during the COVID-19 pandemic, was associated with a significant decrease in ED LOS without a significant increase in 24 hour-RRT activation.
| S-EPMC9338822 | biostudies-literature
Project description:PURPOSE:Sepsis is a common acute life-threatening condition that emergency physicians routinely face. Diagnostic options within the Emergency Department (ED) are limited due to lack of infrastructure, consequently limiting the use of invasive hemodynamic monitoring or imaging tests. The mortality rate due to sepsis can be assessed via multiple scoring systems, for example, mortality in emergency department sepsis (MEDS) score and sepsis patient evaluation in the emergency department (SPEED) score, both of which quantify the variation of mortality rates according to clinical findings, laboratory data, or therapeutic interventions. This study aims to improve the management processes of sepsis patients by comparing SPEED score and MEDS score for predicting the 28-day mortality in cases of emergency sepsis. METHODS:The study is a cross-sectional, prospective study including 61 sepsis patients in ED in Suez Canal University Hospital, Egypt, from August 2017 to June 2018. Patients were selected by two steps: (1) suspected septic patients presenting with at least one of the following abnormal clinical findings: (a) body temperature higher than 38 °C or lower than 36 °C, (b) heart rate higher than 90 beats/min, (c) hyperventilation evidenced by respiratory rate higher than 20 breaths/min or PaCO2 lower than 32 mmHg, and (d) white blood cell count higher than 12,000/μL or lower than 4000/μL; (2) confirmed septic patients with at least a 2-point increase from the baseline total sequential organ failure assessment (SOFA) score following infection. Other inclusion criteria included adult patients with an age ≥18 years regardless of gender and those who had either systemic inflammatory response syndrome or suspected/confirmed infection. Patients were shortly follow-up for the 28-day mortality. Each patient was subject to SPEED score and MEDS score and then the results were compared to detect which of them was more effective in predicting outcome. The receiver operating characteristic curves were also done for MEDS and SPEED scores. RESULTS:Among the 61 patients, 41 died with the mortality rate of 67.2%. The mortality rate increased with a higher SPEED and MEDS scores. Both SPEED and MEDS scores revealed significant difference between the survivors and nonsurvivors (p = 0.004 and p < 0.001, respectively), indicating that both the two systems are effective in predicting the 28-day mortality of sepsis patients. Thereafter, the receiver operating characteristic curves were plotted, which showed that SPEED was better than the MEDS score when applied to the complete study population with an area under the curve being 0.87 (0.788-0.963) as compared with 0.75 (0.634-0.876) for MEDS. Logistic regression analysis revealed that the best fitting predictor of 28-day mortality for sepsis patients was the SPEED scoring system. For every one unit increase in SPEED score, the odds of 28-day mortality increased by 37%. CONCLUSION:SPEED score is more useful and accurate than MEDS score in predicting the 28-day mortality among sepsis patients. Therefore SPEED rather than MEDS should be more widely used in the ED for sepsis patients.
| S-EPMC6921192 | biostudies-literature
Project description:ObjectivesTo investigate the serum surfactant protein D (SP-D) level required to determine the diagnosis and prognosis of coronavirus disease 2019 (COVID-19), and create a new scale for disease prognosis prediction.MethodsThis study was conducted among 64 patients with COVID-19 symptoms and 16 healthy volunteers. The participants were assessed by comparing "Controls/Patients", "PCR-negative/PCR-positive", "Simple COVID-19/Acute respiratory distress syndrome (ARDS)-accompanied COVID-19", "Mild ARDS/Moderate-severe ARDS", and "Survived/Dead" subgroups. Serum SP-D levels and pulmonary infiltration area (volume and percentage) measurements on CT were compared between the groups. A new scale, the "CHARISMA Score", was created by logistic regression method for a complete prognosis assessment. This includes confusion, heart rate, age, respiratory rate, percentage of infiltration on CT, serum SP-D level, mean arterial pressure and SaO2.ResultsSerum SP-D levels differed significantly across the groups. There was a strong correlation between SP-D levels and infiltration volumes. CHARISMA scores were higher in the severe than in the mild ARDS group and in patients who died than in survivors. In the receiver operating characteristic curve analysis of the CHARISMA scores, a cutoff value of 4 indicated mortality.ConclusionSerum SP-D levels can be used to determine COVID-19 diagnosis and prognosis, and the CHARISMA score can be used to predict prognosis and mortality risk in patients with COVID-19 pneumonia.
| S-EPMC11693913 | biostudies-literature
Project description:IntroductionAngioedema represents self-limited, localized swelling of submucosal or subcutaneous tissues. While the underlying etiology may be undeterminable in the emergent setting, nonhistaminergic and histaminergic angioedema respond differently to therapeutic interventions, with implications for empiric treatment. Clinical features and outcome differences among nonhistaminergic vs histaminergic angioedema patients in the emergency department (ED) are poorly characterized. We aim to describe the clinical characteristics and outcomes among ED patients with angioedema by suspected etiology.MethodsThis was a 10-year retrospective study of adult ED patients with angioedema, using data abstracted from the electronic health record. We evaluated univariable associations of select clinical features with etiology and used them to develop a multivariable logistic regression model for nonhistaminergic vs histaminergic angioedema.ResultsAmong 450 adult angioedema patients, the mean +/- standard deviation age was 57 +/- 18 years, and 264 (59%) were female. Among patients, 30% had suspected nonhistaminergic angioedema, 30% had suspected histaminergic angioedema, and 40% were of unknown etiology. As compared to histaminergic angioedema, nonhistaminergic angioedema was associated with angiotensin-converting enzyme inhibitors (ACEI) or use of angiotensin II receptor blockers (ARB) (odds ratio [OR] [60.9]; 95% confidence interval [CI], 23.16-160.14) and time of onset one hour or more prior to ED arrival (OR [5.91]; 95% CI,1.87-18.70) and was inversely associated with urticaria (OR [0.05]; 95% CI, 0.02-0.15), dyspnea (OR [0.23]; 95% CI, 0.08-0.67), and periorbital or lip edema (OR [0.25]; 95% CI, 0.08-0.79 and OR [0.32]; 95% CI, 0.13-0.79, respectively).ConclusionAs compared to histaminergic angioedema, patients with nonhistaminergic angioedema were more likely to present one hour or more after symptom onset and take ACEI or ARB medications, and were less likely to have urticaria, dyspnea, or periorbital or lip angioedema. Identification of characteristics associated with the etiology of angioedema may assist providers in more rapidly initiating targeted therapies.
| S-EPMC6754201 | biostudies-literature
Project description:BackgroundDevelopment of emergency department (ED) triage systems that accurately differentiate and prioritize critically ill from stable patients remains challenging. We used machine learning models to predict clinical outcomes, and then compared their performance with that of a conventional approach-the Emergency Severity Index (ESI).MethodsUsing National Hospital and Ambulatory Medical Care Survey (NHAMCS) ED data, from 2007 through 2015, we identified all adult patients (aged ≥ 18 years). In the randomly sampled training set (70%), using routinely available triage data as predictors (e.g., demographics, triage vital signs, chief complaints, comorbidities), we developed four machine learning models: Lasso regression, random forest, gradient boosted decision tree, and deep neural network. As the reference model, we constructed a logistic regression model using the five-level ESI data. The clinical outcomes were critical care (admission to intensive care unit or in-hospital death) and hospitalization (direct hospital admission or transfer). In the test set (the remaining 30%), we measured the predictive performance, including area under the receiver-operating-characteristics curve (AUC) and net benefit (decision curves) for each model.ResultsOf 135,470 eligible ED visits, 2.1% had critical care outcome and 16.2% had hospitalization outcome. In the critical care outcome prediction, all four machine learning models outperformed the reference model (e.g., AUC, 0.86 [95%CI 0.85-0.87] in the deep neural network vs 0.74 [95%CI 0.72-0.75] in the reference model), with less under-triaged patients in ESI triage levels 3 to 5 (urgent to non-urgent). Likewise, in the hospitalization outcome prediction, all machine learning models outperformed the reference model (e.g., AUC, 0.82 [95%CI 0.82-0.83] in the deep neural network vs 0.69 [95%CI 0.68-0.69] in the reference model) with less over-triages in ESI triage levels 1 to 3 (immediate to urgent). In the decision curve analysis, all machine learning models consistently achieved a greater net benefit-a larger number of appropriate triages considering a trade-off with over-triages-across the range of clinical thresholds.ConclusionsCompared to the conventional approach, the machine learning models demonstrated a superior performance to predict critical care and hospitalization outcomes. The application of modern machine learning models may enhance clinicians' triage decision making, thereby achieving better clinical care and optimal resource utilization.
| S-EPMC6387562 | biostudies-literature