HEART Score Research Articles

Symptoms and coronary risk factors predictive of adverse cardiac events in chest pain patients in an Asian emergency department: the need for a local prediction score.

Clinical assessment is pivotal in diagnosing acute coronary syndrome. Our study aimed to identify clinical characteristics predictive of major adverse cardiac events (MACE) in an Asian population and to derive a risk score for MACE. Patients presenting to the emergency department (ED) with chest pain and non-diagnostic 12-lead electrocardiograms were recruited. Clinical history was recorded in a predesigned template. Random glucose and direct low-density lipoprotein measurements were taken, in addition to serial troponin. We derived the age, coronary risk factors (CRF), sex and symptoms (ACSS) risk score based on multivariate analysis results, considering age, CRF, sex and symptoms and classifying patients into very low, low, moderate and high risk for MACE. Comparison was made with the ED Assessment of Chest Pain Score (EDACS) and the history, electrocardiogram, age, risk factors, troponin (HEART) score. We also modified the HEART score with the CRF that we had identified. The outcomes were 30-day and 1-year MACE. There were a total of 1689 patients, with 172 (10.2%) and 200 (11.8%) having 30-day and 1-year MACE, respectively. Symptoms predictive of MACE included central chest pain, radiation to the jaw/neck, associated diaphoresis, and symptoms aggravated by exertion and relieved by glyceryl trinitrate. The ACSS score had an area under the curve of 0.769 (95% confidence interval [CI]: 0.735-0.803) and 0.760 (95% CI: 0.727-0.793) for 30-day and 1-year MACE, respectively, outperforming EDACS. Those in the very-low-risk and low-risk groups had <1% risk of 30-day MACE. The ACSS risk score shows potential for use in the local ED or primary care setting, potentially reducing unnecessary cardiac investigations and admission.

Clinical decision aids and computed tomography coronary angiography in patients with suspected acute coronary syndrome

BackgroundThe HEART score, the T-MACS model and the GRACE score support early decision-making for acute chest pain, which could be complemented by CT coronary angiography (CTCA). However, their performance has...

Evaluating Cardiovascular Risks: The Platelet Lymphocyte Ratio and the Neutrophil Lymphocyte Ratio As High-Risk Heart Score Predictors in Non-ST Elevation Myocardial Infarction (NSTEMI) and Unstable Angina Patients.

Introduction Acute coronary syndromes (ACS), encompassing non-ST elevation myocardial infarction (NSTEMI) and unstable angina (UA), present significant challenges in risk assessment and management, particularly in resource-constrained environments like India. The burden of cardiovascular diseases in such regions necessitates cost-effective and readily accessible tools for risk stratification. Previous research has emphasized the role of inflammatory markers in coronary artery disease (CAD), prompting investigations into simple and affordable biomarkers for risk assessment. Platelet lymphocyte ratio (PLR) and neutrophil lymphocyte ratio (NLR) have emerged as potential biomarkers for thrombotic activity in cardiac illnesses, offering simplicity, accessibility, and cost-effectiveness in risk assessmentmaking them particularly valuable in resource-poor settings like India, where advanced diagnostic tools may be limited. Objective This study aims to evaluate the effectiveness of PLR and NLR as predictors of high-risk HEART(history, ECG, age, risk factors, and troponin) scores in patients with NSTEMI and UA. Methods A prospective cross-sectional study was conducted at the Saveetha Medical College and Hospitals in Chennai, India, from March 2021 to September 2022. The study included 288 adults diagnosed with NSTEMIor UA, aged 18 years and above. The inclusion criteria comprised patients with confirmed diagnoses of NSTEMI or UA based on clinical symptoms, electrocardiographic findings, and cardiac biomarker elevation. The exclusion criteria encompassed patients with active infections, acute traumatic injuries, end-stage renal disease, malignant neoplasms, and ST-elevation myocardial infarction (STEMI). In addition to the HEART score, PLR, and NLR were computed to assess the prognosis of patients admitted to the Saveetha Medical College and Hospitals. Results The statistical analysis revealed significant correlations between PLR, NLR, and HEART score risk categories. The Pearson's correlation coefficient indicated strong associations between PLR/NLR values and HEART score risk groups, suggesting their potential as predictive markers for adverse clinical outcomes. Additionally, analysis of variance (ANOVA) demonstrated significant differences in PLR/NLR values across different HEART score risk categories, further highlighting their relevance in risk stratification. The effect sizes for these correlations were moderate to large, indicating clinically meaningful associations between PLR/NLR and cardiovascular risk. Conclusion In cases of NSTEMI and UA, PLR and NLR show potential as simple and inexpensive indicators of high-risk patients. By leveraging these inexpensive biomarkers, healthcare providers can enhance risk assessment and prognostication in patients presenting with ACS, facilitating timely interventions and tailored management strategies.

HEART + score: integrating carotid ultrasound to chest pain assessment in the emergency department.

The HEART score is a clinical decision tool that stratifies patients into categories of low, moderate, and high-risk of major adverse cardiac events in the emergency department (ED) but cannot identify underlying cardiovascular disease in patients without prior history. The presence of atherosclerosis can easily be detected at the bedside using carotid ultrasound. Plaque quantification is well established, and plaque composition can be assessed using ultrasound grayscale pixel distribution analysis. This study aimed to determine whether carotid plaque burden and/or composition correlated with risk of events and could improve the sensitivity of the HEART score in risk stratifying ED patients with chest pain. The HEART score was calculated based on history, electrocardiogram, age, risk factors, and initial troponin in patients presenting to the ED with chest pain (n = 321). Focused carotid ultrasound was performed, and maximum plaque height and total plaque area were used to determine plaque burden (quantity). Plaque composition (% blood, fat, muscle, fibrous, calcium-like tissue) was assessed by pixel distribution analysis. Carotid plaque height and area increased with HEART score (p < 0.0001). Carotid plaque % fibrous and % calcium also increased with HEART score. The HEART score had a higher area under the curve (AUC = 0.84) in predicting 30-day events compared to the plaque variables alone (AUCs < 0.70). Integrating plaque quantity into the HEART score slightly increased test sensitivity (62-69%) for 30-day events and reclassified 11 moderate-risk participants to high-risk (score 7-10). Plaque burden with advanced composition features (fibrous and calcium) was associated with increased HEART score. Integrating plaque assessment into the HEART score identified subclinical atherosclerosis in moderate-risk patients.

Are medical history data fit for risk stratification of patients with chest pain in emergency care? Comparing data collected from patients using computerized history taking with data documented by physicians in the electronic health record in the CLEOS-CPDS prospective cohort study.

In acute chest pain management, risk stratification tools, including medical history, are recommended. We compared the fraction of patients with sufficient clinical data obtained using computerized history taking software (CHT) versus physician-acquired medical history to calculate established risk scores and assessed the patient-by-patient agreement between these 2 ways of obtaining medical history information. This was a prospective cohort study of clinically stable patients aged ≥ 18 years presenting to the emergency department (ED) at Danderyd University Hospital (Stockholm, Sweden) in 2017-2019 with acute chest pain and non-diagnostic ECG and serum markers. Medical histories were self-reported using CHT on a tablet. Observations on discrete variables in the risk scores were extracted from electronic health records (EHR) and the CHT database. The patient-by-patient agreement was described by Cohen's kappa statistics. Of the total 1000 patients included (mean age 55.3 ± 17.4 years; 54% women), HEART score, EDACS, and T-MACS could be calculated in 75%, 74%, and 83% by CHT and in 31%, 7%, and 25% by EHR, respectively. The agreement between CHT and EHR was slight to moderate (kappa 0.19-0.70) for chest pain characteristics and moderate to almost perfect (kappa 0.55-0.91) for risk factors. CHT can acquire and document data for chest pain risk stratification in most ED patients using established risk scores, achieving this goal for a substantially larger number of patients, as compared to EHR data. The agreement between CHT and physician-acquired history taking is high for traditional risk factors and lower for chest pain characteristics. ClinicalTrials.gov NCT03439449.

Comparison of prehospital and in-hospital HEART scores in patients with possible myocardial infarction.

Comparison of prehospital and in-hospital HEART scores in patients with possible myocardial infarction.

European Society of Cardiology 0/1-hour algorithm (high-sensitivity cardiac troponin T) performance across distinct age groups

BackgroundTo determine if the European Society of Cardiology 0/1-hour (ESC 0/1-h) algorithm with high-sensitivity cardiac troponin T (hs-cTnT) meets the ≥99% negative predictive value (NPV) safety threshold for 30-day cardiac...

IDENTIFYING AND FIXING UX-FRICTION EMPLOYEE DEVELOPMENT WEBSITE WITH MIXED APPROACH HEART FRAMEWORK AND USABILITY TESTING

The company website has an important role in the business processes that occur in the company, one of which is managing employee productivity. PT.XYZ utilizes the Kaizen website to improve the quality of its employees. Assessment of user experience while operating the website can be a form of evaluation between expectations and actual conditions related to website feasibility. In this study, the researcher intends to find out what indicators are obstacles in the operation of a web-based application called the Kaizen website at PT.XYZ so that there is inefficiency in its use. This research involved 54 respondents who were employees of PT.XYZ. The combination of HEART and Usability Testing methods is used to identify obstacles experienced by users and evaluate user experience when using the Kaizen website. The results of the analysis are then used to improve website performance in order to create a good user experience. The results showed that the HEART score increased by an average of 9% on each variable. Usability testing assessment also shows that the Kaizen website reaches the acceptable category with a score of 90.5 after improvements are made.

Machine learning with electrocardiograms to optimize mortality risk stratification in patients with suspected acute coronary syndrome

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Heart, Lung, and Blood Institute (NHLBI) and National Center for Advancing Translational Sciences (NCATS). Background The importance of risk stratification in patients with suspected acute coronary syndrome (ACS) extends beyond diagnosis and immediate treatment. It influences the precision of care delivery and the allocation of resources to those at highest risk of adverse events. Purpose We sought to evaluate the prognostic value of electrocardiogram (ECG) feature-based machine learning models to risk-stratify long-term mortality in those with suspected ACS. Methods This was a multicenter prospective observational cohort study of consecutive, non-traumatic patients evaluated at the emergency department for suspected ACS. The derivation cohort included 4,015 patients from a University Medical Center (age 59±16 years, 47% women, 80% training with 10-fold cross-validation and 20% testing). Ascertainment of all-cause death was based on numerous data sources, including the Centers for Disease Control and Prevention’s National Death Index registry. We trained six machine learning methods for survival analysis using 73 morphological ECG features from presenting prehospital 10-second 12-lead ECGs. We used predicted risk scores in a variational Bayesian Gaussian mixture model to define three risk groups corresponding to low, moderate, and high-risk and compared the resulting classification performance against the HEART score. The external testing cohort included 3,095 patients from a University (age 59±15 years, 44% women). Results The mortality rate was 20.3% in the derivation cohort after an average follow-up period of 3.53 years (IQR 1.75 - 5.32). Extra Survival Trees outperformed other forecasting models during cross-validation. In the internal testing cohort, the derived risk groups were significantly predictive of survival (Kaplan-Meier log-rank test statistic = 121.14, p&amp;lt;0.001), outperforming the classification performance using the HEART score (Figure 1). Our machine learning-based risk stratification model detected &amp;gt;90% of death events missed by low-risk HEART score (Figure 2). Ruling-out the low-risk group, it achieved a negative predictive value of 93.4% with a sensitivity of 85.9% (compared to 89.0% and 75.0%, respectively, for the HEART score). This classification performance generalized well to our external testing cohort. Compared to those in the low-risk group, patients at moderate (OR = 3.62 [1.35-9.74]) and high (OR = 6.12 [2.38-15.75]) risk had significantly higher odds of 30-day cardiovascular death (n=109, 3.5%). Conclusions Using only features from the 10-second 12-lead ECG, we derived and externally validated a machine learning model that stratifies the mortality risk of patients during long-term follow up. This model outperformed current standard of care based on the HEART score. Future work should validate the potential of this decision-support tool in influencing treatment plans and resource allocation.Figure 1.Clustering performanceFigure 2.Sankey diagram

Cardiac evaluation in free-ranging giant anteaters (Myrmecophaga tridactyla) in the Brazilian Cerrado

The giant anteater (Myrmecophaga tridactyla) is classified as a Vulnerable species, but these animals are regularly brought into rescue centers and are also kept in zoos around the world. They occupy a highly specialized dietary niche with a feeding ecology that is difficult to replicate under human care. This can lead to numerous nutrition-related health problems, including frequent cardiomyopathies. Establishing reference parameters for free-ranging animals is an important tool to assess the health of animals under human care. For this reason, a cardiac assessment through plasma troponin I concentrations, vertebral heart score (VHS) based on thoracic radiographs, and echocardiographic parameters were established for 11 free-ranging giant anteaters in the Cerrado savanna of Mato Grosso do Sul, Brazil. The troponin I concentrations were all lower than 0.2 ng/ml, the cutoff limit for detection in the assay used. The mean VHS in the right lateral projections was 7.49, a value almost identical to cats (7.5) and lower than dogs (9.7). A complete echocardiographic assessment is provided and compared with previous case reports. This paper validates the diet-related origin hypothesis for common dilated cardiomyopathy in giant anteaters. This is the first published study with radiographic and echocardiographic parameters for free-ranging giant anteaters, providing important reference parameters for future giant anteater in situ and ex situ conservation initiatives. This work highlights the benefits of veterinary research comparing health parameters for animals living under and outside of human care.

Vertebral heart size and vertebral left atrial size reference ranges in healthy Miniature Schnauzers.

The inclusion of vertebral heart score (VHS) and, more recently, the inclusion of the vertebral left atrial size (VLAS) in radiographic evaluation have become important screening tools for identifying dogs with occult cardiac disease. Several recent papers have shown there are interbreed variations in the VHS reference range. Our hypothesis is that the Miniature Schnauzer would also have a higher reference range for its VHS. The electronic medical records of IDEXX Telemedicine Consultants were searched for Miniature Schnauzers undergoing thoracic radiographs between March 1, 2022, and February 28, 2023. Dogs were included if they had 3 view thoracic radiographs performed and no evidence of cardiopulmonary disease was detected. Dogs with incomplete radiographic studies or cardiac or extracardiac disease were excluded. The VHS and VLAS measurements were performed by 2 board-certified cardiologists independent of one another. A total of 1,000 radiographs were obtained of which 272 were included for the study. The overall range for the VHS in this cohort was 9.68 to 12.07 with a median of 10.9. For VLAS measurements, a range of 1.71 to 2.4 was documented with a median of 2.0. The VHS for Miniature Schnauzers without cardiac disease was confirmed to be higher than the canine reference range.

Application Value of NLR, PLR, LMR, HEART score, and POCT in Early Warning and Accurate Graded Diagnosis of High-Risk Chest Pain in Emergency Medicine

Objective: To evaluate the application value of neutrophils/lymphocytes (NLR), platelets/lymphocytes (PLR), lymphocytes/monocytes (LMR), HEART (history, electrocardiogram, age, risk factors, and troponin) score, and point-of-care testing (POCT) in the early warning and precise diagnosis of high-risk chest pain in emergency medicine. Methods: A total of 157 patients with acute chest pain who were admitted to the emergency department and chest pain treatment unit of our hospital between August 2022 and September 2023 were selected. Rapid testing of bedside myocardial markers (ultrasensitive troponin (hs-cTnI), myoglobin (MYO), creatine kinase isoenzyme (CK-MB), D-dimer (D-Dimer), and N-terminal B-type natriuretic peptide precursor (NT-proBNP)) was performed on the patients using a POCT device (ThermoKing BioMQ60proB). A HEART score was used to classify the patients into low (n = 53), intermediate (n = 59), and high-risk (n = 45) groups, and the NLR, PLR, and LMR were calculated. The NLR, PLR, and LMR values were compared among the three groups of patients, and the optimal cutoff values as well as sensitivity and specificity were determined based on receiver operating characteristic (ROC) analysis. Results: The HEART scores of patients in the low-risk, intermediate-risk, and high-risk groups were (2.72 ± 0.24), (4.75 ± 0.56), and (5.32 ± 0.73) respectively, and the differences were statistically significant (P &lt; 0.05). Compared with the low-risk group, the intermediate-risk group and high-risk group had a significantly higher NLR and PLR, and a significantly lower LMR; the high-risk group had higher NLR and PLR and lower values of LMR as compared to the other two groups, and the difference was statistically significant (P &lt; 0.05). The ROC curves suggested that the area under the curve, sensitivity, and specificity of the combined diagnosis of NLR, PLR, LMR, HEART score, and POCT were greater than those of LR, PLR, and LMR with HEART score and POCT alone. Conclusion: The combined application of NLR, PLR, LMR, HEART score, and POCT has significant application value in the early warning and precise diagnosis of emergency high-risk chest pain. It provides a more simple, easy-to-access, and efficient assessment index for the clinical prediction and treatment of emergency high-risk chest pain.

ChatGPT provides inconsistent risk-stratification of patients with atraumatic chest pain.

ChatGPT-4 is a large language model with promising healthcare applications. However, its ability to analyze complex clinical data and provide consistent results is poorly known. Compared to validated tools, this study evaluated ChatGPT-4's risk stratification of simulated patients with acute nontraumatic chest pain. Three datasets of simulated case studies were created: one based on the TIMI score variables, another on HEART score variables, and a third comprising 44 randomized variables related to non-traumatic chest pain presentations. ChatGPT-4 independently scored each dataset five times. Its risk scores were compared to calculated TIMI and HEART scores. A model trained on 44 clinical variables was evaluated for consistency. ChatGPT-4 showed a high correlation with TIMI and HEART scores (r = 0.898 and 0.928, respectively), but the distribution of individual risk assessments was broad. ChatGPT-4 gave a different risk 45-48% of the time for a fixed TIMI or HEART score. On the 44-variable model, a majority of the five ChatGPT-4 models agreed on a diagnosis category only 56% of the time, and risk scores were poorly correlated (r = 0.605). While ChatGPT-4 correlates closely with established risk stratification tools regarding mean scores, its inconsistency when presented with identical patient data on separate occasions raises concerns about its reliability. The findings suggest that while large language models like ChatGPT-4 hold promise for healthcare applications, further refinement and customization are necessary, particularly in the clinical risk assessment of atraumatic chest pain patients.

Myxomatous mitral valve disease and associated pulmonary hypertension might increase serum angiopoietin-2 in dogs.

To evaluate the relationships between the severity of myxomatous mitral valve disease (MMVD) and pulmonary hypertension (PH) and serum angiopoietin (Ang)-1 and Ang-2 concentrations in dogs with MMVD. 74 dogs (control, n = 12; MMVD, n = 62) were included. Serum Ang-1 and Ang-2 concentrations were estimated using the canine-specific ELISA kit. The concentrations were compared between dogs with MMVD and healthy dogs, and they were analyzed according to the severity of MMVD and PH. The median serum Ang-1 concentration did not differ among the study groups. The median serum Ang-2 concentration was higher in dogs with stage B2 MMVD (P = .041) and acute congestive heart failure (P = .002) than in control dogs. In addition, the median serum Ang-2 concentration was higher in MMVD dogs with PH than in those without PH (P = .031). Serum Ang-2 concentration was correlated with vertebral heart score (rs = 0.36, P = .004) and vertebral left atrial score (r = 0.50, P < .001) in dogs with MMVD, and correlated with vertebral heart score (r = 0.63, P = .01), maximum E wave amplitude of the diastolic transmitral flow (rs = 0.61, P = .018), ejection fraction (rs = -0.77, P < .001) and fractional shortening (rs = -0.56, P = .032) in dogs with acute congestive heart failure. Circulating Ang-2 levels increase in dogs with the severity of MMVD and the presence of PH.

Integrating Pre-test Probability and Point-of-Care Ultrasound (POCUS) in the Emergency Department (ED) Diagnosis of Small Bowel Obstruction (SBO).

Introduction A computed tomography (CT) scan and point-of-care ultrasound (POCUS) are commonly employed for diagnosing small bowel obstructions (SBOs). Prior studies demonstrated that POCUS has 90-95% sensitivity and specificity compared with CT scanning, which is the gold standard. Unlike other imaging modalities (in which the ordering and performing clinician are not the same), POCUS-performing/interpreting sonologists must recognize the risk of confirmation bias in the POCUS application. Per Bayesian analysis, the likelihood of a diagnosis being true following a diagnostic test is based on the ordering clinician's pre-test probability and the test characteristics (sensitivity and specificity, from which positive and negative likelihood ratios can be calculated). Consequently, establishing pre-test probability is important in informing downstream diagnostic or therapeutic interventions, as pre-test probability influences post-test odds. Little research has been done on the role of POCUS sonologist's pre-test probability and actual POCUS results regarding SBO. This study assessed the role of POCUS, integrating pre-test probability and POCUS results to determine post-test odds. Methods One hundred six patients were recruited on a convenience basis and underwent POCUS and CT between April 2017 and December 2022. All sonographers were credentialed in POCUS. POCUS sonologists' pre-test probabilities and POCUS and CT results were captured, which were compared. Sensitivity, specificity, LR+, and LR- were calculated, and correlations were made between pre-test probability and POCUS and CT results. Results POCUS exhibited a sensitivity of 92% and specificity of 90%, with a corresponding positive likelihood ratio (LR+) of 9.3 and a negative likelihood ratio (LR-) of 0.09 for diagnosing SBO. Among patients with a high pre-test probability of SBO, a negative ultrasound yielded post-test odds of 0.4%, whereas a positive POCUS yielded post-test odds of 39.6%. Among patients with a low pre-test probability, a negative POCUS resulted in post-test odds of 0%, while a positive POCUS led to post-test odds of 2.1%, yielding a number needed to scan (NNS) of ~50 to identify a patient with an SBO on CT. Conclusion This study confirmed POCUS's sensitivity and specificity of ~90-95% and a corresponding LR+ of 9.2 and LR- of 0.9. Pre-test probability substantially affected post-test odds. Patients with a high pre-test probability and a positive POCUS had post-test odds of 39.6 and should have a confirmatory CT, while those with a negative POCUS have very low post-test odds and very likely will not benefit from CT. Patients with low pre-test probability and a positive POCUS have post-test odds of 2.1%, similar to the Wells Score and HEART score; such patients may not benefit from a CT, though clinicians should use their judgment/discretion. Patients with a low pre-test probability and a negative POCUS have post-test odds of 0% and should not have a CT. Among low pre-test probability patients, the NNS was ~50 to identify patients with an SBO on CT.

SVEAT score: Acute chest pain risk stratification

ObjectiveWe aimed to compare the predictive ability of the newly introduced Symptoms, history of Vascular disease, Electrocardiography, Age, and Troponin (SVEAT) score with the widely used History, ECG, Age, Risk factors, and Troponin I (HEART) score in risk stratification for 30-day major adverse cardiac events (MACE) development among patients presenting to the emergency department with acute chest pain complaints. MethodsThis prospective, observational, single-center study was conducted at an emergency department of a tertiary care hospital between June 2022 and January 2023. We recruited all adult patients aged 24 years and above with a primary complaint of non- traumatic chest pain at the critical care unit of the Emergency Department. Inclusion Criteria: Patients aged 24 years and above with a primary complaint of chest pain lasting >5 min. Exclusion Criteria: Patients with STEMI, pregnant individuals, those with traumatic chest pain, and those without 30-day MACE data were excluded. HEART and SVEAT scores were calculated for each participant.The performance of the SVEAT score in identifying the low-risk patient group was compared to that of the HEART score. ResultsIn the study, out of 809 patients, 589 (72.8%) were categorized as low-risk based on the SVEAT score, and 377 (46.6%) based on the HEART score. Out of these 809 patients, 115 (14.2%) experienced MACE. Within the group classified as low risk by the SVEAT score, 6 (0.7%) patients experienced MACE, while within the group classified as low risk by the HEART score, 8 (1%) patients experienced MACE. The SVEAT score had an Area Under the Curve (AUC) of 0.916 (95% CI 0.890 to 0.942), which was found to be higher than the AUC of the HEART score (0.856, 95% CI 0.822 to 0.890). In our study, the sensitivity of the SVEAT and HEART scores was found to be 94.7% (95% CI 88.9%–98.0%) and 93.0% (95% CI 86.7%–96.9%), respectively. The specificity of both scores was 84.1% (95% CI 81.0%–86.6%) and 53.17% (95% CI 49.3%–56.6%), respectively. ConclusionWhile our study indicated a higher predictive power for MACE development with the SVEAT score compared to the HEART score, further extensive studies are necessary for its reliable implementation in emergency departments for chest pain risk classification.

Peripheral Arterial Disease in the Context of Acute Coronary Syndrome: A Comprehensive Analysis of Its Influence on Ejection Fraction Deterioration and the Onset of Acute Heart Failure.

Peripheral artery disease is a condition that causes narrowing of the arteries, impairing circulation to the extremities. Globally, it affects millions of people and is more prevalent in older adults and those with diabetes, high blood pressure, or high cholesterol. There is an overlap specific to polyvascular patients, and almost 50% of patients with PAD have coronary artery disease. Compelling evidence reveals a noteworthy association between PAD and major adverse cardiovascular events (MACEs) in individuals experiencing acute coronary syndrome (ACS) but limited knowledge exists regarding the influence of PAD on left ventricular systolic function during ACS. In a retrospective case-control study, we examined 100 participants who presented with ACS (mean age = 61.03 years, 80 [80%] males). The patients were divided into two groups: the ACS-PAD group (32 subjects, 74% of them with STEMI, 10% with NSTEMI, and 16% with NSTEACS) and the ACS-nonPAD group (68 participants). This study highlighted that PAD negatively impacts patients with non-ST-segment elevation myocardial infarction (NSTEMI). These patients were likely to experience a decline of approximately 19.3% in their left ventricular ejection fraction (LVEF) compared to the ACS-nonPAD group (p = 0.003) and presented a worse clinical status (the PAD group correlated with Killip class IV, p = 0.049). Our analysis indicates that patients diagnosed with NSTEACS and PAD tend to have a higher LVEF of over 55% and a lower HEART score. Patients with PAD tend to have a functionally higher EF but clinically present with more unstable scenarios (pulmonary edema and cardiogenic shock). This is mainly driven by a higher prevalence of HFpEF in the PAD group. Looking closer at the PAD group, they have a higher incidence of comorbidities such as diabetes, hypertension, high cholesterol, CAD, and stroke, as well as being more active smokers.

AI in the ED: Assessing the efficacy of GPT models vs. physicians in medical score calculation

Background and aimsArtificial Intelligence (AI) models like GPT-3.5 and GPT-4 have shown promise across various domains but remain underexplored in healthcare. Emergency Departments (ED) rely on established scoring systems, such as NIHSS and HEART score, to guide clinical decision-making. This study aims to evaluate the proficiency of GPT-3.5 and GPT-4 against experienced ED physicians in calculating five commonly used medical scores. MethodsThis retrospective study analyzed data from 150 patients who visited the ED over one week. Both AI models and two human physicians were tasked with calculating scores for NIH Stroke Scale, Canadian Syncope Risk Score, Alvarado Score for Acute Appendicitis, Canadian CT Head Rule, and HEART Score. Cohen's Kappa statistic and AUC values were used to assess inter-rater agreement and predictive performance, respectively. ResultsThe highest level of agreement was observed between the human physicians (Kappa = 0.681), while GPT-4 also showed moderate to substantial agreement with them (Kappa values of 0.473 and 0.576). GPT-3.5 had the lowest agreement with human scorers. These results highlight the superior predictive performance of human expertise over the currently available automated systems for this specific medical outcome. Human physicians achieved a higher ROC-AUC on 3 of the 5 scores, but none of the differences were statistically significant. ConclusionsWhile AI models demonstrated some level of concordance with human expertise, they fell short in emulating the complex clinical judgments that physicians make. The study suggests that current AI models may serve as supplementary tools but are not ready to replace human expertise in high-stakes settings like the ED. Further research is needed to explore the capabilities and limitations of AI in emergency medicine.

Modified HEART Score Analysis in Patients with NSTEMI and STEMI

Cardiovascular disease is one of the leading causes of death. The utility of the risk score in the emergency department is for the initial examination of patients with chest pain suspected of acute coronary syndrome. HEART score was developed in the Netherlands and utilized to stratify chest pain patients and classify the risk of the patients as well as to identify the possibility of Major Adverse Cardiac Event (MACE) occurrences in 6 weeks. This study aimed to compare STEMI and NSTEMI using the modified HEART score in chest pain patients at the Integrated Heart Center (PJT) Dr. Wahidin Sudirohusodo Government Hospital, Makassar. This study used a cross-sectional approach from January 1 to March 31, 2022. The sample was drawn from patients with chest pain clinically diagnosed NSTEMI or STEMI based on the medical records containing the procedure of initial examination and HEART score risk stratification of each patient. The HEART score was found to be statistically significantly higher in STEMI (7.0 and 7.3) than in NSTEMI (6.0 and 5.6) (p&lt;0,001). AUC 0.809 (p&lt;0.001). The cut-off value of the HEART score can be used as a predictor of STEMI/NSTEMI. A cut-off value of 5.5 and 6.5 (alternative) were obtained from the ROC curve and can provide optimal sensitivity and specificity. The higher the score appeared on the HEART score (high risk), the higher the number of STEMI diagnoses compared to NSTEMI diagnoses. This is characterized by a significant increase of Troponin-I. Hence, the HEART score can be used as a scoring measure to predict whether the patient will be diagnosed with NSTEMI or STEMI.

Assessing risk of major adverse cardiac events among patients with chest pain and cocaine use using the HEART score

IntroductionChest pain (CP), a common presentation in the emergency department (ED) setting, is associated with significant morbidity and mortality if emergency clinicians miss the diagnosis of acute coronary syndrome (ACS). The HEART (History, Electrocardiogram, Age, Risk Factors, Troponin) score had been validated for risk-stratification patients who are at high risk for ACS and major adverse cardiac events (MACE). However, the use of cocaine as a risk factor of the HEART score was controversial. We hypothesized that patients with cocaine-positive (COP) would not be associated with higher risk of 30-day MACE than cocaine-negative (CON) patients. MethodsThis retrospective study included adult patients who presented to 13 EDs of a University's Medical System between August 7, 2017 to August 19, 2021. Patients who had CP and prospectively calculated HEART scores and urine toxicology tests as part of their clinical evaluation were eligible. Areas Under The Receiver Operating Curve (AUROC) were calculated for the performance of HEART score and 30-day MACE for each group. ResultsThis study analyzed 46,210 patients' charts, 663 (1.4%) were COP patients. Mean age was statistically similar between groups but there were fewer females in the COP group (26.2% vs 53.2%, p < 0.001). Mean (+/− SD) HEART score was 3.7 (1.4) comparing to 3.1 (1.8, p < 0.001) between COP vs CON groups, respectively. Although more COP patients (54%) had moderate HEART scores (4–6) vs. CON group (35.2%, p < 0.001), rates of 30-day MACE were 1.1% for both groups. HEART score's AUROC was 0.72 for COP and 0.78 for CON groups. AUROC for the Risk Factor among COP patients, which includes cocaine, was poor (0.54). ConclusionThis study, which utilized prospective calculated HEART scores, demonstrated that overall performance of the HEART score was reasonable. Specifically, our analysis showed that the rate of 30-day MACE was not affected by cocaine use as a risk factor. We would recommend clinicians to consider the HEART score for this patient group.