Cardiac Risk Stratification Scoring Systems for Suspected Acute Coronary Syndromes in the Emergency Department

Prehospital acute life-threatening cardiovascular disease in elderly: an observational, prospective, multicentre, ambulance-based cohort study

OBJECTIVE

The aim was to explore the association of demographic and prehospital parameters with short-term and long-term mortality in acute life-threatening cardiovascular disease by using a hazard model, focusing on elderly individuals, by comparing patients under 75 years versus patients over 75 years of age.

DESIGN

Prospective, multicentre, observational study.

SETTING

Emergency medical services (EMS) delivery study gathering data from two back-to-back studies between 1 October 2019 and 30 November 2021. Six advanced life support (ALS), 43 basic life support and five hospitals in Spain were considered.

PARTICIPANTS

Adult patients suffering from acute life-threatening cardiovascular disease attended by the EMS.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome was in-hospital mortality from any cause within the first to the 365 days following EMS attendance. The main measures included prehospital demographics, biochemical variables, prehospital ALS techniques used and syndromic suspected conditions.

RESULTS

A total of 1744 patients fulfilled the inclusion criteria. The 365-day cumulative mortality in the elderly amounted to 26.1% (229 cases) versus 11.6% (11.6%) in patients under 75 years old. Elderly patients (≥75 years) presented a twofold risk of mortality compared with patients ≤74 years. Life-threatening interventions (mechanical ventilation, cardioversion and defibrillation) were also related to a twofold increased risk of mortality. Importantly, patients suffering from acute heart failure presented a more than twofold increased risk of mortality.

CONCLUSIONS

This study revealed the prehospital variables associated with the long-term mortality of patients suffering from acute cardiovascular disease. Our results provide important insights for the development of specific codes or scores for cardiovascular diseases to facilitate the risk of mortality characterisation.

A Machine Learning Based Discharge Prediction of Cardiovascular Diseases Patients in Intensive Care Units

This paper targets a major challenge of how to effectively allocate medical resources in intensive care units (ICUs). We trained multiple regression models using the Medical Information Mart for Intensive Care III (MIMIC III) database recorded in the period between 2001 and 2012. The training and validation dataset included pneumonia, sepsis, congestive heart failure, hypotension, chest pain, coronary artery disease, fever, respiratory failure, acute coronary syndrome, shortness of breath, seizure and transient ischemic attack, and aortic stenosis patients’ recorded data. Then we tested the models on the unseen data of patients diagnosed with coronary artery disease, congestive heart failure or acute coronary syndrome. We included the admission characteristics, clinical prescriptions, physiological measurements, and discharge characteristics of those patients. We assessed the models’ performance using mean residuals and running times as metrics. We ran multiple experiments to study the data partition’s impact on the learning phase. The total running time of our best-evaluated model is 123,450.9 mS. The best model gives an average accuracy of 98%, highlighting the location of discharge, initial diagnosis, location of admission, drug therapy, length of stay and internal transfers as the most influencing patterns to decide a patient’s readiness for discharge.

Prognostic value of GRACE score for in-hospital and 6 months outcomes after non-ST elevation acute coronary syndrome

Background

The aim of this study was to determine the predictive value of the Global Registry of Acute Coronary Events (GRACE) score for predicting in-hospital and 6 months mortality after non-ST elevation acute coronary syndrome (NSTE-ACS).

Results

In this observational study, 300 patients with NSTE-ACS of age more than 30 years were included; 16 patients died during the hospital stay (5.3%). Of 284 patients at 6 months assessment, 10 patients died (3.5%), 240 survived (84.5%), and 34 were lost to follow-up (12%) respectively. In high risk category, 10.5% of the patients died within hospital stay and 11.8% died within 6 months (p = 0.001 and p = 0.013). In univariate analysis, gender, diabetes mellitus, family history, smoking, and GRACE score were significantly associated with in-hospital mortality whereas age, obesity, dyslipidemia, and GRACE were significantly associated with 6 months mortality. After adjustment, diabetes mellitus, family history, and GRACE score remained significantly associated with in-hospital mortality (p ≤ 0.05) and age remained significantly associated with 6 months mortality.

Conclusion

GRACE risk score has good predictive value for the prediction of in-hospital mortality and 6 months mortality among patients with NSTE-ACS.