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Predictive Factors and Risk Assessment for Hospitalization in Chest Pain Patients Admitted to the Emergency
Nadya Kagansky1,2, David Mazor1,3, Ayashi Wajdi1,2
1Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel.
Insights
The HEART score effectively predicts hospitalization for chest pain patients, outperforming the TIMI score. Key factors include hyperlipidemia, diaphoresis, dyspnea, and hypertension for accurate risk assessment.
Area of Science:
- Emergency Medicine
- Cardiology
- Clinical Decision Support
Background:
- Chest pain is a frequent reason for emergency department visits, often with inconclusive symptoms.
- Managing these patients presents diagnostic challenges and strains emergency department resources.
- This study investigated factors influencing hospitalization decisions for chest pain patients.
Purpose of the Study:
- To identify predictors of hospitalization versus discharge in emergency department patients with chest pain.
- To compare the predictive accuracy of the HEART score and TIMI score for hospitalization.
- To develop a predictive model for chest pain hospitalization risk.
Main Methods:
- A cohort study of 330 patients with chest pain, referred by primary care, was conducted.
- Data collected included demographics, medical history, symptoms, lab results, and risk scores.
- Predictive models were developed and compared using ROC analysis.
Main Results:
- Hospitalization occurred in 41.5% of patients; hospitalized patients were significantly older (median 70 vs. 57 years).
- Hyperlipidemia, diaphoresis, dyspnea, and hypertension were significant predictors of hospitalization.
- The HEART score (AUC 0.807) demonstrated higher predictive accuracy than the TIMI score (AUC 0.742).
Conclusions:
- The HEART score is valuable for rapid risk assessment of chest pain patients.
- A model incorporating hyperlipidemia, diaphoresis, dyspnea, and hypertension is highly predictive of hospitalization.
- Further validation in larger populations is recommended.
Background:
Chest pain is one of the most common reasons for emergency department (ED) visits. Patients presenting with inconclusive symptoms complicate the diagnostic process and add to the burden upon the ED. This study aimed to determine factors possibly influencing ED decisions on hospitalization versus discharge for patients with the diagnosis of chest pain.
Methods:
In the cohort study including 400 patients admitted to the emergency unit with a working diagnosis of chest pain, data on demographics, medical history, symptoms, lab results, and risk scores were collected from the medical records of patients admitted to the ED with a working diagnosis of chest pain. To reduce potential bias, the analysis was restricted to 330 patients who were referred to the ED by a primary care provider or clinic for chest pain.
Results:
Of 330 patients admitted to the ED, 58.5% were discharged, and 41.5% were hospitalized. Hospitalized patients were significantly older, with a median age of 70 versus 57 years for those discharged (p < 0.001). A higher proportion of hospitalizations occurred during the late-night shift. Significant predictors of hospitalization included hyperlipidemia (OR 3.246), diaphoresis (OR 8.525), dyspnea (OR 2.897), and hypertension (OR 1.959). Nursing home residents had a lower risk of hospitalization (OR 0.381). The area under the ROC curve for this model was 0.801 (95% CI: 0.753-0.848), indicating the predictive accuracy of the model in estimating the probability of admission. The HEART (history, ECG, age, risk factors, and troponin level) score was more effective than the TIMI (Thrombolysis in Myocardial Infarction) score in predicting the need for hospitalization, with an area under the curve (AUC) of 0.807 compared to 0.742 for TIMI.
Conclusions:
The HEART score in comparison with TIMI score proved especially valuable for quick risk assessment for hospitalization. The model that included hyperlipidemia, diaphoresis, dyspnea, and hypertension was the most predictive for the risk of hospitalization. Further research with larger populations is needed to validate these findings.
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