Related Experiment Video
Updated: May 21, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
Published on: August 29, 2025
External validation of the GRACE Freedom from Events score
Anne-Maree Kelly1, Prashant Dabee, Sharon Klim
1Joseph Epstein Centre for Emergency Medicine at Western Health, Sunshine Hospital, Furlong Road, St Albans 3021, Australia. anne-maree.kelly@wh.org.au
Insights
The GRACE Freedom-from-Event Score (GFFES) accurately identifies low-risk acute coronary syndrome patients, showing no major adverse cardiac events in this group. This score is a valuable tool for risk stratification in cardiology.
Area of Science:
- Cardiology
- Clinical Risk Prediction
Background:
- Acute coronary syndrome (ACS) presents a significant threat, yet most patients have a low risk of adverse events.
- The GRACE Freedom-from-Event Score (GFFES) was developed in 2005 to identify low-risk ACS patients.
Purpose of the Study:
- To externally validate the predictive performance of the GFFES in a real-world clinical setting.
- To assess the GFFES's ability to identify patients at low risk for major adverse cardiac events (MACE).
Main Methods:
- A prospective observational cohort study included 238 patients admitted with chest pain, unstable angina, or myocardial infarction (MI).
- Data on clinical factors and investigations were collected.
- Major adverse cardiac events (MACE) were defined as death, new MI, stroke, and other severe complications during index admission. Predictive performance was analyzed using ROC curves.
Main Results:
- 32.8% of patients were classified as low risk (GFFES score ≥287).
- No MACE occurred in the low-risk group.
- The GFFES demonstrated good predictive performance with an AUC of 0.74, 100% sensitivity, and 100% negative predictive value for MACE.
Conclusions:
- The GFFES showed good discrimination, sensitivity, and negative predictive value in this external validation.
- The score may serve as a useful tool for risk-based patient management and care allocation in ACS.
- Further multi-center validation could strengthen its clinical utility.
Aim:
Acute coronary syndrome (ACS) is a common life-threatening condition but the majority of patients are at low risk of acute adverse events. In 2005, the GRACE Freedom-from-Event score (GFFES) was developed to identify patients with a low risk of adverse in-hospital events. Our aim was to externally validate this score.
Methods:
A prospective observational cohort of patients was admitted to a cardiology service with admission diagnoses of chest pain, unstable angina or myocardial infarction (MI). Clinical and investigational data were collected. Defined major adverse cardiac events (MACE) were death, new MI, stroke, acute pulmonary oedema, cardiac arrest or sustained ventricular tachycardia, high degree atrioventricular block, cardiogenic shock, pacemaker or intra-aortic balloon pump insertion, assisted ventilation or new acute renal failure occurring during the index admission. The primary outcome of interest was the predictive performance of the GFFES for MACE, by ROC curve and clinical performance analysis.
Results:
238 patients were studied; median age 67, 56.7% were male. Seventy-eight patients (32.8%) were classified as low risk by the score (GFFES score≥287). There were no MACE in the low risk group. The AUC for predictive performance of the GFFES was 0.74 (95% CI 0.62-0.86). Sensitivity was 100% (95% CI 71.7-100%), specificity 34.7% (95% CI 28.5-41.3%) and negative predictive value 100% (95% CI 94.2-100%).
Conclusion:
In this single site prospective validation, GFFES showed good discrimination, sensitivity and negative predictive value. It may be a useful tool for assigning patients to appropriate levels of care based on risk.
Related Concept Videos
Confidence Coefficient
Goodness-of-Fit Test
Self-Evaluation: Self-Enhancement and Self-Verification
Data Validation
Nursing assessment guides are generally based on holistic models rather than medical...
Data Validation
Key parameters for method validation include:
Self-Evaluation Maintenance Model