Validation of Heart Failure Risk Scores MAGGIC, GWTG-HF, and SHFM in Egyptian Patients

External Validation and Recalibration of Heart Failure Risk Models (MAGGIC, GWTG-HF, and SHFM) in Egyptian Patients

Heart failure (HF) affects over 64 million people worldwide and carries high morbidity, frequent hospitalizations, and major economic burden. Accurate risk stratification is essential to guide therapy, follow-up, and advanced care decisions.

Several prognostic models have been developed:

MAGGIC: based on >39,000 patients, predicts mortality from simple clinical variables.

GWTG-HF: derived from >30,000 patients, predicts in-hospital mortality using admission data.

SHFM: estimates 1-3 year survival, incorporating clinical, lab, and treatment factors.

These models, developed mainly in Western cohorts, may not perform well in Arab populations, where HF patients are younger, with more ischemic disease, diabetes, CKD, and limited access to advanced therapies. Such differences risk score miscalibration.

External validation and recalibration are needed to assess predictive accuracy and adjust models for local populations. A head-to-head comparison of MAGGIC, GWTG-HF, and SHFM has never been done in Egypt; such a study would identify the most reliable model for predicting 1-year mortality and 30-day readmission in Egyptian HF patients.

Heart failure (HF) is a major global public health problem, affecting more than 64 million people worldwide [1]. It is associated with high morbidity, frequent hospitalizations, poor quality of life, and substantial economic burden [2]. Accurate risk stratification is central to HF management: it helps guide treatment intensity, identify patients needing closer follow-up, and informs advanced therapy referral and patient counseling.

Over the last two decades, several prognostic models have been developed to predict outcomes in HF patients:

1. Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC): derived from >39,000 patients in 30 cohort studies, predicting mortality based on simple clinical and demographic variables [3]. It has been externally validated in Western and Asian cohorts [4].
2. Get With The Guidelines-Heart Failure (GWTG-HF) risk score: developed from the American Heart Association registry (>30,000 patients), primarily for in-hospital mortality, based on admission data such as SBP, HR, sodium, and BUN [5]. It has shown good predictive value in US and Japanese patients [6,7].
3. Seattle Heart Failure Model (SHFM): a comprehensive model estimating 1-3 year survival in chronic HF patients, integrating demographics, laboratory data, medications, and device therapies [8,9]. It is widely used in advanced HF and transplant referral.

While these models are widely applied, they were derived predominantly from North American and European cohorts. Their performance in Arab populations is uncertain. HF patients in Egypt and the Arab world often present younger, with higher rates of ischemic cardiomyopathy, diabetes, and chronic kidney disease compared with Western cohorts [11-13]. Access to novel therapies (e.g., ARNI, SGLT2 inhibitors) and device-based therapies is lower. and healthcare system constraints may contribute to higher early readmission rates [14]. These differences may lead to miscalibration of existing scores, systematically over- or underestimating risk in this population.

External validation is therefore essential to test model discrimination (ability to distinguish high vs. low risk patients) and calibration (agreement between predicted and observed risk). When miscalibration is found, models can undergo recalibration (adjustment of intercept and/or slope) to improve local performance without discarding their predictive structure .

A direct head-to-head comparison of MAGGIC, GWTG-HF, and SHFM in Egyptian patients has never been conducted. Such validation will provide clinicians with evidence on which model is most accurate for predicting 1-year mortality and 30-day readmission, and whether recalibration is needed to optimize performance for local practice.

1. Inclusion criteria:

   • Age ≥18 years.
   • Clinical HF diagnosis (per ESC/ACC/AHA criteria).
   • Available baseline data to compute ≥1 of the three scores within 24h of index assessment (admission or clinic baseline).
   • For longitudinal endpoints: reachable for follow-up.

2. Exclusion criteria:

   • Cardiogenic shock requiring immediate MCS at presentation (analyzed separately).
   • Isolated right HF from primary pulmonary disease without LV involvement (for main analysis