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

NCT ID: NCT07194889

Last Updated: 2025-10-01

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Total Enrollment: 140 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-10-01
• Study Completion Date: 2027-10-31

Brief Summary

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.

Detailed Description

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.

Conditions

Heart Failure

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Eligibility Criteria

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.

Exclusion Criteria

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Assiut University

OTHER

Sponsor Role: lead

Responsible Party

Antonious Gamal

Resident doctor

Responsibility Role: PRINCIPAL_INVESTIGATOR

References

Hassanin A, Hassanein M, Bendary A, Maksoud MA. Demographics, clinical characteristics, and outcomes among hospitalized heart failure patients across different regions of Egypt. Egypt Heart J. 2020 Aug 13;72(1):49. doi: 10.1186/s43044-020-00082-0.

Reference Type: BACKGROUND

PMID: 32789717 (View on PubMed)

AlHabib KF, Elasfar AA, Alfaleh H, Kashour T, Hersi A, AlBackr H, Alshaer F, AlNemer K, Hussein GA, Mimish L, Almasood A, AlHabeeb W, AlGhamdi S, Alsharari M, Chakra E, Malik A, Soomro R, Ghabashi A, Al-Murayeh M, Abuosa A. Clinical features, management, and short- and long-term outcomes of patients with acute decompensated heart failure: phase I results of the HEARTS database. Eur J Heart Fail. 2014 Apr;16(4):461-9. doi: 10.1002/ejhf.57.

Reference Type: BACKGROUND

PMID: 24515441 (View on PubMed)

Ibrahim MM, Damasceno A. Hypertension in developing countries. Lancet. 2012 Aug 11;380(9841):611-9. doi: 10.1016/S0140-6736(12)60861-7.

Reference Type: BACKGROUND

PMID: 22883510 (View on PubMed)

Sliwa K, Davison BA, Mayosi BM, Damasceno A, Sani M, Ogah OS, Mondo C, Ojji D, Dzudie A, Kouam Kouam C, Suliman A, Schrueder N, Yonga G, Ba SA, Maru F, Alemayehu B, Edwards C, Cotter G. Readmission and death after an acute heart failure event: predictors and outcomes in sub-Saharan Africa: results from the THESUS-HF registry. Eur Heart J. 2013 Oct;34(40):3151-9. doi: 10.1093/eurheartj/eht393. Epub 2013 Sep 18.

Reference Type: BACKGROUND

PMID: 24048728 (View on PubMed)

Pocock SJ, Ariti CA, McMurray JJ, Maggioni A, Kober L, Squire IB, Swedberg K, Dobson J, Poppe KK, Whalley GA, Doughty RN; Meta-Analysis Global Group in Chronic Heart Failure. Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies. Eur Heart J. 2013 May;34(19):1404-13. doi: 10.1093/eurheartj/ehs337. Epub 2012 Oct 24.

Reference Type: BACKGROUND

PMID: 23095984 (View on PubMed)

Aaronson KD, Schwartz JS, Chen TM, Wong KL, Goin JE, Mancini DM. Development and prospective validation of a clinical index to predict survival in ambulatory patients referred for cardiac transplant evaluation. Circulation. 1997 Jun 17;95(12):2660-7. doi: 10.1161/01.cir.95.12.2660.

Reference Type: BACKGROUND

PMID: 9193435 (View on PubMed)

Levy WC, Mozaffarian D, Linker DT, Sutradhar SC, Anker SD, Cropp AB, Anand I, Maggioni A, Burton P, Sullivan MD, Pitt B, Poole-Wilson PA, Mann DL, Packer M. The Seattle Heart Failure Model: prediction of survival in heart failure. Circulation. 2006 Mar 21;113(11):1424-33. doi: 10.1161/CIRCULATIONAHA.105.584102. Epub 2006 Mar 13.

Reference Type: BACKGROUND

PMID: 16534009 (View on PubMed)

Shiraishi Y, Kohsaka S, Abe T, Mizuno A, Goda A, Izumi Y, Yagawa M, Akita K, Sawano M, Inohara T, Takei M, Kohno T, Higuchi S, Yamazoe M, Mahara K, Fukuda K, Yoshikawa T; West Tokyo Heart Failure Registry Investigators. Validation of the Get With The Guideline-Heart Failure risk score in Japanese patients and the potential improvement of its discrimination ability by the inclusion of B-type natriuretic peptide level. Am Heart J. 2016 Jan;171(1):33-9. doi: 10.1016/j.ahj.2015.10.008. Epub 2015 Nov 11.

Reference Type: BACKGROUND

PMID: 26699598 (View on PubMed)

Matsue Y, Damman K, Voors AA, Kagiyama N, Yamaguchi T, Kuroda S, Okumura T, Kida K, Mizuno A, Oishi S, Inuzuka Y, Akiyama E, Matsukawa R, Kato K, Suzuki S, Naruke T, Yoshioka K, Miyoshi T, Baba Y, Yamamoto M, Murai K, Mizutani K, Yoshida K, Kitai T. Time-to-Furosemide Treatment and Mortality in Patients Hospitalized With Acute Heart Failure. J Am Coll Cardiol. 2017 Jun 27;69(25):3042-3051. doi: 10.1016/j.jacc.2017.04.042.

Reference Type: BACKGROUND

PMID: 28641794 (View on PubMed)

Peterson PN, Rumsfeld JS, Liang L, Albert NM, Hernandez AF, Peterson ED, Fonarow GC, Masoudi FA; American Heart Association Get With the Guidelines-Heart Failure Program. A validated risk score for in-hospital mortality in patients with heart failure from the American Heart Association get with the guidelines program. Circ Cardiovasc Qual Outcomes. 2010 Jan;3(1):25-32. doi: 10.1161/CIRCOUTCOMES.109.854877. Epub 2009 Dec 8.

Reference Type: BACKGROUND

PMID: 20123668 (View on PubMed)

Sawano M, Shiraishi Y, Kohsaka S, Nagai T, Goda A, Mizuno A, Sujino Y, Nagatomo Y, Kohno T, Anzai T, Fukuda K, Yoshikawa T. Performance of the MAGGIC heart failure risk score and its modification with the addition of discharge natriuretic peptides. ESC Heart Fail. 2018 Aug;5(4):610-619. doi: 10.1002/ehf2.12278. Epub 2018 Mar 9.

Reference Type: BACKGROUND

PMID: 29520978 (View on PubMed)

Savarese G, Lund LH. Global Public Health Burden of Heart Failure. Card Fail Rev. 2017 Apr;3(1):7-11. doi: 10.15420/cfr.2016:25:2.

Reference Type: BACKGROUND

PMID: 28785469 (View on PubMed)

Groenewegen A, Rutten FH, Mosterd A, Hoes AW. Epidemiology of heart failure. Eur J Heart Fail. 2020 Aug;22(8):1342-1356. doi: 10.1002/ejhf.1858. Epub 2020 Jun 1.

Reference Type: BACKGROUND

PMID: 32483830 (View on PubMed)

Other Identifiers

Validation of HF risk scores

Identifier Type: -

Identifier Source: org_study_id