Comparison of MAGGIC and MECKI risk scores to predict mortality after cardiac rehabilitation among Dutch heart failure patients

Kouwert, Ilse Jm; Bakker, Esmée A.; Snoek, Johan A.; Eijsvogels, Thijs M. H.

doi:10.1177/2047487319865730

Cited by 17 publications

(10 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, risk scores distilled from the models suffer similar problems, limiting their reliability [40]. Such uncertainty has led to the proliferation of new risk scores appearing in the literature in the last years, with mixed results [41][42][43][44][45][46][47]. As a partial solution to improve models' effectiveness, recent published studies included cohorts restricted to specific classes of patients (for example, elderly or diabetic) [48,49].…”

Section: Introductionmentioning

confidence: 99%

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Chicco

Jurman

2020

BMC Med Inform Decis Mak

386

171

View full text Add to dashboard Cite

Background: Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body. Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at highlighting patterns and correlations otherwise undetectable by medical doctors. Machine learning, in particular, can predict patients' survival from their data and can individuate the most important features among those included in their medical records. Methods: In this paper, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning classifiers to both predict the patients survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests, and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone. Results: Our results of these two-feature models show not only that serum creatinine and ejection fraction are sufficient to predict survival of heart failure patients from medical records, but also that using these two features alone can lead to more accurate predictions than using the original dataset features in its entirety. We also carry out an analysis including the follow-up month of each patient: even in this case, serum creatinine and ejection fraction are the most predictive clinical features of the dataset, and are sufficient to predict patients' survival. Conclusions: This discovery has the potential to impact on clinical practice, becoming a new supporting tool for physicians when predicting if a heart failure patient will survive or not. Indeed, medical doctors aiming at understanding if a patient will survive after heart failure may focus mainly on serum creatinine and ejection fraction.

show abstract

Section: Introductionmentioning

confidence: 99%

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Chicco

Jurman

2020

BMC Med Inform Decis Mak

386

171

View full text Add to dashboard Cite

show abstract

“…MECKI score, Metabolic Exercise combined with Cardiac and Kidney Indexes; HFSS, Heart Failure Survival Score) (5,6), is well established. Particularly, the MECKI score, including pVO 2 and ventilatory efficiency together with four non-CPET prognostic variables (EF, haemoglobin, sodium, renal function), has been created (5), recently validated (7)(8)(9) and found, at present, as the most powerful outcome predictor at 1-2 and 4 years of patients with HFrEF (9,10). Accordingly, it might reasonable that also in a multifaceted group, such as the HFmrEF population, the CPET might be extremely useful both to obtain a comprehensive functional and a prognostic assessment.…”

Section: Introductionmentioning

confidence: 99%

Cardiovascular Death Risk in Recovered Mid-Range Ejection Fraction Heart Failure: Insights From Cardiopulmonary Exercise Test

Magrì¹,

Piepoli²,

Corrà³

et al. 2020

Journal of Cardiac Failure

View full text Add to dashboard Cite

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

show abstract

“…The prognostic accuracy of the MECKI score was significantly superior to both alternative scores at both two years and four years. 4 It has also been shown to be superior in predicting mortality compared with the MAGGIC score, notably after participation of heart failure with reduced ejection fraction (HFrEF) patients in a cardiac rehabilitation programme, 5 although this particular cohort may have played to the advantage of the MECKI score because it was restricted to those with good quality peak oxygen consumption (VO 2 ) test results.…”

Section: The Mecki Scorementioning

confidence: 99%

Future developments in the MECKI score initiative

Coats

2020

European Journal of Preventive Cardiology

View full text Add to dashboard Cite

The Metabolic Exercise combined with Cardiac and Kidney Indexes [MECKI) score is a validated prognostic score for heart failure with reduced ejection fraction which combines commonly available clinical and metabolic parameters with two cardiopulmonary exercise test derived prognostic measurements. It has been validated to predict prognosis and to aid clinical decision making and it has been shown to be superior in predicting mortality compared with other commonly used prognostic scores for heart failure. In the future it would be valuable to establish whether the score holds true also in other settings, and in particular in under-represented groups – the elderly, women, and people of different ethnic backgrounds – and in other heart failure syndromes. In future it may be extended to assess its value in the presence of a range of co-morbidities such as chronic obstructive pulmonary disease, pulmonary hypertension and frailty and cachexia as well as in other conditions such as hypertrophic cardiomyopathy, amyloid, asymptomatic left ventricular dysfunction and hypertension. It may also be a candidate end-point for adaptive trials designed to prove an improvement in the MECKI score as an approvable interim end-point whilst larger mortality and morbidity trials are still underway.

show abstract

Comparison of MAGGIC and MECKI risk scores to predict mortality after cardiac rehabilitation among Dutch heart failure patients

Cited by 17 publications

References 14 publications

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Cardiovascular Death Risk in Recovered Mid-Range Ejection Fraction Heart Failure: Insights From Cardiopulmonary Exercise Test

Future developments in the MECKI score initiative

Contact Info

Product

Resources

About