Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies.

By Stuart J. Pocock, C. Ariti, J. McMurray, A. Maggioni, L. Køber, I. Squire, K. Swedberg, J. Dobson, K. Poppe, G. Whalley and 1 more. European Heart Journal, 2013

1,201 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine

View Paper: https://www.semanticscholar.org/paper/4ee2a2b01a0fcef4c35ea1cf6ff5ea3e50ab1d27

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. S. Anker
2. Gerasimos Filippatos
3. J. Januzzi
4. C. Yancy
5. M. Drazner

Citations in the corpus

• Phenomapping for Novel Classification of Heart Failure with Preserved Ejection Fraction (2014, neutral): “Normal diastolic function 21 (16) 9 (8) 2 (1)”
• PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees (2018, methodology): “In addition, we provide high-level (average) results for five additional datasets (with various characteristics): MAGGIC [27], UNOS-Heart wait-list [7], Kaggle cervical cancer dataset [16], UCI ISOLET dataset and UCI Epileptic Seizure Recognition dataset.”
• 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure (2016, neutral): “However, symptom severity correlates poorly with many measures of LV function; although there is a clear relationship between the severity of symptoms and survival, patients with mild symptoms may still have an increased risk of hospitalization and death.8 –10 Sometimes the term ‘advanced HF’ is use”
• Universal Definition and Classification of Heart Failure. (2021, methodology): “A similar parallel in HF is the MAGGIC model for the prediction of mortality and other attempts at scoring to help risk stratify patients who may have worsening HF, rehospitalization, or a greater chance of dying.(79)”