Evidence map›Paper›PMID 39834041›Full record

Observational studyEuropean journal of heart failure2025

Predicting survival in patients with severe heart failure: Risk score validation in the HELP-HF cohort.

Mauro Chiarito, Davide Stolfo, Alessandro Villaschi, Samantha Sartori, Luca Baldetti, Carlo Mario Lombardi, Marianna Adamo, Ferdinando Loiacono, Antonio Maria Sammartino, Mauro Riccardi and 15 more

Abstract readMulticenter StudyObservational StudyValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in European journal of heart failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors.

Mauro ChiaritoHumanitas Research Hospital IRCCS, Rozzano, Milan, Italy.
Davide StolfoCardiovascular Department, Azienda Sanitaria Universitaria Giuliano Isontina (ASUGI), University of Trieste, Trieste, Italy.
Alessandro VillaschiHumanitas Research Hospital IRCCS, Rozzano, Milan, Italy.
Samantha SartoriCenter for Interventional Cardiovascular Research and Clinical Trials, Icahn School Medicine at Mount Sinai, New York, NY, USA.
Luca BaldettiCardiac Intensive Care Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
Carlo Mario LombardiInstitute of Cardiology, ASST Spedali Civili, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy.
Marianna AdamoInstitute of Cardiology, ASST Spedali Civili, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy.
Ferdinando LoiaconoHumanitas Research Hospital IRCCS, Rozzano, Milan, Italy.
Antonio Maria SammartinoInstitute of Cardiology, ASST Spedali Civili, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy.
Mauro RiccardiInstitute of Cardiology, ASST Spedali Civili, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy.
Daniela TomasoniInstitute of Cardiology, ASST Spedali Civili, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy.
Riccardo Maria InciardiInstitute of Cardiology, ASST Spedali Civili, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy.
Marta MaccalliniHumanitas Research Hospital IRCCS, Rozzano, Milan, Italy.
Gaia GaspariniHumanitas Research Hospital IRCCS, Rozzano, Milan, Italy.
Benedetta GrossiDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
Stefano ContessiCardiovascular Department, Azienda Sanitaria Universitaria Giuliano Isontina (ASUGI), University of Trieste, Trieste, Italy.
Daniele CocianniCardiovascular Department, Azienda Sanitaria Universitaria Giuliano Isontina (ASUGI), University of Trieste, Trieste, Italy.
Maria PerottoCardiovascular Department, Azienda Sanitaria Universitaria Giuliano Isontina (ASUGI), University of Trieste, Trieste, Italy.
Giuseppe BaroneCardiac Intensive Care Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
Marco MerloCardiovascular Department, Azienda Sanitaria Universitaria Giuliano Isontina (ASUGI), University of Trieste, Trieste, Italy.
Alberto Maria CappellettiIRCCS San Raffaele Scientific Institute, Milan, Italy.
Gianfranco SinagraCardiovascular Department, Azienda Sanitaria Universitaria Giuliano Isontina (ASUGI), University of Trieste, Trieste, Italy.
Daniela PiniHumanitas Research Hospital IRCCS, Rozzano, Milan, Italy.
Marco MetraInstitute of Cardiology, ASST Spedali Civili, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy.
Matteo PagnesiInstitute of Cardiology, ASST Spedali Civili, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsAccurate selection of patients with severe heart failure (HF) who might benefit from advanced therapies is crucial. The present study investigates the performance of the available risk scores aimed at predicting the risk of mortality in patients with severe HF. METHODS AND

resultsThe risk of 1-year mortality was estimated in patients with severe HF enrolled in the HELP-HF cohort according to the MAGGIC, 3-CHF, ADHF/NT-proBNP, and GWTG-HF risk scores, the number of criteria of the 2018 HFA-ESC definition of advanced HF, I NEED HELP markers, domains fulfilled of the 2019 HFA-ESC definition of frailty, the frailty index, and the INTERMACS profile. In addition, we tested the performance of different machine learning (ML)-based models to predict 1-year mortality. At 1-year follow-up, 265 patients (23.1%) died. The prognostic accuracy, tested in the subgroup of patients with completeness of all data regarding the variables included in the scores (497/1149 patients), resulted moderate for MAGGIC, GWTG-HF, and ADHF/NT-proBNP scores (area under the curve [AUC] ≥0.70) and only poor for the other tools. All the scores lost accuracy in estimating the rate of 1-year mortality in patients at the highest risk. Support vector machine-based model had the best AUC among ML-based models, slightly outperforming most of the tested risk scores.

conclusionMost of the scores used to predict the risk of mortality in HF performed poorly in real-world patients with severe HF and provided inaccurate estimate of the risk of 1-year mortality in patients at the highest risk. ML-based models did not significantly outperform the currently available risk scores and their use must be validated in large cohort of patients.

Indexed as

Heart FailureAgedFemaleHumansMachine LearningMaleMiddle AgedNatriuretic Peptide, BrainPrognosisRisk AssessmentRisk FactorsSeverity of Illness IndexSurvival RateNatriuretic Peptide, BrainAdvanced heart failureHeart failureHELP‐HFRisk scores

Identifiers

PMID39834041
PMCPMC12034438

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.