Evidence map›Paper›PMID 41116068›Full record

ArticleScientific reports2025

Cardiovascular risk assessment enhanced by automated machine learning in a multi-phase study.

Igor Bibi, Daniel Schaffert, Philipp Blanke, Lorenz Illian, Federico Lenzing, Niklas Martin, Jan Leipe, Winfried März, Ksenija Stach, Victor Olsavszky

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Igor BibiDepartment of Dermatology, Venereology and Allergy, Medical Faculty Mannheim, Center of Excellence in Dermatology, University Medical Center, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
Daniel SchaffertDepartment of Dermatology, Venereology and Allergy, Medical Faculty Mannheim, Center of Excellence in Dermatology, University Medical Center, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
Philipp BlankeSYNLAB Academy, SYNLAB Holding Deutschland GmbH, P5, 7, Mannheim, Germany.
Lorenz IllianFifth Department of Medicine (Nephrology/Endocrinology/Rheumatology), Medical Faculty Mannheim, University Medical Center, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
Federico LenzingFifth Department of Medicine (Nephrology/Endocrinology/Rheumatology), Medical Faculty Mannheim, University Medical Center, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
Niklas MartinDepartment of Dermatology, Venereology and Allergy, Medical Faculty Mannheim, Center of Excellence in Dermatology, University Medical Center, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
Jan LeipeUniversity Medical Center, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
Winfried MärzSYNLAB Academy, SYNLAB Holding Deutschland GmbH, P5, 7, Mannheim, Germany.
Ksenija Stach *University Medical Center, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany. Ksenija.Stach-Jablonski@klinikumevb.de.
Victor Olsavszky *Department of Dermatology, Venereology and Allergy, Medical Faculty Mannheim, Center of Excellence in Dermatology, University Medical Center, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany. victor.olsavszky@medma.uni-heidelberg.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, and current predictors such as lipoprotein (a) [Lp(a)] and risk scores have limitations. Automated machine learning (AutoML) offers the potential to improve CVD risk prediction by processing large datasets and developing tailored models without the need for extensive data science expertise. Using clinical datasets from the LURIC (n = 3316) and UMC/M (n = 423) studies, we built AutoML models to predict Lp(a), specific CVDs and CVD-related mortality in three phases. Phase 1 identified key CVD determinants such as age, Lp(a), troponin T, BMI and cholesterol with good accuracy (AUC 0.6249 to 0.9101). Phase 2 validated models in the UMC/M dataset and showed robust performance (AUC 0.7224 to 0.8417), with SHAP analysis highlighting predictors like statin therapy, age and NTproBNP. Phase 3 focused on cardiovascular mortality prediction, achieving high AUC values (0.74 to 0.85) and showed data drift, highlighting the need for model adjustment.

Indexed as

Cardiovascular DiseasesMachine LearningAgedFemaleHeart Disease Risk FactorsHumansLipoprotein(a)MaleMiddle AgedRisk AssessmentRisk FactorsLipoprotein(a)Automated machine learningCardiovascular diseasesClinical datasetsPredictive modelingRisk stratification

Identifiers

PMID41116068
PMCPMC12537956

What Socratic holds

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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.