Evidence map›Paper›PMID 39286486›Full record

ArticleiScience2024

Improving cardiovascular risk stratification through multivariate time-series analysis of cardiopulmonary exercise test data.

Evangelos Ntalianis, Nicholas Cauwenberghs, František Sabovčik, Everton Santana, Francois Haddad, Jomme Claes, Matthijs Michielsen, Guido Claessen, Werner Budts, Kaatje Goetschalckx and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2024. 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

12 authors.

Evangelos NtalianisResearch Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium.
Nicholas CauwenberghsResearch Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium.
František SabovčikResearch Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium.
Everton SantanaResearch Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium.
Francois HaddadStanford Cardiovascular Institute and Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Jomme ClaesRehabilitation in Internal Disorders, KU Leuven Department of Rehabilitation Sciences, University of Leuven, Leuven, Belgium.
Matthijs MichielsenRehabilitation in Internal Disorders, KU Leuven Department of Rehabilitation Sciences, University of Leuven, Leuven, Belgium.
Guido ClaessenDepartment of Cardiology, Hartcentrum, Virga Jessa Hospital, Hasselt, Belgium.
Werner BudtsCardiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium.
Kaatje GoetschalckxCardiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium.
Véronique CornelissenRehabilitation in Internal Disorders, KU Leuven Department of Rehabilitation Sciences, University of Leuven, Leuven, Belgium.
Tatiana KuznetsovaResearch Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nowadays cardiorespiratory fitness (CRF) is assessed using summary indexes of cardiopulmonary exercise tests (CPETs). Yet, raw time-series CPET recordings may hold additional information with clinical relevance. Therefore, we investigated whether analysis of raw CPET data using dynamic time warping combined with k-medoids could identify distinct CRF phenogroups and improve cardiovascular (CV) risk stratification. CPET recordings from 1,399 participants (mean age, 56.4 years; 37.7% women) were separated into 5 groups with distinct patterns. Cluster 5 was associated with the worst CV profile with higher use of antihypertensive medication and a history of CV disease, while cluster 1 represented the most favorable CV profile. Clusters 4 (hazard ratio: 1.30;

Indexed as

Artificial intelligenceCardiovascular medicineKinesiology

Identifiers

PMID39286486
PMCPMC11403400

What Socratic holds

Textmetadata
LicenceCC BY
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.