Predicting left-ventricular recovery and characterizing Cardiorenal risk after Transcatheter aortic valve replacement: A routine-data phenotyping and prediction-model study.

Fuhai Li, Yongchao Zhao, Wei Luo, Jingmin Zhou, Chun Xiao, Junbo Ge

Abstract read

In one paragraph

Article in International journal of cardiology. Heart & vasculature, 2026. 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

6 authors.

Fuhai LiDepartment of Cardiology, the Affiliated Huizhou Hospital, Guangzhou Medical University, Huizhou, Guangdong 516002, China.

Yongchao ZhaoDepartment of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou 563000, China.

Wei LuoDepartment of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing 100037, China.

Jingmin ZhouDepartment of Cardiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Cardiovascular Diseases, Shanghai 200032, China.

Chun XiaoDepartment of Cardiology, the Affiliated Huizhou Hospital, Guangzhou Medical University, Huizhou, Guangdong 516002, China.

Junbo GeDepartment of Cardiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Cardiovascular Diseases, Shanghai 200032, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Ejection fraction (LVEF) underpins risk stratification before transcatheter aortic valve replacement (TAVR), yet it does not indicate whether an impaired ventricle will recover once the stenosis is relieved, or what drives death in high-risk patients. We asked whether routine preprocedural data could address both. Methods and Results: Among 1212 consecutive patients (1213 procedures) who underwent TAVR for severe aortic stenosis (2012-2026), unsupervised clustering of 18 routine variables (principal components, Ward linkage) defined four phenotypes: low-risk, elderly preserved-EF, reduced-EF, and cardiorenal. Five-year mortality ranged from 13.5% to 44.2% ( Conclusions: Routine preprocedural data can estimate which impaired ventricles recover after TAVR and identify a cardiorenal phenotype whose excess, largely noncardiovascular mortality is statistically explained by measurable renal dysfunction rather than captured by ejection fraction. Prospective external validation is warranted.

Indexed as

Aortic stenosisCardiorenal syndromeMachine learningRisk predictionTranscatheter aortic valve replacementVentricular remodeling

Identifiers

PMID42740783

DOI10.1016/j.ijcha.2026.101999

PMCPMC13571956

What OpenQuestion holds

Textmetadata

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 OpenQuestion graph.