Evidence map›Paper›PMID 41395856›Full record

ArticleEchocardiography (Mount Kisco, N.Y.)2025

Clinical, Echocardiographic, and Socioeconomic Predictors of Progression and Outcomes in Patients With Moderate Aortic Stenosis.

Alex D'Amico, Paul Nona, Loren Wagner, Miguel R Sotelo, Chris Rogers, Navin Nanda, Julian Booker, Efstathia Andrikopoulou

Abstract read
In one paragraph

Article in Echocardiography (Mount Kisco, N.Y.), 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
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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

8 authors.

Alex D'AmicoUniversity of Alabama, Birmingham, Alabama, USA.
Paul NonaTempus AI Inc., Chicago, Illinois, USA.ORCID 0000-0002-4758-7211
Loren WagnerTempus AI Inc., Chicago, Illinois, USA.
Miguel R SoteloTempus AI Inc., Chicago, Illinois, USA.
Chris RogersTempus AI Inc., Chicago, Illinois, USA.
Navin NandaUniversity of Alabama, Birmingham, Alabama, USA.
Julian BookerUniversity of Alabama, Birmingham, Alabama, USA.
Efstathia AndrikopoulouUniversity of Washington, Seattle, Washington, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite increased awareness of its clinical consequences, personalized risk stratification in patients with moderate aortic stenosis (AS) remains ambiguous. We studied predictors of progression and clinical outcomes in moderate AS to improve risk stratification and add to the existing literature.

methodsData collected through the Tempus Next care pathway intelligence platform at our tertiary referral center were retrospectively analyzed. The reports of echocardiograms performed from October 2017 to January 2020 were screened for descriptive or quantitative evidence of moderate AS. Follow-up extended until January 2022. Clinical data were extracted following manual chart review. Socioeconomic variables were collected based on zip-code-aggregated United States census data. The endpoints were progression from moderate to severe valvular AS, all-cause mortality, all-cause hospitalization, and heart failure (HF) hospitalization. The final multivariable model was selected using a variable selection algorithm inspired by greedy causal discovery algorithms.

resultsA total of 34 450 echocardiograms (N = 25 204 patients) were screened during the inclusion period; 367 patients met inclusion criteria and were included in the final analysis. Progression to severe AS was noted in 172 patients (median time to progression 16 months). The final predictive models after variable selection exhibited modest predictive power: progression to severe AS, AUC = 0.68; all-cause mortality, 0.797; all-cause hospitalization, 0.629; heart failure hospitalization, 0.744. Variables predictive for the endpoints included comorbidities, echocardiographic variables, and demographics.

conclusionOur findings support further work and exploration of a paradigm shift in the assessment and management of AS, moving beyond traditional measures to a multiparametric model incorporating a broader spectrum of clinical, echocardiographic, and socioeconomic variables.

Indexed as

Aortic Valve StenosisEchocardiographyAgedDisease ProgressionFemaleHospitalizationHumansMaleRetrospective StudiesRisk AssessmentRisk FactorsSeverity of Illness IndexSocioeconomic Factorsaortic stenosisartificial intelligencedisease progressionpersonalized medicinepredictive modeling

Identifiers

PMID41395856
PMCPMC12704040

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Registered trials

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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.