ArticleScientific reports2023
Predicting in-hospital mortality after transcatheter aortic valve replacement using administrative data and machine learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled 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.
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.
Who cites it
13 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Early predictive value of prediction models for mortality after transcatheter aortic valve replacement: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- Unimodal to multimodal: a systematic review of predictive machine learning models for valvular heart diseases.Frontiers in cardiovascular medicine · 2026Pooled it
- The role of artificial intelligence in aortic valve stenosis: a bibliometric analysis.Frontiers in cardiovascular medicine · 2025Pooled it
- Machine Learning for Predicting Mortality Following Transcatheter Aortic Valve Replacement: A Scoping Review of Current Literature.CJC open · 2026Review
- Clinical Applications of Artificial Intelligence in Structural Heart Disease.Journal of the Society for Cardiovascular Angiography & Interventions · 2026Review
- The Application of Multimodal Data Fusion Algorithm MULTINet in Postoperative Risk Assessment of TAVR.Journal of clinical medicine · 2025Article
- Artificial Intelligence in Risk Stratification and Outcome Prediction for Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-Analysis.Journal of personalized medicine · 2025Review
- Futility in TAVI: A scoping review of definitions, predictive criteria, and medical predictive models.PloS one · 2025Article
- Predicting mortality after transcatheter aortic valve replacement using preprocedural CT.Scientific reports · 2024Article
- Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years.Diagnostics (Basel, Switzerland) · 2024Review
- The Current Landscape of Artificial Intelligence in Imaging for Transcatheter Aortic Valve Replacement.Current radiology reports · 2024Article
- Predicting in-hospital mortality after transcatheter aortic valve replacement using administrative data and machine learning.Scientific reports · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transcatheter aortic valve replacement (TAVR) is the gold standard treatment for patients with symptomatic aortic stenosis. The utility of existing risk prediction tools for in-hospital mortality post-TAVR is limited due to two major factors: (a) the predictive accuracy of these tools is insufficient when only preoperative variables are incorporated, and (b) their efficacy is also compromised when solely postoperative variables are employed, subsequently constraining their application in preoperative decision support. This study examined whether statistical/machine learning models trained with solely preoperative information encoded in the administrative National Inpatient Sample database could accurately predict in-hospital outcomes (death/survival) post-TAVR. Fifteen popular binary classification methods were used to model in-hospital survival/death. These methods were evaluated using multiple classification metrics, including the area under the receiver operating characteristic curve (AUC). By analyzing 54,739 TAVRs, the top five classification models had an AUC ≥ 0.80 for two sampling scenarios: random, consistent with previous studies, and time-based, which assessed whether the models could be deployed without frequent retraining. Given the minimal practical differences in the predictive accuracies of the top five models, the L2 regularized logistic regression model is recommended as the best overall model since it is computationally efficient and easy to interpret.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.