Article in Circulation. Arrhythmia and electrophysiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02997254 (COMparison of Physiological Algorithms for Real-time Evaluation of Atrial Fibrillation), which is not on this map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing 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.
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
16 authors.
Prasanth GanesanDivision of Cardiology, Stanford Cardiovascular Institute, Stanford University, Palo Alto, CA, USA (P.G., M.P., R.F., A.J.R., B.D., H.J.C., S.R.-C., V.S., K.A.B., T.B., P.C., P.J.W., S.M.N.).ORCID 0000-0002-1885-0690
Maxime PedronDivision of Cardiology, Stanford Cardiovascular Institute, Stanford University, Palo Alto, CA, USA (P.G., M.P., R.F., A.J.R., B.D., H.J.C., S.R.-C., V.S., K.A.B., T.B., P.C., P.J.W., S.M.N.).
Ruibin FengDivision of Cardiology, Stanford Cardiovascular Institute, Stanford University, Palo Alto, CA, USA (P.G., M.P., R.F., A.J.R., B.D., H.J.C., S.R.-C., V.S., K.A.B., T.B., P.C., P.J.W., S.M.N.).ORCID 0000-0002-0777-8195
Albert J RogersDivision of Cardiology, Stanford Cardiovascular Institute, Stanford University, Palo Alto, CA, USA (P.G., M.P., R.F., A.J.R., B.D., H.J.C., S.R.-C., V.S., K.A.B., T.B., P.C., P.J.W., S.M.N.).ORCID 0000-0001-6585-534X
Brototo DebDivision of Cardiology, Stanford Cardiovascular Institute, Stanford University, Palo Alto, CA, USA (P.G., M.P., R.F., A.J.R., B.D., H.J.C., S.R.-C., V.S., K.A.B., T.B., P.C., P.J.W., S.M.N.).ORCID 0000-0001-8800-0002
Hui Ju ChangDivision of Cardiology, Stanford Cardiovascular Institute, Stanford University, Palo Alto, CA, USA (P.G., M.P., R.F., A.J.R., B.D., H.J.C., S.R.-C., V.S., K.A.B., T.B., P.C., P.J.W., S.M.N.).ORCID 0000-0002-3734-0493
Samuel Ruiperez-CampilloDivision of Cardiology, Stanford Cardiovascular Institute, Stanford University, Palo Alto, CA, USA (P.G., M.P., R.F., A.J.R., B.D., H.J.C., S.R.-C., V.S., K.A.B., T.B., P.C., P.J.W., S.M.N.).ORCID 0000-0002-5425-4175
Viren SrivastavaDivision of Cardiology, Stanford Cardiovascular Institute, Stanford University, Palo Alto, CA, USA (P.G., M.P., R.F., A.J.R., B.D., H.J.C., S.R.-C., V.S., K.A.B., T.B., P.C., P.J.W., S.M.N.).ORCID 0009-0006-8062-2441
Kelly A BrennanDivision of Cardiology, Stanford Cardiovascular Institute, Stanford University, Palo Alto, CA, USA (P.G., M.P., R.F., A.J.R., B.D., H.J.C., S.R.-C., V.S., K.A.B., T.B., P.C., P.J.W., S.M.N.).ORCID 0000-0002-7698-7353
Tina BaykanerDivision of Cardiology, Stanford Cardiovascular Institute, Stanford University, Palo Alto, CA, USA (P.G., M.P., R.F., A.J.R., B.D., H.J.C., S.R.-C., V.S., K.A.B., T.B., P.C., P.J.W., S.M.N.).ORCID 0000-0002-0476-2972
Paul CloptonDivision of Cardiology, Stanford Cardiovascular Institute, Stanford University, Palo Alto, CA, USA (P.G., M.P., R.F., A.J.R., B.D., H.J.C., S.R.-C., V.S., K.A.B., T.B., P.C., P.J.W., S.M.N.).ORCID 0000-0002-0642-0861
Paul J WangDivision of Cardiology, Stanford Cardiovascular Institute, Stanford University, Palo Alto, CA, USA (P.G., M.P., R.F., A.J.R., B.D., H.J.C., S.R.-C., V.S., K.A.B., T.B., P.C., P.J.W., S.M.N.).ORCID 0000-0002-5467-5877
Ulrich SchottenDepartments of Physiology and Cardiology, Maastricht University and Maastricht University Medical Center, the Netherlands (U.S.).ORCID 0000-0003-1532-3315
David E KrummenDepartment of Medicine, University of California San Diego, CA, USA (D.E.K.).ORCID 0000-0003-3123-8970
Sanjiv M NarayanDivision of Cardiology, Stanford Cardiovascular Institute, Stanford University, Palo Alto, CA, USA (P.G., M.P., R.F., A.J.R., B.D., H.J.C., S.R.-C., V.S., K.A.B., T.B., P.C., P.J.W., S.M.N.).ORCID 0000-0001-7552-5053
Funding
The Dynamics of Human Atrial FibrillationR01HL083359 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Sanjiv M Narayan · 2008 to 2026
$7.9M
Patient-Directed Computational Analysis of Atrial FibrillationR01HL122384 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI WOUTER-JAN RAPPEL · 2015 to 2026
$5.3M
Machine Learning in Atrial FibrillationR01HL149134 · NHLBI · STANFORD UNIVERSITY · PI NARAYAN, SANJIV M · 2020 to 2024
$3.8M
Computational Medicine in the Heart, Integrated Training ProgramT32HL166155 · NHLBI · STANFORD UNIVERSITY · PI Alison L Marsden, Sanjiv M Narayan · 2023 to 2026
$2.0M
Deep Neural Networks To Treat Atrial FibrillationK23HL145017 · NHLBI · STANFORD UNIVERSITY · PI BAYKANER, TINA · 2019 to 2023
$968k
Electrical Mapping Signatures of Adverse Structural and Functional Remodeling in Ventricular ArrhythmiaK23HL166977 · NHLBI · STANFORD UNIVERSITY · PI Albert Joseph Rogers · 2023 to 2026
$680k
Novel Atrial Fibrillation Phenotypes Defined by Functional-Anatomical, Machine-Learned ClassificationsF32HL144101 · NHLBI · STANFORD UNIVERSITY · PI ROGERS, ALBERT JOSEPH · 2018 to 2019
backgroundIt is difficult to identify patients with atrial fibrillation (AF) most likely to respond to ablation. While any arrhythmia patient may recur after acutely successful ablation, AF is unusual in that patients may have long-term arrhythmia freedom despite a lack of acute success. We hypothesized that acute and chronic AF ablation outcomes may reflect distinct physiology and used machine learning of multimodal data to identify their phenotypes.
methodsWe studied 561 consecutive patients in the Stanford AF ablation registry (66±10 years, 28% women, 67% nonparoxysmal), from whom we extracted 72 data features of electrograms, electrocardiogram, cardiac structure, lifestyle, and clinical variables. We compared 6 machine learning models to predict acute and long-term end points after ablation and used Shapley explainability analysis to contrast phenotypes. We validated our results in an independent external population of n=77 patients with AF.
resultsThe 1-year success rate was 69.5%, and the acute termination rate was 49.6%, which correlated poorly on a patient-by-patient basis (φ coefficient=0.08). The best model for acute termination (area under the curve=0.86, Random Forest) was more predictive than for long-term outcomes (area under the curve=0.67, logistic regression;
conclusionsLong-term and acute responses to AF ablation reflect distinct clinical and electrical physiology, respectively. This de-linking of phenotypes raises the question of whether long-term success operates through factors such as attenuated AF progression. There remains an urgent need to develop procedural predictors of long-term AF ablation success.
Indexed as
Atrial FibrillationCatheter AblationMachine LearningAction PotentialsAgedElectrocardiographyElectrophysiologic Techniques, CardiacFemaleHeart RateHumansMaleMiddle AgedPhenotypePredictive Value of TestsRegistriesTime Factorsatrial fibrillationelectrocardiographyhearthumanslogistic models
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.
Comparing Phenotypes for Acute and Long-Term Response to Atrial Fibrillation Ablation Using Machine Learning. · full record | OpenQuestion