CoChat
Sign Up
Cited by 5Cardiac Valve Diseases and TreatmentsCardiovascular Function and Risk FactorsAortic Disease and Treatment Approaches

Prognostic Value of Artificial Intelligence–Enabled Electrocardiography–Derived Diastolic Dysfunction Grading and Trajectory in Patients Undergoing Transcatheter Aortic Valve Replacement

Open access
Tsai, Chieh‐Mei, Naser, Jwan A., Tsaban, Gal, Lee, Eunjung, Wood, Julia, Luis, Allen S., Thaden, Jeremy J., Kane, Garvan C. +6 more·2026·Journal of the American Heart Association
DOI 10.1161/jaha.125.046558PMC PMC13055458
Open this paper in CoChat — free account

Abstract

Background: Artificial intelligence (AI)–enabled electrocardiography has emerged as a tool for detecting cardiac dysfunction. The prognostic relevance of AI‐enabled electrocardiography–derived diastolic dysfunction (DD) in patients undergoing transcatheter aortic valve replacement had not been assessed. Methods: We analyzed 3197 patients undergoing transcatheter aortic valve replacement for severe aortic stenosis between 2010 and 2023 with baseline 12‐lead ECGs processed by a validated AI model to classify diastolic function (grades 0–3). Multivariable Cox models and nested model comparisons assessed associations with all‐cause mortality, including a prespecified analysis in patients with indeterminate echocardiographic grading. Trajectories were defined by change in AI‐enabled electrocardiography DD grade (<2 versus ≥2) from baseline to 30‐day or 1‐year follow‐up. Results: At baseline, 21% had grade 3, 57% had grade 2, 8% had grade 1, and 14% had grade 0 AI‐enabled electrocardiography DD. Higher grades were associated with adverse cardiac remodeling and comorbidities. Over a median follow‐up of 3.4 years, grade 3 AI‐enabled electrocardiography DD independently predicted mortality (hazard ratio [HR], 1.80 [95% CI, 1.47–2.20]; P <0.001). AI‐enabled electrocardiography DD improved prognostic discrimination beyond clinical and echocardiographic measures (ΔHarrell concordance statistic, 0.016; Δ χ 2 =57; P <0.001). Among 1259 patients with indeterminate echocardiographic grading, AI‐enabled electrocardiography added prognostic value (ΔHarrell concordance statistic, 0.02; Δ χ 2 =13; P =0.006). Worsening or persistently high‐risk trajectories were associated with increased mortality (HRs, 1.45–1.80; all P <0.05). Conclusions: AI‐enabled electrocardiography–derived DD independently predicts mortality after transcatheter aortic valve replacement, adds value beyond echocardiographic grading, and enables dynamic risk stratification through longitudinal tracking.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1161/jaha.125.046558

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • AI-enabled electrocardiography-derived diastolic dysfunction trajectories after transcatheter aortic valve replacement provide dynamic prognostic stratification.

    “and AI-enabled electrocardiography-derived diastolic dysfunction trajectories after transcatheter aortic valve replacement also provide dynamic prognostic stratification;25”

    From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [25]

  • Valvular and composite SHD AI-ECG models are promising for referral enrichment.

    “Valvular and composite SHD models are promising for referral enrichment”

    From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [25]

    The Discussion (Summary of Findings and Implications; Prospective Clinical Integration) describes an AI-ECG diastolic-dysfunction model for prognosis and follow-up in patients undergoing TAVR. Our copy does not address whether valvular or composite structural-heart-disease models are promising for referral enrichment; searches for those models and referral enrichment found no supporting passage. Page numbers were not provided. (LLM, conf=0.74)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 41

  1. [1]Thellier N, Altes A, Layec J, Castel AL, Delelis F, Hubert T, Outerryck F, Appert L, Tribouilloy C, Maréchaux S. Impact of left atrial and diastolic ventricular dysfunction on mortality in patients with aortic stenosis. Arch Cardiovasc Dis. 2023; 116: 126– 135. doi: 10.1016/j.acvd.2022.12.006 36739188 · DOI
  2. [2]Ong G, Pibarot P, Redfors B, Weissman NJ, Jaber WA, Makkar RR, Lerakis S, Gopal D, Khalique O, Kodali SK, et al. Diastolic function and clinical outcomes after transcatheter aortic valve replacement: PARTNER 2 SAPIEN 3 registry. J Am Coll Cardiol. 2020; 76: 2940– 2951. doi: 10.1016/j.jacc.2020.10.032 33334422 · DOI
  3. [3]Dahl JS, Christensen NL, Videbæk L, Poulsen MK, Carter‐Storch R, Hey TM, Pellikka PA, Steffensen FH, Møller JE. Left ventricular diastolic function is associated with symptom status in severe aortic valve stenosis. Circ Cardiovasc Imaging. 2014; 7: 142– 148. doi: 10.1161/CIRCIMAGING.113.000636 24173271 · DOI
  4. [4]El‐Zein RS, Malik AO, Cohen DJ, Spertus JA, Saxon JT, Pibarot P, Hahn RT, Alu MC, Shang K, Kodali SK, et al. Diastolic dysfunction and health status outcomes after transcatheter aortic valve replacement. Struct Heart. 2024; 8: 100225. doi: 10.1016/j.shj.2023.100225 38283566 PMC10818150 · DOI
  5. [5]Kampaktsis PN, Kokkinidis DG, Wong SC, Vavuranakis M, Skubas NJ, Devereux RB. The role and clinical implications of diastolic dysfunction in aortic stenosis. Heart. 2017; 103: 1481– 1487. doi: 10.1136/heartjnl-2017-311506 28684437 · DOI
  6. [6]van de Bovenkamp AA, Enait V, de Man FS, Oosterveer FTP, Bogaard HJ, Vonk Noordegraaf A, van Rossum AC, Handoko ML. Validation of the 2016 ASE/EACVI guideline for diastolic dysfunction in patients with unexplained dyspnea and a preserved left ventricular ejection fraction. J Am Heart Assoc. 2021; 10: e021165. doi: 10.1161/JAHA.121.021165 34476984 PMC8649534 · DOI
  7. [7]Nagueh SF. Left ventricular diastolic function: understanding pathophysiology, diagnosis, and prognosis with echocardiography. JACC Cardiovasc Imaging. 2020; 13: 228– 244. doi: 10.1016/j.jcmg.2018.10.038 30982669 · DOI
  8. [9]Tsaban G, Lee E, Wopperer S, Abbasi M, Yu HT, Kane GC, Lopez‐Jimenez F, Pislaru SV, Nkomo VT, Deshmukh AJ, et al. Using electrocardiogram to assess diastolic function and prognosis in mitral regurgitation. J Am Coll Cardiol. 2024; 84: 2278– 2289. doi: 10.1016/j.jacc.2024.06.054 39603748 · DOI
  9. [10]Oikonomou EK, Holste G, Yuan N, Coppi A, McNamara RL, Haynes NA, Vora AN, Velazquez EJ, Li F, Menon V, et al. A multimodal video‐based AI biomarker for aortic stenosis development and progression. JAMA Cardiol. 2024; 9: 534– 544. doi: 10.1001/jamacardio.2024.0595 38581644 PMC10999005 · DOI
  10. [11]Oikonomou EK, Craig NJ, Holste G, Vasisht Shankar S, White A, Mahendran M, Newby DE, Dweck MR, Khera R. Artificial intelligence‐enabled echocardiography as a surrogate for multimodality aortic stenosis imaging: post hoc analysis of a clinical trial. Circ Cardiovasc Imaging. 2025; e018353. doi: 10.1161/CIRCIMAGING.125.018353. Epub ahead of print. 41473954 PMC12778975 · DOI
  11. [12]Nagueh SF, Smiseth OA, Appleton CP, et al. Recommendations for the evaluation of left ventricular diastolic function by echocardiography: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. Eur J Echocardiogr. 2016; 17: 1321– 1360. doi: 10.1093/ehjci/jew082 27422899 · DOI
  12. [13]Writing Committee Members, Otto CM, Nishimura RA, Bonow RO, et al. 2020 ACC/AHA guideline for the Management of Patients with Valvular Heart Disease: a report of the American College of Cardiology/American Heart Association joint committee on clinical practice guidelines. J Am Coll Cardiol. 2021; 77: e25– e197. doi: 10.1016/j.jacc.2020.11.018 33342586 · DOI
  13. [14]Otto CM, Kumbhani DJ, Alexander KP, Calhoon JH, Desai MY, Kaul S, Lee JC, Ruiz CE, Vassileva CM. 2017 ACC expert consensus decision pathway for Transcatheter aortic valve replacement in the Management of Adults with Aortic Stenosis: a report of the American College of Cardiology Task Force on clinical expert consensus documents. J Am Coll Cardiol. 2017; 69: 1313– 1346. doi: 10.1016/j.jacc.2016.12.006 28063810 · DOI
  14. [15]Lang RM, Badano LP, Mor‐Avi V, Afilalo J, Armstrong A, Ernande L, Flachskampf FA, Foster E, Goldstein SA, Kuznetsova T, et al. Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr. 2015; 28: 1– 39.e14. doi: 10.1016/j.echo.2014.10.003 25559473 · DOI
  15. [16]Mitchell C, Rahko PS, Blauwet LA, Canaday B, Finstuen JA, Foster MC, Horton K, Ogunyankin KO, Palma RA, Velazquez EJ. Guidelines for performing a comprehensive transthoracic echocardiographic examination in adults: recommendations from the American Society of Echocardiography. J Am Soc Echocardiogr. 2019; 32: 1– 64. doi: 10.1016/j.echo.2018.06.004 30282592 · DOI
  16. [17]Desai A, Pandey A, Suratekar R, Ahmad FS, Alger HM, Anto AG, Nujum GS, Lopez‐Jimenez F, Oh JK, Asfahan S, et al. Predicting heart failure from 12‐Lead ECGs using AI: a HeartShare/AMP‐HF pooled cohort analysis. J Am Coll Cardiol. 2025. doi: 10.1016/j.jacc.2025.10.065. Epub ahead of print. 41493294 · DOI · Paper page
  17. [18]Lin YT, Lin CS, Tsai CS, Tsai D‐J, Lou Y‐S, Fang W‐H, Lee Y‐T, Lin C. Comprehensive clinical application analysis of artificial intelligence‐enabled electrocardiograms for screening multiple valvular heart diseases. Aging (Albany NY). 2024; 16: 8717. doi: 10.18632/aging.205835 38761181 PMC11164519 · DOI
  18. [19]Pandey A, Adedinsewo D. The future of AI‐enhanced ECG interpretation for valvular heart disease screening. J Am Coll Cardiol. 2022; 80: 627– 630. doi: 10.1016/j.jacc.2022.05.034 35926936 · DOI
  19. [20]Naser JA, Lee E, Michelena HI, Lin G, Pellikka PA, Nkomo VT, Noseworthy PA, Friedman PA, Attia ZI, Pislaru SV. Artificial intelligence‐enabled electrocardiogram in the detection of patients at risk of atrial secondary mitral regurgitation. Circ Arrhythm Electrophysiol. 2023; 16: e012033. doi: 10.1161/CIRCEP.123.012033 37565338 · DOI
  20. [21]Ito S, Cohen‐Shelly M, Attia ZI, Lee E, Friedman PA, Nkomo VT, Michelena HI, Noseworthy PA, Lopez‐Jimenez F, Oh JK. Correlation between artificial intelligence‐enabled electrocardiogram and echocardiographic features in aortic stenosis. Eur Heart J Digit Health. 2023; 4: 196– 206. doi: 10.1093/ehjdh/ztad009 37265870 PMC10232245 · DOI
  21. [22]Clavel MA, Magne J, Pibarot P. Low‐gradient aortic stenosis. Eur Heart J. 2016; 37: 2645– 2657. doi: 10.1093/eurheartj/ehw096 27190103 PMC5030681 · DOI
  22. [23]Alkurashi AK, Pislaru SV, Thaden JJ, Collins JD, Foley TA, Greason KL, Eleid MF, Sandhu GS, Alkhouli MA, Asirvatham SJ, et al. Doppler mean gradient is discordant to aortic valve calcium scores in patients with atrial fibrillation undergoing transcatheter aortic valve replacement. J Am Soc Echocardiogr. 2022; 35: 116– 123. doi: 10.1016/j.echo.2021.08.024 34506919 · DOI
  23. [24]Hachicha Z, Dumesnil JG, Bogaty P, Pibarot P. Paradoxical low‐flow, low‐gradient severe aortic stenosis despite preserved ejection fraction is associated with higher afterload and reduced survival. Circulation. 2007; 115: 2856– 2864. doi: 10.1161/circulationaha.106.668681 17533183 · DOI · Paper page
  24. [25]Clavel MA, Dumesnil JG, Capoulade R, Mathieu P, Sénéchal M, Pibarot P. Outcome of patients with aortic stenosis, small valve area, and low‐flow, low‐gradient despite preserved left ventricular ejection fraction. J Am Coll Cardiol. 2012; 60: 1259– 1267. doi: 10.1016/j.jacc.2011.12.054 22657269 · DOI
  25. [26]Pereyra Pietri M, Farina JM, Mahmoud AK, Scalia IG, Galasso F, Killian ME, Suppah M, Kenyon CR, Koepke LM, Padang R, et al. The prognostic value of artificial intelligence to predict cardiac amyloidosis in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement. Eur Heart J Digit Health. 2024; 5: 295– 302. doi: 10.1093/ehjdh/ztae022 38774378 PMC11104461 · DOI
  26. [27]Goto S, Mahara K, Beussink‐Nelson L, Ikura H, Katsumata Y, Endo J, Gaggin HK, Shah SJ, Itabashi Y, MacRae CA, et al. Artificial intelligence‐enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms. Nat Commun. 2021; 12: 2726. doi: 10.1038/s41467-021-22877-8 33976142 PMC8113484 · DOI · Paper page
  27. [28]Shiri I, Balzer S, Baj G, Bernhard B, Hundertmark M, Bakula A, Nakase M, Tomii D, Barbati G, Dobner S, et al. Multi‐modality artificial intelligence‐based transthyretin amyloid cardiomyopathy detection in patients with severe aortic stenosis. Eur J Nucl Med Mol Imaging. 2025; 52: 485– 500. doi: 10.1007/s00259-024-06922-4 39307861 PMC11732884 · DOI
  28. [29]Rosenblum H, Masri A, Narotsky DL, et al. Unveiling outcomes in coexisting severe aortic stenosis and transthyretin cardiac amyloidosis. Eur J Heart Fail. 2021; 23: 250– 258. doi: 10.1002/ejhf.1974 32729170 · DOI
  29. [30]Nitsche C, Dobner S, Rosenblum HR, et al. Cardiac transthyretin amyloidosis treatment improves outcomes after aortic valve replacement for severe stenosis. Eur Heart J. 2025; 46: 4795– 4806. doi: 10.1093/eurheartj/ehaf362 40452225 PMC12634099 · DOI
  30. [31]Christopoulos G, Graff‐Radford J, Lopez CL, et al. Artificial intelligence–electrocardiography to predict incident atrial fibrillation: a population‐based study. Circ Arrhythm Electrophysiol. 2020; 13: e009355. doi: 10.1161/CIRCEP.120.009355 33185118 PMC8127001 · DOI
  31. [32]Khurshid S, Friedman S, Reeder C, Di Achille P, Diamant N, Singh P, Harrington LX, Wang X, Al‐Alusi MA, Sarma G, et al. ECG‐based deep learning and clinical risk factors to predict atrial fibrillation. Circulation. 2022; 145: 122– 133. doi: 10.1161/circulationaha.121.057480 34743566 PMC8748400 · DOI
  32. [33]Verbrugge FH, Reddy YNV, Attia ZI, Friedman PA, Noseworthy PA, Lopez‐Jimenez F, Kapa S, Borlaug BA. Detection of left atrial myopathy using artificial intelligence–enabled electrocardiography. Circ Heart Fail. 2022; 15: e008176. doi: 10.1161/CIRCHEARTFAILURE.120.008176 34911362 PMC8766932 · DOI
  33. [34]Kang E, Lee SW, Ryu H, Kang M, Kim S, Park SK, Jung JY, Lee KB, Han SH, Ahn C, et al. Left ventricular diastolic dysfunction and progression of chronic kidney disease: analysis of KNOW‐CKD data. J Am Heart Assoc. 2022; 11: e025554. doi: 10.1161/JAHA.122.025554 35766277 PMC9333375 · DOI
  34. [35]Otsuka T, Suzuki M, Yoshikawa H, Sugi K. Left ventricular diastolic dysfunction in the early stage of chronic kidney disease. J Cardiol. 2009; 54: 199– 204. doi: 10.1016/j.jjcc.2009.05.002 19782256 · DOI
  35. [36]Treibel TA, Kozor R, Fontana M, Torlasco C, Reant P, Badiani S, Espinoza M, Yap J, Diez J, Hughes AD, et al. Sex dimorphism in the myocardial response to aortic stenosis. JACC Cardiovasc Imaging. 2018; 11: 962– 973. doi: 10.1016/j.jcmg.2017.08.025 29153564 PMC6278887 · DOI
  36. [37]Chen YZ, Qiao SB, Hu FH, Yuan JS, Yang WX, Cui JG, Zhang Y, Zhang CL. Left ventricular remodeling and fibrosis: sex differences and relationship with diastolic function in hypertrophic cardiomyopathy. Eur J Radiol. 2015; 84: 1487– 1492. doi: 10.1016/j.ejrad.2015.04.026 26001434 · DOI
  37. [38]Kashou AH, Medina‐Inojosa JR, Noseworthy PA, Rodeheffer RJ, Lopez‐Jimenez F, Attia IZ, Kapa S, Scott CG, Lee AT, Friedman PA, et al. Artificial intelligence–augmented electrocardiogram detection of left ventricular systolic dysfunction in the general population. Mayo Clin Proc. 2021; 96: 2576– 2586. doi: 10.1016/j.mayocp.2021.02.029 34120755 PMC9904428 · DOI · Paper page
  38. [39]Attia ZI, Kapa S, Lopez‐Jimenez F, McKie P, Ladewig DJ, Satam G, Pellikka PA, Enriquez‐Sarano M, Noseworthy PA, Munger TM, et al. Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nat Med. 2019; 25: 70– 74. doi: 10.1038/s41591-018-0240-2 30617318 · DOI · Paper page
  39. [40]Sanabria M, Tastet L, Pelletier S, Leclercq M, Ohl L, Hermann L, Mattei PA, Precioso F, Coté N, Pibarot P, et al. AI‐enhanced prediction of aortic stenosis progression: insights from the PROGRESSA study. JACC Adv. 2024; 3: 101234. doi: 10.1016/j.jacadv.2024.101234 39309663 PMC11416525 · DOI
  40. [41]Popat A, Saini B, Patel M, Seby N, Patel S, Harikrishnan S, Shah H, Pathak P, Dekhne A, Sen U, et al. Diagnostic accuracy of AI algorithms in aortic stenosis screening: a systematic review and meta‐analysis. Clin Med Res. 2024; 22: 145– 155. doi: 10.3121/cmr.2024.1934 39438148 PMC11495659 · DOI
  41. [42]Moualla SK, McCarthy PM, Thomas JD, Dobbles M, Petrescu OM, Loper T, Barnhart GR, Brennan JM. Artificial intelligence‐enabled predictive model of progression from moderate to severe aortic stenosis. Intell Based Med. 2022; 6: 100062. doi: 10.1016/j.ibmed.2022.100062 · DOI

Cited by

Sorted by citations

Authors
Tsai, C., Naser, J. A., Tsaban, G., Lee, E., Wood, J., Luis, A. S., Thaden, J. J., Kane, G. C., Pislaru, S. V., Simard, T. J., Greason, K. L., Eleid, M. F., Nkomo, V. T., & Oh, J. K.
Affiliations
  • Mayo Clinic (US)
  • Mayo Clinic
Corresponding author
Nkomo, Vuyisile T.
ORCID
Venue
Journal of the American Heart Association
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2026-02-03
Volume
15
Issue
3
Pages
e046558-e046558
ISSN
2047-9980 (ISSN-L)
DOI
10.1161/jaha.125.046558
PMC
PMC13055458
OpenAlex
W7126220393
Cited by
5
References
1
Citations by year
2026: 5
FWCI
24.86
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Journal of the American Heart Association (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac Valve Diseases and Treatments
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Function and Risk Factors
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Aortic Disease and Treatment Approaches
Keywords
artificial intelligence, electrocardiogram, diastolic dysfunction, transcatheter aortic valve implantation, aortic stenosis, all-cause mortality, prognosis, risk stratification, cardiac remodeling, prognostic discrimination, long-term monitoring, 12-lead ECG, dynamic risk
MeSH terms
Intelligent Systems, Aged, Aged, 80 and over, Aortic Valve, Aortic Valve Stenosis, Artificial Intelligence, Diastole, Electrocardiography, Female, Humans, Male, Predictive Value of Tests, Prognosis, Retrospective Studies, Risk Factors, Severity of Illness Index, Ventricular Function, Left, Ventricular Dysfunction, Left, Risk Assessment, Transcatheter Aortic Valve Replacement

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review