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Enregistrement W2914809906 · doi:10.1161/circoutcomes.118.005233

Transcatheter Aortic Valve Replacement in the Era of Quality Assessment

2018· letter· en· W2914809906 sur OpenAlexaboutno aff
Taku Inohara, Sreekanth Vemulapalli

Notice bibliographique

RevueCirculation Cardiovascular Quality and Outcomes · 2018
Typeletter
Langueen
DomaineMedicine
ThématiqueCardiac Valve Diseases and Treatments
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineAortic valve replacementAortic valveValve replacementQuality (philosophy)CardiologyIntensive care medicineSurgeryInternal medicineStenosis

Résumé

récupéré en direct d'OpenAlex

HomeCirculation: Cardiovascular Quality and OutcomesVol. 11, No. 12Transcatheter Aortic Valve Replacement in the Era of Quality Assessment Free AccessEditorialPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessEditorialPDF/EPUBTranscatheter Aortic Valve Replacement in the Era of Quality AssessmentCrossing the Quality Chasm Taku Inohara, MD, PhD and Sreekanth Vemulapalli, MD Taku InoharaTaku Inohara Duke Clinical Research Institute, Duke University Medical Center, Durham, NC. and Sreekanth VemulapalliSreekanth Vemulapalli Sreekanth Vemulapalli, MD, Duke Clinical Research Institute, 2400 Pratt St, Durham, NC 27705. Email E-mail Address: [email protected] Duke Clinical Research Institute, Duke University Medical Center, Durham, NC. Originally published17 Dec 2018https://doi.org/10.1161/CIRCOUTCOMES.118.005233Circulation: Cardiovascular Quality and Outcomes. 2018;11:e005233This article is a commentary on the followingProfiling Hospital Performance Based on Mortality After Transcatheter Aortic Valve Replacement in Ontario, CanadaSee Article by Elbaz-Greener et alThe Donabedian model, which was originally developed by Dr Avedis Donabedian in 1966, is one of the most well-known conceptual models for assessing the quality of health care. In this model, the quality of care can be assessed as a triad of structure, process, and outcome constructs. Structure is defined as the context in which care is delivered (eg, hospital buildings, staff, financing, and equipment). Process denotes what is actually done in giving and receiving care. Outcome refers to the effects of care on the health status of patients and populations.1In the field of percutaneous coronary intervention (PCI), the Physician Consortium for Performance Improvement and 3 professional societies proposed quality measures in 2013.2 They included structural measures, such as operator and hospital PCI volume, and process measures, such as indication of PCI and post-PCI optimal medical therapy. In addition, risk-standardized hospital 30-day mortality and readmission after PCI have been developed by the American College of Cardiology and serve as outcome measures.3–5 Metrics have also been established for cardiovascular surgery by the Society of Thoracic Surgeons. For surgical aortic valve replacement, selection and duration of antibiotic prophylaxis are assessed as process measures, and 30-day mortality and a composite of mortality and major morbidity are evaluated as outcome measures.6Relative to PCI and surgical aortic valve replacement, the quality framework in transcatheter aortic valve replacement (TAVR) is significantly less developed. To date, TAVR volume has been promulgated as a marker of TAVR quality and incorporated as a requirement for launching or maintaining a TAVR program in the United States. In the current 2012 National Coverage Determination by the Centers for Medicare and Medicaid Services, a cardiovascular surgeon and an interventional cardiologist need a combined experience of at least 20 TAVR procedures in the prior year or 40 in the prior 2 years to maintain a TAVR program.7 Similarly, in a 2018 expert consensus statement from 4 US professional societies, operator TAVR experience is described as a requirement for launching a new TAVR program (experience with >100 transfemoral TAVRs lifetime, including 50 TAVRs as primary operator).8 At its essence, procedural volumes represent a structural measure within the Donabedian framework. Structural measures have the benefit of being easy to measure but are essentially surrogates of the metrics of the greatest interest patient outcomes. Quality assessment groups, such as the National Quality Forum, have developed sophisticated and comprehensive frameworks and tools to assess the suitability of structural and process measures as quality indicators.8 However, an ideal feature of a structural quality measure would be an association between that measure and ultimate outcomes of interest. To this end, there are several reports showing the volume-outcome relationship in TAVR.9,10However, there have been few studies evaluating outcome measures of quality of care in TAVR. Concurrent to studies evaluating the volume-outcome relationship, a recent report from the Transcatheter Valve Therapy Registry, including 32 661 TAVR procedures from 188 US sites between 2013 and 2016, derived and validated site-level in-hospital and 30-day risk-adjusted TAVR mortality models.11 The discrimination (c=0.70) and calibration of this model were good for both the overall cohort and key patient subgroups. Importantly, there was no significant variation in risk-adjusted in-hospital and 30-day mortality rates across sites.It is with this background that the present study by Elbaz-Greener et al is published. Their findings were consistent with the work by Arnold et al and showed no significant interhospital variation in risk-adjusted 30-day mortality. Elbaz-Greener et al12 further extended this finding to 1-year mortality in an analysis of 2129 TAVR procedures performed at 10 hospitals in Ontario, Canada. Importantly, the study included all consecutive patients who underwent TAVR in Ontario between April 2012 and March 2016. The number of TAVR procedures varied substantially among sites and ranged from 60 to 376 cases. With regard to patient outcomes, crude all-cause mortalities were 7.0% at 30 days and 16.4% at 1 year, respectively. There were also substantial variations in crude mortality among hospitals, ranging from 4.1% to 8.7% for 30-day mortality and from 8.2% to 22.3% for 1-year mortality, respectively. However, institutional variation in 30-day and 1-year mortality was no longer observed after risk adjustment. Although the study had several limitations, such as limited candidate variables for model development, a small number of sites (10 sites in Ontario), and lack of access to surgical risk scores because of a reliance on administrative claims, when taken together with the results from Arnold et al from the US Transcatheter Valve Therapy Registry, the implication is that low site-level variability in risk-adjusted mortality extends beyond the United States.Despite the lack of site-level variation in risk-adjusted mortality rate for the studied time period, risk-adjusted mortality should play a crucial role in assessing quality of care in TAVR for several reasons. First, one of the purposes of quality reporting is to alert a site about performance and any changes in performance. Because mortality is a key metric in procedural evaluation and in the pathophysiology of valve disease, it is worth assessing for both absolute value and temporal trends at individual sites, regardless of whether it discriminates between sites. Second, even 7 years after the introduction of commercial TAVR in the United States, there are still new TAVR sites being started, and tracking of mortality would be an important metric for these sites during their learning curve. Third, as indications for TAVR migrate toward lower risk patients, even small changes in a site's risk-adjusted mortality rate would take on added significance.However, the lack of variation in risk-adjusted 30-day and 1-year mortality in the study by Elbaz-Greener et al begs the question of whether the performance of TAVR has progressed to the point that something other than mortality may best differentiate between high-performing versus average-performing versus low-performing sites. Although there may be a number of candidate metrics that fill this criteria, certainly procedural complications are feared by both providers and patients.13 Thus, a composite of procedural complications rather than mortality alone may be preferable in evaluating the quality of care in terms of outcome in a contemporary TAVR practice. For several reasons, such as expansion to lower risk patients, more experienced teams, improved technologies and techniques, and optimized patient selection, TAVR outcomes continue to improve. Given the expected lower mortality after TAVR over time, mortality alone may not be sufficient to detect site-level difference in outcomes. Additionally, process should also be evaluated. To date, there is no established list of process-related quality measure metrics to assess the quality of care in TAVR. Candidate process measures may include documentation of appropriate TAVR indication, implementation of heart team approach, or adherence to the recently published appropriate use criteria.14 Taken together, the continued development and validation of structural, process, and outcomes quality measures for TAVR is necessary to complete the framework for quality assessment in aortic valve disease (Figure).Download figureDownload PowerPointFigure. Aortic valve replacement (AVR) quality framework. TVT 30-d mortality,11 STS 30-d mortality and composite,6 procedure volume,7,8 and appropriate use criteria.14 PCI, indicates percutaneous coronary intervention; QoL, quality of life; SAVR, surgical aortic valve replacement; STS, Society of Thoracic Surgeons; TAVR, transcatheter aortic valve replacement; TEE, transesophageal echocardiography; and TVT, transcatheter valve therapy.Finally, Elbaz-Greener et al developed a TAVR-specific model for predicting 1-year mortality. Whether a 1-year quality metric after a procedure is appropriate remains controversial because of the following reasons: (1) when compared with in-hospital or 30-day outcomes, 1-year outcomes are more likely to be affected by factors that are outside of the control of the healthcare provider, such as access to care or care fragmentation and (2) 1-year outcomes may more accurately reflect comorbidities than the pathophysiology for which the procedure is being done. From a patient-centric point of view, however, care fragmentation and the divisions between comorbidities and primary pathophysiology are largely irrelevant to overall goals of care. In this framework, the 1-year timepoint for quality metrics makes sense. One-year site-level cardiovascular procedure quality metrics and risk models are relatively rare in the United States because of difficulty in ascertaining 1-year outcomes in our fragmented healthcare system. Elbaz-Greener et al leveraged a linkage between the CorHealth Ontario TAVR Registry and a population-based administrative database together with the funding support from the Ontario Ministry of Health and Long-Term Care to investigate 1-year risk-adjusted mortality. To pursue 1-year risk models and quality metrics in the United States, similar collaborations and linkages, as well as continued national data collection through mechanisms, such as the Society of Thoracic Surgeons/American College of Cardiology Transcatheter Valve Therapy Registry, will be necessary.Thanks to the work of Arnold et al and Elbaz-Greener et al, TAVR now has out-of-hospital outcomes quality metrics in addition to the structural measure of procedure volume. As Centers for Medicare and Medicaid Services reconsiders the national coverage decision with evidence development for TAVR,15 multiple domains of evidence development for quality assessment remain. For the field of quality measurement in TAVR to continue to evolve beyond structural metrics, such as volume to more patient-centric metrics, such as cardiovascular outcomes and patient-reported quality of life, several key assets are needed: (1) continued national data collection with appropriate minimum volume requirements to assure statistical validity of risk estimates, (2) risk model derivation and validation and (3) identification of valid process measures that can then be used to implement programmatic change to improve outcomes.DisclosuresDr Inohara reports research grant from Japan Society for the Promotion of Science overseas research fellowship and Boston Scientific. Dr Vemulapalli discloses research grants from the American College of Cardiology, Abbott Vascular, Patient-Centered Outcomes Research Institute, Society of Thoracic Surgeons, and Boston Scientific and is a consultant or belongs to the Advisory Board at Premiere Research, Janssen, Boston Scientific, and Novella.FootnotesThe opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.Sreekanth Vemulapalli, MD, Duke Clinical Research Institute, 2400 Pratt St, Durham, NC 27705. Email sreekanth.[email protected]eduReferences1. Donabedian A. The quality of care. How can it be assessed?JAMA. 1988; 260:1743–1748.CrossrefMedlineGoogle Scholar2. Nallamothu BK, Tommaso CL, Anderson HV, Anderson JL, Cleveland JC, Dudley RA, Duffy PL, Faxon DP, Gurm HS, Hamilton LA, Jensen NC, Josephson RA, Malenka DJ, Maniu CV, McCabe KW, Mortimer JD, Patel MR, Persell SD, Rumsfeld JS, Shunk KA, Smith SC, Stanko SJ, Watts B. ACC/AHA/SCAI/AMA-Convened PCPI/NCQA 2013 performance measures for adults undergoing percutaneous coronary intervention: a report of the American College of Cardiology/American Heart Association task force on performance measures, the Society for Cardiovascular Angiography and Interventions, the American Medical Association-Convened Physician Consortium for Performance Improvement, and the National Committee for Quality Assurance.Circulation. 2014; 129:926–949. doi: 10.1161/01.cir.0000441966.31451.3fLinkGoogle Scholar3. Krumholz HM, Keenan PS, Brush JE, Bufalino VJ, Chernew ME, Epstein AJ, Heidenreich PA, Ho V, Masoudi FA, Matchar DB, Normand SL, Rumsfeld JS, Schuur JD, Smith SC, Spertus JA, Walsh MN; American Heart Association Interdisciplinary Council on Quality of Care and Outcomes Research; American College of Cardiology Foundation. Standards for measures used for public reporting of efficiency in health care: a scientific statement from the American Heart Association interdisciplinary council on quality of care and outcomes research and the American College of Cardiology Foundation.Circulation. 2008; 118:1885–1893. doi: 10.1161/CIRCULATIONAHA.108.190500LinkGoogle Scholar4. American College of Cardiology Percutaneous Coronary Intervention (PCI) Readmission Measure. Medicare.org: Hospital compare. http://www.medicare.gov/hospitalcompare/PCIReadmission.html. Accessed November 3, 2018.Google Scholar5. Chui PW, Parzynski CS, Nallamothu BK, Masoudi FA, Krumholz HM, Curtis JP. Hospital performance on percutaneous coronary intervention process and outcomes measures.J Am Heart Assoc. 2017; 6:e004276. doi: 10.1161/JAHA.116.004276LinkGoogle Scholar6. The Society of Thoracic Surgeons performance measures. https://http://www.sts.org/quality-safety/performance-measures. Accessed November 3, 2018.Google Scholar7. The Centers for Medicare & Medicaid Services. National Coverage Determination (NCD) for Transcatheter Aortic Valve Replacement (TAVR) (20.32).https://http://www.cms.gov/medicare-coverage-database/details/ncd-details.aspx?NCDId=355. Accessed November 3, 2018.Google Scholar8. Bavaria JE, Tommaso CL, Brindis RG, Carroll JD, Deeb GM, Feldman TE, Gleason TG, Horlick EM, Kavinsky CJ, Kumbhani DJ, Miller DC, Seals AA, Shahian DM, Shemin RJ, Sundt TM, Thourani VH. 2018 AATS/ACC/SCAI/STS expert consensus systems of care document: operator and institutional recommendations and requirements for transcatheter aortic valve replacement: a joint report of the American Association for Thoracic Surgery, the American College of Cardiology, the Society for Cardiovascular Angiography and Interventions, and the Society of Thoracic Surgeons [published online July 18, 2018].J Am Coll Cardiol. doi: 10.1016/j.jacc.2018.07.002Google Scholar9. Carroll JD, Vemulapalli S, Dai D, Matsouaka R, Blackstone E, Edwards F, Masoudi FA, Mack M, Peterson ED, Holmes D, Rumsfeld JS, Tuzcu EM, Grover F. Procedural experience for transcatheter aortic valve replacement and relation to outcomes: the STS/ACC TVT registry.J Am Coll Cardiol. 2017; 70:29–41. doi: 10.1016/j.jacc.2017.04.056CrossrefMedlineGoogle Scholar10. Wassef AWA, Rodes-Cabau J, Liu Y, Webb JG, Barbanti M, Muñoz-García AJ, Tamburino C, Dager AE, Serra V, Amat-Santos IJ, Alonso Briales JH, San Roman A, Urena M, Himbert D, Nombela-Franco L, Abizaid A, de Brito FS, Ribeiro HB, Ruel M, Lima VC, Nietlispach F, Cheema AN. The learning curve and annual procedure volume standards for optimum outcomes of transcatheter aortic valve replacement: findings from an international registry.JACC Cardiovasc Interv. 2018; 11:1669–1679. doi: 10.1016/j.jcin.2018.06.044CrossrefMedlineGoogle Scholar11. Arnold SV, O'Brien SM, Vemulapalli S, Cohen DJ, Stebbins A, Brennan JM, Shahian DM, Grover FL, Holmes DR, Thourani VH, Peterson ED, Edwards FH, STS/ACC TVT registry. Inclusion of functional status measures in the risk adjustment of 30-day mortality after transcatheter aortic valve replacement: a report from the Society of Thoracic Surgeons/American College of Cardiology TVT registry.JACC Cardiovasc Interv. 2018; 11:581–589. doi: 10.1016/j.jcin.2018.01.242CrossrefMedlineGoogle Scholar12. Elbaz-Greener G, Qui F, Masih S, Fang J, Austin PC, Cantor WJ, Dvir D, Asgar AW, Webb JG, Ko DT, Wijeysundera HC. Profiling hospital performance based on mortality after transcatheter aortic valve replacement in Ontario, Canada.Circ Cardiovasc Qual Outcomes. 2018; 11:e004947. doi: 10.1161/CIRCOUTCOMES.118.004947LinkGoogle Scholar13. Coylewright M, Palmer R, O'Neill ES, Robb JF, Fried TR. Patient-defined goals for the treatment of severe aortic stenosis: a qualitative analysis.Health Expect. 2016; 19:1036–1043. doi: 10.1111/hex.12393CrossrefMedlineGoogle Scholar14. Bonow RO, Brown AS, Gillam LD, Kapadia SR, Kavinsky CJ, Lindman BR, Mack MJ, Thourani VH. ACC/AATS/AHA/ASE/EACTS/HVS/SCA/SCAI/SCCT/SCMR/STS 2017 appropriate use criteria for the treatment of patients with severe aortic stenosis: a report of the American College of Cardiology appropriate use criteria task force, American Association for Thoracic Surgery, American Heart Association, American Society of Echocardiography, European Association for Cardio-Thoracic Surgery, Heart Valve Society, Society of Cardiovascular Anesthesiologists, Society for Cardiovascular Angiography and Interventions, Society of Cardiovascular Computed Tomography, Society for Cardiovascular Magnetic Resonance, and Society of Thoracic Surgeons.J Am Coll Cardiol. 2017; 70:2566–2598. doi: 10.1016/j.jacc.2017.09.018CrossrefMedlineGoogle Scholar15. Medcac Meeting 7/25/2018 - Transcatheter Aortic Valve Replacement (TAVR). https://http://www.cms.gov/medicare-coverage-database/details/medcac-meeting-details.aspx?MEDCACId=75. Accessed November 3, 2018.Google Scholar Previous Back to top Next FiguresReferencesRelatedDetailsRelated articlesProfiling Hospital Performance Based on Mortality After Transcatheter Aortic Valve Replacement in Ontario, CanadaGabby Elbaz-Greener, et al. Circulation: Cardiovascular Quality and Outcomes. 2018;11 December 2018Vol 11, Issue 12 Advertisement Article InformationMetrics © 2018 American Heart Association, Inc.https://doi.org/10.1161/CIRCOUTCOMES.118.005233PMID: 30562074 Originally publishedDecember 17, 2018 Keywordsrisk adjustmentEditorialsoutcome and process assessment (health care)transcatheter aortic valve replacementPDF download Advertisement SubjectsCatheter-Based Coronary and Valvular InterventionsMortality/SurvivalQuality and Outcomes

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Méta-épidémiologie (sens large)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,056
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0020,010
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,065
Tête enseignante GPT0,393
Écart entre enseignants0,328 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2018
Routes d'admission1
Résumé présentoui

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