Cardiac Device Implantation Complications: A Gap in the Quality of Care?
Bibliographic record
Abstract
Editorials3 September 2019Cardiac Device Implantation Complications: A Gap in the Quality of Care?Jorge A. Wong, MD, MPH and P.J. Devereaux, MD, PhDJorge A. Wong, MD, MPHMcMaster University, Hamilton, Ontario, Canada (J.A.W., P.D.)Search for more papers by this author and P.J. Devereaux, MD, PhDMcMaster University, Hamilton, Ontario, Canada (J.A.W., P.D.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M19-1895 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Cardiac implantable electronic devices (CIEDs), including permanent pacemakers (PPMs), implantable cardioverter-defibrillators (ICDs), and cardiac resynchronization therapy devices, reduce adverse cardiovascular outcomes (1). The frequency of CIED implantation has been rising globally (2), and with an aging population and the increasing prevalence of heart failure, the need for CIEDs is expected to keep increasing. Although CIEDs are shown to be beneficial in patients meeting guideline criteria (1), complications associated with CIED implantation are common and may lead to substantial morbidity and even death (3–6). Studies have suggested that the risk for major complications is 3% to 6% with PPM implantation (4, ...References1. Al-Khatib SM, Stevenson WG, Ackerman MJ, et al. 2017 AHA/ACC/HRS guideline for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: a report of the American College of Cardiology/American Heart Association task force on clinical practice guidelines and the Heart Rhythm Society. J Am Coll Cardiol. 2018;72:e91-e220. [PMID: 29097296] doi:10.1016/j.jacc.2017.10.054 CrossrefMedlineGoogle Scholar2. Mond HG, Proclemer A. The 11th world survey of cardiac pacing and implantable cardioverter-defibrillators: calendar year 2009—a World Society of Arrhythmia's project. Pacing Clin Electrophysiol. 2011;34:1013-27. [PMID: 21707667] doi:10.1111/j.1540-8159.2011.03150.x CrossrefMedlineGoogle Scholar3. Al-Khatib SM, Lucas FL, Jollis JG, et al. The relation between patients' outcomes and the volume of cardioverter-defibrillator implantation procedures performed by physicians treating Medicare beneficiaries. J Am Coll Cardiol. 2005;46:1536-40. [PMID: 16226180] CrossrefMedlineGoogle Scholar4. Ellenbogen KA, Hellkamp AS, Wilkoff BL, et al. Complications arising after implantation of DDD pacemakers: the MOST experience. Am J Cardiol. 2003;92:740-1. [PMID: 12972124] CrossrefMedlineGoogle Scholar5. Kirkfeldt RE, Johansen JB, Nohr EA, et al. Complications after cardiac implantable electronic device implantations: an analysis of a complete, nationwide cohort in Denmark. Eur Heart J. 2014;35:1186-94. [PMID: 24347317] doi:10.1093/eurheartj/eht511 CrossrefMedlineGoogle Scholar6. Tang AS, Wells GA, Talajic M, et al; Resynchronization-Defibrillation for Ambulatory Heart Failure Trial Investigators. Cardiac-resynchronization therapy for mild-to-moderate heart failure. N Engl J Med. 2010;363:2385-95. [PMID: 21073365] doi:10.1056/NEJMoa1009540 CrossrefMedlineGoogle Scholar7. Reynolds MR, Cohen DJ, Kugelmass AD, et al. The frequency and incremental cost of major complications among medicare beneficiaries receiving implantable cardioverter-defibrillators. J Am Coll Cardiol. 2006;47:2493-7. [PMID: 16781379] CrossrefMedlineGoogle Scholar8. Dodson JA, Reynolds MR, Bao H, et al; NCDR. Developing a risk model for in-hospital adverse events following implantable cardioverter-defibrillator implantation: a report from the NCDR (National Cardiovascular Data Registry). J Am Coll Cardiol. 2014;63:788-96. [PMID: 24333491] doi:10.1016/j.jacc.2013.09.079 CrossrefMedlineGoogle Scholar9. Ranasinghe I, Labrosciano C, Horton D, et al. Institutional variation in quality of cardiovascular implantable electronic device implantation. A cohort study. Ann Intern Med. 2019;171:309-17. doi:10.7326/M18-2810 LinkGoogle Scholar10. Quan H, Parsons GA, Ghali WA. Validity of information on comorbidity derived from ICD-9-CCM administrative data. Med Care. 2002;40:675-85. [PMID: 12187181] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: McMaster University, Hamilton, Ontario, Canada (J.A.W., P.D.)Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M19-1895.Corresponding Author: Jorge A. Wong, MD, MPH, 237 Barton Street East, DBCVSRI Room C3-13C, Hamilton, Ontario, Canada L8L 2X2; e-mail, Jorge.[email protected]ca.Current Author Addresses: Dr. Wong: 237 Barton Street East, DBCVSRI Room C3-13C, Hamilton, Ontario, Canada L8L 2X2.Dr. Devereaux: Perioperative Medicine, David Braley Research Building, Suite C1, 237 Barton Street East, Hamilton, Ontario, Canada L8L 2X2.This article was published at Annals.org on 30 July 2019. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoInstitutional Variation in Quality of Cardiovascular Implantable Electronic Device Implantation Isuru Ranasinghe , Clementine Labrosciano , Dennis Horton , Anand Ganesan , Jeptha P. Curtis , Harlan M. Krumholz , Andrew McGavigan , Sadia Hossain , Tracy Air , and Saranya Hariharaputhiran Metrics Cited byReduction of CT artifacts from cardiac implantable electronic devices using a combination of virtual monoenergetic images and post-processing algorithmsPrimary Prevention Implantable Cardiac Defibrillators: A Townsville District Perspective 3 September 2019Volume 171, Issue 5Page: 368-369KeywordsCardiovascular implantable electronic devicesDatabasesDecision makingDisclosureHealth care qualityImplantable cardioverter defibrillatorsInformation storage and retrievalObservational studiesPacemakers ePublished: 30 July 2019 Issue Published: 3 September 2019 Copyright & PermissionsCopyright © 2019 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".