Evidence map›Paper›PMID 37124552›Full record

ArticleHeart rhythm O22023

Major drivers of healthcare system costs and cost variability for routine atrial fibrillation ablation.

Brian Zenger, Haojia Li, T Jared Bunch, Candice Crawford, James C Fang, Christopher A Groh, Rachel Hess, Leenhapong Navaravong, Ravi Ranjan, Jeff Young and 2 more

Abstract read
In one paragraph

Article in Heart rhythm O2, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

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

12 authors.

Brian ZengerUniversity of Utah School of Medicine, Salt Lake City, Utah.
Haojia LiDepartment of Internal Medicine, Division of Epidemiology, University of Utah, Salt Lake City, Utah.
T Jared BunchDivision of Cardiovascular Medicine, Department of Internal Medicine, University of Utah, Salt Lake City, Utah.
Candice CrawfordDecision Support, University of Utah Health, Salt Lake City, Utah.
James C FangDivision of Cardiovascular Medicine, Department of Internal Medicine, University of Utah, Salt Lake City, Utah.
Christopher A GrohDivision of Cardiovascular Medicine, Department of Internal Medicine, University of Utah, Salt Lake City, Utah.
Rachel HessDivision of General Internal Medicine, Department of Internal Medicine, University of Utah, Salt Lake City, Utah.
Leenhapong NavaravongDivision of Cardiovascular Medicine, Department of Internal Medicine, University of Utah, Salt Lake City, Utah.
Ravi RanjanDivision of Cardiovascular Medicine, Department of Internal Medicine, University of Utah, Salt Lake City, Utah.
Jeff YoungDecision Support, University of Utah Health, Salt Lake City, Utah.
Yue ZhangDepartment of Internal Medicine, Division of Epidemiology, University of Utah, Salt Lake City, Utah.
Benjamin A SteinbergDivision of Cardiovascular Medicine, Department of Internal Medicine, University of Utah, Salt Lake City, Utah.

Funding

Utah Center for Clinical and Translational ScienceUL1TR002538 · NCATS · UNIVERSITY OF UTAH · PI HESS, RACHEL, MAJERSIK, JENNIFER JUHL · 2018 to 2022
$26.0M
CTSA UM1 Program at University of UtahUM1TR004409 · NCATS · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI RACHEL HESS, Jennifer Juhl Majersik · 2023 to 2026
$21.9M
Optimizing Outcomes for Patients with Heart Failure and Atrial FibrillationK23HL143156 · NHLBI · UNIVERSITY OF UTAH · PI STEINBERG, BENJAMIN ADAM · 2018 to 2022
$951k
Novel Characterization and Detection Techniques for Acute Myocardial IschemiaF30HL149327 · NHLBI · UNIVERSITY OF UTAH · PI ZENGER, BRIAN · 2020 to 2022
$117k
NCATS NIH HHS UL1 TR002538NCATS NIH HHS UM1 TR004409NHLBI NIH HHS F30 HL149327NHLBI NIH HHS K23 HL143156
6 · The paper itself

Abstract

Background: Catheter ablation is an effective treatment for atrial fibrillation (AF) but incurs significant financial costs to payers. Reducing variability may improve cost effectiveness. Objectives: We aimed to measure (1) the components of direct and indirect costs for routine AF ablation procedures, (2) the variability of those costs, and (3) the main factors driving ablation cost variability. Methods: Using data from the University of Utah Health Value Driven Outcomes system, we were able to measure direct, inflation-adjusted costs of uncomplicated, routine AF ablation to the healthcare system. Direct costs were considered costs incurred by pharmacy, disposable supplies, patient labs, implants, and other services categories (primarily anesthesia support) and indirect costs were considered within imaging, facility, and electrophysiology lab management categories. Results: A total of 910 patients with 1060 outpatient ablation encounters were included from January 1, 2013, to December 31, 2020. Disposable supplies accounted for the largest component of cost with 44.8 ± 9.7%, followed by other services (primarily anesthesia support) with 30.4 ± 7.7% and facility costs with 16.1 ± 5.6%; pharmacy, imaging, and implant costs each contributed <5%. Direct costs were larger than indirect costs (82.4 ± 5.6% vs 17.6 ± 5.6%). Multivariable regression showed that procedure operator was the primary factor associated with AF ablation overall cost (up to 12% differences depending on operator). Conclusions: Direct costs and other services (primarily anesthesia) drive the majority costs associated with AF ablations. There is significant variability in costs for these routine, uncomplicated AF ablation procedures. The procedure operator, and not patient characteristic, is the main driver for cost variability.

Indexed as

AblationAtrial fibrillationCostsCost variabilityProvider preferencesValue-driven healthcare

Identifiers

PMID37124552
PMCPMC10134392

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Registered trials

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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.