Evidence map›Paper›PMID 36091675›Full record

ArticleFrontiers in medicine2022

Cost-effectiveness of using protons for breast irradiation aiming at minimizing cardiotoxicity: A risk-stratification analysis.

Guo Li, Yun-Fei Xia, Yi-Xiang Huang, Deniz Okat, Bo Qiu, Jerome Doyen, Pierre-Yves Bondiau, Karen Benezery, Jin Gao, Chao-Nan Qian

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.3field-weighted citation impact, top 50% of its field
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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. 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

10 authors at 6 institutions in 3 countries.

Guo LiDepartment of Radiation Oncology, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China.
Yun-Fei XiaState Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Yi-Xiang HuangDepartment of Health Management, Public Health Institute of Sun Yat-sen University, Guangzhou, China.
Deniz OkatDepartment of Finance, Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China.
Bo QiuState Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Jerome DoyenDepartment of Radiation Oncology, Antoine Lacassagne Cancer Center, University of Nice-Sophia, Nice, France.
Pierre-Yves BondiauDepartment of Radiation Oncology, Antoine Lacassagne Cancer Center, University of Nice-Sophia, Nice, France.
Karen BenezeryDepartment of Radiation Oncology, Antoine Lacassagne Cancer Center, University of Nice-Sophia, Nice, France.
Jin GaoDivision of Life Sciences and Medicine, Department of Radiation Oncology, The First Affiliated Hospital of University of Science and Technology of China, University of Science and Technology of China, Hefei, China.
Chao-Nan QianDepartment of Radiation Oncology, Guangzhou Concord Cancer Center, Guangzhou, China.
Centre Antoine Lacassagne · FRSun Yat-sen University · CNSun Yat-sen University Cancer Center · CNGuangzhou Medical University · CNUniversity of Hong Kong · HKUniversity of Science and Technology of China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Incidental exposure of the heart to ionizing irradiation is associated with an increased risk of ischemic heart disease and subsequent fatality in patients with breast cancer after radiotherapy. Proton beam therapy can limit the heart dose in breast irradiation to a negligible level. However, compared with conventional photon modality, proton breast irradiation is more expensive. In this study, we performed cost-effectiveness analyses to identify the type of patients who would be more suitable for protons. Methods: A Markov decision model was designed to evaluate the cost-effectiveness of protons vs. photons in reducing the risk of irradiation-related ischemic heart disease. A baseline evaluation was performed on a 50-year-old woman patient without the preexisting cardiac risk factor. Furthermore, risk-stratification analyses for photon mean heart dose and preexisting cardiac risk were conducted on 40-, 50-, and 60-year-old women patients under different proton cost and willingness-to-pay (WTP) settings. Results: Using the baseline settings, the incremental effectiveness (protons vs. photons) increased from 0.043 quality-adjusted life-year (QALY) to 0.964 QALY when preexisting cardiac risk increased to 10 times its baseline level. At a proton cost of 50,000 US dollars ($), protons could be cost-effective for ≤ 60-year-old patients with diabetes and ≤50-year-old patients with grade II-III hypertension at the WTP of China ($37,653/QALY); for ≤ 60-year-old patients with diabetes and ≤ 50-year-old patients with grade II-III hypertension or ≥ 2 major cardiac risk factors at a WTP of $50,000/QALY; and for ≤ 60-year-old patients with diabetes, grade II-III hypertension or ≥ 2 major cardiac risk factors and ≤ 50-year-old patients with total cholesterol ≥ 240 mg/dL at a WTP of $100,000/QALY. Conclusion: Patients' preexisting cardiac risk status was a key factor affecting the cardiac benefits gained from protons and should therefore be a major consideration for the clinical decision of using protons; cost-effective scenarios of protons exist in those patients with high risk of developing cardiac diseases.

Indexed as

breast cancercost-effectiveness analysisischemic heart diseaseMarkov modelproton beam therapy

Identifiers

PMID36091675
PMCPMC9452743
OpenAlexW4293223646

What OpenQuestion holds

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LicenceCC BY
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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.