Evidence map›Paper›PMID 37878108›Full record

ArticleHealth economics review2023

Comparative effectiveness and cost-effectiveness of cardioprotective glucose-lowering therapies for type 2 diabetes in Brazil: a Bayesian network model.

Ana Claudia Cavalcante Nogueira, Joaquim Barreto, Filipe A Moura, Beatriz Luchiari, Abrão Abuhab, Isabella Bonilha, Wilson Nadruz, J Michael Gaziano, Thomas Gaziano, Luiz Sergio F de Carvalho and 2 more

Abstract read
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Article in Health economics review, 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. Review
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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.

Ana Claudia Cavalcante NogueiraCardiology Division, Unicamp Medical School, São Paulo, SP, Brazil.
Joaquim BarretoCardiology Division, Unicamp Medical School, São Paulo, SP, Brazil.
Filipe A MouraCardiology Division, Unicamp Medical School, São Paulo, SP, Brazil.
Beatriz LuchiariCardiology Division, Unicamp Medical School, São Paulo, SP, Brazil.
Abrão AbuhabHeart Institute (InCor), Do Hospital das Clínicas - FMUSP, Sao Paulo, Brazil.
Isabella BonilhaCardiology Division, Unicamp Medical School, São Paulo, SP, Brazil.
Wilson NadruzCardiology Division, Unicamp Medical School, São Paulo, SP, Brazil.
J Michael GazianoDepartment of Medicine, Harvard Medical School, Boston, MA, USA.
Thomas GazianoDepartment of Cardiovascular Medicine, Brigham & Women's Hospital, Boston, MA, USA.
Luiz Sergio F de Carvalho *Cardiology Division, Unicamp Medical School, São Paulo, SP, Brazil.
Andrei C Sposito *Cardiology Division, Unicamp Medical School, São Paulo, SP, Brazil. sposito@unicamp.br.ORCID http://orcid.org/0000-0001-7127-2052
Brazilian Heart Study Group

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico 304257/2021-4Conselho Nacional de Desenvolvimento Científico e Tecnológico 437413/2018-7Fundação de Amparo à Pesquisa do Estado de São Paulo 2019/09068-3Fundação de Apoio à Pesquisa do Distrito Federal 841/2015
6 · The paper itself

Abstract

backgroundThe escalating prevalence of type 2 diabetes (T2DM) poses an unparalleled economic catastrophe to developing countries. Cardiovascular diseases remain the primary source of costs among individuals with T2DM, incurring expenses for medications, hospitalizations, and surgical interventions. Compelling evidence suggests that the risk of cardiovascular outcomes can be reduced by three classes of glucose-lowering therapies (GLT), including SGLT2i, GLP-1A, and pioglitazone. However, an evidence-based and cost-effective protocol is still unavailable for many countries. The objective of the current study is to compare the effectiveness and cost-effectiveness of GLT in individuals with T2DM in Brazil.

methodsWe employed Bayesian Networks to calculate the incremental cost-effectiveness ratios (ICER), expressed in international dollars (Int$) per disease-adjusted life years [DALYs] averted. To determine the effectiveness of GLT, we conducted a systematic review with network meta-analysis (NMA) to provide insights for our model. Additionally, we obtained cardiovascular outcome incidence data from two real-world cohorts comprising 851 and 1337 patients in primary and secondary prevention, respectively. Our cost analysis took into account the perspective of the Brazilian public health system, and all values were converted to Int$.

resultsIn the NMA, SGLT2i [HR: 0.81 (95% CI 0.69-0.96)], GLP-1A [HR: 0.79 (95% CI 0.67-0.94)], and pioglitazone [HR: 0.73 (95% CI 0.59-0.91)] demonstrated reduced relative risks of non-fatal cardiovascular events. In the context of primary prevention, pioglitazone yielded 0.2339 DALYs averted, with an ICER of Int$7,082 (95% CI 4,521-10,770) per DALY averted when compared to standard care. SGLT2i and GLP-1A also increased effectiveness, resulting in 0.261 and 0.259 DALYs averted, respectively, but with higher ICERs of Int$12,061 (95% CI: 7,227-18,121) and Int$29,119 (95% CI: 23,811-35,367) per DALY averted. In the secondary prevention scenario, all three classes of treatments were deemed cost-effective at a maximum willingness-to-pay threshold of Int$26,700. Notably, pioglitazone consistently exhibited the highest probability of being cost-effective in both scenarios.

conclusionsIn Brazil, pioglitazone presented a higher probability of being cost-effective both in primary and secondary prevention, followed by SGLT2i and GLP-1A. Our findings support the use of cost-effectiveness models to build optimized and hierarchical therapeutic strategy in the management of T2DM.

trial registrationCRD42020194415.

Indexed as

Cardiovascular diseaseCost-effectivenessGLP1-RAGlucose-lowering therapyPioglitazoneSGLT2i

Identifiers

PMID37878108
PMCPMC10599033

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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.