Evidence mapPaperPMID 33580867Full record

ArticlePharmacoEconomics2021

Macrovascular Risk Equations Based on the CANVAS Program.

Michael Willis, Christian Asseburg, April Slee, Andreas Nilsson, Cheryl Neslusan

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Article in PharmacoEconomics, 2021. 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
0.6field-weighted citation impact, top 33% 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, 7 citations in OpenAlex.

  1. Pooled it
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  3. Review
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

5 authors at 4 institutions in 3 countries.

Michael WillisSwedish Institute for Health Economics, Box 2017, 220 02, Lund, Sweden. mw@ihe.se.ORCID 0000-0002-6565-7025
Christian AsseburgESiOR Oy, Kuopio, Finland.ORCID 0000-0001-7196-3363
April SleeAxio Research, LLC, Seattle, WA, USA.
Andreas NilssonSwedish Institute for Health Economics, Box 2017, 220 02, Lund, Sweden.ORCID 0000-0002-4604-7766
Cheryl NeslusanJanssen Scientific Affairs, LLC, Titusville, NJ, USA.
Swedish Institute for Health Economics · SEAptevo Therapeutics (United states) · USESiOR Oy (Finland) · FIJanssen (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWidely used risk equations for cardiovascular outcomes for individuals with type 2 diabetes mellitus (T2DM) have been incapable of predicting cardioprotective effects observed in recent cardiovascular outcomes trials (CVOTs) involving individuals with T2DM at high risk for or with established cardiovascular disease (CVD).

objectiveWe developed cardiovascular and mortality risk equations using patient-level data from the CANVAS (CANagliflozin cardioVascular Assessment Study) Program to address this shortcoming.

methodsData from 10,142 patients with T2DM at high risk for or with established CVD, randomized to canagliflozin + standard of care (SoC) or SoC alone and followed for a mean duration of 3.6 years in the CANVAS Program were used to derive parametric risk equations for myocardial infarction (MI), stroke, hospitalization for heart failure (HHF), and death. Accumulated knowledge from the widely used UKPDS-OM2 (United Kingdom Prospective Diabetes Study Outcomes Model 2) was leveraged, and any departures in parameterization were limited to those necessary to provide adequate goodness of fit. Candidate explanatory covariates were selected using only the placebo arm to minimize confounding effects. Internal validation was performed separately by study treatment arm.

resultsUKPDS-OM2 predicted CANVAS Program outcomes poorly. Recalibrating UKPDS-OM2 intercepts improved calibration in some cases. Refitting the coefficients but otherwise preserving the UKPDS-OM2 structure improved the fit substantially, which was sufficient for stroke and death. For MI, reselecting UKPDS-OM2 covariates and functional form proved sufficient. For HHF, selection from a broad set of candidate covariates and inclusion of a canagliflozin indicator was required.

conclusionThese risk equations address some of the limitations of widely used risk equations, such as the UKPDS-OM2, for modeling cardioprotective treatments for individuals with T2DM and high cardiovascular risk, including derivation from overly healthy patients treated with agents that lack cardioprotection and have been described as reflecting a different therapeutic era. Future work is needed to examine external validity.

Indexed as

Diabetes Mellitus, Type 2Myocardial InfarctionCanagliflozinHumansProspective StudiesUnited KingdomCanagliflozin

Identifiers

PMID33580867
OpenAlexW3126767572

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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 Socratic graph.