Literature DB >> 33580867

Macrovascular Risk Equations Based on the CANVAS Program.

Michael Willis1, Christian Asseburg2, April Slee3, Andreas Nilsson4, Cheryl Neslusan5.   

Abstract

BACKGROUND: Widely 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).
OBJECTIVE: We developed cardiovascular and mortality risk equations using patient-level data from the CANVAS (CANagliflozin cardioVascular Assessment Study) Program to address this shortcoming.
METHODS: Data 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.
RESULTS: UKPDS-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.
CONCLUSION: These 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.

Entities:  

Year:  2021        PMID: 33580867     DOI: 10.1007/s40273-021-01001-0

Source DB:  PubMed          Journal:  Pharmacoeconomics        ISSN: 1170-7690            Impact factor:   4.981


  32 in total

1.  Model of complications of NIDDM. II. Analysis of the health benefits and cost-effectiveness of treating NIDDM with the goal of normoglycemia.

Authors:  R C Eastman; J C Javitt; W H Herman; E J Dasbach; C Copley-Merriman; W Maier; F Dong; D Manninen; A S Zbrozek; J Kotsanos; S A Garfield; M Harris
Journal:  Diabetes Care       Date:  1997-05       Impact factor: 19.112

2.  Model of complications of NIDDM. I. Model construction and assumptions.

Authors:  R C Eastman; J C Javitt; W H Herman; E J Dasbach; A S Zbrozek; F Dong; D Manninen; S A Garfield; C Copley-Merriman; W Maier; J F Eastman; J Kotsanos; C C Cowie; M Harris
Journal:  Diabetes Care       Date:  1997-05       Impact factor: 19.112

Review 3.  Review of models used in economic analyses of new oral treatments for type 2 diabetes mellitus.

Authors:  Carl V Asche; Stephen E Hippler; Dean T Eurich
Journal:  Pharmacoeconomics       Date:  2014-01       Impact factor: 4.981

4.  Validation of the IMS CORE Diabetes Model.

Authors:  Phil McEwan; Volker Foos; James L Palmer; Mark Lamotte; Adam Lloyd; David Grant
Journal:  Value Health       Date:  2014-09       Impact factor: 5.725

5.  Validation of the Economic and Health Outcomes Model of Type 2 Diabetes Mellitus (ECHO-T2DM).

Authors:  Michael Willis; Pierre Johansen; Andreas Nilsson; Christian Asseburg
Journal:  Pharmacoeconomics       Date:  2017-03       Impact factor: 4.981

6.  Canagliflozin and Cardiovascular and Renal Events in Type 2 Diabetes.

Authors:  Bruce Neal; Vlado Perkovic; Kenneth W Mahaffey; Dick de Zeeuw; Greg Fulcher; Ngozi Erondu; Wayne Shaw; Gordon Law; Mehul Desai; David R Matthews
Journal:  N Engl J Med       Date:  2017-06-12       Impact factor: 91.245

7.  The Mt. Hood challenge: cross-testing two diabetes simulation models.

Authors:  J B Brown; A J Palmer; P Bisgaard; W Chan; K Pedula; A Russell
Journal:  Diabetes Res Clin Pract       Date:  2000-11       Impact factor: 5.602

8.  Validation of the CORE Diabetes Model against epidemiological and clinical studies.

Authors:  Andrew J Palmer; Stéphane Roze; William J Valentine; Michael E Minshall; Volker Foos; Francesco M Lurati; Morten Lammert; Giatgen A Spinas
Journal:  Curr Med Res Opin       Date:  2004-08       Impact factor: 2.580

9.  Cardiovascular disease risk profiles.

Authors:  K M Anderson; P M Odell; P W Wilson; W B Kannel
Journal:  Am Heart J       Date:  1991-01       Impact factor: 4.749

10.  The Challenge of Transparency and Validation in Health Economic Decision Modelling: A View from Mount Hood.

Authors:  Seamus Kent; Frauke Becker; Talitha Feenstra; An Tran-Duy; Iryna Schlackow; Michelle Tew; Ping Zhang; Wen Ye; Shi Lizheng; William Herman; Phil McEwan; Wendelin Schramm; Alastair Gray; Jose Leal; Mark Lamotte; Michael Willis; Andrew J Palmer; Philip Clarke
Journal:  Pharmacoeconomics       Date:  2019-11       Impact factor: 4.981

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  1 in total

Review 1.  Clinical Prediction Models for Heart Failure Hospitalization in Type 2 Diabetes: A Systematic Review and Meta-Analysis.

Authors:  Amir Razaghizad; Emily Oulousian; Varinder Kaur Randhawa; João Pedro Ferreira; James M Brophy; Stephen J Greene; Julian Guida; G Michael Felker; Marat Fudim; Michael Tsoukas; Tricia M Peters; Thomas A Mavrakanas; Nadia Giannetti; Justin Ezekowitz; Abhinav Sharma
Journal:  J Am Heart Assoc       Date:  2022-05-16       Impact factor: 6.106

  1 in total

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