Phenomapping-Derived Tool to Individualize the Effect of Canagliflozin on Cardiovascular Risk in Type 2 Diabetes
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posted on 2022-02-04, 18:44 authored by Evangelos K Oikonomou, Marc A Suchard, Darren K. McGuire, Rohan Khera<b>Objective: </b>Sodium-glucose cotransporter-2
(SGLT2) inhibitors have well-documented cardioprotective effects but are
underused, partly due to high cost. We aimed to develop a machine learning support tool
to individualize the atherosclerotic cardiovascular disease (ASCVD) benefit of
canagliflozin in type 2 diabetes.
<p><b>Research Design and Methods: </b>We constructed a
topological representation of the Canagliflozin Cardiovascular
Assessment Study (CANVAS) using 75 baseline variables collected from 4327 patients with type
2 diabetes randomly assigned 1:1:1 to one of two canagliflozin doses (n=2886) or
placebo (n=1441). Within each patient’s 5% neighborhood, we calculated age- and
sex-adjusted risk estimates for major adverse cardiovascular events (MACE). An
extreme gradient boosting algorithm was trained to predict the personalized ASCVD
effect of canagliflozin using features most predictive
of topological benefit. For validation, this algorithm was applied to the CANVAS-Renal
(CANVAS-R) trial, comprising 5808 patients with type 2 diabetes randomized 1:1
to canagliflozin or placebo.</p>
<p><b>Results: </b>In CANVAS (mean age
60.9±8.1 years, 33.9% women) 1605 (37.1%) patients had a neighborhood hazard
ratio more protective than the effect estimate of 0.86 reported for MACE in the
original trial. A 15-variable tool, INSIGHT, trained to predict the personalized
ASCVD effects of canagliflozin in CANVAS, was tested in CANVAS-R (mean age
62.4±8.4 years, 2164 (37.3%) women) where it identified patient phenotypes with
higher ASCVD effect of canagliflozin (adj. HR 0.60 [95%CI:0.41-0.89] versus
0.99 [95%CI:0.76-1.29]; P for interaction=0.04). </p>
<p><b>Conclusions: </b>We present an
evidence-based, machine learning-guided algorithm to personalize the
prescription of SGLT2 inhibitors for patients with type 2 diabetes for ASCVD effect.<b></b></p>
Funding
This study was supported by research funding awarded by the Yale School of Medicine to Dr. Khera. Dr. Khera also receives support from the National Heart, Lung, and Blood Institute of the National Institutes of Health under the award K23HL153775-01A1, outside of the submitted work. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
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