MACE prediction using high-dimensional machine learning and mechanistic interpretation: A longitudinal cohort study in US veterans Article Swipe
Sayera Dhaubhadel
,
Beauty Kolade
,
Ruy M. Ribeiro
,
Kumkum Ganguly
,
Nicolas Hengartner
,
Tanmoy Bhattacharya
,
Judith D. Cohn
,
Khushbu Agarwal
,
Kelly Cho
,
Lauren Costa
,
Yuk‐Lam Ho
,
Allison Murata
,
Glen H. Murata
,
Jason L. Vassy
,
Daniel Posner
,
J. Michael Gaziano
,
Yan V. Sun
,
Peter W.F. Wilson
,
Ravi Madduri
,
Amy C. Justice
,
Philip S. Tsao
,
Christopher J. O’Donnell
,
Scott M. Damrauer
,
Benjamin H. McMahon
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.10.31.22281742
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.10.31.22281742
High dimensional predictive models of Major Adverse Cardiac Events (MACE), which includes heart attack (AMI), stroke, and death caused by cardiovascular disease (CVD), were built using four longitudinal cohorts of Veterans Administration (VA) patients created from VA medical records. We considered 247 variables / risk factors measured across 7.5 years for millions of patients in order to compare predictions for the first reported MACE event using six distinct modelling methodologies. The best-performing methodology varied across the four cohorts. Model coefficients related to disease pathophysiology and treatment were relatively constant across cohorts, while coefficients dependent upon the confounding variables of age and healthcare utilization varied considerably across cohorts. In particular, models trained on a retrospective case-control (Rcc) cohort (where controls are matched to cases by date of birth cohort and overall level of healthcare utilization) emphasize variables describing pathophysiology and treatment, while predictions based on the cohort of all active patients at the start of 2017 (C-17) rely much more on age and variables reflecting healthcare utilization. In consequence, directly using an Rcc-trained model to evaluate the C-17 cohort resulted in poor performance (C-statistic = 0.65). However, a simple reoptimization of model dependence on age, demographics, and five other variables improved the C-statistic to 0.74, nearly matching the 0.76 obtained on C-17 by a C-17-trained model. Dependence of MACE risk on biomarkers for hypertension, cholesterol, diabetes, body mass index, and renal function in our models was consistent with the literature. At the same time, including medications and procedures provided important indications of both disease severity and the level of treatment. More detailed study designs will be required to disentangle these effects.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2022.10.31.22281742
- https://www.medrxiv.org/content/medrxiv/early/2022/11/01/2022.10.31.22281742.full.pdf
- OA Status
- green
- Cited By
- 3
- References
- 78
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4307859904
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4307859904Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2022.10.31.22281742Digital Object Identifier
- Title
-
MACE prediction using high-dimensional machine learning and mechanistic interpretation: A longitudinal cohort study in US veteransWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-01Full publication date if available
- Authors
-
Sayera Dhaubhadel, Beauty Kolade, Ruy M. Ribeiro, Kumkum Ganguly, Nicolas Hengartner, Tanmoy Bhattacharya, Judith D. Cohn, Khushbu Agarwal, Kelly Cho, Lauren Costa, Yuk‐Lam Ho, Allison Murata, Glen H. Murata, Jason L. Vassy, Daniel Posner, J. Michael Gaziano, Yan V. Sun, Peter W.F. Wilson, Ravi Madduri, Amy C. Justice, Philip S. Tsao, Christopher J. O’Donnell, Scott M. Damrauer, Benjamin H. McMahonList of authors in order
- Landing page
-
https://doi.org/10.1101/2022.10.31.22281742Publisher landing page
- PDF URL
-
https://www.medrxiv.org/content/medrxiv/early/2022/11/01/2022.10.31.22281742.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.medrxiv.org/content/medrxiv/early/2022/11/01/2022.10.31.22281742.full.pdfDirect OA link when available
- Concepts
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Mace, Cohort, Medicine, Confounding, Statistic, Marginal structural model, Retrospective cohort study, Propensity score matching, Gerontology, Demography, Internal medicine, Statistics, Myocardial infarction, Percutaneous coronary intervention, Mathematics, SociologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1, 2023: 1Per-year citation counts (last 5 years)
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78Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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