Evidence map›Paper›PMID 28763374›Full record

ArticleMedical care2017

The Veterans Affairs Cardiac Risk Score: Recalibrating the Atherosclerotic Cardiovascular Disease Score for Applied Use.

Jeremy B Sussman, Wyndy L Wiitala, Matthew Zawistowski, Timothy P Hofer, Douglas Bentley, Rodney A Hayward

Abstract read
In one paragraph

Article in Medical care, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 4 pooled it
–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

24 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  10. Recalibrating the Genetics and Epidemiology of Colorectal Cancer Consortium Environmental Risk Score for Use in US Veterans.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2024
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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

6 authors.

Jeremy B Sussman*Veterans Affairs Center for Clinical Management Research †Department of Internal Medicine and Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI.
Wyndy L Wiitala
Matthew Zawistowski
Timothy P Hofer
Douglas Bentley
Rodney A Hayward

Funding

Research BaseP30DK092926 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MARY ELLEN MICHELE HEISLER, ADESUWA B OLOMU · 2011 to 2026
$10.0M
Improving Analysis of Endogenous Multimodal Treatments for Use in Geriatrics Health Outcomes StudiesI01HX002237 · VA · VA BOSTON HEALTH CARE SYSTEM · PI GARRIDO, MELISSA M · 2018 to 2021
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Implementation research of benefit-based treatment for cardiovascular diseaseIK2HX001368 · VA · VETERANS HEALTH ADMINISTRATION · PI SUSSMAN, JEREMY BRODER · 2015 to 2019
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HSRD VA IK2 HX001368HSRD VA SDR 02-237NIDDK NIH HHS P30 DK092926
6 · The paper itself

Abstract

backgroundAccurately estimating cardiovascular risk is fundamental to good decision-making in cardiovascular disease (CVD) prevention, but risk scores developed in one population often perform poorly in dissimilar populations. We sought to examine whether a large integrated health system can use their electronic health data to better predict individual patients' risk of developing CVD.

methodsWe created a cohort using all patients ages 45-80 who used Department of Veterans Affairs (VA) ambulatory care services in 2006 with no history of CVD, heart failure, or loop diuretics. Our outcome variable was new-onset CVD in 2007-2011. We then developed a series of recalibrated scores, including a fully refit "VA Risk Score-CVD (VARS-CVD)." We tested the different scores using standard measures of prediction quality.

resultsFor the 1,512,092 patients in the study, the Atherosclerotic cardiovascular disease risk score had similar discrimination as the VARS-CVD (c-statistic of 0.66 in men and 0.73 in women), but the Atherosclerotic cardiovascular disease model had poor calibration, predicting 63% more events than observed. Calibration was excellent in the fully recalibrated VARS-CVD tool, but simpler techniques tested proved less reliable.

conclusionsWe found that local electronic health record data can be used to estimate CVD better than an established risk score based on research populations. Recalibration improved estimates dramatically, and the type of recalibration was important. Such tools can also easily be integrated into health system's electronic health record and can be more readily updated.

Indexed as

Health Status IndicatorsAgedAge DistributionAtherosclerosisCardiovascular DiseasesElectronic Health RecordsFemaleHumansMaleMiddle AgedRisk AssessmentRisk FactorsSex DistributionSocioeconomic FactorsUnited StatesUnited States Department of Veterans Affairs

Identifiers

PMID28763374
PMCPMC5561663

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Registered trials

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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.