The use of structured data elements to identify ASCVD patients with statin-associated side effects: Insights from the Department of Veterans Affairs.


Journal

Journal of clinical lipidology
ISSN: 1933-2874
Titre abrégé: J Clin Lipidol
Pays: United States
ID NLM: 101300157

Informations de publication

Date de publication:
Historique:
received: 24 01 2019
revised: 29 07 2019
accepted: 04 08 2019
pubmed: 11 9 2019
medline: 9 7 2020
entrez: 11 9 2019
Statut: ppublish

Résumé

Accurate identification of patients with statin-associated side effects (SASEs) is critical for health care systems to institute strategies to improve guideline-concordant statin use. The objective of this study was to determine whether adverse drug reaction (ADR) entry by clinicians in the electronic medical record can accurately identify SASEs. We identified 1,248,214 atherosclerotic cardiovascular disease (ASCVD) patients seeking care in the Department of Veterans Affairs. Using an ADR data repository, we identified SASEs in 15 major symptom categories. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were assessed using a chart review of 256 ASCVD patients with identified SASEs, who were not on high-intensity statin therapy. We identified 171,189 patients (13.71%) with documented SASEs over a 15-year period (9.9%, 2.7%, and 1.1% to 1, 2, or >2 statins, respectively). Statin use, high-intensity statin use, low-density lipoprotein cholesterol, and non-high-density lipoprotein cholesterol levels were 72%, 28.1%, 99 mg/dL, and 129 mg/dL among those with vs 81%, 31.1%, 84 mg/dL, and 111 mg/dL among those without SASEs. Progressively lower statin and high-intensity statin use, and higher low-density lipoprotein cholesterol and non-high-density lipoprotein cholesterol levels were noted among those with SASEs to 1, 2, or >2 statins. Two-thirds of SASEs were related to muscle symptoms. Sensitivity, specificity, PPV, NPV compared with manual chart review were 63.4%, 100%, 100%, and 85.3%, respectively. A strategy of using ADR entry in the electronic medical record is feasible to identify SASEs with modest sensitivity and NPV but high specificity and PPV. Health care systems can use this strategy to identify ASCVD patients with SASEs and operationalize efforts to improve guideline-concordant lipid-lowering therapy use in such patients. The sensitivity of this approach can be further enhanced by the use of unstructured text data.

Sections du résumé

BACKGROUND
Accurate identification of patients with statin-associated side effects (SASEs) is critical for health care systems to institute strategies to improve guideline-concordant statin use.
OBJECTIVE
The objective of this study was to determine whether adverse drug reaction (ADR) entry by clinicians in the electronic medical record can accurately identify SASEs.
METHODS
We identified 1,248,214 atherosclerotic cardiovascular disease (ASCVD) patients seeking care in the Department of Veterans Affairs. Using an ADR data repository, we identified SASEs in 15 major symptom categories. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were assessed using a chart review of 256 ASCVD patients with identified SASEs, who were not on high-intensity statin therapy.
RESULTS
We identified 171,189 patients (13.71%) with documented SASEs over a 15-year period (9.9%, 2.7%, and 1.1% to 1, 2, or >2 statins, respectively). Statin use, high-intensity statin use, low-density lipoprotein cholesterol, and non-high-density lipoprotein cholesterol levels were 72%, 28.1%, 99 mg/dL, and 129 mg/dL among those with vs 81%, 31.1%, 84 mg/dL, and 111 mg/dL among those without SASEs. Progressively lower statin and high-intensity statin use, and higher low-density lipoprotein cholesterol and non-high-density lipoprotein cholesterol levels were noted among those with SASEs to 1, 2, or >2 statins. Two-thirds of SASEs were related to muscle symptoms. Sensitivity, specificity, PPV, NPV compared with manual chart review were 63.4%, 100%, 100%, and 85.3%, respectively.
CONCLUSION
A strategy of using ADR entry in the electronic medical record is feasible to identify SASEs with modest sensitivity and NPV but high specificity and PPV. Health care systems can use this strategy to identify ASCVD patients with SASEs and operationalize efforts to improve guideline-concordant lipid-lowering therapy use in such patients. The sensitivity of this approach can be further enhanced by the use of unstructured text data.

Identifiants

pubmed: 31501043
pii: S1933-2874(19)30266-1
doi: 10.1016/j.jacl.2019.08.002
pmc: PMC8393880
mid: NIHMS1732943
pii:
doi:

Substances chimiques

Cholesterol, HDL 0
Cholesterol, LDL 0
Hydroxymethylglutaryl-CoA Reductase Inhibitors 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

797-803.e1

Subventions

Organisme : HSRD VA
ID : I01 HX002225
Pays : United States
Organisme : HSRD VA
ID : I01 HX002917
Pays : United States

Informations de copyright

Published by Elsevier Inc.

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Auteurs

Salim S Virani (SS)

Health Policy, Quality & Informatics Program, Michael E. DeBakey VA Medical Center, Health Services Research & Development Center for Innovations, Houston, TX, USA; Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA; Section of Cardiology, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA; Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA. Electronic address: virani@bcm.edu.

Julia M Akeroyd (JM)

Health Policy, Quality & Informatics Program, Michael E. DeBakey VA Medical Center, Health Services Research & Development Center for Innovations, Houston, TX, USA; Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.

Sarah T Ahmed (ST)

Health Policy, Quality & Informatics Program, Michael E. DeBakey VA Medical Center, Health Services Research & Development Center for Innovations, Houston, TX, USA; Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.

Chayakrit Krittanawong (C)

Department of Internal Medicine, Icahn School of Medicine at Mount Sinai St. Luke's and Mount Sinai West, NY, New York, USA.

Lindsey A Martin (LA)

Health Policy, Quality & Informatics Program, Michael E. DeBakey VA Medical Center, Health Services Research & Development Center for Innovations, Houston, TX, USA; Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.

Jason Slagle (J)

Department of Anesthesiology, Center for Research and Innovation in Systems Safety, Vanderbilt University School of Medicine, Nashville, TN, USA; Department of Veterans Affairs, Geriatric Research, Education and Clinical Center, Tennessee Valley Healthcare System, Nashville, TN, USA.

Glenn T Gobbel (GT)

Department of Veterans Affairs, Geriatric Research, Education and Clinical Center, Tennessee Valley Healthcare System, Nashville, TN, USA; Department of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN, USA.

Michael E Matheny (ME)

Department of Veterans Affairs, Geriatric Research, Education and Clinical Center, Tennessee Valley Healthcare System, Nashville, TN, USA; Department of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN, USA.

Christie M Ballantyne (CM)

Section of Cardiology, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA; Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.

Laura A Petersen (LA)

Health Policy, Quality & Informatics Program, Michael E. DeBakey VA Medical Center, Health Services Research & Development Center for Innovations, Houston, TX, USA; Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.

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