Prioritized Clinical Decision Support (CDS) to Reduce Cardiovascular Risk

NCT ID: NCT01420016

Last Updated: 2018-09-21

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 7914 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2012-08-20
• Study Completion Date: 2014-08-19

Brief Summary

The objective of this project is to develop and implement sophisticated point-of-care Electronic Health Record (EHR)-based clinical decision support that (a) identifies and (b) prioritizes all available evidence-based treatment options to reduce a given patient's cardiovascular risk (CVR). After developing the EHR-based decision support intervention, the investigators will test its impact on CVR, the components of CVR, in a group randomized trial that includes 18 primary care clinics, 60 primary care physicians, and 18,000 adults with moderate or high CVR. This approach, if successful, will (a) improve chronic disease outcomes and reduce CVR for about 35% of the U.S. adult population, (b) maximize the clinical return on the massive investments that are increasingly being made in sophisticated outpatient EHR systems, and (c) provide a model for how to use EHR technology support to deliver "personalized medicine" in primary care settings

Detailed Description

This project developed and implemented a sophisticated point-of-care EHR-based clinical decision support that (a) identified and (b) prioritized all available evidence-based treatment options to reduce a given patient's cardiovascular risk (CVR). The prioritized list of treatment options is provided in different formats to both the primary care physician (PCP) and patient at the time of each office visit made by a patient with moderate to high CVR and sub-optimally controlled and potentially reversible CVR factors. Available evidence-based treatment options are prioritized based on the magnitude of potential CVR reduction of each treatment option. This intervention strategy, referred to as Prioritized Clinical Decision Support (CDS), is specifically designed for widespread use in primary care settings and has the potential to substantially augment current efforts to control CVR in the 35% of American adults with 10-year Framingham CVR of 10% or higher.

To assess the ability of the CDS intervention to reduce CVR in adults, we randomized 18 primary care clinics with 60 primary care physicians (PCPs) and approximately 18,000 eligible adults with baseline Framingham 10-year risk of a major CV event (either heart attack or stroke) of 10% or more into one of two experimental conditions: Group 1 includes 9 clinics (with 30 PCPs and 9,000 patients) that received prioritized clinical decision support (CDS) to reduce CVR at the time of each clinical encounter made by an eligible adult. Group 2 includes 9 clinics (with 30 PCPs and 9,000 patients) that received no study intervention and constitute a usual care (UC) control group. The study formally tested the hypothesis that after control for baseline CVR, post-intervention 10-year Framingham CVR will be better in Group 1 than Group 2 at 12 months after start of the intervention. In addition, impact of the intervention on specific components of CVR (BP, lipids, glucose, aspirin use, and smoking) was assessed, and the cost-effectiveness of the intervention will be quantified.

This innovative project builds upon 10 years of prior work by our research team, and extends prior successful EHR clinical decision support interventions by introducing prioritization, by providing decision support to both patients and PCPs at the time of the office visit, and by extending the decision support across the broad and critically important clinical terrain of CVR reduction. The results of this project, whether positive or negative, will extend our understanding of how to maximize the clinical return on massive public and private sector investments now being made in sophisticated outpatient EHR systems. If successful, this decision support tool could be broadly used to both standardize and personalize care delivered by case managers, pharmacists, and other providers in a wide range of care delivery configurations.

Conditions

Hypertension; Hyperlipidemia; Diabetes; Smoking; Cardiovascular Risk Factor; Cardiovascular Diseases

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: HEALTH_SERVICES_RESEARCH
• Blinding Strategy: NONE

Study Groups

Prioritized Clinical Decision Support

The Prioritized Clinical Decision Support (CDS) intervention is a protocol driven CDS system linked within the EMR that identifies patients with high cardiovascular risk and provides tailored, prioritized decision support to the provider and patient at the point of care. The CDS was printed at intervention sites. It i) compiled most recent lab data (A1c, SBP, and LDL), BMI, smoking status, and aspirin use, (ii) calculated a 10-year risk for stroke or heart attack, (iii) prioritized clinical domains based on the absolute risk reduction for each component, (iv) compiled information related to renal and liver function, creatine kinase level, and previous diagnoses (CHF, CVD, DM), and (v) provided recommendations for intensification of therapy for A1c, SBP and/or LDL if not at goal.

Group Type: ACTIVE_COMPARATOR

Prioritized Clinical Decision Support

Intervention Type: OTHER

Eighteen primary care clinics were blocked on size and on patient characteristics. Each clinic was randomly assigned to one of 2 study arms. All consenting PCPs were allocated to the study arm that their clinic was assigned to and the estimated 400 eligible adults with 10-year CVR \>= 10% under the care of each consenting physician were allocated to the same treatment arm as their PCP.

Usual Care

Providers in the usual care arm did not have access to the prioritized clinical decision support tool.

Group Type: NO_INTERVENTION

No interventions assigned to this group

Interventions

Prioritized Clinical Decision Support

Eighteen primary care clinics were blocked on size and on patient characteristics. Each clinic was randomly assigned to one of 2 study arms. All consenting PCPs were allocated to the study arm that their clinic was assigned to and the estimated 400 eligible adults with 10-year CVR \>= 10% under the care of each consenting physician were allocated to the same treatment arm as their PCP.

Intervention Type: OTHER

Other Intervention Names

Cardiovascular Wizard; CV Wizard

Eligibility Criteria

Inclusion Criteria

• Practicing general internist or family physician at HealthPartners Medical Group (HPMG)
• Provide ongoing care for 200 or more adult patients with 10 year CVR >=10%

Exclusion Criteria

• PCP not practicing in HPMG clinic
• Patient age greater than 80 years
• Patient Charlson comorbidity score greater than 3

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

National Heart, Lung, and Blood Institute (NHLBI)

NIH

Sponsor Role: collaborator

HealthPartners Institute

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Patrick J O'Connor, MD, MPH, MA

Role: PRINCIPAL_INVESTIGATOR

HealthPartners Institute

References

Wolfson J, Vock DM, Bandyopadhyay S, Kottke T, Vazquez-Benitez G, Johnson P, Adomavicius G, O'Connor PJ. Use and Customization of Risk Scores for Predicting Cardiovascular Events Using Electronic Health Record Data. J Am Heart Assoc. 2017 Apr 24;6(4):e003670. doi: 10.1161/JAHA.116.003670.

Reference Type: BACKGROUND

PMID: 28438733 (View on PubMed)

O'Connor PJ, Sperl-Hillen JM, Fazio CJ, Averbeck BM, Rank BH, Margolis KL. Outpatient diabetes clinical decision support: current status and future directions. Diabet Med. 2016 Jun;33(6):734-41. doi: 10.1111/dme.13090.

Reference Type: BACKGROUND

PMID: 27194173 (View on PubMed)

O'Connor PJ, Sperl-Hillen JM, Margolis KL, Kottke TE. Strategies to Prioritize Clinical Options in Primary Care. Ann Fam Med. 2017 Jan;15(1):10-13. doi: 10.1370/afm.2027. Epub 2017 Jan 6. No abstract available.

Reference Type: BACKGROUND

PMID: 28376456 (View on PubMed)

Vock DM, Wolfson J, Bandyopadhyay S, Adomavicius G, Johnson PE, Vazquez-Benitez G, O'Connor PJ. Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting. J Biomed Inform. 2016 Jun;61:119-31. doi: 10.1016/j.jbi.2016.03.009. Epub 2016 Mar 16.

Reference Type: BACKGROUND

PMID: 26992568 (View on PubMed)

Wolfson J, Bandyopadhyay S, Elidrisi M, Vazquez-Benitez G, Vock DM, Musgrove D, Adomavicius G, Johnson PE, O'Connor PJ. A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data. Stat Med. 2015 Sep 20;34(21):2941-57. doi: 10.1002/sim.6526. Epub 2015 May 18.

Reference Type: BACKGROUND

PMID: 25980520 (View on PubMed)

O'Connor PJ, Desai JR, Butler JC, Kharbanda EO, Sperl-Hillen JM. Current status and future prospects for electronic point-of-care clinical decision support in diabetes care. Curr Diab Rep. 2013 Apr;13(2):172-6. doi: 10.1007/s11892-012-0350-z.

Reference Type: BACKGROUND

PMID: 23225213 (View on PubMed)

Gilmer TP, O'Connor PJ, Sperl-Hillen JM, Rush WA, Johnson PE, Amundson GH, Asche SE, Ekstrom HL. Cost-effectiveness of an electronic medical record based clinical decision support system. Health Serv Res. 2012 Dec;47(6):2137-58. doi: 10.1111/j.1475-6773.2012.01427.x. Epub 2012 May 11.

Reference Type: BACKGROUND

PMID: 22578085 (View on PubMed)

O'Connor PJ, Sperl-Hillen JM, Rush WA, Johnson PE, Amundson GH, Asche SE, Ekstrom HL, Gilmer TP. Impact of electronic health record clinical decision support on diabetes care: a randomized trial. Ann Fam Med. 2011 Jan-Feb;9(1):12-21. doi: 10.1370/afm.1196.

Reference Type: BACKGROUND

PMID: 21242556 (View on PubMed)

O'Connor P. Opportunities to Increase the Effectiveness of EHR-Based Diabetes Clinical Decision Support. Appl Clin Inform. 2011 Aug 31;2(3):350-4. doi: 10.4338/ACI-2011-05-IE-0032. Print 2011.

Reference Type: BACKGROUND

PMID: 23616881 (View on PubMed)

Sperl-Hillen J, Margolis K, Crain L. Risk and Benefit Information and Use of Aspirin. JAMA Intern Med. 2017 Feb 1;177(2):291. doi: 10.1001/jamainternmed.2016.7988. No abstract available.

Reference Type: BACKGROUND

PMID: 28166337 (View on PubMed)

Sperl-Hillen JM, Crain AL, Margolis KL, Ekstrom HL, Appana D, Amundson G, Sharma R, Desai JR, O'Connor PJ. Clinical decision support directed to primary care patients and providers reduces cardiovascular risk: a randomized trial. J Am Med Inform Assoc. 2018 Sep 1;25(9):1137-1146. doi: 10.1093/jamia/ocy085.

Reference Type: RESULT

PMID: 29982627 (View on PubMed)

Other Identifiers

R01HL102144-01

Identifier Type: NIH

Identifier Source: secondary_id

View Link

09-096

Identifier Type: -

Identifier Source: org_study_id