- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT01420016
Prioritized Clinical Decision Support (CDS) to Reduce Cardiovascular Risk
Study Overview
Status
Conditions
Intervention / Treatment
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.
Study Type
Enrollment (Actual)
Phase
- Not Applicable
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Description
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
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Health Services Research
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: None (Open Label)
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
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Active Comparator: 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.
|
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.
Other Names:
|
|
No Intervention: Usual Care
Providers in the usual care arm did not have access to the prioritized clinical decision support tool.
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Predicted Annual Rate of Change in 10-year Risk of Fatal or Nonfatal Heart Attack or Stroke
Time Frame: Index to 14 months post index
|
Ten year cardiovascular risk was calculated at each post index visit from the most recent clinical and laboratory values in the EMR.
The Framingham lipid equation was used when a lipid value was available in the previous 5 years; otherwise the Framingham BMI equation was used.
The primary outcome was the annualized rate of change (slope) in 10-year CVR, estimated for each treatment group from the time and time-by-treatment parameters of a mixed regression model which predicted post-index CVR values from time elapsed since index, treatment group and the time by treatment interaction.
|
Index to 14 months post index
|
Collaborators and Investigators
Sponsor
Collaborators
Investigators
- Principal Investigator: Patrick J O'Connor, MD, MPH, MA, HealthPartners Institute
Publications and helpful links
General Publications
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Estimate)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
- 09-096
- R01HL102144-01 (U.S. NIH Grant/Contract)
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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