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Prioritized Clinical Decision Support (CDS) to Reduce Cardiovascular Risk

Primary Purpose

Hypertension, Hyperlipidemia, Diabetes

Status
Completed
Phase
Not Applicable
Locations
Study Type
Interventional
Intervention
Prioritized Clinical Decision Support
Sponsored by
HealthPartners Institute
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional health services research trial for Hypertension focused on measuring Cardiovascular risk, Clinical Decision Support, Electronic Health Records, Primary Care, Quality of Care

Eligibility Criteria

18 Years - undefined (Adult, Older Adult)All SexesAccepts Healthy Volunteers

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

Sites / Locations

    Arms of the Study

    Arm 1

    Arm 2

    Arm Type

    Active Comparator

    No Intervention

    Arm Label

    Prioritized Clinical Decision Support

    Usual Care

    Arm Description

    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.

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

    Outcomes

    Primary Outcome Measures

    Predicted Annual Rate of Change in 10-year Risk of Fatal or Nonfatal Heart Attack or Stroke
    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.

    Secondary Outcome Measures

    Full Information

    First Posted
    January 17, 2011
    Last Updated
    September 17, 2018
    Sponsor
    HealthPartners Institute
    Collaborators
    National Heart, Lung, and Blood Institute (NHLBI)
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    1. Study Identification

    Unique Protocol Identification Number
    NCT01420016
    Brief Title
    Prioritized Clinical Decision Support (CDS) to Reduce Cardiovascular Risk
    Official Title
    Prioritized Clinical Decision Support (CDS) to Reduce Cardiovascular Risk
    Study Type
    Interventional

    2. Study Status

    Record Verification Date
    February 2015
    Overall Recruitment Status
    Completed
    Study Start Date
    August 20, 2012 (Actual)
    Primary Completion Date
    August 19, 2014 (Actual)
    Study Completion Date
    August 19, 2014 (Actual)

    3. Sponsor/Collaborators

    Responsible Party, by Official Title
    Sponsor
    Name of the Sponsor
    HealthPartners Institute
    Collaborators
    National Heart, Lung, and Blood Institute (NHLBI)

    4. Oversight

    Studies a U.S. FDA-regulated Drug Product
    No
    Studies a U.S. FDA-regulated Device Product
    No
    Data Monitoring Committee
    Yes

    5. Study Description

    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.

    6. Conditions and Keywords

    Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
    Hypertension, Hyperlipidemia, Diabetes, Smoking, Cardiovascular Risk Factor, Cardiovascular Diseases
    Keywords
    Cardiovascular risk, Clinical Decision Support, Electronic Health Records, Primary Care, Quality of Care

    7. Study Design

    Primary Purpose
    Health Services Research
    Study Phase
    Not Applicable
    Interventional Study Model
    Parallel Assignment
    Masking
    None (Open Label)
    Allocation
    Randomized
    Enrollment
    7914 (Actual)

    8. Arms, Groups, and Interventions

    Arm Title
    Prioritized Clinical Decision Support
    Arm Type
    Active Comparator
    Arm Description
    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.
    Arm Title
    Usual Care
    Arm Type
    No Intervention
    Arm Description
    Providers in the usual care arm did not have access to the prioritized clinical decision support tool.
    Intervention Type
    Other
    Intervention Name(s)
    Prioritized Clinical Decision Support
    Other Intervention Name(s)
    Cardiovascular Wizard, CV Wizard
    Intervention Description
    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.
    Primary Outcome Measure Information:
    Title
    Predicted Annual Rate of Change in 10-year Risk of Fatal or Nonfatal Heart Attack or Stroke
    Description
    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.
    Time Frame
    Index to 14 months post index

    10. Eligibility

    Sex
    All
    Minimum Age & Unit of Time
    18 Years
    Accepts Healthy Volunteers
    Accepts Healthy Volunteers
    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
    Overall Study Officials:
    First Name & Middle Initial & Last Name & Degree
    Patrick J O'Connor, MD, MPH, MA
    Organizational Affiliation
    HealthPartners Institute
    Official's Role
    Principal Investigator

    12. IPD Sharing Statement

    Plan to Share IPD
    No
    Citations:
    PubMed Identifier
    28438733
    Citation
    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.
    Results Reference
    background
    PubMed Identifier
    27194173
    Citation
    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.
    Results Reference
    background
    PubMed Identifier
    28376456
    Citation
    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.
    Results Reference
    background
    PubMed Identifier
    26992568
    Citation
    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.
    Results Reference
    background
    PubMed Identifier
    25980520
    Citation
    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.
    Results Reference
    background
    PubMed Identifier
    23225213
    Citation
    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.
    Results Reference
    background
    PubMed Identifier
    22578085
    Citation
    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.
    Results Reference
    background
    PubMed Identifier
    21242556
    Citation
    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.
    Results Reference
    background
    PubMed Identifier
    23616881
    Citation
    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.
    Results Reference
    background
    PubMed Identifier
    28166337
    Citation
    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.
    Results Reference
    background
    PubMed Identifier
    29982627
    Citation
    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.
    Results Reference
    result
    Links:
    URL
    https://doi.org/10.2337/diaspect.23.3.150
    Description
    Outpatient EHR-Based Diabetes Clinical Decision Support That Works:Lessons Learned From Implementing Diabetes Wizard

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