HypertensionNCT01420016

Prioritized Clinical Decision Support (CDS) to Reduce Cardiovascular Risk

A study testing Prioritized Clinical Decision Support in people with Hypertension and Hyperlipidemia, run by HealthPartners Institute.

Completed

Finished. The study has ended and is not enrolling. Results may be posted on the official record.

Official record

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Trial summary

Who is it for?: Ages 18 years and olderHealthy volunteers can take part
Conditions the study is for: Hypertension, Hyperlipidemia, Diabetes
What is being tested?: Prioritized Clinical Decision Support
Type of treatment: Injection or infusion
Could I receive a placebo?: No, everyone receives an active treatment
How many people: 7,914 participants
Where: No sites listed yet
Study dates: 20 Aug 2012to 19 Aug 2014
Phase: Interventional
Are there results?: Yes, posted in the registrySummary in the doctors' view

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

Ages 18 years and older
All sexes
Healthy volunteers accepted
Adult, Older adult

Who may be able to join

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%

Who cannot join

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

About this study

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

Read the 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
Being tested: Prioritized Clinical Decision Support
Keywords: Cardiovascular risk, Clinical Decision Support, Electronic Health Records, Primary Care, Quality of Care
Medical subject headings: Hypertension, Hyperlipidemias, Diabetes Mellitus, Smoking, Cardiovascular Diseases

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

Prioritized Clinical Decision SupportOther
Also called Cardiovascular Wizard, CV Wizard
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.

Study groups in NCT01420016
Study group	Role	What they receive
Prioritized Clinical Decision Support	Active comparator	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. Receives: Other: Prioritized Clinical Decision Support
Usual Care	No intervention	Providers in the usual care arm did not have access to the prioritized clinical decision support tool.

What is being measured

The primary outcome is the main question the study is designed to answer.

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.
Measured: Index to 14 months post index

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

20 Aug 201219 Aug 2014

First posted19 Aug 2011 (estimated)
Study start20 Aug 2012
Primary completion19 Aug 2014
Study completion19 Aug 2014

Resources

Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
Posted resultsParticipant flow, outcome measures and adverse events reported by the sponsor.
Publications (11)Papers the sponsor has linked to this study.
Other trials for HypertensionStudies of the same condition, open and under way.
What the trial phases meanHow a treatment moves from first tests in people to approval.

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design

Study type: Interventional
Phase: Phase N/A
Primary purpose: Health services research
Allocation: Randomised
Intervention model: Parallel groups
Masking: None (open label)
Enrolment: 7,914 participants (actual)

IDs, dates and publications

ClinicalTrials.gov ID: NCT01420016
Sponsor's protocol ID: 09-096
Other IDs: R01HL102144-01
Status last verified: Feb 2015
First submitted: 17 Jan 2011
Last updated: 21 Sep 2018
Results posted: Yes, on ClinicalTrials.gov
Data monitoring committee: Yes
FDA-regulated drug: No
FDA-regulated device: No
Individual data sharing: No

Publications

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.
PubMed 28438733
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.
PubMed 27194173
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.
PubMed 28376456
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.
PubMed 26992568
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.
PubMed 25980520
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.
PubMed 23225213
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.
PubMed 22578085
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.
PubMed 21242556
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.
PubMed 23616881
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.
PubMed 28166337
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.
PubMed 29982627

Want to know more?

The sponsor has not listed a contact. The official record may have more details.

Talk to your doctor first and take this page with you. Only the study team can confirm whether you can join.

Questions to ask the study team

What is the study trying to find out?
What would I need to do, and how often would I visit?
Could I receive a placebo instead of the treatment?
What are the possible risks and benefits for me?
Will it cost me anything? Are travel or time paid for?
Can I keep seeing my own doctor during the study?
What happens when the study ends?

Official record

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All Hypertension trials

Study at a glance

From the registry record, last updated by the sponsor on 21 Sep 2018. Recruitment status is as the sponsor reported it: confirm with the study team.

Official title: Prioritized Clinical Decision Support (CDS) to Reduce Cardiovascular Risk
ClinicalTrials.gov ID: NCT01420016
Acronym / sponsor ID: 09-096
Conditions: Hypertension, Hyperlipidemia, Diabetes, Smoking, Cardiovascular Risk Factor, Cardiovascular Diseases
Keywords: Cardiovascular risk, Clinical Decision Support, Electronic Health Records, Primary Care, Quality of Care
Study type: Interventional
Recruitment status: Completed
Enrolment: 7,914 participants (actual)
Lead sponsor: HealthPartners InstituteUniversity, hospital or other
Collaborators: National Heart, Lung, and Blood Institute (NHLBI)
Study start: 20 Aug 2012 (actual)
Primary completion: 19 Aug 2014 (actual)
Study completion: 19 Aug 2014 (actual)
First posted: 19 Aug 2011 (estimated)
Results: Posted 21 Sep 2018

Clinical summary

The research question as the sponsor registered it. This describes what the study plans to test, not what it found.

Detailed description

Study design

Primary purpose: Health services research
Allocation: Randomised
Intervention model: Parallel groups
Masking: None (open label)
Number of arms: 2

Arm	Type	Receives
Prioritized Clinical Decision Support	Active comparator	Other: Prioritized Clinical Decision SupportThe 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.
Usual Care	No intervention	–Providers in the usual care arm did not have access to the prioritized clinical decision support tool.

Who may qualify

Ages: 18 years and older
Sex: All
Healthy volunteers: Accepted
Age groups: Adult, Older adult

Key inclusion criteria (2 listed)

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%

Key exclusion criteria (3 listed)

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

Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.

Interventions

Intervention	Type	Arms
Prioritized Clinical Decision SupportAlso: Cardiovascular Wizard, CV WizardEighteen 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	Prioritized Clinical Decision Support

Primary outcome

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.

Results and safety

Results posted to the registry on 21 Sep 2018, as reported by the sponsor. Figures are copied from the record, not re-analysed. Full results on ClinicalTrials.gov.

Participant flow

Group	Started	Completed
Prioritized Clinical Decision Support- Primary Analysis	6,970	4,043
Usual Care- Primary Analysis	6,411	3,871
Prioritized Clinical Decision Support- Safety Analysis	0	0
Usual Care- Safety Analysis	0	0

Primary outcome: 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. Number (95% Confidence Interval). Unit: annualized change in 10 year % cv risk

Measure	Prioritized Clinical Decision Support n = 4043	Usual Care n = 3871
Value	-0.59 (-1.43 to 0.26)	1.66 (0.81 to 2.52)

Prioritized Clinical Decision Support vs Usual Care: Slope Difference (Net): -2.25 (95% CI -3.45 to -1.04), p <0.05, Mixed Models Analysis

Population analysed: The patients whose data were included in the primary outcomes analyses met each of the following eligibility criteria. Each patient had an index visit; their first post-implementation primary care visit in a randomized clinic at which they were eligible for the CV Wizard intervention. Visits were intervention eligible based on the CDS algorithms.

Adverse events

Time frame: We monitored rates of occurrence by study arm (blinded by study arm) every 6 months, from 6 months pre, through the end of the 24-month intervention period. Randomization occurred at the clinic level and all providers at the clinics had access to the CDS tool. We monitored all patients receiving care at randomized clinics regardless of their exposure to the intervention.. Other adverse events reported at 0% frequency or more

Group	Serious adverse events	Other adverse events	Deaths
Prioritized Clinical Decision Support	253 of 6,392 (4%)	601 of 6,392 (9.4%)	Not reported
Usual Care	151 of 5,159 (2.9%)	403 of 5,159 (7.8%)	Not reported

Limitations and caveats (from the sponsor): Because this study was conducted at a single, relatively high-performing medical group, generalizability of results to other care delivery systems or patient populations is uncertain.

Loading the posted results from ClinicalTrials.gov…

Research links

Official record on ClinicalTrials.govLast updated by the sponsor 21 Sep 2018.
Posted resultsParticipant flow, baseline, all outcome measures and adverse events.
Background: PubMed 28438733Wolfson 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.
Background: PubMed 27194173O'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.
Background: PubMed 28376456O'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.
Background: PubMed 26992568Vock 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.
Background: PubMed 25980520Wolfson 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.
Background: PubMed 23225213O'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.
Background: PubMed 22578085Gilmer 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.
Background: PubMed 21242556O'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.
Background: PubMed 23616881O'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.
Background: PubMed 28166337Sperl-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 publication: PubMed 29982627Sperl-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.
Individual participant dataSharing planned: No

Related trials for Hypertension

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Referral and contact

AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.

Patrick J O'Connor, MD, MPH, MA principal investigator

HealthPartners Institute

Questions about this trial

What is this clinical trial studying?

A study testing Prioritized Clinical Decision Support in people with Hypertension and Hyperlipidemia, run by HealthPartners Institute.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 2 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: finished, as of the sponsor's last update on 21 Sep 2018. Status can change between updates, so confirm with the study contact before making plans.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

Yes. Results are posted in the registry (first posted 21 Sep 2018). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.

How can a doctor refer a patient or contact the research team?

The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

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). It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT01420016, which also lists 11 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 21 Sep 2018.