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| ID | Type | Description | Link |
|---|---|---|---|
| R01HL102144-01 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Heart, Lung, and Blood Institute (NHLBI) | NIH |
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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
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
|
| Usual Care | No Intervention | Providers in the usual care arm did not have access to the prioritized clinical decision support tool. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Prioritized Clinical Decision Support | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | Index to 14 months post index |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Patrick J O'Connor, MD, MPH, MA | HealthPartners Institute | Principal Investigator |
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 28438733 | Background | 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. | |
| 27194173 | Background | 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. |
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13381 subjects were assessed for eligibility for the Primary analysis; a partially overlapping population of 11551 subjects were passively monitored for safety outcomes. Consent was waived for all participants. The total number of participants is unknown; therefore, the enrollment only counts participants who were included in the final analysis.
All adult primary care providers (physicians, PAs, NPs) employed full-time as of August 2012 were eligible for the study. Providers received a letter from the study PI inviting them to participate with a link to the consent form. Patient consent was waived by HPI IRB since there was no direct contact between the patient and the study team.
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| ID | Title | Description |
|---|---|---|
| FG000 | Prioritized Clinical Decision Support- Primary Analysis | After entry of BP data, relevant EHR data were extracted from the EHR, encrypted, and processed through Web-based clinical algorithms that (a) determined if the patient met intervention eligibility criteria, (b) identified evidence-based treatment options for any uncontrolled CVR factors, and (c) prioritized treatment recommendations based on potential CV risk reduction. CV risk factors addressed in these study participants were control of lipids, BP, weight, tobacco, and appropriate aspirin use. Personalized treatment recommendations were printed given to PCP and patient immediately before the visit. |
| FG001 | Usual Care- Primary Analysis | Patients and providers at control clinics did not have access to the prioritized clinical decision support system. |
| FG002 | Prioritized Clinical Decision Support- Safety Analysis | The Prioritized Clinical Decision Support (CV Wizard) intervention is a protocol driven clinical decision support 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 CV Wizard was printed at intervention sites and i) compiled lab data (most recent A1c, SBP, and LDL levels), BMI, smoking status, and aspirin use, (ii) calculated a 10-year risk for stroke or heart attack using the Framingham Risk Score, (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. Recommendations were based on evidenced-based protocols including JNC-8, ADA, and Institute for Clinical Systems Improvement (ICSI). |
| FG003 | Usual Care- Safety Analysis | Patients and providers at control clinics did not have access to the CV Wizard clinical decision support system. |
| Title | Milestones | Reasons Not Completed | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary Analysis |
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| Safety Analysis |
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| ID | Title | Description |
|---|---|---|
| BG000 | Prioritized Clinical Decision Support | The Prioritized Clinical Decision Support (CV Wizard) intervention is a protocol driven clinical decision support 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 CV Wizard was printed at intervention sites and i) compiled lab data (most recent A1c, SBP, and LDL levels), BMI, smoking status, and aspirin use, (ii) calculated a 10-year risk for stroke or heart attack using the Framingham Risk Score, (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. Recommendations were based on evidenced-based protocols including JNC-8, ADA, and Institute for Clinical Systems Improvement (ICSI). |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | Mean |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | 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. | 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. | Posted | Number | 95% Confidence Interval | annualized change in 10 year % cv risk | Index to 14 months post index |
|
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.
We conducted passive safety surveillance for all patients in all randomized clinics using routinely collected EMR data to monitor for potential safety events. This means that the safety data population (N=11,551) is a larger population than the analysis population (N=7,914). We identified, a priori, events related to more intensive BP or lipid treatment: hypotensive or hypertensive urgency or emergency, syncope, hyperkalemia, hypokalemia, hyponatremia, myositis, and rhabdomyolysis.
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | Prioritized Clinical Decision Support | The Prioritized Clinical Decision Support (CV Wizard) intervention is a protocol driven clinical decision support 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 CV Wizard was printed at intervention sites and i) compiled lab data (most recent A1c, SBP, and LDL levels), BMI, smoking status, and aspirin use, (ii) calculated a 10-year risk for stroke or heart attack using the Framingham Risk Score, (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. Recommendations were based on evidenced-based protocols including JNC-8, ADA, and Institute for Clinical Systems Improvement (ICSI). |
| Term | Organ System | Source Vocabulary | Assessment Type | Notes | Statistical Information |
|---|---|---|---|---|---|
| Hypotensive emergency, urgency | Vascular disorders | Systematic Assessment |
| Term | Organ System | Source Vocabulary | Assessment Type | Notes | Statistical Information |
|---|---|---|---|---|---|
| Any lipid related event | Cardiac disorders | Non-systematic Assessment |
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.
| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Dr. Patrick O'Connor | HealthPartners Institute | 952-967-5034 | patrick.j.oconnor@healthpartners.com |
| ID | Term |
|---|---|
| D006973 | Hypertension |
| D006949 | Hyperlipidemias |
| D003920 | Diabetes Mellitus |
| D012907 | Smoking |
| D002318 | Cardiovascular Diseases |
| ID | Term |
|---|---|
| D014652 | Vascular Diseases |
| D050171 | Dyslipidemias |
| D052439 | Lipid Metabolism Disorders |
| D008659 | Metabolic Diseases |
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|
|
| 28376456 | Background | 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. |
| 26992568 | Background | 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. |
| 25980520 | Background | 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. |
| 23225213 | Background | 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. |
| 22578085 | Background | 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. |
| 21242556 | Background | 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. |
| 23616881 | Background | 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. |
| 28166337 | Background | 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. |
| 29982627 | Result | 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. |
| COMPLETED |
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| NOT COMPLETED |
|
| BG001 | Usual Care | Usual care. |
| BG002 | Total | Total of all reporting groups |
| years |
|
| Sex: Female, Male | Count of Participants | Participants |
|
| Ethnicity (NIH/OMB) | Count of Participants | Participants |
|
| Race (NIH/OMB) | Count of Participants | Participants |
|
| Systolic Blood Pressure | Mean | Standard Deviation | mmHg |
|
| Diastolic Blood Pressure | Mean | Standard Deviation | mmHg |
|
| Smoking status | Current smoker at index visit | Count of Participants | Participants |
|
| Aspirin use | Aspirin use when indicated | Count of Participants | Participants |
|
| LDL Cholesterol | Mean of LDL cholesterol value as of index date. | Mean | Standard Deviation | mg/dL |
|
| Body Mass Index (BMI) | Body Mass Index (BMI) at index visit | Mean | Standard Deviation | kg/m2 |
|
| Description |
|---|
| OG000 | Prioritized Clinical Decision Support | The Prioritized Clinical Decision Support (CV Wizard) intervention is a protocol driven clinical decision support 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 CV Wizard was printed at intervention sites and i) compiled lab data (most recent A1c, SBP, and LDL levels), BMI, smoking status, and aspirin use, (ii) calculated a 10-year risk for stroke or heart attack using the Framingham Risk Score, (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. Recommendations were based on evidenced-based protocols including JNC-8, ADA, and Institute for Clinical Systems Improvement (ICSI). |
| OG001 | Usual Care | Usual care. |
|
|
|
| 253 |
| 6,392 |
| 601 |
| 6,392 |
| EG001 | Usual Care | Patients and providers at control clinics did not have access to the CV Wizard clinical decision support system. | 151 | 5,159 | 403 | 5,159 |
| Hypertensive emergency, urgency | Vascular disorders | Systematic Assessment |
|
| Acute renal failure | Renal and urinary disorders | Non-systematic Assessment |
|
| Myositis | General disorders | Non-systematic Assessment |
|
| Hypoglycemia | Endocrine disorders | Non-systematic Assessment |
|
| Hyperglycemia | Endocrine disorders | Non-systematic Assessment |
|
| GI bleed | Gastrointestinal disorders | Non-systematic Assessment |
|
| ASA allergy-acute anaphylaxis | General disorders | Non-systematic Assessment |
|
| Any glycemic related event | Endocrine disorders | Non-systematic Assessment |
|
| Hyperkalemia | Metabolism and nutrition disorders | Non-systematic Assessment |
|
| Hypokalemia | Metabolism and nutrition disorders | Non-systematic Assessment |
|
| Hyponatremia | Metabolism and nutrition disorders | Non-systematic Assessment |
|
| Any aspirin related event | General disorders | Non-systematic Assessment |
|
| BP drug interaction | General disorders | Non-systematic Assessment |
|
| Glycemic drug interaction | General disorders | Non-systematic Assessment |
|
| Lipid drug interaction | General disorders | Non-systematic Assessment |
|
| Any blood pressure related event | Vascular disorders | Non-systematic Assessment |
|
| Metabolic disturbance | Metabolism and nutrition disorders | Non-systematic Assessment |
|
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| D009750 |
| Nutritional and Metabolic Diseases |
| D044882 | Glucose Metabolism Disorders |
| D004700 | Endocrine System Diseases |
| D001519 | Behavior |