Computer Alerts for Peripheral Arterial Disease

(PAD-ALERT Trial)

Overseen By Candrika D Kharaini

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Brigham and Women's Hospital

Must not be taking: Statins, Ezetimibe, Bempedoic acid, others

Disqualifiers: None

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

Duration: 3 months

400 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial aims to determine if computer alerts (Alert-Based Computerized Decision Support) can help doctors prescribe cholesterol-lowering medications more effectively for people with peripheral arterial disease (PAD), a condition that narrows blood vessels and reduces blood flow to the limbs. Participants will either receive these alerts or not, and the study will observe whether the alerts lead to more prescriptions of medications like statins, which lower LDL cholesterol. Those with PAD who aren't already on cholesterol-lowering medication might be suitable for this trial. The goal is to improve heart health by ensuring more patients receive the necessary care.

As an unphased trial, this study offers participants a unique opportunity to contribute to innovative research that could enhance treatment strategies for PAD.

Will I have to stop taking my current medications?

If you are currently taking any LDL-C-lowering medications like statins, ezetimibe, bempedoic acid, PCSK9 inhibitors, or inclisiran, you cannot participate in this trial. Otherwise, the protocol does not specify if you need to stop other medications.

What prior data suggests that this alert-based computerized decision support is safe for patients with PAD?

Research has shown that alert-based computerized tools, like Best Practice Advisory alerts, are generally well-received. These alerts help doctors remember important treatment guidelines, such as when to start a patient on cholesterol-lowering medication. Studies indicate that these alerts can improve patient care by helping doctors follow the latest recommendations.

For example, using Best Practice Advisory alerts in other healthcare settings has not resulted in any harmful effects. Doctors find them helpful for making informed decisions about patient care. No reports of negative events directly caused by these alerts suggest that the alert system is safe for clinical use.

Since this trial focuses on using alerts to guide treatment decisions, participants are unlikely to experience any safety issues from the alerts themselves. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the alert-based computerized decision support (CDS) system for peripheral arterial disease (PAD) because it uses technology to enhance clinical decision-making. Unlike traditional treatments that rely solely on a physician's judgment and existing prescriptions, this system provides real-time electronic alerts to clinicians, indicating when a patient with PAD might benefit from LDL-C-lowering therapy. This approach not only prompts clinicians to consider evidence-based recommendations but also streamlines the process of prescribing appropriate treatments, potentially improving patient outcomes and encouraging adherence to current guidelines.

What evidence suggests that this alert-based computerized decision support is effective for increasing LDL-C-lowering therapy in PAD patients?

Research shows that computerized decision support (CDS) tools with alerts can enhance healthcare by reminding doctors to follow proven guidelines. In past studies, these alerts successfully increased the use of recommended treatments, such as statin drugs, for patients who need them but aren't receiving them. In this trial, one group of participants will be managed with an alert-based CDS system, which will notify clinicians to prescribe medications that lower LDL cholesterol for patients with peripheral arterial disease (PAD). This approach is crucial for managing cholesterol and reducing the risk of heart disease. The alert system helps doctors choose the right medications or learn about the latest treatment recommendations. Another group will not receive these alerts. Overall, these CDS tools aim to ensure that patients receive the best care based on the latest evidence. ^{1 2 6 7 8}

Who Is on the Research Team?

Gregory Piazza

Principal Investigator

BWH

Are You a Good Fit for This Trial?

This trial is for patients with Peripheral Artery Disease (PAD) who are not currently taking medication to lower LDL cholesterol. It's designed to see if a computer alert can help improve the use of recommended treatments.

Inclusion Criteria

I have been diagnosed with peripheral artery disease (PAD).

Patients must be seen in Cardiovascular Medicine Clinic, Primary Care, Podiatry, Vascular Surgery, and Diabetology

I am not on any medication to lower my LDL cholesterol.

Exclusion Criteria

I am not currently taking any cholesterol-lowering medications.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Treatment

Participants are exposed to an alert-based computerized decision support tool to increase utilization of LDL-C lowering therapies

3 months

Follow-up

Participants are monitored for safety and effectiveness after treatment, including assessment of major adverse cardiovascular and limb events

6 months

What Are the Treatments Tested in This Trial?

Interventions

• Alert-Based Computerized Decision Support

Trial Overview The study tests an electronic alert system that reminds healthcare providers and patients about the benefits of statins or alternative therapies for lowering cholesterol in PAD patients not on treatment.

How Is the Trial Designed?

Group I: Alert Experimental Treatment 1 Intervention

Alert-based CDS will consist of an on-screen electronic alert that will notify the clinician that the patient has an indication for LDL-C-lowering therapy but is not prescribed any. The clinician will have the opportunity to proceed to an order template through which appropriate lipid-lowering can be prescribed. The clinician could also elect to learn more about current evidence-based recommendations for LDL-C lowering in the PAD population. Finally, the clinician could elect to proceed without ordering oral LDL-C-lowering therapy or reading evidence-based recommendations for LDL-C lowering but would have to provide a rationale for not doing so.

Group II: No Alert Active Control 1 Intervention

No on-screen notification will be issued to the clinician

Who Is Running the Clinical Trial?

Brigham and Women's Hospital

Lead Sponsor

Trials

1,694

Recruited

14,790,000+

Esperion Therapeutics, Inc.

Industry Sponsor

Trials

26

Recruited

21,900+

Founded

2008

Headquarters

Ann Arbor, USA

Known For

Cholesterol Therapies

Top Products

NEXLETOL, NEXLIZET, NILEMDO, NUSTENDI

Published Research Related to This Trial

In a study analyzing 1012 medication regimens in a nephrology clinic, more specific alerting strategies significantly reduced the number of alerts generated, with the most advanced strategy (D) resulting in alerts for only 7.81% of regimens compared to 87.5% with the least specific strategy (A).

The findings suggest that using patient and drug-specific information in alert systems can effectively minimize alert fatigue among physicians, potentially improving the safety of prescribing medications to patients with renal impairment.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Tailoring of alerts substantially reduces the alert burden in computerized clinical decision support for drugs that should be avoided in patients with renal disease. Czock, D., Konias, M., Seidling, HM., et al.[2017]

The study at Jurong Health Campus showed a 59.6% reduction in interruptive Best Practice Advisory (BPA) alerts after implementing optimization strategies, which significantly improved clinician response rates to alerts.

Despite increasing the number of unique BPAs from 54 to 360, the optimized alerts led to a 74% reduction in alerts from seven specific BPAs, saving an estimated 3600 hours of provider time annually and enhancing overall alert compliance.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Optimizing Best Practice Advisory alerts in electronic medical records with a multi-pronged strategy at a tertiary care hospital in Singapore. Ng, HJH., Kansal, A., Abdul Naseer, JF., et al.[2023]

Computerized decision support systems can help healthcare providers choose the best treatment for patients with complex cardiovascular conditions, but they often focus more on effectiveness than on the safety of therapies for individual patients.

Websites like CredibleMeds.org and BrugadaDrugs.org provide valuable safety information about drugs, which could enhance electronic prescribing systems and promote safer, personalized medicine by addressing the risk of drug-induced arrhythmias.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Assessing cardiovascular drug safety for clinical decision-making. Woosley, RL., Romero, K.[2022]

In a study of 375 procedures involving 345 patients with peripheral arterial disease, the greatest improvement in calculated quality of life (cQOL) was observed in patients who underwent endovascular or open revascularization compared to those who had primary amputation or medical therapy.

Patients whose actual treatment matched the surgeon's recommendations or predictions from decision analysis models experienced significantly better outcomes, highlighting the importance of treatment concordance for improving patient quality of life.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Failure of patients with peripheral arterial disease to accept the recommended treatment results in worse outcomes. Brothers, TE.[2022]

The natural language processing (NLP) algorithm demonstrated superior accuracy (91.8%) in identifying cases of lower extremity peripheral arterial disease (PAD) from clinical notes compared to traditional billing code algorithms, which had accuracies of 81.8% and 83%.

The NLP system also showed higher positive predictive value (92.9%) and specificity (92.5%), indicating its effectiveness in accurately identifying PAD cases, which could enhance clinical decision-making and patient care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Mining peripheral arterial disease cases from narrative clinical notes using natural language processing. Afzal, N., Sohn, S., Abram, S., et al.[2022]

Machine learning models were developed using data from over 150,000 patients hospitalized for peripheral artery disease (PAD) to predict in-hospital mortality, showing an in-hospital death rate of 1.8%.

The models demonstrated strong predictive performance, with an area under the curve of 0.83 to 0.85, indicating their potential to identify high-risk patients and guide personalized treatment interventions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Machine Learning Approach to Predict In-Hospital Mortality in Patients Admitted for Peripheral Artery Disease in the United States. Zhang, D., Li, Y., Kalbaugh, CA., et al.[2022]

In a study of 206 patients with peripheral arterial disease, a decision analysis model suggested different treatment plans than those initially proposed by surgeons, indicating that bypass surgery was recommended more often than necessary, while angioplasty and medical management were underutilized.

Surgeons showed greater comfort in their decisions when their initial treatment plans aligned with the model's predictions, suggesting that decision analysis can enhance decision-making processes without significantly altering final treatment choices.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Prospective decision analysis modeling indicates that clinical decisions in vascular surgery often fail to maximize patient expected utility. Brothers, TE., Cox, MH., Robison, JG., et al.[2022]

A study involving 14 pediatric clinicians revealed that many prescribers are unaware of drug-drug interactions (DDIs) for medications they frequently prescribe, highlighting a gap in DDI knowledge that could impact patient safety.

Clinicians preferred receiving DDI alerts during medication selection rather than at order submission, and suggested improvements such as standardized text, risk information, and actionable alerts to enhance the effectiveness and usability of these safety reminders.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Clinician Perceptions of Timing and Presentation of Drug-Drug Interaction Alerts. Humphrey, KE., Mirica, M., Phansalkar, S., et al.[2021]

A survey of 98 healthcare providers revealed that 69% approved of the electronic alert system for acute kidney injury (AKI), indicating a generally positive reception among physicians, pharmacists, and non-physician providers.

Approval of the alert system was strongly linked to the belief that it improved patient care, but notably, approval decreased by 20% for every additional 30 days of trial duration, highlighting the importance of perceived efficacy over time.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Provider acceptance of an automated electronic alert for acute kidney injury. Oh, J., Bia, JR., Ubaid-Ullah, M., et al.[2020]

A model-based billing code algorithm demonstrated high accuracy in identifying patients with peripheral arterial disease (PAD), achieving a c statistic of 0.91 in both training and validation datasets, indicating its effectiveness in clinical settings.

The simpler algorithm, while reasonably accurate for patients in the vascular laboratory, showed significantly lower sensitivity in a community-based sample, highlighting the importance of using more sophisticated methods for accurate PAD identification in broader populations.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Billing code algorithms to identify cases of peripheral artery disease from administrative data. Fan, J., Arruda-Olson, AM., Leibson, CL., et al.[2022]

The implementation of a formal alert management process at NewYork-Presbyterian Hospital has improved the quality of clinical decision support by requiring active involvement from hospital committees and departments in alert development.

In the first year after implementation, the hospital received 10 alert requests, indicating a successful model that has reduced the burden on medical informatics resources and enhanced knowledge management activities.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Managing the alert process at NewYork-Presbyterian Hospital. Kuperman, GJ., Diamente, R., Khatu, V., et al.[2018]

An AI screening tool using the BioMed-RoBERTa model effectively identified patients suitable for deep vein thrombosis (DVT) prophylaxis, achieving high accuracy with a precision-recall area under the curve of 0.82 and a receiver operator curve area of 0.89.

By selectively applying Best Practice Alerts (BPAs), the tool reduced unnecessary alerts by 20% and increased the relevance of alerts by 14.8%, demonstrating the potential of large language models to improve healthcare efficiency and patient safety.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Large Language Model Screening Tool to Target Patients for Best Practice Alerts: Development and Validation. Savage, T., Wang, J., Shieh, L.[2023]

Citations

1. centerwatch.com centerwatch.com/clinical-trials/listings/NCT06300450/randomized-controlled-trial-of-alert-based-computerized-decision-support-for-patients-with-peripheral-artery-disease-not-prescribed-lipid-lowering-therapy

Randomized Controlled Trial of Alert-Based Computerized ... This single-center, 400-patient, randomized controlled trial assesses the impact of a patient- and provider-facing EPIC Best Practice ...

2. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC10393867/

Optimizing Best Practice Advisory alerts in electronic ... Clinical decision support (CDS) alerts can aid in improving patient care. One CDS functionality is the Best Practice Advisory (BPA) alert ...

3. thrombosisresearch.com thrombosisresearch.com/article/S0049-3848(23)00132-9/pdf

A randomized, controlled tria We conducted a randomized controlled trial to evaluate an alert- based CDS program designed to identify consecutive ambulatory care clinic ...

4. ahajournals.org ahajournals.org/doi/10.1161/CIRCOUTCOMES.122.009235

Implementation of an Electronic Health Records–Based ... We created a Best Practice Advisory (BPA) alert in our Epic EHR system, which displayed the patient's safe contrast limit along with CA-AKI ...

5. professional.heart.org professional.heart.org/-/media/PHD-Files-2/Science-News/p/PAD-National-Action-Plan.pdf

PAD National Action Plan - Professional Heart Daily up-to-date evidence-based guidelines for PAD care exist and can easily be integrated into an alert-based computerized decision support program.

6. jacc.org jacc.org/doi/full/10.1016/j.jacadv.2025.101609

Physicians' Perspectives on a Best Practice Alert to ... This study sought to examine perspectives of primary care physicians on MRA utilization and BPA usefulness and to develop a novel conceptual model for guiding ...

7. sciencedirect.com sciencedirect.com/science/article/pii/S2475037922014194

Implementation and sustainment of a best practice alert ... We implemented a best practice alert (BPA) that recommended referral to an anticoagulation clinic before outpatient elective gastrointestinal (GI) endoscopies.

8. formative.jmir.org formative.jmir.org/2023/1/e43963

Development and Integration of Machine Learning Algorithm ... This study aimed to explore the factors that influence the integration of a peripheral arterial disease (PAD) identification algorithm to implement timely ...