Computer Alerts for Peripheral Arterial Disease
(PAD-ALERT Trial)
Recruiting in Palo Alto (17 mi)
Overseen byGregory Piazza
Age: 18+
Sex: Any
Travel: May Be Covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: Brigham and Women's Hospital
Must not be taking: Statins, Ezetimibe, Bempedoic acid, others
Disqualifiers: None
No Placebo Group
Trial Summary
What is the purpose of this trial?This single-center, 400-patient, randomized controlled trial assesses the impact of a patient- and provider-facing EPIC Best Practice Advisory (BPA; alert-based computerized decision support tool) to increase guideline-directed utilization of statin and statin-alternative oral LDL-C lowering therapies in patients with PAD who are not being prescribed LDL-C-lowering therapy.
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 data supports the effectiveness of the treatment Alert-Based Computerized Decision Support for Peripheral Arterial Disease?
Research shows that Best Practice Alerts (BPAs) in electronic health records can improve patient care by encouraging appropriate use of healthcare resources and reducing costs. Tailoring these alerts to specific patient needs can also reduce the burden of excessive alerts, making them more effective.
12345Is the alert-based computerized decision support system safe for humans?
The safety of alert-based computerized decision support systems, like the EPIC Best Practice Advisory, is not well-documented in terms of direct human safety. However, these systems can lead to 'alert fatigue,' where too many alerts cause healthcare providers to ignore them, potentially putting patients at risk.
14567How is the Alert-Based Computerized Decision Support treatment unique for peripheral arterial disease?
This treatment is unique because it uses computer alerts to assist doctors in making better decisions for patients with peripheral arterial disease, potentially improving outcomes by optimizing treatment plans based on individual patient data.
89101112Eligibility Criteria
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).
I am not on any medication to lower my LDL cholesterol.
Patients must be seen in Cardiovascular Medicine Clinic, Primary Care, Podiatry, Vascular Surgery, and Diabetology
+1 more
Exclusion Criteria
I am not currently taking any cholesterol-lowering medications.
Trial Timeline
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
Participant Groups
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.
2Treatment groups
Experimental Treatment
Active Control
Group I: AlertExperimental Treatment1 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 AlertActive Control1 Intervention
No on-screen notification will be issued to the clinician
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
Brigham and Women's HospitalBoston, MA
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Who Is Running the Clinical Trial?
Brigham and Women's HospitalLead Sponsor
Esperion Therapeutics, Inc.Industry Sponsor
References
Optimizing Best Practice Advisory alerts in electronic medical records with a multi-pronged strategy at a tertiary care hospital in Singapore. [2023]Clinical decision support (CDS) alerts can aid in improving patient care. One CDS functionality is the Best Practice Advisory (BPA) alert notification system, wherein BPA alerts are automated alerts embedded in the hospital's electronic medical records (EMR). However, excessive alerts can change clinician behavior; redundant and repetitive alerts can contribute to alert fatigue. Alerts can be optimized through a multipronged strategy. Our study aims to describe these strategies adopted and evaluate the resultant BPA alert optimization outcomes.
Managing the alert process at NewYork-Presbyterian Hospital. [2018]Clinical decision support can improve the quality of care, but requires substantial knowledge management activities. At NewYork-Presbyterian Hospital in New York City, we have implemented a formal alert management process whereby only hospital committees and departments can request alerts. An explicit requestor, who will help resolve the details of the alert logic and the alert message must be identified. Alerts must be requested in writing using a structured alert request form. Alert requests are reviewed by the Alert Committee and then forwarded to the Information Systems department for a software development estimate. The model required that clinical committees and departments become more actively involved in the development of alerts than had previously been necessary. In the 12 months following implementation, 10 alert requests were received. The model has been well received. A lot of the knowledge engineering work has been distributed and burden has been removed from scarce medical informatics resources.
A Large Language Model Screening Tool to Target Patients for Best Practice Alerts: Development and Validation. [2023]Best Practice Alerts (BPAs) are alert messages to physicians in the electronic health record that are used to encourage appropriate use of health care resources. While these alerts are helpful in both improving care and reducing costs, BPAs are often broadly applied nonselectively across entire patient populations. The development of large language models (LLMs) provides an opportunity to selectively identify patients for BPAs.
Tailoring of alerts substantially reduces the alert burden in computerized clinical decision support for drugs that should be avoided in patients with renal disease. [2017]Electronic alerts are often ignored by physicians, which is partly due to the large number of unspecific alerts generated by decision support systems. The aim of the present study was to analyze critical drug prescriptions in a university-based nephrology clinic and to evaluate the effect of different alerting strategies on the alert burden.
Provider acceptance of an automated electronic alert for acute kidney injury. [2020]Clinical decision support systems, including electronic alerts, ideally provide immediate and relevant patient-specific information to improve clinical decision-making. Despite the growing capabilities of such alerts in conjunction with an expanding electronic medical record, there is a paucity of information regarding their perceived usefulness. We surveyed healthcare providers' opinions concerning the practicality and efficacy of a specific text-based automated electronic alert for acute kidney injury (AKI) in a single hospital during a randomized trial of AKI alerts.
Assessing cardiovascular drug safety for clinical decision-making. [2022]Optimal therapeutic decision-making requires integration of patient-specific and therapy-specific information at the point of care, particularly when treating patients with complex cardiovascular conditions. The formidable task for the prescriber is to synthesize information about all therapeutic options and match the best treatment with the characteristics of the individual patient. Computerized decision support systems have been developed with the goal of integrating such information and presenting the acceptable therapeutic options on the basis of their effectiveness, often with limited consideration of their safety for a specific patient. Assessing the safety of therapies relative to each patient is difficult, and sometimes impossible, because the evidence required to make such an assessment is either imperfect or does not exist. In addition, many of the alerts sent to prescribers by decision-support systems are not perceived as credible, and 'alert fatigue' causes warnings to be ignored putting patients at risk of harm. The CredibleMeds.org and BrugadaDrugs.org websites are prototypes for evidence-based sources of safety information that rank drugs for their risk of a specific form of drug toxicity-in these cases, drug-induced arrhythmias. Broad incorporation of this type of information in electronic prescribing algorithms and clinical decision support could speed the evolution of safe personalized medicine.
Clinician Perceptions of Timing and Presentation of Drug-Drug Interaction Alerts. [2021]Alert presentation of clinical decision support recommendations is a common method for providing information; however, many alerts are overridden suggesting presentation design improvements can be made. This study attempts to assess pediatric prescriber information needs for drug-drug interactions (DDIs) alerts and to evaluate the optimal presentation timing and presentation in the medication ordering process.
Failure of patients with peripheral arterial disease to accept the recommended treatment results in worse outcomes. [2022]Strategies available to facilitate decision making for patients with peripheral arterial disease (PAD) include a Markov-based decision analysis (DA) model and the Lower Extremity Grading System (LEGS) score. Both have suggested inferior outcomes when the actual treatment received (ATX) differs from that predicted. This study focuses on patient outcomes when such discordance exists.
Mining peripheral arterial disease cases from narrative clinical notes using natural language processing. [2022]Lower extremity peripheral arterial disease (PAD) is highly prevalent and affects millions of individuals worldwide. We developed a natural language processing (NLP) system for automated ascertainment of PAD cases from clinical narrative notes and compared the performance of the NLP algorithm with billing code algorithms, using ankle-brachial index test results as the gold standard.
Prospective decision analysis modeling indicates that clinical decisions in vascular surgery often fail to maximize patient expected utility. [2022]Applied prospectively to patients with peripheral arterial disease, individualized decision analysis has the potential to improve the surgeon's ability to optimize patient outcome.
Machine Learning Approach to Predict In-Hospital Mortality in Patients Admitted for Peripheral Artery Disease in the United States. [2022]Background Peripheral artery disease (PAD) affects >10 million people in the United States. PAD is associated with poor outcomes, including premature death. Machine learning (ML) has been increasingly used on big data to predict clinical outcomes. This study aims to develop ML models to predict in-hospital mortality in patients hospitalized for PAD based on a national database. Methods and Results Inpatient hospitalization data were obtained from the 2016 to 2019 National Inpatient Sample. A total of 150 921 inpatients were identified with a primary diagnosis of PAD and PAD-related procedures using codes of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) and International Classification of Diseases, Tenth Revision, Procedure Coding System (ICD-10-PCS). Four ML models, including logistic regression, random forest, light gradient boosting, and extreme gradient boosting models, were trained to predict the risk of in-hospital death based on a selection of variables, including patient characteristics, comorbidities, procedures, and hospital-related factors. In-hospital mortality occurred in 1.8% of patients. The performance of the 4 models was comparable, with the area under the receiver operating characteristic curve ranging from 0.83 to 0.85, sensitivity of 77% to 82%, and specificity of 72% to 75%. These results suggest adequate predictability for clinical decision-making. In all 4 models, the total number of diagnoses and procedures, age, endovascular revascularization procedure, congestive heart failure, diabetes, and diabetes with complications were critical predictors of in-hospital mortality. Conclusions This study demonstrates the feasibility of ML in predicting in-hospital mortality in patients with a primary PAD diagnosis. Findings highlight the potential of ML models in identifying high-risk patients for poor outcomes and guiding personalized intervention.
Billing code algorithms to identify cases of peripheral artery disease from administrative data. [2022]To construct and validate billing code algorithms for identifying patients with peripheral arterial disease (PAD).