Clinical Decision Support Tool for Heart Failure

(P3HF Trial)

Recruiting at 1 trial location

Overseen By Nathan Wright, MA

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Yale University

Disqualifiers: Pregnancy, others

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

Compensation: Varies

Number of Visits: Unknown

1485 Participants Needed

Trial Summary

What is the purpose of this trial?

This project will develop and test a novel clinical decision support tool (CDS) that encourages timely referral to palliative care for people hospitalized with heart failure. This intervention will incorporate an existing, validated 1-year mortality risk model into a CDS to deliver prognostic information and evidence-based decision support at the point of care. Thus, this research may lead to improved care-concordant and goal-directed care for people with heart failure.

Do I need to stop taking my current medications for this trial?

The trial information does not specify whether participants need to stop taking their current medications.

What data supports the effectiveness of the treatment Clinical Decision Support Tool for Heart Failure?

The research highlights the importance of patient-reported outcomes (PROs) in heart failure care, suggesting that understanding patient perspectives can improve the quality of care. Although PROs are not yet fully integrated into clinical practice, they have shown precision in evaluating disease status and are associated with better prognosis, indicating potential effectiveness when used in conjunction with clinical decision support tools. ^{1 2 3 4 5}

Is the Clinical Decision Support Tool for Heart Failure safe for humans?

The Clinical Decision Support Tool, part of the SensorART platform, is used to help manage heart failure patients with left ventricular assist devices (LVADs). It includes features for predicting adverse events, which suggests a focus on safety, but specific safety data for humans is not detailed in the available research. ^{6 7 8 9 10}

How does the Clinical Decision Support Tool for Heart Failure differ from other treatments?

The Clinical Decision Support Tool for Heart Failure is unique because it uses technology to help doctors make better treatment decisions by integrating guidelines and expert advice, optimizing medication doses, and predicting hospital readmissions, which can improve patient care and reduce hospital visits. ^{7 11 12 13 14}

Research Team

Shelli Feder, PhD

Principal Investigator

Yale University

Eligibility Criteria

This trial is for hospitalized individuals with heart failure. It aims to improve their care by using a new tool that helps doctors decide when it's time to refer patients to palliative care, which focuses on relieving symptoms and stress.

Inclusion Criteria

Hospital administrator/quality and safety personnel

Not pregnant at the time of admission

Healthcare providers must meet the following criteria:

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

Providers use the Clinical Decision Support (CDS) tool to deliver prognostic information and evidence-based decision support for timely referral to palliative care

15 months

Follow-up

Participants are monitored for outcomes such as consultation by Specialist Palliative Care, advance care plan documentation, and hospital readmissions

15 months

Treatment Details

Interventions

• Clinical Decision Support Tool

Trial Overview The study is testing a clinical decision support tool designed to integrate with hospital systems. This tool uses a risk model to predict mortality within one year and suggests when doctors should consider palliative care for the patient.

Participant Groups

Group I: Level 2 providers Experimental Treatment 1 Intervention

Level 2 providers are providers potentially impacted by the tool. Level 2 providers will participate in one interview.

Group II: Clinical Decision Support (CDS) Tool Experimental Treatment 1 Intervention

Providers will use the CDS tool to deliver prognostic information and evidence-based decision support at the point of care for timely referral to palliative care for participants hospitalized with heart failure.

Group III: Standard of Care Active Control 1 Intervention

Providers will provide usual standard of care for participants hospitalized with heart failure.

Who Is Running the Clinical Trial?

Yale University

Lead Sponsor

Trials

1,963

Recruited

3,046,000+

National Heart, Lung, and Blood Institute (NHLBI)

Collaborator

Trials

3,987

Recruited

47,860,000+

Findings from Research

A systematic review identified 31 patient-reported outcome (PRO) instruments for heart failure, but only 2—the Minnesota Living with Heart Failure and Kansas City Cardiomyopathy questionnaires—met all evaluation criteria for psychometric properties and clinical utility.

Despite the existence of effective PRO instruments, there is a need for better education for healthcare providers on their value and a more streamlined implementation process in clinical settings to enhance patient care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Utility of Patient-Reported Outcome Instruments in Heart Failure. Kelkar, AA., Spertus, J., Pang, P., et al.[2016]

Assessing patients' functional status in chronic heart failure is crucial for understanding the effectiveness of treatments in prolonging life while minimizing disability, bridging the gap between clinical assessments and patient-centered outcomes.

Systematic and unbiased collection of patient-reported outcomes is essential, as it provides a clearer picture of the benefits of medical interventions from the patients' perspective, rather than relying solely on healthcare providers' evaluations.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Outcome assessment: functional status measures as therapeutic endpoints for heart failure. Rector, TS.[2019]

Patient-reported outcomes (PROs) are crucial in heart failure management as they provide insights into how patients feel and function, which can enhance patient-centered care.

The review emphasizes the need to advance PRO measurement and integrate these assessments into national databases and electronic health records to improve the quality of care for heart failure patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Patient-reported outcomes in heart failure: existing measures and future uses. Thompson, LE., Bekelman, DB., Allen, LA., et al.[2022]

Patient-reported outcomes (PROs) are essential for understanding heart failure (HF) from the patient's perspective, as they capture the impact of the disease on daily life and can improve treatment adherence.

Despite their proven effectiveness in evaluating disease status and correlating with prognosis in clinical trials, the use of PROs in everyday clinical practice for heart failure remains limited, highlighting the need for strategies to integrate patient perspectives into care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Use of patient-reported outcomes in heart failure: from clinical trials to routine practice. Savarese, G., Lindenfeld, J., Stolfo, D., et al.[2023]

The Pediatric Quality of Life Inventory (PedsQL) showed strong responsiveness and predictive validity in a study of 4,637 pediatric patients, indicating it can effectively measure changes in health-related quality of life during hospital stays.

Patients with medically complex conditions had lower improvement scores on the PedsQL compared to those without chronic conditions, and lower admission scores were linked to higher risks of prolonged hospital stays and readmissions, highlighting its potential as a tool for assessing patient outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Validity and responsiveness of the pediatric quality of life inventory (PedsQL) 4.0 generic core scales in the pediatric inpatient setting. Desai, AD., Zhou, C., Stanford, S., et al.[2022]

A new tool for clinicians in inpatient oncology units has been developed to prevent adverse events and enhance patient safety, focusing specifically on cancer patients.

The tool includes a catalog of adverse events and a risk map, which helps healthcare providers implement best practices in their daily activities to improve clinical safety.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Improving patient safety in the inpatient setting through risk assessment and mitigation. Reche Navarro, MN.[2016]

The Treatment Tool, part of the SensorART platform, aids specialists in managing heart failure patients with left ventricular assist devices (LVADs) by providing decision support for treatment strategies and patient assessments.

This web-based tool calculates risk scores and predicts adverse events, enhancing the ability of healthcare providers to make informed decisions regarding LVAD management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Adverse event prediction in patients with left ventricular assist devices. Tsipouras, MG., Karvounis, EC., Tzallas, AT., et al.[2020]

In a study of 261 heart failure patients, common adverse drug events (ADEs) included dizziness (26%) and dry mouth (24%), with nearly half of the patients perceiving these ADEs as serious and significantly impacting their daily lives.

Patients expressed a strong belief in healthcare providers' ability to manage ADEs, but many felt that these issues were chronic and caused emotional distress, highlighting the need for better communication between patients and providers regarding ADE management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Perceived adverse drug events in heart failure: patients' perception and related factors. De Smedt, RH., Haaijer-Ruskamp, FM., Groenier, KH., et al.[2011]

The study analyzed serious and fatal adverse drug reactions (ADRs) for 137 commonly-prescribed medicines, revealing significant differences in ADR rates across various therapeutic classes, which highlights the importance of monitoring drug safety.

This research demonstrates the feasibility of creating comparative safety charts by mapping ADR reports to prescription data, which could aid healthcare professionals in making informed decisions about medication safety.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Comparative Safety Analysis of Medicines Based on the UK Pharmacovigilance and General Practice Prescribing Data in England. Mokbel, K., Daniels, R., Weedon, MN., et al.[2022]

In a study of 754 heart failure patients, 50% reported experiencing dizziness and 44% reported a dry cough, with these adverse effects leading to a 38% increased likelihood of medication changes.

Among the reported adverse effects, a dry cough was associated with the highest risk of medication change (1.83 times more likely), while patients reporting gout had a fourfold increase in the likelihood of starting or intensifying alleviating medications.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The impact of perceived adverse effects on medication changes in heart failure patients. De Smedt, RH., Jaarsma, T., Haaijer-Ruskamp, FM., et al.[2013]

The development of clinical decision support (CDS) tools for predicting hospital readmissions in heart failure patients can be enhanced by combining interdisciplinary expertise, ensuring the tools are evidence-based and user-friendly.

Involving clinicians throughout the development process helps tailor the CDS tools to meet end-user needs, facilitating better integration into clinical workflows.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Interdisciplinary Human-Centered AI for Hospital Readmission Prediction of Heart Failure Patients. Soliman, A., Nair, M., Petersson, M., et al.[2023]

The use of a clinical decision support system in telemonitoring heart failure patients allows for real-time medication adjustments based on vital signs, potentially improving patient outcomes and reducing unnecessary hospital visits.

This study evaluates the effectiveness of the decision support system within the INTENSE-HF trial, highlighting its adherence to European Society of Cardiology guidelines while also identifying areas for improvement in telehealth practices.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Telemonitoring in heart failure patients with clinical decision support to optimize medication doses based on guidelines. Kropf, M., Modre-Osprian, R., Hayn, D., et al.[2020]

Clinical decision support software (CDSS) can enhance the treatment of chronic heart failure by providing evidence-based guidelines, but its effectiveness is limited by low computer skills among family physicians and the software's inability to address nonmedical patient needs.

To improve the implementation of CDSS in chronic heart failure management, strategies such as enhancing computer skills among healthcare providers and integrating CDSS into existing referral pathways are recommended.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Clinical decision support software for chronic heart failure. Leslie, SJ., Denvir, MA.[2007]

Electronic health records (EHR) can significantly enhance the management of congestive heart failure (CHF) by standardizing care, promoting adherence to evidence-based guidelines, and improving workflow efficiency, as discussed in a review of various EHR tools.

The review highlights that EHR technologies, such as clinical decision support and telehealth, not only improve patient engagement and performance metrics but also contribute to quality improvement and clinical research in CHF management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Heart Failure Management Innovation Enabled by Electronic Health Records. Kao, DP., Trinkley, KE., Lin, CT.[2021]

References

1. United States pubmed.ncbi.nlm.nih.gov

Utility of Patient-Reported Outcome Instruments in Heart Failure. [2016]

2. United States pubmed.ncbi.nlm.nih.gov

Outcome assessment: functional status measures as therapeutic endpoints for heart failure. [2019]

3. United States pubmed.ncbi.nlm.nih.gov

Patient-reported outcomes in heart failure: existing measures and future uses. [2022]

4. England pubmed.ncbi.nlm.nih.gov

Use of patient-reported outcomes in heart failure: from clinical trials to routine practice. [2023]

5. United States pubmed.ncbi.nlm.nih.gov

Validity and responsiveness of the pediatric quality of life inventory (PedsQL) 4.0 generic core scales in the pediatric inpatient setting. [2022]

6. United States pubmed.ncbi.nlm.nih.gov

Improving patient safety in the inpatient setting through risk assessment and mitigation. [2016]

7. United States pubmed.ncbi.nlm.nih.gov

Adverse event prediction in patients with left ventricular assist devices. [2020]

8. United States pubmed.ncbi.nlm.nih.gov

Perceived adverse drug events in heart failure: patients' perception and related factors. [2011]

9. Greece pubmed.ncbi.nlm.nih.gov

A Comparative Safety Analysis of Medicines Based on the UK Pharmacovigilance and General Practice Prescribing Data in England. [2022]

10. United States pubmed.ncbi.nlm.nih.gov

The impact of perceived adverse effects on medication changes in heart failure patients. [2013]

11. Netherlands pubmed.ncbi.nlm.nih.gov

Interdisciplinary Human-Centered AI for Hospital Readmission Prediction of Heart Failure Patients. [2023]

12. United States pubmed.ncbi.nlm.nih.gov

Telemonitoring in heart failure patients with clinical decision support to optimize medication doses based on guidelines. [2020]

13. United States pubmed.ncbi.nlm.nih.gov

Clinical decision support software for chronic heart failure. [2007]

14. United States pubmed.ncbi.nlm.nih.gov

Heart Failure Management Innovation Enabled by Electronic Health Records. [2021]