Risk EValuation And Its Impact on ClinicAL Decision Making and Outcomes in Heart Failure

NCT ID: NCT03845660

Last Updated: 2022-04-22

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 3124 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2020-01-01
• Study Completion Date: 2021-12-30

Brief Summary

Heart failure is a complex and heterogenous disease with mortality and morbidity that equals more cancers. Numerous studies have examined the ability to improve prognostication from heart failure, ranging from basic statistical methodologies to machine learning, with impressive improvements in both predictive indices. However, no study to date has examined, in a randomized fashion, the impact of providing prognostic information on provider behavior and downstream clinical outcomes. It is for this reason that the investigators plan to perform the Risk EValuation And Its Impact on ClinicAL Decision Making and Outcomes in Heart Failure (REVeAL-HF) Trial within the Yale New Haven Health System. REVeAL-HF is a randomized, single-blind intervention trial that is testing the clinical impact of providing prognostic information to the provider on heart failure outcomes in the inpatient settings. The study hypothesis is that electronic alerting about prognostic information on heart failure patients along with links to guidelines will lead to reductions in all-cause mortality and 30-day HF hospitalizations via improved use of guideline directed medical therapy and more adequate decongestion.

Detailed Description

Heart failure is the major cause of mortality and morbidity in the United States and Western Europe and prognosis in individual patients is highly variable. Quantifying a patient's survival prospects based on their overall risk profile has the potential to help identify those patients in need of more intensive monitoring and help target appropriate populations for therapies. In fact, several comprehensive risk scores in patients with heart failure are currently available for both reduced and preserved ejection fraction but their applicability to contemporary heart failure populations is unknown. Additionally, the impact of knowing a patient's prognostic information on treatment decisions in heart failure has never been studied. Therefore, guidelines do not recommend using risk assessments to decide on therapeutic decision making in heart failure due to a lack of data for this strategy. The investigators have proposed a randomized controlled trial be conducted of an electronic alert system that informs practitioners about their patients risk of inpatient mortality and 1-year predicted mortality using data from the electronic health record (EHR). The primary outcome for the trial will be a composite of all-cause mortality and 30-day risk of heart failure rehospitalization. The secondary outcomes will be length of stay, discharge doses of heart failure therapies, palliative care referral, referral for advanced therapies like transplant or mechanical circulatory support, referral to electrophysiology, and change in weight during hospitalization (aggressiveness of diuresis). The investigators will enroll into the trial across the hospitals that comprise the Yale New Haven Health System.

Conditions

Heart Failure

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: SEQUENTIAL
• Primary Study Purpose: OTHER
• Blinding Strategy: TRIPLE

Study Groups

Usual Care

Providers taking care of patients randomized to this arm will have no alert related to the patients prognosis.

Group Type: NO_INTERVENTION

No interventions assigned to this group

Electronic Alert

Patients randomized to the intervention will have an alert of their prognosis based on the best available prognostic models for inpatient and 1-year mortality generated with information from their electronic health record, which will consist of a "pop-up" when the provider accesses a patient record to enter a progress note after the definitions for inclusions into the study have been registered.

Group Type: EXPERIMENTAL

Electronic Alert

Intervention Type: BEHAVIORAL

Patients randomized to the intervention will have an alert of their prognosis based on the best available prognostic models for inpatient and 1-year mortality generated with information from their electronic health record, which will consist of a "pop-up" when the provider accesses a patient record to enter a progress note after the definitions for inclusions into the study have been registered.

Interventions

Electronic Alert

Patients randomized to the intervention will have an alert of their prognosis based on the best available prognostic models for inpatient and 1-year mortality generated with information from their electronic health record, which will consist of a "pop-up" when the provider accesses a patient record to enter a progress note after the definitions for inclusions into the study have been registered.

Intervention Type: BEHAVIORAL

Eligibility Criteria

Inclusion Criteria

• Age >18
• NTproBNP levels of >500pg/mL within 24 hours of admission
• Intravenous diuretics within 24 hours of admission

Exclusion Criteria

• None

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Yale University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Tariq Ahmad, MD MPH

Role: PRINCIPAL_INVESTIGATOR

Yale University

Nihar R Desai, MD MPH

Role: PRINCIPAL_INVESTIGATOR

Yale University

Francis P Wilson, MD MS

Role: PRINCIPAL_INVESTIGATOR

Yale University

Locations

Yale New Haven Hospital

New Haven, Connecticut, United States

Countries

United States

References

Wilson FP, Greenberg JH. Acute Kidney Injury in Real Time: Prediction, Alerts, and Clinical Decision Support. Nephron. 2018;140(2):116-119. doi: 10.1159/000492064. Epub 2018 Aug 2.

Reference Type: RESULT

PMID: 30071528 (View on PubMed)

Martin M, Wilson FP. Utility of Electronic Medical Record Alerts to Prevent Drug Nephrotoxicity. Clin J Am Soc Nephrol. 2019 Jan 7;14(1):115-123. doi: 10.2215/CJN.13841217. Epub 2018 Apr 5.

Reference Type: RESULT

PMID: 29622668 (View on PubMed)

Ahmad T, Freeman JV, Asselbergs FW. Can advanced analytics fix modern medicine's problem of uncertainty, imprecision, and inaccuracy? Eur J Heart Fail. 2019 Jan;21(1):86-89. doi: 10.1002/ejhf.1370. Epub 2018 Dec 10. No abstract available.

Reference Type: RESULT

PMID: 30537243 (View on PubMed)

Ahmad T, Wilson FP, Desai NR. The Trifecta of Precision Care in Heart Failure: Biology, Biomarkers, and Big Data. J Am Coll Cardiol. 2018 Sep 4;72(10):1091-1094. doi: 10.1016/j.jacc.2018.07.009. No abstract available.

Reference Type: RESULT

PMID: 30165979 (View on PubMed)

Ahmad T, O'Brien EC, Schulte PJ, Stevens SR, Fiuzat M, Kitzman DW, Adams KF, Kraus WE, Pina IL, Donahue MP, Zannad F, Whellan DJ, O'Connor CM, Felker GM. Evaluation of the Incremental Prognostic Utility of Increasingly Complex Testing in Chronic Heart Failure. Circ Heart Fail. 2015 Jul;8(4):709-16. doi: 10.1161/CIRCHEARTFAILURE.114.001996. Epub 2015 Jun 1.

Reference Type: RESULT

PMID: 26034004 (View on PubMed)

Ahmad T, Desai NR, Yamamoto Y, Biswas A, Ghazi L, Martin M, Simonov M, Dhar R, Hsiao A, Kashyap N, Allen L, Velazquez EJ, Wilson FP. Alerting Clinicians to 1-Year Mortality Risk in Patients Hospitalized With Heart Failure: The REVEAL-HF Randomized Clinical Trial. JAMA Cardiol. 2022 Sep 1;7(9):905-912. doi: 10.1001/jamacardio.2022.2496.

Reference Type: DERIVED

PMID: 35947362 (View on PubMed)

Ahmad T, Yamamoto Y, Biswas A, Ghazi L, Martin M, Simonov M, Hsiao A, Kashyap N, Velazquez EJ, Desai NR, Wilson FP. REVeAL-HF: Design and Rationale of a Pragmatic Randomized Controlled Trial Embedded Within Routine Clinical Practice. JACC Heart Fail. 2021 Jun;9(6):409-419. doi: 10.1016/j.jchf.2021.03.006. Epub 2021 May 12.

Reference Type: DERIVED

PMID: 33992566 (View on PubMed)

Other Identifiers

2000025000

Identifier Type: -

Identifier Source: org_study_id