Novel Trial Design: CHIEF-HF.

BACKGROUND: The expense of clinical trials mandates new strategies to efficiently generate evidence and test novel therapies. In this context, we designed a decentralized, patient-centered randomized clinical trial leveraging mobile technologies, rather than in-person site visits, to test the efficacy of 12 weeks of canagliflozin for the treatment of heart failure, regardless of ejection fraction or diabetes status, on the reduction of heart failure symptoms. METHODS: One thousand nine hundred patients will be enrolled with a medical record-confirmed diagnosis of heart failure, stratified by reduced (≤40%) or preserved (>40%) ejection fraction and randomized 1:1 to 100 mg daily of canagliflozin or matching placebo. The primary outcome will be the 12-week change in the total symptom score of the Kansas City Cardiomyopathy Questionnaire. Secondary outcomes will be daily step count and other scales of the Kansas City Cardiomyopathy Questionnaire. RESULTS: The trial is currently enrolling, even in the era of the coronavirus disease 2019 (COVID-19) pandemic. CONCLUSIONS: CHIEF-HF (Canagliflozin: Impact on Health Status, Quality of Life and Functional Status in Heart Failure) is deploying a novel model of conducting a decentralized, patient-centered, randomized clinical trial for a new indication for canagliflozin to improve the symptoms of patients with heart failure. It can model a new method for more cost-effectively testing the efficacy of treatments using mobile technologies with patient-reported outcomes as the primary clinical end point of the trial. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04252287.

The SGLT2 inhibitor canagliflozin in heart failure: the CHIEF-HF remote, patient-centered randomized trial.

Large traditional clinical trials suggest that sodium-glucose co-transporter 2 inhibitors improve symptoms in patients with heart failure and reduced ejection fraction (HFrEF) and in patients with heart failure and preserved ejection fraction (HFpEF). In the midst of the Coronavirus Disease 2019 pandemic, we sought to confirm these benefits in a new type of trial that was patient centered and conducted in a completely remote fashion. In the CHIEF-HF trial ( NCT04252287 ), 476 participants with HF, regardless of EF or diabetes status, were randomized to 100 mg of canagliflozin or placebo. Enrollment was stopped early due to shifting sponsor priorities, without unblinding. The primary outcome was change in the Kansas City Cardiomyopathy Questionnaire Total Symptom Score (KCCQ TSS) at 12 weeks. The 12-week change in KCCQ TSS was 4.3 points (95% confidence interval, 0.8-7.8; P = 0.016) higher with canagliflozin than with placebo, meeting the primary endpoint. Similar effects were observed in participants with HFpEF and in those with HFrEF and in participants with and without diabetes, demonstrating that canagliflozin significantly improves symptom burden in HF, regardless of EF or diabetes status. This randomized, double-blind trial, conducted without in-person interactions between doctor and patient, can serve as a model for future all-virtual clinical trials.

Predicting death from COVID-19 using pre-existing conditions: implications for vaccination triage.

INTRODUCTION: Global shortages in the supply of SARS-CoV-2 vaccines have resulted in campaigns to first inoculate individuals at highest risk for death from COVID-19. Here, we develop a predictive model of COVID-19-related death using longitudinal clinical data from patients in metropolitan Detroit. METHODS: All individuals included in the analysis had a laboratory-confirmed SARS-CoV-2 infection. Thirty-six pre-existing conditions with a false discovery rate p<0.05 were combined with other demographic variables to develop a parsimonious prediction model using least absolute shrinkage and selection operator regression. The model was then prospectively validated in a separate set of individuals with confirmed COVID-19. RESULTS: The study population consisted of 15 502 individuals with laboratory-confirmed SARS-CoV-2. The main prediction model was developed using data from 11 635 individuals with 709 reported deaths (case fatality ratio 6.1%). The final prediction model consisted of 14 variables with 11 comorbidities. This model was then prospectively assessed among the remaining 3867 individuals (185 deaths; case fatality ratio 4.8%). When compared with using an age threshold of 65 years, the 14-variable model detected 6% more of the individuals who would die from COVID-19. However, below age 45 years and its risk equivalent, there was no benefit to using the prediction model over age alone. DISCUSSION: Using a prediction model, such as the one described here, may help identify individuals who would most benefit from COVID-19 inoculation, and thereby may produce more dramatic initial drops in deaths through targeted vaccination.