Evaluation of acebilustat, a selective inhibitor of leukotriene B4 biosynthesis, for treatment of outpatients with mild-moderate COVID-19 disease: A randomized, double-blind, placebo- controlled Phase 2 trial.

BACKGROUND: The vast majority of COVID-19 disease occurs in outpatients where treatment is limited to anti-virals for high-risk subgroups. Acebilustat, a leukotriene B4 (LTB4) inhibitor, has potential to reduce inflammation and symptom duration. METHODS: In a single-center trial spanning Delta and Omicron variants, outpatients were randomized to 100 mg of oral acebilustat or placebo for 28 days. Patients reported daily symptoms via electronic query through Day 28 with phone follow-up on Day 120 and collected nasal swabs on Days 1-10. The primary outcome was sustained symptom resolution to Day 28. Secondary 28-day outcomes included time to first symptom resolution, area under the curve (AUC) of longitudinal daily symptom scores; duration of viral shedding through Day 10; and symptoms on Day 120. RESULTS: Sixty participants were randomized to each study arm. At enrollment, median duration and number of symptoms were 4 (IQR 3-5) days and 9 (IQR 7-11) symptoms. Most patients (90%) were vaccinated with 73% having neutralizing antibodies. A minority (44%) of participants (35% in the acebilustat arm and 53% in placebo) had sustained symptom resolution at Day 28 (HR 0.6, 95% CI 0.34-1.04, p = 0.07 favoring placebo). There was no difference in mean AUC of symptom scores over 28 days (difference in mean of AUC 9.4, 95% CI -42.1-60.9, p=0.72). Acebilustat did not impact viral shedding or symptoms at Day 120. CONCLUSIONS: Sustained symptoms through Day 28 were common in this low-risk population. Despite this, LTB4 antagonism with acebilustat did not shorten symptom duration in outpatients with COVID-19.

A quality-improvement approach to urgent-care antibiotic stewardship for respiratory tract infections during the COVID-19 pandemic: Lessons learned.

OBJECTIVE: We investigated a decrease in antibiotic prescribing for respiratory illnesses in 2 academic urgent-care clinics during the coronavirus disease 2019 (COVID-19) pandemic using semistructured clinician interviews. METHODS: We conducted a quality-improvement project from November 2020 to May 2021. We investigated provider antibiotic decision making using a mixed-methods explanatory design including interviews. We analyzed transcripts using a thematic framework approach to identify emergent themes. Our performance measure was antibiotic prescribing rate (APR) for encounters with respiratory diagnosis billing codes. We extracted billing and prescribing data from the electronic medical record and assessed differences using run charts, p charts and generalized linear regression. RESULTS: We observed significant reductions in the APR early during the COVID-19 pandemic (relative risk [RR], 0.20; 95% confidence interval [CI], 0.17-0.25), which was maintained over the study period (P < .001). The average APRs were 14% before the COVID-19 pandemic, 4% during the QI project, and 7% after the project. All providers prescribed less antibiotics for respiratory encounters during COVID-19, but only 25% felt their practice had changed. Themes from provider interviews included changing patient expectations and provider approach to respiratory encounters during COVID-19, the impact of increased telemedicine encounters, and the changing epidemiology of non-COVID-19 respiratory infections. CONCLUSIONS: Our findings suggest that the decrease in APR was likely multifactorial. The average APR decreased significantly during the pandemic. Although the APR was slightly higher after the QI project, it did not reach prepandemic levels. Future studies should explore how these factors, including changing patient expectations, can be leveraged to improve urgent-care antibiotic stewardship.

Favipiravir for Treatment of Outpatients With Asymptomatic or Uncomplicated Coronavirus Disease 2019: A Double-Blind, Randomized, Placebo-Controlled, Phase 2 Trial.

BACKGROUND: Favipiravir, an oral, RNA-dependent RNA polymerase inhibitor, has in vitro activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite limited data, favipiravir is administered to patients with coronavirus disease 2019 (COVID-19) in several countries. METHODS: We conducted a phase 2, double-blind, randomized controlled outpatient trial of favipiravir in asymptomatic or mildly symptomatic adults with a positive SARS-CoV-2 reverse-transcription polymerase chain reaction assay (RT-PCR) within 72 hours of enrollment. Participants were randomized to receive placebo or favipiravir (1800 mg twice daily [BID] day 1, 800 mg BID days 2-10). The primary outcome was SARS-CoV-2 shedding cessation in a modified intention-to-treat (mITT) cohort of participants with positive enrollment RT-PCRs. Using SARS-CoV-2 amplicon-based sequencing, we assessed favipiravir's impact on mutagenesis. RESULTS: We randomized 149 participants with 116 included in the mITT cohort. The participants' mean age was 43 years (standard deviation, 12.5 years) and 57 (49%) were women. We found no difference in time to shedding cessation overall (hazard ratio [HR], 0.76 favoring placebo [95% confidence interval {CI}, .48-1.20]) or in subgroups (age, sex, high-risk comorbidities, seropositivity, or symptom duration at enrollment). We detected no difference in time to symptom resolution (initial: HR, 0.84 [95% CI, .54-1.29]; sustained: HR, 0.87 [95% CI, .52-1.45]) and no difference in transition mutation accumulation in the viral genome during treatment. CONCLUSIONS: Our data do not support favipiravir at commonly used doses in outpatients with uncomplicated COVID-19. Further research is needed to ascertain if higher favipiravir doses are effective and safe for patients with COVID-19. CLINICAL TRIALS REGISTRATION: NCT04346628.

An Evaluation of Point-of-Care HbA1c, HbA1c Home Kits, and Glucose Management Indicator: Potential Solutions for Telehealth Glycemic Assessments.

During the COVID-19 pandemic, fewer in-person clinic visits resulted in fewer point-of-care (POC) HbA1c measurements. In this sub-study, we assessed the performance of alternative glycemic measures that can be obtained remotely, such as HbA1c home kits and Glucose Management Indicator (GMI) values from Dexcom Clarity. Home kit HbA1c (n = 99), GMI, (n = 88), and POC HbA1c (n = 32) were collected from youth with T1D (age 9.7 ± 4.6 years). Bland-Altman analyses and Lin's concordance correlation coefficient (ρc) were used to characterize the agreement between paired HbA1c measures. Both the HbA1c home kit and GMI showed a slight positive bias (mean difference 0.18% and 0.34%, respectively) and strong concordance with POC HbA1c (ρc = 0.982 [0.965, 0.991] and 0.823 [0.686, 0.904], respectively). GMI showed a slight positive bias (mean difference 0.28%) and fair concordance (ρc = 0.750 [0.658, 0.820]) to the HbA1c home kit. In conclusion, the strong concordance of GMI and home kits to POC A1c measures suggest their utility in telehealth visits assessments. Although these are not candidates for replacement, these measures can facilitate telehealth visits, particularly in the context of other POC HbA1c measurements from an individual.

Incidence and Prevalence of Coronavirus Disease 2019 Within a Healthcare Worker Cohort During the First Year of the Severe Acute Respiratory Syndrome Coronavirus 2 Pandemic.

BACKGROUND: Preventing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2_ infections in healthcare workers (HCWs) is critical for healthcare delivery. We aimed to estimate and characterize the prevalence and incidence of coronavirus disease 2019 (COVID-19) in a US HCW cohort and to identify risk factors associated with infection. METHODS: We conducted a longitudinal cohort study of HCWs at 3 Bay Area medical centers using serial surveys and SARS-CoV-2 viral and orthogonal serological testing, including measurement of neutralizing antibodies. We estimated baseline prevalence and cumulative incidence of COVID-19. We performed multivariable Cox proportional hazards models to estimate associations of baseline factors with incident infections and evaluated the impact of time-varying exposures on time to COVID-19 using marginal structural models. RESULTS: A total of 2435 HCWs contributed 768 person-years of follow-up time. We identified 21 of 2435 individuals with prevalent infection, resulting in a baseline prevalence of 0.86% (95% confidence interval [CI], .53%-1.32%). We identified 70 of 2414 incident infections (2.9%), yielding a cumulative incidence rate of 9.11 cases per 100 person-years (95% CI, 7.11-11.52). Community contact with a known COVID-19 case was most strongly correlated with increased hazard for infection (hazard ratio, 8.1 [95% CI, 3.8-17.5]). High-risk work-related exposures (ie, breach in protective measures) drove an association between work exposure and infection (hazard ratio, 2.5 [95% CI, 1.3-4.8). More cases were identified in HCWs when community case rates were high. CONCLUSIONS: We observed modest COVID-19 incidence despite consistent exposure at work. Community contact was strongly associated with infections, but contact at work was not unless accompanied by high-risk exposure.

Anti-nucleocapsid antibody levels and pulmonary comorbid conditions are linked to post-COVID-19 syndrome.

BACKGROUNDProlonged symptoms after SARS-CoV-2 infection are well documented. However, which factors influence development of long-term symptoms, how symptoms vary across ethnic groups, and whether long-term symptoms correlate with biomarkers are points that remain elusive.METHODSAdult SARS-CoV-2 reverse transcription PCR-positive (RT-PCR-positive) patients were recruited at Stanford from March 2020 to February 2021. Study participants were seen for in-person visits at diagnosis and every 1-3 months for up to 1 year after diagnosis; they completed symptom surveys and underwent blood draws and nasal swab collections at each visit.RESULTSOur cohort (n = 617) ranged from asymptomatic to critical COVID-19 infections. In total, 40% of participants reported at least 1 symptom associated with COVID-19 six months after diagnosis. Median time from diagnosis to first resolution of all symptoms was 44 days; median time from diagnosis to sustained symptom resolution with no recurring symptoms for 1 month or longer was 214 days. Anti-nucleocapsid IgG level in the first week after positive RT-PCR test and history of lung disease were associated with time to sustained symptom resolution. COVID-19 disease severity, ethnicity, age, sex, and remdesivir use did not affect time to sustained symptom resolution.CONCLUSIONWe found that all disease severities had a similar risk of developing post-COVID-19 syndrome in an ethnically diverse population. Comorbid lung disease and lower levels of initial IgG response to SARS-CoV-2 nucleocapsid antigen were associated with longer symptom duration.TRIAL REGISTRATIONClinicalTrials.gov, NCT04373148.FUNDINGNIH UL1TR003142 CTSA grant, NIH U54CA260517 grant, NIEHS R21 ES03304901, Sean N Parker Center for Allergy and Asthma Research at Stanford University, Chan Zuckerberg Biohub, Chan Zuckerberg Initiative, Sunshine Foundation, Crown Foundation, and Parker Foundation.

COVIDNearTerm: A simple method to forecast COVID-19 hospitalizations.

Introduction: COVID-19 has caused tremendous death and suffering since it first emerged in 2019. Soon after its emergence, models were developed to help predict the course of various disease metrics, and these models have been relied upon to help guide public health policy. Methods: Here we present a method called COVIDNearTerm to "forecast" hospitalizations in the short term, two to four weeks from the time of prediction. COVIDNearTerm is based on an autoregressive model and utilizes a parametric bootstrap approach to make predictions. It is easy to use as it requires only previous hospitalization data, and there is an open-source R package that implements the algorithm. We evaluated COVIDNearTerm on San Francisco Bay Area hospitalizations and compared it to models from the California COVID Assessment Tool (CalCAT). Results: We found that COVIDNearTerm predictions were more accurate than the CalCAT ensemble predictions for all comparisons and any CalCAT component for a majority of comparisons. For instance, at the county level our 14-day hospitalization median absolute percentage errors ranged from 16 to 36%. For those same comparisons, the CalCAT ensemble errors were between 30 and 59%. Conclusion: COVIDNearTerm is a simple and useful tool for predicting near-term COVID-19 hospitalizations.

Race-ethnicity and COVID-19 Vaccination Beliefs and Intentions: A Cross-Sectional Study among the General Population in the San Francisco Bay Area.

OBJECTIVE: The study was designed to compare intentions to receive COVID-19 vaccination by race-ethnicity, to identify beliefs that may mediate the association between race-ethnicity and intention to receive the vaccine and to identify the demographic factors and beliefs most strongly predictive of intention to receive a vaccine. DESIGN: Cross-sectional survey conducted from November 2020 to January 2021, nested within a longitudinal cohort study of the prevalence and incidence of SARS-CoV-2 among a general population-based sample of adults in six San Francisco Bay Area counties (called TrackCOVID). Study Cohort: In total, 3161 participants among the 3935 in the TrackCOVID parent cohort responded. RESULTS: Rates of high vaccine willingness were significantly lower among Black (41%), Latinx (55%), Asian (58%), Multi-racial (59%), and Other race (58%) respondents than among White respondents (72%). Black, Latinx, and Asian respondents were significantly more likely than White respondents to endorse lack of trust of government and health agencies as a reason not to get vaccinated. Participants' motivations and concerns about COVID-19 vaccination only partially explained racial-ethnic differences in vaccination willingness. Concerns about a rushed government vaccine approval process and potential bad reactions to the vaccine were the two most important factors predicting vaccination intention. CONCLUSIONS: Vaccine outreach campaigns must ensure that the disproportionate toll of COVID-19 on historically marginalized racial-ethnic communities is not compounded by inequities in vaccination. Efforts must emphasize messages that speak to the motivations and concerns of groups suffering most from health inequities to earn their trust to support informed decision making.

Inflammatory but not respiratory symptoms are associated with ongoing upper airway viral shedding in outpatients with uncomplicated COVID-19.

Although the vast majority of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infections are uncomplicated, our understanding of predictors of symptom resolution and viral shedding cessation remains limited. We characterized symptom trajectories and oropharyngeal viral shedding among 120 outpatients with uncomplicated Coronavirus Disease of 2019 (COVID-19) enrolled in a clinical trial of Peginterferon Lambda, which demonstrated no clinical or virologic benefit compared with placebo. In the combined trial cohort, objective fever was uncommon, inflammatory symptoms (myalgias, fatigue) peaked at 4 to 5 days postsymptom onset, and cough peaked at 9 days. The median time to symptom resolution from earliest symptom onset was 17 days (95% confidence interval 14-18). SARS-CoV-2 IgG seropositivity at enrollment was associated with hastened resolution of viral shedding (hazard ratio 1.80, 95% confidence interval 1.05-3.1, P = 0.03), but not with symptom resolution. Inflammatory symptoms were associated with a significantly greater odds of oropharyngeal SARS-CoV-2 RNA detection; respiratory symptoms were not. These findings have important implications for COVID-19 screening approaches and trial design.

Design of a population-based longitudinal cohort study of SARS-CoV-2 incidence and prevalence among adults in the San Francisco Bay Area.

PURPOSE: We describe the design of a longitudinal cohort study to determine SARS-CoV-2 incidence and prevalence among a population-based sample of adults living in six San Francisco Bay Area counties. METHODS: Using an address-based sample, we stratified households by county and by census-tract risk. Risk strata were determined by using regression models to predict infections by geographic area using census-level sociodemographic and health characteristics. We disproportionately sampled high and medium risk strata, which had smaller population sizes, to improve precision of estimates, and calculated a desired sample size of 3400. Participants were primarily recruited by mail and were followed monthly with PCR testing of nasopharyngeal swabs, testing of venous blood samples for antibodies to SARS-CoV-2 spike and nucleocapsid antigens, and testing of the presence of neutralizing antibodies, with completion of questionnaires about socio-demographics and behavior. Estimates of incidence and prevalence will be weighted by county, risk strata and sociodemographic characteristics of non-responders, and will take into account laboratory test performance. RESULTS: We enrolled 3842 adults from August to December 2020, and completed follow-up March 31, 2021. We reached target sample sizes within most strata. CONCLUSIONS: Our stratified random sampling design will allow us to recruit a robust general population cohort of adults to determine the incidence of SARS-CoV-2 infection. Identifying risk strata was unique to the design and will help ensure precise estimates, and high-performance testing for presence of virus and antibodies will enable accurate ascertainment of infections.

Good Concordance between Home Kit A1C, Glucose Management Indicator, and Point-of-Care A1C: Potential Solutions to Glycemic Assessment during the SARS CoV-2 Pandemic

Due to the SARS CoV-2 pandemic, fewer in-person clinic visits have resulted in fewer point-of-care (POC) A1c measurements in youth with T1D. Therefore, there is an increased need to use alternate methods to assess A1c, including continuous glucose monitoring-derived Glucose Management Indicator (GMI) and home kit A1c. The University of Minnesota's home kit A1c (n=59), GMI (n=56), and POC A1c (n=16) were collected from youth with T1D (age 10.0 [5.3, 13.0] years, 42% female, and baseline A1c 12.4 ± 2.2%). Matched pairs were used for Bland Altman analyses and Lin's concordance correlation coefficient (pc) to evaluate the agreement between A1c measures. GMI data (up to 90 days) was captured using Dexcom Clarity. In relation to POC A1c, both home kit A1c (panel A) and GMI (panel B) showed a slight positive bias (mean difference 0.13 and 0.22%, respectively). Home kit A1c and GMI showed strong concordance to POC A1c (pc = 0.987 [0.963, 0.995] and 0.930 [0.835, 0.971], respectively). GMI (panel C) also showed a slight positive bias (mean difference 0.26%) and good concordance (pc = 0.803 [0.703, 0.871]) to home kit A1c. These data demonstrate that home kit A1c and GMI show strong concordance with POC A1c. Overall, home kit A1c and GMI may be potential solutions to glycemic assessment for telehealth visits, including during the SARS CoV-2 pandemic.

The COVID-19 Outpatient Pragmatic Platform Study (COPPS): Study design of a multi-center pragmatic platform trial.

More than 3000 clinical trials related to COVID-19 have been registered through clinicaltrials.gov. With so many trials, there is a risk that many will be inconclusive due to being underpowered or due to an inability to recruit patients. At academic medical centers, multiple trials are competing for the same resources; the success of one may come at the expense of another. The COVID-19 Outpatient Pragmatic Protocol Study (COPPS) is a flexible phase 2, multi-site, randomized, blinded trial based at Stanford University designed to overcome these issues by simultaneously evaluating multiple COVID-19 treatments in the outpatient setting in one common platform with shared controls. This approach reduces the overall number of patients required for statistical power, while improving the likelihood that any enrolled patient receives active treatment. The platform study has two main domains designed to evaluate COVID-19 treatments by assessing their ability to reduce viral shedding (Viral Domain), measured with self-collected nasal swabs, or improve clinical outcomes (Clinical Domain), measured through self-reported symptomology data. Data are collected on both domains for all participants enrolled. Participants are followed over a 28-day period. COPPS has the advantage of pragmatism created around its workflow that is also appealing to potential participants because of a lower probability of inactive treatment. At the conclusion of this clinical trial we expect to have identified potentially effective therapeutic strategy/ies for treating COVID-19 in the outpatient setting, which will have a transformative impact on medicine and public health.

Asthma phenotypes, associated comorbidities, and long-term symptoms in COVID-19.

BACKGROUND: It is unclear whether asthma and its allergic phenotype are risk factors for hospitalization or severe disease from SARS-CoV-2. METHODS: All patients over 28 days old testing positive for SARS-CoV-2 between March 1 and September 30, 2020, were retrospectively identified and characterized through electronic analysis at Stanford. A sub-cohort was followed prospectively to evaluate long-term COVID-19 symptoms. RESULTS: 168,190 patients underwent SARS-CoV-2 testing, and 6,976 (4.15%) tested positive. In a multivariate analysis, asthma was not an independent risk factor for hospitalization (OR 1.12 [95% CI 0.86, 1.45], p = .40). Among SARS-CoV-2-positive asthmatics, allergic asthma lowered the risk of hospitalization and had a protective effect compared with non-allergic asthma (OR 0.52 [0.28, 0.91], p = .026); there was no association between baseline medication use as characterized by GINA and hospitalization risk. Patients with severe COVID-19 disease had lower eosinophil levels during hospitalization compared with patients with mild or asymptomatic disease, independent of asthma status (p = .0014). In a patient sub-cohort followed longitudinally, asthmatics and non-asthmatics had similar time to resolution of COVID-19 symptoms, particularly lower respiratory symptoms. CONCLUSIONS: Asthma is not a risk factor for more severe COVID-19 disease. Allergic asthmatics were half as likely to be hospitalized with COVID-19 compared with non-allergic asthmatics. Lower levels of eosinophil counts (allergic biomarkers) were associated with a more severe COVID-19 disease trajectory. Recovery was similar among asthmatics and non-asthmatics with over 50% of patients reporting ongoing lower respiratory symptoms 3 months post-infection.

The ARREST Pneumonia Clinical Trial. Rationale and Design.

Patients hospitalized for pneumonia are at high risk for mortality. Effective therapies are therefore needed. Recent randomized clinical trials suggest that systemic steroids can reduce the length of hospital stays among patients hospitalized for pneumonia. Furthermore, preliminary findings from a feasibility study demonstrated that early treatment with a combination of an inhaled corticosteroid and a bronchodilator can improve oxygenation and reduce risk of respiratory failure in patients at risk of acute respiratory distress syndrome. Whether such a combination administered early is effective in reducing acute respiratory failure (ARF) among patients hospitalized with pneumonia is unknown. Here we describe the ARREST Pneumonia (Arrest Respiratory Failure due to Pneumonia) trial designed to address this question. ARREST Pneumonia is a two-arm, randomized, double-blinded, placebo-controlled trial designed to test the efficacy of a combination of an inhaled corticosteroid and a ß-agonist compared with placebo for the prevention of ARF in hospitalized participants with severe pneumonia. The primary outcome is ARF within 7 days of randomization, defined as a composite endpoint of intubation and mechanical ventilation; need for high-flow nasal cannula oxygen therapy or noninvasive ventilation for >36 hours (each alone or combined); or death within 36 hours of being placed on respiratory support. The planned enrollment is 600 adult participants at 10 academic medical centers. In addition, we will measure selected plasma biomarkers to better understand mechanisms of action. The trial is funded by the U.S. National Heart Lung and Blood Institute.Clinical trial registered with www.clinicaltrials.gov (NCT04193878).

Peginterferon Lambda-1a for treatment of outpatients with uncomplicated COVID-19: a randomized placebo-controlled trial.

Type III interferons have been touted as promising therapeutics in outpatients with coronavirus disease 2019 (COVID-19). We conducted a randomized, single-blind, placebo-controlled trial (NCT04331899) in 120 outpatients with mild to moderate COVID-19 to determine whether a single, 180 mcg subcutaneous dose of Peginterferon Lambda-1a (Lambda) within 72 hours of diagnosis could shorten the duration of viral shedding (primary endpoint) or symptoms (secondary endpoint). In both the 60 patients receiving Lambda and 60 receiving placebo, the median time to cessation of viral shedding was 7 days (hazard ratio [HR] = 0.81; 95% confidence interval [CI] 0.56 to 1.19). Symptoms resolved in 8 and 9 days in Lambda and placebo, respectively, and symptom duration did not differ significantly between groups (HR 0.94; 95% CI 0.64 to 1.39). Both Lambda and placebo were well-tolerated, though liver transaminase elevations were more common in the Lambda vs. placebo arm (15/60 vs 5/60; p = 0.027). In this study, a single dose of subcutaneous Peginterferon Lambda-1a neither shortened the duration of SARS-CoV-2 viral shedding nor improved symptoms in outpatients with uncomplicated COVID-19.

Clinical trials in a COVID-19 pandemic: Shared infrastructure for continuous learning in a rapidly changing landscape.

BACKGROUND: Clinical trials, conducted efficiently and with the utmost integrity, are a key component in identifying effective vaccines, therapies, and other interventions urgently needed to solve the COVID-19 crisis. Yet launching and implementing trials with the rigor necessary to produce convincing results is a complicated and time-consuming process. Balancing rigor and efficiency involves relying on designs that employ flexible features to respond to a fast-changing landscape, measuring valid endpoints that result in translational actions and disseminating findings in a timely manner. We describe the challenges involved in creating infrastructure with potential utility for shared learning. METHODS: We have established a shared infrastructure that borrows strength across multiple trials. The infrastructure includes an endpoint registry to aid in selecting appropriate endpoints, a registry to facilitate establishing a Data & Safety Monitoring Board, common data collection instruments, a COVID-19 dedicated design and analysis team, and a pragmatic platform protocol, among other elements. RESULTS: The authors have relied on the shared infrastructure for six clinical trials for which they serve as the Data Coordinating Center and have a design and analysis team comprising 15 members who are dedicated to COVID-19. The authors established a pragmatic platform to simultaneously investigate multiple treatments for the outpatient with adaptive features to add or drop treatment arms. CONCLUSION: The shared infrastructure provides appealing opportunities to evaluate disease in a more robust manner with fewer resources and is especially valued during a pandemic where efficiency in time and resources is crucial. The most important element of the shared infrastructure is the pragmatic platform. While it may be the most challenging of the elements to establish, it may provide the greatest benefit to both patients and researchers.