Use of electronic healthcare records in large-scale simple randomized trials at the point of care for the documentation of value-based medicine

Toward Precision Medicine: Circadian Rhythm of Blood Pressure and Chronotherapy for Hypertension - 2021 NHLBI Workshop Report

Healthy individuals exhibit blood pressure variation over a 24-hour period with higher blood pressure during wakefulness and lower blood pressure during sleep. Loss or disruption of the blood pressure circadian rhythm has been linked to adverse health outcomes, for example, cardiovascular disease, dementia, and chronic kidney disease. However, the current diagnostic and therapeutic approaches lack sufficient attention to the circadian rhythmicity of blood pressure. Sleep patterns, hormone release, eating habits, digestion, body temperature, renal and cardiovascular function, and other important host functions as well as gut microbiota exhibit circadian rhythms, and influence circadian rhythms of blood pressure. Potential benefits of nonpharmacologic interventions such as meal timing, and pharmacologic chronotherapeutic interventions, such as the bedtime administration of antihypertensive medications, have recently been suggested in some studies. However, the mechanisms underlying circadian rhythm-mediated blood pressure regulation and the efficacy of chronotherapy in hypertension remain unclear. This review summarizes the results of the National Heart, Lung, and Blood Institute workshop convened on October 27 to 29, 2021 to assess knowledge gaps and research opportunities in the study of circadian rhythm of blood pressure and chronotherapy for hypertension.

Embedded point of care randomisation for evaluating comparative effectiveness questions: PROSPECTOR-critical care feasibility study protocol

INTRODUCTION

Many routinely administered treatments lack evidence as to their effectiveness. When treatments lack evidence, patients receive varying care based on the preferences of clinicians. Standard randomised controlled trials are unsuited to comparisons of different routine treatment strategies, and there remains little economic incentive for change.Integrating clinical trial infrastructure into electronic health record systems offers the potential for routine treatment comparisons at scale, through reduced trial costs. To date, embedded trials have automated data collection, participant identification and eligibility screening, but randomisation and consent remain manual and therefore costly tasks.This study will investigate the feasibility of using computer prompts to allow flexible randomisation at the point of clinical decision making. It will compare the effectiveness of two prompt designs through the lens of a candidate research question-comparing liberal or restrictive magnesium supplementation practices for critical care patients. It will also explore the acceptability of two consent models for conducting comparative effectiveness research.

METHODS AND ANALYSIS

We will conduct a single centre, mixed-methods feasibility study, aiming to recruit 50 patients undergoing elective surgery requiring postoperative critical care admission. Participants will be randomised to either 'Nudge' or 'Preference' designs of electronic point-of-care randomisation prompt, and liberal or restrictive magnesium supplementation.We will judge feasibility through a combination of study outcomes. The primary outcome will be the proportion of prompts displayed resulting in successful randomisation events (compliance with the allocated magnesium strategy). Secondary outcomes will evaluate the acceptability of both prompt designs to clinicians and ascertain the acceptability of pre-emptive and opt-out consent models to patients.

ETHICS AND DISSEMINATION

This study was approved by Riverside Research Ethics Committee (Ref: 21/LO/0785) and will be published on completion.

TRIAL REGISTRATION NUMBER

NCT05149820.

Why We Are Losing the War Against COVID-19 on the Data Front and How to Reverse the Situation

With over 117 million COVID-19–positive cases declared and the death count approaching 3 million, we would expect that the highly digitalized health systems of high-income countries would have collected, processed, and analyzed large quantities of clinical data from patients with COVID-19. Those data should have served to answer important clinical questions such as: what are the risk factors for becoming infected? What are good clinical variables to predict prognosis? What kinds of patients are more likely to survive mechanical ventilation? Are there clinical subphenotypes of the disease? All these, and many more, are crucial questions to improve our clinical strategies against the epidemic and save as many lives as possible. One might assume that in the era of big data and machine learning, there would be an army of scientists crunching petabytes of clinical data to answer these questions. However, nothing could be further from the truth. Our health systems have proven to be completely unprepared to generate, in a timely manner, a flow of clinical data that could feed these analyses. Despite gigabytes of data being generated every day, the vast quantity is locked in secure hospital data servers and is not being made available for analysis. Routinely collected clinical data are, by and large, regarded as a tool to inform decisions about individual patients, and not as a key resource to answer clinical questions through statistical analysis. The initiatives to extract COVID-19 clinical data are often promoted by private groups of individuals and not by health systems, and are uncoordinated and inefficient. The consequence is that we have more clinical data on COVID-19 than on any other epidemic in history, but we have failed to analyze this information quickly enough to make a difference. In this viewpoint, we expose this situation and suggest concrete ideas that health systems could implement to dynamically analyze their routine clinical data, becoming learning health systems and reversing the current situation.

COllaborative open platform E-cohorts for research acceleration in trials and epidemiology

BACKGROUND

The current clinical research system relies on a "one-off" project-by-project model involving a costly and time-wasting permanent construction and deconstruction of the research infrastructure. We propose a new model of research relying on collaborative principles: the COllaborative Open Platform (COOP') e-cohort.

DEVELOPMENT

The COOP' e-cohort aims at building a large community of patients willing to participate in research by contributing to the generation of a large database of patient-reported data, passively enriched, at the individual level, by linkage with routinely collected care and/or medico-administrative data. Approved teams can use the platform and benefit from already enrolled participants or collected data or add new online questionnaires to perform observational or interventional studies to answer a broad range of research questions.

APPLICATION

The Community of Patients for Research (ComPaRe) is a proof-of-concept COOP' e-cohort in the field of chronic conditions that was launched in 2017. As of April 2020, 36,000 patients have joined the project and contributed to more than 4 million data points. Patient-reported data will be enriched by linkage with the French national health system databases and with hospital data for patients receiving care in the Paris region. Since 2017, 150 researchers have used the platform for research projects. Three clinical trials nested in ComPaRe have been funded.

CONCLUSION

By moving from myriad independent studies to a large collaborative infrastructure of research, COOP' e-cohorts will accelerate the research process by avoiding the redundancy of many steps common to all research projects and by limiting waste of research.

The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement

Heterogeneity of treatment effect (HTE) refers to the nonrandom variation in the magnitude or direction of a treatment effect across levels of a covariate, as measured on a selected scale, against a clinical outcome. In randomized controlled trials (RCTs), HTE is typically examined through a subgroup analysis that contrasts effects in groups of patients defined "1 variable at a time" (for example, male vs. female or old vs. young). The authors of this statement present guidance on an alternative approach to HTE analysis, "predictive HTE analysis." The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risks with versus without the intervention, taking into account all relevant patient attributes simultaneously. The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed using a multidisciplinary technical expert panel, targeted literature reviews, simulations to characterize potential problems with predictive approaches, and a deliberative process engaging the expert panel. The authors distinguish 2 categories of predictive HTE approaches: a "risk-modeling" approach, wherein a multivariable model predicts the risk for an outcome and is applied to disaggregate patients within RCTs to define risk-based variation in benefit, and an "effect-modeling" approach, wherein a model is developed on RCT data by incorporating a term for treatment assignment and interactions between treatment and baseline covariates. Both approaches can be used to predict differential absolute treatment effects, the most relevant scale for clinical decision making. The authors developed 4 sets of guidance: criteria to determine when risk-modeling approaches are likely to identify clinically important HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. The PATH Statement, together with its explanation and elaboration document, may guide future analyses and reporting of RCTs.

The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration

The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed to promote the conduct of, and provide guidance for, predictive analyses of heterogeneity of treatment effects (HTE) in clinical trials. The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risk with versus without the intervention, taking into account all relevant patient attributes simultaneously, to support more personalized clinical decision making than can be made on the basis of only an overall average treatment effect. The authors distinguished 2 categories of predictive HTE approaches (a "risk-modeling" and an "effect-modeling" approach) and developed 4 sets of guidance statements: criteria to determine when risk-modeling approaches are likely to identify clinically meaningful HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. They discuss limitations of these methods and enumerate research priorities for advancing methods designed to generate more personalized evidence. This explanation and elaboration document describes the intent and rationale of each recommendation and discusses related analytic considerations, caveats, and reservations.

The impact of hospital grade, hospital-volume, and surgeon-volume on outcomes for adults undergoing appendicectomy

INTRODUCTION

Acute Appendicitis and appendicectomy are common surgical emergencies worldwide. However, there is a lack of published data on the impact of hospital grade, surgeon- and hospital-volumes on patient outcomes following appendicectomy.

AIM

To establish if hospital grade, hospital-volume, or surgeon-volume impacted patient outcomes following appendicectomy.

METHODS

Using the National Quality Assurance and Improvement System (NQAIS) data for all appendicectomies performed in Ireland between January 2014 and November 2017 were examined. Data relating to patient demographics, type of surgery (open/laparoscopic/laparoscopic converted to open), length of stay (LOS), mortality, admission to critical care and re-admission rates were collected and analysed.

RESULTS

During the study period, 15,896 adult appendicectomies were performed, 14,521 were laparoscopic procedures. Patients treated in district general hospitals (DGHs) had lower LOS (2.96 v 3.37 days, p < 0.0001) than patients treated in tertiary referral hospitals (TRHs), had lower rates of laparoscopic procedures (87.38% v 95.56% p < 0.0001) and higher admission rates to critical care (1.91% v 0.75% p < 0.0001). No significant outcome difference was seen between those treated by high-volume (>62 cases/year) or low volume surgeons (<20 cases/year). Patients treated in high-volume hospitals (>260 cases/year) had higher rates of laparoscopic procedures (94.9% v 83.5%, p < 0.0001), lower rates of admission to critical care (0.85% v 2.25%, p < 0.0001) and lower 7-day re-admission rates (2.54% v 3.55%, p = 0.02) than those operated in low-volume hospitals (<161 cases/year).

CONCLUSION

Patients operated on in high-volume hospitals benefit from higher rates of laparoscopic surgery and fewer critical care admissions. No significant difference in outcome was noted in those patients operated upon by high- or low-volume surgeons or based on hospital grade.

Design and implementation of pragmatic clinical trials using the electronic medical record and an adaptive design

Objectives

To demonstrate the feasibility of pragmatic clinical trials comparing the effectiveness of treatments using the electronic medical record (EMR) and an adaptive assignment design.

Methods

We have designed and are implementing pragmatic trials at the point-of-care using custom-designed structured clinical documentation support and clinical decision support tools within our physician's typical EMR workflow. We are applying a subgroup based adaptive design (SUBA) that enriches treatment assignments based on baseline characteristics and prior outcomes. SUBA uses information from a randomization phase (phase 1, equal randomization, 120 patients), to adaptively assign treatments to the remaining participants (at least 300 additional patients total) based on a Bayesian hierarchical model. Enrollment in phase 1 is underway in our neurology clinical practices for 2 separate trials using this method, for migraine and mild cognitive impairment (MCI).

Results

We are successfully collecting structured data, in the context of the providers' clinical workflow, necessary to conduct our trials. We are currently enrolling patients in 2 point-of-care trials of non-inferior treatments. As of March 1, 2018, we have enrolled 36% of eligible patients into our migraine study and 63% of eligible patients into our MCI study. Enrollment is ongoing and validation of outcomes has begun.

Discussion

This proof of concept article demonstrates the feasibility of conducting pragmatic trials using the EMR and an adaptive design.

Conclusion

The demonstration of successful pragmatic clinical trials based on a customized EMR and adaptive design is an important next step in achieving personalized medicine and provides a framework for future studies of comparative effectiveness.

Opportunities and barriers for pragmatic embedded trials: Triumphs and tribulations

RESULTS

Embedded pragmatic clinical trials (PCTs) are set in routine health care, have broad eligibility criteria, and use routinely collected electronic data. Many consider them a breakthrough innovation in clinical research and a necessary step in clinical trial development. To identify barriers and success factors, we reviewed published embedded PCTs and interviewed 30 researchers and clinical leaders in 7 US delivery systems.

LITERATURE

We searched PubMed, the Cochrane library, and clinicaltrials.gov for studies reporting embedded PCTs. We identified 108 embedded PCTs published in the last 10 years. The included studies had a median of 5540 randomized patients, addressed a variety of diseases, and practice settings covering a broad range of interventions. Eighty-one used cluster randomization. The median cost per patient was $97 in the 64 trials for which it was possible to obtain cost data.

INTERVIEWS

Delivery systems required research studies to align with operational priorities, existing information technology capabilities, and standard quality improvement procedures. Barriers that were identified included research governance, requirements for processes that were incompatible with clinical operations, and unrecoverable costs.

CONCLUSIONS

Embedding PCTs in delivery systems can provide generalizable knowledge that is directly applicable to practice settings at much lower cost than conventional trials. Successful embedding trials require accommodating delivery systems' needs and priorities.

Evaluation of biases present in the cohort multiple randomised controlled trial design: a simulation study

Background

The cohort multiple randomised controlled trial (cmRCT) design provides an opportunity to incorporate the benefits of randomisation within clinical practice; thus reducing costs, integrating electronic healthcare records, and improving external validity. This study aims to address a key concern of the cmRCT design: refusal to treatment is only present in the intervention arm, and this may lead to bias and reduce statistical power.

Methods

We used simulation studies to assess the effect of this refusal, both random and related to event risk, on bias of the effect estimator and statistical power. A series of simulations were undertaken that represent a cmRCT trial with time-to-event endpoint. Intention-to-treat (ITT), per protocol (PP), and instrumental variable (IV) analysis methods, two stage predictor substitution and two stage residual inclusion, were compared for various refusal scenarios.

Results

We found the IV methods provide a less biased estimator for the causal effect when refusal is present in the intervention arm, with the two stage residual inclusion method performing best with regards to minimum bias and sufficient power. We demonstrate that sample sizes should be adapted based on expected and actual refusal rates in order to be sufficiently powered for IV analysis.

Conclusion

We recommend running both an IV and ITT analyses in an individually randomised cmRCT as it is expected that the effect size of interest, or the effect we would observe in clinical practice, would lie somewhere between that estimated with ITT and IV analyses. The optimum (in terms of bias and power) instrumental variable method was the two stage residual inclusion method. We recommend using adaptive power calculations, updating them as refusal rates are collected in the trial recruitment phase in order to be sufficiently powered for IV analysis.

Calculating Total Health Service Utilisation and Costs from Routinely Collected Electronic Health Records Using the Example of Patients with Irritable Bowel Syndrome Before and After Their First Gastroenterology Appointment

INTRODUCTION

Health economic models are increasingly important in funding decisions but most are based on data, which may therefore not represent the general population. We sought to establish the potential of real-world data available within the Clinical Practice Research Datalink (CPRD) and linked Hospital Episode Statistics (HES) to determine comprehensive healthcare utilisation and costs as input variables for economic modelling.

METHODS

A cohort of patients with irritable bowel syndrome (IBS) who first saw a gastroenterologist in 2008 or 2009, and with 3 years of data before and after their appointment, was created in the CPRD. Primary care, outpatient, inpatient, prescription and colonoscopy data were extracted from the linked CPRD and HES. The appropriate cost to the NHS was attached to each event. Total and stratified annual healthcare utilisation rates and costs were calculated before and after the gastroenterology appointment with distribution parameters. Absolute differences were calculated with 95% confidence intervals.

RESULTS

Total annual healthcare costs over 3 years increase by £935 (95% CI £928-941) following a gastroenterology appointment for IBS. We derived utilisation and cost data with parameter distributions stratified by demographics and time. Women, older patients, smokers and patients with greater comorbidity utilised more healthcare resources, which generated higher costs.

CONCLUSIONS

These linked datasets provide comprehensive primary and secondary care data for large numbers of patients, which allows stratification of outcomes. It is possible to derive input parameters appropriate for economic models and their distributions directly from the population of interest.