From a decentralized clinical trial to a decentralized and clinical-trial-in-a-box platform: Towards patient-centric and equitable trials.

Background/Objective: Despite the intuitive attractiveness of bringing research to participants rather than making them come to central study sites, widespread decentralized enrollment has not been common in clinical trials. Methods: The need for clinical research in the context of the COVID-19 pandemic, along with innovations in technology, led us to use a decentralized trial approach in our Phase 2 COVID-19 trial. We used real-time acquisition and transmission of health-related data using home-based monitoring devices and mobile applications to assess outcomes. This approach not only avoids spreading COVID-19 but it also can support inclusion of participants in more diverse socioeconomic circumstances and in rural settings. Results: Our team developed and deployed a decentralized trial platform to support patient engagement and adverse event reporting. Clinicians, engineers, and informaticians on our research team developed a Clinical-Trial-in-a-Box tool to optimally collect and analyze data from multiple decentralized platforms. Conclusion: Applying the decentralized model in Long COVID, using digital health technology and personal devices integrated with our telehealth platform, we share the lessons learned from our work, along with challenges and future possibilities.

Development and validation of a multivariable model and online decision-support calculator to aid in preoperative discrimination of benign from malignant splenic masses in dogs.

OBJECTIVE: To develop a multivariable model and online decision-support calculator to aid in preoperative discrimination of benign from malignant splenic masses in dogs. ANIMALS: 522 dogs that underwent splenectomy because of splenic masses. PROCEDURES: A multivariable model was developed with preoperative clinical data obtained retrospectively from the records of 422 dogs that underwent splenectomy. Inclusion criteria were the availability of complete abdominal ultrasonographic examination images and splenic histologic slides or histology reports for review. Variables considered potentially predictive of splenic malignancy were analyzed. A receiver operating characteristic curve was created for the final multivariable model, and area under the curve was calculated. The model was externally validated with data from 100 dogs that underwent splenectomy subsequent to model development and was used to create an online calculator to estimate probability of splenic malignancy in individual dogs. RESULTS: The final multivariable model contained 8 clinical variables used to estimate splenic malignancy probability: serum total protein concentration, presence (vs absence) of ≥ 2 nRBCs/100 WBCs, ultrasonographically assessed splenic mass diameter, number of liver nodules (0, 1, or ≥ 2), presence (vs absence) of multiple splenic masses or nodules, moderate to marked splenic mass inhomogeneity, moderate to marked abdominal effusion, and mesenteric, omental, or peritoneal nodules. Areas under the receiver operating characteristic curves for the development and validation populations were 0.80 and 0.78, respectively. CONCLUSIONS AND CLINICAL RELEVANCE: The online calculator (T-STAT.net or T-STAT.org) developed in this study can be used as an aid to estimate the probability of malignancy in dogs with splenic masses and has potential to facilitate owners' decisions regarding splenectomy.

Stakeholder engagement in methodological research: Development of a clinical decision support tool.

INTRODUCTION: Shared patient-clinician decision-making is central to choosing between medical treatments. Decision support tools can have an important role to play in these decisions. We developed a decision support tool for deciding between nonsurgical treatment and surgical total knee replacement for patients with severe knee osteoarthritis. The tool aims to provide likely outcomes of alternative treatments based on predictive models using patient-specific characteristics. To make those models relevant to patients with knee osteoarthritis and their clinicians, we involved patients, family members, patient advocates, clinicians, and researchers as stakeholders in creating the models. METHODS: Stakeholders were recruited through local arthritis research, advocacy, and clinical organizations. After being provided with brief methodological education sessions, stakeholder views were solicited through quarterly patient or clinician stakeholder panel meetings and incorporated into all aspects of the project. RESULTS: Participating in each aspect of the research from determining the outcomes of interest to providing input on the design of the user interface displaying outcome predications, 86% (12/14) of stakeholders remained engaged throughout the project. Stakeholder engagement ensured that the prediction models that form the basis of the Knee Osteoarthritis Mathematical Equipoise Tool and its user interface were relevant for patient-clinician shared decision-making. CONCLUSIONS: Methodological research has the opportunity to benefit from stakeholder engagement by ensuring that the perspectives of those most impacted by the results are involved in study design and conduct. While additional planning and investments in maintaining stakeholder knowledge and trust may be needed, they are offset by the valuable insights gained.

Optimization of Electronic Medical Records for Data Mining Using a Common Data Model.

The increasing use of electronic health records (EHRs) in veterinary medicine creates an opportunity to utilize the high volume of electronic patient data for mining and data-driven analytics with the goal of improving patient care and outcomes. A central focus of the Clinical and Translational Science Award One Health Alliance (COHA) is to integrate efforts across multiple disciplines to better understand shared diseases in animals and people. The ability to combine veterinary and human medical data provides a unique resource to study the interactions and relationships between animals, humans, and the environment. However, to effectively answer these questions, veterinary EHR data must first be prepared in the same way it is now commonly being done in human medicine to enable data mining and development of analytics to facilitate knowledge formation and solutions that advance our understanding of disease processes, with the ultimate goal of improving outcomes for veterinary patients and their owners. As a first step, COHA member institutions implemented a Common Data Model to standardize EHR data. Herein we present the approach executed within the COHA framework to prepare and optimize veterinary EHRs for data mining and knowledge formation based on the adoption of the Observational Health Data Sciences and Informatics' Observational Medical Outcomes Partnership Common Data Model.

TRANSLATOR Database-A Vision for a Multi-Institutional Research Network.

The formation of the CTSI One Health Alliance (COHA) network has generated the infrastructure necessary to support "Big Data" collaborative comparative and translational research in veterinary medicine. We describe the first step in the design, implementation, and dissemination of a collaborative information technology infrastructure that will serve the public and clinicians (COHA public/member based web site at https://ctsaonehealthalliance.org/) and its research focused COHA Research Workbench application. The core research infrastructure, TRANSLATOR (TRanslational ANimal Shared ColLAboraTive Observational Research), represents the foundation of a federated research-capable network to enable pooling large datasets from both electronic health records and publications. The public facing COHA website is a mechanism for both the dissemination of knowledge to the public and to foster collaborations amongst veterinary clinician scientists and the greater medical research community.

The use of patient-specific equipoise to support shared decision-making for clinical care and enrollment into clinical trials.

BACKGROUND: To enhance enrollment into randomized clinical trials (RCTs), we proposed electronic health record-based clinical decision support for patient-clinician shared decision-making about care and RCT enrollment, based on "mathematical equipoise." OBJECTIVES: As an example, we created the Knee Osteoarthritis Mathematical Equipoise Tool (KOMET) to determine the presence of patient-specific equipoise between treatments for the choice between total knee replacement (TKR) and nonsurgical treatment of advanced knee osteoarthritis. METHODS: With input from patients and clinicians about important pain and physical function treatment outcomes, we created a database from non-RCT sources of knee osteoarthritis outcomes. We then developed multivariable linear regression models that predict 1-year individual-patient knee pain and physical function outcomes for TKR and for nonsurgical treatment. These predictions allowed detecting mathematical equipoise between these two options for patients eligible for TKR. Decision support software was developed to graphically illustrate, for a given patient, the degree of overlap of pain and functional outcomes between the treatments and was pilot tested for usability, responsiveness, and as support for shared decision-making. RESULTS: The KOMET predictive regression model for knee pain had four patient-specific variables, and an r 2 value of 0.32, and the model for physical functioning included six patient-specific variables, and an r 2 of 0.34. These models were incorporated into prototype KOMET decision support software and pilot tested in clinics, and were generally well received. CONCLUSIONS: Use of predictive models and mathematical equipoise may help discern patient-specific equipoise to support shared decision-making for selecting between alternative treatments and considering enrollment into an RCT.

An example of medical device-based projection of clinical trial enrollment: Use of electrocardiographic data to identify candidates for a trial in acute coronary syndromes.

BACKGROUND: To identify potential participants for clinical trials, electronic health records (EHRs) are searched at potential sites. As an alternative, we investigated using medical devices used for real-time diagnostic decisions for trial enrollment. METHODS: To project cohorts for a trial in acute coronary syndromes (ACS), we used electrocardiograph-based algorithms that identify ACS or ST elevation myocardial infarction (STEMI) that prompt clinicians to offer patients trial enrollment. We searched six hospitals' electrocardiograph systems for electrocardiograms (ECGs) meeting the planned trial's enrollment criterion: ECGs with STEMI or > 75% probability of ACS by the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI). We revised the ACI-TIPI regression to require only data directly from the electrocardiograph, the e-ACI-TIPI using the same data used for the original ACI-TIPI (development set n = 3,453; test set n = 2,315). We also tested both on data from emergency department electrocardiographs from across the US (n = 8,556). We then used ACI-TIPI and e-ACI-TIPI to identify potential cohorts for the ACS trial and compared performance to cohorts from EHR data at the hospitals. RESULTS: Receiver-operating characteristic (ROC) curve areas on the test set were excellent, 0.89 for ACI-TIPI and 0.84 for the e-ACI-TIPI, as was calibration. On the national electrocardiographic database, ROC areas were 0.78 and 0.69, respectively, and with very good calibration. When tested for detection of patients with > 75% ACS probability, both electrocardiograph-based methods identified eligible patients well, and better than did EHRs. CONCLUSION: Using data from medical devices such as electrocardiographs may provide accurate projections of available cohorts for clinical trials.

Risk Prediction for Progression of Macular Degeneration: 10 Common and Rare Genetic Variants, Demographic, Environmental, and Macular Covariates.

PURPOSE: To determine the association between genetic variants and transition to advanced age-related macular degeneration (AMD), and to develop a predictive model and online application to assist in clinical decision making. METHODS: Among 2951 subjects in the Age-Related Eye Disease Study, 834 progressed from no AMD, early AMD, or intermediate AMD to advanced disease. Survival analysis was used to assess which genetic, demographic, environmental, and macular covariates were independently associated with progression. Attributable risk, area under the curve statistics (AUCs), and reclassification odds ratios (ORs) were calculated. Split-sample validation was performed. An online risk calculator was developed and is available in the public domain at www.seddonamdriskscore.org. RESULTS: Ten genetic loci were independently associated with progression, including newly identified rare variant C3 K155Q (hazard ratio: 1.7, 95% confidence interval: 1.2-2.5, P = 0.002), three variants in CFH, and six variants in ARMS2/HTRA1, CFB, C3, C2, COL8A1, and RAD51B. Attributable risk calculations revealed that 80% of incident AMD is attributable to genetic factors, adjusting for demographic covariates and baseline macular phenotypes. In a model including 10 genetic loci, age, sex, education, body mass index, smoking, and baseline AMD status, the AUC for progression to advanced AMD over 10 years was 0.911. Split-sample validation showed a similar AUC (0.907). Reclassification analyses indicated that subjects were categorized into a more accurate risk category if genetic information was included (OR 3.2, P < 0.0001). CONCLUSIONS: Rare variant C3 K155Q was independently associated with AMD progression. The comprehensive model may be useful for identifying and monitoring high-risk patients, selecting appropriate therapies, and designing clinical trials.

Improving use of prehospital 12-lead ECG for early identification and treatment of acute coronary syndrome and ST-elevation myocardial infarction.

BACKGROUND: Performance of prehospital ECGs expedites identification of ST-elevation myocardial infarction and reduces door-to-balloon times for patients receiving reperfusion therapy. To fully realize this benefit, emergency medical service performance must be measured and used in feedback reporting and quality improvement. METHODS AND RESULTS: This quasi-experimental design trial tested an approach to improving emergency medical service prehospital ECGs using feedback reporting and quality improvement interventions in 2 cities' emergency medical service agencies and receiving hospitals. All patients age > or =30 years, calling 9-1-1 with possible acute coronary syndrome, were included. In total, 6994 patients were included: 1589 patients in the baseline period without feedback and 5405 in the intervention period when there were feedback reports and quality improvement interventions. Mean age was 66+/-17 years, and women represented 51%. Feedback and quality improvement increased prehospital ECG performance for patients with acute coronary syndrome from 76% to 93% (P=<0.0001) and for patients with ST-elevation myocardial infarction from 77% to 99% (P=<0.0001). Aspirin administration increased from 75% to 82% (P=0.001), but the median total emergency medical service run time remained the same at 22 minutes. The proportion of patients with door-to-balloon times of < or =90 minutes increased from 27% to 67% (P=0.006). CONCLUSIONS: Feedback reports and quality improvement improved prehospital ECG performance for patients with acute coronary syndrome and ST-elevation myocardial infarction and increased aspirin administration without prehospital transport delays. Improvements in door-to-balloon times were also seen.