Patient-prioritized primary endpoints in clinical trials

Patient and public involvement in Nordic healthcare research: a scoping review of contemporary practice

BACKGROUND

Over the past decades, there has been a growing international interest in user involvement in healthcare research. However, evidence on the management and impact of patient and public involvement in Nordic healthcare research remains limited.

OBJECTIVE

The aim was to explore and delineate the current state, practice, and impact of patient and public involvement in healthcare research across different areas of healthcare and patient populations in the Nordic countries.

METHODS

We conducted a scoping review using nine scientific databases and gray literature from 1992-2023. Sources were categorized as empirical or non-empirical. We used the Guidance for Reporting Involvement of Patients and the Public Short Form 2 checklist for reporting of patient and public involvement in healthcare research and the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews.

RESULTS

A total of 56 publications were included, consisting of 39 empirical and 17 non-empirical sources. Gray literature varied among countries and institutions encompassing different types of documents. We found an increase in the number of publications on patient and public involvement in Nordic healthcare research. This was evidenced by the growing number of references and institutional initiatives intended at involving the public, indicating the increasing emphasis on patient and public involvement in Nordic healthcare research. The terminology used to describe patient and public involvement varied over time. However, there has been a gradual narrowing down of terms as the concept of PPI has become more integrated into research practices, particularly with the involvement of funding agencies.

CONCLUSION

The utilization of patient and public involvement in Nordic healthcare research has substantially increased, proliferated, and gained widespread acceptance across diverse healthcare domains. The variety of approaches challenged our scoping review in terms of systematic description and impact. Patient and public involvement was applied in one or more research stages using different methodologies and terms. International agreement on terms and definitions is needed for reliable interpretation of the use of patient and public involvement in Nordic healthcare research.

Research Question, Objectives, and Endpoints in Clinical and Oncological Research: A Comprehensive Review

Clinical research is a systematic process of conducting research work to find solutions for human health-related problems. It is applied to understand the disease process and assist in the diagnosis, treatment, and prevention. Currently, we are experiencing global unrest caused by the coronavirus disease (COVID-19) pandemic. The novel severe acute respiratory syndrome coronavirus (SARS-CoV-2) has been responsible for the deaths of more than 50 million people worldwide. Also, it has resulted in severe morbidity among the affected population. The cause of such a huge amount of influence on human health by the pandemic was the unavailability of drugs and therapeutic interventions to treat and manage the disease. Cancer is a disease condition wherein the normal cell function is deranged, and the cells multiply in an uncontrolled manner. Based on recent reports by the World Health Organization (WHO), cancer is the second leading cause of death globally. Moreover, the rates of cancers have shown an increasing trend in the past decade. Therefore, it is essential to improve the understanding concerning clinical research to address the health concerns of humans. In this review, we comprehensively discuss critical aspects of clinical research that include the research question, research objectives, patient-reported outcome measures (PROMs), intention-to-treat and per-protocol analysis, and endpoints in clinical and oncological research.

Development and validation of a novel model for characterizing migraine outcomes within real-world data

BACKGROUND

In disease areas with 'soft' outcomes (i.e., the subjective aspects of a medical condition or its management) such as migraine or depression, extraction and validation of real-world evidence (RWE) from electronic health records (EHRs) and other routinely collected data can be challenging due to how the data are collected and recorded. In this study, we aimed to define and validate a scalable framework model to measure outcomes of migraine treatment and prevention by use of artificial intelligence (AI) algorithms within EHR data.

METHODS

Headache specialists defined descriptive features based on routinely collected clinical data. Data elements were weighted to define a 10-point scale encompassing headache severity (1-7 points) and associated features (0-3 points). A test data set was identified, and a reference standard was manually produced by trained annotators. Automation (i.e., AI) was used to extract features from the unstructured data of patient encounters and compared to the reference standard. A threshold of 70% close agreement (within 1 point) between the automated score and the human annotator was considered to be a sufficient extraction accuracy. The accuracy of AI in identifying features used to construct the outcome model was also evaluated and success was defined as achieving an F1 score (i.e., the weighted harmonic mean of the precision and recall) of 80% in identifying encounters.

RESULTS

Using data from 2,006 encounters, 11 features were identified and included in the model; the average F1 scores for automated extraction were 92.0% for AI applied to unstructured data. The outcome model had excellent accuracy in characterizing migraine status with an exact match for 77.2% of encounters and a close match (within 1 point) for 82.2%, compared with manual extraction scores-well above the 70% match threshold set prior to the study.

CONCLUSION

Our findings indicate the feasibility of technology-enabled models for validated determination of soft outcomes such as migraine progression using the data elements typically captured in the real-world clinical setting, providing a scalable approach to credible EHR-based clinical studies.