Abstract
Objective
In the past few months, a large number of clinical studies on the novel coronavirus disease (COVID-19) have been initiated worldwide to find effective therapeutics, vaccines, and preventive strategies for COVID-19. In this study, we aim to understand the landscape of COVID-19 clinical research and identify the issues that may cause recruitment difficulty or reduce study generalizability.
Methods
We analyzed 3,765 COVID-19 studies registered in the largest public registry - ClinicalTrials.gov, leveraging natural language processing and using descriptive, association, and clustering analyses. We first characterized COVID-19 studies by study features such as phase and tested intervention. We then took a deep dive and analyzed their eligibility criteria to understand whether these studies: (1) considered the reported underlying health conditions that may lead to severe illnesses, and (2) excluded older adults, either explicitly or implicitly, which may reduce the generalizability of these studies to the older adults population.
Results
Our analysis included 2295 interventional studies and 1470 observational studies. Most trials did not explicitly exclude older adults with common chronic conditions. However, known risk factors such as diabetes and hypertension were considered by less than 5% of trials based on their trial description. Pregnant women were excluded by 34.9% of the studies.
Conclusions
Most COVID-19 clinical studies included both genders and older adults. However, risk factors such as diabetes, hypertension, and pregnancy were under-represented, likely skewing the population that was sampled. A careful examination of existing COVID-19 studies can inform future COVID-19 trial design towards balanced internal validity and generalizability.
Lay Summary
Since the outbreak of COVID-19 in early 2020, thousands of clinical studies have been conducted to evaluate the efficacy and safety of various types of treatments and vaccines in human. COVID-19 clinical studies play a crucial role in controlling the virus. Yet it is unclear what types of patients were considered by these studies. This study analyzed 3,765 COVID-19 clinical study summaries downloaded from a major clinical trial registry ClinicalTrials.gov. We employed natural language processing techniques to parse the study description and eligibility criteria of these studies and then performed descriptive and clustering analysis on the parsing results. We found that older adults were not systematically excluded but pregnant women were often excluded. It was also found that the known risk factors such as diabetes, hypertension, obesity, and asthma, which may lead to serious illnesses, were considered by less than 5% of the studies according to their study description and eligibility criteria. This study provides an evidence that natural language processing can be applied to examine the design of clinical studies and identify issues that may cause delays in patient recruitment and the lack of real-world population representativeness.
Identifying Late-Stage Cancer and Chronic Kidney Disease Patients for Palliative Care Research and Practice: Computable Phenotypes and Natural Language Processing (S824)