Dataset Information

Towards clinical data-driven eligibility criteria optimization for interventional COVID-19 clinical trials.

ABSTRACT:

Objective

This research aims to evaluate the impact of eligibility criteria on recruitment and observable clinical outcomes of COVID-19 clinical trials using electronic health record (EHR) data.

Materials and methods

On June 18, 2020, we identified frequently used eligibility criteria from all the interventional COVID-19 trials in ClinicalTrials.gov (n?=?288), including age, pregnancy, oxygen saturation, alanine/aspartate aminotransferase, platelets, and estimated glomerular filtration rate. We applied the frequently used criteria to the EHR data of COVID-19 patients in Columbia University Irving Medical Center (CUIMC) (March 2020-June 2020) and evaluated their impact on patient accrual and the occurrence of a composite endpoint of mechanical ventilation, tracheostomy, and in-hospital death.

Results

There were 3251 patients diagnosed with COVID-19 from the CUIMC EHR included in the analysis. The median follow-up period was 10 days (interquartile range 4-28 days). The composite events occurred in 18.1% (n?=?587) of the COVID-19 cohort during the follow-up. In a hypothetical trial with common eligibility criteria, 33.6% (690/2051) were eligible among patients with evaluable data and 22.2% (153/690) had the composite event.

Discussion

By adjusting the thresholds of common eligibility criteria based on the characteristics of COVID-19 patients, we could observe more composite events from fewer patients.

Conclusions

This research demonstrated the potential of using the EHR data of COVID-19 patients to inform the selection of eligibility criteria and their thresholds, supporting data-driven optimization of participant selection towards improved statistical power of COVID-19 trials.

SUBMITTER: Kim JH

PROVIDER: S-EPMC7798960 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Towards clinical data-driven eligibility criteria optimization for interventional COVID-19 clinical trials.

Kim Jae Hyun JH Ta Casey N CN Liu Cong C Sung Cynthia C Butler Alex M AM Stewart Latoya A LA Ena Lyudmila L Rogers James R JR Lee Junghwan J Ostropolets Anna A Ryan Patrick B PB Liu Hao H Lee Shing M SM Elkind Mitchell S V MSV Weng Chunhua C

Journal of the American Medical Informatics Association : JAMIA 20210101 1

<h4>Objective</h4>This research aims to evaluate the impact of eligibility criteria on recruitment and observable clinical outcomes of COVID-19 clinical trials using electronic health record (EHR) data.<h4>Materials and methods</h4>On June 18, 2020, we identified frequently used eligibility criteria from all the interventional COVID-19 trials in ClinicalTrials.gov (n = 288), including age, pregnancy, oxygen saturation, alanine/aspartate aminotransferase, platelets, and estimated glomerular filtr ...[more]

PMID: 33260201

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Project description:BackgroundBreast cancer (BC) is the most common cancer type in women. The purpose of this study was to assess the eligibility criteria in recent clinical trials in BC, especially those that can limit the enrollment of older patients as well as those with comorbidities and poor performance status.MethodsData on clinical trials in BC were extracted from ClinicalTrials.gov. Co-primary outcomes were proportions of trials with different types of the eligibility criteria. Associations between trial characteristics and the presence of certain types of these criteria (binary variable) were determined with univariate and multivariate logistic regression.ResultsOur analysis included 522 trials of systemic anticancer treatments started between 2020 and 2022. Upper age limits, strict exclusion criteria pertaining to comorbidities, and those referring to inadequate performance status of the patient were used in 204 (39%), 404 (77%), and 360 (69%) trials, respectively. Overall, 493 trials (94%) had at least one of these criteria. The odds of the presence of each type of the exclusion criteria were significantly associated with investigational site location and trial phase. We also showed that the odds of the upper age limits and the exclusion criteria involving the performance status were significantly higher in the cohort of recent trials compared with cohort of 309 trials started between 2010 and 2012 (39% vs 19% and 69% vs 46%, respectively; p < 0.001 for univariate and multivariate analysis in both comparisons). The proportion of trials with strict exclusion criteria was comparable between the two cohorts (p > 0.05). Only three of recent trials (1%) enrolled solely patients aged 65 or 70 and older.ConclusionsMany recent clinical trials in BC exclude large groups of patients, especially older adults, individuals with different comorbidities, and those with poor performance status. Careful modification of some of the eligibility criteria in these trials should be considered to allow investigators to assess the benefits and harms of investigational treatments in participants with characteristics typically encountered in clinical practice.

Dataset Information

Towards clinical data-driven eligibility criteria optimization for interventional COVID-19 clinical trials.

Objective

Materials and methods

Results

Discussion

Conclusions

Publications

Towards clinical data-driven eligibility criteria optimization for interventional COVID-19 clinical trials.

Similar Datasets

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