Deep Learning Approach to Parse Eligibility Criteria in Dietary Supplements Clinical Trials Following OMOP Common Data Model.
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ABSTRACT:
SUBMITTER: Bompelli A
PROVIDER: S-EPMC8075443 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Eligibility criteria are important for clinical research protocols or clinical practice guidelines for determining who qualify for studies and to whom clinical evidence is applicable, but the free-text format is not amenable for computational processing. In this paper, we described a practical method for transforming free-text clinical research eligibility criteria of Alzheimer's clinical trials into a structured relational database compliant with standards for medical terminologies and clinical data models. We utilized a hybrid natural language processing system and a concept normalization tool to extract medical terms in clinical research eligibility criteria and represent them using the OMOP Common Data Model (CDM) v5. We created a database schema design to store syntactic relations to facilitate efficient cohort queries. We further discussed the potential of applying this method to trials on other diseases and the promise of using it to accelerate clinical research with electronic health records.
| S-EPMC5893219 | biostudies-literature
Project description:To automatically identify and cluster clinical trials with similar eligibility features.Using the public repository ClinicalTrials.gov as the data source, we extracted semantic features from the eligibility criteria text of all clinical trials and constructed a trial-feature matrix. We calculated the pairwise similarities for all clinical trials based on their eligibility features. For all trials, by selecting one trial as the center each time, we identified trials whose similarities to the central trial were greater than or equal to a predefined threshold and constructed center-based clusters. Then we identified unique trial sets with distinctive trial membership compositions from center-based clusters by disregarding their structural information.From the 145,745 clinical trials on ClinicalTrials.gov, we extracted 5,508,491 semantic features. Of these, 459,936 were unique and 160,951 were shared by at least one pair of trials. Crowdsourcing the cluster evaluation using Amazon Mechanical Turk (MTurk), we identified the optimal similarity threshold, 0.9. Using this threshold, we generated 8806 center-based clusters. Evaluation of a sample of the clusters by MTurk resulted in a mean score 4.331±0.796 on a scale of 1-5 (5 indicating "strongly agree that the trials in the cluster are similar").We contribute an automated approach to clustering clinical trials with similar eligibility features. This approach can be potentially useful for investigating knowledge reuse patterns in clinical trial eligibility criteria designs and for improving clinical trial recruitment. We also contribute an effective crowdsourcing method for evaluating informatics interventions.
| S-EPMC4119097 | biostudies-literature
Project description:To assess the effect of eligibility criteria on exclusion rates, generalizability, and outcome heterogeneity in amyotrophic lateral sclerosis (ALS) clinical trials and to assess the value of a risk-based inclusion criterion. A literature search was performed to summarize the eligibility criteria of clinical trials. The extracted criteria were applied to an incidence cohort of 2,904 consecutive patients with ALS to quantify their effects on generalizability and outcome heterogeneity. We evaluated the effect of a risk-based selection approach on trial design using a personalized survival prediction model. We identified 38 trials. A large variability exists between trials in all patient characteristics for enrolled patients (p < 0.001), except for the proportion of men (p = 0.21). Exclusion rates varied widely (from 14% to 95%; mean 59.8%; 95% confidence interval 52.6%-66.7%). Stratification of the eligible populations into prognostic subgroups showed that eligibility criteria lead to exclusion of patients in all prognostic groups. Eligibility criteria neither reduce heterogeneity in survival time (from 22.0 to 20.5 months, p = 0.09) nor affect between-patient variability in functional decline (from 0.62 to 0.65, p = 0.25). In none of the 38 trials were the eligibility criteria found to be more efficient than the prediction model in optimizing sample size and eligibility rate. The majority of patients with ALS are excluded from trial participation, which questions the generalizability of trial results. Eligibility criteria only minimally improve homogeneity in trial endpoints. An individualized risk-based criterion could be used to balance the gains in trial design and loss in generalizability.
| S-EPMC6369899 | biostudies-literature
Project description:Clinical trials are essential for determining whether new interventions are effective. In order to determine the eligibility of patients to enroll into these trials, clinical trial coordinators often perform a manual review of clinical notes in the electronic health record of patients. This is a very time-consuming and exhausting task. Efforts in this process can be expedited if these coordinators are directed toward specific parts of the text that are relevant for eligibility determination. In this study, we describe the creation of a dataset that can be used to evaluate automated methods capable of identifying sentences in a note that are relevant for screening a patient's eligibility in clinical trials. Using this dataset, we also present results for four simple methods in natural language processing that can be used to automate this task. We found that this is a challenging task (maximum F-score=26.25), but it is a promising direction for further research.
| S-EPMC4978353 | biostudies-literature
Project description:ObjectivesTreatments for metastatic renal cell carcinoma (mRCC) are often compared across trials, but trial eligibility criteria and endpoints differ. In an effort to better align trials, the Definition for the Assessment of Time to event Endpoints in CANcer trials (DATECAN) project published recommendations in 2015 to be used in mRCC clinical trial design. We analyzed mRCC trial criteria to determine if DATECAN's recommendations were followed.Materials and methodsWe compared eligibility criteria across 29 phase 3 mRCC trials conducted between 2003 and 2019. We then evaluated endpoints used in 10 phase 3 trials activated between 2015 and 2019 to determine their compliance with DATECAN's recommendations.ResultsAmong the 29 trials, performance status, renal function, and disease characteristics differed in terms of requirements and measures used. In terms of endpoints, the 10 trials did not entirely follow DATECAN's recommendations. In total, 7/10 trials' primary endpoint was progression-free survival (PFS) as recommended; 4/9 trials used PFS as an endpoint but did not publish their definition of PFS, and the 5 that did, included "death from any cause" instead of DATECAN's recommendation of "death from kidney cancer."ConclusionsKey eligibility criteria were somewhat inconsistent across the phase 3 mRCC trials studied. Endpoints in the newer trials did not align with DATECAN's recommendations. Not only is greater standardization needed to facilitate meta-analyses and cross-trial comparisons, but as evident from lack of adherence to DATECAN's recommendations, greater promotion and adoption of recommendations are needed to better harmonize trial design.
| S-EPMC7515769 | biostudies-literature
Project description:IntroductionTo generalize safety and efficacy findings, it is essential that diverse populations are well represented in Alzheimer's disease (AD) drug trials. In this review, we aimed to investigate participant diversity in disease-modifying AD trials over time, and the frequencies of participant eligibility criteria.MethodsA systematic review was performed using Medline, Embase, the Cochrane Library, and Clinicaltrials.gov, identifying 2247 records.ResultsIn the 101 included AD trials, participants were predominantly White (median percentage: 94.7%, interquartile range: 81.0-96.7%); and this percentage showed no significant increase or decrease over time (2001-2019). Eligibility criteria such as exclusion of persons with psychiatric illness (78.2%), cardiovascular disease (71.3%) and cerebrovascular disease (68.3%), obligated caregiver attendance (80.2%), and specific Mini-Mental State Examination scores (90.1%; no significant increase/decrease over time) may have led to a disproportionate exclusion of ethnoracially diverse individuals.DiscussionEthnoracially diverse participants continue to be underrepresented in AD clinical trials. Several recommendations are provided to broaden eligibility criteria.
| S-EPMC8964823 | biostudies-literature
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.
| S-EPMC10318672 | biostudies-literature
Project description:There is a growing focus on making clinical trials more inclusive but the design of trial eligibility criteria remains challenging1-3. Here we systematically evaluate the effect of different eligibility criteria on cancer trial populations and outcomes with real-world data using the computational framework of Trial Pathfinder. We apply Trial Pathfinder to emulate completed trials of advanced non-small-cell lung cancer using data from a nationwide database of electronic health records comprising 61,094 patients with advanced non-small-cell lung cancer. Our analyses reveal that many common criteria, including exclusions based on several laboratory values, had a minimal effect on the trial hazard ratios. When we used a data-driven approach to broaden restrictive criteria, the pool of eligible patients more than doubled on average and the hazard ratio of the overall survival decreased by an average of 0.05. This suggests that many patients who were not eligible under the original trial criteria could potentially benefit from the treatments. We further support our findings through analyses of other types of cancer and patient-safety data from diverse clinical trials. Our data-driven methodology for evaluating eligibility criteria can facilitate the design of more-inclusive trials while maintaining safeguards for patient safety.
| S-EPMC9007176 | biostudies-literature
Project description:Commerical Probioitc Composition
| PRJNA297050 | ENA
Project description:Dietary supplements Metagenome
| PRJNA291686 | ENA