Optimizing eligibility criteria and clinical trial conduct to enhance clinical trial participation for primary brain tumor patients.
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ABSTRACT:
SUBMITTER: Lee EQ
PROVIDER: S-EPMC7229255 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Brain metastases cause significant morbidity and mortality in patients with advanced cancer. In the era of precision oncology and immunotherapy, there are rapidly evolving systemic treatment options. These novel therapies may have variable intracranial efficacy, and patients with brain metastases remain a population of special interest. Typically, only patients with stable, asymptomatic and/or treated brain metastases are enrolled in clinical trials, or may be excluded altogether, particularly in the setting of leptomeningeal carcinomatosis. Consequently, this leads to significant concerns on the external validity of clinical trial evidence to real-world clinical practice. Here we describe the current trends in cancer clinical trial eligibility for patients with brain metastases in both early and late phase trials, with a focus on targeted and immunotherapies. We evaluate recent newly FDA approved therapies and the clinical trial evidence base leading to approval. This includes analysis of inclusion and exclusion criteria, requirements for baseline screening for brain metastases, surveillance cerebral imaging and incorporation of trial endpoints for patients with brain metastases. Finally, the use of alternative sources of data such as real-world evidence with registries and collaborative studies will be discussed.
| S-EPMC8761732 | biostudies-literature
Project description:We present Chia, a novel, large annotated corpus of patient eligibility criteria extracted from 1,000 interventional, Phase IV clinical trials registered in ClinicalTrials.gov. This dataset includes 12,409 annotated eligibility criteria, represented by 41,487 distinctive entities of 15 entity types and 25,017 relationships of 12 relationship types. Each criterion is represented as a directed acyclic graph, which can be easily transformed into Boolean logic to form a database query. Chia can serve as a shared benchmark to develop and test future machine learning, rule-based, or hybrid methods for information extraction from free-text clinical trial eligibility criteria.
| S-EPMC7452886 | biostudies-literature
Project description:Knowledge reuse of cancer trial designs may benefit from a temporal understanding of the evolution of the target populations of cancer studies over time. Therefore, we conducted a retrospective analysis of the trends of cancer trial eligibility criteria between 1999 and 2014. The yearly distributions of eligibility concepts for chemicals and drugs, procedures, observations, and medical conditions extracted from free-text eligibility criteria of 32,000 clinical trials for 89 cancer types were analyzed. We identified the concepts that trend upwards or downwards in all or selected cancer types, and the concepts that show anomalous trends for some cancers. Later, concept trends were studied in a disease-specific manner and illustrated for breast cancer. Criteria trends observed in this study are also validated and interpreted using evidence from the existing medical literature. This study contributes a method for concept trend analysis and original knowledge of the trends in cancer clinical trial eligibility criteria.
| S-EPMC5001741 | biostudies-literature
Project description:The rapid adoption of electronic health records (EHRs) systems has made clinical data available in electronic format for research and for many downstream applications. Electronic screening of potentially eligible patients using these clinical databases for clinical trials is a critical need to improve trial recruitment efficiency. Nevertheless, manually translating free-text eligibility criteria into database queries is labor intensive and inefficient. To facilitate automated screening, free-text eligibility criteria must be structured and coded into a computable format using controlled vocabularies. Named entity recognition (NER) is thus an important first step. In this study, we evaluate 4 state-of-the-art transformer-based NER models on two publicly available annotated corpora of eligibility criteria released by Columbia University (i.e., the Chia data) and Facebook Research (i.e.the FRD data). Four transformer-based models (i.e., BERT, ALBERT, RoBERTa, and ELECTRA) pretrained with general English domain corpora vs. those pretrained with PubMed citations, clinical notes from the MIMIC-III dataset and eligibility criteria extracted from all the clinical trials on ClinicalTrials.gov were compared. Experimental results show that RoBERTa pretrained with MIMIC-III clinical notes and eligibility criteria yielded the highest strict and relaxed F-scores in both the Chia data (i.e., 0.658/0.798) and the FRD data (i.e., 0.785/0.916). With promising NER results, further investigations on building a reliable natural language processing (NLP)-assisted pipeline for automated electronic screening are needed.
| S-EPMC8373041 | biostudies-literature
Project description:Objective:To develop an open-source information extraction system called Eligibility Criteria Information Extraction (EliIE) for parsing and formalizing free-text clinical research eligibility criteria (EC) following Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) version 5.0. Materials and Methods:EliIE parses EC in 4 steps: (1) clinical entity and attribute recognition, (2) negation detection, (3) relation extraction, and (4) concept normalization and output structuring. Informaticians and domain experts were recruited to design an annotation guideline and generate a training corpus of annotated EC for 230 Alzheimer's clinical trials, which were represented as queries against the OMOP CDM and included 8008 entities, 3550 attributes, and 3529 relations. A sequence labeling-based method was developed for automatic entity and attribute recognition. Negation detection was supported by NegEx and a set of predefined rules. Relation extraction was achieved by a support vector machine classifier. We further performed terminology-based concept normalization and output structuring. Results:In task-specific evaluations, the best F1 score for entity recognition was 0.79, and for relation extraction was 0.89. The accuracy of negation detection was 0.94. The overall accuracy for query formalization was 0.71 in an end-to-end evaluation. Conclusions:This study presents EliIE, an OMOP CDM-based information extraction system for automatic structuring and formalization of free-text EC. According to our evaluation, machine learning-based EliIE outperforms existing systems and shows promise to improve.
| S-EPMC6259668 | biostudies-literature
Project description:BackgroundClinical trials are important for patients, for researchers and for companies. One of the major bottlenecks is patient recruitment. This task requires the matching of a large volume of information about the patient with numerous eligibility criteria, in a logically-complex combination. Moreover, some of the patient's information necessary to determine the status of the eligibility criteria may not be available at the time of pre-screening.ResultsWe showed that the classic approach based on negation as failure over-estimates rejection when confronted with partially-known information about the eligibility criteria because it ignores the distinction between a trial for which patient eligibility should be rejected and trials for which patient eligibility cannot be asserted. We have also shown that 58.64% of the values were unknown in the 286 prostate cancer cases examined during the weekly urology multidisciplinary meetings at Rennes' university hospital between October 2008 and March 2009.We propose an OWL design pattern for modeling eligibility criteria based on the open world assumption to address the missing information problem. We validate our model on a fictitious clinical trial and evaluate it on two real clinical trials. Our approach successfully distinguished clinical trials for which the patient is eligible, clinical trials for which we know that the patient is not eligible and clinical trials for which the patient may be eligible provided that further pieces of information (which we can identify) can be obtained.ConclusionsOWL-based reasoning based on the open world assumption provides an adequate framework for distinguishing those patients who can confidently be rejected from those whose status cannot be determined. The expected benefits are a reduction of the workload of the physicians and a higher efficiency by allowing them to focus on the patients whose eligibility actually require expertise.
| S-EPMC3852288 | biostudies-literature
Project description:A major challenge in using electronic health record repositories for research is the difficulty matching subject eligibility criteria to query capabilities of the repositories. We propose categories for study criteria corresponding to the effort needed for querying those criteria: "easy" (supporting automated queries), mixed (initial automated querying with manual review), "hard" (fully manual record review), and "impossible" or "point of enrollment" (not typically in health repositories). We obtained a sample of 292 criteria from 20 studies from ClinicalTrials.gov. Six independent reviewers, three each from two academic research institutions, rated criteria according to our four types. We observed high interrater reliability both within and between institutions. The analysis demonstrated typical features of criteria that map with varying levels of difficulty to repositories. We propose using these features to improve enrollment workflow through more standardized study criteria, self-service repository queries, and analyst-mediated retrievals.
| S-EPMC5977684 | 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:BackgroundMost clinical trials on colorectal cancer (CRC) exclude cases who have history of a prior malignancy. However, no prior research studied this history's actual impact on the survival of CRC. In the paper, we study the effects of having a malignancy preceding CRC diagnosis on its survival outcomes.MethodsCRC patients diagnosed during 1973-2008 were reviewed using the SEER 18 database. We calculated overall survival and cancer-specific survival of subsequent CRC, and more specifically stage IV CRC, using Kaplan-Meier test and adjusted Cox models.ResultsA total 550,325 CRC patients were reviewed, of whom 31,663 had history of a prior malignancy. The most commonly reported sites of a prior malignancy were: prostate, breast, urinary bladder, lung, and endometrium. Patients with history of a prior non-leukemic malignancy or history of a prior leukemia were found to have worse overall survival (HR = 1.165 95%CI = 1.148-1.183, P < 0.001) and (HR = 1.825 95%CI = 1.691-1.970, P < 0.001), respectively. However, CRC patients with history of a prior non-leukemic malignancy showed an improved colorectal cancer-specific survival (HR = .930 95%CI = .909-.952, P < 0.001). Analysis of stage IV CRC patients showed that patients with history of any non-leukemic malignancy did not have a significant change in overall survival. Whereas, patients with a prior leukemia showed a worse overall survival (HR = 1.535, 95%CI = 1.303-1.809, P < 0.001). When analyzed separately, right CRC and left CRC showed similar survival patterns.ConclusionA prior malignancy before CRC -in general- can be associated with worse clinical survival outcomes. These worse outcomes are not observed in stage IV CRC. Considering these results when including/excluding stage IV CRC patients with prior malignancies in clinical trials may play help improve their generalizability.
| S-EPMC6716811 | 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