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A rule-based approach to identify patient eligibility criteria for clinical trials from narrative longitudinal records.

ABSTRACT: Objective:Achieving unbiased recognition of eligible patients for clinical trials from their narrative longitudinal clinical records can be time consuming. We describe and evaluate a knowledge-driven method that identifies whether a patient meets a selected set of 13 eligibility clinical trial criteria from their longitudinal clinical records, which was one of the tasks of the 2018 National NLP Clinical Challenges. Materials and Methods:The approach developed uses rules combined with manually crafted dictionaries that characterize the domain. The rules are based on common syntactical patterns observed in text indicating or describing explicitly a criterion. Certain criteria were classified as "met" only when they occurred within a designated time period prior to the most recent narrative of a patient record and were dealt through their position in text. Results:The system was applied to an evaluation set of 86 unseen clinical records and achieved a microaverage F1-score of 89.1% (with a micro F1-score of 87.0% and 91.2% for the patients that met and did not meet the criteria, respectively). Most criteria returned reliable results (drug abuse, 92.5%; Hba1c, 91.3%) while few (eg, advanced coronary artery disease, 72.0%; myocardial infarction within 6 months of the most recent narrative, 47.5%) proved challenging enough. Conclusion:Overall, the results are encouraging and indicate that automated text mining methods can be used to process clinical records to recognize whether a patient meets a set of clinical trial criteria and could be leveraged to reduce the workload of humans screening patients for trials.

SUBMITTER: Karystianis G

PROVIDER: S-EPMC6993990 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

Project description:IntroductionDementia prevention trials have so far shown little benefit of multidomain interventions against cognitive decline. Recruitment strategies in these trials often centre around dementia risk or cardiovascular risk profile, but it is uncertain whether this leads to inclusion of individuals who may benefit most from the intervention. We determined the effects of eligibility criteria on the recruitment of potential trial participants in the general population.MethodsIn a systematic search until January 1, 2022, we identified all published and ongoing large (≥500 participants), phase-3 multidomain trials for the prevention of cognitive decline or dementia. We applied trial eligibility criteria to 5,381 participants of the population-based Rotterdam Study (mean age: 72 years, 58% women), to compare participant characteristics, predicted risk of cardiovascular disease, and dementia risk, between trial eligible and ineligible persons.ResultsWe identified 10 trials, of which 5 had been published (DR's EXTRA, FINGER, preDIVA, MAPT, and HATICE) and 5 are ongoing (US-POINTER, MIND-CHINA, MYB, AgeWell.de, and J-Mint). Among all Rotterdam Study participants, eligibility across published trials ranged from 48% for MAPT to 87% for preDIVA, in line with original trial reports. Variability in eligibility was wider for ongoing trials, from 1% for US-POINTER to over 94% for MYB trial. Over 70% of trial eligible individuals are recommended preventive intervention in routine care based on their cardiovascular risk, similar for lipid-lowering (71%) and blood pressure-lowering treatment (73%). Ten-year risks of dementia were similar for eligible compared to ineligible individuals (12 vs. 11%).ConclusionMultidomain dementia prevention trials fail to preferentially include those at the highest risk of dementia and mostly include individuals who qualify for interventions already on the basis of cardiovascular prevention guidelines. These findings call for better targeted enrolment of individuals for whom trial results can improve clinical decision-making.

Project description:PurposeTo develop a statistical approach that compares patient selection strategies across clinical trials and apply this approach to acute ischemic stroke clinical trials to identify the optimal inclusion criteria.MethodsWe developed a statistical approach that compares the number needed to treat to achieve one success (NNT) along with the number needed to screen to achieve one success (NNS) and assesses if there are significant differences in inclusion criteria, treatment course, and clinical outcome among patients that may have been included/excluded in the trials. We applied this approach to the study population from four recent positive acute stroke clinical trials: MR CLEAN, EXTEND-IA, ESCAPE, and SWIFT PRIME, applying published trial criteria to an independent registry of 612 acute stroke patients, since we did not have access to the complete trial data.ResultsAlthough reported NNT were similar for EXTEND-IA, SWIFT PRIME and ESCAPE, and somewhat higher for MR CLEAN, NNS varied across the trials from 21 for EXTEND-IA, 27 for MR CLEAN, to 46 for ESCAPE and 64 for SWIFT PRIME, reflecting less and more stringent inclusion criteria, respectively. Although there were significant differences in imaging biomarkers and other clinical characteristics among patients that may have been included/excluded in the trials, these differences did not translate to significant differences in treatment course or clinical outcomes.ConclusionsOur study proposes a robust statistical approach that can be applied to a larger pooled trial dataset, if made available, to objectively compare across clinical trials and inform inclusion criteria of future trials. Pooled analysis of the acute stroke trial data is needed to determine which imaging biomarker inclusion criteria are critical and which may be relaxed. If this procedure were applied across the pooled trial data, it could decrease costs and refine the design of future trials to be the most efficacious for the greatest number of patients.

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A rule-based approach to identify patient eligibility criteria for clinical trials from narrative longitudinal records.

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