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The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0.

ABSTRACT: OBJECTIVE:The population representativeness of a clinical study is influenced by how real-world patients qualify for the study. We analyze the representativeness of eligible patients for multiple type 2 diabetes trials and the relationship between representativeness and other trial characteristics. METHODS:Sixty-nine study traits available in the electronic health record data for 2034 patients with type 2 diabetes were used to profile the target patients for type 2 diabetes trials. A set of 1691 type 2 diabetes trials was identified from ClinicalTrials.gov, and their population representativeness was calculated using the published Generalizability Index of Study Traits 2.0 metric. The relationships between population representativeness and number of traits and between trial duration and trial metadata were statistically analyzed. A focused analysis with only phase 2 and 3 interventional trials was also conducted. RESULTS:A total of 869 of 1691 trials (51.4%) and 412 of 776 phase 2 and 3 interventional trials (53.1%) had a population representativeness of <5%. The overall representativeness was significantly correlated with the representativeness of the Hba1c criterion. The greater the number of criteria or the shorter the trial, the less the representativeness. Among the trial metadata, phase, recruitment status, and start year were found to have a statistically significant effect on population representativeness. For phase 2 and 3 interventional trials, only start year was significantly associated with representativeness. CONCLUSIONS:Our study quantified the representativeness of multiple type 2 diabetes trials. The common low representativeness of type 2 diabetes trials could be attributed to specific study design requirements of trials or safety concerns. Rather than criticizing the low representativeness, we contribute a method for increasing the transparency of the representativeness of clinical trials.

SUBMITTER: Sen A

PROVIDER: S-EPMC7378875 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0.

Sen Anando A Goldstein Andrew A Chakrabarti Shreya S Shang Ning N Kang Tian T Yaman Anil A Ryan Patrick B PB Weng Chunhua C

Journal of the American Medical Informatics Association : JAMIA 20180301 3

<h4>Objective</h4>The population representativeness of a clinical study is influenced by how real-world patients qualify for the study. We analyze the representativeness of eligible patients for multiple type 2 diabetes trials and the relationship between representativeness and other trial characteristics.<h4>Methods</h4>Sixty-nine study traits available in the electronic health record data for 2034 patients with type 2 diabetes were used to profile the target patients for type 2 diabetes trials ...[more]

PMID: 29025047

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Project description:BackgroundTrials often struggle to achieve their target sample size with only half doing so. Some researchers have turned to Electronic Health Records (EHRs), seeking a more efficient way of recruitment. The Scottish Health Research Register (SHARE) obtained patients' consent for their EHRs to be used as a searching base from which researchers can find potential participants. However, due to the fact that EHR data is not complete, sufficient or accurate, a database search strategy may not generate the best case-finding result. The current study aims to evaluate the performance of a case-based reasoning method in identifying participants for population-based clinical studies recruiting through SHARE, and assess the difference between its resultant cohort and the original one deriving from searching EHRs.MethodsA case-based reasoning framework was applied to 119 participants in nine projects using two-fold cross-validation, with records from a further 86,292 individuals used for testing. A prediction score for study participation was derived from the diagnosis, procedure, pharmaceutical prescription, and laboratory test results attributes of each participant. Evaluation was conducted by calculating Area Under the ROC Curve and information retrieval metrics for the ranking list of the test set by prediction score. We compared the most likely participants as identified by searching a database to those ranked highest by our model.ResultsThe average ROCAUC for nine projects was 81% indicating strong predictive ability for these data. However, the derived ranking lists showed lower predictive performance, with only 21% of the persons ranked within top 50 positions being the same as identified by searching databases.ConclusionsCase-based reasoning is may be more effective than a database search strategy for participant identification for clinical studies using population EHRs. The lower performance of ranking lists derived from case-based reasoning means that patients identified as highly suitable for study participation may still not be recruited. This suggests that further study is needed into improvements in the collection and curation of population EHRs, such as use of free text data to aid reliable identification of people more likely to be recruited to clinical trials.

Project description:BACKGROUND: About 10-15% of adult gastrointestinal stromal tumors (GIST) and the vast majority of pediatric GIST do not harbour KIT or platelet-derived growth factor receptor alpha (PDGFRA) mutations (J Clin Oncol 22:3813-3825, 2004; Hematol Oncol Clin North Am 23:15-34, 2009). The molecular biology of these GIST, originally defined as KIT/PDGFRA wild-type (WT), is complex due to the existence of different subgroups with distinct molecular hallmarks, including defects in the succinate dehydrogenase (SDH) complex and mutations of neurofibromatosis type 1 (NF1), BRAF, or KRAS genes (RAS-pathway or RAS-P).In this extremely heterogeneous landscape, the clinical profile and molecular abnormalities of the small subgroup of WT GIST suitably referred to as quadruple wild-type GIST (quadrupleWT or KITWT/PDGFRAWT/SDHWT/RAS-PWT) remains undefined. The aim of this study is to investigate the genomic profile of KITWT/PDGFRAWT/SDHWT/RAS-PWT GIST, by using a massively parallel sequencing and microarray approach, and compare it with the genomic profile of other GIST subtypes. METHODS: We performed a whole genome analysis using a massively parallel sequencing approach on a total of 16 GIST cases (2 KITWT/PDGFRAWT/SDHWT and SDHBIHC+/SDHAIHC+, 2 KITWT/PDGFRAWT/SDHAmut and SDHBIHC-/SDHAIHC- and 12 cases of KITmut or PDGFRAmut GIST). To confirm and extend the results, whole-genome gene expression analysis by microarray was performed on 9 out 16 patients analyzed by RNAseq and an additional 20 GIST patients (1 KITWT/PDGFRAWTSDHAmut GIST and 19 KITmut or PDGFRAmut GIST). The most impressive data were validated by quantitave PCR and Western Blot analysis. RESULTS: We found that both cases of quadrupleWT GIST had a genomic profile profoundly different from both either KIT/PDGFRA mutated or SDHA-mutated GIST. In particular, the quadrupleWT GIST tumors are characterized by the overexpression of molecular markers (CALCRL and COL22A1) and of specific oncogenes including tyrosine and cyclin- dependent kinases (NTRK2 and CDK6) and one member of the ETS-transcription factor family (ERG). CONCLUSION: We report for the first time an integrated genomic picture of KITWT/PDGFRAWT/SDHWT/RAS-PWT GIST, using massively parallel sequencing and gene expression analyses, and found that quadrupleWT GIST have an expression signature that is distinct from SDH-mutant GIST as well as GIST harbouring mutations in KIT or PDGFRA. Our findings suggest that quadrupleWT GIST represent another unique group within the family of gastrointestintal stromal tumors.

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The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0.

Publications

The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0.

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