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Published in: BMC Medical Informatics and Decision Making 1/2020

Open Access 01-12-2020 | Research article

Efficient identification of patients eligible for clinical studies using case-based reasoning on Scottish Health Research register (SHARE)

Authors: Wen Shi, Tom Kelsey, Frank Sullivan

Published in: BMC Medical Informatics and Decision Making | Issue 1/2020

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Abstract

Background

Trials 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.

Methods

A 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.

Results

The 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.

Conclusions

Case-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.
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Literature
1.
6.
go back to reference UK HARP-III Collaborative Group. Randomized multicentre pilot study of sacubitril/valsartan versus irbesartan in patients with chronic kidney disease: United Kingdom heart and renal protection (HARP)- III-rationale, trial design and baseline data. Nephrol Dial Transplant. 2017;32(12):2043–51. https://doi.org/10.1093/ndt/gfw321.CrossRef UK HARP-III Collaborative Group. Randomized multicentre pilot study of sacubitril/valsartan versus irbesartan in patients with chronic kidney disease: United Kingdom heart and renal protection (HARP)- III-rationale, trial design and baseline data. Nephrol Dial Transplant. 2017;32(12):2043–51. https://​doi.​org/​10.​1093/​ndt/​gfw321.CrossRef
16.
go back to reference Niedner CD. The entity-attribute-value data model in radiology informatics. In: Proceedings of the 10th conference on computer applications in radiology. Anaheim: Symposia Foundation; 1990. p. 50–60. Niedner CD. The entity-attribute-value data model in radiology informatics. In: Proceedings of the 10th conference on computer applications in radiology. Anaheim: Symposia Foundation; 1990. p. 50–60.
19.
go back to reference Li B, Han L. Distance Weighted Cosine Similarity Measure for Text Classification. In: Yin H, Tang K, Gao Y, Klawonn F, Lee M, Weise T, et al., editors. Intelligent Data Engineering and Automated Learning – IDEAL 2013. 8206. Berlin: Springer; 2013. p. 611–8.CrossRef Li B, Han L. Distance Weighted Cosine Similarity Measure for Text Classification. In: Yin H, Tang K, Gao Y, Klawonn F, Lee M, Weise T, et al., editors. Intelligent Data Engineering and Automated Learning – IDEAL 2013. 8206. Berlin: Springer; 2013. p. 611–8.CrossRef
21.
go back to reference Safari S, Baratloo A, Elfil M, Negida A. Evidence based emergency medicine; part 5 receiver operating curve and area under the curve. Emerg (Tehran). 2016 Spring;4(2):111–3. Safari S, Baratloo A, Elfil M, Negida A. Evidence based emergency medicine; part 5 receiver operating curve and area under the curve. Emerg (Tehran). 2016 Spring;4(2):111–3.
23.
go back to reference Craswell N. Precision at n. In: Liu L, ÖZsu MT, editors. Encyclopedia of database systems. Boston: Springer US; 2009. p. 2127–8. Craswell N. Precision at n. In: Liu L, ÖZsu MT, editors. Encyclopedia of database systems. Boston: Springer US; 2009. p. 2127–8.
24.
go back to reference Zhang E, Zhang Y. Average precision. In: Liu L, ÖZsu MT, editors. Encyclopedia of database systems. Boston: Springer US; 2009. p. 192–3. Zhang E, Zhang Y. Average precision. In: Liu L, ÖZsu MT, editors. Encyclopedia of database systems. Boston: Springer US; 2009. p. 192–3.
25.
go back to reference Craswell N. Mean Reciprocal Rank. In: Liu L, MT ÖZ, editors. Encyclopedia of Database Systems. Boston: Springer US; 2009. p. 1703. Craswell N. Mean Reciprocal Rank. In: Liu L, MT ÖZ, editors. Encyclopedia of Database Systems. Boston: Springer US; 2009. p. 1703.
Metadata
Title
Efficient identification of patients eligible for clinical studies using case-based reasoning on Scottish Health Research register (SHARE)
Authors
Wen Shi
Tom Kelsey
Frank Sullivan
Publication date
01-12-2020
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2020
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-020-1091-6

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