Estimation of Patient Accrual Rates in Clinical Trials Based on Routine Data from Hospital Information Systems

Summary

Background: Delayed patient recruitment is a common problem in clinical trials. According to the literature, only about a third of medical research studies recruit their planned number of patients within the time originally specified.

Objectives: To provide a method to estimate patient accrual rates in clinical trials based on routine data from hospital information systems (HIS).

Methods: Based on inclusion and exclusion criteria for each trial, a specific HIS report is generated to list potential trial subjects. Because not all information relevant for assessment of patient eligibility is available as coded HIS items, a sample of this patient list is reviewed manually by study physicians. Proportions of matching and non-matching patients are analyzed with a Chi-squared test. An estimation formula for patient accrual rate is derived from this data.

Results: The method is demonstrated with two datasets from cardiology and oncology. HIS reports should account for previous disease episodes and eliminate duplicate persons.

Conclusion: HIS data in combination with manual chart review can be applied to estimate patient recruitment for clinical trials.

Keywords

Patient accrual rate - hospital information system - clinical trial

• References

• 1 Charlson ME, Horwitz RI. Applying results of randomised trials to clinical practice: impact of losses before randomisation. Br Med J (Clin Res Ed) 1984; 289 6454 1281-1284.
• 2 Campbell MK, Snowdon C, Francis D, Elbourne D, McDonald AM, Knight R, Entwistle V, Garcia J, Roberts I, Grant A. Recruitment to randomised trials: strategies for trial enrolment and participation study. The STEPS study. Health Technol Assess. 2007 11 (48).
• 3 Mapstone J, Elbourne D. Roberts. Strategies to improve recruitment to research studies (Review). Cochrane Database Syst Rev 2007; 2: MR000013.
• 4 Dugas M, Lange M, Berdel WE, Müller-Tidow C. Workflow to improve patient recruitment for clinical trials within hospital information systems – a case-study. Trials 2008; 9: 2.
• 5 R: A language and environment for statistical computing. http://www.R-project.org.
• 6 Clopper CJ, Pearson ES. The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 1934; 26: 404-413.
• 7 Kirchhof P, Auricchio A, Bax J, Crijns H, Camm J, Diener HC, Goette A, Hindricks G, Hohnloser S, Kappenberger L, Kuck KH, Lip GY, Olsson B, Meinertz T, Priori S, Ravens U, Steinbeck G, Svernhage E, Tijssen J, Vincent A, Breithardt G. Outcome parameters for trials in atrial fibrillation: executive summary. Eur Heart J 2007; 28 (22) 2803-2817.
• 8 Büchner T, Hiddemann W, Berdel WE, Wörmann B, Schoch C, Fonatsch C, Löffler H, Haferlach T, Ludwig WD, Maschmeyer G, Staib P, Aul C, Gruneisen A, Lengfelder E, Frickhofen N, Kern W, Serve HL, Mesters RM, Sauerland MC, Heinecke A. German AML Cooperative Group.. 6-Thioguanine, cytarabine, and daunorubicin (TAD) and high-dose cytarabine and mitoxantrone (HAM) for induction, TAD for consolidation, and either prolonged maintenance by reduced monthly TAD or TAD-HAM-TAD and one course of intensive consolidation by sequential HAM in adult patients at all ages with de novo acute myeloid leukemia (AML): a randomized trial of the German AML Cooperative Group. J Clin Oncol 2003; 21 (24) 4496-4504.
• 9 Büchner T, Berdel WE, Schoch C, Haferlach T, Serve HL, Kienast J, Schnittger S, Kern W, Tchinda J, Reichle A, Lengfelder E, Staib P, Ludwig WD, Aul C, Eimermacher H, Balleisen L, Sauerland MC, Heinecke A, Wörmann B. Hiddemann.. Double induction containing either two courses or one course of high-dose cytarabine plus mitoxantrone and post-remission therapy by either autologous stem-cell transplantation or by prolonged maintenance for acute myeloid leukemia. J Clin Oncol 2006; 24 (16) 2480-2489.
• 10 Lasagna L. Problems in publication of clinical trial methodology. Clin Pharmacol Ther 1979; 25 5 Pt 2 751-753.
• 11 Collins JF, Williford WO, Weiss DG, Bingham SF, Klett C. Planning patient recruitment: fantasy and reality. Stat Med 1984; 3 (04) 435-443.
• 12 Korn EL, Simon R. Data monitoring committees and problems of lower-than-expected accrual or events rates. Control Clin Trials 1996; 17 (06) 526-535.
• 13 Carter RE, Sonne SC, Brady KT. Practical considerations for estimating clinical trial accrual periods: application to a multi-center effectiveness study. BMC Medical Research Methodology 2005; 5: 11.
• 14 Dorda W, Gall W, Duftschmid G. Clinical data retrieval: 25 years of temporal query management at the University of Vienna Medical School. Methods Inf Med 2002; 41 (02) 89-97.
• 15 Williams JG, Cheung WY, Cohen DR, Hutchings HA, Longo MF, Russell IT. Can randomised trials rely on existing electronic data? A feasibility study to explore the value of routine data in health technology assessment. Health Technol Assess 2003; 7 (26) iii v-x, 1-117.
• 16 Kush R, Alschuler L, Ruggeri R, Cassells S, Gupta N, Bain L, Claise K, Shah M, Nahm M. Implementing Single Source: the STARBRITE proof-of-concept study. J Am Med Inform Assoc 2007; 14 (05) 662-673.