Classifying the diagnosis of study participants in clinical trials: a structured and efficient approach.

Autor: van Engelen TSR; Department of Internal Medicine, Center for Experimental and Molecular Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Room: T1-0-240, Amsterdam, The Netherlands. t.s.vanengelen@amsterdamumc.nl., Kanglie MMNP; Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands., van den Berk IAH; Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands., Bouwman MLJ; Department of Internal Medicine, Center for Experimental and Molecular Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Room: T1-0-240, Amsterdam, The Netherlands., Suhooli HJM; Department of Internal Medicine, Center for Experimental and Molecular Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Room: T1-0-240, Amsterdam, The Netherlands., Heckert SL; Department of Internal Medicine, Center for Experimental and Molecular Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Room: T1-0-240, Amsterdam, The Netherlands., Stoker J; Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands., Bossuyt PMM; Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands., Prins JM; Department of Internal Medicine, Division of Infectious Diseases, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
Jazyk: angličtina
Zdroj: European radiology experimental [Eur Radiol Exp] 2020 Jul 17; Vol. 4 (1), pp. 44. Date of Electronic Publication: 2020 Jul 17.
DOI: 10.1186/s41747-020-00169-y
Abstrakt: Background: A challenge in imaging research is a diagnostic classification of study participants. We hypothesised that a structured approach would be efficient and that classification by medical students, residents, and an expert panel whenever necessary would be as valid as classification of all patients by experts.
Methods: OPTIMACT is a randomised trial designed to evaluate the effectiveness of replacing chest x-ray for ultra-low-dose chest computed tomography (CT) at the emergency department. We developed a handbook with diagnostic guidelines and randomly selected 240 cases from 2,418 participants enrolled in OPTIMACT. Each case was independently classified by two medical students and, if they disagreed, by the students and a resident in a consensus meeting. Cases without consensus and cases classified as complex were assessed by a panel of medical specialists. To evaluate the validity, 60 randomly selected cases not referred to the panel by the students and the residents were reassessed by the specialists.
Results: Overall, the students and, if necessary, residents were able to assign a diagnosis in 183 of the 240 cases (76% concordance; 95% confidence interval [CI] 71-82%). We observed agreement between students and residents versus medical specialists in 50/60 cases (83% concordance; 95% CI 74-93%).
Conclusions: A structured approach in which study participants are assigned diagnostic labels by assessors with increasing levels of medical experience was an efficient and valid classification method, limiting the workload for medical specialists. We presented a viable option for classifying study participants in large-scale imaging trials (Netherlands National Trial Register number NTR6163).
Databáze: MEDLINE
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