Analysis of errors made on in utero MR studies of the foetal brain in the MERIDIAN study.
Objectives: In utero magnetic resonance (iuMR) imaging to diagnose foetal brain abnormalities has been established and is supported by meta-analyses of retrospective and prospective studies. In this paper we describe and classify the iuMR errors made in the largest diagnostic accuracy study to date...
| Publicado en: | European Radiology Vol. 29; no. 1; pp. 195 - 202 |
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| Autores principales: | , , , , , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=133531093&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133531093 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jan2019 vid: 29 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 133531093 133531093 NLM29948083 133531093 10.1007/s00330-018-5508-x NLM29948083 133531093 ppf: 195 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Analysis of errors made on in utero MR studies of the foetal brain in the MERIDIAN study. aug: au: Batty, Ruth Gawne-Cain, Mary L. Mooney, Cara Mandefield, Laura Bradburn, Michael Mason, Gerald Griffiths, Paul D. affil: Department of Radiology, Sheffield Teaching Hospitals Trust, Sheffield, UK sug: subj: Brain Diagnostic Errors Statistics and Numerical Data Prenatal Diagnosis Methods Algorithms Retrospective Design Pregnancy Magnetic Resonance Imaging Methods Magnetic Resonance Imaging Standards Brain Abnormalities Prospective Studies Prenatal Diagnosis Standards Female Clinical Competence Human Fetus Fetus, conception to birth Female ab: Objectives: In utero magnetic resonance (iuMR) imaging to diagnose foetal brain abnormalities has been established and is supported by meta-analyses of retrospective and prospective studies. In this paper we describe and classify the iuMR errors made in the largest diagnostic accuracy study to date (MERIDIAN). We also correlate the error rates and types with the prior experience of the reporting radiologists in order to inform how to provide a national programme with the best diagnostic accuracy achievable.Methods: The MERIDIAN cohort of 570 foetus formed the basis of this study and included 40 cases with a confirmed diagnostic error, compared with the Outcome Reference Diagnosis. Analysis included the potential clinical effect of the error and classification of error type through an Expert Neuroradiological Panel re-reporting the study. Assessments were made regarding radiologists experience prior to MERIDIAN.Results: The overall confirmed error rate for iuMR was 7·0% and it was considered that there would have been an adverse effect on prognostic information in 22/40 cases if the iuMR had informed counselling. The experienced central reporter made statistically significant fewer errors than the less experienced non-central reporters (3·8% v 11·0%) and the central reporter made fewer clinically significant errors. Furthermore, the type of cognitive errors differed between central and non-central reporters.Conclusions: Although iuMR imaging improves the diagnostic accuracy of detecting foetal brain abnormalities there remains a substantial error rate, which can have major clinical significance. We have shown that error rates are lower for more experienced reporting radiologists with fewer potential deleterious clinical implications. We discuss the implications of these findings in terms of providing a uniform national service.Key Points: • Overall confirmed error rate for iuMR diagnosing foetal brain abnormalities was 7·0%. • IuMR reports had an adverse effect on counselling in 55% of error cases. • Error rates are consistently lower for more experienced radiologists. • Collaboration between radiologists, dual reporting, overseeing scan and formal training can reduce errors. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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