Top

Published in:

18-04-2022 | Magnetic Resonance Imaging | Emergency Radiology

Evaluation of neuroradiology emergency MRI interpretations: low discrepancy rates between on-call radiology residents’ preliminary interpretations and neuroradiologists’ final reports

Authors: Diana Salca, François Lersy, Thibault Willaume, Marie Stoessel, Agnieszka Lefèvre, François-Daniel Ardellier, Caroline Nicolaï, Abtine Nouri, Seyyid Baloglu, Guillaume Bierry, Agathe Chammas, Stéphane Kremer

Published in: European Radiology | Issue 10/2022

Abstract

Objectives

To evaluate the performance of on-call radiology residents in interpreting alone brain and spine MRI studies performed after hours, to describe their mistakes, and to identify influencing factors that increased the occurrence of errors.

Methods

A total of 328 MRI examinations performed during a 13-month period (from December 1, 2019, to January 1, 2021) were prospectively included. Discrepancies between the preliminary interpretation of on-call radiology residents and the final reports of attending neuroradiologists were noted and classified according to a three-level score: level 1 (perfect interpretation or minor correction), level 2 (important correction without immediate change in patient management), or level 3 (major correction with immediate change in patient management). Categorical data were compared using Fisher’s exact test.

Results

The overall discrepancy rate (level-2 and level-3 errors) was 16%; the rate of major discrepancies (only level-3 errors) was 5.5%. The major-discrepancy rate of second-year residents, when compared with that of senior residents, was significantly higher (p = 0.02). Almost all of the level-3 errors concerned cerebrovascular pathology. The most common level-2 errors involved undescribed aneurysms. We found no significant difference in the major-discrepancy rate regarding time since the beginning of the shift.

Conclusions

The great majority of examinations were correctly interpreted. The rate of major discrepancies in our study was comparable to the data in the literature, and there was no adverse clinical outcome. The level of residency has an effect on the rate of serious errors in residents’ reports.

Key Points

• The rate of major discrepancies between preliminary MRI interpretations by on-call radiology residents and final reports by attending neuroradiologists is low, and comparable to discrepancy rates reported for head CT interpretations.

• The youngest residents made significantly more serious errors when compared to senior residents.

• There was no adverse clinical outcome in patient morbidity as a result of an initial misdiagnosis.

Wildman-Tobriner B, Allen BC, Maxfield CM (2019) Common resident errors when interpreting computed tomography of the abdomen and pelvis: a review of types, pitfalls, and strategies for improvement. Curr Probl Diagn Radiol 48(1):4–9. https://doi.org/10.1067/j.cpradiol.2017.12.010CrossRefPubMed

Yaniv G, Mozes O, Greenberg G, Bakon M, Hoffmann C (2013) Common sites and etiologies of residents’ misinterpretation of head CT scans in the emergency department of a level I trauma center. Isr Med Assoc J 15(5):221–225

Filippi CG, Schneider B, Burbank HN, Alsofrom GF, Linnell G, Ratkovits B (2008) Discrepancy rates of radiology resident interpretations of on-call neuroradiology MR imaging studies. Radiology. 249(3):972–979. https://doi.org/10.1148/radiol.2493071543CrossRefPubMed

Cooper VF, Goodhartz LA, Nemcek AA, Ryu RK (2008) Radiology resident interpretations of on-call imaging studies. Acad Radiol 15(9):1198–1204. https://doi.org/10.1016/j.acra.2008.02.011CrossRefPubMed

Mellnick V, Raptis C, McWilliams S, Picus D, Wahl R (2016) On-call radiology resident discrepancies: categorization by patient location and severity. J Am Coll Radiol 13(10):1233–1238. https://doi.org/10.1016/j.jacr.2016.04.020CrossRefPubMed

Ruchman RB, Jaeger J, Wiggins EF et al (2007) Preliminary radiology resident interpretations versus final attending radiologist interpretations and the impact on patient care in a community hospital. AJR Am J Roentgenol 189(3):523–526. https://doi.org/10.2214/AJR.07.2307

Stevens KJ, Griffiths KL, Rosenberg J, Mahadevan S, Zatz LM, Leung ANC (2008) Discordance rates between preliminary and final radiology reports on cross-sectional imaging studies at a level 1 trauma center. Academic Radiology. 15(10):1217–1226. https://doi.org/10.1016/j.acra.2008.03.017CrossRefPubMed

Weinberg BD, Richter MD, Champine JG, Morriss MC, Browning T (2015) Radiology Resident preliminary reporting in an independent call environment: multiyear assessment of volume, timeliness, and accuracy. Journal of the American College of Radiology. janv 12(1):95–100. https://doi.org/10.1016/j.jacr.2014.08.005CrossRef

Walls J, Hunter N, Brasher PMA, Ho SGF (2009) The DePICTORS Study: discrepancies in preliminary interpretation of CT scans between on-call residents and staff. Emerg Radiol 16(4):303–308. https://doi.org/10.1007/s10140-009-0795-9CrossRefPubMed

10.

Erly WK, Berger WG, Krupinski E, Seeger JF, Guisto JA (2002) Radiology resident evaluation of head CT scan orders in the emergency department. AJNR Am J Neuroradiol 23(1):103–107

11.

Lal NR, Murray UM, Eldevik OP, Desmond JS (2000) Clinical consequences of misinterpretations of neuroradiologic CT scans by on-call radiology residents. AJNR Am J Neuroradiol 21(1):124–129

12.

Filippi CG, Meyer RE, Cauley K et al (2010) The misinterpretation rates of radiology residents on emergent neuroradiology magnetic resonance (MR) angiogram studies: correlation with level of residency training. Emerg Radiol 17(1):45–50. https://doi.org/10.1007/s10140-009-0820-z

13.

Ruma J, Klein KA, Chong S et al (2011) Cross-sectional examination interpretation discrepancies between on-call diagnostic radiology residents and subspecialty faculty radiologists: analysis by imaging modality and subspecialty. J Am Coll Radiol 8(6):409–414. https://doi.org/10.1016/j.jacr.2011.01.012CrossRefPubMed

14.

Tieng N, Grinberg D, Li SF (2007) Discrepancies in interpretation of ED body computed tomographic scans by radiology residents. Am J Emerg Med 25(1):45–48. https://doi.org/10.1016/j.ajem.2006.04.008CrossRefPubMed

15.

Seltzer S, Hessel S, Herman P, Swensson R, Sheriff C (1981) Resident film interpretations and staff review. AJR Am J Roentgenol 137(1):129–133. https://doi.org/10.2214/ajr.137.1.129

16.

Goradia D, Blackmore CC, Talner LB, Bittles M, Meshberg E (2005) Predicting radiology resident errors in diagnosis of cervical spine fractures. Academic Radiology. 12(7):6. https://doi.org/10.1016/j.acra.2005.04.004CrossRef

17.

Caranci F, Tedeschi E, Leone G et al (2015) Errors in neuroradiology. Radiol Med 120(9):795–801. https://doi.org/10.1007/s11547-015-0564-7

18.

Carney E, Kempf J, DeCarvalho V, Yudd A, Nosher J (2003) Preliminary interpretations of after-hours CT and sonography by radiology residents versus final interpretations by body imaging radiologists at a level 1 trauma center. AJR Am J Roentgenol 181(2):367–373. https://doi.org/10.2214/ajr.181.2.1810367

19.

Patel AG, Pizzitola VJ, Johnson CD, Zhang N, Patel MD (2020) Radiologists make more errors interpreting off-hours body CT studies during overnight assignments as compared with daytime assignments. Radiology 297(2):374–379. https://doi.org/10.1148/radiol.2020201558CrossRefPubMed

20.

Verdoorn JT, Hunt CH, Luetmer MT et al (2014) Increasing neuroradiology exam volumes on-call do not result in increased major discrepancies in primary reads performed by residents. Open Neuroimag J 8(1):11–15. https://doi.org/10.2174/1874440001408010011

21.

Lee CS, Nagy PG, Weaver SJ, Newman-Toker DE (2013) Cognitive and system factors contributing to diagnostic errors in radiology. AJR Am J Roentgenol 201(3):611–617. https://doi.org/10.2214/AJR.12.10375

22.

Maskell G (2019) Error in radiology—where are we now? BJR 92(1096):20180845. https://doi.org/10.1259/bjr.20180845CrossRefPubMed

23.

Elliott J, Williamson K (2020) The radiology impact of healthcare errors during shift work. Radiography 26(3):248–253. https://doi.org/10.1016/j.radi.2019.12.007CrossRefPubMed

24.

Krupinski EA, Berbaum KS, Caldwell RT, Schartz KM, Kim J (2010) Long radiology workdays reduce detection and accommodation accuracy. J Am Coll Radiol 7(9):698–704. https://doi.org/10.1016/j.jacr.2010.03.004CrossRefPubMedPubMedCentral

25.

Gaba DM, Howard SK (2002) Fatigue among clinicians and the safety of patients. N Engl J Med 347(16):1249–1255. https://doi.org/10.1056/NEJMsa020846CrossRefPubMed

26.

Itri JN, Kim W, Scanlon MH (2011) Orion: A web-based application designed to monitor resident and fellow performance on-call. J Digit Imaging 24(5):897–907. https://doi.org/10.1007/s10278-011-9360-7CrossRefPubMedPubMedCentral

Title: Evaluation of neuroradiology emergency MRI interpretations: low discrepancy rates between on-call radiology residents’ preliminary interpretations and neuroradiologists’ final reports
Authors: Diana Salca
François Lersy
Thibault Willaume
Marie Stoessel
Agnieszka Lefèvre
François-Daniel Ardellier
Caroline Nicolaï
Abtine Nouri
Seyyid Baloglu
Guillaume Bierry
Agathe Chammas
Stéphane Kremer
Publication date: 18-04-2022
Publisher: Springer Berlin Heidelberg
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Magnetic Resonance Imaging of the Spine
Published in: European Radiology / Issue 10/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-022-08789-1

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Evaluation of neuroradiology emergency MRI interpretations: low discrepancy rates between on-call radiology residents’ preliminary interpretations and neuroradiologists’ final reports

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Please log in to get access to this content

Other articles of this Issue 10/2022

ASPECTS-based net water uptake predicts poor reperfusion and poor clinical outcomes in patients with ischemic stroke

Preoperative prediction of inadvertent enterotomy during adhesive small bowel obstruction surgery using combination of CT features

Contrast-enhanced CT radiomics for prediction of recurrence-free survival in gallbladder carcinoma after surgical resection

A deep learning model combining multimodal radiomics, clinical and imaging features for differentiating ocular adnexal lymphoma from idiopathic orbital inflammation

Puncture accuracy of an optical tracked robotic aiming device—a phantom study

Systematic review and meta-analysis of MRI features for differentiating autoimmune pancreatitis from pancreatic adenocarcinoma