Startseite Systematic review and meta-analysis of observational studies evaluating glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCHL1) as blood biomarkers of mild acute traumatic brain injury (mTBI) or sport-related concussion (SRC) in adult subjects
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

Systematic review and meta-analysis of observational studies evaluating glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCHL1) as blood biomarkers of mild acute traumatic brain injury (mTBI) or sport-related concussion (SRC) in adult subjects

  • Paolo Mastandrea ORCID logo EMAIL logo , Silvia Mengozzi und Sergio Bernardini
Veröffentlicht/Copyright: 20. August 2024
Diagnosis
Aus der Zeitschrift Diagnosis Band 12 Heft 1

Abstract

Introduction

Neurotrauma is the leading cause of death in individuals <45 years old. Many of the published articles on UCHL1 and GFAP lack rigorous methods and reporting.

Content

Due to the high heterogeneity between studies, we evaluated blood GFAP and UCHL1 levels in the same subjects. We determined the biomarker congruence among areas under the ROC curves (AUCs), sensitivities, specificities, and laboratory values in ng/L to avoid spurious results. The definitive meta-analysis included 1,880 subjects in eight studies. The items with the highest risk of bias were as follows: cut-off not prespecified and case-control design not avoided. The AUC of GFAP was greater than the AUC of UCHL1, with a lower prediction interval (PI) limit of 50.1 % for GFAP and 37.3 % for UCHL1, and a significantly greater percentage of GFAP Sp. The PI of laboratory results for GFAP and UCHL1 were 0.517–7,518 ng/L (diseased), 1.2–255 ng/L (nondiseased), and 3–4,180 vs. 3.2–1,297 ng/L, respectively.

Summary

Only the GFAP positive cut-off (255 ng/L) appears to be reliable. The negative COs appear unreliable.

Outlook

GFAP needs better standardization. However, the AUCs of the phospho-Tau and phospho-Tau/Tau proteins resulted not significantly lower than AUC of GFAP, but this result needs further verifications.

Corresponding author: Paolo Mastandrea, Department of Clinical Pathology, Azienda Ospedaliera di Rilievo Nazionale e di Alta Specialità San Giuseppe Moscati, Discesa Campanile 52, 84085 Mercato S. Severino, Salerno, Italy, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: (1) Substantial contributions to the conception, design, acquisition, analysis and interpretation of data of the work: Mastandrea, Mengozzi, Bernardini; (2) drafting of the work and revising it critically for important intellectual content: Mastandrea, Mengozzi, Bernardini; (3) final approval of the version to be published: Mastandrea, Mengozzi Bernardini; (4) agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved: Mastandrea, Mengozzi, Bernardini. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Competing interests: The authors state no conflict of interest.

  5. Research funding: None declared.

  6. Data availability: Not applicable.

References

1. Linsenmaier, U, Wirth, S, Kanz, KG, Geyer, L. Imaging minor head injury in emergency radiology: MRI highlights additional intracranial findings after measurement of trauma biomarker S-100 B in patients with normal CCT. Br J Radiol 2016;89:20150827. https://doi.org/10.1259/bjr.20150827.Suche in Google Scholar PubMed PubMed Central

2. Wang, KKW, Kobeissy, FH, Shakkour, Z, Tyndall, JA. Thorough overview of ubiquitin C-terminal hydrolase-L 1 and glial fibrillary acidic protein as tandem biomarkers recently cleared by US Food and Drug Administration for the evaluation of intracranial injuries among patients with traumatic brain injury. Acute Med Surg 2021;8:e622. https://doi.org/10.1002/ams2.622.Suche in Google Scholar PubMed PubMed Central

3. Okonkwo, DO, Puffer, RC, Puccio, AM, Yuh, EL, Yue, JK, Diaz-Arrastia, R, et al.. Point-of-care platform bòood biomarker testing of glial fibrillary acidic protein versus S100 Calcium-binding protein B for prediction of traumatic brain injuries: a transforming research and clinic knowledge in traumatic brain injury study. J Neurotrauma 2020;37:2460–67. https://doi.org/10.1089/neu.2020.7140.Suche in Google Scholar PubMed PubMed Central

4. Papa, L, Zonfrillo, M, Ramirez, J, Silvestri, S, Giordano, PH, Braga, C, et al.. Performance of glial Fibrillary acidic protein in detecting traumatic intracranial lesions on computed tomography in children and youth with mild head trauma. Acad Emerg Med 2015;22:1274–82. https://doi.org/10.1111/acem.12795.Suche in Google Scholar PubMed PubMed Central

5. McCrea, M, Broglio, SP, McAllister, TW, Gill, J, Giza, CC, Huber, DL, et al.. Association of blood biomarkers with acute sport-related concussion in collegiate athletes: findings from NCAA and Department of Defense CARE Consortium. JAMA Netw Open 2020;3:e1919771. https://doi.org/10.1001/jamanetworkopen.2019.19771.Suche in Google Scholar PubMed PubMed Central

6. Papa, L, Rosenthal, K, Cook, L, Caire, M, Thundiyl, JG, Ladde, JG, et al.. Concussion severity and functional outcome using biomarkers in children and youth involved in organized sports, recreational activities and non-sport related incidents. Brain Inj 2022;36:939–47. https://doi.org/10.1080/02699052.2022.2106383.Suche in Google Scholar PubMed

7. Nishimura, K, Cordeiro, JG, Ahmed, A, Yokobori, S, Gajavelli, S. Advances in traumatic brain injury biomarkers. Cureus 2022;14:e23804. https://doi.org/10.7759/cureus.23804.Suche in Google Scholar PubMed PubMed Central

8. Gill, J, Latour, L, Diaz-Arrastia, R, Motamedi, V, Turtzo, CH, Shahim, P, et al.. Glial fibrillary acidic protein elevations relate to neuroimaging abnormalities after mild TBI. Neurology 2018;91:e1385–9. https://doi.org/10.1212/wnl.0000000000006321.Suche in Google Scholar PubMed PubMed Central

9. Metting, Z, Rodiger, LA, De Keyser, J, van der Naalt, J. Structural and functional neuroimaging in mild-to-moderate head injury. Lancet Neurol 2007;6:699–710. https://doi.org/10.1016/s1474-4422(07)70191-6.Suche in Google Scholar PubMed

10. Papa, L, Wang, KKW. Raising the bar for traumatic brain injury biomarker research: methods make a difference. J Neurotrauma 2017;34:2187–9. https://doi.org/10.1089/neu.2017.5030.Suche in Google Scholar PubMed PubMed Central

11. Bazarian, JJ, Biberthaler, P, Welch, RD, Lewis, LM, Barzo, P, Bogner-Flatz, B, et al.. Serum GFAP and UCH-L1 for prediction of absence of intracranial injuries on head CT (ALERT-TBI): a multicentre observational study. Lancet Neurol 2018;17:782–9. https://doi.org/10.1016/s1474-4422(18)30231-x.Suche in Google Scholar

12. Stroup, DF, Berlin, JA, Morton, SC, Olkin, I, Williamson, GD, Rennie, D, et al.. Meta-analysis of observational studies in epidemiology. A proposal for reporting. MOOSE group. J Am Med Assoc 2000;283:2008–12. https://doi.org/10.1001/jama.283.15.2008.Suche in Google Scholar PubMed

13. Whiting, PF, Rutjes, AWS, Westwood, ME, Mallet, S, Deeks, JJ, Reitsma, JB, et al.. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011;155:529–36. https://doi.org/10.7326/0003-4819-155-8-201110180-00009.Suche in Google Scholar PubMed

14. Teasdale, G, Maas, A, Lecky, F, Manley, G, Stocchetti, N, Murray, G. The Glasgow coma scale at 40 years: standing the test of time. Lancet Neurol 2014;13:844–54. https://doi.org/10.1016/s1474-4422(14)70120-6.Suche in Google Scholar PubMed

15. McCrory, P, Meewisse, W, Dvorak, J, Aubry, M, Bailes, J, Broglio, S, et al.. Consensus statement on concussion in sport – the 5th International Conference on concussion in sport held in Berlin, October 2016. Br J Sports Med 2017;31:838–47.10.1136/bjsports-2017-097699Suche in Google Scholar PubMed

16. Borenstein, M, Hedges, L, Higgins, J, Rothstein, H, editors. Comprehensive meta-analysis version 4. Available from: https://www.Meta-Analysis.com/downloads/MAManual.pdf.Suche in Google Scholar

17. Borenstein, M, Hedges, L, Higgins, J, Rothstein, H, editors. Introduction to meta-analysis. Chichester: Wiley; 2009.10.1002/9780470743386Suche in Google Scholar

18. Borenstein, M. In a meta-analysis, the I-squared statistic does not tell us how much the effect size varies. J Clin Epidemiol 2022;152:281–4. https://doi.org/10.1016/j.jclinepi.2022.10.003.Suche in Google Scholar PubMed

19. IntHout, J, Ioannidis, JPA, Rovers, MM, Goeman, JJ. Plea for routinely presenting predicton intervals in meta-analysis. BMJ Open 2016;6:e010247. https://doi.org/10.1136/bmjopen-2015-010247.Suche in Google Scholar PubMed PubMed Central

20. Higgins, JPT, Li, T, Deeks, JJ. Choosing effect measures and computing estimates of effect. In: Cochrane handbook for systematic reviews of interventions, Higgins, JPT, editor. Glasgow, UK: Thomas J. Wiley; 2019.10.1002/9781119536604.ch6Suche in Google Scholar

21. Wan, X, Wang, W, Liu, J, Tong, T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol 2014;14:135. https://doi.org/10.1186/1471-2288-14-135.Suche in Google Scholar PubMed PubMed Central

22. Reitsma, JB, Glas, AS, Rutjes, AWS, Scholten, RJPM, Bossuyt, PM, Zwinderman, AH. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol 2005;58:982–90. https://doi.org/10.1016/j.jclinepi.2005.02.022.Suche in Google Scholar PubMed

23. Harbord, RM, Whiting, P. Metandi: meta-analysis of diagnostic accuracy using hierarchical logistic regression. In: Palmer, TM, Sterne, JAC, editors. Meta-analysis in Stata. Lakeway drive. College Station, Texas: Stata Press; 2016.Suche in Google Scholar

24. Wang, MC, Bushman, BJ. Conducting and reporting the results of a meta-analysis. In: Wang, MG, Bushman, BJ, editors. Integrating results through meta-analytic review using SAS software. Cary, NC, USA: SAS Institute Inc; 1999.Suche in Google Scholar

25. Diaz-Arrastia, R, Wang, KKW, Papa, L, Sorani, MD, Yue, JK, Puccio, AM, et al.. Acute biomarkers of traumatic brain injury relationship between plasma levels of ubiquitin C-terminal hydrolase-L1 and glial fibrillary acidic protein. J Neurotrauma 2014;31:19–25. https://doi.org/10.1089/neu.2013.3040.Suche in Google Scholar PubMed PubMed Central

26. Welch, RD, Ayaz, SI, Lewis, LM, Unden, J, Chen, JY, Mika, VH, et al.. Ability of serum glial fibrillary acidic protein , ubiquitin C-terminal hydrolase L1, and S100B to differentiate normal and abnormal head computed tomography findings in patients with suspected mild or moderate traumatic brain injury. J Neurotrauma 2016;33:203–16. https://doi.org/10.1089/neu.2015.4149.Suche in Google Scholar PubMed PubMed Central

27. Lewis, LM, Schloemann, DT, Papa, L, Fucetola, RP, Bazarian, J, Lindburg, M, et al.. Utility of serum biomarkers in the diagnosis and stratification of mild traumatic brain injury. Acad Emerg Med 2017;24:710–20. https://doi.org/10.1111/acem.13174.Suche in Google Scholar PubMed

28. Asken, BM, Bauer, RM, DeKosky, ST, Svingos, AM, Hromas, G, Boone, JK, et al.. Concussion basics III: serum biomarker changes following sport-related concussion. Neurology 2018;91:S2133–43. https://doi.org/10.1212/wnl.0000000000006617.Suche in Google Scholar PubMed

29. Mahan, MY, Thorpe, M, Ahmadi, A, Abdallah, T, Casey, H, Sturtevant, D, et al.. Glial fibrillary acidic protein outperforms S100 calcium-binding protein B and ubiquitin C-terminal hydrolase L1 as predictor for positive computed tomography of the head in trauma subjects. World Neurosurg 2019;128:S434–44. https://doi.org/10.1016/j.wneu.2019.04.170.Suche in Google Scholar PubMed

30. Meier, TB, Huber, DL, Bohorquez-Montoya, L, Nitta, ME, Savitz, J, Teague, TK, et al.. A prospective study of acute blood-based biomarkers for sport-related concussion. Ann Neurol 2020;87:907–20. https://doi.org/10.1002/ana.25725.Suche in Google Scholar PubMed PubMed Central

31. Asken, BM, Yang, Z, Xu, H, Weber, AG, Hayes, RL, Bauer, RM, et al.. Acute effects of sport-related concussion on serum glial fibrillary acidic protein, ubiquitin C-terminal hydrolase L1, total tau, and neurofilament light measured by a multiplex assay. J Neurotrauma 2020;37:1537–45. https://doi.org/10.1089/neu.2019.6831.Suche in Google Scholar PubMed

32. Papa, L, Ladde, JG, O’Brien, JF, Thundiyill, JG, Tesar, J, Leech, S, et al.. Evaluation of glial and neuronal blood biomarkers compared with clinical decision rules in assessing the need for computed tomography in patients with mild traumatic brain injury. JAMA Netw Open 2022;5:e221302. https://doi.org/10.1001/jamanetworkopen.2022.1302.Suche in Google Scholar PubMed PubMed Central

33. Sterne, JAC, Harbord, RM. Investigating bias in meta-analysis. In: Palmer, TM, Sterne, JAC, editors. Meta-analysis in Stata. Lakeway Drive, Texas: Stata Press.Suche in Google Scholar

34. Papa, L, Mittal, MK, Ramirez, J, Ramia, M, Kirby, S, Silvestri, S, et al.. In children and youth with mild and moderate traumatic brain injury, glial fibrillary acidic protein out-performs S100B in detecting traumatic intracranial lesions on computed tomography. J Neurotrauma 2016;33:58–64. https://doi.org/10.1089/neu.2015.3869.Suche in Google Scholar PubMed PubMed Central

35. Reitsma, JB, Rutjes, AW, Whiting, P, Yang, B, Leeflang, MM, Bossuyt, PM, et al.. Assessing risk of bias and applicability. In: Deeks, JJ, Bossuyt, PM, Leeflang, MM, Takwoingi, Y, editors. Cochrane handbook for systematic reviews of diagnostic test accuracy. Chichester, UK: Wiley; 2023.Suche in Google Scholar

36. Leeflang, MM, Moons, KG, Reitsma, JB, Zwinderman, AH. Bias in sensitivity and specificity caused by data-driven selection of optimal cut-off values: mechanisms, magnitude, and solutions. Clin Chem 2008;54:729–37. https://doi.org/10.1373/clinchem.2007.096032.Suche in Google Scholar PubMed

37. Nahm, FS. Receiver operating characteristic curve: overview and practical use for clinicians. Korean J Anesthesiol 2022:25–36. https://doi.org/10.4097/kja.21209.Suche in Google Scholar PubMed PubMed Central

38. Deeks, JJ, Takwoingi, Y, Macaskill, P, Bossuyt, PM. Understanding test accuracy measures. In: Deeks, JJ, Bossuyt, PM, Leeflang, MM, Takwoingi, Y, editors. Cochrane handbook for systematic reviews of diagnostic test accuracy. Chichester, UK: Wiley; 2023.10.1002/9781119756194Suche in Google Scholar

39. Cooper, JG, Stukas, S, Ghodsi, M, Ahmed, N, Diaz-Arrastia, R, Holmes, DT, et al.. Age specific reference intervals for plasma biomarkers of neurotrauma in a Canadian population. Clin Biochem 2023:121–2. https://doi.org/10.1016/j.clinbiochem.2023.110680. Epub 2023 Oct 24.Suche in Google Scholar PubMed

40. Amoo, M, Henry, J, O’Halloran, PJ, Brennan, P, Husien, MB, Campbell, M, et al.. S100B, GFAP, UCH-L1 and NSE as predictors of abnormalities on CT imaging following mild traumatic brain injury: a systematic review and meta-analysis of diagnostic test accuracy. Neurosurg Rev 2022;45:1171–93. https://doi.org/10.1007/s10143-021-01678-z.Suche in Google Scholar PubMed

41. Bossuyt, PM. Understanding the design of test accuracy studies. In: Deeks, JJ, Bossuyt, PM, Leeflang, MM, Takwoingi, Y, editors. Cochrane handbook for systematic reviews of diagnostic test accuracy. Chichester, UK: Wiley; 2023.10.1002/14651858.ED000163Suche in Google Scholar PubMed PubMed Central

42. Papa, L, Mittal, MK, Ramirez, J, Silvestri, S, Giordano, P, Braga, CF, et al.. Neuronal biomarker UCHL1 detects traumatic intracranial lesions on CT in children and youth with mild TBI. J Neurotrauma 2017;34:2132–40. https://doi.org/10.1089/neu.2016.4806.Suche in Google Scholar PubMed PubMed Central

43. Meier, TB, Nelson, LD, Huber, DL, Bazarian, JJ, Hayes, RL, McCrea, MA. Prospective assessment of acute blood markers of brain injury in sport-related concussion. J Neurotrauma 2017;34:3134–42. https://doi.org/10.1089/neu.2017.5046.Suche in Google Scholar PubMed PubMed Central

44. Lagares, A, Payen, JF, Biberthaler, P, Poca, MA, Méjan, O, Pavlov, V, et al.. Study protocol for investigating the performance of an automated blood test for glial fibrillary acidic protein and ubiquitin carboxy-terminal hydrolase L1 blood concentrations in elderly patients measuring GFAP and UCHL1 in elderly patients with mild traumatic brain injury and reference values (BRAINI-2 Elderly European study): a prospective multicentre observational study. BMJ Open 2023;13:e071467. https://doi.org/10.1136/bmjopen-2022-071467.Suche in Google Scholar PubMed PubMed Central

45. Wilson, L, Boase, K, Nelson, LD, Temkin, NR, Giacino, JT, Markowitz, AJ, et al.. A manual for the Glasgow outcome scale-extended interview. J Neurotrauma 2021;38:2435–46. https://doi.org/10.1089/neu.2020.7527.Suche in Google Scholar PubMed PubMed Central

46. Korley, FK, Jain, S, Sun, X, Puccio, AM, Yue, JK, Gardner, RC, et al.. Prognostic value of day-of-injury plasma GFAP and UCHL1 levels for predicting functional recovery in the TRACK-TBI cohort: an observational cohort study. Lancet Neurol 2022;21:803–13. https://doi.org/10.1016/s1474-4422(22)00256-3.Suche in Google Scholar PubMed PubMed Central

47. Dey, S, Gangadharan, J, Deepika, A, Kumar, JK, Christopher, R, Ramesh, SS, et al.. Correlation of ubiquitin C terminal hydrolase and S100B with cognitive deficits in young adults with mild traumatic brain injury. Neurol India 2017;65:761–6. https://doi.org/10.4103/neuroindia.ni_884_15.Suche in Google Scholar

48. Hier, DB, Obafemi-Ajayi, T, Thimgan, MS, Olbricht, GR, Azizi, S, Allen, B, et al.. Blood biomarkers for mild traumatic brain injury: a selective review of unresolved issues. Biomark Res 2021;9:70. https://doi.org/10.1186/s40364-021-00325-5.Suche in Google Scholar PubMed PubMed Central

49. Czeiter, E, Amrein, K, Gravesteijn, BY, Lecky, F, Menon, DK, Mondello, S, et al.. Blood biomarkers on admission in acute traumatic brain injury: relations to severity, CT findings and care path in the CENTER-TBI study. EBioMedicine 2020;56:102785. https://doi.org/10.1016/j.ebiom.2020.102785.Suche in Google Scholar PubMed PubMed Central

50. Papa, L, Brophy, GM, Welch, RD, Lewis, LM, Braga, CF, Tan, CN, et al.. Time course and diagnostic accuracy of glial and neuronal blood biomarkers GFAP and UCHL1 in a large cohort of trauma patients with and without mild traumatic brain injury. JAMA Neurol 2016;73:551–60. https://doi.org/10.1001/jamaneurol.2016.0039.Suche in Google Scholar PubMed PubMed Central

51. Papa, L, Zonfrillo, MR, Welch, RD, Lewis, LM, Braga, CF, Tau, CN, et al.. Evaluating glial and neuronal blood biomarkers GFAP and UCHL1 as gradients of brain injury in concussive, subconcussive and non-concussive trauma: a prospective cohort study. BMJ Paediatr Open 2019;3:e000473. https://doi.org/10.1136/bmjpo-2019-000473.Suche in Google Scholar PubMed PubMed Central

52. Posti, JP, Takala, RSK, Runtti, H, Newcombe, V, Outtrim, JRN, Katila, AJ, et al.. The levels of GFAP and UCHL1 during the first week after a traumatic brain injury: correlation with clinical and imaging findings. Neurosurgery 2016;79:456–74.10.1227/NEU.0000000000001226Suche in Google Scholar PubMed

53. Yue, JK, Upadhyayula, PS, Avalos, LN, Deng, H, Wang, KKW. The role of blood biomarkers for magnetic resonance imaging diagnosis of traumatic brain injury. Review. Medicina 2020;56:87. https://doi.org/10.3390/medicina56020087.Suche in Google Scholar PubMed PubMed Central

54. Rubenstein, R, Chang, B, Yue, JK, Chiu, A, Winkler, EA, Puccio, AM, et al.. Comparing plasma phospho tau, total tau and phospho tau-total tau ratio as acute and chronic traumatic brain injury biomarkers. JAMA Neurol 2017;74:1063–72. https://doi.org/10.1001/jamaneurol.2017.0655.Suche in Google Scholar PubMed PubMed Central

55. Gardner, RC, Rubenstein, R, Wang, KKW, Korley, FK, Yue, JK, Yuh, EL, et al.. Age-related differences in diagnostic accuracy of plasma glial fibrillary acidic protein and tau for identifying acute intracranial trauma on computed tomography: a TRACK-TBI study. J Neurotrauma 2018;35:2341–50. https://doi.org/10.1089/neu.2018.5694.Suche in Google Scholar PubMed PubMed Central

56. Macaskill, P, Takwoingi, Y, Deeks, JJ, Gatsonis, C. Understanding meta-analysis. In: Deeks, JJ, Bossuyt, PM, Leeflang, MM, Takwoingi, Y, editors. Cochrane handbook for systematic reviews of diagnostic test accuracy. Chichester, UK: Wiley; 2023.10.1002/9781119756194.ch9Suche in Google Scholar

57. Devoto, CH, Vorn, R, Mithani, S, Meier, TB, Lai, CH, Broglio, SP, et al.. Plasma phosphorylated Tau-181 as a biomarker of mild TBI: findings from THINC and NCAA-DoD CARE Consortium prospective cohorts. Front Neurol 2023;14:1202967. https://doi.org/10.3389/fneur.2023.1202967.Suche in Google Scholar PubMed PubMed Central

Received: 2024-04-23
Accepted: 2024-07-14
Published Online: 2024-08-20

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Review
  3. Systematic review and meta-analysis of observational studies evaluating glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCHL1) as blood biomarkers of mild acute traumatic brain injury (mTBI) or sport-related concussion (SRC) in adult subjects
  4. Opinion Papers
  5. From stable teamwork to dynamic teaming in the ambulatory care diagnostic process
  6. Bringing team science to the ambulatory diagnostic process: how do patients and clinicians develop shared mental models?
  7. Vitamin D assay and supplementation: still debatable issues
  8. Original Articles
  9. Developing a framework for understanding diagnostic reconciliation based on evidence review, stakeholder engagement, and practice evaluation
  10. Validity and reliability of Brier scoring for assessment of probabilistic diagnostic reasoning
  11. Impact of disclosing a working diagnosis during simulated patient handoff presentation in the emergency department: correctness matters
  12. Implementation of a bundle to improve diagnosis in hospitalized patients: lessons learned
  13. Time pressure in diagnosing written clinical cases: an experimental study on time constraints and perceived time pressure
  14. A decision support system to increase the compliance of diagnostic imaging examinations with imaging guidelines: focused on cerebrovascular diseases
  15. Bridging the divide: addressing discrepancies between clinical guidelines, policy guidelines, and biomarker utilization
  16. Unnecessary repetitions of C-reactive protein and leukocyte count at the emergency department observation unit contribute to higher hospital admission rates
  17. Quality control of ultrasonography markers for Down’s syndrome screening: a retrospective study by the laboratory
  18. Short Communications
  19. Unclassified green dots on nucleated red blood cells (nRBC) plot in DxH900 from a patient with hyperviscosity syndrome
  20. Bayesian intelligence for medical diagnosis: a pilot study on patient disposition for emergency medicine chest pain
  21. Case Report – Lessons in Clinical Reasoning
  22. A delayed diagnosis of hyperthyroidism in a patient with persistent vomiting in the presence of Chiari type 1 malformation
  23. Letters to the Editor
  24. Mpox (monkeypox) diagnostic kits – September 2024
  25. Barriers to diagnostic error reduction in Japan
  26. Superwarfarin poisoning: a challenging diagnosis
  27. Reviewer Acknowledgment
  28. Reviewer Acknowledgment
https://doi.org/10.1515/dx-2024-0078

Artikel in diesem Heft

  1. Frontmatter
  2. Review
  3. Systematic review and meta-analysis of observational studies evaluating glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCHL1) as blood biomarkers of mild acute traumatic brain injury (mTBI) or sport-related concussion (SRC) in adult subjects
  4. Opinion Papers
  5. From stable teamwork to dynamic teaming in the ambulatory care diagnostic process
  6. Bringing team science to the ambulatory diagnostic process: how do patients and clinicians develop shared mental models?
  7. Vitamin D assay and supplementation: still debatable issues
  8. Original Articles
  9. Developing a framework for understanding diagnostic reconciliation based on evidence review, stakeholder engagement, and practice evaluation
  10. Validity and reliability of Brier scoring for assessment of probabilistic diagnostic reasoning
  11. Impact of disclosing a working diagnosis during simulated patient handoff presentation in the emergency department: correctness matters
  12. Implementation of a bundle to improve diagnosis in hospitalized patients: lessons learned
  13. Time pressure in diagnosing written clinical cases: an experimental study on time constraints and perceived time pressure
  14. A decision support system to increase the compliance of diagnostic imaging examinations with imaging guidelines: focused on cerebrovascular diseases
  15. Bridging the divide: addressing discrepancies between clinical guidelines, policy guidelines, and biomarker utilization
  16. Unnecessary repetitions of C-reactive protein and leukocyte count at the emergency department observation unit contribute to higher hospital admission rates
  17. Quality control of ultrasonography markers for Down’s syndrome screening: a retrospective study by the laboratory
  18. Short Communications
  19. Unclassified green dots on nucleated red blood cells (nRBC) plot in DxH900 from a patient with hyperviscosity syndrome
  20. Bayesian intelligence for medical diagnosis: a pilot study on patient disposition for emergency medicine chest pain
  21. Case Report – Lessons in Clinical Reasoning
  22. A delayed diagnosis of hyperthyroidism in a patient with persistent vomiting in the presence of Chiari type 1 malformation
  23. Letters to the Editor
  24. Mpox (monkeypox) diagnostic kits – September 2024
  25. Barriers to diagnostic error reduction in Japan
  26. Superwarfarin poisoning: a challenging diagnosis
  27. Reviewer Acknowledgment
  28. Reviewer Acknowledgment
Jetzt anmelden und 20% sparen
Institutioneller Zugang
Wie funktioniert Authentifizierung?
Haben Sie eine Idee, wie wir unsere Website verbessern können?
Bitte schreiben Sie uns.
© 2025 De Gruyter Brill
Heruntergeladen am 22.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/dx-2024-0078/html
Button zum nach oben scrollen