Selection of memory clinic patients for CSF biomarker assessment can be restricted to a quarter of cases by using computerized decision support, without compromising diagnostic accuracy


Autoři: Hanneke F. M. Rhodius-Meester aff001;  Ingrid S. van Maurik aff001;  Juha Koikkalainen aff004;  Antti Tolonen aff005;  Kristian S. Frederiksen aff006;  Steen G. Hasselbalch aff006;  Hilkka Soininen aff007;  Sanna-Kaisa Herukka aff007;  Anne M. Remes aff007;  Charlotte E. Teunissen aff010;  Frederik Barkhof aff011;  Yolande A. L. Pijnenburg aff001;  Philip Scheltens aff001;  Jyrki Lötjönen aff004;  Wiesje M. van der Flier aff001
Působiště autorů: Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands aff001;  Department of Internal Medicine, Geriatric Medicine section, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands aff002;  Department of Epidemiology and Biostatistics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands aff003;  Combinostics Ltd., Tampere, Finland aff004;  VTT Technical Research Centre of Finland Ltd., Tampere, Finland aff005;  Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark aff006;  Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland aff007;  Department of Research Neurology, Unit of Clinical Neuroscience, University of Oulu, Oulu, Finland aff008;  MRC Oulu, Oulu University Hospital, Oulu, Finland aff009;  Neurochemistry Lab and Biobank, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, the Netherlands aff010;  Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, the Netherlands aff011;  Institutes of Neurology and Healthcare Engineering, UCL, London, England, United Kingdom aff012
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226784

Souhrn

Introduction

An accurate and timely diagnosis for Alzheimer’s disease (AD) is important, both for care and research. The current diagnostic criteria allow the use of CSF biomarkers to provide pathophysiological support for the diagnosis of AD. How these criteria should be operationalized by clinicians is unclear. Tools that guide in selecting patients in which CSF biomarkers have clinical utility are needed. We evaluated computerized decision support to select patients for CSF biomarker determination.

Methods

We included 535 subjects (139 controls, 286 Alzheimer’s disease dementia, 82 frontotemporal dementia and 28 vascular dementia) from three clinical cohorts. Positive (AD like) and negative (normal) CSF biomarker profiles were simulated to estimate whether knowledge of CSF biomarkers would impact (confidence in) diagnosis. We applied these simulated CSF values and combined them with demographic, neuropsychology and MRI data to initiate CSF testing (computerized decision support approach). We compared proportion of CSF measurements and patients diagnosed with sufficient confidence (probability of correct class ≥0.80) based on an algorithm with scenarios without CSF (only neuropsychology, MRI and APOE), CSF according to the appropriate use criteria (AUC) and CSF for all patients.

Results

The computerized decision support approach recommended CSF testing in 140 (26%) patients, which yielded a diagnosis with sufficient confidence in 379 (71%) of all patients. This approach was more efficient than CSF in none (0% CSF, 308 (58%) diagnosed), CSF selected based on AUC (295 (55%) CSF, 350 (65%) diagnosed) or CSF in all (100% CSF, 348 (65%) diagnosed).

Conclusions

We used a computerized decision support with simulated CSF results in controls and patients with different types of dementia. This approach can support clinicians in making a balanced decision in ordering additional biomarker testing. Computer-supported prediction restricts CSF testing to only 26% of cases, without compromising diagnostic accuracy.

Klíčová slova:

Alzheimer's disease – Biomarkers – Cerebrospinal fluid – Diagnostic medicine – Magnetic resonance imaging – Neuropsychology – Vascular dementia – Frontotemporal dementia


Zdroje

1. 2016 Alzheimer's disease facts and figures. Alzheimers Dement. 2016;12(4):459–509. Epub 2016/08/30. 27570871.

2. Jack CR Jr., Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimers Dement. 2018;14(4):535–62. Epub 2018/04/15. doi: 10.1016/j.jalz.2018.02.018 29653606.

3. Mattsson N, Lonneborg A, Boccardi M, Blennow K, Hansson O. Clinical validity of cerebrospinal fluid Abeta42, tau, and phospho-tau as biomarkers for Alzheimer's disease in the context of a structured 5-phase development framework. Neurobiol Aging. 2017;52:196–213. Epub 2017/03/21. doi: 10.1016/j.neurobiolaging.2016.02.034 28317649.

4. McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr., Kawas CH, et al. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011;7(3):263–9. S1552-5260(11)00101-4 [pii]; doi: 10.1016/j.jalz.2011.03.005 21514250

5. Frisoni GB, Boccardi M, Barkhof F, Blennow K, Cappa S, Chiotis K, et al. Strategic roadmap for an early diagnosis of Alzheimer's disease based on biomarkers. Lancet Neurol. 2017;16(8):661–76. Epub 2017/07/20. doi: 10.1016/S1474-4422(17)30159-X 28721928.

6. Shaw LM, Arias J, Blennow K, Galasko D, Molinuevo JL, Salloway S, et al. Appropriate use criteria for lumbar puncture and cerebrospinal fluid testing in the diagnosis of Alzheimer's disease. Alzheimers Dement. 2018;14(11):1505–21. Epub 2018/10/15. doi: 10.1016/j.jalz.2018.07.220 30316776.

7. Shortliffe EH, Sepulveda MJ. Clinical Decision Support in the Era of Artificial Intelligence. Jama. 2018;320(21):2199–200. Epub 2018/11/07. doi: 10.1001/jama.2018.17163 30398550.

8. Mattila J, Koikkalainen J, Virkki A, Simonsen A, van GM, Waldemar G, et al. A disease state fingerprint for evaluation of Alzheimer's disease. J Alzheimers Dis. 2011;27(1):163–76. KG54325631131N10 [pii]; doi: 10.3233/JAD-2011-110365 21799247

9. Koikkalainen J, Rhodius-Meester H, Tolonen A, Barkhof F, Tijms B, Lemstra AW, et al. Differential diagnosis of neurodegenerative diseases using structural MRI data. Neuroimage Clin. 2016;11:435–49. doi: 10.1016/j.nicl.2016.02.019 S2213-1582(16)30040-7 [pii]. 27104138

10. Tolonen A, F. M. Rhodius-Meester H, Bruun M, Koikkalainen J, Barkhof F, Lemstra A, et al. Data-Driven Differential Diagnosis of Dementia Using Multiclass Disease State Index Classifier. Front Aging Neurosci. 2018;10:111. doi: 10.3389/fnagi.2018.00111 29922145

11. Bruun M, Rhodius-Meester HFM, Koikkalainen J, Baroni M, Gjerum L, Lemstra AW, et al. Evaluating combinations of diagnostic tests to discriminate different dementia types. Alzheimers Dement (Amst). 2018;10:509–18. Epub 2018/10/16. doi: 10.1016/j.dadm.2018.07.003 30320203; PubMed Central PMCID: PMC6180596.

12. Rhodius-Meester HFM, Liedes H, Koikkalainen J, Wolfsgruber S, Coll-Padros N, Kornhuber J, et al. Computer-assisted prediction of clinical progression in the earliest stages of AD. Alzheimers Dement (Amst). 2018;10:726–36. Epub 2019/01/09. doi: 10.1016/j.dadm.2018.09.001 30619929; PubMed Central PMCID: PMC6310913.

13. Bruun M, Frederiksen KS, Rhodius-Meester HFM, Baroni M, Gjerum L, Koikkalainen J, et al. Impact of a Clinical Decision Support Tool on Dementia Diagnostics in Memory Clinics: The PredictND Validation Study. Current Alzheimer research. 2019;16(2):91–101. Epub 2019/01/04. doi: 10.2174/1567205016666190103152425 30605060.

14. van der Flier WM, Pijnenburg YA, Prins N, Lemstra AW, Bouwman FH, Teunissen CE, et al. Optimizing patient care and research: the Amsterdam Dementia Cohort. J Alzheimers Dis. 2014;41(1):313–27. LR61J34616453267 [pii]; doi: 10.3233/JAD-132306 24614907

15. van der Flier WM, Scheltens P. Amsterdam Dementia Cohort: Performing Research to Optimize Care. J Alzheimers Dis. 2018;62(3):1091–111. Epub 2018/03/23. doi: 10.3233/JAD-170850 29562540.

16. Rascovsky K, Hodges JR, Knopman D, Mendez MF, Kramer JH, Neuhaus J, et al. Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain. 2011;134(Pt 9):2456–77. awr179 [pii]; doi: 10.1093/brain/awr179 21810890

17. Gorno-Tempini ML, Hillis AE, Weintraub S, Kertesz A, Mendez M, Cappa SF, et al. Classification of primary progressive aphasia and its variants. Neurology. 2011;76(11):1006–14. WNL.0b013e31821103e6 [pii]; doi: 10.1212/WNL.0b013e31821103e6 21325651

18. Roman GC, Tatemichi TK, Erkinjuntti T, Cummings JL, Masdeu JC, Garcia JH, et al. Vascular dementia: diagnostic criteria for research studies. Report of the NINDS-AIREN International Workshop. Neurology. 1993;43(2):250–60. doi: 10.1212/wnl.43.2.250 8094895

19. Folstein MF, Folstein SE, McHugh PR. "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–98. 0022-3956(75)90026-6 [pii]. doi: 10.1016/0022-3956(75)90026-6 1202204

20. Schmand B, Houx P, de Koning I. Normen van psychologische tests voor gebruik in de klinische neuropsychologie (in Dutch). Nederlands Instituut van Psychologen. 2012;www.psynip.nl/website/sectoren-en-secties/sector-gezondheidszorg/neuropsychologie.

21. Reitan R. Validity of the Trail Making Test as an indicator of organic brain damage. Percept Mot Skills. 1958;8:271–6.

22. Van der Elst W, Van Boxtel MP, Van Breukelen GJ, Jolles J. Normative data for the Animal, Profession and Letter M Naming verbal fluency tests for Dutch speaking participants and the effects of age, education, and sex. J Int Neuropsychol Soc. 2006;12(1):80–9. S1355617706060115 [pii]; doi: 10.1017/S1355617706060115 16433947

23. Cummings JL, Mega M, Gray K, Rosenberg-Thompson S, Carusi DA, Gornbein J. The Neuropsychiatric Inventory: comprehensive assessment of psychopathology in dementia. Neurology. 1994;44(12):2308–14. doi: 10.1212/wnl.44.12.2308 7991117

24. Scheltens P, Leys D, Barkhof F, Huglo D, Weinstein HC, Vermersch P, et al. Atrophy of medial temporal lobes on MRI in "probable" Alzheimer's disease and normal ageing: diagnostic value and neuropsychological correlates. J Neurol Neurosurg Psychiatry. 1992;55(10):967–72. doi: 10.1136/jnnp.55.10.967 1431963

25. Koikkalainen JR, Rhodius-Meester HFM, Frederiksen KS, Bruun M, Hasselbalch SG, Baroni M, et al. Automatically computed rating scales from MRI for patients with cognitive disorders. European radiology. 2019. Epub 2019/02/24. doi: 10.1007/s00330-019-06067-1 30796570.

26. Lotjonen JM, Wolz R, Koikkalainen JR, Thurfjell L, Waldemar G, Soininen H, et al. Fast and robust multi-atlas segmentation of brain magnetic resonance images. Neuroimage. 2010;49(3):2352–65. Epub 2009/10/28. doi: 10.1016/j.neuroimage.2009.10.026 19857578.

27. Ashburner J, Friston KJ. Voxel-based morphometry—the methods. Neuroimage. 2000;11(6 Pt 1):805–21. Epub 2000/06/22. doi: 10.1006/nimg.2000.0582 10860804.

28. Vos SJ, Visser PJ, Verhey F, Aalten P, Knol D, Ramakers I, et al. Variability of CSF Alzheimer's disease biomarkers: implications for clinical practice. PLoS One. 2014;9(6):e100784. doi: 10.1371/journal.pone.0100784 PONE-D-14-16936 [pii]. 24959687

29. Bruun M, Koikkalainen J, Rhodius-Meester HFM, Baroni M, Gjerum L, van Gils M, et al. Detecting frontotemporal dementia syndromes using MRI biomarkers. NeuroImage Clinical. 2019;22:101711. Epub 2019/02/12. doi: 10.1016/j.nicl.2019.101711 30743135; PubMed Central PMCID: PMC6369219.

30. Wang Y, Catindig JA, Hilal S, Soon HW, Ting E, Wong TY, et al. Multi-stage segmentation of white matter hyperintensity, cortical and lacunar infarcts. Neuroimage. 2012;60(4):2379–88. Epub 2012/03/06. doi: 10.1016/j.neuroimage.2012.02.034 22387175.

31. Buckner RL, Head D, Parker J, Fotenos AF, Marcus D, Morris JC, et al. A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. Neuroimage. 2004;23(2):724–38. Epub 2004/10/19. doi: 10.1016/j.neuroimage.2004.06.018 15488422.

32. Cole TJ, Green PJ. Smoothing reference centile curves: the LMS method and penalized likelihood. Statistics in medicine. 1992;11(10):1305–19. Epub 1992/07/01. doi: 10.1002/sim.4780111005 1518992.

33. Girdler-Brown BV, Dzikiti LN. Hypothesis tests for the difference between two population proportions using Stata. Southern African Journal of Public Health. 2018;2(3):63–8. http://dx.doi.org/10.7196/SHS.2018.v2.i3.71.

34. Cluitmans L, Mattila J, Runtti H, van Gils M, Lotjonen J. A MATLAB toolbox for classification and visualization of heterogenous multi-scale human data using the Disease State Fingerprint method. Stud Health Technol Inform. 2013;189:77–82. Epub 2013/06/07. 23739361.

35. Da X, Toledo JB, Zee J, Wolk DA, Xie SX, Ou Y, et al. Integration and relative value of biomarkers for prediction of MCI to AD progression: spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers. NeuroImage Clinical. 2014;4:164–73. Epub 2013/12/29. doi: 10.1016/j.nicl.2013.11.010 24371799; PubMed Central PMCID: PMC3871290.

36. Visser LNC, Kunneman M, Murugesu L. Clinician-patient communication during the diagnostic work-up: the ABIDE project. Alzheimer's & Dementia: The Journal of the Alzheimer's Association. 2019;11:520–8.

37. Prestia A, Caroli A, Wade SK, van der Flier WM, Ossenkoppele R, Van BB, et al. Prediction of AD dementia by biomarkers following the NIA-AA and IWG diagnostic criteria in MCI patients from three European memory clinics. Alzheimers Dement. 2015. S1552-5260(14)02890-8 [pii]; doi: 10.1016/j.jalz.2014.12.001 25646957

38. Insel PS, Palmqvist S, Mackin RS, Nosheny RL, Hansson O, Weiner MW, et al. Assessing risk for preclinical beta-amyloid pathology with APOE, cognitive, and demographic information. Alzheimers Dement (Amst). 2016;4:76–84. Epub 2016/10/11. doi: 10.1016/j.dadm.2016.07.002 27722193; PubMed Central PMCID: PMC5045949.

39. Verberk I, Slot RE, Verfaillie SC, Heijst H, Prins ND, Van Berckel B, et al. Plasma-amyloid as pre-screener for the earliest Alzheimer's pathological changes. Ann Neurol. 2018;in press.

40. Palmqvist S, Insel PS, Zetterberg H, Blennow K, Brix B, Stomrud E, et al. Accurate risk estimation of beta-amyloid positivity to identify prodromal Alzheimer's disease: Cross-validation study of practical algorithms. Alzheimers Dement. 2019;15(2):194–204. Epub 2018/10/27. doi: 10.1016/j.jalz.2018.08.014 30365928; PubMed Central PMCID: PMC6374284.

41. Zekry D, Hauw JJ, Gold G. Mixed dementia: epidemiology, diagnosis, and treatment. J Am Geriatr Soc. 2002;50(8):1431–8. Epub 2002/08/08. doi: 10.1046/j.1532-5415.2002.50367.x 12165002.

42. Zwan M, van Harten A, Ossenkoppele R, Bouwman F, Teunissen C, Adriaanse S, et al. Concordance between cerebrospinal fluid biomarkers and [11C]PIB PET in a memory clinic cohort. J Alzheimers Dis. 2014;41(3):801–7. Epub 2014/04/08. doi: 10.3233/JAD-132561 24705549.

43. van Maurik IS, Zwan MD, Tijms BM, Bouwman FH, Teunissen CE, Scheltens P, et al. Interpreting Biomarker Results in Individual Patients With Mild Cognitive Impairment in the Alzheimer's Biomarkers in Daily Practice (ABIDE) Project. JAMA Neurol. 2017;74(12):1481–91. Epub 2017/10/20. doi: 10.1001/jamaneurol.2017.2712 29049480; PubMed Central PMCID: PMC5822193.

44. Van der Flier W, Kunneman M, Bouwman FH, Petersen R, Smets EMA. Diagnostic dilemmas in Alzheimer's disease: Room for shared decision making. Alzheimers Dement. 2017;3(3):301–4. http://dx.doi.org/10.1016/j.trci.2017.03.008.

45. Stiggelbout AM, Pieterse AH, De Haes JC. Shared decision making: Concepts, evidence, and practice. Patient education and counseling. 2015;98(10):1172–9. Epub 2015/07/29. doi: 10.1016/j.pec.2015.06.022 26215573.

46. Grill JD, Apostolova LG, Bullain S, Burns JM, Cox CG, Dick M, et al. Communicating mild cognitive impairment diagnoses with and without amyloid imaging. Alzheimers Res Ther. 2017;9(1):35. Epub 2017/05/06. doi: 10.1186/s13195-017-0261-y 28472970; PubMed Central PMCID: PMC5418690.

47. Kunneman M, Pel-Littel R, Bouwman FH, Gillissen F, Schoonenboom NSM, Claus JJ, et al. Patients' and caregivers' views on conversations and shared decision making in diagnostic testing for Alzheimer's disease: The ABIDE project. Alzheimer's & dementia (New York, N Y). 2017;3(3):314–22. Epub 2017/10/27. doi: 10.1016/j.trci.2017.04.002 29067338; PubMed Central PMCID: PMC5651429.


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