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Common pre-diagnostic features in individuals with different rare diseases represent a key for diagnostic support with computerized pattern recognition?


Autoři: Lorenz Grigull aff001;  Sandra Mehmecke aff002;  Ann-Katrin Rother aff003;  Susanne Blöß aff004;  Christian Klemann aff005;  Ulrike Schumacher aff006;  Urs Mücke aff001;  Xiaowei Kortum aff007;  Werner Lechner aff008;  Frank Klawonn aff007
Působiště autorů: Department of Pediatric Hematology and Oncology, Hannover Medical School, Hannover, Germany aff001;  Nursing Council (Pflegekammer) Lower Saxony, Hannover, Germany aff002;  Department of Pediatrics and Adolescent Medicine, University of Cologne, Cologne, Germany aff003;  Department of Hematology, Hemostasis, Oncology and Stem Cell Transplantation, Hannover Medical School, Hannover, Germany aff004;  Department of Pediatric Pneumology, Allergy and Neonatology, Hannover Medical School, Hannover, Germany aff005;  DRK Clementinenkrankenhaus, Hannover, Germany aff006;  Department of Computer Science, Ostfalia University of Applied Sciences, Wolfenbuettel, Germany aff007;  Improved Medical Diagnostics IMD GmbH, Donauwoerth, Germany aff008;  Biostatistics, Helmholtz Centre for Infection Research, Braunschweig, Germany aff009
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0222637

Souhrn

Background

Rare diseases (RD) result in a wide variety of clinical presentations, and this creates a significant diagnostic challenge for health care professionals. We hypothesized that there exist a set of consistent and shared phenomena among all individuals affected by (different) RD during the time before diagnosis is established.

Objective

We aimed to identify commonalities between different RD and developed a machine learning diagnostic support tool for RD.

Methods

20 interviews with affected individuals with different RD, focusing on the time period before their diagnosis, were performed and qualitatively analyzed. Out of these pre-diagnostic experiences, we distilled key phenomena and created a questionnaire which was then distributed among individuals with the established diagnosis of i.) RD, ii.) other common non-rare diseases (NRO) iii.) common chronic diseases (CD), iv.), or psychosomatic/somatoform disorders (PSY). Finally, four combined single machine learning methods and a fusion algorithm were used to distinguish the different answer patterns of the questionnaires.

Results

The questionnaire contained 53 questions. A total sum of 1763 questionnaires (758 RD, 149 CD, 48 PSY, 200 NRO, 34 healthy individuals and 574 not evaluable questionnaires) were collected. Based on 3 independent data sets the 10-fold stratified cross-validation method for the answer-pattern recognition resulted in sensitivity values of 88.9% to detect the answer pattern of a RD, 86.6% for NRO, 87.7% for CD and 84.2% for PSY.

Conclusion

Despite the great diversity in presentation and pathogenesis of each RD, patients with RD share surprisingly similar pre-diagnosis experiences. Our questionnaire and data-mining based approach successfully detected unique patterns in groups of individuals affected by a broad range of different rare diseases. Therefore, these results indicate distinct patterns that may be used for diagnostic support in RD.

Klíčová slova:

Diagnostic medicine – Machine learning – Patient advocacy – Physicians – Psychometrics – Questionnaires – Acromegaly – Fabry disease


Zdroje

1. http://www.eurordis.org/IMG/pdf/voice_12000_patients/EURORDISCARE_FULLBOOKr.pdf; http://www.webcitation.org/70nuNiKp7

2. Berody S, Galeotti C, Koné-Paut I, Piram M: A retrospective survey of patients's journey before the diagnosis of mevalonate kinase deficiency. Joint Bone Spine 2015;82(4):240–244. doi: 10.1016/j.jbspin.2014.12.011 25677409

3. Bhattacharya K, Balasubramaniam S, Choy YS, Fietz M, Fu A, Jin DK: Overcoming the barriers to diagnosis of Morquio A syndrome. Orphanet J Rare Dis. 2014;9:192. doi: 10.1186/s13023-014-0192-7 25433535

4. Brown LM, Chen H, Halpern S, Taichman D, McGoon MD, Farber HW: Delay in Recognition of Pulmonary Arterial Hypertension: Factors Identified From the REVEAL Registry. Chest. 2011; 140(1): 19–26. doi: 10.1378/chest.10-1166 21393391

5. Demily C, Sedel F: Psychiatric manifestations of treatable hereditary metabolic disorders in adults. Ann Gen Psychiatry. 2014; 13: 27. doi: 10.1186/s12991-014-0027-x 25478001

6. Bonnot O, Klünemann HH, Sedel F, Tordjman S, Cohen D, Walterfang M: Diagnostic and treatment implications of psychosis secondary to treatable metabolic disorders in adults: a systematic review. Orphanet J Rare Dis. 2014; 9: 65. doi: 10.1186/1750-1172-9-65 24775716

7. Dasouki M, Jawdat O, Almadhoun O, Pasnoor M, McVey AL, Abuzinadah A: Neurol Clin. 2014; 32(3): 751–76. doi: 10.1016/j.ncl.2014.04.010 25037089

8. Rohrbach M, Vandersteen A, Yiş U, Serdaroglu G, Ataman E, Chopra M: Phenotypic variability of the kyphoscoliotic type of Ehlers-Danlos syndrome (EDS VIA): clinical, molecular and biochemical delineation. Orphanet J Rare Dis. 2011; 6: 46. doi: 10.1186/1750-1172-6-46 21699693

9. Bouwman MG, Teunissen QG, Wijburg FA, Linthorst GE: Doctor Google’ ending the diagnostic odyssey in lysosomal storage disorders: parents using internet search engines as an efficient diagnostic strategy in rare diseases. Arch Dis Child 2010;95:642–644. doi: 10.1136/adc.2009.171827 20418338

10. Kuehni CE, Frischer T, Strippoli MP, Maurer E, Bush A, Nielsen KG: Factors influencing age at diagnosis of primary ciliary dyskinesia in European children. Eur Respir J. 2010;36(6):1248–58. doi: 10.1183/09031936.00001010 20530032

11. Gathmann B, Mahlaoui N; CEREDIH, Gérard L, Oksenhendler E, Warnatz K: Clinical picture and treatment of 2212 patients with common variable immunodeficiency. J Allergy Clin Immunol. 2014;134(1):116–26. doi: 10.1016/j.jaci.2013.12.1077 24582312

12. Molster C, Urwin D, Di Pietro L, Fookes M, Petrie D, van der Laan S, Dawkins H: Survey of healthcare experiences of Australian adults living with rare diseases. Orphanet J Rare Dis. 2016; 11: 30. doi: 10.1186/s13023-016-0409-z 27012247

13. http://ec.europa.eu/health/rare_diseases/docs/2015_factsheet_en.pdf; URL http://www.webcitation.org/70nu1pcXW

14. Rother A-K, Schwerk N, Brinkmann F, Klawonn F, Lechner W, Grigull L.: Diagnostic Support for Selected Paediatric Pulmonary Diseases Using Answer-Pattern Recognition in Questionnaires Based on Combined Data Mining Applications—A Monocentric Observational Pilot Study. PLoS One. 2015; 10(8): e0135180. doi: 10.1371/journal.pone.0135180 26267801

15. Grigull L, Lechner W, Petri S, Kollewe K, Dengler R, Mehmecke S: Diagnostic support for selected neuromuscular diseases using answer-pattern recognition and data mining techniques: a proof of concept multicenter prospective trial. BMC Med Inform Decis Mak. 2016; 16: 31. doi: 10.1186/s12911-016-0268-5 26957320

16. Blöss S, Klemann C, Rother AK, Mehmecke S, Schumacher U, Mücke U, Grigull L.: Diagnostic support for rare diseases and shared prediagnostic phenomena: Results of a German-wide expert Delphi survey. PLosOne, 2017; PLoS One. 2017;12(2):e0172532. doi: 10.1371/journal.pone.0172532 28234950

17. Wirihana L, Welch A, Williamson M, Christensen M, Bakon S, Craft J: Using Colaizzi's method of data analysis to explore the experiences of nurse academics teaching on satellite campuses. Nurse Res. 2018 Mar 16;25(4):30–34. doi: 10.7748/nr.2018.e1516 29546965

18. Tsalik EL, Henao R, Nichols M, Burke T, Ko ER, McClain MT: Host gene expression classifiers diagnose acute respiratory illness etiology. Science Translational Medicine. 2016; 8(322): doi: 10.1126/scitranslmed.aad6873 26791949

19. Turner MR, Talbot K.: Mimics and chameleons in motor neurone disease. Pract Neurol. 2013;13:153–64. doi: 10.1136/practneurol-2013-000557 23616620

20. Agarwal PK, Mansfield DC, Mechan D, Al-Shahi Salman R, Davenport RJ, Connor M, Metcalfe R: Delayed diagnosis of oculopharyngeal muscular dystrophy in Scotland. Br J Ophthalmol. 2012;96:281–3. doi: 10.1136/bjo.2010.200378 21602480

21. Spuler S, Stroux A, Kuschel F, Kuhlmey A, Kendel F.: Delay in diagnosis of muscle disorders depends on the subspecialty of the initially consulted physician. BMC Health Serv Res. 2011;11:91. doi: 10.1186/1472-6963-11-91 21542919

22. Müller-Felber W, Horvath R, Gempel K, Podskarbi T, Shin Y, Pongratz D: Late onset Pompe disease: clinical and neurophysiological spectrum of 38 patients including long-term follow-up in 18 patients. Neuromuscul Disord. 2007;17(9–10):698–706. doi: 10.1016/j.nmd.2007.06.002 17643989

23. Lohmann E, Krüger S, Hauser AK, Hanagasi H, Guven G, Erginel-Unaltuna N: Clinical variability in ataxia-telangiectasia. J Neurol. 2015; 262(7):1724–7. doi: 10.1007/s00415-015-7762-z 25957637

24. Rigoldi M, Concolino D, Morrone A, Pieruzzi F, Ravaglia R, Furlan F: Intrafamilial phenotypic variability in four families with Anderson-Fabry disease. Clin Genet. 2014;86(3):258–63. doi: 10.1111/cge.12261 23980562

25. Kraemer M, Buerger M, Berlit P: Diagnostic problems and delay of diagnosis in amyotrophic lateral sclerosis. Clin Neurol Neurosurg 2010, 112:103–105. doi: 10.1016/j.clineuro.2009.10.014 19931253

26. Pavletic AJ, HNatiuk O: Puzzling dyspnea caused by respiratory muscle weakness. J Am Board Fam Med. 2012;25:396–7. doi: 10.3122/jabfm.2012.03.110220 22570404

27. Comi GP, Prelle A, Bresolin N, Moggio M, Bardoni A, Gallanti A: Clinical variability in Becker muscular dystrophy. Genetic, biochemical and immunohistochemical correlates. Brain. 1994;117:1–14. doi: 10.1093/brain/117.1.1-a 8149204

28. Barnett GO, Cimino JJ, Hupp JA, Hoffer EP: DXplain. An evolving diagnostic decision-support system. JAMA. 1987; 258:67–74. doi: 10.1001/jama.258.1.67 3295316

29. Feldman MJ, Hoffer EP, Barnett GO, Kim RJ, Famiglietti KT, Chueh HC: Impact of a Computer-Based Diagnostic Decision Support Tool on the Differential Diagnoses of Medicine Residents. J Grad Med Educ. 2012; 4 (2):227–231. doi: 10.4300/JGME-D-11-00180.1 23730446

30. Graber ML, Franklin N, Gordon R. Diagnostic error in internal medicine. Arch Intern Med. 2005;165:1493–9. doi: 10.1001/archinte.165.13.1493 16009864

31. Van Karnebeek CD, Houben RF, Lafek M, Giannasi W, Stockler S. The treatable intellectual disability APP www.treatable-id.org: a digital tool to enhance diagnosis & care for rare diseases. Orphanet J Rare Dis. 2012 23;7:47. doi: 10.1186/1750-1172-7-47 22824307

32. Ronicke S, Hirsch MC, Türk E, Larionov K, Tientcheu D, Wagner AD Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study. Orphanet J Rare Dis. 2019;14(1):69. doi: 10.1186/s13023-019-1040-6 30898118

33. Jia J, Wang R, An Z, Guo Y, Ni X, Shi T.RDAD: A Machine Learning System to Support Phenotype-Based Rare Disease Diagnosis. Front Genet. 2018https://doi.org/10.3389/fgene.2018.00587

34. Greulich T, Nell C, Herr C, Vogelmeier C, Kotke V, Wiedmann S: Results from a large targeted screening program for alpha-1-antitrypsin deficiency: 2003–2015. Orphanet J Rare Dis. 2016;11(1):75. doi: 10.1186/s13023-016-0453-8 27282198

35. Danilowicz K, Fainstein Day P, Manavela MP, Herrera CJ, Deheza ML, Isaac G: Implementing a screening program for acromegaly in Latin America: necessity versus feasibility. Pituitary. 2016;19(4):370–4. doi: 10.1007/s11102-016-0714-5 27130456

36. Wilcox WR, Oliveira JP, Hopkin RJ, Ortiz A, Banikazemi M, Feldt-Rasmussen U: Females with Fabry disease frequently have major organ involvement: lessons from the Fabry Registry. Mol Genet Metab. 2008;93(2):112–28. doi: 10.1016/j.ymgme.2007.09.013 18037317

37. Hawley DP, Baildam EM, Amin TS, Cruikshank MK, Davidson JE, Dixon J: Access to care for children and young people diagnosed with localized scleroderma or juvenile SSc in the UK. Rheumatology (Oxford). 2012;51(7):1235–9. doi: 10.1093/rheumatology/ker521 22344577

38. Pierucci P, Lenato GM, Suppressa P, Lastella P, Triggiani V, Valerio R: A long diagnostic delay in patients with Hereditary Haemorrhagic Telangiectasia: a questionnaire-based retrospective study. Orphanet J Rare Dis. 2012;7:33. doi: 10.1186/1750-1172-7-33 22676497


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