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Open Access 01-12-2022 | Artificial Intelligence | Research article

Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model

Authors: Qiang Liu, Nemanja Vaci, Ivan Koychev, Andrey Kormilitzin, Zhenpeng Li, Andrea Cipriani, Alejo Nevado-Holgado

Published in: BMC Medicine | Issue 1/2022

Abstract

Background

Donepezil, galantamine, rivastigmine and memantine are potentially effective interventions for cognitive impairment in dementia, but the use of these drugs has not been personalised to individual patients yet. We examined whether artificial intelligence-based recommendations can identify the best treatment using routinely collected patient-level information.

Methods

Six thousand eight hundred four patients aged 59–102 years with a diagnosis of dementia from two National Health Service (NHS) Foundation Trusts in the UK were used for model training/internal validation and external validation, respectively. A personalised prescription model based on the Recurrent Neural Network machine learning architecture was developed to predict the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores post-drug initiation. The drug that resulted in the smallest decline in cognitive scores between prescription and the next visit was selected as the treatment of choice. Change of cognitive scores up to 2 years after treatment initiation was compared for model evaluation.

Results

Overall, 1343 patients with MMSE scores were identified for internal validation and 285 [21.22%] took the drug recommended. After 2 years, the reduction of mean [standard deviation] MMSE score in this group was significantly smaller than the remaining 1058 [78.78%] patients (0.60 [0.26] vs 2.80 [0.28]; P = 0.02). In the external validation cohort (N = 1772), 222 [12.53%] patients took the drug recommended and reported a smaller MMSE reduction compared to the 1550 [87.47%] patients who did not (1.01 [0.49] vs 4.23 [0.60]; P = 0.01). A similar performance gap was seen when testing the model on patients prescribed with AChEIs only.

Conclusions

It was possible to identify the most effective drug for the real-world treatment of cognitive impairment in dementia at an individual patient level. Routine care patients whose prescribed medications were the best fit according to the model had better cognitive performance after 2 years.

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Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020;396(10248):413–46. https://doi.org/10.1016/S0140-6736(20)30367-6.CrossRefPubMedPubMedCentral

Dementia - World Health Organization 2 September 2021 [cited 2021 31 October]. Available from: https://www.who.int/news-room/fact-sheets/detail/dementia.

Yunusa I, Alsahali S, Ranes A, Eguale T. Comparative value of cholinesterase inhibitors and memantine in persons with moderate-to-severe Alzheimer’s disease in the United States: a cost-effectiveness analysis. J Alzheimers Dis Rep. 2021;5(1):705–13. https://doi.org/10.3233/ADR-210307.CrossRefPubMedPubMedCentral

Rabins PV, Rovner BW, Rummans T, Schneider LS, Tariot PN. Guideline watch (October 2014): practice guideline for the treatment of patients with Alzheimer’s disease and other dementias. Focus (Am Psychiatr Publ). 2017;15(1):110–28. https://doi.org/10.1176/appi.focus.15106.CrossRefPubMedPubMedCentral

Pink J, O’Brien J, Robinson L, Longson D. Dementia: assessment, management and support: summary of updated NICE guidance. BMJ. 2018;361:k2438. https://doi.org/10.1136/bmj.k2438.CrossRefPubMed

Moore A, Patterson C, Lee L, Vedel I, Bergman H. Fourth Canadian Consensus Conference on the Diagnosis and Treatment of Dementia: recommendations for family physicians. Can Fam Physician. 2014;60(5):433–8.PubMedPubMedCentral

Vaci N, Koychev I, Kim C-H, Kormilitzin A, Liu Q, Lucas C, et al. Real-world effectiveness, its predictors and onset of action of cholinesterase inhibitors and memantine in dementia: retrospective health record study. Br J Psychiatry. 2021;218(5):261–7. https://doi.org/10.1192/bjp.2020.136.CrossRefPubMed

Seibert M, Mühlbauer V, Holbrook J, Voigt-Radloff S, Brefka S, Dallmeier D, et al. Efficacy and safety of pharmacotherapy for Alzheimer’s disease and for behavioural and psychological symptoms of dementia in older patients with moderate and severe functional impairments: a systematic review of controlled trials. Alzheimers Res Ther. 2021;13(1):1–20. https://doi.org/10.1186/s13195-021-00867-8.CrossRef

Wong-Lin K, McClean PL, McCombe N, Kaur D, Sanchez-Bornot JM, Gillespie P, et al. Shaping a data-driven era in dementia care pathway through computational neurology approaches. BMC Med. 2020;18(1):1–10. https://doi.org/10.1186/s12916-020-01841-1.CrossRef

10.

Miah Y, Prima CNE, Seema SJ, Mahmud M, Kaiser MS. Performance comparison of machine learning techniques in identifying dementia from open access clinical datasets. Adv Smart Soft Comput Springer. 2021:79–89. https://doi.org/10.1007/978-981-15-6048-4_8.

11.

Sepehrband F, Barisano G, Sheikh-Bahaei N, Choupan J, Cabeen RP, Crawford MS, et al. Alteration of perivascular spaces in early cognitive decline: neuroimaging/optimal neuroimaging measures for early detection. Alzheimers Dement. 2020;16(S5):e045605. https://doi.org/10.1002/alz.045605.CrossRef

12.

Forouzannezhad P, Abbaspour A, Li C, Fang C, Williams U, Cabrerizo M, et al. A Gaussian-based model for early detection of mild cognitive impairment using multimodal neuroimaging. J Neurosci Methods. 2020;333:108544. https://doi.org/10.1016/j.jneumeth.2019.108544.CrossRefPubMed

13.

Erdoğan O, Esme M, Balci C, Rafatov S, Cankurtaran M, Yavuz BB, et al. Identification of genomic biomarkers with machine learning for early and differential diagnosis of late-onset Alzheimer’s disease (LOAD). Alzheimers Dement. 2020;16(S2):e042558. https://doi.org/10.1002/alz.042558.CrossRef

14.

Buch VH, Ahmed I, Maruthappu M. Artificial intelligence in medicine: current trends and future possibilities. Br J Gen Pract. 2018;68(668):143–4. https://doi.org/10.3399/bjgp18X695213.CrossRefPubMedPubMedCentral

15.

Kumar S, Oh I, Schindler S, Lai AM, Payne PR, Gupta A. Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review. JAMIA Open. 2021;4(3):ooab052.CrossRef

16.

Amini S, Zhang L, Hao B, Gupta A, Song M, Karjadi C, et al. An artificial intelligence-assisted method for dementia detection using images from the clock drawing test. J Alzheimers Dis. 2021;(Preprint):1–9.

17.

Zhang Z, Jiang Y, Cao X, Yang X, Zhu C, Li Y, et al. Deep learning based gait analysis for contactless dementia detection system from video camera. IEEE Int Symp Circuits Syst Proc. 2021; IEEE.

18.

Mahajan P, Baths V. Acoustic and language based deep learning approaches for Alzheimer's dementia detection from spontaneous speech. Front Aging Neurosci. 2021;13. https://doi.org/10.3389/fnagi.2021.623607.

19.

Ozdemir D, Cibulka J, Stepankova O, Holmerova I. Design and implementation framework of social assistive robotics for people with dementia-a scoping review. Health Technol (Berl). 2021:1–12.

20.

Khan AA. Robotics in dementia: caregiving needs addressed by an assistive robot in combination with wearable sensors. Alzheimers Dement. 2020;16(S11):e037855. https://doi.org/10.1002/alz.037855.CrossRef

21.

Astell AJ, Bouranis N, Hoey J, Lindauer A, Mihailidis A, Nugent C, et al. Technology and dementia: the future is now. Dement Geriatr Cogn Disord. 2019;47(3):131–9. https://doi.org/10.1159/000497800.CrossRefPubMed

22.

Müllers P, Taubert M, Müller NG. Physical exercise as personalized medicine for dementia prevention? Front Physiol. 2019;10:672. https://doi.org/10.3389/fphys.2019.00672.CrossRefPubMedPubMedCentral

23.

Evans SC, Garabedian C, Bray J. ‘Now he sings’. The My Musical Memories Reminiscence programme: personalised interactive reminiscence sessions for people living with dementia. Dementia (London). 2019;18(3):1181-1198, https://doi.org/10.1177/1471301217710531.

24.

Denis M. U.K. clinical record interactive search (CRIS). Alzheimers Dement. 2017;13(7).

25.

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. https://doi.org/10.1016/0022-3956(75)90026-6.CrossRefPubMed

26.

Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695–9. https://doi.org/10.1111/j.1532-5415.2005.53221.x.CrossRefPubMed

27.

Goodday S, Kormilitzin A, Vaci N, Liu Q, Cipriani A, Smith T, et al. Maximizing the use of social and behavioural information from secondary care mental health electronic health records. J Biomed Inform. 2020;107:103429. https://doi.org/10.1016/j.jbi.2020.103429.CrossRefPubMed

28.

Senior M, Burghart M, Yu R, Kormilitzin A, Liu Q, Vaci N, et al. Identifying predictors of suicide in severe mental illness: a feasibility study of a clinical prediction rule (Oxford Mental Illness and Suicide Tool or OxMIS). Front Psychiatry. 2020;11:268. https://doi.org/10.3389/fpsyt.2020.00268.CrossRefPubMedPubMedCentral

29.

Vaci N, Liu Q, Kormilitzin A, De Crescenzo F, Kurtulmus A, Harvey J, et al. Natural language processing for structuring clinical text data on depression using UK-CRIS. Evid Based Ment Health. 2020;23(1):21–6. https://doi.org/10.1136/ebmental-2019-300134.CrossRefPubMed

30.

Gehrmann S, Dernoncourt F, Li Y, Carlson ET, Wu JT, Welt J, et al. Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives. PloS ONE. 2018;13(2):e0192360. https://doi.org/10.1371/journal.pone.0192360.CrossRefPubMedPubMedCentral

31.

Savva GM, Wharton SB, Ince PG, Forster G, Matthews FE, Brayne C. Age, neuropathology, and dementia. N Engl J Med. 2009;360(22):2302–9. https://doi.org/10.1056/NEJMoa0806142.CrossRefPubMed

32.

Zuliani G, Polastri M, Romagnoli T, Marabini L, Seripa D, Cervellati C, et al. Clinical and demographic parameters predict the progression from mild cognitive impairment to dementia in elderly patients. Aging Clin Exp Res. 2021;33(7):1895–902. https://doi.org/10.1007/s40520-020-01697-8.CrossRefPubMed

33.

Perera G, Khondoker M, Broadbent M, Breen G, Stewart R. Factors associated with response to acetylcholinesterase inhibition in dementia: a cohort study from a secondary mental health care case register in London. PloS One. 2014;9(11):e109484. https://doi.org/10.1371/journal.pone.0109484.CrossRefPubMedPubMedCentral

34.

Ruitenberg A, Ott A, van Swieten JC, Hofman A, Breteler MM. Incidence of dementia: does gender make a difference? Neurobiol Aging. 2001;22(4):575–80. https://doi.org/10.1016/S0197-4580(01)00231-7.CrossRefPubMed

35.

Mukadam N, Cooper C, Livingston G. A systematic review of ethnicity and pathways to care in dementia. Int J Geriatr Psychiatry. 2011;26(1):12–20. https://doi.org/10.1002/gps.2484.CrossRefPubMed

36.

Sundström A, Westerlund O, Kotyrlo E. Marital status and risk of dementia: a nationwide population-based prospective study from Sweden. BMJ Open. 2016;6(1):e008565.CrossRef

37.

Zhang Z, Liu H. Choi S-wE. Marital loss and risk of dementia: do race and gender matter. Soc Sci Med. 2021;275:113808.CrossRef

38.

Mielke MM. Sex and gender differences in Alzheimer’s disease dementia. Psychiatr Times. 2018;35(11):14.PubMedPubMedCentral

39.

Zhou Z, Wang P, Fang Y. Loneliness and the risk of dementia among older Chinese adults: gender differences. Aging Ment Health. 2018;22(4):519–25. https://doi.org/10.1080/13607863.2016.1277976.CrossRefPubMed

40.

Arevalo-Rodriguez I, Smailagic N, i Figuls MR, Ciapponi A, Sanchez-Perez E, Giannakou A, et al. Mini-Mental State Examination (MMSE) for the detection of Alzheimer’s disease and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev. 2015;3. https://doi.org/10.1002/14651858.CD010783.pub2.

41.

Davis DH, Creavin ST, Yip JL, Noel-Storr AH, Brayne C, Cullum S. Montreal Cognitive Assessment for the detection of dementia. Cochrane Database Syst Rev. 2021;7(7).

42.

Pezzotti P, Scalmana S, Mastromattei A, Di Lallo D. The accuracy of the MMSE in detecting cognitive impairment when administered by general practitioners: a prospective observational study. BMC Fam Pract. 2008;9(1):1–11. https://doi.org/10.1186/1471-2296-9-29.CrossRef

43.

Roalf DR, Moberg PJ, Xie SX, Wolk DA, Moelter ST, Arnold SE. Comparative accuracies of two common screening instruments for classification of Alzheimer’s disease, mild cognitive impairment, and healthy aging. Alzheimers Dement. 2013;9(5):529–37. https://doi.org/10.1016/j.jalz.2012.10.001.CrossRefPubMed

44.

Kumar S, Sharma R, Tsunoda T, Kumarevel T, Sharma A. Forecasting the spread of COVID-19 using LSTM network. BMC Bioinforma. 2021;22(6):1–9. https://doi.org/10.1186/s12859-021-04224-2.CrossRef

45.

Liu Q, Li R, Hu H, Gu D. Using unsupervised deep learning technique for monocular visual odometry. IEEE Access. 2019;7:18076–88. https://doi.org/10.1109/ACCESS.2019.2896988.CrossRef

46.

Guo A, Beheshti R, Khan YM, Langabeer JR, Foraker RE. Predicting cardiovascular health trajectories in time-series electronic health records with LSTM models. BMC Med Inform Decis Mak. 2021;21(1):1–10. https://doi.org/10.1186/s12911-020-01345-1.CrossRef

47.

Zhao J, Feng Q, Wu P, Lupu RA, Wilke RA, Wells QS, et al. Learning from longitudinal data in electronic health record and genetic data to improve cardiovascular event prediction. Sci Rep. 2019;9(1):1–10. https://doi.org/10.1038/s41598-018-36745-x.CrossRef

48.

Putt RVD, Dineen C, Janes D, Series H, McShane R. Effectiveness of acetylcholinesterase inhibitors: diagnosis and severity as predictors of response in routine practice. Int J Geriatr Psychiatry. 2006;21(8):755–60. https://doi.org/10.1002/gps.1557.CrossRef

49.

Wattmo C, Wallin ÅK, Minthon L. Functional response to cholinesterase inhibitor therapy in a naturalistic Alzheimer’s disease cohort. BMC Neurol. 2012;12(1):1–10. https://doi.org/10.1186/1471-2377-12-134.CrossRef

50.

Harutyunyan H, Khachatrian H, Kale DC, Ver Steeg G, Galstyan A. Multitask learning and benchmarking with clinical time series data. Sci Data. 2019;6(1):1–18. https://doi.org/10.1038/s41597-019-0103-9.CrossRef

51.

Altmann A, Toloşi L, Sander O, Lengauer T. Permutation importance: a corrected feature importance measure. Bioinformatics. 2010;26(10):1340–7. https://doi.org/10.1093/bioinformatics/btq134.CrossRefPubMed

52.

Ramachandran KM, Tsokos CP. Mathematical statistics with applications in R 3rd Edition: Academic Press; 2020. https://doi.org/10.1016/C2018-0-02285-9.

53.

Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, et al. TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv. 2016:160304467.

54.

Chollet F. Deep learning with Python: Simon and Schuster; 2021. https://www.manning.com/books/deep-learning-with-python-second-edition

55.

McShane R, Westby MJ, Roberts E, Minakaran N, Schneider L, Farrimond LE, et al. Memantine for dementia. Cochrane Database Syst Rev. 2019;3. https://doi.org/10.1002/14651858.CD003154.pub6.

56.

Robinson DM, Keating GM. Memantine: a review of its use in Alzheimer’s disease. Drugs. 2006;66(11):1515–34. https://doi.org/10.2165/00003495-200666110-00015.CrossRefPubMed

57.

Cui C-C, Sun Y, Wang X-Y, Zhang Y, Xing Y. The effect of anti-dementia drugs on Alzheimer disease-induced cognitive impairment: a network meta-analysis. Medicine (Baltimore). 2019;98(27):e16091.CrossRef

58.

Whitehead A, Perdomo C, Pratt RD, Birks J, Wilcock GK, Evans JG. Donepezil for the symptomatic treatment of patients with mild to moderate Alzheimer’s disease: a meta-analysis of individual patient data from randomised controlled trials. Int J Geriatr Psychiatry. 2004;19(7):624–33. https://doi.org/10.1002/gps.1133.CrossRefPubMed

59.

Knight R, Khondoker M, Magill N, Stewart R, Landau S. A systematic review and meta-analysis of the effectiveness of acetylcholinesterase inhibitors and memantine in treating the cognitive symptoms of dementia. Dement Geriatr Cogn Disord. 2018;45(3-4):131–51. https://doi.org/10.1159/000486546.CrossRefPubMed

60.

Hazif-Thomas C. Lefebvre des Noëttes V. Issues of the delisting of anti-Alzheimer’s drugs in France: between the law and ethics. Geriatr Psychol Neuropsychiatr Vieil. 2020;18(1):97–102. https://doi.org/10.1684/pnv.2020.0843.CrossRefPubMed

61.

Biundo R, Weis L, Bostantjopoulou S, Stefanova E, Falup-Pecurariu C, Kramberger M, et al. MMSE and MoCA in Parkinson’s disease and dementia with Lewy bodies: a multicenter 1-year follow-up study. J Neural Transm. 2016;123(4):431–8. https://doi.org/10.1007/s00702-016-1517-6.CrossRefPubMed

62.

Dufouil C, Clayton D, Brayne C, Chi L-Y, Dening TR, Paykel E, et al. Population norms for the MMSE in the very old: estimates based on longitudinal data. Neurology. 2000;55(11):1609–13. https://doi.org/10.1212/WNL.55.11.1609.CrossRefPubMed

63.

Perera G, Mueller C, Stewart R. Factors associated with slow progression of cognitive impairment following first dementia diagnosis. Int J Geriatr Psychiatry. 2021;36(2):271–85. https://doi.org/10.1002/gps.5420.CrossRefPubMed

64.

Bae J, Stocks J, Heywood A, Jung Y, Jenkins L, Hill V, et al. Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer’s type based on a three-dimensional convolutional neural network. Neurobiol Aging. 2021;99:53–64. https://doi.org/10.1016/j.neurobiolaging.2020.12.005.CrossRefPubMed

65.

De Crescenzo F, Garriga C, Tomlinson A, Coupland C, Efthimiou O, Fazel S, et al. Real-world effect of antidepressants for depressive disorder in primary care: protocol of a population-based cohort study. Evid Based Ment Health. 2020;23(3):122–6. https://doi.org/10.1136/ebmental-2020-300149.CrossRefPubMed

66.

Mateos-Álvarez R, Ramos-Ríos R, López-Moríñigo J. Comparative analysis between the MMSE and the RUDAS for dementia screening in low educated people in a Spanish psychogeriatric clinic. Eur J Psychiatry. 2017;31(3):119–26. https://doi.org/10.1016/j.ejpsy.2017.06.003.CrossRef

67.

Mitchell AJ. The Mini-Mental State Examination (MMSE): update on its diagnostic accuracy and clinical utility for cognitive disorders. Cogn Screen Instrum: Springer. 2017:37–48. https://doi.org/10.1007/978-3-319-44775-9_3.

68.

Arevalo-Rodriguez I, Smailagic N, Roqué-Figuls M, Ciapponi A, Sanchez-Perez E, Giannakou A, et al. Mini-Mental State Examination (MMSE) for the early detection of dementia in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev. 2021;7(7). https://doi.org/10.1002/14651858.CD010783.pub3.

69.

Piersma D, Fuermaier A, de Waard D, De Deyn P, Davidse R, De Groot J, et al. The MMSE should not be the sole indicator of fitness to drive in mild Alzheimer’s dementia. Acta Neurol Belg. 2018;118(4):637–42. https://doi.org/10.1007/s13760-018-1036-3.CrossRefPubMedPubMedCentral

70.

O’Brien JT, Holmes C, Jones M, Jones R, Livingston G, McKeith I, et al. Clinical practice with anti-dementia drugs: a revised (third) consensus statement from the British Association for Psychopharmacology. J Psychopharmacol. 2017;31(2):147–68. https://doi.org/10.1177/0269881116680924.CrossRefPubMed

71.

Levine SZ, Yoshida K, Goldberg Y, Samara M, Cipriani A, Efthimiou O, et al. Linking the Mini-Mental State Examination, the Alzheimer’s Disease Assessment Scale–Cognitive Subscale and the Severe Impairment Battery: evidence from individual participant data from five randomised clinical trials of donepezil. Evid Based Ment Health. 2021;24(2):56–61. https://doi.org/10.1136/ebmental-2020-300184.CrossRefPubMed

Title: Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model
Authors: Qiang Liu
Nemanja Vaci
Ivan Koychev
Andrey Kormilitzin
Zhenpeng Li
Andrea Cipriani
Alejo Nevado-Holgado
Publication date: 01-12-2022
Publisher: BioMed Central
Keywords: Artificial Intelligence
Dementia
Dementia
Published in: BMC Medicine / Issue 1/2022
Electronic ISSN: 1741-7015
DOI: https://doi.org/10.1186/s12916-022-02250-2

At a glance: The STEP trials

Springer Medicine

Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model

Abstract

Background

Methods

Results

Conclusions

At a glance: The STEP trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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