Treffer: Predicting functional impairment trajectories in amyotrophic lateral sclerosis: a probabilistic, multifactorial model of disease progression.
Title:
Predicting functional impairment trajectories in amyotrophic lateral sclerosis: a probabilistic, multifactorial model of disease progression.
Authors:
Tavazzi E; Department of Information Engineering, University of Padova, Padua, Italy., Daberdaku S; Department of Information Engineering, University of Padova, Padua, Italy., Zandonà A; Department of Information Engineering, University of Padova, Padua, Italy., Vasta R; Department of Neuroscience, University of Torino, 'Rita Levi Montalcini', Turin, Italy., Nefussy B; Tel Aviv Sourasky Medical Center, Tel Aviv, Israel., Lunetta C; Centro Clinico Nemo Milano, Fondazione Serena Onlus, Milan, Italy., Mora G; Istituti Clinici Scientifici Maugeri IRCCS, Milan, Italy., Mandrioli J; Azienda Ospedaliero Universitaria di Modena, Modena, Italy., Grisan E; Department of Information Engineering, University of Padova, Padua, Italy.; School of Engineering, London South Bank University, London, UK., Tarlarini C; Centro Clinico Nemo Milano, Fondazione Serena Onlus, Milan, Italy., Calvo A; Department of Neuroscience, University of Torino, 'Rita Levi Montalcini', Turin, Italy., Moglia C; Department of Neuroscience, University of Torino, 'Rita Levi Montalcini', Turin, Italy., Drory V; Tel Aviv Sourasky Medical Center, Tel Aviv, Israel., Gotkine M; Hadassah University Hospital Medical Center, Jerusalem, Israel., Chiò A; Department of Neuroscience, University of Torino, 'Rita Levi Montalcini', Turin, Italy., Di Camillo B; Department of Information Engineering, University of Padova, Padua, Italy. barbara.dicamillo@unipd.it.; Department of Comparative Biomedicine and Food Science, University of Padova, Via Gradenigo 6/B, 35131, Padua, Italy. barbara.dicamillo@unipd.it.
Corporate Authors:
Source:
Journal of neurology [J Neurol] 2022 Jul; Vol. 269 (7), pp. 3858-3878. Date of Electronic Publication: 2022 Mar 10.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 0423161 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-1459 (Electronic) Linking ISSN: 03405354 NLM ISO Abbreviation: J Neurol Subsets: MEDLINE
Imprint Name(s):
Original Publication: Berlin ; New York, Springer-Verlag
MeSH Terms:
References:
Talbot K (2009) Motor neuron disease. Pract Neurol 9(5):303–309. (PMID: 1976289410.1136/jnnp.2009.188151)
Chio A, Calvo A, Moglia C, Mazzini L, Mora G, PARALS Study Group (2011) Phenotypic heterogeneity of amyotrophic lateral sclerosis: a population based study. J Neurol Neurosurg Psychiatry 82(7):740–746. https://doi.org/10.1136/jnnp.2010.235952. (PMID: 2140274310.1136/jnnp.2010.235952)
Al-Chalabi A, Jones A, Troakes C, King A, Al-Sarraj S, van den Berg LH (2012) The genetics and neuropathology of amyotrophic lateral sclerosis. Acta Neuropathol 124(3):339–352. (PMID: 2290339710.1007/s00401-012-1022-4)
Al-Chalabi A, Hardiman O, Kiernan MC, Chiò A, Rix-Brooks B, van den Berg LH (2016) Amyotrophic lateral sclerosis: moving towards a new classification system. Lancet Neurol 15(11):1182–1194. (PMID: 2764764610.1016/S1474-4422(16)30199-5)
Küffner R et al (2015) Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression. Nat Biotechnol 33(1):51–57. (PMID: 2536224310.1038/nbt.3051)
Taylor AA et al (2016) Predicting disease progression in amyotrophic lateral sclerosis. Ann Clin Transl Neurol 3(11):866–875. (PMID: 27844032509953210.1002/acn3.348)
Xu L et al (2021) Prognostic models for amyotrophic lateral sclerosis: a systematic review. J Neurol. https://doi.org/10.1007/s00415-021-10508-7. (PMID: 34839456841827910.1007/s00415-021-10508-7)
Westeneng H-J et al (2018) Prognosis for patients with amyotrophic lateral sclerosis: development and validation of a personalised prediction model. Lancet Neurol 17(5):423–433. https://doi.org/10.1016/s1474-4422(18)30089-9. (PMID: 2959892310.1016/s1474-4422(18)30089-9)
Hothorn T, Jung HH (2014) RandomForest4Life: a random forest for predicting ALS disease progression. Amyotroph Lateral Scler Frontotemporal Degener 15(5–6):444–452. (PMID: 2514107610.3109/21678421.2014.893361)
Gomeni R, Fava M, Pooled Resource Open-Access ALS Clinical Trials Consortium (2014) Amyotrophic lateral sclerosis disease progression model. Amyotroph. Lateral Scler Frontotemporal Degener 15(1–2):119–129. (PMID: 2407040410.3109/21678421.2013.838970)
Ong M-L, Tan PF, Holbrook JD (2017) Predicting functional decline and survival in amyotrophic lateral sclerosis. PLoS ONE 12(4):e0174925. (PMID: 28406915539099310.1371/journal.pone.0174925)
Kueffner R et al (2019) Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach. Sci Rep 9(1):690. (PMID: 30679616634593510.1038/s41598-018-36873-4)
Tang M et al (2019) Model-based and model-free techniques for amyotrophic lateral sclerosis diagnostic prediction and patient clustering. Neuroinformatics 17(3):407–421. (PMID: 30460455652750510.1007/s12021-018-9406-9)
Atassi N et al (2014) The PRO-ACT database: design, initial analyses, and predictive features. Neurology 83(19):1719–1725. https://doi.org/10.1212/wnl.0000000000000951. (PMID: 25298304423983410.1212/wnl.0000000000000951)
Chio A et al (2011) ALS clinical trials: do enrolled patients accurately represent the ALS population? Neurology 77(15):1432–1437. https://doi.org/10.1212/wnl.0b013e318232ab9b. (PMID: 2195672310.1212/wnl.0b013e318232ab9b)
Carreiro AV, Amaral PMT, Pinto S, Tomás P, de Carvalho M, Madeira SC (2015) Prognostic models based on patient snapshots and time windows: predicting disease progression to assisted ventilation in amyotrophic lateral sclerosis. J Biomed Inform 58:133–144. (PMID: 2645526510.1016/j.jbi.2015.09.021)
Grollemund V et al (2021) Manifold learning for amyotrophic lateral sclerosis functional loss assessment: Development and validation of a prognosis model. J Neurol 268(3):825–850. (PMID: 3288625210.1007/s00415-020-10181-2)
Chiò A et al (2017) Secular trends of amyotrophic lateral sclerosis: the Piemonte and Valle d’Aosta Register. JAMA Neurol 74(9):1097–1104. (PMID: 28692730571018110.1001/jamaneurol.2017.1387)
Mandrioli J et al (2014) Epidemiology of amyotrophic lateral sclerosis in Emilia Romagna Region (Italy): a population based study. Amyotr Lateral Scler Frontotemporal Degener 15(3–4):262–268. https://doi.org/10.3109/21678421.2013.865752. (PMID: 10.3109/21678421.2013.865752)
Brooks BR, Miller RG, Swash M, Munsat TL (2000) El Escorial revisited: Revised criteria for the diagnosis of amyotrophic lateral sclerosis. Amyotroph Lateral Scler Other Motor Neuron Disord 1(5):293–299. https://doi.org/10.1080/146608200300079536. (PMID: 1146484710.1080/146608200300079536)
Cedarbaum JM et al (1999) The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function. BDNF ALS Study Group (Phase III). J Neurol Sci 169(1–2):13–21. (PMID: 1054000210.1016/S0022-510X(99)00210-5)
Chiò A, Hammond ER, Mora G, Bonito V, Filippini G (2015) Development and evaluation of a clinical staging system for amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry 86(1):38–44. (PMID: 2433681010.1136/jnnp-2013-306589)
Koller D, Friedman N, Bach F (2009) Probabilistic graphical models: principles and techniques. MIT Press, Cambridge.
Abkai C, Hesser J (2009) Virtual intensive care unit (ICU): real-time simulation environment applying hybrid approach using dynamic Bayesian networks and ODEs. Stud Health Technol Inform 142:1–6. (PMID: 19377100)
Exarchos KP et al (2015) A multiscale approach for modeling atherosclerosis progression. IEEE J Biomed Health Inform 19(2):709–719. (PMID: 2483522910.1109/JBHI.2014.2323935)
Marini S et al (2015) A dynamic Bayesian network model for long-term simulation of clinical complications in type 1 diabetes. J Biomed Inform 57:369–376. (PMID: 2632529510.1016/j.jbi.2015.08.021)
Zandonà A, Vasta R, Chiò A, Di Camillo B (2019) A dynamic Bayesian network model for the simulation of amyotrophic lateral sclerosis progression. BMC Bioinform 20(Suppl 4):118. (PMID: 10.1186/s12859-019-2692-x)
Franzin A, Sambo F, Di Camillo B (2017) bnstruct: an R package for Bayesian network structure learning in the presence of missing data. Bioinformatics 33(8):1250–1252. (PMID: 28003263)
Tsamardinos I, Brown LE, Aliferis CF (2006) The max-min hill-climbing Bayesian network structure learning algorithm. Mach Learn 65(1):31–78. https://doi.org/10.1007/s10994-006-6889-7. (PMID: 10.1007/s10994-006-6889-7)
Calvo A et al (2017) Factors predicting survival in ALS: a multicenter Italian study. J Neurol 264(1):54–63. https://doi.org/10.1007/s00415-016-8313-y. (PMID: 2777815610.1007/s00415-016-8313-y)
Longato E, Vettoretti M, Di Camillo B (2020) A practical perspective on the concordance index for the evaluation and selection of prognostic time-to-event models. J Biomed Inform 108:103496. (PMID: 3265223610.1016/j.jbi.2020.103496)
Hardiman O et al (2017) Amyotrophic lateral sclerosis. Nat Rev Dis Primers 3:17085. (PMID: 2905261110.1038/nrdp.2017.85)
McCombe PA, Henderson RD (2010) Effects of gender in amyotrophic lateral sclerosis. Gend Med 7(6):557–570. https://doi.org/10.1016/j.genm.2010.11.010. (PMID: 2119535610.1016/j.genm.2010.11.010)
Turner MR, Barnwell J, Al-Chalabi A, Eisen A (2012) Young-onset amyotrophic lateral sclerosis: historical and other observations. Brain 135(Pt 9):2883–2891. (PMID: 2266174610.1093/brain/aws144)
Mandrioli J et al (2018) Riluzole and other prognostic factors in ALS: a population-based registry study in Italy. J Neurol 265(4):817–827. https://doi.org/10.1007/s00415-018-8778-y. (PMID: 2940473510.1007/s00415-018-8778-y)
Czaplinski A (2005) Forced vital capacity (FVC) as an indicator of survival and disease progression in an ALS clinic population. J Neurol Neurosurg Psychiatry 77(3):390–392. https://doi.org/10.1136/jnnp.2005.072660. (PMID: 10.1136/jnnp.2005.072660)
Poser CM et al (1983) New diagnostic criteria for multiple sclerosis: guidelines for research protocols. Ann Neurol 13(3):227–231. (PMID: 684713410.1002/ana.410130302)
Kraemer M, Buerger M, Berlit P (2010) Diagnostic problems and delay of diagnosis in amyotrophic lateral sclerosis. Clin Neurol Neurosurg 112(2):103–105. (PMID: 1993125310.1016/j.clineuro.2009.10.014)
Turner MR, Scaber J, Goodfellow JA, Lord ME, Marsden R, Talbot K (2010) The diagnostic pathway and prognosis in bulbar-onset amyotrophic lateral sclerosis. J Neurol Sci 294(1–2):81–85. (PMID: 2045262410.1016/j.jns.2010.03.028)
Turner M, Al-Chalabi A (2002) Early symptom progression rate is related to ALS outcome: a prospective population-based study. Neurology 59(12):2012–2013 (author reply 2013). (PMID: 1249951310.1212/WNL.59.12.2012-a)
Cellura E, Spataro R, Taiello AC, La Bella V (2012) Factors affecting the diagnostic delay in amyotrophic lateral sclerosis. Clin Neurol Neurosurg 114(6):550–554. (PMID: 2216915810.1016/j.clineuro.2011.11.026)
Rosen D (1993) Mutations in Cu/Zn superoxide dismutase gene are associated with familial amyotrophic lateral sclerosis. Nature 364(6435):362–362. https://doi.org/10.1038/364362c0. (PMID: 833219710.1038/364362c0)
Sreedharan J et al (2008) TDP-43 mutations in familial and sporadic amyotrophic lateral sclerosis. Science 319(5870):1668–1672. (PMID: 18309045711665010.1126/science.1154584)
Renton AE, Chiò A, Traynor BJ (2014) State of play in amyotrophic lateral sclerosis genetics. Nat Neurosci 17(1):17–23. (PMID: 2436937310.1038/nn.3584)
Mackenzie IRA, Rademakers R, Neumann M (2010) TDP-43 and FUS in amyotrophic lateral sclerosis and frontotemporal dementia. Lancet Neurol 9(10):995–1007. https://doi.org/10.1016/s1474-4422(10)70195-2. (PMID: 2086405210.1016/s1474-4422(10)70195-2)
Majounie E et al (2012) Frequency of the C9orf72 hexanucleotide repeat expansion in patients with amyotrophic lateral sclerosis and frontotemporal dementia: a cross-sectional study. Lancet Neurol 11(4):323–330. (PMID: 22406228332242210.1016/S1474-4422(12)70043-1)
Gorges M et al (2017) Hypothalamic atrophy is related to body mass index and age at onset in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry 88(12):1033–1041. https://doi.org/10.1136/jnnp-2017-315795. (PMID: 2859625110.1136/jnnp-2017-315795)
Murphy NA, Arthur KC, Tienari PJ, Houlden H, Chiò A, Traynor BJ (2017) Age-related penetrance of the C9orf72 repeat expansion. Sci Rep 7(1):2116. (PMID: 28522837543703310.1038/s41598-017-02364-1)
Chiò A et al (2018) The multistep hypothesis of ALS revisited: the role of genetic mutations. Neurology 91(7):e635–e642. (PMID: 30045958610504010.1212/WNL.0000000000005996)
Tibshirani R (1997) The lasso method for variable selection in the Cox model. Stat Med 16(4):385–395. (PMID: 904452810.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3)
Web Application Framework for R [R package shiny version 1.6.0]. 2021 [Online]. https://CRAN.R-project.org/package=shiny . Accessed 10 Aug 2021.
Chio A, Calvo A, Moglia C, Mazzini L, Mora G, PARALS Study Group (2011) Phenotypic heterogeneity of amyotrophic lateral sclerosis: a population based study. J Neurol Neurosurg Psychiatry 82(7):740–746. https://doi.org/10.1136/jnnp.2010.235952. (PMID: 2140274310.1136/jnnp.2010.235952)
Al-Chalabi A, Jones A, Troakes C, King A, Al-Sarraj S, van den Berg LH (2012) The genetics and neuropathology of amyotrophic lateral sclerosis. Acta Neuropathol 124(3):339–352. (PMID: 2290339710.1007/s00401-012-1022-4)
Al-Chalabi A, Hardiman O, Kiernan MC, Chiò A, Rix-Brooks B, van den Berg LH (2016) Amyotrophic lateral sclerosis: moving towards a new classification system. Lancet Neurol 15(11):1182–1194. (PMID: 2764764610.1016/S1474-4422(16)30199-5)
Küffner R et al (2015) Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression. Nat Biotechnol 33(1):51–57. (PMID: 2536224310.1038/nbt.3051)
Taylor AA et al (2016) Predicting disease progression in amyotrophic lateral sclerosis. Ann Clin Transl Neurol 3(11):866–875. (PMID: 27844032509953210.1002/acn3.348)
Xu L et al (2021) Prognostic models for amyotrophic lateral sclerosis: a systematic review. J Neurol. https://doi.org/10.1007/s00415-021-10508-7. (PMID: 34839456841827910.1007/s00415-021-10508-7)
Westeneng H-J et al (2018) Prognosis for patients with amyotrophic lateral sclerosis: development and validation of a personalised prediction model. Lancet Neurol 17(5):423–433. https://doi.org/10.1016/s1474-4422(18)30089-9. (PMID: 2959892310.1016/s1474-4422(18)30089-9)
Hothorn T, Jung HH (2014) RandomForest4Life: a random forest for predicting ALS disease progression. Amyotroph Lateral Scler Frontotemporal Degener 15(5–6):444–452. (PMID: 2514107610.3109/21678421.2014.893361)
Gomeni R, Fava M, Pooled Resource Open-Access ALS Clinical Trials Consortium (2014) Amyotrophic lateral sclerosis disease progression model. Amyotroph. Lateral Scler Frontotemporal Degener 15(1–2):119–129. (PMID: 2407040410.3109/21678421.2013.838970)
Ong M-L, Tan PF, Holbrook JD (2017) Predicting functional decline and survival in amyotrophic lateral sclerosis. PLoS ONE 12(4):e0174925. (PMID: 28406915539099310.1371/journal.pone.0174925)
Kueffner R et al (2019) Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach. Sci Rep 9(1):690. (PMID: 30679616634593510.1038/s41598-018-36873-4)
Tang M et al (2019) Model-based and model-free techniques for amyotrophic lateral sclerosis diagnostic prediction and patient clustering. Neuroinformatics 17(3):407–421. (PMID: 30460455652750510.1007/s12021-018-9406-9)
Atassi N et al (2014) The PRO-ACT database: design, initial analyses, and predictive features. Neurology 83(19):1719–1725. https://doi.org/10.1212/wnl.0000000000000951. (PMID: 25298304423983410.1212/wnl.0000000000000951)
Chio A et al (2011) ALS clinical trials: do enrolled patients accurately represent the ALS population? Neurology 77(15):1432–1437. https://doi.org/10.1212/wnl.0b013e318232ab9b. (PMID: 2195672310.1212/wnl.0b013e318232ab9b)
Carreiro AV, Amaral PMT, Pinto S, Tomás P, de Carvalho M, Madeira SC (2015) Prognostic models based on patient snapshots and time windows: predicting disease progression to assisted ventilation in amyotrophic lateral sclerosis. J Biomed Inform 58:133–144. (PMID: 2645526510.1016/j.jbi.2015.09.021)
Grollemund V et al (2021) Manifold learning for amyotrophic lateral sclerosis functional loss assessment: Development and validation of a prognosis model. J Neurol 268(3):825–850. (PMID: 3288625210.1007/s00415-020-10181-2)
Chiò A et al (2017) Secular trends of amyotrophic lateral sclerosis: the Piemonte and Valle d’Aosta Register. JAMA Neurol 74(9):1097–1104. (PMID: 28692730571018110.1001/jamaneurol.2017.1387)
Mandrioli J et al (2014) Epidemiology of amyotrophic lateral sclerosis in Emilia Romagna Region (Italy): a population based study. Amyotr Lateral Scler Frontotemporal Degener 15(3–4):262–268. https://doi.org/10.3109/21678421.2013.865752. (PMID: 10.3109/21678421.2013.865752)
Brooks BR, Miller RG, Swash M, Munsat TL (2000) El Escorial revisited: Revised criteria for the diagnosis of amyotrophic lateral sclerosis. Amyotroph Lateral Scler Other Motor Neuron Disord 1(5):293–299. https://doi.org/10.1080/146608200300079536. (PMID: 1146484710.1080/146608200300079536)
Cedarbaum JM et al (1999) The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function. BDNF ALS Study Group (Phase III). J Neurol Sci 169(1–2):13–21. (PMID: 1054000210.1016/S0022-510X(99)00210-5)
Chiò A, Hammond ER, Mora G, Bonito V, Filippini G (2015) Development and evaluation of a clinical staging system for amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry 86(1):38–44. (PMID: 2433681010.1136/jnnp-2013-306589)
Koller D, Friedman N, Bach F (2009) Probabilistic graphical models: principles and techniques. MIT Press, Cambridge.
Abkai C, Hesser J (2009) Virtual intensive care unit (ICU): real-time simulation environment applying hybrid approach using dynamic Bayesian networks and ODEs. Stud Health Technol Inform 142:1–6. (PMID: 19377100)
Exarchos KP et al (2015) A multiscale approach for modeling atherosclerosis progression. IEEE J Biomed Health Inform 19(2):709–719. (PMID: 2483522910.1109/JBHI.2014.2323935)
Marini S et al (2015) A dynamic Bayesian network model for long-term simulation of clinical complications in type 1 diabetes. J Biomed Inform 57:369–376. (PMID: 2632529510.1016/j.jbi.2015.08.021)
Zandonà A, Vasta R, Chiò A, Di Camillo B (2019) A dynamic Bayesian network model for the simulation of amyotrophic lateral sclerosis progression. BMC Bioinform 20(Suppl 4):118. (PMID: 10.1186/s12859-019-2692-x)
Franzin A, Sambo F, Di Camillo B (2017) bnstruct: an R package for Bayesian network structure learning in the presence of missing data. Bioinformatics 33(8):1250–1252. (PMID: 28003263)
Tsamardinos I, Brown LE, Aliferis CF (2006) The max-min hill-climbing Bayesian network structure learning algorithm. Mach Learn 65(1):31–78. https://doi.org/10.1007/s10994-006-6889-7. (PMID: 10.1007/s10994-006-6889-7)
Calvo A et al (2017) Factors predicting survival in ALS: a multicenter Italian study. J Neurol 264(1):54–63. https://doi.org/10.1007/s00415-016-8313-y. (PMID: 2777815610.1007/s00415-016-8313-y)
Longato E, Vettoretti M, Di Camillo B (2020) A practical perspective on the concordance index for the evaluation and selection of prognostic time-to-event models. J Biomed Inform 108:103496. (PMID: 3265223610.1016/j.jbi.2020.103496)
Hardiman O et al (2017) Amyotrophic lateral sclerosis. Nat Rev Dis Primers 3:17085. (PMID: 2905261110.1038/nrdp.2017.85)
McCombe PA, Henderson RD (2010) Effects of gender in amyotrophic lateral sclerosis. Gend Med 7(6):557–570. https://doi.org/10.1016/j.genm.2010.11.010. (PMID: 2119535610.1016/j.genm.2010.11.010)
Turner MR, Barnwell J, Al-Chalabi A, Eisen A (2012) Young-onset amyotrophic lateral sclerosis: historical and other observations. Brain 135(Pt 9):2883–2891. (PMID: 2266174610.1093/brain/aws144)
Mandrioli J et al (2018) Riluzole and other prognostic factors in ALS: a population-based registry study in Italy. J Neurol 265(4):817–827. https://doi.org/10.1007/s00415-018-8778-y. (PMID: 2940473510.1007/s00415-018-8778-y)
Czaplinski A (2005) Forced vital capacity (FVC) as an indicator of survival and disease progression in an ALS clinic population. J Neurol Neurosurg Psychiatry 77(3):390–392. https://doi.org/10.1136/jnnp.2005.072660. (PMID: 10.1136/jnnp.2005.072660)
Poser CM et al (1983) New diagnostic criteria for multiple sclerosis: guidelines for research protocols. Ann Neurol 13(3):227–231. (PMID: 684713410.1002/ana.410130302)
Kraemer M, Buerger M, Berlit P (2010) Diagnostic problems and delay of diagnosis in amyotrophic lateral sclerosis. Clin Neurol Neurosurg 112(2):103–105. (PMID: 1993125310.1016/j.clineuro.2009.10.014)
Turner MR, Scaber J, Goodfellow JA, Lord ME, Marsden R, Talbot K (2010) The diagnostic pathway and prognosis in bulbar-onset amyotrophic lateral sclerosis. J Neurol Sci 294(1–2):81–85. (PMID: 2045262410.1016/j.jns.2010.03.028)
Turner M, Al-Chalabi A (2002) Early symptom progression rate is related to ALS outcome: a prospective population-based study. Neurology 59(12):2012–2013 (author reply 2013). (PMID: 1249951310.1212/WNL.59.12.2012-a)
Cellura E, Spataro R, Taiello AC, La Bella V (2012) Factors affecting the diagnostic delay in amyotrophic lateral sclerosis. Clin Neurol Neurosurg 114(6):550–554. (PMID: 2216915810.1016/j.clineuro.2011.11.026)
Rosen D (1993) Mutations in Cu/Zn superoxide dismutase gene are associated with familial amyotrophic lateral sclerosis. Nature 364(6435):362–362. https://doi.org/10.1038/364362c0. (PMID: 833219710.1038/364362c0)
Sreedharan J et al (2008) TDP-43 mutations in familial and sporadic amyotrophic lateral sclerosis. Science 319(5870):1668–1672. (PMID: 18309045711665010.1126/science.1154584)
Renton AE, Chiò A, Traynor BJ (2014) State of play in amyotrophic lateral sclerosis genetics. Nat Neurosci 17(1):17–23. (PMID: 2436937310.1038/nn.3584)
Mackenzie IRA, Rademakers R, Neumann M (2010) TDP-43 and FUS in amyotrophic lateral sclerosis and frontotemporal dementia. Lancet Neurol 9(10):995–1007. https://doi.org/10.1016/s1474-4422(10)70195-2. (PMID: 2086405210.1016/s1474-4422(10)70195-2)
Majounie E et al (2012) Frequency of the C9orf72 hexanucleotide repeat expansion in patients with amyotrophic lateral sclerosis and frontotemporal dementia: a cross-sectional study. Lancet Neurol 11(4):323–330. (PMID: 22406228332242210.1016/S1474-4422(12)70043-1)
Gorges M et al (2017) Hypothalamic atrophy is related to body mass index and age at onset in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry 88(12):1033–1041. https://doi.org/10.1136/jnnp-2017-315795. (PMID: 2859625110.1136/jnnp-2017-315795)
Murphy NA, Arthur KC, Tienari PJ, Houlden H, Chiò A, Traynor BJ (2017) Age-related penetrance of the C9orf72 repeat expansion. Sci Rep 7(1):2116. (PMID: 28522837543703310.1038/s41598-017-02364-1)
Chiò A et al (2018) The multistep hypothesis of ALS revisited: the role of genetic mutations. Neurology 91(7):e635–e642. (PMID: 30045958610504010.1212/WNL.0000000000005996)
Tibshirani R (1997) The lasso method for variable selection in the Cox model. Stat Med 16(4):385–395. (PMID: 904452810.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3)
Web Application Framework for R [R package shiny version 1.6.0]. 2021 [Online]. https://CRAN.R-project.org/package=shiny . Accessed 10 Aug 2021.
Grant Information:
ERRALS register grant Regione Emilia-Romagna; 259867 Seventh Framework Programme; "Departments of Excellence" (Law 232/2016) Ministero dell'Istruzione, dell'Università e della Ricerca; PRIN, grant 2017SNW5MB Ministero dell'Istruzione, dell'Università e della Ricerca; CompALS project Ministero degli Affari Esteri e della Cooperazione Internazionale; Ricerca Sanitaria Finalizzata, grant RF-2016-02362 Ministero della Salute; CompALS project Ministry of Science, Technology and Space of the State of Israel
Contributed Indexing:
Investigator: A Chiò; RL Montalcini; A Calvo; C Moglia; A Canosa; U Manera; R Vasta; F Palumbo; A Bombaci; M Grassano; M Brunetti; F Casale; G Fuda; P Salomone; B Iazzolino; L Peotta; P Cugnasco; G De Marco; MC Torrieri; S Gallone; M Barberis; L Sbaiz; S Gentile; A Mauro; L Mazzini; F Marchi; L Corrado; S D'Alfonso; A Bertolotto; M Gionco; D Leotta; E Oddenino; R Cavallo; M De Mattei; G Gusmaroli; C Comi; C Labate; F Poglio; L Ruiz; D Ferrandi; L Testa; E Rota; M Aguggia; N Di Vito; P Meineri; P Ghiglione; N Launaro; M Dotta; A Sapio; M Giovanni; J Mandrioli; J Mandrioli; N Fini; I Martinelli; E Zucchi; G Gianferrari; C Simonini; M Vinceti; S Meletti; V Vacchiano; R Liguori; F Salvi; I Bartolomei; R Michelucci; P Cortelli; AM Borghi; A Zini; R Rinaldi; P Cortelli; E Sette; V Tugnoli; M Pugliatti; E Canali; L Codeluppi; F Valzania; L Zinno; G Pavesi; D Medici; G Pilurzi; E Terlizzi; D Guidetti; S Pasqua; M Santangelo; M Bracaglia; P DeMassis; M Casmiro; P Querzani; S Morresi; M Longoni; A Patuelli; S Malagù; M Longoni; M Currò Dossi; S Vidale
Keywords: Amyotrophic lateral sclerosis; Artificial intelligence; Clinical trajectories; Dynamic Bayesian Networks; Population model; Prognosis modelling
Keywords: Amyotrophic lateral sclerosis; Artificial intelligence; Clinical trajectories; Dynamic Bayesian Networks; Population model; Prognosis modelling
Entry Date(s):
Date Created: 20220310 Date Completed: 20220624 Latest Revision: 20220826
Update Code:
20250114
PubMed Central ID:
PMC9217910
DOI:
10.1007/s00415-022-11022-0
PMID:
35266043
Database:
MEDLINE
Weitere Informationen
Objective: To employ Artificial Intelligence to model, predict and simulate the amyotrophic lateral sclerosis (ALS) progression over time in terms of variable interactions, functional impairments, and survival.
Methods: We employed demographic and clinical variables, including functional scores and the utilisation of support interventions, of 3940 ALS patients from four Italian and two Israeli registers to develop a new approach based on Dynamic Bayesian Networks (DBNs) that models the ALS evolution over time, in two distinct scenarios of variable availability. The method allows to simulate patients' disease trajectories and predict the probability of functional impairment and survival at different time points.
Results: DBNs explicitly represent the relationships between the variables and the pathways along which they influence the disease progression. Several notable inter-dependencies were identified and validated by comparison with literature. Moreover, the implemented tool allows the assessment of the effect of different markers on the disease course, reproducing the probabilistically expected clinical progressions. The tool shows high concordance in terms of predicted and real prognosis, assessed as time to functional impairments and survival (integral of the AU-ROC in the first 36 months between 0.80-0.93 and 0.84-0.89 for the two scenarios, respectively).
Conclusions: Provided only with measurements commonly collected during the first visit, our models can predict time to the loss of independence in walking, breathing, swallowing, communicating, and survival and it can be used to generate in silico patient cohorts with specific characteristics. Our tool provides a comprehensive framework to support physicians in treatment planning and clinical decision-making.
(© 2022. The Author(s).)