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Mar J, Gorostiza A, Arrospide A, Larrañaga I, Alberdi A, Cernuda C, Iruin Á, Tainta M, Mar-Barrutia L, Ibarrondo O. Estimation of the epidemiology of dementia and associated neuropsychiatric symptoms by applying machine learning to real-world data. Rev Psiquiatr Salud Ment (Engl Ed) 2022; 15:167-175. [PMID: 36272739 DOI: 10.1016/j.rpsmen.2022.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 03/14/2021] [Indexed: 06/16/2023]
Abstract
INTRODUCTION Incidence rates of dementia-related neuropsychiatric symptoms (NPS) are not known and this hampers the assessment of their population burden. The objective of this study was to obtain an approximate estimate of the population incidence and prevalence of both dementia and NPS. METHODS Given the dynamic nature of the population with dementia, a retrospective study was conducted within the database of the Basque Health Service (real-world data) at the beginning and end of 2019. Validated random forest models were used to identify separately depressive and psychotic clusters according to their presence in the electronic health records of all patients diagnosed with dementia. RESULTS Among the 631,949 individuals over 60 years registered, 28,563 were diagnosed with dementia, of whom 15,828 (55.4%) showed psychotic symptoms and 19,461 (68.1%) depressive symptoms. The incidence of dementia in 2019 was 6.8/1000 person-years. Most incident cases of depressive (72.3%) and psychotic (51.9%) NPS occurred in cases of incident dementia. The risk of depressive-type NPS grows with years since dementia diagnosis, living in a nursing home, and female sex, but falls with older age. In the psychotic cluster model, the effects of male sex, and older age are inverted, both increasing the probability of this type of symptoms. CONCLUSIONS The stigmatization factor conditions the social and attitudinal environment, delaying the diagnosis of dementia, preventing patients from receiving adequate care and exacerbating families' suffering. This study evidences the synergy between big data and real-world data for psychiatric epidemiological research.
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Affiliation(s)
- Javier Mar
- Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Gipuzkoa, Spain; Kronikgune Institute for Health Service Research, Barakaldo, Bizkaia, Spain; Biodonostia Health Research Institute, Donostia-San Sebastián, Gipuzkoa, Spain; Health Services Research on Chronic Patients Network (REDISSEC), Bilbao, Bizkaia, Spain.
| | - Ania Gorostiza
- Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Gipuzkoa, Spain; Kronikgune Institute for Health Service Research, Barakaldo, Bizkaia, Spain
| | - Arantzazu Arrospide
- Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Gipuzkoa, Spain; Kronikgune Institute for Health Service Research, Barakaldo, Bizkaia, Spain; Biodonostia Health Research Institute, Donostia-San Sebastián, Gipuzkoa, Spain; Health Services Research on Chronic Patients Network (REDISSEC), Bilbao, Bizkaia, Spain
| | - Igor Larrañaga
- Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Gipuzkoa, Spain; Kronikgune Institute for Health Service Research, Barakaldo, Bizkaia, Spain
| | - Ane Alberdi
- Mondragon Unibertsitatea, Faculty of Engineering, Electronics and Computing Department, Arrasate-Mondragón, Gipuzkoa, Spain
| | - Carlos Cernuda
- Mondragon Unibertsitatea, Faculty of Engineering, Electronics and Computing Department, Arrasate-Mondragón, Gipuzkoa, Spain
| | - Álvaro Iruin
- Basque Health Service (Osakidetza), Gipuzkoa Mental Health Network, Donostia-San Sebastián, Gipuzkoa, Spain; Biodonostia Health Research Institute, Donostia-San Sebastián, Gipuzkoa, Spain
| | - Mikel Tainta
- Basque Health Service (Osakidetza), Goierri-Urola Garaia Integrated Healthcare Organisation, Department of Neurology, Zumarraga, Gipuzkoa, Spain; Fundación CITA-Alzheimer Fundazioa, Donostia-San Sebastián, Gipuzkoa, Spain
| | - Lorea Mar-Barrutia
- Psiquiatry Service, Hospital Bellvitge, Hospitalet de Llobregat, Barcelona, Spain
| | - Oliver Ibarrondo
- Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Gipuzkoa, Spain; Biodonostia Health Research Institute, Donostia-San Sebastián, Gipuzkoa, Spain; RS-Statistics, Arrasate-Mondragón, Gipuzkoa, Spain
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Mar J, Gorostiza A, Arrospide A, Larrañaga I, Alberdi A, Cernuda C, Iruin Á, Tainta M, Mar-Barrutia L, Ibarrondo O. Estimation of the epidemiology of dementia and associated neuropsychiatric symptoms by applying machine learning to real-world data. Rev Psiquiatr Salud Ment (Engl Ed) 2021; 15:S1888-9891(21)00032-X. [PMID: 33774222 DOI: 10.1016/j.rpsm.2021.03.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 03/14/2021] [Indexed: 11/17/2022]
Abstract
INTRODUCTION Incidence rates of dementia-related neuropsychiatric symptoms (NPS) are not known and this hampers the assessment of their population burden. The objective of this study was to obtain an approximate estimate of the population incidence and prevalence of both dementia and NPS. METHODS Given the dynamic nature of the population with dementia, a retrospective study was conducted within the database of the Basque Health Service (real-world data) at the beginning and end of 2019. Validated random forest models were used to identify separately depressive and psychotic clusters according to their presence in the electronic health records of all patients diagnosed with dementia. RESULTS Among the 631,949 individuals over 60 years registered, 28,563 were diagnosed with dementia, of whom 15,828 (55.4%) showed psychotic symptoms and 19,461 (68.1%) depressive symptoms. The incidence of dementia in 2019 was 6.8/1000 person-years. Most incident cases of depressive (72.3%) and psychotic (51.9%) NPS occurred in cases of incident dementia. The risk of depressive-type NPS grows with years since dementia diagnosis, living in a nursing home, and female sex, but falls with older age. In the psychotic cluster model, the effects of male sex, and older age are inverted, both increasing the probability of this type of symptoms. CONCLUSIONS The stigmatization factor conditions the social and attitudinal environment, delaying the diagnosis of dementia, preventing patients from receiving adequate care and exacerbating families' suffering. This study evidences the synergy between big data and real-world data for psychiatric epidemiological research.
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Affiliation(s)
- Javier Mar
- Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Gipuzkoa, Spain; Kronikgune Institute for Health Service Research, Barakaldo, Bizkaia, Spain; Biodonostia Health Research Institute, Donostia-San Sebastián, Gipuzkoa, Spain; Health Services Research on Chronic Patients Network (REDISSEC), Bilbao, Bizkaia, Spain.
| | - Ania Gorostiza
- Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Gipuzkoa, Spain; Kronikgune Institute for Health Service Research, Barakaldo, Bizkaia, Spain
| | - Arantzazu Arrospide
- Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Gipuzkoa, Spain; Kronikgune Institute for Health Service Research, Barakaldo, Bizkaia, Spain; Biodonostia Health Research Institute, Donostia-San Sebastián, Gipuzkoa, Spain; Health Services Research on Chronic Patients Network (REDISSEC), Bilbao, Bizkaia, Spain
| | - Igor Larrañaga
- Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Gipuzkoa, Spain; Kronikgune Institute for Health Service Research, Barakaldo, Bizkaia, Spain
| | - Ane Alberdi
- Mondragon Unibertsitatea, Faculty of Engineering, Electronics and Computing Department, Arrasate-Mondragón, Gipuzkoa, Spain
| | - Carlos Cernuda
- Mondragon Unibertsitatea, Faculty of Engineering, Electronics and Computing Department, Arrasate-Mondragón, Gipuzkoa, Spain
| | - Álvaro Iruin
- Basque Health Service (Osakidetza), Gipuzkoa Mental Health Network, Donostia-San Sebastián, Gipuzkoa, Spain; Biodonostia Health Research Institute, Donostia-San Sebastián, Gipuzkoa, Spain
| | - Mikel Tainta
- Basque Health Service (Osakidetza), Goierri-Urola Garaia Integrated Healthcare Organisation, Department of Neurology, Zumarraga, Gipuzkoa, Spain; Fundación CITA-Alzheimer Fundazioa, Donostia-San Sebastián, Gipuzkoa, Spain
| | - Lorea Mar-Barrutia
- Psiquiatry Service, Hospital Bellvitge, Hospitalet de Llobregat, Barcelona, Spain
| | - Oliver Ibarrondo
- Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Gipuzkoa, Spain; Biodonostia Health Research Institute, Donostia-San Sebastián, Gipuzkoa, Spain; RS-Statistics, Arrasate-Mondragón, Gipuzkoa, Spain
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Mar J, Gorostiza A, Ibarrondo O, Cernuda C, Alberdi A, Iruin Á, Tainta M. Validation and calibration of machine‐learning predictive models aimed to identify dementia‐related neuropsychiatric symptoms on real‐world data (RWD). Alzheimers Dement 2020. [DOI: 10.1002/alz.039104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Javier Mar
- Alto Deba Hospital Arrasate‐Mondragón Spain
| | | | | | | | - Ane Alberdi
- Mondragon University Arrasate‐Mondragón Spain
| | - Álvaro Iruin
- Gipuzkoa Mental Health Network Donostia‐San Sebastian Spain
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Mar J, Gorostiza A, Ibarrondo O, Cernuda C, Arrospide A, Iruin Á, Larrañaga I, Tainta M, Ezpeleta E, Alberdi A. Validation of Random Forest Machine Learning Models to Predict Dementia-Related Neuropsychiatric Symptoms in Real-World Data. J Alzheimers Dis 2020; 77:855-864. [PMID: 32741825 PMCID: PMC7592688 DOI: 10.3233/jad-200345] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/24/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND Neuropsychiatric symptoms (NPS) are the leading cause of the social burden of dementia but their role is underestimated. OBJECTIVE The objective of the study was to validate predictive models to separately identify psychotic and depressive symptoms in patients diagnosed with dementia using clinical databases representing the whole population to inform decision-makers. METHODS First, we searched the electronic health records of 4,003 patients with dementia to identify NPS. Second, machine learning (random forest) algorithms were applied to build separate predictive models for psychotic and depressive symptom clusters in the training set (N = 3,003). Third, calibration and discrimination were assessed in the test set (N = 1,000) to assess the performance of the models. RESULTS Neuropsychiatric symptoms were noted in the electronic health record of 58% of patients. The area under the receiver operating curve reached 0.80 for the psychotic cluster model and 0.74 for the depressive cluster model. The Kappa index and accuracy also showed better discrimination in the psychotic model. Calibration plots indicated that both types of model had less predictive accuracy when the probability of neuropsychiatric symptoms was <25%. The most important variables in the psychotic cluster model were use of risperidone, level of sedation, use of quetiapine and haloperidol and the number of antipsychotics prescribed. In the depressive cluster model, the most important variables were number of antidepressants prescribed, escitalopram use, level of sedation, and age. CONCLUSION Given their relatively good performance, the predictive models can be used to estimate prevalence of NPS in population databases.
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Affiliation(s)
- Javier Mar
- Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Guipúzcoa, Spain
- Kronikgune Institute for Health Service Research, Barakaldo, Spain
- Biodonostia Health Research Institute, Donostia-San Sebastán, Guipúzcoa, Spain
- Health Services Research on Chronic Patients Network (REDISSEC), Bilbao, Vizcaya, Spain
| | - Ania Gorostiza
- Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Guipúzcoa, Spain
- Kronikgune Institute for Health Service Research, Barakaldo, Spain
| | - Oliver Ibarrondo
- Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Guipúzcoa, Spain
- Kronikgune Institute for Health Service Research, Barakaldo, Spain
- Biodonostia Health Research Institute, Donostia-San Sebastán, Guipúzcoa, Spain
| | - Carlos Cernuda
- Mondragon Unibertsitatea, Faculty of Engineering, Electronics and Computing Department, Arrasate-Mondragon, Gipuzkoa, Spain
| | - Arantzazu Arrospide
- Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Guipúzcoa, Spain
- Kronikgune Institute for Health Service Research, Barakaldo, Spain
- Biodonostia Health Research Institute, Donostia-San Sebastán, Guipúzcoa, Spain
- Health Services Research on Chronic Patients Network (REDISSEC), Bilbao, Vizcaya, Spain
| | - Álvaro Iruin
- Biodonostia Health Research Institute, Donostia-San Sebastán, Guipúzcoa, Spain
- Basque Health Service (Osakidetza), Gipuzkoa Mental Health Network, Donostia-San Sebastián, Guipúzcoa, Spain
| | - Igor Larrañaga
- Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Guipúzcoa, Spain
- Kronikgune Institute for Health Service Research, Barakaldo, Spain
| | - Mikel Tainta
- Kronikgune Institute for Health Service Research, Barakaldo, Spain
- Department of Neurology, Basque Health Service (Osakidetza), Goierri-Urola Garaia Integrated Healthcare Organisation, Zumarraga, Guipúzcoa, Spain
- Fundación CITA-Alzheimer Fundazioa, Donostia-San Sebastián, Guipúzcoa, Spain
| | - Enaitz Ezpeleta
- Mondragon Unibertsitatea, Faculty of Engineering, Electronics and Computing Department, Arrasate-Mondragon, Gipuzkoa, Spain
| | - Ane Alberdi
- Mondragon Unibertsitatea, Faculty of Engineering, Electronics and Computing Department, Arrasate-Mondragon, Gipuzkoa, Spain
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Cernuda C, Lughofer E, Klein H, Forster C, Pawliczek M, Brandstetter M. Improved quantification of important beer quality parameters based on nonlinear calibration methods applied to FT-MIR spectra. Anal Bioanal Chem 2016; 409:841-857. [DOI: 10.1007/s00216-016-9785-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Revised: 06/08/2016] [Accepted: 07/08/2016] [Indexed: 11/24/2022]
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Cernuda C, Lughofer E, Suppan L, Röder T, Schmuck R, Hintenaus P, Märzinger W, Kasberger J. Evolving chemometric models for predicting dynamic process parameters in viscose production. Anal Chim Acta 2012; 725:22-38. [DOI: 10.1016/j.aca.2012.03.012] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2011] [Revised: 01/19/2012] [Accepted: 03/07/2012] [Indexed: 11/28/2022]
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