Automated tests for diagnosing and monitoring cognitive impairment: a diagnostic accuracy review

Background: Cognitive impairment is a growing public health concern, and is one of the most distinctive characteristics of all dementias. The timely recognition of dementia syndromes can be beneficial, as some causes of dementia are treatable and are fully or partially reversible. Several automated cognitive assessment tools for assessing mild cognitive impairment (MCI) and early dementia are now available. Proponents of these tests cite as benefits the tests’ repeatability and robustness and the saving of clinicians’ time. However, the use of these tools to diagnose and/or monitor progressive cognitive impairment or response to treatment has not yet been evaluated. Objectives: The aim of this review was to determine whether or not automated computerised tests could accurately identify patients with progressive cognitive impairment in MCI and dementia and, if so, to investigate their role in monitoring disease progression and/or response to treatment. Data sources: Five electronic databases (MEDLINE, EMBASE, The Cochrane Library, ISI Web of Science and PsycINFO), plus ProQuest, were searched from 2005 to August 2015. The bibliographies of retrieved citations were also examined. Trial and research registers were searched for ongoing studies and reviews. A second search was run to identify individual test costs and acquisition costs for the various tools identified in the review. Review methods: Two reviewers independently screened all titles and abstracts to identify potentially relevant studies for inclusion in the review. Full-text copies were assessed independently by two reviewers. Data were extracted and assessed for risk of bias by one reviewer and independently checked for accuracy by a second. The results of the data extraction and quality assessment for each study are presented in structured tables and as a narrative summary. Results: The electronic searching of databases, including ProQuest, resulted in 13,542 unique citations. The titles and abstracts of these were screened and 399 articles were shortlisted for full-text assessment. Sixteen studies were included in the diagnostic accuracy review. No studies were eligible for inclusion in the review of tools for monitoring progressive disease. Eleven automated computerised tests were assessed in the 16 included studies. The overall quality of the studies was good; however, the wide range of tests assessed and the non-standardised reporting of diagnostic accuracy outcomes meant that meaningful synthesis or statistical analysis was not possible. Limitations: The main limitation of this review is the substantial heterogeneity of the tests assessed in the included studies. As a result, no meta-analyses could be undertaken. Conclusion: The quantity of information available is insufficient to be able to make recommendations on the clinical use of the computerised tests for diagnosing and monitoring MCI and early dementia progression. The value of these tests also depends on the costs of acquisition, training, administration and scoring. Future work: Research is required to establish stable cut-off points for automated computerised tests that are used to diagnose patients with MCI or early dementia. Additionally, the costs associated with acquiring and using these tests in clinical practice should be estimated. Study registration: The study is registered as PROSPERO CRD42015025410. Funding: The National Institute for Health Research Health Technology Assessment programme. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Rabeea’h W Aslam [verfasserIn] Vickie Bates [verfasserIn] Yenal Dundar [verfasserIn] Juliet Hounsome [verfasserIn] Marty Richardson [verfasserIn] Ashma Krishan [verfasserIn] Rumona Dickson [verfasserIn] Angela Boland [verfasserIn] Eleanor Kotas [verfasserIn] Joanne Fisher [verfasserIn] Sudip Sikdar [verfasserIn] Louise Robinson [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	dementia alzheimer’s disease cognitive impairment automated tests systematic review diagnostic accuracy

Übergeordnetes Werk:	In: Health Technology Assessment - NIHR Journals Library, 2018, 20(2016), 77
Übergeordnetes Werk:	volume:20 ; year:2016 ; number:77

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.3310/hta20770

Katalog-ID:	DOAJ038633450

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520			\|a Background: Cognitive impairment is a growing public health concern, and is one of the most distinctive characteristics of all dementias. The timely recognition of dementia syndromes can be beneficial, as some causes of dementia are treatable and are fully or partially reversible. Several automated cognitive assessment tools for assessing mild cognitive impairment (MCI) and early dementia are now available. Proponents of these tests cite as benefits the tests’ repeatability and robustness and the saving of clinicians’ time. However, the use of these tools to diagnose and/or monitor progressive cognitive impairment or response to treatment has not yet been evaluated. Objectives: The aim of this review was to determine whether or not automated computerised tests could accurately identify patients with progressive cognitive impairment in MCI and dementia and, if so, to investigate their role in monitoring disease progression and/or response to treatment. Data sources: Five electronic databases (MEDLINE, EMBASE, The Cochrane Library, ISI Web of Science and PsycINFO), plus ProQuest, were searched from 2005 to August 2015. The bibliographies of retrieved citations were also examined. Trial and research registers were searched for ongoing studies and reviews. A second search was run to identify individual test costs and acquisition costs for the various tools identified in the review. Review methods: Two reviewers independently screened all titles and abstracts to identify potentially relevant studies for inclusion in the review. Full-text copies were assessed independently by two reviewers. Data were extracted and assessed for risk of bias by one reviewer and independently checked for accuracy by a second. The results of the data extraction and quality assessment for each study are presented in structured tables and as a narrative summary. Results: The electronic searching of databases, including ProQuest, resulted in 13,542 unique citations. The titles and abstracts of these were screened and 399 articles were shortlisted for full-text assessment. Sixteen studies were included in the diagnostic accuracy review. No studies were eligible for inclusion in the review of tools for monitoring progressive disease. Eleven automated computerised tests were assessed in the 16 included studies. The overall quality of the studies was good; however, the wide range of tests assessed and the non-standardised reporting of diagnostic accuracy outcomes meant that meaningful synthesis or statistical analysis was not possible. Limitations: The main limitation of this review is the substantial heterogeneity of the tests assessed in the included studies. As a result, no meta-analyses could be undertaken. Conclusion: The quantity of information available is insufficient to be able to make recommendations on the clinical use of the computerised tests for diagnosing and monitoring MCI and early dementia progression. The value of these tests also depends on the costs of acquisition, training, administration and scoring. Future work: Research is required to establish stable cut-off points for automated computerised tests that are used to diagnose patients with MCI or early dementia. Additionally, the costs associated with acquiring and using these tests in clinical practice should be estimated. Study registration: The study is registered as PROSPERO CRD42015025410. Funding: The National Institute for Health Research Health Technology Assessment programme.
650		4	\|a dementia
650		4	\|a alzheimer’s disease
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allfields	10.3310/hta20770 doi (DE-627)DOAJ038633450 (DE-599)DOAJ9138927dd7714c38b9b8592fe10168b2 DE-627 ger DE-627 rakwb eng R855-855.5 Rabeea’h W Aslam verfasserin aut Automated tests for diagnosing and monitoring cognitive impairment: a diagnostic accuracy review 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Cognitive impairment is a growing public health concern, and is one of the most distinctive characteristics of all dementias. The timely recognition of dementia syndromes can be beneficial, as some causes of dementia are treatable and are fully or partially reversible. Several automated cognitive assessment tools for assessing mild cognitive impairment (MCI) and early dementia are now available. Proponents of these tests cite as benefits the tests’ repeatability and robustness and the saving of clinicians’ time. However, the use of these tools to diagnose and/or monitor progressive cognitive impairment or response to treatment has not yet been evaluated. Objectives: The aim of this review was to determine whether or not automated computerised tests could accurately identify patients with progressive cognitive impairment in MCI and dementia and, if so, to investigate their role in monitoring disease progression and/or response to treatment. Data sources: Five electronic databases (MEDLINE, EMBASE, The Cochrane Library, ISI Web of Science and PsycINFO), plus ProQuest, were searched from 2005 to August 2015. The bibliographies of retrieved citations were also examined. Trial and research registers were searched for ongoing studies and reviews. A second search was run to identify individual test costs and acquisition costs for the various tools identified in the review. Review methods: Two reviewers independently screened all titles and abstracts to identify potentially relevant studies for inclusion in the review. Full-text copies were assessed independently by two reviewers. Data were extracted and assessed for risk of bias by one reviewer and independently checked for accuracy by a second. The results of the data extraction and quality assessment for each study are presented in structured tables and as a narrative summary. Results: The electronic searching of databases, including ProQuest, resulted in 13,542 unique citations. The titles and abstracts of these were screened and 399 articles were shortlisted for full-text assessment. Sixteen studies were included in the diagnostic accuracy review. No studies were eligible for inclusion in the review of tools for monitoring progressive disease. Eleven automated computerised tests were assessed in the 16 included studies. The overall quality of the studies was good; however, the wide range of tests assessed and the non-standardised reporting of diagnostic accuracy outcomes meant that meaningful synthesis or statistical analysis was not possible. Limitations: The main limitation of this review is the substantial heterogeneity of the tests assessed in the included studies. As a result, no meta-analyses could be undertaken. Conclusion: The quantity of information available is insufficient to be able to make recommendations on the clinical use of the computerised tests for diagnosing and monitoring MCI and early dementia progression. The value of these tests also depends on the costs of acquisition, training, administration and scoring. Future work: Research is required to establish stable cut-off points for automated computerised tests that are used to diagnose patients with MCI or early dementia. Additionally, the costs associated with acquiring and using these tests in clinical practice should be estimated. Study registration: The study is registered as PROSPERO CRD42015025410. Funding: The National Institute for Health Research Health Technology Assessment programme. dementia alzheimer’s disease cognitive impairment automated tests systematic review diagnostic accuracy Medical technology Vickie Bates verfasserin aut Yenal Dundar verfasserin aut Juliet Hounsome verfasserin aut Marty Richardson verfasserin aut Ashma Krishan verfasserin aut Rumona Dickson verfasserin aut Angela Boland verfasserin aut Eleanor Kotas verfasserin aut Joanne Fisher verfasserin aut Sudip Sikdar verfasserin aut Louise Robinson verfasserin aut In Health Technology Assessment NIHR Journals Library, 2018 20(2016), 77 (DE-627)335259553 (DE-600)2059206-1 20464924 nnns volume:20 year:2016 number:77 https://doi.org/10.3310/hta20770 kostenfrei https://doaj.org/article/9138927dd7714c38b9b8592fe10168b2 kostenfrei https://doi.org/10.3310/hta20770 kostenfrei https://doaj.org/toc/1366-5278 Journal toc kostenfrei https://doaj.org/toc/2046-4924 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2016 77
spelling	10.3310/hta20770 doi (DE-627)DOAJ038633450 (DE-599)DOAJ9138927dd7714c38b9b8592fe10168b2 DE-627 ger DE-627 rakwb eng R855-855.5 Rabeea’h W Aslam verfasserin aut Automated tests for diagnosing and monitoring cognitive impairment: a diagnostic accuracy review 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Cognitive impairment is a growing public health concern, and is one of the most distinctive characteristics of all dementias. The timely recognition of dementia syndromes can be beneficial, as some causes of dementia are treatable and are fully or partially reversible. Several automated cognitive assessment tools for assessing mild cognitive impairment (MCI) and early dementia are now available. Proponents of these tests cite as benefits the tests’ repeatability and robustness and the saving of clinicians’ time. However, the use of these tools to diagnose and/or monitor progressive cognitive impairment or response to treatment has not yet been evaluated. Objectives: The aim of this review was to determine whether or not automated computerised tests could accurately identify patients with progressive cognitive impairment in MCI and dementia and, if so, to investigate their role in monitoring disease progression and/or response to treatment. Data sources: Five electronic databases (MEDLINE, EMBASE, The Cochrane Library, ISI Web of Science and PsycINFO), plus ProQuest, were searched from 2005 to August 2015. The bibliographies of retrieved citations were also examined. Trial and research registers were searched for ongoing studies and reviews. A second search was run to identify individual test costs and acquisition costs for the various tools identified in the review. Review methods: Two reviewers independently screened all titles and abstracts to identify potentially relevant studies for inclusion in the review. Full-text copies were assessed independently by two reviewers. Data were extracted and assessed for risk of bias by one reviewer and independently checked for accuracy by a second. The results of the data extraction and quality assessment for each study are presented in structured tables and as a narrative summary. Results: The electronic searching of databases, including ProQuest, resulted in 13,542 unique citations. The titles and abstracts of these were screened and 399 articles were shortlisted for full-text assessment. Sixteen studies were included in the diagnostic accuracy review. No studies were eligible for inclusion in the review of tools for monitoring progressive disease. Eleven automated computerised tests were assessed in the 16 included studies. The overall quality of the studies was good; however, the wide range of tests assessed and the non-standardised reporting of diagnostic accuracy outcomes meant that meaningful synthesis or statistical analysis was not possible. Limitations: The main limitation of this review is the substantial heterogeneity of the tests assessed in the included studies. As a result, no meta-analyses could be undertaken. Conclusion: The quantity of information available is insufficient to be able to make recommendations on the clinical use of the computerised tests for diagnosing and monitoring MCI and early dementia progression. The value of these tests also depends on the costs of acquisition, training, administration and scoring. Future work: Research is required to establish stable cut-off points for automated computerised tests that are used to diagnose patients with MCI or early dementia. Additionally, the costs associated with acquiring and using these tests in clinical practice should be estimated. Study registration: The study is registered as PROSPERO CRD42015025410. Funding: The National Institute for Health Research Health Technology Assessment programme. dementia alzheimer’s disease cognitive impairment automated tests systematic review diagnostic accuracy Medical technology Vickie Bates verfasserin aut Yenal Dundar verfasserin aut Juliet Hounsome verfasserin aut Marty Richardson verfasserin aut Ashma Krishan verfasserin aut Rumona Dickson verfasserin aut Angela Boland verfasserin aut Eleanor Kotas verfasserin aut Joanne Fisher verfasserin aut Sudip Sikdar verfasserin aut Louise Robinson verfasserin aut In Health Technology Assessment NIHR Journals Library, 2018 20(2016), 77 (DE-627)335259553 (DE-600)2059206-1 20464924 nnns volume:20 year:2016 number:77 https://doi.org/10.3310/hta20770 kostenfrei https://doaj.org/article/9138927dd7714c38b9b8592fe10168b2 kostenfrei https://doi.org/10.3310/hta20770 kostenfrei https://doaj.org/toc/1366-5278 Journal toc kostenfrei https://doaj.org/toc/2046-4924 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2016 77
allfields_unstemmed	10.3310/hta20770 doi (DE-627)DOAJ038633450 (DE-599)DOAJ9138927dd7714c38b9b8592fe10168b2 DE-627 ger DE-627 rakwb eng R855-855.5 Rabeea’h W Aslam verfasserin aut Automated tests for diagnosing and monitoring cognitive impairment: a diagnostic accuracy review 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Cognitive impairment is a growing public health concern, and is one of the most distinctive characteristics of all dementias. The timely recognition of dementia syndromes can be beneficial, as some causes of dementia are treatable and are fully or partially reversible. Several automated cognitive assessment tools for assessing mild cognitive impairment (MCI) and early dementia are now available. Proponents of these tests cite as benefits the tests’ repeatability and robustness and the saving of clinicians’ time. However, the use of these tools to diagnose and/or monitor progressive cognitive impairment or response to treatment has not yet been evaluated. Objectives: The aim of this review was to determine whether or not automated computerised tests could accurately identify patients with progressive cognitive impairment in MCI and dementia and, if so, to investigate their role in monitoring disease progression and/or response to treatment. Data sources: Five electronic databases (MEDLINE, EMBASE, The Cochrane Library, ISI Web of Science and PsycINFO), plus ProQuest, were searched from 2005 to August 2015. The bibliographies of retrieved citations were also examined. Trial and research registers were searched for ongoing studies and reviews. A second search was run to identify individual test costs and acquisition costs for the various tools identified in the review. Review methods: Two reviewers independently screened all titles and abstracts to identify potentially relevant studies for inclusion in the review. Full-text copies were assessed independently by two reviewers. Data were extracted and assessed for risk of bias by one reviewer and independently checked for accuracy by a second. The results of the data extraction and quality assessment for each study are presented in structured tables and as a narrative summary. Results: The electronic searching of databases, including ProQuest, resulted in 13,542 unique citations. The titles and abstracts of these were screened and 399 articles were shortlisted for full-text assessment. Sixteen studies were included in the diagnostic accuracy review. No studies were eligible for inclusion in the review of tools for monitoring progressive disease. Eleven automated computerised tests were assessed in the 16 included studies. The overall quality of the studies was good; however, the wide range of tests assessed and the non-standardised reporting of diagnostic accuracy outcomes meant that meaningful synthesis or statistical analysis was not possible. Limitations: The main limitation of this review is the substantial heterogeneity of the tests assessed in the included studies. As a result, no meta-analyses could be undertaken. Conclusion: The quantity of information available is insufficient to be able to make recommendations on the clinical use of the computerised tests for diagnosing and monitoring MCI and early dementia progression. The value of these tests also depends on the costs of acquisition, training, administration and scoring. Future work: Research is required to establish stable cut-off points for automated computerised tests that are used to diagnose patients with MCI or early dementia. Additionally, the costs associated with acquiring and using these tests in clinical practice should be estimated. Study registration: The study is registered as PROSPERO CRD42015025410. Funding: The National Institute for Health Research Health Technology Assessment programme. dementia alzheimer’s disease cognitive impairment automated tests systematic review diagnostic accuracy Medical technology Vickie Bates verfasserin aut Yenal Dundar verfasserin aut Juliet Hounsome verfasserin aut Marty Richardson verfasserin aut Ashma Krishan verfasserin aut Rumona Dickson verfasserin aut Angela Boland verfasserin aut Eleanor Kotas verfasserin aut Joanne Fisher verfasserin aut Sudip Sikdar verfasserin aut Louise Robinson verfasserin aut In Health Technology Assessment NIHR Journals Library, 2018 20(2016), 77 (DE-627)335259553 (DE-600)2059206-1 20464924 nnns volume:20 year:2016 number:77 https://doi.org/10.3310/hta20770 kostenfrei https://doaj.org/article/9138927dd7714c38b9b8592fe10168b2 kostenfrei https://doi.org/10.3310/hta20770 kostenfrei https://doaj.org/toc/1366-5278 Journal toc kostenfrei https://doaj.org/toc/2046-4924 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2016 77
allfieldsGer	10.3310/hta20770 doi (DE-627)DOAJ038633450 (DE-599)DOAJ9138927dd7714c38b9b8592fe10168b2 DE-627 ger DE-627 rakwb eng R855-855.5 Rabeea’h W Aslam verfasserin aut Automated tests for diagnosing and monitoring cognitive impairment: a diagnostic accuracy review 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Cognitive impairment is a growing public health concern, and is one of the most distinctive characteristics of all dementias. The timely recognition of dementia syndromes can be beneficial, as some causes of dementia are treatable and are fully or partially reversible. Several automated cognitive assessment tools for assessing mild cognitive impairment (MCI) and early dementia are now available. Proponents of these tests cite as benefits the tests’ repeatability and robustness and the saving of clinicians’ time. However, the use of these tools to diagnose and/or monitor progressive cognitive impairment or response to treatment has not yet been evaluated. Objectives: The aim of this review was to determine whether or not automated computerised tests could accurately identify patients with progressive cognitive impairment in MCI and dementia and, if so, to investigate their role in monitoring disease progression and/or response to treatment. Data sources: Five electronic databases (MEDLINE, EMBASE, The Cochrane Library, ISI Web of Science and PsycINFO), plus ProQuest, were searched from 2005 to August 2015. The bibliographies of retrieved citations were also examined. Trial and research registers were searched for ongoing studies and reviews. A second search was run to identify individual test costs and acquisition costs for the various tools identified in the review. Review methods: Two reviewers independently screened all titles and abstracts to identify potentially relevant studies for inclusion in the review. Full-text copies were assessed independently by two reviewers. Data were extracted and assessed for risk of bias by one reviewer and independently checked for accuracy by a second. The results of the data extraction and quality assessment for each study are presented in structured tables and as a narrative summary. Results: The electronic searching of databases, including ProQuest, resulted in 13,542 unique citations. The titles and abstracts of these were screened and 399 articles were shortlisted for full-text assessment. Sixteen studies were included in the diagnostic accuracy review. No studies were eligible for inclusion in the review of tools for monitoring progressive disease. Eleven automated computerised tests were assessed in the 16 included studies. The overall quality of the studies was good; however, the wide range of tests assessed and the non-standardised reporting of diagnostic accuracy outcomes meant that meaningful synthesis or statistical analysis was not possible. Limitations: The main limitation of this review is the substantial heterogeneity of the tests assessed in the included studies. As a result, no meta-analyses could be undertaken. Conclusion: The quantity of information available is insufficient to be able to make recommendations on the clinical use of the computerised tests for diagnosing and monitoring MCI and early dementia progression. The value of these tests also depends on the costs of acquisition, training, administration and scoring. Future work: Research is required to establish stable cut-off points for automated computerised tests that are used to diagnose patients with MCI or early dementia. Additionally, the costs associated with acquiring and using these tests in clinical practice should be estimated. Study registration: The study is registered as PROSPERO CRD42015025410. Funding: The National Institute for Health Research Health Technology Assessment programme. dementia alzheimer’s disease cognitive impairment automated tests systematic review diagnostic accuracy Medical technology Vickie Bates verfasserin aut Yenal Dundar verfasserin aut Juliet Hounsome verfasserin aut Marty Richardson verfasserin aut Ashma Krishan verfasserin aut Rumona Dickson verfasserin aut Angela Boland verfasserin aut Eleanor Kotas verfasserin aut Joanne Fisher verfasserin aut Sudip Sikdar verfasserin aut Louise Robinson verfasserin aut In Health Technology Assessment NIHR Journals Library, 2018 20(2016), 77 (DE-627)335259553 (DE-600)2059206-1 20464924 nnns volume:20 year:2016 number:77 https://doi.org/10.3310/hta20770 kostenfrei https://doaj.org/article/9138927dd7714c38b9b8592fe10168b2 kostenfrei https://doi.org/10.3310/hta20770 kostenfrei https://doaj.org/toc/1366-5278 Journal toc kostenfrei https://doaj.org/toc/2046-4924 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2016 77
allfieldsSound	10.3310/hta20770 doi (DE-627)DOAJ038633450 (DE-599)DOAJ9138927dd7714c38b9b8592fe10168b2 DE-627 ger DE-627 rakwb eng R855-855.5 Rabeea’h W Aslam verfasserin aut Automated tests for diagnosing and monitoring cognitive impairment: a diagnostic accuracy review 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Cognitive impairment is a growing public health concern, and is one of the most distinctive characteristics of all dementias. The timely recognition of dementia syndromes can be beneficial, as some causes of dementia are treatable and are fully or partially reversible. Several automated cognitive assessment tools for assessing mild cognitive impairment (MCI) and early dementia are now available. Proponents of these tests cite as benefits the tests’ repeatability and robustness and the saving of clinicians’ time. However, the use of these tools to diagnose and/or monitor progressive cognitive impairment or response to treatment has not yet been evaluated. Objectives: The aim of this review was to determine whether or not automated computerised tests could accurately identify patients with progressive cognitive impairment in MCI and dementia and, if so, to investigate their role in monitoring disease progression and/or response to treatment. Data sources: Five electronic databases (MEDLINE, EMBASE, The Cochrane Library, ISI Web of Science and PsycINFO), plus ProQuest, were searched from 2005 to August 2015. The bibliographies of retrieved citations were also examined. Trial and research registers were searched for ongoing studies and reviews. A second search was run to identify individual test costs and acquisition costs for the various tools identified in the review. Review methods: Two reviewers independently screened all titles and abstracts to identify potentially relevant studies for inclusion in the review. Full-text copies were assessed independently by two reviewers. Data were extracted and assessed for risk of bias by one reviewer and independently checked for accuracy by a second. The results of the data extraction and quality assessment for each study are presented in structured tables and as a narrative summary. Results: The electronic searching of databases, including ProQuest, resulted in 13,542 unique citations. The titles and abstracts of these were screened and 399 articles were shortlisted for full-text assessment. Sixteen studies were included in the diagnostic accuracy review. No studies were eligible for inclusion in the review of tools for monitoring progressive disease. Eleven automated computerised tests were assessed in the 16 included studies. The overall quality of the studies was good; however, the wide range of tests assessed and the non-standardised reporting of diagnostic accuracy outcomes meant that meaningful synthesis or statistical analysis was not possible. Limitations: The main limitation of this review is the substantial heterogeneity of the tests assessed in the included studies. As a result, no meta-analyses could be undertaken. Conclusion: The quantity of information available is insufficient to be able to make recommendations on the clinical use of the computerised tests for diagnosing and monitoring MCI and early dementia progression. The value of these tests also depends on the costs of acquisition, training, administration and scoring. Future work: Research is required to establish stable cut-off points for automated computerised tests that are used to diagnose patients with MCI or early dementia. Additionally, the costs associated with acquiring and using these tests in clinical practice should be estimated. Study registration: The study is registered as PROSPERO CRD42015025410. Funding: The National Institute for Health Research Health Technology Assessment programme. dementia alzheimer’s disease cognitive impairment automated tests systematic review diagnostic accuracy Medical technology Vickie Bates verfasserin aut Yenal Dundar verfasserin aut Juliet Hounsome verfasserin aut Marty Richardson verfasserin aut Ashma Krishan verfasserin aut Rumona Dickson verfasserin aut Angela Boland verfasserin aut Eleanor Kotas verfasserin aut Joanne Fisher verfasserin aut Sudip Sikdar verfasserin aut Louise Robinson verfasserin aut In Health Technology Assessment NIHR Journals Library, 2018 20(2016), 77 (DE-627)335259553 (DE-600)2059206-1 20464924 nnns volume:20 year:2016 number:77 https://doi.org/10.3310/hta20770 kostenfrei https://doaj.org/article/9138927dd7714c38b9b8592fe10168b2 kostenfrei https://doi.org/10.3310/hta20770 kostenfrei https://doaj.org/toc/1366-5278 Journal toc kostenfrei https://doaj.org/toc/2046-4924 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2016 77
language	English
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authorswithroles_txt_mv	Rabeea’h W Aslam @@aut@@ Vickie Bates @@aut@@ Yenal Dundar @@aut@@ Juliet Hounsome @@aut@@ Marty Richardson @@aut@@ Ashma Krishan @@aut@@ Rumona Dickson @@aut@@ Angela Boland @@aut@@ Eleanor Kotas @@aut@@ Joanne Fisher @@aut@@ Sudip Sikdar @@aut@@ Louise Robinson @@aut@@
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title_auth	Automated tests for diagnosing and monitoring cognitive impairment: a diagnostic accuracy review
abstract	Background: Cognitive impairment is a growing public health concern, and is one of the most distinctive characteristics of all dementias. The timely recognition of dementia syndromes can be beneficial, as some causes of dementia are treatable and are fully or partially reversible. Several automated cognitive assessment tools for assessing mild cognitive impairment (MCI) and early dementia are now available. Proponents of these tests cite as benefits the tests’ repeatability and robustness and the saving of clinicians’ time. However, the use of these tools to diagnose and/or monitor progressive cognitive impairment or response to treatment has not yet been evaluated. Objectives: The aim of this review was to determine whether or not automated computerised tests could accurately identify patients with progressive cognitive impairment in MCI and dementia and, if so, to investigate their role in monitoring disease progression and/or response to treatment. Data sources: Five electronic databases (MEDLINE, EMBASE, The Cochrane Library, ISI Web of Science and PsycINFO), plus ProQuest, were searched from 2005 to August 2015. The bibliographies of retrieved citations were also examined. Trial and research registers were searched for ongoing studies and reviews. A second search was run to identify individual test costs and acquisition costs for the various tools identified in the review. Review methods: Two reviewers independently screened all titles and abstracts to identify potentially relevant studies for inclusion in the review. Full-text copies were assessed independently by two reviewers. Data were extracted and assessed for risk of bias by one reviewer and independently checked for accuracy by a second. The results of the data extraction and quality assessment for each study are presented in structured tables and as a narrative summary. Results: The electronic searching of databases, including ProQuest, resulted in 13,542 unique citations. The titles and abstracts of these were screened and 399 articles were shortlisted for full-text assessment. Sixteen studies were included in the diagnostic accuracy review. No studies were eligible for inclusion in the review of tools for monitoring progressive disease. Eleven automated computerised tests were assessed in the 16 included studies. The overall quality of the studies was good; however, the wide range of tests assessed and the non-standardised reporting of diagnostic accuracy outcomes meant that meaningful synthesis or statistical analysis was not possible. Limitations: The main limitation of this review is the substantial heterogeneity of the tests assessed in the included studies. As a result, no meta-analyses could be undertaken. Conclusion: The quantity of information available is insufficient to be able to make recommendations on the clinical use of the computerised tests for diagnosing and monitoring MCI and early dementia progression. The value of these tests also depends on the costs of acquisition, training, administration and scoring. Future work: Research is required to establish stable cut-off points for automated computerised tests that are used to diagnose patients with MCI or early dementia. Additionally, the costs associated with acquiring and using these tests in clinical practice should be estimated. Study registration: The study is registered as PROSPERO CRD42015025410. Funding: The National Institute for Health Research Health Technology Assessment programme.
abstractGer	Background: Cognitive impairment is a growing public health concern, and is one of the most distinctive characteristics of all dementias. The timely recognition of dementia syndromes can be beneficial, as some causes of dementia are treatable and are fully or partially reversible. Several automated cognitive assessment tools for assessing mild cognitive impairment (MCI) and early dementia are now available. Proponents of these tests cite as benefits the tests’ repeatability and robustness and the saving of clinicians’ time. However, the use of these tools to diagnose and/or monitor progressive cognitive impairment or response to treatment has not yet been evaluated. Objectives: The aim of this review was to determine whether or not automated computerised tests could accurately identify patients with progressive cognitive impairment in MCI and dementia and, if so, to investigate their role in monitoring disease progression and/or response to treatment. Data sources: Five electronic databases (MEDLINE, EMBASE, The Cochrane Library, ISI Web of Science and PsycINFO), plus ProQuest, were searched from 2005 to August 2015. The bibliographies of retrieved citations were also examined. Trial and research registers were searched for ongoing studies and reviews. A second search was run to identify individual test costs and acquisition costs for the various tools identified in the review. Review methods: Two reviewers independently screened all titles and abstracts to identify potentially relevant studies for inclusion in the review. Full-text copies were assessed independently by two reviewers. Data were extracted and assessed for risk of bias by one reviewer and independently checked for accuracy by a second. The results of the data extraction and quality assessment for each study are presented in structured tables and as a narrative summary. Results: The electronic searching of databases, including ProQuest, resulted in 13,542 unique citations. The titles and abstracts of these were screened and 399 articles were shortlisted for full-text assessment. Sixteen studies were included in the diagnostic accuracy review. No studies were eligible for inclusion in the review of tools for monitoring progressive disease. Eleven automated computerised tests were assessed in the 16 included studies. The overall quality of the studies was good; however, the wide range of tests assessed and the non-standardised reporting of diagnostic accuracy outcomes meant that meaningful synthesis or statistical analysis was not possible. Limitations: The main limitation of this review is the substantial heterogeneity of the tests assessed in the included studies. As a result, no meta-analyses could be undertaken. Conclusion: The quantity of information available is insufficient to be able to make recommendations on the clinical use of the computerised tests for diagnosing and monitoring MCI and early dementia progression. The value of these tests also depends on the costs of acquisition, training, administration and scoring. Future work: Research is required to establish stable cut-off points for automated computerised tests that are used to diagnose patients with MCI or early dementia. Additionally, the costs associated with acquiring and using these tests in clinical practice should be estimated. Study registration: The study is registered as PROSPERO CRD42015025410. Funding: The National Institute for Health Research Health Technology Assessment programme.
abstract_unstemmed	Background: Cognitive impairment is a growing public health concern, and is one of the most distinctive characteristics of all dementias. The timely recognition of dementia syndromes can be beneficial, as some causes of dementia are treatable and are fully or partially reversible. Several automated cognitive assessment tools for assessing mild cognitive impairment (MCI) and early dementia are now available. Proponents of these tests cite as benefits the tests’ repeatability and robustness and the saving of clinicians’ time. However, the use of these tools to diagnose and/or monitor progressive cognitive impairment or response to treatment has not yet been evaluated. Objectives: The aim of this review was to determine whether or not automated computerised tests could accurately identify patients with progressive cognitive impairment in MCI and dementia and, if so, to investigate their role in monitoring disease progression and/or response to treatment. Data sources: Five electronic databases (MEDLINE, EMBASE, The Cochrane Library, ISI Web of Science and PsycINFO), plus ProQuest, were searched from 2005 to August 2015. The bibliographies of retrieved citations were also examined. Trial and research registers were searched for ongoing studies and reviews. A second search was run to identify individual test costs and acquisition costs for the various tools identified in the review. Review methods: Two reviewers independently screened all titles and abstracts to identify potentially relevant studies for inclusion in the review. Full-text copies were assessed independently by two reviewers. Data were extracted and assessed for risk of bias by one reviewer and independently checked for accuracy by a second. The results of the data extraction and quality assessment for each study are presented in structured tables and as a narrative summary. Results: The electronic searching of databases, including ProQuest, resulted in 13,542 unique citations. The titles and abstracts of these were screened and 399 articles were shortlisted for full-text assessment. Sixteen studies were included in the diagnostic accuracy review. No studies were eligible for inclusion in the review of tools for monitoring progressive disease. Eleven automated computerised tests were assessed in the 16 included studies. The overall quality of the studies was good; however, the wide range of tests assessed and the non-standardised reporting of diagnostic accuracy outcomes meant that meaningful synthesis or statistical analysis was not possible. Limitations: The main limitation of this review is the substantial heterogeneity of the tests assessed in the included studies. As a result, no meta-analyses could be undertaken. Conclusion: The quantity of information available is insufficient to be able to make recommendations on the clinical use of the computerised tests for diagnosing and monitoring MCI and early dementia progression. The value of these tests also depends on the costs of acquisition, training, administration and scoring. Future work: Research is required to establish stable cut-off points for automated computerised tests that are used to diagnose patients with MCI or early dementia. Additionally, the costs associated with acquiring and using these tests in clinical practice should be estimated. Study registration: The study is registered as PROSPERO CRD42015025410. Funding: The National Institute for Health Research Health Technology Assessment programme.
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container_issue	77
title_short	Automated tests for diagnosing and monitoring cognitive impairment: a diagnostic accuracy review
url	https://doi.org/10.3310/hta20770 https://doaj.org/article/9138927dd7714c38b9b8592fe10168b2 https://doaj.org/toc/1366-5278 https://doaj.org/toc/2046-4924
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author2	Vickie Bates Yenal Dundar Juliet Hounsome Marty Richardson Ashma Krishan Rumona Dickson Angela Boland Eleanor Kotas Joanne Fisher Sudip Sikdar Louise Robinson
author2Str	Vickie Bates Yenal Dundar Juliet Hounsome Marty Richardson Ashma Krishan Rumona Dickson Angela Boland Eleanor Kotas Joanne Fisher Sudip Sikdar Louise Robinson
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doi_str	10.3310/hta20770
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up_date	2024-07-03T19:01:35.887Z
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