Ensemble machine learning classification of daily living abilities among older people with HIV
Background: clinically relevant methods to identify individuals at risk for impaired daily living abilities secondary to neurocognitive impairment (ADLs) remain elusive. This is especially true for complex clinical conditions such as HIV-Associated Neurocognitive Disorders (HAND). The aim of this st...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-05-01
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| Series: | EClinicalMedicine |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589537021001255 |
| _version_ | 1818645660987031552 |
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| author | Robert Paul Torie Tsuei Kyu Cho Andrew Belden Benedetta Milanini Jacob Bolzenius Shireen Javandel Joseph McBride Lucette Cysique Samantha Lesinski Victor Valcour |
| author_facet | Robert Paul Torie Tsuei Kyu Cho Andrew Belden Benedetta Milanini Jacob Bolzenius Shireen Javandel Joseph McBride Lucette Cysique Samantha Lesinski Victor Valcour |
| author_sort | Robert Paul |
| collection | DOAJ |
| description | Background: clinically relevant methods to identify individuals at risk for impaired daily living abilities secondary to neurocognitive impairment (ADLs) remain elusive. This is especially true for complex clinical conditions such as HIV-Associated Neurocognitive Disorders (HAND). The aim of this study was to identify novel and modifiable factors that have potential to improve diagnostic accuracy of ADL risk, with the long-term goal of guiding future interventions to minimize ADL disruption. Methods: study participants included 79 people with HIV (PWH; mean age = 63; range = 55–80) enrolled in neuroHIV studies at University California San Francisco (UCSF) between 2016 and 2019. All participants were virally suppressed and exhibited objective evidence of neurocognitive impairment. ADL status was defined as either normative (n = 39) or at risk (n = 40) based on a task-based protocol. Gradient boosted multivariate regression (GBM) was employed to identify the combination of variables that differentiated ADL subgroup classification. Predictor variables included demographic factors, HIV disease severity indices, brain white matter integrity quantified using diffusion tensor imaging, cognitive test performance, and health co-morbidities. Model performance was examined using average Area Under the Curve (AUC) with repeated five-fold cross validation. Findings: the univariate GBM yielded an average AUC of 83% using Wide Range Achievement test 4 (WRAT-4) reading score, self-reported thought confusion and difficulty reading, radial diffusivity (RD) in the left external capsule, fractional anisotropy (FA) in the left cingulate gyrus, and Stroop performance. The model allowing for two-way interactions modestly improved classification performance (AUC of 88%) and revealed synergies between race, reading ability, cognitive performance, and neuroimaging metrics in the genu and uncinate fasciculus. Conversion of Neuropsychological Assessment Battery Daily Living Module (NAB-DLM) performance from raw scores into T scores amplified differences between White and non-White study participants. Interpretation: demographic and sociocultural factors are critical determinants of ADL risk status among older PWH who meet diagnostic criteria for neurocognitive impairment. Task-based ADL assessment that relies heavily on reading proficiency may artificially inflate the frequency/severity of ADL impairment among diverse clinical populations. Culturally relevant measures of ADL status are needed for individuals with acquired neurocognitive disorders, including HAND. |
| first_indexed | 2024-12-17T00:34:17Z |
| format | Article |
| id | doaj.art-a3be0ac9c4a34f3689d5713e1853b0d3 |
| institution | Directory Open Access Journal |
| issn | 2589-5370 |
| language | English |
| last_indexed | 2024-12-17T00:34:17Z |
| publishDate | 2021-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EClinicalMedicine |
| spelling | doaj.art-a3be0ac9c4a34f3689d5713e1853b0d32022-12-21T22:10:11ZengElsevierEClinicalMedicine2589-53702021-05-0135100845Ensemble machine learning classification of daily living abilities among older people with HIVRobert Paul0Torie Tsuei1Kyu Cho2Andrew Belden3Benedetta Milanini4Jacob Bolzenius5Shireen Javandel6Joseph McBride7Lucette Cysique8Samantha Lesinski9Victor Valcour10Missouri Institute of Mental Health, University of Missouri-St. Louis, St. Louis, MO 63121-4400, United States; Department of Psychological Sciences, University of Missouri-St. Louis, St. Louis, MO, United States; Corresponding author at: Missouri Institute of Mental Health, University of Missouri-St. Louis, St. Louis, MO 63121-4400, United States.Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, United StatesMissouri Institute of Mental Health, University of Missouri-St. Louis, St. Louis, MO 63121-4400, United StatesMissouri Institute of Mental Health, University of Missouri-St. Louis, St. Louis, MO 63121-4400, United StatesMemory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, United StatesMissouri Institute of Mental Health, University of Missouri-St. Louis, St. Louis, MO 63121-4400, United StatesMemory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, United StatesMissouri Institute of Mental Health, University of Missouri-St. Louis, St. Louis, MO 63121-4400, United StatesSchool of Psychology, University of New South Wales, Sydney, AustraliaMissouri Institute of Mental Health, University of Missouri-St. Louis, St. Louis, MO 63121-4400, United StatesMemory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, United States; Global Brain Health Institute, University of California San Francisco, San Francisco, CA, United StatesBackground: clinically relevant methods to identify individuals at risk for impaired daily living abilities secondary to neurocognitive impairment (ADLs) remain elusive. This is especially true for complex clinical conditions such as HIV-Associated Neurocognitive Disorders (HAND). The aim of this study was to identify novel and modifiable factors that have potential to improve diagnostic accuracy of ADL risk, with the long-term goal of guiding future interventions to minimize ADL disruption. Methods: study participants included 79 people with HIV (PWH; mean age = 63; range = 55–80) enrolled in neuroHIV studies at University California San Francisco (UCSF) between 2016 and 2019. All participants were virally suppressed and exhibited objective evidence of neurocognitive impairment. ADL status was defined as either normative (n = 39) or at risk (n = 40) based on a task-based protocol. Gradient boosted multivariate regression (GBM) was employed to identify the combination of variables that differentiated ADL subgroup classification. Predictor variables included demographic factors, HIV disease severity indices, brain white matter integrity quantified using diffusion tensor imaging, cognitive test performance, and health co-morbidities. Model performance was examined using average Area Under the Curve (AUC) with repeated five-fold cross validation. Findings: the univariate GBM yielded an average AUC of 83% using Wide Range Achievement test 4 (WRAT-4) reading score, self-reported thought confusion and difficulty reading, radial diffusivity (RD) in the left external capsule, fractional anisotropy (FA) in the left cingulate gyrus, and Stroop performance. The model allowing for two-way interactions modestly improved classification performance (AUC of 88%) and revealed synergies between race, reading ability, cognitive performance, and neuroimaging metrics in the genu and uncinate fasciculus. Conversion of Neuropsychological Assessment Battery Daily Living Module (NAB-DLM) performance from raw scores into T scores amplified differences between White and non-White study participants. Interpretation: demographic and sociocultural factors are critical determinants of ADL risk status among older PWH who meet diagnostic criteria for neurocognitive impairment. Task-based ADL assessment that relies heavily on reading proficiency may artificially inflate the frequency/severity of ADL impairment among diverse clinical populations. Culturally relevant measures of ADL status are needed for individuals with acquired neurocognitive disorders, including HAND.http://www.sciencedirect.com/science/article/pii/S2589537021001255ADLsHIVAgingMachine learning |
| spellingShingle | Robert Paul Torie Tsuei Kyu Cho Andrew Belden Benedetta Milanini Jacob Bolzenius Shireen Javandel Joseph McBride Lucette Cysique Samantha Lesinski Victor Valcour Ensemble machine learning classification of daily living abilities among older people with HIV EClinicalMedicine ADLs HIV Aging Machine learning |
| title | Ensemble machine learning classification of daily living abilities among older people with HIV |
| title_full | Ensemble machine learning classification of daily living abilities among older people with HIV |
| title_fullStr | Ensemble machine learning classification of daily living abilities among older people with HIV |
| title_full_unstemmed | Ensemble machine learning classification of daily living abilities among older people with HIV |
| title_short | Ensemble machine learning classification of daily living abilities among older people with HIV |
| title_sort | ensemble machine learning classification of daily living abilities among older people with hiv |
| topic | ADLs HIV Aging Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2589537021001255 |
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