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2. Comparisons Between Hypothesis- and Data-Driven Approaches for Multimorbidity Frailty Index: A Machine Learning Approach

Author

Peng, Li-Ning, Hsiao, Fei-Yuan, Lee, Wei-Ju, Huang, Shih-Tsung, and Chen, Liang-Kung

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundUsing big data and the theory of cumulative deficits to develop the multimorbidity frailty index (mFI) has become a widely accepted approach in public health and health care services. However, constructing the mFI using the most critical determinants and stratifying different risk groups with dose-response relationships remain major challenges in clinical practice. ObjectiveThis study aimed to develop the mFI by using machine learning methods that select variables based on the optimal fitness of the model. In addition, we aimed to further establish 4 entities of risk using a machine learning approach that would achieve the best distinction between groups and demonstrate the dose-response relationship. MethodsIn this study, we used Taiwan’s National Health Insurance Research Database to develop a machine learning multimorbidity frailty index (ML-mFI) using the theory of cumulative diseases/deficits of an individual older person. Compared to the conventional mFI, in which the selection of diseases/deficits is based on expert opinion, we adopted the random forest method to select the most influential diseases/deficits that predict adverse outcomes for older people. To ensure that the survival curves showed a dose-response relationship with overlap during the follow-up, we developed the distance index and coverage index, which can be used at any time point to classify the ML-mFI of all subjects into the categories of fit, mild frailty, moderate frailty, and severe frailty. Survival analysis was conducted to evaluate the ability of the ML-mFI to predict adverse outcomes, such as unplanned hospitalizations, intensive care unit (ICU) admissions, and mortality. ResultsThe final ML-mFI model contained 38 diseases/deficits. Compared with conventional mFI, both indices had similar distribution patterns by age and sex; however, among people aged 65 to 69 years, the mean mFI and ML-mFI were 0.037 (SD 0.048) and 0.0070 (SD 0.0254), respectively. The difference may result from discrepancies in the diseases/deficits selected in the mFI and the ML-mFI. A total of 86,133 subjects aged 65 to 100 years were included in this study and were categorized into 4 groups according to the ML-mFI. Both the Kaplan-Meier survival curves and Cox models showed that the ML-mFI significantly predicted all outcomes of interest, including all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions at 1, 5, and 8 years of follow-up (P

Published
2020
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31. Altered cerebellar and caudate gray‐matter volumes and structural covariance networks preceding dual cognitive and mobility impairments in older people.

Author

Lee, Pei‐Lin, Chou, Kun‐Hsien, Lee, Wei‐Ju, Peng, Li‐Ning, Chen, Liang‐Kung, Lin, Ching‐Po, Liang, Chih‐Kuang, and Chung, Chih‐Ping

Abstract

INTRODUCTION: The neuroanatomical changes driving both cognitive and mobility impairments, an emerging preclinical dementia syndrome, are not fully understood. We examined gray‐matter volumes (GMVs) and structural covariance networks (SCNs) abnormalities in community‐based older people preceding the conversion to physio‐cognitive decline syndrome (PCDS). METHODS: Voxel‐wise brain GMV and established SCNs were compared between PCDS and non‐PCDS converters. RESULTS: The study included 343 individuals (60.2 ± 6.9 years, 49.6% men) with intact cognitive and mobility functions. Over an average 5.6‐year follow‐up, 116 transitioned to PCDS. Identified regions with abnormal GMVs in PCDS converters were over cerebellum and caudate, which served as seeds for SCNs establishment. Significant differences in cerebellum‐based (to right frontal pole and left middle frontal gyrus) and caudate‐based SCNs (to right caudate putamen, right planum temporale, left precentral gyrus, right postcentral gyrus, and left parietal operculum) between converters and nonconverters were observed. DISCUSSION: This study reveals early neuroanatomic changes, emphasizing the cerebellum's role, in dual cognitive and mobility impairments. Highlights: Neuroanatomic precursors of dual cognitive and mobility impairments are identified.Cerebellar GMV reductions and increased right caudate GMV precede the onset of PCDS.Altered cerebellum‐ and caudate‐based SCNs drive PCDS transformation.This research establishes a foundation for understanding PCDS as a specific dementia syndrome. [ABSTRACT FROM AUTHOR]

Published
2024
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