OBJECTIVE: To assess the role of Machine Learning (ML) in identification critical factors of dementia and mild cognitive impairment.
METHODS: 371 elderly individuals were ultimately included in the ML analysis. Demographic information (including gender, age, parity, visual acuity, auditory function, mobility, and medication history) and 35 features from 10 assessment scales were used for modeling. Five machine learning classifiers were used for evaluation, employing a procedure involving feature extraction, selection, model training, and performance assessment to identify key indicative factors.
RESULTS: The Random Forest model, after data preprocessing, Information Gain, and Meta-analysis, utilized three training features and four meta-features, achieving an area under the curve of 0.961 and a accuracy of 0.894, showcasing exceptional accuracy for the identification of dementia and mild cognitive impairment.
CONCLUSIONS: ML serves as a identification tool for dementia and mild cognitive impairment. Using Information Gain and Meta-feature analysis, Clinical Dementia Rating (CDR) and Neuropsychiatric Inventory (NPI) scale information emerged as crucial for training the Random Forest model.

To compare the neuropsychiatric symptoms (NPS) of patients with mild cognitive impairment with Lewy bodies (MCI-LB) with those with mild cognitive impairment due to Alzheimer\'s disease (MCI-AD).
Subjects with probable MCI-LB (n = 53), MCI-AD (n = 60), dementia with Lewy bodies (DLB) (n = 97) and AD (n = 202) were recruited. All were older than 60 years. Neuropsychiatric profiles were evaluated by the Neuropsychiatric Inventory (NPI). The Lewy Body Neuropsychiatric Supportive Symptom Count (LBNSSC) was used to assess give supportive clinical features of DLB (systematized delusions, hallucinations in non-visual modalities, apathy, anxiety, and depression).
Compared with MCI-AD, those with MCI-LB had higher total NPI scores on prevalence and severity, as were prevalence and severity in visual hallucinations and rapid eye movement sleep behavior disorder (RBD). The MCI-LB group had a higher LBNSSC than did the MCI-AD group. Compared with 20% of those with MCI-AD, 41.5% of those with MCI-LB had two or more supportive NPS, (likelihood ratio = 2.08, p = 0.013). MCI-LB subjects showed a high prevalence in apathy, depression, and appetite/eating disorders items.
The NPI is a useful tool to detect NPS in those with MCI-LB. The MCI-LB group had a higher prevalence of core features (visual hallucinations and RBD) and a higher LBNSSC than the MCI-AD group. These features could help to differentiate MCI-LB and MCI-AD.

暂无摘要。

Cerebral small vessel disease (SVD) and neuropsychiatric symptoms (NPS) independently increase the risk of cognitive decline. While their co-existence has been reported in the preclinical stage of dementia, longitudinal data establishing the prognosis of their associations, especially in an Asian context remains limited.
This study investigated the role of SVD and NPS progressions on cognitive outcomes over 2 years in a dementia-free elderly cohort.
170 dementia-free elderly with baseline and 2-year neuropsychological assessments and MRI scans were included in this study. White matter hyperintensities (WMH), lacunes, and microbleeds (CMBs) were graded as markers of SVD. The Neuropsychiatric Inventory (NPI) was used to measure NPS. Generalized estimating equations modelling evaluated the relationship between NPI change and SVD progression. Logistic regression evaluated the risk of incident cognitive decline with both SVD and NPS. All models were adjusted for demographics, baseline cerebrovascular diease, and medial temporal lobe atrophy.
Higher NPI scores were associated with higher SVD burden at baseline. Subjects with WMH progression had greater increase in total NPI (β[SE] = 0.46[0.19], p = 0.016), driven by hyperactivity subsyndrome (β[SE] = 0.88[0.34], p = 0.007). Subjects with incident CMBs had greater increase in psychosis subsyndrome (β[SE] = 0.89[0.30], p < 0.001). Subjects with progressions in both SVD and NPS were more likely to develop cognitive decline over 2 years (OR[95% CI] = 4.17[1.06-16.40], p < 0.05).
Our findings support worsening of NPS as a clinical indicator of SVD progression and are associated with cognitive decline over 2 years. Early detection of NPS and targeted interventions on SVD burden may improve NPS outcomes.

BACKGROUND: With the increasing number of dementia patients in China, there is a pressing need for a reliable and valid Chinese instrument that can measure neuropsychiatric symptoms in institutionalized dementia patients. This study examined the reliability and structural validity of the Chinese version of the Neuropsychiatric Inventory, Nursing Home version (NPI-NH), in a sample of institutionalized dementia patients in China.
METHODS: A total of 112 residents with dementia (Clinical Dementia Rating = 1: 10.7%; Clinical Dementia Rating = 2: 39.3%; Clinical Dementia Rating = 3: 50.0%) and 30 informants participated in this cross-sectional study. Reliability was tested using Cronbach\'s α and intra-class correlation coefficient. Principal component analysis was used to evaluate the factor structure of the inventory.
RESULTS: Of the patients, 92.9% had at least one neuropsychiatric symptom. Apathy (57%) was the most common symptom. The Chinese version of the Neuropsychiatric Inventory, Nursing Home version, showed acceptable internal consistency (Cronbach\'s α for the total scale, frequency, severity, and disturbance subscales were 0.64, 0.70, 0.73, and 0.80, respectively) and test-retest reliability (intra-class correlation coefficient for the total scale, frequency, severity, and disturbance subscales were 0.93, 0.92, 0.89, and 0.91, respectively). Five factors-psychomotor behaviour, affective symptoms, psychosis, sleep disorders, and eating disorders-were identified for the total scale. The cluster symptoms aggression and irritability, depression and anxiety, and delusions and hallucinations were three of the optimally stable groups of symptoms.
CONCLUSIONS: This study demonstrated that the Chinese version of the Neuropsychiatric Inventory, Nursing Home version, is a valid and reliable instrument for evaluating neuropsychiatric symptoms in institutionalized dementia patients.

BACKGROUND: Neuropsychiatric deficits can induce marked disability in patients with dementia and increase caregiver distress. Several studies have found that neuropsychiatric symptoms are common both in patients with Alzheimer\'s disease (AD) and patients with vascular dementia (VaD). However, there are few studies of the neuropsychiatric disturbances in large clinical samples of patients with mixed (cortical - subcortical) VaD from mainland China. This study aimed to investigate the neuropsychiatric symptoms in VaD patients in mainland China.
METHODS: Eighty patients with mixed VaD for over 6 months duration, and their caregivers (VaD group), were recruited for interview in Zhongnan Hospital of Wuhan University, from June 2010 to June 2012. Eighty age- and sex-matched normal volunteers (control group) were interviewed at the same time. The Mini Mental State Examination (MMSE) and the Neuropsychiatric Inventory (NPI) were administered to the VaD patients, their caregivers, and normal volunteers. Group differences were analyzed using the unpaired t-test.
RESULTS: The total mean scores of the NPI in the VaD group were higher than in the control group (P < 0.01). The subscale scores of NPI, including delusions, hallucinations, depression, apathy, irritability, agitation, aberrant motor behavior, and change in appetite were significantly higher in the VaD group than in the control group (P < 0.05-0.01). Compared with the mild VaD subgroup, the NPI subscale scores of apathy, irritability and total scores were significantly higher in the moderate VaD subgroup (P < 0.05-0.01); the NPI subscale scores of anxiety, apathy, irritability, and total scores were significantly higher in the severe VaD subgroup (P < 0.01). Compared with the moderate VaD subgroup, the NPI subscale scores of anxiety and apathy were significantly higher in the severe VaD subgroup (P < 0.05-0.01).
CONCLUSIONS: Neuropsychiatric symptoms, such as hallucination, anxiety, apathy, irritability and aberrant action behavior, are common in patients with mixed VaD from mainland China; anxiety and apathy were more pronounced in the subgroup of severe VaD patients.

BACKGROUND: Many studies reported that physiotherapy interventions are available to treat Alzheimer\'s disease (AD), but the efficacy remains uncertain.
OBJECTIVE: To evaluate the effectiveness of physiotherapy intervention on AD.
METHODS: The data sources were searched from literature databases, journals, and reference lists from 1 January 1990 to the end of 1 April 2014. Randomized and non-randomized controlled trials with physiotherapy intervention were included in our meta-analysis. Jadad score and Newcastle-Ottawa scale were used to assess the quality of included trials. Outcome measures were cognition function, physical function, activity of daily life (ADL) and neuropsychiatric inventory (NPI).
RESULTS: 23 trials met the inclusion standard finally. Significant changes were seen in cognitive function: Mini-Mental State Examination score (weighted mean difference (WMD): 1.84, 95% confidence interval (CI): [0.76, to, 2.93], p < 0.0001), and verbal fluency (standard mean difference (SMD): 0.34, 95% CI: [0.01 to 0.66], p = 0.04). Other outcomes are also significant, they were timed up and go test (SMD: 0.56, 95% CI: [0.30 to 0.83], p < 0.0001), berg functional balance scale (SMD: 1.11, 95% CI: [0.37 to 1.84], p = 0.003), 6-min walk distance test (SMD: 141.45, 95% CI: [11.72 to 271.18], p = 0.03), ADL (SMD: 0.78, 95% CI: [0.33 to 1.23], p = 0.0007) and NPI (SMD: -0.69, 95% CI: [-1.31 to -0.07], p = 0.03).
CONCLUSIONS: The available data indicate that physiotherapy intervention may have benefits in AD. However, current data are not definitive; more carefully designed and conducted observational studies are needed to definitively establish that whether physiotherapy intervention can effectively alleviate symptoms of AD.