Estimation of Human Cerebral Atrophy Based on Systemic Metabolic Status Using Machine Learning

Estimation of cognitive impairment in chronic pain patients and characteristics of estimated mild cognitive impairment

Background

Patients with chronic pain suffer from psychological effects such as anxiety due to the pain itself. Pain can not only impair activities of daily living (ADL) and quality of life (QOL), but also impair cognitive function. Therefore, in this study, we aimed to estimate the cognitive function of chronic pain patients using a deep neural network (DNN) model that has already been implemented in society. We investigated the characteristics of patients presumed to have mild cognitive impairment (MCI) and, at the same time, verified the relationship with the questionnaire commonly used in chronic pain research, which is administered by 43 university affiliated hospitals and medical institutions participating in the chronic pain research group of the Ministry of Health, Labor and Welfare in Japan (assessment batteries).

Method

The study included 114 outpatients from a multidisciplinary pain clinic, and we estimated their Mini-Mental State Examination (MMSE) scores based on age and basic blood test data (23 items). Furthermore, we classified the estimated MMSE scores of chronic pain patients into two groups based on a cutoff score of 27, which indicates MCI, and compared the blood data and assessment batteries. Additionally, we used a control group of 252 healthy adults aged 45 years or older who visited a dementia prevention outpatient clinic for comparison with the MMSE scores of chronic pain patients.

Result

The MMSE scores in chronic pain patients were below the cutoff for MCI. When classified into two groups based on the estimated MMSE score of 27 points, WBC, RBC, Hb, Hct, PLT, UA, BUN, creatinine, Triglyceride, and γ-GT were significantly higher in the blood data. In the MCI group, PDAS values were significantly lower. Furthermore, only in the non-MCI group, a significant correlation was found between the estimated MMSE value and BPI, PDAS, and Locomo. The estimated MMSE scores were significantly lower in chronic pain patients than in healthy adults (p = 0.04).

Conclusion

Patients with chronic pain may exhibit cognitive impairment due to systemic metabolic disturbances. This suggests that chronic pain affects activities of daily living, resulting in systemic metabolic disorders.

Prevalence and risk factors of brain atrophy and associated confusion state among adults from three hospitals in northern Tanzania

Introduction

brain atrophy is the reduction of brain volume often accompanied with cognitive changes. Despite the availability of computerized-tomography (CT) scanners in Tanzania, little is known about the magnitude of brain atrophy, its associated confusion state and the risk factors in adults. This study aimed to fill those knowledge gaps.

Methods

a retrospective cross-sectional hospital-based survey was conducted in northern Tanzania using a sample size of 384 CT images of adults who underwent brain CT scans in three referral hospitals. CT images were evaluated using a diagonal brain fraction (DBF) method to determine the presence of brain atrophy. Data for other covariates were also collected.

Results

we report a prevalence of 60.67% for brain atrophy and 35% for the associated confusion state. Association between confusion state and brain atrophy was statistically significant (χ² = 21.954, p<0.001). Brain atrophy was prognosticated by: age (adjusted OR: 1.11; 95% CI [1.05, 1.20], p<0.001), smoking (adjusted OR: 6.97; 95% CI [2.12, 26.19], p<0.001), alcohol-consumption (adjusted OR: 11.87; 95% CI [3.44, 40.81], p<0.001), hypertension (adjusted OR: 61.21; 95 CI [15.20, 349.43], p<0.001), type-2 diabetes mellitus (adjusted OR: 15.67; 95% CI [5.32, 52.77], p<0.001) and white matter demyelination (adjusted OR: 13.45; 95% CI [4.66, 44.25], p<0.001).

Conclusion

there is high prevalence of brain atrophy and associated confusion state among hospitalized adults in northern Tanzania. Reported prognostic factors for brain atrophy such as age, smoking, alcohol consumption, hypertension, type-2 diabetes mellitus and white matter demyelination could help focus interventions in this area.

Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population

The rising incidence of fatty liver disease (FLD) poses a health challenge, and is expected to be the leading global cause of liver-related morbidity and mortality in the near future. Early case identification is crucial for disease intervention. A retrospective cross-sectional study was performed on 31,930 Taiwanese subjects (25,544 training and 6386 testing sets) who had received health check-ups and abdominal ultrasounds in Changhua Christian Hospital from January 2009 to January 2019. Clinical and laboratory factors were included for analysis by different machine-learning algorithms. In addition, the performance of the machine-learning algorithms was compared with that of the fatty liver index (FLI). Totally, 6658/25,544 (26.1%) and 1647/6386 (25.8%) subjects had moderate-to-severe liver disease in the training and testing sets, respectively. Five machine-learning models were examined and demonstrated exemplary performance in predicting FLD. Among these models, the xgBoost model revealed the highest area under the receiver operating characteristic (AUROC) (0.882), accuracy (0.833), F1 score (0.829), sensitivity (0.833), and specificity (0.683) compared with those of neural network, logistic regression, random forest, and support vector machine-learning models. The xgBoost, neural network, and logistic regression models had a significantly higher AUROC than that of FLI. Body mass index was the most important feature to predict FLD according to the feature ranking scores. The xgBoost model had the best overall prediction ability for diagnosing FLD in our study. Machine-learning algorithms provide considerable benefits for screening candidates with FLD.