Calf circumference as a screening tool for low skeletal muscle mass: Cut-off values in independent Thai older adults.

BACKGROUND: Calf circumference is recommended as a marker for low muscle mass and as a case finding in the diagnosis of sarcopenia. However, the cut-off value differed by ethic and region. Currently there is no study among Thai population. Therefore, we aimed to identify the optimal cutoff value of calf circumference as a screening tool for low skeletal muscle mass in independent Thai older adults. Subgroup analysis was performed for obesity and adults over 75 years. METHODS: This cross-sectional cohort studied in an outpatient geriatric check-up clinic. Participants, aged 60 and above, needed to be independent in basic activities of daily living to meet the inclusion criteria. Exclusion criteria comprised active malignancy, cardiac, pulmonary, or neurovascular diseases necessitating hospitalization in the preceding three months, chronic renal diseases requiring renal replacement therapy, and unstable psychiatric disorders. We measured the maximum calf circumference and appendicular skeletal muscle mass (ASMI) using bioelectrical impedance analysis (BIA). Low muscle mass is defined according to the Asian Working Group of Sarcopenia (AWGS) 2019 consensus. RESULTS: We enrolled 6,404 elderly adults (mean age 67.3 ± 5.1 years), with a 47% prevalence of low muscle mass in women and 25% in men. Lower muscle mass significantly correlated with reduced BMI and waist circumference in both genders (p < 0.001). Optimal cut-off values for low muscle mass screening were < 33 cm (sensitivity 80.1%, specificity 60.5%) for women and < 34 cm (sensitivity 85.4%, specificity 70.2%) for men. Subgroup analysis for those with BMI ≥ 25 kg/m² suggested raising the cut-off for women to < 34 cm (sensitivity 80.6%, specificity 54.0%) and for men to < 35 cm (sensitivity 88.7%, specificity 55.2%) to enhance specificity without substantial sensitivity loss. In the older-old adult subgroup (≥ 75 years), optimal cut-off values were < 33 cm (sensitivity 84.6%, specificity 79.9%) for women and < 34 cm (sensitivity 75.6%, specificity 87.0%) for men. CONCLUSIONS: There is a strong correlation between calf circumference and ASMI in independent Thai older adults. Calf circumference can serve as a screening tool for identifying low muscle mass. The recommended cut-off values for men and women are 34 cm and 33 cm, respectively in alignment with AWGS 2019 recommendation. Incorporating a 1-cm higher cut-off value for obese older adults improves the accuracy of muscle mass screening. TRIAL REGISTRATION: Thai clinical trial registry: TCTR20200511003.

Biomechanics of sit-to-stand with dual tasks in older adults with and without mild cognitive impairment.

BACKGROUND: The decline in cognitive function in older adults with mild cognitive impairment (MCI) may contribute to a change in movement pattern during sit-to-stand transitions (STS). However, when comparing older adults with MCI to older adults without MCI, there is a lack of evidence of kinematic and kinetic data during STS. Furthermore, while significant cognitive dual-task interference has been demonstrated in older adults with MCI, studies on the effects of dual motor tasks in MCI, particularly during STS, have not been reported. RESEARCH QUESTION: Are there any differences in the movement time, joint angles, and maximum joint moments while performing STS under single- and dual-task conditions in older adults with and without MCI? METHODS: In a cross-sectional study, 70 participants were divided into two groups: older adults with MCI and without MCI. Motion analysis and a force plate system were used to collect and analyze the STS movement. All participants were asked to do the STS movement alone and the STS with a dual motor task with the self-selected pattern on an adjustable bench. RESULTS: Older adults with MCI had greater maximum trunk flexion during STS with a dual task than older adults without MCI and greater than STS alone. Furthermore, older adults with MCI had a greater ankle plantar flexion moment during STS with a dual task than during STS alone. SIGNIFICANCE: Even though the STS task is one of the simplest functional activities, different strategies to achieve the STS action with dual tasks were found among older adults with and without MCI in terms of joint angle and joint moments.

Machine learning-based predictive risk models for 30-day and 1-year mortality in severe aortic stenosis patients undergoing transcatheter aortic valve implantation.

BACKGROUND: Predictive risk score for mortality plays an important role in the decision-making in patient selection and risk stratification for TAVI. Existing established predictive risk scores had poor discrimination performance in the prediction of mortality after the TAVI. OBJECTIVES: The present study aimed to develop machine learning-based predictive models for 30-day and 1-year mortality in severe aortic stenosis patients undergoing TAVI. METHODS: A total of 186 patients in a retrospective cohort study were analyzed. The models were fitted by a decision tree. Each model was tested in 100 iterations of 80:20 stratified random splitting into training/testing samples and 10-fold cross-validation. RESULTS: Variables that predict 30-day mortality are a set of factors driven mainly by height, chronic lung disease, STS score, preoperative LVEF, age, and preoperative LVOT VTI. Variables that predict 1-year mortality are a set of factors consisting of preoperative LVEF, STS score, heart rate, systolic blood pressure, home oxygen use, serum creatinine level, and preoperative LVOT Vmax. This decision tree-generated predictive models for 30-day and 1- year mortality provided the most precise accuracy of 0.97 and 0.90 with the AUC-ROC curves of 0.83 and 0.71 on 30-day and 1-year mortality on testing data and had better discrimination performance compared to the existing established TAVI predictive risk scores. CONCLUSIONS: These machine learning models show excellent accuracy and have a better prediction for 30-day and 1-year mortality than the existing established TAVI predictive risk scores. A customized predictive model deems to be properly developed for better risk discrimination among cohorts.

Attentive pairwise interaction network for AI-assisted clock drawing test assessment of early visuospatial deficits.

Dementia is a debilitating neurological condition which impairs the cognitive function and the ability to take care of oneself. The Clock Drawing Test (CDT) is widely used to detect dementia, but differentiating normal from borderline cases requires years of clinical experience. Misclassifying mild abnormal as normal will delay the chance to investigate for potential reversible causes or slow down the progression. To help address this issue, we propose an automatic CDT scoring system that adopts Attentive Pairwise Interaction Network (API-Net), a fine-grained deep learning model that is designed to distinguish visually similar images. Inspired by how humans often learn to recognize different objects by looking at two images side-by-side, API-Net is optimized using image pairs in a contrastive manner, as opposed to standard supervised learning, which optimizes a model using individual images. In this study, we extend API-Net to infer Shulman CDT scores from a dataset of 3108 subjects. We compare the performance of API-Net to that of convolutional neural networks: VGG16, ResNet-152, and DenseNet-121. The best API-Net achieves an F1-score of 0.79, which is a 3% absolute improvement over ResNet-152's F1-score of 0.76. The code for API-Net and the dataset used have been made available at https://github.com/cccnlab/CDT-API-Network .

Machine Learning-Based Predictive Model of Aortic Valve Replacement Modality Selection in Severe Aortic Stenosis Patients.

The current recommendation for bioprosthetic valve replacement in severe aortic stenosis (AS) is either surgical aortic valve replacement (SAVR) or transcatheter aortic valve replacement (TAVR). We evaluated the performance of a machine learning-based predictive model using existing periprocedural variables for valve replacement modality selection. We analyzed 415 patients in a retrospective longitudinal cohort of adult patients undergoing aortic valve replacement for aortic stenosis. A total of 72 clinical variables including demographic data, patient comorbidities, and preoperative investigation characteristics were collected on each patient. We fit models using LASSO (least absolute shrinkage and selection operator) and decision tree techniques. The accuracy of the prediction on confusion matrix was used to assess model performance. The most predictive independent variable for valve selection by LASSO regression was frailty score. Variables that predict SAVR consisted of low frailty score (value at or below 2) and complex coronary artery diseases (DVD/TVD). Variables that predicted TAVR consisted of high frailty score (at or greater than 6), history of coronary artery bypass surgery (CABG), calcified aorta, and chronic kidney disease (CKD). The LASSO-generated predictive model achieved 98% accuracy on valve replacement modality selection from testing data. The decision tree model consisted of fewer important parameters, namely frailty score, CKD, STS score, age, and history of PCI. The most predictive factor for valve replacement selection was frailty score. The predictive models using different statistical learning methods achieved an excellent concordance predictive accuracy rate of between 93% and 98%.

Identification of a novel gut microbiota signature associated with colorectal cancer in Thai population.

Colorectal cancer (CRC) is the third most common cancer worldwide. Dysbiosis of human gut microbiota has been linked to sporadic CRC. This study aimed to compare the gut microbiota profiles of 80 Thai volunteers over 50 years of age among 25 CRC patients, 33 patients with adenomatous polyp, and 22 healthy controls. The 16S rRNA sequencing was utilized to characterize the gut microbiome in both mucosal tissue and stool samples. The results revealed that the luminal microbiota incompletely represented the intestinal bacteria at the mucus layer. The mucosal microbiota in beta diversity differed significantly among the three groups. The stepwise increase of Bacteroides and Parabacteroides according to the adenomas-carcinomas sequence was found. Moreover, linear discriminant analysis effect size showed a higher level of Erysipelatoclostridium ramosum (ER), an opportunistic pathogen in the immunocompromised host, in both sample types of CRC patients. These findings indicated that the imbalance of intestinal microorganisms might involve in CRC tumorigenesis. Additionally, absolute quantitation of bacterial burden by quantitative real-time PCR (qPCR) confirmed the increasing ER levels in both sample types of cancer cases. Using ER as a stool-based biomarker for CRC detection by qPCR could predict CRC in stool samples with a specificity of 72.7% and a sensitivity of 64.7%. These results suggested ER might be a potential noninvasive marker for CRC screening development. However, a larger sample size is required to validate this candidate biomarker in diagnosing CRC.

Summary of the Thai Osteoporosis Foundation (TOPF) Clinical Practice Guideline on the diagnosis and management of osteoporosis 2021.

Objectives: The Thai Osteoporosis Foundation (TOPF) is an academic organization that consists of a multidisciplinary group of healthcare professionals managing osteoporosis. The first clinical practice guideline for diagnosing and managing osteoporosis in Thailand was published by the TOPF in 2010, then updated in 2016 and 2021. This paper presents important updates of the guideline for the diagnosis and management of osteoporosis in Thailand. Methods: A panel of experts in the field of osteoporosis was recruited by the TOPF to review and update the TOPF position statement from 2016. Evidence was searched using the MEDLINE database through PubMed. Primary writers submitted their first drafts, which were reviewed, discussed, and integrated into the final document. Recommendations are based on reviews of the clinical evidence and experts' opinions. The recommendations are classified using the Grading of Recommendations, Assessment, Development, and Evaluation classification system. Results: The updated guideline comprises 90 recommendations divided into 12 main topics. This paper summarizes the recommendations focused on 4 main topics: the diagnosis and evaluation of osteoporosis, fracture risk assessment and indications for bone mineral density measurement, fracture risk categorization, management according to fracture risk, and pharmacological management of osteoporosis. Conclusions: This updated clinical practice guideline is a practical tool to assist healthcare professionals in diagnosing, evaluating, and managing osteoporosis in Thailand.

How Microbiomes Affect Skin Aging: The Updated Evidence and Current Perspectives.

The skin has a multifactorial aging process, caused by both intrinsic and extrinsic factors. A major theory of aging involves cellular senescence or apoptosis resulting from oxidative damage as the skin's antioxidant system tends to weaken with age. The human microbiota is a complex ecosystem that is made up of microorganisms (bacteria, fungi, and viruses). Both gut and skin microbiota have essential roles in the protection against invading pathogens, mediating inflammatory conditions, and the modulation of the immune system which is involved in both innate and adaptive immune responses. However, the human microbiome could be changed during the life stage and affected by various perturbations. An alteration of the intestinal bacteria results in "microbial dysbiosis" which is associated with the influence of various diseases, including aging. The skin interactome is a novel integration of the "genome-microbiome-exposome" that plays a significant role in skin aging and skin health. Mitigating the negative impacts of factors influencing the skin interactome should be the future strategy to protect, prevent, and delay skin aging along with preserving healthy skin conditions. This review summarizes the current evidence on how human microbiomes affect skin aging and demonstrates the possible interventions, relating to human microbiomes, to modulate skin health and aging. Probiotics-based products are currently available mainly for the add-on treatment of many dermatologic conditions. However, at this point, there are limited clinical studies on skin anti-aging purposes and more are required as this evolving concept is on the rise and might provide an insight into future therapeutic options.

Prediction of nonviable myocardium by ECG Q-Wave parameters: A 3.0 T cardiovascular magnetic resonance study.

INTRODUCTION: The presence of a Q-wave on a 12-lead electrocardiogram (ECG) has been considered a marker of a large myocardial infarction (MI). However, the correlation between the presence of Q-waves and nonviable myocardium is still controversial. The aims of this study were to 1) test QWA, a novel ECG approach, to predict transmural extent and scar volume using a 3.0 Tesla scanner, and 2) assess the accuracy of QWA and transmural extent. METHODS: Consecutive patients with a history of coronary artery disease who came for myocardial viability assessment by CMR were retrospectively enrolled. Q-wave measurements parameters including duration and maximal amplitude were performed from each surface lead. A 3.0 Tesla CMR was performed to assess LGE and viability. RESULTS: Total of 248 patients were enrolled in the study (with presence (n = 76) and absence of pathologic Q-wave (n = 172)). Overall prevalence of pathologic Q-waves was 27.2% (for LAD infarction patients), 20.0 % (for LCX infarction patients), and 16.8% (for RCA infarction patients). Q-wave area demonstrated high performance for predicting the presence of a nonviable segment in LAD territory (AUC 0.85, 0.77-0.92) and a lower, but still significant performance in LCX (0.63, 0.51-0.74) and RCA territory (0.66, 0.55-0.77). Q-wave area greater than 6 ms mV demonstrated high performance in predicting the presence of myocardium scar larger than 10% (AUC 0.82, 0.76-0.89). CONCLUSION: Q-wave area, a novel Q-wave parameter, can predict non-viable myocardial territories and the presence of a significant myocardial scar extension.

An explainable self-attention deep neural network for detecting mild cognitive impairment using multi-input digital drawing tasks.

BACKGROUND: Mild cognitive impairment (MCI) is an early stage of cognitive decline which could develop into dementia. An early detection of MCI is a crucial step for timely prevention and intervention. Recent studies have developed deep learning models to detect MCI and dementia using a bedside task like the classic clock drawing test (CDT). However, it remains a challenge to predict the early stage of the disease using the CDT data alone. Moreover, the state-of-the-art deep learning techniques still face black box challenges, making it questionable to implement them in a clinical setting. METHODS: We recruited 918 subjects from King Chulalongkorn Memorial Hospital (651 healthy subjects and 267 MCI patients). We propose a novel deep learning framework that incorporates data from the CDT, cube-copying, and trail-making tests. Soft label and self-attention were applied to improve the model performance and provide a visual explanation. The interpretability of the visualization of our model and the Grad-CAM approach were rated by experienced medical personnel and quantitatively evaluated using intersection over union (IoU) between the models' heat maps and the regions of interest. RESULTS: Rather than using a single CDT image in the baseline VGG16 model, using multiple drawing tasks as inputs into our proposed model with soft label significantly improves the classification performance between the healthy aging controls and the MCI patients. In particular, the classification accuracy increases from 0.75 (baseline model) to 0.81. The F1-score increases from 0.36 to 0.65, and the area under the receiver operating characteristic curve (AUC) increases from 0.74 to 0.84. Compared to the multi-input model that also offers interpretable visualization, i.e., Grad-CAM, our model receives higher interpretability scores given by experienced medical experts and higher IoUs. CONCLUSIONS: Our model achieves better classification performance at detecting MCI compared to the baseline model. In addition, the model provides visual explanations that are superior to those of the baseline model as quantitatively evaluated by experienced medical personnel. Thus, our work offers an interpretable machine learning model with high classification performance, both of which are crucial aspects of artificial intelligence in medical diagnosis.

Prevalence and Associated Factors of Drug-Related Problems Among Older People: A Cross-Sectional Study at King Chulalongkorn Memorial Hospital in Bangkok.

BACKGROUND AND OBJECTIVE: The use of multiple medications and altered pharmacokinetics/pharmacodynamics may lead to drug-related problems in members of the older population. The aim of this study is to evaluate the prevalence of, and factors related to, drug-related problems in older urban-living Thai people. METHODS: We conducted a cross-sectional study involving 466 participants (aged ≥ 65 years) whose first-time health screening at the Geriatric Excellence Center, King Chulalongkorn Memorial Hospital, Bangkok was between May and October 2019. Participants were interviewed and assessed for drug-related problems by clinical pharmacists. RESULTS: In total, 54.5% (254) of the participants were aged 65-69 years and 77.0% (359) of the participants were women. Of the participants, 56.7% had three or more health conditions such as hyperlipidemia (62%), hypertension (46%), and cataract (18%). Fifty-five percent of the participants took five or more health products (polypharmacy) and 16% took ten or more products on a regular basis. Of the 2633 products used, 68% were prescription drugs and 32% were over-the-counter products. The prevalence of drug-related problems according to the criteria suggested by Cipolle-Strand-Morley (2012) was 63.3% (587 drug-related problems). Most of the problems came from: (a) non-adherence (28.6%); (b) needs for additional drug therapy (26.4%); and (c) adverse drug reactions (17.4%). Factors associated with drug-related problems were polypharmacy (odds ratio 2.50, 95% confidence interval 1.60-3.89) and multiple comorbidities [three or more conditions] (odds ratio 2.20, 95% confidence interval 1.41-3.43). CONCLUSIONS: The prevalence of drug-related problems in urban-living older people at King Chulalongkorn Memorial Hospital in Bangkok was high. Polypharmacy and multiple comorbidities were significantly related to drug-related problems. To decrease the number of drug-related problems, pharmacists should collaborate with healthcare teams and suggest how to correctly reduce the number of health products being consumed by older people.

Factors associated with unintentional weight loss among older adults in a geriatric outpatient clinic of university hospital.

BACKGROUND: Unintentional weight loss (UWL) is defined as unintentional reduction of more than 5% of baseline body weight over 6 to 12 months. UWL is a common problem in the older adults, resulting in increased rate of morbidity and mortality. With specific reference to Thailand, no information on factors associated with UWL in older adults could be traced. The aims of this research were to identify the factors associated with UWL and to assess the common causes of UWL among older adults in the geriatric outpatient clinic of university hospital. METHODS: A case-control study was conducted from June 1st, 2020 to December 31st, 2020. Eighty older adults aged 60 years or older were enrolled in the UWL group while the non-UWL group consisted of 160 participants. Data collection was performed by structural questionnaire including baseline characteristics, psychosocial factors, health information, lifestyle behaviors, and medications. The factors associated with UWL were analyzed by using univariate and multivariate logistic regression analysis. Causes of UWL were recorded from electronic medical records. RESULTS: The mean age of the 240 participants was 79.6 years (SD 7.4). Most patients were female (79.2%) and had fewer than 12 years of education (62.6%). The three common causes of UWL were reduced appetite (20.1%), dementia and behavioral and psychological symptoms of dementia (13.7%) and medications (11.0%). Multivariate logistic regression analysis showed that a Charlson Comorbidity Index (CCI) score of >1 (OR 2.55, 95% CI 1.37-4.73; P = 0.003), vitamin D deficiency (OR 4.01, 95% CI 1.62-9.97; P = 0.003), and hemoglobin level of <12 g/dL (OR 2.47, 95% CI 1.32-4.63; P = 0.005) were factors significantly associated with UWL. CONCLUSIONS: Factors associated with UWL were CCI score >1, vitamin D deficiency, and hemoglobin level of <12 g/dl. The early detection of these associated factors, reduced appetite, dementia and polypharmacy may be important in UWL prevention in older adults.

Prevalence and risk factors of primary sarcopenia in community-dwelling outpatient elderly: a cross-sectional study.

No previous study has investigated the prevalence and risk factors for primary sarcopenia in outpatient setting. This study aims to evaluate the prevalence and factors associated with primary sarcopenia in outpatient elderly. Additionally, we compared the severity of sarcopenia based on the 2014 and 2019 Asian Working Group for Sarcopenia (AWGS) criteria. This cross-sectional study was performed in 330 subjects aged over 60 years in an outpatient setting. The muscle strength, muscle performance and muscle mass were assessed using the handheld dynamometer, 6-m gait speed, and bioelectrical impedance analysis, respectively. The prevalence of sarcopenia was 10% as per the 2014 and 2019 AWGS criteria. The development of sarcopenia was positively correlated with the age with an odds ratio (OR) of 6.87 [95% confidence interval (CI) 1.63-28.88] in the middle-old group (70-79 years), and 13.71 (95%CI 3.66-51.41; p = 0.009) in the very old group (≥ 80 years). Prefrailty and low physical activity were significantly associated with sarcopenia with an OR of 4.75 (95%CI 1.90-11.89) in prefrailty, 15.35 (95%CI 1.69-139.47) in the middle activity group, and 17.99 (95%CI 1.95-165.73) in the lowest activity group. In conclusion, primary sarcopenia was found in one-tenth of outpatient elderly. Age, prefrailty, and low activity were independent factors associated with sarcopenia.

Furosemide Stress Test as a Predicting Biomarker for Delayed Graft Function in Kidney Transplantation.

BACKGROUND: Delayed graft function (DGF) could worsen early and long-term outcomes of kidney transplantation (KT). DGF is caused by several pre-transplantation and perioperative factors in both donors and recipients. At present, there are no biomarkers or tests during the immediate post-KT period that can accurately predict the development of DGF. MATERIALS AND METHODS: This prospective study was conducted in deceased donor KT (DDKT) at King Chulalongkorn Memorial Hospital, Thailand. All recipients underwent furosemide stress test (FST) by receiving a single dose of intravenous furosemide, 1.5 mg/kg at 3 h after allograft reperfusion. We determined the correlations between DGF (requiring dialysis within the first week after transplantation) and the values of urine volume recorded hourly after FST until 6 h, the parameters of postoperative dynamic tests, including resistive index (RI) of renal arteries and effective renal plasma flow (ERPF), and urine neutrophil gelatinase-associated lipocalin (NGAL). RESULTS: Of the 59 total DDKT recipients enrolled, 24 developed DGF. The FST is a more accurate biomarker than urine NGAL, RI of renal arteries, and ERPF in the prediction of DGF. The 4-h urine volume less than 350 mL (FST non-responsive) was the best cut-off value in predicting DGF with 87.5% sensitivity, 82.9% specificity, and 82.5% accuracy. Multiple logistic regression analyses showed an odds ratio of 0.993 (0.986-0.999, p = 0.035) for the 4-h urine volume to predict DGF. CONCLUSIONS: The FST is a simple and accurate biomarker for predicting DGF in early post-KT period. Close monitoring and well prepared dialysis are suggested in patients with urine volume < 350 mL after 4 h of FST. The FST non-responsive patients could be the target for further DGF preventive intervention. ClinicalTrials.gov identifier: NCT03071536.

Sensorineural Organs Dysfunction and Cognitive Decline: A Review Article.

Vision, hearing, olfaction, and cognitive function are essential components of healthy and successful aging. Multiple studies demonstrate relationship between these conditions with cognitive function. The present article focuses on hearing loss, visual impairment, olfactory loss, and dual sensory impairments in relation to cognitive declination and neurodegenerative disorders. Sensorineural organ impairment is a predictive factor for mild cognitive impairment and neurodegenerative disorders in the elderly. We recommend early detection of sensorineural dysfunction by history, physical examination, and screening tests. Assisted device and early cognitive rehabilitation may be beneficial. Future research is warranted in order to explore advanced treatment options and method to slow progression for cognitive declination and sensorineural organ impairment.