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To identify factors associated with malnutrition (undernutrition and overnutrition) and determine appropriate cut-off values for mid-arm circumference (MAC) and calf circumference (CC) among community-dwelling Indian older adults.
Data from the first wave of harmonised diagnostic assessment of dementia for Longitudinal Ageing Study in India (LASI-DAD) were used. Various sociodemographic factors, comorbidities, geriatric syndromes, childhood financial and health status were included. Anthropometric measurements included body mass index (BMI), MAC and CC.
Nationally representative cohort study including 36 Indian states and union territories.
4096 older adults aged >60 years from LASI DAD.
The outcome variable was BMI, categorised as low (2), normal (18.5–22.9 kg/m2) and high (>23 kg/m2). The cut-off values of MAC and CC were derived using ROC curve with BMI as the gold standard.
902 (weighted percentage 20.55%) had low BMI, 1742 (44.25%) had high BMI. Undernutrition was associated with age, wealth-quintile and impaired cognition, while overnutrition was associated with higher education, urban living and comorbidities such as hypertension, diabetes and chronic heart disease. For CC, the optimal lower and upper cut-offs for males were 28.1 cm and >31.5 cm, respectively, while for females, the corresponding values were 26 cm and >29 cm. Similarly, the optimal lower and upper cut-offs for MAC in males were 23.9 cm and >26.9 cm, and for females, they were 22.5 cm and >25 cm.
Our study identifies a high BMI prevalence, especially among females, individuals with higher education, urban residents and those with comorbidities. We establish gender-specific MAC and CC cut-off values with significant implications for healthcare, policy and research. Tailored interventions can address undernutrition and overnutrition in older adults, enhancing standardised nutritional assessment and well-being.
As part of the ‘Suicidality: Treatment Occurring in Paediatrics (STOP)’ study, we developed and performed psychometric validation of an electronic-clinical-outcome-assessment (eCOA), which included a patient-reported-outcome (ePRO), an observer-rated-outcome (eObsRO) for parents/carers and a clinician-reported-outcome (eClinRO) that allows identification and monitoring of medication-related suicidality (MRS) in adolescents.
STOP: Prospective study: A two phase validation study to assess the impact of medication on suicidal ideations.
Six participating countries: Netherlands, UK, Germany, France, Spain and Italy that were part of the Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 261411.
Cohort 1 consisted of 41 adolescent-completions, 50 parent-completions and 56 clinician-completions. Cohort 2 consisted of 244 adolescent-completions, 198 parent-completions and 240 clinician-completions from across the six countries. The scale was administered only to participants who have screened positive for the STOP-Suicidality Assessment Scale (STOP-SAS).
A total of 24 items for the development of the STOP-Medication Suicidality Side Effects Scale (STOP-MS3) were identified and three versions (for patients, parents and clinicians) of the STOP-MS3 were developed and validated in two separate study cohorts comprising of adolescents, their parents and clinicians. Cronbach’s α coefficients were above 0.85 for all domains. The inter-rater reliability of the STOP-MS3 was good and significant for the adolescent (ePRO), clinician (eClinRO) (r=0.613), parent (eObsRO) versions of the scale (r=0.394) and parent and clinician (r=0.347). Exploratory factor analysis identified a 3-factor model across 24 items for the adolescent and parent version of the scale: (1) Emotional Dysregulation, (2) Somatic Dysregulation and (3) Behavioural Dysregulation. For the clinician version, a 4-factor model defined the scale structure: (1) Somatic Dysregulation, (2) Emotional Dysregulation, (3) Behavioural Dysregulation and (4) Mood Dysregulation.
These findings suggest that the STOP-MS3 scale, a web-based eCOA, allows identification and monitoring of MRS in the adolescent population and shows good reliability and validity.
by Sona M. Al Younis, Leontios J. Hadjileontiadis, Aamna M. Al Shehhi, Cesare Stefanini, Mohanad Alkhodari, Stergios Soulaidopoulos, Petros Arsenos, Ioannis Doundoulakis, Konstantinos A. Gatzoulis, Konstantinos Tsioufis, Ahsan H. Khandoker
Heart Failure (HF) significantly impacts approximately 26 million people worldwide, causing disruptions in the normal functioning of their hearts. The estimation of left ventricular ejection fraction (LVEF) plays a crucial role in the diagnosis, risk stratification, treatment selection, and monitoring of heart failure. However, achieving a definitive assessment is challenging, necessitating the use of echocardiography. Electrocardiogram (ECG) is a relatively simple, quick to obtain, provides continuous monitoring of patient’s cardiac rhythm, and cost-effective procedure compared to echocardiography. In this study, we compare several regression models (support vector machine (SVM), extreme gradient boosting (XGBOOST), gaussian process regression (GPR) and decision tree) for the estimation of LVEF for three groups of HF patients at hourly intervals using 24-hour ECG recordings. Data from 303 HF patients with preserved, mid-range, or reduced LVEF were obtained from a multicentre cohort (American and Greek). ECG extracted features were used to train the different regression models in one-hour intervals. To enhance the best possible LVEF level estimations, hyperparameters tuning in nested loop approach was implemented (the outer loop divides the data into training and testing sets, while the inner loop further divides the training set into smaller sets for cross-validation). LVEF levels were best estimated using rational quadratic GPR and fine decision tree regression models with an average root mean square error (RMSE) of 3.83% and 3.42%, and correlation coefficients of 0.92 (p
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