Current Aging Science - Volume 12, Issue 2, 2019

Background: Older age has a significant association with anaemia. However, this has not been adequately investigated in the context of specific comorbidities such as Type 2 Diabetes Mellitus (T2DM). Objectives: To investigate the role of age in Normocytic Anaemia (NCA) and the adverse outcomes of NCA in T2DM. Methods: Patients with NCA, either unexplained or related to Chronic Kidney Disease (CKD), were recruited from a diabetic clinic over six months. Anaemia was defined as a haemoglobin(Hb) < 130g/l for men and <120g/l for women. The relevant data were obtained by interviewing patients and review of medical records. Patients were followed for 12 months for pre-defined adverse outcomes. Results: Of the 354 patients assessed, 203 were included (mean age 63.12 ± 13.62 years, males 49.8%). The prevalence of NCA was 24% (49). Older age had a significant univariate association with NCA (p < 0.001) and this remained significant (adjusted Odds Ratio (OR) 1.24, 95% CI 1.16- 5.29) after adjusting for estimated Glomerular Filtration Rate (eGFR) < 60ml/min/1.73m2, albuminuria and other potential confounders. Adjusting for eGFR as a continuous variable also confirmed this significant association (OR1.15, 95% CI 1.10-7.01). In the subgroup of patients aged ≥ 75 years, only older age was significantly associated with NCA. The incidence of all-cause mortality and composite cardiovascular/cerebrovascular events was similar in the anaemic and nonanaemic groups. Conclusion: NCA is common in T2DM and has a significant association with older age independent of CKD. The anaemia is mild in most patients and appears to have a benign course.

Introduction: Population aging and related issues are among the most important challenges in different countries. It seems that the use of remote technology can provide the elderly with better healthcare services and can help them to manage chronic diseases. This research aimed to investigate the experts' views about the feasibility of using mobile-based technology to manage chronic diseases in the elderly. Methods: This was a survey study which was completed in 2018. The participants were the faculty members of the department of geriatrics across the country (n=33). In order to collect data, two questionnaires were used. The first questionnaire contained questions related to the five dimensions of a feasibility study and the second questionnaire consisted of five open questions about the opportunities, threats, strengths and weaknesses of using mobile-based technology in chronic disease management. The qualitative data were analyzed by using content analysis method and the quantitative data were analyzed by using descriptive statistics. Results: From the participants’ perspectives, it was feasible to use mobile-based technology in chronic disease management for elderly people. The mean values for five feasibility dimensions were as follows: ethico-legal dimension (4.09±1.01), scheduling dimension (3.70±1.03), economic dimension (3.47±1.04), operational dimension (3.42±1.12) and technical dimension (3.07±1.27). While such a technology can help increasing the accessibility of healthcare services, raising health literacy, and saving time and costs, the main threats, namely; the misinterpretation of information and the possibility of breeching confidentiality should not be underestimated. Conclusion: The results showed that mHealth and remote medical technologies can be used for chronic disease management among elderly people. However, these technologies have some strengths and weaknesses. In order to succeed in the development and implementation of these projects, it is essential to consider probable threats and weaknesses before implementing the applications.

Background: Augmentation Index (AIx) is considered as an important predictor of cardiovascular disease. So, quantification of AIx may provide a rapid cost-effective and non-invasive means of cardiovascular risk stratification. At present, WHO/ISH risk prediction charts are used to predict 10-year risk of a fatal or nonfatal major cardiovascular event, an assessment which requires laboratory support for blood chemistry and thus making it ill-suited for resource-limited settings. Objectives: In this study, we examined the association of AIx with cardiovascular risk as determined by the WHO/ISH chart and identified AIx cut-offs to stratify patients into different risk categories. Methods: A case-control study with 162 cases and 61 controls was conducted in a tertiary care hospital in eastern India. Data were obtained for demographic, anthropometric, cardiovascular, and biochemical parameters. Cardiovascular risk assessment was carried out by WHO/ISH risk model in R. Statistical analysis was done for examining the association of AIx with WHO/ISH cardiovascular risk and for identifying AIx cut-offs to stratify patients into different risk categories. Results: Box and whisker plots for assessing the correlation between AIx and WHO/ISH cardiovascular risk showed an increase in the median value of AIx with increasing risk in both cases and controls. Heart rate corrected AIx showed a steady increase with increasing risk in males. AIx cutoffs showed good sensitivity and specificity for each risk category. Conclusion: AIx is remarkably associated with cardiovascular risk as assessed by the WHO/ISH chart and the AIx cut-offs obtained in the study can be used as an efficient, non-invasive surrogate biomarker of cardiovascular risk even in resource-limited settings.

Background: Together with the Alzheimer’s disease, Parkinson’s disease is considered as one of the two serious known neurodegenerative diseases. Physicians find it hard to predict whether a given patient has already developed or is expected to develop the Parkinson’s disease in the future. To overcome this difficulty, it is possible to develop a computing model, which analyzes the data related to a given patient and predicts with acceptable accuracy when he/she is anticipated to develop the Parkinson’s disease. Objectives: This paper contributes an attractive prediction framework based on some machine learning approaches for distinguishing people with Parkinsonism from healthy individuals. Methods: Several fuzzy classifiers such as Inductive Fuzzy Classifier, Fuzzy Rough Classifier and two types of neuro-fuzzy classifiers have been employed. Results: The fuzzy classifiers utilized in this study have been tested using the “Parkinson Speech Dataset with Multiple Types of Sound Recordings Data Set” of 40 subjects available on the UCI repository. Conclusion: The results achieved show that FURIA, MLP- Bagging - SGD, genfis2 and scg1 performed the best among the fuzzy rough, WEKA, adaptive neuro-fuzzy and neuro-fuzzy classifiers, respectively. The worst performance belongs to nearest neighborhood, IBK, genfis3 and scg3 among the formerly mentioned classifiers. The results reported in this paper are better in comparison to the results reported in Sakar et al., where the same dataset was used, with utilization of different classifiers. This demonstrates the applicability and effectiveness of the fuzzy classifiers used in this study as compared to the non-fuzzy classifiers used by Sakar et al.

Background: Polypharmacy remains problematic for individuals ≥65. Objective: To summarise the percentages of patients meeting 2015 STOPP criteria for Potentially Inappropriate Prescriptions (PIPs), 2015 Beers criteria for Potentially Inappropriate Medications (PIMs), and START criteria Potential Prescribing Omissions (PPOs). Methods: Searches conducted on 2 January 2019 in Medline, Embase, and PubMed identified 562 studies and 62 studies were retained for review. Data were abstracted independently. Results: 62 studies (n=1,854,698) included two RCTs and 60 non-randomised studies. For thirty STOPP/START studies (n=1,245,974) average percentages for ≥1 PIP weighted by study size were 42.8% for 1,242,010 community patients and 51.8% for 3,964 hospitalised patients. For nineteen Beers studies (n = 595,811) the average percentages for ≥1 PIM were 58% for 593,389 community patients and 55.5% for 2,422 hospitalised patients. For thirteen studies (n=12,913) assessing both STOPP/START and Beers criteria the average percentages for ≥1 STOPP PIP were 33.9% and Beers PIMs 46.8% for 8,238 community patients, and for ≥ 1 STOPP PIP were 42.4% and for ≥1 Beers PIM 60.5% for 4,675 hospitalised patients. Only ten studies assessed changes over time and eight found positive changes. Conclusion: PIP/PIM/PPO rates are high in community and hospitalised patients in many countries. RCTs are needed for interventions to: reduce new/existing PIPs/PIMs/PPO prescriptions, reduce prescriptions causing adverse effects, and enable regulatory authorities to monitor and reduce inappropriate prescriptions in real time. Substantial differences between Beers and STOPP/START assessments need to be investigated whether they are due to the criteria, differential medication availability between countries, or data availability to assess the criteria.