Health-SigQOLM is a versatile scale for measuring various aspects of health-related quality of life.

OBJECTIVE: The "Health" element is one of the elements in Significant Quality of Life Measure (SigQOLM) that measures quality of life and well-being of people. This study aims to evaluate the Health element (Health-SigQOLM) as a generic and dynamic scale to measure health-related quality of life (HRQOL) with a broader spectrum of coverage. This study used a secondary data that developed SigQOLM. Only the "Health" element with 33 items is used for analysis. RESULTS: The construct of Health-SigQOLM has a minimum factor loading of 0.425 with excellent model fit. The health status among healthcare workers is significantly associated with the Health-SigQOLM (p < 0.001). The Health-SigQOLM score can clearly distinguish between healthy people and those who have been afflicted with some diseases but have never been hospitalized due to disease progression or other associated complications (p = 0.002). The Health-SigQOLM is a generic and dynamic tool for assessing various aspects of health-related quality of life.

Measuring population health and quality of life: Developing and testing of the significant quality of life measure (SigQOLM).

Quality of life (QOL) should ideally be determined by a broader spectrum of measurable parameters. This study aims to develop and validate a study instrument that is designed to determine a holistic measure of health and non-health aspects of QOL, and it is called the 'Significant Quality of Life Measure' (SigQOLM). This study involves five phases which aim to (i) explore and understand the subject matter content, (ii) develop a questionnaire, (iii) assess its content validity and face validity, (iv) conduct a pilot study, and lastly (v) perform a field-test by using the questionnaire. For the field-testing phase, a cross-sectional study was conducted which elicited responses from healthcare workers via a self-administered survey for all the SigQOLM items. Based on the results, the overall framework of the SigQOLM consists of four elements, 18 domains with 69 items. The element of "Health" is measured by nine domains, while "Relationships", "Functional activities, and "Survival" are measured by three domains respectively. The SigQOLM has been developed successfully and then validated with a high level of reliability, validity, and overall model fit. Therefore, the SigQOLM will provide researchers and policymakers another viable option to elicit a more comprehensive outcome measure of QOL which shall then enable them to implement specific interventions for improving the QOL of all the people, both healthy or otherwise.

ACORN (A Clinically-Oriented Antimicrobial Resistance Surveillance Network) II: protocol for case based antimicrobial resistance surveillance.

Background: Antimicrobial resistance surveillance is essential for empiric antibiotic prescribing, infection prevention and control policies and to drive novel antibiotic discovery. However, most existing surveillance systems are isolate-based without supporting patient-based clinical data, and not widely implemented especially in low- and middle-income countries (LMICs). Methods: A Clinically-Oriented Antimicrobial Resistance Surveillance Network (ACORN) II is a large-scale multicentre protocol which builds on the WHO Global Antimicrobial Resistance and Use Surveillance System to estimate syndromic and pathogen outcomes along with associated health economic costs. ACORN-healthcare associated infection (ACORN-HAI) is an extension study which focuses on healthcare-associated bloodstream infections and ventilator-associated pneumonia. Our main aim is to implement an efficient clinically-oriented antimicrobial resistance surveillance system, which can be incorporated as part of routine workflow in hospitals in LMICs. These surveillance systems include hospitalised patients of any age with clinically compatible acute community-acquired or healthcare-associated bacterial infection syndromes, and who were prescribed parenteral antibiotics. Diagnostic stewardship activities will be implemented to optimise microbiology culture specimen collection practices. Basic patient characteristics, clinician diagnosis, empiric treatment, infection severity and risk factors for HAI are recorded on enrolment and during 28-day follow-up. An R Shiny application can be used offline and online for merging clinical and microbiology data, and generating collated reports to inform local antibiotic stewardship and infection control policies. Discussion: ACORN II is a comprehensive antimicrobial resistance surveillance activity which advocates pragmatic implementation and prioritises improving local diagnostic and antibiotic prescribing practices through patient-centred data collection. These data can be rapidly communicated to local physicians and infection prevention and control teams. Relative ease of data collection promotes sustainability and maximises participation and scalability. With ACORN-HAI as an example, ACORN II has the capacity to accommodate extensions to investigate further specific questions of interest.

Development of a Quality-of-Life Instrument to Measure Current Health Outcomes: Health-Related Quality of Life with Six Domains (HRQ-6D).

Health-related quality of life (HRQOL) is one of the most important outcome measures to be assessed by medical research. This study aims to develop and validate an instrument called the "health-related quality of life with six domains" (HRQ-6D), which aims to measure an individual's health-related quality of life within a 24 h period of time. This is a questionnaire development study involving five phases, which are (i) to explore the subject matter content for gaining a better understanding of the topic, (ii) to develop the questionnaire, (iii) to assess both its content validity and face validity, (iv) to conduct a pilot study, and finally, (v) to undertake a field testing of the questionnaire. For the field-testing phase, a cross-sectional study involving a self-administered survey for HRQ-6D items was conducted among healthcare workers with various health conditions. Exploratory factor analysis was initially applied to construct the major dimensions of the HRQ-6D. Confirmatory factor analysis was subsequently applied to evaluate the model fit of the overall framework of the HRQ-6D. The clinical utility of this HRQ-6D was also assessed via its association with actual clinical evidence. A total of 406 respondents participated in the survey. Six domains were identified from the analysis, namely "pain", "physical strength", "emotion", "self-care", "mobility", and "perception of future health" comprising two items in each domain. Each domain was reported to have a minimum value of Cronbach's alpha of 0.731, and the model fit for the overall framework of the HRQ-6D was also found to be excellent. Exploratory factor analysis was undertaken for the 12 items of the HRQ-6D. All the domains can be categorized into three major dimensions, namely "health", "body function", and "future perception", with a minimum value for their factor loadings of at least 0.507. A notable finding was that the HRQ-6D was significantly associated with an individual's existing comorbidities and current status of health (p < 0.05). This study successfully validated the HRQ-6D, and we found it to possess both excellent levels of reliability and validity and a satisfactory degree of model fit; it was also significantly associated with actual clinical evidence.

The Effect of DPP4 Inhibitor on Glycemic Variability in Patients with Type 2 Diabetes treated with twice-daily Premixed Human Insulin.

OBJECTIVE: To evaluate the effect of adding DPP4 inhibitor (DPP4-i) on glycemic variability (GV) in patients with type 2 diabetes mellitus (T2DM) treated with premixed human insulin (MHI). METHODOLOGY: We conducted a prospective study in patients with T2DM on twice-daily MHI with or without metformin therapy. Blinded continuous glucose monitoring was performed at baseline and following 6 weeks of Vildagliptin therapy. RESULTS: Twelve patients with mean (SD) age of 55.8 (13.1) years and duration of disease of 14.0 (6.6) years were recruited. The addition of Vildagliptin significantly reduced GV indices (mmol/L): SD from 2.73 (IQR 2.12-3.66) to 2.11 (1.76-2.55), p=0.015; mean amplitude of glycemic excursions (MAGE) 6.94(2.61) to 5.72 (1.87), p=0.018 and CV 34.05 (8.76) to 28.19 (5.36), p=0.010. In addition, % time in range (3.9-10 mmol/l) improved from 61.17 (20.50) to 79.67 (15.33)%, p=0.001; % time above range reduced from 32.92 (23.99) to 18.50 (15.62)%, p=0.016; with reduction in AUC for hyperglycemia from 1.24 (1.31) to 0.47 (0.71) mmol/day, p=0.015. Hypoglycemic events were infrequent and the reduction in time below range and AUC for hypoglycemia did not reach statistical significance. CONCLUSION: The addition of DPP4-I to commonly prescribed twice-daily MHI in patients with T2DM improves GV and warrants further exploration.

A family of three with haemoglobin Cheverly: low oxygen saturation in stable patients.

Haemoglobin (Hb) Cheverly is a rare, low oxygen affinity haemoglobinopathy. It is a result of point mutation at the 45 codon of the beta globin genes that leads to substitution of phenylalanine by serine. It is characterised by spuriously low peripheral oxygen saturation with normal arterial oxygen saturation. We describe a family of three with Hb Cheverly in Sarawak General Hospital, Malaysia. It was discovered through incidental finding during hospital admission for unrelated complaints. Laboratory testing revealed abnormal haemoglobin detected at the C window of the high performance liquid chromatography. Subsequent DNA analysis detected replacement of thymidine by cytosine at the beta globin genes. Hb Cheverly may or may not have clinical significance as most of the patients live a normal life; however, it is crucial for us to make early diagnosis to prevent unnecessary extensive investigations for hypoxaemia detected via pulse oximetry, especially in the midst of COVID-19 pandemic.

A comparison between the effectiveness of a gamified approach with the conventional approach in point-of-care ultrasonographic training.

BACKGROUND: Although gamification increases user engagement, its effectiveness in point-of-care ultrasonographic training has yet to be fully established. This study was conducted with the primary outcome of evaluating its effectiveness in point-of-care ultrasonographic training as compared to conventional approach. METHODS: Participants consisting of junior doctors were randomized into either the (1) gamified or the (2) conventional educational approach for ultrasonographic training. RESULTS: A total of 31 junior doctors participated in this study (16 participants in gamified arm, 15 in the conventional arm after one participant from the conventional arm dropped out due to work commitment). Two-way mixed ANOVA test showed that there was no statistically significant interaction between the types of educational approach and time of testing (pre-test, post-test, 2 months post-training) for both theoretical knowledge score and practical skills score, with F(2, 58) = 39.6, p < 0.001, partial η2 = 0.4 and F(2, 58) = 3.06, p = 0.06, partial η2 = 0.095, respectively. For theoretical knowledge score, pairwise comparisons showed that the mean 2 months post-training scores (20.28 +/- 0.70, 95% CI 18.87-21.69) and mean post-test scores (20.27 +/- 0.65, 95% CI 18.94-21.60) were better than the pre-test scores (12.99 +/- 0.50, 95% CI 11.97-14.00) with p-values < 0.001 for both comparisons respectively. Similarly, for practical skill score, pairwise comparisons showed that the mean 2 months post-training scores (20.28 +/- 0.70, 95% CI 18.87-21.69) and mean post-test scores (20.27 +/- 0.65, 95% CI 18.94-21.60) were also better than the pre-test scores (12.99 +/- 0.50, 95% CI 11.97-14.00) with p-values < 0.001 for both comparisons respectively. Participants in the gamification arm generally perceived the various game elements and game mechanics as useful in contributing and motivating them to learn ultrasonography. CONCLUSIONS: Gamification approach could be an effective alternative to conventional approach in point-of-care ultrasonographic training.

The All-Cause Mortality and a Screening Tool to Determine High-Risk Patients among Prevalent Type 2 Diabetes Mellitus Patients.

AIMS: This study aims to determine the all-cause mortality and the associated risk factors for all-cause mortality among the prevalent type 2 diabetes mellitus (T2DM) patients within five years' period and to develop a screening tool to determine high-risk patients. METHODS: This is a cohort study of T2DM patients in the national diabetes registry, Malaysia. Patients' particulars were derived from the database between 1st January 2009 and 31st December 2009. Their records were matched with the national death record at the end of year 2013 to determine the status after five years. The factors associated with mortality were investigated, and a prognostic model was developed based on logistic regression model. RESULTS: There were 69,555 records analyzed. The mortality rate was 1.4 persons per 100 person-years. The major cause of death were diseases of the circulatory system (28.4%), infectious and parasitic diseases (19.7%), and respiratory system (16.0%). The risk factors of mortality within five years were age group (p < 0.001), body mass index category (p < 0.001), duration of diabetes (p < 0.001), retinopathy (p = 0.001), ischaemic heart disease (p < 0.001), cerebrovascular (p = 0.007), nephropathy (p = 0.001), and foot problem (p = 0.001). The sensitivity and specificity of the proposed model was fairly strong with 70.2% and 61.3%, respectively. CONCLUSIONS: The elderly and underweight T2DM patients with complications have higher risk for mortality within five years. The model has moderate accuracy; the prognostic model can be used as a screening tool to classify T2DM patients who are at higher risk for mortality within five years.