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Background: The treatment options for Diabetes mellitus and their prescribing has increased over years. This needs appropriate selection of drugs. The main objective of this study was to highlight the current prescribing trends in Diabetes mellitus patients with other co-morbid conditions. Methods:A prospective observational study was conducted on inpatients admitted to various wards in a tertiary care hospital for period of 6 months between October 2016-March 2017. Prescriptions of the patients are collected in a designed questionnaire form and the relevant information is recorded and analysed.Results: 235 patient prescription patterns were studied, out of which 62.97% were males and 37.02% were females. Most of the patients were in the age group of 41-60. Hypertension was the most commonly found co-morbid condition. Rapid acting insulin was mostly prescribed during hospital stay. Metformin was the commonly prescribed oral hypoglycemic agent followed by glimeperide. Conclusion:The adverse drug reactions can be minimized by replacing the drugs with novel therapeutic agents like Glucagon-like peptide agonist, Dipeptidyl peptidase inhibitors and Sodium-glucose transport inhibitors. The management of drug interactions should be done by clinical significance and correlation.

The aim of this study was to demonstrate the construct validity of a newly developed cancer screening perception scale as a measure of the perception of cancer screening in general among high-risk but healthy asymptomatic groups.The cancer screening perception scale (CSPS) was developed based on extensive literature reviews guided by The Health Belief Model. Fifty-five written items were initially pooled, reviewed by experts for face validity, pretested by 25 healthcare workers and translated into Malay using simple back translation. The scale was then distributed to 300 respondents from two health clinics for construct validation purposes. The obtained data were analyzed using the varimax rotation method for exploratory factor analysis (EFA). The data was submitted for further confirmatory factor analysis using AMOS software.Based on EFA, the results produced five constructs as predicted: perceived severity, perceived susceptibility, perceived benefits, perceived barriers, and cues for action. Two items with low factor loading and unrelated to the recovered domains were removed. Perceived barriers and cues for action had three and two sub-domains respectively which were further confirmed to fit the measurement and structural models. CFA demonstrated the scale fitted GFI = 0.936, CFI = 0.935, RMSEA = 0.076, NORMEDCHISQ = 2.162. The scale discriminated between the domains. Cronbach's alpha for perceived severity, perceived susceptibility, perceived benefits, perceived barrier, and cues for action were 0.907, 0.877, 0.940, 0.864 and 0.938, respectively.The cancer screening perception scale with its promising psychometric properties is now available to measure risks to high-risk but healthy, asymptomatic groups aged 18 and above and can also be used for larger scale study purposes.

Introduction: Preparedness for the prevention of travel-related infectious diseases among Malaysian international travelers has yet to be explored. With no such data, health programs to empower travelers on behavioral responses towards travel-related illnesses will be ineffective. The current study aimed to develop and validate a new scoring-based instrument measuring Malaysian international travelers' preparedness in terms of their risk perception (RP), attitude, and practices (RisPAK-Q) towards travel-related infectious diseases using factor analysis. Methods: The newly developed instrument was tested among 200 Malaysian international travelers based on the systematic random sampling method. The number of domains, model-fit index, construct validity, and internal consistency for this instrument were determined using exploratory and confirmatory factor analysis (CFA). Results: Twenty-two out of 34 questions were retained, and the following 5 domains were extracted: RP, pre-travel attitude (PTA), duringtravel attitude (DTA), general traveling practice, and food practice (FP). All 22 questions had factor loadings of above 0.6. All 5 domains achieved a stable model fit index with good convergent and discriminant construct validity of above 0.5 indicated by the average variance extracted (AVE) with all of the maximum shared variance (MSV) values below their corresponding AVEs. All domains also had high internal consistency with a composite reliability (CR) of above 0.7. Conclusion: The RisPAK-Q containing 22 questions in 5 domains is a valid and reliable instrument for measuring the preparedness of Malaysian travelers for travel-related infectious diseases and can be used in a subsequent larger study.

Background Chronic kidney disease has become a major health problem around the world. It displays no symptoms until the later stages. Therefore, its early detection is crucial, and a suitable intervention is necessary to halt its development. The aim of this study was to develop and validate a recently formulated Chronic Kidney Disease Perception Scale (CKDPS) for diabetic patients based on Social Psychology, and their perceptions based on the Health Belief Model (HBM). Methods The newly developed CKDPS instrument was tested on 300 patients with diabetes mellitus in a cross-sectional study. The number of domains, model-fit index, construct validity, and internal consistency of this instrument were determined using exploratory (EFA) and confirmatory factor analysis (CFA). Results The EFA yielded nine domains: illness identity, timeline motivation, medical practice and co-operation for Social Psychology, and perceived benefit, perceived barriers, perceived susceptibility, perceived severity, and perceived cue to action for HBM. Four items with low factor loading were removed. CFA yielded the following fit indices for Social Psychology: the goodness of fit index (GFI) = 0.889, comparative fit index (CFI) = 0.934, root mean square error of approximation (RMSEA) = 0.053, normed chi-square (NC) = 1.831; and the following for HBM: GFI = 0.834, CFI = 0.957, RMSEA = 0.053, NC = 1.830. Values of Cronbach’s α ranged between 0.760 and 0.909. Conclusions The CKDPS includes 61 questions across nine domains, divided under two categories of Social Psychology and HBM. It is also a valid and reliable tool for measuring diabetic patients’ perception of CKD prevention that can be used in larger studies.

of the population are at risk of dying from cancer before the age of 75, and in 5-years time, the number of cases will rise to 384.1 per 100 000 population. The five most frequent cancers by total number of cases for both sexes are breast, colorectal, lung, cervix uteri and nasopharynx (Ferley et al., 2013). Studies have shown that there is a lack of awareness among the public in taking tests such as pap smear for detecting cervical cancer (Wong et al., 2009),

Background According to the World Bank, Malaysia reported an estimated 97 tuberculosis cases per 100,000 people in 2021. Chest x-ray (CXR) remains the best conventional method for the early detection of pulmonary tuberculosis (PTB) infection. The intervention of artificial intelligence (AI) in PTB diagnosis could efficiently aid human interpreters and reduce health professionals’ work burden. To date, no AI studies have been evaluated in Malaysia. Objective This study aims to evaluate the performance of Putralytica and Qure.ai software for CXR screening and PTB diagnosis among the Malaysian population. Methods We will conduct a retrospective case-control study at the Respiratory Medicine Institute, National Cancer Institute, and Sungai Buloh Health Clinic. A total of 1500 CXR images of patients who completed treatments or check-ups will be selected and categorized into three groups: (1) abnormal PTB cases, (2) abnormal non-PTB cases, and (3) normal cases. These CXR images, along with their clinical findings, will be the reference standard in this study. All patient data, including sociodemographic characteristics and clinical history, will be collected prior to screening via Putralytica and Qure.ai software and readers’ interpretation, which are the index tests for this study. Interpretation from all 3 index tests will be compared with the reference standard, and significant statistical analysis will be computed. Results Data collection is expected to commence in August 2023. It is anticipated that 1 year will be needed to conduct the study. Conclusions This study will measure the accuracy of Putralytica and Qure.ai software and whether their findings will concur with readers’ interpretation and the reference standard, thus providing evidence toward the effectiveness of implementing AI in the medical setting. International Registered Report Identifier (IRRID) PRR1-10.2196/36121

BACKGROUND Tuberculosis (TB) profile in Malaysia showed an average annual growth rate of 2.23%, with an estimated 92 cases per 100,000 people reported in 2018. CXR remains the best conventional method for the early detection of pulmonary TB infection. The intervention of AI in TB diagnosis could efficiently aid human interpreters and reduce health professionals' work burden. To date, no evaluation of AI studies has been carried out in Malaysia. OBJECTIVE This study aims to determine the diagnostic accuracy and evaluate the performance of Qure.ai and Putra Analytica AI software. METHODS We will conduct a retrospective case-control study in Respiratory Medicine Institute (IPR), Kuala Lumpur Health Clinic and Bandar Botanik Klang Health Clinic. Patients' medical reports on TB investigation will be retrieved by accessing electronic and hardcopy medical records and collecting demographic data. Prior to conducting the study, patients' PTB status will be obtained by identifying MTB culture (reference standard) results in order to create a case and a control group. A total of 2000 CXR images will be retrieved, of which 1000 images will be the case (abnormality). Normal and abnormal CXR will be categorized into film and digital CXR, which will be screened onto the said AI software (index tests). RESULTS Results obtained from the AI software will be compared with the reference standard, and significant statistical analysis will be computed CONCLUSIONS We hope that the findings of this evaluation study will provide sufficient information for stakeholders and to implement AI technology in the medical imaging field for better management of TB in hospital and clinic settings.