PATIENTS AND METHODS: An observational retrospective study was conducted at Cardiology Centre, Hospital Serdang, from 1st January to 30th April 2021. Data were collected from medication charts, medical progress notes, laboratory and operative charts through electronic Health Information System (eHIS). The types and causes of DRPs were identified and classified based on Pharmaceutical Care Network of Europe's (PCNE) classification system V9.02. The data were analyzed using descriptive statistics.
RESULTS: All patients (100%) experienced at least one DRP. Total number of DRPs identified was 120 encounters which were associated with 503 causes. The majority of problems were related to treatment effectiveness (59.1%) and treatment safety (33.4%). The causes of DRPs are mainly related to inappropriate monitoring including therapeutic drug monitoring (18.6%), inappropriate combination of drugs, or drugs and dietary/herbal supplement (10.3%), drug dose was too high (8.9%), drug dose was too low (8.2%) and inappropriate timing of administration or dosing intervals (7.7%).
CONCLUSION: The percentage of DRP occurrence was high in the studied population. Treatment effectiveness and treatment safety issues were the main DRPs identified with various preventable causes. The findings may be useful to guide the planning of measures to prevent and solve future DRPs in the population.
PURPOSE: The study aimed to evaluate the budget impact of increasing the uptake of denosumab for the management of postmenopausal osteoporosis in Malaysia.
METHODS: A Markov budget impact model was developed to estimate the financial impact of osteoporosis treatment. We modelled a scenario in which the uptake of denosumab would increase each year compared with a static scenario. A 5-year time horizon from the perspective of a Malaysian MOH healthcare provider was used. Model inputs were based on Malaysian sources where available. Sensitivity analyses were performed to examine the robustness of the modelled results.
RESULTS: An increase in denosumab uptake of 8% per year over a 5-year time horizon would result in an additional budget impact, from MYR 0.26 million (USD 0.06 million) in the first year to MYR 3.25 million (USD 0.78 million) in the fifth year. When expressed as cost per-member-per-month (PMPM), these were less than MYR 0.01 across all five years of treatment. In sensitivity analyses, the acquisition cost of denosumab and medication persistence had the largest impact on the budget.
CONCLUSION: From the perspective of a Malaysian MOH healthcare provider, moderately increasing uptake of denosumab would have a minimal additional budget impact, partially offset by savings in fracture treatment costs. Increasing the use of denosumab appears affordable to reduce the economic burden of osteoporosis in Malaysia.
METHODS: We conducted a retrospective cohort study by retrieving 4 years (2018-2021) of TB patients' records at 10 public health clinics in Sarawak, Malaysia. Adult patients (≥18 years) with drug-susceptible TB were selected. Treatment interruption was defined as ≥2 weeks of cumulative interruption during treatment. The Chi-square test, Mann-Whitney U test, Kaplan-Meier and Cox proportional hazards regression were used to analyse the data, with p
OBJECTIVES: This study aimed to identify risk factors of TB treatment interruption and construct a predictive scoring model that enables objective risk stratification for better prediction of treatment interruption.
METHODS: A multicentre retrospective cohort study was conducted at public health clinics in Sarawak, Malaysia over 11 months from March 2022 to January 2023, involving adult patients aged ≥18 years with drug-susceptible TB diagnosed between 2018 and 2021. Cumulative missed doses or discontinuation of TB medications for ≥2 weeks, either consecutive or non-consecutive, was considered as treatment interruption. The model was developed and internally validated using the split-sample method. Multiple logistic regression analysed 18 pre-defined variables to identify the predictors of TB treatment interruption. The Hosmer-Lemeshow test and area under the receiver operating characteristic curve (AUC) were employed to evaluate model performance.
RESULTS: Of 2953 cases, two-thirds (1969) were assigned to the derivation cohort, and one-third (984) formed the validation cohort. Positive predictors included smoking, previously treated cases, and adverse drug reactions, while concurrent diabetes was protective. Based on the validation dataset, the model demonstrated good calibration (P = 0.143) with acceptable discriminative ability (AUC = 0.775). A cutoff score of 2.5 out of 11 achieved a sensitivity of 81 % and a specificity of 64.4 %. Risk stratification into low (0-2), medium (3-5), and high-risk (≥6) categories showed ascending interruption rates of 5.3 %, 18.1 %, and 41.3 %, respectively (P
PURPOSE: To develop and validate a risk assessment tool for the therapeutic outcomes of ASM therapy.
PATIENTS AND METHODS: A cross-sectional study was carried out in a hospital-based specialist clinic from September 2022 to August 2023. Data was analyzed from patients' medical records and face-to-face assessments. The seizure control domain was determined from the patients' medical records while seizure severity (SS) and adverse effects (AE) of ASM were assessed using the Seizure Severity Questionnaire and the Liverpool Adverse Event Profile respectively. The developed tool was devised from prediction models using logistic and linear regressions. Concurrent validity and interrater reliability methods were employed for validity assessments.
RESULTS: A total of 397 patients were included in the analysis. For seizure control, the identified predictors include ≥10 years' epilepsy duration (OR:1.87,95% CI:1.10-3.17), generalized onset (OR:7.42,95% CI:2.95-18.66), focal onset seizure (OR:8.24,95% CI:2.98-22.77), non-adherence (OR:3.55,95% CI:1.52-8.27) and having ≥3 ASM (OR:3.29,95% CI:1.32-8.24). Younger age at epilepsy onset (≤40) (OR:3.29,95% CI:1.32-8.24) and neurological deficit (OR:3.55,95% CI:1.52-8.27) were significant predictors for SS. For AE, the positive predictors were age >35 (OR:0.12,95% CI:0.03-0.20), <13 years epilepsy duration (OR:2.89,95% CI:0.50-5.29) and changes in ASM regimen (OR:2.93,95% CI: 0.24-5.62). The seizure control domain showed a good discriminatory ability with a c-index of 0.711. From the Bonferroni (ANOVA) analysis, only SS predicted scores generated a linear plot against the mean of the actual scores. The AE domain was omitted from the final tool because it did not meet the requirements for validity assessment.
CONCLUSION: This newly developed tool (RAS-TO) is a promising tool that could help healthcare providers in determining optimal treatment strategies for adults with epilepsy.
METHODS: Through 25 semi-structured in-depth interviews, themes were identified using thematic analysis, guided by the Technology Readiness and Acceptance Model (TRAM).
RESULTS: Anticipated convenience and benefits, openness to new technologies acted as drivers, while limited digital literacy and concerns about data privacy and security served as inhibitors of readiness to adopt health apps. Acceptance was influenced by elements related to medication, patient, healthcare professional, family and app aspects. The identified barriers were related to patient, smartphone and monetary factors. Patients perceived the need to adopt digital apps were for those with poor adherence, complex medication regimen and forgetfulness issues. However, concerns about effectively implementing this approach were noted as T2DM patients were predominantly late middle-aged adults who faced technical challenges, leading to combination approach between digital technology and conventional patient education and counselling.
CONCLUSION: The findings highlighted the factors influencing patient's readiness, acceptance, and barriers on effective utilisation of digital health solutions in managing adherence issues.
PRACTICAL IMPLICATIONS: The elements of TRAM provide guidance for strategic actions to enhance digital health technology adoption among T2DM patients.
METHODS: Study subjects include patients with various levels of renal function recruited from the nephrology clinic and wards of a tertiary hospital. The blood samples collected were analyzed for serum cystatin C and creatinine levels by particle-enhanced turbidimetric immunoassay and kinetic alkaline picrate method, respectively. DNA was extracted using a commercially available kit. -Polymerase chain reaction results were confirmed by direct DNA Sanger sequencing.
RESULTS: The genotype percentage (G/G = 73%, G/A = 24.1%, and A/A = 2.9%) adhere to the Hardy-Weinberg equilibrium. The dominant allele found in our population was CST3 73G allele (85%). The regression lines' slope of serum cystatin C against creatinine and cystatin C-based eGFR against creatinine-based eGFR, between G and A allele groups, showed a statistically significant difference (z-score = 3.457, p < 0.001 and z-score = 2.158, p = 0.015, respectively). Patients with A allele had a lower serum cystatin C level when the values were extrapolated at a fixed serum creatinine value, suggesting the influence of genetic factor.
CONCLUSION: Presence of CST3 gene G73A polymorphism affects serum cystatin C levels.
METHODS: Patients were recruited from four hospitals. Clinical data were recorded and blood samples were taken for PK and genetic studies. Population PK parameters were estimated by nonlinear mixed-effects modelling in Monolix®. Models were evaluated using the difference in objective function value, goodness-of-fit plots, visual predictive check and bootstrap analysis. Monte Carlo simulation was conducted to evaluate different dosing regimens for IVIG.
RESULTS: A total of 30 blood samples were analysed from 10 patients. The immunoglobulin G concentration data were best described by a one-compartment model with linear elimination. The final model included both volume of distribution (Vd) and clearance (CL) based on patient's individual weight. Goodness-of-fit plots indicated that the model fit the data adequately, with minor model mis-specification. Genetic polymorphism of the FcRn gene and the presence of bronchiectasis did not affect the PK of IVIG. Simulation showed that 3-4-weekly dosing intervals were sufficient to maintain IgG levels of 5 g L-1 , with more frequent intervals needed to achieve higher trough levels.
CONCLUSIONS: Body weight significantly affects the PK parameters of IVIG. Genetic and other clinical factors investigated did not affect the disposition of IVIG.