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.
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: 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.
OBJECTIVES: This review investigates the effects of TDM-guided piperacillin dosing on pharmacokinetic target attainment and clinical outcomes in CRRT patients, analyses correlations with clinical outcomes, provides optimal dosing strategies for piperacillin and identifies future research areas.
METHODS: A systematic search of PubMed, Scopus and Web of Science was conducted until December 2023, identifying studies on piperacillin pharmacokinetics and clinical outcomes in adult CRRT patients. Data on study characteristics, piperacillin exposures, TDM use, target attainment rates, mortality and length of stay were extracted. The risk of bias was assessed using the Newcastle-Ottawa Scale.
RESULTS: Eleven observational studies were included. High pharmacokinetic variability was evident, with piperacillin target non-attainment in up to 74% of cases without TDM. Two studies with routine TDM showed increased target attainment rates of 80%-100%. Mortality ranged from 17% to 56%, with supratherapeutic concentrations (≥100 mg/L) associated with higher mortality. The impact of optimized piperacillin exposures on outcomes was inconclusive. Most studies demonstrated a low risk of bias.
CONCLUSIONS: TDM-guided piperacillin dosing in CRRT patients improved target attainment rates (≥80%). Mortality rates ranged from 17% to 56%, with inconsistent correlations between drug exposures and survival. Supratherapeutic concentrations were linked to higher mortality. Standardized TDM protocols are needed. Future research should establish clear exposure-response relationships and the impact of TDM on clinical outcomes.
DESIGN: Prospective direct observational study.
METHODS: The study was conducted in the neonatal intensive care units of five public hospitals in Malaysia from April 2022 to March 2023. The preparation and administration of medications were observed using a standardized data collection form followed by chart review. After data collection, error identification was independently performed by two clinical pharmacists. Multivariable logistic regression was used to identify factors associated with medication administration errors.
RESULTS: A total of 743 out of 1093 observed doses had at least one error, affecting 92.4% (157/170) neonates. The rate of medication administration errors was 68.0%. The top three most frequently occurring types of medication administration errors were wrong rate of administration (21.2%), wrong drug preparation (17.9%) and wrong dose (17.0%). Factors significantly associated with medication administration errors were medications administered intravenously, unavailability of a protocol, the number of prescribed medications, nursing experience, non-ventilated neonates and gestational age in weeks.
CONCLUSION: Medication administration errors among neonates in the neonatal intensive care units are still common. The intravenous route of administration, absence of a protocol, younger gestational age, non-ventilated neonates, higher number of medications prescribed and increased years of nursing experience were significantly associated with medication administration errors.
IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: The findings of this study will enable the implementation of effective and sustainable interventions to target the factors identified in reducing medication administration errors among neonates in the neonatal intensive care unit.
REPORTING METHOD: We adhered to the STROBE checklist.
PATIENT OR PUBLIC CONTRIBUTION: An expert panel consisting of healthcare professionals was involved in the identification of independent variables.
METHODS: Fifty-nine chemo-naive patients receiving either olanzapine or aprepitant were randomly recruited and completed the EQ-5D-5L before and day 5 after HEC. HRQoL utility scores were analyzed according to the Malaysian valuation set. The economic evaluation was conducted from a healthcare payer perspective with a 5-day time horizon. Quality-adjusted life days (QALD) and the rate of successfully treated patients were used to measure health effects. The incremental cost-effectiveness ratio is assessed as the mean difference between groups' costs per mean difference in health effects. A one-way sensitivity analysis was performed to assess variations that might affect outcomes.
RESULTS: Aprepitant and olanzapine arms' patients had comparable baseline mean HRQoL utility scores of 0.920 (SD = 0.097) and 0.930 (SD = 0.117), respectively; however, on day 5, a significant difference (P value = .006) was observed with mean score of 0.778 (SD = 0.168) for aprepitant and 0.889 (SD = 0.133) for olanzapine. The cost per successfully treated patient in the aprepitant arm was 60 times greater than in the olanzapine arm (Malaysian Ringgit [MYR] 927 vs MYR 14.83). Likewise, the cost per QALD gain in the aprepitant arm was 36 times higher than in the olanzapine arm (MYR 57.05 vs MYR 1.57). Incremental cost-effectiveness ratio of MYR -937.00 (USD -200.98) per successfully treated patient and MYR -391.84 (USD -85.43) per QALD gained for olanzapine compared with the aprepitant-based regimen.
CONCLUSIONS: An olanzapine-based regimen is a cost-effective therapeutic substitution in patients receiving HEC in Malaysia.
METHODS: This national-level, multicentre, prospective direct observational study was conducted in neonatal intensive care units (NICUs) of five public hospitals in Malaysia. Randomly selected nurses were directly observed during medication preparation and administration. Each observation was independently assessed for errors. Ten machine learning (ML) algorithms were applied with features derived from systematic reviews, incident reports, and expert consensus. Model performance, prioritising F1-score for MAEs, was evaluated using various measures. Feature importance was determined using the permutation-feature importance for robust comparison across ML algorithms.
RESULTS: A total of 1093 doses were administered to 170 neonates, with mean age and birth weight of 33.43 (SD ± 5.13) weeks and 1.94 (SD ± 0.95) kg, respectively. F1-scores for the ten models ranged from 76.15% to 83.28%. Adaptive boosting (AdaBoost) emerged as the best-performing model (F1-score: 83.28%, accuracy: 77.63%, area under the receiver operating characteristic: 82.95%, precision: 84.72%, sensitivity: 81.88% and negative predictive value: 64.00%). The most influential features in AdaBoost were the intravenous route of administration, working hours, and nursing experience.
CONCLUSIONS: This study developed and validated an ML-based model to predict the presence of MAEs among neonates in NICUs. AdaBoost was identified as the best-performing algorithm. Utilising the model's predictions, healthcare providers can potentially reduce MAE occurrence through timely interventions.