AIM: The aim of this study was to determine the characteristics of medication complexity and polypharmacy and determine their relationship with drug-related problems (DRP) and control of iron overload in transfusion-dependent thalassaemia patients.
METHOD: Data were derived from a cross-sectional observational study on characteristics of DRPs conducted at a Malaysian tertiary hospital. The medication regimen complexity index (MRCI) was determined using a validated tool, and polypharmacy was defined as the chronic use of five or more medications. The receiver operating characteristic curve analysis was used to determine the optimal cut-off value for MRCI, and logistic regression analysis was conducted.
RESULTS: The study enrolled 200 adult patients. The MRCI cut-off point was proposed to be 17.5 (Area Under Curve = 0.722; sensitivity of 73.3% and specificity of 62.0%). Approximately 73% and 64.5% of the patients had polypharmacy and high MRCI, respectively. Findings indicated that DRP was a full mediator in the association between MRCI and iron overload.
CONCLUSION: Transfusion-dependent thalassaemia patients have high MRCI and suboptimal control of iron overload conditions in the presence of DRPs. Thus, future interventions should consider MRCI and DRP as factors in serum iron control.
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: 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.