OBJECTIVE: This study aimed to estimate and critically appraise the evidence on the prevalence, causes and severity of medication administration errors (MAEs) amongst neonates in Neonatal Intensive Care Units (NICUs).
METHODS: A systematic review and meta-analysis was conducted by searching nine electronic databases and the grey literature for studies, without language and publication date restrictions. The pooled prevalence of MAEs was estimated using a random-effects model. Data on error causation were synthesised using Reason's model of accident causation.
RESULTS: Twenty unique studies were included. Amongst direct observation studies reporting total opportunity for errors as the denominator for MAEs, the pooled prevalence was 59.3% (95% confidence interval [CI] 35.4-81.3, I2 = 99.5%). Whereas, the non-direct observation studies reporting medication error reports as the denominator yielded a pooled prevalence of 64.8% (95% CI 46.6-81.1, I2 = 98.2%). The common reported causes were error-provoking environments (five studies), while active failures were reported by three studies. Only three studies examined the severity of MAEs, and each utilised a different method of assessment.
CONCLUSIONS: This is the first comprehensive systematic review and meta-analysis estimating the prevalence, causes and severity of MAEs amongst neonates. There is a need to improve the quality and reporting of studies to produce a better estimate of the prevalence of MAEs amongst neonates. Important targets such as wrong administration-technique, wrong drug-preparation and wrong time errors have been identified to guide the implementation of remedial measures.
METHODS AND ANALYSIS: This is a prospective direct observational study that will be conducted in five neonatal intensive care units. A minimum sample size of 820 drug preparations and administrations will be observed. Data including patient characteristics, drug preparation-related and administration-related information and other procedures will be recorded. After each round of observation, the observers will compare his/her observations with the prescriber's medication order, hospital policies and manufacturer's recommendations to determine whether MAE has occurred. To ensure reliability, the error identification will be independently performed by two clinical pharmacists after the completion of data collection for all study sites. Any disagreements will be discussed with the research team for consensus. To reduce overfitting and improve the quality of risk predictions, we have prespecified a priori the analytical plan, that is, prespecifying the candidate predictor variables, handling missing data and validation of the developed model. The model's performance will also be assessed. Finally, various modes of presentation formats such as a simplified scoring tool or web-based electronic risk calculators will be considered.
METHODS: A total of 6221 tweets related to breast cancer posted between 2018 and 2022 were collected. An oncologist and two pharmacists coded the tweets to differentiate between true information and misinformation, and to analyse the misinformation content. Binary logistic regression was conducted to identify determinants of misinformation.
RESULTS: There were 780 tweets related to breast cancer prevention and treatment, and 456 (58.5%) contain misinformation, with significantly more misinformation in Malay compared to English tweets (OR = 6.18, 95% CI: 3.45-11.07, p
OBJECTIVE: To explore the perceptions of Malaysian hospital pharmacists and patients on the barriers and facilitators of a PCC approach in pharmacist consultations.
DESIGN: This study employed a qualitative, explorative semi-structured interview design.
SETTING AND PARTICIPANTS: Interviews were conducted with 17 patients and 18 pharmacists from three tertiary hospitals in Malaysia. The interviews were audiotaped and transcribed verbatim. Emerging themes were developed through a constant comparative approach and thematic analysis.
RESULTS: Three themes were identified in this study: (i) patient-related factors (knowledge, role expectations, and sociocultural characteristics), (ii) pharmacist-related factors (personalities and communication), and (iii) healthcare institutional and system-related factors (resources, continuity of care, and interprofessional collaboration). Pharmacists and patients mentioned that factors such as patients' knowledge and attitudes and pharmacists' personality traits and communication styles can affect patients' engagement in the consultation. Long waiting time and insufficient manpower were perceived as barriers to the practice of PCC. Continuity of care and interprofessional collaboration were viewed as crucial in providing supportive and tailored care to patients.
CONCLUSION: The study findings outlined the potential factors of PCC that may influence its implementation in pharmacist consultations. Strategic approaches can be undertaken by policymakers, healthcare institutions, and pharmacists themselves to address the identified barriers to more fully support the implementation of PCC in the pharmacy setting.
METHODS: This was a cross-sectional study of patients with chronic diseases in two tertiary hospitals in Selangor, Malaysia. Patients who agreed to participate in the study were asked to answer questions in the following areas: 1) perceived group and higher authority cultural orientations; 2) religiosity: organizational and non-organizational religious activities, and intrinsic religiosity; 3) perceived social support; and 4) self-reported medication adherence. Patients' medication adherence was modeled using multiple logistic regressions, and only variables with a P-value of <0.25 were included in the analysis.
RESULTS: A total of 300 patients completed the questionnaire, with the exception of 40 participants who did not complete the cultural orientation question. The mean age of the patients was 57.6±13.5. Group cultural orientation, organizational religious activity, non-organizational religious activity, and intrinsic religiosity demonstrated significant associations with patients' perceived social support (r=0.181, P=0.003; r=0.230, P<0.001; r=0.135, P=0.019; and r=0.156, P=0.007, respectively). In the medication adherence model, only age, duration of treatment, organizational religious activity, and disease type (human immunodeficiency virus) were found to significantly influence patients' adherence to medications (adjusted odds ratio [OR] 1.05, P=0.002; OR 0.99, P=0.025; OR 1.19, P=0.038; and OR 9.08, P<0.05, respectively).
CONCLUSION: When examining religious practice and cultural orientation, social support was not found to have significant influence on patients' medication adherence. Only age, duration of treatment, organizational religious activity, and disease type (human immunodeficiency virus) had significant influence on patients' adherence.
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: 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.
PARTICIPANTS AND METHODS: A cross-sectional study was conducted involving physicians and newly diagnosed breast cancer patients from three public/teaching hospitals in Malaysia. The Control Preference Scale (CPS) was administered to patients and physicians, and the Krantz Health Opinion Survey (KHOS) was completed by the patients alone. Binary logistic regression was used to determine the association between sociodemographic characteristics, the patients' involvement in treatment decision-making, and patients' preference for behavioral involvement and information related to their disease.
RESULTS: The majority of patients preferred to share decision-making with their physicians (47.5%), while the second largest group preferred being passive (42.6%) and a small number preferred being active (9.8%). However, the physicians perceived that the majority of patients preferred active decision-making (56.9%), followed by those who desired shared decision-making (32.8%), and those who preferred passive decision-making (10.3%). The overall concordance was 26.5% (54 of 204 patient-physician dyads). The median of preference for information score and behavioral involvement score was 4 (interquartile range [IQR] =3-5) and 2 (IQR =2-3), respectively. In univariate analysis, the ethnicity and educational qualification of patients were significantly associated with the patients' preferred role in the process of treatment decision-making and the patients' preference for information seeking (p>0.05). However, only educational qualification (p=0.004) was significantly associated with patients' preference for information seeking in multivariate analysis.
CONCLUSION: Physicians failed to understand patients' perspectives and preferences in treatment decision-making. The concordance between physicians' perception and patients' perception was quite low as the physicians perceived that more than half of the patients were active in treatment decision-making. In actuality, more than half of patients perceived that they shared decision-making with their physicians.
DESIGN: Qualitative study; semistructured interviews. To identify emerging themes relating to information needs, open coding and thematic analysis were employed.
SETTING: Participants were recruited from a tertiary care children's hospital in Kuala Lumpur, Malaysia and a specialist hospital in Riyadh, Saudi Arabia.
PARTICIPANTS: Thirty one children with a mean age of 11.5 years (SD=1.9) and their caregivers were interviewed. Seventeen participants were from Malaysia and 14 were from Saudi Arabia.
RESULTS: Four themes of information emerged from the interviews, including information related to (1) hypoglycaemia and hyperglycaemia, (2) insulin therapy, (3) injection technique and (4) other information needs pertaining to continuous glucose monitoring, access to peer groups and future advances in insulin therapy.
CONCLUSION: This study provided valuable insights into the information needs related to T1DM and insulin therapy among children and adolescents with T1DM that should be considered by stakeholders in the development of age-appropriate education materials. Such materials will assist children and adolescents to better manage their life-long T1DM condition from adolescence until adulthood.
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.