METHODS: This was a 4-year cross-sectional study of snakebite patients from January 2013 to December 2016 in Hospital Sultanah Nur Zahirah (HSNZ), Terengganu. Data was extracted from the Pharmacy Record on the usage of antivenom and patients of snakebites treated with antivenom were identified. Data of patients were then obtained from the electronic medical records.' Demographic details, clinical features and characteristics of antivenom reactions of patients were recorded in standardized data collection forms and analyzed using chi-square or Mann- Whitney U tests.
RESULTS: Of the 44 patients who received antivenom, 24 (54.5%) developed hypersensitivity reaction. All patients developed reaction early. No patient developed delayed (serum-sickness) reaction. Of the 24 patients, 14 (58.3%) had moderate to severe hypersensitivity reaction and 9 (37.5%) patients had mild reactions. Only one (4.2%) patient presented with bradycardia.
CONCLUSION: The prevalence of early hypersensitivity reaction to snake antivenom in HSNZ was relatively high. Healthcare providers should be aware of the appropriate method of preparing and administering antivenom, and the management for acute hypersensitivity reactions. This will optimize the management of snakebite and ensure patient safety.
METHODS: This cross-sectional study included data from 344 older (173 inpatients and 171 outpatients) patients, aged 60 years and above, through validated questionnaires. Medication appropriateness was assessed via Medication Appropriateness Index (MAI) tool, whereas Beers and Screening Tool of Older Person's Potentially Inappropriate Prescribing (STOPP) criteria were used to evaluate PIMs and potentially inappropriate prescribing (PIP), respectively. The Drug Burden Index (DBI) and polypharmacy, as well as PROs, included Groningen Frailty Indicator (GFI), Katz Index of Independence in Activities of Daily Living (Katz ADL) and Older People's Quality of Life (OPQOL) were also evaluated.
RESULTS: Overall, inpatients received significantly higher medications (6.90 ± 2.70 vs 4.49 ± 3.20) than outpatients. A significantly higher proportion of inpatients received at least one PIM (65% vs 57%) or PIP (57.4% vs 17.0%) and higher mean MAI score (1.76 ± 1.08 and 1.10 ± 0.34) and DBI score (2.67 ± 1.28 vs 1.49 ± 1.17) than outpatients. Inpatients had significantly higher total OPQOL (118.53 vs 79.95) and GFI score (5.44 vs 3.78) than outpatients. We only found significant correlations between GFI and DBI and total OPQOL and the number of PIMs.
CONCLUSIONS: Proportions of PIMs and DBI exposure were significantly higher in an inpatient setting. No significant correlations between exposures to inappropriate medications or drug burden and PROs were observed.
OBJECTIVE: To understand the psychological processes involved in the experiencing of suffering at the end phase of life.
METHODS: Semistructured interviews were conducted with 20 palliative care inpatients from an academic medical centre in Kuala Lumpur, Malaysia. The transcripts were thematically analysed with NVIVO9.
RESULTS: 5 themes of psychological processes of suffering were generated: (1) perceptions, (2) cognitive appraisals, (3) hope and the struggles with acceptance, (4) emotions and (5) clinging. A model of suffering formation was constructed.
CONCLUSION: The findings may inform the development of mechanism-based interventions in the palliation of suffering.
METHODS: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case-control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks.
RESULTS: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools.
Methods: In this cross-sectional study, data from 147 ACS patients aged less than 45 years were analysed.
Results: The mean age was 39.1 (4.9) years, the male to female ratio was 3:1; 21.2% of patients presented with unstable angina, 58.5% had non-ST elevation myocardial infarction and 20.4% had ST elevation myocardial infarction. The most frequent risk factor of ACS was dyslipidaemia (65.3%), followed by hypertension (43.5%). In total, 49.7% of patients had inpatient complication(s), with the most common being heart failure (35.4%), followed by arrhythmia (20.4%). The significant factors associated with ACS complications were current smoking [adjusted odds ratio (AOR) 4.03; 95% confidence interval (CI): 1.33, 12.23;P-value = 0.014], diabetic mellitus [AOR 3.03; 95% CI: 1.19, 7.71;P-value = 0.020], treatments of fondaparinux [AOR 0.18; 95% CI: 0.08, 0.39;P-value < 0.001] and oral nitrates [AOR 0.18; 95% CI: 0.08, 0.42;P-value < 0.001].
Conclusions: Smoking status and diabetes mellitus were modifiable risk factors while pharmacological treatment was an important protective factor for ACS complications in young patients.