METHODS: A total 98 in-hospital first ever acute stroke patients were recruited, and their Barthel Index scores were measured at the time of discharge, at 1 month and 3 months post-discharge. The Barthel Index was scored through telephone interviews. We employed the random intercept model from linear mixed effect regression to model the change of Barthel Index scores during the three months intervals. The prognostic factors included in the model were acute stroke subtypes, age, sex and time of measurement (at discharge, at 1 month and at 3 month post-discharge).
RESULTS: The crude mean Barthel Index scores showed an increased trend. The crude mean Barthel Index at the time of discharge, at 1-month post-discharge and 3 months post-discharge were 35.1 (SD = 39.4), 64.4 (SD = 39.5) and 68.8 (SD = 38.9) respectively. Over the same period, the adjusted mean Barthel Index scores estimated from the linear mixed effect model increased from 39.6 to 66.9 to 73.2. The adjusted mean Barthel Index scores decreased as the age increased, and haemorrhagic stroke patients had lower adjusted mean Barthel Index scores compared to the ischaemic stroke patients.
CONCLUSION: Overall, the crude and adjusted mean Barthel Index scores increase from the time of discharge up to 3-month post-discharge among acute stroke patients. Time after discharge, age and stroke subtypes are the significant prognostic factors for Barthel Index score changes over the period of 3 months.
OBJECTIVE: This study aimed to evaluate the research output of the top 100 publications and further identify a research theme of breast cancer and machine-learning studies.
METHODS: Databases of Scopus and Web of Science were used to extract the top 100 publications. These publications were filtered based on the total citation of each paper. Additionally, a bibliometric analysis was applied to the top 100 publications.
RESULTS: The top 100 publications were published between 1993 and 2019. The most productive author was Giger ML, and the top two institutions were the University of Chicago and the National University of Singapore. The most active countries were the USA, Germany and China. Ten clusters were identified as both basic and specialised themes of breast cancer and machine learning.
CONCLUSION: Various countries demonstrated comparable interest in breast cancer and machine-learning research. A few Asian countries, such as China, India and Singapore, were listed in the top 10 countries based on the total citation. Additionally, the use of deep learning and breast imaging data was trending in the past 10 years in the field of breast cancer and machine-learning research.
OBJECTIVE: This study aimed to establish the interrater reliability between multiple telephone interviewers when assessing long-term stroke outcomes.
METHODS: Patients alive at discharge selected in a retrospective cohort stroke project were recruited in this study. Their contact numbers were obtained from the medical record unit. The patients and/or proxies were interviewed based on a standardized script in Malay or English. Stroke outcomes assessed were modified Rankin Scale (mRS) and Barthel Index (BI) at 1-year post discharge. Fully crossed design was applied and 3 assessors collected the data simultaneously. Data was analysed using the software R version 3.4.4.
RESULTS: Out of 207 subjects recruited, 132 stroke survivors at the time of interview were analysed. We found a significant excellent interrater reliability between telephone interviewers assessing BI, with intraclass correlation coefficient at 0.996 (95% CI 0.995-0.997). Whereas substantial agreement between the telephone interviewers was revealed in assessing mRS, with Fleiss', Conger's and Light's Kappa statistics reporting 0.719 and the Nelson's model-based κm kappa statistic reporting 0.689 (95% CI 0.667-0.711).
CONCLUSION: It is reliable to get multiple raters in assessing mRS and BI using the telephone system. It is worthwhile to make use of a telephone interview to update clinicians on their acute clinical management towards long-term stroke prognosis.
Method: All serologically confirmed dengue patients in Kelantan, a northeastern state in Malaysia, registered in the eDengue system with an onset of disease from January 2016 to December 2018, were included in the study with the exclusion of duplicate entry. Using a generalized additive model, climate data collected from the Kota Bharu weather station (latitude 6°10'N, longitude 102°18'E) was analysed with dengue data.
Result: A cyclical pattern of dengue cases was observed with annual peaks coinciding with the intermonsoon period. Our analysis reveals that maximum temperature, mean temperature, rainfall, and wind speed have a significant nonlinear effect on dengue cases in Kelantan. Our model can explain approximately 8.2% of dengue incidence variabilities.
Conclusion: Weather variables affect nearly 10% of the dengue incidences in Northeast Malaysia, thereby making it a relevant variable to be included in a dengue early warning system. Interventions such as vector control activities targeting the intermonsoon period are recommended.
METHODS: This study incorporated data from the national dengue monitoring system (eDengue system). Confirmed dengue cases registered in Kelantan with disease onset between January 1, 2016 and December 31, 2018 were included in the study. Yearly changes in dengue incidence were mapped by using ArcGIS. Hotspot analysis was performed using Getis-Ord Gi to track changes in the trends of dengue spatial clustering.
RESULTS: A total of 10 645 dengue cases were recorded in Kelantan between 2016 and 2018, with an average of 10 dengue cases reported daily (standard deviation, 11.02). Areas with persistently high dengue incidence were seen mainly in the coastal region for the 3-year period. However, the hotspots shifted over time with a gradual dispersion of hotspots to their adjacent districts.
CONCLUSIONS: A notable shift in the spatial patterns of dengue was observed. We were able to glimpse the shift of dengue from an urban to peri-urban disease with the possible effect of a state-wide population movement that affects dengue transmission.
METHODS: A cross-sectional study was done. The translation was conducted in ten steps including forwards and back translation. Six experts were recruited for content validation of clarity, comprehension and relevancy of the items. Ten subjects from the target population were recruited for face validation of clarity and comprehension. For confirmatory factor analysis (CFA), all eligible caregivers were recruited until the sample size of 222 subjects was satisfied.
RESULTS: For content validity, two items had low Item-level Content Validity Index and were suggested for rephrasing. The following face validity had an acceptable Item-level Face Validity Index between 0.9 and 1 for all items. In CFA, four items were suggested to be removed due to low factor loading. The final model had Robust Comparative Fit Index of 0.889, Robust Tucker-Lewis Index of 0.877, Robust Root Mean Square Error of Approximation of 0.066 (95%CI: 0.059,0.072) and Standardized Root Mean Square Residual of 0.064. The composite reliability for all factors were between 0.77 and 0.91 respectively. Significant difference was observed when the mean total quality of life score was compared among caregivers with different depression, anxiety and stress status. As for test-retest reliability, it was found that the questionnaire had good intraclass correlation coefficient of 0.86 (95%CI: 1.08,4.35).
CONCLUSIONS: The Malay version of AC-QoL demonstrates satisfactory validity and reliability, suitable for evaluating the quality of life among informal caregivers of stroke survivors in Malaysia. The questionnaire serves as a comprehensive tool for one-time assessments and holds promise for gauging intervention effectiveness.