METHODS: 3-T brain MRI and DTI (diffusion tensor imaging) were performed on 26 PD and 13 MSA patients. Regions of interest (ROIs) were the putamen, substantia nigra, pons, middle cerebellar peduncles (MCP) and cerebellum. Linear, volumetry and DTI (fractional anisotropy and mean diffusivity) were measured. A three-node decision tree was formulated, with design goals being 100 % specificity at node 1, 100 % sensitivity at node 2 and highest combined sensitivity and specificity at node 3.
RESULTS: Nine parameters (mean width, fractional anisotropy (FA) and mean diffusivity (MD) of MCP; anteroposterior diameter of pons; cerebellar FA and volume; pons and mean putamen volume; mean FA substantia nigra compacta-rostral) showed statistically significant (P < 0.05) differences between MSA and PD with mean MCP width, anteroposterior diameter of pons and mean FA MCP chosen for the decision tree. Threshold values were 14.6 mm, 21.8 mm and 0.55, respectively. Overall performance of the decision tree was 92 % sensitivity, 96 % specificity, 92 % PPV and 96 % NPV. Twelve out of 13 MSA patients were accurately classified.
CONCLUSION: Formation of the decision tree using these parameters was both descriptive and predictive in differentiating between MSA and PD.
KEY POINTS: • Parkinson's disease and multiple system atrophy can be distinguished on MR imaging. • Combined conventional MRI and diffusion tensor imaging improves the accuracy of diagnosis. • A decision tree is descriptive and predictive in differentiating between clinical entities. • A decision tree can reliably differentiate Parkinson's disease from multiple system atrophy.
OBJECTIVE: The objectives of this paper are to gain a better understanding of the key presenting symptoms of COVID-19 in HCWs in a district specialist hospital, to establish the proportion of symptomatic COVID-19 cases among HCWs and its severity and to determine the time taken from onset of symptoms or perceived exposure to diagnostic testing.
METHODOLOGY: This is a retrospective descriptive analysis of clinical characteristics of subjects infected with COVID-19 among HCW in HTI. Their demography and clinical characteristics were recorded.
RESULTS: There were 47 HCW in HTI who tested positive for COVID-19. The mean age of the patients was 37.5 years old. 7 patients (15.2%) had at least more than one comorbidity. Average duration of time from perceived close contact to onset of symptom was 4.5 days, while the mean duration of time from symptoms to first positive RT-PCR result was 3.4 days. Six patients (13.0%) were asymptomatic throughout, whereas 40 (87.0%) had at least one symptom prior to hospitalization. The most commonly reported symptoms were fever (65.2%), sore throat (39.1%) and cough (37.0%). In terms of severity of symptoms, the majority of patients experienced mild symptoms (Group 2, 52.2%). Two patients (4.3%) with multiple comorbidities had severe disease requiring ICU admission and mechanical ventilation. There were no mortalities, and the longest staying patient was hospitalized for 18 days. The high rates of infectivity among HCW in HTI can be attributed to working in close proximity while in the asymptomatic incubation phase, while no HCW directly involved in the care of COVID-19 positive patients were tested positive.
CONCLUSION: We report that HCW share similar clinical characteristics of COVID-19 infection as those of non HCW patients in earlier studies. The infection can spread rapidly within healthcare settings via close contacts among infected HCWs. As such, we advocate distancing when working and usage of personal protective equipment when treating patients with respiratory illness to reduce transmission of COVID-19.
METHOD: A quasi-randomized controlled trial was conducted recruiting students from two different higher learning institutions in Kuantan, Pahang, Malaysia. Students are selected after fulfilling the criteria such as body mass index (BMI) of ≥23kg/m2, no chronic diseases that may influence by exercise, no significant changes in body weight within two months and not taking any medications or supplements. One institution was purposely chosen as a simulation-based group and another one control group. In the simulation-based group, participants were given a booklet and CD to do aerobic and resistance exercise for a minimum of 25min per day, three times a week for 10 weeks. No exercise was given to the control group. Participants were measured with the International Physical Activity Questionnaire (IPAQ), BMI, waist circumference (WC), body fat percentage before and after 10 weeks of simulation-based exercise.
RESULTS: A total of 52 (control: 25, simulation-based: 27) participants involved in the study. There was no baseline characteristics difference between the two groups (p>0.005). All 27 participants in the simulation-based group reported performing the exercise based on the recommendation. The retention rate at three months was 100%. No adverse events were reported throughout the study. Better outcomes (p<0.001) were reported among participants in the simulation-based group for BMI, WC and body fat percentage.
CONCLUSIONS: The findings of this study indicate that the simulation-based exercise programme may be feasible for an overweight adult in higher learning institutes. As a feasibility study this is not powered to detect significant differences on the outcomes. However, participants reported positive views towards the recommended exercise with significant improvements in body mass index, body fat percentage and reduced the waist circumference.
METHODS: We included ischaemic stroke cases admitted to Sarawak General Hospital between June 2013 and August 2018 from Malaysia National Stroke Registry. We performed descriptive analyses on patient demographics, cardiovascular risk factors, prior medications, smoking status, arrival time, thrombolysis rate, Get With The Guidelines (GWTG)-Stroke measures, and outcomes at discharge. We also numerically compared the results from Sarawak with the published data from selected national and international cohorts.
RESULTS: We analysed 1435 ischaemic stroke cases. The mean age was 60.1±13.2 years old; 64.9% were male; median baseline National Institute of Health Stroke Scale was seven points. Hypertension was the most prevalent risk factor of ischaemic stroke; 12.7% had recurrent stroke; 13.7% were active smokers. The intravenous thrombolysis rate was 18.8%. We achieved 80-90% in three GWTG-Stroke performance measures and 90-98% in four additional quality measures in our ischaemic stroke management. At discharge, 57% had modified Rankin Scale of 0-2; 6.7% died during hospitalisation. When compared with selected national and international data, patients in Sarawak were the youngest; Sarawak had more male and more first-ever stroke. Thrombolysis rate in Sarawak was higher compared with most studies in the comparison. Functional outcome at discharge in Sarawak was better than national cohort but still lagging behind when compared with the developed countries. In-hospital mortality rate in Sarawak was slightly lower than the national data but higher when compared with other countries.
CONCLUSION: Our study described characteristics, management, and outcomes of ischaemic stroke in Sarawak. We achieved high compliance with most of GTWG-Stroke performance and quality indicators. Sarawak had better outcomes than the national results on ischaemic stroke. However, there is still room for improvement when compared with other countries. Actions are needed to reduce the cardiovascular burdens for stroke prevention, enhance healthcare resources for stroke care, and improve intravenous thrombolysis treatment in Sarawak.
METHODS: The derivation cohort included 90 Malaysian GBS patients with two sets of NCS performed early (1-20days) and late (3-8 weeks). Potential predictors of AIDP were considered in univariate and multivariate logistic regression models to develop a predictive model. The model was externally validated in 102 Japanese GBS patients.
RESULTS: Median motor conduction velocity (MCV), ulnar distal motor latency (DML) and abnormal ulnar/normal sural pattern were independently associated with AIDP at both timepoints (median MCV: p = 0.038, p = 0.014; ulnar DML: p = 0.002, p = 0.003; sural sparing: p = 0.033, p = 0.009). There was good discrimination of AIDP (area under the curve (AUC) 0.86-0.89) and this was valid in the validation cohort (AUC 0.74-0.94). Scores ranged from 0 to 6, and corresponded to AIDP probabilities of 15-98% at early NCS and 6-100% at late NCS.
CONCLUSION: The probabilities of AIDP could be reliably predicted based on median MCV, ulnar DML and ulnar/sural sparing pattern that were determined at early and late stages of GBS.
SIGNIFICANCE: A simple and valid model was developed which can accurately predict the probability of AIDP.