METHODS: A cross-sectional study was conducted of consecutive diagnostic EGDs performed at a university-affiliated, teaching hospital, which has an open-access endoscopy system for doctors who work in the hospital. The main indication(s) for EGD was recorded and assessed as appropriate or inappropriate by using American Society for Gastrointestinal Endoscopy criteria. EGD findings were recorded and classified as positive or negative. Referrals were categorized as being from endoscopists, primary care physicians, and others.
RESULTS: Of 1076 referrals for EGD, 88.3% were deemed appropriate. The group with the highest rate of appropriate referral was endoscopists (90.2%), followed by primary care physicians (89.6%) and "others" (81.9%). The rate of appropriate referrals was significantly higher for endoscopists and primary care physicians compared with "others" (respectively, p=0.001 and p=0.022). The most common appropriate indication was "upper abdominal distress that persists despite an appropriate trial of therapy" (35.4%). The most common inappropriate indication was "dyspepsia in patients aged 45 years or below without adequate empirical medical treatment" (48.4%); 42.2% with an appropriate indication had positive findings compared with only 25.6% of those with inappropriate indications (p=0.006). On multivariate analysis, the following were identified as independent predictive factors for positive findings at EGD: male gender (p=0.005), age over 45 years (p=0.011), smoking (p=0.005), none/primary education (p<0.001), and secondary education (p=0.026).
CONCLUSIONS: The proportion of patients referred for open-access EGD with an appropriate indication(s) was high for all doctor groups in a large university-affiliated medical center in Asia. EGDs performed for appropriate indications had a higher yield of positive findings. Independent predictive factors of positive findings were male gender, age over 45 years, lower education level, and referral by an endoscopist.
Objective: To determine the additional relationship between factors discovered by searching for sociodemographic and metastasis factors, as well as treatment outcomes, which could help improve the prediction of the survival rate in cancer patients. Material and Methods. A total of 56 patients were recruited from the ambulatory clinic at the Hospital Universiti Sains Malaysia (USM). In this retrospective study, advanced computational statistical modeling techniques were used to evaluate data descriptions of several variables such as treatment, age, and distant metastasis. The R-Studio software and syntax were used to implement and test the hazard ratio. The statistics for each sample were calculated using a combination model that included methods such as bootstrap and multiple linear regression (MLR).
Results: The statistical strategy showed R demonstrates that regression modeling outperforms an R-squared. It demonstrated that when data is partitioned into a training and testing dataset, the hybrid model technique performs better at predicting the outcome. The variable validation was determined using the well-established bootstrap-integrated MLR technique. In this case, three variables are considered: age, treatment, and distant metastases. It is important to note that three things affect the hazard ratio: age (β 1: -0.006423; p < 2e - 16), treatment (β 2: -0.355389; p < 2e - 16), and distant metastasis (β 3: -0.355389; p < 2e - 16). There is a 0.003469102 MSE for the linear model in this scenario.
Conclusion: In this study, a hybrid approach combining bootstrapping and multiple linear regression will be developed and extensively tested. The R syntax for this methodology was designed to ensure that the researcher completely understood the illustration. In this case, a hybrid model demonstrates how this critical conclusion enables us to better understand the utility and relative contribution of the hybrid method to the outcome. The statistical technique used in this study, R, demonstrates that regression modeling outperforms R-squared values of 0.9014 and 0.00882 for the predicted mean squared error, respectively. The conclusion of the study establishes the superiority of the hybrid model technique used in the study.
RESULTS: A total of 21 distinct metabolic differences between HAE patients and healthy individuals were identified, and they are associated with perturbations in amino acid metabolism, energy metabolism, glyoxylate and dicarboxylate metabolism. Furthermore, the present results showed that the Fischer ratio, which is the molar ratio of branched-chain amino acids to aromatic amino acids, was significantly lower (P
METHODS: A cross sectional study was conducted among 2120 cancer patients in Peninsular Malaysia, between April 2016 to January 2017. All cancer patients aged 18 years old and above, Malaysian citizens and undergoing cancer treatment at government hospitals were approached to participate in this study and requested to complete a set of validated questionnaires. Inferential statistical tests such as t-test and one-way ANOVA were used to determine the differences between demographic variables, physical effects, clinical factors, psychological effects and self-esteem with the quality of life of cancer patients. Predictor(s) of quality of life were determined by using Multivariate linear regression models.
RESULT: A total 1620 out of 2120 cancer patients participated in this study, giving a response rate of 92%. The majority of cancer patients were female 922 (56.9%), Malays 1031 (63.6%), Muslim 1031 (63.6%), received chemotherapy treatment 1483 (91.5%). Overall, 1138 (70.2%) of the patients had depression and 1500 (92.6%) had anxiety. Statistically significant associations were found between QOL and clinical factors, physical side effects of cancer, psychological effects and self-esteem (p
METHODS: This is a cohort study of T2DM patients in the national diabetes registry, Malaysia. Patients' particulars were derived from the database between 1st January 2009 and 31st December 2009. Their records were matched with the national death record at the end of year 2013 to determine the status after five years. The factors associated with mortality were investigated, and a prognostic model was developed based on logistic regression model.
RESULTS: There were 69,555 records analyzed. The mortality rate was 1.4 persons per 100 person-years. The major cause of death were diseases of the circulatory system (28.4%), infectious and parasitic diseases (19.7%), and respiratory system (16.0%). The risk factors of mortality within five years were age group (p < 0.001), body mass index category (p < 0.001), duration of diabetes (p < 0.001), retinopathy (p = 0.001), ischaemic heart disease (p < 0.001), cerebrovascular (p = 0.007), nephropathy (p = 0.001), and foot problem (p = 0.001). The sensitivity and specificity of the proposed model was fairly strong with 70.2% and 61.3%, respectively.
CONCLUSIONS: The elderly and underweight T2DM patients with complications have higher risk for mortality within five years. The model has moderate accuracy; the prognostic model can be used as a screening tool to classify T2DM patients who are at higher risk for mortality within five years.