METHODS: Questionnaire of dietary changes was modified from WHEL study and adapted to typical Malay's food intake in Malaysia. A total of 23 items were listed and categorized by types of food and cooking methods. Four categories of changes "increased", "decreased", "no changes" or "stopped" were used to determine the changes in dietary practices. Score one (+1) is given to positive changes by reference to WCRF/AICR and Malaysia Dietary Guideline healthy eating recommendations. Malay EORTC QLQ-C30 were used to determine the QoL. Sociodemographic, clinical characteristics and anthropometric measurement were also collected.
RESULTS: The mean age of the subjects (n=77) was 50.7±7.8 years old with duration of survivorship 4.0±3.1 years. Subjects mean BMI was 27.8±4.9 kg/m2 which indicate subjects were 31.2% overweight and 32.5% obese. The percentage score of positive dietary changes was 34.7±16.4%. Positive dietary changes were increased intake of green leafy vegetable (49.4%), cruciferous vegetable (46.8%) and boiling cooking methods (45.5%). Subjects reduced their intake of red meat (42.9%), sugar (53.2%) and fried cooking method (44.2%). Subjects stopped consuming milk (41.6%), c 2008-5862 heese (33.8%) and sweetened condensed milk (33.8%). With increasing positive dietary changes, there was a significant improvement on emotional function (rs=0.27; p=0.016) and reduced fatigue symptoms (rs=-0.24; p=0.033).
CONCLUSION: Positive changes in dietary intake improved emotional function and reduced fatigue symptoms after cancer treatment. By knowing the trend of food changes after cancer treatment, enables the formation of healthy food intervention implemented more effective.
METHODS: Patients with AIH-ACLF without baseline infection/hepatic encephalopathy were identified from APASL ACLF research consortium (AARC) database. Diagnosis of AIH-ACLF was based mainly on histology. Those treated with steroids were assessed for non-response (defined as death or liver transplant at 90 days for present study). Laboratory parameters, AARC, and model for end-stage liver disease (MELD) scores were assessed at baseline and day 3 to identify early non-response. Utility of dynamic SURFASA score [- 6.80 + 1.92*(D0-INR) + 1.94*(∆%3-INR) + 1.64*(∆%3-bilirubin)] was also evaluated. The performance of early predictors was compared with changes in MELD score at 2 weeks.
RESULTS: Fifty-five out of one hundred and sixty-five patients (age-38.2 ± 15.0 years, 67.2% females) with AIH-ACLF [median MELD 24 (IQR: 22-27); median AARC score 7 (6-9)] given oral prednisolone 40 (20-40) mg per day were analyzed. The 90 day transplant-free survival in this cohort was 45.7% with worse outcomes in those with incident infections (56% vs 28.0%, p = 0.03). The AUROC of pre-therapy AARC score [0.842 (95% CI 0.754-0.93)], MELD [0.837 (95% CI 0.733-0.94)] score and SURFASA score [0.795 (95% CI 0.678-0.911)] were as accurate as ∆MELD at 2 weeks [0.770 (95% CI 0.687-0.845), p = 0.526] and better than ∆MELD at 3 days [0.541 (95% CI 0.395, 0.687), p 6, MELD score > 24 with SURFASA score ≥ - 1.2, could identify non-responders at day 3 (concomitant- 75% vs either - 42%, p
METHODS: Prospectively collected data of ACLF patients from APASL-ACLF Research Consortium (AARC) was analyzed for 30-day outcomes. The models evaluated at days 0, 4, and 7 of presentation for 30-day mortality were: AARC (model and score), CLIF-C (ACLF score, and OF score), NACSELD-ACLF (model and binary), SOFA, APACHE-II, MELD, MELD-Lactate, and CTP. Evaluation parameters were discrimination (c-indices), calibration [accuracy, sensitivity, specificity, and positive/negative predictive values (PPV/NPV)], Akaike/Bayesian Information Criteria (AIC/BIC), Nagelkerke-R2, relative prediction errors, and odds ratios.
RESULTS: Thirty-day survival of the cohort (n = 2864) was 64.9% and was lowest for final-AARC-grade-III (32.8%) ACLF. Performance parameters of all models were best at day 7 than at day 4 or day 0 (p 12 had the lowest 30-day survival (5.7%).
CONCLUSIONS: APASL-ACLF is often a progressive disease, and models assessed up to day 7 of presentation reliably predict 30-day mortality. Day-7 AARC model is a statistically robust tool for classifying risk of death and accurately predicting 30-day outcomes with relatively lower prediction errors. Day-7 AARC score > 12 may be used as a futility criterion in APASL-ACLF patients.
METHODS: A total of 1402 ACLF patients, enrolled in the APASL-ACLF Research Consortium (AARC) with 90-day follow-up, were analyzed. An ACLF score was developed in a derivation cohort (n = 480) and was validated (n = 922).
RESULTS: The overall survival of ACLF patients at 28 days was 51.7%, with a median of 26.3 days. Five baseline variables, total bilirubin, creatinine, serum lactate, INR and hepatic encephalopathy, were found to be independent predictors of mortality, with AUROC in derivation and validation cohorts being 0.80 and 0.78, respectively. AARC-ACLF score (range 5-15) was found to be superior to MELD and CLIF SOFA scores in predicting mortality with an AUROC of 0.80. The point scores were categorized into grades of liver failure (Gr I: 5-7; II: 8-10; and III: 11-15 points) with 28-day cumulative mortalities of 12.7, 44.5 and 85.9%, respectively. The mortality risk could be dynamically calculated as, with each unit increase in AARC-ACLF score above 10, the risk increased by 20%. A score of ≥11 at baseline or persisting in the first week was often seen among nonsurvivors (p = 0.001).
CONCLUSIONS: The AARC-ACLF score is easy to use, dynamic and reliable, and superior to the existing prediction models. It can reliably predict the need for interventions, such as liver transplant, within the first week.
METHODOLOGY: One thousand two hundred and sixteen prospectively enrolled patients with ACLF (males 98%, mean age 42.5 ± 9.4 years, mean CTP, MELD and AARC scores of 12 ± 1.4, 29.7 ± 7 and 9.8 ± 2 respectively) from the Asian Pacific Association for the Study of the Liver (APASL) ACLF Research Consortium (AARC) database were analysed retrospectively. Patients with or without metabolic risk factors were compared for severity (CTP, MELD, AARC scores) and day 30 and 90 mortality. Information on overweight/obesity, type 2 diabetes mellitus (T2DM), hypertension and dyslipidaemia were available in 1028 (85%), 1019 (84%), 1017 (84%) and 965 (79%) patients respectively.
RESULTS: Overall, 392 (32%) patients died at day 30 and 528 (43%) at day 90. Overweight/obesity, T2DM, hypertension and dyslipidaemia were present in 154 (15%), 142 (14%), 66 (7%) and 141 (15%) patients, respectively, with no risk factors in 809 (67%) patients. Patients with overweight/obesity had higher MELD scores (30.6 ± 7.1 vs 29.2 ± 6.9, P = .007) and those with dyslipidaemia had higher AARC scores (10.4 ± 1.2 vs 9.8 ± 2, P = .014). Overweight/obesity was associated with increased day 30 mortality (HR 1.54, 95% CI 1.06-2.24, P = .023). None of other metabolic risk factors, alone or in combination, had any impact on disease severity or mortality. On multivariate analysis, overweight or obesity was significantly associated with 30-day mortality (aHR 1.91, 95% CI 1.41-2.59, P
METHODS: We identified drugs as precipitants of ACLF among prospective cohort of patients with ACLF from the Asian Pacific Association of Study of Liver (APASL) ACLF Research Consortium (AARC) database. Drugs were considered precipitants after exclusion of known causes together with a temporal association between exposure and decompensation. Outcome was defined as death from decompensation.
RESULTS: Of the 3,132 patients with ACLF, drugs were implicated as a cause in 329 (10.5%, mean age 47 years, 65% men) and other nondrug causes in 2,803 (89.5%) (group B). Complementary and alternative medications (71.7%) were the commonest insult, followed by combination antituberculosis therapy drugs (27.3%). Alcoholic liver disease (28.6%), cryptogenic liver disease (25.5%), and non-alcoholic steatohepatitis (NASH) (16.7%) were common causes of underlying liver diseases. Patients with drug-induced ACLF had jaundice (100%), ascites (88%), encephalopathy (46.5%), high Model for End-Stage Liver Disease (MELD) (30.2), and Child-Turcotte-Pugh score (12.1). The overall 90-day mortality was higher in drug-induced (46.5%) than in non-drug-induced ACLF (38.8%) (P = 0.007). The Cox regression model identified arterial lactate (P < 0.001) and total bilirubin (P = 0.008) as predictors of mortality.
DISCUSSION: Drugs are important identifiable causes of ACLF in Asia-Pacific countries, predominantly from complementary and alternative medications, followed by antituberculosis drugs. Encephalopathy, bilirubin, blood urea, lactate, and international normalized ratio (INR) predict mortality in drug-induced ACLF.
METHODS: Data was collected from 13 Asian countries on patients with CLD, known or newly diagnosed, with confirmed COVID-19.
RESULTS: Altogether, 228 patients [185 CLD without cirrhosis and 43 with cirrhosis] were enrolled, with comorbidities in nearly 80%. Metabolism associated fatty liver disease (113, 61%) and viral etiology (26, 60%) were common. In CLD without cirrhosis, diabetes [57.7% vs 39.7%, OR = 2.1 (1.1-3.7), p = 0.01] and in cirrhotics, obesity, [64.3% vs. 17.2%, OR = 8.1 (1.9-38.8), p = 0.002] predisposed more to liver injury than those without these. Forty three percent of CLD without cirrhosis presented as acute liver injury and 20% cirrhotics presented with either acute-on-chronic liver failure [5 (11.6%)] or acute decompensation [4 (9%)]. Liver related complications increased (p
METHODS AND RESULTS: We compared clinical characteristics and treatment approaches between patients with or without a history of COPD, before and after 1:2 propensity matching (for age, sex, geographical region, income level, and ethnic group) in 5232 prospectively recruited patients with HF and reduced ejection fraction (HFrEF, <40%) from 11 Asian regions (Northeast Asia: South Korea, Japan, Taiwan, Hong Kong, and China; South Asia: India; Southeast Asia: Thailand, Malaysia, Philippines, Indonesia, and Singapore). Among the 5232 patients with HFrEF, a history of COPD was present in 8.3% (n = 434), with significant variation in geography (11.0% in Northeast Asia vs. 4.7% in South Asia), regional income level (9.7% in high income vs. 5.8% in low income), and ethnicity (17.0% in Filipinos vs. 5.2% in Indians) (all P
METHODS: This is a controlled, intervention based study. It was run on three phases: before, during, and after Ramadan on 262 type 2 diabetes patients. The intervention group (n = 140) received RFEP on medications doses & timing adjustment before and after Ramadan, while the control group (n = 122) received standard care.
RESULTS: The dose of insulin glargine was reduced from 42.51 ± 22.16 at the baseline to 40.11 ± 18.51-units during Ramadan (p = 0.002) in the intervention group while it remained the same in the control group before Ramadan and during Ramadan (38.51 ± 18.63 and 38.14 ± 18.46, P = 0.428, respectively). The hypoglycemia score was 14.2 ± (8.5) pre-Ramadan in the intervention and reduced to 6.36 ± 6.17 during Ramadan (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.
METHODS: A cross sectional study was conducted in five health clinics under Kota Kinabalu district, Sabah, Malaysia Borneo involving 162 attendees with age of 50 years old and above. A validated self-administered questionnaire was used to collect the data. Multiple logistic regression analysis was used to determine the predictors of NS-FOBT.
RESULTS: The prevalence of NS-FOBT was 85.8% (n=139). Important predictors of NS-FOBT were age (aOR: 0.922; 95% CI: 0.855, 0.995; p=0.035), Bumiputera ethnicity (vs Non Bumiputera; aOR: 4.285; 95% CI: 1.384, 13.263; p=0.012), knowledge score (aOR: 0.921; 95% CI: 0.856, 0.99; p=0.027), and attitude score (aOR: 0.801; 95% CI: 0.702, 0.913; p=0.001).
CONCLUSION: There is high prevalence of NS-FOBT. Age, ethnicity, knowledge, and attitude were important predictors of NS-FOBT. Strategies are needed to improve FOBT screening rate among the public. Socio-culturally tailored health promotion strategies as well as strengthening the communication, collaboration, and education to enhance the role of family physician is vital in improving the CRC prevention and care.
METHODS: A cross-sectional study was conducted among 95 breast and gynaecology cancer survivor subjects. The Malay International Physical Activity Questionnaire (IPAQ) was used to assess physical activity and sitting time. Quality of life was assessed using the Malay EORTC QLQ-C30 questionnaire. Sociodemographic, clinical characteristics and anthropometric measurements were also obtained in this study.
RESULTS: The mean age of the subject was 51.8 ± 7.7 years old and the duration of survivorship was 4.3 ± 3.4 years. A total of 76.8% of subjects were categorized as having low physical activity level with a mean MET 403.5 ± 332.7 minutes/week and sitting time of 416.9 ± 151.0 minutes/day. Overall, subjects aged 50 years and above (p=0.006), widowed (p=0.032), retired (p=0.029) and had other non-communicable diseases (p=0.005) showed lower levels of physical activity. Increased physical activity had a positive effect on physical function (r=0.2, p=0.038), reduced insomnia (r=-0.3, p <0.001) and constipation symptoms (r=-0.3, p=0.012) domains of quality of life. The longer the sitting period showed more severe insomnia symptoms (r=0.2, p=0.03) but improved social function (r=0.2, p=0.012).
CONCLUSIONS: Increasing physical activity and reducing sitting time have a positive effect on the quality of life of cancer survivors. The focus of health education should be prioritized to older adults (50 years and above), widows, retirees, and those with other comorbidities as they are at risk of being not physically active.
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