METHODS: A cross-sectional study was conducted among 134 geriatric patients with a mean age of 68.9 ± 8.4 who stayed at acute care wards in Hospital Tuanku Ampuan Rahimah, Klang from July 2017 to August 2017. The SGA, MNA, and GNRI were administered through face-to-face interviews with all the participants who gave their consent. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the GNRI and MNA were analyzed against the SGA. Receiver-operating characteristic (ROC) curve analysis was used to obtain the area under the curve (AUC) and suitable optimal cutoff values for both the GNRI and MNA.
RESULTS: According to the SGA, MNA, and GNRI, 26.9%, 42.5%, and 44.0% of the participants were malnourished, respectively. The sensitivity, specificity, PPV, and NPV for the GNRI were 0.622, 0.977, 0.982, and 0.558, respectively, while those for the MNA were 0.611, 0.909, 0.932, and 0.533, respectively. The AUC of the GNRI was comparable to that of the MNA (0.831 and 0.898, respectively). Moreover, the optimal malnutrition cutoff value for the GNRI was 94.95.
CONCLUSIONS: The prevalence of malnutrition remains high among hospitalized elderly patients. Validity of the GNRI is comparable to that of the MNA, and use of the GNRI to assess the nutritional status of this group is proposed with the new suggested cutoff value (GNRI ≤ 94.95), as it is simpler and more efficient. Underdiagnosis of malnutrition can be prevented, possibly reducing the prevalence of malnourished hospitalized elderly patients and improving the quality of the nutritional care process practiced in Malaysia.
METHODS: We recruited 164 healthy controls (HC) and 120 cognitively impaired (CI) subjects- 47 mild cognitive impairment (MCI) and 73 mild Alzheimer's disease (AD) dementia participants, from four countries between January 2015 and August 2016 to determine the usefulness of a single version of the VCAT, without translation or adaptation, in a multinational, multilingual population. The VCAT was administered along with established cognitive evaluation.
RESULTS: The VCAT, without local translation or adaptation, was effective in discriminating between HC and CI subjects (MCI and mild AD dementia). Mean (SD) VCAT scores for HC and CI subjects were 22.48 (3.50) and 14.17 (5.05) respectively. Areas under the curve for Montreal Cognitive Assessment (0.916, 95% CI 0.884-0.948) and the VCAT (0.905, 95% CI 0.870-0.940) in discriminating between HCs and CIs were comparable. The multiple languages used to administer VCAT in four countries did not significantly influence test scores.
CONCLUSIONS: The VCAT without the need for language translation or cultural adaptation showed satisfactory discriminative ability and was effective in a multinational, multilingual Southeast Asian population.
METHODS: We did an individual patient data meta-analysis, in which we searched PubMed and Web of Science for studies published from database inception until April 30, 2019. Studies reporting original biopsy-controlled data of CAP for non-invasive grading of steatosis were eligible. Probe recommendation was based on automated selection, manual assessment of skin-to-liver-capsule distance, and a body-mass index (BMI) criterion. Receiver operating characteristic methods and mixed models were used to assess diagnostic properties and covariates. Patients with non-alcoholic fatty liver disease (NAFLD) were analysed separately because they are the predominant patient group when using the XL probe. This study is registered with PROSPERO, CRD42018099284.
FINDINGS: 16 studies reported histology-controlled CAP including the XL probe, and individual data from 13 papers and 2346 patients were included. Patients with a mean age of 46·5 years (SD 14·5) were recruited from 20 centres in nine countries. 2283 patients had data for BMI; 673 (29%) were normal weight (BMI <25 kg/m2), 530 (23%) were overweight (BMI ≥25 to <30 kg/m2), and 1080 (47%) were obese (BMI ≥30 kg/m2). 1277 (54%) patients had NAFLD, 474 (20%) had viral hepatitis, 285 (12%) had alcohol-associated liver disease, and 310 (13%) had other liver disease aetiologies. The XL probe was recommended in 1050 patients, 930 (89%) of whom had NAFLD; among the patients with NAFLD, the areas under the curve were 0·819 (95% CI 0·769-0·869) for S0 versus S1 to S3 and 0·754 (0·720-0·787) for S0 to S1 versus S2 to S3. CAP values were independently affected by aetiology, diabetes, BMI, aspartate aminotransferase, and sex. Optimal cutoffs differed substantially across aetiologies. Risk of bias according to QUADAS-2 was low.
INTERPRETATION: CAP cutoffs varied according to cause, and can effectively recognise significant steatosis in patients with viral hepatitis. CAP cannot grade steatosis in patients with NAFLD adequately, but its value in a NAFLD screening setting needs to be studied, ideally with methods beyond the traditional histological reference standard.
FUNDING: The German Federal Ministry of Education and Research and Echosens.
OBJECTIVE: Apply machine learning for the prediction and identification of factors associated with short and long-term mortality in Asian STEMI patients and compare with a conventional risk score.
METHODS: The National Cardiovascular Disease Database for Malaysia registry, of a multi-ethnic, heterogeneous Asian population was used for in-hospital (6299 patients), 30-days (3130 patients), and 1-year (2939 patients) model development. 50 variables were considered. Mortality prediction was analysed using feature selection methods with machine learning algorithms and compared to Thrombolysis in Myocardial Infarction (TIMI) score. Invasive management of varying degrees was selected as important variables that improved mortality prediction.
RESULTS: Model performance using a complete and reduced variable produced an area under the receiver operating characteristic curve (AUC) from 0.73 to 0.90. The best machine learning model for in-hospital, 30 days, and 1-year outperformed TIMI risk score (AUC = 0.88, 95% CI: 0.846-0.910; vs AUC = 0.81, 95% CI:0.772-0.845, AUC = 0.90, 95% CI: 0.870-0.935; vs AUC = 0.80, 95% CI: 0.746-0.838, AUC = 0.84, 95% CI: 0.798-0.872; vs AUC = 0.76, 95% CI: 0.715-0.802, p < 0.0001 for all). TIMI score underestimates patients' risk of mortality. 90% of non-survival patients are classified as high risk (>50%) by machine learning algorithm compared to 10-30% non-survival patients by TIMI. Common predictors identified for short- and long-term mortality were age, heart rate, Killip class, fasting blood glucose, prior primary PCI or pharmaco-invasive therapy and diuretics. The final algorithm was converted into an online tool with a database for continuous data archiving for algorithm validation.
CONCLUSIONS: In a multi-ethnic population, patients with STEMI were better classified using the machine learning method compared to TIMI scoring. Machine learning allows for the identification of distinct factors in individual Asian populations for better mortality prediction. Ongoing continuous testing and validation will allow for better risk stratification and potentially alter management and outcomes in the future.