OBJECTIVE: The aim was to understand the local prevalence and factors associated with returning to work in Malaysia after a cardiac event.
METHODS: A cross sectional design was used. All patients attending the cardiac rehabilitation program after major cardiac event during an 11-months period (2011-2012) were included. Data relating to socio-demographic, work-related, risk factors and acute myocardial infarction were collected. The SF-36 questionnaire was used to assess quality of life. Regression analysis was used to determine the predicting factors to return to work.
RESULTS: A total of 398 files were screened, 112 respondents agreed to participate giving a response rate of 47.3%. The prevalence of returned to work (RTW) was 66.1% [95% CI: 57.2-75.0]. Factors associated with work resumption were age (Adj. OR: 0.92 (95% CI: 0.84-0.99), diabetes mellitus (Adj. OR: 3.70, 95% CI: 1.35-10.12), Mental Component Summary (MCS) score (Adj. OR: 1.05 (95% CI: 1.01-1.09) and baseline angiography findings. Patients with single vessel and two vessel disease were 8.9 times and 3.78 times more likely to return to work compared to those with 3 vessels (Adj. OR: 8.90 (95% CI: 2.29-34.64) and Adj. OR: 3.78, (95% CI: 1.12, 12.74).
CONCLUSIONS: We proposed a cardiac rehabilitation program to emphasize mental health as it may improve successful return to work after cardiac event.
METHODS: We examined 81 common treeshrews from 14 areas in nine states and the Federal Territory of Peninsular Malaysia for filarial parasites. Once any filariae that were found had been isolated, we examined their morphological characteristics and determined the partial sequences of their mitochondrial cytochrome c oxidase subunit 1 (cox1) and 12S rRNA genes. Polymerase chain reaction (PCR) products of the internal transcribed spacer 1 (ITS1) region were then cloned into the pGEM-T vector, and the recombinant plasmids were used as templates for sequencing.
RESULTS: Malayfilaria sofiani Uni, Mat Udin & Takaoka, n. g., n. sp. is described based on the morphological characteristics of adults and microfilariae found in common treeshrews from Jeram Pasu, Kelantan, Malaysia. The Kimura 2-parameter distance between the cox1 gene sequences of the new species and W. bancrofti was 11.8%. Based on the three gene sequences, the new species forms a monophyletic clade with W. bancrofti and Brugia spp. The adult parasites were found in tissues surrounding the lymph nodes of the neck of common treeshrews.
CONCLUSIONS: The newly described species appears most closely related to Wuchereria spp. and Brugia spp., but differs from these in several morphological characteristics. Molecular analyses based on the cox1 and 12S rRNA genes and the ITS1 region indicated that this species differs from both W. bancrofti and Brugia spp. at the genus level. We thus propose a new genus, Malayfilaria, along with the new species M. sofiani.
OBJECTIVE: To derive a single algorithm using deep learning and machine learning for the prediction and identification of factors associated with in-hospital mortality in Asian patients with ACS and to compare performance to a conventional risk score.
METHODS: The Malaysian National Cardiovascular Disease Database (NCVD) registry, is a multi-ethnic, heterogeneous database spanning from 2006-2017. It was used for in-hospital mortality model development with 54 variables considered for patients with STEMI and Non-STEMI (NSTEMI). Mortality prediction was analyzed using feature selection methods with machine learning algorithms. Deep learning algorithm using features selected from machine learning was compared to Thrombolysis in Myocardial Infarction (TIMI) score.
RESULTS: A total of 68528 patients were included in the analysis. Deep learning models constructed using all features and selected features from machine learning resulted in higher performance than machine learning and TIMI risk score (p < 0.0001 for all). The best model in this study is the combination of features selected from the SVM algorithm with a deep learning classifier. The DL (SVM selected var) algorithm demonstrated the highest predictive performance with the least number of predictors (14 predictors) for in-hospital prediction of STEMI patients (AUC = 0.96, 95% CI: 0.95-0.96). In NSTEMI in-hospital prediction, DL (RF selected var) (AUC = 0.96, 95% CI: 0.95-0.96, reported slightly higher AUC compared to DL (SVM selected var) (AUC = 0.95, 95% CI: 0.94-0.95). There was no significant difference between DL (SVM selected var) algorithm and DL (RF selected var) algorithm (p = 0.5). When compared to the DL (SVM selected var) model, the TIMI score underestimates patients' risk of mortality. TIMI risk score correctly identified 13.08% of the high-risk patient's non-survival vs 24.7% for the DL model and 4.65% vs 19.7% of the high-risk patient's non-survival for NSTEMI. Age, heart rate, Killip class, cardiac catheterization, oral hypoglycemia use and antiarrhythmic agent were found to be common predictors of in-hospital mortality across all ML feature selection models in this study. The final algorithm was converted into an online tool with a database for continuous data archiving for prospective validation.
CONCLUSIONS: ACS patients were better classified using a combination of machine learning and deep learning in a multi-ethnic Asian population when compared to TIMI scoring. Machine learning enables the identification of distinct factors in individual Asian populations to improve mortality prediction. Continuous testing and validation will allow for better risk stratification in the future, potentially altering management and outcomes.
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.