RESULTS: We present an automated gene prediction pipeline, Seqping that uses self-training HMM models and transcriptomic data. The pipeline processes the genome and transcriptome sequences of the target species using GlimmerHMM, SNAP, and AUGUSTUS pipelines, followed by MAKER2 program to combine predictions from the three tools in association with the transcriptomic evidence. Seqping generates species-specific HMMs that are able to offer unbiased gene predictions. The pipeline was evaluated using the Oryza sativa and Arabidopsis thaliana genomes. Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis showed that the pipeline was able to identify at least 95% of BUSCO's plantae dataset. Our evaluation shows that Seqping was able to generate better gene predictions compared to three HMM-based programs (MAKER2, GlimmerHMM and AUGUSTUS) using their respective available HMMs. Seqping had the highest accuracy in rice (0.5648 for CDS, 0.4468 for exon, and 0.6695 nucleotide structure) and A. thaliana (0.5808 for CDS, 0.5955 for exon, and 0.8839 nucleotide structure).
CONCLUSIONS: Seqping provides researchers a seamless pipeline to train species-specific HMMs and predict genes in newly sequenced or less-studied genomes. We conclude that the Seqping pipeline predictions are more accurate than gene predictions using the other three approaches with the default or available HMMs.
Methods: A total of 7180 STEMI male patients from the National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry for the years 2006-2013 were enrolled. In the development of univariate and multivariate logistic regression model for the STEMI patients, Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied. The performance of the model was assessed through convergence diagnostics, overall model fit, model calibration and discrimination.
Results: A set of six risk factors for cardiovascular death among STEMI male patients were identified from the Bayesian multivariate logistic model namely age, diabetes mellitus, family history of CVD, Killip class, chronic lung disease and renal disease respectively. Overall model fit, model calibration and discrimination were considered good for the proposed model.
Conclusion: Bayesian risk prediction model for CVD male patients identified six risk factors associated with mortality. Among the highest risks were Killip class (OR=18.0), renal disease (2.46) and age group (OR=2.43) respectively.
METHODS: A validated computer simulation model (the IMS CORE Diabetes Model) was used to estimate the long-term projection of costs and clinical outcomes. The model was populated with published characteristics of Thai patients with type 2 diabetes. Baseline risk factors were obtained from Thai cohort studies, while relative risk reduction was derived from a meta-analysis study conducted by the Canadian Agency for Drugs and Technology in Health. Only direct costs were taken into account. Costs of diabetes management and complications were obtained from hospital databases in Thailand. Both costs and outcomes were discounted at 3 % per annum and presented in US dollars in terms of 2014 dollar value. Incremental cost-effectiveness ratio (ICER) was calculated. One-way and probabilistic sensitivity analyses were also performed.
RESULTS: IGlar is associated with a slight gain in quality-adjusted life years (0.488 QALYs), an additional life expectancy (0.677 life years), and an incremental cost of THB119,543 (US$3522.19) compared with NPH insulin. The ICERs were THB244,915/QALY (US$7216.12/QALY) and THB176,525/life-year gained (LYG) (US$5201.09/LYG). The ICER was sensitive to discount rates and IGlar cost. At the acceptable willingness to pay of THB160,000/QALY (US$4714.20/QALY), the probability that IGlar was cost effective was less than 20 %.
CONCLUSIONS: Compared to treatment with NPH insulin, treatment with IGlar in type 2 diabetes patients who had uncontrolled blood glucose with oral anti-diabetic drugs did not represent good value for money at the acceptable threshold in Thailand.
OBJECTIVES: To determine the cost-effectiveness of universal HLA-B*15:02 screening in preventing carbamazepine-induced Stevens-Johnson syndrome/toxic epidermal necrolysis in an ethnically diverse Malaysian population.
METHODS: A hybrid model of a decision tree and Markov model was developed to evaluate three strategies for treating newly diagnosed epilepsy among adults: (i) carbamazepine initiation without HLA-B*15:02 screening (current practice); (ii) universal HLA-B*15:02 screening prior to carbamazepine initiation; and (iii) alternative treatment [sodium valproate (VPA)] prescribing without HLA-B*15:02 screening. Base-case analysis and sensitivity analyses were performed over a lifetime time horizon. Incremental cost-effectiveness ratios were calculated.
RESULTS: Both universal HLA-B*15:02 screening and VPA prescribing were dominated by current practice. Compared with current practice, universal HLA-B*15:02 screening resulted in a loss of 0·0255 quality-adjusted life years (QALYs) at an additional cost of 707 U.S. dollars (USD); VPA prescribing resulted in a loss of 0·2622 QALYs at an additional cost of USD 4127, owing to estimated differences in antiepileptic treatment efficacy.
CONCLUSIONS: Universal HLA-B*15:02 screening is unlikely to be a cost-effective intervention in Malaysia. However, with the emergence of an ethnically diverse population in many other countries, this may render HLA-B*15:02 screening a viable intervention when an increasing proportion of the population is at risk and an equally effective yet safer antiepileptic drug is available.
MATERIAL AND METHODS: This was a prospective cohort study conducted in a tertiary referral hospital in Sydney, Australia. In all, 212 women with a low-risk pregnancy or with gestational diabetes were recruited including 158 nulliparous and 54 parous women. Maternal demographic, clinical and ultrasound characteristics were collected at 37 weeks of gestation. Semi-Bayesian logistic regression and Markov chain Monte Carlo simulation were used to assess the relation between cervical length and cesarean section in labor.
RESULTS: Rates of cesarean section were 5% (2/55) for cervical length ≤20 mm, 17% (17/101) for cervical length 20-32 mm, and 27% (13/56) for cervical length >32 mm. These rates were 4, 22 and 33%, respectively, in nulliparous women. In the semi-Bayesian analysis, the odds ratio for cesarean section was 6.2 (95% confidence interval 2.2-43) for cervical length 20-32 mm and 10 (95% confidence interval 4.8-74) for cervical length >32 mm compared with the lowest quartile of cervical length, after adjusting for maternal age, parity, height, prepregnancy body mass index, gestational diabetes, induction of labor, neonatal sex and birthweight centile.
CONCLUSIONS: Cervical length at 37 weeks of gestation is associated with intrapartum cesarean section.
METHODS: We developed a decision analytic model to estimate the lifetime costs and quality-adjusted life-years (QALYs) accrued through BRCA mutation testing or routine clinical surveillance (RCS) for a hypothetical cohort of 1000 early-stage breast cancer patients aged 40 years. In the model, patients would decide whether to accept testing and to undertake risk-reducing mastectomy, oophorectomy, tamoxifen, combinations or neither. We calculated the incremental cost-effectiveness ratio (ICER) from the health system perspective. A series of sensitivity analyses were performed.
RESULTS: In the base case, testing generated 11.2 QALYs over the lifetime and cost US$4815 per patient whereas RCS generated 11.1 QALYs and cost US$4574 per patient. The ICER of US$2725/QALY was below the cost-effective thresholds. The ICER was sensitive to the discounting of cost, cost of BRCA mutation testing and utility of being risk-free, but the ICERs remained below the thresholds. Probabilistic sensitivity analysis showed that at a threshold of US$9500/QALY, 99.9% of simulations favoured BRCA mutation testing over RCS.
CONCLUSIONS: Offering BRCA mutation testing to early-stage breast cancer patients identified using a locally-validated risk-assessment tool may be cost effective compared to RCS in Malaysia.