ABSTRACT
Introduction: Timely palliative care and surgical interventions improve symptoms, health-related quality of life (HRQoL), and reduce medical cost for seriously ill adults at end of life (EOL). However, there is still poor delivery and underutilization of these palliative services. We hypothesize that the sub-optimal delivery is due to limited understanding among healthcare providers. Methods: A nationwide cross-sectional online survey was conducted among primary and tertiary healthcare providers. The survey assessed challenges faced, palliative education, confidence in managing palliative patients, and knowledge on palliative surgery. Overall palliative care awareness and knowledge was assessed using a 6-point score. Likelihood of considering various palliative interventions at EOL was also determined using a threshold score (higher score = higher threshold). Results: There were 145 healthcare providers who completed the survey (81.9% response rate); majority reported significant challenges in providing various aspects of palliative care: 57% (n = 82) in the provision of emotional support. Sixty-nine percent (n = 97) in managing social issues, and 71% (n = 103) in managing family expectations. Most expressed inadequate palliative care training in both under-graduate and post-graduate training and lack confidence in managing EOL issues. Up to 57% had misconceptions regarding potential benefits, morbidity and mortality after palliative surgery. In general, most providers had high thresholds for Intensive Care Unit admissions and palliative surgery, and were more likely to recommend endoscopic or interventional radiology procedures at EOL. Conclusion: Healthcare providers in Singapore have poor knowledge and misconceptions about palliative care and surgery. Improving awareness and education among those caring for seriously ill adults is essential.
ABSTRACT
Introduction: Pharmacogenetics currently supports clinical decision-making on the basis of a limited number of variants in a few genes and may benefit paediatric prescribing where there is a need for more precise dosing. Integrating genomic information such as methylation into pharmacogenetic models holds the potential to improve their accuracy and consequently prescribing decisions. Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene conventionally associated with the metabolism of commonly used drugs and endogenous substrates. We thus sought to predict epigenetic loci from single nucleotide polymorphisms (SNPs) related to CYP2D6 in children from the GUSTO cohort. Methods: Buffy coat DNA methylation was quantified using the Illumina Infinium Methylation EPIC beadchip. CpG sites associated with CYP2D6 were used as outcome variables in Linear Regression, Elastic Net and XGBoost models. We compared feature selection of SNPs from GWAS mQTLs, GTEx eQTLs and SNPs within 2 MB of the CYP2D6 gene and the impact of adding demographic data. The samples were split into training (75%) sets and test (25%) sets for validation. In Elastic Net model and XGBoost models, optimal hyperparameter search was done using 10-fold cross validation. Root Mean Square Error and R-squared values were obtained to investigate each models' performance. When GWAS was performed to determine SNPs associated with CpG sites, a total of 15 SNPs were identified where several SNPs appeared to influence multiple CpG sites. Results: Overall, Elastic Net models of genetic features appeared to perform marginally better than heritability estimates and substantially better than Linear Regression and XGBoost models. The addition of nongenetic features appeared to improve performance for some but not all feature sets and probes. The best feature set and Machine Learning (ML) approach differed substantially between CpG sites and a number of top variables were identified for each model. Discussion: The development of SNP-based prediction models for CYP2D6 CpG methylation in Singaporean children of varying ethnicities in this study has clinical application. With further validation, they may add to the set of tools available to improve precision medicine and pharmacogenetics-based dosing.
