Development and Validation of a Risk Score for Predicting Non-Adherence to Antiretroviral Therapy.

Several patient-related factors that influence adherence to antiretroviral therapy (ART) have been described. However, studies that propose a practical and simple tool to predict nonadherence after ART initiation are still scarce. In this study, we develop and validate a score to predict the risk of nonadherence in people starting ART. The model/score was developed and validated using a cohort of people living with HIV starting ART at the Hospital del Mar, Barcelona, between 2012 and 2015 (derivation cohort) and between 2016 and 2018 (validation cohort),. Adherence was evaluated every 2 months using both pharmacy refills and patient self-reports. Nonadherence was defined as taking <90% of the prescribed dose and/or ART interruption for more than 1 week. Predictive factors for nonadherence were identified by logistic regression. Beta coefficients were used to develop a predictive score. Optimal cutoffs were identified using the bootstrapping methodology, and performance was evaluated with the C statistic. Our study is based on 574 patients: 349 in the derivation cohort and 225 in the validation cohort. A total of 104 patients (29.8%) of the derivation cohort were nonadherent. Nonadherence predictors were patient prejudgment; previous medical appointment failures; cultural and/or idiomatic barriers; heavy alcohol use; substance abuse; unstable housing; and severe mental illness. The cutoff point (receiver operating characteristic curve) for nonadherence was 26.3 (sensitivity 0.87 and specificity 0.86). The C statistic (95% confidence interval) was 0.91 (0.87-0.94). These results were consistent with those predicted by the score in the validation cohort. This easy-to-use, highly sensitive, and specific tool could be easily used to identify patients at highest risk for nonadherence, thus allowing resource optimization and achieving optimal treatment goals.

ISOGO: Functional annotation of protein-coding splice variants.

The advent of RNA-seq technologies has switched the paradigm of genetic analysis from a genome to a transcriptome-based perspective. Alternative splicing generates functional diversity in genes, but the precise functions of many individual isoforms are yet to be elucidated. Gene Ontology was developed to annotate gene products according to their biological processes, molecular functions and cellular components. Despite a single gene may have several gene products, most annotations are not isoform-specific and do not distinguish the functions of the different proteins originated from a single gene. Several approaches have tried to automatically annotate ontologies at the isoform level, but this has shown to be a daunting task. We have developed ISOGO (ISOform + GO function imputation), a novel algorithm to predict the function of coding isoforms based on their protein domains and their correlation of expression along 11,373 cancer patients. Combining these two sources of information outperforms previous approaches: it provides an area under precision-recall curve (AUPRC) five times larger than previous attempts and the median AUROC of assigned functions to genes is 0.82. We tested ISOGO predictions on some genes with isoform-specific functions (BRCA1, MADD,VAMP7 and ITSN1) and they were coherent with the literature. Besides, we examined whether the main isoform of each gene -as predicted by APPRIS- was the most likely to have the annotated gene functions and it occurs in 99.4% of the genes. We also evaluated the predictions for isoform-specific functions provided by the CAFA3 challenge and results were also convincing. To make these results available to the scientific community, we have deployed a web application to consult ISOGO predictions (https://biotecnun.unav.es/app/isogo). Initial data, website link, isoform-specific GO function predictions and R code is available at https://gitlab.com/icassol/isogo.