ABSTRACT
Oncology inpatients are at high risk of malnutrition. Identification of at risk patients by nutrition screening requires a practical and easy to use tool. The aim of this study was to determine the validity of the Bach Mai Boston Tool (BBT) compared to a 'gold standard' full nutrition assessment using the Patient-Generated Subjective Global Assessment (PG-SGA). A cross-sectional study was conducted on 270 oncology inpatients from January to December 2016. Cohen's Kappa, sensitivity, specificity and ROC analyses were performed. 270 inpatients were included in this study with a mean age of 56.3 ± 12.1 years old. Of these patients, 51.8% were male, and 74.1% had gastrointestinal cancer. The mean body mass index of patients was 20.6 ± 3.0 kg/m2. The PG-SGA tool identified 146 (54.1%) malnourished patients, while the BBT identified 105 (39.9%) malnourished patients. The BBT had a medium consistency, with a Kappa value of 0.6. Using a cut-off point of ≥ 4, the BBT had a sensitivity of 87.7% and a specificity of 72.6%. On the other hand, a BBT with a cut-off point ≥ 5 resulted in a sensitivity of 67.1%, a specificity of 94.4%, and an AUC of 0.81. The BBT is a practical, informative and valid tool for detecting malnutrition in hospitalized oncology patients. We recommend using a cut-off point of 4 for screening the risk of malnutrition for oncology inpatients.
Subject(s)
Early Detection of Cancer/methods , Inpatients/statistics & numerical data , Medical Oncology/methods , Neoplasms/diagnosis , Nutrition Assessment , Aged , Cross-Sectional Studies , Female , Gastrointestinal Neoplasms/diagnosis , Gastrointestinal Neoplasms/therapy , Humans , Male , Malnutrition/diagnosis , Malnutrition/therapy , Middle Aged , Neoplasms/therapy , Nutritional Status , ROC CurveABSTRACT
Precision medicine and sequence-based clinical diagnostics seek to predict disease risk or to identify causative variants from sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. In the past, few CAGI challenges have addressed the impact of sequence variants on splicing. In CAGI5, two challenges (Vex-seq and MaPSY) involved prediction of the effect of variants, primarily single-nucleotide changes, on splicing. Although there are significant differences between these two challenges, both involved prediction of results from high-throughput exon inclusion assays. Here, we discuss the methods used to predict the impact of these variants on splicing, their performance, strengths, and weaknesses, and prospects for predicting the impact of sequence variation on splicing and disease phenotypes.
Subject(s)
Alternative Splicing , Computational Biology/methods , Mutation , Proteins/genetics , Animals , Congresses as Topic , Genetic Fitness , Humans , Models, Genetic , Sequence Homology, Nucleic AcidABSTRACT
Pathogenic genetic variants often primarily affect splicing. However, it remains difficult to quantitatively predict whether and how genetic variants affect splicing. In 2018, the fifth edition of the Critical Assessment of Genome Interpretation proposed two splicing prediction challenges based on experimental perturbation assays: Vex-seq, assessing exon skipping, and MaPSy, assessing splicing efficiency. We developed a modular modeling framework, MMSplice, the performance of which was among the best on both challenges. Here we provide insights into the modeling assumptions of MMSplice and its individual modules. We furthermore illustrate how MMSplice can be applied in practice for individual genome interpretation, using the MMSplice VEP plugin and the Kipoi variant interpretation plugin, which are directly applicable to VCF files.
Subject(s)
Computational Biology/methods , Genetic Variation , RNA Splicing , Congresses as Topic , Exons , Genetic Predisposition to Disease , Humans , Introns , Models, Genetic , SoftwareABSTRACT
Predicting the effects of genetic variants on splicing is highly relevant for human genetics. We describe the framework MMSplice (modular modeling of splicing) with which we built the winning model of the CAGI5 exon skipping prediction challenge. The MMSplice modules are neural networks scoring exon, intron, and splice sites, trained on distinct large-scale genomics datasets. These modules are combined to predict effects of variants on exon skipping, splice site choice, splicing efficiency, and pathogenicity, with matched or higher performance than state-of-the-art. Our models, available in the repository Kipoi, apply to variants including indels directly from VCF files.
