Dataset Information

Using Patient Completed Screening Tools to Predict Risk of Malnutrition in Patients With Inflammatory Bowel Disease.

ABSTRACT:

Background

Malnutrition is associated with adverse clinical outcomes in patients with inflammatory bowel disease (IBD), however, malnutrition screening is not routinely performed. This study aimed to identify the prevalence of malnutrition in patients with IBD and compare the accuracy of patient completed screens to a gold-standard malnutrition assessment tool: the dietitian-completed subjective global assessment (SGA).

Methods

This cross-sectional study was conducted at 2 hospitals and 2 ambulatory care clinics in Alberta, Canada. Patients with IBD completed 4 malnutrition screening tools: abridged patient-generated SGA (abPG-SGA), Malnutrition Universal Screening Tool (MUST), Canadian Nutrition Screening Tool (CNST), and Saskatchewan IBD-nutrition risk (SaskIBD-NR). Risk of malnutrition was calculated for each tool and differences were compared between IBD subtype and body mass index (BMI) categories. Sensitivity and specificity, negative and positive predictive values (NPV and PPV), and area under the receiver operating characteristic curve (AUC) were calculated compared to SGA.

Results

Patients with Crohn's disease (n = 149) and ulcerative colitis (n = 96) participated in this study. Overall prevalence of malnutrition using SGA was 23% and malnutrition risk for CNST, abPG-SGA, SaskIBD-NR, and MUST was 37%, 36%, 36%, and 27%, respectively. Overall, the abPG-SGA had the highest sensitivity (83%), PPV (53%), and NPV (94%), and largest AUC (0.837) compared to SGA. For patients with a BMI ≥25 kg/m², sensitivity and PPV of the abPG-SGA decreased to 73% and 41%, respectively, with a AUC of 0.841.

Conclusions

Malnutrition is prevalent in patients with IBD and using malnutrition risk screening tools such as the abPG-SGA may be useful to identify patients who would benefit from further assessment.

SUBMITTER: Taylor LM

PROVIDER: S-EPMC9802362 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Publications

Using Patient Completed Screening Tools to Predict Risk of Malnutrition in Patients With Inflammatory Bowel Disease.

Taylor Lorian M LM Eslamparast Tannaz T Farhat Kamal K Kroeker Karen K Halloran Brendan B Shommu Nusrat N Kumar Ankush A Fitzgerald Quinn Q Gramlich Leah L Abraldes Juan G JG Tandon Puneeta P Raman Maitreyi M

Crohn's & colitis 360 20210707 3

<h4>Background</h4>Malnutrition is associated with adverse clinical outcomes in patients with inflammatory bowel disease (IBD), however, malnutrition screening is not routinely performed. This study aimed to identify the prevalence of malnutrition in patients with IBD and compare the accuracy of patient completed screens to a gold-standard malnutrition assessment tool: the dietitian-completed subjective global assessment (SGA).<h4>Methods</h4>This cross-sectional study was conducted at 2 hospita ...[more]

PMID: 36776646

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Project description:Inflammatory bowel disease (IBD) is characterized by complex etiology and a disrupted colonic ecosystem. We provide a framework for the analysis of multi-omic data, which we apply to study the gut ecosystem in IBD. Specifically, we train and validate models using data on the metagenome, metatranscriptome, virome, and metabolome from the Human Microbiome Project 2 IBD multi-omic database, with 1,785 repeated samples from 130 individuals (103 cases and 27 controls). After splitting the participants into training and testing groups, we used mixed-effects least absolute shrinkage and selection operator regression to select features for each omic. These features, with demographic covariates, were used to generate separate single-omic prediction scores. All four single-omic scores were then combined into a final regression to assess the relative importance of the individual omics and the predictive benefits when considered together. We identified several species, pathways, and metabolites known to be associated with IBD risk, and we explored the connections between data sets. Individually, metabolomic and viromic scores were more predictive than metagenomics or metatranscriptomics, and when all four scores were combined, we predicted disease diagnosis with a Nagelkerke's R2 of 0.46 and an area under the curve of 0.80 (95% confidence interval: 0.63, 0.98). Our work supports that some single-omic models for complex traits are more predictive than others, that incorporating multiple omic data sets may improve prediction, and that each omic data type provides a combination of unique and redundant information. This modeling framework can be extended to other complex traits and multi-omic data sets.IMPORTANCEComplex traits are characterized by many biological and environmental factors, such that multi-omic data sets are well-positioned to help us understand their underlying etiologies. We applied a prediction framework across multiple omics (metagenomics, metatranscriptomics, metabolomics, and viromics) from the gut ecosystem to predict inflammatory bowel disease (IBD) diagnosis. The predicted scores from our models highlighted key features and allowed us to compare the relative utility of each omic data set in single-omic versus multi-omic models. Our results emphasized the importance of metabolomics and viromics over metagenomics and metatranscriptomics for predicting IBD status. The greater predictive capability of metabolomics and viromics is likely because these omics serve as markers of lifestyle factors such as diet. This study provides a modeling framework for multi-omic data, and our results show the utility of combining multiple omic data types to disentangle complex disease etiologies and biological signatures.

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Dataset Information

Using Patient Completed Screening Tools to Predict Risk of Malnutrition in Patients With Inflammatory Bowel Disease.

Background

Methods

Results

Conclusions

Publications

Using Patient Completed Screening Tools to Predict Risk of Malnutrition in Patients With Inflammatory Bowel Disease.

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