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Identifying KDIGO Trajectory Phenotypes Associated with Increased Inpatient Mortality.

ABSTRACT: Acute kidney injury (AKI) is a complex systemic syndrome associated with high morbidity and mortality and risk for the subsequent development of renal and non-renal complications. Nearly 50% of patients in the ICU experience AKI. AKI severity is a key metric for evaluating patients risk of hospital mortality. Current AKI stratification is based on absolute changes in Serum Creatinine (SCr) and the maximal increase relative to the patients baseline value. However, such measurement does not consider either the progression or duration of AKI, both of which are associated with adverse outcomes post-AKI. In this article, by leveraging a large volume of SCr temporal variabilities, we present a novel model called Trajectory of Acute Kidney Injury (TAKI) for the identification of AKI trajectory subtypes. Experimental results demonstrate that TAKI is better than the existing trajectory subtyping methods on both the inpatient mortality stratification and the post-7-day AKI progression estimation. With TAKI, it is found that the trend of KDIGO trajectory appears to be more highly associated with inpatient mortality rates than the maximum KDIGO score.

SUBMITTER: Smith TD

PROVIDER: S-EPMC7444813 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Identifying KDIGO Trajectory Phenotypes Associated with Increased Inpatient Mortality.

Smith Taylor D TD Soriano Victor Ortiz VO Neyra Javier A JA Chen Jin J

Proceedings. IEEE International Conference on Healthcare Informatics 20190601

Acute kidney injury (AKI) is a complex systemic syndrome associated with high morbidity and mortality and risk for the subsequent development of renal and non-renal complications. Nearly 50% of patients in the ICU experience AKI. AKI severity is a key metric for evaluating patients risk of hospital mortality. Current AKI stratification is based on absolute changes in Serum Creatinine (SCr) and the maximal increase relative to the patients baseline value. However, such measurement does not consid ...[more]

PMID: 32844151

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Project description:Chronic obstructive pulmonary disease (COPD) is characterized by expiratory airflow limitation and symptoms such as shortness of breath. Although many studies have demonstrated dysregulated microRNA (miRNA) and gene (mRNA) expression in the pathogenesis of COPD, how miRNAs and mRNAs systematically interact and contribute to COPD development is still not clear. To gain a deeper understanding of the gene regulatory network underlying COPD pathogenesis, we used Sparse Multiple Canonical Correlation Network (SmCCNet) to integrate whole blood miRNA and RNA-sequencing data from 404 participants in the COPDGene study to identify novel miRNA-mRNA networks associated with COPD-related phenotypes including lung function and emphysema. We hypothesized that phenotype-directed interpretable miRNA-mRNA networks from SmCCNet would assist in the discovery of novel biomarkers that traditional single biomarker discovery methods (such as differential expression) might fail to discover. Additionally, we investigated whether adjusting -omics and clinical phenotypes data for covariates prior to integration would increase the statistical power for network identification. Our study demonstrated that partial covariate adjustment for age, sex, race, and CT scanner model (in the quantitative emphysema networks) improved network identification when compared with no covariate adjustment. However, further adjustment for current smoking status and relative white blood cell (WBC) proportions sometimes weakened the power for identifying lung function and emphysema networks, a phenomenon which may be due to the correlation of smoking status and WBC counts with the COPD-related phenotypes. With partial covariate adjustment, we found six miRNA-mRNA networks associated with COPD-related phenotypes. One network consists of 2 miRNAs and 28 mRNAs which had a 0.33 correlation (p = 5.40E-12) to forced expiratory volume in 1 s (FEV1) percent predicted. We also found a network of 5 miRNAs and 81 mRNAs that had a 0.45 correlation (p = 8.80E-22) to percent emphysema. The miRNA-mRNA networks associated with COPD traits provide a systems view of COPD pathogenesis and complements biomarker identification with individual miRNA or mRNA expression data.

Dataset Information

Identifying KDIGO Trajectory Phenotypes Associated with Increased Inpatient Mortality.

Publications

Identifying KDIGO Trajectory Phenotypes Associated with Increased Inpatient Mortality.

Similar Datasets

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