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Gastroenterology ; 162(7):S-383, 2022.
Article in English | EMBASE | ID: covidwho-1967304


Introduction: The SARS-CoV-2 pandemic highlighted the need for a way to predict progression to critical illness and ICU admission amongst infected patients. Previous liver disease is a known risk for progression to critical illness. Attempts to identify biomarkers for progression to critical illness suggest inflammatory markers and coagulation markers as useful. We used a machine learning approach to compare the admission liver panel and inflammatory biomarker assays in hospitalized COVID-19 patients with extant mild or severe hepatic disease who progressed to critical illness (ICU admission) versus those who were progression-free. Methods: We included data ed under IRB exemption from electronic medical records (EMR) for SARS-CoV-2 patients admitted to the hospital with chronic liver disease ICD-10-CM codes. Demographics, laboratory results and administrative data were archived and analyzed (SAS, Cary, NC). Generalized regression identified inflammatory and liver panel biomarkers assayed within 8h of hospital admission associated (p<.05) with progression to critical illness. Retained biomarkers underwent bootstrap forest analysis forming a receiver operating characteristic (ROC) that optimized area under ROC (AUROC) estimating model accuracy (precision). Continuous data summarized with median [IQR] were compared using Kruskal-Wallis Test. Discrete data summarized as counts or proportions were compared with chi-squared test. Two-tailed p<.05 was significant. Results: Out of the 4411 COVID-19 patients who were discharged between March 14, 2020 and September 30, 2021, 333 with a previous diagnosis chronic liver disease were included in this study. Demographics for this population are presented in Table 1. Statistical values for biomarkers and progression to critical illness are seen in table 1. Statistically significant markers are compared via explained variance and ROC curve in Figure 1. Although AST and D-dimer were statistically significant markers of progression to critical illness, when modelled as a predictive biomarker, they were not informative in the aggregated ensemble. Therefore, they were not included in the modeling analysis. Conclusion: Hypoalbuminemia, inflammatory markers, D-dimer, and AST were significantly associated with progression to critical illness. Indexing liver specific synthetic function (albumin) to CoV-2 evoked inflammatory markers improves explained variance for progression to critical illness. Alternative liver synthetic function biomarker (INR), ALT, and ALP were not a significant prognostic indicator for progression to severe illness. To our knowledge, this is debut of modeling hypoalbuminemia indexed with multiple routinely assayed inflammatory biomarkers for baseline risk assessment in COVID-19 patients with liver disease. (Table Presented) (Figure Presented)