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Gastroenterology ; 162(7):S-1280, 2022.
Article in English | EMBASE | ID: covidwho-1967446


Background & Aims: Prior studies have indicated the presence of hepatic inflammation (as signified by elevated liver function test (LFT) values), as conferring an escalated risk toward adverse outcomes in patients admitted with COVID-19. In line with this hypothesis, we study the various thresholds of LFTs and its associated prognostic risks toward COVID- 19 related hospital deaths Method: This was a single-center retrospective study involving patients admitted with COVID-19. Univariate Cox regression analysis identified the LFT variables significantly associated with our primary endpoint, in-hospital death. Subsequently, 500 iterations of thresholds were generated for each biomarker to estimate the prognostic relationship between biomarker and endpoint. Multivariate Cox regression and event-analyses were performed for each threshold to identify the minimal cutoffs at which the prognostic relationship was significant. Event curves were drawn for each significant relationship. Results: A total of 858 patients with COVID-19 were included with a median follow-up time of 5 days from admission. From the total, 90 patients passed away during admission (10.5%). The deceased cases were more likely to be older (66.2 vs 55.3y p<0.001);however, there was no difference in gender (male: 66 vs 56.2% p=0.11). Between the cases and controls (no-death), deceased cases had higher incidence of nonalcoholic fatty liver disease (7.78 vs 2.99% p=0.042), COPD (18.9 vs 7.80% p=0.001), lung cancer (4.44 vs 0.65% p= 0.009), ICU admissions (81.1 vs 26% p<0.001), and intubation events (84.4 vs 19.5% p<0.001), however there was no difference in alcohol use (21.1 vs 30.6% p=0.083) and alcoholic liver disease (5.56 vs 2.08% p=0.097). Upon univariate Cox analysis, the following LFT parameters were associated with in-hospital death: Bilirubin (p<0.001), AST (p<0.001), ALT (p<0.001). However, alkaline phosphatase (p=0.449) was not associated with the primary endpoint. The iterations of event regression analyses using 500 sequences of LFT thresholds showed the following cutoffs to be significantly associated with in-hospital death (minimally significant values): ALT (281.71 IU/L), AST (120.94 IU/L), bilirubin (2.615 mg/ dL). On the multivariate analysis, while controlling for demographics and cardiopulmonary/ medical comorbidities, the following adjusted hazard ratios were derived for each cutoff: ALT (aHR: 6.43 95%CI 1.85-22.40), AST (aHR: 3.35 95%CI 1.84-6.11), and bilirubin (aHR: 2.77 95%CI 1.15-6.65). Conclusion: The delineated cutoffs for AST, ALT, and bilirubin levels can serve as clinical benchmarks to help determine when a COVID-19 infection poses significant risk. Given this finding, the cutoffs can be used as part of a risk assessment for patients to support early preventative therapies and medical management. (Table Presented)

Gastroenterology ; 162(7):S-1279-S-1280, 2022.
Article in English | EMBASE | ID: covidwho-1967445


Background and Aims: While the relationship between elevated liver enzymes and COVID- 19 related adverse events is well-established, a liver-dependent prognostic model that predicts the risk of death is helpful to accurately stratify admitted patients. In this study, we use a bootstrapping-enhanced method of regression modeling to predict COVID-19 related deaths in admitted patients. Method: This was a single-center, retrospective study. Univariate and multivariate Cox regression analyses were performed using 30-day mortality as the primary endpoint to establish associated hepatic risk factors. Regression-based prediction models were constructed using a series of modeling iterations with an escalating number of categorical terms. Model performance was evaluated using receiver operating characteristic (ROC) curves. Model accuracy was internally validated using bootstrapping-enhanced iterations. Results: 858 patients admitted to hospital with COVID-19 were included. 78 were deceased by 30 days (9.09%). Cox regression (greater than 20 variables) showed the following core variables to be significant: INR (aHR 1.26 95%CI 1.06-1.49), AST (aHR 1.00 95%CI 1.00- 1.00), age (aHR 1.05 95%CI 1.02-1.08), WBC (aHR 1.07 95%CI 1.03-1.11), lung cancer (aHR 3.38 95%CI 1.15-9.90), COPD (aHR 2.26 95%CI 1.21-4.22). Using these core variables and additional categorical terms, the following model iterations were constructed with their respective AUC;model 1 (core only): 0.82 95%CI 0.776-0.82, model 2 (core + demographics): 0.828 95%CI 0.785-0.828, model 3 (prior terms + additional biomarkers): 0.842 95%CI 0.799-0.842, model 4 (prior terms + comorbidities): 0.851 95%CI 0.809-0.851, model 5 (prior terms + life-sustaining therapies): 0.933 95%CI 0.91-0.933, model 6 (prior terms + COVID-19 medications): 0.934 95%CI 0.91-0.934. Model 1 demonstrated the following parameters at 0.91 TPR: 0.54 specificity, 0.17 PPV, 0.98 NPV. Bootstrapped iterations showed the following AUC for the respective models: model 1: 0.82 95%CI 0.765-0.882, model 2 0.828 95%CI 0.764-0.885, model 3 0.842 95%CI 0.779-0.883, model 4: 0.851 95%CI 0.808-0.914, model 5: 0.933 95%CI 0.901-0.957, model 6: 0.934 95%CI 0.901- 0.961. Conclusion: Model 1 displays high prediction performance (AUC >0.8) in both regression-based and bootstrapping-enhanced modeling iterations. Therefore, this model can be adopted for clinical use as a calculator to evaluate the risk of 30-day mortality in patients admitted with COVID-19. (Table Presented)