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Objectives: The aim of this multicenter retrospective study was to investigate safety and efficacy of direct acting antiviral (DAA) treatment in the rare subgroup of patients with HCV/HIV-coinfection and advanced liver cirrhosis on the liver transplant waiting list or after liver transplantation, respectively.
Methods: When contacting 54 German liver centers (including all 23 German liver transplant centers), 12 HCV/HIV-coinfected patients on antiretroviral combination therapy were reported having received additional DAA therapy while being on the waiting list for liver transplantation (patient characteristics: Child-Pugh A (n = 6), B (n = 5), C (n = 1); MELD range 7–21; HCC (n = 2); HCV genotype 1a (n = 8), 1b (n = 2), 4 (n = 2)). Furthermore, 2 HCV/HIV-coinfected patients were denoted having received DAA therapy after liver transplantation (characteristics: HCV genotype 1a (n = 1), 4 (n = 1)).
Results: Applied DAA regimens were SOF/DAC (n = 7), SOF/LDV/RBV (n = 3), SOF/RBV (n = 3), PTV/r/OBV/DSV (n = 1), or PTV/r/OBV/DSV/RBV (n = 1), respectively. All patients achieved SVR 12, in the end. In one patient, HCV relapse occurred after 24 weeks of SOF/DAC therapy; subsequent treatment with 12 weeks PTV/r/OBV/DSV achieved SVR 12. One patient underwent liver transplantation while on DAA treatment. Analysis of liver function revealed either stable parameters or even significant improvement during DAA therapy and in follow-up. MELD scores were found to improve in 9/13 therapies in patients on the waiting list for liver transplantation; in only 2 patients a moderate increase of MELD scores persisted at the end of follow-up.
Conclusion: DAA treatment was safe and highly effective in this nation-wide cohort of patients with HCV/HIV-coinfection awaiting liver transplantation or being transplanted.
Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.