Myosteatosis is closely associated with sarcopenia and significantly worse outcomes in patients with cirrhosis

Background & aims: Sarcopenia and myosteatosis are common in patients with cirrhosis. This study aimed to determine the prevalence of these muscle changes, their interrelations and their prognostic impact over a 12-month period. Methods: We conducted a prospective multicentre study involving 433 patients. Sarcopenia and myosteatosis were evaluated using computed tomography scans. The 1-year cumulative incidence of relevant events was assessed by competing risk analysis. We used a Fine-Gray model adjusted for known prognostic factors to evaluate the impact of sarcopenia and myosteatosis on mortality, hospitalization, and liver decompensation. Results: At enrolment, 166 patients presented with isolated myosteatosis, 36 with isolated sarcopenia, 135 with combined sarcopenia and myosteatosis and 96 patients showed no muscle changes. The 1-year cumulative incidence of death in patients with either sarcopenia and myosteatosis (13.8%) or isolated myosteatosis (13.4%) was over twice that of patients without muscle changes (5.2%) or with isolated sarcopenia (5.6%). The adjusted sub-hazard ratio for death in patients with muscle changes was 1.36 (95% CI 0.99-1.86, p = 0.058). The cumulative incidence of hospitalization was significantly higher in patients with combined sarcopenia and myosteatosis than in patients without muscle changes (adjusted sub-hazard ratio 1.18, 95% CI 1.04-1.35). The cumulative incidence of liver decompensation was greater in patients with combined sarcopenia and myosteatosis (p = 0.018) and those with isolated sarcopenia (p = 0.046) than in patients without muscle changes. Lastly, we found a strong correlation of function tests and frailty scores with the presence of muscle changes. Conclusions: Myosteatosis, whether alone or combined with sarcopenia, is highly prevalent in patients with cirrhosis and is associated with significantly worse outcomes. The prognostic role of sarcopenia should always be evaluated in relation to the presence of myosteatosis. Impact and implications: This study investigates the prognostic role of muscle changes in patients with cirrhosis. The novelty of this study is its multicentre, prospective nature and the fact that it distinguishes between the impact of individual muscle changes and their combination on prognosis in cirrhosis. This study highlights the prognostic role of myosteatosis, especially when combined with sarcopenia. On the other hand, the relevance of sarcopenia could be mitigated when considered together with myosteatosis. The implication from these findings is that sarcopenia should never be evaluated individually and that myosteatosis may play a dominant role in the prognosis of patients with cirrhosis

Machine Learning to Calculate Heparin Dose in COVID-19 Patients with Active Cancer

To realize a machine learning (ML) model to estimate the dose of low molecular weight heparin to be administered, preventing thromboembolism events in COVID-19 patients with active cancer. Methods: We used a dataset comprising 131 patients with active cancer and COVID-19. We considered five ML models: logistic regression, decision tree, random forest, support vector machine and Gaussian naive Bayes. We decided to implement the logistic regression model for our study. A model with 19 variables was analyzed. Data were randomly split into training (70%) and testing (30%) sets. Model performance was assessed by confusion matrix metrics on the testing data for each model as positive predictive value, sensitivity and F1-score. Results: We showed that the five selected models outperformed classical statistical methods of predictive validity and logistic regression was the most effective, being able to classify with an accuracy of 81%. The most relevant result was finding a patient-proof where python function was able to obtain the exact dose of low weight molecular heparin to be administered and thereby to prevent the occurrence of VTE. Conclusions: The world of machine learning and artificial intelligence is constantly developing. The identification of a specific LMWH dose for preventing VTE in very high-risk populations, such as the COVID-19 and active cancer population, might improve with the use of new training ML-based algorithms. Larger studies are needed to confirm our exploratory results