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.

Stratification of COVID-19 Patients with Moderate-to-Severe Hypoxemic Respiratory Failure for Response to High-Flow Nasal Cannula: A Retrospective Observational Study.

Background and Objectives: In patients with COVID-19, high-flow nasal cannula (HFNC) and continuous positive airway pressure (CPAP) are widely applied as initial treatments for moderate-to-severe acute hypoxemic respiratory failure. The aim of the study was to assess which respiratory supports improve 28-day mortality and to identify a predictive index of treatment response. Materials and Methods: This is a single-center retrospective observational study including 159 consecutive adult patients with COVID-19 and moderate-to-severe hypoxemic acute respiratory failure. Results: A total of 159 patients (82 in the CPAP group and 77 in the HFNC group) were included in the study. Mortality within 28 days was significantly lower with HFNC compared to CPAP (16.8% vs. 50%), while ICU admission and tracheal intubation within 28 days were significantly higher with CPAP compared to HFNC treatment (32% vs. 13%). We identified an index for survival in HFNC by including three variables easily available at admission (LDH, age, and respiratory rate) and the PaO2/FiO2 ratio at 48 h. The index showed high discrimination for survival with an AUC of 0.88, a negative predictive value of 86%, and a positive predictive value of 95%. Conclusions: Treatment with HFNC appears to be associated with greater survival and fewer ICU admission than CPAP. LDH, respiratory rate, age, and PaO2/FiO2 at 48 h were independently associated with survival and an index based on these variables allows for the prediction of treatment success and the assessment of patient allocation to the appropriate intensity of care after 48 h. Further research is warranted to determine effects on other outcomes and to assess the performance of the index in larger cohorts.

Fatty Liver as Potential Biomarker of Atherosclerotic Damage in Familial Combined Hyperlipidemia.

Familial combined hyperlipidemia (FCH) is a very common inherited lipid disorder, characterized by a high risk of developing cardiovascular (CV) disease and metabolic complications, including insulin resistance (IR) and type 2 diabetes mellitus (T2DM). The prevalence of non-alcoholic fatty liver disease (NAFLD) is increased in FCH patients, especially in those with IR or T2DM. However, it is unknown how precociously metabolic and cardiovascular complications appear in FCH patients. We aimed to evaluate the prevalence of NAFLD and to assess CV risk in newly diagnosed insulin-sensitive FCH patients. From a database including 16,504 patients, 110 insulin-sensitive FCH patients were selected by general practitioners and referred to the Lipid Center. Lipid profile, fasting plasma glucose and insulin were determined by standard methods. Based on the results of the hospital screening, 96 patients were finally included (mean age 52.2 ± 9.8 years; 44 males, 52 females). All participants underwent carotid ultrasound to assess carotid intima media thickness (cIMT), presence or absence of plaque, and pulse wave velocity (PWV). Liver steatosis was assessed by both hepatic steatosis index (HSI) and abdomen ultrasound (US). Liver fibrosis was non-invasively assessed by transient elastography (TE) and by fibrosis 4 score (FIB-4) index. Carotid plaque was found in 44 out of 96 (45.8%) patients, liver steatosis was found in 68 out of 96 (70.8%) and in 41 out of 96 (42.7%) patients by US examination and HSI, respectively. Overall, 72 subjects (75%) were diagnosed with steatosis by either ultrasound or HSI, while 24 (25%) had steatosis excluded (steatosis excluded by both US and HSI). Patients with liver steatosis had a significantly higher body mass index (BMI) compared to those without (p < 0.05). Steatosis correlated with fasting insulin (p < 0.05), liver stiffness (p < 0.05), BMI (p < 0.001), and inversely with high-density lipoprotein cholesterol (p < 0.05). Fibrosis assessed by TE was significantly associated with BMI (p < 0.001) and cIMT (p < 0.05); fibrosis assessed by FIB-4 was significantly associated with sex (p < 0.05), cIMT (p < 0.05), and atherosclerotic plaque (p < 0.05). The presence of any grade of liver fibrosis was significantly associated with atherosclerotic plaque in the multivariable model, independent of alcohol habit, sex, HSI score, and liver stiffness by TE (OR 6.863, p < 0.001). In our cohort of newly diagnosed, untreated, insulin-sensitive FCH patients we found a high prevalence of liver steatosis. Indeed, the risk of atherosclerotic plaque was significantly increased in patients with liver fibrosis, suggesting a possible connection between liver disease and CV damage in dyslipidemic patients beyond the insulin resistance hypothesis.

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.