Blood ammonia levels in liver cirrhosis: a clue for the presence of portosystemic collateral veins

<p>Abstract</p> <p>Background</p> <p>Portal hypertension leads to the formation of portosystemic collateral veins in liver cirrhosis. The resulting shunting is responsible for the development of portosystemic encephalopathy. Although ammonia plays a certain role in determining portosystemic encephalopathy, the venous ammonia level has not been found to correlate with the presence or severity of this entity. So, it has become partially obsolete. Realizing the need for non-invasive markers mirroring the presence of esophageal varices in order to reduce the number of endoscopy screening, we came back to determine whether there was a correlation between blood ammonia concentrations and the detection of portosystemic collateral veins, also evaluating splenomegaly, hypersplenism (thrombocytopenia) and the severity of liver cirrhosis.</p> <p>Methods</p> <p>One hundred and fifty three consecutive patients with hepatic cirrhosis of various etiologies were recruited to participate in endoscopic and ultrasonography screening for the presence of portosystemic collaterals mostly esophageal varices, but also portal hypertensive gastropathy and large spontaneous shunts.</p> <p>Results</p> <p>Based on Child-Pugh classification, the median level of blood ammonia was 45 mcM/L in 64 patients belonging to class A, 66 mcM/L in 66 patients of class B and 108 mcM/L in 23 patients of class C respectively (p < 0.001).</p> <p>The grade of esophageal varices was concordant with venous ammonia levels (rho 0.43, p < 0.001). The best area under the curve was given by ammonia concentrations, i, e., 0.78, when comparing areas of ammonia levels, platelet count and spleen longitudinal diameter at ultrasonography. Ammonia levels predicted hepatic decompensation and ascites presence (Odds Ratio 1.018, p < 0.001).</p> <p>Conclusion</p> <p>Identifying cirrhotic patients with high blood ammonia concentrations could be clinically useful, as high levels would lead to suspicion of being in presence of collaterals, in clinical practice of esophageal varices, and pinpoint those patients requiring closer follow-up and endoscopic screening.</p

Machine learning for genetic prediction of psychiatric disorders: a systematic review

Machine learning methods have been employed to make predictions in psychiatry from genotypes, with the potential to bring improved prediction of outcomes in psychiatric genetics; however, their current performance is unclear. We aim to systematically review machine learning methods for predicting psychiatric disorders from genetics alone and evaluate their discrimination, bias and implementation. Medline, PsycInfo, Web of Science and Scopus were searched for terms relating to genetics, psychiatric disorders and machine learning, including neural networks, random forests, support vector machines and boosting, on 10 September 2019. Following PRISMA guidelines, articles were screened for inclusion independently by two authors, extracted, and assessed for risk of bias. Overall, 63 full texts were assessed from a pool of 652 abstracts. Data were extracted for 77 models of schizophrenia, bipolar, autism or anorexia across 13 studies. Performance of machine learning methods was highly varied (0.48–0.95 AUC) and differed between schizophrenia (0.54–0.95 AUC), bipolar (0.48–0.65 AUC), autism (0.52–0.81 AUC) and anorexia (0.62–0.69 AUC). This is likely due to the high risk of bias identified in the study designs and analysis for reported results. Choices for predictor selection, hyperparameter search and validation methodology, and viewing of the test set during training were common causes of high risk of bias in analysis. Key steps in model development and validation were frequently not performed or unreported. Comparison of discrimination across studies was constrained by heterogeneity of predictors, outcome and measurement, in addition to sample overlap within and across studies. Given widespread high risk of bias and the small number of studies identified, it is important to ensure established analysis methods are adopted. We emphasise best practices in methodology and reporting for improving future studies