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- Ihne-Schubert, Sandra Michaela, et al.
(författare)
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Cardio-Hepatic Interaction in Cardiac Amyloidosis
- 2024
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Ingår i: Journal of Clinical Medicine. - 2077-0383. ; 13:5
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Tidskriftsartikel (refereegranskat)abstract
- Background: Congestion is associated with poor prognosis in cardiac amyloidosis (CA). The cardio-hepatic interaction and the prognostic impact of secondary liver affection by cardiac congestion in CA are poorly understood and require further characterisation. Methods: Participants of the amyloidosis cohort study AmyKoS at the Interdisciplinary Amyloidosis Centre of Northern Bavaria with proven transthyretin (ATTR-CA) and light chain CA (AL-CA) underwent serial work-up including laboratory tests, echocardiography, and in-depth hepatic assessment by vibration-controlled transient elastography (VCTE) and 13C-methacetin breath test. Results: In total, 74 patients with AL-CA (n = 17), ATTR-CA (n = 26) and the controls (n = 31) were analysed. ATTR-CA patients showed decreased microsomal liver function expressed by maximal percentage of dose rate (PDRpeak) related to hepatic congestion. Reduced PDRpeak in AL-CA could result from altered pharmacokinetics due to changed hepatic blood flow. Liver stiffness as a combined surrogate of chronic liver damage and congestion was identified as a predictor of all-cause mortality. Statistical modelling of the cardio-hepatic interaction revealed septum thickness, NT-proBNP and PDRpeak as predictors of liver stiffness in both CA subtypes; dilatation of liver veins and the fibrosis score FIB-4 were only significant for ATTR-CA. Conclusions: Non-invasive methods allow us to characterise CA-associated hepatic pathophysiology. Liver stiffness might be promising for risk stratification in CA.
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| 2. |
- Mcteer, Matthew, et al.
(författare)
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Machine learning approaches to enhance diagnosis and staging of patients with MASLD using routinely available clinical information
- 2024
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Ingår i: PLOS ONE. - : PUBLIC LIBRARY SCIENCE. - 1932-6203. ; 19:2
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Tidskriftsartikel (refereegranskat)abstract
- Aims Metabolic dysfunction Associated Steatotic Liver Disease (MASLD) outcomes such as MASH (metabolic dysfunction associated steatohepatitis), fibrosis and cirrhosis are ordinarily determined by resource-intensive and invasive biopsies. We aim to show that routine clinical tests offer sufficient information to predict these endpoints.Methods Using the LITMUS Metacohort derived from the European NAFLD Registry, the largest MASLD dataset in Europe, we create three combinations of features which vary in degree of procurement including a 19-variable feature set that are attained through a routine clinical appointment or blood test. This data was used to train predictive models using supervised machine learning (ML) algorithm XGBoost, alongside missing imputation technique MICE and class balancing algorithm SMOTE. Shapley Additive exPlanations (SHAP) were added to determine relative importance for each clinical variable.Results Analysing nine biopsy-derived MASLD outcomes of cohort size ranging between 5385 and 6673 subjects, we were able to predict individuals at training set AUCs ranging from 0.719-0.994, including classifying individuals who are At-Risk MASH at an AUC = 0.899. Using two further feature combinations of 26-variables and 35-variables, which included composite scores known to be good indicators for MASLD endpoints and advanced specialist tests, we found predictive performance did not sufficiently improve. We are also able to present local and global explanations for each ML model, offering clinicians interpretability without the expense of worsening predictive performance.Conclusions This study developed a series of ML models of accuracy ranging from 71.9-99.4% using only easily extractable and readily available information in predicting MASLD outcomes which are usually determined through highly invasive means.
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