HCC risk stratification after cure of hepatitis C in patients with compensated advanced chronic liver disease

Background&Aims: Hepatocellular carcinoma (HCC) is a main cause of morbidity and mortality in patients with advanced chronic liver disease (ACLD) due to chronic hepatitis C and who have achieved sustained virologic response (SVR). We elaborated risk stratification algorithms for de-novo-HCC-development after SVR and validated them in an independent cohort. Methods: Derivation cohort: 527 patients with pre-treatment ACLD and SVR to interferon-free therapy were evaluated for de-novo-HCC-development. Among others, alpha-fetoprotein (AFP) and non-invasive surrogates of portal hypertension including liver stiffness measurement (LSM) were assessed pre-/post-treatment. Validation cohort: 1500 patients with compensated ACLD (cACLD) from other European centers. Results: During a median follow-up (FU) of 41 months, 22/475 cACLD (4.6%) (1.45/100patient-years)vs.12/52 decompensated patients (23.1%, 7.00/100patient-years, p<0.001) developed de-novo-HCC. Since decompensated patients were at substantial HCC-risk, we focused on cACLD for all further analyses. In cACLD, post-treatment-values showed a higher discriminative ability for patients with/without de-novo-HCC-development during FU than pre-treatment-values or absolute/relative changes. Models based on post-treatment AFP≥4.6ngxmL-1-3points, alcohol consumption (males:>30g/d/females:>20g/d)-2points (optional), age≥59year-2points, LSM≥19.0kPa-1point, and albumin<42gxL-1-1point, accurately predicted de-novo-HCC-development (bootstrapped Harrel’s C with and without considering alcohol:0.893 and 0.836). Importantly, these parameters also provided independent prognostic information in competing risk analysis and accurately stratified patients into low-(0-3points; ≈2/3 of patients) and high-risk (≥4points; ≈1/3) groups in the derivation (algorithm with alcohol consumption; 4-year HCC-risk:0%vs.16.5%) and validation (3.3%/17.5%) cohorts. An alternative approach based on age/alcohol (optional)/FU-LSM/FU-albumin (i.e., without FU-AFP) also showed a robust performance. Conclusions: Simple algorithms based on post-treatment age/albumin/LSM, and optionally, AFP and alcohol, accurately stratified de-novo-HCC-risk in cACLD patients with SVR. Approximately 2/3 were identified as having an HCC-risk <1%/y in both the derivation and validation cohort, thereby clearly falling below the cost-effectiveness threshold for HCC-surveillance. LAY SUMMARY: Simple algorithms based on age, alcohol consumption, results of blood tests (albumin and α-fetoprotein), as well as liver stiffness measurement after the end of hepatitis C treatment identify a large proportion (approximately 2/3) of patients with advanced but still asymptomatic liver disease who are at very low risk (<1%/year) of liver cancer development, and thus, might not need to undergo 6-monthly liver ultrasound

Assessment of portal hypertension severity using machine learning models in patients with compensated cirrhosis

Background &amp; Aims: In individuals with compensated advanced chronic liver disease (cACLD), the severity of portal hypertension (PH) determines the risk of decompensation. Invasive measurement of the hepatic venous pressure gradient (HVPG) is the diagnostic gold standard for PH. We evaluated the utility of machine learning models (MLMs) based on standard laboratory parameters to predict the severity of PH in individuals with cACLD. Methods: A detailed laboratory workup of individuals with cACLD recruited from the Vienna cohort (NCT03267615) was utilised to predict clinically significant portal hypertension (CSPH, i.e., HVPG ≥10 mmHg) and severe PH (i.e., HVPG ≥16 mmHg). The MLMs were then evaluated in individual external datasets and optimised in the merged cohort. Results: Among 1,232 participants with cACLD, the prevalence of CSPH/severe PH was similar in the Vienna (n = 163, 67.4%/35.0%) and validation (n = 1,069, 70.3%/34.7%) cohorts. The MLMs were based on 3 (3P: platelet count, bilirubin, international normalised ratio) or 5 (5P: +cholinesterase, +gamma-glutamyl transferase, +activated partial thromboplastin time replacing international normalised ratio) laboratory parameters. The MLMs performed robustly in the Vienna cohort. 5P-MLM had the best AUCs for CSPH (0.813) and severe PH (0.887) and compared favourably to liver stiffness measurement (AUC: 0.808). Their performance in external validation datasets was heterogeneous (AUCs: 0.589-0.887). Training on the merged cohort optimised model performance for CSPH (AUCs for 3P and 5P: 0.775 and 0.789, respectively) and severe PH (0.737 and 0.828, respectively). Conclusions: Internally trained MLMs reliably predicted PH severity in the Vienna cACLD cohort but exhibited heterogeneous results on external validation. The proposed 3P/5P online tool can reliably identify individuals with CSPH or severe PH, who are thus at risk of hepatic decompensation. Impact and implications: We used machine learning models based on widely available laboratory parameters to develop a non-invasive model to predict the severity of portal hypertension in individuals with compensated cirrhosis, who currently require invasive measurement of hepatic venous pressure gradient. We validated our findings in a large multicentre cohort of individuals with advanced chronic liver disease (cACLD) of any cause. Finally, we provide a readily available online calculator, based on 3 (platelet count, bilirubin, international normalised ratio) or 5 (platelet count, bilirubin, activated partial thromboplastin time, gamma-glutamyltransferase, choline-esterase) widely available laboratory parameters, that clinicians can use to predict the likelihood of their patients with cACLD having clinically significant or severe portal hypertension

Electric field and grain size dependence of Meyer–Neldel energy in C60 films

AbstractMeyer–Neldel rule for charge carrier mobility measured in C60-based organic field-effect transistors (OFETs) at different applied source drain voltages and at different morphologies of semiconducting fullerene films was systematically studied. A decrease in the Meyer–Neldel energy EMN from 36meV to 32meV was observed with changing electric field in the channel. Concomitantly a decrease from 34meV to 21meV was observed too by increasing the grain size and the crystallinity of the active C60 layer in the device. These empiric findings are in agreement with the hopping-transport model for the temperature dependent charge carrier mobility in organic semiconductors with a Gaussian density of states (DOS). Experimental results along with theoretical descriptions are presented

Molecular structure of the substrate-induced thin-film phase of tetracene

We present a combined experimental and theoretical study to solve the unit-cell and molecular arrangement of the tetracene thin film (TF) phase. TF phases, also known as substrate induced phases (SIP), are polymorphs that exist at interfaces and decisively impact the functionality of organic thin films, e.g., in a transistor channel, but also change the optical spectra due to the different molecular packing. As SIPs only exist in textured ultrathin films, their structure determination remains challenging compared to bulk materials. Here, we use grazing incidence Xray diffraction and atomistic simulations to extract the TF unit-cell parameters of tetracene together with the atomic positions within the unit-cell

Effect of source-drain electric field on the Meyer–Neldel energy in organic field effect transistors

We studied the influence of the lateral source-drain electric field on the Meyer–Neldel phenomenon observed for the charge mobility measured in C60-based organic field effect transistors (OFETs). It was found that the characteristic Meyer-Neldel temperature notably shifts with applied source drain electric field. This finding is in excellent agreement with an analytic model recently extended to account also for the field dependence of the charge carrier mobility in materials with a Gaussian density-of-states distribution. As the theoretical model to predict charge carrier mobility is not limited to zero-electric field, it provides a more accurate evaluation of energetic disorder parameters from experimental data measured at arbitrary electric fields