The MELD score in patients awaiting liver transplant: strengths and weaknesses.

Adoption of the Model for End-stage Liver Disease (MELD) to select and prioritize patients for liver transplantation represented a turning point in organ allocation. Prioritization of transplant recipients switched from time accrued on the waiting list to the principle of "sickest first". The MELD score incorporates three simple laboratory parameters (serum creatinine and bilirubin, and INR for prothrombin time) and stratifies patients according to their disease severity in an objective and continuous ranking scale. Concordance statistics have demonstrated its high accuracy in stratifying patients according to their risk of dying in the short-term (three months). Further validations of MELD as a predictor of survival at various temporal end-points have been obtained in independent patient cohorts with a broad spectrum of chronic liver disease. The MELD-based liver graft allocation policy has led to a reduction in waitlist new registrations and mortality, shorter waiting times, and an increase in transplants, without altering overall graft and patient survival rates after transplantation. MELD limitations are related either to the inter-laboratory variability of the parameters included in the score, or to the inability of the formula to predict mortality accurately in specific settings. For some conditions, such as hepatocellular carcinoma, widely accepted MELD corrections have been devised. For others, such as persistent ascites and hyponatremia, attempts to improve MELD's predicting power are currently underway, but await definite validation

Feature Selection for SUNNY: a Study on the Algorithm Selection Library

International audienceGiven a collection of algorithms, the Algorithm Selection (AS) problem consists in identifying which of them is the best one for solving a given problem. The selection depends on a set of numerical features that characterize the problem to solve. In this paper we show the impact of feature selection techniques on the performance of the SUNNY algorithm selector, taking as reference the benchmarks of the AS library (ASlib). Results indicate that a handful of features is enough to reach similar, if not better, performance of the original SUNNY approach that uses all the available features. We also present sunny-as: a tool for using SUNNY on a generic ASlib scenario

SUNNY for Algorithm Selection: A Preliminary Study

National audienceGiven a collection of algorithms, the Algorithm Selection (AS) problem consists in identifying which of them is the best one for solving a given problem. In this paper we show how we adapted the algorithm selector SUNNY, originally tailored for constraint solving, to deal with general AS problems. Preliminary investigations based on the AS Library benchmarks already show some promising results: for some scenarios SUNNY is able to outperform AS state-of-the-art approaches

Years of life that could be saved from prevention of hepatocellular carcinoma

BACKGROUND: Hepatocellular carcinoma (HCC) causes premature death and loss of life expectancy worldwide. Its primary and secondary prevention can result in a significant number of years of life saved. AIM: To assess how many years of life are lost after HCC diagnosis. METHODS: Data from 5346 patients with first HCC diagnosis were used to estimate lifespan and number of years of life lost after tumour onset, using a semi-parametric extrapolation having as reference an age-, sex- and year-of-onset-matched population derived from national life tables. RESULTS: Between 1986 and 2014, HCC lead to an average of 11.5 years-of-life lost for each patient. The youngest age-quartile group (18-61 years) had the highest number of years-of-life lost, representing approximately 41% of the overall benefit obtainable from prevention. Advancements in HCC management have progressively reduced the number of years-of-life lost from 12.6 years in 1986-1999, to 10.7 in 2000-2006 and 7.4 years in 2007-2014. Currently, an HCC diagnosis when a single tumour <2 cm results in 3.7 years-of-life lost while the diagnosis when a single tumour 65 2 cm or 2/3 nodules still within the Milan criteria, results in 5.0 years-of-life lost, representing the loss of only approximately 5.5% and 7.2%, respectively, of the entire lifespan from birth. CONCLUSIONS: Hepatocellular carcinoma occurrence results in the loss of a considerable number of years-of-life, especially for younger patients. In recent years, the increased possibility of effectively treating this tumour has improved life expectancy, thus reducing years-of-life lost

Development and Validation of a New Prognostic System for Patients with Hepatocellular Carcinoma

Background: Prognostic assessment in patients with hepatocellular carcinoma (HCC) remains controversial. Using the Italian Liver Cancer (ITA.LI.CA) database as a training set, we sought to develop and validate a new prognostic system for patients with HCC. Methods and Findings: Prospective collected databases from Italy (training cohort, n = 3,628; internal validation cohort, n = 1,555) and Taiwan (external validation cohort, n = 2,651) were used to develop the ITA.LI.CA prognostic system. We first defined ITA.LI.CA stages (0, A, B1, B2, B3, C) using only tumor characteristics (largest tumor diameter, number of nodules, intra- and extrahepatic macroscopic vascular invasion, extrahepatic metastases). A parametric multivariable survival model was then used to calculate the relative prognostic value of ITA.LI.CA tumor stage, Eastern Cooperative Oncology Group (ECOG) performance status, Child–Pugh score (CPS), and alpha-fetoprotein (AFP) in predicting individual survival. Based on the model results, an ITA.LI.CA integrated prognostic score (from 0 to 13 points) was constructed, and its prognostic power compared with that of other integrated systems (BCLC, HKLC, MESIAH, CLIP, JIS). Median follow-up was 58 mo for Italian patients (interquartile range, 26–106 mo) and 39 mo for Taiwanese patients (interquartile range, 12–61 mo). The ITA.LI.CA integrated prognostic score showed optimal discrimination and calibration abilities in Italian patients. Observed median survival in the training and internal validation sets was 57 and 61 mo, respectively, in quartile 1 (ITA.LI.CA score ≤ 1), 43 and 38 mo in quartile 2 (ITA.LI.CA score 2–3), 23 and 23 mo in quartile 3 (ITA.LI.CA score 4–5), and 9 and 8 mo in quartile 4 (ITA.LI.CA score > 5). Observed and predicted median survival in the training and internal validation sets largely coincided. Although observed and predicted survival estimations were significantly lower (log-rank test, p < 0.001) in Italian than in Taiwanese patients, the ITA.LI.CA score maintained very high discrimination and calibration features also in the external validation cohort. The concordance index (C index) of the ITA.LI.CA score in the internal and external validation cohorts was 0.71 and 0.78, respectively. The ITA.LI.CA score’s prognostic ability was significantly better (p < 0.001) than that of BCLC stage (respective C indexes of 0.64 and 0.73), CLIP score (0.68 and 0.75), JIS stage (0.67 and 0.70), MESIAH score (0.69 and 0.77), and HKLC stage (0.68 and 0.75). The main limitations of this study are its retrospective nature and the intrinsically significant differences between the Taiwanese and Italian groups. Conclusions: The ITA.LI.CA prognostic system includes both a tumor staging—stratifying patients with HCC into six main stages (0, A, B1, B2, B3, and C)—and a prognostic score—integrating ITA.LI.CA tumor staging, CPS, ECOG performance status, and AFP. The ITA.LI.CA prognostic system shows a strong ability to predict individual survival in European and Asian populations

Clinical patterns of hepatocellular carcinoma in nonalcoholic fatty liver disease: A multicenter prospective study

107noNonalcoholic fatty liver disease (NAFLD) represents the hepatic manifestation of metabolic syndrome and may evolve into hepatocellular carcinoma (HCC). Only scanty clinical information is available on HCC in NAFLD. The aim of this multicenter observational prospective study was to assess the clinical features of patients with NAFLD-related HCC (NAFLD-HCC) and to compare them to those of hepatitis C virus (HCV)-related HCC. A total of 756 patients with either NAFLD (145) or HCV-related chronic liver disease (611) were enrolled in secondary care Italian centers. Survival was modeled according to clinical parameters, lead-time bias, and propensity analysis. Compared to HCV, HCC in NAFLD patients had a larger volume, showed more often an infiltrative pattern, and was detected outside specific surveillance. Cirrhosis was present in only about 50% of NAFLD-HCC patients, in contrast to the near totality of HCV-HCC. Regardless of tumor stage, survival was significantly shorter (P = 0.017) in patients with NAFLD-HCC, 25.5 months (95% confidence interval 21.9-29.1), than in those with HCV-HCC, 33.7 months (95% confidence interval 31.9-35.4). To eliminate possible confounders, a propensity score analysis was performed, which showed no more significant difference between the two groups. Additionally, analysis of patients within Milan criteria submitted to curative treatments did not show any difference in survival between NAFLD-HCC and HCV-HCC (respectively, 38.6 versus 41.0 months, P = nonsignificant) Conclusions: NAFLD-HCC is more often detected at a later tumor stage and could arise also in the absence of cirrhosis, but after patient matching, it has a similar survival rate compared to HCV infection; a future challenge will be to identify patients with NAFLD who require more stringent surveillance in order to offer the most timely and effective treatment. (Hepatology 2016;63:827-838)openopenPiscaglia F.; Svegliati-Baroni G.; Barchetti A.; Pecorelli A.; Marinelli S.; Tiribelli C.; Bellentani S.; Bernardi M.; Biselli M.; Caraceni P.; Domenicali M.; Garuti F.; Gramenzi A.; Lenzi B.; Magalotti D.; Cescon M.; Ravaioli M.; Del Poggio P.; Olmi S.; Rapaccini G.L.; Balsamo C.; Di Nolfo M.A.; Vavassori E.; Alberti A.; Benvegnau L.; Gatta A.; Giacomin A.; Vanin V.; Pozzan C.; Maddalo G.; Giampalma E.; Cappelli A.; Golfieri R.; Mosconi C.; Renzulli M.; Roselli P.; Dell'isola S.; Ialungo A.M.; Risso D.; Marenco S.; Sammito G.; Bruzzone L.; Bosco G.; Grieco A.; Pompili M.; Rinninella E.; Siciliano M.; Chiaramonte M.; Guarino M.; Camma C.; Maida M.; Costantino A.; Barcellona M.R.; Schiada L.; Gemini S.; Lanzi A.; Stefanini G.F.; Dall'aglio A.C.; Cappa F.M.; Suzzi A.; Mussetto A.; Treossi O.; Missale G.; Porro E.; Mismas V.; Vivaldi C.; Bolondi L.; Zoli M.; Granito A.; Malagotti D.; Tovoli F.; Trevisani F.; Venerandi L.; Brandi G.; Cucchetti A.; Bugianesi E.; Vanni E.; Mezzabotta L.; Cabibbo G.; Petta S.; Fracanzani A.; Fargion S.; Marra F.; Fani B.; Biasini E.; Sacco R.; Morisco F.; Caporaso N.; Colombo M.; D'ambrosio R.; Croce L.S.; Patti R.; Giannini E.G.; Loria P.; Lonardo A.; Baldelli E.; Miele L.; Farinati F.; Borzio M.; Dionigi E.; Soardo G.; Caturelli E.; Ciccarese F.; Virdone R.; Affronti A.; Foschi F.G.; Borzio F.Piscaglia, F.; Svegliati-Baroni, G.; Barchetti, A.; Pecorelli, A.; Marinelli, S.; Tiribelli, C.; Bellentani, S.; Bernardi, M.; Biselli, M.; Caraceni, P.; Domenicali, M.; Garuti, F.; Gramenzi, A.; Lenzi, B.; Magalotti, D.; Cescon, M.; Ravaioli, M.; Del Poggio, P.; Olmi, S.; Rapaccini, G. L.; Balsamo, C.; Di Nolfo, M. A.; Vavassori, E.; Alberti, A.; Benvegnau, L.; Gatta, A.; Giacomin, A.; Vanin, V.; Pozzan, C.; Maddalo, G.; Giampalma, E.; Cappelli, A.; Golfieri, R.; Mosconi, C.; Renzulli, M.; Roselli, P.; Dell'Isola, S.; Ialungo, A. M.; Risso, D.; Marenco, S.; Sammito, G.; Bruzzone, L.; Bosco, G.; Grieco, A.; Pompili, M.; Rinninella, E.; Siciliano, M.; Chiaramonte, M.; Guarino, M.; Camma, C.; Maida, M.; Costantino, A.; Barcellona, M. R.; Schiada, L.; Gemini, S.; Lanzi, A.; Stefanini, G. F.; Dall'Aglio, A. C.; Cappa, F. M.; Suzzi, A.; Mussetto, A.; Treossi, O.; Missale, G.; Porro, E.; Mismas, V.; Vivaldi, C.; Bolondi, L.; Zoli, M.; Granito, A.; Malagotti, D.; Tovoli, F.; Trevisani, F.; Venerandi, L.; Brandi, G.; Cucchetti, A.; Bugianesi, E.; Vanni, E.; Mezzabotta, L.; Cabibbo, G.; Petta, S.; Fracanzani, A.; Fargion, S.; Marra, F.; Fani, B.; Biasini, E.; Sacco, R.; Morisco, F.; Caporaso, N.; Colombo, M.; D'Ambrosio, R.; Croce, L. S.; Patti, R.; Giannini, E. G.; Loria, P.; Lonardo, A.; Baldelli, E.; Miele, L.; Farinati, F.; Borzio, M.; Dionigi, E.; Soardo, G.; Caturelli, E.; Ciccarese, F.; Virdone, R.; Affronti, A.; Foschi, F. G.; Borzio, F

Discutindo a educação ambiental no cotidiano escolar: desenvolvimento de projetos na escola formação inicial e continuada de professores

A presente pesquisa buscou discutir como a Educação Ambiental (EA) vem sendo trabalhada, no Ensino Fundamental e como os docentes desta escola compreendem e vem inserindo a EA no cotidiano escolar., em uma escola estadual do município de Tangará da Serra/MT, Brasil. Para tanto, realizou-se entrevistas com os professores que fazem parte de um projeto interdisciplinar de EA na escola pesquisada. Verificou-se que o projeto da escola não vem conseguindo alcançar os objetivos propostos por: desconhecimento do mesmo, pelos professores; formação deficiente dos professores, não entendimento da EA como processo de ensino-aprendizagem, falta de recursos didáticos, planejamento inadequado das atividades. A partir dessa constatação, procurou-se debater a impossibilidade de tratar do tema fora do trabalho interdisciplinar, bem como, e principalmente, a importância de um estudo mais aprofundado de EA, vinculando teoria e prática, tanto na formação docente, como em projetos escolares, a fim de fugir do tradicional vínculo “EA e ecologia, lixo e horta”.Facultad de Humanidades y Ciencias de la Educació

Hepatocellular carcinoma surveillance: an open question

No abstrac

Feature selection for SUNNY: A study on the algorithm selection library

Given a collection of algorithms, the Algorithm Selection (AS) problem consists in identifying which of them is the best one for solving a given problem. The selection depends on a set of numerical features that characterize the problem to solve. In this paper we show the impact of feature selection techniques on the performance of the SUNNY algorithm selector, taking as reference the benchmarks of the AS library (ASlib). Results indicate that a handful of features is enough to reach similar, if not better, performance of the original SUNNY approach that uses all the available features. We also present sunny-as: a tool for using SUNNY on a generic ASlib scenario