A multicenter comparison between Child Pugh and ALBI scores in patients treated with sorafenib for hepatocellular carcinoma

Background & aims: The ALBI grade was proposed as an objective means to evaluate liver function in patients with Hepatocellular Carcinoma (HCC). ALBI grade 1 vs 2 were proposed as stratification factors within the Child Pugh (CP) A class. However, the original publication did not provide comparison with the sub-classification by points (5 to 15) within the CP classification. Methods: We retrospectively analyzed data from patients treated with sorafenib for HCC from 17 centers in United Kingdom and France. Overall survival (OS) was analyzed with the Kaplan-Meier method and a Cox regression model. Discriminatory abilities of the classifications were assessed with the log likelihood ratio, Harrell’s C statistics and Akaike information criterion. Results: Data from 1,019 patients were collected, of which 905 could be assessed for both scores. 92% of ALBI grade 1 were CP A5 while ALBI 2 included a broad range of CP scores of which 44% were CP A6. Median OS was 10.2, 7.0 and 3.6 months for CP scores A5, A6 and >A6, respectively (P<0.001), Hazard Ratio (HR)=1.60 (95%CI: 1.35-1.89, P<0.001) for A6 vs A5. Median OS was 10.9, 6.6 and 3.0 months for ALBI grade 1, 2 and 3, respectively (P<0.001), HR=1.68 (1.43-1.97, P<0.001) for grade 2 vs 1. Discriminatory abilities of CP and ALBI were similar in the CP A population, but better for CP in the overall population. Conclusions: Our findings support the use CP class A as an inclusion criterion, and ALBI as a stratification factor in trials of systemic therapy

Apport de la régression logistique dans un système à base de cas (une application à la prédiction des inscriptions sur liste d'attente de greffe rénale)

Les systèmes à base de cas (SBC) utilisent l'expérience acquise pour résoudre des problèmes. Ils rapprochent à l'aide de fonctions de similarité les nouveaux cas à d'anciens cas déjà résolus. L'objectif de ce travail est d'optimiser à partir d'un modèle de régression logistique (RL) le calcul de similarité d'un SBC utilisé pour prédire lles inscriptions sur la liste d'attente de greffe rénale. L'étude consiste à analyser la base des cas par RL et à utiliser ces résultats pour la construction du SBC: pondération des cas et pondération des attributs décrivant les cas. Nous comparons ensuite les prédictions obtenues par la RL et le SBC seul ou en association avec la RL. Les performances de la RL et des différentes constructions du SBC sont très proches, toutefois les meilleurs résultats sont retrouvés pour le SBC avec pondération des attributs et des cas. Cette étude montre que la RL peut servir à optimiser le calcul de similarité et améliorer ainsi les performances d'un SBC.Case-based reasoning (CBR) systems use experience and similarity functions to solve problems by matching new cases to old cases already solved. Our main objective is to optimize by logistic models (LM) similarity easures of a CBR system used to predict access to the french waiting list of kidney transplantation. The study plan is to analyze the case database by a LM and to use model results to build the CBR system: weighting of cases and weighting of attributes describing the cases. Then, we compare predictions obtained by the LM, the standalone CBR system and the CBR system coupled with LM. Performances of the LM and the different constructions of the CBR system are very close, but the best results are found for the CBR system with weighting of attributes and cases. This study shows that LM can be used to optimize similarity algorithms and thus to improve performance of CBR systems.RENNES1-BU Santé (352382103) / SudocSudocFranceF

Personalised dosimetry for SIRT: new standard or bridge to surgical resection? - Authors’ reply

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Improving case-based reasoning systems by combining k-nearest neighbour algorithm with logistic regression in the prediction of patients' registration on the renal transplant waiting list.

Case-based reasoning (CBR) is an emerging decision making paradigm in medical research where new cases are solved relying on previously solved similar cases. Usually, a database of solved cases is provided, and every case is described through a set of attributes (inputs) and a label (output). Extracting useful information from this database can help the CBR system providing more reliable results on the yet to be solved cases.We suggest a general framework where a CBR system, viz. K-Nearest Neighbour (K-NN) algorithm, is combined with various information obtained from a Logistic Regression (LR) model, in order to improve prediction of access to the transplant waiting list.LR is applied, on the case database, to assign weights to the attributes as well as the solved cases. Thus, five possible decision making systems based on K-NN and/or LR were identified: a standalone K-NN, a standalone LR and three soft K-NN algorithms that rely on the weights based on the results of the LR. The evaluation was performed under two conditions, either using predictive factors known to be related to registration, or using a combination of factors related and not related to registration.The results show that our suggested approach, where the K-NN algorithm relies on both weighted attributes and cases, can efficiently deal with non relevant attributes, whereas the four other approaches suffer from this kind of noisy setups. The robustness of this approach suggests interesting perspectives for medical problem solving tools using CBR methodology

Negative phase 3 study of Y-90 microspheres versus sorafenib in HCC

International audienceCorrespondence: It is with great interest that we read the report of the SARAH trial by Valérie Vilgrain and colleagues, recently published The Lancet Oncology..

Negative phase 3 study of 90Y microspheres versus sorafenib in HCC

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Risk factors for neurosurgical site infection after neurosurgery in Rennes, France: comparison of logistic and Cox models

International audienceThe logistic model is widely used to assess the risk factors for surgical site infections (SSIs). An alternative to the logistic model is the Cox model. The objective of this study was to compare these 2 models to identify the risk factors of SSIs in neurosurgery. The Cox model is a valid alternative for assessing the risk factors of SSIs

Full-text automated detection of surgical site infections secondary to neurosurgery in Rennes, France.

International audienceThe surveillance of Surgical Site Infections (SSI) contributes to the management of risk in French hospitals. Manual identification of infections is costly, time-consuming and limits the promotion of preventive procedures by the dedicated teams. The introduction of alternative methods using automated detection strategies is promising to improve this surveillance. The present study describes an automated detection strategy for SSI in neurosurgery, based on textual analysis of medical reports stored in a clinical data warehouse. The method consists firstly, of enrichment and concept extraction from full-text reports using NOMINDEX, and secondly, text similarity measurement using a vector space model. The text detection was compared to the conventional strategy based on self-declaration and to the automated detection using the diagnosis-related group database. The text-mining approach showed the best detection accuracy, with recall and precision equal to 92% and 40% respectively, and confirmed the interest of reusing full-text medical reports to perform automated detection of SSI