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- Beumer, B. R., et al.
(författare)
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Impact of muscle mass on survival of patients with hepatocellular carcinoma after liver transplantation beyond the Milan criteria
- 2022
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Ingår i: Journal of Cachexia, Sarcopenia and Muscle. - : Wiley. - 2190-5991 .- 2190-6009. ; 13:5, s. 2373-2382
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Tidskriftsartikel (refereegranskat)abstract
- Background: Access to the liver transplant waitlist for patients with hepatocellular carcinoma (HCC) depends on tumour presentation, biology, and response to treatments. The Milan Criteria (MC) represent the benchmark for expanded criteria that incorporate additional prognostic factors. The purpose of this study was to determine the added value of skeletal muscle index (SMI) in HCC patients beyond the MC. Method: Patients with HCC that were transplanted beyond the MC were included in this retrospective multicentre study. SMI was quantified using the Computed Tomography (CT) within 3months prior to transplantation. Cox regression models were used to identify predictors of overall survival (OS). The discriminative performance of SMI extended Metroticket 2.0 and AFP models was also assessed. Results: Out of 889 patients transplanted outside the MC, 528 had a CT scan within 3months prior to liver transplantation (LT), of whom 176 (33%) were classified as sarcopenic. The median time between assessment of the SMI and LT was 1.8months (IQR: 0.77–2.67). The median follow-up period was 5.1 95% CI [4.7–5.5] years, with a total of 177 recorded deaths from any cause. In a linear regression model with SMI as the dependent variable, only male gender (8.55 95% CI [6.51–10.59], P<0.001) and body mass index (0.74 95% CI [0.59–0.89], P<0.001) were significant. Univariable survival analysis of patients with sarcopenia versus patients without sarcopenia showed a significant difference in OS (HR 1.44 95% CI [1.07−1.94], P=0.018). Also the SMI was significant (HR 0.98 95% CI [0.96–0.99], P=0.014). The survival difference between the lowest SMI quartile versus the highest SMI quartile was significant (log-rank: P=0.005) with 5year OS of 57% and 71%, respectively. Data from 423 patients, describing 139 deaths, was used for multivariate analysis. Both sarcopenia (HR 1.45 95% CI [1.02−2.05], P=0.036) and SMI were (HR 0.98 95% CI [0.95–0.99], P=0.035) significant. On the survival scale this translates to a 5year OS difference of 11% between sarcopenia and no sarcopenia. Whereas for SMI, this translates to a survival difference of 8% between first and third quartiles for both genders. Conclusions: Overall, we can conclude that higher muscle mass contributes to a better long-term survival. However, for individual patients, low muscle mass should not be considered an absolute contra-indication for LT as its discriminatory performance was limited.
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- Ravelli, A, et al.
(författare)
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2016 Classification Criteria for Macrophage Activation Syndrome Complicating Systemic Juvenile Idiopathic Arthritis: A European League Against Rheumatism/American College of Rheumatology/Paediatric Rheumatology International Trials Organisation Collaborative Initiative
- 2016
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Ingår i: Annals of the rheumatic diseases. - : BMJ. - 1468-2060 .- 0003-4967. ; 75:3, s. 481-489
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Tidskriftsartikel (refereegranskat)abstract
- To develop criteria for the classification of macrophage activation syndrome (MAS) in patients with systemic juvenile idiopathic arthritis (JIA). A multistep process, based on a combination of expert consensus and analysis of real patient data, was conducted. A panel of 28 experts was first asked to classify 428 patient profiles as having or not having MAS, based on clinical and laboratory features at the time of disease onset. The 428 profiles comprised 161 patients with systemic JIA—associated MAS and 267 patients with a condition that could potentially be confused with MAS (active systemic JIA without evidence of MAS, or systemic infection). Next, the ability of candidate criteria to classify individual patients as having MAS or not having MAS was assessed by evaluating the agreement between the classification yielded using the criteria and the consensus classification of the experts. The final criteria were selected in a consensus conference. Experts achieved consensus on the classification of 391 of the 428 patient profiles (91.4%). A total of 982 candidate criteria were tested statistically. The 37 best-performing criteria and 8 criteria obtained from the literature were evaluated at the consensus conference. During the conference, 82% consensus among experts was reached on the final MAS classification criteria. In validation analyses, these criteria had a sensitivity of 0.73 and a specificity of 0.99. Agreement between the classification (MAS or not MAS) obtained using the criteria and the original diagnosis made by the treating physician was high (κ=0.76). We have developed a set of classification criteria for MAS complicating systemic JIA and provided preliminary evidence of its validity. Use of these criteria will potentially improve understanding of MAS in systemic JIA and enhance efforts to discover effective therapies, by ensuring appropriate patient enrollment in studies.
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| 7. |
- Degas, A., et al.
(författare)
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A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management : Current Trends and Development with Future Research Trajectory
- 2022
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 12:3
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Forskningsöversikt (refereegranskat)abstract
- Air Traffic Management (ATM) will be more complex in the coming decades due to the growth and increased complexity of aviation and has to be improved in order to maintain aviation safety. It is agreed that without significant improvement in this domain, the safety objectives defined by international organisations cannot be achieved and a risk of more incidents/accidents is envisaged. Nowadays, computer science plays a major role in data management and decisions made in ATM. Nonetheless, despite this, Artificial Intelligence (AI), which is one of the most researched topics in computer science, has not quite reached end users in ATM domain. In this paper, we analyse the state of the art with regards to usefulness of AI within aviation/ATM domain. It includes research work of the last decade of AI in ATM, the extraction of relevant trends and features, and the extraction of representative dimensions. We analysed how the general and ATM eXplainable Artificial Intelligence (XAI) works, analysing where and why XAI is needed, how it is currently provided, and the limitations, then synthesise the findings into a conceptual framework, named the DPP (Descriptive, Predictive, Prescriptive) model, and provide an example of its application in a scenario in 2030. It concludes that AI systems within ATM need further research for their acceptance by end-users. The development of appropriate XAI methods including the validation by appropriate authorities and end-users are key issues that needs to be addressed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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