| 1. |
- Janko, Matthew R., et al.
(författare)
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In-situ bypass is associated with superior infection-free survival compared with extra-anatomic bypass for the management of secondary aortic graft infections without enteric involvement
- 2022
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Ingår i: Journal of Vascular Surgery. - : Elsevier. - 0741-5214 .- 1097-6809. ; 76:2, s. 546-
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Tidskriftsartikel (refereegranskat)abstract
- Objective: The optimal revascularization modality following complete resection of aortic graft infection (AGI) without enteric involvement remains unclear. The purpose of this investigation is to determine the revascularization approach associated with the lowest morbidity and mortality using real-world data in patients undergoing complete excision of AGI. Methods: A retrospective, multi-institutional study of AGI from 2002 to 2014 was performed using a standardized database. Baseline demographics, comorbidities, and perioperative variables were recorded. The primary outcome was infection-free survival. Descriptive statistics, Kaplan-Meier survival analysis, and univariate and multivariable analyses were performed. Results: A total of 241 patients at 34 institutions from seven countries presented with AGI during the study period (median age, 68 years; 75% male). The initial aortic procedures that resulted in AGI were 172 surgical grafts (71%), 66 endografts (27%), and three unknown (2%). Of the patients, 172 (71%) underwent complete excision of infected aortic graft material followed by in situ (in-line) bypass (ISB), including antibiotic-treated prosthetic graft (35%), autogenous femoral vein (neo-aortoiliac surgery) (24%), and cryopreserved allograft (41%). Sixty-nine patients (29%) underwent extra-anatomic bypass (EAB). Overall median Kaplan-Meier estimated survival was 5.8 years. Perioperative mortality was 16%. When stratified by ISB vs EAB, there was a significant difference in Kaplan-Meier estimated infection-free survival (2910 days; interquartile range, 391-3771 days vs 180 days; interquartile range, 27-3750 days; P <.001). There were otherwise no significant differences in presentation, comorbidities, or perioperative variables. Multivariable Cox regression showed lower infection-free survival among patients with EAB (hazard ratio [HR], 2.4; 95% confidence interval [CI], 1.6-3.6; P <.001), polymicrobial infection (HR, 2.2; 95% CI, 1.4-3.5; P = .001), methicillin-resistant Staphylococcus aureus infection (HR, 1.7; 95% CI, 1.1-2.7; P = .02), as well as the protective effect of omental/muscle flap coverage (HR, 0.59; 95% CI, 0.37-0.92; P = .02). Conclusions: After complete resection of AGI, perioperative mortality is 16% and median overall survival is 5.8 years. EAB is associated with nearly a two and one-half-fold higher reinfection/mortality compared with ISB. Omental and/or muscle flap coverage of the repair appear protective.
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| 3. |
- Singh, Vijay Kumar, et al.
(författare)
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Novel Genetic Algorithm (GA) based hybrid machine learning-pedotransfer Function (ML-PTF) for prediction of spatial pattern of saturated hydraulic conductivity
- 2022
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Ingår i: Engineering Applications of Computational Fluid Mechanics. - : Taylor & Francis. - 1994-2060 .- 1997-003X. ; 16:1, s. 1082-1099
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Tidskriftsartikel (refereegranskat)abstract
- Saturated hydraulic conductivity (Ks) is an important soil characteristic that controls water moves through the soil. On the other hand, its measurement is difficult, time-consuming, and expensive; hence Pedotransfer Functions (PTFs) are commonly used for its estimation. Despite significant development over the years, the PTFs showed poor performance in predicting Ks. Using Genetic Algorithm (GA), two hybrid Machine Learning based PTFs (ML-PTF), i.e. a combination of GA with Multilayer Perceptron (MLP-GA) and Support Vector Machine (SVM-GA), were proposed in this study. We compared the performances of four machine learning algorithms for different sets of predictors. The predictor combination containing sand, clay, Field Capacity, and Wilting Point showed the highest accuracy for all the ML-PTFs. Among the ML-PTFs, the SVM-GA algorithm outperformed the rest of the PTFs. It was noticed that the SVM-GA PTF demonstrated higher efficiency than the MLP-GA algorithm. The reference model for hydraulic conductivity prediction was selected as the SVM-GA PTF paired with the K-5 predictor variables. The proposed PTFs were compared with 160 models from past literature. It was found that the algorithms advocated were an improvement over these PTFs. The current model would help in efficient spatio-temporal measurement of hydraulic conductivity using pre-available databases.
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