Simple scoring system to predict in-hospital mortality after surgery for infective endocarditis

BACKGROUND: Aspecific scoring systems are used to predict the risk of death postsurgery in patients with infective endocarditis (IE). The purpose of the present study was both to analyze the risk factors for in-hospital death, which complicates surgery for IE, and to create a mortality risk score based on the results of this analysis. METHODS AND RESULTS: Outcomes of 361 consecutive patients (mean age, 59.1\ub115.4 years) who had undergone surgery for IE in 8 European centers of cardiac surgery were recorded prospectively, and a risk factor analysis (multivariable logistic regression) for in-hospital death was performed. The discriminatory power of a new predictive scoring system was assessed with the receiver operating characteristic curve analysis. Score validation procedures were carried out. Fifty-six (15.5%) patients died postsurgery. BMI >27 kg/m2 (odds ratio [OR], 1.79; P=0.049), estimated glomerular filtration rate 55 mm Hg (OR, 1.78; P=0.032), and critical state (OR, 2.37; P=0.017) were independent predictors of in-hospital death. A scoring system was devised to predict in-hospital death postsurgery for IE (area under the receiver operating characteristic curve, 0.780; 95% CI, 0.734-0.822). The score performed better than 5 of 6 scoring systems for in-hospital death after cardiac surgery that were considered. CONCLUSIONS: A simple scoring system based on risk factors for in-hospital death was specifically created to predict mortality risk postsurgery in patients with IE

LDA filter: A Latent Dirichlet Allocation preprocess method for Weka.

This work presents an alternative method to represent documents based on LDA (Latent Dirichlet Allocation) and how it affects to classification algorithms, in comparison to common text representation. LDA assumes that each document deals with a set of predefined topics, which are distributions over an entire vocabulary. Our main objective is to use the probability of a document belonging to each topic to implement a new text representation model. This proposed technique is deployed as an extension of the Weka software as a new filter. To demonstrate its performance, the created filter is tested with different classifiers such as a Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Naive Bayes in different documental corpora (OHSUMED, Reuters-21578, 20Newsgroup, Yahoo! Answers, YELP Polarity, and TREC Genomics 2015). Then, it is compared with the Bag of Words (BoW) representation technique. Results suggest that the application of our proposed filter achieves similar accuracy as BoW but greatly improves classification processing times

LDA filter: A Latent Dirichlet Allocation preprocess method for Weka

This work presents an alternative method to represent documents based on LDA (Latent Dirichlet Allocation) and how it affects to classification algorithms, in comparison to common text representation. LDA assumes that each document deals with a set of predefined topics, which are distributions over an entire vocabulary. Our main objective is to use the probability of a document belonging to each topic to implement a new text representation model. This proposed technique is deployed as an extension of the Weka software as a new filter. To demonstrate its performance, the created filter is tested with different classifiers such as a Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Naive Bayes in different documental corpora (OHSUMED, Reuters-21578, 20Newsgroup, Yahoo! Answers, YELP Polarity, and TREC Genomics 2015). Then, it is compared with the Bag of Words (BoW) representation technique. Results suggest that the application of our proposed filter achieves similar accuracy as BoW but greatly improves classification processing times.</jats:p

Current patterns of infective endocarditis in congenital heart disease

OBJECTIVE: To assess the changing profile of infective endocarditis in patients with congenital heart disease. METHODS: All cases diagnosed from 1966 to 2001 (revised Duke criteria) were retrospectively reviewed and categorised in periods I (< 1990) and II (⩾ 1990). RESULTS: 153 episodes occurred, 81 in period I and 72 in period II. Mean age of affected patients was higher in period II. Non‐operated ventricular septal defect, Rastelli correction and palliated cyanotic heart disease increased. Infective endocarditis in corrective surgery changed to patients with prosthetic material. Post‐surgical cases decreased. Dental problems were the leading cause (period I 20% v II 33% of cases) with a large variety of pathological organisms (multiple species of Streptococcus). Cutaneous causative infections increased (5% to 17%) with different species of Staphylococcus. Negative blood cultures lessened (20% to 7%, p  =  0.03). Streptococci were the most common causative organisms in both periods. Severe heart failure and cardiac complications lessened (20% to 4% and 31% to 18% during periods I and II, respectively). Early surgery was more frequent in period II (32% v 18.5%, p  =  0.02). One‐ and 10‐year survival was 91% v 97% in period I and 89% v 97% in period II, respectively (NS). CONCLUSION: Current targets include complex cyanotic disease, congenital heart disease corrected with prosthetic material and small ventricular septal defect. Postoperative cases lessened; dental and cutaneous causes increased. Survival was unchanged. Prophylactic measures targeted at dental and cutaneous sources should be emphasised