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3. Aortic diastolic flow displacement from phase-contrast CMR is associated with exercise capacity in healthy adults

Author

Zhao, X, primary, Garg, P, additional, Tan, R S, additional, Chai, P, additional, Yeo, T J, additional, Leng, S, additional, Bryant, J, additional, Teo, L, additional, Koh, A S, additional, Zhang, S, additional, Van Der Geest, R J, additional, Yip, J, additional, Tan, J L, additional, Plein, S, additional, and Zhong, L, additional

Published
2023
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4. Quantification of Coronary Artery Cross Section Lumen Area and Area Stenosis with 3D Centerline-Centric Straightening

Author

Cui, H. F., Wang, D. S., Wan, M., Zhang, J. M., Zhao, X. D., Tan, S. Y., Wong, A. S. L., Tan, R. S., Huang, W. M., Xiong, W., Duan, Y. P., Zhou, J. Y., Chi, Y. L., Zhong, L., MAGJAREVIC, Ratko, Editor-in-chief, Ładyżyński, Piotr, Series editor, Ibrahim, Fatimah, Series editor, Lacković, Igor, Series editor, Rock, Emilio Sacristan, Series editor, Usman, Juliana, editor, Mohktar, Mas Sahidayana, editor, and Ahmad, Mohd Yazed, editor

Published
2016
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5. Attenuation of 3-Dimensional Epicardial Strain from Cardiac Magnetic Resonance Associated with Obstructive Hypertrophic Cardiomyopathy

Author

Zhao, X. D., Tan, R. S., Tang, H. C., Leng, S., Zhang, J.-M., Su, B. Y., Zhong, L., MAGJAREVIC, Ratko, Editor-in-chief, Ładyżyński, Piotr, Series editor, Ibrahim, Fatimah, Series editor, Lacković, Igor, Series editor, Rock, Emilio Sacristan, Series editor, Usman, Juliana, editor, Mohktar, Mas Sahidayana, editor, and Ahmad, Mohd Yazed, editor

Published
2016
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6. Numerical Modeling of Blood Flow in Patient-Specific Right Ventricle with Pulmonary Arterial Hypertension Based on MRI

Author

Su, B., Tan, R. S., Tan, J. L., Guo, K. W. Q., Zhao, X., Leng, S., Zhang, J. M., Zhong, L., MAGJAREVIC, Ratko, Editor-in-chief, Ładyżyński, Piotr, Series editor, Ibrahim, Fatimah, Series editor, Lacković, Igor, Series editor, Rock, Emilio Sacristan, Series editor, Usman, Juliana, editor, Mohktar, Mas Sahidayana, editor, and Ahmad, Mohd Yazed, editor

Published
2016
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7. Automated Adrenal Gland Disease Classes Using Patch-Based Center Symmetric Local Binary Pattern Technique with CT Images.

Author

Sut, SK, Koc, M, Zorlu, G, Serhatlioglu, I, Barua, PD, Dogan, S, Baygin, M, Tuncer, T, Tan, R-S, Acharya, UR, Sut, SK, Koc, M, Zorlu, G, Serhatlioglu, I, Barua, PD, Dogan, S, Baygin, M, Tuncer, T, Tan, R-S, and Acharya, UR

Abstract

Incidental adrenal masses are seen in 5% of abdominal computed tomography (CT) examinations. Accurate discrimination of the possible differential diagnoses has important therapeutic and prognostic significance. A new handcrafted machine learning method has been developed for the automated and accurate classification of adrenal gland CT images. A new dataset comprising 759 adrenal gland CT image slices from 96 subjects were analyzed. Experts had labeled the collected images into four classes: normal, pheochromocytoma, lipid-poor adenoma, and metastasis. The images were preprocessed, resized, and the image features were extracted using the center symmetric local binary pattern (CS-LBP) method. CT images were next divided into 16 × 16 fixed-size patches, and further feature extraction using CS-LBP was performed on these patches. Next, extracted features were selected using neighborhood component analysis (NCA) to obtain the most meaningful ones for downstream classification. Finally, the selected features were classified using k-nearest neighbor (kNN), support vector machine (SVM), and neural network (NN) classifiers to obtain the optimum performing model. Our proposed method obtained an accuracy of 99.87%, 99.21%, and 98.81% with kNN, SVM, and NN classifiers, respectively. Hence, the kNN classifier yielded the highest classification results with no pathological image misclassified as normal. Our developed fixed patch CS-LBP-based automatic classification of adrenal gland pathologies on CT images is highly accurate and has low time complexity [Formula: see text]. It has the potential to be used for screening of adrenal gland disease classes with CT images.

Published
2023

8. Multilevel hybrid accurate handcrafted model for myocardial infarction classification using ECG signals.

Author

Barua, PD, Aydemir, E, Dogan, S, Kobat, MA, Demir, FB, Baygin, M, Tuncer, T, Oh, SL, Tan, R-S, Acharya, UR, Barua, PD, Aydemir, E, Dogan, S, Kobat, MA, Demir, FB, Baygin, M, Tuncer, T, Oh, SL, Tan, R-S, and Acharya, UR

Abstract

Myocardial infarction (MI) is detected using electrocardiography (ECG) signals. Machine learning (ML) models have been used for automated MI detection on ECG signals. Deep learning models generally yield high classification performance but are computationally intensive. We have developed a novel multilevel hybrid feature extraction-based classification model with low time complexity for MI classification. The study dataset comprising 12-lead ECGs belonging to one healthy and 10 MI classes were downloaded from a public ECG signal databank. The model architecture comprised multilevel hybrid feature extraction, iterative feature selection, classification, and iterative majority voting (IMV). In the hybrid handcrafted feature (HHF) generation phase, both textural and statistical feature extraction functions were used to extract features from ECG beats but only at a low level. A new pooling-based multilevel decomposition model was presented to enable them to create features at a high level. This model used average and maximum pooling to create decomposed signals. Using these pooling functions, an unbalanced tree was obtained. Therefore, this model was named multilevel unbalanced pooling tree transformation (MUPTT). On the feature extraction side, two extractors (functions) were used to generate both statistical and textural features. To generate statistical features, 20 commonly used moments were used. A new, improved symmetric binary pattern function was proposed to generate textural features. Both feature extractors were applied to the original MI signal and the decomposed signals generated by the MUPTT. The most valuable features from among the extracted feature vectors were selected using iterative neighborhood component analysis (INCA). In the classification phase, a one-dimensional nearest neighbor classifier with ten-fold cross-validation was used to obtain lead-wise results. The computed lead-wise results derived from all 12 leads of the same beat were input to t

Published
2023

9. Automated Urine Cell Image Classification Model Using Chaotic Mixer Deep Feature Extraction.

Author

Erten, M, Tuncer, I, Barua, PD, Yildirim, K, Dogan, S, Tuncer, T, Tan, R-S, Fujita, H, Acharya, UR, Erten, M, Tuncer, I, Barua, PD, Yildirim, K, Dogan, S, Tuncer, T, Tan, R-S, Fujita, H, and Acharya, UR

Abstract

Microscopic examination of urinary sediments is a common laboratory procedure. Automated image-based classification of urinary sediments can reduce analysis time and costs. Inspired by cryptographic mixing protocols and computer vision, we developed an image classification model that combines a novel Arnold Cat Map (ACM)- and fixed-size patch-based mixer algorithm with transfer learning for deep feature extraction. Our study dataset comprised 6,687 urinary sediment images belonging to seven classes: Cast, Crystal, Epithelia, Epithelial nuclei, Erythrocyte, Leukocyte, and Mycete. The developed model consists of four layers: (1) an ACM-based mixer to generate mixed images from resized 224 × 224 input images using fixed-size 16 × 16 patches; (2) DenseNet201 pre-trained on ImageNet1K to extract 1,920 features from each raw input image, and its six corresponding mixed images were concatenated to form a final feature vector of length 13,440; (3) iterative neighborhood component analysis to select the most discriminative feature vector of optimal length 342, determined using a k-nearest neighbor (kNN)-based loss function calculator; and (4) shallow kNN-based classification with ten-fold cross-validation. Our model achieved 98.52% overall accuracy for seven-class classification, outperforming published models for urinary cell and sediment analysis. We demonstrated the feasibility and accuracy of deep feature engineering using an ACM-based mixer algorithm for image preprocessing combined with pre-trained DenseNet201 for feature extraction. The classification model was both demonstrably accurate and computationally lightweight, making it ready for implementation in real-world image-based urine sediment analysis applications.

Published
2023

10. Primate brain pattern-based automated Alzheimer's disease detection model using EEG signals.

Author

Dogan, S, Baygin, M, Tasci, B, Loh, HW, Barua, PD, Tuncer, T, Tan, R-S, Acharya, UR, Dogan, S, Baygin, M, Tasci, B, Loh, HW, Barua, PD, Tuncer, T, Tan, R-S, and Acharya, UR

Abstract

Electroencephalography (EEG) may detect early changes in Alzheimer's disease (AD), a debilitating progressive neurodegenerative disease. We have developed an automated AD detection model using a novel directed graph for local texture feature extraction with EEG signals. The proposed graph was created from a topological map of the macroscopic connectome, i.e., neuronal pathways linking anatomo-functional brain segments involved in visual object recognition and motor response in the primate brain. This primate brain pattern (PBP)-based model was tested on a public AD EEG signal dataset. The dataset comprised 16-channel EEG signal recordings of 12 AD patients and 11 healthy controls. While PBP could generate 448 low-level features per one-dimensional EEG signal, combining it with tunable q-factor wavelet transform created a multilevel feature extractor (which mimicked deep models) to generate 8,512 (= 448 × 19) features per signal input. Iterative neighborhood component analysis was used to choose the most discriminative features (the number of optimal features varied among the individual EEG channels) to feed to a weighted k-nearest neighbor (KNN) classifier for binary classification into AD vs. healthy using both leave-one subject-out (LOSO) and tenfold cross-validations. Iterative majority voting was used to compute subject-level general performance results from the individual channel classification outputs. Channel-wise, as well as subject-level general results demonstrated exemplary performance. In addition, the model attained 100% and 92.01% accuracy for AD vs. healthy classification using the KNN classifier with tenfold and LOSO cross-validations, respectively. Our developed multilevel PBP-based model extracted discriminative features from EEG signals and paved the way for further development of models inspired by the brain connectome.

Published
2023

12. Automated BI-RADS classification of lesions using pyramid triple deep feature generator technique on breast ultrasound images

Author

Kaplan, E, Chan, WY, Dogan, S, Barua, PD, Bulut, HT, Tuncer, T, Cizik, M, Tan, R-S, and Acharya, UR

Subjects
Machine Learning, Biomedical Engineering, Humans, Breast Neoplasms, Female, Breast, Ultrasonography, Mammary, 02 Physical Sciences, 09 Engineering, 11 Medical and Health Sciences, Ultrasonography
Abstract

Ultrasound (US) is an important imaging modality used to assess breast lesions for malignant features. In the past decade, many machine learning models have been developed for automated discrimination of breast cancer versus normal on US images, but few have classified the images based on the Breast Imaging Reporting and Data System (BI-RADS) classes. This work aimed to develop a model for classifying US breast lesions using a BI-RADS classification framework with a new multi-class US image dataset. We proposed a deep model that combined a novel pyramid triple deep feature generator (PTDFG) with transfer learning based on three pre-trained networks for creating deep features. Bilinear interpolation was applied to decompose the input image into four images of successively smaller dimensions, constituting a four-level pyramid for downstream feature generation with the pre-trained networks. Neighborhood component analysis was applied to the generated features to select each network's 1,000 most informative features, which were fed to support vector machine classifier for automated classification using a ten-fold cross-validation strategy. Our proposed model was validated using a new US image dataset containing 1,038 images divided into eight BI-RADS classes and histopathological results. We defined three classification schemes: Case 1 involved the classification of all images into eight categories; Case 2, classification of breast US images into five BI-RADS classes; and Case 3, classification of BI-RADS 4 lesions into benign versus malignant classes. Our PTDFG-based transfer learning model attained accuracy rates of 79.29%, 80.42%, and 88.67% for Case 1, Case 2, and Case 3, respectively.

Published
2022

13. DKPNet41: Directed knight pattern network-based cough sound classification model for automatic disease diagnosis

Author

Kuluozturk, M, Kobat, MA, Barua, PD, Dogan, S, Tuncer, T, Tan, R-S, Ciaccio, EJ, and Acharya, UR

Subjects
Machine Learning, Support Vector Machine, Cough, Biomedical Engineering, Humans, COVID-19, 02 Physical Sciences, 09 Engineering, 11 Medical and Health Sciences, Asthma
Abstract

PROBLEM: Cough-based disease detection is a hot research topic for machine learning, and much research has been published on the automatic detection of Covid-19. However, these studies are useful for the diagnosis of different diseases. AIM: In this work, we collected a new and large (n=642 subjects) cough sound dataset comprising four diagnostic categories: 'Covid-19', 'heart failure', 'acute asthma', and 'healthy', and used it to train, validate, and test a novel model designed for automatic detection. METHOD: The model consists of four main components: novel feature generation based on a specifically directed knight pattern (DKP), signal decomposition using four pooling methods, feature selection using iterative neighborhood analysis (INCA), and classification using the k-nearest neighbor (kNN) classifier with ten-fold cross-validation. Multilevel multiple pooling decomposition combined with DKP yielded 41 feature vectors (40 extracted plus one original cough sound). From these, the ten best feature vectors were selected. Based on each vector's misclassification rate, redundant feature vectors were eliminated and then merged. The merged vector's most informative features automatically selected using INCA were input to a standard kNN classifier. RESULTS: The model, called DKPNet41, attained a high accuracy of 99.39% for cough sound-based multiclass classification of the four categories. CONCLUSIONS: The results obtained in the study showed that the DKPNet41 model automatically and efficiently classifies cough sounds for disease diagnosis.

Published
2022

14. Novel nested patch-based feature extraction model for automated Parkinson's Disease symptom classification using MRI images.

Author

Kaplan, E, Altunisik, E, Ekmekyapar Firat, Y, Datta Barua, P, Dogan, S, Baygin, M, Burak Demir, F, Tuncer, T, Palmer, E, Tan, R-S, Yu, P, Soar, J, Fujita, H, Rajendra Acharya, U, Kaplan, E, Altunisik, E, Ekmekyapar Firat, Y, Datta Barua, P, Dogan, S, Baygin, M, Burak Demir, F, Tuncer, T, Palmer, E, Tan, R-S, Yu, P, Soar, J, Fujita, H, and Rajendra Acharya, U

Abstract

OBJECTIVE: Parkinson's disease (PD) is a common neurological disorder with variable clinical manifestations and magnetic resonance imaging (MRI) findings. We propose a handcrafted image classification model that can accurately (i) classify different PD stages, (ii) detect comorbid dementia, and (iii) discriminate PD-related motor symptoms. METHODS: Selected image datasets from three PD studies were used to develop the classification model. Our proposed novel automated system was developed in four phases: (i) texture features are extracted from the non-fixed size patches. In the feature extraction phase, a pyramid histogram-oriented gradient (PHOG) image descriptor is used. (ii) In the feature selection phase, four feature selectors: neighborhood component analysis (NCA), Chi2, minimum redundancy maximum relevancy (mRMR), and ReliefF are used to generate four feature vectors. (iii) Two classifiers: k-nearest neighbor (kNN) and support vector machine (SVM) are used in the classification step. A ten-fold cross-validation technique is used to validate the results. (iv) Eight predicted vectors are generated using four selected feature vectors and two classifiers. Finally, iterative majority voting (IMV) is used to attain general classification results. Therefore, this model is named nested patch-PHOG-multiple feature selectors and multiple classifiers-IMV (NP-PHOG-MFSMCIMV). RESULTS: Our presented NP-PHOG-MFSMCIMV model achieved 99.22, 98.70, and 99.53% accuracies for the collected PD stages, PD dementia, and PD symptoms classification datasets, respectively. SIGNIFICANCE: The obtained accuracies (over 98% for all states) demonstrated the performance of developed NP-PHOG-MFSMCIMV model in automated PD state classification.

Published
2022

15. Automated detection of pain levels using deep feature extraction from shutter blinds-based dynamic-sized horizontal patches with facial images.

Author

Barua, PD, Baygin, N, Dogan, S, Baygin, M, Arunkumar, N, Fujita, H, Tuncer, T, Tan, R-S, Palmer, E, Azizan, MMB, Kadri, NA, Acharya, UR, Barua, PD, Baygin, N, Dogan, S, Baygin, M, Arunkumar, N, Fujita, H, Tuncer, T, Tan, R-S, Palmer, E, Azizan, MMB, Kadri, NA, and Acharya, UR

Abstract

Pain intensity classification using facial images is a challenging problem in computer vision research. This work proposed a patch and transfer learning-based model to classify various pain intensities using facial images. The input facial images were segmented into dynamic-sized horizontal patches or "shutter blinds". A lightweight deep network DarkNet19 pre-trained on ImageNet1K was used to generate deep features from the shutter blinds and the undivided resized segmented input facial image. The most discriminative features were selected from these deep features using iterative neighborhood component analysis, which were then fed to a standard shallow fine k-nearest neighbor classifier for classification using tenfold cross-validation. The proposed shutter blinds-based model was trained and tested on datasets derived from two public databases-University of Northern British Columbia-McMaster Shoulder Pain Expression Archive Database and Denver Intensity of Spontaneous Facial Action Database-which both comprised four pain intensity classes that had been labeled by human experts using validated facial action coding system methodology. Our shutter blinds-based classification model attained more than 95% overall accuracy rates on both datasets. The excellent performance suggests that the automated pain intensity classification model can be deployed to assist doctors in the non-verbal detection of pain using facial images in various situations (e.g., non-communicative patients or during surgery). This system can facilitate timely detection and management of pain.

Published
2022

16. Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography

Author

Tuncer, I, Barua, PD, Dogan, S, Baygin, M, Tuncer, T, Tan, R-S, Yeong, CH, Acharya, UR, Tuncer, I, Barua, PD, Dogan, S, Baygin, M, Tuncer, T, Tan, R-S, Yeong, CH, and Acharya, UR

Abstract

Background: Chest computed tomography (CT) has a high sensitivity for detecting COVID-19 lung involvement and is widely used for diagnosis and disease monitoring. We proposed a new image classification model, swin-textural, that combined swin-based patch division with textual feature extraction for automated diagnosis of COVID-19 on chest CT images. The main objective of this work is to evaluate the performance of the swin architecture in feature engineering.Material and method: We used a public dataset comprising 2167, 1247, and 757 (total 4171) transverse chest CT images belonging to 80, 80, and 50 (total 210) subjects with COVID-19, other non-COVID lung conditions, and normal lung findings. In our model, resized 420 × 420 input images were divided using uniform square patches of incremental dimensions, which yielded ten feature extraction layers. At each layer, local binary pattern and local phase quantization operations extracted textural features from individual patches as well as the undivided input image. Iterative neighborhood component analysis was used to select the most informative set of features to form ten selected feature vectors and also used to select the 11th vector from among the top selected feature vectors with accuracy 97.5%. The downstream kNN classifier calculated 11 prediction vectors. From these, iterative hard majority voting generated another nine voted prediction vectors. Finally, the best result among the twenty was determined using a greedy algorithm.Results: Swin-textural attained 98.71% three-class classification accuracy, outperforming published deep learning models trained on the same dataset. The model has linear time complexity.Conclusions: Our handcrafted computationally lightweight swin-textural model can detect COVID-19 accurately on chest CT images with low misclassification rates. The model can be implemented in hospitals for efficient automated screening of COVID-19 on chest CT images. Moreover, findings demonstrate that o

Published
2022

17. Automated Diabetic Retinopathy Detection Using Horizontal and Vertical Patch Division-Based Pre-Trained DenseNET with Digital Fundus Images.

Author

Kobat, SG, Baygin, N, Yusufoglu, E, Baygin, M, Barua, PD, Dogan, S, Yaman, O, Celiker, U, Yildirim, H, Tan, R-S, Tuncer, T, Islam, N, Acharya, UR, Kobat, SG, Baygin, N, Yusufoglu, E, Baygin, M, Barua, PD, Dogan, S, Yaman, O, Celiker, U, Yildirim, H, Tan, R-S, Tuncer, T, Islam, N, and Acharya, UR

Abstract

Diabetic retinopathy (DR) is a common complication of diabetes that can lead to progressive vision loss. Regular surveillance with fundal photography, early diagnosis, and prompt intervention are paramount to reducing the incidence of DR-induced vision loss. However, manual interpretation of fundal photographs is subject to human error. In this study, a new method based on horizontal and vertical patch division was proposed for the automated classification of DR images on fundal photographs. The novel sides of this study are given as follows. We proposed a new non-fixed-size patch division model to obtain high classification results and collected a new fundus image dataset. Moreover, two datasets are used to test the model: a newly collected three-class (normal, non-proliferative DR, and proliferative DR) dataset comprising 2355 DR images and the established open-access five-class Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 dataset comprising 3662 images. Two analysis scenarios, Case 1 and Case 2, with three (normal, non-proliferative DR, and proliferative DR) and five classes (normal, mild DR, moderate DR, severe DR, and proliferative DR), respectively, were derived from the APTOS 2019 dataset. These datasets and these cases have been used to demonstrate the general classification performance of our proposal. By applying transfer learning, the last fully connected and global average pooling layers of the DenseNet201 architecture were used to extract deep features from input DR images and each of the eight subdivided horizontal and vertical patches. The most discriminative features are then selected using neighborhood component analysis. These were fed as input to a standard shallow cubic support vector machine for classification. Our new DR dataset obtained 94.06% and 91.55% accuracy values for three-class classification with 80:20 hold-out validation and 10-fold cross-validation, respectively. As can be seen from steps of the proposed model, a new patch-b

Published
2022

18. ViVGG19: Novel exemplar deep feature extraction-based shoulder rotator cuff tear and biceps tendinosis detection using magnetic resonance images.

Author

Key, S, Demir, S, Gurger, M, Yilmaz, E, Barua, PD, Dogan, S, Tuncer, T, Arunkumar, N, Tan, R-S, Acharya, UR, Key, S, Demir, S, Gurger, M, Yilmaz, E, Barua, PD, Dogan, S, Tuncer, T, Arunkumar, N, Tan, R-S, and Acharya, UR

Abstract

BACKGROUND AND PURPOSE: Rotator cuff tear (RCT) and biceps tendinosis (BT) are the two most common shoulder disorders worldwide. These disorders can be diagnosed using magnetic resonance imaging (MRI), but the expert interpretation is manual, time-consuming, and subjected to human errors. Therefore, a fixed-size feature extraction model was created to objectively and accurately perform automated binary classification of RCT vs. normal and BT vs. normal on MRI images. MATERIALS AND METHODS: We have developed an exemplar deep feature extraction model to diagnose RCT and BT disorders. The model was tested on a new MR image dataset comprising transverse, sagittal, and coronal MRI images of the shoulder that had been organized into three cases. BT was studied on transverse MRI images (Case 1), while RCT was studied on sagittal (Case 2) and coronal MRI images (Case 3). Our model comprised deep feature generation using a pre-trained VGG19, feature selection using iterative neighborhood component analysis (INCA), and classification using shallow standard classifiers k-nearest neighbors (KNN), support vector machine (SVM), and artificial neural network (ANN). In the feature extraction phase, two fully connected layers were used to extract deep features from the original image, and sixteen fixed-size patches obtained by the division of the original image. This model was named Vision VGG19 (ViVGG), analogous to vision transformers (ViT). The feature vector is extracted from the raw image dataset, and 16 feature vectors are extracted from each fixed-size patch. Seventeen feature vectors obtained from each image are obtained from fc6 and fc7 layers of the pre-trained VGG19, are merged to obtain final feature vector. INCA was used to choose the top features from the created features, and the chosen features were classified using shallow classifiers. RESULTS: We defined three cases to evaluate the proposed ViVGG19 to diagnose RT and BCT disorders. Our proposed ViVGG19 model achiev

Published
2022

19. Heart rate variability for medical decision support systems: A review.

Author

Faust, O, Hong, W, Loh, HW, Xu, S, Tan, R-S, Chakraborty, S, Barua, PD, Molinari, F, Acharya, UR, Faust, O, Hong, W, Loh, HW, Xu, S, Tan, R-S, Chakraborty, S, Barua, PD, Molinari, F, and Acharya, UR

Abstract

Heart Rate Variability (HRV) is a good predictor of human health because the heart rhythm is modulated by a wide range of physiological processes. This statement embodies both challenges to and opportunities for HRV analysis. Opportunities arise from the wide-ranging applicability of HRV analysis for disease detection. The availability of modern high-quality sensors and the low data rate of heart rate signals make HRV easy to measure, communicate, store, and process. However, there are also significant obstacles that prevent a wider use of this technology. HRV signals are both nonstationary and nonlinear and, to the human eye, they appear noise-like. This makes them difficult to analyze and indeed the analysis findings are difficult to explain. Moreover, it is difficult to discriminate between the influences of different complex physiological processes on the HRV. These difficulties are compounded by the effects of aging and the presence of comorbidities. In this review, we have looked at scientific studies that have addressed these challenges with advanced signal processing and Artificial Intelligence (AI) methods.

Published
2022

20. Application of photoplethysmography signals for healthcare systems: An in-depth review.

Author

Loh, HW, Xu, S, Faust, O, Ooi, CP, Barua, PD, Chakraborty, S, Tan, R-S, Molinari, F, Acharya, UR, Loh, HW, Xu, S, Faust, O, Ooi, CP, Barua, PD, Chakraborty, S, Tan, R-S, Molinari, F, and Acharya, UR

Abstract

BACKGROUND AND OBJECTIVES: Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, heart rate variability, blood pressure, etc. Hence, PPG signals can produce a wide variety of biological information that can be useful for the detection and diagnosis of various health problems. In this review, we are interested in the possible health disorders that can be detected using PPG signals. METHODS: We applied PRISMA guidelines to systematically search various journal databases and identified 43 PPG studies that fit the criteria of this review. RESULTS: Twenty-five health issues were identified from these studies that were classified into six categories: cardiac, blood pressure, sleep health, mental health, diabetes, and miscellaneous. Various routes were employed in these PPG studies to perform the diagnosis: machine learning, deep learning, and statistical routes. The studies were reviewed and summarized. CONCLUSIONS: We identified limitations such as poor standardization of sampling frequencies and lack of publicly available PPG databases. We urge that future work should consider creating more publicly available databases so that a wide spectrum of health problems can be covered. We also want to promote the use of PPG signals as a potential precision medicine tool in both ambulatory and hospital settings.

Published
2022

21. PFP-LHCINCA: Pyramidal Fixed-Size Patch-Based Feature Extraction and Chi-Square Iterative Neighborhood Component Analysis for Automated Fetal Sex Classification on Ultrasound Images.

Author

Hashmi, MF, Kaplan, E, Ekinci, T, Kaplan, S, Barua, PD, Dogan, S, Tuncer, T, Tan, R-S, Arunkumar, N, Acharya, UR, Hashmi, MF, Kaplan, E, Ekinci, T, Kaplan, S, Barua, PD, Dogan, S, Tuncer, T, Tan, R-S, Arunkumar, N, and Acharya, UR

Abstract

OBJECTIVES: Fetal sex determination with ultrasound (US) examination is indicated in pregnancies at risk of X-linked genetic disorders or ambiguous genitalia. However, misdiagnoses often arise due to operator inexperience and technical difficulties while acquiring diagnostic images. We aimed to develop an efficient automated US-based fetal sex classification model that can facilitate efficient screening and reduce misclassification. METHODS: We have developed a novel feature engineering model termed PFP-LHCINCA that employs pyramidal fixed-size patch generation with average pooling-based image decomposition, handcrafted feature extraction based on local phase quantization (LPQ), and histogram of oriented gradients (HOG) to extract directional and textural features and used Chi-square iterative neighborhood component analysis feature selection (CINCA), which iteratively selects the most informative feature vector for each image that minimizes calculated feature parameter-derived k-nearest neighbor-based misclassification rates. The model was trained and tested on a sizeable expert-labeled dataset comprising 339 males' and 332 females' fetal US images. One transverse fetal US image per subject zoomed to the genital area and standardized to 256 × 256 size was used for analysis. Fetal sex was annotated by experts on US images and confirmed postnatally. RESULTS: Standard model performance metrics were compared using five shallow classifiers-k-nearest neighbor (kNN), decision tree, naïve Bayes, linear discriminant, and support vector machine (SVM)-with the hyperparameters tuned using a Bayesian optimizer. The PFP-LHCINCA model achieved a sex classification accuracy of ≥88% with all five classifiers and the best accuracy rates (>98%) with kNN and SVM classifiers. CONCLUSIONS: US-based fetal sex classification is feasible and accurate using the presented PFP-LHCINCA model. The salutary results support its clinical use for fetal US image screening for sex classification. The

Published
2022

24. Quantification of Coronary Artery Cross Section Lumen Area and Area Stenosis with 3D Centerline-Centric Straightening

Author

Cui, H. F., primary, Wang, D. S., additional, Wan, M., additional, Zhang, J. M., additional, Zhao, X. D., additional, Tan, S. Y., additional, Wong, A. S. L., additional, Tan, R. S., additional, Huang, W. M., additional, Xiong, W., additional, Duan, Y. P., additional, Zhou, J. Y., additional, Chi, Y. L., additional, and Zhong, L., additional

Published
2015
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29. Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization

Author

Gudigar, A, Nayak, S, Samanth, J, Raghavendra, U, Ashwal, AJ, Barua, PD, Hasan, N, Ciaccio, EJ, Tan, R-S, Acharya, UR, Gudigar, A, Nayak, S, Samanth, J, Raghavendra, U, Ashwal, AJ, Barua, PD, Hasan, N, Ciaccio, EJ, Tan, R-S, and Acharya, UR

Abstract

Coronary artery disease is a major cause of morbidity and mortality worldwide. Its underlying histopathology is the atherosclerotic plaque, which comprises lipid, fibrous and—when chronic—calcium components. Intravascular ultrasound (IVUS) and intravascular optical coherence tomography (IVOCT) performed during invasive coronary angiography are reference standards for characterizing the atherosclerotic plaque. Fine image spatial resolution attainable with contemporary coronary computed tomographic angiography (CCTA) has enabled noninvasive plaque assessment, including identifying features associated with vulnerable plaques known to presage acute coronary events. Manual interpretation of IVUS, IVOCT and CCTA images demands scarce physician expertise and high time cost. This has motivated recent research into and development of artificial intelligence (AI)-assisted methods for image processing, feature extraction, plaque identification and characterization. We performed parallel searches of the medical and technical literature from 1995 to 2021 focusing respectively on human plaque characterization using various imaging modalities and the use of AI-assisted computer aided diagnosis (CAD) to detect and classify atherosclerotic plaques, including their composition and the presence of high-risk features denoting vulnerable plaques. A total of 122 publications were selected for evaluation and the analysis was summarized in terms of data sources, methods—machine versus deep learning—and performance metrics. Trends in AI-assisted plaque characterization are detailed and prospective research challenges discussed. Future directions for the development of accurate and efficient CAD systems to characterize plaque noninvasively using CCTA are proposed.

Published
2021

30. Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds.

Author

Kobat, MA, Kivrak, T, Barua, PD, Tuncer, T, Dogan, S, Tan, R-S, Ciaccio, EJ, Acharya, UR, Kobat, MA, Kivrak, T, Barua, PD, Tuncer, T, Dogan, S, Tan, R-S, Ciaccio, EJ, and Acharya, UR

Abstract

COVID-19 and heart failure (HF) are common disorders and although they share some similar symptoms, they require different treatments. Accurate diagnosis of these disorders is crucial for disease management, including patient isolation to curb infection spread of COVID-19. In this work, we aim to develop a computer-aided diagnostic system that can accurately differentiate these three classes (normal, COVID-19 and HF) using cough sounds. A novel handcrafted model was used to classify COVID-19 vs. healthy (Case 1), HF vs. healthy (Case 2) and COVID-19 vs. HF vs. healthy (Case 3) automatically using deoxyribonucleic acid (DNA) patterns. The model was developed using the cough sounds collected from 241 COVID-19 patients, 244 HF patients, and 247 healthy subjects using a hand phone. To the best our knowledge, this is the first work to automatically classify healthy subjects, HF and COVID-19 patients using cough sounds signals. Our proposed model comprises a graph-based local feature generator (DNA pattern), an iterative maximum relevance minimum redundancy (ImRMR) iterative feature selector, with classification using the k-nearest neighbor classifier. Our proposed model attained an accuracy of 100.0%, 99.38%, and 99.49% for Case 1, Case 2, and Case 3, respectively. The developed system is completely automated and economical, and can be utilized to accurately detect COVID-19 versus HF using cough sounds.

Published
2021

38. P1925Machine learning versus classic electrocardiographic criteria for left ventricular hypertrophy in a young pre-participation cohort: results from the SAFE protocol study

Author

Sng, G, primary, Lim, D Y Z, additional, Sia, C H, additional, Lee, J S W, additional, Shen, X Y, additional, Lee, E C Y, additional, Dalakoti, M, additional, Wang, K J, additional, Kwan, C K W, additional, Chow, W E, additional, Tan, R S, additional, Lam, C S P, additional, Chua, T, additional, Yeo, T J, additional, and Chong, D T T, additional

Published
2019
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40. Comparison of Fatal or Irreversible Events With Extended-Duration Betrixaban Versus Standard Dose Enoxaparin in Acutely III Medical Patients: An APEX Trial Substudy

Author

Gibson, C. M., Korjian, S., Chi, G., Daaboul, Y., Jain, P., Arbetter, D., Goldhaber, S. Z., Hull, R., Hernandez, A. F., Lopes, R. D., Gold, A., Cohen, A. T., Harrington, R. A., Bello, F., Ferrari, A. E., Jure, H., Macin, S., Oliva, M., Parody, M., Poy, C., Baker, R., Colquhoun, D., Coughlin, P., Finfer, S., Hammerschlag, G., Rubinfeld, A., Huber, K., Konig, J., Mathies, R., Pilger, E., Schoenerr, H., Adzerikho, I., Koryk, V., Mikhailova, E., Mitkovskaya, N., Pimanov, S., Polonetsy, L., Soroka, N., Blockmans, D., Delforge, M., Dive, A., Lienart, F., Motte, S., Annichino Bizzacchi, J., Fiss, E., Freire, A., Manenti, E., Ramacciotti, E., Raymuno, S., Rocha, A., Saraiva, J. F., Dimov, B., Grigorov, M., Kalpachki, R., Kamenova, Z., Kostadinova, M., Milanova, M., Mincheva, V., Pencheva, G., Raev, D., Runev, N., Stoeva, N., Stoyanov, M., Syulemzova, S., Todorov, G., Tokmakova, M., Dhar, A., Douketis, J., Kahn, S., Le Gal, G., Pearce, M., Provencher, S., Verreault, S., Arias Alarcon, M., Olivares Canon, C., Opazo Lazcano, M., Potthoff Cardenas, S., Butkovic-Soldo, S., Car, S., Ciglenecki, N., Francetic, I., Jakopovic, M., Kalinic-Grgorinic, H., Knezevic, A., Malojcic, B., Marusic, S., Sikic Vagic, J., Skerk, V., Cermak, O., Cervinka, P., Chlumsky, J., Chochola, J., Cizek, V., Dunaj, M., Dusek, J., Francek, L., Havelka, J., Herold, M., Holaj, R., Horny, I., Hubac, J., Jajtner, P., Kolman, P., Lang, P., Mayer, O., Mikulova, J., Podpera, I., Reiterer, P., Spacek, R., Vejvoda, J., Vyhnanek, M., Christensen, H., Lassen, M., Storgaard, M., Tuxen, C., Urhammer, S., Lember, M., Marandi, T., Uuetoa, T., Airaksinen, J., Honkaniemi, J., Kaaja, R., Lassila, R., Saarinen, J., Tatlisumak, T., Vikman, S., Agraou, B., Aquilanti, S., Belhassane, A., Brisot, D., De Geeter, G., Debourdeau, P., Decoulx, E., El Kouri, D., Falvo, N., Grange, C., Lacroix, P., Messas, E., Mismetti, P., Montaclair, K., Mottier, D., Paleiron, N., Payot, L., Pernod, G., Pottier, P., Proust, A., Quere, I., Roy, P. -M., Schmidt, J., Simoneau, G., Datikashvili-David, I., Khabeishvili, G., Khintibidze, I., Kobulia, B., Megreladze, I., Pagava, Z., Paposhvili, K., Shaburishvili, T., Amann, B., Berrouschot, J., Beyer-Westendorf, J., Blessing, E., Bott, M., Dengler, T., Diehm, C., Dziewas, R., Genth-Zotz, S., Hamann, F., Horacek, T., Klimpe, S., Kroning, R., Lapp, H., Lawall, H., Licka, M., Rizos, T., Schellong, S., Schmidt-Lucke, J., Singer, C., Tiefenbacher, C., Veltkamp, R., Weimar, C., Zeymer, U., Alkonyi, B., Falukozy, J., Futo, L., Katona, A., Kirschner, R., Kristof, P., Lakatos, F., Laszlo, Z., Lupkovics, G., Merkely, B., Nagy Andras, C., Nemeth, L., Papp, A., Soltesz, P., Sudar, Z., Szabo, G., Szegedi, N., Timar, G., Valco, J., Vertes, A., Efrati, S., Elias, M., Gafter, A., Hayek, T., Hussein, O., Lishner, M., Lugassy, G., Zeltser, D., Ageno, W., Cerveri, I., D'Angelo, A., De Pellegrin, A., Imberti, D., Landolfi, R., Lembo, G., Lodigiani, C., Luisetti, M., Moia, M., Molteni, M., Mumoli, N., Novo, S., Orlandini, F., Parisi, R., Pizzini, A., Pomero, F., Salvi, A., Schenone, A., Visona, A., Krievins, D., Martinova, V., Pontaga, N., Rozitis, V., Stukena, I., Alekniene, B., Bagdonas, A., Basijokiene, V., Butkiene, Z., Griskeviciene, V., Naudziunas, A., Norvaisiene, R., Norviliene, R., Petrauskiene, R., Poskiene, R., Susinskiene, D., Valavicius, A., Castillo Leon, R., Cotrina Pereyra, R., Farjardo Karlo, L., Horna, M., Lema Osores, J., Salas, M., Toche Yanez, L., Fryze, W., Gaciong, Z., Gniot, J., Gorecka, D., Gruenpeter, P., Grzelakowski, P., Jastrzebski, D., Kucharski, L., Mirek-Bryniarska, E., Pulkowski, G., Sobkowicz, B., Sulik, P., Tomkowski, W., Walasek, L., Waldemar, K., Wrzesinski, K., Balogh, Z. E., Bojinca, M., Marin, I., Mot, S., Musetescu, R., Podoleanu, C., Popescu, M., Stamate, S., Stanciulescu, G., Vida-Simiti, L., Abashev, A., Andreev, D., Apartsin, K., Arkhipov, M., Averkov, O., Barbarash, O., Belskaya, G., Bogdanov, E., Boldueva, S., Chefranova, Z., Dovgalevskiy, Y., Ershova, O., Goloshchekin, B., Khachatryan, N., Khurs, E., Klein, G., Kobalava, Z., Kosmacheva, E., Kostenko, V., Malygin, A., Martynenko, T., Martynenko, V., Maslova, N., Mordovin, V., Nikolaev, K., Nilk, R., Panchenko, E., Popov, D., Privalova, E., Reshetko, O., Sergeeva, E., Shapovalova, Y., Shpagina, L., Shvarts, Y., Simanenkov, V., Solovyov, O., Vishneva, E., Vishnevskiy, A., Apostolovic, S., Celic, V., Ilic, S., Kovacevic-Kuzmanovic, A., Miloradovic, V., Tan, R. S., Bodikova, S., Cervenakova, A., Dvorak, M., Gaspar, L., Herman, O., Hrubon, A., Kokles, M., Krastev, G., Payer, J., Prokop, D., Spisakova, M., Adler, D., Basson, M., Breedt, J., Engelbrecht, J., Jacobson, B., Mitha, I., Van Dyk, C., Alvarez Sala, L. A., Barbagelata Lopez, C., Bisbe, J., Castro Guardiola, A., Cepeda, J. M., Cereto, F., Diaz Santos, E., Ferrer, R., Gomez Cerezo, J., Gonzales-Porras, J. R., Grandes, J., Jimenez, D., Martin Loeches, I., Mellibovsky, L., Richart, C., Riera, A., Trujillo, J., Vargas Nunez, J. A., Villalta, J., Akgul, O., Guneri, S., Kilichesmez, K., Kirma, C., Kutluk, H., Nazliel, B., Okumus, G., Ongen, G., Tigen, K., Topcuoglu, M., Tuncay, E., Abrahamovych, O., Batushkin, V., Brozhyk, J., Burmak, I., Godlevska, O., Goloborodko, A., Goloborodko, B., Goncharova, Y., Gryb, V., Karpenko, O., Kopytsya, M., Koshlia, V., Krakhmalova, O., Kyrychenko, I., Legkonogov, O., Malynovsky, Y., Maslovaskyi, V., Nikonov, V., Parkhomenko, O., Perepeliuk, M., Reshotko, D., Rudenko, L., Ryabichenko, T., Svyridova, I., Svyshchenko, Y., Tseluyko, V., Ursol, G., Vakaliuk, I., Vishnivestsky, I., Voronkov, L., Yagensky, A., Body, R., Chandra, D., Davis, M., Kesteven, P., Maccallum, P., Mccollum, C., Natarajan, I., Almasri, E., Amin, M., Anderson, C., Baker, S., Barney, J., Bastani, B. B. A., Bercz, P., Bidair, M., Carman, T., Chang, H., Clark, C., Concha, M., Cornell, J., Dhingra, R., Doshi, A., Ebrahimi, R., Farley, B., Fermann, G., Foster, G., Fraiz, J., Fulmer, J., Gaggin, H., Goytia-Leos, D., Hahn, B., Haidar, A., Hamad, A., Hazelrigg, M., Ioachimescu, O., Johnson, B., Kabler, H., Kao, C. -K., Kazimir, M., Kouras, F., Kung, M., Lerner, R., Lopez, J., Macchiavelli, A., Mahal, S., Margolis, B., Mclaren, G., Milling, T., Mittal, M., Nadar, V., Ohaju, V., Ortel, T., Overcash, J., Parthiban, K., Pearl, R., Pineda, L., Pratt, R., Pullman, J., Quintana, O., Rajan, R., Rastogi, P., Rees, C., Rodriguez, W., Saba, F., Shammas, N., Sharma, S., Sokol, S., Stoltz, S., Subich, D., Tuck, M., Updegrove, J., Warner, A., Welch, M., Welker, J., Whitman, B., Wichman, T., Yousef, K., Yusen, R., and Zakai, N.

Subjects
Male, pulmonary embolism, Time Factors, Pyridines, Intracranial hemorrhage, Kaplan-Meier Estimate, 030204 cardiovascular system & hematology, law.invention, chemistry.chemical_compound, 0302 clinical medicine, Randomized controlled trial, law, Risk Factors, Clinical Studies, Medicine, 030212 general & internal medicine, Myocardial infarction, Original Research, Ischemic stroke, Hazard ratio, Absolute risk reduction, Venous Thromboembolism, Middle Aged, Interventional Cardiology, Pulmonary embolism, Death, myocardial infarction, Treatment Outcome, Cardiovascular Diseases, Anesthesia, Acute Disease, Benzamides, Number needed to treat, Female, Cardiology and Cardiovascular Medicine, Intracranial Hemorrhages, Adult, venous thromboembolism, Hemorrhage, 03 medical and health sciences, Double-Blind Method, death, ischemic stroke, Humans, Enoxaparin, Aged, Proportional Hazards Models, Inpatients, Venous thromboembolism, business.industry, Settore MED/09 - MEDICINA INTERNA, Anticoagulants, Thrombosis, medicine.disease, Clinical trial, chemistry, Betrixaban, business, Acute Coronary Syndromes, intracranial hemorrhage, Factor Xa Inhibitors
Abstract

Background Extended‐duration betrixaban showed a significant reduction in venous thromboembolism in the APEX trial (Acute Medically Ill VTE Prevention With Extended Duration Betrixaban Study). Given the variable clinical impact of different efficacy and safety events, one approach to assess net clinical outcomes is to include only those events that are either fatal or cause irreversible harm. Methods and Results This was a post hoc analysis of the APEX trial—a multicenter, double‐blind, randomized controlled trial comparing extended‐duration betrixaban versus standard‐of‐care enoxaparin. A composite of all fatal or irreversible safety (fatal bleeding or intracranial hemorrhage) and efficacy events (cardiopulmonary death, myocardial infarction, pulmonary embolism, and ischemic stroke) was evaluated in a time‐to‐first event analysis. In patients with positive D‐dimer results, betrixaban reduced fatal or irreversible events at 35 to 42 days (4.80% versus 3.54%; hazard ratio, 0.73; absolute risk reduction, 1.26%; number needed to treat, 79 [ P =0.033]) and at study end at 77 days (6.27% versus 4.36%; hazard ratio, 0.70; absolute risk reduction, 1.91%; number needed to treat, 52 [ P =0.005]) versus enoxaparin. In all patients, betrixaban reduced fatal or irreversible events at 35 to 42 days (4.08% versus 2.90%; hazard ratio, 0.71; absolute risk reduction, 1.18%; number needed to treat, 86 [ P =0.006]) and 77 days (5.17% versus 3.64%; hazard ratio, 0.70; absolute risk reduction, 1.53%; number needed to treat, 65 [ P =0.002]). Conclusions Among hospitalized medically ill patients, extended‐duration betrixaban demonstrated an ≈30% reduction in fatal or irreversible ischemic or bleeding events compared with standard‐duration enoxaparin. A total of 65 patients would require treatment with betrixaban to prevent 1 fatal or irreversible event versus enoxaparin. Clinical Trial Registration URL : http://www.ClinicalTrials.gov . Unique identifier: NCT 01583218.

Published
2017

45. Extended thromboprophylaxis with betrixaban in acutely ill medical patients

Author

Cohen, A. T., Harrington, R. A., Goldhaber, S. Z., Hull, R. D., Wiens, B. L., Gold, A., Hernandez, A. F., Gibson, C. M., Bello, F., Ferrari, A. E., Jure, H., Macin, S., Oliva, M., Parody, M., Poy, C., Baker, R., Colquhoun, D., Coughlin, P., Finfer, S., Rubinfeld, A., Huber, K., Konig, J., Mathies, R., Pilger, E., Schoenerr, H., Adzerikho, I., Koryk, V., Mikhailova, E., Mitkovskaya, N., Pimanov, S., Polonetsy, L., Soroka, N., Blockmans, D., Delforge, M., Dive, A., Lienart, F., Motte, S., Bizzacchi, J., Fiss, E., Freire, A., Manenti, E., Ramacciotti, E., Raymuno, S., Rocha, A., Saraiva, J. F., Dimov, B., Grigorov, M., Kalpachki, R., Kamenova, Z., Kostadinova, M., Milanova, M., Mincheva, V., Pencheva, G., Raev, D., Runev, N., Stoeva, N., Stoyanov, M., Syulemzova, S., Todorov, G., Tokmakova, M., Dhar, A., Douketis, J., Kahn, S., Le Gal, G., Pearce, M., Provencher, S., Verreault, S., Alarcon, M., Canon, C., Lazcano, M., Cardenas, S., Butkovic-Soldo, S., Car, S., Ciglenecki, N., Francetic, I., Jakopovic, M., Kalinic-Grgorinic, H., Knezevic, A., Malojcic, B., Marusic, S., Vagic, J., Skerk, V., Cermak, O., Chlumsky, J., Chochola, J., Cizek, V., Dunaj, M., Dusek, J., Francek, L., Havelka, J., Herold, M., Holaj, R., Horny, I., Hubac, J., Jajtner, P., Kolman, P., Lang, P., Mayer, O., Mikulova, J., Podpera, I., Reiterer, P., Spacek, R., Vejvoda, J., Vyhnanek, M., Christensen, H., Lassen, M., Storgaard, M., Tuxen, C., Urhammer, S., Lember, M., Marandi, T., Uuetoa, T., Airaksinen, J., Honkaniemi, J., Kaaja, R., Lassila, R., Saarinen, J., Tatlisumak, T., Vikman, S., Agraou, B., Aquilanti, S., Belhassane, A., Brisot, D., De Geeter, G., Debourdeau, P., Decoulx, E., El Kouri, D., Falvo, N., Grange, C., Lacroix, P., Messas, E., Mismetti, P., Montaclair, K., Mottier, D., Paleiron, N., Payot, L., Pernod, G., Pottier, P., Proust, A., Quere, I., Roy, P. M., Schmidt, J., Simoneau, G., Datikashvili-David, I., Khabeishvili, G., Khintibidze, I., Kobulia, B., Megreladze, I., Pagava, Z., Paposhvili, K., Shaburishvili, T., Amann, B., Berrouschot, J., Beyer-Westendorf, J., Blessing, E., Bott, M., Dengler, T., Dziewas, R., Genth-Zotz, S., Hamann, F., Horacek, T., Klimpe, S., Kroning, R., Lapp, H., Lawall, H., Licka, M., Rizos, T., Schellong, S., Tiefenbacher, C., Weimar, C., Zeymer, U., Alkonyi, B., Falukozy, J., Futo, L., Katona, A., Kirschner, R., Kristof, P., Lakatos, F., Laszlo, Z., Lupkovics, G., Merkely, B., Andras, C., Nemeth, L., Papp, A., Soltesz, P., Sudar, Z., Szabo, G., Szegedi, N., Timar, G., Valco, J., Vertes, A., Efrati, S., Elias, M., Gafter, A., Hayek, T., Hussein, O., Lishner, M., Lugassy, G., Zeltser, D., Ageno, W., Cerveri, I., D'Angelo, A., De Pellegrin, A., Imberti, D., Landolfi, Raffaele, Lembo, G., Lodigiani, C., Moia, M., Molteni, M., Mumoli, N., Novo, S., Orlandini, F., Parisi, R., Pizzini, A., Pomero, F., Salvi, A., Schenone, A., Visona, A., Krievins, D., Martinova, V., Pontaga, N., Rozitis, V., Stukena, I., Alekniene, B., Bagdonas, A., Basijokiene, V., Butkiene, Z., Griskeviciene, V., Naudziunas, A., Norvaisiene, R., Norviliene, R., Petrauskiene, R., Poskiene, R., Susinskiene, D., Valavicius, A., Leon, R., Pereyra, R., Karlo, L., Horna, M., Osores, J., Salas, M., Yanez, L., Fryze, W., Gaciong, Z., Gniot, J., Gorecka, D., Gruenpeter, P., Grzelakowski, P., Jastrzebski, D., Kucharski, L., Mirek-Bryniarska, E., Pulkowski, G., Sobkowicz, B., Sulik, P., Tomkowski, W., Walasek, L., Waldemar, K., Wrzesinski, K., Balogh, Z. E., Bojinca, M., Marin, I., Mot, S., Musetescu, R., Podoleanu, C., Popescu, M., Stamate, S., Stanciulescu, G., Vida-Simiti, L., Abashev, A., Andreev, D., Apartsin, K., Arkhipov, M., Averkov, O., Barbarash, O., Belskaya, G., Bogdanov, E., Boldueva, S., Chefranova, Z., Dovgalevskiy, Y., Ershova, O., Goloshchekin, B., Khachatryan, N., Klein, G., Kobalava, Z., Kosmacheva, E., Kostenko, V., Malygin, A., Martynenko, T., Martynenko, V., Maslova, N., Mordovin, V., Nikolaev, K., Nilk, R., Panchenko, E., Popov, D., Privalova, E., Reshetko, O., Sergeeva, E., Shapovalova, Y., Shpagina, L., Shvarts, Y., Simanenkov, V., Solovyov, O., Vishneva, E., Vishnevskiy, A., Apostolovic, S., Celic, V., Ilic, S., Kovacevic-Kuzmanovic, A., Miloradovic, V., Tan, R. S., Bodikova, S., Cervenakova, A., Dvorak, M., Gaspar, L., Herman, O., Hrubon, A., Kokles, M., Krastev, G., Payer, J., Prokop, D., Spisakova, M., Adler, D., Basson, M., Breedt, J., Engelbrecht, J., Jacobson, B., Mitha, I., Van Dyk, C., Sala, L. A., Lopez, C., Bisbe, J., Guardiola, A., Cepeda, J. M., Cereto, F., Santos, E., Ferrer, R., Cerezo, J., Gonzales-Porras, J. R., Grandes, J., Jimenez, D., Mellibovsky, L., Richart, C., Riera, A., Trujillo, J., Nunez, J. A., Villalta, J., Akgul, O., Guneri, S., Kilichesmez, K., Kirma, C., Kutluk, H., Nazliel, B., Okumus, G., Ongen, G., Tigen, K., Topcuoglu, M., Tuncay, E., Abrahamovych, O., Batushkin, V., Brozhyk, J., Burmak, I., Godlevska, O., Goloborodko, A., Goloborodko, B., Goncharova, Y., Gryb, V., Karpenko, O., Kopytsya, M., Koshlia, V., Krakhmalova, O., Kyrychenko, I., Legkonogov, O., Malynovsky, Y., Maslovaskyi, V., Nikonov, V., Parkhomenko, O., Perepeliuk, M., Reshotko, D., Rudenko, L., Ryabichenko, T., Svyridova, I., Svyshchenko, Y., Tseluyko, V., Ursol, G., Vakaliuk, I., Vishnivestsky, I., Voronkov, L., Yagensky, A., Body, R., Davis, M., Maccallum, P., Mccollum, C., Natarajan, I., Almasri, E., Amin, M., Anderson, C., Baker, S., Barney, J., Bastani, A., Bercz, P., Bidair, M., Carman, T., Chang, H., Clark, C., Concha, M., Cornell, J., Dhingra, R., Doshi, A., Farley, B., Fermann, G., Fraiz, J., Fulmer, J., Gaggin, H., Goytia-Leos, D., Hahn, B., Haidar, A., Hamad, A., Hazelrigg, M., Ioachimescu, O., Kabler, H., Kao, C. K., Kazimir, M., Koura, F., Kung, M., Lerner, R., Lopez, J., Macchiavelli, A., Mahal, S., Margolis, B., Mclaren, G., Milling, T., Mittal, M., Nadar, V., Ohaju, V., Ortel, T., Overcash, J., Parthiban, K., Pearl, R., Pineda, L., Pratt, R., Pullman, J., Quintana, O., Rajan, R., Rastogi, P., Rees, C., Rodriguez, W., Saba, F., Shammas, N., Sharma, S., Sokol, S., Stoltz, S., Tuck, M., Updegrove, J., Warner, A., Welch, M., Welker, J., Whitman, B., Wichman, T., Yousef, K., Yusen, R., Zakai, N., Landolfi R. (ORCID:0000-0002-7913-8576), Cohen, A. T., Harrington, R. A., Goldhaber, S. Z., Hull, R. D., Wiens, B. L., Gold, A., Hernandez, A. F., Gibson, C. M., Bello, F., Ferrari, A. E., Jure, H., Macin, S., Oliva, M., Parody, M., Poy, C., Baker, R., Colquhoun, D., Coughlin, P., Finfer, S., Rubinfeld, A., Huber, K., Konig, J., Mathies, R., Pilger, E., Schoenerr, H., Adzerikho, I., Koryk, V., Mikhailova, E., Mitkovskaya, N., Pimanov, S., Polonetsy, L., Soroka, N., Blockmans, D., Delforge, M., Dive, A., Lienart, F., Motte, S., Bizzacchi, J., Fiss, E., Freire, A., Manenti, E., Ramacciotti, E., Raymuno, S., Rocha, A., Saraiva, J. F., Dimov, B., Grigorov, M., Kalpachki, R., Kamenova, Z., Kostadinova, M., Milanova, M., Mincheva, V., Pencheva, G., Raev, D., Runev, N., Stoeva, N., Stoyanov, M., Syulemzova, S., Todorov, G., Tokmakova, M., Dhar, A., Douketis, J., Kahn, S., Le Gal, G., Pearce, M., Provencher, S., Verreault, S., Alarcon, M., Canon, C., Lazcano, M., Cardenas, S., Butkovic-Soldo, S., Car, S., Ciglenecki, N., Francetic, I., Jakopovic, M., Kalinic-Grgorinic, H., Knezevic, A., Malojcic, B., Marusic, S., Vagic, J., Skerk, V., Cermak, O., Chlumsky, J., Chochola, J., Cizek, V., Dunaj, M., Dusek, J., Francek, L., Havelka, J., Herold, M., Holaj, R., Horny, I., Hubac, J., Jajtner, P., Kolman, P., Lang, P., Mayer, O., Mikulova, J., Podpera, I., Reiterer, P., Spacek, R., Vejvoda, J., Vyhnanek, M., Christensen, H., Lassen, M., Storgaard, M., Tuxen, C., Urhammer, S., Lember, M., Marandi, T., Uuetoa, T., Airaksinen, J., Honkaniemi, J., Kaaja, R., Lassila, R., Saarinen, J., Tatlisumak, T., Vikman, S., Agraou, B., Aquilanti, S., Belhassane, A., Brisot, D., De Geeter, G., Debourdeau, P., Decoulx, E., El Kouri, D., Falvo, N., Grange, C., Lacroix, P., Messas, E., Mismetti, P., Montaclair, K., Mottier, D., Paleiron, N., Payot, L., Pernod, G., Pottier, P., Proust, A., Quere, I., Roy, P. M., Schmidt, J., Simoneau, G., Datikashvili-David, I., Khabeishvili, G., Khintibidze, I., Kobulia, B., Megreladze, I., Pagava, Z., Paposhvili, K., Shaburishvili, T., Amann, B., Berrouschot, J., Beyer-Westendorf, J., Blessing, E., Bott, M., Dengler, T., Dziewas, R., Genth-Zotz, S., Hamann, F., Horacek, T., Klimpe, S., Kroning, R., Lapp, H., Lawall, H., Licka, M., Rizos, T., Schellong, S., Tiefenbacher, C., Weimar, C., Zeymer, U., Alkonyi, B., Falukozy, J., Futo, L., Katona, A., Kirschner, R., Kristof, P., Lakatos, F., Laszlo, Z., Lupkovics, G., Merkely, B., Andras, C., Nemeth, L., Papp, A., Soltesz, P., Sudar, Z., Szabo, G., Szegedi, N., Timar, G., Valco, J., Vertes, A., Efrati, S., Elias, M., Gafter, A., Hayek, T., Hussein, O., Lishner, M., Lugassy, G., Zeltser, D., Ageno, W., Cerveri, I., D'Angelo, A., De Pellegrin, A., Imberti, D., Landolfi, Raffaele, Lembo, G., Lodigiani, C., Moia, M., Molteni, M., Mumoli, N., Novo, S., Orlandini, F., Parisi, R., Pizzini, A., Pomero, F., Salvi, A., Schenone, A., Visona, A., Krievins, D., Martinova, V., Pontaga, N., Rozitis, V., Stukena, I., Alekniene, B., Bagdonas, A., Basijokiene, V., Butkiene, Z., Griskeviciene, V., Naudziunas, A., Norvaisiene, R., Norviliene, R., Petrauskiene, R., Poskiene, R., Susinskiene, D., Valavicius, A., Leon, R., Pereyra, R., Karlo, L., Horna, M., Osores, J., Salas, M., Yanez, L., Fryze, W., Gaciong, Z., Gniot, J., Gorecka, D., Gruenpeter, P., Grzelakowski, P., Jastrzebski, D., Kucharski, L., Mirek-Bryniarska, E., Pulkowski, G., Sobkowicz, B., Sulik, P., Tomkowski, W., Walasek, L., Waldemar, K., Wrzesinski, K., Balogh, Z. E., Bojinca, M., Marin, I., Mot, S., Musetescu, R., Podoleanu, C., Popescu, M., Stamate, S., Stanciulescu, G., Vida-Simiti, L., Abashev, A., Andreev, D., Apartsin, K., Arkhipov, M., Averkov, O., Barbarash, O., Belskaya, G., Bogdanov, E., Boldueva, S., Chefranova, Z., Dovgalevskiy, Y., Ershova, O., Goloshchekin, B., Khachatryan, N., Klein, G., Kobalava, Z., Kosmacheva, E., Kostenko, V., Malygin, A., Martynenko, T., Martynenko, V., Maslova, N., Mordovin, V., Nikolaev, K., Nilk, R., Panchenko, E., Popov, D., Privalova, E., Reshetko, O., Sergeeva, E., Shapovalova, Y., Shpagina, L., Shvarts, Y., Simanenkov, V., Solovyov, O., Vishneva, E., Vishnevskiy, A., Apostolovic, S., Celic, V., Ilic, S., Kovacevic-Kuzmanovic, A., Miloradovic, V., Tan, R. S., Bodikova, S., Cervenakova, A., Dvorak, M., Gaspar, L., Herman, O., Hrubon, A., Kokles, M., Krastev, G., Payer, J., Prokop, D., Spisakova, M., Adler, D., Basson, M., Breedt, J., Engelbrecht, J., Jacobson, B., Mitha, I., Van Dyk, C., Sala, L. A., Lopez, C., Bisbe, J., Guardiola, A., Cepeda, J. M., Cereto, F., Santos, E., Ferrer, R., Cerezo, J., Gonzales-Porras, J. R., Grandes, J., Jimenez, D., Mellibovsky, L., Richart, C., Riera, A., Trujillo, J., Nunez, J. A., Villalta, J., Akgul, O., Guneri, S., Kilichesmez, K., Kirma, C., Kutluk, H., Nazliel, B., Okumus, G., Ongen, G., Tigen, K., Topcuoglu, M., Tuncay, E., Abrahamovych, O., Batushkin, V., Brozhyk, J., Burmak, I., Godlevska, O., Goloborodko, A., Goloborodko, B., Goncharova, Y., Gryb, V., Karpenko, O., Kopytsya, M., Koshlia, V., Krakhmalova, O., Kyrychenko, I., Legkonogov, O., Malynovsky, Y., Maslovaskyi, V., Nikonov, V., Parkhomenko, O., Perepeliuk, M., Reshotko, D., Rudenko, L., Ryabichenko, T., Svyridova, I., Svyshchenko, Y., Tseluyko, V., Ursol, G., Vakaliuk, I., Vishnivestsky, I., Voronkov, L., Yagensky, A., Body, R., Davis, M., Maccallum, P., Mccollum, C., Natarajan, I., Almasri, E., Amin, M., Anderson, C., Baker, S., Barney, J., Bastani, A., Bercz, P., Bidair, M., Carman, T., Chang, H., Clark, C., Concha, M., Cornell, J., Dhingra, R., Doshi, A., Farley, B., Fermann, G., Fraiz, J., Fulmer, J., Gaggin, H., Goytia-Leos, D., Hahn, B., Haidar, A., Hamad, A., Hazelrigg, M., Ioachimescu, O., Kabler, H., Kao, C. K., Kazimir, M., Koura, F., Kung, M., Lerner, R., Lopez, J., Macchiavelli, A., Mahal, S., Margolis, B., Mclaren, G., Milling, T., Mittal, M., Nadar, V., Ohaju, V., Ortel, T., Overcash, J., Parthiban, K., Pearl, R., Pineda, L., Pratt, R., Pullman, J., Quintana, O., Rajan, R., Rastogi, P., Rees, C., Rodriguez, W., Saba, F., Shammas, N., Sharma, S., Sokol, S., Stoltz, S., Tuck, M., Updegrove, J., Warner, A., Welch, M., Welker, J., Whitman, B., Wichman, T., Yousef, K., Yusen, R., Zakai, N., and Landolfi R. (ORCID:0000-0002-7913-8576)

Abstract

BACKGROUND: Patients with acute medical illnesses are at prolonged risk for venous thrombosis. However, the appropriate duration of thromboprophylaxis remains unknown. METHODS: Patients who were hospitalized for acute medical illnesses were randomly assigned to receive subcutaneous enoxaparin (at a dose of 40 mg once daily) for 10±4 days plus oral betrixaban placebo for 35 to 42 days or subcutaneous enoxaparin placebo for 10±4 days plus oral betrixaban (at a dose of 80 mg once daily) for 35 to 42 days. We performed sequential analyses in three prespecified, progressively inclusive cohorts: patients with an elevated D-dimer level (cohort 1), patients with an elevated D-dimer level or an age of at least 75 years (cohort 2), and all the enrolled patients (overall population cohort). The statistical analysis plan specified that if the between-group difference in any analysis in this sequence was not significant, the other analyses would be considered exploratory. The primary efficacy outcome was a composite of asymptomatic proximal deep-vein thrombosis and symptomatic venous thromboembolism. The principal safety outcome was major bleeding. RESULTS: A total of 7513 patients underwent randomization. In cohort 1, the primary efficacy outcome occurred in 6.9% of patients receiving betrixaban and 8.5% receiving enoxaparin (relative risk in the betrixaban group, 0.81; 95% confidence interval [CI], 0.65 to 1.00; P = 0.054). The rates were 5.6% and 7.1%, respectively (relative risk, 0.80; 95% CI, 0.66 to 0.98; P = 0.03) in cohort 2 and 5.3% and 7.0% (relative risk, 0.76; 95% CI, 0.63 to 0.92; P = 0.006) in the overall population. (The last two analyses were considered to be exploratory owing to the result in cohort 1.) In the overall population, major bleeding occurred in 0.7% of the betrixaban group and 0.6% of the enoxaparin group (relative risk, 1.19; 95% CI, 0.67 to 2.12; P = 0.55). CONCLUSIONS: Among acutely ill medical patients with an elevated D-dimer level, there was no

Published
2016

48. Impact of early valve surgery on outcome of staphylococcus aureus prosthetic valve infective endocarditis: Analysis in the international collaboration of endocarditis-prospective cohort study.

Author

Vincelj J., Sexton D.J., Corey G.R., Chu V.H., Wang A., Erpelding M.-L., Durante-Mangoni E., Fernandez-Hidalgo N., Giannitsioti E., Hannan M.M., Lejko-Zupanc T., Pare C., Pericas J., Ramirez J., Rovira I., Sitges M., Anguera I., Font B., Guma J.R., Bermejo J., Bouza E., Fernandez M.A.G., Gonzalez-Ramallo V., Marin M., Pedromingo M., Roda J., Rodriguez-Creixems M., Solis J., Almirante B., Tornos P., De Alarcon A., Parra R., Alestig E., Johansson M., Olaison L., Snygg-Martin U., Pachirat O., Pachirat P., Pussadhamma B., Senthong V., Casey A., Elliott T., Lambert P., Watkin R., Eyton C., Klein J.L., Bradley S., Kauffman C., Bedimo R., Crowley A.L., Douglas P., Drew L., Holland T., Lalani T., Mudrick D., Samad Z., Stryjewski M., Woods C.W., Lerakis S., Cantey R., Steed L., Wray D., Dickerman S.A., Bonilla H., Di Persio J., Salstrom S.-J., Baddley J., Patel M., Peterson G., Stancoven A., Afonso L., Kulman T., Levine D., Rybak M., Cabell C.H., Baloch K., Dixon C.C., Harding T., Jones-Richmond M., Pappas P., Park L.P., Redick T., Stafford J., Anstrom K., Bayer A.S., Karchmer A.W., Durack D.T., Eykyn S., Moreillon P., Chirouze C., Alla F., Fowler V.G., Miro J.M., Munoz P., Murdoch D.R., Tattevin P., Tribouilloy C., Hoen B., Clara L., Sanchez M., Nacinovich F., Oses P.F., Ronderos R., Sucari A., Thierer J., Casabe J., Cortes C., Altclas J., Kogan S., Spelman D., Athan E., Harris O., Kennedy K., Tan R., Gordon D., Papanicolas L., Eisen D., Grigg L., Street A., Korman T., Kotsanas D., Dever R., Konecny P., Lawrence R., Rees D., Ryan S., Feneley M.P., Harkness J., Jones P., Post J., Reinbott P., Gattringer R., Wiesbauer F., Andrade A.R., De Brito A.C.P., Guimaraes A.C., Grinberg M., Mansur A.J., Siciliano R.F., Strabelli T.M.V., Vieira M.L.C., De Medeiros Tranchesi R.A., Paiva M.G., Fortes C.Q., De Oliveira Ramos A., Ferraiuoli G., Golebiovski W., Lamas C., Santos M., Weksler C., Karlowsky J.A., Keynan Y., Morris A.M., Rubinstein E., Jones S.B., Garcia P., Cereceda M., Fica A., Mella R.M., Barsic B., Bukovski S., Krajinovic V., Pangercic A., Rudez I., Freiberger T., Pol J., Zaloudikova B., Zainab A., El Kholy A., Mishaal M., Rizk H., Aissa N., Alauzet C., Campagnac C., Doco-Lecompte T., Selton-Suty C., Casalta J.-P., Fournier P.-E., Habib G., Raoult D., Thuny F., Delahaye F., Delahaye A., Vandenesch F., Donal E., Donnio P.Y., Michelet C., Revest M., Violette J., Chevalier F., Jeu A., Rusinaru D.M.D., Sorel C., Bernard Y., Leroy J., Plesiat P., Naber C., Neuerburg C., Mazaheri B., Athanasia S., Deliolanis I., Giamarellou H., Tsaganos T., Mylona E., Paniara O., Papanicolaou K., Pyros J., Skoutelis A., Sharma G., Francis J., Nair L., Thomas V., Venugopal K., Hurley J., Gilon D., Israel S., Korem M., Strahilevitz J., Casillo R., Cuccurullo S., Dialetto G., Irene M., Ragone E., Tripodi M.F., Utili R., Cecchi E., De Rosa F., Forno D., Imazio M., Trinchero R., Tebini A., Grossi P., Lattanzio M., Toniolo A., Goglio A., Raglio A., Ravasio V., Rizzi M., Suter F., Carosi G., Magri S., Signorini L., Baban T., Kanafani Z., Kanj S.S., Yasmine M., Abidin I., Tamin S.S., Martinez E.R., Nieto G.I.S., Van Der Meer J.T.M., Chambers S., Holland D., Morris A., Raymond N., Read K., Dragulescu S., Ionac A., Mornos C., Butkevich O.M., Chipigina N., Kirill O., Vadim K., Vinogradova T., Edathodu J., Halim M., Lum L.-N., Tan R.-S., Logar M., Mueller-Premru M., Commerford P., Commerford A., Deetlefs E., Hansa C., Ntsekhe M., Almela M., Armero Y., Azqueta M., Castaneda X., Cervera C., Del Rio A., Falces C., Garcia-De-La-Maria C., Fita G., Gatell J.M., Marco F., Mestres C.A., Moreno A., Ninot S., Vincelj J., Sexton D.J., Corey G.R., Chu V.H., Wang A., Erpelding M.-L., Durante-Mangoni E., Fernandez-Hidalgo N., Giannitsioti E., Hannan M.M., Lejko-Zupanc T., Pare C., Pericas J., Ramirez J., Rovira I., Sitges M., Anguera I., Font B., Guma J.R., Bermejo J., Bouza E., Fernandez M.A.G., Gonzalez-Ramallo V., Marin M., Pedromingo M., Roda J., Rodriguez-Creixems M., Solis J., Almirante B., Tornos P., De Alarcon A., Parra R., Alestig E., Johansson M., Olaison L., Snygg-Martin U., Pachirat O., Pachirat P., Pussadhamma B., Senthong V., Casey A., Elliott T., Lambert P., Watkin R., Eyton C., Klein J.L., Bradley S., Kauffman C., Bedimo R., Crowley A.L., Douglas P., Drew L., Holland T., Lalani T., Mudrick D., Samad Z., Stryjewski M., Woods C.W., Lerakis S., Cantey R., Steed L., Wray D., Dickerman S.A., Bonilla H., Di Persio J., Salstrom S.-J., Baddley J., Patel M., Peterson G., Stancoven A., Afonso L., Kulman T., Levine D., Rybak M., Cabell C.H., Baloch K., Dixon C.C., Harding T., Jones-Richmond M., Pappas P., Park L.P., Redick T., Stafford J., Anstrom K., Bayer A.S., Karchmer A.W., Durack D.T., Eykyn S., Moreillon P., Chirouze C., Alla F., Fowler V.G., Miro J.M., Munoz P., Murdoch D.R., Tattevin P., Tribouilloy C., Hoen B., Clara L., Sanchez M., Nacinovich F., Oses P.F., Ronderos R., Sucari A., Thierer J., Casabe J., Cortes C., Altclas J., Kogan S., Spelman D., Athan E., Harris O., Kennedy K., Tan R., Gordon D., Papanicolas L., Eisen D., Grigg L., Street A., Korman T., Kotsanas D., Dever R., Konecny P., Lawrence R., Rees D., Ryan S., Feneley M.P., Harkness J., Jones P., Post J., Reinbott P., Gattringer R., Wiesbauer F., Andrade A.R., De Brito A.C.P., Guimaraes A.C., Grinberg M., Mansur A.J., Siciliano R.F., Strabelli T.M.V., Vieira M.L.C., De Medeiros Tranchesi R.A., Paiva M.G., Fortes C.Q., De Oliveira Ramos A., Ferraiuoli G., Golebiovski W., Lamas C., Santos M., Weksler C., Karlowsky J.A., Keynan Y., Morris A.M., Rubinstein E., Jones S.B., Garcia P., Cereceda M., Fica A., Mella R.M., Barsic B., Bukovski S., Krajinovic V., Pangercic A., Rudez I., Freiberger T., Pol J., Zaloudikova B., Zainab A., El Kholy A., Mishaal M., Rizk H., Aissa N., Alauzet C., Campagnac C., Doco-Lecompte T., Selton-Suty C., Casalta J.-P., Fournier P.-E., Habib G., Raoult D., Thuny F., Delahaye F., Delahaye A., Vandenesch F., Donal E., Donnio P.Y., Michelet C., Revest M., Violette J., Chevalier F., Jeu A., Rusinaru D.M.D., Sorel C., Bernard Y., Leroy J., Plesiat P., Naber C., Neuerburg C., Mazaheri B., Athanasia S., Deliolanis I., Giamarellou H., Tsaganos T., Mylona E., Paniara O., Papanicolaou K., Pyros J., Skoutelis A., Sharma G., Francis J., Nair L., Thomas V., Venugopal K., Hurley J., Gilon D., Israel S., Korem M., Strahilevitz J., Casillo R., Cuccurullo S., Dialetto G., Irene M., Ragone E., Tripodi M.F., Utili R., Cecchi E., De Rosa F., Forno D., Imazio M., Trinchero R., Tebini A., Grossi P., Lattanzio M., Toniolo A., Goglio A., Raglio A., Ravasio V., Rizzi M., Suter F., Carosi G., Magri S., Signorini L., Baban T., Kanafani Z., Kanj S.S., Yasmine M., Abidin I., Tamin S.S., Martinez E.R., Nieto G.I.S., Van Der Meer J.T.M., Chambers S., Holland D., Morris A., Raymond N., Read K., Dragulescu S., Ionac A., Mornos C., Butkevich O.M., Chipigina N., Kirill O., Vadim K., Vinogradova T., Edathodu J., Halim M., Lum L.-N., Tan R.-S., Logar M., Mueller-Premru M., Commerford P., Commerford A., Deetlefs E., Hansa C., Ntsekhe M., Almela M., Armero Y., Azqueta M., Castaneda X., Cervera C., Del Rio A., Falces C., Garcia-De-La-Maria C., Fita G., Gatell J.M., Marco F., Mestres C.A., Moreno A., and Ninot S.

Abstract

Background. The impact of early valve surgery (EVS) on the outcome of Staphylococcus aureus (SA) prosthetic valve infective endocarditis (PVIE) is unresolved. The objective of this study was to evaluate the association between EVS, performed within the first 60 days of hospitalization, and outcome of SA PVIE within the International Collaboration on Endocarditis-Prospective Cohort Study. Methods. Participants were enrolled between June 2000 and December 2006. Cox proportional hazards modeling that included surgery as a time-dependent covariate and propensity adjustment for likelihood to receive cardiac surgery was used to evaluate the impact of EVS and 1-year all-cause mortality on patients with definite left-sided S. aureus PVIE and no history of injection drug use. Results. EVS was performed in 74 of the 168 (44.3%) patients. One-year mortality was significantly higher among patients with S. aureus PVIE than in patients with non-S. aureus PVIE (48.2% vs 32.9%; P = .003). Staphylococcus aureus PVIE patients who underwent EVS had a significantly lower 1-year mortality rate (33.8% vs 59.1%; P = .001). In multivariate, propensity-adjusted models, EVS was not associated with 1-year mortality (risk ratio, 0.67 [95% confidence interval, .39-1.15]; P = .15). Conclusions. In this prospective, multinational cohort of patients with S. aureus PVIE, EVS was not associated with reduced 1-year mortality. The decision to pursue EVS should be individualized for each patient, based upon infection-specific characteristics rather than solely upon the microbiology of the infection causing PVIE.Copyright © The Author 2014.

Published
2015

49. Candida infective endocarditis: An observational cohort study with a focus on therapy.

Author

Campagnac C., Llopis J., Marco F., Mestres C.A., Moreno A., Ninot S., Pare C., Pericas J.M., Ramirez J., Rovira I., Sitges M., Anguera I., Font B., Guma J.R., Bermejo J., Bouza E., Leoni M.E.G., Robles J.A.G., Ramallo V.G., Cruz A.F., Kestler M., Marin M., Selles M.M., Abella H.R., Roda J.R., Lopez R.A., Pinilla B., Pinto A., Valerio M., Vazquez P., Verde E., Almirante B., De Alarcon A., Parra R., Alestig E., Johansson M., Olaison L., Snygg-Martin U., Pachirat O., Pachirat P., Pussadhamma B., Senthong V., Casey A., Elliott T., Lambert P., Watkin R., Eyton C., Klein J.L., Kauffman C., Bedimo R., Corey G.R., Crowley A.L., Douglas P., Drew L., Fowler V.G., Holland T., Lalani T., Mudrick D., Samad Z., Sexton D., Stryjewski M., Wang A., Woods C.W., Lerakis S., Cantey R., Steed L., Wray D., Dickerman S.A., Bonilla H., DiPersio J., Salstrom S.-J., Baddley J., Patel M., Peterson G., Stancoven A., Levine D., Riddle J., Rybak M., Cabell C.H., Baloch K., Dixon C.C., Harding T., Jones-Richmond M., Park L.P., Sanderford B., Stafford J., Anstrom K., Karchmer A.W., Sexton D.J., Durack D.T., Eykyn S., Moreillon P., Sexton. D.J., Arnold C.J., Johnson M., Bayer A.S., Bradley S., Giannitsioti E., Miro J.M., Tornos P., Tattevin P., Strahilevitz J., Spelman D., Athan E., Nacinovich F., Lamas C., Barsic B., Fernandez-Hidalgo N., Munoz P., Chu V.H., Clara L., Sanchez M., Casabe J., Cortes C., Oses P.F., Ronderos R., Sucari A., Thierer J., Altclas J., Kogan S., Harris O., Kennedy K., Tan R., Gordon D., Papanicolas L., Korman T., Kotsanas D., Dever R., Jones P., Konecny P., Lawrence R., Rees D., Ryan S., Feneley M.P., Harkness J., Post J., Reinbott P., Gattringer R., Wiesbauer F., Andrade A.R., De Brito A.C.P., Guimaraes A.C., Grinberg M., Mansur A.J., Siciliano R.F., Strabelli T.M.V., Vieira M.L.C., De Medeiros Tranchesi R.A., Paiva M.G., Fortes C.Q., De Oliveira Ramos A., Weksler C., Ferraiuoli G., Golebiovski W., Karlowsky J.A., Keynan Y., Morris A.M., Rubinstein E., Jones S.B., Garcia P., Cereceda M., Fica A., Mella R.M., Fernandez R., Franco L., Gonzalez J., Jaramillo A.N., Bukovski S., Krajinovic V., Pangercic A., Rudez I., Vincelj J., Freiberger T., Pol J., Zaloudikova B., Ashour Z., Kholy A.E., Mishaal M., Osama D., Rizk H., Aissa N., Alauzet C., Alla F., Doco-Lecompte T., Selton-Suty C., Casalta J.-P., Fournier P.-E., Habib G., Raoult D., Thuny F., Delahaye F., Delahaye A., Vandenesch F., Donal E., Donnio P.Y., Flecher E., Michelet C., Revest M., Chevalier F., Jeu A., Remadi J.P., Rusinaru D., Tribouilloy C., Bernard Y., Chirouze C., Hoen B., Leroy J., Plesiat P., Naber C., Neuerburg C., Mazaheri B., Helen G., Sofia A., Ioannis D., Thomas T., Mylona E., Paniara O., Papanicolaou K., Pyros J., Skoutelis A., Sharma G., Francis J., Nair L., Thomas V., Venugopal K., Hannan M.M., Hurley J.P., Cahan A., Gilon D., Israel S., Korem M., Durante-Mangoni E., Mattucci I., Pinto D., Agrusta F., Senese A., Ragone E., Utili R., Cecchi E., De Rosa F., Forno D., Imazio M., Trinchero R., Grossi P., Lattanzio M., Toniolo A., Goglio A., Raglio A., Ravasio V., Rizzi M., Suter F., Carosi G., Magri S., Signorini L., Kanafani Z., Kanj S.S., Sharif-Yakan A., Abidin I., Tamin S.S., Martinez E.R., Nieto G.I.S., Van Der Meer J.T.M., Chambers S., Holland D., Morris A., Raymond N., Read K., Murdoch D.R., Dragulescu S., Ionac A., Mornos C., Butkevich O.M., Chipigina N., Kirill O., Vadim K., Vinogradova T., Edathodu J., Halim M., Liew Y.-Y., Tan R.-S., Lejko-Zupanc T., Logar M., Mueller-Premru M., Commerford P., Commerford A., Deetlefs E., Hansa C., Ntsekhe M., Almela M., Armero Y., Azqueta M., Castaneda X., Cervera C., Falces C., Garcia-De-La-Maria C., Fita G., Gatell J.M., Heras M., Campagnac C., Llopis J., Marco F., Mestres C.A., Moreno A., Ninot S., Pare C., Pericas J.M., Ramirez J., Rovira I., Sitges M., Anguera I., Font B., Guma J.R., Bermejo J., Bouza E., Leoni M.E.G., Robles J.A.G., Ramallo V.G., Cruz A.F., Kestler M., Marin M., Selles M.M., Abella H.R., Roda J.R., Lopez R.A., Pinilla B., Pinto A., Valerio M., Vazquez P., Verde E., Almirante B., De Alarcon A., Parra R., Alestig E., Johansson M., Olaison L., Snygg-Martin U., Pachirat O., Pachirat P., Pussadhamma B., Senthong V., Casey A., Elliott T., Lambert P., Watkin R., Eyton C., Klein J.L., Kauffman C., Bedimo R., Corey G.R., Crowley A.L., Douglas P., Drew L., Fowler V.G., Holland T., Lalani T., Mudrick D., Samad Z., Sexton D., Stryjewski M., Wang A., Woods C.W., Lerakis S., Cantey R., Steed L., Wray D., Dickerman S.A., Bonilla H., DiPersio J., Salstrom S.-J., Baddley J., Patel M., Peterson G., Stancoven A., Levine D., Riddle J., Rybak M., Cabell C.H., Baloch K., Dixon C.C., Harding T., Jones-Richmond M., Park L.P., Sanderford B., Stafford J., Anstrom K., Karchmer A.W., Sexton D.J., Durack D.T., Eykyn S., Moreillon P., Sexton. D.J., Arnold C.J., Johnson M., Bayer A.S., Bradley S., Giannitsioti E., Miro J.M., Tornos P., Tattevin P., Strahilevitz J., Spelman D., Athan E., Nacinovich F., Lamas C., Barsic B., Fernandez-Hidalgo N., Munoz P., Chu V.H., Clara L., Sanchez M., Casabe J., Cortes C., Oses P.F., Ronderos R., Sucari A., Thierer J., Altclas J., Kogan S., Harris O., Kennedy K., Tan R., Gordon D., Papanicolas L., Korman T., Kotsanas D., Dever R., Jones P., Konecny P., Lawrence R., Rees D., Ryan S., Feneley M.P., Harkness J., Post J., Reinbott P., Gattringer R., Wiesbauer F., Andrade A.R., De Brito A.C.P., Guimaraes A.C., Grinberg M., Mansur A.J., Siciliano R.F., Strabelli T.M.V., Vieira M.L.C., De Medeiros Tranchesi R.A., Paiva M.G., Fortes C.Q., De Oliveira Ramos A., Weksler C., Ferraiuoli G., Golebiovski W., Karlowsky J.A., Keynan Y., Morris A.M., Rubinstein E., Jones S.B., Garcia P., Cereceda M., Fica A., Mella R.M., Fernandez R., Franco L., Gonzalez J., Jaramillo A.N., Bukovski S., Krajinovic V., Pangercic A., Rudez I., Vincelj J., Freiberger T., Pol J., Zaloudikova B., Ashour Z., Kholy A.E., Mishaal M., Osama D., Rizk H., Aissa N., Alauzet C., Alla F., Doco-Lecompte T., Selton-Suty C., Casalta J.-P., Fournier P.-E., Habib G., Raoult D., Thuny F., Delahaye F., Delahaye A., Vandenesch F., Donal E., Donnio P.Y., Flecher E., Michelet C., Revest M., Chevalier F., Jeu A., Remadi J.P., Rusinaru D., Tribouilloy C., Bernard Y., Chirouze C., Hoen B., Leroy J., Plesiat P., Naber C., Neuerburg C., Mazaheri B., Helen G., Sofia A., Ioannis D., Thomas T., Mylona E., Paniara O., Papanicolaou K., Pyros J., Skoutelis A., Sharma G., Francis J., Nair L., Thomas V., Venugopal K., Hannan M.M., Hurley J.P., Cahan A., Gilon D., Israel S., Korem M., Durante-Mangoni E., Mattucci I., Pinto D., Agrusta F., Senese A., Ragone E., Utili R., Cecchi E., De Rosa F., Forno D., Imazio M., Trinchero R., Grossi P., Lattanzio M., Toniolo A., Goglio A., Raglio A., Ravasio V., Rizzi M., Suter F., Carosi G., Magri S., Signorini L., Kanafani Z., Kanj S.S., Sharif-Yakan A., Abidin I., Tamin S.S., Martinez E.R., Nieto G.I.S., Van Der Meer J.T.M., Chambers S., Holland D., Morris A., Raymond N., Read K., Murdoch D.R., Dragulescu S., Ionac A., Mornos C., Butkevich O.M., Chipigina N., Kirill O., Vadim K., Vinogradova T., Edathodu J., Halim M., Liew Y.-Y., Tan R.-S., Lejko-Zupanc T., Logar M., Mueller-Premru M., Commerford P., Commerford A., Deetlefs E., Hansa C., Ntsekhe M., Almela M., Armero Y., Azqueta M., Castaneda X., Cervera C., Falces C., Garcia-De-La-Maria C., Fita G., Gatell J.M., and Heras M.

Abstract

Candida infective endocarditis is a rare disease with a high mortality rate. Our understanding of this infection is derived from case series, case reports, and small prospective cohorts. The purpose of this study was to evaluate the clinical features and use of different antifungal treatment regimens for Candida infective endocarditis. This prospective cohort study was based on 70 cases of Candida infective endocarditis from the International Collaboration on Endocarditis (ICE)-Prospective Cohort Study and ICE-Plus databases collected between 2000 and 2010. The majority of infections were acquired nosocomially (67%). Congestive heart failure (24%), prosthetic heart valve (46%), and previous infective endocarditis (26%) were common comorbidities. Overall mortality was high, with 36% mortality in the hospital and 59% at 1 year. On univariate analysis, older age, heart failure at baseline, persistent candidemia, nosocomial acquisition, heart failure as a complication, and intracardiac abscess were associated with higher mortality. Mortality was not affected by use of surgical therapy or choice of antifungal agent. A subgroup analysis was performed on 33 patients for whom specific antifungal therapy information was available. In this subgroup, 11 patients received amphotericin B-based therapy and 14 received echinocandin-based therapy. Despite a higher percentage of older patients and nosocomial infection in the echinocandin group, mortality rates were similar between the two groups. In conclusion, Candida infective endocarditis is associated with a high mortality rate that was not impacted by choice of antifungal therapy or by adjunctive surgical intervention. Additionally, echinocandin therapy was as effective as amphotericin B-based therapy in the small subgroup analysis.Copyright © 2015, American Society for Microbiology.

Published
2015

50. One-year outcome following biological or mechanical valve replacement for infective endocarditis.

Author

Pangercic A., Munoz P., Pedromingo M., Roda J., Rodriguez-Creixems M., Solis J., Almirante B., Fernandez-Hidalgo N., Tornos P., de Alarcon A., Parra R., Alestig E., Johansson M., Olaison L., Snygg-Martin U., Pachirat P., Pussadhamma B., Senthong V., Casey A., Elliott T., Lambert P., Watkin R., Eyton C., Klein J.L., Bradley S., Kauffman C., Bedimo R., Corey G.R., Crowley A.L., Douglas P., Drew L., Fowler V.G., Holland T., Lalani T., Mudrick D., Samad Z., Sexton D.J., Stryjewski M., Woods C.W., Lerakis S., Cantey R., Steed L., Wray D., Dickerman S.A., Bonilla H., Dipersio J., Salstrom S.-J., Baddley J., Patel M., Peterson G., Stancoven A., Levine D., Riddle J., Rybak M., Cabell C.H., Baloch K., Dixon C.C., Harding T., Jones-Richmond M., Park L.P., Sanderford B., Stafford J., Anstrom K., Bayer A.S., Karchmer A.W., Durack D.T., Eykyn S., Moreillon P., Delahaye F., Chu V.H., Altclas J., Barsic B., Delahaye A., Freiberger T., Gordon D.L., Hannan M.M., Hoen B., Kanj S.S., Lejko-Zupanc T., Mestres C.A., Pachirat O., Pappas P., Lamas C., Selton-Suty C., Tan R., Tattevin P., Wang A., Clara L., Sanchez M., Casabe J., Cortes C., Nacinovich F., Oses P.F., Ronderos R., Sucari A., Thierer J., Kogan S., Spelman D., Athan E., Harris O., Kennedy K., Gordon D., Papanicolas L., Eisen D., Grigg L., Street A., Korman T., Kotsanas D., Dever R., Jones P., Konecny P., Lawrence R., Rees D., Ryan S., Feneley M.P., Harkness J., Post J., Reinbott P., Gattringer R., Wiesbauer F., Andrade A.R., de Brito A.C.P., Guimaraes A.C., Grinberg M., Mansur A.J., Siciliano R.F., Strabelli T.M.V., Vieira M.L.C., Tranchesi R.A.M., Paiva M.G., Fortes C.Q., Ramos A.O., Ferraiuoli G., Golebiovski W., Weksler C., Santos M., Karlowsky J.A., Keynan Y., Morris A.M., Rubinstein E., Jones S.B., Garcia P., Cereceda M., Fica A., Mella R.M., Fernandez R., Franco L., Gonzalez J., Jaramillo A.N., Bukovski S., Krajinovic V., Sharma G., Rudez I., Vincelj J., Pol J., Zaloudikova B., Ashour Z., El Kholy A., Mishaal M., Osama D., Rizk H., Aissa N., Alauzet C., Alla F., Campagnac C., Doco-Lecompte T., Goehringer F., Casalta J.-P., Fournier P.-E., Habib G., Raoult D., Thuny F., Vandenesch F., Donal E., Donnio P.Y., Flecher E., Michelet C., Revest M., Chevalier F., Jeu A., Remadi J.P., Rusinaru D., Tribouilloy C., Bernard Y., Chirouze C., Leroy J., Plesiat P., Naber C., Neuerburg C., Mazaheri B., Thomas T., Giannitsioti E., Mylona E., Paniara O., Papanicolaou K., Pyros J., Skoutelis A., Papanikolaou K., Francis J., Nair L., Thomas V., Venugopal K., Hannan M., Hurley J., Cahan A., Gilon D., Israel S., Korem M., Strahilevitz J., Durante-Mangoni E., Mattucci I., Pinto D., Agrusta F., Senese A., Ragone E., Utili R., Cecchi E., De Rosa F., Forno D., Imazio M., Trinchero R., Tebini A., Grossi P., Lattanzio M., Toniolo A., Goglio A., Raglio A., Ravasio V., Rizzi M., Suter F., Carosi G., Magri S., Signorini L., Anouti K., Chahoud J., Kanafani Z., Abidin I., Tamin S.S., Martinez E.R., Nieto G.I.S., van der Meer J.T.M., Chambers S., Holland D., Morris A., Raymond N., Read K., Murdoch D.R., Dragulescu S., Ionac A., Mornos C., Butkevich O.M., Chipigina N., Kirill O., Vadim K., Vinogradova T., Edathodu J., Halim M., Liew Y.-Y., Tan R.-S., Logar M., Mueller-Premru M., Commerford P., Commerford A., Deetlefs E., Hansa C., Ntsekhe M., Almela M., Armero Y., Azqueta M., Castaneda X., Cervera C., Del Rio A., Falces C., Garcia-De-la-maria C., Fita G., Gatell J.M., Heras M., Llopis J., Marco F., Miro J.M., Moreno A., Ninot S., Pare C., Pericas J.M., Ramirez J., Rovira I., Sitges M., Anguera I., Font B., Guma J.R., Bermejo J., Bouza E., Fernandez M.A.G., Gonzalez-Ramallo V., Marin M., Pangercic A., Munoz P., Pedromingo M., Roda J., Rodriguez-Creixems M., Solis J., Almirante B., Fernandez-Hidalgo N., Tornos P., de Alarcon A., Parra R., Alestig E., Johansson M., Olaison L., Snygg-Martin U., Pachirat P., Pussadhamma B., Senthong V., Casey A., Elliott T., Lambert P., Watkin R., Eyton C., Klein J.L., Bradley S., Kauffman C., Bedimo R., Corey G.R., Crowley A.L., Douglas P., Drew L., Fowler V.G., Holland T., Lalani T., Mudrick D., Samad Z., Sexton D.J., Stryjewski M., Woods C.W., Lerakis S., Cantey R., Steed L., Wray D., Dickerman S.A., Bonilla H., Dipersio J., Salstrom S.-J., Baddley J., Patel M., Peterson G., Stancoven A., Levine D., Riddle J., Rybak M., Cabell C.H., Baloch K., Dixon C.C., Harding T., Jones-Richmond M., Park L.P., Sanderford B., Stafford J., Anstrom K., Bayer A.S., Karchmer A.W., Durack D.T., Eykyn S., Moreillon P., Delahaye F., Chu V.H., Altclas J., Barsic B., Delahaye A., Freiberger T., Gordon D.L., Hannan M.M., Hoen B., Kanj S.S., Lejko-Zupanc T., Mestres C.A., Pachirat O., Pappas P., Lamas C., Selton-Suty C., Tan R., Tattevin P., Wang A., Clara L., Sanchez M., Casabe J., Cortes C., Nacinovich F., Oses P.F., Ronderos R., Sucari A., Thierer J., Kogan S., Spelman D., Athan E., Harris O., Kennedy K., Gordon D., Papanicolas L., Eisen D., Grigg L., Street A., Korman T., Kotsanas D., Dever R., Jones P., Konecny P., Lawrence R., Rees D., Ryan S., Feneley M.P., Harkness J., Post J., Reinbott P., Gattringer R., Wiesbauer F., Andrade A.R., de Brito A.C.P., Guimaraes A.C., Grinberg M., Mansur A.J., Siciliano R.F., Strabelli T.M.V., Vieira M.L.C., Tranchesi R.A.M., Paiva M.G., Fortes C.Q., Ramos A.O., Ferraiuoli G., Golebiovski W., Weksler C., Santos M., Karlowsky J.A., Keynan Y., Morris A.M., Rubinstein E., Jones S.B., Garcia P., Cereceda M., Fica A., Mella R.M., Fernandez R., Franco L., Gonzalez J., Jaramillo A.N., Bukovski S., Krajinovic V., Sharma G., Rudez I., Vincelj J., Pol J., Zaloudikova B., Ashour Z., El Kholy A., Mishaal M., Osama D., Rizk H., Aissa N., Alauzet C., Alla F., Campagnac C., Doco-Lecompte T., Goehringer F., Casalta J.-P., Fournier P.-E., Habib G., Raoult D., Thuny F., Vandenesch F., Donal E., Donnio P.Y., Flecher E., Michelet C., Revest M., Chevalier F., Jeu A., Remadi J.P., Rusinaru D., Tribouilloy C., Bernard Y., Chirouze C., Leroy J., Plesiat P., Naber C., Neuerburg C., Mazaheri B., Thomas T., Giannitsioti E., Mylona E., Paniara O., Papanicolaou K., Pyros J., Skoutelis A., Papanikolaou K., Francis J., Nair L., Thomas V., Venugopal K., Hannan M., Hurley J., Cahan A., Gilon D., Israel S., Korem M., Strahilevitz J., Durante-Mangoni E., Mattucci I., Pinto D., Agrusta F., Senese A., Ragone E., Utili R., Cecchi E., De Rosa F., Forno D., Imazio M., Trinchero R., Tebini A., Grossi P., Lattanzio M., Toniolo A., Goglio A., Raglio A., Ravasio V., Rizzi M., Suter F., Carosi G., Magri S., Signorini L., Anouti K., Chahoud J., Kanafani Z., Abidin I., Tamin S.S., Martinez E.R., Nieto G.I.S., van der Meer J.T.M., Chambers S., Holland D., Morris A., Raymond N., Read K., Murdoch D.R., Dragulescu S., Ionac A., Mornos C., Butkevich O.M., Chipigina N., Kirill O., Vadim K., Vinogradova T., Edathodu J., Halim M., Liew Y.-Y., Tan R.-S., Logar M., Mueller-Premru M., Commerford P., Commerford A., Deetlefs E., Hansa C., Ntsekhe M., Almela M., Armero Y., Azqueta M., Castaneda X., Cervera C., Del Rio A., Falces C., Garcia-De-la-maria C., Fita G., Gatell J.M., Heras M., Llopis J., Marco F., Miro J.M., Moreno A., Ninot S., Pare C., Pericas J.M., Ramirez J., Rovira I., Sitges M., Anguera I., Font B., Guma J.R., Bermejo J., Bouza E., Fernandez M.A.G., Gonzalez-Ramallo V., and Marin M.

Abstract

Methods and results Among 5591 patients included in the International Collaboration on Endocarditis Prospective Cohort Study, 1467 patients with definite IE were operated on during the active phase and had a biological (37%) or mechanical (63%) valve replacement. Patients who received bioprostheses were older (62 vs 54 years), more often had a history of cancer (9% vs 6%), and had moderate or severe renal disease (9% vs 4%); proportion of health care-associated IE was higher (26% vs 17%); intracardiac abscesses were more frequent (30% vs 23%). In-hospital and 1-year death rates were higher in the bioprosthesis group, 20.5% vs 14.0% (p = 0.0009) and 25.3% vs 16.6% (p <.0001), respectively. In multivariable analysis, mechanical prostheses were less commonly implanted in older patients (odds ratio: 0.64 for every 10 years), and in patients with a history of cancer (0.72), but were more commonly implanted in mitral position (1.60). Bioprosthesis was independently associated with 1-year mortality (hazard ratio: 1.298). Conclusions Patients with IE who receive a biological valve replacement have significant differences in clinical characteristics compared to patients who receive a mechanical prosthesis. Biological valve replacement is independently associated with a higher in-hospital and 1-year mortality, a result which is possibly related to patient characteristics rather than valve dysfunction. Background Nearly half of patients require cardiac surgery during the acute phase of infective endocarditis (IE). We describe the characteristics of patients according to the type of valve replacement (mechanical or biological), and examine whether the type of prosthesis was associated with in-hospital and 1-year mortality.Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Published
2014

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