Search

Your search keyword '"JONNAGADDALA, Jitendra"' showing total 351 results
Start Over Author "JONNAGADDALA, Jitendra"
351 results on '"JONNAGADDALA, Jitendra"'

1. Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study

Author

Reps, Jenna M, Kim, Chungsoo, Williams, Ross D, Markus, Aniek F, Yang, Cynthia, Duarte-Salles, Talita, Falconer, Thomas, Jonnagaddala, Jitendra, Williams, Andrew, Fernández-Bertolín, Sergio, DuVall, Scott L, Kostka, Kristin, Rao, Gowtham, Shoaibi, Azza, Ostropolets, Anna, Spotnitz, Matthew E, Zhang, Lin, Casajust, Paula, Steyerberg, Ewout W, Nyberg, Fredrik, Kaas-Hansen, Benjamin Skov, Choi, Young Hwa, Morales, Daniel, Liaw, Siaw-Teng, Abrahão, Maria Tereza Fernandes, Areia, Carlos, Matheny, Michael E, Lynch, Kristine E, Aragón, María, Park, Rae Woong, Hripcsak, George, Reich, Christian G, Suchard, Marc A, You, Seng Chan, Ryan, Patrick B, Prieto-Alhambra, Daniel, and Rijnbeek, Peter R

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundSARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the “prediction model risk of bias assessment” criteria, and it has not been externally validated. ObjectiveThe aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases. MethodsWe followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia. ResultsThe internal validation performance of the C-19 index had a C statistic of 0.73, and the calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data, the model obtained C statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US, and South Korean data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68. ConclusionsOur results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, Spain, and South Korea. These results suggest that C-19 should not be used to aid decision-making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.

Published
2021
Full Text
View/download PDF

2. Family History Information Extraction With Neural Attention and an Enhanced Relation-Side Scheme: Algorithm Development and Validation

Author

Dai, Hong-Jie, Lee, You-Qian, Nekkantti, Chandini, and Jonnagaddala, Jitendra

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundIdentifying and extracting family history information (FHI) from clinical reports are significant for recognizing disease susceptibility. However, FHI is usually described in a narrative manner within patients’ electronic health records, which requires the application of natural language processing technologies to automatically extract such information to provide more comprehensive patient-centered information to physicians. ObjectiveThis study aimed to overcome the 2 main challenges observed in previous research focusing on FHI extraction. One is the requirement to develop postprocessing rules to infer the member and side information of family mentions. The other is to efficiently utilize intrasentence and intersentence information to assist FHI extraction. MethodsWe formulated the task as a sequential labeling problem and propose an enhanced relation-side scheme that encodes the required family member properties to not only eliminate the need for postprocessing rules but also relieve the insufficient training instance issues. Moreover, an attention-based neural network structure was proposed to exploit cross-sentence information to identify FHI and its attributes requiring cross-sentence inference. ResultsThe dataset released by the 2019 n2c2/OHNLP family history extraction task was used to evaluate the performance of the proposed methods. We started by comparing the performance of the traditional neural sequence models with the ordinary scheme and enhanced scheme. Next, we studied the effectiveness of the proposed attention-enhanced neural networks by comparing their performance with that of the traditional networks. It was observed that, with the enhanced scheme, the recall of the neural network can be improved, leading to an increase in the F score of 0.024. The proposed neural attention mechanism enhanced both the recall and precision and resulted in an improved F score of 0.807, which was ranked fourth in the shared task. ConclusionsWe presented an attention-based neural network along with an enhanced tag scheme that enables the neural network model to learn and interpret the implicit relationship and side information of the recognized family members across sentences without relying on heuristic rules.

Published
2020
Full Text
View/download PDF

3. DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecture

Author

Cen, Min, Li, Xingyu, Guo, Bangwei, Jonnagaddala, Jitendra, Zhang, Hong, and Xu, Xu Steven

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

In digital pathology tasks, transformers have achieved state-of-the-art results, surpassing convolutional neural networks (CNNs). However, transformers are usually complex and resource intensive. In this study, we developed a novel and efficient digital pathology classifier called DPSeq, to predict cancer biomarkers through fine-tuning a sequencer architecture integrating horizon and vertical bidirectional long short-term memory (BiLSTM) networks. Using hematoxylin and eosin (H&E)-stained histopathological images of colorectal cancer (CRC) from two international datasets: The Cancer Genome Atlas (TCGA) and Molecular and Cellular Oncology (MCO), the predictive performance of DPSeq was evaluated in series of experiments. DPSeq demonstrated exceptional performance for predicting key biomarkers in CRC (MSI status, Hypermutation, CIMP status, BRAF mutation, TP53 mutation and chromosomal instability [CING]), outperforming most published state-of-the-art classifiers in a within-cohort internal validation and a cross-cohort external validation. Additionally, under the same experimental conditions using the same set of training and testing datasets, DPSeq surpassed 4 CNN (ResNet18, ResNet50, MobileNetV2, and EfficientNet) and 2 transformer (ViT and Swin-T) models, achieving the highest AUROC and AUPRC values in predicting MSI status, BRAF mutation, and CIMP status. Furthermore, DPSeq required less time for both training and prediction due to its simple architecture. Therefore, DPSeq appears to be the preferred choice over transformer and CNN models for predicting cancer biomarkers.

Published
2023

4. Time to Embrace Natural Language Processing (NLP)-based Digital Pathology: Benchmarking NLP- and Convolutional Neural Network-based Deep Learning Pipelines

Author

Cen, Min, Li, Xingyu, Guo, Bangwei, Jonnagaddala, Jitendra, Zhang, Hong, and Xu, Xu Steven

Subjects
Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Quantitative Methods
Abstract

NLP-based computer vision models, particularly vision transformers, have been shown to outperform CNN models in many imaging tasks. However, most digital pathology artificial-intelligence models are based on CNN architectures, probably owing to a lack of data regarding NLP models for pathology images. In this study, we developed digital pathology pipelines to benchmark the five most recently proposed NLP models (vision transformer (ViT), Swin Transformer, MobileViT, CMT, and Sequencer2D) and four popular CNN models (ResNet18, ResNet50, MobileNetV2, and EfficientNet) to predict biomarkers in colorectal cancer (microsatellite instability, CpG island methylator phenotype, and BRAF mutation). Hematoxylin and eosin-stained whole-slide images from Molecular and Cellular Oncology and The Cancer Genome Atlas were used as training and external validation datasets, respectively. Cross-study external validations revealed that the NLP-based models significantly outperformed the CNN-based models in biomarker prediction tasks, improving the overall prediction and precision up to approximately 10% and 26%, respectively. Notably, compared with existing models in the current literature using large training datasets, our NLP models achieved state-of-the-art predictions for all three biomarkers using a relatively small training dataset, suggesting that large training datasets are not a prerequisite for NLP models or transformers, and NLP may be more suitable for clinical studies in which small training datasets are commonly collected. The superior performance of Sequencer2D suggests that further research and innovation on both transformer and bidirectional long short-term memory architectures are warranted in the field of digital pathology. NLP models can replace classic CNN architectures and become the new workhorse backbone in the field of digital pathology.

Published
2023

5. Prognostic Significance of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images in Colorectal Cancers

Author

Liu, Anran, Li, Xingyu, Wu, Hongyi, Guo, Bangwei, Jonnagaddala, Jitendra, Zhang, Hong, and Xu, Xu Steven

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Purpose Tumor-infiltrating lymphocytes (TILs) have significant prognostic values in cancers. However, very few automated, deep-learning-based TIL scoring algorithms have been developed for colorectal cancers (CRC). Methods We developed an automated, multiscale LinkNet workflow for quantifying cellular-level TILs for CRC tumors using H&E-stained images. The predictive performance of the automatic TIL scores (TIL) for disease progression and overall survival was evaluate using two international datasets, including 554 CRC patients from The Cancer Genome Atlas (TCGA) and 1130 CRC patients from Molecular and Cellular Oncology (MCO). Results The LinkNet model provided an outstanding precision (0.9508), recall (0.9185), and overall F1 score (0.9347). Clear dose-response relationships were observed between TILs and risk of disease progression or death decreased in both TCGA and MCO cohorts. Both univariate and multivariate Cox regression analyses for the TCGA data demonstrated that patients with high TILs had significant (approx. 75%) reduction of risk for disease progression. In both MCO and TCGA studies, the TIL-high group was significantly associated with improved overall survival in univariate analysis (30% and 54% reduction in risk, respectively). However, potential confounding was observed in the MCO dataset. The favorable effects of high TILs were consistently observed in different subgroups according to know risk factors. Conclusion A deep-learning workflow for automatic TIL quantification based on LinkNet was successfully developed.

Published
2022

6. Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: Achieving SOTA predictive performance with fewer data using Swin Transformer

Author

Guo, Bangwei, Li, Xingyu, Jonnagaddala, Jitendra, Zhang, Hong, and Xu, Xu Steven

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Artificial intelligence (AI) models have been developed for predicting clinically relevant biomarkers, including microsatellite instability (MSI), for colorectal cancers (CRC). However, the current deep-learning networks are data-hungry and require large training datasets, which are often lacking in the medical domain. In this study, based on the latest Hierarchical Vision Transformer using Shifted Windows (Swin-T), we developed an efficient workflow for biomarkers in CRC (MSI, hypermutation, chromosomal instability, CpG island methylator phenotype, BRAF, and TP53 mutation) that only required relatively small datasets, but achieved the state-of-the-art (SOTA) predictive performance. Our Swin-T workflow not only substantially outperformed published models in an intra-study cross-validation experiment using TCGA-CRC-DX dataset (N = 462), but also showed excellent generalizability in cross-study external validation and delivered a SOTA AUROC of 0.90 for MSI using the MCO dataset for training (N = 1065) and the same TCGA-CRC-DX for testing. Similar performance (AUROC=0.91) was achieved by Echle and colleagues using approximately 8000 training samples (ResNet18) on the same testing dataset. Swin-T was extremely efficient using small training datasets and exhibits robust predictive performance with only 200-500 training samples. These data indicate that Swin-T may be 5-10 times more efficient than the current state-of-the-art algorithms for MSI based on ResNet18 and ShuffleNet. Furthermore, the Swin-T models showed promise as pre-screening tests for MSI status and BRAF mutation status, which could exclude and reduce the samples before the subsequent standard testing in a cascading diagnostic workflow to allow turnaround time reduction and cost saving.

Published
2022

7. Assessing Video Games to Improve Driving Skills: A Literature Review and Observational Study

Author

Sue, Damian, Ray, Pradeep, Talaei-Khoei, Amir, Jonnagaddala, Jitendra, and Vichitvanichphong, Suchada

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundFor individuals, especially older adults, playing video games is a promising tool for improving their driving skills. The ease of use, wide availability, and interactivity of gaming consoles make them an attractive simulation tool. ObjectiveThe objective of this study was to look at the feasibility and effects of installing video game consoles in the homes of individuals looking to improve their driving skills. MethodsA systematic literature review was conducted to assess the effect of playing video games on improving driving skills. An observatory study was performed to evaluate the feasibility of using an Xbox 360 Kinect console for improving driving skills. ResultsTwenty–nine articles, which discuss the implementation of video games in improving driving skills were found in literature. On our study, it was found the Xbox 360 with Kinect is capable of improving physical and mental activities. Xbox Video games were introduced to engage players in physical, visual and cognitive activities including endurance, postural sway, reaction time, eyesight, eye movement, attention and concentration, difficulties with orientation, and semantic fluency. However, manual dexterity, visuo-spatial perception and binocular vision could not be addressed by these games. It was observed that Xbox Kinect (by incorporating Kinect sensor facilities) combines physical, visual and cognitive engagement of players. These results were consistent with those from the literature review. ConclusionsFrom the research that has been carried out, we can conclude that video game consoles are a viable solution for improving user’s physical and mental state. In future we propose to carry a thorough evaluation of the effects of video games on driving skills in elderly people.

Published
2014
Full Text
View/download PDF

8. Colorectal cancer survival prediction using deep distribution based multiple-instance learning

Author

Li, Xingyu, Jonnagaddala, Jitendra, Cen, Min, Zhang, Hong, and Xu, Xu Steven

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Several deep learning algorithms have been developed to predict survival of cancer patients using whole slide images (WSIs).However, identification of image phenotypes within the WSIs that are relevant to patient survival and disease progression is difficult for both clinicians, and deep learning algorithms. Most deep learning based Multiple Instance Learning (MIL) algorithms for survival prediction use either top instances (e.g., maxpooling) or top/bottom instances (e.g., MesoNet) to identify image phenotypes. In this study, we hypothesize that wholistic information of the distribution of the patch scores within a WSI can predict the cancer survival better. We developed a distribution based multiple-instance survival learning algorithm (DeepDisMISL) to validate this hypothesis. We designed and executed experiments using two large international colorectal cancer WSIs datasets - MCO CRC and TCGA COAD-READ. Our results suggest that the more information about the distribution of the patch scores for a WSI, the better is the prediction performance. Including multiple neighborhood instances around each selected distribution location (e.g., percentiles) could further improve the prediction. DeepDisMISL demonstrated superior predictive ability compared to other recently published, state-of-the-art algorithms. Furthermore, our algorithm is interpretable and could assist in understanding the relationship between cancer morphological phenotypes and patients cancer survival risk.

Published
2022
Full Text
View/download PDF

9. A Retrospective Analysis using Deep-Learning Models for Prediction of Survival Outcome and Benefit of Adjuvant Chemotherapy in Stage II/III Colorectal Cancer

Author

Li, Xingyu, Jonnagaddala, Jitendra, Yang, Shuhua, Zhang, Hong, and Xu, Xu Steven

Subjects
Computer Science - Machine Learning
Abstract

Most early-stage colorectal cancer (CRC) patients can be cured by surgery alone, and only certain high-risk early-stage CRC patients benefit from adjuvant chemotherapies. However, very few validated biomarkers are available to accurately predict survival benefit from postoperative chemotherapy. We developed a novel deep-learning algorithm (CRCNet) using whole-slide images from Molecular and Cellular Oncology (MCO) to predict survival benefit of adjuvant chemotherapy in stage II/III CRC. We validated CRCNet both internally through cross-validation and externally using an independent cohort from The Cancer Genome Atlas (TCGA). We showed that CRCNet can accurately predict not only survival prognosis but also the treatment effect of adjuvant chemotherapy. The CRCNet identified high-risk subgroup benefits from adjuvant chemotherapy most and significant longer survival is observed among chemo-treated patients. Conversely, minimal chemotherapy benefit is observed in the CRCNet low- and medium-risk subgroups. Therefore, CRCNet can potentially be of great use in guiding treatments for Stage II/III CRC.

Published
2021

12. Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

Author

Phelps, Nowell H, Singleton, Rosie K, Zhou, Bin, Heap, Rachel A, Mishra, Anu, Bennett, James E, Paciorek, Christopher J, Lhoste, Victor PF, Carrillo-Larco, Rodrigo M, Stevens, Gretchen A, Rodriguez-Martinez, Andrea, Bixby, Honor, Bentham, James, Di Cesare, Mariachiara, Danaei, Goodarz, Rayner, Archie W, Barradas-Pires, Ana, Cowan, Melanie J, Savin, Stefan, Riley, Leanne M, Aguilar-Salinas, Carlos A, Baker, Jennifer L, Barkat, Amina, Bhutta, Zulfiqar A, Branca, Francesco, Caixeta, Roberta B, Cuschieri, Sarah, Farzadfar, Farshad, Ganapathy, Shubash, Ikeda, Nayu, Iotova, Violeta, Kengne, Andre P, Khang, Young-Ho, Laxmaiah, Avula, Lin, Hsien-Ho, Ma, Jun, Mbanya, Jean Claude N, Miranda, J Jaime, Pradeepa, Rajendra, Rodríguez-Artalejo, Fernando, Sorić, Maroje, Turley, Maria, Wang, Limin, Webster-Kerr, Karen, Aarestrup, Julie, Abarca-Gómez, Leandra, Abbasi-Kangevari, Mohsen, Abdeen, Ziad A, Abdrakhmanova, Shynar, Abdul Ghaffar, Suhaila, Abdul Rahim, Hanan F, Abdurrahmonova, Zulfiya, Abu-Rmeileh, Niveen M, Abubakar Garba, Jamila, Acosta-Cazares, Benjamin, Adam, Ishag, Adamczyk, Marzena, Adams, Robert J, Adu-Afarwuah, Seth, Aekplakorn, Wichai, Afsana, Kaosar, Afzal, Shoaib, Agbor, Valirie N, Agdeppa, Imelda A, Aghazadeh-Attari, Javad, Ågren, Åsa, Aguenaou, Hassan, Agyemang, Charles, Ahmad, Mohamad Hasnan, Ahmad, Noor Ani, Ahmadi, Ali, Ahmadi, Naser, Ahmadi, Nastaran, Ahmed, Imran, Ahmed, Soheir H, Ahrens, Wolfgang, Aitmurzaeva, Gulmira, Ajlouni, Kamel, Al-Hazzaa, Hazzaa M, Al-Hinai, Halima, Al-Lahou, Badreya, Al-Lawati, Jawad A, Al-Raddadi, Rajaa, Al Asfoor, Deena, Al Hourani, Huda M, Al Qaoud, Nawal M, Alarouj, Monira, AlBuhairan, Fadia, AlDhukair, Shahla, Aldwairji, Maryam A, Alexius, Sylvia, Ali, Mohamed M, Alieva, Anna V, Alkandari, Abdullah, Alkerwi, Ala'a, Alkhatib, Buthaina M, Allin, Kristine, Alomary, Shaker A, Alomirah, Husam F, Alshangiti, Arwa M, Alvarez-Pedrerol, Mar, Aly, Eman, Amarapurkar, Deepak N, Amiano Etxezarreta, Pilar, Amoah, John, Amougou, Norbert, Amouyel, Philippe, Andersen, Lars Bo, Anderssen, Sigmund A, Androutsos, Odysseas, Ängquist, Lars, Anjana, Ranjit Mohan, Ansari-Moghaddam, Alireza, Anufrieva, Elena, Aounallah-Skhiri, Hajer, Araújo, Joana, Ariansen, Inger, Aris, Tahir, Arku, Raphael E, Arlappa, Nimmathota, Aryal, Krishna K, Assefa, Nega, Aspelund, Thor, Assah, Felix K, Assembekov, Batyrbek, Assunção, Maria Cecília F, Aung, May Soe, Aurélio de Valois, Correia Júnior Marco, Auvinen, Juha, Avdičová, Mária, Avi, Shina, Azad, Kishwar, Azevedo, Ana, Azimi-Nezhad, Mohsen, Azizi, Fereidoun, Babu, Bontha V, Bacopoulou, Flora, Bæksgaard Jørgensen, Maja, Baharudin, Azli, Bahijri, Suhad, Bajramovic, Izet, Bakacs, Marta, Balakrishna, Nagalla, Balanova, Yulia, Bamoshmoosh, Mohamed, Banach, Maciej, Banegas, José R, Baran, Joanna, Baran, Rafał, Barbagallo, Carlo M, Barbosa Filho, Valter, Barceló, Alberto, Baretić, Maja, Barnoya, Joaquin, Barrera, Lena, Barreto, Marta, Barros, Aluisio JD, Barros, Mauro Virgílio Gomes, Bartosiewicz, Anna, Basit, Abdul, Bastos, Joao Luiz, Bata, Iqbal, Batieha, Anwar M, Batista, Aline P, Batista, Rosangela L, Battakova, Zhamilya, Baur, Louise A, Bayauli, Pascal M, Beaglehole, Robert, Bel-Serrat, Silvia, Belavendra, Antonisamy, Ben Romdhane, Habiba, Benedek, Theodora, Benedics, Judith, Benet, Mikhail, Benitez Rolandi, Gilda Estela, Benzeval, Michaela, Bere, Elling, Berger, Nicolas, Bergh, Ingunn Holden, Berhane, Yemane, Berkinbayev, Salim, Bernabe-Ortiz, Antonio, Bernotiene, Gailute, Berrios Carrasola, Ximena, Bettiol, Heloísa, Beutel, Manfred E, Beybey, Augustin F, Bezerra, Jorge, Bhagyalaxmi, Aroor, Bharadwaj, Sumit, Bhargava, Santosh K, Bi, Hongsheng, Bi, Yufang, Bia, Daniel, Biasch, Katia, Bika Lele, Elysée Claude, Bikbov, Mukharram M, Bista, Bihungum, Bjelica, Dusko J, Bjerregaard, Anne A, Bjerregaard, Peter, Bjertness, Espen, Bjertness, Marius B, Björkelund, Cecilia, Bloch, Katia V, Blokstra, Anneke, Blychfeld Magnazu, Moran, Bo, Simona, Bobak, Martin, Boddy, Lynne M, Boehm, Bernhard O, Boer, Jolanda MA, Boggia, Jose G, Bogova, Elena, Boissonnet, Carlos P, Bojesen, Stig E, Bonaccio, Marialaura, Bongard, Vanina, Bonilla-Vargas, Alice, Bopp, Matthias, Borghs, Herman, Botomba, Steve, Bourne, Rupert RA, Bovet, Pascal, Boymatova, Khadichamo, Braeckevelt, Lien, Braeckman, Lutgart, Bragt, Marjolijn CE, Braithwaite, Tasanee, Brajkovich, Imperia, Breckenkamp, Juergen, Breda, João, Brenner, Hermann, Brewster, Lizzy M, Brian, Garry R, Briceño, Yajaira, Brinduse, Lacramioara, Bringolf-Isler, Bettina, Brito, Miguel, Brophy, Sinead, Brug, Johannes, Bruno, Graziella, Bugge, Anna, Buoncristiano, Marta, Burazeri, Genc, Burns, Con, Cabrera de León, Antonio, Cacciottolo, Joseph, Cai, Hui, Cama, Tilema, Cameron, Christine, Camolas, José, Can, Günay, Cândido, Ana Paula c, Cañete, Felicia, Capanzana, Mario V, Čapková, Naděžda, Capuano, Eduardo, Capuano, Rocco, Capuano, Vincenzo, Cardol, Marloes, Cardoso, Viviane C, Carlsson, Axel C, Carmuega, Esteban, Carvalho, Joana, Casajús, José A, Casanueva, Felipe F, Casas, Maribel, Celikcan, Ertugrul, Censi, Laura, Cervantes-Loaiza, Marvin, Cesar, Juraci A, Chamnan, Parinya, Chamukuttan, Snehalatha, Chan, Angelique, Chan, Queenie, Charchar, Fadi J, Charles, Marie-Aline, Chaturvedi, Himanshu K, Chaturvedi, Nish, Che Abdul Rahim, Norsyamlina, Chee, Miao Li, Chen, Chien-Jen, Chen, Fangfang, Chen, Huashuai, Chen, Long-Sheng, Chen, Shuohua, Chen, Zhengming, Cheng, Ching-Yu, Cheng, Yiling J, Cheraghian, Bahman, Chetrit, Angela, Chikova-Iscener, Ekaterina, Chinapaw, Mai JM, Chinnock, Anne, Chiolero, Arnaud, Chiou, Shu-Ti, Chirita-Emandi, Adela, Chirlaque, María-Dolores, Cho, Belong, Christensen, Kaare, Christofaro, Diego G, Chudek, Jerzy, Cifkova, Renata, Cilia, Michelle, Cinteza, Eliza, Cirillo, Massimo, Claessens, Frank, Clare, Philip, Clarke, Janine, Clays, Els, Cohen, Emmanuel, Cojocaru, Cosmin R, Colorado-Yohar, Sandra, Compañ-Gabucio, Laura-María, Concin, Hans, Confortin, Susana C, Cooper, Cyrus, Coppinger, Tara C, Corpeleijn, Eva, Cortés, Lilia Yadira, Costanzo, Simona, Cottel, Dominique, Cowell, Chris, Craig, Cora L, Crampin, Amelia C, Cross, Amanda J, Crujeiras, Ana B, Cruz, Juan J, Csányi, Tamás, Csilla, Semánová, Cucu, Alexandra M, Cui, Liufu, Cureau, Felipe V, Czenczek-Lewandowska, Ewelina, D'Arrigo, Graziella, d'Orsi, Eleonora, da Silva, Alanna G, Dacica, Liliana, Dahm, Christina C, Dallongeville, Jean, Damasceno, Albertino, Damsgaard, Camilla T, Dankner, Rachel, Dantoft, Thomas M, Dasgupta, Parasmani, Dastgiri, Saeed, Dauchet, Luc, Davletov, Kairat, de Assis Guedes de Vasconcelos, Francisco, de Assis, Maria Alice Altenburg, De Backer, Guy, De Bacquer, Dirk, De Bacquer, Jaco, de Bont, Jeroen, De Curtis, Amalia, de Fragas Hinnig, Patrícia, de Gaetano, Giovanni, De Henauw, Stefaan, De Miguel-Etayo, Pilar, De Neve, Jan-Walter, Duarte de Oliveira, Paula, De Ridder, David, De Ridder, Karin, de Rooij, Susanne R, de Sá, Ana Carolina MGN, De Smedt, Delphine, Deepa, Mohan, Deev, Alexander D, DeGennaro, Vincent Jr, Delisle, Hélène, Delpeuch, Francis, Demarest, Stefaan, Dennison, Elaine, Dereń, Katarzyna, Deschamps, Valérie, Devrishov, Ruslan D, Dhimal, Meghnath, Di Castelnuovo, Augusto, Dias-da-Costa, Juvenal Soares, Díaz-Sánchez, María Elena, Diaz, Alejandro, Díaz Fernández, Pedro, Díez Ripollés, María Pilar, Dika, Zivka, Djalalinia, Shirin, Djordjic, Visnja, Do, Ha TP, Dobson, Annette J, Dominguez, Liria, Donati, Maria Benedetta, Donfrancesco, Chiara, Dong, Guanghui, Dong, Yanhui, Donoso, Silvana P, Döring, Angela, Dorobantu, Maria, Dorosty, Ahmad Reza, Dörr, Marcus, Doua, Kouamelan, Dragano, Nico, Drygas, Wojciech, Du, Shufa, Duan, Jia Li, Duante, Charmaine A, Duboz, Priscilla, Duleva, Vesselka L, Dulskiene, Virginija, Dumith, Samuel C, Dushpanova, Anar, Dwyer, Terence, Dyussupova, Azhar, Dzerve, Vilnis, Dziankowska-Zaborszczyk, Elzbieta, Ebrahimi, Narges, Echeverría, Guadalupe, Eddie, Ricky, Eftekhar, Ebrahim, Efthymiou, Vasiliki, Egbagbe, Eruke E, Eggertsen, Robert, Eghtesad, Sareh, Eiben, Gabriele, Ekelund, Ulf, El-Khateeb, Mohammad, El Ammari, Laila, El Ati, Jalila, Eldemire-Shearer, Denise, Elliott, Paul, Enang, Ofem, Endevelt, Ronit, Engle-Stone, Reina, Erasmus, Rajiv T, Erem, Cihangir, Ergor, Gul, Eriksen, Louise, Eriksson, Johan G, Escobedo-de la Peña, Jorge, Eslami, Saeid, Esmaeili, Ali, Evans, Alun, Evans, Roger G, Faeh, David, Fagherazzi, Guy, Fakhradiyev, Ildar, Fakhretdinova, Albina A, Fall, Caroline H, Faramarzi, Elnaz, Farjam, Mojtaba, Farrugia Sant'Angelo, Victoria, Farzi, Yosef, Fattahi, Mohammad Reza, Fawwad, Asher, Fawzi, Wafaie W, Felix-Redondo, Francisco J, Ferguson, Trevor S, Fernandes, Romulo A, Fernández-Bergés, Daniel, Ferrante, Daniel, Ferrao, Thomas, Ferrari, Gerson, Ferrari, Marika, Ferrario, Marco M, Ferreccio, Catterina, Ferreira, Haroldo S, Ferrer, Eldridge, Ferrieres, Jean, Figueiró, Thamara Hubler, Fijalkowska, Anna, Fink, Günther, Fisberg, Mauro, Fischer, Krista, Foo, Leng Huat, Forsner, Maria, Fottrell, Edward F, Fouad, Heba M, Francis, Damian K, Franco, Maria do Carmo, Fras, Zlatko, Fraser, Brooklyn, Frontera, Guillermo, Fuchs, Flavio D, Fuchs, Sandra C, Fujiati, Isti I, Fujita, Yuki, Fumihiko, Matsuda, Furdela, Viktoriya, Furusawa, Takuro, Gabriela, Stefan Adela, Gaciong, Zbigniew, Gafencu, Mihai, Galán Cuesta, Manuel, Galbarczyk, Andrzej, Galcheva, Sonya V, Galenkamp, Henrike, Galeone, Daniela, Galfo, Myriam, Galvano, Fabio, Gao, Jingli, Gao, Pei, Garcia-de-la-Hera, Manoli, García Mérida, María José, García Solano, Marta, Gareta, Dickman, Garnett, Sarah P, Gaspoz, Jean-Michel, Gasull, Magda, Gaya, Adroaldo Cesar Araujo, Gaya, Anelise Reis, Gazzinelli, Andrea, Gehring, Ulrike, Geiger, Harald, Geleijnse, Johanna M, George, Ronnie, Gerdts, Eva, Ghaderi, Ebrahim, Ghamari, Seyyed-Hadi, Ghanbari, Ali, Ghasemi, Erfan, Gheorghe-Fronea, Oana-Florentina, Gialluisi, Alessandro, Giampaoli, Simona, Gianfagna, Francesco, Gieger, Christian, Gill, Tiffany K, Giovannelli, Jonathan, Gironella, Glen, Giwercman, Aleksander, Gkiouras, Konstantinos, Glushkova, Natalya, Godara, Ramesh, Godos, Justyna, Gogen, Sibel, Goldberg, Marcel, Goltzman, David, Gómez, Georgina, Gómez Gómez, Jesús Humberto, Gomez, Luis F, Gómez, Santiago F, Gomula, Aleksandra, Gonçalves Cordeiro da Silva, Bruna, Gonçalves, Helen, Gonçalves, Mauer, González-Alvarez, Ana D, Gonzalez-Chica, David A, González-Gil, Esther M, Gonzalez-Gross, Marcela, González-Leon, Margot, González-Rivas, Juan P, González-Villalpando, Clicerio, González-Villalpando, María-Elena, Gonzalez, Angel R, Gottrand, Frederic, Graça, Antonio Pedro, Grafnetter, Dušan, Grajda, Aneta, Grammatikopoulou, Maria G, Gregg, Edward W, Gregor, Ronald D, Gregório, Maria João, Grøholt, Else Karin, Grøntved, Anders, Grosso, Giuseppe, Gruden, Gabriella, Gu, Dongfeng, Guajardo, Viviana, Gualdi-Russo, Emanuela, Guallar-Castillón, Pilar, Gualtieri, Andrea, Gudmundsson, Elias F, Gudnason, Vilmundur, Guerchet, Maëlenn, Guerrero, Ramiro, Guessous, Idris, Guimaraes, Andre L, Gujral, Unjali P, Gulliford, Martin C, Gunnlaugsdottir, Johanna, Gunter, Marc J, Guo, Xiu-Hua, Guo, Yin, Gupta, Prakash C, Gupta, Rajeev, Gureje, Oye, Gurinović, Mirjana A, Gutiérrez González, Enrique, Gutierrez, Laura, Gutzwiller, Felix, Gwee, Xinyi, Ha, Seongjun, Hadaegh, Farzad, Hadjigeorgiou, Charalambos A, Haghshenas, Rosa, Hakimi, Hamid, Halkjær, Jytte, Hambleton, Ian R, Hamzeh, Behrooz, Hanekom, Willem A, Hange, Dominique, Hanif, Abu AM, Hantunen, Sari, Hao, Jie, Hardman, Carla Menêses, Hardy, Louise, Hari Kumar, Rachakulla, Harmer Lassen, Tina, Harooni, Javad, Hashemi-Shahri, Seyed Mohammad, Hassapidou, Maria, Hata, Jun, Haugsgjerd, Teresa, Hayes, Alison J, He, Jiang, He, Yuan, He, Yuna, Heidinger-Felső, Regina, Heier, Margit, Heinen, Mirjam, Hejgaard, Tatjana, Hendriks, Marleen Elisabeth, Henrique, Rafael dos Santos, Henriques, Ana, Hernandez Cadena, Leticia, Herrala, Sauli, Herrera-Cuenca, Marianella, Herrera, Victor M, Herter-Aeberli, Isabelle, Herzig, Karl-Heinz, Heshmat, Ramin, Heude, Barbara, Hill, Allan G, Ho, Sai Yin, Ho, Suzanne C, Hobbs, Michael, Höfelmann, Doroteia A, Holdsworth, Michelle, Homayounfar, Reza, Homs, Clara, Hoogendijk, Emiel, Hopman, Wilma M, Horimoto, Andrea RVR, Hormiga, Claudia M, Horta, Bernardo L, Houti, Leila, Howitt, Christina, Htay, Thein Thein, Htet, Aung Soe, Htike, Maung Maung Than, Hu, Yonghua, Huerta, José María, Huhtaniemi, Ilpo Tapani, Huiart, Laetitia, Huidumac Petrescu, Constanta, Husseini, Abdullatif, Huu, Chinh Nguyen, Huybrechts, Inge, Hwalla, Nahla, Hyska, Jolanda, Iacoviello, Licia, Iakupova, Ellina M, Ibarluzea, Jesús, Ibrahim, Mohsen M, Ibrahim Wong, Norazizah, Igland, Jannicke, Ijoma, Chinwuba, Ikram, M Arfan, Iñiguez, Carmen, Irazola, Vilma E, Ishida, Takafumi, Isiguzo, Godsent C, Islam, Muhammad, Islam, Sheikh Mohammed Shariful, Islek, Duygu, Ittermann, Till, Ivanova-Pandourska, Ivaila Y, Iwasaki, Masanori, Jääskeläinen, Tuija, Jackson, Rod T, Jacobs, Jeremy M, Jadoul, Michel, Jafar, Tazeen, Jallow, Bakary, James, Kenneth, Jamil, Kazi M, Jamrozik, Konrad, Jan, Nataša, Jansson, Anna, Janszky, Imre, Janus, Edward, Jarani, Juel, Jarnig, Gerald, Jarvelin, Marjo-Riitta, Jasienska, Grazyna, Jelaković, Ana, Jelaković, Bojan, Jennings, Garry, Jiang, Chao Qiang, Jimenez, Ramon O, Jöckel, Karl-Heinz, Joffres, Michel, Jokelainen, Jari J, Jonas, Jost B, Jonnagaddala, Jitendra, Jøran Kjerpeseth, Lars, Jørgensen, Torben, Joshi, Pradeep, Joshi, Rohina, Josipović, Josipa, Joukar, Farahnaz, Jóźwiak, Jacek J, Judge, Debra S, Juolevi, Anne, Jurak, Gregor, Jurca Simina, Iulia, Juresa, Vesna, Kaaks, Rudolf, Kaducu, Felix O, Kadvan, Agnes L, Kafatos, Anthony, Kaj, Mónika, Kajantie, Eero O, Kakutia, Natia, Kállayová, Daniela, Kalmatayeva, Zhanna, Kalter-Leibovici, Ofra, Kameli, Yves, Kanala, Kodanda R, Kannan, Srinivasan, Kapantais, Efthymios, Karaglani, Eva, Karakosta, Argyro, Kårhus, Line L, Karki, Khem B, Karlsson, Omat, Kassi Anicet, Adoubi, Katchunga, Philippe B, Katibeh, Marzieh, Katz, Joanne, Katzmarzyk, Peter T, Kauhanen, Jussi, Kaur, Prabhdeep, Kavousi, Maryam, Kazakbaeva, Gyulli M, Kaze, François F, Kazembe, Benson M, Ke, Calvin, Keil, Ulrich, Keinan Boker, Lital, Keinänen-Kiukaanniemi, Sirkka, Kelishadi, Roya, Kelleher, Cecily, Kemper, Han CG, Keramati, Maryam, Kerimkulova, Alina, Kersting, Mathilde, Key, Timothy, Khader, Yousef Saleh, Khaledifar, Arsalan, Khalili, Davood, Kheiri, Bahareh, Kheradmand, Motahareh, Khosravi, Alireza, Khouw, Ilse MSL, Kiechl-Kohlendorfer, Ursula, Kiechl, Sophia J, Kiechl, Stefan, Killewo, Japhet, Kim, Hyeon Chang, Kim, Jeongseon, Kindblom, Jenny M, Kingston, Andrew, Klakk, Heidi, Klanarong, Suntara, Klanova, Jana, Klimek, Magdalena, Klimont, Jeannette, Klumbiene, Jurate, Knoflach, Michael, Kobel, Susanne, Koirala, Bhawesh, Kolle, Elin, Kolo, Sanda M, Kolsteren, Patrick, König, Jürgen, Korpelainen, Raija, Korrovits, Paul, Korzycka, Magdalena, Kos, Jelena, Koskinen, Seppo, Kouda, Katsuyasu, Koussoh Simone, Malik, Kovács, Éva, Kovacs, Viktoria Anna, Kovalskys, Irina, Kowlessur, Sudhir, Koziel, Slawomir, Kratenova, Jana, Kratzer, Wolfgang, Kriaucioniene, Vilma, Kriemler, Susi, Kristensen, Peter Lund, Krizan, Helena, Kroker-Lobos, Maria F, Krokstad, Steinar, Kromhout, Daan, Kruger, Herculina S, Kruger, Ruan, Kryst, Łukasz, Kubinova, Ruzena, Kuciene, Renata, Kujala, Urho M, Kujundzic, Enisa, Kulaga, Zbigniew, Kulimbet, Mukhtar, Kulothungan, Vaitheeswaran, Kumar, R Krishna, Kumari, Meena, Kunešová, Marie, Kurjata, Pawel, Kusuma, Yadlapalli S, Kutsenko, Vladimir, Kuulasmaa, Kari, Kyobutungi, Catherine, La, Quang Ngoc, Laamiri, Fatima Zahra, Laatikainen, Tiina, Labadarios, Demetre, Lachat, Carl, Lackner, Karl J, Lai, Daphne, Laid, Youcef, Lall, Lachmie, Lam, Tai Hing, Landaeta Jimenez, Maritza, Landais, Edwige, Lankila, Tiina, Lanska, Vera, Lappas, Georg, Larijani, Bagher, Larissa, Simo Pone, Lateva, Mina P, Latt, Tint Swe, Laurenzi, Martino, Lauria, Laura, Lazo-Porras, Maria, Le Coroller, Gwenaëlle, Le Nguyen Bao, Khanh, Le Port, Agnès, Le, Tuyen D, Lee, Jeannette, Lee, Jeonghee, Lee, Paul H, Lehtimäki, Terho, Lemogoum, Daniel, Leong, Elvynna, Leskošek, Branimir, Leszczak, Justyna, Leth-Møller, Katja B, Leung, Gabriel M, Levitt, Naomi S, Li, Yanping, Liivak, Merike, Lilly, Christa L, Lim, Charlie, Lim, Wei-Yen, Lima-Costa, M Fernanda, Lin, Xu, Lind, Lars, Lingam, Vijaya, Linkohr, Birgit, Linneberg, Allan, Lissner, Lauren, Litwin, Mieczyslaw, Liu, Jing, Liu, Lijuan, Liu, Liping, Liu, Xiaotian, Lo, Wei-Cheng, Loit, Helle-Mai, Long, Khuong Quynh, Longo Abril, Guadalupe, Lopes, Luis, Lopes, Marcus SS, Lopes, Oscar, Lopez-Garcia, Esther, Lopez, Tania, Lotufo, Paulo A, Lozano, José Eugenio, Lukrafka, Janice L, Luksiene, Dalia, Lundqvist, Annamari, Lunet, Nuno, Lunogelo, Charles, Lustigová, Michala, Łuszczki, Edyta, M'Buyamba-Kabangu, Jean-René, Ma, Guansheng, Ma, Xu, Machado-Coelho, George LL, Machado-Rodrigues, Aristides M, Macia, Enguerran, Macieira, Luisa M, Madar, Ahmed A, Madraisau, Sherilynn, Madsen, Anja L, Maestre, Gladys E, Maggi, Stefania, Magliano, Dianna J, Magnacca, Sara, Magriplis, Emmanuella, Mahasampath, Gowri, Maire, Bernard, Majer, Marjeta, Makdisse, Marcia, Mäki, Päivi, Malekpour, Mohammad-Reza, Malekzadeh, Fatemeh, Malekzadeh, Reza, Malhotra, Rahul, Mallikharjuna Rao, Kodavanti, Malta, Deborah C, Malyutina, Sofia K, Maniego, Lynell V, Manios, Yannis, Mann, Jim I, Mannix, Masimango Imani, Mansour-Ghanaei, Fariborz, Manyanga, Taru, Manzato, Enzo, Mapatano, Mala Ali, Marcil, Anie, Margozzini, Paula, Maria-Magdalena, Rosu, Mariño, Joany, Markaki, Anastasia, Markey, Oonagh, Markidou Ioannidou, Eliza, Marques-Vidal, Pedro, Marques, Larissa Pruner, Marrugat, Jaume, Martin-Prevel, Yves, Martin, Rosemarie, Martorell, Reynaldo, Martos, Eva, Maruf, Fatai A, Maruszczak, Katharina, Marventano, Stefano, Masala, Giovanna, Mascarenhas, Luis P, Masinaei, Masoud, Masoodi, Shariq R, Mathiesen, Ellisiv B, Mathur, Prashant, Matijasevich, Alicia, Matłosz, Piotr, Matsha, Tandi E, Matsudo, Victor, Matteo, Giletta, Maulik, Pallab K, Mavrogianni, Christina, Mazur, Artur, McFarlane, Shelly R, McGarvey, Stephen T, McKee, Martin, McLean, Rachael M, McLean, Scott B, McNairy, Margaret L, McNulty, Breige A, Mediene Benchekor, Sounnia, Medzioniene, Jurate, Mehlig, Kirsten, Mehrparvar, Amir Houshang, Meirhaeghe, Aline, Meisfjord, Jørgen, Meisinger, Christa, Melgarejo, Jesus D, Melkumova, Marina, Mello, Júlio, Méndez, Fabián, Mendivil, Carlos O, Menezes, Ana Maria B, Menon, Geetha R, Mensink, Gert BM, Menzano, Maria Teresa, Meshram, Indrapal I, Meto, Diane T, Meyer, Haakon E, Mi, Jie, Michaelsen, Kim F, Michels, Nathalie, Mikkel, Kairit, Miłkowska, Karolina, Miller, Jody C, Milushkina, Olga, Minderico, Cláudia S, Mini, GK, Miquel, Juan Francisco, Mirjalili, Mohammad Reza, Mirkopoulou, Daphne, Mirrakhimov, Erkin, Mišigoj-Duraković, Marjeta, Mistretta, Antonio, Mocanu, Veronica, Modesti, Pietro A, Moghaddam, Sahar Saeedi, Mohamed, Shukri F, Mohammad, Kazem, Mohammadi, Mohammad Reza, Mohammadi, Zahra, Mohammadifard, Noushin, Mohammadpourhodki, Reza, Mohan, Viswanathan, Mohanna, Salim, Mohd Yusoff, Muhammad Fadhli, Mohebbi, Iraj, Moitry, Marie, Møllehave, Line T, Møller, Niels C, Molnár, Dénes, Momenan, Amirabbas, Mondo, Charles K, Monroy-Valle, Michele, Montenegro Mendoza, Roger A, Monterrubio-Flores, Eric, Monyeki, Kotsedi Daniel K, Moon, Jin Soo, Moosazadeh, Mahmood, Mopa, Hermine T, Moradpour, Farhad, Moreira, Leila B, Morejon, Alain, Moreno, Luis A, Morey, Francis, Morgan, Karen, Morin, Suzanne N, Mortensen, Erik Lykke, Moschonis, George, Moslem, Alireza, Mosquera, Mildrey, Mossakowska, Malgorzata, Mostafa, Aya, Mostafavi, Seyed-Ali, Mota-Pinto, Anabela, Mota, Jorge, Motlagh, Mohammad Esmaeel, Motta, Jorge, Moura-dos-Santos, Marcos André, Movsesyan, Yeva, Mridha, Malay K, Msyamboza, Kelias P, Mu, Thet Thet, Muc, Magdalena, Muca, Florian, Mugoša, Boban, Muiesan, Maria L, Müller-Nurasyid, Martina, Münzel, Thomas, Mursu, Jaakko, Murtagh, Elaine M, Musa, Kamarul Imran, Musić Milanović, Sanja, Musil, Vera, Musinguzi, Geofrey, Muyer, Muel Telo, Nabipour, Iraj, Nagel, Gabriele, Najafi, Farid, Nakamura, Harunobu, Nalecz, Hanna, Námešná, Jana, Nang, Ei Ei K, Nangia, Vinay B, Nankap, Martin, Narake, Sameer, Narayan, KM Venkat, Nardone, Paola, Naseri, Take, Nathalie, Michels, Neal, William A, Neelapaichit, Nareemarn, Nejatizadeh, Azim, Nekkantti, Chandini, Nelis, Keiu, Nenko, Ilona, Neovius, Martin, Nervi, Flavio, Ng, Tze Pin, Nguyen, Chung T, Nguyen, Nguyen D, Nguyen, Quang Ngoc, Ni, Michael Y, Nicolescu, Rodica, Nie, Peng, Nieto-Martínez, Ramfis E, Nikitin, Yury P, Ning, Guang, Ninomiya, Toshiharu, Nishi, Nobuo, Nishtar, Sania, Noale, Marianna, Noboa, Oscar A, Nogueira, Helena, Nordendahl, Maria, Nordestgaard, Børge G, Norton, Kevin I, Noto, Davide, Nowak-Szczepanska, Natalia, Nsour, Mohannad Al, Nuhoğlu, Irfan, Nunes, Baltazar, Nurk, Eha, Nuwaha, Fred, Nyirenda, Moffat, O'Neill, Terence W, O'Reilly, Dermot, Obreja, Galina, Ochimana, Caleb, Ochoa-Avilés, Angélica M, Oda, Eiji, Odili, Augustine N, Oh, Kyungwon, Ohara, Kumiko, Ohlsson, Claes, Ohtsuka, Ryutaro, Olafsson, Örn, Oldenburg, Brian, Olinto, Maria Teresa A, Oliveira, Isabel O, Omar, Mohd Azahadi, Omar, Saeed M, Onat, Altan, Ong, Sok King, Onland-Moret, N Charlotte, Ono, Lariane M, Onodugo, Obinna, Ordunez, Pedro, Ornelas, Rui, Ortiz, Ana P, Ortiz, Pedro J, Osler, Merete, Osmond, Clive, Ostojic, Sergej M, Ostovar, Afshin, Otero, Johanna A, Ottendahl, Charlotte B, Otu, Akaninyene, Overvad, Kim, Owusu-Dabo, Ellis, Oyeyemi, Adetoyeje Y, Oyeyemi, Adewale L, Paccaud, Fred Michel, Padez, Cristina P, Pagkalos, Ioannis, Pahomova, Elena, de Paiva, Karina Mary, Pająk, Andrzej, Pajula, Natalja, Palloni, Alberto, Palmieri, Luigi, Pan, Wen-Harn, Panda-Jonas, Songhomitra, Pandey, Arvind, Pang, Zengchang, Panza, Francesco, Paoli, Mariela, Papadopoulou, Sousana K, Papandreou, Dimitrios, Pareja, Rossina G, Park, Soon-Woo, Park, Suyeon, Parnell, Winsome R, Parsaeian, Mahboubeh, Pascanu, Ionela M, Pasquet, Patrick, Patel, Nikhil D, Pattussi, Marcos, Pavlyshyn, Halyna, Pechlaner, Raimund, Pećin, Ivan, Pednekar, Mangesh S, Pedro, João M, Peer, Nasheeta, Peixoto, Sergio Viana, Peltonen, Markku, Pereira, Alexandre C, Peres, Marco A, Perez-Londoño, Agustín, Pérez, Cynthia M, Peterkova, Valentina, Peters, Annette, Petkeviciene, Janina, Petrauskiene, Ausra, Petrovna Kovtun, Olga, Pettenuzzo, Emanuela, Peykari, Niloofar, Pfeiffer, Norbert, Phall, Modou Cheyassin, Pham, Son Thai, Phiri, Felix P, Pichardo, Rafael N, Pierannunzio, Daniela, Pierre-Marie, Preux, Pigeot, Iris, Pikhart, Hynek, Pilav, Aida, Piler, Pavel, Pilotto, Lorenza, Pistelli, Francesco, Pitakaka, Freda, Piwonska, Aleksandra, Pizarro, Andreia N, Plans-Rubió, Pedro, Platonova, Alina G, Poh, Bee Koon, Pohlabeln, Hermann, Polka, Nadija S, Pop, Raluca M, Popkin, Barry M, Popovic, Stevo R, Porta, Miquel, Posch, Georg, Poudyal, Anil, Poulimeneas, Dimitrios, Pouraram, Hamed, Pourfarzi, Farhad, Pourshams, Akram, Poustchi, Hossein, Price, Alison J, Price, Jacqueline F, Prista, Antonio, Providencia, Rui, Puder, Jardena J, Pudule, Iveta, Puhakka, Solie, Puiu, Maria, Punab, Margus, Qadir, Muhammed S, Qasrawi, Radwan F, Qiao, Qing, Qorbani, Mostafa, Quintana, Hedley K, Quiroga-Padilla, Pedro J, Quoc Bao, Tran, Rach, Stefan, Radic, Ivana, Radisauskas, Ricardas, Rahimikazerooni, Salar, Rahman, Mahfuzar, Rahman, Mahmudur, Raitakari, Olli, Raj, Manu, Rajabov, Tamerlan, Rakhmatulloev, Sherali, Rakovac, Ivo, Ramachandra Rao, Sudha, Ramachandran, Ambady, Ramadan, Otim PC, Ramires, Virgílio V, Ramirez-Zea, Manuel, Ramke, Jacqueline, Ramos, Elisabete, Ramos, Rafel, Rampal, Lekhraj, Rampal, Sanjay, Ramsay, Sheena E, Rangelova, Lalka S, Rarra, Vayia, Rascon-Pacheco, Ramon A, Rashidi, Mohammad-Mahdi, Rech, Cassiano Ricardo, Redon, Josep, Reganit, Paul Ferdinand M, Regecová, Valéria, Renner, Jane DP, Repasy, Judit A, Reuter, Cézane P, Revilla, Luis, Reynolds, Andrew, Rezaei, Negar, Rezaianzadeh, Abbas, Rho, Yeunsook, Ribas-Barba, Lourdes, Ribeiro, Robespierre, Riboli, Elio, Rigo, Fernando, Rigotti, Attilio, Rinaldo, Natascia, Rinke de Wit, Tobias F, Risérus, Ulf, Rito, Ana I, Ritti-Dias, Raphael M, Rivera, Juan A, Roa, Reina G, Robinson, Louise, Roccaldo, Romana, Rodrigues, Daniela, Rodriguez-Perez, María del Cristo, Rodríguez-Villamizar, Laura A, Rodríguez, Andrea Y, Roggenbuck, Ulla, Rohloff, Peter, Rohner, Fabian, Rojas-Martinez, Rosalba, Rojroongwasinkul, Nipa, Romaguera, Dora, Romeo, Elisabetta L, Rosario, Rafaela V, Rosengren, Annika, Rouse, Ian, Rouzier, Vanessa, Roy, Joel GR, Ruano, Maira H, Rubinstein, Adolfo, Rühli, Frank J, Ruidavets, Jean-Bernard, Ruiz-Betancourt, Blanca Sandra, Ruiz-Castell, Maria, Ruiz Moreno, Emma, Rusakova, Iuliia A, Rusek, Wojciech, Russell Jonsson, Kenisha, Russo, Paola, Rust, Petra, Rutkowski, Marcin, Saamel, Marge, Saar, Crizian G, Sabanayagam, Charumathi, Sabbaghi, Hamideh, Sacchini, Elena, Sachdev, Harshpal S, Sadjadi, Alireza, Safarpour, Ali Reza, Safi, Sare, Safiri, Saeid, Saghi, Mohammad Hossien, Saidi, Olfa, Saieva, Calogero, Sakata, Satoko, Saki, Nader, Šalaj, Sanja, Salanave, Benoit, Salazar Martinez, Eduardo, Salhanova, Akkumis, Salmerón, Diego, Salomaa, Veikko, Salonen, Jukka T, Salvetti, Massimo, Samoutian, Margarita, Sánchez-Abanto, Jose, Sánchez Rodríguez, Inés, Sandjaja, Sans, Susana, Santa-Marina, Loreto, Santacruz, Ethel, Santos, Diana A, Santos, Ina S, Santos, Lèlita C, Santos, Maria Paula, Santos, Osvaldo, Santos, Rute, Santos, Tamara R, Saramies, Jouko L, Sardinha, Luis B, Sarrafzadegan, Nizal, Sathish, Thirunavukkarasu, Saum, Kai-Uwe, Savva, Savvas, Savy, Mathilde, Sawada, Norie, Sbaraini, Mariana, Scazufca, Marcia, Schaan, Beatriz D, Schaffrath Rosario, Angelika, Schargrodsky, Herman, Schienkiewitz, Anja, Schindler, Karin, Schipf, Sabine, Schmidt, Börge, Schmidt, Carsten O, Schmidt, Ida Maria, Schneider, Andrea, Schnohr, Peter, Schöttker, Ben, Schramm, Sara, Schramm, Stine, Schröder, Helmut, Schultsz, Constance, Schultz, Gry, Schulze, Matthias B, Schutte, Aletta E, Sebert, Sylvain, Sedaghattalab, Moslem, Selamat, Rusidah, Sember, Vedrana, Sen, Abhijit, Senbanjo, Idowu O, Sepanlou, Sadaf G, Sequera, Guillermo, Serra-Majem, Luis, Servais, Jennifer, Ševčíková, Ľudmila, Sewpaul, Ronel, Shalnova, Svetlana, Shamah-Levy, Teresa, Shamshirgaran, Seyed Morteza, Shanthirani, Coimbatore Subramaniam, Sharafkhah, Maryam, Sharma, Sanjib K, Sharman, Almaz, Shaw, Jonathan E, Shayanrad, Amaneh, Shayesteh, Ali Akbar, Shengelia, Lela, Shi, Zumin, Shibuya, Kenji, Shimizu-Furusawa, Hana, Shimony, Tal, Shiri, Rahman, Shrestha, Namuna, Si-Ramlee, Khairil, Siani, Alfonso, Siantar, Rosalynn, Sibai, Abla M, Sidossis, Labros S, Silitrari, Natalia, Silva, Antonio M, Silva, Caroline Ramos de Moura, Silva, Diego Augusto Santos, Silva, Kelly Samara, Sim, Xueling, Simon, Mary, Simons, Judith, Simons, Leon A, Sjöberg, Agneta, Sjöström, Michael, Skoblina, Elena V, Skoblina, Natalia A, Slazhnyova, Tatyana, Slowikowska-Hilczer, Jolanta, Slusarczyk, Przemysław, Smeeth, Liam, So, Hung-Kwan, Soares, Fernanda Cunha, Sobek, Grzegorz, Sobngwi, Eugène, Sodemann, Morten, Söderberg, Stefan, Soekatri, Moesijanti YE, Soemantri, Agustinus, Sofat, Reecha, Solfrizzi, Vincenzo, Solovieva, Yuliya V, Somi, Mohammad Hossein, Sonestedt, Emily, Song, Yi, Soofi, Sajid, Sørensen, Thorkild IA, Sørgjerd, Elin P, Sossa Jérome, Charles, Soto-Rojas, Victoria E, Soumaré, Aïcha, Sousa-Poza, Alfonso, Sovic, Slavica, Sparboe-Nilsen, Bente, Sparrenberger, Karen, Spencer, Phoebe R, Spinelli, Angela, Spiroski, Igor, Staessen, Jan A, Stamm, Hanspeter, Stang, Andreas, Starc, Gregor, Staub, Kaspar, Stavreski, Bill, Steene-Johannessen, Jostein, Stehle, Peter, Stein, Aryeh D, Steinsbekk, Silje, Stergiou, George S, Stessman, Jochanan, Stevanović, Ranko, Stieber, Jutta, Stöckl, Doris, Stokwiszewski, Jakub, Stoyanova, Ekaterina, Stratton, Gareth, Stronks, Karien, Strufaldi, Maria Wany, Sturua, Lela, Suárez-Medina, Ramón, Suarez-Ortegón, Milton F, Suebsamran, Phalakorn, Sugiyama, Mindy, Suka, Machi, Sulo, Gerhard, Sun, Chien-An, Sun, Liang, Sund, Malin, Sundström, Johan, Sung, Yn-Tz, Sunyer, Jordi, Suriyawongpaisal, Paibul, Sweis, Nabil William G, Swinburn, Boyd A, Sy, Rody G, Sylva, René Charles, Szponar, Lucjan, Tabone, Lorraine, Tai, E Shyong, Takuro, Furusawa, Tambalis, Konstantinos D, Tammesoo, Mari-Liis, Tamosiunas, Abdonas, Tan, Eng Joo, Tang, Xun, Tanrygulyyeva, Maya, Tanser, Frank, Tao, Yong, Tarawneh, Mohammed Rasoul, Tarp, Jakob, Tarqui-Mamani, Carolina B, Taxová Braunerová, Radka, Taylor, Anne, Taylor, Julie, Tchibindat, Félicité, Te Velde, Saskia, Tebar, William R, Tell, Grethe S, Tello, Tania, Tessema, Masresha, Tham, Yih Chung, Thankappan, KR, Theobald, Holger, Theodoridis, Xenophon, Thomas, Nihal, Thorand, Barbara, Thrift, Amanda G, Tichá, Ľubica, Timmermans, Erik J, Tjandrarini, Dwi Hapsari, Tjonneland, Anne, Tolonen, Hanna K, Tolstrup, Janne S, Tomaszewski, Maciej, Topbas, Murat, Topór-Mądry, Roman, Torheim, Liv Elin, Tornaritis, Michael J, Torrent, Maties, Torres-Collado, Laura, Toselli, Stefania, Touloumi, Giota, Traissac, Pierre, Tran, Thi Tuyet-Hanh, Tremblay, Mark S, Triantafyllou, Areti, Trichopoulos, Dimitrios, Trichopoulou, Antonia, Trinh, Oanh TH, Trivedi, Atul, Tshepo, Lechaba, Tsigga, Maria, Tsintavis, Panagiotis, Tsugane, Shoichiro, Tuitele, John, Tuliakova, Azaliia M, Tulloch-Reid, Marshall K, Tullu, Fikru, Tuomainen, Tomi-Pekka, Tuomilehto, Jaakko, Twig, Gilad, Tynelius, Per, Tzala, Evangelia, Tzotzas, Themistoklis, Tzourio, Christophe, Udoji, Nwannedimma, Ueda, Peter, Ugel, Eunice, Ukoli, Flora AM, Ulmer, Hanno, Unal, Belgin, Usupova, Zhamyila, Uusitalo, Hannu MT, Uysal, Nalan, Vaitkeviciute, Justina, Valdivia, Gonzalo, Vale, Susana, Valvi, Damaskini, van Dam, Rob M, van den Born, Bert-Jan, Van der Heyden, Johan, van der Schouw, Yvonne T, Van Herck, Koen, Van Lippevelde, Wendy, Van Minh, Hoang, Van Schoor, Natasja M, van Valkengoed, Irene GM, Vanderschueren, Dirk, Vanuzzo, Diego, Varbo, Anette, Varela-Moreiras, Gregorio, Vargas, Luz Nayibe, Varona-Pérez, Patricia, Vasan, Senthil K, Vasques, Daniel G, Vatasescu, Radu, Vega, Tomas, Veidebaum, Toomas, Velasquez-Melendez, Gustavo, Velika, Biruta, Verloigne, Maïté, Veronesi, Giovanni, Verschuren, WM Monique, Victora, Cesar G, Viegi, Giovanni, Viet, Lucie, Vik, Frøydis N, Vilar, Monica, Villalpando, Salvador, Vioque, Jesus, Viriyautsahakul, Napaphan, Virtanen, Jyrki K, Visser, Marjolein, Visvikis-Siest, Sophie, Viswanathan, Bharathi, Vladulescu, Mihaela, Vlasoff, Tiina, Vocanec, Dorja, Vollenweider, Peter, Völzke, Henry, Vourli, Georgia, Voutilainen, Ari, Vrijheid, Martine, Vrijkotte, Tanja GM, Vuletić, Silvije, Wade, Alisha N, Waldhör, Thomas, Walton, Janette, Wambiya, Elvis OA, Wan Bebakar, Wan Mohamad, Wan Mohamud, Wan Nazaimoon, Wanderley Júnior, Rildo de Souza, Wang, Chongjian, Wang, Huijun, Wang, Ming-Dong, Wang, Ningli, Wang, Qian, Wang, Xiangjun, Wang, Ya Xing, Wang, Ying-Wei, Wannamethee, S Goya, Wareham, Nicholas, Wartha, Olivia, Weber, Adelheid, Wedderkopp, Niels, Weghuber, Daniel, Wei, Wenbin, Weres, Aneta, Werner, Bo, Westbury, Leo D, Whincup, Peter H, Wichstrøm, Lars, Wickramasinghe, Kremlin, Widhalm, Kurt, Widyahening, Indah S, Więcek, Andrzej, Wild, Philipp S, Wilks, Rainford J, Willeit, Johann, Willeit, Peter, Williams, Julianne, Wilsgaard, Tom, Wirth, James P, Wojtyniak, Bogdan, Woldeyohannes, Meseret, Wolf, Kathrin, Wong-McClure, Roy A, Wong, Andrew, Wong, Emily B, Wong, Jyh Eiin, Wong, Tien Yin, Woo, Jean, Woodward, Mark, Wu, Frederick C, Wu, Hon-Yen, Wu, Jianfeng, Wu, Li Juan, Wu, Shouling, Wyszyńska, Justyna, Xu, Haiquan, Xu, Liang, Yaacob, Nor Azwany, Yamborisut, Uruwan, Yan, Li, Yan, Weili, Yang, Ling, Yang, Xiaoguang, Yang, Yang, Yardim, Nazan, Yasuharu, Tabara, Yépez García, Martha, Yiallouros, Panayiotis K, Yngve, Agneta, Yoosefi, Moein, Yoshihara, Akihiro, Yotov, Yoto, You, Qi Sheng, You, San-Lin, Younger-Coleman, Novie O, Yu, Yu-Ling, Yu, Yunjiang, Yusof, Safiah Md, Yusoff, Ahmad Faudzi, Zaccagni, Luciana, Zafiropulos, Vassilis, Zainuddin, Ahmad A, Zakavi, Seyed Rasoul, Zamani, Farhad, Zambon, Sabina, Zampelas, Antonis, Zamrazilová, Hana, Zapata, Maria Elisa, Zargar, Abdul Hamid, Zaw, Ko Ko, Zayed, Ayman A, Zdrojewski, Tomasz, Żegleń, Magdalena, Zejglicova, Kristyna, Zeljkovic Vrkic, Tajana, Zeng, Yi, Zentai, Andrea, Zhang, Bing, Zhang, Luxia, Zhang, Zhen-Yu, Zhao, Dong, Zhao, Ming-Hui, Zhao, Wenhua, Zhecheva, Yanitsa V, Zhen, Shiqi, Zheng, Wei, Zheng, Yingfeng, Zholdin, Bekbolat, Zhou, Maigeng, Zhu, Dan, Zimmet, Paul, Zins, Marie, Zitt, Emanuel, Zocalo, Yanina, Zoghlami, Nada, Zuñiga Cisneros, Julio, Zuziak, Monika, and Ezzati, Majid

Published
2024
Full Text
View/download PDF

13. Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network.

Author

Williams, Ross, Markus, Aniek, Yang, Cynthia, Duarte-Salles, Talita, DuVall, Scott, Falconer, Thomas, Jonnagaddala, Jitendra, Kim, Chungsoo, Rho, Yeunsook, Williams, Andrew, Machado, Amanda, An, Min, Aragón, María, Areia, Carlos, Burn, Edward, Choi, Young, Drakos, Iannis, Abrahão, Maria, Fernández-Bertolín, Sergio, Hripcsak, George, Kaas-Hansen, Benjamin, Kandukuri, Prasanna, Kors, Jan, Kostka, Kristin, Liaw, Siaw-Teng, Lynch, Kristine, Machnicki, Gerardo, Matheny, Michael, Morales, Daniel, Nyberg, Fredrik, Park, Rae, Prats-Uribe, Albert, Pratt, Nicole, Rao, Gowtham, Reich, Christian, Rivera, Marcela, Seinen, Tom, Shoaibi, Azza, Spotnitz, Matthew, Steyerberg, Ewout, Suchard, Marc, You, Seng, Zhang, Lin, Zhou, Lili, Ryan, Patrick, Prieto-Alhambra, Daniel, Reps, Jenna, and Rijnbeek, Peter

Subjects
COVID-19, Patient-level prediction modelling, Risk score, COVID-19, COVID-19 Testing, Humans, Influenza, Human, Pneumonia, SARS-CoV-2, United States
Abstract

BACKGROUND: We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patients risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. METHODS: We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. RESULTS: Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.72-0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. CONCLUSIONS: This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use.

Published
2022

14. COVID-19 in patients with autoimmune diseases: characteristics and outcomes in a multinational network of cohorts across three countries

Author

Tan, Eng Hooi, Sena, Anthony G, Prats-Uribe, Albert, You, Seng Chan, Ahmed, Waheed-Ul-Rahman, Kostka, Kristin, Reich, Christian, Duvall, Scott L, Lynch, Kristine E, Matheny, Michael E, Duarte-Salles, Talita, Bertolin, Sergio Fernandez, Hripcsak, George, Natarajan, Karthik, Falconer, Thomas, Spotnitz, Matthew, Ostropolets, Anna, Blacketer, Clair, Alshammari, Thamir M, Alghoul, Heba, Alser, Osaid, Lane, Jennifer CE, Dawoud, Dalia M, Shah, Karishma, Yang, Yue, Zhang, Lin, Areia, Carlos, Golozar, Asieh, Recalde, Martina, Casajust, Paula, Jonnagaddala, Jitendra, Subbian, Vignesh, Vizcaya, David, Lai, Lana YH, Nyberg, Fredrik, Morales, Daniel R, Posada, Jose D, Shah, Nigam H, Gong, Mengchun, Vivekanantham, Arani, Abend, Aaron, Minty, Evan P, Suchard, Marc, Rijnbeek, Peter, Ryan, Patrick B, and Prieto-Alhambra, Daniel

Subjects
Infectious Diseases, Lung, Emerging Infectious Diseases, Pneumonia & Influenza, Clinical Research, Influenza, Autoimmune Disease, Cardiovascular, Good Health and Well Being, Adult, Aged, Aged, 80 and over, Autoimmune Diseases, COVID-19, Cohort Studies, Female, Hospitalization, Humans, Influenza, Human, Male, Middle Aged, Prevalence, Prognosis, Republic of Korea, SARS-CoV-2, Spain, United States, Young Adult, autoimmune condition, mortality, hospitalization, open science, Observational Health Data Sciences and Informatics, Observational Medical Outcomes Partnership, Clinical Sciences, Immunology, Public Health and Health Services, Arthritis & Rheumatology
Abstract

ObjectivePatients with autoimmune diseases were advised to shield to avoid coronavirus disease 2019 (COVID-19), but information on their prognosis is lacking. We characterized 30-day outcomes and mortality after hospitalization with COVID-19 among patients with prevalent autoimmune diseases, and compared outcomes after hospital admissions among similar patients with seasonal influenza.MethodsA multinational network cohort study was conducted using electronic health records data from Columbia University Irving Medical Center [USA, Optum (USA), Department of Veterans Affairs (USA), Information System for Research in Primary Care-Hospitalization Linked Data (Spain) and claims data from IQVIA Open Claims (USA) and Health Insurance and Review Assessment (South Korea). All patients with prevalent autoimmune diseases, diagnosed and/or hospitalized between January and June 2020 with COVID-19, and similar patients hospitalized with influenza in 2017-18 were included. Outcomes were death and complications within 30 days of hospitalization.ResultsWe studied 133 589 patients diagnosed and 48 418 hospitalized with COVID-19 with prevalent autoimmune diseases. Most patients were female, aged ≥50 years with previous comorbidities. The prevalence of hypertension (45.5-93.2%), chronic kidney disease (14.0-52.7%) and heart disease (29.0-83.8%) was higher in hospitalized vs diagnosed patients with COVID-19. Compared with 70 660 hospitalized with influenza, those admitted with COVID-19 had more respiratory complications including pneumonia and acute respiratory distress syndrome, and higher 30-day mortality (2.2-4.3% vs 6.32-24.6%).ConclusionCompared with influenza, COVID-19 is a more severe disease, leading to more complications and higher mortality.

Published
2021

15. Characteristics and Outcomes of Over 300,000 Patients with COVID-19 and History of Cancer in the United States and SpainCharacteristics of 300,000 COVID-19 Individuals with Cancer

Author

Roel, Elena, Pistillo, Andrea, Recalde, Martina, Sena, Anthony G, Fernández-Bertolín, Sergio, Aragón, Maria, Puente, Diana, Ahmed, Waheed-Ul-Rahman, Alghoul, Heba, Alser, Osaid, Alshammari, Thamir M, Areia, Carlos, Blacketer, Clair, Carter, William, Casajust, Paula, Culhane, Aedin C, Dawoud, Dalia, DeFalco, Frank, DuVall, Scott L, Falconer, Thomas, Golozar, Asieh, Gong, Mengchun, Hester, Laura, Hripcsak, George, Tan, Eng Hooi, Jeon, Hokyun, Jonnagaddala, Jitendra, Lai, Lana YH, Lynch, Kristine E, Matheny, Michael E, Morales, Daniel R, Natarajan, Karthik, Nyberg, Fredrik, Ostropolets, Anna, Posada, José D, Prats-Uribe, Albert, Reich, Christian G, Rivera, Donna R, Schilling, Lisa M, Soerjomataram, Isabelle, Shah, Karishma, Shah, Nigam H, Shen, Yang, Spotniz, Matthew, Subbian, Vignesh, Suchard, Marc A, Trama, Annalisa, Zhang, Lin, Zhang, Ying, Ryan, Patrick B, Prieto-Alhambra, Daniel, Kostka, Kristin, and Duarte-Salles, Talita

Subjects
Biomedical and Clinical Sciences, Health Services and Systems, Health Sciences, Clinical Sciences, Oncology and Carcinogenesis, Breast Cancer, Patient Safety, Infectious Diseases, Rare Diseases, Hematology, Cancer, Urologic Diseases, Prevention, Clinical Research, Aetiology, 2.4 Surveillance and distribution, Good Health and Well Being, Adolescent, Adult, Aged, Aged, 80 and over, COVID-19, Child, Cohort Studies, Comorbidity, Databases, Factual, Female, Hospitalization, Humans, Immunosuppression Therapy, Influenza, Human, Male, Middle Aged, Neoplasms, Outcome Assessment, Health Care, Pandemics, Prevalence, Risk Factors, SARS-CoV-2, Spain, United States, Young Adult, Medical and Health Sciences, Epidemiology, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundWe described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and coronavirus disease 2019 (COVID-19). Second, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza.MethodsWe conducted a cohort study using eight routinely collected health care databases from Spain and the United States, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: (i) diagnosed with COVID-19, (ii) hospitalized with COVID-19, and (iii) hospitalized with influenza in 2017 to 2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes.ResultsWe included 366,050 and 119,597 patients diagnosed and hospitalized with COVID-19, respectively. Prostate and breast cancers were the most frequent cancers (range: 5%-18% and 1%-14% in the diagnosed cohort, respectively). Hematologic malignancies were also frequent, with non-Hodgkin's lymphoma being among the five most common cancer subtypes in the diagnosed cohort. Overall, patients were aged above 65 years and had multiple comorbidities. Occurrence of death ranged from 2% to 14% and from 6% to 26% in the diagnosed and hospitalized COVID-19 cohorts, respectively. Patients hospitalized with influenza (n = 67,743) had a similar distribution of cancer subtypes, sex, age, and comorbidities but lower occurrence of adverse events.ConclusionsPatients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematologic malignancies were frequent.ImpactThis study provides epidemiologic characteristics that can inform clinical care and etiologic studies.

Published
2021

16. End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study

Author

Jiang, Xiaofeng, Hoffmeister, Michael, Brenner, Hermann, Muti, Hannah Sophie, Yuan, Tanwei, Foersch, Sebastian, West, Nicholas P, Brobeil, Alexander, Jonnagaddala, Jitendra, Hawkins, Nicholas, Ward, Robyn L, Brinker, Titus J, Saldanha, Oliver Lester, Ke, Jia, Müller, Wolfram, Grabsch, Heike I, Quirke, Philip, Truhn, Daniel, and Kather, Jakob Nikolas

Published
2024
Full Text
View/download PDF

17. Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks

Author

Yao, Jiawen, Zhu, Xinliang, Jonnagaddala, Jitendra, Hawkins, Nicholas, and Huang, Junzhou

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Traditional image-based survival prediction models rely on discriminative patch labeling which make those methods not scalable to extend to large datasets. Recent studies have shown Multiple Instance Learning (MIL) framework is useful for histopathological images when no annotations are available in classification task. Different to the current image-based survival models that limit to key patches or clusters derived from Whole Slide Images (WSIs), we propose Deep Attention Multiple Instance Survival Learning (DeepAttnMISL) by introducing both siamese MI-FCN and attention-based MIL pooling to efficiently learn imaging features from the WSI and then aggregate WSI-level information to patient-level. Attention-based aggregation is more flexible and adaptive than aggregation techniques in recent survival models. We evaluated our methods on two large cancer whole slide images datasets and our results suggest that the proposed approach is more effective and suitable for large datasets and has better interpretability in locating important patterns and features that contribute to accurate cancer survival predictions. The proposed framework can also be used to assess individual patient's risk and thus assisting in delivering personalized medicine. Codes are available at https://github.com/uta-smile/DeepAttnMISL_MEDIA., Comment: 22 pages, 13 figures, published in Medical Image Analysis 65, 101789

Published
2020
Full Text
View/download PDF

19. Use of repurposed and adjuvant drugs in hospital patients with covid-19: multinational network cohort study.

Author

Prats-Uribe, Albert, Sena, Anthony G, Lai, Lana Yin Hui, Ahmed, Waheed-Ul-Rahman, Alghoul, Heba, Alser, Osaid, Alshammari, Thamir M, Areia, Carlos, Carter, William, Casajust, Paula, Dawoud, Dalia, Golozar, Asieh, Jonnagaddala, Jitendra, Mehta, Paras P, Gong, Mengchun, Morales, Daniel R, Nyberg, Fredrik, Posada, Jose D, Recalde, Martina, Roel, Elena, Shah, Karishma, Shah, Nigam H, Schilling, Lisa M, Subbian, Vignesh, Vizcaya, David, Zhang, Lin, Zhang, Ying, Zhu, Hong, Liu, Li, Cho, Jaehyeong, Lynch, Kristine E, Matheny, Michael E, You, Seng Chan, Rijnbeek, Peter R, Hripcsak, George, Lane, Jennifer Ce, Burn, Edward, Reich, Christian, Suchard, Marc A, Duarte-Salles, Talita, Kostka, Kristin, Ryan, Patrick B, and Prieto-Alhambra, Daniel

Subjects
Humans, Ceftriaxone, Azithromycin, Ritonavir, Hydroxychloroquine, Fluoroquinolones, Adrenal Cortex Hormones, Enoxaparin, Vitamin D, Drug Combinations, Treatment Outcome, Chemotherapy, Adjuvant, Cohort Studies, Safety, Adolescent, Adult, Aged, Aged, 80 and over, Middle Aged, Child, Child, Preschool, Infant, Infant, Newborn, Inpatients, United States, China, Spain, Female, Male, Young Adult, Republic of Korea, Electronic Health Records, Drug Repositioning, Lopinavir, Administrative Claims, Healthcare, COVID-19, SARS-CoV-2, Administrative Claims, Healthcare, and over, Chemotherapy, Adjuvant, Preschool, Newborn, General & Internal Medicine, Clinical Sciences, Public Health and Health Services
Abstract

ObjectiveTo investigate the use of repurposed and adjuvant drugs in patients admitted to hospital with covid-19 across three continents.DesignMultinational network cohort study.SettingHospital electronic health records from the United States, Spain, and China, and nationwide claims data from South Korea.Participants303 264 patients admitted to hospital with covid-19 from January 2020 to December 2020.Main outcome measuresPrescriptions or dispensations of any drug on or 30 days after the date of hospital admission for covid-19.ResultsOf the 303 264 patients included, 290 131 were from the US, 7599 from South Korea, 5230 from Spain, and 304 from China. 3455 drugs were identified. Common repurposed drugs were hydroxychloroquine (used in from

Published
2021

20. Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study

Author

Church, David, Domingo, Enric, Edwards, Joanne, Glimelius, Bengt, Gogenur, Ismail, Harkin, Andrea, Hay, Jen, Iveson, Timothy, Jaeger, Emma, Kelly, Caroline, Kerr, Rachel, Maka, Noori, Morgan, Hannah, Oien, Karin, Orange, Clare, Palles, Claire, Roxburgh, Campbell, Sansom, Owen, Saunders, Mark, Tomlinson, Ian, Wagner, Sophia J., Reisenbüchler, Daniel, West, Nicholas P., Niehues, Jan Moritz, Zhu, Jiefu, Foersch, Sebastian, Veldhuizen, Gregory Patrick, Quirke, Philip, Grabsch, Heike I., van den Brandt, Piet A., Hutchins, Gordon G.A., Richman, Susan D., Yuan, Tanwei, Langer, Rupert, Jenniskens, Josien C.A., Offermans, Kelly, Mueller, Wolfram, Gray, Richard, Gruber, Stephen B., Greenson, Joel K., Rennert, Gad, Bonner, Joseph D., Schmolze, Daniel, Jonnagaddala, Jitendra, Hawkins, Nicholas J., Ward, Robyn L., Morton, Dion, Seymour, Matthew, Magill, Laura, Nowak, Marta, Hay, Jennifer, Koelzer, Viktor H., Church, David N., Matek, Christian, Geppert, Carol, Peng, Chaolong, Zhi, Cheng, Ouyang, Xiaoming, James, Jacqueline A., Loughrey, Maurice B., Salto-Tellez, Manuel, Brenner, Hermann, Hoffmeister, Michael, Truhn, Daniel, Schnabel, Julia A., Boxberg, Melanie, Peng, Tingying, and Kather, Jakob Nikolas

Published
2023
Full Text
View/download PDF

26. Graph Attention Multi-instance Learning for Accurate Colorectal Cancer Staging

Author

Raju, Ashwin, Yao, Jiawen, Haq, Mohammad MinHazul, Jonnagaddala, Jitendra, Huang, Junzhou, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Martel, Anne L., editor, Abolmaesumi, Purang, editor, Stoyanov, Danail, editor, Mateus, Diana, editor, Zuluaga, Maria A., editor, Zhou, S. Kevin, editor, Racoceanu, Daniel, editor, and Joskowicz, Leo, editor

Published
2020
Full Text
View/download PDF

31. A Report on the Workshop on Biobanking Informatics

Author

Jonnagaddala, Jitendra and Sue, Damian

Subjects
Quantitative Biology - Tissues and Organs
Abstract

The Workshop on Biobanking Informatics in NSW 2013 (WBIN13) was held on Friday, 10 May 2013 at The Wallace Wurth Building in the University of New South Wales. This report summarises the keynotes, presentations and discussions in WBIN13 which discusses current research in the field of Biobanking Informatics in Australia and internationally., Comment: 5 Pages, Workshop

Published
2015

32. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study

Author

Roth, Gregory A., Mensah, George A., Johnson, Catherine O., Addolorato, Giovanni, Ammirati, Enrico, Baddour, Larry M., Barengo, Noel C., Beaton, Andrea, Benjamin, Emelia J., Benziger, Catherine P., Bonny, Aime, Brauer, Michael, Brodmann, Marianne, Cahill, Thomas J., Carapetis, Jonathan R., Catapano, Alberico L., Chugh, Sumeet, Cooper, Leslie T., Coresh, Josef, Criqui, Michael H., DeCleene, Nicole K., Eagle, Kim A., Emmons-Bell, Sophia, Feigin, Valery L., Fernández-Sola, Joaquim, Fowkes, F. Gerry R., Gakidou, Emmanuela, Grundy, Scott M., He, Feng J., Howard, George, Hu, Frank, Inker, Lesley, Karthikeyan, Ganesan, Kassebaum, Nicholas J., Koroshetz, Walter J., Lavie, Carl, Lloyd-Jones, Donald, Lu, Hong S., Mirijello, Antonio, Misganaw, Awoke T., Mokdad, Ali H., Moran, Andrew E., Muntner, Paul, Narula, Jagat, Neal, Bruce, Ntsekhe, Mpiko, Oliveira, Gláucia M.M., Otto, Catherine M., Owolabi, Mayowa O., Pratt, Michael, Rajagopalan, Sanjay, Reitsma, Marissa B., Ribeiro, Antonio Luiz P., Rigotti, Nancy A., Rodgers, Anthony, Sable, Craig A., Shakil, Saate S., Sliwa, Karen, Stark, Benjamin A., Sundström, Johan, Timpel, Patrick, Tleyjeh, Imad I., Valgimigli, Marco, Vos, Theo, Whelton, Paul K., Yacoub, Magdi, Zuhlke, Liesl J., Abbasi-Kangevari, Mohsen, Abdi, Alireza, Abedi, Aidin, Aboyans, Victor, Abrha, Woldu A., Abu-Gharbieh, Eman, Abushouk, Abdelrahman I., Acharya, Dilaram, Adair, Tim, Adebayo, Oladimeji M., Ademi, Zanfina, Advani, Shailesh M., Afshari, Khashayar, Afshin, Ashkan, Agarwal, Gina, Agasthi, Pradyumna, Ahmad, Sohail, Ahmadi, Sepideh, Ahmed, Muktar B., Aji, Budi, Akalu, Yonas, Akande-Sholabi, Wuraola, Aklilu, Addis, Akunna, Chisom J., Alahdab, Fares, Al-Eyadhy, Ayman, Alhabib, Khalid F., Alif, Sheikh M., Alipour, Vahid, Aljunid, Syed M., Alla, François, Almasi-Hashiani, Amir, Almustanyir, Sami, Al-Raddadi, Rajaa M., Amegah, Adeladza K., Amini, Saeed, Aminorroaya, Arya, Amu, Hubert, Amugsi, Dickson A., Ancuceanu, Robert, Anderlini, Deanna, Andrei, Tudorel, Andrei, Catalina Liliana, Ansari-Moghaddam, Alireza, Anteneh, Zelalem A., Antonazzo, Ippazio Cosimo, Antony, Benny, Anwer, Razique, Appiah, Lambert T., Arabloo, Jalal, Ärnlöv, Johan, Artanti, Kurnia D., Ataro, Zerihun, Ausloos, Marcel, Avila-Burgos, Leticia, Awan, Asma T., Awoke, Mamaru A., Ayele, Henok T., Ayza, Muluken A., Azari, Samad, B, Darshan B., Baheiraei, Nafiseh, Baig, Atif A., Bakhtiari, Ahad, Banach, Maciej, Banik, Palash C., Baptista, Emerson A., Barboza, Miguel A., Barua, Lingkan, Basu, Sanjay, Bedi, Neeraj, Béjot, Yannick, Bennett, Derrick A., Bensenor, Isabela M., Berman, Adam E., Bezabih, Yihienew M., Bhagavathula, Akshaya S., Bhaskar, Sonu, Bhattacharyya, Krittika, Bijani, Ali, Bikbov, Boris, Birhanu, Mulugeta M., Boloor, Archith, Brant, Luisa C., Brenner, Hermann, Briko, Nikolay I., Butt, Zahid A., Caetano dos Santos, Florentino Luciano, Cahill, Leah E., Cahuana-Hurtado, Lucero, Cámera, Luis A., Campos-Nonato, Ismael R., Cantu-Brito, Carlos, Car, Josip, Carrero, Juan J., Carvalho, Felix, Castañeda-Orjuela, Carlos A., Catalá-López, Ferrán, Cerin, Ester, Charan, Jaykaran, Chattu, Vijay Kumar, Chen, Simiao, Chin, Ken L., Choi, Jee-Young J., Chu, Dinh-Toi, Chung, Sheng-Chia, Cirillo, Massimo, Coffey, Sean, Conti, Sara, Costa, Vera M., Cundiff, David K., Dadras, Omid, Dagnew, Baye, Dai, Xiaochen, Damasceno, Albertino A.M., Dandona, Lalit, Dandona, Rakhi, Davletov, Kairat, De la Cruz-Góngora, Vanessa, De la Hoz, Fernando P., De Neve, Jan-Walter, Denova-Gutiérrez, Edgar, Derbew Molla, Meseret, Derseh, Behailu T., Desai, Rupak, Deuschl, Günther, Dharmaratne, Samath D., Dhimal, Meghnath, Dhungana, Raja Ram, Dianatinasab, Mostafa, Diaz, Daniel, Djalalinia, Shirin, Dokova, Klara, Douiri, Abdel, Duncan, Bruce B., Duraes, Andre R., Eagan, Arielle W., Ebtehaj, Sanam, Eftekhari, Aziz, Eftekharzadeh, Sahar, Ekholuenetale, Michael, El Nahas, Nevine, Elgendy, Islam Y., Elhadi, Muhammed, El-Jaafary, Shaimaa I., Esteghamati, Sadaf, Etisso, Atkilt E., Eyawo, Oghenowede, Fadhil, Ibtihal, Faraon, Emerito Jose A., Faris, Pawan S., Farwati, Medhat, Farzadfar, Farshad, Fernandes, Eduarda, Fernandez Prendes, Carlota, Ferrara, Pietro, Filip, Irina, Fischer, Florian, Flood, David, Fukumoto, Takeshi, Gad, Mohamed M., Gaidhane, Shilpa, Ganji, Morsaleh, Garg, Jalaj, Gebre, Abadi K., Gebregiorgis, Birhan G., Gebregzabiher, Kidane Z., Gebremeskel, Gebreamlak G., Getacher, Lemma, Obsa, Abera Getachew, Ghajar, Alireza, Ghashghaee, Ahmad, Ghith, Nermin, Giampaoli, Simona, Gilani, Syed Amir, Gill, Paramjit S., Gillum, Richard F., Glushkova, Ekaterina V., Gnedovskaya, Elena V., Golechha, Mahaveer, Gonfa, Kebebe B., Goudarzian, Amir Hossein, Goulart, Alessandra C., Guadamuz, Jenny S., Guha, Avirup, Guo, Yuming, Gupta, Rajeev, Hachinski, Vladimir, Hafezi-Nejad, Nima, Haile, Teklehaimanot G., Hamadeh, Randah R., Hamidi, Samer, Hankey, Graeme J., Hargono, Arief, Hartono, Risky K., Hashemian, Maryam, Hashi, Abdiwahab, Hassan, Shoaib, Hassen, Hamid Y., Havmoeller, Rasmus J., Hay, Simon I., Hayat, Khezar, Heidari, Golnaz, Herteliu, Claudiu, Holla, Ramesh, Hosseini, Mostafa, Hosseinzadeh, Mehdi, Hostiuc, Mihaela, Hostiuc, Sorin, Househ, Mowafa, Huang, Junjie, Humayun, Ayesha, Iavicoli, Ivo, Ibeneme, Charles U., Ibitoye, Segun E., Ilesanmi, Olayinka S., Ilic, Irena M., Ilic, Milena D., Iqbal, Usman, Irvani, Seyed Sina N., Islam, Sheikh Mohammed Shariful, Islam, Rakibul M., Iso, Hiroyasu, Iwagami, Masao, Jain, Vardhmaan, Javaheri, Tahereh, Jayapal, Sathish Kumar, Jayaram, Shubha, Jayawardena, Ranil, Jeemon, Panniyammakal, Jha, Ravi P., Jonas, Jost B., Jonnagaddala, Jitendra, Joukar, Farahnaz, Jozwiak, Jacek J., Jürisson, Mikk, Kabir, Ali, Kahlon, Tanvir, Kalani, Rizwan, Kalhor, Rohollah, Kamath, Ashwin, Kamel, Ibrahim, Kandel, Himal, Kandel, Amit, Karch, André, Kasa, Ayele Semachew, Katoto, Patrick D.M.C., Kayode, Gbenga A., Khader, Yousef S., Khammarnia, Mohammad, Khan, Muhammad S., Khan, Md Nuruzzaman, Khan, Maseer, Khan, Ejaz A., Khatab, Khaled, Kibria, Gulam M.A., Kim, Yun Jin, Kim, Gyu Ri, Kimokoti, Ruth W., Kisa, Sezer, Kisa, Adnan, Kivimäki, Mika, Kolte, Dhaval, Koolivand, Ali, Korshunov, Vladimir A., Koulmane Laxminarayana, Sindhura Lakshmi, Koyanagi, Ai, Krishan, Kewal, Krishnamoorthy, Vijay, Kuate Defo, Barthelemy, Kucuk Bicer, Burcu, Kulkarni, Vaman, Kumar, G. Anil, Kumar, Nithin, Kurmi, Om P., Kusuma, Dian, Kwan, Gene F., La Vecchia, Carlo, Lacey, Ben, Lallukka, Tea, Lan, Qing, Lasrado, Savita, Lassi, Zohra S., Lauriola, Paolo, Lawrence, Wayne R., Laxmaiah, Avula, LeGrand, Kate E., Li, Ming-Chieh, Li, Bingyu, Li, Shanshan, Lim, Stephen S., Lim, Lee-Ling, Lin, Hualiang, Lin, Ziqiang, Lin, Ro-Ting, Liu, Xuefeng, Lopez, Alan D., Lorkowski, Stefan, Lotufo, Paulo A., Lugo, Alessandra, M, Nirmal K., Madotto, Fabiana, Mahmoudi, Morteza, Majeed, Azeem, Malekzadeh, Reza, Malik, Ahmad A., Mamun, Abdullah A., Manafi, Navid, Mansournia, Mohammad Ali, Mantovani, Lorenzo G., Martini, Santi, Mathur, Manu R., Mazzaglia, Giampiero, Mehata, Suresh, Mehndiratta, Man Mohan, Meier, Toni, Menezes, Ritesh G., Meretoja, Atte, Mestrovic, Tomislav, Miazgowski, Bartosz, Miazgowski, Tomasz, Michalek, Irmina Maria, Miller, Ted R., Mirrakhimov, Erkin M., Mirzaei, Hamed, Moazen, Babak, Moghadaszadeh, Masoud, Mohammad, Yousef, Mohammad, Dara K., Mohammed, Shafiu, Mohammed, Mohammed A., Mokhayeri, Yaser, Molokhia, Mariam, Montasir, Ahmed A., Moradi, Ghobad, Moradzadeh, Rahmatollah, Moraga, Paula, Morawska, Lidia, Moreno Velásquez, Ilais, Morze, Jakub, Mubarik, Sumaira, Muruet, Walter, Musa, Kamarul Imran, Nagarajan, Ahamarshan J., Nalini, Mahdi, Nangia, Vinay, Naqvi, Atta Abbas, Narasimha Swamy, Sreenivas, Nascimento, Bruno R., Nayak, Vinod C., Nazari, Javad, Nazarzadeh, Milad, Negoi, Ruxandra I., Neupane Kandel, Sandhya, Nguyen, Huong L.T., Nixon, Molly R., Norrving, Bo, Noubiap, Jean Jacques, Nouthe, Brice E., Nowak, Christoph, Odukoya, Oluwakemi O., Ogbo, Felix A., Olagunju, Andrew T., Orru, Hans, Ortiz, Alberto, Ostroff, Samuel M., Padubidri, Jagadish Rao, Palladino, Raffaele, Pana, Adrian, Panda-Jonas, Songhomitra, Parekh, Utsav, Park, Eun-Cheol, Parvizi, Mojtaba, Pashazadeh Kan, Fatemeh, Patel, Urvish K., Pathak, Mona, Paudel, Rajan, Pepito, Veincent Christian F., Perianayagam, Arokiasamy, Perico, Norberto, Pham, Hai Q., Pilgrim, Thomas, Piradov, Michael A., Pishgar, Farhad, Podder, Vivek, Polibin, Roman V., Pourshams, Akram, Pribadi, Dimas R.A., Rabiee, Navid, Rabiee, Mohammad, Radfar, Amir, Rafiei, Alireza, Rahim, Fakher, Rahimi-Movaghar, Vafa, Ur Rahman, Mohammad Hifz, Rahman, Muhammad Aziz, Rahmani, Amir Masoud, Rakovac, Ivo, Ram, Pradhum, Ramalingam, Sudha, Rana, Juwel, Ranasinghe, Priyanga, Rao, Sowmya J., Rathi, Priya, Rawal, Lal, Rawasia, Wasiq F., Rawassizadeh, Reza, Remuzzi, Giuseppe, Renzaho, Andre M.N., Rezapour, Aziz, Riahi, Seyed Mohammad, Roberts-Thomson, Ross L., Roever, Leonardo, Rohloff, Peter, Romoli, Michele, Roshandel, Gholamreza, Rwegerera, Godfrey M., Saadatagah, Seyedmohammad, Saber-Ayad, Maha M., Sabour, Siamak, Sacco, Simona, Sadeghi, Masoumeh, Saeedi Moghaddam, Sahar, Safari, Saeed, Sahebkar, Amirhossein, Salehi, Sana, Salimzadeh, Hamideh, Samaei, Mehrnoosh, Samy, Abdallah M., Santos, Itamar S., Santric-Milicevic, Milena M., Sarrafzadegan, Nizal, Sarveazad, Arash, Sathish, Thirunavukkarasu, Sawhney, Monika, Saylan, Mete, Schmidt, Maria I., Schutte, Aletta E., Senthilkumaran, Subramanian, Sepanlou, Sadaf G., Sha, Feng, Shahabi, Saeed, Shahid, Izza, Shaikh, Masood A., Shamali, Mahdi, Shamsizadeh, Morteza, Shawon, Md Shajedur Rahman, Sheikh, Aziz, Shigematsu, Mika, Shin, Min-Jeong, Shin, Jae Il, Shiri, Rahman, Shiue, Ivy, Shuval, Kerem, Siabani, Soraya, Siddiqi, Tariq J., Silva, Diego A.S., Singh, Jasvinder A., Mtech, Ambrish Singh, Skryabin, Valentin Y., Skryabina, Anna A., Soheili, Amin, Spurlock, Emma E., Stockfelt, Leo, Stortecky, Stefan, Stranges, Saverio, Suliankatchi Abdulkader, Rizwan, Tadbiri, Hooman, Tadesse, Eyayou G., Tadesse, Degena B., Tajdini, Masih, Tariqujjaman, Md, Teklehaimanot, Berhane F., Temsah, Mohamad-Hani, Tesema, Ayenew K., Thakur, Bhaskar, Thankappan, Kavumpurathu R., Thapar, Rekha, Thrift, Amanda G., Timalsina, Binod, Tonelli, Marcello, Touvier, Mathilde, Tovani-Palone, Marcos R., Tripathi, Avnish, Tripathy, Jaya P., Truelsen, Thomas C., Tsegay, Guesh M., Tsegaye, Gebiyaw W., Tsilimparis, Nikolaos, Tusa, Biruk S., Tyrovolas, Stefanos, Umapathi, Krishna Kishore, Unim, Brigid, Unnikrishnan, Bhaskaran, Usman, Muhammad S., Vaduganathan, Muthiah, Valdez, Pascual R., Vasankari, Tommi J., Velazquez, Diana Z., Venketasubramanian, Narayanaswamy, Vu, Giang T., Vujcic, Isidora S., Waheed, Yasir, Wang, Yanzhong, Wang, Fang, Wei, Jingkai, Weintraub, Robert G., Weldemariam, Abrha H., Westerman, Ronny, Winkler, Andrea S., Wiysonge, Charles S., Wolfe, Charles D.A., Wubishet, Befikadu Legesse, Xu, Gelin, Yadollahpour, Ali, Yamagishi, Kazumasa, Yan, Lijing L., Yandrapalli, Srikanth, Yano, Yuichiro, Yatsuya, Hiroshi, Yeheyis, Tomas Y., Yeshaw, Yigizie, Yilgwan, Christopher S., Yonemoto, Naohiro, Yu, Chuanhua, Yusefzadeh, Hasan, Zachariah, Geevar, Zaman, Sojib Bin, Zaman, Muhammed S., Zamanian, Maryam, Zand, Ramin, Zandifar, Alireza, Zarghi, Afshin, Zastrozhin, Mikhail S., Zastrozhina, Anasthasia, Zhang, Zhi-Jiang, Zhang, Yunquan, Zhang, Wangjian, Zhong, Chenwen, Zou, Zhiyong, Zuniga, Yves Miel H., Murray, Christopher J.L., Fuster, Valentin, Barengo, Noël C., Beaton, Andrea Z., Bonny, Aimé, Carapetis, Jonathan, Chugh, Sumeet S., Criqui, Michael, DeCleene, Nicole, Fernández-Solà, Joaquim, Fowkes, Gerry, Kassebaum, Nicholas, Koroshetz, Walter, Temesgen, Awoke Misganaw, Mokdad, Ali, Moraes de Oliveira, Glaucia, Otto, Catherine, Owolabi, Mayowa, Reitsma, Marissa, Rigotti, Nancy, Sable, Craig, Shakil, Saate, Sliwa-Hahnle, Karen, Stark, Benjamin, Tleyjeh, Imad M., Zuhlke, Liesl, and Murray, Christopher

Published
2020
Full Text
View/download PDF

38. Preliminary Evaluation of Fine-Tuning the OpenDeLD Deidentification Pipeline Across Multi-Center Corpora.

Author

GUPTA, Shalini, Jiaxing LIU, Zoie Shiu-Yee WONG, and JONNAGADDALA, Jitendra

Abstract

Automatic deidentification of Electronic Health Records (EHR) is a crucial step in secondary usage for biomedical research. This study introduces evaluation of an intricate hybrid deidentification strategy to enhance patient privacy in secondary usage of EHR. Specifically, this study focuses on assessing automatic deidentification using OpenDeID pipeline across diverse corpora for safeguarding sensitive information within EHR datasets by incorporating diverse corpora. Three distinct corpora were utilized: the OpenDeID v2 corpus containing pathology reports from Australian hospitals, the 2014 i2b2/UTHealth deidentification corpus with clinical narratives from the USA, and the 2016 CEGS N-GRID identification corpus comprising psychiatric notes. The OpenDeID pipeline employs a hybrid approach based on deep learning and contextual rules. Pre-processing steps involved harmonizing and addressing encoding and format issues. Precision, Recall, Fmeasure metrics were used to assess the performance. The evaluation metrics demonstrated the superior performance of the Discharge Summary BioBERT model. Trained on three corpora with a total of 4,038 reports, the best performing model exhibited robust deidentification capabilities when applied to EHR. It achieved impressive micro-averaged F1-scores of 0.9248 and 0.9692 for strict and relaxed settings, respectively. These results offer valuable insights into the model's efficacy and its potential role in safeguarding patient privacy in secondary usage of EHR. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

39. Approaches for Evaluating Visit-to-Visit Blood Pressure Variability as a Cardiovascular Disease Risk Factor: A Scoping Review.

Author

LUKITASARI, Mifetika, JONNAGADDALA, Jitendra, LIAW, Siaw-Teng, and JALALUDIN, Bin

Abstract

Visit-to-visit blood pressure variability (BPV) is associated with cardiovascular disease (CVD) and its mortality, independent of mean blood pressure (BP). However, in real world clinical practice this phenomenon is under-appreciated by clinicians. Serial BPV measured at clinical visits are frequently considered random fluctuations. This scoping review aims to review methodologies for estimating BPV, including metrics, frequency of BP measurements, BPV observation and follow-up durations. The review also compares studies that used electronic health record (EHR) data and those that used non-EHR data to assess BPV. We found little or no consensus on metrics used for BPV estimation in either study using EHR or non-EHR data. The non-EHR studies followed a stricter protocol for BP measurement than the EHR-based studies. Both groups of studies used comparable methodologies to estimate BPV. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

40. Strategies to Address Statin Medication Intolerance Among Patients at Risk of Cardiovascular Disease Identified Through Electronic Health Records: A Literature Review and Pooled Analysis.

Author

RAKHSHANDA, Shagoofa, LIAW, Siaw-Teng, RHEE, Joel, RYE, Kerry-Anne, and JONNAGADDALA, Jitendra

Abstract

Statins are a group of medications that lower lipid and are used for primary and secondary prevention of cardiovascular diseases (CVD). Patients can either be partially (15%) or completely (5%) intolerant to statins. Symptoms of statin intolerance can include muscle aches (myalgia), weakness, cramps, myopathy, diabetes mellitus, and elevated creatine kinase levels. Decreasing statin intolerance also improves statin adherence, which in turn results in lower number of CVD events among patients. Studies on statin intolerance is often embedded within studies of statin adherence. However, relevant data can be obtained from digital health systems. This preliminary literature review looks at studies from the past 10 years to identify and determine the effectiveness of strategies to address statin intolerance. The NLA definition for statin intolerance was used in this review. The initial search results on EMBASE, PubMed, SCOPUS, and CINAHL showed 91 articles and applying the inclusion and exclusion criteria, four articles were used in this review and pooled analysis. The study patients were identified through electronic health records. The pooled analysis was done using the Metafor package in R, applying a random-effect model to estimate pooled effect size. The findings suggest that using fixed dose combination therapy and switching from a lipophilic statin to a hydrophilic statin, while correcting metabolic abnormalities, or initiating evolocumab alongside statin can address statin intolerance. The overall relative risk (RR) was 0.40 (95% CI, 0.09 to 1.70) with I² 90%, and the overall odds ratio (OR) was 0.11 (95% CI, 0.01 to 1.59) with I² 94%, suggesting that the interventions work well in addressing statin intolerance. Since statin intolerance is has a vast range of effects, further research works may be done on exploring the possibility of using digital health systems to identify and provide targeted interventions to patients. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

41. Association Between Visit to Visit Blood Pressure Variability and Cardiovascular Outcome: A Meta-Analysis.

Author

Lukitasari, Mifetika, Jonnagaddala, Jitendra, Siaw-Teng Liaw, and Jalaludin, Bin

Abstract

Visit-to-visit (VVV) blood pressure variability (BPV) is associated with cardiovascular disease. However, in practice, BPV at sequential clinic visits is often regarded as mere random fluctuations and frequently under-appreciated by the clinicians. Therefore, this meta-analysis aims to compare the effect size of VVV BPV on cardiovascular outcome, by comparing studies that have used the electronic health record (EHR) and non-EHR data. The pooled hazard ratio for VVV BPV is comparable between studies using EHR and non-EHR data. Studies using EHR reported a pooled hazard ratio (HR) for VVV systolic BPV of 1.22 (95% CI: 1.07-1.38), while non-EHR studies had a HR of 1.16 (95% CI: 1.10-1.22). The pooled HR for VVV diastolic BPV in EHR studies was 1.09 (95% CI: 0.86-1.39), whereas non-EHR studies showed a HR of 1.10 (95% CI: 1.04-1.17). EHR data is a reliable source for assessing BPV, which in turn can predict the CVD outcomes. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

46. Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study

Author

Wagner, Sophia J., primary, Reisenbüchler, Daniel, additional, West, Nicholas P., additional, Niehues, Jan Moritz, additional, Zhu, Jiefu, additional, Foersch, Sebastian, additional, Veldhuizen, Gregory Patrick, additional, Quirke, Philip, additional, Grabsch, Heike I., additional, van den Brandt, Piet A., additional, Hutchins, Gordon G.A., additional, Richman, Susan D., additional, Yuan, Tanwei, additional, Langer, Rupert, additional, Jenniskens, Josien C.A., additional, Offermans, Kelly, additional, Mueller, Wolfram, additional, Gray, Richard, additional, Gruber, Stephen B., additional, Greenson, Joel K., additional, Rennert, Gad, additional, Bonner, Joseph D., additional, Schmolze, Daniel, additional, Jonnagaddala, Jitendra, additional, Hawkins, Nicholas J., additional, Ward, Robyn L., additional, Morton, Dion, additional, Seymour, Matthew, additional, Magill, Laura, additional, Nowak, Marta, additional, Hay, Jennifer, additional, Koelzer, Viktor H., additional, Church, David N., additional, Matek, Christian, additional, Geppert, Carol, additional, Peng, Chaolong, additional, Zhi, Cheng, additional, Ouyang, Xiaoming, additional, James, Jacqueline A., additional, Loughrey, Maurice B., additional, Salto-Tellez, Manuel, additional, Brenner, Hermann, additional, Hoffmeister, Michael, additional, Truhn, Daniel, additional, Schnabel, Julia A., additional, Boxberg, Melanie, additional, Peng, Tingying, additional, Kather, Jakob Nikolas, additional, Church, David, additional, Domingo, Enric, additional, Edwards, Joanne, additional, Glimelius, Bengt, additional, Gogenur, Ismail, additional, Harkin, Andrea, additional, Hay, Jen, additional, Iveson, Timothy, additional, Jaeger, Emma, additional, Kelly, Caroline, additional, Kerr, Rachel, additional, Maka, Noori, additional, Morgan, Hannah, additional, Oien, Karin, additional, Orange, Clare, additional, Palles, Claire, additional, Roxburgh, Campbell, additional, Sansom, Owen, additional, Saunders, Mark, additional, and Tomlinson, Ian, additional

Published
2023
Full Text
View/download PDF

48. Integrated Management Systems (IMS) to Support and Sustain Quality One Health Services: International Lessons from the COVID-19 Pandemic by the IMIA Primary Care Working Group

Author

Jonnagaddala, Jitendra, additional, Hoang, Uy, additional, Wensaas, Knut-Arne, additional, Tu, Karen, additional, Ortigoza, Angela, additional, Silva-Valencia, Javier, additional, Cuba-Fuentes, María Sofía, additional, Godinho, Myron Anthony, additional, de Lusignan, Simon, additional, and Liaw, Siaw-Teng, additional

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results