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Author Correction: Federated learning enables big data for rare cancer boundary detection
Pati, Sarthak; Baid, Ujjwal; Edwards, Brandon; Sheller, Micah; Wang, Shih-Han; Reina, G Anthony; Foley, Patrick; Gruzdev, Alexey; Karkada, Deepthi; Davatzikos, Christos; Sako, Chiharu; Ghodasara, Satyam; Bilello, Michel; Mohan, Suyash; Vollmuth, Philipp; Brugnara, Gianluca; Preetha, Chandrakanth J; Sahm, Felix; Maier-Hein, Klaus; Zenk, Maximilian; Bendszus, Martin; Wick, Wolfgang; Calabrese, Evan; Rudie, Jeffrey; Villanueva-Meyer, Javier; Cha, Soonmee; Ingalhalikar, Madhura; Jadhav, Manali; Pandey, Umang; Saini, Jitender; Garrett, John; Larson, Matthew; Jeraj, Robert; Currie, Stuart; Frood, Russell; Fatania, Kavi; Huang, Raymond Y; Chang, Ken; Balaña, Carmen; Capellades, Jaume; Puig, Josep; Trenkler, Johannes; Pichler, Josef; Necker, Georg; Haunschmidt, Andreas; Meckel, Stephan; Shukla, Gaurav; Liem, Spencer; Alexander, Gregory S; Lombardo, Joseph; Palmer, Joshua D; Flanders, Adam E; Dicker, Adam P; Sair, Haris I; Jones, Craig K; Venkataraman, Archana; Jiang, Meirui; So, Tiffany Y; Chen, Cheng; Heng, Pheng Ann; Dou, Qi; Kozubek, Michal; Lux, Filip; Michálek, Jan; Matula, Petr; Keřkovský, Miloš; Kopřivová, Tereza; Dostál, Marek; Vybíhal, Václav; Vogelbaum, Michael A; Mitchell, J Ross; Farinhas, Joaquim; Maldjian, Joseph A; Yogananda, Chandan Ganesh Bangalore; Pinho, Marco C; Reddy, Divya; Holcomb, James; Wagner, Benjamin C; Ellingson, Benjamin M; Cloughesy, Timothy F; Raymond, Catalina; Oughourlian, Talia; Hagiwara, Akifumi; Wang, Chencai; To, Minh-Son; Bhardwaj, Sargam; Chong, Chee; Agzarian, Marc; Falcão, Alexandre Xavier; Martins, Samuel B; Teixeira, Bernardo C A; Sprenger, Flávia; Menotti, David; Lucio, Diego R; LaMontagne, Pamela; Marcus, Daniel; Wiestler, Benedikt; Kofler, Florian; Ezhov, Ivan; Metz, Marie; Jain, Rajan; Lee, Matthew; Lui, Yvonne W; McKinley, Richard; Slotboom, Johannes; Radojewski, Piotr; Meier, Raphael; Wiest, Roland; Murcia, Derrick; Fu, Eric; Haas, Rourke; Thompson, John; Ormond, David Ryan; Badve, Chaitra; Sloan, Andrew E; Vadmal, Vachan; Waite, Kristin; Colen, Rivka R; Pei, Linmin; Ak, Murat; Srinivasan, Ashok; Bapuraj, J Rajiv; Rao, Arvind; Wang, Nicholas; Yoshiaki, Ota; Moritani, Toshio; Turk, Sevcan; Lee, Joonsang; Prabhudesai, Snehal; Morón, Fanny; Mandel, Jacob; Kamnitsas, Konstantinos; Glocker, Ben; Dixon, Luke V M; Williams, Matthew; Zampakis, Peter; Panagiotopoulos, Vasileios; Tsiganos, Panagiotis; Alexiou, Sotiris; Haliassos, Ilias; Zacharaki, Evangelia I; Moustakas, Konstantinos; Kalogeropoulou, Christina; Kardamakis, Dimitrios M; Choi, Yoon Seong; Lee, Seung-Koo; Chang, Jong Hee; Ahn, Sung Soo; Luo, Bing; Poisson, Laila; Wen, Ning; Tiwari, Pallavi; Verma, Ruchika; Bareja, Rohan; Yadav, Ipsa; Chen, Jonathan; Kumar, Neeraj; Smits, Marion; van der Voort, Sebastian R; Alafandi, Ahmed; Incekara, Fatih; Wijnenga, Maarten M J; Kapsas, Georgios; Gahrmann, Renske; Schouten, Joost W; Dubbink, Hendrikus J; Vincent, Arnaud J P E; van den Bent, Martin J; French, Pim J; Klein, Stefan; Yuan, Yading; Sharma, Sonam; Tseng, Tzu-Chi; Adabi, Saba; Niclou, Simone P; Keunen, Olivier; Hau, Ann-Christin; Vallières, Martin; Fortin, David; Lepage, Martin; Landman, Bennett; Ramadass, Karthik; Xu, Kaiwen; Chotai, Silky; Chambless, Lola B; Mistry, Akshitkumar; Thompson, Reid C; Gusev, Yuriy; Bhuvaneshwar, Krithika; Sayah, Anousheh; Bencheqroun, Camelia; Belouali, Anas; Madhavan, Subha; Booth, Thomas C; Chelliah, Alysha; Modat, Marc; Shuaib, Haris; Dragos, Carmen; Abayazeed, Aly; Kolodziej, Kenneth; Hill, Michael; Abbassy, Ahmed; Gamal, Shady; Mekhaimar, Mahmoud; Qayati, Mohamed; Reyes, Mauricio; Park, Ji Eun; Yun, Jihye; Kim, Ho Sung; Mahajan, Abhishek; Muzi, Mark; Benson, Sean; Beets-Tan, Regina G H; Teuwen, Jonas; Herrera-Trujillo, Alejandro; Trujillo, Maria; Escobar, William; Abello, Ana; Bernal, Jose; Gómez, Jhon; Choi, Joseph; Baek, Stephen; Kim, Yusung; Ismael, Heba; Allen, Bryan; Buatti, John M; Kotrotsou, Aikaterini; Li, Hongwei; Weiss, Tobias; Weller, Michael; Bink, Andrea; Pouymayou, Bertrand; Shaykh, Hassan F; Saltz, Joel; Prasanna, Prateek; Shrestha, Sampurna; Mani, Kartik M; Payne, David; Kurc, Tahsin; Pelaez, Enrique; Franco-Maldonado, Heydy; Loayza, Francis; Quevedo, Sebastian; Guevara, Pamela; Torche, Esteban; Mendoza, Cristobal; Vera, Franco; Ríos, Elvis; López, Eduardo; Velastin, Sergio A; Ogbole, Godwin; Soneye, Mayowa; Oyekunle, Dotun; Odafe-Oyibotha, Olubunmi; Osobu, Babatunde; Shu'aibu, Mustapha; Dorcas, Adeleye; Dako, Farouk; Simpson, Amber L; Hamghalam, Mohammad; Peoples, Jacob J; Hu, Ricky; Tran, Anh; Cutler, Danielle; Moraes, Fabio Y; Boss, Michael A; Gimpel, James; Veettil, Deepak Kattil; Schmidt, Kendall; Bialecki, Brian; Marella, Sailaja; Price, Cynthia; Cimino, Lisa; Apgar, Charles; Shah, Prashant; Menze, Bjoern; Barnholtz-Sloan, Jill S; Martin, Jason; Bakas, Spyridon
PMID: 36702828
ISSN: 2041-1723
CID: 5426632
Acute Neurological Complications of Coronavirus Disease
Chang, Sanders; Schecht, Michael; Jain, Rajan; Belani, Puneet
The coronavirus disease (COVID-19) pandemic has impacted many lives globally. Neurologic manifestations have been observed among individuals at various stages and severity of the disease, the most common being stroke. Prompt identification of these neurologic diagnoses can affect patient management and prognosis. This article discusses the acute neuroradiological features typical of COVID-19, including cerebrovascular disease, intracerebral hemorrhage, leukoencephalopathy, and sensory neuropathies.
PMID: 36404047
ISSN: 1557-9867
CID: 5371912
AI-based decision support improves reproducibility of tumor response assessment in neuro-oncology: an international multi-reader study
Vollmuth, Philipp; Foltyn, Martha; Huang, Raymond Y; Galldiks, Norbert; Petersen, Jens; Isensee, Fabian; van den Bent, Martin J; Barkhof, Frederik; Park, Ji Eun; Park, Yae Won; Ahn, Sung Soo; Brugnara, Gianluca; Meredig, Hagen; Jain, Rajan; Smits, Marion; Pope, Whitney B; Maier-Hein, Klaus; Weller, Michael; Wen, Patrick Y; Wick, Wolfgang; Bendszus, Martin
BACKGROUND:To assess whether AI-based decision support allows more reproducible and standardized assessment of treatment response on MRI in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden using the RANO criteria. METHODS:A series of 30 patients (15 lower-grade gliomas, 15 glioblastoma) with availability of consecutive MRI scans was selected. The time to progression (TTP) on MRI was separately evaluated for each patient by 15 investigators over two rounds. In the 1 st round the TTP was evaluated based on the RANO-criteria, whereas in the 2 nd round the TTP was evaluated by incorporating additional information from AI-enhanced MRI-sequences depicting the longitudinal changes in tumor volumes. The agreement of the TTP-measurements between investigators was evaluated using concordance correlation coefficients (CCC) with confidence intervals (CI) and p-values obtained using bootstrap resampling. RESULTS:The CCC of TTP-measurements between investigators was 0.77 (95%CI=0.69,0.88) with RANO alone and increased to 0.91 (95%CI=0.82,0.95) with AI-based decision support (p=0.005). This effect was significantly greater (p=0.008) for patients with lower-grade gliomas (CCC=0.70 [95%CI=0.56,0.85] without vs. 0.90 [95%CI=0.76,0.95] with AI-based decision support) as compared to glioblastoma (CCC=0.83 [95%CI=0.75,0.92] without vs. 0.86 [95%CI=0.78,0.93] with AI-based decision support). Investigators with less years of experience judged the AI-based decision as more helpful (p=0.02). CONCLUSIONS:AI-based decision support has the potential to yield more reproducible and standardized assessment of treatment response in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden, particularly in patients with lower-grade gliomas. A fully-functional version of this AI-based processing pipeline is provided as open-source (https://github.com/NeuroAI-HD/HD-GLIO-XNAT).
PMID: 35917833
ISSN: 1523-5866
CID: 5287962
MRI features predict tumor grade in isocitrate dehydrogenase (IDH)-mutant astrocytoma and oligodendroglioma
Joyner, David A; Garrett, John; Batchala, Prem P; Rama, Bharath; Ravicz, Joshua R; Patrie, James T; Lopes, Maria-B; Fadul, Camilo E; Schiff, David; Jain, Rajan; Patel, Sohil H
PURPOSE/OBJECTIVE:Nearly all literature for predicting tumor grade in astrocytoma and oligodendroglioma pre-dates the molecular classification system. We investigated the association between contrast enhancement, ADC, and rCBV with tumor grade separately for IDH-mutant astrocytomas and molecularly-defined oligodendrogliomas. METHODS:For this retrospective study, 44 patients with IDH-mutant astrocytomas (WHO grades II, III, or IV) and 39 patients with oligodendrogliomas (IDH-mutant and 1p/19q codeleted) (WHO grade II or III) were enrolled. Two readers independently assessed preoperative MRI for contrast enhancement, ADC, and rCBV. Inter-reader agreement was calculated, and statistical associations between MRI metrics and WHO grade were determined per reader. RESULTS:For IDH-mutant astrocytomas, both readers found a stepwise positive association between contrast enhancement and WHO grade (Reader A: OR 7.79 [1.97, 30.80], p = 0.003; Reader B: OR 6.62 [1.70, 25.82], p = 0.006); both readers found that ADC was negatively associated with WHO grade (Reader A: OR 0.74 [0.61, 0.90], p = 0.002); Reader B: OR 0.80 [0.66, 0.96], p = 0.017), and both readers found that rCBV was positively associated with WHO grade (Reader A: OR 2.33 [1.35, 4.00], p = 0.002; Reader B: OR 2.13 [1.30, 3.57], p = 0.003). For oligodendrogliomas, both readers found a positive association between contrast enhancement and WHO grade (Reader A: OR 15.33 [2.56, 91.95], p = 0.003; Reader B: OR 20.00 [2.19, 182.45], p = 0.008), but neither reader found an association between ADC or rCBV and WHO grade. CONCLUSIONS:Contrast enhancement predicts WHO grade for IDH-mutant astrocytomas and oligodendrogliomas. ADC and rCBV predict WHO grade for IDH-mutant astrocytomas, but not for oligodendrogliomas.
PMID: 35953567
ISSN: 1432-1920
CID: 5287192
Imaging-based stratification of adult gliomas prognosticates survival and correlates with the 2021 WHO classification
Kamble, Akshaykumar N; Agrawal, Nidhi K; Koundal, Surabhi; Bhargava, Salil; Kamble, Abhaykumar N; Joyner, David A; Kalelioglu, Tuba; Patel, Sohil H; Jain, Rajan
BACKGROUND:Because of the lack of global accessibility, delay, and cost-effectiveness of genetic testing, there is a clinical need for an imaging-based stratification of gliomas that can prognosticate survival and correlate with the 2021-WHO classification. METHODS:In this retrospective study, adult primary glioma patients with pre-surgery/pre-treatment MRI brain images having T2, FLAIR, T1, T1 post-contrast, DWI sequences, and survival information were included in TCIA training-dataset (n = 275) and independent validation-dataset (n = 200). A flowchart for imaging-based stratification of adult gliomas(IBGS) was created in consensus by three authors to encompass all adult glioma types. Diagnostic features used were T2-FLAIR mismatch sign, central necrosis with peripheral enhancement, diffusion restriction, and continuous cortex sign. Roman numerals (I, II, and III) denote IBGS types. Two independent teams of three and two radiologists, blinded to genetic, histology, and survival information, manually read MRI into three types based on the flowchart. Overall survival-analysis was done using age-adjusted Cox-regression analysis, which provided both hazard-ratio (HR) and area-under-curve (AUC) for each stratification system(IBGS and 2021-WHO). The sensitivity and specificity of each IBSG type were analyzed with cross-table to identify the corresponding 2021-WHO genotype. RESULTS:Imaging-based stratification was statistically significant in predicting survival in both datasets with good inter-observer agreement (age-adjusted Cox-regression, AUC > 0.5, k > 0.6, p < 0.001). IBGS type-I, type-II, and type-III gliomas had good specificity in identifying IDHmut 1p19q-codel oligodendroglioma (training - 97%, validation - 85%); IDHmut 1p19q non-codel astrocytoma (training - 80%, validation - 85.9%); and IDHwt glioblastoma (training - 76.5%, validation- 87.3%) respectively (p-value < 0.01). CONCLUSIONS:Imaging-based stratification of adult diffuse gliomas predicted patient survival and correlated well with 2021-WHO glioma classification.
PMID: 35876874
ISSN: 1432-1920
CID: 5276232
Neuroimaging of cerebrovascular complications in cancer patients
Chapter by: Kwofie, Michael; Nagpal, Prashant; Ellika, Shehanaz; Jain, Rajan
in: Handbook of Neuro-Oncology Neuroimaging by
[S.l.] : Elsevier, 2022
pp. 935-954
ISBN: 9780128229958
CID: 5500792
Federated learning enables big data for rare cancer boundary detection
Pati, Sarthak; Baid, Ujjwal; Edwards, Brandon; Sheller, Micah; Wang, Shih-Han; Reina, G Anthony; Foley, Patrick; Gruzdev, Alexey; Karkada, Deepthi; Davatzikos, Christos; Sako, Chiharu; Ghodasara, Satyam; Bilello, Michel; Mohan, Suyash; Vollmuth, Philipp; Brugnara, Gianluca; Preetha, Chandrakanth J; Sahm, Felix; Maier-Hein, Klaus; Zenk, Maximilian; Bendszus, Martin; Wick, Wolfgang; Calabrese, Evan; Rudie, Jeffrey; Villanueva-Meyer, Javier; Cha, Soonmee; Ingalhalikar, Madhura; Jadhav, Manali; Pandey, Umang; Saini, Jitender; Garrett, John; Larson, Matthew; Jeraj, Robert; Currie, Stuart; Frood, Russell; Fatania, Kavi; Huang, Raymond Y; Chang, Ken; Quintero, Carmen Balaña; Capellades, Jaume; Puig, Josep; Trenkler, Johannes; Pichler, Josef; Necker, Georg; Haunschmidt, Andreas; Meckel, Stephan; Shukla, Gaurav; Liem, Spencer; Alexander, Gregory S; Lombardo, Joseph; Palmer, Joshua D; Flanders, Adam E; Dicker, Adam P; Sair, Haris I; Jones, Craig K; Venkataraman, Archana; Jiang, Meirui; So, Tiffany Y; Chen, Cheng; Heng, Pheng Ann; Dou, Qi; Kozubek, Michal; Lux, Filip; Michálek, Jan; Matula, Petr; Keřkovský, Miloš; Kopřivová, Tereza; Dostál, Marek; Vybíhal, Václav; Vogelbaum, Michael A; Mitchell, J Ross; Farinhas, Joaquim; Maldjian, Joseph A; Yogananda, Chandan Ganesh Bangalore; Pinho, Marco C; Reddy, Divya; Holcomb, James; Wagner, Benjamin C; Ellingson, Benjamin M; Cloughesy, Timothy F; Raymond, Catalina; Oughourlian, Talia; Hagiwara, Akifumi; Wang, Chencai; To, Minh-Son; Bhardwaj, Sargam; Chong, Chee; Agzarian, Marc; Falcão, Alexandre Xavier; Martins, Samuel B; Teixeira, Bernardo C A; Sprenger, Flávia; Menotti, David; Lucio, Diego R; LaMontagne, Pamela; Marcus, Daniel; Wiestler, Benedikt; Kofler, Florian; Ezhov, Ivan; Metz, Marie; Jain, Rajan; Lee, Matthew; Lui, Yvonne W; McKinley, Richard; Slotboom, Johannes; Radojewski, Piotr; Meier, Raphael; Wiest, Roland; Murcia, Derrick; Fu, Eric; Haas, Rourke; Thompson, John; Ormond, David Ryan; Badve, Chaitra; Sloan, Andrew E; Vadmal, Vachan; Waite, Kristin; Colen, Rivka R; Pei, Linmin; Ak, Murat; Srinivasan, Ashok; Bapuraj, J Rajiv; Rao, Arvind; Wang, Nicholas; Yoshiaki, Ota; Moritani, Toshio; Turk, Sevcan; Lee, Joonsang; Prabhudesai, Snehal; Morón, Fanny; Mandel, Jacob; Kamnitsas, Konstantinos; Glocker, Ben; Dixon, Luke V M; Williams, Matthew; Zampakis, Peter; Panagiotopoulos, Vasileios; Tsiganos, Panagiotis; Alexiou, Sotiris; Haliassos, Ilias; Zacharaki, Evangelia I; Moustakas, Konstantinos; Kalogeropoulou, Christina; Kardamakis, Dimitrios M; Choi, Yoon Seong; Lee, Seung-Koo; Chang, Jong Hee; Ahn, Sung Soo; Luo, Bing; Poisson, Laila; Wen, Ning; Tiwari, Pallavi; Verma, Ruchika; Bareja, Rohan; Yadav, Ipsa; Chen, Jonathan; Kumar, Neeraj; Smits, Marion; van der Voort, Sebastian R; Alafandi, Ahmed; Incekara, Fatih; Wijnenga, Maarten M J; Kapsas, Georgios; Gahrmann, Renske; Schouten, Joost W; Dubbink, Hendrikus J; Vincent, Arnaud J P E; van den Bent, Martin J; French, Pim J; Klein, Stefan; Yuan, Yading; Sharma, Sonam; Tseng, Tzu-Chi; Adabi, Saba; Niclou, Simone P; Keunen, Olivier; Hau, Ann-Christin; Vallières, Martin; Fortin, David; Lepage, Martin; Landman, Bennett; Ramadass, Karthik; Xu, Kaiwen; Chotai, Silky; Chambless, Lola B; Mistry, Akshitkumar; Thompson, Reid C; Gusev, Yuriy; Bhuvaneshwar, Krithika; Sayah, Anousheh; Bencheqroun, Camelia; Belouali, Anas; Madhavan, Subha; Booth, Thomas C; Chelliah, Alysha; Modat, Marc; Shuaib, Haris; Dragos, Carmen; Abayazeed, Aly; Kolodziej, Kenneth; Hill, Michael; Abbassy, Ahmed; Gamal, Shady; Mekhaimar, Mahmoud; Qayati, Mohamed; Reyes, Mauricio; Park, Ji Eun; Yun, Jihye; Kim, Ho Sung; Mahajan, Abhishek; Muzi, Mark; Benson, Sean; Beets-Tan, Regina G H; Teuwen, Jonas; Herrera-Trujillo, Alejandro; Trujillo, Maria; Escobar, William; Abello, Ana; Bernal, Jose; Gómez, Jhon; Choi, Joseph; Baek, Stephen; Kim, Yusung; Ismael, Heba; Allen, Bryan; Buatti, John M; Kotrotsou, Aikaterini; Li, Hongwei; Weiss, Tobias; Weller, Michael; Bink, Andrea; Pouymayou, Bertrand; Shaykh, Hassan F; Saltz, Joel; Prasanna, Prateek; Shrestha, Sampurna; Mani, Kartik M; Payne, David; Kurc, Tahsin; Pelaez, Enrique; Franco-Maldonado, Heydy; Loayza, Francis; Quevedo, Sebastian; Guevara, Pamela; Torche, Esteban; Mendoza, Cristobal; Vera, Franco; Ríos, Elvis; López, Eduardo; Velastin, Sergio A; Ogbole, Godwin; Soneye, Mayowa; Oyekunle, Dotun; Odafe-Oyibotha, Olubunmi; Osobu, Babatunde; Shu'aibu, Mustapha; Dorcas, Adeleye; Dako, Farouk; Simpson, Amber L; Hamghalam, Mohammad; Peoples, Jacob J; Hu, Ricky; Tran, Anh; Cutler, Danielle; Moraes, Fabio Y; Boss, Michael A; Gimpel, James; Veettil, Deepak Kattil; Schmidt, Kendall; Bialecki, Brian; Marella, Sailaja; Price, Cynthia; Cimino, Lisa; Apgar, Charles; Shah, Prashant; Menze, Bjoern; Barnholtz-Sloan, Jill S; Martin, Jason; Bakas, Spyridon
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.
PMCID:9722782
PMID: 36470898
ISSN: 2041-1723
CID: 5381682
A Unified Approach to Analysis of MRI Radiomics of Glioma Using Minimum Spanning Trees
Simon, Olivier B.; Jain, Rajan; Choi, Yoon-Seong; Goerg, Carsten; Suresh, Krithika; Severn, Cameron; Ghosh, Debashis
ISI:000797934200001
ISSN: 2296-424x
CID: 5284532
Prognostic indicators and outcomes of hospitalised COVID-19 patients with neurological disease: An individual patient data meta-analysis
Singh, Bhagteshwar; Lant, Suzannah; Cividini, Sofia; Cattrall, Jonathan W S; Goodwin, Lynsey C; Benjamin, Laura; Michael, Benedict D; Khawaja, Ayaz; Matos, Aline de Moura Brasil; Alkeridy, Walid; Pilotto, Andrea; Lahiri, Durjoy; Rawlinson, Rebecca; Mhlanga, Sithembinkosi; Lopez, Evelyn C; Sargent, Brendan F; Somasundaran, Anushri; Tamborska, Arina; Webb, Glynn; Younas, Komal; Al Sami, Yaqub; Babu, Heavenna; Banks, Tristan; Cavallieri, Francesco; Cohen, Matthew; Davies, Emma; Dhar, Shalley; Fajardo Modol, Anna; Farooq, Hamzah; Harte, Jeffrey; Hey, Samuel; Joseph, Albert; Karthikappallil, Dileep; Kassahun, Daniel; Lipunga, Gareth; Mason, Rachel; Minton, Thomas; Mond, Gabrielle; Poxon, Joseph; Rabas, Sophie; Soothill, Germander; Zedde, Marialuisa; Yenkoyan, Konstantin; Brew, Bruce; Contini, Erika; Cysique, Lucette; Zhang, Xin; Maggi, Pietro; van Pesch, Vincent; Lechien, Jérome; Saussez, Sven; Heyse, Alex; Brito Ferreira, Maria Lúcia; Soares, Cristiane N; Elicer, Isabel; EugenÃn-von Bernhardi, Laura; Ñancupil Reyes, Waleng; Yin, Rong; Azab, Mohammed A; Abd-Allah, Foad; Elkady, Ahmed; Escalard, Simon; Corvol, Jean-Christophe; Delorme, Cécile; Tattevin, Pierre; Bigaut, Kévin; Lorenz, Norbert; Hornuss, Daniel; Hosp, Jonas; Rieg, Siegbert; Wagner, Dirk; Knier, Benjamin; Lingor, Paul; Winkler, Andrea Sylvia; Sharifi-Razavi, Athena; Moein, Shima T; SeyedAlinaghi, SeyedAhmad; JamaliMoghadamSiahkali, Saeidreza; Morassi, Mauro; Padovani, Alessandro; Giunta, Marcello; Libri, Ilenia; Beretta, Simone; Ravaglia, Sabrina; Foschi, Matteo; Calabresi, Paolo; Primiano, Guido; Servidei, Serenella; Biagio Mercuri, Nicola; Liguori, Claudio; Pierantozzi, Mariangela; Sarmati, Loredana; Boso, Federica; Garazzino, Silvia; Mariotto, Sara; Patrick, Kimani N; Costache, Oana; Pincherle, Alexander; Klok, Frederikus A; Meza, Roger; Cabreira, Verónica; Valdoleiros, Sofia R; Oliveira, Vanessa; Kaimovsky, Igor; Guekht, Alla; Koh, Jasmine; Fernández DÃaz, Eva; Barrios-López, José María; Guijarro-Castro, Cristina; Beltrán-Corbellini, Ãlvaro; Martínez-Poles, Javier; Diezma-MartÃn, Alba María; Morales-Casado, Maria Isabel; García García, Sergio; Breville, Gautier; Coen, Matteo; Uginet, Marjolaine; Bernard-Valnet, Raphaël; Du Pasquier, Renaud; Kaya, Yildiz; Abdelnour, Loay H; Rice, Claire; Morrison, Hamish; Defres, Sylviane; Huda, Saif; Enright, Noelle; Hassell, Jane; D'Anna, Lucio; Benger, Matthew; Sztriha, Laszlo; Raith, Eamon; Chinthapalli, Krishna; Nortley, Ross; Paterson, Ross; Chandratheva, Arvind; Werring, David J; Dervisevic, Samir; Harkness, Kirsty; Pinto, Ashwin; Jillella, Dinesh; Beach, Scott; Gunasekaran, Kulothungan; Rocha Ferreira Da Silva, Ivan; Nalleballe, Krishna; Santoro, Jonathan; Scullen, Tyler; Kahn, Lora; Kim, Carla Y; Thakur, Kiran T; Jain, Rajan; Umapathi, Thirugnanam; Nicholson, Timothy R; Sejvar, James J; Hodel, Eva Maria; Tudur Smith, Catrin; Solomon, Tom
BACKGROUND:Neurological COVID-19 disease has been reported widely, but published studies often lack information on neurological outcomes and prognostic risk factors. We aimed to describe the spectrum of neurological disease in hospitalised COVID-19 patients; characterise clinical outcomes; and investigate factors associated with a poor outcome. METHODS:We conducted an individual patient data (IPD) meta-analysis of hospitalised patients with neurological COVID-19 disease, using standard case definitions. We invited authors of studies from the first pandemic wave, plus clinicians in the Global COVID-Neuro Network with unpublished data, to contribute. We analysed features associated with poor outcome (moderate to severe disability or death, 3 to 6 on the modified Rankin Scale) using multivariable models. RESULTS:We included 83 studies (31 unpublished) providing IPD for 1979 patients with COVID-19 and acute new-onset neurological disease. Encephalopathy (978 [49%] patients) and cerebrovascular events (506 [26%]) were the most common diagnoses. Respiratory and systemic symptoms preceded neurological features in 93% of patients; one third developed neurological disease after hospital admission. A poor outcome was more common in patients with cerebrovascular events (76% [95% CI 67-82]), than encephalopathy (54% [42-65]). Intensive care use was high (38% [35-41]) overall, and also greater in the cerebrovascular patients. In the cerebrovascular, but not encephalopathic patients, risk factors for poor outcome included breathlessness on admission and elevated D-dimer. Overall, 30-day mortality was 30% [27-32]. The hazard of death was comparatively lower for patients in the WHO European region. INTERPRETATION:Neurological COVID-19 disease poses a considerable burden in terms of disease outcomes and use of hospital resources from prolonged intensive care and inpatient admission; preliminary data suggest these may differ according to WHO regions and country income levels. The different risk factors for encephalopathy and stroke suggest different disease mechanisms which may be amenable to intervention, especially in those who develop neurological symptoms after hospital admission.
PMCID:9162376
PMID: 35653330
ISSN: 1932-6203
CID: 5277632
Federated Learning Enables Big Data for Rare Cancer Boundary Detection [PrePrint]
Pati, Sarthak; Baid, Ujjwal; Edwards, Brandon; Sheller, Micah; Wang, Shi-Han; Jain, Rajan; et al
ORIGINAL:0015699
ISSN: 2331-8422
CID: 5284542
