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1. Editorial

Author

Norgaard, Martin and Schmidt, Andrew

Published
2024

2. Interaction between the gut microbiota and colonic enteroendocrine cells regulates host metabolism

Author

Tan, Shuai, Santolaya, Jacobo L., Wright, Tiffany Freeney, Liu, Qi, Fujikawa, Teppei, Chi, Sensen, Bergstrom, Colin P., Lopez, Adam, Chen, Qing, Vale, Goncalo, McDonald, Jeffrey G., Schmidt, Andrew, Vo, Nguyen, Kim, Jiwoong, Baniasadi, Hamid, Li, Li, Zhu, Gaohui, He, Tong-Chuan, Zhan, Xiaowei, Obata, Yuuki, Jin, Aishun, Jia, Da, Elmquist, Joel K., Sifuentes-Dominguez, Luis, and Burstein, Ezra

Published
2024
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3. TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation

Author

Cousins, David Bruce, Polyakov, Yuriy, Badawi, Ahmad Al, French, Matthew, Schmidt, Andrew, Jacob, Ajey, Reynwar, Benedict, Canida, Kellie, Jaiswal, Akhilesh, Mathew, Clynn, Gamil, Homer, Neda, Negar, Soni, Deepraj, Maniatakos, Michail, Reagen, Brandon, Zhang, Naifeng, Franchetti, Franz, Brinich, Patrick, Johnson, Jeremy, Broderick, Patrick, Franusich, Mike, Zhang, Bo, Cheng, Zeming, and Pedram, Massoud

Subjects
Computer Science - Cryptography and Security, Computer Science - Hardware Architecture, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Performance
Abstract

Secure computation is of critical importance to not only the DoD, but across financial institutions, healthcare, and anywhere personally identifiable information (PII) is accessed. Traditional security techniques require data to be decrypted before performing any computation. When processed on untrusted systems the decrypted data is vulnerable to attacks to extract the sensitive information. To address these vulnerabilities Fully Homomorphic Encryption (FHE) keeps the data encrypted during computation and secures the results, even in these untrusted environments. However, FHE requires a significant amount of computation to perform equivalent unencrypted operations. To be useful, FHE must significantly close the computation gap (within 10x) to make encrypted processing practical. To accomplish this ambitious goal the TREBUCHET project is leading research and development in FHE processing hardware to accelerate deep computations on encrypted data, as part of the DARPA MTO Data Privacy for Virtual Environments (DPRIVE) program. We accelerate the major secure standardized FHE schemes (BGV, BFV, CKKS, FHEW, etc.) at >=128-bit security while integrating with the open-source PALISADE and OpenFHE libraries currently used in the DoD and in industry. We utilize a novel tile-based chip design with highly parallel ALUs optimized for vectorized 128b modulo arithmetic. The TREBUCHET coprocessor design provides a highly modular, flexible, and extensible FHE accelerator for easy reconfiguration, deployment, integration and application on other hardware form factors, such as System-on-Chip or alternate chip areas., Comment: 6 pages, 5 figures and 2 tables

Published
2023

4. RPU: The Ring Processing Unit

Author

Soni, Deepraj, Neda, Negar, Zhang, Naifeng, Reynwar, Benedict, Gamil, Homer, Heyman, Benjamin, Nabeel, Mohammed, Badawi, Ahmad Al, Polyakov, Yuriy, Canida, Kellie, Pedram, Massoud, Maniatakos, Michail, Cousins, David Bruce, Franchetti, Franz, French, Matthew, Schmidt, Andrew, and Reagen, Brandon

Subjects
Computer Science - Hardware Architecture, Computer Science - Cryptography and Security
Abstract

Ring-Learning-with-Errors (RLWE) has emerged as the foundation of many important techniques for improving security and privacy, including homomorphic encryption and post-quantum cryptography. While promising, these techniques have received limited use due to their extreme overheads of running on general-purpose machines. In this paper, we present a novel vector Instruction Set Architecture (ISA) and microarchitecture for accelerating the ring-based computations of RLWE. The ISA, named B512, is developed to meet the needs of ring processing workloads while balancing high-performance and general-purpose programming support. Having an ISA rather than fixed hardware facilitates continued software improvement post-fabrication and the ability to support the evolving workloads. We then propose the ring processing unit (RPU), a high-performance, modular implementation of B512. The RPU has native large word modular arithmetic support, capabilities for very wide parallel processing, and a large capacity high-bandwidth scratchpad to meet the needs of ring processing. We address the challenges of programming the RPU using a newly developed SPIRAL backend. A configurable simulator is built to characterize design tradeoffs and quantify performance. The best performing design was implemented in RTL and used to validate simulator performance. In addition to our characterization, we show that a RPU using 20.5mm2 of GF 12nm can provide a speedup of 1485x over a CPU running a 64k, 128-bit NTT, a core RLWE workload

Published
2023

6. Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning

Author

Dominic, Jeffrey, Bhaskhar, Nandita, Desai, Arjun D., Schmidt, Andrew, Rubin, Elka, Gunel, Beliz, Gold, Garry E., Hargreaves, Brian A., Lenchik, Leon, Boutin, Robert, and Chaudhari, Akshay S.

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

Although supervised learning has enabled high performance for image segmentation, it requires a large amount of labeled training data, which can be difficult to obtain in the medical imaging field. Self-supervised learning (SSL) methods involving pretext tasks have shown promise in overcoming this requirement by first pretraining models using unlabeled data. In this work, we evaluate the efficacy of two SSL methods (inpainting-based pretext tasks of context prediction and context restoration) for CT and MRI image segmentation in label-limited scenarios, and investigate the effect of implementation design choices for SSL on downstream segmentation performance. We demonstrate that optimally trained and easy-to-implement inpainting-based SSL segmentation models can outperform classically supervised methods for MRI and CT tissue segmentation in label-limited scenarios, for both clinically-relevant metrics and the traditional Dice score., Comment: Submitted to Radiology: Artificial Intelligence

Published
2022

7. P2M-DeTrack: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Tracking

Author

Datta, Gourav, Kundu, Souvik, Yin, Zihan, Mathai, Joe, Liu, Zeyu, Wang, Zixu, Tian, Mulin, Lu, Shunlin, Lakkireddy, Ravi T., Schmidt, Andrew, Abd-Almageed, Wael, Jacob, Ajey P., Jaiswal, Akhilesh R., and Beerel, Peter A.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Today's high resolution, high frame rate cameras in autonomous vehicles generate a large volume of data that needs to be transferred and processed by a downstream processor or machine learning (ML) accelerator to enable intelligent computing tasks, such as multi-object detection and tracking. The massive amount of data transfer incurs significant energy, latency, and bandwidth bottlenecks, which hinders real-time processing. To mitigate this problem, we propose an algorithm-hardware co-design framework called Processing-in-Pixel-in-Memory-based object Detection and Tracking (P2M-DeTrack). P2M-DeTrack is based on a custom faster R-CNN-based model that is distributed partly inside the pixel array (front-end) and partly in a separate FPGA/ASIC (back-end). The proposed front-end in-pixel processing down-samples the input feature maps significantly with judiciously optimized strided convolution and pooling. Compared to a conventional baseline design that transfers frames of RGB pixels to the back-end, the resulting P2M-DeTrack designs reduce the data bandwidth between sensor and back-end by up to 24x. The designs also reduce the sensor and total energy (obtained from in-house circuit simulations at Globalfoundries 22nm technology node) per frame by 5.7x and 1.14x, respectively. Lastly, they reduce the sensing and total frame latency by an estimated 1.7x and 3x, respectively. We evaluate our approach on the multi-object object detection (tracking) task of the large-scale BDD100K dataset and observe only a 0.5% reduction in the mean average precision (0.8% reduction in the identification F1 score) compared to the state-of-the-art., Comment: 6 pages, 4 figures, 4 tables

Published
2022

8. SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation

Author

Desai, Arjun D, Schmidt, Andrew M, Rubin, Elka B, Sandino, Christopher M, Black, Marianne S, Mazzoli, Valentina, Stevens, Kathryn J, Boutin, Robert, Ré, Christopher, Gold, Garry E, Hargreaves, Brian A, and Chaudhari, Akshay S

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

Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging. However, long image acquisition times, the need for qualitative expert analysis, and the lack of (and difficulty extracting) quantitative indicators that are sensitive to tissue health have curtailed widespread clinical and research studies. While recent machine learning methods for MRI reconstruction and analysis have shown promise for reducing this burden, these techniques are primarily validated with imperfect image quality metrics, which are discordant with clinically-relevant measures that ultimately hamper clinical deployment and clinician trust. To mitigate this challenge, we present the Stanford Knee MRI with Multi-Task Evaluation (SKM-TEA) dataset, a collection of quantitative knee MRI (qMRI) scans that enables end-to-end, clinically-relevant evaluation of MRI reconstruction and analysis tools. This 1.6TB dataset consists of raw-data measurements of ~25,000 slices (155 patients) of anonymized patient MRI scans, the corresponding scanner-generated DICOM images, manual segmentations of four tissues, and bounding box annotations for sixteen clinically relevant pathologies. We provide a framework for using qMRI parameter maps, along with image reconstructions and dense image labels, for measuring the quality of qMRI biomarker estimates extracted from MRI reconstruction, segmentation, and detection techniques. Finally, we use this framework to benchmark state-of-the-art baselines on this dataset. We hope our SKM-TEA dataset and code can enable a broad spectrum of research for modular image reconstruction and image analysis in a clinically informed manner. Dataset access, code, and benchmarks are available at https://github.com/StanfordMIMI/skm-tea., Comment: Accepted to NeurIPS Datasets & Benchmarks (2021)

Published
2022

10. Do Transtibial Amputations Outperform Amputations of the Hind- and Midfoot Following Severe Limb Trauma?: A Secondary Analysis of the OUTLET Study

Author

Fram, Brianna R., Bosse, Michael J., Odum, Susan M., Reider, Lisa, Gary, Joshua L., Gordon, Wade T., Teague, David, Alkhoury, Dana, MacKenzie, Ellen J., Seymour, Rachel B., Karunakar, Madhav A., Fox, W. Everett, Hsu, Joseph R., Kempton, Laurence, Robinson, Katherine Sample, Sims, Stephen H., Churchill, Christine, Teasdall, Robert D., Carroll, Eben A., Scott, Aaron T., Halvorson, Jason J., Pilson, Holly, Goodman, James Brett, Holden, Martha B., McAndrew, Christopher M., Gardner, Michael J., Miller, Anna N., Hughes, Amanda Spraggs, Stinner, Daniel J., Rivera, Jessica C., Osborn, Patrick M., Nadeau, Jason T., Howes, Cameron, Schenker, Mara L., Mir, Hassan, Taylor, Benjamin C., Schmidt, Andrew H., Mullis, Brian H., Shively, Karl D., Sorkin, Anthony T., Virkus, Walter, Konda, Sanjit R., Choo, Andrew, Munz, John W., Boutte, Sterling, Breslin, Mary A., Toledano, James E., Langford, Joshua Robert, Horne, Andrea, O’Toole, Robert V., Boulton, Christina, Manson, Theodore, Nascone, Jason, Pollak, Andrew N., Sciadini, Marcus F., Degani, Yasmin, Howe, Andrea L., Zych, Gregory A., Cannada, Lisa K., Dawson, Sarah A., Jones, Clifford B., Sietsema, Debra L., Miclau, Theodore, Morshed, Saam, Wilken, Jason M., Bergin, Patrick F., Graves, Matt L., Spitler, Clay A., Jones, LaRita C., Ertl, William, Moloney, Gele B., Evans, Andrew R., Weiss, David B., Yarboro, Seth R., Lester-Ballard, Veronica, McVey, Eric D., Firoozabadi, Reza, Agel, Julie, Obremskey, William, Archer, Kristin R., Burgos, Eduardo J., Gajari, Vamshi, Rodriguez-Buitrago, Andres, Tummuru, Rajesh R., Trochez, Karen M., D’Alleyrand, Jean-Claude G., Castillo, Renan C., Allen, Lauren E., and Carlini, Anthony R.

Published
2024
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11. vlang: Mapping Verilog Netlists to Modern Technologies

Author

Giamblanco, Nicholas V. and Schmidt, Andrew

Subjects
Computer Science - Hardware Architecture, Computer Science - Programming Languages
Abstract

Portability of hardware designs between Programmable Logic Devices (PLD) can be accomplished through the use of device-agnostic hardware description languages (HDL) such as Verilog or VHDL. Hardware designers can use HDLs to migrate hardware designs between devices and explore performance, area and power tradeoffs, as well as, port designs to an alternative device. However, if design files are corrupt or missing, the portability of the design is lost. While reverse engineering efforts may be able to recover an HDL-netlist of the original design, HDL-netlists use device-specific primitives, restricting portability. Additionally, the recovered design may benefit from other computational technologies (e.g., $\mu$P, GPGPUs), but is restricted to the domain of PLDs. In this work, we provide a new framework, vlang, which automatically maps Verilog-netlists into LLVM's intermediate representation (IR). The remapped design can use the LLVM-framework to target many device technologies such as: x86-64 assembly, RISC-V, ARM or to other PLDs with a modern high-level synthesis tool. Our framework is able to preserve the exact functionality of the original design within the software executable. The vlang-produced software executable can be used with other software programs, or to verify the functionality and correctness of the remapped design. We evaluate our work with a suite of hardware designs from OpenCores. We compare our framework against state-of-the-art simulators, thereby outlining our framework's ability to produce a fully-functional, cycle accurate software-executable. We also explore the usage of vlang as a front-end for high-level synthesis tools.

Published
2021

13. Patients recently treated for B-lymphoid malignancies show increased risk of severe COVID-19: a CCC19 registry analysisImpact of B-cell malignancy therapy on COVID-19 outcomes

Author

Rubinstein, Samuel M, Bhutani, Divaya, Lynch, Ryan C, Hsu, Chih-Yuan, Shyr, Yu, Advani, Shailesh, Mesa, Ruben A, Mishra, Sanjay, Mundt, Daniel P, Shah, Dimpy P, Sica, R Alejandro, Stockerl-Goldstein, Keith E, Stratton, Catherine, Weiss, Matthias, Beeghly-Fadiel, Alicia, Accordino, Melissa, Assouline, Sarit E, Awosika, Joy, Bakouny, Ziad, Bashir, Babar, Berg, Stephanie, Bilen, Mehmet Asim, Castellano, Cecilia A, Cogan, Jacob C, Kc, Devendra, Friese, Christopher R, Gupta, Shilpa, Hausrath, Daniel, Hwang, Clara, Johnson, Nathalie A, Joshi, Monika, Kasi, Anup, Klein, Elizabeth J, Koshkin, Vadim S, Kuderer, Nicole M, Kwon, Daniel H, Labaki, Chris, Latif, Tahir, Lau, Eric, Li, Xuanyi, Lyman, Gary H, McKay, Rana R, Nagaraj, Gayathri, Nizam, Amanda, Nonato, Taylor K, Olszewski, Adam J, Polimera, Hyma V, Portuguese, Andrew J, Puc, Matthew M, Razavi, Pedram, Rosovski, Rachel, Schmidt, Andrew, Shah, Sumit A, Shastri, Aditi, Su, Christopher, Torka, Pallawi, Wise-Draper, Trisha M, Zubiri, Leyre, Warner, Jeremy L, and Thompson, Michael A

Subjects
Rare Diseases, Clinical Research, Prevention, Cancer, Aetiology, 2.1 Biological and endogenous factors, COVID-19, COVID-19 Testing, Humans, Lymphatic Diseases, Neoplasms, Risk Factors, SARS-CoV-2, COVID-19 and Cancer Consortium
Abstract

Patients with B-lymphoid malignancies have been consistently identified as a population at high risk of severe COVID-19. Whether this is exclusively due to cancer-related deficits in humoral and cellular immunity, or whether risk of severe COVID-19 is increased by anticancer therapy, is uncertain. Using data derived from the COVID-19 and Cancer Consortium (CCC19), we show that patients treated for B-lymphoid malignancies have an increased risk of severe COVID-19 compared with control populations of patients with non-B-lymphoid malignancies. Among patients with B-lymphoid malignancies, those who received anticancer therapy within 12 months of COVID-19 diagnosis experienced increased COVID-19 severity compared with patients with non-recently treated B-lymphoid malignancies, after adjustment for cancer status and several other prognostic factors. Our findings suggest that patients recently treated for a B-lymphoid malignancy are at uniquely high risk for severe COVID-19.SignificanceOur study suggests that recent therapy for a B-lymphoid malignancy is an independent risk factor for COVID-19 severity. These findings provide rationale to develop mitigation strategies targeted at the uniquely high-risk population of patients with recently treated B-lymphoid malignancies. This article is highlighted in the In This Issue feature, p. 171.

Published
2022

14. Geriatric risk factors for serious COVID-19 outcomes among older adults with cancer: a cohort study from the COVID-19 and Cancer Consortium

Author

Elkrief, Arielle, Hennessy, Cassandra, Kuderer, Nicole M, Rubinstein, Samuel M, Wulff-Burchfield, Elizabeth, Rosovsky, Rachel P, Vega-Luna, Karen, Thompson, Michael A, Panagiotou, Orestis A, Desai, Aakash, Rivera, Donna R, Khaki, Ali Raza, Tachiki, Lisa, Lynch, Ryan C, Stratton, Catherine, Elias, Rawad, Batist, Gerald, Kasi, Anup, Shah, Dimpy P, Bakouny, Ziad, Cabal, Angelo, Clement, Jessica, Crowell, Jennifer, Dixon, Becky, Friese, Christopher R, Fry, Stacy L, Grover, Punita, Gulati, Shuchi, Gupta, Shilpa, Hwang, Clara, Khan, Hina, Kim, Soo Jung, Klein, Elizabeth J, Labaki, Chris, McKay, Rana R, Nizam, Amanda, Pennell, Nathan A, Puc, Matthew, Schmidt, Andrew L, Shahrokni, Armin, Shaya, Justin A, Su, Christopher T, Wall, Sarah, Williams, Nicole, Wise-Draper, Trisha M, Mishra, Sanjay, Grivas, Petros, French, Benjamin, Warner, Jeremy L, Wildes, Tanya M, and Consortium, COVID-19 and Cancer

Subjects
Epidemiology, Public Health, Health Sciences, Clinical Research, Aging, Cancer, Prevention, Good Health and Well Being, Aged, COVID-19, COVID-19 Testing, Cohort Studies, Humans, Middle Aged, Neoplasms, Risk Factors, SARS-CoV-2, COVID-19 and Cancer Consortium, Public health
Abstract

BackgroundOlder age is associated with poorer outcomes of SARS-CoV-2 infection, although the heterogeneity of ageing results in some older adults being at greater risk than others. The objective of this study was to quantify the association of a novel geriatric risk index, comprising age, modified Charlson comorbidity index, and Eastern Cooperative Oncology Group performance status, with COVID-19 severity and 30-day mortality among older adults with cancer.MethodsIn this cohort study, we enrolled patients aged 60 years and older with a current or previous cancer diagnosis (excluding those with non-invasive cancers and premalignant or non-malignant conditions) and a current or previous laboratory-confirmed COVID-19 diagnosis who reported to the COVID-19 and Cancer Consortium (CCC19) multinational, multicentre, registry between March 17, 2020, and June 6, 2021. Patients were also excluded for unknown age, missing data resulting in unknown geriatric risk measure, inadequate data quality, or incomplete follow-up resulting in unknown COVID-19 severity. The exposure of interest was the CCC19 geriatric risk index. The primary outcome was COVID-19 severity and the secondary outcome was 30-day all-cause mortality; both were assessed in the full dataset. Adjusted odds ratios (ORs) and 95% CIs were estimated from ordinal and binary logistic regression models.Findings5671 patients with cancer and COVID-19 were included in the analysis. Median follow-up time was 56 days (IQR 22-120), and median age was 72 years (IQR 66-79). The CCC19 geriatric risk index identified 2365 (41·7%) patients as standard risk, 2217 (39·1%) patients as intermediate risk, and 1089 (19·2%) as high risk. 36 (0·6%) patients were excluded due to non-calculable geriatric risk index. Compared with standard-risk patients, high-risk patients had significantly higher COVID-19 severity (adjusted OR 7·24; 95% CI 6·20-8·45). 920 (16·2%) of 5671 patients died within 30 days of a COVID-19 diagnosis, including 161 (6·8%) of 2365 standard-risk patients, 409 (18·5%) of 2217 intermediate-risk patients, and 350 (32·1%) of 1089 high-risk patients. High-risk patients had higher adjusted odds of 30-day mortality (adjusted OR 10·7; 95% CI 8·54-13·5) than standard-risk patients.InterpretationThe CCC19 geriatric risk index was strongly associated with COVID-19 severity and 30-day mortality. Our CCC19 geriatric risk index, based on readily available clinical factors, might provide clinicians with an easy-to-use risk stratification method to identify older adults most at risk for severe COVID-19 as well as mortality.FundingUS National Institutes of Health National Cancer Institute Cancer Center.

Published
2022

16. Coinfections in Patients With Cancer and COVID-19: A COVID-19 and Cancer Consortium (CCC19) Study

Author

Satyanarayana, Gowri, Enriquez, Kyle T, Sun, Tianyi, Klein, Elizabeth J, Abidi, Maheen, Advani, Shailesh M, Awosika, Joy, Bakouny, Ziad, Bashir, Babar, Berg, Stephanie, Bernardes, Marilia, Egan, Pamela C, Elkrief, Arielle, Feldman, Lawrence E, Friese, Christopher R, Goel, Shipra, Gomez, Cyndi Gonzalez, Grant, Keith L, Griffiths, Elizabeth A, Gulati, Shuchi, Gupta, Shilpa, Hwang, Clara, Jain, Jayanshu, Jani, Chinmay, Kaltsas, Anna, Kasi, Anup, Khan, Hina, Knox, Natalie, Koshkin, Vadim S, Kwon, Daniel H, Labaki, Chris, Lyman, Gary H, McKay, Rana R, McNair, Christopher, Nagaraj, Gayathri, Nakasone, Elizabeth S, Nguyen, Ryan, Nonato, Taylor K, Olszewski, Adam J, Panagiotou, Orestis A, Puc, Matthew, Razavi, Pedram, Robilotti, Elizabeth V, Santos-Dutra, Miriam, Schmidt, Andrew L, Shah, Dimpy P, Shah, Sumit A, Vieira, Kendra, Weissmann, Lisa B, Wise-Draper, Trisha M, Wu, Ulysses, Wu, Julie Tsu-Yu, Choueiri, Toni K, Mishra, Sanjay, Warner, Jeremy L, French, Benjamin, and Farmakiotis, Dimitrios

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer, Infectious Diseases, Prevention, Lung, Clinical Research, Emerging Infectious Diseases, 2.2 Factors relating to the physical environment, Aetiology, Infection, Good Health and Well Being, bacterial infections, CAPA, COVID-19, mucormycoses, viral infections, Clinical sciences, Medical microbiology
Abstract

BackgroundThe frequency of coinfections and their association with outcomes have not been adequately studied among patients with cancer and coronavirus disease 2019 (COVID-19), a high-risk group for coinfection.MethodsWe included adult (≥18 years) patients with active or prior hematologic or invasive solid malignancies and laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) infection, using data from the COVID-19 and Cancer Consortium (CCC19, NCT04354701). We captured coinfections within ±2 weeks from diagnosis of COVID-19, identified factors cross-sectionally associated with risk of coinfection, and quantified the association of coinfections with 30-day mortality.ResultsAmong 8765 patients (hospitalized or not; median age, 65 years; 47.4% male), 16.6% developed coinfections: 12.1% bacterial, 2.1% viral, 0.9% fungal. An additional 6.4% only had clinical diagnosis of a coinfection. The adjusted risk of any coinfection was positively associated with age >50 years, male sex, cardiovascular, pulmonary, and renal comorbidities, diabetes, hematologic malignancy, multiple malignancies, Eastern Cooperative Oncology Group Performance Status, progressing cancer, recent cytotoxic chemotherapy, and baseline corticosteroids; the adjusted risk of superinfection was positively associated with tocilizumab administration. Among hospitalized patients, high neutrophil count and C-reactive protein were positively associated with bacterial coinfection risk, and high or low neutrophil count with fungal coinfection risk. Adjusted mortality rates were significantly higher among patients with bacterial (odds ratio [OR], 1.61; 95% CI, 1.33-1.95) and fungal (OR, 2.20; 95% CI, 1.28-3.76) coinfections.ConclusionsViral and fungal coinfections are infrequent among patients with cancer and COVID-19, with the latter associated with very high mortality rates. Clinical and laboratory parameters can be used to guide early empiric antimicrobial therapy, which may improve clinical outcomes.

Published
2022

18. The Bioburden Associated with Severe Open Tibial Fracture Wounds at the Time of Definitive Closure or Coverage: The BIOBURDEN Study

Author

Bosse, Michael J., Joshi, Manjari, Carlini, Anthony R., Firoozabadi, Reza, Murray, Clinton K., Manson, Theodore, Yun, Heather C., Gary, Joshua L., Wenke, Joseph C., Castillo, Renan C., Carroll, Eben A., Goodman, James Brett, Holden, Martha B., Miller, Anna N., Spraggs-Hughes, Amanda, Tornetta, Paul, III, Osborn, Patrick M., DeLeon, Stephanie, Hsu, Joseph R., Karunakar, Madhav A., Seymour, Rachel B., Sims, Stephen H., Churchill, Christine, Fraychineaud, Hannah Gissel, Trujillo, Corey Henderson, Zura, Robert D., Howes, Cameron, Mir, Hassan, Taylor, Benjamin C., Schmidt, Andrew H., Yoon, Patrick, McKinley, Todd O., Sorkin, Anthony, Virkus, Walter W., Krause, Peter C., Lee, Olivia C., Achor, Timothy S., Brinker, Mark R., Choo, Andrew, Munz, John W., Galpin, Matthew C., Frisch, H. Michael, Kaufman, Adam M., Large, Thomas M., LeCroy, C. Michael, Smith, Christopher S., Crickard, Colin V., Langford, Joshua, Reid, J. Spence, Horne, Andrea, O’Toole, Robert V., Eglseder, W. Andrew, Pensy, Raymond A., Crisco, M.J., Zych, Gregory A., Cannada, FAOA; Lisa K., Gardner, Michael J., Rehman, Saqib, Jones, Clifford B., Sietsema, Debra L., Ly, Thuan V., Miclau, Theodore, Morshed, Saam, Kwong, Jonny, Rothberg, David L., Bergin, Patrick F., Bhanat, Eldrin, Graves, Matt L., Spitler, Clay A., Teague, David, Ertl, William, Gorczyca, John T., Lester-Ballard, Veronica, McVey, Eric D., Agel, Julie, Obremskey, William, Burgos, Eduardo J., Gajari, Vamshi, Jahangir, A. Alex, Rodriguez-Buitrago, Andres, Trochez, Karen M., D’Alleyrand, Jean-Claude G., Gordon, Wade T., Ceniceros, Xochitl, Waggoner, Sandra L., and Collins, Susan C.

Published
2024
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19. The CoVID‐TE risk assessment model for venous thromboembolism in hospitalized patients with cancer and COVID‐19

Author

Li, Ang, Kuderer, Nicole M, Hsu, Chih‐Yuan, Shyr, Yu, Warner, Jeremy L, Shah, Dimpy P, Kumar, Vaibhav, Shah, Surbhi, Kulkarni, Amit A, Fu, Julie, Gulati, Shuchi, Zon, Rebecca L, Li, Monica, Desai, Aakash, Egan, Pamela C, Bakouny, Ziad, Devendra, KC, Hwang, Clara, Akpan, Imo J, McKay, Rana R, Girard, Jennifer, Schmidt, Andrew L, Halmos, Balazs, Thompson, Michael A, Patel, Jaymin M, Pennell, Nathan A, Peters, Solange, Elshoury, Amro, de Lima Lopes, Gilbero, Stover, Daniel G, Grivas, Petros, Rini, Brian I, Painter, Corrie A, Mishra, Sanjay, Connors, Jean M, Lyman, Gary H, Rosovsky, Rachel P, and consortium, the CCC19

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Clinical Research, Prevention, Hematology, Cancer, COVID-19, Cohort Studies, Humans, Neoplasms, Risk Assessment, SARS-CoV-2, Venous Thromboembolism, clinical decision rules, thrombosis, venous thromboembolism, CCC19 consortium, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundHospitalized patients with COVID-19 have increased risks of venous (VTE) and arterial thromboembolism (ATE). Active cancer diagnosis and treatment are well-known risk factors; however, a risk assessment model (RAM) for VTE in patients with both cancer and COVID-19 is lacking.ObjectivesTo assess the incidence of and risk factors for thrombosis in hospitalized patients with cancer and COVID-19.MethodsAmong patients with cancer in the COVID-19 and Cancer Consortium registry (CCC19) cohort study, we assessed the incidence of VTE and ATE within 90 days of COVID-19-associated hospitalization. A multivariable logistic regression model specifically for VTE was built using a priori determined clinical risk factors. A simplified RAM was derived and internally validated using bootstrap.ResultsFrom March 17, 2020 to November 30, 2020, 2804 hospitalized patients were analyzed. The incidence of VTE and ATE was 7.6% and 3.9%, respectively. The incidence of VTE, but not ATE, was higher in patients receiving recent anti-cancer therapy. A simplified RAM for VTE was derived and named CoVID-TE (Cancer subtype high to very-high risk by original Khorana score +1, VTE history +2, ICU admission +2, D-dimer elevation +1, recent systemic anti-cancer Therapy +1, and non-Hispanic Ethnicity +1). The RAM stratified patients into two cohorts (low-risk, 0-2 points, n = 1423 vs. high-risk, 3+ points, n = 1034) where VTE occurred in 4.1% low-risk and 11.3% high-risk patients (c statistic 0.67, 95% confidence interval 0.63-0.71). The RAM performed similarly well in subgroups of patients not on anticoagulant prior to admission and moderately ill patients not requiring direct ICU admission.ConclusionsHospitalized patients with cancer and COVID-19 have elevated thrombotic risks. The CoVID-TE RAM for VTE prediction may help real-time data-driven decisions in this vulnerable population.

Published
2021

20. 2023 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations: Summary From the Basic Life Support; Advanced Life Support; Pediatric Life Support; Neonatal Life Support; Education, Implementation, and Teams; and First Aid Task Forces

Author

Barcala-Furelos, Roberto, Beerman, Stephen B., Bruckner, Marlies, Castrén, Maaret, Chong, ShuLing, Claesson, Andreas, Dunne, Cody L., Finan, Emer, Fukuda, Tatsuma, Lalgudi Ganesan, Saptharishi, Gately, Callum, Gois, Aecio, Gray, Seth, Halamek, Louis P., Hoover, Amber V., Hurst, Cameron, Josephsen, Justin, Kollander, Louise, Omar Kamlin, C., Kool, Mirjam, Li, Lei, Mecrow, Thomas S., Montgomery, William, Ristau, Patrick, Jayashree, Muralidharan, Schmidt, Andrew, Scquizzato, Tommaso, Seesink, Jeroen, Sempsrott, Justin, Lee Solevåg, Anne, Strand, Marya L., Szpilman, David, Szyld, Edgardo, Thom, Ogilvie, Tobin, Joshua M., Trang, Jacinta, Webber, Jonathon, Webster, Hannah K., Wellsford, Michelle, Berg, Katherine M., Bray, Janet E., Ng, Kee-Chong, Liley, Helen G., Greif, Robert, Carlson, Jestin N., Morley, Peter T., Drennan, Ian R., Smyth, Michael, Scholefield, Barnaby R., Weiner, Gary M., Cheng, Adam, Djärv, Therese, Abelairas-Gómez, Cristian, Acworth, Jason, Andersen, Lars W., Atkins, Dianne L., Berry, David C., Bhanji, Farhan, Bierens, Joost, Bittencourt Couto, Thomaz, Borra, Vere, Böttiger, Bernd W., Bradley, Richard N., Breckwoldt, Jan, Cassan, Pascal, Chang, Wei-Tien, Charlton, Nathan P., Chung, Sung Phil, Considine, Julie, Costa-Nobre, Daniela T., Couper, Keith, Dainty, Katie N., Dassanayake, Vihara, Davis, Peter G., Dawson, Jennifer A., Fernanda de Almeida, Maria, De Caen, Allan R., Deakin, Charles D., Dicker, Bridget, Douma, Matthew J., Eastwood, Kathryn, El-Naggar, Walid, Fabres, Jorge G., Fawke, Joe, Fijacko, Nino, Finn, Judith C., Flores, Gustavo E., Foglia, Elizabeth E., Folke, Fredrik, Gilfoyle, Elaine, Goolsby, Craig A., Granfeldt, Asger, Guerguerian, Anne-Marie, Guinsburg, Ruth, Hatanaka, Tetsuo, Hirsch, Karen G., Holmberg, Mathias J., Hosono, Shigeharu, Hsieh, Ming-Ju, Hsu, Cindy H., Ikeyama, Takanari, Isayama, Tetsuya, Johnson, Nicholas J., Kapadia, Vishal S., Daripa Kawakami, Mandira, Kim, Han-Suk, Kleinman, Monica E., Kloeck, David A., Kudenchuk, Peter, Kule, Amy, Kurosawa, Hiroshi, Lagina, Anthony T., Lauridsen, Kasper G., Lavonas, Eric J., Lee, Henry C., Lin, Yiqun, Lockey, Andrew S., Macneil, Finlay, Maconochie, Ian K., John Madar, R., Malta Hansen, Carolina, Masterson, Siobhan, Matsuyama, Tasuku, McKinlay, Christopher J.D., Meyran, Daniel, Monnelly, Vix, Nadkarni, Vinay, Nakwa, Firdose L., Nation, Kevin J., Nehme, Ziad, Nemeth, Michael, Neumar, Robert W., Nicholson, Tonia, Nikolaou, Nikolaos, Nishiyama, Chika, Norii, Tatsuya, Nuthall, Gabrielle A., Ohshimo, Shinchiro, Olasveengen, Theresa M., Gene Ong, Yong-Kwang, Orkin, Aaron M., Parr, Michael J., Patocka, Catherine, Perkins, Gavin D., Perlman, Jeffrey M., Rabi, Yacov, Raitt, James, Ramachandran, Shalini, Ramaswamy, Viraraghavan V., Raymond, Tia T., Reis, Amelia G., Reynolds, Joshua C., Ristagno, Giuseppe, Rodriguez-Nunez, Antonio, Roehr, Charles C., Rüdiger, Mario, Sakamoto, Tetsuya, Sandroni, Claudio, Sawyer, Taylor L., Schexnayder, Steve M., Schmölzer, Georg M., Schnaubelt, Sebastian, Semeraro, Federico, Singletary, Eunice M., Skrifvars, Markus B., Smith, Christopher M., Soar, Jasmeet, Stassen, Willem, Sugiura, Takahiro, Tijssen, Janice A., Topjian, Alexis A., Trevisanuto, Daniele, Vaillancourt, Christian, Wyckoff, Myra H., Wyllie, Jonathan P., Yang, Chih-Wei, Yeung, Joyce, Zelop, Carolyn M., Zideman, David A., and Nolan, Jerry P.

Published
2024
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23. 2023 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations: Summary From the Basic Life Support; Advanced Life Support; Pediatric Life Support; Neonatal Life Support; Education, Implementation, and Teams; and First Aid Task Forces

Author

Berg, Katherine M., Bray, Janet E., Ng, Kee-Chong, Liley, Helen G., Greif, Robert, Carlson, Jestin N., Morley, Peter T., Drennan, Ian R., Smyth, Michael, Scholefield, Barnaby R., Weiner, Gary M., Cheng, Adam, Djärv, Therese, Abelairas-Gómez, Cristian, Acworth, Jason, Andersen, Lars W., Atkins, Dianne L., Berry, David C., Bhanji, Farhan, Bierens, Joost, Bittencourt Couto, Thomaz, Borra, Vere, Böttiger, Bernd W., Bradley, Richard N., Breckwoldt, Jan, Cassan, Pascal, Chang, Wei-Tien, Charlton, Nathan P., Chung, Sung Phil, Considine, Julie, Costa-Nobre, Daniela T., Couper, Keith, Dainty, Katie N., Dassanayake, Vihara, Davis, Peter G., Dawson, Jennifer A., de Almeida, Maria Fernanda, De Caen, Allan R., Deakin, Charles D., Dicker, Bridget, Douma, Matthew J., Eastwood, Kathryn, El-Naggar, Walid, Fabres, Jorge G., Fawke, Joe, Fijacko, Nino, Finn, Judith C., Flores, Gustavo E., Foglia, Elizabeth E., Folke, Fredrik, Gilfoyle, Elaine, Goolsby, Craig A., Granfeldt, Asger, Guerguerian, Anne-Marie, Guinsburg, Ruth, Hatanaka, Tetsuo, Hirsch, Karen G., Holmberg, Mathias J., Hosono, Shigeharu, Hsieh, Ming-Ju, Hsu, Cindy H., Ikeyama, Takanari, Isayama, Tetsuya, Johnson, Nicholas J., Kapadia, Vishal S., Kawakami, Mandira Daripa, Kim, Han-Suk, Kleinman, Monica E., Kloeck, David A., Ko, Ying-Chih, Kudenchuk, Peter, Kule, Amy, Kurosawa, Hiroshi, Lagina, Anthony T., Lauridsen, Kasper G., Lavonas, Eric J., Lee, Henry C., Lin, Yiqun, Lockey, Andrew S., Macneil, Finlay, Maconochie, Ian K., Madar, R. John, Malta Hansen, Carolina, Masterson, Siobhan, Matsuyama, Tasuku, McKinlay, Christopher J.D., Meyran, Daniel, Monnelly, Vix, Morrison, Laurie J., Nadkarni, Vinay, Nakwa, Firdose L., Nation, Kevin J., Nehme, Ziad, Nemeth, Michael, Neumar, Robert W., Nicholson, Tonia, Nikolaou, Nikolaos, Nishiyama, Chika, Norii, Tatsuya, Nuthall, Gabrielle A., Ohshimo, Shinchiro, Olasveengen, Theresa M., Ong, Yong-Kwang Gene, Orkin, Aaron M., Parr, Michael J., Patocka, Catherine, Perkins, Gavin D., Perlman, Jeffrey M., Rabi, Yacov, Raitt, James, Ramachandran, Shalini, Ramaswamy, Viraraghavan V., Raymond, Tia T., Reis, Amelia G., Reynolds, Joshua C., Ristagno, Giuseppe, Rodriguez-Nunez, Antonio, Roehr, Charles C., Rüdiger, Mario, Sakamoto, Tetsuya, Sandroni, Claudio, Sawyer, Taylor L., Schexnayder, Steve M., Schmölzer, Georg M., Schnaubelt, Sebastian, Semeraro, Federico, Singletary, Eunice M., Skrifvars, Markus B., Smith, Christopher M., Soar, Jasmeet, Stassen, Willem, Sugiura, Takahiro, Tijssen, Janice A., Topjian, Alexis A., Trevisanuto, Daniele, Vaillancourt, Christian, Wyckoff, Myra H., Wyllie, Jonathan P., Yang, Chih-Wei, Yeung, Joyce, Zelop, Carolyn M., Zideman, David A., Nolan, Jerry P., Barcala-Furelos, Roberto, Beerman, Stephen B., Castrén, Maaret, Chong, ShuLing, Claesson, Andreas, Dunne, Cody L., Ersdal, Hege L., Finan, Emer, Fuerch, Janene, Fukuda, Tatsuma, Ganesan, Saptharishi Lalgudi, Gately, Callum, Gray, Seth, Halamek, Louis P., Hoover, Amber V., Kollander, Louise, Kamlin, C. Omar, Koo, Mirjam, Li, Lei, Leone, Tina A., Mecrow, s, Montgomery, William, Ristau, Patrick, Jayashree, Muralidharan, Quek, Bin Huey, Schmidt, Andrew, Scquizzato, Tommaso, Seesink, Jeroen, Sempsrott, Justin, Shah, Birju A., Strand, Marya L., Szpilman, David, Szyld, Edgardo, Thio, Marta, Thom, Ogilvie, Tobin, Joshua M., Udaeta, Enrique, Webber, Jonathon, Webster, Hannah K., Wellsford, Michelle, and Yamada, Nicole K.

Published
2023
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24. Rocking on

Author

Schmidt, Andrew

Published
1997

26. Germline variants associated with toxicity to immune checkpoint blockade

Author

Groha, Stefan, Alaiwi, Sarah Abou, Xu, Wenxin, Naranbhai, Vivek, Nassar, Amin H., Bakouny, Ziad, El Zarif, Talal, Saliby, Renee Maria, Wan, Guihong, Rajeh, Ahmad, Adib, Elio, Nuzzo, Pier V., Schmidt, Andrew L., Labaki, Chris, Ricciuti, Biagio, Alessi, Joao Victor, Braun, David A., Shukla, Sachet A., Keenan, Tanya E., Van Allen, Eliezer, Awad, Mark M., Manos, Michael, Rahma, Osama, Zubiri, Leyre, Villani, Alexandra-Chloe, Fairfax, Benjamin, Hammer, Christian, Khan, Zia, Reynolds, Kerry, Semenov, Yevgeniy, Schrag, Deborah, Kehl, Kenneth L., Freedman, Matthew L., Choueiri, Toni K., and Gusev, Alexander

Published
2022
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27. Empowering lives through commitment to health and well-being: Amway transforms communities with a personal touch and innovative health solutions

Author

Schmidt, Andrew

Subjects
Health -- Psychological aspects, Dietary supplements -- Psychological aspects, Business, Business, regional
Abstract

For over sixty years, Amway business owners have been more than just providers of nutrition supplements and well-being products, they've been promoters of healthy routines and lifestyles for individuals, families [...]

Published
2024

29. Toward Efficient Evaluation of Logic Encryption Schemes: Models and Metrics

Author

Hu, Yinghua, Menon, Vivek V., Schmidt, Andrew, Monson, Joshua, French, Matthew, and Nuzzo, Pierluigi

Subjects
Computer Science - Cryptography and Security, Electrical Engineering and Systems Science - Systems and Control
Abstract

Research in logic encryption over the last decade has resulted in various techniques to prevent different security threats such as Trojan insertion, intellectual property leakage, and reverse engineering. However, there is little agreement on a uniform set of metrics and models to efficiently assess the achieved security level and the trade-offs between security and overhead. This paper addresses the above challenges by relying on a general logic encryption model that can encompass all the existing techniques, and a uniform set of metrics that can capture multiple, possibly conflicting, security concerns. We apply our modeling approach to four state-of-the-art encryption techniques, showing that it enables fast and accurate evaluation of design trade-offs, average prediction errors that are at least 2X smaller than previous approaches, and the evaluation of compound encryption methods., Comment: This report is an extended version of "Y. Hu, V. Venugopalan, A. Schmidt, J. Monson, M. French, and P. Nuzzo. Security-driven metrics and models for efficient evaluation of logic encryption schemes. In 2019 17th ACM/IEEE International Conference on Formal Methods and Models for System Design (MEMOCODE). ACM, 2019."

Published
2019

32. A systematic review of interventions for resuscitation following drowning

Author

Bierens, Joost, Bray, Janet, Abelairas-Gomez, Cristian, Barcala-Furelos, Roberto, Beerman, Stephen, Claesson, Andreas, Dunne, Cody, Fukuda, Tatsuma, Jayashree, Muralidharan, T Lagina, Anthony, Li, Lei, Mecrow, Tom, Morgan, Patrick, Schmidt, Andrew, Seesink, Jeroen, Sempsrott, Justin, Szpilman, David, Thom, Ogilvie, Tobin, Joshua, Webber, Jonathon, Johnson, Samantha, and Perkins, Gavin D

Published
2023
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42. Hungry mouths

Author

Schmidt, Andrew and Fowler, Jessica

Published
1994

43. Clinical impact of COVID-19 on patients with cancer (CCC19): a cohort study

Author

Kuderer, Nicole M, Choueiri, Toni K, Shah, Dimpy P, Shyr, Yu, Rubinstein, Samuel M, Rivera, Donna R, Shete, Sanjay, Hsu, Chih-Yuan, Desai, Aakash, de Lima Lopes, Gilberto, Grivas, Petros, Painter, Corrie A, Peters, Solange, Thompson, Michael A, Bakouny, Ziad, Batist, Gerald, Bekaii-Saab, Tanios, Bilen, Mehmet A, Bouganim, Nathaniel, Larroya, Mateo Bover, Castellano, Daniel, Del Prete, Salvatore A, Doroshow, Deborah B, Egan, Pamela C, Elkrief, Arielle, Farmakiotis, Dimitrios, Flora, Daniel, Galsky, Matthew D, Glover, Michael J, Griffiths, Elizabeth A, Gulati, Anthony P, Gupta, Shilpa, Hafez, Navid, Halfdanarson, Thorvardur R, Hawley, Jessica E, Hsu, Emily, Kasi, Anup, Khaki, Ali R, Lemmon, Christopher A, Lewis, Colleen, Logan, Barbara, Masters, Tyler, McKay, Rana R, Mesa, Ruben A, Morgans, Alicia K, Mulcahy, Mary F, Panagiotou, Orestis A, Peddi, Prakash, Pennell, Nathan A, Reynolds, Kerry, Rosen, Lane R, Rosovsky, Rachel, Salazar, Mary, Schmidt, Andrew, Shah, Sumit A, Shaya, Justin A, Steinharter, John, Stockerl-Goldstein, Keith E, Subbiah, Suki, Vinh, Donald C, Wehbe, Firas H, Weissmann, Lisa B, Wu, Julie Tsu-Yu, Wulff-Burchfield, Elizabeth, Xie, Zhuoer, Yeh, Albert, Yu, Peter P, Zhou, Alice Y, Zubiri, Leyre, Mishra, Sanjay, Lyman, Gary H, Rini, Brian I, Warner, Jeremy L, Consortium, COVID-19 and Cancer, Abidi, Maheen, Acoba, Jared D, Agarwal, Neeraj, Ahmad, Syed, Ajmera, Archana, Altman, Jessica, Angevine, Anne H, Azad, Nilo, Bar, Michael H, Bardia, Aditya, Barnholtz-Sloan, Jill, Barrow, Briana, Bashir, Babar, Belenkaya, Rimma, Berg, Stephanie, Bernicker, Eric H, Bestvina, Christine, Bishnoi, Rohit, Boland, Genevieve, Bonnen, Mark, Bouchard, Gabrielle, Bowles, Daniel W, Busser, Fiona, Cabal, Angelo, Caimi, Paolo, and Carducci, Theresa

Subjects
Biomedical and Clinical Sciences, Health Sciences, Clinical Sciences, Oncology and Carcinogenesis, Clinical Research, Aging, Prevention, Cancer, Lung, Good Health and Well Being, Aged, Antiviral Agents, Azithromycin, Betacoronavirus, COVID-19, Cause of Death, Comorbidity, Coronavirus Infections, Databases, Factual, Female, Humans, Hydroxychloroquine, Male, Middle Aged, Neoplasms, Pandemics, Pneumonia, Viral, Prognosis, Risk Factors, SARS-CoV-2, COVID-19 Drug Treatment, COVID-19 and Cancer Consortium, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundData on patients with COVID-19 who have cancer are lacking. Here we characterise the outcomes of a cohort of patients with cancer and COVID-19 and identify potential prognostic factors for mortality and severe illness.MethodsIn this cohort study, we collected de-identified data on patients with active or previous malignancy, aged 18 years and older, with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection from the USA, Canada, and Spain from the COVID-19 and Cancer Consortium (CCC19) database for whom baseline data were added between March 17 and April 16, 2020. We collected data on baseline clinical conditions, medications, cancer diagnosis and treatment, and COVID-19 disease course. The primary endpoint was all-cause mortality within 30 days of diagnosis of COVID-19. We assessed the association between the outcome and potential prognostic variables using logistic regression analyses, partially adjusted for age, sex, smoking status, and obesity. This study is registered with ClinicalTrials.gov, NCT04354701, and is ongoing.FindingsOf 1035 records entered into the CCC19 database during the study period, 928 patients met inclusion criteria for our analysis. Median age was 66 years (IQR 57-76), 279 (30%) were aged 75 years or older, and 468 (50%) patients were male. The most prevalent malignancies were breast (191 [21%]) and prostate (152 [16%]). 366 (39%) patients were on active anticancer treatment, and 396 (43%) had active (measurable) cancer. At analysis (May 7, 2020), 121 (13%) patients had died. In logistic regression analysis, independent factors associated with increased 30-day mortality, after partial adjustment, were: increased age (per 10 years; partially adjusted odds ratio 1·84, 95% CI 1·53-2·21), male sex (1·63, 1·07-2·48), smoking status (former smoker vs never smoked: 1·60, 1·03-2·47), number of comorbidities (two vs none: 4·50, 1·33-15·28), Eastern Cooperative Oncology Group performance status of 2 or higher (status of 2 vs 0 or 1: 3·89, 2·11-7·18), active cancer (progressing vs remission: 5·20, 2·77-9·77), and receipt of azithromycin plus hydroxychloroquine (vs treatment with neither: 2·93, 1·79-4·79; confounding by indication cannot be excluded). Compared with residence in the US-Northeast, residence in Canada (0·24, 0·07-0·84) or the US-Midwest (0·50, 0·28-0·90) were associated with decreased 30-day all-cause mortality. Race and ethnicity, obesity status, cancer type, type of anticancer therapy, and recent surgery were not associated with mortality.InterpretationAmong patients with cancer and COVID-19, 30-day all-cause mortality was high and associated with general risk factors and risk factors unique to patients with cancer. Longer follow-up is needed to better understand the effect of COVID-19 on outcomes in patients with cancer, including the ability to continue specific cancer treatments.FundingAmerican Cancer Society, National Institutes of Health, and Hope Foundation for Cancer Research.

Published
2020

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