Importance: Cancer treatment delay has been reported to variably impact cancer-specific survival and coronavirus disease 2019 (COVID-19)-specific mortality during the severe acute respiratory syndrome coronavirus 2 pandemic. During the pandemic, treatment delay is being recommended in a nonquantitative, nonobjective, and nonpersonalized manner, and this approach may be associated with suboptimal outcomes. Quantitative integration of cancer mortality estimates and data on the consequences of treatment delay is needed to aid treatment decisions and improve patient outcomes. Objective: To obtain quantitative integration of cancer-specific and COVID-19-specific mortality estimates that can be used to make optimal decisions for individual patients and optimize resource allocation. Design, Setting, and Participants: In this decision analytical model, age-specific and stage-specific estimates of overall survival pre-COVID-19 were adjusted by the probability of COVID-19 (individualized by county, treatment-specific variables, hospital exposure frequency, and COVID-19 infectivity estimates), COVID-19 mortality (individualized by age-specific, comorbidity-specific, and treatment-specific variables), and delay of cancer treatment (impact and duration). These model estimates were integrated into a web application (OncCOVID) to calculate estimates of the cumulative overall survival and restricted mean survival time of patients who received immediate vs delayed cancer treatment. Using currently available information about COVID-19, a susceptible-infected-recovered model that accounted for the increased risk among patients at health care treatment centers was developed. This model integrated the data on cancer mortality and the consequences of treatment delay to aid treatment decisions. Age-specific and cancer stage-specific estimates of overall survival pre-COVID-19 were extracted from the Surveillance, Epidemiology, and End Results database for 691 854 individuals with 25 cancer types who received cancer diagnoses in 2005 to 2006. Data from 5 436 896 individuals in the National Cancer Database were used to estimate the independent impact of treatment delay by cancer type and stage. In addition, data from 275 patients in a nested case-control study were used to estimate the COVID-19 mortality rate by age group and number of comorbidities. Data were analyzed from March 17 to May 21, 2020. Exposures: COVID-19 and cancer. Main Outcomes and Measures: Estimates of restricted mean survival time after the receipt of immediate vs delayed cancer treatment. Results: At the time of the study, the OncCOVID web application allowed for the selection of up to 47 individualized variables to assess net survival for an individual patient with cancer. Substantial heterogeneity was found regarding the association between delayed cancer treatment and net survival among patients with a given cancer type and stage, and these 2 variables were insufficient to discriminate the net impact of immediate vs delayed treatment. Individualized overall survival estimates were associated with patient age, number of comorbidities, treatment received, and specific local community estimates of COVID-19 risk. Conclusions and Relevance: This decision analytical modeling study found that the OncCOVID web-based application can quantitatively aid in the resource allocation of individualized treatment for patients with cancer during the COVID-19 global pandemic.
Importance: Cancer treatment delay has been reported to variably impact cancer-specific survival and coronavirus disease 2019 (COVID-19)-specific mortality during the severe acute respiratory syndrome coronavirus 2 pandemic. During the pandemic, treatment delay is being recommended in a nonquantitative, nonobjective, and nonpersonalized manner, and this approach may be associated with suboptimal outcomes. Quantitative integration of cancermortality estimates and data on the consequences of treatment delay is needed to aid treatment decisions and improve patient outcomes. Objective: To obtain quantitative integration of cancer-specific and COVID-19-specific mortality estimates that can be used to make optimal decisions for individual patients and optimize resource allocation. Design, Setting, and Participants: In this decision analytical model, age-specific and stage-specific estimates of overall survival pre-COVID-19 were adjusted by the probability of COVID-19 (individualized by county, treatment-specific variables, hospital exposure frequency, and COVID-19 infectivity estimates), COVID-19mortality (individualized by age-specific, comorbidity-specific, and treatment-specific variables), and delay of cancer treatment (impact and duration). These model estimates were integrated into a web application (OncCOVID) to calculate estimates of the cumulative overall survival and restricted mean survival time of patients who received immediate vs delayed cancer treatment. Using currently available information about COVID-19, a susceptible-infected-recovered model that accounted for the increased risk among patients at health care treatment centers was developed. This model integrated the data on cancermortality and the consequences of treatment delay to aid treatment decisions. Age-specific and cancer stage-specific estimates of overall survival pre-COVID-19 were extracted from the Surveillance, Epidemiology, and End Results database for 691 854 individuals with 25 cancer types who received cancer diagnoses in 2005 to 2006. Data from 5 436 896 individuals in the National Cancer Database were used to estimate the independent impact of treatment delay by cancer type and stage. In addition, data from 275 patients in a nested case-control study were used to estimate the COVID-19mortality rate by age group and number of comorbidities. Data were analyzed from March 17 to May 21, 2020. Exposures: COVID-19 and cancer. Main Outcomes and Measures: Estimates of restricted mean survival time after the receipt of immediate vs delayed cancer treatment. Results: At the time of the study, the OncCOVID web application allowed for the selection of up to 47 individualized variables to assess net survival for an individual patient with cancer. Substantial heterogeneity was found regarding the association between delayed cancer treatment and net survival among patients with a given cancer type and stage, and these 2 variables were insufficient to discriminate the net impact of immediate vs delayed treatment. Individualized overall survival estimates were associated with patient age, number of comorbidities, treatment received, and specific local community estimates of COVID-19 risk. Conclusions and Relevance: This decision analytical modeling study found that the OncCOVID web-based application can quantitatively aid in the resource allocation of individualized treatment for patients with cancer during the COVID-19 global pandemic.
Authors: Safiya Richardson; Jamie S Hirsch; Mangala Narasimhan; James M Crawford; Thomas McGinn; Karina W Davidson; Douglas P Barnaby; Lance B Becker; John D Chelico; Stuart L Cohen; Jennifer Cookingham; Kevin Coppa; Michael A Diefenbach; Andrew J Dominello; Joan Duer-Hefele; Louise Falzon; Jordan Gitlin; Negin Hajizadeh; Tiffany G Harvin; David A Hirschwerk; Eun Ji Kim; Zachary M Kozel; Lyndonna M Marrast; Jazmin N Mogavero; Gabrielle A Osorio; Michael Qiu; Theodoros P Zanos Journal: JAMA Date: 2020-05-26 Impact factor: 56.272
Authors: Hyunsoon Cho; Angela B Mariotto; Bhupinder S Mann; Carrie N Klabunde; Eric J Feuer Journal: Am J Epidemiol Date: 2013-07-03 Impact factor: 4.897
Authors: Farhaan S Vahidy; David W Bernard; Marc L Boom; Ashley L Drews; Paul Christensen; Jeremy Finkelstein; Roberta L Schwartz Journal: JAMA Netw Open Date: 2020-07-01
Authors: Gilles R Dagenais; Darryl P Leong; Sumathy Rangarajan; Fernando Lanas; Patricio Lopez-Jaramillo; Rajeev Gupta; Rafael Diaz; Alvaro Avezum; Gustavo B F Oliveira; Andreas Wielgosz; Shameena R Parambath; Prem Mony; Khalid F Alhabib; Ahmet Temizhan; Noorhassim Ismail; Jephat Chifamba; Karen Yeates; Rasha Khatib; Omar Rahman; Katarzyna Zatonska; Khawar Kazmi; Li Wei; Jun Zhu; Annika Rosengren; K Vijayakumar; Manmeet Kaur; Viswanathan Mohan; AfzalHussein Yusufali; Roya Kelishadi; Koon K Teo; Philip Joseph; Salim Yusuf Journal: Lancet Date: 2019-09-03 Impact factor: 79.321
Authors: Matthias Guckenberger; Claus Belka; Andrea Bezjak; Jeffrey Bradley; Megan E Daly; Dirk DeRuysscher; Rafal Dziadziuszko; Corinne Faivre-Finn; Michael Flentje; Elizabeth Gore; Kristin A Higgins; Puneeth Iyengar; Brian D Kavanagh; Sameera Kumar; Cecile Le Pechoux; Yolande Lievens; Karin Lindberg; Fiona McDonald; Sara Ramella; Ramesh Rengan; Umberto Ricardi; Andreas Rimner; George B Rodrigues; Steven E Schild; Suresh Senan; Charles B Simone; Ben J Slotman; Martin Stuschke; Greg Videtic; Joachim Widder; Sue S Yom; David Palma Journal: Radiother Oncol Date: 2020-04-06 Impact factor: 6.280
Authors: Mara Antonoff; Leah Backhus; Daniel J Boffa; Stephen R Broderick; Lisa M Brown; Phillip Carrott; James M Clark; David Cooke; Elizabeth David; Matt Facktor; Farhood Farjah; Eric Grogan; James Isbell; David R Jones; Biniam Kidane; Anthony W Kim; Shaf Keshavjee; Seth Krantz; Natalie Lui; Linda Martin; Robert A Meguid; Shari L Meyerson; Tim Mullett; Heidi Nelson; David D Odell; Joseph D Phillips; Varun Puri; Valerie Rusch; Lawrence Shulman; Thomas K Varghese; Elliot Wakeam; Douglas E Wood Journal: J Thorac Cardiovasc Surg Date: 2020-04-09 Impact factor: 5.209
Authors: Amit Sud; Bethany Torr; Michael E Jones; John Broggio; Stephen Scott; Chey Loveday; Alice Garrett; Firza Gronthoud; David L Nicol; Shaman Jhanji; Stephen A Boyce; Matthew Williams; Elio Riboli; David C Muller; Emma Kipps; James Larkin; Neal Navani; Charles Swanton; Georgios Lyratzopoulos; Ethna McFerran; Mark Lawler; Richard Houlston; Clare Turnbull Journal: Lancet Oncol Date: 2020-07-20 Impact factor: 41.316
Authors: Alexander Kutikov; David S Weinberg; Martin J Edelman; Eric M Horwitz; Robert G Uzzo; Richard I Fisher Journal: Ann Intern Med Date: 2020-03-27 Impact factor: 25.391
Authors: David J Thomson; David Palma; Matthias Guckenberger; Panagiotis Balermpas; Jonathan J Beitler; Pierre Blanchard; David Brizel; Wilfred Budach; Jimmy Caudell; June Corry; Renzo Corvo; Mererid Evans; Adam S Garden; Jordi Giralt; Vincent Gregoire; Paul M Harari; Kevin Harrington; Ying J Hitchcock; Jorgen Johansen; Johannes Kaanders; Shlomo Koyfman; J A Langendijk; Quynh-Thu Le; Nancy Lee; Danielle Margalit; Michelle Mierzwa; Sandro Porceddu; Yoke Lim Soong; Ying Sun; Juliette Thariat; John Waldron; Sue S Yom Journal: Int J Radiat Oncol Biol Phys Date: 2020-04-14 Impact factor: 7.038
Authors: Chris Labaki; Ziad Bakouny; Andrew Schmidt; Stuart R Lipsitz; Timothy R Rebbeck; Quoc-Dien Trinh; Toni K Choueiri Journal: Cancer Cell Date: 2021-07-01 Impact factor: 38.585
Authors: Johanna Kirchberg; Anke Rentsch; Anna Klimova; Vasyl Vovk; Sebastian Hempel; Gunnar Folprecht; Mechthild Krause; Verena Plodeck; Thilo Welsch; Jürgen Weitz; Johannes Fritzmann Journal: Front Public Health Date: 2021-11-23
Authors: Oscar Arrieta; Luis Lara-Mejía; Elysse Bautista-GonzÁlez; David Heredia; Jenny G Turcott; Feliciano BarrÓn; Maritza Ramos-Ramírez; Luis Cabrera-Miranda; Miguel Ángel Salinas Padilla; Mercedes Aguerrebere; Andrés F Cardona; Christian Rolfo; Marisol Arroyo-HernÁndez; Enrique Soto-Pérez-de-Celis; Renata Baéz-Saldaña Journal: Oncologist Date: 2021-09-09