Elective surgery cancellations due to the COVID-19 pandemic: global predictive modelling to inform surgical recovery plans
Research output: Contribution to journal › Journal article › Research › peer-review
Standard
Elective surgery cancellations due to the COVID-19 pandemic : global predictive modelling to inform surgical recovery plans. / Blichfeldt, Louise; Moller, Kirsten; Nielsen, Jeppe Sylvest; Simonsen, Martin; Hansen, Frank; Bestle, Morten; Hansen, Christian Steen; Afshari, Arash; Matos, Ricardo; Chew, Michelle S.; Wernerman, Jan; Hughes, Thomas; Parker, Robert; Thomas, Richard; Alexander, David; Rasmussen, Lars; Matos, Ricardo; Chew, Michelle S.; Yang, Yang; Wang, Zhipeng; Tang, Jing; Wang, Jun; Zhang, Min; Zhang, Yuan; Sun, Yu; Zhao, Lei; Li, Hui; Zhang, Jie; Rasmussen, Bodil Steen; Gätke, Mona Ring; Lange, Kai Henrik Wiborg; Larsen, Michael; Rasmussen, Lars; Pedersen, Karen; Ekelund, Kim; Chew, Michelle S.; Madsen, A. S.M.; COVIDSurg Collaborative.
In: British Journal of Surgery, Vol. 107, No. 11, 2020, p. 1440-1449.Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Elective surgery cancellations due to the COVID-19 pandemic
T2 - global predictive modelling to inform surgical recovery plans
AU - Blichfeldt, Louise
AU - Moller, Kirsten
AU - Nielsen, Jeppe Sylvest
AU - Simonsen, Martin
AU - Hansen, Frank
AU - Bestle, Morten
AU - Hansen, Christian Steen
AU - Afshari, Arash
AU - Matos, Ricardo
AU - Chew, Michelle S.
AU - Wernerman, Jan
AU - Hughes, Thomas
AU - Parker, Robert
AU - Thomas, Richard
AU - Alexander, David
AU - Rasmussen, Lars
AU - Matos, Ricardo
AU - Chew, Michelle S.
AU - Yang, Yang
AU - Wang, Zhipeng
AU - Tang, Jing
AU - Wang, Jun
AU - Zhang, Min
AU - Zhang, Yuan
AU - Sun, Yu
AU - Zhao, Lei
AU - Li, Hui
AU - Zhang, Jie
AU - Rasmussen, Bodil Steen
AU - Gätke, Mona Ring
AU - Lange, Kai Henrik Wiborg
AU - Larsen, Michael
AU - Rasmussen, Lars
AU - Pedersen, Karen
AU - Ekelund, Kim
AU - Chew, Michelle S.
AU - Madsen, A. S.M.
AU - COVIDSurg Collaborative
PY - 2020
Y1 - 2020
N2 - Background: The COVID-19 pandemic has disrupted routine hospital services globally. This study estimated the total number of adult elective operations that would be cancelled worldwide during the 12 weeks of peak disruption due to COVID-19. Methods: A global expert response study was conducted to elicit projections for the proportion of elective surgery that would be cancelled or postponed during the 12 weeks of peak disruption. A Bayesian β-regression model was used to estimate 12-week cancellation rates for 190 countries. Elective surgical case-mix data, stratified by specialty and indication (surgery for cancer versus benign disease), were determined. This case mix was applied to country-level surgical volumes. The 12-week cancellation rates were then applied to these figures to calculate the total number of cancelled operations. Results: The best estimate was that 28 404 603 operations would be cancelled or postponed during the peak 12 weeks of disruption due to COVID-19 (2 367 050 operations per week). Most would be operations for benign disease (90·2 per cent, 25 638 922 of 28 404 603). The overall 12-week cancellation rate would be 72·3 per cent. Globally, 81·7 per cent of operations for benign conditions (25 638 922 of 31 378 062), 37·7 per cent of cancer operations (2 324 070 of 6 162 311) and 25·4 per cent of elective caesarean sections (441 611 of 1 735 483) would be cancelled or postponed. If countries increased their normal surgical volume by 20 per cent after the pandemic, it would take a median of 45 weeks to clear the backlog of operations resulting from COVID-19 disruption. Conclusion: A very large number of operations will be cancelled or postponed owing to disruption caused by COVID-19. Governments should mitigate against this major burden on patients by developing recovery plans and implementing strategies to restore surgical activity safely.
AB - Background: The COVID-19 pandemic has disrupted routine hospital services globally. This study estimated the total number of adult elective operations that would be cancelled worldwide during the 12 weeks of peak disruption due to COVID-19. Methods: A global expert response study was conducted to elicit projections for the proportion of elective surgery that would be cancelled or postponed during the 12 weeks of peak disruption. A Bayesian β-regression model was used to estimate 12-week cancellation rates for 190 countries. Elective surgical case-mix data, stratified by specialty and indication (surgery for cancer versus benign disease), were determined. This case mix was applied to country-level surgical volumes. The 12-week cancellation rates were then applied to these figures to calculate the total number of cancelled operations. Results: The best estimate was that 28 404 603 operations would be cancelled or postponed during the peak 12 weeks of disruption due to COVID-19 (2 367 050 operations per week). Most would be operations for benign disease (90·2 per cent, 25 638 922 of 28 404 603). The overall 12-week cancellation rate would be 72·3 per cent. Globally, 81·7 per cent of operations for benign conditions (25 638 922 of 31 378 062), 37·7 per cent of cancer operations (2 324 070 of 6 162 311) and 25·4 per cent of elective caesarean sections (441 611 of 1 735 483) would be cancelled or postponed. If countries increased their normal surgical volume by 20 per cent after the pandemic, it would take a median of 45 weeks to clear the backlog of operations resulting from COVID-19 disruption. Conclusion: A very large number of operations will be cancelled or postponed owing to disruption caused by COVID-19. Governments should mitigate against this major burden on patients by developing recovery plans and implementing strategies to restore surgical activity safely.
U2 - 10.1002/bjs.11746
DO - 10.1002/bjs.11746
M3 - Journal article
C2 - 32395848
AN - SCOPUS:85085308541
VL - 107
SP - 1440
EP - 1449
JO - British Journal of Surgery
JF - British Journal of Surgery
SN - 0007-1323
IS - 11
ER -
ID: 260042071