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10.1016/j.cjca.2020.05.024 http://scihub22266oqcxt.onion/10.1016/j.cjca.2020.05.024 32447059!7241392!32447059     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Can+J+Cardiol 2020 ; 36 (8): 1308-1312 Nephropedia Template TP {{tp|p=32447059|t=2020. The Use of Decision Modelling to Inform Timely Policy Decisions on Cardiac Resource Capacity During the COVID-19 Pandemic |pdf=|usr=}}{{32447059}} gab.com Text The Use of Decision Modelling to Inform Timely Policy Decisions on Cardiac Resource Capacity During the COVID-19 Pandemic JO: Can J Cardiol http://www.kidney.de/pget.php?&tw=1&pmid=32447059 #FOAvip In Ontario on March 16, 2020, a directive was issued to all acute care hospitals to halt nonessential procedures in anticipation of a potential surge in COVID-19 patients. This included scheduled outpatient cardiac surgical and interventional procedures that required the use of intensive care units, ventilators, and skilled critical care personnel, given that these procedures would draw from the same pool of resources required for critically ill COVID-19 patients. We adapted the COVID-19 Resource Estimator (CORE) decision analytic model by adding a cardiac component to determine the impact of various policy decisions on the incremental waitlist growth and estimated waitlist mortality for 3 key groups of cardiovascular disease patients: coronary artery disease, valvular heart disease, and arrhythmias. We provided predictions based on COVID-19 epidemiology available in real-time, in 3 phases. First, in the initial crisis phase, in a worst case scenario, we showed that the potential number of waitlist related cardiac deaths would be orders of magnitude less than those who would die of COVID-19 if critical cardiac care resources were diverted to the care of COVID-19 patients. Second, with better local epidemiology data, we predicted that across 5 regions of Ontario, there may be insufficient resources to resume all elective outpatient cardiac procedures. Finally in the recovery phase, we showed that the estimated incremental growth in waitlist for all cardiac procedures is likely substantial. These outputs informed timely data-driven decisions during the COVID-19 pandemic regarding the provision of cardiovascular care. Twit Text FOAVip The Use of Decision Modelling to Inform Timely Policy Decisions on Cardiac Resource Capacity During the COVID-19 Pandemic ????Can J Cardiol http://www.kidney.de/pget.php?&tw=1&pmid=32447059 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32447059 #FOAvip English Wikipedia <ref>{{Cite pmid|32447059}}</ref> The Use of Decision Modelling to Inform Timely Policy Decisions on Cardiac Resource Capacity During the COVID-19 Pandemic #MMPMID32447059 Tam DY; Naimark D; Natarajan MK; Woodward G; Oakes G; Rahal M; Barrett K; Khan YA; Ximenes R; Mac S; Sander B; Wijeysundera HC Can J Cardiol 2020[Aug]; 36 (8): 1308-1312 PMID32447059show ga In Ontario on March 16, 2020, a directive was issued to all acute care hospitals to halt nonessential procedures in anticipation of a potential surge in COVID-19 patients. This included scheduled outpatient cardiac surgical and interventional procedures that required the use of intensive care units, ventilators, and skilled critical care personnel, given that these procedures would draw from the same pool of resources required for critically ill COVID-19 patients. We adapted the COVID-19 Resource Estimator (CORE) decision analytic model by adding a cardiac component to determine the impact of various policy decisions on the incremental waitlist growth and estimated waitlist mortality for 3 key groups of cardiovascular disease patients: coronary artery disease, valvular heart disease, and arrhythmias. We provided predictions based on COVID-19 epidemiology available in real-time, in 3 phases. First, in the initial crisis phase, in a worst case scenario, we showed that the potential number of waitlist related cardiac deaths would be orders of magnitude less than those who would die of COVID-19 if critical cardiac care resources were diverted to the care of COVID-19 patients. Second, with better local epidemiology data, we predicted that across 5 regions of Ontario, there may be insufficient resources to resume all elective outpatient cardiac procedures. Finally in the recovery phase, we showed that the estimated incremental growth in waitlist for all cardiac procedures is likely substantial. These outputs informed timely data-driven decisions during the COVID-19 pandemic regarding the provision of cardiovascular care. |*Ambulatory Care/organization & administration/trends[MESH] |*Cardiology Service, Hospital/organization & administration/trends[MESH] |*Cardiovascular Diseases/epidemiology/therapy[MESH] |*Coronavirus Infections/epidemiology/prevention & control[MESH] |*Pandemics/prevention & control[MESH] |*Pneumonia, Viral/epidemiology/prevention & control[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Decision Support Techniques[MESH] |Health Care Rationing/*methods[MESH] |Humans[MESH] |Ontario/epidemiology[MESH] |Organizational Innovation[MESH] |Policy Making[MESH] |SARS-CoV-2[MESH]The Use of Decision Modelling to Inform Timely Policy Decisions on Car(epmc).pdf DeepDyve Pubget Overpricing