key: cord-0691614-jlinwjp4
authors: Tam, Derrick Y.; Naimark, David; Natarajan, Madhu K.; Woodward, Graham; Oakes, Garth; Rahal, Mirna; Barrett, Kali; Khan, Yasin A.; Ximenes, Raphael; Mac MBiotech, Stephen; Sander, Beate; Wijeysundera, Harindra C.
title: The Use of Decision Modelling to Inform Timely Policy Decisions on Cardiac Resource Capacity during the COVID-19 Pandemic.
date: 2020-05-21
journal: Can J Cardiol
DOI: 10.1016/j.cjca.2020.05.024
sha: f4a3c93ec6a9f56cea1965cca7bf3bd1d91c9f28
doc_id: 691614
cord_uid: jlinwjp4

In Ontario, on March 16(th), 2020, a directive was issued to all acute care hospitals to halt non-essential procedures in anticipation for 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 three 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 five 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.

First, in the initial crisis phase, in a worst case scenario, we showed that the potential number of waitlist 82 related cardiac deaths would be orders of magnitude less than those who would die of COVID-19 if 83 critical cardiac care resources were diverted to the care of COVID-19 patients. Second, with better local 84 epidemiology data, we predicted that across five regions of Ontario, there may be insufficient resources 85 to resume all elective outpatient cardiac procedures. Finally in the recovery phase, we showed that the 86 estimated incremental growth in waitlist for all cardiac procedures is likely substantial. an approach to modeling coronary artery disease, electrophysiology procedures and valvular 107 heart disease using historic referral and procedural volume data from a provincial clinical 108 registry along with ICU and ward length of stay data from provincial administrative data. Our 109 overall goals were to better understand the consequences of initially curtailing and subsequently 110 resuming cardiac procedures during the pandemic, and to be able to provide timely, data-111 informed predictions to facilitate policy decisions. We provide an example of how such 112 modelling was used through the arc of the COVID-19 pandemic in Ontario, through the initial 113 crisis phase of the pandemic with substantial unknowns, a second phase with local epidemiology 114 data available, and then finally the recovery phase. The cardiac modeling group consisted of a multidisciplinary team of graduate students, clinician-119 scientists, and academics with expertise in cardiology/cardiac surgery, infectious disease, and 120 advanced decision analytic modeling. We engaged our primary stakeholder, CorHealth Ontario 121 to understand the needs of the type of predictions required to make timely policy decisions. (Figure 2A, Supplementary Table S1 ), the increasing rate of 171 COVID-19 cases in Ontario raised substantial concerns that the system was at an inflection 172 point, whereby a surge of critically ill patients could overwhelm the acute care system. 3 We 173 compared the potential deaths from COVID-19 patients in a hypothetical ICU and ventilator 174 shortage scenario to the potential deaths of elective patients waiting for their cardiac procedures. 175

We showed that the potential number of deaths for patients awaiting their elective cardiac 176 procedures would be orders of magnitude less than those who would die of COVID-19 if there 177 was a depletion of critical care resources ( Figure 2B ). This led to the urgent release of CorHealth Currently, we are working on estimating the necessary resource capacity and time required to 200 bring the waitlist back to pre-COVID-19 levels. As real-world data is made available, we will be 201 able to validate model outputs against observed outcomes and make iterative improvements to 202 allow for more accurate predictions in the future. This is an important point given the concern for 203 multiple waves of the pandemic in the absence of a vaccine for COVID-19. 5 In this first response 204 to the pandemic, the public health mandate was prioritized over other concerns; we recognize 205 that in hindsight, resources were left unused in Ontario. This reinforces the need for prediction 206 models to be improved for the next phase of the pandemic, such that subsequent policy decisions 207 are improved and spillover impacts on non-COVID-19 care is reduced 208 conditions, such as cardiac disease, result in significant sequelae; these spillover impacts are also 246 critical consequences of the pandemic. The use of decision analytic modeling that is iterated on 247 rapidly evolving data is a tool to help inform health policy decisions to address these difficult 248 trade-offs. 249 

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