Minimizing Population Health Loss in Times of Scarce Surgical Capacity
Abstract
Background
COVID-19 has put unprecedented pressure on healthcare systems worldwide, leading to a reduction of the available healthcare capacity. Our objective was to develop a decision model that supports prioritization of care from a utilitarian perspective, which is to minimize population health loss.
Methods
A cohort state-transition model was developed and applied to 43 semi-elective non-paediatric surgeries commonly performed in academic hospitals. Scenarios of delaying surgery from two weeks were compared with delaying up to one year, and no surgery at all. Model parameters were based on registries, scientific literature, and the World Health Organization global burden of disease study. For each surgery, the urgency was estimated as the average expected loss of Quality-Adjusted Life-Years (QALYs) per month.
Results
Given the best available evidence, the two most urgent surgeries were bypass surgery for Fontaine III/IV peripheral arterial disease (0.23 QALY loss/month, 95%-CI: 0.09-0.24) and transaortic valve implantation (0.15 QALY loss/month, 95%-CI: 0.09-0.24). The two least urgent surgeries were placing a shunt for dialysis (0.01, 95%-CI: 0.005-0.01) and thyroid carcinoma resection (0.01, 95%-CI: 0.01-0.02): these surgeries were associated with a limited amount of health lost on the waiting list.
Conclusion
Expected health loss due to surgical delay can be objectively calculated with our decision model based on best available evidence, which can guide prioritization of surgeries to minimize population health loss in times of scarcity. This tool should yet be placed in the context of different ethical perspectives and combined with capacity management tools to facilitate large-scale implementation.
Summary box
What is already known on this topic
The perspective of maximizing population health, a utilitarian ethical perspective, has been described to be most defendable in times of scarcity. To prioritize surgical patients, literature mainly discusses approaches which are intra-disciplinary (e.g. within gynecological or oncological surgery) and mostly existed of narrative reviews of the literature. Some decision tools were developed, which rely on the consensus of experts on various measures of urgency (e.g. health benefit, or time until inoperable). No approach was found which transparently weighs objective factors in order to quantify a clinically relevant measure of urgency.
What this study adds
In contrast to previously developed approaches, our approach transparently and consistently aggregates best available objective evidence across disciplines. This novel aggregated urgency measure can be easily linked with capacity management tools. Our approach can help to minimize health losses when trying to overcome delay in surgeries in times of surgical scarcity, during the COVID-19 pandemic and beyond.
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