Dataset Information

Minimizing Population Health Loss in Times of Scarce Surgical Capacity During the Coronavirus Disease 2019 Crisis and Beyond: A Modeling Study.

ABSTRACT:

Objectives

Coronavirus disease 2019 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 to estimate the impact of postponing semielective surgical procedures on health, to support prioritization of care from a utilitarian perspective.

Methods

A cohort state-transition model was developed and applied to 43 semielective nonpediatric surgical procedures commonly performed in academic hospitals. Scenarios of delaying surgery from 2 weeks were compared with delaying up to 1 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 surgical procedure, the model estimated the average expected disability-adjusted life-years (DALYs) per month of delay.

Results

Given the best available evidence, the 2 surgical procedures associated with most DALYs owing to delay were bypass surgery for Fontaine III/IV peripheral arterial disease (0.23 DALY/month, 95% confidence interval [CI]: 0.13-0.36) and transaortic valve implantation (0.15 DALY/month, 95% CI: 0.09-0.24). The 2 surgical procedures with the least DALYs 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).

Conclusion

Expected health loss owing to surgical delay can be objectively calculated with our decision model based on best available evidence, which can guide prioritization of surgical procedures to minimize population health loss in times of scarcity. The model results should be placed in the context of different ethical perspectives and combined with capacity management tools to facilitate large-scale implementation.

SUBMITTER: Gravesteijn B

PROVIDER: S-EPMC7933792 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:To establish the feasibility of empirically testing crisis standards of care guidelines.DesignRetrospective single-center study.SettingICUs at a large academic medical center in the United States.SubjectsAdult, critically ill patients admitted to ICU, with 27 patients admitted for acute respiratory failure due to coronavirus disease 2019 and 37 patients admitted for diagnoses other than coronavirus disease 2019.InterventionsReview of electronic health record.Measurements and main resultsMany U.S. states released crisis standards of care guidelines with algorithms to allocate scarce healthcare resources during the coronavirus disease 2019 pandemic. We compared state guidelines that represent different approaches to incorporating disease severity and comorbidities: New York, Maryland, Pennsylvania, and Colorado. Following each algorithm, we calculated priority scores at the time of ICU admission for a cohort of patients with primary diagnoses of coronavirus disease 2019 and diseases other than coronavirus disease 2019 (n = 64). We assessed discrimination of 28-day mortality by area under the receiver operating characteristic curve. We simulated real-time decision-making by applying the triage algorithms to groups of two, five, or 10 patients. For prediction of 28-day mortality by priority scores, area under the receiver operating characteristic curve was 0.56, 0.49, 0.53, 0.66, and 0.69 for New York, Maryland, Pennsylvania, Colorado, and raw Sequential Organ Failure Assessment score algorithms, respectively. For groups of five patients, the percentage of decisions made without deferring to a lottery were 1%, 57%, 80%, 88%, and 95% for New York, Maryland, Pennsylvania, Colorado, and raw Sequential Organ Failure Assessment score algorithms, respectively. The percentage of decisions made without lottery was higher in the subcohort without coronavirus disease 2019, compared with the subcohort with coronavirus disease 2019.ConclusionsInclusion of comorbidities does not consistently improve an algorithm's performance in predicting 28-day mortality. Crisis standards of care algorithms result in a substantial percentage of tied priority scores. Crisis standards of care algorithms operate differently in cohorts with and without coronavirus disease 2019. This proof-of-principle study demonstrates the feasibility and importance of empirical testing of crisis standards of care guidelines to understand whether they meet their goals.