Minimizing population health loss due to scarcity in OR capacity: validation of quality of life input.

OBJECTIVES: A previously developed decision model to prioritize surgical procedures in times of scarce surgical capacity used quality of life (QoL) primarily derived from experts in one center. These estimates are key input of the model, and might be more context-dependent than the other input parameters (age, survival). The aim of this study was to validate our model by replicating these QoL estimates. METHODS: The original study estimated QoL of patients in need of commonly performed procedures in live expert-panel meetings. This study replicated this procedure using a web-based Delphi approach in a different hospital. The new QoL scores were compared with the original scores using mixed effects linear regression. The ranking of surgical procedures based on combined QoL values from the validation and original study was compared to the ranking based solely on the original QoL values. RESULTS: The overall mean difference in QoL estimates between the validation study and the original study was - 0.11 (95% CI: -0.12 - -0.10). The model output (DALY/month delay) based on QoL data from both studies was similar to the model output based on the original data only: The Spearman's correlation coefficient between the ranking of all procedures before and after including the new QoL estimates was 0.988. DISCUSSION: Even though the new QoL estimates were systematically lower than the values from the original study, the ranking for urgency based on health loss per unit of time delay of procedures was consistent. This underscores the robustness and generalizability of the decision model for prioritization of surgical procedures.

Minimising population health loss in times of scarce surgical capacity: a modelling study for surgical procedures performed in nonacademic hospitals.

BACKGROUND: The burden of the COVID-19 pandemic resulted in a reduction of available health care capacity for regular care. To guide prioritisation of semielective surgery in times of scarcity, we previously developed a decision model to quantify the expected health loss due to delay of surgery, in an academic hospital setting. The aim of this study is to validate our decision model in a nonacademic setting and include additional elective surgical procedures. METHODS: In this study, we used the previously published three-state cohort state-transition model, to evaluate the health effects of surgery postponement for 28 surgical procedures commonly performed in nonacademic hospitals. Scientific literature and national registries yielded nearly all input parameters, except for the quality of life (QoL) estimates which were obtained from experts using the Delphi method. Two expert panels, one from a single nonacademic hospital and one from different nonacademic hospitals in the Netherlands, were invited to estimate QoL weights. We compared estimated model results (disability adjusted life years (DALY)/month of surgical delay) based on the QoL estimates from the two panels by calculating the mean difference and the correlation between the ranks of the different surgical procedures. The eventual model was based on the combined QoL estimates from both panels. RESULTS: Pacemaker implantation was associated with the most DALY/month of surgical delay (0.054 DALY/month, 95% CI: 0.025-0.103) and hemithyreoidectomy with the least DALY/month (0.006 DALY/month, 95% CI: 0.002-0.009). The overall mean difference of QoL estimates between the two panels was 0.005 (95% CI -0.014-0.004). The correlation between ranks was 0.983 (p < 0.001). CONCLUSIONS: Our study provides an overview of incurred health loss due to surgical delay for surgeries frequently performed in nonacademic hospitals. The quality of life estimates currently used in our model are robust and validate towards a different group of experts. These results enrich our earlier published results on academic surgeries and contribute to prioritising a more complete set of surgeries.

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

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.