Prioritization of surgical patients during the COVID-19 pandemic and beyond:A qualitative exploration of patients’ perspectives

IntroductionDuring the COVID-19 pandemic, prioritizing certain surgical patients became inevitable due to limited surgical capacity. This study aims to identify which factors patients value in priority setting, and to evaluate their perspective on a decision model for surgical prioritization.MethodsWe enacted a qualitative exploratory study and conducted semi-structured interviews with N = 15 patients. Vignettes were used as guidance. The interviews were transcribed and iteratively analyzed using thematic analysis.ResultsWe unraveled three themes: 1) general attitude towards surgical prioritization: patients showed understanding for the difficult decisions to be made, but demanded greater transparency and objectivity; 2) patient-related factors that some participants considered should, or should not, influence the prioritization: age, physical functioning, cognitive functioning, behavior, waiting time, impact on survival and quality of life, emotional consequences, and resource usage; and 3) patients’ perspective on a decision model: usage of such a model for prioritization decisions is favorable if the model is simple, uses trustworthy data, and its output is supervised by physicians. The model could also be used as a communication tool to explain prioritization dilemmas to patients.ConclusionSupport for the various factors and use of a decision model varied among patients. Therefore, it seems unrealistic to immediately incorporate these factors in decision models. Instead, this study calls for more research to identify feasible avenues and seek consensus

Patients' perspectives on ethical principles to fairly allocate scarce surgical resources during the COVID-19 pandemic in the Netherlands:a Q-methodology study

OBJECTIVES: During the COVID-19 pandemic, healthcare professionals were faced with prioritisation dilemmas due to limited surgical capacity. While the views of healthcare professionals on fair allocation have been given considerable attention, the views of patients have been overlooked. To address this imbalance, our study aimed to identify which ethical principles are most supported by patients regarding the fair allocation of surgical resources. DESIGN: A Q-methodology study was conducted. Participants ranked ordered 20 statements covering different viewpoints on fair allocation according to their point of view, followed by an interview. Principal component analysis followed by varimax rotation was used to identify subgroups who broadly agreed in terms of their rankings. SETTING: The setting of this study was in the Netherlands. PARTICIPANTS: 16 patient representatives were purposively sampled. RESULTS: Two perspectives were identified, both of which supported utilitarianism. In perspective 1, labelled as 'clinical needs and outcomes', resource allocation should aim to maximise the health gains based on individual patient characteristics. In perspective 2, labelled as 'population outcomes and contribution to society', allocation should maximise health gains as with perspective 1, but this should also consider societal gains. CONCLUSIONS: There was a broad agreement among patient representatives that utilitarianism should be the guiding ethical principle for fair allocation of scarce surgical resources. The insights gained from this study should be integrated into policymaking and prioritisation strategies in future healthcare crises.</p

Surgical prioritization based on decision model outcomes is not sensitive to differences between the health-related quality of life values estimates of physicians and citizens

Purpose: Decision models can be used to support allocation of scarce surgical resources. These models incorporate health-related quality of life (HRQoL) values that can be determined using physician panels. The predominant opinion is that one should use values obtained from citizens. We investigated whether physicians give different HRQoL values to citizens and evaluate whether such differences impact decision model outcomes. Methods: A two-round Delphi study was conducted. Citizens estimated HRQoL of pre- and post-operative health states for ten surgeries using a visual analogue scale. These values were compared using Bland–Altman analysis with HRQoL values previously obtained from physicians. Impact on decision model outcomes was evaluated by calculating the correlation between the rankings of surgeries established using the physicians’ and the citizens’ values.Results: A total of 71 citizens estimated HRQoL. Citizens’ values on the VAS scale were − 0.07 points (95% CI − 0.12 to − 0.01) lower than the physicians’ values. The correlation between the rankings of surgeries based on citizens’ and physicians’ values was 0.96 (p &lt; 0.001). Conclusion: Physicians put higher values on health states than citizens. However, these differences only result in switches between adjacent entries in the ranking. It would seem that HRQoL values obtained from physicians are adequate to inform decision models during crises.</p

Use of Patient-Reported Outcome Measures in the Surgical Treatment of Hidradenitis Suppurativa: A Systematic Review

BACKGROUND: Surgery is considered to be the best treatment for recurrent hidradenitis suppurativa (HS). Although it is necessary to assess the effect on health-related quality of life (HR-QoL), patient-reported outcome measures (PROMs) are scarce and heterogeneously used in the literature about the surgical treatment of HS. OBJECTIVE: The aim of this study was to provide a review of the complete literature for different PROMs used in the surgical treatment of HS and to assess their methodological qualities. METHODS: A systematic literature search of PubMed, Medline, Cochrane, CINAHL, and Embase with an assessment following the COnsensus-based standards for the Selection of health status Measurement INstrument criteria. RESULTS: The search identified 218 articles, with the inclusion of 6 studies for analysis. Identified PROMs were as follows: the Dermatology Life Quality Index (DLQI), the Derriford Appearance Scale-24 (DAS-24), and the Work Productivity and Activity Impairment (WPAI). These non-disease-specific PROMs seem to have poor results concerning development and content validation. CONCLUSION: The DLQI, WPAI, and DAS-24 are generic PROMs with poor methodological qualities for PROM development and content validation. Hidradenitis suppurativa-specific instruments are not used in available studies because they have been developed recently and, therefore, partially validated. More research is needed to further investigate methodological qualities of HS-specific instruments

Update on a Model to Minimize Population Health Loss in Times of Scarce Surgical Capacity During the COVID-19 Crisis and Beyond

Development and application of statistical models for medical scientific researc

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

Abstract 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

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

Abstract 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

Additional file 1 of Minimizing population health loss due to scarcity in OR capacity: validation of quality of life input

Additional file 1: Fig. S1. Calibrated visual analog scale based on the Global burden of disease study. Table S1. Description of input parameters. Table S2. Form for participants in the Delphi rounds in the developmental study

Additional file 3 of Minimizing population health loss due to scarcity in OR capacity: validation of quality of life input

Additional file 3: Fig. S1. The structure of the previously developed cohort state-transition model. Preop: preoperative state; Postop: postoperative state (6). Fig. S2. The model estimates for urgency based on the original quality of life estimates (upper panel) and the updated scores from both the original and the validation study (bottom panel). Fig. S3. The random effects of procedure on the standard deviation of the QoL estimates. These estimates are the random intercept values for procedure in a model with as independent variable the standard deviations of surgical procedures, also including hospital and pre- or postoperative as fixed effects (supplementary table 2). A random intercept above 0 indicates a higher than expected standard deviation, which we interpret as lower consensus between experts. A random intercept below 0 indicates a lower than expected standard deviation, which we interpret as higher consensus between experts. The overall standard deviation of the random effect was 0.005. Table S1. The estimates from the first mixed effects linear regression model. The dependent variable is the utility scores scored by the expert panel. Table S2. The estimates from the second mixed effects linear regression model. The dependent variable is the standard deviation of the utility scores per study center, pre- and postoperative state, and procedure. Table S3. The quality of life estimates and 95% CI derived from the original study and the validation study, stratified for preoperative and postoperative state, corresponding to figure 1 in the manuscript. Table S4. The difference in urgency of surgical procedures between the original and the updated quality of life estimates. Only the diseases which now include the new scores from the validation study are shown. This table corresponds to figure 4 in the manuscript