Resuming elective surgery after COVID-19: A simulation modelling framework for guiding the phased opening of operating rooms

A Robust ORMS Framework for Taiwanese Healthcare: Taguchi's Dynamic Method in Action

The study focused on the design of an ORMS in a medical center in central Taiwan, which also functions as a teaching hospital. Background/Objectives: The research objectives were to design an ORMS simulation system based on the status quo of the operating room planning and scheduling in the medical center, obtain the optimal parameter setting in the ORMS, and find improvement strategies according to the sensitivity analysis based on the optimal parameter setting for total performance. Methods: Taguchi's dynamic method was adopted to design the ORMS under human and material resource constraints. The scope of the study was internal medicine patients of the ORMS. A neural network was used to construct a relationship between parameters and performances. A genetic algorithm was used to obtain the optimal parameter setting for optimal performance. Results: This study successfully established a robust operating room management system (ORMS) to help hospital manager to plan and schedule operating rooms and take the ORMS into account to meet patient needs. Decision-makers can use the insights from the sensitivity analysis to refine their strategies effectively. The sensitivity analysis showed that the impact power (the percentage change in d) of the "number of circulating nurses (-0.15 to -1.25; -0.25 to -1.85)" factor was less than (<) that of the "number of holding nurses (-0.85 to -2.04; -0.91 to -2.07)" factor < that of the "number of preoperative beds (-2.57 to -4.53; -2.23 to -4.10)" factor < that of the "number of anesthetists (-3.13 to -7.50)" factor. Conclusions: In the optimal parameter setting obtained, the number of holding nurses was 18, the number of circulating nurses was 20, the number of anesthetists was 15, and the number of preoperative beds was 12. The optimal performance was 0.91.

The Impact of COVID-19 Pandemic on the Diagnosis, Treatment, and Outcomes of Colorectal Cancer in Singapore

Background and Objectives: During the COVID-19 pandemic, many countries implemented lockdowns and social distancing measures, which may delay the early diagnosis of colorectal cancer (CRC). This study aims to review the impact of the pandemic on the diagnosis and treatment outcomes of CRC. Materials and Methods: Patients who underwent colonoscopy or surgery for CRC were included. The study was divided into the pre-COVID-19 (January 2019-January 2020), early COVID-19 (February-May 2020), recovery (June-December 2020), and heightened alert (January-December 2021) periods. Cox regression was used to model the waiting time to colonoscopy. Multivariable logistic regression identified associations between time periods and incidence of CRC diagnosed. The characteristics and outcomes of the surgical procedures that were performed were compared across the time periods. Results: A total of 18,662 colonoscopies and 1462 surgical procedures were performed in the study period. Compared to the pre-COVID-19 period, there was a longer time to colonoscopy during the recovery (HR: 0.91; 95% CI: 0.87, 0.94) and heightened alert periods (HR: 0.88; 95% CI 0.85, 0.91). The early COVID-19 (OR: 1.36; 95% CI: 1.04, 1.77) and recovery (OR: 1.20; 95% CI: 1.01, 1.43) periods were associated with higher odds of diagnosing CRC. Compared to the pre-COVID-19 period, there was a higher proportion of ASA 4 patients (4.3% vs. 1.3%; p < 0.001) and stage 4 CRC patients (22.2% vs. 16.9%; p = 0.001) that required surgery during the heightened alert period. Similarly, there was a higher proportion of emergency surgeries (22% vs. 13.3%; p = 0.002); diverting stomas (13.5% vs. 10.5%; p = 0.005), and Hartmann's procedures (4.4% vs. 0.4%; p = 0.001) performed during the heightened alert period. Conclusions: The pandemic was associated with a higher proportion of metastatic CRC patients requiring surgery. Healthcare policies should facilitate early cancer screening, diagnosis, and treatment to reduce cancer-related morbidity for future pandemics.

A systematic literature review on public health and healthcare resources for pandemic preparedness planning

BACKGROUND

Generating insights into resource demands during outbreaks is an important aspect of pandemic preparedness. The EU PANDEM-2 project used resource modelling to explore the demand profile for key resources during pandemic scenarios. This review aimed to identify public health and healthcare resources needed to respond to pandemic threats and the ranges of parameter values on the use of these resources for pandemic influenza (including the novel influenza A(H1N1)pdm09 pandemic) and the COVID-19 pandemic, to support modelling activities.

METHODS

We conducted a systematic literature review and searched Embase and Medline databases (1995 - June 2023) for articles that included a model, scenario, or simulation of pandemic resources and/or describe resource parameters, for example personal protective equipment (PPE) usage, length of stay (LoS) in intensive care unit (ICU), or vaccine efficacy. Papers with data on resource parameters from all countries were included.

RESULTS

We identified 2754 articles of which 147 were included in the final review. Forty-six different resource parameters with values related to non-ICU beds (n = 43 articles), ICU beds (n = 57), mechanical ventilation (n = 39), healthcare workers (n = 12), pharmaceuticals (n = 21), PPE (n = 8), vaccines (n = 26), and testing and tracing (n = 19). Differences between resource types related to pandemic influenza and COVID-19 were observed, for example on mechanical ventilation (mostly for COVID-19) and testing & tracing (all for COVID-19).

CONCLUSION

This review provides an overview of public health and healthcare resources with associated parameters in the context of pandemic influenza and the COVID-19 pandemic. Providing insight into the ranges of plausible parameter values on the use of public health and healthcare resources improves the accuracy of results of modelling different scenarios, and thus decision-making by policy makers and hospital planners. This review also highlights a scarcity of published data on important public health resources.

Forecasting disease trajectories in critical illness: comparison of probabilistic dynamic systems to static models to predict patient status in the intensive care unit

OBJECTIVE

Conventional prediction models fail to integrate the constantly evolving nature of critical illness. Alternative modelling approaches to study dynamic changes in critical illness progression are needed. We compare static risk prediction models to dynamic probabilistic models in early critical illness.

DESIGN

We developed models to simulate disease trajectories of critically ill COVID-19 patients across different disease states. Eighty per cent of cases were randomly assigned to a training and 20% of the cases were used as a validation cohort. Conventional risk prediction models were developed to analyse different disease states for critically ill patients for the first 7 days of intensive care unit (ICU) stay. Daily disease state transitions were modelled using a series of multivariable, multinomial logistic regression models. A probabilistic dynamic systems modelling approach was used to predict disease trajectory over the first 7 days of an ICU admission. Forecast accuracy was assessed and simulated patient clinical trajectories were developed through our algorithm.

SETTING AND PARTICIPANTS

We retrospectively studied patients admitted to a Cleveland Clinic Healthcare System in Ohio, for the treatment of COVID-19 from March 2020 to December 2022.

RESULTS

5241 patients were included in the analysis. For ICU days 2-7, the static (conventional) modelling approach, the accuracy of the models steadily decreased as a function of time, with area under the curve (AUC) for each health state below 0.8. But the dynamic forecasting approach improved its ability to predict as a function of time. AUC for the dynamic forecasting approach were all above 0.90 for ICU days 4-7 for all states.

CONCLUSION

We demonstrated that modelling critical care outcomes as a dynamic system improved the forecasting accuracy of the disease state. Our model accurately identified different disease conditions and trajectories, with a <10% misclassification rate over the first week of critical illness.

The impact of the COVID-19 pandemic on paediatric surgical volumes in Africa: A retrospective observational study

BACKGROUND

The aim of this study is to investigate the impact that COVID-19 had on the pattern and trend of surgical volumes, urgency and reason for surgery during the first 6 months of the pandemic in sub-Saharan Africa.

METHODS

This retrospective facility-based study involved collection of paediatric operation data from operating theatre records across 5 hospitals from 3 countries: Zimbabwe, Zambia and Nigeria over the first half of 2019 and 2020 for comparison. Data concerning diagnosis, procedure, anaesthesia, grade, speciality, NCEPOD classification and indication was collected. The respective dates of enactment of cancellation policies in each country were used to compare changes in weekly median surgical case volume before cancellation using the Wilcoxon Sign-Rank Test.

RESULTS

A total of 1821 procedures were recorded over the study period. Surgical volumes experienced a precipitous drop overall from a median of 100 cases/week to 50 cases/week coinciding with cancellation of surgical electives. Median accumulated weekly procedures before COVID-related cancellation were significantly different from those after cancellation (p = 0.027). Emergency surgery fell by 23.3% while electives fell by 78,9% (P = 0.042). The most common primary indication for surgery was injury which experienced a 30.5% drop in number of procedures, only exceeded by congenital surgery which dropped 34.7%.

CONCLUSIONS

The effects of surgical cancellations during the covid-19 pandemic are particularly devastating in African countries where the unmet need and surgical caseload are high. Continued cancellations that have since occurred will cause similar drops in surgical case volume that these health systems may not have the resilience to recover from.

LEVEL OF EVIDENCE

Level II.

Impact of ICU strain on outcomes

PURPOSE OF REVIEW

Acute surge events result in health capacity strain, which can result in deviations from normal care, activation of contingencies and decisions related to resource allocation. This review discusses the impact of health capacity strain on patient centered outcomes.

RECENT FINDINGS

This manuscript discusses the lack of validated metrics for ICU strain capacity and a need for understanding the complex interrelationships of strain with patient outcomes. Recent work through the coronavirus disease 2019 pandemic has shown that acute surge events are associated with significant increase in hospital mortality. Though causal data on the differential impact of surge actions and resource availability on patient outcomes remains limited the overall signal consistently highlights the link between ICU strain and critical care outcomes in both normal and surge conditions.

SUMMARY

An understanding of ICU strain is fundamental to the appropriate clinical care for critically ill patients. Accounting for stain on outcomes in critically ill patients allows for minimization of variation in care and an ability of a given healthcare system to provide equitable, and quality care even in surge scenarios.