The Use of Decision Modelling to Inform Timely Policy Decisions on Cardiac Resource Capacity During the COVID-19 Pandemic

Decision analysis in cardiac surgery: a scoping review and methodological primer

OBJECTIVES

Randomized controlled trials are the gold standard for evidence generation in medicine but are limited by their real-world generalizability, resource needs, shorter follow-up durations and inability to be conducted for all clinical questions. Decision analysis (DA) models may simulate trials and observational studies by using existing data and evidence- and expert-informed assumptions and extend analyses over longer time horizons, different study populations and specific scenarios, helping to translate population outcomes to patient-specific clinical and economic outcomes. Here, we present a scoping review and methodological primer on DA for cardiac surgery research.

METHODS

A scoping review was performed using the PubMed/MEDLINE, EMBASE and Web of Science databases for cardiac surgery DA studies published until December 2021. Articles were summarized descriptively to quantify trends and ascertain methodological consistency.

RESULTS

A total of 184 articles were identified, among which Markov models (N = 92, 50.0%) were the most commonly used models. The most common outcomes were costs (N = 107, 58.2%), quality-adjusted life-years (N = 96, 52.2%) and incremental cost-effectiveness ratios (N = 89, 48.4%). Most (N = 165, 89.7%) articles applied sensitivity analyses, most frequently in the form of deterministic sensitivity analyses (N = 128, 69.6%). Reporting of guidelines to inform the model development and/or reporting was present in 22.3% of articles.

CONCLUSION

DA methods are increasing but remain limited and highly variable in cardiac surgery. A methodological primer is presented and may provide researchers with the foundation to start with or improve DA, as well as provide readers and reviewers with the fundamental concepts to review DA studies.

Corona virus 19(COVID-19) Conceptual Modeling a Single-Center Prospective: Cross-Sectional Study

BACKGROUND

Conceptual models are abstract representations of the real world. They are used to refine medical and non-medical healthcare scopes of service. During the covid 19 pandemic numerous analytic predictive models were generated aiming to evaluate the impact of policies implemented on the mitigating of COVID-19 pandemic, the psycho-social factors that might govern general population adherence to these policies, identify factors that might affect COVID-19 vaccine uptake and allocation. The outcomes of these analytic models helped set priorities when vaccines were available, and predicted readiness to resume non-COVID-19 healthcare services.

OBJECTIVE

The objective of our research was to implement a descriptive-analytical conceptual model that analyzes the data of all COVID-19-positive cases admitted to our hospital 1st of March to the 31st of May 2020, the initial wave of the pandemic, the time interval during which local policies and clinical guidelines were constantly updated to mitigate the local effects of SARS-CoV-2, minimize mortality, ICU admission, and ensure the safety of healthcare providers. The primary outcome of interest was to identify factors that might affect mortality and ICU admission, and the impact of the policy implemented on SARS-CoV-2 positivity among healthcare providers. The secondary outcome of interest was to evaluate the sensitivity of the SARS-coV-2 visual score implemented by the Saudi MOH for COVID-19- risk assessment as well as CURB-65 scores in predicting ICU admission or mortality among the study population.

METHODS

This was a cross-sectional study. The relevant attributes were constructed based on research findings from the first wave of the pandemic and were electronically retrieved from the hospital database. Analysis of the conceptual model was based on the International Society for Pharmacoeconomics and Outcomes Research guidelines and the Society for Medical Decision-Making.

RESULTS

275 were SARS-CoV-2- positive within the study design interval. The conceptualization model revealed a low-risk population based on the following attributes: the mean age was 42 ± 19.2 years, 19% of the study population were senior adults ≥ 60 years, 80% had a CURB-65 score < 4, 53% had no comorbidities, 5% had extreme obesity, and 2% had a significant hematological abnormality. The overall rate of ICU admission for the study population was 5%, with a 1.5% overall mortality. The multivariate correlation analysis revealed that high selectivity was adopted, wherein patients with complex medical problems were not sent to MOH isolation facilities. Furthermore, 5% of healthcare providers were SARS-CoV-2-positive, and none were healthcare providers allocated to the COVID-19 screening areas indicating the effectiveness of the policy implemented to ensure the safety of healthcare providers.

CONCLUSIONS

Based on the conceptual model outcome, the selectivity applied to retaining high-risk populations within the hospital might have contributed to the low mortality rate observed without increasing the risk to attending healthcare providers.

CLINICALTRIAL

Not applicable.

Patient Throughput Initiatives in Ambulatory Care Organizations during the COVID-19 Pandemic: A Systematic Review

BACKGROUND AND OBJECTIVES

Ambulatory (outpatient) health care organizations continue to respond to the COVID-19 global pandemic using an array of initiatives to provide a continuity of care for both COVID-19 and non-COVID-19 patients. The purpose of this study is to systematically identify the facilitators and barriers experienced by outpatient health care organizations in an effort to maintain effective and efficient patient throughput during the pandemic.

MATERIALS AND METHODS

This study systematically reviewed articles focused on initiatives taken by ambulatory care organizations to maintain optimal outpatient throughput levels while balancing pandemic precautions, published during 2020.

RESULTS

Among the 30 articles that met the inclusion criteria, three initiatives healthcare organizations have taken to maintain throughput were identified: the use (and enhanced use) of telehealth, protocol development, and health care provider training. The research team also identified three barriers to patient throughput: lack of telehealth, lack of resources, and overall lack of knowledge.

CONCLUSIONS

To maintain patient throughput during the COVID-19 pandemic, healthcare organizations need to develop strategies such as the use of virtual consultation and follow-up, new guidelines to move patients along the care delivery value-chain, and ongoing training of providers. Additionally, the availability of required technology for telehealth, availability of resources, and adequate knowledge are vital for continuous patient throughput to ensure continuity of care during a pandemic.

The Impact of the COVID-19 Pandemic on Cardiac Procedure Wait List Mortality in Ontario, Canada

Background

The novel SARS-CoV-2 (COVID-19) pandemic has dramatically altered the delivery of healthcare services, resulting in significant referral pattern changes, delayed presentations, and procedural delays. Our objective was to determine the effect of the COVID-19 pandemic on all-cause mortality in patients awaiting commonly performed cardiac procedures.

Methods

Clinical and administrative data sets were linked to identify all adults referred for: (1) percutaneous coronary intervention; (2) coronary artery bypass grafting; (3) valve surgery; and (4) transcatheter aortic valve implantation, from January 2014 to September 2020 in Ontario, Canada. Piece-wise regression models were used to determine the effect of the COVID-19 pandemic on referrals and procedural volume. Multivariable Cox proportional hazards models were used to determine the effect of the pandemic on waitlist mortality for the 4 procedures.

Results

We included 584,341 patients who were first-time referrals for 1 of the 4 procedures, of whom 37,718 (6.4%) were referred during the pandemic. The pandemic period was associated with a significant decline in the number of referrals and procedures completed compared with the prepandemic period. Referral during the pandemic period was a significant predictor for increased all-cause mortality for the percutaneous coronary intervention (hazard ratio, 1.83; 95% confidence interval, 1.47-2.27) and coronary artery bypass grafting (hazard ratio, 1.96; 95% confidence interval, 1.28-3.01), but not for surgical valve or transcatheter aortic valve implantation referrals. Procedural wait times were shorter during the pandemic period compared with the prepandemic period.

Conclusions

There was a significant decrease in referrals and procedures completed for cardiac procedures during the pandemic period. Referral during the pandemic was associated with increased all-cause mortality while awaiting coronary revascularization.

The ophthalmic surgical backlog associated with the COVID-19 pandemic: a population-based and microsimulation modelling study

BACKGROUND

Jurisdictions worldwide ramped down ophthalmic surgeries to mitigate the effects of COVID-19, creating a global surgical backlog. We sought to predict the long-term impact of COVID-19 on the timely delivery of non-emergent ophthalmology sub-specialty surgical care in Ontario.

METHODS

This is a microsimulation modelling study. We used provincial population-based administrative data from the Wait Time Information System database in Ontario for January 2019 to May 2021 and facility-level data for March 2018 to May 2021 to estimate the backlog size and wait times associated with the COVID-19 pandemic. For the postpandemic recovery phase, we estimated the resources required to clear the backlog of patients accumulated on the wait-list during the pandemic. Outcomes were accrued over a time horizon of 3 years.

RESULTS

A total of 56 923 patients were on the wait-list in the province of Ontario awaiting non-emergency ophthalmic surgery as of Mar. 15, 2020. The number of non-emergency surgeries performed in the province decreased by 97% in May 2020 and by 80% in May 2021 compared with the same months in 2019. By 2 years and 3 years since the start of the pandemic, the overall estimated number of patients awaiting surgery grew by 129% and 150%, respectively. The estimated mean wait time for patients for all subspecialty surgeries increased to 282 (standard deviation [SD] 91) days in March 2023 compared with 94 (SD 97) days in 2019. The provincial monthly additional resources required to clear the backlog by March 2023 was estimated to be a 34% escalation from the prepandemic volumes (4626 additional surgeries).

INTERPRETATION

The estimates from this microsimulation modelling study suggest that the magnitude of the ophthalmic surgical backlog from the COVID-19 pandemic has important implications for the recovery phase. This model can be adapted to other jurisdictions to assist with recovery planning for vision-saving surgeries.

COVID-19 Vaccination Prioritization on the Basis of Cardiovascular Risk Factors and Number Needed to Vaccinate to Prevent Death

The supply limitations of COVID-19 vaccines have led to the need to prioritize vaccine distribution. Obesity, diabetes, and hypertension have been associated with an increased risk of severe COVID-19 infection. Approximately half as many individuals with a cardiovascular risk factor need to be vaccinated against COVID-19 to prevent related death compared with individuals without a risk factor. Adults with body mass index ≥ 30, diabetes, or hypertension should be of a similar priority for COVID-19 vaccination to adults 10 years older with a body mass index of 20 to < 30, no diabetes, and no hypertension.

Reprise du retard en chirurgie causé par la COVID-19 en Ontario : étude de modélisation de séries chronologiques

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Clearing the surgical backlog caused by COVID-19 in Ontario: a time series modelling study

BACKGROUND

To mitigate the effects of coronavirus disease 2019 (COVID-19), jurisdictions worldwide ramped down nonemergent surgeries, creating a global surgical backlog. We sought to estimate the size of the nonemergent surgical backlog during COVID-19 in Ontario, Canada, and the time and resources required to clear the backlog.

METHODS

We used 6 Ontario or Canadian population administrative sources to obtain data covering part or all of the period between Jan. 1, 2017, and June 13, 2020, on historical volumes and operating room throughput distributions by surgery type and region, and lengths of stay in ward and intensive care unit (ICU) beds. We used time series forecasting, queuing models and probabilistic sensitivity analysis to estimate the size of the backlog and clearance time for a +10% (+1 day per week at 50% capacity) surge scenario.

RESULTS

Between Mar. 15 and June 13, 2020, the estimated backlog in Ontario was 148 364 surgeries (95% prediction interval 124 508-174 589), an average weekly increase of 11 413 surgeries. Estimated backlog clearance time is 84 weeks (95% confidence interval [CI] 46-145), with an estimated weekly throughput of 717 patients (95% CI 326-1367) requiring 719 operating room hours (95% CI 431-1038), 265 ward beds (95% CI 87-678) and 9 ICU beds (95% CI 4-20) per week.

INTERPRETATION

The magnitude of the surgical backlog from COVID-19 raises serious implications for the recovery phase in Ontario. Our framework for modelling surgical backlog recovery can be adapted to other jurisdictions, using local data to assist with planning.

Surgical management of thoracic aortic emergency with pre- and postoperative COVID-19 disease

The nuance of operative decision making for those in need of emergent operation during coronavirus disease 2019 (COVID‐19) pandemic is increasingly complex in the absence of robust data or guidelines. We present two cases of thoracic aortic emergencies with COVID‐19 disease who survived high‐risk operations to highlight the potential for successful outcomes even in situations compounding patient disease, morbid operation, and the added risk associated with virulent disease in the pandemic time.