Modeling the Recovery of Elective Waiting Lists Following COVID-19: Scenario Projections for England

Transcatheter Aortic Valve Implantation Wait-Time Management: Derivation and Validation of the Canadian TAVI Triage Tool (CAN3T)

BACKGROUND

Transcatheter aortic valve implantation (TAVI) has seen indication expansion and thus exponential growth in demand over the past decade. In many jurisdictions, the growing demand has outpaced capacity, increasing wait times and preprocedural adverse events. In this study, we derived prediction models that estimate the risk of adverse events on the waitlist and developed a triage tool to identify patients who should be prioritized for TAVI.

METHODS AND RESULTS

We included adult patients in Ontario, Canada referred for TAVI and followed up until one of the following events first occurred: death, TAVI procedure, removal from waitlist, or end of the observation period. We used subdistribution hazards models to find significant predictors for each of the following outcomes: (1) all-cause death while on the waitlist; (2) all-cause hospitalization while on the waitlist; (3) receipt of urgent TAVI; and (4) a composite outcome. The median predicted risk at 12 weeks was chosen as a threshold for a maximum acceptable risk while on the waitlist and incorporated in the triage tool to recommend individualized wait times. Of 13 128 patients, 586 died while on the waitlist, and 4343 had at least 1 hospitalization. A total of 6854 TAVIs were completed, of which 1135 were urgent procedures. We were able to create parsimonious models for each outcome that included clinically relevant predictors.

CONCLUSIONS

The Canadian TAVI Triage Tool (CAN3T) is a triage tool to assist clinicians in the prioritization of patients who should have timely access to TAVI. We anticipate that the CAN3T will be a valuable tool as it may improve equity in access to care, reduce preventable adverse events, and improve system efficiency.

Factors associated with poorer outcomes for posterior lumbar decompression and or/or discectomy: an exploratory analysis of administrative data

PURPOSE

This study aimed to identify factors associated with poorer patient outcomes for lumbar decompression and/or discectomy (PLDD).

METHODS

We extracted data from the Hospital Episodes Statistics database for the 5 years from 1st April 2014 to 31st March 2019. Patients undergoing an elective one- or two-level PLDD aged ≥ 17 years and without evidence of revision surgery during the index stay were included. The primary patient outcome measure was readmission within 90 days post-discharge.

RESULTS

Data for 93,813 PLDDs across 111 hospital trusts were analysed. For the primary outcome, greater age [< 40 years vs 70-79 years odds ratio (OR) 1.28 (95% confidence interval (CI) 1.14 to 1.42), < 40 years vs ≥ 80 years OR 2.01 (95% CI 1.76-2.30)], female sex [OR 1.09 (95% CI 1.02-1.16)], surgery over two spinal levels [OR 1.16 (95% CI 1.06-1.26)] and the comorbidities chronic pulmonary disease, connective tissue disease, liver disease, diabetes, hemi/paraplegia, renal disease and cancer were all associated with emergency readmission within 90 days. Other outcomes studied had a similar pattern of associations.

CONCLUSIONS

A high-throughput PLDD pathway will not be suitable for all patients. Extra care should be taken for patients aged ≥ 70 years, females, patients undergoing surgery over two spinal levels and those with specific comorbidities or generalised frailty.

The impact of the COVID-19 pandemic on delayed care of cardiovascular diseases in Europe: a systematic review

AIMS

Cardiovascular diseases (CVD) are the leading cause of death worldwide. The coronavirus disease 2019 (COVID-19) pandemic has disrupted healthcare systems, causing delays in essential medical services, and potentially impacting CVD treatment. This study aims to estimate the impact of the pandemic on delayed CVD care in Europe by providing a systematic overview of the available evidence.

METHODS AND RESULTS

PubMed, Embase, and Web of Science were searched until mid-September 2022 for studies focused on the impact of delayed CVD care due to the pandemic in Europe among adult patients. Outcomes were changes in hospital admissions, mortality rates, delays in seeking medical help after symptom onset, delays in treatment initiation, and change in the number of treatment procedures. We included 132 studies, of which all were observational retrospective. Results were presented in five disease groups: ischaemic heart diseases (IHD), cerebrovascular accidents (CVA), cardiac arrests (CA), heart failures (HF), and others, including broader CVD groups. There were significant decreases in hospital admissions for IHD, CVA, HF and urgent and elective cardiac procedures, and significant increases for CA. Mortality rates were higher for IHD and CVA.

CONCLUSION

The pandemic led to reduced acute CVD hospital admissions and increased mortality rates. Delays in seeking medical help were observed, while urgent and elective cardiac procedures decreased. Adequate resource allocation, clear guidelines on how to handle care during health crises, reduced delays, and healthy lifestyle promotion should be implemented. The long-term impact of pandemics on delayed CVD care, and the health-economic impact of COVID-19 should be further evaluated.

Machine learning models to predict surgical case duration compared to current industry standards: scoping review

BACKGROUND

Surgical waiting lists have risen dramatically across the UK as a result of the COVID-19 pandemic. The effective use of operating theatres by optimal scheduling could help mitigate this, but this requires accurate case duration predictions. Current standards for predicting the duration of surgery are inaccurate. Artificial intelligence (AI) offers the potential for greater accuracy in predicting surgical case duration. This study aimed to investigate whether there is evidence to support that AI is more accurate than current industry standards at predicting surgical case duration, with a secondary aim of analysing whether the implementation of the models used produced efficiency savings.

METHOD

PubMed, Embase, and MEDLINE libraries were searched through to July 2023 to identify appropriate articles. PRISMA extension for scoping reviews and the Arksey and O'Malley framework were followed. Study quality was assessed using a modified version of the reporting guidelines for surgical AI papers by Farrow et al. Algorithm performance was reported using evaluation metrics.

RESULTS

The search identified 2593 articles: 14 were suitable for inclusion and 13 reported on the accuracy of AI algorithms against industry standards, with seven demonstrating a statistically significant improvement in prediction accuracy (P < 0.05). The larger studies demonstrated the superiority of neural networks over other machine learning techniques. Efficiency savings were identified in a RCT. Significant methodological limitations were identified across most studies.

CONCLUSION

The studies suggest that machine learning and deep learning models are more accurate at predicting the duration of surgery; however, further research is required to determine the best way to implement this technology.

Retailoring training programmes in anaesthesia and intensive care after the coronavirus disease 2019 outbreak

PURPOSE OF REVIEW

In this review, we want to collect all the adaptations that anaesthesiology training has faced because of the health crisis and social distancing measures resulting from coronavirus 2019 disease (COVID-19). We reviewed new teaching tools launched during the COVID-19 outbreak worldwide and particularly those implemented by the European Society of Anaesthesiology and Intensive Care (ESAIC) and the European Association of Cardiothoracic Anaesthesiology and Intensive Care (EACTAIC).

RECENT FINDINGS

Globally, COVID-19 has interrupted health services and all aspects of training programmes. These unprecedented changes have led to teaching and trainee support innovation tools, focusing on online learning and simulation programmes. Airway management, critical care and regional anaesthesia, have been enhanced during the pandemic, while there were major obstacles in paediatrics, obstetrics and pain medicine.

SUMMARY

The COVID-19 pandemic has altered profoundly the functioning of health systems worldwide. Anaesthesiologists and trainees have fought on the front lines of the battle against COVID-19. As a result, training in anaesthesiology during the last 2 years has focused on managing patients in intensive care. New training programmes have been designed to continue teaching residents of this speciality, focusing on e-learning and advanced simulation. It is necessary to present a review describing the impact that this turbulent period has had on the different subsections of anaesthesiology and to review the innovative measures that have been implemented to address these possible deficits in education and training.

No time to waste; the impact of the COVID-19 pandemic on hip, knee, and shoulder arthroplasty surgeries in the Netherlands and Denmark

AIMS

This study aimed to investigate the estimated change in primary and revision arthroplasty rate in the Netherlands and Denmark for hips, knees, and shoulders during the COVID-19 pandemic in 2020 (COVID-period). Additional points of focus included the comparison of patient characteristics and hospital type (2019 vs COVID-period), and the estimated loss of quality-adjusted life years (QALYs) and impact on waiting lists.

METHODS

All hip, knee, and shoulder arthroplasties (2014 to 2020) from the Dutch Arthroplasty Register, and hip and knee arthroplasties from the Danish Hip and Knee Arthroplasty Registries, were included. The expected number of arthroplasties per month in 2020 was estimated using Poisson regression, taking into account changes in age and sex distribution of the general Dutch/Danish population over time, calculating observed/expected (O/E) ratios. Country-specific proportions of patient characteristics and hospital type were calculated per indication category (osteoarthritis/other elective/acute). Waiting list outcomes including QALYs were estimated by modelling virtual waiting lists including 0%, 5% and 10% extra capacity.

RESULTS

During COVID-period, fewer arthroplasties were performed than expected (Netherlands: 20%; Denmark: 5%), with the lowest O/E in April. In the Netherlands, more acute indications were prioritized, resulting in more American Society of Anesthesiologists grade III to IV patients receiving surgery. In both countries, no other patient prioritization was present. Relatively more arthroplasties were performed in private hospitals. There were no clinically relevant differences in revision arthroplasties between pre-COVID and COVID-period. Estimated total health loss depending on extra capacity ranged from: 19,800 to 29,400 QALYs (Netherlands): 1,700 to 2,400 QALYs (Denmark). With no extra capacity it will take > 30 years to deplete the waiting lists.

CONCLUSION

The COVID-19 pandemic had an enormous negative effect on arthroplasty rates, but more in the Netherlands than Denmark. In the Netherlands, hip and shoulder patients with acute indications were prioritized. Private hospitals filled in part of the capacity gap. QALY loss due to postponed arthroplasty surgeries is considerable.Cite this article: Bone Jt Open 2022;3(12):977-990.