Development and application of simulation modelling for orthopaedic elective resource planning in England.

OBJECTIVES: This study aimed to develop a simulation model to support orthopaedic elective capacity planning. METHODS: An open-source, generalisable discrete-event simulation was developed, including a web-based application. The model used anonymised patient records between 2016 and 2019 of elective orthopaedic procedures from a National Health Service (NHS) Trust in England. In this paper, it is used to investigate scenarios including resourcing (beds and theatres) and productivity (lengths of stay, delayed discharges and theatre activity) to support planning for meeting new NHS targets aimed at reducing elective orthopaedic surgical backlogs in a proposed ring-fenced orthopaedic surgical facility. The simulation is interactive and intended for use by health service planners and clinicians. RESULTS: A higher number of beds (65-70) than the proposed number (40 beds) will be required if lengths of stay and delayed discharge rates remain unchanged. Reducing lengths of stay in line with national benchmarks reduces bed utilisation to an estimated 60%, allowing for additional theatre activity such as weekend working. Further, reducing the proportion of patients with a delayed discharge by 75% reduces bed utilisation to below 40%, even with weekend working. A range of other scenarios can also be investigated directly by NHS planners using the interactive web app. CONCLUSIONS: The simulation model is intended to support capacity planning of orthopaedic elective services by identifying a balance of capacity across theatres and beds and predicting the impact of productivity measures on capacity requirements. It is applicable beyond the study site and can be adapted for other specialties.

The impact of restricted provision of publicly funded elective hip and knee joints replacement during the COVID-19 pandemic in England.

AIMS: Elective hip and knee replacement operations were suspended in April 2020 due to the COVID-19 pandemic. The impact of this suspension and continued disruption to the delivery of joint replacement surgery is still emerging. We describe the impact of the pandemic on the provision of publicly funded elective hip and knee replacement surgery at one teaching hospital in England and on which patients had surgery. METHODS: We included all elective primary and revision hip and knee replacements performed at one hospital between January 2016 and June 2021. Using data for the years 2016-2019, we estimated the expected number of operations and beds occupied per month in January 2020 to June 2021 using time series linear models (adjusting for season and trend). We compared the predictions with the real data for January 2020 to June 2021 to assess the impact of the pandemic on the provision of elective hip and knee replacements. We compared the length of stay and characteristics (age, gender, number of comorbidities, index of multiple deprivation) of patients who had surgery before the pandemic with those who had surgery during the pandemic. RESULTS: We included 6,964 elective primary and revision hip and knee replacements between January 2016 and June 2021. Between January 2020 and June 2021 primary hip replacement volume was 59% of predicted, and 47% for primary knee replacements. Revision hip replacement volume was 77% of predicted, and 42% for revision knee replacement. Median length of stay was one day shorter for primary (4 vs 3 days) and revision (6 vs 5 days) operations during the pandemic compared with before. Patients operated on during the pandemic were younger and had slightly more comorbidities than those operated on before the pandemic. CONCLUSIONS: The restricted provision of elective hip and knee replacements during the COVID-19 pandemic changed the patient casemix, but did not introduce new inequalities in access to these operations. Patients were younger, had more comorbidities, and stayed in hospital for less time than those treated before the pandemic. Approximately half the number of operations were performed during the pandemic than would have been expected and the effect was greatest for revision knee replacements.

Impact of pausing elective hip and knee replacement surgery during 2017 on subsequent service provision at a major NHS Trust: a descriptive observational study using interrupted time series.

OBJECTIVES: To explore the impact of a temporary cancellation of elective surgery in winter 2017 on trends in primary hip and knee replacement at a major National Health Service (NHS) Trust, and whether lessons can be learnt about efficient surgery provision. DESIGN AND SETTING: Observational descriptive study using interrupted time series analysis of hospital records to explore trends in primary hip and knee replacement surgery at a major NHS Trust, as well as patient characteristics, 2016-2019. INTERVENTION: A temporary cancellation of elective services for 2 months in winter 2017. OUTCOMES: NHS-funded hospital admissions for primary hip or knee replacement, length of stay and bed occupancy. Additionally, we explored the ratio of elective to emergency admissions at the Trust as a measure of elective capacity, and the ratio of public to private provision of NHS-funded hip and knee surgery. RESULTS: After winter 2017, there was a sustained reduction in the number of knee replacements, a decrease in the proportion of most deprived people having knee replacements and an increase in average age for knee replacement and comorbidity for both types of surgery. The ratio of public to private provision dropped after winter 2017, and elective capacity generally has reduced over time. There was clear seasonality in provision of elective surgery, with less complex patients admitted during winter. CONCLUSIONS: Declining elective capacity and seasonality has a marked effect on the provision of joint replacement, despite efficiency improvements in hospital treatment. The Trust has outsourced less complex patients to independent providers, and/or treated them during winter when capacity is most limited. There is a need to explore whether these are strategies that could be used explicitly to maximise the use of limited elective capacity, provide benefit to patients and value for money for taxpayers.

Identification of risk factors associated with prolonged hospital stay following primary knee replacement surgery: a retrospective, longitudinal observational study.

OBJECTIVES: To identify risk factors associated with prolonged length of hospital stay and staying in hospital longer than medically necessary following primary knee replacement surgery. DESIGN: Retrospective, longitudinal observational study. SETTING: Elective knee replacement surgeries between 2016 and 2019 were identified using routinely collected data from an NHS Trust in England. PARTICIPANTS: There were 2295 knee replacement patients with complete data included in analysis. The mean age was 68 (SD 11) and 60% were female. OUTCOME MEASURES: We assessed a binary length of stay outcome (>7 days), a continuous length of stay outcome (≤30 days) and a binary measure of whether patients remained in hospital when they were medically fit for discharge. RESULTS: The mean length of stay was 5.0 days (SD 3.9), 15.4% of patients were in hospital for >7 days and 7.1% remained in hospital when they were medically fit for discharge. Longer length of stay was associated with older age (b=0.08, 95% CI 0.07 to 0.09), female sex (b=0.36, 95% CI 0.06 to 0.67), high deprivation (b=0.98, 95% CI 0.47 to 1.48) and more comorbidities (b=2.48, 95% CI 0.15 to 4.81). Remaining in hospital beyond being medically fit for discharge was associated with older age (OR=1.07, 95% CI 1.05 to 1.09), female sex (OR=1.71, 95% CI 1.19 to 2.47) and high deprivation (OR=2.27, 95% CI 1.27 to 4.06). CONCLUSIONS: The regression models could be used to identify which patients are likely to occupy hospital beds for longer. This could be helpful in scheduling operations to aid hospital efficiency by planning these patients' operations for when the hospital is less busy.

Can we accurately forecast non-elective bed occupancy and admissions in the NHS? A time-series MSARIMA analysis of longitudinal data from an NHS Trust.

OBJECTIVES: The main objective of the study was to develop more accurate and precise short-term forecasting models for admissions and bed occupancy for an NHS Trust located in Bristol, England. Subforecasts for the medical and surgical specialties, and for different lengths of stay were realised DESIGN: Autoregressive integrated moving average models were specified on a training dataset of daily count data, then tested on a 6-week forecast horizon. Explanatory variables were included in the models: day of the week, holiday days, lagged temperature and precipitation. SETTING: A secondary care hospital in an NHS Trust in South West England. PARTICIPANTS: Hospital admissions between September 2016 and March 2020, comprising 1291 days. PRIMARY AND SECONDARY OUTCOME MEASURES: The accuracy of the forecasts was assessed through standard measures, as well as compared with the actual data using accuracy thresholds of 10% and 20% of the mean number of admissions or occupied beds. RESULTS: The overall Autoregressive Integrated Moving Average (ARIMA) admissions forecast was compared with the Trust's forecast, and found to be more accurate, namely, being closer to the actual value 95.6% of the time. Furthermore, it was more precise than the Trust's. The subforecasts, as well as those for bed occupancy, tended to be less accurate compared with the overall forecasts. All of the explanatory variables improved the forecasts. CONCLUSIONS: ARIMA models can forecast non-elective admissions in an NHS Trust accurately on a 6-week horizon, which is an improvement on the current predictive modelling in the Trust. These models can be readily applied to other contexts, improving patient flow.

Preclinical evaluation of cellular immune responses elicited by a polyvalent DNA prime/protein boost HIV-1 vaccine.

While DNA vaccines have been shown to prime cellular immune responses, levels are often low in nonhuman primates or humans. Hence, efforts have been directed toward boosting responses by combining DNA with different vaccination modalities. To this end, a polyvalent DNA prime/protein boost vaccine, consisting of codon optimized HIV-1 env (A, B, C, E) and gag (C) and homologous gp120 proteins in QS-21, was evaluated in rhesus macaques and BALB/c mice. Humoral and cellular responses, detected following DNA immunization, were increased following protein boost in macaques and mice. In dissecting cellular immune responses in mice, protein-enhanced responses were found to be mediated by CD4+ and CD8+ T cells with a Th1 cytokine bias. Our study reveals that, in addition to augmenting humoral responses, protein boosting of DNA-primed animals augments cellular immune responses mediated by CD8+ CTL, CD4+ T-helper cells and Th1 cytokines; thus, offering much promise in controlling HIV-1 in vaccinees.