Importance of patient bed pathways and length of stay differences in predicting COVID-19 bed occupancy in England

  1. Quentin J. Leclerc
  2. Naomi M. Fuller
  3. Ruth H. Keogh
  4. Karla Diaz-Ordaz
  5. Richard Sekula
  6. Malcolm G Semple
  7. ISARIC4C Investigators
  8. CMMID COVID-19 Working Group
  9. Katherine E. Atkins
  10. Simon R. Procter
  11. Gwenan M. Knight

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Abstract

Objectives

Predicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient’s “bed pathway” - the sequence of transfers between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy.

Design

We obtained data from University College Hospital (UCH) and the ISARIC4C COVID-19 Clinical Information Network (CO-CIN) on hospitalised patients with COVID-19 who required care in general ward or critical care (CC) beds to determine possible bed pathways and LoS. We developed a discrete-time model to examine the implications of using either bed pathways or only average LoS by bed type to forecast bed occupancy. We compared model-predicted bed occupancy to publicly available bed occupancy data on COVID-19 in England between March and August 2020.

Results

In both the UCH and CO-CIN datasets, 82% of hospitalised patients with COVID-19 only received care in general ward beds. We identified four other bed pathways, present in both datasets: “Ward, CC, Ward”, “Ward, CC”, “CC” and “CC, Ward”. Mean LoS varied by bed type, pathway, and dataset, between 1.78 and 13.53 days.

For UCH, we found that using bed pathways improved the accuracy of bed occupancy predictions, while only using an average LoS for each bed type underestimated true bed occupancy. However, using the CO-CIN LoS dataset we were not able to replicate past data on bed occupancy in England, suggesting regional LoS heterogeneities.

Conclusions

We identified five bed pathways, with substantial variation in LoS by bed type, pathway, and geography. This might be caused by local differences in patient characteristics, clinical care strategies, or resource availability, and suggests that national LoS averages may not be appropriate for local forecasts of bed occupancy for COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2021.01.14.21249791: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All of the analyses below were conducted in R (14), using the following packages: openxlsx (15), here (16), dplyr (17), reshape2 (18), linelist (19), ggplot2 (20), cowplot (21), knitr (22), rlist (23) and mstate (24).
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: Our results are subject to limitations, notably linked to our model assumptions. Firstly, we chose to round-up sampled LoS estimates to the next day instead of rounding to the closest. In the case of UCH, this improved the fit of the model-predicted bed occupancy to the data (Supplementary Table 3), however in other settings this assumption could lead to overestimating bed occupancy. Nevertheless, we consider this to be a “safer” approach, as it implies that a bed needed for a fraction of a day is needed for that entire day. For capacity planning, slightly overestimating bed occupancy will likely be preferred to underestimating it, which could have severe consequences on clinical care. Secondly, we did not include a maximum bed capacity - this could improve accuracy if reaching limits frequently prevents patients from moving between bed types. Thirdly, we assume that behaviours remain constant through time, which means that the average LoS estimates and proportion of patients going through each bed pathway do not change with time. This may not be realistic, as hospital discharge rates could vary with time, depending on capacity and changes in organisation. Similarly, the proportions of patients requiring critical care could change over time, depending on the public health measures in place which may affect the demographics of people most likely to be infected. Finally, we assume independence within bed pathways, meaning that the LoS for each stage is drawn random...

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    ISRCTN66726260NANA

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2021.01.14.21249791v1 on medRxiv