COVID-19 length of hospital stay: a systematic review and data synthesis

  1. Eleanor M. Rees
  2. Emily S. Nightingale
  3. Yalda Jafari
  4. Naomi R. Waterlow
  5. Samuel Clifford
  6. Carl A. B. Pearson
  7. CMMID Working Group
  8. Thibaut Jombart
  9. Simon R. Procter
  10. Gwenan M. Knight

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Abstract

Background

The COVID-19 pandemic has placed an unprecedented strain on health systems, with rapidly increasing demand for healthcare in hospitals and intensive care units (ICUs) worldwide. As the pandemic escalates, determining the resulting needs for healthcare resources (beds, staff, equipment) has become a key priority for many countries. Projecting future demand requires estimates of how long patients with COVID-19 need different levels of hospital care.

Methods

We performed a systematic review of early evidence on length of stay (LoS) of patients with COVID-19 in hospital and in ICU. We subsequently developed a method to generate LoS distributions which combines summary statistics reported in multiple studies, accounting for differences in sample sizes. Applying this approach, we provide distributions for total hospital and ICU LoS from studies in China and elsewhere, for use by the community.

Results

We identified 52 studies, the majority from China (46/52). Median hospital LoS ranged from 4 to 53 days within China, and 4 to 21 days outside of China, across 45 studies. ICU LoS was reported by eight studies—four each within and outside China—with median values ranging from 6 to 12 and 4 to 19 days, respectively. Our summary distributions have a median hospital LoS of 14 (IQR 10–19) days for China, compared with 5 (IQR 3–9) days outside of China. For ICU, the summary distributions are more similar (median (IQR) of 8 (5–13) days for China and 7 (4–11) days outside of China). There was a visible difference by discharge status, with patients who were discharged alive having longer LoS than those who died during their admission, but no trend associated with study date.

Conclusion

Patients with COVID-19 in China appeared to remain in hospital for longer than elsewhere. This may be explained by differences in criteria for admission and discharge between countries, and different timing within the pandemic. In the absence of local data, the combined summary LoS distributions provided here can be used to model bed demands for contingency planning and then updated, with the novel method presented here, as more studies with aggregated statistics emerge outside China.

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  1. Version published to 10.1186/s12916-020-01726-3
  2. ScreenIT

    SciScore for 10.1101/2020.04.30.20084780: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableAverage patient age and sex distribution (% male) were summarised across all studies by weighted mean and standard deviation (mean (sd)), according to study sample size.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We searched the bibliographic databases Embase and Medline, as well as the online preprint archive medRxiv.
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    Medline
    suggested: (MEDLINE, RRID:SCR_002185)

    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 and biases: Having been the first country to observe this novel coronavirus, published data on COVID-19 patient outcomes in China is more widely available than from countries to which the epidemic spread later on. The set of studies found in this review reflects this bias towards evidence obtained from China, particularly Wuhan. As more studies emerge from a broader range of settings it would be important to re-evaluate LoS estimates, as there are likely to be between-country differences that we have not captured here. Furthermore, a number of studies include patients from the same hospital over the same period, for example, Yang et al. [51] and Wu et al. [52] who both reported patients from Jin Yin Tan hospital in Wuhan), and it is possible that these studies had overlapping study populations. Furthermore, Guan et al. [36] was a national study conducted in China and ISARIC [27] included 25 countries world-wide, therefore these studies may also include patients previously described. The effect of this double-counting would be to bias the summary statistics towards the LoS from these settings. Although this is acknowledged as an issue, this was not considered as an exclusion criteria as it would have resulted in the exclusion of many studies. The overall benefit of inclusion was deemed to out-weigh the potential biases which may arise as a result of overlapping patient populations. In this review we were only able to distinguish between “general hospital LoS” and “...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    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.

    About SciScore

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  3. Version published to 10.1101/2020.04.30.20084780 on medRxiv