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

Leclerc, Quentin J.; Fuller, Naomi M.; Keogh, Ruth H.; Díaz-Ordaz, Karla; Sekula, Richard; Semple, Malcolm G; Investigators, Isaric C; Atkins, Katherine E.; Procter, Simon R.; Knight, Gwenan M.

doi:10.1101/2021.01.14.21249791

Cited by 9 publications

(16 citation statements)

References 24 publications

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“…Among publications on the topic of hospital stay time for COVID-19 patients, some of them only address the overall statistics or difference between countries, on bed types and not on factors which might impact the stay time. [35] For example, it was reported that median hospital stay is 14 days in China, whereas it is 5 days outside of China. [36] Another overview paper estimates the majority of ICU hospital stay time is between 7 to 11 days.…”

Section: Discussionmentioning

confidence: 99%

COVID-19 Tanısı ile Hastanede Yatan Hastalarda Hastanede Kalış Süresini Etkileyen Faktörler Üzerine Bir Araştırma

Çetin

Ülgen

Şıvgın

et al. 2021

Journal of Contemporary Medicine

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Knowing the typical length of hospital stay of COVID-19 patientsand which factors affecting the stay time are important for hospital management.A factor can impact the stay time in conflicting ways: a condition may,by the same mechanism, increase the stay time for a severe patient while decrease the time for an even more severe and deceased patient.Material and Method: 3184 COVID-19 patients from the Tokat State Hospital were examined on arrival to determine if they are required further treatment in hospital or not. Among them,404 are inpatients whose hospital stay time is available. By a simple and conditional linear regressions, we examine which factors statistically impact the hospital stay time,both overall and after taking into account the patient's age or eventual survival status.We also use causespecific Cox proportional-hazard regression for competing risk to examine contribution from a factor to the rate of event of mortality, and to the event of discharge.Results: Surviving ICU patients have longer hospital stay time than nonsurviving ICU patients, which in turn longer than non-ICU patients. Older age within acertain range is always correlated with a longer hospital stay. The factors with strongest signal in our dataset are C-reactive protein (CRP), hemoglobin (HGB)and calcium level: increased CRP level, decreased HGB level and calcium level are associated with longer hospital stay, independent from the contribution from surviving status. Other potentially staytime-impacting factors include d-dimer, urea, glucose, white blood cell (WBC) count, neutrophil, but these signals may not be robust against logtransformation of the stay time, or confounding with age. We also observed that glucose is more important than HbA1C or diabetes status in its influence on hospital stay time. Conclusion:Almost all factors we collected contribute to a faster/slower mortality or discharge rate, in particular C-reactive protein and hemoglobin. Measurement of the associated factors in COVID-19 patients could be used for a better hospital bed management.

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Section: Discussionmentioning

confidence: 99%

COVID-19 Tanısı ile Hastanede Yatan Hastalarda Hastanede Kalış Süresini Etkileyen Faktörler Üzerine Bir Araştırma

Çetin

Ülgen

Şıvgın

et al. 2021

Journal of Contemporary Medicine

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“…Besides informing situational awareness at a local level, more robust forecasts of hospital admissions can improve forecasts of bed or ICU needs [10,11,14,15], although occupancy forecasts will also depend on patient demographics, patient pathways, ICU requirements and bed availability and length-of-stay distributions [11].…”

Section: Discussionmentioning

confidence: 99%

“…Forecasts of hospital admissions are also an essential step to forecasting bed or ICU demand (e.g. [11,14,15]).…”

Section: Introductionmentioning

confidence: 99%

“…Forecasting healthcare requirements during an epidemic is critical for planning and resource allocation [5][6][7], and short-term forecasts of COVID-19 hospital activity have been widely used during the COVID-19 pandemic to support public health policy (e.g. [8][9][10][11]). While national or regional forecasts provide a big-picture summary of the expected trajectory of COVID-19 activity, they can mask spatial heterogeneity that arises through localised interventions or demographic heterogeneity in the risk of exposure or severity [12].…”

Section: Introductionmentioning

confidence: 99%

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Comparative assessment of methods for short-term forecasts of COVID-19 hospital admissions in England at the local level

Meakin

Abbott

Bosse

et al. 2021

Preprint

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Background: Forecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. Ideally, forecasts should be robust across time and locations. During the COVID-19 pandemic in England, it is an ongoing concern that demand for hospital care for COVID-19 patients in England will exceed available resources. Methods: We made weekly forecasts of daily COVID-19 hospital admissions for National Health Service (NHS) Trusts in England between August 2020 and April 2021 using three disease-agnostic forecasting models: a mean ensemble of autoregressive time series models, a linear regression model with 7-day-lagged local cases as a predictor, and a scaled convolution of local cases and a delay distribution. We compared their point and probabilistic accuracy to a mean-ensemble of them all, and to a simple baseline model of no change from the last day of admissions. We measured predictive performance using the Weighted Interval Score (WIS) and considered how this changed in different scenarios (the length of the predictive horizon, the date on which the forecast was made, and by location), as well as how much admissions forecasts improved when future cases were known. Results: All models outperformed the baseline in the majority of scenarios. Forecasting accuracy varied by forecast date and location, depending on the trajectory of the outbreak, and all individual models had instances where they were the top- or bottom-ranked model. Forecasts produced by the mean-ensemble were both the most accurate and most consistently accurate forecasts amongst all the models considered. Forecasting accuracy was improved when using future observed, rather than forecast, cases, especially at longer forecast horizons. Conclusions: Assuming no change in current admissions is rarely better than including at least a trend. Using confirmed COVID-19 cases as a predictor can improve admissions forecasts in some scenarios, but this is variable and depends on the ability to make consistently good case forecasts. However, ensemble forecasts can make forecasts that make consistently more accurate forecasts across time and locations. Given minimal requirements on data and computation, our admissions forecasting ensemble could be used to anticipate healthcare needs in future epidemic or pandemic settings.

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“…Anticipation of admitted patients with COVID-19 who are candidates for early discharge may allow hospitals to develop protocols to further reduce length-of-stay, involving strategies such as early discharge planning, protocolized delivery of therapeutic medications, and the use of dedicated observation units and personnel [ 7 ]. Improved identification of short-stay patients also enables continuous quality improvement.…”

Section: Introductionmentioning

confidence: 99%

Short-Stay Hospitalizations for Patients with COVID-19: A Retrospective Cohort Study

Kilaru

Lee

Grossman

et al. 2021

JCM

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Objective: Patients requiring hospital care for COVID-19 may be stable for discharge soon after admission. This study sought to describe patient characteristics associated with short-stay hospitalization for COVID-19. Methods: We performed a retrospective cohort study of patients with COVID-19 admitted to five United States hospitals from March to December 2020. We used multivariable logistic regression to identify patient characteristics associated with short hospital length-of-stay. Results: Of 3103 patients, 648 (20.9%) were hospitalized for less than 48 h. These patients were significantly less likely to have an age greater than 60, diabetes, chronic kidney disease; emergency department vital sign abnormalities, or abnormal initial diagnostic testing. For patients with no significant risk factors, the adjusted probability of short-stay hospitalization was 62.4% (95% CI 58.9–69.6). Conclusion: Identification of candidates for early hospital discharge may allow hospitals to streamline throughput using protocols that optimize the efficiency of hospital care and coordinate post-discharge monitoring.

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

Cited by 9 publications

References 24 publications

COVID-19 Tanısı ile Hastanede Yatan Hastalarda Hastanede Kalış Süresini Etkileyen Faktörler Üzerine Bir Araştırma

COVID-19 Tanısı ile Hastanede Yatan Hastalarda Hastanede Kalış Süresini Etkileyen Faktörler Üzerine Bir Araştırma

Comparative assessment of methods for short-term forecasts of COVID-19 hospital admissions in England at the local level

Short-Stay Hospitalizations for Patients with COVID-19: A Retrospective Cohort Study

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