International variation in length of stay in intensive care units and the impact of patient-to-nurse ratios.

OBJECTIVE: To assess variation in ICU length of stay between countries with varying patient-to-nurse ratios; to compare ICU length of stay of individual countries against an international benchmark. DESIGN: Secondary analysis of the DecubICUs trial (performed on 15 May 2018). SETTING: The study cohort included 12,794 adult ICU patients (57 countries). Only countries with minimally twenty patients discharged (or deceased) within 30 days of ICU admission were included. MAIN OUTCOME MEASURE: Multivariate Cox regression was used to evaluate ICU length of stay, censored at 30 days, across countries and for patient-to-nurse ratio, adjusted for sex, age, admission type and Simplified Acute Physiology Score II. The resulting hazard ratios for countries, indicating longer or shorter length of stay than average, were plotted on a forest plot. Results by country were benchmarked against the overall length of stay using Kaplan-Meier curves. RESULTS: Patients had a median ICU length of stay of 11 days (interquartile range, 4-27). Hazard ratio by country ranged from minimally 0.42 (95% confidence interval 0.35-0.51) for Greece, to maximaly1.94 (1.28-2.93) for Lithuania. The hazard ratio for patient-to-nurse was 0.96 (0.94-0.98), indicating that higher patient-to-nurse ratio results in longer length of stay. CONCLUSIONS: Despite adjustment for case-mix, we observed significant heterogeneity of ICU length of stay in-between countries, and a significantly longer length of stay when patient-to-nurse ratio increases. Future studies determining underlying characteristics of individual ICUs and broader organisation of healthcare infrastructure within countries may further explain the observed heterogeneity in ICU length of stay.

Prediction of hospital bed capacity during the COVID- 19 pandemic.

BACKGROUND: Prediction of the necessary capacity of beds by ward type (e.g. ICU) is essential for planning purposes during epidemics, such as the COVID- 19 pandemic. The COVID- 19 taskforce within the Ghent University hospital made use of ten-day forecasts on the required number of beds for COVID- 19 patients across different wards. METHODS: The planning tool combined a Poisson model for the number of newly admitted patients on each day with a multistate model for the transitions of admitted patients to the different wards, discharge or death. These models were used to simulate the required capacity of beds by ward type over the next 10 days, along with worst-case and best-case bounds. RESULTS: Overall, the models resulted in good predictions of the required number of beds across different hospital wards. Short-term predictions were especially accurate as these are less sensitive to sudden changes in number of beds on a given ward (e.g. due to referrals). Code snippets and details on the set-up are provided to guide the reader to apply the planning tool on one's own hospital data. CONCLUSIONS: We were able to achieve a fast setup of a planning tool useful within the COVID- 19 pandemic, with a fair prediction on the needed capacity by ward type. This methodology can also be applied for other epidemics.