BACKGROUND: The COVID-19 pandemic has provided an unprecedented scenario to deepen knowledge of surge capacity (SC), assessment of which remains a challenge. This study reports a large-scale experience of a multi-hospital network, with the aim of evaluating the characteristics of different hospitals involved in the response and of measuring a real-time SCbased on two complementary modalities (actual, base) referring to the intensive care units (ICU). METHODS: Data analysis referred to two consecutive pandemic waves (March-December 2020). Regarding SC, two different levels of analysis are considered: single hospital category (referring to a six-level categorization based on the number of hospital beds) and multi-hospital wide (referring to the response of the entire hospital network). RESULTS: During the period of 114 days, the analysis revealed a key role of the biggest hospitals (>Category-4) in terms of involvement in the pandemic response. In terms of SC, Category-4 hospitals showed the highest mean SCvalues, irrespective of the calculation method and level of analysis. At the multi-hospital level, the analysis revealed an overall ICU-SC(base) of 84.4% and an ICU-SC(actual) of 106.5%. CONCLUSIONS: The results provide benchmarks to better understand ICU hospital response capacity, highlighting the need for a more flexible approach to SC definition.
Nocci, M., Ragazzoni, L., Barone-Adesi, F., Hubloue, I., Romagnoli, S., Peris, A., et al. (2022). Dynamic assessment of surge capacity in a large hospital network during COVID-19 pandemic. MINERVA ANESTESIOLOGICA, 88(11), 928-938 [10.23736/S0375-9393.22.16460-6].
Dynamic assessment of surge capacity in a large hospital network during COVID-19 pandemic
Nocci M.;Ragazzoni L.;Scolletta S.;
2022-01-01
Abstract
BACKGROUND: The COVID-19 pandemic has provided an unprecedented scenario to deepen knowledge of surge capacity (SC), assessment of which remains a challenge. This study reports a large-scale experience of a multi-hospital network, with the aim of evaluating the characteristics of different hospitals involved in the response and of measuring a real-time SCbased on two complementary modalities (actual, base) referring to the intensive care units (ICU). METHODS: Data analysis referred to two consecutive pandemic waves (March-December 2020). Regarding SC, two different levels of analysis are considered: single hospital category (referring to a six-level categorization based on the number of hospital beds) and multi-hospital wide (referring to the response of the entire hospital network). RESULTS: During the period of 114 days, the analysis revealed a key role of the biggest hospitals (>Category-4) in terms of involvement in the pandemic response. In terms of SC, Category-4 hospitals showed the highest mean SCvalues, irrespective of the calculation method and level of analysis. At the multi-hospital level, the analysis revealed an overall ICU-SC(base) of 84.4% and an ICU-SC(actual) of 106.5%. CONCLUSIONS: The results provide benchmarks to better understand ICU hospital response capacity, highlighting the need for a more flexible approach to SC definition.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11365/1265184