TY - JOUR
T1 - Dynamic assessment of surge capacity in a large hospital network during COVID-19 pandemic
AU - Nocci, Matteo
AU - Ragazzoni, Luca
AU - Barone-Adesi, Francesco
AU - Hubloue, Ives
AU - Romagnoli, Stefano
AU - Peris, Adriano
AU - Bertini, Pietro
AU - Scolletta, Sabino
AU - Cipollini, Fabrizio
AU - Mechi, Maria T.
AU - Dellacorte, Francesco
N1 - Publisher Copyright:
© 2022 Edizioni Minerva Medica. All rights reserved.
PY - 2022/11
Y1 - 2022/11
N2 - 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.
AB - 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.
KW - Disaster medicine
KW - Intensive care units.
KW - Pandemics
KW - Surge capacity
UR - http://www.scopus.com/inward/record.url?scp=85141936045&partnerID=8YFLogxK
U2 - 10.23736/S0375-9393.22.16460-6
DO - 10.23736/S0375-9393.22.16460-6
M3 - Article
SN - 0375-9393
VL - 88
SP - 928
EP - 938
JO - Minerva Anestesiologica
JF - Minerva Anestesiologica
IS - 11
ER -