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Minerva Anestesiologica 2022 November;88(11):928-38

DOI: 10.23736/S0375-9393.22.16460-6

Copyright © 2022 EDIZIONI MINERVA MEDICA

language: English

Dynamic assessment of surge capacity in a large hospital network during COVID-19 pandemic

Matteo NOCCI 1, 2 , Luca RAGAZZONI 3, 4, Francesco BARONE-ADESI 3, Ives HUBLOUE 5, 6, Stefano ROMAGNOLI 1, Adriano PERIS 7, Pietro BERTINI 8, Sabino SCOLLETTA 9, Fabrizio CIPOLLINI 10, Maria T. MECHI 11, Francesco DELLA CORTE 3

1 Section of Anesthesia and Critical Care, Department of Anesthesia and Critical Care, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Florence, Italy; 2 PhD Candidate International Joint PhD in Global Health, Humanitarian Aid and Disaster Medicine, Università del Piemonte Orientale, Novara, Italy; 3 CRIMEDIM - Center for Research and Training in Disaster Medicine, Humanitarian Aid and Global Health, Università del Piemonte Orientale, Novara, Italy; 4 Department of Sustainable Development and Ecological Transition, Università del Piemonte Orientale, Novara, Italy; 5 Department of Emergency Medicine, Universitair Ziekenhuis Brussel, Brussels, Belgium; 6 Research Group on Emergency and Disaster Medicine, Vrije Universiteit Brussels, Brussels, Belgium; 7 Intensive Care Unit and Regional ECMO Referral Center, Careggi University Hospital, Florence, Italy; 8 Unit of Cardiothoracic and Vascular Anesthesia and Intensive Care, Department of Anesthesia and Critical Care Medicine, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy; 9 Unit of Anesthesia and Intensive Care, Department of Emergency, Urgency and Transplantation, University Hospital of Siena, Siena, Italy; 10 DiSIA - Department of Statistics, Informatics, Applications, University of Florence, Florence, Italy; 11 Hospital Health Direction, Careggi University Hospital, Florence, Italy



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 SC based 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 SC values, 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.


KEY WORDS: Surge capacity; Disaster medicine; Pandemics; Intensive care units

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