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Original Paper   

Minerva Anestesiologica 2022 Jul 05

DOI: 10.23736/S0375-9393.22.16460-6


lingua: Inglese

Dynamic assessment of Surge Capacity in a large hospital network during Covid-19 pandemic

Matteo NOCCI 1, 2 , Luca RAGAZZONI 3, 4, 5, Francesco BARONE-ADESI 3, 4, 5, Ives HUBLOUE 6, 7, Stefano ROMAGNOLI 1, Adriano PERIS 8, Pietro BERTINI 9, Sabino SCOLLETTA 10, Fabrizio CIPOLLINI 11, Maria T. MECHI 12, Francesco DELLA CORTE 3, 4, 5

1 Section of Anesthesia and Critical Care, Department of Anesthesia and Critical Care, Careggi University Hospital, Florence, Italy; 2 University of Eastern Piedmont, Novara, Italy; 3 Vrije Universiteit Brussel, Brussels, Belgium; 4 CRIMEDIM - Center for Research and Training in Disaster Medicine, Humanitarian Aid and Global Health, University of Eastern Piedmont, Novara, Italy; 5 Department of Translational Medicine, University of Eastern Piedmont, Novara, Italy; 6 Department of Emergency Medicine, Universitair Ziekenhuis Brussel, Brussels, Belgium; 7 Research Group on Emergency and Disaster Medicine, Vrije Universiteit Brussels, Brussels, Belgium; 8 Intensive Care Unit and Regional ECMO Referral Center, Careggi University Hospital, Florence, Italy; 9 Unit of Cardiothoracic and Vascular Anesthesia and Intensive Care, Department of Anesthesia and Critical Care Medicine, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy; 10 Unit of Anesthesia and Intensive Care, Department of Emergency, Urgency and Transplantation, University Hospital of Siena, Siena, Italy; 11 DiSIA - Department of Statistics, Informatics, Applications, University of Florence, Florence, Italy; 12 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 surge capacity 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 surge capacity definition.

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

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