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Bicocca Open Archive
Background: Up to 30% of hospitalised patients with COVID-19 require advanced respiratory support, including high-flow nasal cannulas (HFNC), non-invasive mechanical ventilation (NIV), or invasive mechanical ventilation (IMV). We aimed to describe the clinical characteristics, outcomes and risk factors for failing non-invasive respiratory support in patients treated with severe COVID-19 during the first two years of the pandemic in high-income countries (HICs) and low middle-income countries (LMICs). Methods: This is a multinational, multicentre, prospective cohort study embedded in the ISARIC-WHO COVID-19 Clinical Characterisation Protocol. Patients with laboratory-confirmed SARS-CoV-2 infection who required hospital admission were recruited prospectively. Patients treated with HFNC, NIV, or IMV within the first 24 h of hospital admission were included in this study. Descriptive statistics, random forest, and logistic regression analyses were used to describe clinical characteristics and compare clinical outcomes among patients treated with the different types of advanced respiratory support. Results: A total of 66,565 patients were included in this study. Overall, 82.6% of patients were treated in HIC, and 40.6% were admitted to the hospital during the first pandemic wave. During the first 24 h after hospital admission, patients in HICs were more frequently treated with HFNC (48.0%), followed by NIV (38.6%) and IMV (13.4%). In contrast, patients admitted in lower- and middle-income countries (LMICs) were less frequently treated with HFNC (16.1%) and the majority received IMV (59.1%). The failure rate of non-invasive respiratory support (i.e. HFNC or NIV) was 15.5%, of which 71.2% were from HIC and 28.8% from LMIC. The variables most strongly associated with non-invasive ventilation failure, defined as progression to IMV, were high leukocyte counts at hospital admission (OR [95%CI]; 5.86 [4.83–7.10]), treatment in an LMIC (OR [95%CI]; 2.04 [1.97–2.11]), and tachypnoea at hospital admission (OR [95%CI]; 1.16 [1.14–1.18]). Patients who failed HFNC/NIV had a higher 28-day fatality ratio (OR [95%CI]; 1.27 [1.25–1.30]). Conclusions: In the present international cohort, the most frequently used advanced respiratory support was the HFNC. However, IMV was used more often in LMIC. Higher leucocyte count, tachypnoea, and treatment in LMIC were risk factors for HFNC/NIV failure. HFNC/NIV failure was related to worse clinical outcomes, such as 28-day mortality. Trial registration This is a prospective observational study; therefore, no health care interventions were applied to participants, and trial registration is not applicable.
Reyes, L., Murthy, S., Garcia-Gallo, E., Merson, L., Ibanez-Prada, E., Rello, J., et al. (2022). Respiratory support in patients with severe COVID-19 in the International Severe Acute Respiratory and Emerging Infection (ISARIC) COVID-19 study: a prospective, multinational, observational study. CRITICAL CARE, 26(1) [10.1186/s13054-022-04155-1].
Respiratory support in patients with severe COVID-19 in the International Severe Acute Respiratory and Emerging Infection (ISARIC) COVID-19 study: a prospective, multinational, observational study
Reyes L. F.;Murthy S.;Garcia-Gallo E.;Merson L.;Ibanez-Prada E. D.;Rello J.;Fuentes Y. V.;Martin-Loeches I.;Bozza F.;Duque S.;Taccone F. S.;Fowler R. A.;Kartsonaki C.;Goncalves B. P.;Citarella B. W.;Aryal D.;Burhan E.;Cummings M. J.;Delmas C.;Diaz R.;Figueiredo-Mello C.;Hashmi M.;Panda P. K.;Jimenez M. P.;Rincon D. F. B.;Thomson D.;Nichol A.;Marshall J. C.;Olliaro P. L.;Abbas A.;Abdukahil S. A.;Abe R.;Abel L.;Absil L.;Acharya S.;Acker A.;Adriao D.;Al Ageel S.;Ahmed S.;Ainscough K.;Aisa T.;Hssain A. A.;Tamlihat Y. A.;Akimoto T.;Akmal E.;Al Qasim E.;Alalqam R.;Al-dabbous T.;Alegesan S.;Alegre C.;Alessi M.;Alex B.;Alexandre K.;Al-Fares A.;Alfoudri H.;Ali I.;Shah N. A.;Alidjnou K. E.;Aliudin J.;Alkhafajee Q.;Allavena C.;Allou N.;Altaf A.;Alves J.;Alves J. M.;Alves R.;Cabrita J. A.;Amaral M.;Ampaw P.;Andini R.;Andrejak C.;Angheben A.;Angoulvant F.;Ansart S.;Antonelli M.;de Brito C. A. 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S.;Shadowitz E.;Shamsah M.;Sharjeel S.;Sharma P.;Shaw C. A.;Shaw V.;Sheharyar A.;Shetty R. M.;Shi H.;Shiekh M.;Shimizu K.;Shrapnel S.;Shrestha S. K.;Shrestha P. S.;Shum H. P.;Mohammed N. S.;Sibiude J.;Siddiqui A.;Sigfrid L.;Sillaots P.;Silva C.;Silva M. J.;Silva R.;Sin W. C.;Singh B. C.;Singh P.;Sitompul P. A.;Skogen V.;Smith S.;Smood B.;Smyth C.;Smyth M.;Smyth M.;Snacken M.;So D.;Solomon J.;Solomon T.;Somers E.;Sommet A.;Song M. J.;Song R.;Song T.;Chia J. S.;Sotto A.;Soum E.;Sousa A. C.;Sousa M.;Uva M. S.;Sperry A.;Spinuzza E.;Darshana B. P. S. R. S.;Sriskandan S.;Stabler S.;Staudinger T.;Stecher S. -S.;Stienstra Y.;Stiksrud B.;Stolz E.;Stone A.;Streinu-Cercel A.;Streinu-Cercel A.;Stuart A.;Stuart D.;Suen G.;Suen J. Y.;Sultana A.;Summers C.;Supic D.;Surovcova M.;Svistunov A.;Syrigos K.;Sztajnbok J.;Szuldrzynski K.;Tabrizi S.;Tagherset L.;Taleb S.;Talsma J.;Tampubolon M. L.;Tanaka H.;Taqdees H.;Taqi A.;Tardivon C.;Tattevin P.;Taufik M. A.;Tawfik H.;Tedder R. S.;Teixeira J.;Tejada S.;Tellier M. -C.;Teoule F.;Terpstra P.;Terrier O.;Terzi N.;Tessier-Grenier H.;Tey A.;Thakur A.;Thibault V.;Thiberville S. -D.;Thill B.;Thompson S.;Thomson E. C.;Thwaites R. S.;Tierney P.;Tieroshyn V.;Timashev P. S.;Timsit J. -F.;Vijayaraghavan B. K. T.;Tissot N.;Toki M.;Tonby K.;Torre M.;Torres A.;Torres M.;Torres-Zevallos H.;Towers M.;Trapani T.;Treoux T.;Tromeur C.;Trontzas I.;Trouillon T.;Truong J.;Tual C.;Tubiana S.;Tuite H.;Turmel J. -M.;Turtle L. C. W.;Twardowski P.;Uchiyama M.;Udayanga P. G. I.;Udy A.;Ullrich R.;Uribe A.;Usman A.;Uyeki T. M.;Vajdovics C.;Val-Flores L.;Valran A.;Van de Velde S.;van den Berge M.;van der Palen J.;van der Valk P.;Van Der Vekens N.;Van der Voort P.;Van Der Werf S.;van Gulik L.;Van Hattem J.;van Netten C.;van Veen I.;Vanel N.;Vanoverschelde H.;Varghese P.;Vauchy C.;Veislinger A.;Vencken S.;Ventura S.;Verbon A.;Vickers J.;Vidal J. E.;Vieira C.;Vijayan D.;Villanueva J. A.;Villar J.;Villeneuve P. -M.;Villoldo A.;Visseaux B.;Visser H.;Vitiello C.;Vonkeman H.;Vuotto F.;Shukeri W. F. W. M.;Wang C. -H.;Webb S.;Wei J.;Weil K.;Wesselius S.;West T. E.;Wham M.;Whelan B.;White N.;Wicky P. H.;Wiedemann A.;Wijaya S. O.;Wille K.;Willems S.;Williams V.;Wils E. -J.;Yiu N. W.;Wong C.;Xynogalas I.;Yamazaki M.;Yazdanpanah Y.;Yelnik C.;Yerkovich S.;Yokoyama T.;Yonis H.;Yousif O.;Yuliarto S.;Zaaqoq A.;Zabbe M.;Zahran M.;Zambon M.;Zanella A.;Zayyad H.;Zoufaly A.;Zucman D.
2022
Abstract
Background: Up to 30% of hospitalised patients with COVID-19 require advanced respiratory support, including high-flow nasal cannulas (HFNC), non-invasive mechanical ventilation (NIV), or invasive mechanical ventilation (IMV). We aimed to describe the clinical characteristics, outcomes and risk factors for failing non-invasive respiratory support in patients treated with severe COVID-19 during the first two years of the pandemic in high-income countries (HICs) and low middle-income countries (LMICs). Methods: This is a multinational, multicentre, prospective cohort study embedded in the ISARIC-WHO COVID-19 Clinical Characterisation Protocol. Patients with laboratory-confirmed SARS-CoV-2 infection who required hospital admission were recruited prospectively. Patients treated with HFNC, NIV, or IMV within the first 24 h of hospital admission were included in this study. Descriptive statistics, random forest, and logistic regression analyses were used to describe clinical characteristics and compare clinical outcomes among patients treated with the different types of advanced respiratory support. Results: A total of 66,565 patients were included in this study. Overall, 82.6% of patients were treated in HIC, and 40.6% were admitted to the hospital during the first pandemic wave. During the first 24 h after hospital admission, patients in HICs were more frequently treated with HFNC (48.0%), followed by NIV (38.6%) and IMV (13.4%). In contrast, patients admitted in lower- and middle-income countries (LMICs) were less frequently treated with HFNC (16.1%) and the majority received IMV (59.1%). The failure rate of non-invasive respiratory support (i.e. HFNC or NIV) was 15.5%, of which 71.2% were from HIC and 28.8% from LMIC. The variables most strongly associated with non-invasive ventilation failure, defined as progression to IMV, were high leukocyte counts at hospital admission (OR [95%CI]; 5.86 [4.83–7.10]), treatment in an LMIC (OR [95%CI]; 2.04 [1.97–2.11]), and tachypnoea at hospital admission (OR [95%CI]; 1.16 [1.14–1.18]). Patients who failed HFNC/NIV had a higher 28-day fatality ratio (OR [95%CI]; 1.27 [1.25–1.30]). Conclusions: In the present international cohort, the most frequently used advanced respiratory support was the HFNC. However, IMV was used more often in LMIC. Higher leucocyte count, tachypnoea, and treatment in LMIC were risk factors for HFNC/NIV failure. HFNC/NIV failure was related to worse clinical outcomes, such as 28-day mortality. Trial registration This is a prospective observational study; therefore, no health care interventions were applied to participants, and trial registration is not applicable.
Reyes, L., Murthy, S., Garcia-Gallo, E., Merson, L., Ibanez-Prada, E., Rello, J., et al. (2022). Respiratory support in patients with severe COVID-19 in the International Severe Acute Respiratory and Emerging Infection (ISARIC) COVID-19 study: a prospective, multinational, observational study. CRITICAL CARE, 26(1) [10.1186/s13054-022-04155-1].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/479021
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simulazione ASN
Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2023-2025 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
La presente simulazione è stata realizzata sulla base delle specifiche raccolte sul tavolo ER del Focus Group IRIS coordinato dall’Università di Modena e Reggio Emilia e delle regole riportate nel DM 598/2018 e allegata Tabella A. Cineca, l’Università di Modena e Reggio Emilia e il Focus Group IRIS non si assumono alcuna responsabilità in merito all’uso che il diretto interessato o terzi faranno della simulazione. Si specifica inoltre che la simulazione contiene calcoli effettuati con dati e algoritmi di pubblico dominio e deve quindi essere considerata come un mero ausilio al calcolo svolgibile manualmente o con strumenti equivalenti.
