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1.
Antibiotics ; 12(1):125, 2023.
Article in English | MDPI | ID: covidwho-2199677

ABSTRACT

(1) Background: Colistin-only susceptible (COS) Acinetobacter baumannii (AB) ventilator-associated pneumonia (VAP) represents a clinical challenge in the Intensive Care Unit (ICU) due to the negligible lung diffusion of this molecule and the low-grade evidence on efficacy of its nebulization. (2) Methods: We conducted a prospective observational study on 134 ICU patients with COS-AB VAP to describe the 'real life' clinical use of high-dose (5 MIU q8) aerosolized colistin, using a vibrating mesh nebulizer. Lung pharmacokinetics and microbiome features were investigated. (3) Results: Patients were enrolled during the COVID-19 pandemic with the ICU presenting a SAPS II of 42 [32-57]. At VAP diagnosis, the median PaO2/FiO2 was 120 [100-164], 40.3% were in septic shock, and 24.6% had secondary bacteremia. The twenty-eight day mortality was 50.7% with 60.4% and 40.3% rates of clinical cure and microbiological eradication, respectively. We did not observe any drug-related adverse events. Epithelial lining fluid colistin concentrations were far above the CRAB minimal-inhibitory concentration and the duration of nebulized therapy was an independent predictor of microbiological eradication (12 [9.75-14] vs. 7 [4-13] days, OR (95% CI): 1.069 (1.003-1.138), p = 0.039). (4) Conclusions: High-dose and prolonged colistin nebulization, using a vibrating mesh, was a safe adjunctive therapeutic strategy for COS-AB VAP. Its right place and efficacy in this setting warrant investigation in interventional studies.

3.
Ann Intensive Care ; 12(1): 94, 2022 Oct 14.
Article in English | MEDLINE | ID: covidwho-2108949

ABSTRACT

INTRODUCTION: Helmet noninvasive support may provide advantages over other noninvasive oxygenation strategies in the management of acute hypoxemic respiratory failure. In this narrative review based on a systematic search of the literature, we summarize the rationale, mechanism of action and technicalities for helmet support in hypoxemic patients. MAIN RESULTS: In hypoxemic patients, helmet can facilitate noninvasive application of continuous positive-airway pressure or pressure-support ventilation via a hood interface that seals at the neck and is secured by straps under the arms. Helmet use requires specific settings. Continuous positive-airway pressure is delivered through a high-flow generator or a Venturi system connected to the inspiratory port of the interface, and a positive end-expiratory pressure valve place at the expiratory port of the helmet;  alternatively, pressure-support ventilation is delivered by connecting the helmet to a mechanical ventilator through a bi-tube circuit. The helmet interface allows continuous treatments with high positive end-expiratory pressure with good patient comfort. Preliminary data suggest that helmet noninvasive ventilation (NIV) may provide physiological benefits compared to other noninvasive oxygenation strategies (conventional oxygen, facemask NIV, high-flow nasal oxygen) in non-hypercapnic patients with moderate-to-severe hypoxemia (PaO2/FiO2 ≤ 200 mmHg), possibly because higher positive end-expiratory pressure (10-15 cmH2O) can be applied for prolonged periods with good tolerability. This improves oxygenation, limits ventilator inhomogeneities, and may attenuate the potential harm of lung and diaphragm injury caused by vigorous inspiratory effort. The potential superiority of helmet support for reducing the risk of intubation has been hypothesized in small, pilot randomized trials and in a network metanalysis. CONCLUSIONS: Helmet noninvasive support represents a promising tool for the initial management of patients with severe hypoxemic respiratory failure. Currently, the lack of confidence with this and technique and the absence of conclusive data regarding its efficacy render helmet use limited to specific settings, with expert and trained personnel. As per other noninvasive oxygenation strategies, careful clinical and physiological monitoring during the treatment is essential to early identify treatment failure and avoid delays in intubation.

4.
Crit Care ; 26(1): 338, 2022 11 04.
Article in English | MEDLINE | ID: covidwho-2108872

ABSTRACT

We conducted a proof of concept study where Anapnoguard endotracheal tubes and its control unit were used in 15 patients with COVID-19 acute respiratory distress syndrome. Anapnoguard system provides suction, venting, rinsing of subglottic space and controls cuff pressure detecting air leakage through the cuff. Alpha-amylase and pepsin levels, as oropharyngeal and gastric microaspiration markers, were assessed from 85 tracheal aspirates in the first 72 h after connection to the system. Oropharyngeal microaspiration occurred in 47 cases (55%). Episodes of gastric microaspiration were not detected. Patient positioning, either prone or supine, did not affect alpha-amylase and pepsin concentration in tracheal secretions. Ventilator-associated pneumonia (VAP) rate was 40%. The use of the AG system provided effective cuff pressure control and subglottic secretions drainage. Despite this, no reduction in the incidence of VAP has been demonstrated, compared to data reported in the current COVID-19 literature. The value of this new technology is worth of being evaluated for the prevention of ventilator-associated respiratory tract infections.


Subject(s)
COVID-19 , Pneumonia, Ventilator-Associated , Respiratory Distress Syndrome , Humans , Intensive Care Units , Pepsin A , Pronation , Equipment Design , Pneumonia, Ventilator-Associated/etiology , Intubation, Intratracheal/adverse effects , alpha-Amylases
5.
Ultraschall Med ; 2021 Nov 03.
Article in English | MEDLINE | ID: covidwho-2077144

ABSTRACT

PURPOSE: The goal of this survey was to describe the use and diffusion of lung ultrasound (LUS), the level of training received before and during the COVID-19 pandemic, and the clinical impact LUS has had on COVID-19 cases in intensive care units (ICU) from February 2020 to May 2020. MATERIALS AND METHODS: The Italian Lung Ultrasound Survey (ITALUS) was a nationwide online survey proposed to Italian anesthesiologists and intensive care physicians carried out after the first wave of the COVID-19 pandemic. It consisted of 27 questions, both quantitative and qualitative. RESULTS: 807 responded to the survey. The median previous LUS experience was 3 years (IQR 1.0-6.0). 473 (60.9 %) reported having attended at least one training course on LUS before the COVID-19 pandemic. 519 (73.9 %) reported knowing how to use the LUS score. 404 (52 %) reported being able to use LUS without any supervision. 479 (68.2 %) said that LUS influenced their clinical decision-making, mostly with respect to patient monitoring. During the pandemic, the median of patients daily evaluated with LUS increased 3-fold (p < 0.001), daily use of general LUS increased from 10.4 % to 28.9 % (p < 0.001), and the daily use of LUS score in particular increased from 1.6 % to 9.0 % (p < 0.001). CONCLUSION: This survey showed that LUS was already extensively used during the first wave of the COVID-19 pandemic by anesthesiologists and intensive care physicians in Italy, and then its adoption increased further. Residency programs are already progressively implementing LUS teaching. However, 76.7 % of the sample did not undertake any LUS certification.

6.
J Clin Med ; 11(19)2022 Oct 10.
Article in English | MEDLINE | ID: covidwho-2066210

ABSTRACT

Background: Cardiovascular sequelae after COVID-19 are frequent. However, the predictors for their occurrence are still unknown. In this study, we aimed to assess whether myocardial injury during COVID-19 hospitalization is associated to CV sequelae and death after hospital discharge. Methods: In this prospective observational study, consecutive patients who were admitted for COVID-19 in a metropolitan COVID-19 hub in Italy, between March 2021 and January 2022, with a ≥ 1 assessment of high sensitivity cardiac troponin I (hs-cTnI) were included in the study, if they were alive at hospital discharge. Myocardial injury was defined as elevation hs-cTnI > 99th percentile of the upper reference limit. The incidence of all-cause mortality and major adverse cardiovascular and cerebrovascular events (MACCE, including cardiovascular death, admission for acute or chronic coronary syndrome, hospitalization for heart failure, and stroke/transient ischemic attack) at follow-up were the primary outcomes. Arrhythmias, inflammatory heart diseases, and/or thrombotic disorders were analyzed as well. Results: Among the 701 COVID-19 survivors (mean age 66.4 ± 14.4 years, 40.2% female), myocardial injury occurred in 75 (10.7%) patients. At a median follow-up of 270 days (IQR 165, 380), all-cause mortality (21.3% vs. 6.1%, p < 0.001), MACCE (25.3% vs. 4.5%, p < 0.001), arrhythmias (9.3% vs. 5.0%, p = 0.034), and inflammatory heart disease (8.0% vs. 1.1%, p < 0.001) were more frequent in patients with myocardial injury compared to those without. At multivariate analysis, myocardial injury (HR 1.95 [95% CI:1.05-3.61]), age (HR 1.09 [95% CI:1.06-1.12]), and chronic kidney disease (HR 2.63 [95% CI:1.33-5.21]) were independent predictors of death. Myocardial injury (HR 3.92 [95% CI:2.07-7.42]), age (HR 1.05 [95% CI:1.02-1.08]), and diabetes (HR 2.35 [95% CI:1.25-4.43]) were independent predictors of MACCE. Conclusion: In COVID-19 survivors, myocardial injury during the hospital stay portends a higher risk of mortality and cardiovascular sequelae and could be considered for the risk stratification of COVID-19 sequelae in patients who are successfully discharged.

7.
Baruch, Joaquin, Rojek, Amanda, Kartsonaki, Christiana, Vijayaraghavan, Bharath K. T.; Gonçalves, Bronner P.; Pritchard, Mark G.; Merson, Laura, Dunning, Jake, Hall, Matthew, Sigfrid, Louise, Citarella, Barbara W.; Murthy, Srinivas, Yeabah, Trokon O.; Olliaro, Piero, Abbas, Ali, Abdukahil, Sheryl Ann, Abdulkadir, Nurul Najmee, Abe, Ryuzo, Abel, Laurent, Absil, Lara, Acharya, Subhash, Acker, Andrew, Adam, Elisabeth, Adrião, Diana, Al Ageel, Saleh, Ahmed, Shakeel, Ainscough, Kate, Airlangga, Eka, Aisa, Tharwat, Hssain, Ali Ait, Tamlihat, Younes Ait, Akimoto, Takako, Akmal, Ernita, Al Qasim, Eman, Alalqam, Razi, Alberti, Angela, Al‐dabbous, Tala, Alegesan, Senthilkumar, Alegre, Cynthia, Alessi, Marta, Alex, Beatrice, Alexandre, Kévin, Al‐Fares, Abdulrahman, Alfoudri, Huda, Ali, Imran, Ali, Adam, Shah, Naseem Ali, Alidjnou, Kazali Enagnon, Aliudin, Jeffrey, Alkhafajee, Qabas, Allavena, Clotilde, Allou, Nathalie, Altaf, Aneela, Alves, João, Alves, Rita, Alves, João Melo, Amaral, Maria, Amira, Nur, Ampaw, Phoebe, Andini, Roberto, Andréjak, Claire, Angheben, Andrea, Angoulvant, François, Ansart, Séverine, Anthonidass, Sivanesen, Antonelli, Massimo, de Brito, Carlos Alexandre Antunes, Apriyana, Ardiyan, Arabi, Yaseen, Aragao, Irene, Araujo, Carolline, Arcadipane, Antonio, Archambault, Patrick, Arenz, Lukas, Arlet, Jean‐Benoît, Arora, Lovkesh, Arora, Rakesh, Artaud‐Macari, Elise, Aryal, Diptesh, Asensio, Angel, Ashraf, Muhammad, Asif, Namra, Asim, Mohammad, Assie, Jean Baptiste, Asyraf, Amirul, Atique, Anika, Attanyake, A. M. Udara Lakshan, Auchabie, Johann, Aumaitre, Hugues, Auvet, Adrien, Axelsen, Eyvind W.; Azemar, Laurène, Azoulay, Cecile, Bach, Benjamin, Bachelet, Delphine, Badr, Claudine, Bævre‐Jensen, Roar, Baig, Nadia, Baillie, J. Kenneth, Baird, J. Kevin, Bak, Erica, Bakakos, Agamemnon, Bakar, Nazreen Abu, Bal, Andriy, Balakrishnan, Mohanaprasanth, Balan, Valeria, Bani‐Sadr, Firouzé, Barbalho, Renata, Barbosa, Nicholas Yuri, Barclay, Wendy S.; Barnett, Saef Umar, Barnikel, Michaela, Barrasa, Helena, Barrelet, Audrey, Barrigoto, Cleide, Bartoli, Marie, Baruch, Joaquín, Bashir, Mustehan, Basmaci, Romain, Basri, Muhammad Fadhli Hassin, Battaglini, Denise, Bauer, Jules, Rincon, Diego Fernando Bautista, Dow, Denisse Bazan, Beane, Abigail, Bedossa, Alexandra, Bee, Ker Hong, Begum, Husna, Behilill, Sylvie, Beishuizen, Albertus, Beljantsev, Aleksandr, Bellemare, David, Beltrame, Anna, Beltrão, Beatriz Amorim, Beluze, Marine, Benech, Nicolas, Benjiman, Lionel Eric, Benkerrou, Dehbia, Bennett, Suzanne, Bento, Luís, Berdal, Jan‐Erik, Bergeaud, Delphine, Bergin, Hazel, Sobrino, José Luis Bernal, Bertoli, Giulia, Bertolino, Lorenzo, Bessis, Simon, Bevilcaqua, Sybille, Bezulier, Karine, Bhatt, Amar, Bhavsar, Krishna, Bianco, Claudia, Bidin, Farah Nadiah, Singh, Moirangthem Bikram, Humaid, Felwa Bin, Kamarudin, Mohd Nazlin Bin, Bissuel, François, Bitker, Laurent, Bitton, Jonathan, Blanco‐Schweizer, Pablo, Blier, Catherine, Bloos, Frank, Blot, Mathieu, Boccia, Filomena, Bodenes, Laetitia, Bogaarts, Alice, Bogaert, Debby, Boivin, Anne‐Hélène, Bolze, Pierre‐Adrien, Bompart, François, Bonfasius, Aurelius, Borges, Diogo, Borie, Raphaël, Bosse, Hans Martin, Botelho‐Nevers, Elisabeth, Bouadma, Lila, Bouchaud, Olivier, Bouchez, Sabelline, Bouhmani, Dounia, Bouhour, Damien, Bouiller, Kévin, Bouillet, Laurence, Bouisse, Camile, Boureau, Anne‐Sophie, Bourke, John, Bouscambert, Maude, Bousquet, Aurore, Bouziotis, Jason, Boxma, Bianca, Boyer‐Besseyre, Marielle, Boylan, Maria, Bozza, Fernando Augusto, Braconnier, Axelle, Braga, Cynthia, Brandenburger, Timo, Monteiro, Filipa Brás, Brazzi, Luca, Breen, Patrick, Breen, Dorothy, Breen, Patrick, Brickell, Kathy, Browne, Shaunagh, Browne, Alex, Brozzi, Nicolas, Brunvoll, Sonja Hjellegjerde, Brusse‐Keizer, Marjolein, Buchtele, Nina, Buesaquillo, Christian, Bugaeva, Polina, Buisson, Marielle, Buonsenso, Danilo, Burhan, Erlina, Burrell, Aidan, Bustos, Ingrid G.; Butnaru, Denis, Cabie, André, Cabral, Susana, Caceres, Eder, Cadoz, Cyril, Calligy, Kate, Calvache, Jose Andres, Camões, João, Campana, Valentine, Campbell, Paul, Campisi, Josie, Canepa, Cecilia, Cantero, Mireia, Caraux‐Paz, Pauline, Cárcel, Sheila, Cardellino, Chiara Simona, Cardoso, Sofia, Cardoso, Filipe, Cardoso, Filipa, Cardoso, Nelson, Carelli, Simone, Carlier, Nicolas, Carmoi, Thierry, Carney, Gayle, Carqueja, Inês, Carret, Marie‐Christine, Carrier, François Martin, Carroll, Ida, Carson, Gail, Casanova, Maire‐Laure, Cascão, Mariana, Casey, Siobhan, Casimiro, José, Cassandra, Bailey, Castañeda, Silvia, Castanheira, Nidyanara, Castor‐Alexandre, Guylaine, Castrillón, Henry, Castro, Ivo, Catarino, Ana, Catherine, François‐Xavier, Cattaneo, Paolo, Cavalin, Roberta, Cavalli, Giulio Giovanni, Cavayas, Alexandros, Ceccato, Adrian, Cervantes‐Gonzalez, Minerva, Chair, Anissa, Chakveatze, Catherine, Chan, Adrienne, Chand, Meera, Auger, Christelle Chantalat, Chapplain, Jean‐Marc, Chas, Julie, Chatterjee, Allegra, Chaudry, Mobin, Iñiguez, Jonathan Samuel Chávez, Chen, Anjellica, Chen, Yih‐Sharng, Cheng, Matthew Pellan, Cheret, Antoine, Chiarabini, Thibault, Chica, Julian, Chidambaram, Suresh Kumar, Tho, Leong Chin, Chirouze, Catherine, Chiumello, Davide, Cho, Sung‐Min, Cholley, Bernard, Chopin, Marie‐Charlotte, Chow, Ting Soo, Chow, Yock Ping, Chua, Jonathan, Chua, Hiu Jian, Cidade, Jose Pedro, Herreros, José Miguel Cisneros, Citarella, Barbara Wanjiru, Ciullo, Anna, Clarke, Jennifer, Clarke, Emma, Granado, Rolando Claure‐Del, Clohisey, Sara, Cobb, Perren J.; Codan, Cassidy, Cody, Caitriona, Coelho, Alexandra, Coles, Megan, Colin, Gwenhaël, Collins, Michael, Colombo, Sebastiano Maria, Combs, Pamela, Connor, Marie, Conrad, Anne, Contreras, Sofía, Conway, Elaine, Cooke, Graham S.; Copland, Mary, Cordel, Hugues, Corley, Amanda, Cornelis, Sabine, Cornet, Alexander Daniel, Corpuz, Arianne Joy, Cortegiani, Andrea, Corvaisier, Grégory, Costigan, Emma, Couffignal, Camille, Couffin‐Cadiergues, Sandrine, Courtois, Roxane, Cousse, Stéphanie, Cregan, Rachel, Croonen, Sabine, Crowl, Gloria, Crump, Jonathan, Cruz, Claudina, Bermúdez, Juan Luis Cruz, Rojo, Jaime Cruz, Csete, Marc, Cullen, Ailbhe, Cummings, Matthew, Curley, Gerard, Curlier, Elodie, Curran, Colleen, Custodio, Paula, da Silva Filipe, Ana, Da Silveira, Charlene, Dabaliz, Al‐Awwab, Dagens, Andrew, Dahl, John Arne, Dahly, Darren, Dalton, Heidi, Dalton, Jo, Daly, Seamus, Daneman, Nick, Daniel, Corinne, Dankwa, Emmanuelle A.; Dantas, Jorge, D'Aragon, Frédérick, de Loughry, Gillian, de Mendoza, Diego, De Montmollin, Etienne, de Oliveira França, Rafael Freitas, de Pinho Oliveira, Ana Isabel, De Rosa, Rosanna, De Rose, Cristina, de Silva, Thushan, de Vries, Peter, Deacon, Jillian, Dean, David, Debard, Alexa, Debray, Marie‐Pierre, DeCastro, Nathalie, Dechert, William, Deconninck, Lauren, Decours, Romain, Defous, Eve, Delacroix, Isabelle, Delaveuve, Eric, Delavigne, Karen, Delfos, Nathalie M.; Deligiannis, Ionna, Dell'Amore, Andrea, Delmas, Christelle, Delobel, Pierre, Delsing, Corine, Demonchy, Elisa, Denis, Emmanuelle, Deplanque, Dominique, Depuydt, Pieter, Desai, Mehul, Descamps, Diane, Desvallées, Mathilde, Dewayanti, Santi, Dhanger, Pathik, Diallo, Alpha, Diamantis, Sylvain, Dias, André, Diaz, Juan Jose, Diaz, Priscila, Diaz, Rodrigo, Didier, Kévin, Diehl, Jean‐Luc, Dieperink, Wim, Dimet, Jérôme, Dinot, Vincent, Diop, Fara, Diouf, Alphonsine, Dishon, Yael, Djossou, Félix, Docherty, Annemarie B.; Doherty, Helen, Dondorp, Arjen M.; Donnelly, Maria, Donnelly, Christl A.; Donohue, Sean, Donohue, Yoann, Donohue, Chloe, Doran, Peter, Dorival, Céline, D'Ortenzio, Eric, Douglas, James Joshua, Douma, Renee, Dournon, Nathalie, Downer, Triona, Downey, Joanne, Downing, Mark, Drake, Tom, Driscoll, Aoife, Dryden, Murray, Fonseca, Claudio Duarte, Dubee, Vincent, Dubos, François, Ducancelle, Alexandre, Duculan, Toni, Dudman, Susanne, Duggal, Abhijit, Dunand, Paul, Dunning, Jake, Duplaix, Mathilde, Durante‐Mangoni, Emanuele, Durham, Lucian, Dussol, Bertrand, Duthoit, Juliette, Duval, Xavier, Dyrhol‐Riise, Anne Margarita, Ean, Sim Choon, Echeverria‐Villalobos, Marco, Egan, Siobhan, Eggesbø, Linn Margrete, Eira, Carla, El Sanharawi, Mohammed, Elapavaluru, Subbarao, Elharrar, Brigitte, Ellerbroek, Jacobien, Ellingjord‐Dale, Merete, Eloy, Philippine, Elshazly, Tarek, Elyazar, Iqbal, Enderle, Isabelle, Endo, Tomoyuki, Eng, Chan Chee, Engelmann, Ilka, Enouf, Vincent, Epaulard, Olivier, Escher, Martina, Esperatti, Mariano, Esperou, Hélène, Esposito‐Farese, Marina, Estevão, João, Etienne, Manuel, Ettalhaoui, Nadia, Everding, Anna Greti, Evers, Mirjam, Fabre, Marc, Fabre, Isabelle, Faheem, Amna, Fahy, Arabella, Fairfield, Cameron J.; Fakar, Zul, Fareed, Komal, Faria, Pedro, Farooq, Ahmed, Fateena, Hanan, Fatoni, Arie Zainul, Faure, Karine, Favory, Raphaël, Fayed, Mohamed, Feely, Niamh, Feeney, Laura, Fernandes, Jorge, Fernandes, Marília Andreia, Fernandes, Susana, Ferrand, François‐Xavier, Devouge, Eglantine Ferrand, Ferrão, Joana, Ferraz, Mário, Ferreira, Sílvia, Ferreira, Isabel, Ferreira, Benigno, Ferrer‐Roca, Ricard, Ferriere, Nicolas, Ficko, Céline, Figueiredo‐Mello, Claudia, Finlayson, William, Fiorda, Juan, Flament, Thomas, Flateau, Clara, Fletcher, Tom, Florio, Letizia Lucia, Flynn, Deirdre, Foley, Claire, Foley, Jean, Fomin, Victor, Fonseca, Tatiana, Fontela, Patricia, Forsyth, Simon, Foster, Denise, Foti, Giuseppe, Fourn, Erwan, Fowler, Robert A.; Fraher, Marianne, Franch‐Llasat, Diego, Fraser, John F.; Fraser, Christophe, Freire, Marcela Vieira, Ribeiro, Ana Freitas, Friedrich, Caren, Fry, Stéphanie, Fuentes, Nora, Fukuda, Masahiro, Argin, G.; Gaborieau, Valérie, Gaci, Rostane, Gagliardi, Massimo, Gagnard, Jean‐Charles, Gagneux‐Brunon, Amandine, Gaião, Sérgio, Skeie, Linda Gail, Gallagher, Phil, Gamble, Carrol, Gani, Yasmin, Garan, Arthur, Garcia, Rebekha, Barrio, Noelia García, Garcia‐Diaz, Julia, Garcia‐Gallo, Esteban, Garimella, Navya, Garot, Denis, Garrait, Valérie, Gauli, Basanta, Gault, Nathalie, Gavin, Aisling, Gavrylov, Anatoliy, Gaymard, Alexandre, Gebauer, Johannes, Geraud, Eva, Morlaes, Louis Gerbaud, Germano, Nuno, Ghisulal, Praveen Kumar, Ghosn, Jade, Giani, Marco, Gibson, Jess, Gigante, Tristan, Gilg, Morgane, Gilroy, Elaine, Giordano, Guillermo, Girvan, Michelle, Gissot, Valérie, Glikman, Daniel, Glybochko, Petr, Gnall, Eric, Goco, Geraldine, Goehringer, François, Goepel, Siri, Goffard, Jean‐Christophe, Goh, Jin Yi, Golob, Jonathan, Gomez, Kyle, Gómez‐Junyent, Joan, Gominet, Marie, Gonçalves, Bronner P.; Gonzalez, Alicia, Gordon, Patricia, Gorenne, Isabelle, Goubert, Laure, Goujard, Cécile, Goulenok, Tiphaine, Grable, Margarite, Graf, Jeronimo, Grandin, Edward Wilson, Granier, Pascal, Grasselli, Giacomo, Green, Christopher A.; Greene, Courtney, Greenhalf, William, Greffe, Segolène, Grieco, Domenico Luca, Griffee, Matthew, Griffiths, Fiona, Grigoras, Ioana, Groenendijk, Albert, Lordemann, Anja Grosse, Gruner, Heidi, Gu, Yusing, Guedj, Jérémie, Guego, Martin, Guellec, Dewi, Guerguerian, Anne‐Marie, Guerreiro, Daniela, Guery, Romain, Guillaumot, Anne, Guilleminault, Laurent, Guimarães de Castro, Maisa, Guimard, Thomas, Haalboom, Marieke, Haber, Daniel, Habraken, Hannah, Hachemi, Ali, Hackmann, Amy, Hadri, Nadir, Haidri, Fakhir, Hakak, Sheeba, Hall, Adam, Hall, Matthew, Halpin, Sophie, Hameed, Jawad, Hamer, Ansley, Hamers, Raph L.; Hamidfar, Rebecca, Hammarström, Bato, Hammond, Terese, Han, Lim Yuen, Haniffa, Rashan, Hao, Kok Wei, Hardwick, Hayley, Harrison, Ewen M.; Harrison, Janet, Harrison, Samuel Bernard Ekow, Hartman, Alan, Hasan, Mohd Shahnaz, Hashmi, Junaid, Hayat, Muhammad, Hayes, Ailbhe, Hays, Leanne, Heerman, Jan, Heggelund, Lars, Hendry, Ross, Hennessy, Martina, Henriquez‐Trujillo, Aquiles, Hentzien, Maxime, Hernandez‐Montfort, Jaime, Hershey, Andrew, Hesstvedt, Liv, Hidayah, Astarini, Higgins, Eibhilin, Higgins, Dawn, Higgins, Rupert, Hinchion, Rita, Hinton, Samuel, Hiraiwa, Hiroaki, Hirkani, Haider, Hitoto, Hikombo, Ho, Yi Bin, Ho, Antonia, Hoctin, Alexandre, Hoffmann, Isabelle, Hoh, Wei Han, Hoiting, Oscar, Holt, Rebecca, Holter, Jan Cato, Horby, Peter, Horcajada, Juan Pablo, Hoshino, Koji, Houas, Ikram, Hough, Catherine L.; Houltham, Stuart, Hsu, Jimmy Ming‐Yang, Hulot, Jean‐Sébastien, Huo, Stella, Hurd, Abby, Hussain, Iqbal, Ijaz, Samreen, Illes, Hajnal‐Gabriela, Imbert, Patrick, Imran, Mohammad, Sikander, Rana Imran, Imtiaz, Aftab, Inácio, Hugo, Dominguez, Carmen Infante, Ing, Yun Sii, Iosifidis, Elias, Ippolito, Mariachiara, Isgett, Sarah, Isidoro, Tiago, Ismail, Nadiah, Isnard, Margaux, Istre, Mette Stausland, Itai, Junji, Ivulich, Daniel, Jaafar, Danielle, Jaafoura, Salma, Jabot, Julien, Jackson, Clare, Jamieson, Nina, Jaquet, Pierre, Jaud‐Fischer, Coline, Jaureguiberry, Stéphane, Jaworsky, Denise, Jego, Florence, Jelani, Anilawati Mat, Jenum, Synne, Jimbo‐Sotomayor, Ruth, Joe, Ong Yiaw, Jorge García, Ruth N.; Jørgensen, Silje Bakken, Joseph, Cédric, Joseph, Mark, Joshi, Swosti, Jourdain, Mercé, Jouvet, Philippe, Jung, Hanna, Jung, Anna, Juzar, Dafsah, Kafif, Ouifiya, Kaguelidou, Florentia, Kaisbain, Neerusha, Kaleesvran, Thavamany, Kali, Sabina, Kalicinska, Alina, Kalleberg, Karl Trygve, Kalomoiri, Smaragdi, Kamaluddin, Muhammad Aisar Ayadi, Kamaruddin, Zul Amali Che, Kamarudin, Nadiah, Kamineni, Kavita, Kandamby, Darshana Hewa, Kandel, Chris, Kang, Kong Yeow, Kanwal, Darakhshan, Karpayah, Pratap, Kartsonaki, Christiana, Kasugai, Daisuke, Kataria, Anant, Katz, Kevin, Kaur, Aasmine, Kay, Christy, Keane, Hannah, Keating, Seán, Kedia, Pulak, Kelly, Claire, Kelly, Yvelynne, Kelly, Andrea, Kelly, Niamh, Kelly, Aoife, Kelly, Sadie, Kelsey, Maeve, Kennedy, Ryan, Kennon, Kalynn, Kernan, Maeve, Kerroumi, Younes, Keshav, Sharma, Khalid, Imrana, Khalid, Osama, Khalil, Antoine, Khan, Coralie, Khan, Irfan, Khan, Quratul Ain, Khanal, Sushil, Khatak, Abid, Khawaja, Amin, Kherajani, Krish, Kho, Michelle E.; 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Y.; Oosthuyzen, Wilna, Opavsky, Anne, Openshaw, Peter, Orakzai, Saijad, Orozco‐Chamorro, Claudia Milena, Ortoleva, Jamel, Osatnik, Javier, O'Shea, Linda, O'Sullivan, Miriam, Othman, Siti Zubaidah, Ouamara, Nadia, Ouissa, Rachida, Oziol, Eric, Pagadoy, Maïder, Pages, Justine, Palacios, Mario, Palacios, Amanda, Palmarini, Massimo, Panarello, Giovanna, Panda, Prasan Kumar, Paneru, Hem, Pang, Lai Hui, Panigada, Mauro, Pansu, Nathalie, Papadopoulos, Aurélie, Parke, Rachael, Parker, Melissa, Parra, Briseida, Pasha, Taha, Pasquier, Jérémie, Pastene, Bruno, Patauner, Fabian, Patel, Drashti, Pathmanathan, Mohan Dass, Patrão, Luís, Patricio, Patricia, Patrier, Juliette, Patterson, Lisa, Pattnaik, Rajyabardhan, Paul, Mical, Paul, Christelle, Paulos, Jorge, Paxton, William A.; Payen, Jean‐François, Peariasamy, Kalaiarasu, Jiménez, Miguel Pedrera, Peek, Giles J.; Peelman, Florent, Peiffer‐Smadja, Nathan, Peigne, Vincent, Pejkovska, Mare, Pelosi, Paolo, Peltan, Ithan D.; Pereira, Rui, Perez, Daniel, Periel, Luis, Perpoint, Thomas, Pesenti, Antonio, Pestre, Vincent, Petrou, Lenka, Petrovic, Michele, Petrov‐Sanchez, Ventzislava, Pettersen, Frank Olav, Peytavin, Gilles, Pharand, Scott, Picard, Walter, Picone, Olivier, de Piero, Maria, Pierobon, Carola, Piersma, Djura, Pimentel, Carlos, Pinto, Raquel, Pires, Catarina, Pironneau, Isabelle, Piroth, Lionel, Pitaloka, Ayodhia, Pius, Riinu, Plantier, Laurent, Png, Hon Shen, Poissy, Julien, Pokeerbux, Ryadh, Pokorska‐Spiewak, Maria, Poli, Sergio, Pollakis, Georgios, Ponscarme, Diane, Popielska, Jolanta, Porto, Diego Bastos, Post, Andra‐Maris, Postma, Douwe F.; Povoa, Pedro, Póvoas, Diana, Powis, Jeff, Prapa, Sofia, Preau, Sébastien, Prebensen, Christian, Preiser, Jean‐Charles, Prinssen, Anton, Pritchard, Mark G.; Priyadarshani, Gamage Dona Dilanthi, Proença, Lucia, Pudota, Sravya, Puéchal, Oriane, Semedi, Bambang Pujo, Pulicken, Mathew, Purcell, Gregory, Quesada, Luisa, Quinones‐Cardona, Vilmaris, González, Víctor Quirós, Quist‐Paulsen, Else, Quraishi, Mohammed, Rabaa, Maia, Rabaud, Christian, Rabindrarajan, Ebenezer, Rafael, Aldo, Rafiq, Marie, Rahardjani, Mutia, Rahman, Rozanah Abd, Rahman, Ahmad Kashfi Haji Ab, Rahutullah, Arsalan, Rainieri, Fernando, Rajahram, Giri Shan, Ramachandran, Pratheema, Ramakrishnan, Nagarajan, Ramli, Ahmad Afiq, Rammaert, Blandine, Ramos, Grazielle Viana, Rana, Asim, Rangappa, Rajavardhan, Ranjan, Ritika, Rapp, Christophe, Rashan, Aasiyah, Rashan, Thalha, Rasheed, Ghulam, Rasmin, Menaldi, Rätsep, Indrek, Rau, Cornelius, Ravi, Tharmini, Raza, Ali, Real, Andre, Rebaudet, Stanislas, Redl, Sarah, Reeve, Brenda, Rehman, Attaur, Reid, Liadain, Reikvam, Dag Henrik, Reis, Renato, Rello, Jordi, Remppis, Jonathan, Remy, Martine, Ren, Hongru, Renk, Hanna, Resseguier, Anne‐Sophie, Revest, Matthieu, Rewa, Oleksa, Reyes, Luis Felipe, Reyes, Tiago, Ribeiro, Maria Ines, Ricchiuto, Antonia, Richardson, David, Richardson, Denise, Richier, Laurent, Ridzuan, Siti Nurul Atikah Ahmad, Riera, Jordi, Rios, Ana L.; Rishu, Asgar, Rispal, Patrick, Risso, Karine, Nuñez, Maria Angelica Rivera, Rizer, Nicholas, Robba, Chiara, Roberto, André, Roberts, Stephanie, Robertson, David L.; Robineau, Olivier, Roche‐Campo, Ferran, Rodari, Paola, Rodeia, Simão, Abreu, Julia Rodriguez, Roessler, Bernhard, Roger, Pierre‐Marie, Roger, Claire, Roilides, Emmanuel, Rojek, Amanda, Romaru, Juliette, Roncon‐Albuquerque, Roberto, Roriz, Mélanie, Rosa‐Calatrava, Manuel, Rose, Michael, Rosenberger, Dorothea, Roslan, Nurul Hidayah Mohammad, Rossanese, Andrea, Rossetti, Matteo, Rossignol, Bénédicte, Rossignol, Patrick, Rousset, Stella, Roy, Carine, Roze, Benoît, Rusmawatiningtyas, Desy, Russell, Clark D.; Ryan, Maria, Ryan, Maeve, Ryckaert, Steffi, Holten, Aleksander Rygh, Saba, Isabela, Sadaf, Sairah, Sadat, Musharaf, Sahraei, Valla, Saint‐Gilles, Maximilien, Sakiyalak, Pranya, Salahuddin, Nawal, Salazar, Leonardo, Saleem, Jodat, Sales, Gabriele, Sallaberry, Stéphane, Salmon Gandonniere, Charlotte, Salvator, Hélène, Sanchez, Olivier, Sanchez‐Miralles, Angel, Sancho‐Shimizu, Vanessa, Sandhu, Gyan, Sandhu, Zulfiqar, Sandrine, Pierre‐François, Sandulescu, Oana, Santos, Marlene, Sarfo‐Mensah, Shirley, Banheiro, Bruno Sarmento, Sarmiento, Iam Claire E.; 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Influenza and Other Respiratory Viruses ; 2022.
Article in English | Web of Science | ID: covidwho-2019369

ABSTRACT

Introduction: Case definitions are used to guide clinical practice, surveillance and research protocols. However, how they identify COVID-19-hospitalised patients is not fully understood. We analysed the proportion of hospitalised patients with laboratory-confirmed COVID-19, in the ISARIC prospective cohort study database, meeting widely used case definitions. Methods: Patients were assessed using the Centers for Disease Control (CDC), European Centre for Disease Prevention and Control (ECDC), World Health Organization (WHO) and UK Health Security Agency (UKHSA) case definitions by age, region and time. Case fatality ratios (CFRs) and symptoms of those who did and who did not meet the case definitions were evaluated. Patients with incomplete data and non-laboratory-confirmed test result were excluded. Results: A total of 263,218 of the patients (42%) in the ISARIC database were included. Most patients (90.4%) were from Europe arid Central Asia. The proportions of patients meeting the case definitions were 56.8% (WHO), 74.4% (UKHSA), 81.6% (ECDC) and 82.3% (CDC). For each case definition, patients at the extremes of age distribution met the criteria less frequently than those aged 30 to 70 years;geographical and time variations were also observed. Estimated CFRs were similar for the patients who met the case definitions. However, when more patients did riot meet the case definition, the CFR increased. Conclusions: The performance of case definitions might be different in different regions and may change over time. Similarly concerning is the fact that older patients often did not meet case definitions, risking delayed medical care. While epidemiologists must balance their analytics with field applicability, ongoing revision of case definitions is necessary to improve patient care through early diagnosis and limit potential nosocomial spread.

8.
Healthcare (Basel) ; 10(7)2022 Jun 22.
Article in English | MEDLINE | ID: covidwho-1911292

ABSTRACT

Background. To evaluate relationships between lung aeration assessed by lung ultrasound (LUS) with viscoelastic profiles obtained by thromboelastography (TEG) in COVID-19 respiratory failure. Methods. Retrospective analysis in a tertiary ICU in Rome, Italy. Forty invasively ventilated adults with COVID-19 underwent LUS and TEG assessment. A simplified LUS protocol consisting in scanning six areas, three per side, was adopted. A score from 0 to 3 was assigned to each area. TEG®6s was used to obtain viscoelastic hemostatic assay parameters which were compared to LUS score. Results. There was a significant inverse correlation between LUS score and static compliance of the respiratory system (Crs, rs -0.75; p < 0.001). We found a significant association between LUS and functional fibrinogen maximum amplitude (FF-MA): among 18 patients with LUS score ≤ 12, median FF-MA was 31 mm [IQR 28-39] whilst, among 22 patients with LUS score > 12, it was 46.3 mm [IQR 40-53], p = 0.0004. Median of the citrated recalcified kaolin-activated maximum amplitude (CK-MA) was 66.1 mm [64.4-68] in the LUS score ≤ 12 group, and 69.6 [68.5-70.7] when LUS score > 12, p < 0.002. Conclusions. The hypercoagulable profile as defined by elevated FF-MA and CK-MA may be associated with a low degree of lung aeration as assessed by LUS.

9.
Intensive Care Med ; 48(6): 690-705, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1899123

ABSTRACT

PURPOSE: To accommodate the unprecedented number of critically ill patients with pneumonia caused by coronavirus disease 2019 (COVID-19) expansion of the capacity of intensive care unit (ICU) to clinical areas not previously used for critical care was necessary. We describe the global burden of COVID-19 admissions and the clinical and organizational characteristics associated with outcomes in critically ill COVID-19 patients. METHODS: Multicenter, international, point prevalence study, including adult patients with SARS-CoV-2 infection confirmed by polymerase chain reaction (PCR) and a diagnosis of COVID-19 admitted to ICU between February 15th and May 15th, 2020. RESULTS: 4994 patients from 280 ICUs in 46 countries were included. Included ICUs increased their total capacity from 4931 to 7630 beds, deploying personnel from other areas. Overall, 1986 (39.8%) patients were admitted to surge capacity beds. Invasive ventilation at admission was present in 2325 (46.5%) patients and was required during ICU stay in 85.8% of patients. 60-day mortality was 33.9% (IQR across units: 20%-50%) and ICU mortality 32.7%. Older age, invasive mechanical ventilation, and acute kidney injury (AKI) were associated with increased mortality. These associations were also confirmed specifically in mechanically ventilated patients. Admission to surge capacity beds was not associated with mortality, even after controlling for other factors. CONCLUSIONS: ICUs responded to the increase in COVID-19 patients by increasing bed availability and staff, admitting up to 40% of patients in surge capacity beds. Although mortality in this population was high, admission to a surge capacity bed was not associated with increased mortality. Older age, invasive mechanical ventilation, and AKI were identified as the strongest predictors of mortality.


Subject(s)
Acute Kidney Injury , COVID-19 , Adult , Critical Illness , Humans , Intensive Care Units , Respiration, Artificial , SARS-CoV-2
10.
Int J Environ Res Public Health ; 19(10)2022 05 12.
Article in English | MEDLINE | ID: covidwho-1855593

ABSTRACT

The Prospective Study of Intensivists and COVID-19 (PSIC) is a longitudinal study that besides investigating a cohort of intensivists from one of the two COVID-19 hub hospitals in Central Italy since the beginning of the pandemic (first wave, April 2020), has conducted a new survey at each successive wave. In addition to the variables investigated in previous surveys (job changes due to the pandemic, justice of safety procedures, job stress, sleep quality, satisfaction, happiness, anxiety, depression, burnout, and intention to quit), the latest fourth wave (December 2021) study has evaluated discomfort in caring for anti-vax patients. A multivariate logistic regression model confirmed that high levels of occupational stress (distressed 75.8%) were associated with isolation, monotony, lack of time for meditation, and poor relationships with anti-vaccination patients. Compared to the first phase, there was a reduction in levels of insomnia and anxiety, but the percentage of intensivists manifesting symptoms of depression remained high (58.9%). The study underlined the efficacy of organizational interventions and psychological support.


Subject(s)
Burnout, Professional , COVID-19 , Occupational Stress , Burnout, Professional/epidemiology , Burnout, Professional/psychology , COVID-19/epidemiology , Humans , Job Satisfaction , Longitudinal Studies , Occupational Stress/epidemiology , Prospective Studies
11.
PLoS One ; 17(4): e0267038, 2022.
Article in English | MEDLINE | ID: covidwho-1817490

ABSTRACT

INTRODUCTION: Remdesivir and Dexamethasone represent the cornerstone of therapy for critically ill patients with acute hypoxemic respiratory failure caused by Coronavirus Disease 2019 (COVID-19). However, clinical efficacy and safety of concomitant administration of Remdesivir and Dexamethasone (Rem-Dexa) in severe COVID-19 patients on high flow oxygen therapy (HFOT) or non-invasive ventilation (NIV) remains unknown. MATERIALS AND METHODS: Prospective cohort study that was performed in two medical Intensive Care Units (ICUs) of a tertiary university hospital. The clinical impact of Rem-Dexa administration in hypoxemic patients with COVID-19, who required NIV or HFOT and selected on the simplified acute physiology score II, the sequential organ failure assessment score and the Charlson Comorbidity Index score, was investigated. The primary outcome was 28-day intubation rate; secondary outcomes were end-of-treatment clinical improvement and PaO2/FiO2 ratio, laboratory abnormalities and clinical complications, ICU and hospital length of stay, 28-day and 90-day mortality. RESULTS: We included 132 patients and found that 28-day intubation rate was significantly lower among Rem-Dexa group (19.7% vs 48.5%, p<0.01). Although the end-of-treatment clinical improvement was larger among Rem-Dexa group (69.7% vs 51.5%, p = 0.05), the 28-day and 90-day mortalities were similar (4.5% and 10.6% vs. 15.2% and 16.7%; p = 0.08 and p = 0.45, respectively). The logistic regression and Cox-regression models showed that concomitant Rem-Dexa therapy was associated with a reduction of 28-day intubation rate (OR 0.22, CI95% 0.05-0.94, p = 0.04), in absence of laboratory abnormalities and clinical complications (p = ns). CONCLUSIONS: In COVID-19 critically ill patients receiving HFO or NIV, 28-day intubation rate was lower in patients who received Rem-Dexa and this finding corresponded to lower end-of-treatment clinical improvement. The individual contribution of either Remdesevir or Dexamethasone to the observed clinical effect should be further investigated.


Subject(s)
COVID-19 , Noninvasive Ventilation , Adenosine Monophosphate/analogs & derivatives , Alanine/analogs & derivatives , COVID-19/drug therapy , Cohort Studies , Critical Illness , Dexamethasone/therapeutic use , Humans , Oxygen , Prospective Studies
13.
Minerva Anestesiol ; 88(1-2): 6-7, 2022.
Article in English | MEDLINE | ID: covidwho-1716373
14.
Ind Health ; 60(1): 75-78, 2022 Feb 08.
Article in English | MEDLINE | ID: covidwho-1677635

ABSTRACT

We aimed to evaluate the impact of the COVID-19 pandemic on anaesthesiology residents in a COVID-19 hub hospital in Latium and ascertain their level of perceived justice and work-related stress. Residents and specialist anaesthesiologists were recruited during April-May 2020. Informational and procedural justice were measured with the Organizational Justice questionnaire; work-related stress was measured with the Effort Reward Imbalance questionnaire. Interns perceived a significantly lower level of informational justice than specialists. Organizational justice protected from occupational stress (OR=0.860, CI95% 0.786-0.940). Our findings suggest that it would be useful to improve knowledge of safety measures in trainees, increasing their confidence in work organization and reducing stress.


Subject(s)
COVID-19 , Anesthetists , Humans , Organizational Culture , Pandemics , SARS-CoV-2 , Social Justice , Surveys and Questionnaires
16.
Comput Methods Programs Biomed ; 217: 106655, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1654240

ABSTRACT

BACKGROUND: The COVID-19 pandemic affected healthcare systems worldwide. Predictive models developed by Artificial Intelligence (AI) and based on timely, centralized and standardized real world patient data could improve management of COVID-19 to achieve better clinical outcomes. The objectives of this manuscript are to describe the structure and technologies used to construct a COVID-19 Data Mart architecture and to present how a large hospital has tackled the challenge of supporting daily management of COVID-19 pandemic emergency, by creating a strong retrospective knowledge base, a real time environment and integrated information dashboard for daily practice and early identification of critical condition at patient level. This framework is also used as an informative, continuously enriched data lake, which is a base for several on-going predictive studies. METHODS: The information technology framework for clinical practice and research was described. It was developed using SAS Institute software analytics tool and SAS® Vyia® environment and Open-Source environment R ® and Python ® for fast prototyping and modeling. The included variables and the source extraction procedures were presented. RESULTS: The Data Mart covers a retrospective cohort of 5528 patients with SARS-CoV-2 infection. People who died were older, had more comorbidities, reported more frequently dyspnea at onset, had higher d-dimer, C-reactive protein and urea nitrogen. The dashboard was developed to support the management of COVID-19 patients at three levels: hospital, single ward and individual care level. INTERPRETATION: The COVID-19 Data Mart based on integration of a large collection of clinical data and an AI-based integrated framework has been developed, based on a set of automated procedures for data mining and retrieval, transformation and integration, and has been embedded in the clinical practice to help managing daily care. Benefits from the availability of a Data Mart include the opportunity to build predictive models with a machine learning approach to identify undescribed clinical phenotypes and to foster hospital networks. A real-time updated dashboard built from the Data Mart may represent a valid tool for a better knowledge of epidemiological and clinical features of COVID-19, especially when multiple waves are observed, as well as for epidemic and pandemic events of the same nature (e. g. with critical clinical conditions leading to severe pulmonary inflammation). Therefore, we believe the approach presented in this paper may find several applications in comparable situations even at region or state levels. Finally, models predicting the course of future waves or new pandemics could largely benefit from network of DataMarts.


Subject(s)
COVID-19 , Artificial Intelligence , COVID-19/epidemiology , Clinical Decision-Making , Humans , Pandemics , Retrospective Studies , SARS-CoV-2
17.
Respir Physiol Neurobiol ; 298: 103844, 2022 04.
Article in English | MEDLINE | ID: covidwho-1620996

ABSTRACT

BACKGROUND: Use of high positive end-expiratory pressure (PEEP) and prone positioning is common in patients with COVID-19-induced acute respiratory failure. Few data clarify the hemodynamic effects of these interventions in this specific condition. We performed a physiologic study to assess the hemodynamic effects of PEEP and prone position during COVID-19 respiratory failure. METHODS: Nine adult patients mechanically ventilated due to COVID-19 infection and fulfilling moderate-to-severe ARDS criteria were studied. Respiratory mechanics, gas exchange, cardiac output, oxygen consumption, systemic and pulmonary pressures were recorded through pulmonary arterial catheterization at PEEP of 15 and 5 cmH2O, and after prone positioning. Recruitability was assessed through the recruitment-to-inflation ratio. RESULTS: High PEEP improved PaO2/FiO2 ratio in all patients (p = 0.004), and significantly decreased pulmonary shunt fraction (p = 0.012), regardless of lung recruitability. PEEP-induced increases in PaO2/FiO2 changes were strictly correlated with shunt fraction reduction (rho=-0.82, p = 0.01). From low to high PEEP, cardiac output decreased by 18 % (p = 0.05) and central venous pressure increased by 17 % (p = 0.015). As compared to supine position with low PEEP, prone positioning significantly decreased pulmonary shunt fraction (p = 0.03), increased PaO2/FiO2 (p = 0.03) and mixed venous oxygen saturation (p = 0.016), without affecting cardiac output. PaO2/FiO2 was improved by prone position also when compared to high PEEP (p = 0.03). CONCLUSIONS: In patients with moderate-to-severe ARDS due to COVID-19, PEEP and prone position improve arterial oxygenation. Changes in cardiac output contribute to the effects of PEEP but not of prone position, which appears the most effective intervention to improve oxygenation with no hemodynamic side effects.


Subject(s)
Blood Pressure/physiology , COVID-19/physiopathology , COVID-19/therapy , Heart Rate/physiology , Outcome and Process Assessment, Health Care , Oxygen Consumption/physiology , Positive-Pressure Respiration , Prone Position , Vascular Resistance/physiology , Aged , Aged, 80 and over , Female , Hemodynamic Monitoring , Humans , Intensive Care Units , Italy , Male , Middle Aged , Prone Position/physiology
18.
Microbiol Spectr ; 9(3): e0069521, 2021 12 22.
Article in English | MEDLINE | ID: covidwho-1597074

ABSTRACT

Bacterial pneumonia is a challenging coronavirus disease 2019 (COVID-19) complication for intensive care unit (ICU) clinicians. Upon its implementation, the FilmArray pneumonia plus (FA-PP) panel's practicability for both the diagnosis and antimicrobial therapy management of bacterial pneumonia was assessed in ICU patients with COVID-19. Respiratory samples were collected from patients who were mechanically ventilated at the time bacterial etiology and antimicrobial resistance were determined using both standard-of-care (culture and antimicrobial susceptibility testing [AST]) and FA-PP panel testing methods. Changes to targeted and/or appropriate antimicrobial therapy were reviewed. We tested 212 samples from 150 patients suspected of bacterial pneumonia. Etiologically, 120 samples were positive by both methods, two samples were culture positive but FA-PP negative (i.e., negative for on-panel organisms), and 90 were negative by both methods. FA-PP detected no culture-growing organisms (mostly Staphylococcus aureus or Pseudomonas aeruginosa) in 19 of 120 samples or antimicrobial resistance genes in two culture-negative samples for S. aureus organisms. Fifty-nine (27.8%) of 212 samples were from empirically treated patients. Antibiotics were discontinued in 5 (33.3%) of 15 patients with FA-PP-negative samples and were escalated/deescalated in 39 (88.6%) of 44 patients with FA-PP-positive samples. Overall, antibiotics were initiated in 87 (72.5%) of 120 pneumonia episodes and were not administered in 80 (87.0%) of 92 nonpneumonia episodes. Antimicrobial-resistant organisms caused 78 (60.0%) of 120 episodes. Excluding 19 colistin-resistant Acinetobacter baumannii episodes, AST confirmed appropriate antibiotic receipt in 101 (84.2%) of 120 episodes for one or more FA-PP-detected organisms. Compared to standard-of-care testing, the FA-PP panel may be of great value in the management of COVID-19 patients at risk of developing bacterial pneumonia in the ICU. IMPORTANCE Since bacterial pneumonia is relatively frequent, suspicion of it in COVID-19 patients may prompt ICU clinicians to overuse (broad-spectrum) antibiotics, particularly when empirical antibiotics do not cover the suspected pathogen. We showed that a PCR-based, culture-independent laboratory assay allows not only accurate diagnosis but also streamlining of antimicrobial therapy for bacterial pneumonia episodes. We report on the actual implementation of rapid diagnostics and its real-life impact on patient treatment, which is a gain over previously published studies on the topic. A better understanding of the role of that or similar PCR assays in routine ICU practice may lead us to appreciate the effectiveness of their implementation during the COVID-19 pandemic.


Subject(s)
COVID-19/complications , Hospitals , Multiplex Polymerase Chain Reaction/methods , Pneumonia, Bacterial/diagnosis , Pneumonia, Bacterial/drug therapy , Aged , Anti-Bacterial Agents/therapeutic use , Bacteria/genetics , COVID-19/diagnosis , COVID-19 Testing/methods , Critical Illness , Female , Humans , Intensive Care Units , Male , Middle Aged , Pandemics , Patient Acuity , Pneumonia, Bacterial/microbiology , SARS-CoV-2/genetics , SARS-CoV-2/isolation & purification
19.
Medicina (Kaunas) ; 57(12)2021 Dec 12.
Article in English | MEDLINE | ID: covidwho-1572559

ABSTRACT

Background and Objectives: The COVID-19 pandemic has been shaking lives around the world for nearly two years. The discovery of highly effective vaccines has not been able to stop the transmission of the virus. SARS-CoV-2 shows completely different clinical manifestations. A large percentage (about 40%) of admitted patients require treatment in an intensive care unit (ICU). This study investigates the factors associated with admission of COVID-19 patients to the ICU and whether it is possible to obtain a score that can help the emergency physician to select the hospital ward. Materials and Methods: We retrospectively recorded 313 consecutive patients who were presented to the emergency department (ED) of our hospital and had a diagnosis of COVID-19 confirmed by polymerase chain reaction (PCR) on an oropharyngeal swab. We used multiple logistic regression to evaluate demographic, clinical, and laboratory data statistically associated with ICU admission. These variables were used to create a prognostic score for ICU admission. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and receiver-operating characteristic curve (ROC) of the score for predicting ICU admission during hospitalization were calculated. Results: Of the variables evaluated, only blood type A (p = 0.003), PaO2/FiO2 (p = 0.002), LDH (p = 0.004), lactate (p = 0.03), dyspnea (p = 0.03) and SpO2 (p = 0.0228) were significantly associated with ICU admission after adjusting for sex, age and comorbidity using multiple logistic regression analysis. We used these variables to create a prognostic score called GOL2DS (group A, PaO2/FiO2, LDH, lactate and dyspnea, and SpO2), which had high accuracy in predicting ICU admission (AUROC 0.830 [95% CI, 0.791-0.892). Conclusions: In our single-center experience, the GOL2DS score could be useful in identifying patients at high risk for ICU admission.


Subject(s)
COVID-19 , Hospitalization , Humans , Intensive Care Units , Pandemics , ROC Curve , Retrospective Studies , SARS-CoV-2
20.
Am J Respir Crit Care Med ; 205(4): 431-439, 2022 02 15.
Article in English | MEDLINE | ID: covidwho-1551111

ABSTRACT

Rationale: The "Berlin definition" of acute respiratory distress syndrome (ARDS) does not allow inclusion of patients receiving high-flow nasal oxygen (HFNO). However, several articles have proposed that criteria for defining ARDS should be broadened to allow inclusion of patients receiving HFNO. Objectives: To compare the proportion of patients fulfilling ARDS criteria during HFNO and soon after intubation, and 28-day mortality between patients treated exclusively with HFNO and patients transitioned from HFNO to invasive mechanical ventilation (IMV). Methods: From previously published studies, we analyzed patients with coronavirus disease (COVID-19) who had PaO2/FiO2 of ⩽300 while treated with ⩾40 L/min HFNO, or noninvasive ventilation (NIV) with positive end-expiratory pressure of ⩾5 cm H2O (comparator). In patients transitioned from HFNO/NIV to invasive mechanical ventilation (IMV), we compared ARDS severity during HFNO/NIV and soon after IMV. We compared 28-day mortality in patients treated exclusively with HFNO/NIV versus patients transitioned to IMV. Measurements and Main Results: We analyzed 184 and 131 patients receiving HFNO or NIV, respectively. A total of 112 HFNO and 69 NIV patients transitioned to IMV. Of those, 104 (92.9%) patients on HFNO and 66 (95.7%) on NIV continued to have PaO2/FiO2 ⩽300 under IMV. Twenty-eight-day mortality in patients who remained on HFNO was 4.2% (3/72), whereas in patients transitioned from HFNO to IMV, it was 28.6% (32/112) (P < 0.001). Twenty-eight-day mortality in patients who remained on NIV was 1.6% (1/62), whereas in patients who transitioned from NIV to IMV, it was 44.9% (31/69) (P < 0.001). Overall mortality was 19.0% (35/184) and 24.4% (32/131) for HFNO and NIV, respectively (P = 0.2479). Conclusions: Broadening the ARDS definition to include patients on HFNO with PaO2/FiO2 ⩽300 may identify patients at earlier stages of disease but with lower mortality.


Subject(s)
COVID-19/therapy , Hypoxia/therapy , Oxygen Inhalation Therapy/methods , Respiratory Distress Syndrome/therapy , Aged , COVID-19/mortality , COVID-19/physiopathology , Female , Humans , Hypoxia/diagnosis , Hypoxia/mortality , Hypoxia/virology , Italy/epidemiology , Male , Middle Aged , Oxygen Inhalation Therapy/mortality , Patient Acuity , Respiration, Artificial/methods , Respiration, Artificial/mortality , Respiratory Distress Syndrome/diagnosis , Respiratory Distress Syndrome/mortality , Respiratory Distress Syndrome/virology , Treatment Outcome
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