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1. Mechanical ventilation settings during weaning from venovenous extracorporeal membrane oxygenation

Author

Passarelli, M, Petit, M, Garberi, R, Lebreton, G, Luyt, C, Pineton De Chambrun, M, Chommeloux, J, Hekimian, G, Rezoagli, E, Foti, G, Combes, A, Giani, M, Schmidt, M, Passarelli M. T., Petit M., Garberi R., Lebreton G., Luyt C. E., Pineton De Chambrun M., Chommeloux J., Hekimian G., Rezoagli E., Foti G., Combes A., Giani M., Schmidt M., Passarelli, M, Petit, M, Garberi, R, Lebreton, G, Luyt, C, Pineton De Chambrun, M, Chommeloux, J, Hekimian, G, Rezoagli, E, Foti, G, Combes, A, Giani, M, Schmidt, M, Passarelli M. T., Petit M., Garberi R., Lebreton G., Luyt C. E., Pineton De Chambrun M., Chommeloux J., Hekimian G., Rezoagli E., Foti G., Combes A., Giani M., and Schmidt M.

Abstract

Background: The optimal timing of weaning from venovenous extracorporeal membrane oxygenation (VV ECMO) and its modalities have been rarely studied. Methods: Retrospective, multicenter cohort study over 7 years in two tertiary ICUs, high-volume ECMO centers in France and Italy. Patients with ARDS on ECMO and successfully weaned from VV ECMO were classified based on their mechanical ventilation modality during the sweep gas-off trial (SGOT) with either controlled mechanical ventilation or spontaneous breathing (i.e. pressure support ventilation). The primary endpoint was the time to successful weaning from mechanical ventilation within 90 days post-ECMO weaning. Results: 292 adult patients with severe ARDS were weaned from controlled ventilation, and 101 were on spontaneous breathing during SGOT. The 90-day probability of successful weaning from mechanical ventilation was not significantly different between the two groups (sHR [95% CI], 1.23 [0.84–1.82]). ECMO-related complications were not statistically different between patients receiving these two mechanical ventilation strategies. After adjusting for covariates, older age, higher pre-ECMO sequential organ failure assessment score, pneumothorax, ventilator-associated pneumonia, and renal replacement therapy, but not mechanical ventilation modalities during SGOT, were independently associated with a lower probability of successful weaning from mechanical ventilation after ECMO weaning. Conclusions: Time to successful weaning from mechanical ventilation within 90 days post-ECMO was not associated with the mechanical ventilation strategy used during SGOT. Further research is needed to assess the optimal ventilation strategy during weaning off VV ECMO and its impact on short- and long-term outcomes.

Published
2024

2. Dynamic inflation prevents and standardized lung recruitment reverts volume loss associated with percutaneous tracheostomy during volume control ventilation: results from a Neuro-ICU population

Author

Bastia, L, Garberi, R, Querci, L, Cipolla, C, Curto, F, Rezoagli, E, Fumagalli, R, Chieregato, A, Bastia L., Garberi R., Querci L., Cipolla C., Curto F., Rezoagli E., Fumagalli R., Chieregato A., Bastia, L, Garberi, R, Querci, L, Cipolla, C, Curto, F, Rezoagli, E, Fumagalli, R, Chieregato, A, Bastia L., Garberi R., Querci L., Cipolla C., Curto F., Rezoagli E., Fumagalli R., and Chieregato A.

Abstract

To determine how percutaneous tracheostomy (PT) impacts on respiratory system compliance (Crs) and end-expiratory lung volume (EELV) during volume control ventilation and to test whether a recruitment maneuver (RM) at the end of PT may reverse lung derecruitment. This is a single center, prospective, applied physiology study. 25 patients with acute brain injury who underwent PT were studied. Patients were ventilated in volume control ventilation. Electrical impedance tomography (EIT) monitoring and respiratory mechanics measurements were performed in three steps: (a) baseline, (b) after PT, and (c) after a standardized RM (10 sighs of 30 cmH2O lasting 3 s each within 1 min). End-expiratory lung impedance (EELI) was used as a surrogate of EELV. PT determined a significant EELI loss (mean reduction of 432 arbitrary units p = 0.049) leading to a reduction in Crs (55 ± 13 vs. 62 ± 13 mL/cmH2O; p < 0.001) as compared to baseline. RM was able to revert EELI loss and restore Crs (68 ± 15 vs. 55 ± 13 mL/cmH2O; p < 0.001). In a subgroup of patients (N = 8, 31%), we observed a gradual but progressive increase in EELI. In this subgroup, patients did not experience a decrease of Crs after PT as compared to patients without dynamic inflation. Dynamic inflation did not cause hemodynamic impairment nor raising of intracranial pressure. We propose a novel and explorative hyperinflation risk index (HRI) formula. Volume control ventilation did not prevent the PT-induced lung derecruitment. RM could restore the baseline lung volume and mechanics. Dynamic inflation is common during PT, it can be monitored real-time by EIT and anticipated by HRI. The presence of dynamic inflation during PT may prevent lung derecruitment.

Published
2024

3. Time course of risk factors associated with mortality of 1260 critically ill patients with COVID-19 admitted to 24 Italian intensive care units

Author

Zanella, A., Florio, G., Antonelli, M., Bellani, G., Berselli, A., Bove, T., Cabrini, L., Carlesso, E., Castelli, G. P., Cecconi, M., Citerio, G., Coloretti, I., Corti, D., Dalla Corte, F., De Robertis, E., Foti, G., Fumagalli, R., Girardis, M., Giudici, R., Guiotto, L., Langer, T., Mirabella, L., Pasero, D., Protti, A., Ranieri, M. V., Rona, R., Scudeller, L., Severgnini, P., Spadaro, S., Stocchetti, N., Vigano, M., Pesenti, A., Grasselli, G., Aspesi, M., Baccanelli, F., Bassi, F., Bet, A., Biagioni, E., Biondo, A., Bonenti, C., Bottino, N., Brazzi, L., Buquicchio, I., Busani, S., Calini, A., Calligaro, P., Cantatore, L. P., Carelli, S., Carsetti, A., Cavallini, S., Cimicchi, G., Coppadoro, A., Dall'Ara, L., Di Gravio, V., Erba, M., Evasi, G., Facchini, A., Fanelli, V., Feliciotti, G., Fusarini, C. F., Ferraro, G., Garberi, R., Gay, H., Giacche, L., Grieco, D., Guzzardella, A., Longhini, F., Manzan, A., Maraggia, D., Milani, A., Mischi, A., Montalto, C., Mormina, S., Noseda, V., Paleari, C., Pedeferri, M., Pezzi, A., Pizzilli, G., Pozzi, M., Properzi, P., Rauseo, M., Russotto, V., Saccarelli, L., Servillo, G., Spano, S., Tagliabue, P., Tonetti, T., Tullo, L., Vetrugno, L., Vivona, L., Volta, C. A., Zambelli, V., Zanoni, A, Brazzi, L, Gagliardi, G., Zanella A., Florio G., Antonelli M., Bellani G., Berselli A., Bove T., Cabrini L., Carlesso E., Castelli G.P., Cecconi M., Citerio G., Coloretti I., Corti D., Dalla Corte F., De Robertis E., Foti G., Fumagalli R., Girardis M., Giudici R., Guiotto L., Langer T., Mirabella L., Pasero D., Protti A., Ranieri M.V., Rona R., Scudeller L., Severgnini P., Spadaro S., Stocchetti N., Vigano M., Pesenti A., Grasselli G., Aspesi M., Baccanelli F., Bassi F., Bet A., Biagioni E., Biondo A., Bonenti C., Bottino N., Brazzi L., Buquicchio I., Busani S., Calini A., Calligaro P., Cantatore L.P., Carelli S., Carsetti A., Cavallini S., Cimicchi G., Coppadoro A., Dall'Ara L., Di Gravio V., Erba M., Evasi G., Facchini A., Fanelli V., Feliciotti G., Fusarini C.F., Ferraro G., Gagliardi G., Garberi R., Gay H., Giacche L., Grieco D., Guzzardella A., Longhini F., Manzan A., Maraggia D., Milani A., Mischi A., Montalto C., Mormina S., Noseda V., Paleari C., Pedeferri M., Pezzi A., Pizzilli G., Pozzi M., Properzi P., Rauseo M., Russotto V., Saccarelli L., Servillo G., Spano S., Tagliabue P., Tonetti T., Tullo L., Vetrugno L., Vivona L., Volta C.A., Zambelli V., Zanoni A., Zanella, A, Florio, G, Antonelli, M, Bellani, G, Berselli, A, Bove, T, Cabrini, L, Carlesso, E, Castelli, G, Cecconi, M, Citerio, G, Coloretti, I, Corti, D, Dalla Corte, F, De Robertis, E, Foti, G, Fumagalli, R, Girardis, M, Giudici, R, Guiotto, L, Langer, T, Mirabella, L, Pasero, D, Protti, A, Ranieri, M, Rona, R, Scudeller, L, Severgnini, P, Spadaro, S, Stocchetti, N, Viganò, M, Pesenti, A, and Grasselli, G

Subjects
Male, Original, Critical Illness, Socio-culturale, Retrospective Studie, Risk Factors, Humans, Intensive care unit, Prospective Studies, Mortality, Aged, Retrospective Studies, COVID-19, Longitudinal models, Predictors, Time course, SARS-CoV-2, Risk Factor, Respiration, Intensive Care Units, Italy, Middle Aged, Respiration, Artificial, Prospective Studie, Longitudinal model, Artificial, Predictor, Human
Abstract

Purpose To evaluate the daily values and trends over time of relevant clinical, ventilatory and laboratory parameters during the intensive care unit (ICU) stay and their association with outcome in critically ill patients with coronavirus disease 19 (COVID-19). Methods In this retrospective–prospective multicentric study, we enrolled COVID-19 patients admitted to Italian ICUs from February 22 to May 31, 2020. Clinical data were daily recorded. The time course of 18 clinical parameters was evaluated by a polynomial maximum likelihood multilevel linear regression model, while a full joint modeling was fit to study the association with ICU outcome. Results 1260 consecutive critically ill patients with COVID-19 admitted in 24 ICUs were enrolled. 78% were male with a median age of 63 [55–69] years. At ICU admission, the median ratio of arterial oxygen partial pressure to fractional inspired oxygen (PaO2/FiO2) was 122 [89–175] mmHg. 79% of patients underwent invasive mechanical ventilation. The overall mortality was 34%. Both the daily values and trends of respiratory system compliance, PaO2/FiO2, driving pressure, arterial carbon dioxide partial pressure, creatinine, C-reactive protein, ferritin, neutrophil, neutrophil–lymphocyte ratio, and platelets were associated with survival, while for lactate, pH, bilirubin, lymphocyte, and urea only the daily values were associated with survival. The trends of PaO2/FiO2, respiratory system compliance, driving pressure, creatinine, ferritin, and C-reactive protein showed a higher association with survival compared to the daily values. Conclusion Daily values or trends over time of parameters associated with acute organ dysfunction, acid–base derangement, coagulation impairment, or systemic inflammation were associated with patient survival. Supplementary Information The online version contains supplementary material available at 10.1007/s00134-021-06495-y.

Published
2021

4. Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data

Author

Langer, T, Favarato, M, Giudici, R, Bassi, G, Garberi, R, Villa, F, Gay, H, Zeduri, A, Bragagnolo, S, Molteni, A, Beretta, A, Corradin, M, Moreno, M, Vismara, C, Perno, C, Buscema, M, Grossi, E, Fumagalli, R, Langer, Thomas, Favarato, Martina, Giudici, Riccardo, Bassi, Gabriele, Garberi, Roberta, Villa, Fabiana, Gay, Hedwige, Zeduri, Anna, Bragagnolo, Sara, Molteni, Alberto, Beretta, Andrea, Corradin, Matteo, Moreno, Mauro, Vismara, Chiara, Perno, Carlo Federico, Buscema, Massimo, Grossi, Enzo, Fumagalli, Roberto, Langer, T, Favarato, M, Giudici, R, Bassi, G, Garberi, R, Villa, F, Gay, H, Zeduri, A, Bragagnolo, S, Molteni, A, Beretta, A, Corradin, M, Moreno, M, Vismara, C, Perno, C, Buscema, M, Grossi, E, Fumagalli, R, Langer, Thomas, Favarato, Martina, Giudici, Riccardo, Bassi, Gabriele, Garberi, Roberta, Villa, Fabiana, Gay, Hedwige, Zeduri, Anna, Bragagnolo, Sara, Molteni, Alberto, Beretta, Andrea, Corradin, Matteo, Moreno, Mauro, Vismara, Chiara, Perno, Carlo Federico, Buscema, Massimo, Grossi, Enzo, and Fumagalli, Roberto

Abstract

Background: Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) diagnosis currently requires quite a long time span. A quicker and more efficient diagnostic tool in emergency departments could improve management during this global crisis. Our main goal was assessing the accuracy of artificial intelligence in predicting the results of RT-PCR for SARS-COV-2, using basic information at hand in all emergency departments. Methods: This is a retrospective study carried out between February 22, 2020 and March 16, 2020 in one of the main hospitals in Milan, Italy. We screened for eligibility all patients admitted with influenza-like symptoms tested for SARS-COV-2. Patients under 12 years old and patients in whom the leukocyte formula was not performed in the ED were excluded. Input data through artificial intelligence were made up of a combination of clinical, radiological and routine laboratory data upon hospital admission. Different Machine Learning algorithms available on WEKA data mining software and on Semeion Research Centre depository were trained using both the Training and Testing and the K-fold cross-validation protocol. Results: Among 199 patients subject to study (median [interquartile range] age 65 [46–78] years; 127 [63.8%] men), 124 [62.3%] resulted positive to SARS-COV-2. The best Machine Learning System reached an accuracy of 91.4% with 94.1% sensitivity and 88.7% specificity. Conclusion: Our study suggests that properly trained artificial intelligence algorithms may be able to predict correct results in RT-PCR for SARS-COV-2, using basic clinical data. If confirmed, on a larger-scale study, this approach could have important clinical and organizational implications.

Published
2020

5. Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data

Author

Thomas Langer, Martina Favarato, Riccardo Giudici, Gabriele Bassi, Roberta Garberi, Fabiana Villa, Hedwige Gay, Anna Zeduri, Sara Bragagnolo, Alberto Molteni, Andrea Beretta, Matteo Corradin, Mauro Moreno, Chiara Vismara, Carlo Federico Perno, Massimo Buscema, Enzo Grossi, Roberto Fumagalli, Langer, T, Favarato, M, Giudici, R, Bassi, G, Garberi, R, Villa, F, Gay, H, Zeduri, A, Bragagnolo, S, Molteni, A, Beretta, A, Corradin, M, Moreno, M, Vismara, C, Perno, C, Buscema, M, Grossi, E, and Fumagalli, R

Subjects
Male, Influenza-like symptoms, Artificial intelligence, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Critical Care and Intensive Care Medicine, Machine learning, computer.software_genre, Sensitivity and Specificity, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Emergency service, hospital, Interquartile range, Medicine, Humans, Severe acute respiratory syndrome coronavirus 2, In patient, 030212 general & internal medicine, Diagnosis, Computer-Assisted, Supervised machine learning, Pandemics, 030304 developmental biology, Original Research, Aged, Retrospective Studies, Protocol (science), 0303 health sciences, Pandemic, business.industry, Reverse Transcriptase Polymerase Chain Reaction, SARS-CoV-2, lcsh:Medical emergencies. Critical care. Intensive care. First aid, COVID-19, Retrospective cohort study, lcsh:RC86-88.9, Middle Aged, Critical care, Research centre, Radiological weapon, Emergency Medicine, Female, business, computer, Software
Abstract

Background Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) diagnosis currently requires quite a long time span. A quicker and more efficient diagnostic tool in emergency departments could improve management during this global crisis. Our main goal was assessing the accuracy of artificial intelligence in predicting the results of RT-PCR for SARS-COV-2, using basic information at hand in all emergency departments. Methods This is a retrospective study carried out between February 22, 2020 and March 16, 2020 in one of the main hospitals in Milan, Italy. We screened for eligibility all patients admitted with influenza-like symptoms tested for SARS-COV-2. Patients under 12 years old and patients in whom the leukocyte formula was not performed in the ED were excluded. Input data through artificial intelligence were made up of a combination of clinical, radiological and routine laboratory data upon hospital admission. Different Machine Learning algorithms available on WEKA data mining software and on Semeion Research Centre depository were trained using both the Training and Testing and the K-fold cross-validation protocol. Results Among 199 patients subject to study (median [interquartile range] age 65 [46–78] years; 127 [63.8%] men), 124 [62.3%] resulted positive to SARS-COV-2. The best Machine Learning System reached an accuracy of 91.4% with 94.1% sensitivity and 88.7% specificity. Conclusion Our study suggests that properly trained artificial intelligence algorithms may be able to predict correct results in RT-PCR for SARS-COV-2, using basic clinical data. If confirmed, on a larger-scale study, this approach could have important clinical and organizational implications.

Published
2020

6. Mechanical ventilation settings during weaning from venovenous extracorporeal membrane oxygenation.

Author

Passarelli MT, Petit M, Garberi R, Lebreton G, Luyt CE, Pineton De Chambrun M, Chommeloux J, Hékimian G, Rezoagli E, Foti G, Combes A, Giani M, and Schmidt M

Abstract

Background: The optimal timing of weaning from venovenous extracorporeal membrane oxygenation (VV ECMO) and its modalities have been rarely studied., Methods: Retrospective, multicenter cohort study over 7 years in two tertiary ICUs, high-volume ECMO centers in France and Italy. Patients with ARDS on ECMO and successfully weaned from VV ECMO were classified based on their mechanical ventilation modality during the sweep gas-off trial (SGOT) with either controlled mechanical ventilation or spontaneous breathing (i.e. pressure support ventilation). The primary endpoint was the time to successful weaning from mechanical ventilation within 90 days post-ECMO weaning., Results: 292 adult patients with severe ARDS were weaned from controlled ventilation, and 101 were on spontaneous breathing during SGOT. The 90-day probability of successful weaning from mechanical ventilation was not significantly different between the two groups (sHR [95% CI], 1.23 [0.84-1.82]). ECMO-related complications were not statistically different between patients receiving these two mechanical ventilation strategies. After adjusting for covariates, older age, higher pre-ECMO sequential organ failure assessment score, pneumothorax, ventilator-associated pneumonia, and renal replacement therapy, but not mechanical ventilation modalities during SGOT, were independently associated with a lower probability of successful weaning from mechanical ventilation after ECMO weaning., Conclusions: Time to successful weaning from mechanical ventilation within 90 days post-ECMO was not associated with the mechanical ventilation strategy used during SGOT. Further research is needed to assess the optimal ventilation strategy during weaning off VV ECMO and its impact on short- and long-term outcomes., (© 2024. The Author(s).)

Published
2024
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7. Dynamic inflation prevents and standardized lung recruitment reverts volume loss associated with percutaneous tracheostomy during volume control ventilation: results from a Neuro-ICU population.

Author

Bastia L, Garberi R, Querci L, Cipolla C, Curto F, Rezoagli E, Fumagalli R, and Chieregato A

Abstract

To determine how percutaneous tracheostomy (PT) impacts on respiratory system compliance (C rs ) and end-expiratory lung volume (EELV) during volume control ventilation and to test whether a recruitment maneuver (RM) at the end of PT may reverse lung derecruitment. This is a single center, prospective, applied physiology study. 25 patients with acute brain injury who underwent PT were studied. Patients were ventilated in volume control ventilation. Electrical impedance tomography (EIT) monitoring and respiratory mechanics measurements were performed in three steps: (a) baseline, (b) after PT, and (c) after a standardized RM (10 sighs of 30 cmH 2 O lasting 3 s each within 1 min). End-expiratory lung impedance (EELI) was used as a surrogate of EELV. PT determined a significant EELI loss (mean reduction of 432 arbitrary units p = 0.049) leading to a reduction in C rs (55 ± 13 vs. 62 ± 13 mL/cmH 2 O; p < 0.001) as compared to baseline. RM was able to revert EELI loss and restore C rs (68 ± 15 vs. 55 ± 13 mL/cmH 2 O; p < 0.001). In a subgroup of patients (N = 8, 31%), we observed a gradual but progressive increase in EELI. In this subgroup, patients did not experience a decrease of C rs after PT as compared to patients without dynamic inflation. Dynamic inflation did not cause hemodynamic impairment nor raising of intracranial pressure. We propose a novel and explorative hyperinflation risk index (HRI) formula. Volume control ventilation did not prevent the PT-induced lung derecruitment. RM could restore the baseline lung volume and mechanics. Dynamic inflation is common during PT, it can be monitored real-time by EIT and anticipated by HRI. The presence of dynamic inflation during PT may prevent lung derecruitment., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Published
2024
Full Text
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8. Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data.

Author

Langer T, Favarato M, Giudici R, Bassi G, Garberi R, Villa F, Gay H, Zeduri A, Bragagnolo S, Molteni A, Beretta A, Corradin M, Moreno M, Vismara C, Perno CF, Buscema M, Grossi E, and Fumagalli R

Subjects
Aged, Female, Humans, Male, Middle Aged, Retrospective Studies, Reverse Transcriptase Polymerase Chain Reaction, SARS-CoV-2 genetics, Sensitivity and Specificity, COVID-19 diagnosis, Diagnosis, Computer-Assisted, Machine Learning, Software
Abstract

Background: Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) diagnosis currently requires quite a long time span. A quicker and more efficient diagnostic tool in emergency departments could improve management during this global crisis. Our main goal was assessing the accuracy of artificial intelligence in predicting the results of RT-PCR for SARS-COV-2, using basic information at hand in all emergency departments., Methods: This is a retrospective study carried out between February 22, 2020 and March 16, 2020 in one of the main hospitals in Milan, Italy. We screened for eligibility all patients admitted with influenza-like symptoms tested for SARS-COV-2. Patients under 12 years old and patients in whom the leukocyte formula was not performed in the ED were excluded. Input data through artificial intelligence were made up of a combination of clinical, radiological and routine laboratory data upon hospital admission. Different Machine Learning algorithms available on WEKA data mining software and on Semeion Research Centre depository were trained using both the Training and Testing and the K-fold cross-validation protocol., Results: Among 199 patients subject to study (median [interquartile range] age 65 [46-78] years; 127 [63.8%] men), 124 [62.3%] resulted positive to SARS-COV-2. The best Machine Learning System reached an accuracy of 91.4% with 94.1% sensitivity and 88.7% specificity., Conclusion: Our study suggests that properly trained artificial intelligence algorithms may be able to predict correct results in RT-PCR for SARS-COV-2, using basic clinical data. If confirmed, on a larger-scale study, this approach could have important clinical and organizational implications.

Published
2020
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9. Spontaneous activity level and life span in rotifers: lack of support for the rate of living theory.

Author

Enesco HE, McTavish A, and Garberi R

Subjects
Aging physiology, Animals, Body Constitution, Locomotion physiology, Rotifera metabolism, Energy Metabolism physiology, Longevity physiology, Physical Exertion physiology, Rotifera physiology
Abstract

Spontaneous activity level was measured in rotifers of the species Asplanchna brightwelli on each day of their 5-day life span. No correlation was seen in group data between activity level and life span. However, data from individuals showed a positive correlation between life span and activity level in the latter half of the life span on 3 days out of 5. This tendency toward greater activity in the longer-lived rotifers is not consistent with the rate of living theory of aging.

Published
1990
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