Static Compliance and Driving Pressure are Associated with ICU Mortality in Intubated COVID-19 ARDS

Boscolo, Annalisa; Sella, Nicolò; Lorenzoni, Giulia; Pettenuzzo, Tommaso; Pasin, Laura; Pretto, Chiara; Tocco, Martina; Tamburini, Enrico; Cassai, Alessandro De; Rosi, Paolo; Polati, Enrico; Donadello, Katia; Gottin, Leonardo; Rosa, Silvia De; Baratto, Fabio; Toffoletto, Fabio; Ranieri, V. Marco; Gregori, Darío; Navalesi, Paolo

doi:10.21203/rs.3.rs-399976/v1

Cited by 5 publications

(7 citation statements)

References 17 publications

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“…This finding is in line with current literature, describing a great impact of age on mortality in COVID-19 patients undergoing invasive and non-invasive ventilation [6,[11][12][13][14][15]. Karagiannidis C et al, in the widest cohort of hospitalized COVID-19 patients, showed that mortality has been high for patients receiving mechanical ventilation, particularly for patients aged 80 years or older and those requiring dialysis, and has been considerably lower for patients younger than 60 years [11].…”

Section: Discussionsupporting

confidence: 90%

“…Similar findings were reported by Boscolo et cumulative incidence of mortality at 60 days higher in the older ones [6,12]. Worth noting, from the beginning of the pandemic, several tools have been proposed for mortality prediction of COVID-19 patients; however, it is difficult to compare their performance because each model was developed on patients with different characteristics, using different sets of variables, and using different techniques for model development.…”

Section: Discussionsupporting

confidence: 76%

“…We prospectively screened the records of all adult patients with confirmed SARS-CoV-2 infection admitted to the ICUs of the COVID-19 VENETO ICU network, between 28th of February 2020 and 4th of April 2021 [5,6]. COVID-19 diagnosis was made according to the World Health Organization interim guidance (http:// www.who.int/docs/default-source/coronaviruse/clinicalmanagement-of-novel-cov.pdf).…”

Section: Methodsmentioning

confidence: 99%

“…5 Anaesthesia and Intensive Care Unit, Ospedale di San Donà di Piave e Jesolo, San Donà di Piave, Italy. 6 Anaesthesia and Critical Care Unit, San Bortolo Hospital, Vicenza, Italy. 7 Anaesthesia and Intensive Care Unit, Ospedale Dell'Angelo, AULSS 3 Serenissima, Mestre, Italy.…”

Section: Acknowledgementsmentioning

confidence: 99%

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COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm

Lorenzoni

Sella

Boscolo

et al. 2021

J Anesth Analg Crit Care

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Background Since the beginning of coronavirus disease 2019 (COVID-19), the development of predictive models has sparked relevant interest due to the initial lack of knowledge about diagnosis, treatment, and prognosis. The present study aimed at developing a model, through a machine learning approach, to predict intensive care unit (ICU) mortality in COVID-19 patients based on predefined clinical parameters. Results Observational multicenter cohort study. All COVID-19 adult patients admitted to 25 ICUs belonging to the VENETO ICU network (February 28th 2020-april 4th 2021) were enrolled. Patients admitted to the ICUs before 4th March 2021 were used for model training (“training set”), while patients admitted after the 5th of March 2021 were used for external validation (“test set 1”). A further group of patients (“test set 2”), admitted to the ICU of IRCCS Ca’ Granda Ospedale Maggiore Policlinico of Milan, was used for external validation. A SuperLearner machine learning algorithm was applied for model development, and both internal and external validation was performed. Clinical variables available for the model were (i) age, gender, sequential organ failure assessment score, Charlson Comorbidity Index score (not adjusted for age), Palliative Performance Score; (ii) need of invasive mechanical ventilation, non-invasive mechanical ventilation, O2 therapy, vasoactive agents, extracorporeal membrane oxygenation, continuous venous-venous hemofiltration, tracheostomy, re-intubation, prone position during ICU stay; and (iii) re-admission in ICU. One thousand two hundred ninety-three (80%) patients were included in the “training set”, while 124 (8%) and 199 (12%) patients were included in the “test set 1” and “test set 2,” respectively. Three different predictive models were developed. Each model included different sets of clinical variables. The three models showed similar predictive performances, with a training balanced accuracy that ranged between 0.72 and 0.90, while the cross-validation performance ranged from 0.75 to 0.85. Age was the leading predictor for all the considered models. Conclusions Our study provides a useful and reliable tool, through a machine learning approach, for predicting ICU mortality in COVID-19 patients. In all the estimated models, age was the variable showing the most important impact on mortality.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionsupporting

confidence: 76%

Section: Methodsmentioning

confidence: 99%

Section: Acknowledgementsmentioning

confidence: 99%

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COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm

Lorenzoni

Sella

Boscolo

et al. 2021

J Anesth Analg Crit Care

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“…The concept of reducing the driving pressure to titrate ventilation is currently tested in in a few clinical trials. 31,32 There are a few limitations using this approach. The accurate measurement of the driving pressure requires a sedated and paralysed patient and the measurement of the plateau pressure (obtained by an end inspiratory pause).…”

Section: State Of the Art: The Concept To Reduce Driving Pressurementioning

confidence: 99%

From state-of-the-art ventilation to closed loop ventilation

Schibler¹,

Staay²,

Remus³

2022

JMV

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Recent emphasis on energy load delivered during each ventilatory breath has opened a new insight to reduce harmful ventilatory induced lung injury, but no robust clinical evidence of patient benefit produced yet. Closed loop ventilation is a strategy to adjust respiratory support using physiological feedback data obtained for each delivered cycle of respiratory support. Dependent on the model assumption used, closed loop ventilation aims to identify the ideal combination of tidal volume size, reduced driving pressure or respiratory frequency ultimately reducing the energy loading of the lung. This review aims to discuss the current state-of-the-art ventilation concepts and their integration in closed loop ventilation.

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Early Physiologic Effects of Prone Positioning in COVID-19 Acute Respiratory Distress Syndrome

et al. 2022

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Background. The mechanisms underlying oxygenation improvement after prone positioning in COVID-19 ARDS have not been fully elucidated yet. We hypothesized that the oxygenation increase with prone positioning is secondary to the improvement of ventilation-perfusion matching. Methods. In a series of consecutive intubated COVID-19 ARDS patients receiving volume-controlled ventilation, we prospectively assessed the percent variation of ventilation-perfusion matching by electrical impedance tomography (EIT) before and 90 minutes after the first cycle of prone positioning (primary endpoint). We also assessed changes in the distribution and homogeneity of lung ventilation and perfusion, lung overdistention and collapse, respiratory system compliance, driving pressure, optimal positive end-expiratory pressure (PEEP), as assessed by EIT, and the ratio of partial pressure to fractional inspired oxygen (PaO2/FiO2) (secondary endpoints). Data are reported as medians [25th - 75th] or percentages. Results. We enrolled 30 consecutive patients, all analyzed without missing data. Compared to supine position, prone positioning overall improved ventilation-perfusion matching from 58 [43-69] % to 68 [56-75] % (p=0.042), with a median difference of 8.0% (95% Confidence Interval 0.1 to 16.0%) . Dorsal ventilation increased from 39 [31-43] % to 52 [44-62] % (p<0.001), while dorsal perfusion did not significantly vary. Prone positioning also reduced lung overdistension from 9 [4-11] % to 4 [2-6] % (p=0.025), while it did not significantly affect ventilation and perfusion homogeneity, lung collapse, static respiratory system compliance, driving pressure, and optimal PEEP. PaO2/FiO2 overall improved from 141 [104-182] mmHg to 235 [164-267] mmHg (p=0.019). However, 9 (30%) patients were non-responders, experiencing an increase in PaO2/FiO2 <20% with respect to baseline. Conclusions. In COVID-19 ARDS patients, prone positioning overall produced an early increase in ventilation-perfusion matching and dorsal ventilation. These effects were, however, heterogeneous among patients.

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Static Compliance and Driving Pressure are Associated with ICU Mortality in Intubated COVID-19 ARDS

Cited by 5 publications

References 17 publications

COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm

COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm

From state-of-the-art ventilation to closed loop ventilation

Early Physiologic Effects of Prone Positioning in COVID-19 Acute Respiratory Distress Syndrome

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