No abstract

The efficacy of non-invasive ventilation (NIV) in acute respiratory failure secondary to SARS-CoV-2 infection remains controversial. Current literature mainly examined efficacy, safety and potential predictors of NIV failure provided out of the intensive care unit (ICU). On the contrary, the outcomes of ICU patients, intubated after NIV failure, remain to be explored. The aims of the present study are: (1) investigating in-hospital mortality in coronavirus disease 2019 (COVID-19) ICU patients receiving endotracheal intubation after NIV failure and (2) assessing whether the length of NIV application affects patient survival. This observational multicenter study included all consecutive COVID-19 adult patients, admitted into the twenty-five ICUs of the COVID-19 VENETO ICU network (February–April 2020), who underwent endotracheal intubation after NIV failure. Among the 704 patients admitted to ICU during the study period, 280 (40%) presented the inclusion criteria and were enrolled. The median age was 69 [60–76] years; 219 patients (78%) were male. In-hospital mortality was 43%. Only the length of NIV application before ICU admission (OR 2.03 (95% CI 1.06–4.98), p = 0.03) and age (OR 1.18 (95% CI 1.04–1.33), p < 0.01) were identified as independent risk factors of in-hospital mortality; whilst the length of NIV after ICU admission did not affect patient outcome. In-hospital mortality of ICU patients intubated after NIV failure was 43%. Days on NIV before ICU admission and age were assessed to be potential risk factors of greater in-hospital mortality.

Background Pathophysiological features of coronavirus disease 2019-associated acute respiratory distress syndrome (COVID-19 ARDS) were indicated to be somewhat different from those described in nonCOVID-19 ARDS, because of relatively preserved compliance of the respiratory system despite marked hypoxemia. We aim ascertaining whether respiratory system static compliance (Crs), driving pressure (DP), and tidal volume normalized for ideal body weight (VT/kg IBW) at the 1st day of controlled mechanical ventilation are associated with intensive care unit (ICU) mortality in COVID-19 ARDS. Methods Observational multicenter cohort study. All consecutive COVID-19 adult patients admitted to 25 ICUs belonging to the COVID-19 VENETO ICU network (February 28th–April 28th, 2020), who received controlled mechanical ventilation, were screened. Only patients fulfilling ARDS criteria and with complete records of Crs, DP and VT/kg IBW within the 1st day of controlled mechanical ventilation were included. Crs, DP and VT/kg IBW were collected in sedated, paralyzed and supine patients. Results A total of 704 COVID-19 patients were screened and 241 enrolled. Seventy-one patients (29%) died in ICU. The logistic regression analysis showed that: (1) Crs was not linearly associated with ICU mortality (p value for nonlinearity = 0.01), with a greater risk of death for values < 48 ml/cmH2O; (2) the association between DP and ICU mortality was linear (p value for nonlinearity = 0.68), and increasing DP from 10 to 14 cmH2O caused significant higher odds of in-ICU death (OR 1.45, 95% CI 1.06–1.99); (3) VT/kg IBW was not associated with a significant increase of the risk of death (OR 0.92, 95% CI 0.55–1.52). Multivariable analysis confirmed these findings. Conclusions Crs < 48 ml/cmH2O was associated with ICU mortality, while DP was linearly associated with mortality. DP should be kept as low as possible, even in the case of relatively preserved Crs, irrespective of VT/kg IBW, to reduce the risk of death.

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.