Patient-ventilator asynchrony is a common problem in mechanically ventilated patients with acute respiratory failure. It is assumed that asynchronies worsen lung function and prolong the duration of mechanical ventilation (MV). Neurally Adjusted Ventilatory Assist (NAVA) is a novel approach to MV based on neural respiratory center output that is able to trigger, cycle, and regulate the ventilatory cycle. We hypothesized that the use of NAVA compared to conventional lung-protective MV will result in a reduction of the duration of MV. It is further hypothesized that NAVA compared to conventional lung-protective MV will result in a decrease in the length of ICU and hospital stay, and mortality. This is a prospective, multicenter, randomized controlled trial in 306 mechanically ventilated patients with acute respiratory failure from several etiologies. Only patients ventilated for less than 5 days, and who are expected to require prolonged MV for an additional 72 h or more and are able to breathe spontaneously, will be considered for enrollment. Eligible patients will be randomly allocated to two ventilatory arms: (1) conventional lung-protective MV (n = 153) and conventional lung-protective MV with NAVA (n = 153). Primary outcome is the number of ventilator-free days, defined as days alive and free from MV at day 28 after endotracheal intubation. Secondary outcomes are total length of MV, and ICU and hospital mortality. This is the first randomized clinical trial examining, on a multicenter scale, the beneficial effects of NAVA in reducing the dependency on MV of patients with acute respiratory failure. ClinicalTrials.gov website ( NCT01730794 ). Registered on 15 November 2012.

The incidence of secondary infections in critically ill coronavirus disease 2019 (COVID-19) patients is worrisome. We investigated whether selective digestive decontamination (SDD) added to infection control measures during an intensive care unit (ICU) stay modified these infection rates. Methods: A retrospective observational cohort study was carried out in four ICUs in Spain. All consecutive ventilated patients with a SARS-CoV-2 infection engaged in national infection control programs between 1 March and 10 December 2020 were investigated. Patients were grouped into two cohorts according to the site of ICU admission. Secondary relevant infections were included. Infection densities corresponding to ventilator-associated pneumonia (VAP), catheter bacteremia, secondary bacteremia, and multi-resistant germs were obtained as the number of events per 1000 days of exposure and were compared between SDD and non-SDD groups using Poisson regression. Factors that had an independent association with mortality were identified using multidimensional logistic analysis. Results: There were 108 patients in the SDD cohort and 157 in the non-SDD cohort. Patients in the SDD cohort showed significantly lower rates (p < 0.001) of VAP (1.9 vs. 9.3 events per 1000 ventilation days) and MDR infections (0.57 vs. 2.28 events per 1000 ICU days) and a non-significant reduction in secondary bacteremia (0.6 vs. 1.41 events per 1000 ICU days) compared with those in the non-SDD cohort. Infections caused by MDR pathogens occurred in 5 patients in the SDD cohort and 21 patients in the non-SDD cohort (p = 0.006). Differences in mortality according to SDD were not found. Conclusion: The implementation of SDD in infection control programs significantly reduced the incidence of VAP and MDR infections in critically ill SARS-CoV-2 infected patients.

Background: Early prediction of ICU death in acute hypoxemic respiratory failure (AHRF) could inform clinicians for targeting therapies to reduce harm and increase survival. We sought to determine clinical modifiable and non-modifiable features during the first 24 h of AHRF associated with ICU death. Methods: This is a development, testing, and validation study using data from a prospective, multicenter, nation-based, observational cohort of 1241 patients with AHRF (defined as PaO₂/FiO₂ ≤ 300 mmHg on mechanical ventilation [MV] with positive end-expiratory pressure [PEEP] ≥ 5 cmH₂O and FiO₂ ≥ 0.3) from any etiology. Using relevant features captured at AHRF diagnosis and within 24 h, we developed a logistic regression model following variable selection by genetic algorithm and machine learning (ML) approaches. Results: We analyzed 1193 patients, after excluding 48 patients with no data at 24 h after AHRF diagnosis. Using repeated random sampling, we selected 75% (n = 900) for model development and testing, and 25% (n = 293) for final validation. Risk modeling identified six major predictors of ICU death, including patient’s age, and values at 24 h of PEEP, FiO₂, plateau pressure, tidal volume, and number of extrapulmonary organ failures. Performance with ML methods was similar to logistic regression and achieved a high area under the receiver operating characteristic curve (AUROC) of 0.88, 95%CI 0.86–0.90. Validation confirmed adequate model performance (AUROC 0.83, 95%CI 0.78–0.88). Conclusions: ML and traditional methods led to an encouraging model to predict ICU death in ventilated AHRF as early as 24 h after diagnosis. More research is needed to identify modifiable factors to prevent ICU deaths.