Background
Switching from controlled to assisted ventilation is crucial in the trajectory of intensive care unit (ICU) stay, but no guidelines exist. We described current practices, analyzed patient characteristics associated with switch success or failure, and explored the feasibility to predict switch failure.

Methods
In this retrospective study, we obtained highly granular longitudinal ICU data sets from three medical centers, covering demographics, severity scores, vital signs, ventilation, and laboratory parameters. The primary endpoint was switch success, considering a switch attempt to be successful if a patient did not return to controlled ventilation for the next 72 h while alive, and to be failed otherwise. We compared the characteristics of patients with successful vs. failed first switch attempts at ICU admission, immediately before, and 3 h after the attempt. We trained LASSO logistic regression models to predict switch failure.

Results
In 4524/6715 (67%) patients attempting a switch, the first attempt failed. The first switch attempt, regardless of success or failure, was generally made at normalized PaCO2 and pH levels, with PEEP < 10 cmH2O and PaO2/FiO2 indicating mild injury. Despite very similar baseline disease severity, switch failure was associated with significantly worse outcomes, including a 28-day mortality of 27% vs. 16% and median ventilator-free days of 16 vs. 22 (p < 0.001). Failed attempts were initiated significantly earlier than successful ones (median 1.8 vs. 1.3 days, p < 0.001). Before the switch, PaO2/FiO2, if measured at PEEP > 10 cmH2O, and respiratory system compliance was lower in patients with switch failure (median 185 vs. 205 mmHg, p < 0.001; 39 vs. 41 mL/cmH2O, P = 0.001), and post-switch, patients with switch failure experienced greater deterioration in gas exchange and minimal improvement in ventilatory parameters post-switch. Contrary to our hypotheses, patient characteristics for failed vs. successful switches were surprisingly similar, resulting in prediction models with limited discriminative performance.

Conclusions
Approximately two-thirds of attempts to switch patients to assisted ventilation fail, which are associated with significantly worse clinical outcomes, despite similar baseline disease severity. Contrary to our hypotheses, patients with successful and failed attempts showed similar characteristics, making switch failure difficult to predict. These findings underscore the importance of preventing switch failures and, given the retrospective nature of this study, highlight the need for prospective studies to better understand the reasons for switch failure and when spontaneous breathing can be safely initiated.

In her pioneering work, Calfee et al. [1] addressed the clinical and biological heterogeneity of acute respiratory distress syndrome (ARDS), a factor likely contributing to the poor track record of randomized trials (RCTs) in this patient population. Using latent class (or profile) analysis (LCA), a method for identifying unobserved subgroups from observed data, they identified two distinct ARDS sub-phenotypes (hypo- and hyperinflammatory), which showed association with clinical outcomes and, crucially, heterogeneity of treatment effect (HTE) [2], demonstrating different responses to higher vs. lower PEEP regimes. [...]

Objective. The respiratory rate (RR) is considered one of the most informative vital signals. A well-validated standard for RR measurement in mechanically ventilated patient is capnography; a noninvasive technique for expiratory CO2measurements. Reliable RR measurements in spontaneously breathing patients remains a challenge as continuous mainstream capnography measurements are not available. This study aimed to assess the accuracy of RR measurement using electrical impedance tomography (EIT) in healthy volunteers and intensive care unit (ICU) patients on mechanical ventilation and spontaneously breathing post-extubation. Comparator methods included RR derived from both capnography and bioimpedance electrocardiogram (ECG) measurements.Approach. Twenty healthy volunteers wore an EIT belt and ECG electrodes while breathing through a capnometer within a 10-40 breaths per minute (BPM) range. Nineteen ICU patients underwent similar measurements during pressure support ventilation and spontaneously breathing after extubation from mechanical ventilation. Stable periods with regular breathing and no artefacts were selected, and agreement between measurement methods was assessed using Bland-Altman analysis for repeated measurements.Main result. Bland-Altman analysis revealed a bias less than 0.2 BPM, with tight limits of agreement (LOA) ±1.5 BPM in healthy volunteers and ventilated ICU patients when comparing EIT to capnography. Spontaneously breathing ICU patients had wider LOA (±2.5 BPM) when comparing EIT to ECG bioimpedance, but gold standard comparison was unavailable. RR measurements were stable for 91% of the time for capnography, 68% for EIT, and 64% of the ECG bioimpedance signals. After extubation, the percentage of stable periods decreased to 48% for EIT signals and to 55% for ECG bioimpedance.Significance. In periods of stable breathing, EIT demonstrated excellent RR measurement accuracy in healthy volunteers and ICU patients. However, stability of both EIT and ECG bioimpedance RR measurements declined in spontaneously breathing patients to approximately 50% of the time.

The accuracy of diaphragm electromyogram (EMGdi) derived parameters, as used in critically ill intensive care unit (ICU) patients, can be compromised due to electrocardiographic (ECG) interference in the EMGdi signal. Removal of ECG contamination from the esophageal recordings of the EMGdi is challenging due to spectral overlapping of EMG and ECG signals and because of variability in ECG shape and amplitude. Therefore, we designed an Estimated ECG Subtraction (EES) method, based on three steps: (1) identification of the timing of the ECG artifact without an ECG reference channel, (2) estimation of the normalized ECG, considering the EMGdi as noise, and (3) subtraction of the denormalized ECG estimate from the EMGdi recordings. We evaluated the EES method against the use of a single wavelet-based adaptive filter. Using EMGdi signals of ten ICU patients and simulated contaminated EMG, we demonstrated that the EES method yields uncontaminated EMGdi, and showed that it is more effective than a wavelet-based adaptive filter only. Implementation of this technique may offer means to improve diaphragm activity monitoring and control in clinical practice.