AWA provides a less invasive and easy-to-use alternative for CO measurement. The validity of AWA devices has been verified in a variety of patients and circumstances, but their performance is compromised in the presence of hemodynamic instability, cardiac arrhythmias, or other factors disturbing the arterial pressure waveform. The definitive role of dynamic preload parameters like SVV and PPV is a matter of research. Large trials in which the value of early goal-directed therapy using this technology is studied in relation to outcome are urgently needed.

To explore if change in the extent of emphysema correlated with change in lung function, the effect of resection of emphysematous tissue was studied by computed tomography (CT) densitometry. In addition, the current authors studied how surgery-induced change in emphysema related to lung density in control subjects.In total, 30 patients (14 females; mean¡SD age 59¡10 yrs) with severe emphysema before and 3 months after lung volume reduction surgery (LVRS), 48 patients with moderate emphysema and 76 control subjects were investigated. Lung density (15th percentile point) of both lungs and heterogeneity of lung density between 12 isovolumetric partitions in each lung were calculated from chest CT images.The 15th percentile point and its heterogeneity could distinguish controls from subjects with moderate emphysema with a sensitivity and specificity of .95%. LVRS significantly increased lung density by 5.0¡10.9 g?L -1 (n530). Improvement in the diffusing capacity of the lung for carbon monoxide and in residual volume significantly correlated with an increase in lung density (n520 and 28, respectively). Change in forced expiratory volume in one second did not correlate with change in lung density.In conclusion, lung density 15th percentile point is a valuable surrogate marker for detection of both the extent of and reduction in emphysema.

Introduction Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19. Methods We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primary and secondary outcomes were extubation without reintubation or death within the next 7 days and within 48 h, respectively. We trained and validated multiple machine learning algorithms using fivefold nested cross-validation. Predictor importance was estimated using Shapley additive explanations, while cutoff values for the relative probability of failed extubation were estimated through partial dependence plots. Results A total of 883 patients were included in the model derivation. The reintubation rate was 13.4% within 48 h and 18.9% at day 7, with a mortality rate of 0.6% and 1.0% respectively. The grandient-boost model performed best (area under the curve of 0.70) and was used to calculate predictor importance. Ventilatory characteristics and settings were the most important predictors. More specifically, a controlled mode duration longer than 4 days, a last fraction of inspired oxygen higher than 35%, a mean tidal volume per kg ideal body weight above 8 ml/kg in the day before extubation, and a shorter duration in assisted mode (< 2 days) compared to their median values. Additionally, a higher C-reactive protein and leukocyte count, a lower thrombocyte count, a lower Glasgow coma scale and a lower body mass index compared to their medians were associated with extubation failure. Conclusion The most important predictors for extubation failure in critically ill COVID-19 patients include ventilatory settings, inflammatory parameters, neurological status, and body mass index. These predictors should therefore be routinely captured in electronic health records.

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