Explore the vast range of titles available.

Background In critically ill patients, changes in the velocity-time integral (VTI) of the left ventricular outflow tract, measured by transthoracic echocardiography (TTE), are often used to non-invasively assess the response to fluid administration or for performing tests assessing fluid responsiveness. However, the precision of TTE measurements has not yet been investigated in such patients. First, we aimed at assessing how many measurements should be averaged within one TTE examination to reach a sufficient precision for various variables. Second, we aimed at identifying the least significant change (LSC) of these variables between successive TTE examinations. Methods We prospectively included 100 haemodynamically stable patients in whom TTE examination was planned. Three TTE examinations were performed, the first and the third by one operator and the second by another one. We calculated the precision and LSC (1) within one examination depending on the number of averaged measurements and (2) between measurements performed in two successive examinations. Results In patients in sinus rhythm, averaging three measurements within an examination was enough for obtaining an acceptable precision (interquartile range highest value < 10%) for VTI. In patients with atrial fibrillation, averaging five measurements was necessary. The precision of some other common TTE variables depending on the number of measurements is provided. Between two successive examinations performed by the same operator, the LSC was 11 [5–18]% for VTI. If two operators performed the examinations, the LSC for VTI significantly increased to 14 [8–26]%. The LSC between two examinations for other TTE variables is also provided. Conclusions Averaging three measurements within one TTE examination is enough for obtaining precise measurements for VTI in patients in sinus rhythm but not in patients with atrial fibrillation. Between two TTE examinations performed by the same operator, the LSC of VTI is compatible with the assessment of the effects of a 500-mL fluid infusion but is not precise enough for assessing the effects of some tests predicting preload responsiveness.

Modalities of fluid management in patients sustaining the acute respiratory distress syndrome (ARDS) are challenging and controversial. Optimal fluid management should provide adequate oxygen delivery to the body, while avoiding inadvertent increase in lung edema which further impairs gas exchange. In ARDS patients, positive fluid balance has been associated with prolonged mechanical ventilation, longer ICU and hospital stay, and higher mortality. Accordingly, a restrictive strategy has been compared to a more liberal approach in randomized controlled trials conducted in various clinical settings. Restrictive strategies included fluid restriction guided by the monitoring of extravascular lung water, pulmonary capillary wedge or central venous pressure, and furosemide targeted to diuresis and/or albumin replacement in hypoproteinemic patients. Overall, restrictive strategies improved oxygenation significantly and reduced duration of mechanical ventilation, but had no significant effect on mortality. Fluid management may require different approaches depending on the time course of ARDS (i.e., early vs. late period). The effects of fluid strategy management according to ARDS phenotypes remain to be evaluated. Since ARDS is frequently associated with sepsis-induced acute circulatory failure, the prediction of fluid responsiveness is crucial in these patients to avoid hemodynamically inefficient—hence respiratory detrimental—fluid administration. Specific hemodynamic indices of fluid responsiveness or mini-fluid challenges should be preferably used. Since the positive airway pressure contributes to positive fluid balance in ventilated ARDS patients, it should be kept as low as possible. As soon as the hemodynamic status is stabilized, correction of cumulated fluid retention may rely on diuretics administration or renal replacement therapy.

The renin–angiotensin system (RAS) has long been described in the field of cardiovascular physiology as the main player in blood pressure homeostasis. However, other effects have since been described, and include proliferation, fibrosis, and inflammation. To illustrate the immunomodulatory properties of the RAS, we chose three distinct fields in which RAS may play a critical role and be the subject of specific treatments. In oncology, RAS hyperactivation has been associated with tumor migration, survival, cell proliferation, and angiogenesis; preliminary data showed promise of the benefit of RAS blockers in patients treated for certain types of cancer. In intensive care medicine, vasoplegic shock has been associated with severe macro- and microcirculatory imbalance. A relative insufficiency in angiotensin II (AngII) was associated to lethal outcomes and synthetic AngII has been suggested as a specific treatment in these cases. Finally, in solid organ transplantation, both AngI and AngII have been associated with increased rejection events, with a regional specificity in the RAS activity. These elements emphasize the complexity of the direct and indirect interactions of RAS with immunomodulatory pathways and warrant further research in the field.

Managing patients with acute respiratory distress syndrome (ARDS) requires frequent changes in mechanical ventilator respiratory settings to optimize arterial oxygenation assessed by arterial oxygen partial pressure (PaO 2 ) and saturation (SaO 2 ). Pulse oxymetry (SpO 2 ) has been suggested as a non-invasive surrogate for arterial oxygenation however its accuracy in COVID-19 patients is unknown. In this study, we aimed to investigate the influence of COVID-19 status on the association between SpO 2 and arterial oxygenation. We prospectively included patients with ARDS and compared COVID-19 to non-COVID-19 patients, regarding SpO 2 and concomitant arterial oxygenation (SaO 2 and PaO 2 ) measurements, and their association. Bias was defined as mean difference between SpO 2 and SaO 2 measurements. Occult hypoxemia was defined as a SpO 2  ≥ 92% while concomitant SaO 2  < 88%. Multiple linear regression models were built to account for confounders. We also assessed concordance between positive end-expiratory pressure (PEEP) trial-induced changes in SpO 2 and in arterial oxygenation. We included 55 patients, among them 26 (47%) with COVID-19. Overall, SpO 2 and SaO 2 measurements were correlated (r = 0.70; p  < 0.0001), however less so in COVID-19 than in non-COVID-19 patients (r = 0.55, p  < 0.0001 vs. r = 0.84, p  < 0.0001, p  = 0.002 for intergroup comparison). Bias was + 1.1%, greater in COVID-19 than in non-COVID-19 patients (2.0 vs. 0.3%; p  = 0.02). In multivariate analysis, bias was associated with COVID-19 status (unstandardized β = 1.77, 95%CI = 0.38–3.15, p  = 0.01), ethnic group and ARDS severity. Occult hypoxemia occurred in 5.5% of measurements (7.7% in COVID-19 patients vs. 3.4% in non-COVID-19 patients, p  = 0.42). Concordance rate between PEEP trial-induced changes in SpO 2 and SaO 2 was 84%, however less so in COVID-19 than in non-COVID-19 patients (69% vs. 97%, respectively). Similar results were observed for PaO 2 regarding correlations, bias, and concordance with SpO 2 changes. In patients with ARDS, SpO 2 was associated with arterial oxygenation, but COVID-19 status significantly altered this association.

Background High-flow nasal oxygen therapy (HFNC) may be an attractive first-line ventilatory support in COVID-19 patients. However, HNFC use for the management of COVID-19 patients and risk factors for HFNC failure remain to be determined. Methods In this retrospective study, we included all consecutive COVID-19 patients admitted to our intensive care unit (ICU) in the first (Mars-May 2020) and second (August 2020- February 202) French pandemic waves. Patients with limitations for intubation were excluded. HFNC failure was defined as the need for intubation after ICU admission. The impact of HFNC use was analyzed in the whole cohort and after constructing a propensity score. Risk factors for HNFC failure were identified through a landmark time-dependent cause-specific Cox model. The ability of the 6-h ROX index to detect HFNC failure was assessed by generating receiver operating characteristic (ROC) curve. Results 200 patients were included: HFNC was used in 114(57%) patients, non-invasive ventilation in 25(12%) patients and 145(72%) patients were intubated with a median delay of 0 (0–2) days after ICU admission. Overall, 78(68%) patients had HFNC failure. Patients with HFNC failure had a higher ICU mortality rate (34 vs. 11%, p = 0.02) than those without. At landmark time of 48 and 72 h, SAPS-2 score, extent of CT-Scan abnormalities > 75% and HFNC duration (cause specific hazard ratio (CSH) = 0.11, 95% CI (0.04–0.28), per + 1 day, p < 0.001 at 48 h and CSH = 0.06, 95% CI (0.02–0.23), per + 1 day, p < 0.001 at 72 h) were associated with HFNC failure. The 6-h ROX index was lower in patients with HFNC failure but could not reliably predicted HFNC failure with an area under ROC curve of 0.65 (95% CI(0.52–0.78), p = 0.02). In the matched cohort, HFNC use was associated with a lower risk of intubation (CSH = 0.32, 95% CI (0.19–0.57), p < 0.001). Conclusions In critically-ill COVID-19 patients, while HFNC use as first-line ventilatory support was associated with a lower risk of intubation, more than half of patients had HFNC failure. Risk factors for HFNC failure were SAPS-2 score and extent of CT-Scan abnormalities > 75%. The risk of HFNC failure could not be predicted by the 6-h ROX index but decreased after a 48-h HFNC duration.

Background Weaning from mechanical ventilation (MV) is a key phase in the management of intensive care unit (ICU) patient. According to the WEAN SAFE study, weaning from MV initiation is defined as the first attempt to separate a patient from the ventilator and the success is the absence of reintubation (or death) within 7 days of extubation. Mortality rates increase with the difficulty of weaning, reaching 38% for the most challenging cases. Predicting the success of weaning is difficult, due to the complexity of factors involved. The many biosignals that are measured in patients during ventilation may be considered “weak signals”, a concept rarely used in medicine. The aim of this research is to investigate the performance of machine learning (ML) models based on biosignals to predict spontaneous breathing trial success (SBT) using biosignals and to identify the most important variables. Methods This retrospective study used data from two centers (Nice University Hospital, Archet and Pasteur) collected from 232 intensive care patients who underwent MV (149 successfully and 83 unsuccessfully) between January, 2020 and April, 2023. The study focuses on the development of ML algorithms to predict the success of the spontaneous breathing trial based on a combination of discrete variables and biosignals (time series) recorded during the 24 h prior to the SBT. Results For the models tested, the best results were obtained with Support Vector Classifier model: AUC-PR 0.963 (0.936–0.970, p  = 0.001), AUROC 0.922 (0.871–0.940, p  < 0.001). Conclusions We found that ML models are effective in predicting the success of SBT based on biosignals. Predicting weaning from mechanical ventilation thus appears to be a promising area for the application of AI, through the development of multidimensional models to analyze weak signals.

Background We assessed the effect of noninvasive ventilation (NIV) on mortality and length of stay after high flow nasal oxygenation (HFNO) failure among patients with severe hypoxemic COVID-19 pneumonia. Methods In this multicenter, retrospective study, we enrolled COVID-19 patients admitted in intensive care unit (ICU) for severe COVID-19 pneumonia with a HFNO failure from December 2020 to January 2022. The primary outcome was to compare the 90-day mortality between patients who required a straight intubation after HFNO failure and patients who received NIV after HFNO failure. Secondary outcomes included ICU and hospital length of stay. A propensity score analysis was performed to control for confounding factors between groups. Exploratory outcomes included a subgroup analysis for 90-day mortality. Results We included 461 patients with HFNO failure in the analysis, 233 patients in the straight intubation group and 228 in the NIV group. The 90-day mortality did not significantly differ between groups, 58/228 (25.4%) int the NIV group compared with 59/233 (25.3%) in the straight intubation group, with an adjusted hazard ratio (HR) after propensity score weighting of 0.82 [95%CI, 0.50–1.35] ( p  = 0.434). ICU length of stay was significantly shorter in the NIV group compared to the straight intubation group, 10.0 days [IQR, 7.0-19.8] versus 18.0 days [IQR,11.0–31.0] with a propensity score weighted HR of 1.77 [95%CI, 1.29–2.43] ( p  < 0.001). A subgroup analysis showed a significant increase in mortality rate for intubated patients in the NIV group with 56/122 (45.9%), compared to 59/233 (25.3%) for patients in the straight intubation group ( p  < 0.001). Conclusions In severely hypoxemic COVID-19 patients, no significant differences were observed on 90-day mortality between patients receiving straight intubation and those receiving NIV after HFNO failure. NIV strategy was associated with a significant reduction in ICU length of stay, despite an increase in mortality in the subgroup of patients finally intubated.

Large language models (LLMs) have emerged as promising tools in medical education due to their ability to understand, generate, and reason with natural language. Their ability to simulate expert reasoning suggests a potential for supporting quality control in assessment design. In this study, the use of LLMs in identifying ambiguous or poorly constructed exam items in critical care academic assessments was evaluated. The study aimed to develop automated ambiguity and quality scores to objectively assess individual questions and entire exam components. We analyzed 264 questions from academic exams conducted over 3 academic years (2023-2025) at the Medical School of Université Côte d'Azur. Questions were drawn from 4 docimological formats: progressive clinical cases (PCC), mini-PCC, key feature problems, and isolated question sequences (IQS). Each element was submitted to 4 LLMs (ChatGPT, Gemini Pro, Le Chat, and DeepSeek) without prompt engineering. Performance was evaluated using the official correction key. We applied 4 binary diagnostic tags based on model agreement and self-reported ambiguity: ambiguity, low performance, incoherence, and subjective ambiguity. These tags generated a composite ambiguity score and contributed to a weighted quality score for each exam component. LLMs achieved mean scores in the same range as students, with no significant differences across academic years and significantly higher performance on the mini-PCC and IQS formats (P=.049 and P=.04, respectively). IQS items had the highest ambiguity scores (54 items received a score of 2 in both 2023 and 2024, and 53 items retained the same score). Tag patterns revealed frequent issues with ambiguity and inconsistency. Quality scores varied across academic years. IQS predominantly showed moderate ambiguity (score 2), with occasional instances of strong signals. There was no significant difference in quality based on author specialty or seniority (P=.08 and P=.44, respectively). In this pilot study, LLMs may offer a preliminary framework to proactively detect ambiguous exam questions and estimate the overall quality of an exam. Integrating these tools into the assessment design process could potentially reduce the need for postexam corrections and may help improve fairness and clarity in medical evaluations.

[This corrects the article DOI: 10.1371/journal.pone.0307723.].