Intensive Care Medicine Experimental最新文献

{"title":"Plasma endostatin at intensive care admission is independently associated with acute kidney injury, dialysis, and mortality in COVID-19.","authors":"Hazem Koozi, Jonas Engström, Ahmad Zwawi, Martin Spångfors, Ingrid Didriksson, Anders Larsson, Hans Friberg, Attila Frigyesi","doi":"10.1186/s40635-025-00748-6","DOIUrl":"10.1186/s40635-025-00748-6","url":null,"abstract":"<p><strong>Background: </strong>Critical COVID-19 is associated with high mortality, and acute kidney injury (AKI) is common. Endostatin has emerged as a promising prognostic biomarker for predicting AKI and mortality in intensive care. This study aimed to investigate plasma endostatin at intensive care unit (ICU) admission as a biomarker for AKI, renal replacement therapy (RRT), and 90-day mortality in COVID-19.</p><p><strong>Methods: </strong>A pre-planned retrospective analysis of a prospectively collected cohort of admissions with a primary SARS-CoV-2 infection to six ICUs in southern Sweden between May 2020 and May 2021 was undertaken. Endostatin at ICU admission was evaluated with multivariable logistic regression analyses adjusted for age, sex, C-reactive protein, and creatinine. Net reclassification index analyses were also performed.</p><p><strong>Results: </strong>Four hundred eighty-four patients were included. Endostatin showed a non-linear association with AKI, RRT, and 90-day mortality. Endostatin levels of 100-200 ng/mL were associated with AKI on ICU day 1 (OR 5.1, 95% CI 1.5-18, p = 0.0097), RRT during the ICU stay (OR 3.5, 95% CI 1.1-12, p = 0.039), and 90-day mortality (OR 4.2, 95% CI 1.6-11, p = 0.0037). Adding endostatin to creatinine improved prediction of AKI on ICU day 1, while adding it to a model containing age, sex, CRP, and creatinine improved prediction of both AKI on ICU day 1 and 90-day mortality, but not RRT.</p><p><strong>Conclusions: </strong>Endostatin at ICU admission was independently associated with AKI, RRT, and 90-day mortality in ICU patients with COVID-19. In addition, endostatin improved the prediction of AKI and 90-day mortality, highlighting its potential as a biomarker for early risk stratification in intensive care.</p>","PeriodicalId":13750,"journal":{"name":"Intensive Care Medicine Experimental","volume":"13 1","pages":"42"},"PeriodicalIF":2.8,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11968582/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143772272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comment: Admission neutrophil-to-lymphocyte ratio to predict mortality in burn patients: a meta-analysis.","authors":"Nosaibah Razaqi, Rachana Mehta, Shubham Kumar, Ranjana Sah","doi":"10.1186/s40635-025-00734-y","DOIUrl":"10.1186/s40635-025-00734-y","url":null,"abstract":"","PeriodicalId":13750,"journal":{"name":"Intensive Care Medicine Experimental","volume":"13 1","pages":"41"},"PeriodicalIF":2.8,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11950594/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143718731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expiratory time constants in mechanically ventilated patients: rethinking the old concept-a narrative review.","authors":"Filip Depta, Richard H Kallet, Michael A Gentile, Elias N Baedorf Kassis","doi":"10.1186/s40635-025-00745-9","DOIUrl":"10.1186/s40635-025-00745-9","url":null,"abstract":"<p><p>The expiratory time constant (RC_EXP) plays an important role in understanding the mechanical properties of the respiratory system in patients receiving mechanical ventilation. Initially conceived as a tool to illustrate nonlinearity in lung emptying, RC_EXP has transitioned from a theoretical concept to a clinically relevant parameter, particularly within the realm of intelligent ventilation strategies. This narrative review explores the historical development of RC_EXP, starting with its foundational definition based on fixed values of respiratory system resistance and compliance (i.e., the single-compartmental model). This early approach to RC_EXP largely overlooked the intricate viscoelastic characteristics of the lungs. The inherent limitations of this simplified model are discussed. The review then shifts its focus to clinical evidence describing the severity of deviations in RC_EXP from the ''ideal'' state in both acute lung injury and obstructive lung disease. This includes an analysis of which portions of the expiratory phase are most affected and how adjustments in tidal volume and positive end-expiratory pressure can potentially improve the homogeneity of lung emptying. The review concludes with a discussion of the clinical applications of RC_EXP and proposes future directions for its integration into ventilator management.</p>","PeriodicalId":13750,"journal":{"name":"Intensive Care Medicine Experimental","volume":"13 1","pages":"40"},"PeriodicalIF":2.8,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11947344/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143718739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Haemoadsorption to remove inflammatory mediators in sepsis: past, present, and future.","authors":"Nicole J B Waalders, Matthijs Kox, Peter Pickkers","doi":"10.1186/s40635-025-00740-0","DOIUrl":"10.1186/s40635-025-00740-0","url":null,"abstract":"<p><p>While a dysregulated immune response is at the center of the sepsis definition, standard care is still solely focussed on prompt administration of antimicrobial therapy, source control, resuscitation and organ supportive therapies. Extracorporeal blood purification therapies, such as haemoadsorption, have been proposed as a possible adjunctive therapy to standard care in sepsis. These adsorption devices aim to rebalance the dysregulated immune response by removal of excessive amounts of circulating inflammatory mediators, including cytokines and endotoxins. Thus far, the effects of haemoadsorption on clinical outcomes have been insufficiently studied and although its routine use is not justified based on the current evidence, multiple centers use these devices in patients with severe septic shock. This narrative review describes the most well-studied adsorption devices as well as a novel selective adsorption device called the 'IL-6-Sieve', including in vitro data showing its capturing potential. Finally, it addresses important considerations for future trials on haemoadsorption in septic patients.</p>","PeriodicalId":13750,"journal":{"name":"Intensive Care Medicine Experimental","volume":"13 1","pages":"38"},"PeriodicalIF":2.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11928715/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143673883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Let's get in sync: current standing and future of AI-based detection of patient-ventilator asynchrony.","authors":"Thijs P Rietveld, Björn J P van der Ster, Abraham Schoe, Henrik Endeman, Anton Balakirev, Daria Kozlova, Diederik A M P J Gommers, Annemijn H Jonkman","doi":"10.1186/s40635-025-00746-8","DOIUrl":"10.1186/s40635-025-00746-8","url":null,"abstract":"<p><strong>Background: </strong>Patient-ventilator asynchrony (PVA) is a mismatch between the patient's respiratory drive/effort and the ventilator breath delivery. It occurs frequently in mechanically ventilated patients and has been associated with adverse events and increased duration of ventilation. Identifying PVA through visual inspection of ventilator waveforms is highly challenging and time-consuming. Automated PVA detection using Artificial Intelligence (AI) has been increasingly studied, potentially offering real-time monitoring at the bedside. In this review, we discuss advances in automatic detection of PVA, focusing on developments of the last 15 years.</p><p><strong>Results: </strong>Nineteen studies were identified. Multiple forms of AI have been used for the automated detection of PVA, including rule-based algorithms, machine learning and deep learning. Three licensed algorithms are currently reported. Results of algorithms are generally promising (average reported sensitivity, specificity and accuracy of 0.80, 0.93 and 0.92, respectively), but most algorithms are only available offline, can detect a small subset of PVAs (focusing mostly on ineffective effort and double trigger asynchronies), or remain in the development or validation stage (84% (16/19 of the reviewed studies)). Moreover, only in 58% (11/19) of the studies a reference method for monitoring patient's breathing effort was available. To move from bench to bedside implementation, data quality should be improved and algorithms that can detect multiple PVAs should be externally validated, incorporating measures for breathing effort as ground truth. Last, prospective integration and model testing/finetuning in different ICU settings is key.</p><p><strong>Conclusions: </strong>AI-based techniques for automated PVA detection are increasingly studied and show potential. For widespread implementation to succeed, several steps, including external validation and (near) real-time employment, should be considered. Then, automated PVA detection could aid in monitoring and mitigating PVAs, to eventually optimize personalized mechanical ventilation, improve clinical outcomes and reduce clinician's workload.</p>","PeriodicalId":13750,"journal":{"name":"Intensive Care Medicine Experimental","volume":"13 1","pages":"39"},"PeriodicalIF":2.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11928342/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143676628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating the plasma volume by infusing albumin: a retrospective feasibility study.","authors":"Robert G Hahn, Joachim H Zdolsek","doi":"10.1186/s40635-025-00743-x","DOIUrl":"10.1186/s40635-025-00743-x","url":null,"abstract":"<p><strong>Background: </strong>The combined changes in plasma albumin and blood hemoglobin can probably be used to estimate the plasma volume (PV) when albumin is infused. However, the optimal setup, timing of the blood sampling, and the importance of capillary leakage to the calculations are unclear.</p><p><strong>Methods: </strong>In this technical vignette, we estimated the PV using retrospective data on plasma albumin and blood hemoglobin obtained during intravenous infusion of 3 mL/kg of 20% albumin over 30 min in 41 volunteers and 45 patients. We used a manual and a kinetic correction for capillary leakage of albumin. The results were compared to the mean of two anthropometric equations derived via tracer methods.</p><p><strong>Results: </strong>The anthropometric PV was 3.00 ± 0.63 L (mean ± SD). The strongest linearity between the albumin-derived and anthropometric PV was obtained at the end, and 10 min after the end, of the 30-min infusions; the correlation coefficient was 0.75 over this time frame. The difference between the two measures (the prediction error) was 0.31 ± 0.56 L but the SD was only half as high for PVs< 2.5 L than for larger PVs. There was slightly stronger linearity and better accuracy, but no better precision, when data were corrected for capillary leakage.</p><p><strong>Conclusion: </strong>This study suggests how an evaluation of this method using isotopes can be conducted. Changes in plasma albumin and blood hemoglobin have the best chance to accurately indicate the PV at the end of, or 10 min after, a 30-min infusion of albumin. Subtraction of 0.3 L from the PV is sufficient to correct for capillary leakage of albumin.</p>","PeriodicalId":13750,"journal":{"name":"Intensive Care Medicine Experimental","volume":"13 1","pages":"35"},"PeriodicalIF":2.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11923349/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143663378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced glomerular thrombosis in pronated animals with ARDS.","authors":"Silvia Marchesi, Elin Lundström, Elin Lindström, Jonas Ödmark, Mark Lubberink, Håkan Ahlström, Miklós Lipcsey","doi":"10.1186/s40635-025-00747-7","DOIUrl":"10.1186/s40635-025-00747-7","url":null,"abstract":"<p><strong>Background: </strong>Prone positioning is part of the management of acute respiratory distress syndrome (ARDS) and has been demonstrated to successfully improve the ventilation-perfusion match and reduce mortality in patients with severe respiratory failure. However, the effect of pronation on other organs than the lungs has not been widely studied. This study aimed to compare abdominal edema, perfusion and inflammation in supine and prone positioning in a porcine ARDS model.</p><p><strong>Methods: </strong>Seventeen piglets were randomized into two groups: a supine group (n = 9) and a prone group (n = 8). Both groups received endotoxemic infusion and were observed for 6 h. Three animals per group underwent positron emission tomography-magnetic resonance imaging (PET-MRI) for imaging acquisition. Hemodynamic and respiratory parameters were recorded throughout the protocol. Inflammation was assessed by measuring cytokine concentrations in blood, ascites and the abdominal organs' tissue. The edema in abdominal organs was assessed by wet-dry ratio and pathophysiological analysis of tissue samples and by MRI and PET measurements from volumes of interest (VOIs) delineated in abdominal organ in MRI and PET images. The abdominal organs' perfusion was also assessed by MRI and PET measurements.</p><p><strong>Results: </strong>The prone group had a faster CO₂ washout and needed a lower positive end-expiratory pressure to maintain the desired oxygenation. In the prone group duodenal edema was lower (measured with wet-dry ratio) and renal perfusion, by both MRI and PET measurements, was lower than half compared to the supine group (MRI, perfusion fraction, f: supine group 0.13; prone group 0.03; p-value 0.002. PET Flow: supine group 1.7; prone group 0.4 ml/cm³/min; p-value 0.002). In addition, the histopathological samples of the kidneys showed a higher incidence and extent of glomerular thrombosis in the prone group.</p><p><strong>Conclusions: </strong>In a porcine ARDS model, prone positioning was associated with enhanced glomerular thrombosis and low renal perfusion.</p>","PeriodicalId":13750,"journal":{"name":"Intensive Care Medicine Experimental","volume":"13 1","pages":"36"},"PeriodicalIF":2.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926287/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised clustering for sepsis identification in large-scale patient data: a model development and validation study.","authors":"Na Li, Kiarash Riazi, Jie Pan, Kednapa Thavorn, Jennifer Ziegler, Bram Rochwerg, Hude Quan, Hallie C Prescott, Peter M Dodek, Bing Li, Alain Gervais, Allan Garland","doi":"10.1186/s40635-025-00744-w","DOIUrl":"10.1186/s40635-025-00744-w","url":null,"abstract":"<p><strong>Background: </strong>Sepsis is a major global health problem. However, it lacks a true reference standard for case identification, complicating epidemiologic surveillance. Consensus definitions have changed multiple times, clinicians struggle to identify sepsis at the bedside, and differing identification algorithms generate wide variation in incidence rates. The two current identification approaches use codes from administrative data, or electronic health record (EHR)-based algorithms such as the Center for Disease Control Adult Sepsis Event (ASE); both have limitations. Here our primary purpose is to report initial steps in developing a novel approach to identifying sepsis using unsupervised clustering methods. Secondarily, we report preliminary analysis of resulting clusters, using identification by ASE criteria as a familiar comparator.</p><p><strong>Methods: </strong>This retrospective cohort study used hospital administrative and EHR data on adults admitted to intensive care units (ICUs) at five Canadian medical centres (2015-2017), with split development and validation cohorts. After preprocessing 592 variables (demographics, encounter characteristics, diagnoses, medications, laboratory tests, and clinical management) and applying data reduction, we presented 55 principal components to eight different clustering algorithms. An automated elbow method determined the optimal number of clusters, and the optimal algorithm was selected based on clustering metrics for consistency, separation, distribution and stability. Cluster membership in the validation cohort was assigned using an XGBoost model trained to predict cluster membership in the development cohort. For cluster analysis, we prospectively subdivided clusters by their fractions meeting ASE criteria (≥ 50% ASE-majority clusters vs. ASE-minority clusters), and compared their characteristics.</p><p><strong>Results: </strong>There were 3660 patients in the development cohort and 3012 in the validation cohort, of which 21.5% (development) and 19.1% (validation) were ASE (+). The Robust and Sparse K-means Clustering (RSKC) method performed best. In the development cohort, it identified 48 clusters of hospitalizations; 11 ASE-majority clusters contained 22.4% of all patients but 77.8% of all ASE (+) patients. 34.9% of the 209 ASE (-) patients in the ASE-majority clusters met more liberal ASE criteria for sepsis. Findings were consistent in the validation cohort.</p><p><strong>Conclusions: </strong>Unsupervised clustering applied to diverse, large-scale medical data offers a promising approach to the identification of sepsis phenotypes for epidemiological surveillance.</p>","PeriodicalId":13750,"journal":{"name":"Intensive Care Medicine Experimental","volume":"13 1","pages":"37"},"PeriodicalIF":2.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11925832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using weak signals to predict spontaneous breathing trial success: a machine learning approach.","authors":"Romain Lombardi, Mathieu Jozwiak, Jean Dellamonica, Claude Pasquier","doi":"10.1186/s40635-025-00724-0","DOIUrl":"10.1186/s40635-025-00724-0","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":13750,"journal":{"name":"Intensive Care Medicine Experimental","volume":"13 1","pages":"34"},"PeriodicalIF":2.8,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11920562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of the capnodynamic method to calculate mixed venous oxygen saturation in postoperative cardiac patients.","authors":"Mats Wallin, Magnus Hallback, Hareem Iftikhar, Elise Keleher, Anders Aneman","doi":"10.1186/s40635-025-00741-z","DOIUrl":"10.1186/s40635-025-00741-z","url":null,"abstract":"<p><strong>Background: </strong>Cardiac output and mixed venous oxygen saturation are key variables in monitoring adequate oxygen delivery and have typically been measured using pulmonary artery catheterisation. The capnodynamic method measures effective pulmonary blood flow utilising carbon dioxide kinetics in ventilated patients. Combined with breath-by-breath measurements of carbon dioxide elimination, a non-invasive approximation of mixed venous oxygen saturation can be calculated.</p><p><strong>Methods: </strong>This study primarily investigated the agreement between mixed venous oxygen saturation calculated using the capnodynamic method and blood gas analysis of mixed venous blood sampled via a pulmonary artery catheter in 47 haemodynamically stable postoperative cardiac patients. Both measurements were synchronised and performed during alveolar recruitment by stepwise changes to the level of positive end-expiratory pressure. Simultaneously, we studied the agreement between effective pulmonary blood flow and thermodilution cardiac output. The Bland-Altman method for repeated measurements and calculation of percentage error were used to examine agreement. Measurements before and after alveolar recruitment were analysed by a paired t test. The study hypothesis for agreement was a limit of difference of ten percentage points between mixed venous oxygen saturation using the capnodynamic algorithm vs. catheter blood gas analysis.</p><p><strong>Results: </strong>Capnodynamic calculation of mixed venous saturation compared to blood gas analysis showed a bias of -0.02 [95% CI - 0.96-0.91] % and limits of agreement at 8.8 [95% CI 7.7-10] % and - 8.9 [95% CI -10-- 7.8] %. The percentage error was < 20%. The effective pulmonary blood flow compared to thermodilution showed a bias of - 0.41 [95% CI - 0.55-- 0.28] l.min^-1 and limits of agreement at 0.56 [95% CI 0.41-0.75] l.min^-1 and - 1.38 [95% CI - 1.57--1.24] l.min^-1. The percentage error was < 30%. Only effective pulmonary blood flow increased by 0.38 [95% CI 0.20-0.56] l.min^-1 (p < 0.01) after alveolar recruitment.</p><p><strong>Conclusions: </strong>In this study, minimal bias and limits of agreement < 10% between mixed venous oxygen saturation calculated by the capnodynamic method and pulmonary arterial blood gas analysis confirmed the agreement hypothesis in stable postoperative patients. The effective pulmonary blood flow agreed with thermodilution cardiac output, while influenced by pulmonary shunt flow.</p>","PeriodicalId":13750,"journal":{"name":"Intensive Care Medicine Experimental","volume":"13 1","pages":"32"},"PeriodicalIF":2.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11889286/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}