Critical care explorations最新文献

{"title":"Quantifying Healthcare Provider Perceptions of a Novel Deep Learning Algorithm to Predict Sepsis: Electronic Survey.","authors":"Karthik Ramesh, Aaron Boussina, Supreeth P Shashikumar, Atul Malhotra, Christopher A Longhurst, Christopher S Josef, Kimberly Quintero, Jake Del Rosso, Shamim Nemati, Gabriel Wardi","doi":"10.1097/CCE.0000000000001276","DOIUrl":"10.1097/CCE.0000000000001276","url":null,"abstract":"<p><strong>Importance: </strong>Sepsis is a major cause of morbidity and mortality, with early intervention shown to improve outcomes. Predictive modeling and artificial intelligence (AI) can aid in early sepsis recognition, but there remains a gap between algorithm development and clinical use. Despite the importance of user experience in adopting clinical predictive models, few studies have focused on provider acceptance and feedback.</p><p><strong>Objectives: </strong>To evaluate healthcare worker perception and acceptance of a deep learning sepsis prediction model in the emergency department (ED).</p><p><strong>Design, setting, and participants: </strong>COnformal Multidimensional Prediction Of SEpsis Risk (COMPOSER), a deep learning algorithm, is used at two EDs of a large academic medical center to predict sepsis before clear clinical presentation. An internally developed survey following the Checklist for Reporting Results of Internet E-Surveys was distributed to team members who received a COMPOSER alert.</p><p><strong>Analysis: </strong>Mann-Whitney U testing was performed on results stratified by provider experience.</p><p><strong>Results: </strong>A total of 114 responses were received: 76 from doctors of medicine/doctors of osteopathic medicine, 34 from registered nurses, and four from nurse practicioners/physician assistants. Of these, 53% were from providers with fewer than 5 years of experience. Seventy-seven percent of respondents had a positive or neutral perception of the alert's usefulness. Providers with 0-5 years of experience were more likely to expect sepsis after the alert (p = 0.021) and found the alert more useful (p = 0.016) compared with those with 6+ years of experience. Additionally, physicians with 0-5 years of experience were more likely to say the alert changed their patient management (p = 0.048).</p><p><strong>Conclusions: </strong>Less experienced providers were more likely to perceive benefit from the alert, which was overall received favorably. Future AI implementations might consider tailored alert patterns and education to enhance reception and reduce fatigue.</p>","PeriodicalId":93957,"journal":{"name":"Critical care explorations","volume":"7 6","pages":"e1276"},"PeriodicalIF":0.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12140755/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144227985","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":"Positive End-Expiratory Pressure Titration Based on Lung Mechanics May Improve Pulse Pressure Variation Interpretation in Acute Respiratory Distress Syndrome Patients.","authors":"Vasiliki Tsolaki, George E Zakynthinos, Nikitas Karavidas, Maria Eirini Papadonta, Ilias Dimeas, Kyriaki Parisi, Theofilos Amanatidis, Epaminondas Zakynthinos","doi":"10.1097/CCE.0000000000001273","DOIUrl":"10.1097/CCE.0000000000001273","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the effects of positive end-expiratory pressure (PEEP) on pulse pressure variation (PPV) in patients with moderate/severe acute respiratory distress syndrome (ARDS).</p><p><strong>Design: </strong>Prospective interventional self-controlled study.</p><p><strong>Setting: </strong>University Hospital of Larissa.</p><p><strong>Patients: </strong>ARDS patients admitted intubated in the ICU (from August 2020 to March 2022).</p><p><strong>Interventions: </strong>None.</p><p><strong>Measurements and main results: </strong>PPV and inferior vena cava (IVC) respiratory variability were evaluated at two PEEP levels (first value mainly based on PEEP/Fio2 and second value based on respiratory system compliance). Additionally, respiratory mechanics, hemodynamics, and echocardiographic indices assessing right ventricular (RV) size (RV end-diastolic area/left ventricular end-diastolic area [RVEDA/LVEDA]), RV systolic function, and RV afterload (pulmonary artery systolic pressure [PASP] and PASP/left ventricular outflow tract velocity time integral [PASP/VTILVOT]) were recorded. Ninety-five patients were evaluated. PPV decreased after PEEP reduction (11.7 ± 0.2 to 7.9% ± 0.2%), whereas IVC respiratory variability increased (9.1 ± 0.9 to 14.6% ± 0.1%) and central venous pressure decreased (all p < 0.0001). RV afterload indices decreased (p < 0.0001), simultaneously with RV size (< 0.0001) and systolic function indices' improvements (< 0.05); shock warranted less noradrenaline doses. The change in PPV correlated significantly to respiratory variability in IVC diameter distensibility (p < 0.0001) and moderately to changes in RV size and systolic function (change in RVEDA/change in LVEDA, change in tricuspid annular plane systolic excursion); RV afterload (change in PASP [ΔPASP], ΔPASP/VTILVOT); and change in Paco2 (all p < 0.05).</p><p><strong>Conclusions: </strong>PPV alteration with PEEP decrease, associated with IVC distensibility increases, may indicate the presence of RV dysfunction and increased pulmonary vascular resistances. Whether the patients are in need for fluid loading, fluid responsiveness assessment may be further warranted.</p>","PeriodicalId":93957,"journal":{"name":"Critical care explorations","volume":"7 6","pages":"e1273"},"PeriodicalIF":0.0,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12122179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176177","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":"Predictions With a Purpose: Elevating Standards for Clinical Modeling Research.","authors":"Patrick G Lyons","doi":"10.1097/CCE.0000000000001268","DOIUrl":"10.1097/CCE.0000000000001268","url":null,"abstract":"","PeriodicalId":93957,"journal":{"name":"Critical care explorations","volume":"7 6","pages":"e1268"},"PeriodicalIF":0.0,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12119043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176183","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":"Predictions With a Purpose: Elevating Standards for Clinical Modeling Research.","authors":"Patrick G Lyons","doi":"10.1097/CCE.0000000000001268","DOIUrl":"10.1097/CCE.0000000000001268","url":null,"abstract":"","PeriodicalId":93957,"journal":{"name":"Critical care explorations","volume":"7 6","pages":"e1268"},"PeriodicalIF":0.0,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12119043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144183119","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":"Machine Learning Accurately Predicts Need for Critical Care Support in Patients Admitted to Hospital for Community-Acquired Pneumonia.","authors":"George S Chen, Terry Lee, Jennifer L Y Tsang, Alexandra Binnie, Anne McCarthy, Juthaporn Cowan, Patrick Archambault, Francois Lellouche, Alexis F Turgeon, Jennifer Yoon, Francois Lamontagne, Allison McGeer, Josh Douglas, Peter Daley, Robert Fowler, David M Maslove, Brent W Winston, Todd C Lee, Karen C Tran, Matthew P Cheng, Donald C Vinh, John H Boyd, Keith R Walley, Joel Singer, John C Marshall, James A Russell","doi":"10.1097/CCE.0000000000001262","DOIUrl":"10.1097/CCE.0000000000001262","url":null,"abstract":"<p><strong>Objectives: </strong>Hospitalized community-acquired pneumonia (CAP) patients are admitted for ventilation, vasopressors, and renal replacement therapy (RRT). This study aimed to develop a machine learning (ML) model that predicts the need for such interventions and compare its accuracy to that of logistic regression (LR).</p><p><strong>Design: </strong>This retrospective observational study trained separate models using random-forest classifier (RFC), support vector machines (SVMs), Extreme Gradient Boosting (XGBoost), and multilayer perceptron (MLP) to predict three endpoints: eventual use of invasive ventilation, vasopressors, and RRT during hospitalization. RFC-based models were overall most accurate in a derivation COVID-19 CAP cohort and were validated in one COVID-19 CAP and two non-COVID-19 CAP cohorts.</p><p><strong>Setting: </strong>This study is part of the Community-Acquired Pneumonia: Toward InnoVAtive Treatment (CAPTIVATE) Research program.</p><p><strong>Patients: </strong>Two thousand four hundred twenty COVID-19 and 1909 non-COVID-19 CAP patients over 18 years old hospitalized and not needing invasive ventilation, vasopressors, and RRT on the day of admission were included.</p><p><strong>Interventions: </strong>None.</p><p><strong>Measurements and main results: </strong>Performance was evaluated with area under the receiver operating characteristic curve (AUROC) and accuracy. RFCs performed better than XGBoost, SVM, and MLP models. For comparison, we evaluated LR models in the same cohorts. AUROC was very high ranging from 0.74 to 0.95 in predicting ventilation, vasopressors, and RRT use in our derivation and validation cohorts. ML used and variables such as Fio₂, Glasgow Coma Scale, and mean arterial pressure to predict ventilator, vasopressor use, creatinine, and potassium to predict RRT use. LR was less accurate than ML, with AUROC ranging 0.66 to 0.8.</p><p><strong>Conclusions: </strong>A ML algorithm more accurately predicts need of invasive ventilation, vasopressors, or RRT in hospitalized non-COVID-19 CAP and COVID-19 patients than regression models and could augment clinician judgment for triage and care of hospitalized CAP patients.</p>","PeriodicalId":93957,"journal":{"name":"Critical care explorations","volume":"7 6","pages":"e1262"},"PeriodicalIF":0.0,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12119046/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144183146","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":"Machine Learning Accurately Predicts Need for Critical Care Support in Patients Admitted to Hospital for Community-Acquired Pneumonia.","authors":"George S Chen, Terry Lee, Jennifer L Y Tsang, Alexandra Binnie, Anne McCarthy, Juthaporn Cowan, Patrick Archambault, Francois Lellouche, Alexis F Turgeon, Jennifer Yoon, Francois Lamontagne, Allison McGeer, Josh Douglas, Peter Daley, Robert Fowler, David M Maslove, Brent W Winston, Todd C Lee, Karen C Tran, Matthew P Cheng, Donald C Vinh, John H Boyd, Keith R Walley, Joel Singer, John C Marshall, James A Russell","doi":"10.1097/CCE.0000000000001262","DOIUrl":"10.1097/CCE.0000000000001262","url":null,"abstract":"<p><strong>Objectives: </strong>Hospitalized community-acquired pneumonia (CAP) patients are admitted for ventilation, vasopressors, and renal replacement therapy (RRT). This study aimed to develop a machine learning (ML) model that predicts the need for such interventions and compare its accuracy to that of logistic regression (LR).</p><p><strong>Design: </strong>This retrospective observational study trained separate models using random-forest classifier (RFC), support vector machines (SVMs), Extreme Gradient Boosting (XGBoost), and multilayer perceptron (MLP) to predict three endpoints: eventual use of invasive ventilation, vasopressors, and RRT during hospitalization. RFC-based models were overall most accurate in a derivation COVID-19 CAP cohort and were validated in one COVID-19 CAP and two non-COVID-19 CAP cohorts.</p><p><strong>Setting: </strong>This study is part of the Community-Acquired Pneumonia: Toward InnoVAtive Treatment (CAPTIVATE) Research program.</p><p><strong>Patients: </strong>Two thousand four hundred twenty COVID-19 and 1909 non-COVID-19 CAP patients over 18 years old hospitalized and not needing invasive ventilation, vasopressors, and RRT on the day of admission were included.</p><p><strong>Interventions: </strong>None.</p><p><strong>Measurements and main results: </strong>Performance was evaluated with area under the receiver operating characteristic curve (AUROC) and accuracy. RFCs performed better than XGBoost, SVM, and MLP models. For comparison, we evaluated LR models in the same cohorts. AUROC was very high ranging from 0.74 to 0.95 in predicting ventilation, vasopressors, and RRT use in our derivation and validation cohorts. ML used and variables such as Fio2, Glasgow Coma Scale, and mean arterial pressure to predict ventilator, vasopressor use, creatinine, and potassium to predict RRT use. LR was less accurate than ML, with AUROC ranging 0.66 to 0.8.</p><p><strong>Conclusions: </strong>A ML algorithm more accurately predicts need of invasive ventilation, vasopressors, or RRT in hospitalized non-COVID-19 CAP and COVID-19 patients than regression models and could augment clinician judgment for triage and care of hospitalized CAP patients.</p>","PeriodicalId":93957,"journal":{"name":"Critical care explorations","volume":"7 6","pages":"e1262"},"PeriodicalIF":0.0,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prevalence and Predictors of Home Discharge from the PICU.","authors":"Leslie A Dervan, Daniel Garros, Waylon Howard, Nadia Roumeliotis","doi":"10.1097/CCE.0000000000001266","DOIUrl":"10.1097/CCE.0000000000001266","url":null,"abstract":"<p><strong>Importance: </strong>Discharge directly home from PICU is increasingly described, but its prevalence and predictors are not known.</p><p><strong>Objectives: </strong>To describe the prevalence and predictors of discharge directly home from the PICU.</p><p><strong>Design: </strong>Cohort of admissions between January 1, 2015, and June 30, 2021.</p><p><strong>Setting: </strong>Multicenter study of 142 North American PICUs participating in Virtual Pediatric Systems dataset.</p><p><strong>Participants: </strong>Hospitalized children younger than 18 years admitted to a participating PICU who were discharged home either directly or after transfer to the acute care ward. We excluded admissions with unclear discharge disposition, and repeat admissions within a year.</p><p><strong>Main outcomes and measures: </strong>Demographic, PICU admission, and unit characteristics were measured according to discharge disposition. A multilevel adjusted logistic regression model, clustered by patient and center, was used to evaluate predictors of discharge directly home.</p><p><strong>Results: </strong>The cohort included 612,471 admissions (339,818 [55%] males), of which 141,427 (23.1%) were discharged directly home from the PICU. Across 142 sites, the proportion of those discharged directly home ranged from 3% to 100%. In adjusted models, predictors associated with discharge home included prior discharge home (odds ratio [OR], 30.77; 95% CI, 27.92-33.92), age 2-5 years (OR, 2.35; 95% CI, 2.08-2.66), surgical admission (OR, 2.68; 95% CI, 1.34-5.33), and respiratory (OR, 2.08; 95% CI, 1.64-2.63) and cardiovascular (OR, 1.56; 95% CI, 1.18-2.06) complex chronic conditions. Large center size was associated with a lower likelihood of discharge home (OR, 0.58; 95% CI, 0.49-0.69).</p><p><strong>Conclusions and relevance: </strong>About one in four children are discharged directly home after a PICU admission. While the practice is common, there is a large variability between centers. Selected patients, including those with acute surgical admission and those with complex chronic respiratory and cardiovascular conditions, are more likely to go directly home from the PICU.</p>","PeriodicalId":93957,"journal":{"name":"Critical care explorations","volume":"7 5","pages":"e1266"},"PeriodicalIF":0.0,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12088628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144082450","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":"Impact of Glucagon-Like Peptide-1 Receptor Agonist Exposure on Gastrointestinal Outcomes Among ICU Patients: A Multicenter Matched Cohort Study.","authors":"Cameron G Gmehlin, Marko Nemet, Zeeshan M Rizwan, Sumera Ahmad, Ognjen Gajic, Aysun Tekin","doi":"10.1097/CCE.0000000000001263","DOIUrl":"10.1097/CCE.0000000000001263","url":null,"abstract":"<p><strong>Importance: </strong>Patients admitted to the ICU often experience gastrointestinal complications. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have become increasingly prevalent in the treatment of type 2 diabetes mellitus and obesity, and there is evidence that their use may be associated with an increased risk of clinically significant gastrointestinal events. However, their impact on critically ill patients admitted to the medical ICU is unknown.</p><p><strong>Objectives: </strong>This study examined whether pre-ICU use of GLP-1RAs was associated with increased incidence of gastrointestinal complications and hospitalization outcomes.</p><p><strong>Design, setting and participants: </strong>Multicenter, retrospective cohort study of critically ill patients admitted to academic and community hospitals of Mayo Clinic Health System from January 1, 2018, to December 31, 2023. Patients who were admitted to surgical ICUs and those who were exposed to GLP-1RA medications before but did not have an active prescription within 30 days of admission were excluded. Patients exposed to GLP-1RA were matched with those nonexposed in a 1:1 fashion based on demographic factors, factors affecting gastrointestinal motility, overall illness burden, and clinical acuity.</p><p><strong>Main outcomes and measures: </strong>Outcomes of interest were the development of gastrointestinal dysfunction, ICU- and hospital-free days, and mortality.</p><p><strong>Results: </strong>A total of 31,327 patients with diabetes or obesity were identified of whom these, 631 were exposed to GLP-1RA before admission. In the matched cohort of 1262 patients, baseline variables were evenly distributed between the two groups. There were no significant differences in the odds of developing nausea/vomiting, constipation, ileus, obstruction, impaction, or aspiration pneumonia between the GLP-1RA exposed and unexposed groups. Similarly, ICU and hospital mortality rates were comparable across the two groups. However, GLP-1RA exposed patients had significantly more hospital-free days compared with unexposed patients (estimate, 1.19; 95% CI, 0.38-2.0; p = 0.004).</p><p><strong>Conclusions and relevance: </strong>GLP-1RA exposure was not associated with increased odds of clinically significant gastrointestinal complications in nonsurgical critically ill patients. Increased hospital-free days observed among GLP-1RA exposed patients requires further study.</p>","PeriodicalId":93957,"journal":{"name":"Critical care explorations","volume":"7 5","pages":"e1263"},"PeriodicalIF":0.0,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12080681/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058819","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":"Critical Care Ultrasonography for Volume Management: A Systematic Review, Meta-Analysis, and Trial Sequential Analysis of Randomized Trials.","authors":"Sameer Sharif, Holden Flindall, John Basmaji, Enyo Ablordeppey, José L Díaz-Gómez, Michael Lanspa, Sara Nikravan, Joshua Piticaru, Kimberley Lewis","doi":"10.1097/CCE.0000000000001261","DOIUrl":"10.1097/CCE.0000000000001261","url":null,"abstract":"<p><strong>Objectives: </strong>To determine the safety and efficacy of critical care ultrasonography (CCUS) guided volume management in acutely ill patients.</p><p><strong>Data sources: </strong>We searched MEDLINE, Embase, Wiley CENTRAL, and unpublished sources from inception to February 6, 2024.</p><p><strong>Study selection: </strong>We included randomized controlled trials (RCTs) of acutely ill adult patients randomized to receive CCUS as compared with no CCUS to guide fluid management.</p><p><strong>Data extraction: </strong>Reviewers screened abstracts, full texts, and extracted data independently and in duplicate. We pooled data using a random-effects model, assessed the risk of bias using the modified Cochrane tool and assessed the certainty of evidence using the Grading Recommendations Assessment, Development, and Evaluation approach.</p><p><strong>Data synthesis: </strong>We included 17 RCTs (n = 1765 patients) in this review. Pooled analyses found that the use of CCUS for volume management in acutely ill patients may decrease mortality at the longest reported time period (relative risk [RR], 0.79; 95% CI, 0.67-0.95; low certainty) and decreases the fluid balance up to 72 hours after admission (mean difference [MD], 0.72 L lower; 95% CI, 1.5 L lower to 0.07 L higher; low certainty). CCUS had an uncertain effect on duration of mechanical ventilation (MD, 1.14 d fewer; 95% CI, 3.35 d fewer to 1.07 d more; very low certainty), ICU length of stay (LOS) (MD, 0.01 d fewer; 95% CI, 1.12 d fewer to 1.09 d more; very low certainty), the need for vasopressors (RR, 0.39; 95% CI, 0.10-1.62; very low certainty), acute kidney injury (AKI) (RR, 0.94; 95% CI, 0.32-2.72; very low certainty), and the need for renal replacement therapy (RRT) (RR, 0.79; 95% CI, 0.17-3.66; very low certainty).</p><p><strong>Conclusions: </strong>In acutely ill adult patients, CCUS for the use of targeted volume management may reduce mortality and fluid balance up to 72 hours after admission. CCUS has an uncertain effect on ICU LOS, duration of mechanical ventilation, duration of vasopressor use, AKI, and the need for RRT. However, this evidence is limited by imprecision and indirectness.</p>","PeriodicalId":93957,"journal":{"name":"Critical care explorations","volume":"7 5","pages":"e1261"},"PeriodicalIF":0.0,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12080698/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144053864","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":"Determination of Glomerular Filtration Rate After Contrast-Enhanced CT Among Critically Ill Patients: Support for a New Procedure.","authors":"Bertil Kågedal, Anders Helldén, Dženeta Nezirević Dernroth, Anders Lindgaard Andersen, Andreas Ekman, Mats Haglund, Bharti Kataria, Frida Oskarsson, Lovisa Tobieson, Åse Östholm, Håkan Hanberger","doi":"10.1097/CCE.0000000000001269","DOIUrl":"10.1097/CCE.0000000000001269","url":null,"abstract":"<p><strong>Objectives: </strong>To measure glomerular filtration rate using iohexol plasma clearance (mGFRiohexol) in critically ill patients using the high doses of iohexol administered at CT and to evaluate its agreements with urinary creatinine clearance (uClcr) and estimated glomerular filtration rates (eGFRs), calculated from plasma concentrations of creatinine (eGFRcr) and cystatin C (eGFRcys).</p><p><strong>Design: </strong>Prospective observational cohort study.</p><p><strong>Setting: </strong>ICUs across Southeast Sweden.</p><p><strong>Patients: </strong>Critically ill adult patients.</p><p><strong>Interventions and measurements: </strong>Twenty-six ICU patients were given high doses of iohexol (range, 27-140 mL) for contrast-enhanced CT, whereafter blood samples were taken in the elimination phase for determination of mGFRiohexol. Plasma iohexol concentrations were determined by high-performance liquid chromatography and mGFRiohexol was calculated. Standard dose (5 mL) of iohexol was administered the following days to compare low-dose clearance results with the high-dose clearance results. Six-hour uClcr was performed four times a day and averaged.</p><p><strong>Main results: </strong>Mean ± sd mGFRiohexol after CT was 77.4 ± 38.1 mL/min (n = 26), and uClcr was 97.3 ± 58.2 mL/min (n = 25) in the critically ill patients. There was a strong positive correlation between mGFRiohexol determined with high and low doses of iohexol in patients with normal or high mGFRiohexol (coefficient of determination [R2] = 0.88; p < 0.001) and between mGFRiohexol and uClcr (R2 = 0.87; p < 0.001). eGFRcr overestimated mGFRiohexol and eGFRcys underestimated mGFRiohexol.</p><p><strong>Conclusions: </strong>mGFRiohexol after contrast-enhanced CT compares well with mGFRiohexol after standard low-dose iohexol respectively uClcr. Over- and underestimation of mGFRiohexol by eGFRcr and eGFRcys is probably explained by increased tubular secretion of creatinine and increased production of cystatin C in intensive care patients.</p>","PeriodicalId":93957,"journal":{"name":"Critical care explorations","volume":"7 5","pages":"e1269"},"PeriodicalIF":0.0,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12080682/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144038340","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}