medRxiv - Intensive Care and Critical Care Medicine最新文献

{"title":"Myonuclear apoptosis underlies diaphragm atrophy in mechanically ventilated ICU patients.","authors":"Wout J. Claassen, Marloes van den Berg, Rianne J Baelde, Sylvia J.P. Bogaards, Luuk Bonis, Heleen C. Hakkeling, Gerben Schaaf, Albertus Beishuizen, Chris Dickhoff, Reinier A. Boon, Leo Heunks, Tyler J. Kirby, Coen A.C. Ottenheijm","doi":"10.1101/2024.07.23.24310792","DOIUrl":"https://doi.org/10.1101/2024.07.23.24310792","url":null,"abstract":"Abstract (236 words)\u0000Rationale. Intensive care unit (ICU) acquired diaphragm weakness is a common consequence of mechanical ventilation (MV). It contributes to difficult weaning, which is associated with increased morbidity and mortality. Diaphragm weakness is caused by a combination of atrophy and dysfunction of myofibers, large syncytial cells that are maintained by a population of myonuclei. Each myonucleus provides gene transcripts to a finite fiber volume, termed the myonuclear domain. Myonuclear loss in myofibers undergoing atrophy is subject to debate. Myonuclear number is a determinant of transcriptional capacity, and therefore critical for muscle regeneration after atrophy. Objectives. Our objective was to investigate if and how myonuclear number is altered in the diaphragm of mechanically ventilated ICU patients. Methods. We used a combination of confocal microscopy, transcriptomics, and immunohistochemistry techniques to study myonuclear alterations in diaphragm and quadriceps biopsies from MV ICU patients. Measurements and Main Results. Patients with established diaphragm atrophy had a reduced myonuclear number and myonuclear domain. Intrinsic apoptotic pathway activation was identified as a potential mechanism underlying myonuclear removal in the diaphragm of mechanically ventilated ICU patients. Total transcription of myofibers decreased with myonuclear loss. Furthermore, muscle stem cell number was reduced in the patients with diaphragm atrophy.\u0000Conclusion. We identified myonuclear loss due to intrinsic apoptotic pathway activation as a mechanism underlying diaphragm atrophy in mechanically ventilated patients. The loss of myonuclei may contribute to difficult weaning due to impaired regrowth of myofibers after atrophy.","PeriodicalId":501249,"journal":{"name":"medRxiv - Intensive Care and Critical Care Medicine","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778101","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":"Microvascular dysfunction induces a hyperdynamic circulation; a mathematical exploration","authors":"Ivor Popovich","doi":"10.1101/2024.07.22.24310841","DOIUrl":"https://doi.org/10.1101/2024.07.22.24310841","url":null,"abstract":"Abstract Background: The discordance between the macrocirculation and microcirculation in septic shock has been recognised but never explained. I present a novel mathematical hypothesis as to how heterogenous microcirculatory flow distribution directly induces a hyperdynamic circulation and how elevated central venous pressure induces microcirculatory dysfunction. Methods: I explore the tube law and modified Poiseuille resistance for compliant blood vessels. Using these equations a new equation is developed incorporating time constants, elastance of the vessel, unstressed volume and wave reflections that demonstrates the relationship between volume of a microcirculatory vessel and total flow through it. Results: The relationship is demonstrated to be constant at zero until the unstressed volume is reached after which it increases exponentially. By considering n of these vessels in parallel, I demonstrate that the summed flow is minimised when flow is equally distributed among the n vessels, while it is maximised when all flow goes through one vessel alone, thereby demonstrating that heterogenous microvascular perfusion leads to increased total flow. It is shown that if conditions of wave reflection are right then a hyperdynamic circulation with high cardiac output develops. It is also demonstrated that high central venous pressure increases wave reflections and necessarily leads to microvascular perfusion heterogeneity if cardiac output is to be maintained. Conclusions: Microvascular impairment in septic shock directly leads to a hyperdynamic circulation with high cardiac output. High central venous pressures impair the microcirculation. Decades of clinical findings can now be explained mathematically. Implications for hemodynamic therapy for septic shock are discussed.","PeriodicalId":501249,"journal":{"name":"medRxiv - Intensive Care and Critical Care Medicine","volume":"78 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778038","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":"Time-Dependent Variation in Immunoparalysis Biomarkers Among Patients with Sepsis and Critical Illness","authors":"Abigail Samuelsen, Parker Burrows, Erik Lehman, Anthony S Bonavia","doi":"10.1101/2024.07.11.24310285","DOIUrl":"https://doi.org/10.1101/2024.07.11.24310285","url":null,"abstract":"Immunoparalysis is a significant concern in patients with sepsis and critical illness, potentially leading to increased risk of secondary infections. This study aimed to perform a longitudinal assessment of immune function over the initial two weeks following the onset of sepsis and critical illness. We compared ex vivo stimulated cytokine release to traditional markers of immunoparalysis, including monocyte Human Leukocyte Antigen (mHLA)-DR expression and absolute lymphocyte count (ALC). A total of 64 critically ill patients were recruited in a tertiary care academic medical setting, including 31 septic and 33 non-septic patients. Results showed that while mHLA-DR expression significantly increased over time, this was primarily driven by the non-septic subset of critically ill patients. ALC recovery was more prominent in septic patients. Ex vivo stimulation revealed significant increases in TNF and IL-6 production over time in septic patients. However, IFNg production varied with the stimulant used and did not show significant recovery when normalized to cell count. No significant correlation was found between mHLA-DR expression and other immunoparalysis biomarkers. These findings suggest the need for more nuanced immune monitoring approaches beyond the traditional 'sepsis' versus 'non-sepsis' classifications in critically ill patients. It also provided further evidence of a potential window for targeted immunotherapeutic interventions in the first week of critical illness.","PeriodicalId":501249,"journal":{"name":"medRxiv - Intensive Care and Critical Care Medicine","volume":"80 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141611936","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":"Awake prone positioning effectiveness in moderate to severe COVID-19 a randomized controlled trial.","authors":"Phong Nguyen Thanh, Duc Hong Du, Ho Bich Hai, Nguyen Thanh Nguyen, Le Dinh Van Khoa, Le Thuy Thuy Khanh, Luu Hoai Bao Tran, Nguyen Thi My Linh, Cao Thi Cam Van, Dang Phuong Thao, Nguyen Thi Diem Trinh, Pham Tieu Kieu, Nguyen Thanh Truong, Vo Tan Hoang, Nguyen Thanh Ngoc, Tran Thi Dong Vien, Vo Trieu Ly, Tran Dang Khoa, Abi Beane, James T Anibal, Guy Thwaites, Ronald B Geskus, David Clifton, Nguyen Thi Phuong Dung, Evelyne Kestelyn, Guy Glover, Le Van Tan, Lam Minh Yen, Nguyen Le Nhu Tung, Nguyen Thanh Dung, C. Louise Thwaites","doi":"10.1101/2024.06.30.24309722","DOIUrl":"https://doi.org/10.1101/2024.06.30.24309722","url":null,"abstract":"Objectives: We evaluated the efficacy and acceptability of awake-prone positioning (APP) in a randomised controlled trial, using a dedicated APP implementation team and wearable continuous-monitoring devices to monitor position and oximetry.\u0000Methods: The trial was performed at a tertiary level hospital in Ho Chi Minh City, Vietnam, recruiting adults (≥18 years) hospitalised with moderate or severe COVID-19 and receiving supplemental oxygen therapy via nasal/facemask systems or high-flow nasal canulae. Participants were randomized (1:1) to standard care or APP. The primary outcome was escalation of respiratory support within 28 days of randomisation.\u0000Results: Ninety-three patients were enrolled between March 2022 and March 2023; 80 (86%) had received ≥2 doses of SARS-CoV2 vaccine. Significantly greater mean daily APP times were achieved in those allocated to APP, although most did not achieve the target 8 hours/day. We did not detect significant differences in the primary outcome (RR 0.85, 95% CI 0.40-1.78, p=0.67) or secondary outcomes, including intubation rate and 28-day mortality. Particpants reported prone positioning was comfortable, although almost all preferred supine positioning. No adverse events associated with the intervention were reported.\u0000Conclusions: APP was not associated with benefit, but was safe. Continuous monitoring with wearable devices was feasible and acceptable to patients.","PeriodicalId":501249,"journal":{"name":"medRxiv - Intensive Care and Critical Care Medicine","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500656","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":"Developing and validating a machine learning model to predict successful next-day extubation in the ICU","authors":"Samuel W Fenske, Alec Peltekian, Mengija Kang, Nikolay S Markov, Mengou Zhu, Kevin Grudzinski, Melissa J Bak, Anna Pawlowski, Vishu Gupta, Yuwei Mao, Stanislav Bratchikov, Thomas Stoeger, Luke V Rasmussen, Alok N Choudhary, Alexander V Misharin, Benjamin D Singer, GR Scott Budinger, Richard D Wunderink, Ankit Agrawal, Catherine A Gao, NU Script Study Investigators","doi":"10.1101/2024.06.28.24309547","DOIUrl":"https://doi.org/10.1101/2024.06.28.24309547","url":null,"abstract":"Background: Criteria to identify patients who are ready to be liberated from mechanical ventilation are imprecise, often\u0000resulting in prolonged mechanical ventilation or reintubation, both of which are associated with adverse outcomes. Daily\u0000protocol-driven assessment of the need for mechanical ventilation leads to earlier extubation but requires dedicated\u0000personnel. We sought to determine whether machine learning applied to the electronic health record could predict\u0000successful extubation.\u0000Methods: We examined 37 clinical features from patients from a single-center prospective cohort study of patients in our\u0000quaternary care medical ICU who required mechanical ventilation and underwent a bronchoalveolar lavage for known or\u0000suspected pneumonia. We also tested our models on an external test set from a community hospital ICU in our health care\u0000system. We curated electronic health record data aggregated from midnight to 8AM and labeled extubation status. We\u0000deployed three data encoding/imputation strategies and built XGBoost, LightGBM, logistic regression, LSTM, and RNN\u0000models to predict successful next-day extubation. We evaluated each model's performance using Area Under the Receiver\u0000Operating Characteristic (AUROC), Area Under the Precision Recall Curve (AUPRC), Sensitivity (Recall), Specificity, PPV\u0000(Precision), Accuracy, and F1-Score.\u0000Results: Our internal cohort included 696 patients and 9,828 ICU days, and our external cohort had 333 patients and 2,835\u0000ICU days. The best model (LSTM) predicted successful extubation on a given ICU day with an AUROC 0.87 (95% CI 0.834-\u00000.902) and the internal test set and 0.87 (95% CI 0.848-0.885) on the external test set. A Logistic Regression model\u0000performed similarly (AUROC 0.86 internal test, 0.83 external test). Across multiple model types, measures previously\u0000demonstrated to be important in determining readiness for extubation were found to be most informative, including plateau\u0000pressure and Richmond Agitation Sedation Scale (RASS) score. Our model often predicted patients to be stable for\u0000extubation in the days preceding their actual extubation, with 63.8% of predicted extubations occurring within three days of\u0000true extubation. We also tested the best model on cases of failed extubations (requiring reintubation within two days) not\u0000seen by the model during training. Our best model would have identified 35.4% (17/48) of these cases in the internal test\u0000set and 48.1% (13/27) cases in the external test set as unlikely to be successfully extubated.\u0000Conclusions: Machine learning models can accurately predict the likelihood of extubation on a given ICU day from data\u0000available in the electronic health record. Predictions from these models are driven by clinical features that have been\u0000associated with successful extubation in clinical trials.","PeriodicalId":501249,"journal":{"name":"medRxiv - Intensive Care and Critical Care Medicine","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141524683","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":"An artificial-intelligence interpretable tool to predict risk of deep vein thrombosis after endovenous thermal ablation","authors":"Yu Ma, Azadeh Tabari, Jesus Alfonso Juarez Palazuelos, Anthony Gebran, Haytham Kaafarani, Dimitris Bertsimas, Dania Daye","doi":"10.1101/2024.06.19.24309166","DOIUrl":"https://doi.org/10.1101/2024.06.19.24309166","url":null,"abstract":"Introduction: Endovenous thermal ablation (EVTA) stands as one of the primary treatments for superficial venous insufficiency. Concern exists about the potential for thromboembolic complications following this procedure. Although rare, those complications can be severe, necessitating early identification of patients prone to increased thrombotic risks. This study aims to leverage AI-based algorithms to forecast patients' likelihood of developing deep vein thrombosis (DVT) within 30 days following EVTA.\u0000Materials and Methods: From 2007 to 2017, all patients who underwent EVTA were identified using the American College of Surgeons National Surgical Quality Improvement Program database. We developed and validated 4 machine learning models using demographics, comorbidities, and laboratory values to predict the risk of postoperative deep vein thrombosis: Classification and Regression Trees (CART), Optimal Classification Trees (OCT), Random Forests, and Extreme Gradient Boosting (XGBoost). The models were trained using all the available variables. SHAP analysis was adopted to interpret model outcomes and offer medical insights into feature importance and interactions.\u0000Results: A total of 21,549 patients were included (mean age of 54 +- SD years, 67% female). In this cohort, 1.59% developed DVT. The XGBoost model had good discriminative power for predicting DVT risk with AUC of 0.711 in the hold-out test set for all-variable model. Stratification of the test set by age, BMI, preoperative white blood cell and platelet count shows that the model performs equally well across these groups. Conclusion: We developed and validated an interpretable model that enables physicians to predict which patients with superficial venous insufficiency has higher risk of developing deep vein thrombosis within 30 days following endovenous thermal ablation.","PeriodicalId":501249,"journal":{"name":"medRxiv - Intensive Care and Critical Care Medicine","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500780","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":"A core outcome set for adult general ICU patients","authors":"Maj-Brit Nørregaard Kjær, Camilla Rahbek Lysholm Bruun, Anders Granholm, Morten Hylander Møller, Bodil Steen Rasmussen, Camilla Bekker Mortensen, Lone Musaeus Poulsen, Thomas Strøm, Eva Laerkner, Anne Craveiro Brøchner, Trine Haberlandt, Anne-Marie Gellert Bunzel, Louise Stenbryggen Herløv, Anna Holm, Praleene Sivapalan, Stine Estrup, Maria Cronhjort, Anna Schandl, Jon Henrik Laake, Kristin Hofsø, Fredrike Blokzijl, Frederic Keus, Carmen Andrea Pfortmueller, Marlies Ostermann, Jade M Cole, Matt P Wise, Wojciech Szczeklik, Anna Wludarczyk, Tomas Jovaiša, Maurizio Cecconi, Martin Ingi Sigurdsson, Marek Nalos, Johanna Hästbacka, Marja Mäkinen, Naomi Hammond, Edward Litton, Kimberley Haines, Sheila Nainan Myatra, Bharath Kumar Tirupakuzhi Vijayaraghavan, Kavita Yadav, Vivekanand Jha, Balasubramanian Venkatesh, Ingrid Egerod, Anders Perner, Marie O Collet","doi":"10.1101/2024.05.29.24308094","DOIUrl":"https://doi.org/10.1101/2024.05.29.24308094","url":null,"abstract":"<strong>Purpose</strong> Randomised clinical trials should ideally use harmonised outcomes that are important to patients and to facilitate meta-analyses and ensuring generalisability. Core outcome sets for specific subsets of ICU patients exist, e.g., respiratory failure, delirium, and COVID-19, but not for ICU patients in general. Accordingly, we aimed to develop a core outcome set for adult general ICU patients.","PeriodicalId":501249,"journal":{"name":"medRxiv - Intensive Care and Critical Care Medicine","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141188847","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":"Filling the gaps: leveraging large language models for temporal harmonization of clinical text across multiple medical visits for clinical prediction","authors":"Inyoung Choi, Qi Long, Emily Getzen","doi":"10.1101/2024.05.06.24306959","DOIUrl":"https://doi.org/10.1101/2024.05.06.24306959","url":null,"abstract":"Electronic health records offer great promise for early disease detection, treatment evaluation, information discovery, and other important facets of precision health. Clinical notes, in particular, may contain nuanced information about a patient’s condition, treatment plans, and history that structured data may not capture. As a result, and with advancements in natural language processing, clinical notes have been increasingly used in supervised prediction models. To predict long-term outcomes such as chronic disease and mortality, it is often advantageous to leverage data occurring at multiple time points in a patient’s history. However, these data are often collected at irregular time intervals and varying frequencies, thus posing an analytical challenge. Here, we propose the use of large language models (LLMs) for robust temporal harmonization of clinical notes across multiple visits. We compare multiple state-of-the-art LLMs in their ability to generate useful information during time gaps, and evaluate performance in supervised deep learning models for clinical prediction.","PeriodicalId":501249,"journal":{"name":"medRxiv - Intensive Care and Critical Care Medicine","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935378","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":"Neutrophil percentages in bronchoalveolar lavage fluid: Implications for diagnosing bacterial pneumonia in patients with immunocompromise and neutropenia","authors":"Kevin M. Grudzinski, Samuel Fenske, Richard G. Wunderink, Catherine A. Gao, NU SCRIPT Study Investigators","doi":"10.1101/2024.05.04.24306709","DOIUrl":"https://doi.org/10.1101/2024.05.04.24306709","url":null,"abstract":"<strong>Background</strong> Pneumonia is the leading cause of infectious deaths and the most common infection identified in ICU patients. Assessment of bronchoalveolar lavage fluid (BALF) cellularity can aid in pneumonia diagnosis. Low percentages (&lt;50%) of BALF neutrophils have a high negative predictive value for bacterial pneumonia in a general medical ICU population, but unclear operating characteristics in patients with immunocompromise and neutropenia remain unknown.","PeriodicalId":501249,"journal":{"name":"medRxiv - Intensive Care and Critical Care Medicine","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935310","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":"Factors influencing long-term recovery in critically ill COVID-19 survivors: A prospective multicentre cohort study","authors":"Ingrid Didriksson, Attila Frigyesi, Martin Spångfors, Märta Leffler, Anton Reepalu, Anna Nilsson, Martin Annborn, Anna Lybeck, Hans Friberg, Gisela Lilja","doi":"10.1101/2024.05.01.24306267","DOIUrl":"https://doi.org/10.1101/2024.05.01.24306267","url":null,"abstract":"<strong>Background</strong> Long-term outcomes after critical COVID-19 have not been sufficiently studied. This study aimed to describe changes in functional outcome and health-related quality of life (HRQoL) assessed at 3 and 12 months in a cohort of critically ill COVID-19 survivors. A secondary aim was to investigate factors associated with good functional outcome and HRQoL at 12 months.","PeriodicalId":501249,"journal":{"name":"medRxiv - Intensive Care and Critical Care Medicine","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140888782","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}