Journal of Clinical Monitoring and Computing最新文献

{"title":"Not all anesthetic techniques fit the questions: editorial fit reflects scientific fit.","authors":"Susana Vacas, Michele Introna, Francisco Lobo","doi":"10.1007/s10877-026-01462-8","DOIUrl":"10.1007/s10877-026-01462-8","url":null,"abstract":"","PeriodicalId":15513,"journal":{"name":"Journal of Clinical Monitoring and Computing","volume":" ","pages":"961-962"},"PeriodicalIF":2.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391686/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148345600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A temporal deep learning algorithm for prediction of extubation failures in critical care patients.","authors":"Jiawei Shen, Shengye Lu, Yaqin Wu, Lihong Liu, Yue Wang, Youzhong An, Fengxue Zhu, Buzhou Tang, Huiying Zhao","doi":"10.1007/s10877-026-01439-7","DOIUrl":"10.1007/s10877-026-01439-7","url":null,"abstract":"<p><p>Extubation failure in ICU patients is associated with poor outcomes. Existing prediction models often rely on static data, missing dynamic disease fluctuations. This study introduces TrAcE, a deep learning-based model integrating static and temporal data for improved extubation failure prediction with explainable results. The model was trained and validated using MIMIC-III (Medical Information Mart for Intensive Care-III) data and tested on the LOCAL-Ext (a local database for extubation) dataset. A Transformer-based neural network with temporal fusion was used to screen extubation records (Patients planned for post-extubation non-invasive ventilation or tracheostomy were excluded). Model performance was assessed using AUROC (area under the receiver operating curve) and AUPRC (area under the precision recall curve). Explainability was ensured via Captum's occlusion method, identifying feature attributions at both population and individual levels. TrAcE's extubation timing was compared to the spontaneous breathing test (SBT) in LOCAL-Ext. From MIMIC-III, 5,895 patients (4,126 for training, 1,729 for validation) were selected. LOCAL-Ext included 6,765 test patients. TrAcE outperformed other models, achieving AUROCs of 0.823 (95% CI: 0.808-0.838) in validation and 0.859 (95% CI: 0.836-0.881) in testing, with favorable AUPRCs (0.734 and 0.757, respectively). Compared to SBT in LOCAL-Ext, TrAcE identified suitable extubation days earlier (4.32 [IQR 0-20] vs. 5.65 [IQR 0-23], p < 0.01). In the present study, we developed a deep learning model (TrAcE) for predicting extubation failure in critical care patients. This model can process both static and dynamic data, provides risk prediction and explainability in real-time. Furthermore, it demonstrated an advantage over SBT procedures in promptly identifying patients ready for extubation.</p>","PeriodicalId":15513,"journal":{"name":"Journal of Clinical Monitoring and Computing","volume":" ","pages":"945-956"},"PeriodicalIF":2.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391780/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147838254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From prediction to practice: closing the translation gap in artificial intelligence for anesthesia.","authors":"Janardhan Baliga, Niranjan Seshadri","doi":"10.1007/s10877-026-01434-y","DOIUrl":"10.1007/s10877-026-01434-y","url":null,"abstract":"","PeriodicalId":15513,"journal":{"name":"Journal of Clinical Monitoring and Computing","volume":" ","pages":"933-943"},"PeriodicalIF":2.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391707/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147772858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-assisted videolaryngoscopy improves intubation success: results from a randomized simulation trial.","authors":"Ariadna Comaniciu, David Schurter, Ajsi Kanapari, Giulia Lorenzoni, Asimina Lazaridou, Michael Thomas Ganter, Dario Gregori, Christoph Karl Hofer","doi":"10.1007/s10877-026-01431-1","DOIUrl":"10.1007/s10877-026-01431-1","url":null,"abstract":"<p><p>Video laryngoscopy has become an integral part of today's airway management. Despite its advantages, loss of depth perception and increased cognitive load remain a challenge and not all airway scenarios can be successfully managed and resolved. This study aims to evaluate the efficacy of larynGuide, an AI-based software integrated with the C-MAC video laryngoscope, in improving tracheal intubation success rates. This prospective, randomized, 2 × 2 crossover, simulation-based study included 74 medical professionals, 35 experienced and 39 inexperienced participants. Each participant performed tracheal intubations using a C-MAC video laryngoscope with and without the larynGuide AI overlay in both easy and difficult airway scenarios. The primary outcome was the first attempt success rate. Secondary outcomes included overall success rate, time to intubation, procedural performance scores, and incidence of dental injury. Data were analyzed using mixed effects models with binomial response and non-parametric tests as appropriate. The overall first attempt success rate was 89% with larynGuide and 85% with standard C-MAC, the odds of successful intubation were 72% higher with larynGuide (OR = 1.72, 95%CI = 1.08, 2.73; p = 0.022). Among experienced participants, the benefit was more pronounced (OR = 3.37, 95%CI = 1.2, 8.9; p = 0.016). Time to intubation was longer with larynGuide (median = 29 s) compared to standard C-MAC (median = 23 s, p < 0.01). The incidence of dental damage was lower with larynGuide (22%) compared to C-MAC (27%), although not statistically significant (p = 0.095). The larynGuide AI-powered software improved tracheal intubation success rates compared to standard C-MAC videolaryngoscopy in this simulation-based study, highlighting the potential role of AI in enhancing airway management. Further clinical studies are needed to confirm these results and support broader integration into practice. TRIAL REGISTRATION: ClinicalTrials.gov ID NCT06657417.</p>","PeriodicalId":15513,"journal":{"name":"Journal of Clinical Monitoring and Computing","volume":" ","pages":"905-915"},"PeriodicalIF":2.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391665/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147574177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Anesthesia Preparedeness Clinic (APC) triage score tool: an integrated electronic health record tool for improving resource allocation for preoperative care clinics.","authors":"Minhthy N Meineke, Alice Chien, Sophie W Mawdsley, Megan Meyer, Brian H Park, Elizabeth Kim, Rodney A Gabriel","doi":"10.1007/s10877-026-01438-8","DOIUrl":"10.1007/s10877-026-01438-8","url":null,"abstract":"<p><p>Pre-anesthesia clinics have been associated with improved patient outcomes, but with the rising surgical volume, there is a need to improve allocation of preoperative evaluations. We developed the Anesthesia Preparedeness Clinic (APC) Triage Score tool that was integrated into the electronic health record system and calculated a score based on preoperative factors. It was used to allocate preoperative evaluations for patients to either in-person, telehealth, or nursing phone screens. A retrospective cohort analysis was performed compare how resources were allocated prior to and after implementation (Pre-APC and Post-APC, respectively). There were a total of 9,986 and 10,487 surgical patients included in the analysis in the Pre-APC and Post-APC cohorts, respectively. The correlation coefficient between the triage score and American Society of Anesthesiologists Physical Status (ASA PS) score based on Spearmen test was 0.59 (P < 0.001). The median (quartiles) APC Triage Score in patients who were ASA PS 1 was 0 (0, 1.5), ASA PS 2 was 3.0 (1.0, 6.0), ASA PS 3 was 9.5 (4.5, 17.0) and ASA PS 4 was 21.0 (11.5, 29.5). Patients that received an in-person visit tended to have higher APC Triage Scores in the Post-APC versus Pre-APC cohort with a median (quartiles) score of 17.0 (12.0, 25.5) versus 10.0 (6.0, 19.5), respectively (P < 0.001). Patients that received a telehealth visit tended to have higher APC Triage Scores in the Post-APC versus Pre-APC cohort with a median (quartiles) score of 8.0 (5.0, 13.5) versus 7.0 (3.5, 13.0), respectively (P < 0.001). Finally, patients that received a phone visit tended to have a lower APC Triage Score in the Post-APC versus Pre-APC cohorts with a median (quartiles) score of 3.0 (1.0, 7.0) versus 4.0 (1.0, 8.5), respectively (P < 0.001). Our study demonstrated that implementation of the tool was associated with improved preoperative allocations.</p>","PeriodicalId":15513,"journal":{"name":"Journal of Clinical Monitoring and Computing","volume":" ","pages":"923-932"},"PeriodicalIF":2.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391741/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147699048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain monitoring during anesthesia and critical care: a call for research, collaboration, and implementation.","authors":"Michele Introna, M Kreuzer, A R Absalom","doi":"10.1007/s10877-026-01450-y","DOIUrl":"10.1007/s10877-026-01450-y","url":null,"abstract":"","PeriodicalId":15513,"journal":{"name":"Journal of Clinical Monitoring and Computing","volume":" ","pages":"771-773"},"PeriodicalIF":2.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391656/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148138044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nociception index-monitored extubation is associated with improved hemodynamic stability and recovery efficiency in the post-anesthesia care unit: a single-center randomized controlled trial.","authors":"Qian Qian, Yingjie Zhou, Lulu Chou, Wenmin Hu, Weilian Cai, Huijia Lin, Yinghui Wang, Liuqing Chen, Ping Zhou","doi":"10.1007/s10877-026-01432-0","DOIUrl":"10.1007/s10877-026-01432-0","url":null,"abstract":"","PeriodicalId":15513,"journal":{"name":"Journal of Clinical Monitoring and Computing","volume":" ","pages":"833-844"},"PeriodicalIF":2.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391682/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147592367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Respiratory variation in carotid artery blood flow peak velocity combined with tidal volume challenge in predicting fluid responsiveness of lung-protective ventilation patients: a feasibility study.","authors":"Xiaoying Wang, Qun Zhou, Jingqi Ma, Ju Gao, Tianfeng Huang","doi":"10.1007/s10877-026-01429-9","DOIUrl":"10.1007/s10877-026-01429-9","url":null,"abstract":"","PeriodicalId":15513,"journal":{"name":"Journal of Clinical Monitoring and Computing","volume":" ","pages":"821-832"},"PeriodicalIF":2.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391717/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147457688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relationship between esophageal and central venous pressure variation during respiratory activity: the best choice among four different methods to measure the amplitude of central venous pressure changes.","authors":"Giorgia Ogliari, Letizia Mazzuca Mari, Serena Crisci, Giulia Pradissitto, Federica Fusina, Filippo Albani, Alessandro Morandi, Michele Bertelli, Giuseppe Natalini","doi":"10.1007/s10877-026-01425-z","DOIUrl":"10.1007/s10877-026-01425-z","url":null,"abstract":"","PeriodicalId":15513,"journal":{"name":"Journal of Clinical Monitoring and Computing","volume":" ","pages":"775-784"},"PeriodicalIF":2.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391684/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147355232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NIRS monitoring during steep trendelenburg in robotic prostatectomy.","authors":"L Campiglia, B Matta, Y Santoro, F Becherucci, Iacopo Cappellini, V Pavoni","doi":"10.1007/s10877-026-01443-x","DOIUrl":"10.1007/s10877-026-01443-x","url":null,"abstract":"<p><p>Robotic-assisted laparoscopic prostatectomy (RALP) requires steep Trendelenburg positioning and CO₂ pneumoperitoneum, which may significantly influence cerebral hemodynamic homeostasis. While postoperative neurological complications are uncommon, there is concern regarding increased intracranial pressure and potential edema during prolonged procedures. Near-infrared spectroscopy (NIRS) allows continuous monitoring of cerebral oxygenation, rSO2 reflects the balance between oxygen delivery and consumption, integrating changes of oxygenated hemoglobin (ΔHbO2) and deoxygenated hemoglobin (ΔHHb). This study aimed to evaluate phase-dependent changes in cerebral oxygenation (rSO₂, ΔO₂Hb, ΔHHb) and optic nerve sheath diameter (ONSD) during RALP, and to assess their temporal correlation. This prospective observational study enrolled forty adult patients (ASA I-III) undergoing RALP in steep Trendelenburg position (30 degrees) at Nuovo Ospedale di Prato, Italy, between November 2024 and May 2025. Cerebral oxygenation was continuously monitored using near-infrared spectroscopy (NIRS). ONSD was measured bilaterally using ultrasound at predefined time points: baseline (T0), 45 min after Trendelenburg positioning (T1), 120 min after Trendelenburg (T2), during vesicourethral anastomosis (T3), and before extubation in neutral position (T4). Pearson correlation coefficients were calculated to assess the association between NIRS-derived parameters and ONSD changes. Compared with baseline, Trendelenburg positioning was associated with significant phase-dependent changes in cerebral oxygenation and hemoglobin-derived parameters. rSO₂ and ΔO₂Hb increased during early and prolonged Trendelenburg phases, peaking during vesicourethral anastomosis (T3), and partially decreased after return to supine position (T4). ΔHHb demonstrated a progressive increase during later intraoperative phases, reaching highest values at T3 and T4. ONSD increased progressively across phases on both sides (right: 3.79 ± 0.36 mm at T0 to 4.59 ± 0.47 mm at T3; left: 3.80 ± 0.42 mm to 4.73 ± 0.47 mm). Correlation analysis demonstrated a moderate-to-strong association between ΔHHb and ONSD (right: r = 0.63, p < 10⁻¹⁶; left: r = 0.63, p < 10⁻¹⁶), supporting temporal coupling between cerebral deoxygenated hemoglobin accumulation and optic nerve sheath expansion. This study demonstrates distinct phase-dependent cerebral oxygenation patterns during RALP. The late-phase increase in ΔHHb occurring alongside ONSD enlargement may reflect cerebral venous congestion, a mechanism potentially involved in cerebral edema development during prolonged steep Trendelenburg positioning.</p>","PeriodicalId":15513,"journal":{"name":"Journal of Clinical Monitoring and Computing","volume":" ","pages":"875-886"},"PeriodicalIF":2.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13391443/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148030840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}