Journal of Clinical Monitoring and Computing

I am very happy to have Chiara Robba on our team of Associate Editors and to witness her successful career!

The JCMC is proud to announce that Chiara Robba currently a member of our editorial board as AE, has been appointed Deputy EIC of the prestigious journal Intensive Care Medicine 🎉🌟 Despite her new and important duties, Chiara will continue to serve our journal, highlighting her incredible commitment and dedication to the JCMC 🙌💪 Congratulations, Chiara! 👏🎊 Your achievements inspire us all! 🚀✨ We are honored to be part of the Springer Nature family 🌍📚

🌟 Noninvasive estimation of PaCO2 from volumetric capnography in animals with injured lungs: an Artificial Intelligence approach 🌬️💻 by G Tusman, Gustavo Meschino, Fernando Suarez Sipmann et al. from 🇪🇸 🇦🇷 🇩🇪 https://lnkd.in/dU4zVvDs 🧐This study evaluates the feasibility of estimating arterial partial pressure of carbon dioxide (PaCO2) non-invasively using an Adaptive Neuro-Fuzzy Inference System (ANFIS) model. The model utilizes parameters from volumetric capnography (VCap), offering a potential breakthrough in respiratory monitoring. 📊 Methods:  - Conducted on 14 lung-lavaged pigs under mechanical ventilation.  - PaCO2 was measured with an optical intravascular catheter while VCap parameters were collected breath-by-breath.  - Data:   - 8599 data pairs: Each pair included one PaCO2 value and 12 VCap/ventilatory parameters.   - Split: 85% for training (7370 points) and 15% for testing (1229 points).  - ANFIS modeling: Performed 10 independent iterations with randomized data.  - Analysis included:   - Bland-Altman plots for accuracy/precision.   - Root Mean Square Error (RMSE) for prediction quality.   - Four-quadrant and polar plots for trending ability. 📈 Results:  - Accuracy and Precision:   - Mean bias: 0.03 ± 0.03 mmHg.   - Limits of agreement: 2.25 ± 0.42 mmHg   - RMSE: 1.15 ± 0.06 mmHg - Trending ability:   - Four-quadrant concordance: 95.5%  - Polar plot concordance: 94.3% 🌟 Conclusions:  The ANFIS model, using non-invasive VCap parameters, accurately estimates PaCO2 with high precision and reliable trending ability in an animal lung injury model. 💡 Implications:  This approach could transform **respiratory monitoring**, minimizing invasiveness while maintaining accuracy, especially in critical care or operating room settings. #ArtificialIntelligence 🤖 #RespiratoryCare 🌬️ #Capnography 📈 #InnovativeMonitoring #CriticalCare

⚡️ ¡Comenzamos el 2025 con más fuerza que nunca! ⚡️ Volvemos con nuestras esperadas píldoras semanales, y qué mejor manera de arrancar este año que compartiendo un artículo brutal: ”Electroencephalogram Monitoring During Anesthesia and Critical Care: A Guide for the Clinician" (Journal of Clinical Monitoring and Computing, 2024). 🧠 Este artículo, recién publicado en diciembre en Journal of Clinical Monitoring and Computing, es una visión renovada de otros artículos clásicos. Un trabajo multicéntrico liderado por referentes como Dr. Nitin Manohara, Dr. Francisco A Lobo, Dr. Matthias Kreuzer, entre otros. 🔍 ¿Qué lo hace único? Esta nueva revisión amplía el horizonte con conceptos avanzados como el alfa dropout, el beta arousal, y el delta arousal, fundamentales para entender el balance nocicepción-antinocicepción, y explica otros básicos como el SEF, MF, BSR, o los spindles. Además, hace una descripción detallada de los monitores más comúnmente utilizados y aborda diferentes aspectos clínicos fundamentales como el delirio en UCI. ✨ Este artículo se convertirá y es ya una referencia imprescindible para anestesiólogos e intensivistas de todo el mundo. Una guía completa que combina fundamentos básicos con aplicaciones prácticas avanzadas para mejorar la seguridad de nuestros pacientes. 💡 ¡Y esto es solo el comienzo! Este año, en las Jornadas de Anestesia Multimodal 2025, no solo contaremos con expertos internacionales como el Dr. Emery N. Brown, sino que renovamos con expertos como el Dr. Pablo Sepúlveda y la Dra. Cristina Honorato. Además, el Dr. Matthias Kreuzer, coautor del artículo, junto al Dr. Alex Barroso, liderará el bootcamp de EEG del Safe Brain Initiative - SBI, explorando la monitorización cerebral avanzada en anestesia y cuidados críticos. 🔗 Accede al artículo completo, compártelo y guárdalo como referencia: https://lnkd.in/d6EAeVx7 📆 Guarda la fecha: 23 al 25 de octubre de 2025 en Madrid. 🖥️ Más información: https://lnkd.in/duJv6Une 📧 Dudas o consultas: info@anestesiamultimodal.com ¡La comunidad sigue creciendo y pronto estará disponible el programa preliminar! ¡Feliz 2025! 🧠⚡️ #AnestesiaMultimodal #Neurociencias #MedicinaPerioperatoria #SEDAR #EEG #Madrid #AMM2025

🎉Take on fewer projects, but don’t hesitate in the face of challenges. Rush less, practice patience. Live passionately for what makes you happy. If you ever leave something good in this world, let it be that. I’m still learning. And that’s okay. Welcome, 2025 Cit. Antonio Messina , associate editor Happy new year from the JCMC editorial board! 🎊🥳

🫀Automated and reference methods for the calculation of left ventricular outflow tract velocity time integral or ejection fraction by non-cardiologists: a systematic review on the agreement of the two methods by FA Gonzalez, Mateusz Zawadka, Rita Varudo, Cristina Santonocito, Slama Michel, Filippo Sanfilippo 🇵🇹 🇫🇷 🇮🇹 https://lnkd.in/dFMwj6uK 🔍 Echocardiography is vital for assessing clinical deterioration and diagnosing shock through measurements like Left Ventricular Ejection Fraction (LVEF) and Velocity Time Integral (VTI). Traditional bedside calculations can be time-consuming and variable. This review evaluates the use of AI-powered automation for measuring LVEF and VTI in non-cardiology settings, comparing them to reference methods such as Simpson’s method and manual tracing. 📚 Methods: A systematic literature review was conducted using PubMed. Eight studies were included: Four on automated-LVEF, three on automated-VTI, and one addressing both. 📊 Findings: Feasibility: Automated measurements were feasible in 78.4% to 93.3% of cases. LVEF Accuracy: Mean bias: 0 to 2.9% for experienced operators, 0 to -10.2% for non-experienced operators. Correlation with reference methods: Experienced operators: 0.63–0.86 Non-experienced operators: 0.56–0.81 Limits of Agreement (LoA) were wide. VTI Accuracy: Mean bias: −1.7 cm to −1.9 cm. Correlation: 0.86 (experienced) and 0.79 (non-experienced). 💡 Conclusions: AI-based automation shows potential for bedside echocardiography, with accuracy improving under experienced operators. Despite variability, these methods can be useful in time-sensitive, non-cardiology settings, aiding clinical decisions when interpreted within the clinical context.

🧠 Entropy of difference works similarly to permutation entropy for the assessment of anesthesia and sleep EEG despite the lower computational effort By Alexander Edthofer , Andreas Körner , Dr. Matthias Kreuzer et al from Technical University of Munich 🇩🇪 https://lnkd.in/d9xMsprV 📊 Purpose: Electroencephalogram (EEG) monitoring is essential for anesthesia and sleep disorder diagnosis. This study compared two entropic measures: 1️⃣ Permutation Entropy (widely used but computationally intense, especially for higher orders). 2️⃣ Entropy of Difference (a newer, potentially faster method). The goal was to evaluate: • Computational efficiency. • Ability to distinguish vigilance states. • Practicality for higher-order analysis. 🧪 Methods: • Algorithms for pattern encoding and entropy calculation were analyzed. • Runtime comparisons for both methods were performed at different orders. • Mutual information extracted by each method was assessed using AUC in a linear discriminant analysis classifier. 📊 Results: • Entropy of Difference showed significantly reduced calculation times compared to Permutation Entropy, especially for higher orders: • Some higher orders were only computable with Entropy of Difference. • Performance: Both methods performed similarly in distinguishing vigilance states, as indicated by comparable AUC values. 💡 Conclusion: • Entropy of Difference is a promising method for analyzing EEG data, offering faster computation without sacrificing accuracy. • It enables higher-order analysis more efficiently, broadening possibilities for advanced EEG investigations. 🚀 Implications: This method could enhance EEG analysis in clinical settings, making real-time monitoring during anesthesia or sleep studies more feasible.

🎊❄️Season’s greatings from the editorial board of the Journal of Clinical Monitoring and Computing!! ❄️🎊🎁🌈

🩸 Clot formation risk in the clearing fluid after arterial catheter blood sampling: coagulation factors consumption over time - a prospective pilot study🩸 by Jerome Dauvergne, Bertrand Rozec, et al. from Nantes Université 🇫🇷 🔍 Reinjecting the clearing fluid (a mix of saline and blood) after arterial blood sampling can help reduce blood loss but raises concerns about potential clot-related complications. This study explored: 1️⃣ Fibrinogen consumption as an early indicator of clot formation. 2️⃣ Presence of macroscopic clots and changes in coagulation factors like prothrombin time, factors II and V. 🧪 Methods: 📍 Conducted in 30 ICU patients with radial arterial catheters. Used a locally developed closed blood sampling system. Analyzed hemostasis parameters in pure blood (reference) and clearing fluid at 2, 3, and 5 minutes after reservoir filling. Evaluated the impact of anticoagulant therapy (e.g., unfractionated heparin, antiplatelet agents). 📊 Results: Fibrinogen levels showed a significant decline over time in clearing fluid: 📉 Reference: 4.3 [IQR: 3.1–5.9] g/L 2 min: 3.6 [IQR: 2.7–4.7] g/L (p < 0.001) 3 min: 3.4 [IQR: 2.1–4.3] g/L (p < 0.001) 5 min: 3.0 [IQR: 1.7–4.1] g/L (p < 0.001) ❌ No macroscopic clots were observed in any samples. Antiplatelet agents were used in 37% of patients, while 57% had elevated heparin anti-Xa activity (> 0.10 UI/ml). Clearing fluid showed progressive decreases in coagulation factors, indicating the initiation of clot formation even without visible clots. 🩺 Conclusion: Although macroscopic clots were absent, fibrinogen and coagulation factor reductions suggest potential clot initiation in clearing fluid. This finding raises safety concerns regarding its reinjection and underscores the need for further research into its role in patient blood management strategies. https://lnkd.in/dp9vF_BN

🩸 Clot formation risk in the clearing fluid after arterial catheter blood sampling: coagulation factors consumption over time - a prospective pilot study🩸 by Jerome Dauvergne, Bertrand Rozec, et al. from Nantes Université 🇫🇷 🔍 Reinjecting the clearing fluid (a mix of saline and blood) after arterial blood sampling can help reduce blood loss but raises concerns about potential clot-related complications. This study explored: 1️⃣ Fibrinogen consumption as an early indicator of clot formation. 2️⃣ Presence of macroscopic clots and changes in coagulation factors like prothrombin time, factors II and V. 🧪 Methods: 📍 Conducted in 30 ICU patients with radial arterial catheters. Used a locally developed closed blood sampling system. Analyzed hemostasis parameters in pure blood (reference) and clearing fluid at 2, 3, and 5 minutes after reservoir filling. Evaluated the impact of anticoagulant therapy (e.g., unfractionated heparin, antiplatelet agents). 📊 Results: Fibrinogen levels showed a significant decline over time in clearing fluid: 📉 Reference: 4.3 [IQR: 3.1–5.9] g/L 2 min: 3.6 [IQR: 2.7–4.7] g/L (p < 0.001) 3 min: 3.4 [IQR: 2.1–4.3] g/L (p < 0.001) 5 min: 3.0 [IQR: 1.7–4.1] g/L (p < 0.001) ❌ No macroscopic clots were observed in any samples. Antiplatelet agents were used in 37% of patients, while 57% had elevated heparin anti-Xa activity (> 0.10 UI/ml). Clearing fluid showed progressive decreases in coagulation factors, indicating the initiation of clot formation even without visible clots. 🩺 Conclusion: Although macroscopic clots were absent, fibrinogen and coagulation factor reductions suggest potential clot initiation in clearing fluid. This finding raises safety concerns regarding its reinjection and underscores the need for further research into its role in patient blood management strategies. https://lnkd.in/dp9vF_BN