{"title":"Low baseline Tc-MEP amplitude is associated with false-positive alerts in spine surgery: a ROC-based analysis across spinal pathologies.","authors":"Shuyun Jiang, Hiroki Ushirozako, Go Yoshida, Tomohiko Hasegawa, Tomohiro Banno, Hideyuki Arima, Shin Oe, Tomohiro Yamada, Koichiro Ide, Kouki Yamaguchi, Shigeru Aketa, Yukihiro Matsuyama, Yu Yamato","doi":"10.1007/s10877-026-01505-0","DOIUrl":"https://doi.org/10.1007/s10877-026-01505-0","url":null,"abstract":"<p><p>This study aimed to determine whether absolute baseline transcranial motor evoked potential (Tc-MEP) amplitude is associated with false-positive (FP) alerts during intraoperative neurophysiological monitoring and to estimate pathology-specific cutoff values. We retrospectively reviewed 307 high-risk spine surgery cases monitored with Tc-MEPs from 2018 to 2020. For waveform-level analysis, 1,748 muscle waveforms from 287 FP/true-negative cases were analyzed. Alerts were defined as final amplitude ≤ 30% of baseline, and FP alerts were defined as alerts without new postoperative motor deficit. Receiver operating characteristic analyses were performed to estimate baseline-amplitude cutoff values for FP alerts in spinal cord tumor surgery, spinal deformity surgery, ossification of the posterior longitudinal ligament surgery, and the pooled cohort. FP alerts occurred in 253 of 1,748 waveforms (14.5%). Baseline Tc-MEP amplitude was lower in FP than in true-negative waveforms (351 ± 533 µV vs. 604 ± 1007 µV). FP alerts were more frequent in lower-extremity recordings. Exploratory ROC-derived cutoff values were 122 µV for spinal cord tumor surgery, 180 µV for spinal deformity surgery, 160 µV for ossification of the posterior longitudinal ligament surgery, and 170 µV for the pooled cohort; the corresponding AUCs were 0.68, 0.56, 0.61, and 0.61, respectively. Lower baseline Tc-MEP amplitudes were associated with FP alerts. Baseline amplitude assessment may help identify low-amplitude signals that warrant optimization and cautious interpretation before percentage-based alarm criteria are applied.</p>","PeriodicalId":15513,"journal":{"name":"Journal of Clinical Monitoring and Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149074930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yoon Jung Kim, Juseong Jin, Youmin Shin, Chang-Hoon Koo, Seung-Bo Lee, Young-Gon Kim, Hee-Soo Kim
{"title":"Deep-learning-based postoperative desaturation prediction using spirometry image data.","authors":"Yoon Jung Kim, Juseong Jin, Youmin Shin, Chang-Hoon Koo, Seung-Bo Lee, Young-Gon Kim, Hee-Soo Kim","doi":"10.1007/s10877-026-01483-3","DOIUrl":"https://doi.org/10.1007/s10877-026-01483-3","url":null,"abstract":"<p><p>Postoperative desaturation, a significant concern following laparoscopic and robot-assisted procedures, is associated with increased mortality rates and prolonged stays in the recovery room. Although modern anesthesia workstations offer real-time spirometry pressure-volume loops for each breath, their clinical utility remains limited. This study aimed to develop a deep learning model using a convolutional neural network to analyze spirometry pressure-volume loops in patients undergoing laparoscopic or robot-assisted surgeries and to design a clinically applicable index using gradient-weighted class activation maps to enhance interpretability. This retrospective study was conducted across two tertiary hospitals, involving data from 207 patients who underwent laparoscopic or robot-assisted abdominal surgeries for model training. Data from 45 patients at an independent hospital were used for external validation. Thus, data from a total of 252 patients were used. The primary outcome measure was the model's ability to predict postoperative desaturation. The performance of the model, along with that of the derived clinical index, was evaluated using the area under the receiver operating characteristic curve (AUROC). The deep learning model predicted postoperative desaturation with AUROC (95% confidence interval) values of 0.825 [0.744-0.905] and 0.688 [0.656-0.720] on internal and external validation datasets, respectively. The desaturation predictive index demonstrated moderate predictive performance on both internal and external datasets (AUROC: 0.774 and 0.722, respectively). These findings support the efficacy of both the deep learning model and the predictive index in forecasting postoperative desaturation, thus highlighting the potential of real-time spirometry in postoperative monitoring research.</p>","PeriodicalId":15513,"journal":{"name":"Journal of Clinical Monitoring and Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149069711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Nociception monitoring during cardiac surgery with cardiopulmonary bypass: limitations and future directions.","authors":"Vladislav Rogozov, Tomáš Vaněk, Zuzana Blažejová","doi":"10.1007/s10877-026-01478-0","DOIUrl":"https://doi.org/10.1007/s10877-026-01478-0","url":null,"abstract":"<p><p>Adequate suppression of nociception remains a fundamental objective of modern cardiac anaesthesia. Interpretation of nociception monitoring during cardiac surgery involving cardiopulmonary bypass (CPB), however, is complicated by profound physiological alterations including non-pulsatile circulation, cardioplegic arrest, temperature modulation, vasoactive and inotropic support, pacing, haemodilution, and systemic inflammatory responses. Current monitoring technologies rely on surrogate autonomic, vascular, pupillary, and electroencephalography-derived signals, many of which become difficult to interpret during CPB. Although several modalities demonstrate responsiveness to surgical stimulation under relatively stable perioperative conditions, cardiac-specific validation remains limited and extrapolation of available evidence into CPB conditions remains uncertain. This article discusses the principal physiological limitations affecting intraoperative nociception monitoring during adult cardiac surgery and highlights priorities for future cardiac-specific validation and context-aware analytical approaches.</p>","PeriodicalId":15513,"journal":{"name":"Journal of Clinical Monitoring and Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149069624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Peter Raivio, Liisa Petäjä, Anu Granvik, Tiina Ojala, Erika Wilkman, Mikko Jormalainen, Henri Haapanen, Mikko Lax, Sami Soljanlahti, Suvi Tuohinen, Christoffer Stark, Eetu Haavisto, Esko Kankuri, Kalle Kotilahti, Mihkel Meinberg, Tatu Juvonen, Tommi Pätilä
{"title":"Intraoperative monitoring of myocardial oxygenation during open-heart surgery: feasibility and initial performance.","authors":"Peter Raivio, Liisa Petäjä, Anu Granvik, Tiina Ojala, Erika Wilkman, Mikko Jormalainen, Henri Haapanen, Mikko Lax, Sami Soljanlahti, Suvi Tuohinen, Christoffer Stark, Eetu Haavisto, Esko Kankuri, Kalle Kotilahti, Mihkel Meinberg, Tatu Juvonen, Tommi Pätilä","doi":"10.1007/s10877-026-01504-1","DOIUrl":"https://doi.org/10.1007/s10877-026-01504-1","url":null,"abstract":"<p><p>Myocardial protection during cardiopulmonary bypass relies on intermittent administrations of cardioplegia without continuous, tissue‑level feedback on myocardial oxygen supply and mitochondrial metabolic status. Extended near‑infrared spectroscopy (eNIRS) can enable real-time intramyocardial monitoring of hemoglobin/myoglobin oxygen saturation (S<sub>Hb/Mb</sub>) and cytochrome c oxidase oxidation (O<sub>CcO</sub>) for assessment of cardiac oxygen availability and utilization during surgery. In this prospective, single-center feasibility study, myocardial eNIRS monitoring was carried out in 15 adults undergoing aortic valve replacement with or without coronary artery bypass grafting. The study focused on initial feasibility and performance. Clinical performance was explored by relating changes in O<sub>CcO</sub> responses to intermittently administered blood cardioplegia volumes. Myocardial eNIRS consistently recorded S<sub>Hb/Mb</sub>, and O<sub>CcO</sub> responses to cross-clamp, cardioplegia, and reperfusion throughout all operations. Mitochondrial oxygenation status improvement (O<sub>CcO</sub> area under the curve) correlated with the intermittently administered volume of blood cardioplegia (r = 0.68, p < 0.0001). No device-related adverse events were observed. Feedback on usability was favorable (surgeons 87%; anesthesiologists 85%; nurses 74%). Intraoperative myocardial eNIRS monitoring is feasible. It provides continuous assessment of oxygen availability and mitochondrial redox status during surgery. Real‑time monitoring of ischemia-reperfusion and cardioplegia effects may support intraoperative decision‑making for myocardial protection. Clinical trial registration number ClinicalTrials.gov NCT05479968.</p>","PeriodicalId":15513,"journal":{"name":"Journal of Clinical Monitoring and Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149038996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kent Rice, Stanley Skinner, Ernest M Hoffman, Tatsuya Oishi, Lanjun Guo, Ronald Leppanen
{"title":"Intraoperative electromyography (EMG): an updated position statement by the American Society of Neurophysiological Monitoring.","authors":"Kent Rice, Stanley Skinner, Ernest M Hoffman, Tatsuya Oishi, Lanjun Guo, Ronald Leppanen","doi":"10.1007/s10877-026-01489-x","DOIUrl":"https://doi.org/10.1007/s10877-026-01489-x","url":null,"abstract":"<p><p>Electromyography (EMG) is used to assess the functional status of peripheral motor axons during surgical procedures and can help protect patients' neurological integrity intraoperatively. This is a position statement on intraoperative EMG monitoring from the American Society of Neurophysiological Monitoring (ASNM) and updates prior ASNM position statement on EMG from 2005. This position statement is endorsed by ASNM and serves as an educational service to the neurophysiological community on the recommended use of EMG as a neurophysiological monitoring tool. It presents the rationale for EMG utilization and its clinical applications. It also covers the relevant anatomy, technical methodology for setup and signal acquisition, signal interpretation, anesthesia and physiological considerations, and documentation and credentialing requirements to optimize EMG monitoring to aid in protecting the peripheral nervous system during surgery.</p>","PeriodicalId":15513,"journal":{"name":"Journal of Clinical Monitoring and Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148990328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Robert Zanner, Gisela Untergehrer, Denis Jordan, Matthias Kreuzer, Gerhard Schneider
{"title":"Contact heat evoked potentials as a measure of the analgesic component of anesthesia - a patient study.","authors":"Robert Zanner, Gisela Untergehrer, Denis Jordan, Matthias Kreuzer, Gerhard Schneider","doi":"10.1007/s10877-026-01501-4","DOIUrl":"https://doi.org/10.1007/s10877-026-01501-4","url":null,"abstract":"<p><p>To assess the applicability of contact heat evoked potentials (CHEPs) as a direct measure of analgesia, we analyzed the following endpoints. (1) Correlation between remifentanil-induced changes in VAS and CHEP amplitudes. (2) Correlation between CHEP amplitude reduction and remifentanil infusion rate. Furthermore, we investigated the sedative effects of remifentanil and the effect of propofol-induced loss of responsiveness (PI-LOR) on CHEPs. After determination of the individual pain threshold (visual analog scale (VAS) of 10), 120 adult ASA physical status I or II patients randomly received remifentanil in one of four predefined infusion rates (0.05, 0.1, 0.2, or 0.4 [Formula: see text]g/kg/min). After equilibration, a heat stimulus 20% above the individual pain threshold was applied to the volar forearm. Eventually, propofol was infused until LOR, and the heat stimulus was applied again. CHEPs, VAS, and the Observer's Assessment of Alertness/Sedation (OAA/S) scale, as well as middle latency auditory evoked potentials (MLAEPs), were assessed. A total of 119 patients were included in the analysis. CHEP amplitudes could not be detected for all patients at all times. Remifentanil dosage and VAS showed a strong inverse correlation (r=-0.46; p<0.001). We also detected a correlation between remifentanil dosage and CHEP amplitudes (r=0.29; p=0.003) but not between VAS and CHEPs (r=-0.17; p=0.868). Both the OAA/S score and MLAEPs indicated a correlation between remifentanil dosage and degree of sedation. After PI-LOR, CHEPs were no longer detectable. The exact mechanism for the detected CHEP reduction remains to be determined. CHEPs monitoring appears unsuitable for direct assessment of the analgesic component of general anesthesia.Trial registration: German Clinical Trials Register ID DRKS00003300, Oct 24, 2011.</p>","PeriodicalId":15513,"journal":{"name":"Journal of Clinical Monitoring and Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148990183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
William J McIver, Sadie Dillon, Keelie Schofield, Jack Merritt, Elizabeth Morgan, Mansoor N Bangash, Dhruv Parekh
{"title":"The utility of focused transthoracic echocardiography in preoperative assessment: a systematic review and narrative synthesis of current evidence.","authors":"William J McIver, Sadie Dillon, Keelie Schofield, Jack Merritt, Elizabeth Morgan, Mansoor N Bangash, Dhruv Parekh","doi":"10.1007/s10877-026-01503-2","DOIUrl":"https://doi.org/10.1007/s10877-026-01503-2","url":null,"abstract":"<p><p>We aimed to evaluate the feasibility, diagnostic yield, accuracy, impact on management and clinical outcomes of focused transthoracic echocardiography (fTTE) in preoperative assessment. Medline, Embase and Cochrane Trials were searched to 19 January 2026. Studies evaluating preoperative fTTE were included. Cardiac surgery, examinations performed by cardiologists or cardiac physiologists, and intraoperative or postoperative examinations were excluded. Screening and data extraction were performed independently by two reviewers. Risk of bias was assessed for comparative effectiveness studies. Thirty-five studies involving 3728 patients were included. Study populations, scanning protocols and operator experience were highly heterogeneous. Median feasibility was 92.5%, with examination times of 3-16 min. Cardiac abnormalities were identified in 22-64% of patients. No formal diagnostic-accuracy studies were identified; agreement with formal TTE was variable and clinically important missed diagnoses were reported. Step-up and step-down changes in management occurred in 48 (21-54) % and 20 (1-49) % of patients, respectively. Three studies comparing plans before and after fTTE found no overall change in anaesthetic management. Individual changes in management included increased arterial-line use and reduced postoperative intensive-care admission. Four randomised trials found that echocardiography-guided pre-induction fluid therapy reduced post-induction hypotension. Three randomised trials in emergency surgical populations showed no improvement in mortality or morbidity compared with standard care. Preoperative fTTE is feasible and can identify clinically relevant cardiac and haemodynamic abnormalities and influence perioperative management. However, performance is operator- and setting-dependent, diagnostic-accuracy evidence is sparse, and benefit for patient-centred outcomes remains unproven.</p>","PeriodicalId":15513,"journal":{"name":"Journal of Clinical Monitoring and Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148990296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Predicting patient care pathway deviations and their consequences in anaesthesia using machine learning.","authors":"Rémi Florquin, Philippe Dony","doi":"10.1007/s10877-026-01496-y","DOIUrl":"https://doi.org/10.1007/s10877-026-01496-y","url":null,"abstract":"<p><p>Deviation from the intended perioperative care pathway, particularly unplanned admission after day-case surgery, affects patient safety and the efficient organisation of care. We aimed to develop and compare interpretable machine-learning models predicting such deviations from information available before surgery. In this retrospective study, 51,112 consecutive ambulatory anaesthesia procedures from a Belgian network were analysed, with unplanned admission as the outcome. Data were partitioned into training, validation, and test sets; class imbalance was addressed with SMOTENC on the training data alone. Seven algorithms: logistic regression, decision tree, random forest, XGBoost, LightGBM, histogram-based gradient boosting, and explainable boosting machine (EBM) were compared using discrimination, precision-recall, and calibration metrics at two operating points (fixed sensitivity and fixed specificity). Unplanned admission occurred in 9.08% of patients. Discrimination varied markedly, the highest area under the receiver operating characteristic curve achieved by the explainable boosting machine (EBM, 0.826), the lowest by logistic regression (0.595). Given the rarity of the outcome, precision-recall was most informative: the EBM attained both the highest average precision (0.472) and F1-score (0.392), exceeding the black-box ensembles while remaining intrinsically interpretable. Performance was stable between validation and test sets, arguing against overfitting, although all models over-estimated risk and need recalibration. Machine learning applied to routinely collected data can identify patients at risk of deviating from their intended day-case pathway with stable performance and the potential for clinical usefulness. The explainable boosting machine is especially attractive, combining competitive accuracy with transparency. Prospective validation and recalibration of the predicted probabilities will be required before clinical deployment.</p>","PeriodicalId":15513,"journal":{"name":"Journal of Clinical Monitoring and Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148911390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A comparison between two indices derived from automated pupillometry devices.","authors":"Carlotta Bandoni, Khalil Mounir, Francesca Iannuzzi, Andrea Berardino, Fabio Tarantino, Mohammed Rabi Andaloussi, Fabio Silvio Taccone, Denise Battaglini, Nicolo ' Antonino Patroniti, Chiara Robba","doi":"10.1007/s10877-026-01490-4","DOIUrl":"https://doi.org/10.1007/s10877-026-01490-4","url":null,"abstract":"<p><p>Automated pupillometry provides a standardised, quantitative, highly reproducible measurement of the pupillary light reactivity and other pupillary variables. The Neurologic pupillary index (NPi) has shown good prognostic value in patients with acute brain injury and cardiac arrest. However, few data on the comparison of NPi with a recently introduced index (quantitative pupillary index, QPI) are available. To compare the performance of two commercially available automated pupillometry devices, and in particular QPI and NPi, in a cohort of neuro-critically ill patients. Single-center observational study, including adult (> 18 years) patients admitted to a neuro-intensive care unit over a 6-month period. Pupillary reactivity was assessed in both eyes of each patient using the two pupillometers currently available, the Neuroptics NPi-300 (Neuroptics, Irvine, CA, USA) and the NeuroLight (ID-Med, Marseilles, France). A total of 73 patients were included in the final analysis, with a median age of 65 years (interquartile range [IQR] 54-77). The mean bias between the two pupillometers was 0.44 mm (95% Limit of Agreement [LoA] - 0.43 mm to 1.31 mm) for pupil diameter, 0.57% (95% LoA - 7.63% to 8.77%) for pupillary reactivity, and - 0.82 (95% LoA - 2.70 to 1.05) for NPi vs. QPI comparison. A very strong correlation was found between the two devices in terms of pupil diameter measurement (ρ = 0.91; 95% Confidence Interval [CI] 0.86 to 0.95; p < 0.0001) and pupil reactivity (ρ = 0.92; 95% CI 0.87 to 0.95; p < 0.0001). NPi and QPI were well correlated (ρ = 0.75; 95% CI, 0.62-0.84; p < 0.0001). In this cohort study, a significant statistical correlation between the measurements provided by two available pupilometers was found; however, NPi and QPI might not be clinically interchangeable.</p>","PeriodicalId":15513,"journal":{"name":"Journal of Clinical Monitoring and Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148857380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ian Piper, Rob Donald, Katarina Sandall, Annemarie Docherty
{"title":"Accelerometers improve detection of movement related artifact in bedside ICU data.","authors":"Ian Piper, Rob Donald, Katarina Sandall, Annemarie Docherty","doi":"10.1007/s10877-026-01487-z","DOIUrl":"https://doi.org/10.1007/s10877-026-01487-z","url":null,"abstract":"<p><p>Routinely collected real time data from ICU bedside monitors is a valuable source of information which can assist the clinical team caring for a patient. This data almost always contains artifacts caused by clinical staff attending to the patient throughout the day, e.g. blood samples being taken, body washing, administration of IV medication. We present a small (10 patient) study but with accurately annotated clinical event data constructed by observing standard clinical procedures and merging this information with the physiologic data (ECG, ABP, CVP) from bedside monitors. In addition, to assess the feasibility for using accelerometers in the ICU environment, we received ethical approval to place an accelerometer [1] on the chest of all patients and the X, Y & Z accelerometer plane data was merged with the physiological and event annotation data. The non-trivial methods required to capture the data and initial characterisation of results are detailed and discussed. A baseline model cross-correlating ECG and ABP waveform signals over a 10 s window was developed and features derived from this model were used in both GLM and Random Forest (RF) models for predicting events that routinely cause movement related artifact in waveform quality time-series data. RF model performance improved when accelerometer data was added to the model for most movement related artifact types. For the GLM models, only selected models (ANY Type, Turns, Bed Movement and Blood Sample) showed an improvement in model fit with the addition of the accelerometer data (P < 0.01). Implications for observational ICU research and potential \"Closed Loop\" clinical modelling studies are discussed.</p>","PeriodicalId":15513,"journal":{"name":"Journal of Clinical Monitoring and Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148857443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}