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 (SHb/Mb) and cytochrome c oxidase oxidation (OCcO) 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 OCcO responses to intermittently administered blood cardioplegia volumes. Myocardial eNIRS consistently recorded SHb/Mb, and OCcO responses to cross-clamp, cardioplegia, and reperfusion throughout all operations. Mitochondrial oxygenation status improvement (OCcO 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.

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.

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.

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.

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.

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.

Early detection of clinical deterioration on surgical wards is vital to prevent adverse outcomes. Conventional ward monitoring using the Modified Early Warning Score (MEWS) relies on intermittent nurse-measured vital signs, but its role may change when continuous wearable monitoring is already available. This study evaluated the transition from mandatory to discretionary nurse-measured MEWS assessment in a continuously monitored surgical ward and compared the diagnostic performance of two wearable-derived Early Warning Scores (EWS) with conventional MEWS. In this single-center cohort study conducted between October 2024 and June 2025, postoperative patients were continuously monitored using a wearable sensor measuring heart rate, respiratory rate, and oxygen saturation. Continuous wearable monitoring was available throughout the entire study period. During Phase 1, nurse-measured MEWS assessments were mandatory according to local protocol. During Phase 2, nurse-measured MEWS assessments were performed at staff discretion. Two wearable-derived algorithms were evaluated: the Continuous Remote EWS (CREWS), based on a previously developed algorithm, and Remote 3 EWS (R3EWS), a modified version of the hospital's standard MEWS using three parameters. The primary outcome was diagnostic accuracy for clinical deterioration, defined as postoperative complications graded Clavien-Dindo ≥ II. A total of 544 admissions (516 patients) were included, of which 98 admissions (18%) experienced clinical deterioration (comprising 154 clinical deterioration events). Following the transition from mandatory to discretionary nurse-measured MEWS assessment, intermittent nurse-measured vital signs were performed less frequently, while MEWS ≥ 3 occurred more often, resulting in higher MEWS sensitivity and lower specificity. In the separate diagnostic performance analysis, R3EWS showed slightly higher AUROC values (0.61) compared with MEWS (0.58) and CREWS (0.59), although differences were modest. Wearable-derived alarm frequency increased 6-8 h before clinical deterioration. In a surgical ward where continuous wearable monitoring was already available, transition from mandatory to discretionary nurse-measured MEWS assessment was associated with fewer intermittent observations, higher MEWS sensitivity, and lower specificity. Wearable-derived EWS algorithms showed comparable but modest discriminative performance relative to conventional MEWS. The increased number of alerts highlights the need for prospective validation and clear protocols for integrating continuous monitoring data into ward escalation workflows.

To demonstrate a method for the identification of safe cerebral perfusion pressure (CPP) in patients with severe traumatic brain injury using brain tissue oxygen (PbtO2) readings as a surrogate marker of cerebral blood flow. Retrospective analysis of prospectively collected data from patients with severe TBI (sTBI). A total of 9 patients with sTBI enrolled in the BONANZA-GT trial with continuous monitoring of mean arterial blood pressure (MAP), intracranial pressure (ICP), cerebral perfusion pressure (CPP) and PbtO2. MAP, ICP, CPP and high frequency PbtO2 signals were continuously recorded. stoCPP was calculated as the coefficient of determination (r[2]) of short-term changes in PbtO2 regressed on short-term changes in CPP within non-overlapping 144-second correlation windows, assigned to 5-mmHg CPP bins using a window-median binning approach. Algorithm parameters were optimised using Bayesian optimisation with a composite scoring function benchmarked against CPPopt. Per-subject r[2] thresholds were derived by block-shuffle calibration (median 0.12, range 0.06-0.26). stoCPP was successfully generated for all 9 patients from raw unfiltered data. The lower limit of the stoCPP range (LLstoCPP) was defined in approximately 22% of analysis windows, as it is identifiable only during episodes of CPP perturbation below the stable range. CPPopt point estimates fell within the stoCPP range in 76.2% of observations, with a mean stoCPP width of 22.8 mmHg. Bland-Altman analysis of lower bounds (n = 102 independent estimates) demonstrated a mean bias of 4.1 mmHg (SD 7.3 mmHg; 95% limits of agreement - 10.1 to + 18.4 mmHg). Trough depth characteristics of the stoCPP range degraded significantly with temporal misalignment of the PbtO2 signal (Kendall's W = 0.557, p < 0.001), supporting the physiological basis of the method. stoCPP was defined in all patients from raw unfiltered data using Bayesian-optimised parameters and per-subject calibrated r[2] thresholds, and demonstrated close correspondence with CPPopt. We propose further validation of this method utilising alternative PbtO2 probe types and larger patient cohorts to assess the validity of time below LLstoCPP as a marker of ischaemia and determinant of outcome in sTBI.

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.

The purpose was to describe patient monitor alarm burden in a cardiac surgical intensive care unit and explore whether routinely available patient characteristics are associated with alarm frequency, type, and duration. Secondary objectives were to assess indirect signs of alarm fatigue and compare alarm rates with two previous studies using identical monitors in different clinical units. We retrospectively collected patient-monitor alarm data and patient characteristics over four consecutive weeks in the cardiac surgical intensive care unit of Helsinki University Hospital. The study population comprised 81 patients. Alarm data were analysed descriptively and modelled using negative-binomial regression. Patients produced 45,044 alarms, with a median alarm rate of 6.8 [4.8 to 10.8] alarms per patient per hour. Older age was associated with fewer alarms (IRR = 0.90, 95% CI 0.83 to 0.98, P < 0.05). ASA 5 classification was associated with more low- ETCO2 alarms (IRR = 9.05, 95% CI 3.37 to 27.88, P < 0.05). Smoking was associated with more low invasive blood pressure alarms (IRR = 1.94, 95% CI 1.05 to 3.90, P < 0.05). Alarm load was high, clinician interaction with alarms was limited, and many alarms were momentary. Alarm rates were similar across three clinically different units using identical monitors. Routinely available patient characteristics were associated with selected alarm patterns, but the findings are hypothesis-generating. Similar alarm rates across different units suggest that default settings may not reflect unit- or patient-specific monitoring needs. The results suggest low positive predictive value and possible alarm fatigue, supporting unit-level alarm review that incorporates patient-level alarm patterns and signal-quality improvement, with prospective validation before patient-specific alarm-management changes.

The increasing availability of advanced monitoring technologies in anaesthesia and critical care has enabled continuous assessment of neurological, haemodynamic, and oxygen delivery parameters. Despite this, integrating multiple data streams into coherent, physiology-guided clinical decisions remains challenging, and unified interpretive frameworks remain limited. To introduce the HOPFIT framework - a conceptual model linking continuous neuro-cardiovascular monitoring to structured, physiology-guided clinical reasoning across six interdependent domains. HOPFIT organises six therapeutic domains - Hypnotics, Opioids, Pressors, Fluids, Inotropes, and Transfusion - around the interdependence of brain function, cardiovascular physiology, and oxygen delivery. Rather than introducing new monitoring modalities, the framework provides a unifying structure for bedside interpretation of existing data, including processed electroencephalography, cerebral oximetry, dynamic indices of fluid responsiveness, non-invasive haemoglobin, and cardiac output monitoring. An original pharmacodynamic risk surface illustrates the interaction between hypnotic and analgesic agents, and a conceptual hypotension framework integrating EEG and haemodynamic data illustrates how multimodal information may support clinical reasoning. Tables summarise monitoring parameters, commonly used ranges, and potential clinical applications across patient groups. HOPFIT is intended as a conceptual and educational tool to support integration of multimodal physiological data at the bedside, rather than a prescriptive clinical protocol. Prospective studies are required to determine its impact on clinical decision-making and patient outcomes.

Spinal anesthesia during cesarean delivery frequently causes maternal hypotension. Early detection of hemodynamic compromise before blood pressure decline could enable timely intervention. This study evaluates the use of serial transthoracic echocardiography (TTE) as a monitoring tool for early detection of hemodynamic fluctuation. To characterize the temporal sequence of hemodynamic changes using serial TTE, with emphasis on the relationship between stroke volume (SV) reduction and subsequent hypotension, and to assess the potential of TTE‑derived parameters as physiological indicators of preload changes. Prospective observational study. Single-center trial. Thirty healthy term pregnant women (ASA I‑II) undergoing elective cesarean delivery under spinal anesthesia. Serial TTE examinations were performed at 13 predefined time points. Left ventricular diameters (LVIDd, LVIDs) were measured and volumes (EDV, ESV) were derived using the Teichholz method, from which stroke volume, cardiac output, cardiac index, and ejection fraction were calculated. Temporal relationship between SV decline and hypotension onset. After spinal anesthesia, left ventricular end-diastolic volume (EDV) decreased by 34.6%, stroke volume (SV) by 36.2%, cardiac output (CO) by 38.7% (all P < 0.001). Hypotension (SBP drop > 20%) occurred in 76.7%. Crucially, SV reduction preceded blood pressure fall: at the first post‑spinal measurement, SV had already decreased by 30.6%, while systolic BP had fallen by only 12.2%. Left ventricular ejection fraction (LVEF) remained stable throughout. After fetal delivery, all parameters partially recovered. Serial TTE monitoring detects a significant reduction in SV consistently before hypotension becomes clinically apparent, demonstrating the potential of TTE‑derived parameters as physiological indicators of preload changes. These findings establish a monitoring‑based physiological baseline for future hypothesis‑testing in obstetric anesthesia.Trial registration: Chinese Clinical Trial Registry (ChiCTR2500111576; registered 3 November 2025).

Perioperative anaesthetic complications are a major public health concern, as they are associated with increased mortality, prolonged hospital stay, higher healthcare costs, and compromised patient safety. Despite advances in perioperative monitoring and risk stratification, early and personalised identification of patients at risk remains limited. Artificial intelligence (AI) has emerged as a promising approach to improve prediction of perioperative anaesthetic complications, although current evidence is heterogeneous. To synthesise the available evidence on the use of artificial intelligence models to predict perioperative anaesthetic complications in adult surgical patients. A scoping review was conducted following Joanna Briggs Institute methodology and reported according to PRISMA-ScR guidelines. The protocol was registered in the Open Science Framework (DOI: https://doi.org/10.17605/OSF.IO/QU6WY). PubMed, Scopus, and Web of Science were searched for studies published between 2015 and 2025. Studies applying machine learning, deep learning, or hybrid models to predict perioperative anaesthetic complications were included. Evidence was synthesised using conceptual mapping and thematic analysis. Fifty-one studies were included. Haemodynamic and cardiovascular outcomes, particularly intraoperative hypotension, were most frequently studied, followed by renal, neurological, and respiratory complications, postoperative pain, and perioperative resource utilisation. Most models showed moderate to high predictive performance, while external validation and evaluation of clinical impact were uncommon. Artificial intelligence has substantial potential to support prediction of perioperative anaesthetic complications. However, clinical implementation is limited by methodological weaknesses, including predominantly retrospective designs, limited external validation, and scarce assessment of real-world impact. Prospective multicentre studies are required.