Journal of Clinical Monitoring and Computing最新文献

A recent study introduced a patient-specific algorithm designed to reduce the acquisition time required for obtaining somatosensory evoked potentials during spinal surgery. While the approach is promising, its reliance on amplitude and latency thresholds may overlook subtle waveform features that are crucial in high-risk patients. Broader validation, integration of waveform morphology, and cautious application in clinically compromised populations are warranted. Optimizing intraoperative neurophysiological monitoring requires not only speed but also diagnostic fidelity.

Baseline cerebral regional oxygen saturation (rSO₂) measured with the INVOS 5100C near-infrared spectroscopy (NIRS) device has been reported to correlate primarily with preoperative B-type natriuretic peptide (BNP) and hemoglobin levels. It has also been reported to be associated with postoperative mortality. This study evaluated whether similar associations exist for other NIRS-derived indicators, including tissue oxygenation index (TOI) and tissue oxygen saturation (StO₂), measured with the NIRO-200NX and FORESIGHT Elite devices, respectively. We retrospectively analyzed 510, 468, and 510 non-dialysis adult patients undergoing cardiac surgery in whom baseline rSO₂, TOI, and StO₂, respectively, were measured on the forehead before anesthesia and mixed venous oxygen saturation (SmvO₂) was measured after induction of anesthesia. Correlations between 37 preoperative blood test variables and NIRS or SmvO₂ values were evaluated using Spearman's correlation coefficient. Associations between baseline NIRS values and postoperative in-hospital mortality were assessed using logistic regression. Across all three devices, baseline NIRS values and SmvO₂ values were most significantly correlated with BNP and hemoglobin (all p < 0.00001) of the 37 preoperative blood test variables. Baseline rSO₂, TOI, and StO₂ values were each significantly associated with postoperative mortality (p = 0.00101, 0.00111, and 0.01122, respectively). For all NIRS-derived indicators examined, baseline NIRS values before anesthesia and SmvO₂ values after induction of anesthesia were primarily correlated with BNP and hemoglobin levels. In addition, baseline NIRS values showed a significant association with postoperative in-hospital mortality, suggesting their potential utility as a prognostic marker, although this requires confirmation in larger studies.

Purpose: Traditional assessments using carboxyhemoglobin (COHb) levels alone often do not adequately predict clinical course of carbon monoxide (CO) poisoning cases. Perfusion index (PI) and pleth variability index (PVI) offer non-invasive, continuous monitoring of peripheral perfusion, potentially improving patient management. The objective of this study is to evaluate whether perfusion indices can assist in triage and monitoring of patients with CO poisoning.

Methods: All patients aged 18 years and older, diagnosed with CO poisoning were consecutively enrolled in this prospective observational study from January 2019 to May 2023. Perfusion indices, COHb and lactate levels were measured at diagnosis (values denoted by 1) and after 60-min hyperbaric or normobaric oxygen therapy (HBOT or NBOT) (values denoted by 2).

Results: PI-1 showed significant moderate negative correlation with COHb-1 levels in all patients and AUC value of PI-1 in predicting the necessity for HBOT was 0.935. Patients requiring HBOT had significantly lower PI-1 and higher COHb-1, lactate-1, and PVI-1 compared to those receiving NBOT. Following treatment, PI increased, and PVI, lactate, and COHb decreased significantly in both treatment groups (p<0.001 for all).

Conclusions:  Perfusion indices, especially PI, may reflect changes in COHb levels and could provide additional information to support triage and monitoring in CO poisoning.

Acute kidney injury (AKI) affects 40-50% of cardiac surgery patients and is closely linked to renal medullary hypoxia. Although urinary oxygen partial pressure (PuO₂) offers real-time insight into renal oxygenation, variable urine transit times through the urinary catheter can impair measurement accuracy. This study aimed to develop an algorithm that calculates transit time by modeling urine flow as discrete particles and to assess whether it improves PuO₂ estimation. The proposed algorithm models urine flow as discrete particles, tracking transit time through the urinary catheter. The transit time allows correcting oxygen measurements at the catheter exit, mitigating distortions from variable flow rates. Validation used a bench-top system with a flow sensor, a 30-cm glass tube simulating a catheter, and optode-based oxygen sensors positioned inside a flask and at the catheter entry and exit. Flow rates spanned 20-450 mL/h, and flask oxygen 15-120 mmHg, with exit compared to entrance values. Without adjustment, the root mean square error (RMSE) between entrance and exit oxygen measurements was 15.71 mmHg. Incorporating the transit time correction reduced the RMSE to 5.82 mmHg. This marked improvement indicates that the corrected measurements more accurately reflect the true oxygen levels entering the catheter across various flow conditions. By accounting for dynamic urine transit times, the proposed algorithm substantially enhances the accuracy of urinary oxygen monitoring. This improvement in estimating renal oxygenation may facilitate noninvasive detection of renal hypoxia and allow for timely interventions to reduce the incidence and severity of AKI in cardiac surgery patients.

The Analgesia Nociception Index (ANI) is based on respiratory sinus arrhythmia and is a validated surrogate marker of the nociception-antinociception balance. Along with the ANI, the monitor provides a measure of overall heart rate variability modulation named "Energy" and which is closely related to the standard deviation of normal R-R intervals. The objective of the present study was to evaluate variations in "Energy" during general anesthesia, sedation, and spinal anesthesia. We retrospectively analyzed data stored in the anesthesia data warehouse at Lille University Medical Center (Lille, France). Eligible cases involved general anesthesia, spinal anesthesia, or sedation over the period 2012-2024. Patients with arrhythmia or missing baseline data were excluded. Three periods were defined: pre-induction (P1), post-induction (P2), and intraoperative (P3). Linear mixed models were adjusted for age, the American Society of Anesthesiologists score, norepinephrine use, and sex. 2226 procedures were included. The decrease in "Energy" after induction was significantly greater for general anesthesia after adjustment between P1 and P2 (Mean (SD) -0.306 (-0.321; -0.292), p < 0.001) and between P1 and P3 (-0.334 (-0.348; -0.319), p < 0.001). Same results were found for sedation (P1-P2: -0.120 (-0.176; -0.064), p < 0.001; P1-P3: -0.113 (-0.168; -0.056), p < 0.001) and spinal anesthesia (P1-P2: 0.082 (0.017; 0.146), p = 0.012; P1-P3: 0.089 (0.025; 0.153), p = 0.006) after adjustment. Changes during sedation and spinal anesthesia were not clinically relevant. "Energy" decreases after the induction of general anesthesia and sedation and thus reflects a lower degree of autonomic modulation.

Vegetative reactions are common during neurosurgical procedures. Known effects are mainly cardiovascular, including tachy- and bradyarrhythmia, hyper- and hypotonia as well as cardiac arrest. Computer-assisted real-time analysis of heart rate variability (HRV), baroreflex-sensitivity (BRS) allows for continuous evaluation of the autonomic nervous system (ANS). We analyzed ANS parameters during intracranial neurosurgical procedures. In this pilot study, we aim to provide proof-of-concept that ANS monitoring during surgery is feasible and yields stable results.We included 129 consecutive patients undergoing neurosurgery for intracranial pathologies over a period of four months. Heart rate (HR) and mean arterial pressure (MAP) were continuously monitored during routine anesthesiology care. Data were recorded via ICM + software. HRV, BRS and other vegetative parameters were calculated continuously. Intraoperative events such as hypo-/hypertonia or brady-/tachycardia were monitored.Mean age was 47.2 ± 17.7 years. Of all patients, 54.3% were male (n = 70). For every patient, four intraoperative episodes were defined: start of anesthesia until incision - start of incision until craniotomy - craniotomy until end of resection or intracranial manipulation - end phase until skin closure. BRS continuously decreased during cranial surgery, indicating stabilized autonomic function. Furthermore, blood pressure variability was increased during semi-sitting surgery.Autonomic system monitoring during neurosurgical procedures is safe and feasible. Intraoperatively, an increasing sympathetic activity has been observed without clear disctinction between surgical or anesthesiological events as underlying cause. Monitoring results are reproducible and may be of importance for the detection and prevention of intraoperative cardiovascular events.

Purpose: Intensive care units (ICUs) handle mechanically ventilated patients with life-threatening conditions, who require intensive monitoring and treatment. In a low physician-patient ratio setting, providing consistent care to all patients is challenging. A survival prediction model using machine-learning can potentially improve prognosis evaluation and resource allocation. This study aims to develop a machine-learning model to predict survival/mortality in mechanically ventilated patients using clinical features recorded at the time of ICU admission and compare its performance with the Sequential Organ Failure Assessment (SOFA) score as a standalone predictor.

Methods: A dataset consisting of 660 mechanically ventilated patients and 98 clinical parameters (n = 660, Male: Female = 365:295, Age = 44.45 ± 19.36 years) from three ICUs at AIIMS, Delhi, was retrospectively evaluated after institutional ethical approval. Binary classification models were trained using 10-fold cross-validation with 70% data and 30% reserved for testing. The outcome was based on the survival/death of the patient during their ICU stay.

Results: A total of 39 features were selected using Shapley-Additive-Explanations (SHAP) and Random Forest model. The top three features were SOFA score, International normalized ratio (INR) and respiratory rate with feature importance values of 7.3%, 4.5% and 3.4% respectively. The K-nearest-neighbour (KNN) model using SHAP-selected features achieved the best test performance with an accuracy = 0.80, area-under-receiver-operating-characteristics-curve (AUROC) = 0.84, sensitivity = 0.82, specificity = 0.77, positive-predictive-value (PPV) = 0.78 and negative-predictive-value (NPV) = 0.82, compared to the SOFA-only model showing accuracy = 0.73, AUROC = 0.73, sensitivity = 0.82, specificity = 0.63, PPV = 0.69 and NPV = 0.78.

Conclusion: The automated machine-learning method for prognosis prediction may assist clinicians in the early triage of patients. These models may offer valuable support to ICU physicians for timely alerts and informed clinical judgment. The study also highlights the continued utility of the SOFA score used by clinicians as the first assessment tool in ICUs, while suggesting that carefully developed machine-learning models may offer complementary support in high-risk ICU settings.