Journal of Clinical Monitoring and Computing最新文献

Fluids are given with the purpose of increasing cardiac output (CO), but approximately only 50% of critically ill patients are fluid responders. Since the effect of a fluid bolus is time-sensitive, it diminuish within few hours, following the initial fluid resuscitation. Several functional hemodynamic tests (FHTs), consisting of maneuvers affecting heart-lung interactions, have been conceived to discriminate fluid responders from non-responders. Three main variables affect the reliability of FHTs in predicting fluid responsiveness: (1) tidal volume; (2) spontaneous breathing activity; (3) cardiac arrythmias. Most FTHs have been validated in sedated or even paralyzed ICU patients, since, historically, controlled mechanical ventilation with high tidal volumes was the preferred mode of ventilatory support. The transition to contemporary methods of invasive mechanical ventilation with spontaneous breathing activity impacts heart-lung interactions by modifying intrathoracic pressure, tidal volumes and transvascular pressure in lung capillaries. These alterations and the heterogeneity in respiratory mechanics (that is present both in healthy and injured lungs) subsequently influence venous return and cardiac output. Cardiac arrythmias are frequently present in critically ill patients, especially atrial fibrillation, and intuitively impact on FHTs. This is due to the random CO fluctuations. Finally, the presence of continuous CO monitoring in ICU patients is not standard and the assessment of fluid responsiveness with surrogate methods is clinically useful, but also challenging. In this review we provide an algorithm for the use of FHTs in different subgroups of ICU patients, according to ventilatory setting, cardiac rhythm and the availability of continuous hemodynamic monitoring.

The measurement of nociception and the optimisation of intraoperative antinociceptive medication could potentially improve the conduct of anaesthesia, especially in the older population. The Surgical Pleth Index (SPI) is one of the monitoring methods presently used for the detection of nociceptive stimulus. Eighty patients aged 50 years and older who were scheduled to undergo major abdominal surgery were randomised and divided into a study group and a control group. In the study group, the SPI was used to guide the administration of remifentanil during surgery. In the control group, the SPI value was concealed, and remifentanil administration was based on the clinical evaluation of the attending anaesthesiologist. The primary endpoint of this study was intraoperative remifentanil consumption. In addition, we compared the durations of intraoperative hypotension and hypertension. No difference in intraoperative remifentanil consumption (4.5 µg kg^- 1h^- 1 vs. 5.6 µg kg^- 1h^- 1, p = 0.14) was found. Furthermore, there was no difference in the proportion of hypotensive time (mean arterial pressure, MAP < 65) (3.7% vs. 1.6%, p = 0.40). However, in the subgroup of patients who underwent operation with invasive blood pressure monitoring, there was less severe hypotension (MAP < 55) (0.3% vs. 0.0%, p = 0.02) and intermediate hypotension (MAP < 65) (10.2% vs. 2.6%, p = 0.07) in the treatment group, even though remifentanil consumption was higher (3.5 µg kg^- 1h^- 1 vs. 5.1 µg kg^- 1h^- 1p = 0.03). The use of SPI guidance for the administration of remifentanil during surgery did not help to reduce the remifentanil consumption. However, the results from invasively monitored study group suggest more timely administered opioid when SPI was used.

Intravenous fluid is administered during high-risk surgery to optimize stroke volume (SV). To assess ongoing need for fluids, the hemodynamic response to a fluid bolus is evaluated using a fluid challenge technique. The Acumen Assisted Fluid Management (AFM) system is a decision support tool designed to ease the application of fluid challenges and thus improve fluid administration during high-risk surgery. In this post hoc analysis of data from a randomized controlled trial, we compared the rates of fluid responsiveness (defined as an increase in SV of ≥ 10%) after AFM-guided or clinician-initiated (control) fluid challenges. Patients undergoing high-risk abdominal surgery were randomly allocated to AFM-guided or clinician-initiated groups for fluid challenges titration, which consisted of 250-mL boluses of crystalloid or albumin given over 5 min. The fluid responsiveness rates and the mean SV increase in the two groups were compared. The original study included 86 patients (44 in the AFM group and 42 in the clinician-initiated group) and this sub-study analysed 85 patients with a total of 448 fluid challenges. The median rate of fluid responsiveness was greater in the AFM than in the control group (50 [44-71] % vs 33 [20-40] %, p<0.001). The mean increase in SV after fluid challenge was also higher in the AFM than in the control group (12 [9-16] % vs 6 [3-10] %, p<0.001). AFM-initiated fluid challenges were more often associated with the desired increase in SV than were clinician-initiated fluid challenges, and absolute SV increases were greater.

Echocardiography is crucial for evaluating patients at risk of clinical deterioration. Left ventricular ejection fraction (LVEF) and velocity time integral (VTI) aid in diagnosing shock, but bedside calculations can be time-consuming and prone to variability. Artificial intelligence technology shows promise in providing assistance to clinicians performing point-of-care echocardiography. We conducted a systematic review, utilizing a comprehensive literature search on PubMed, to evaluate the interchangeability of LVEF and/or VTI measurements obtained through automated mode as compared to the echocardiographic reference methods in non-cardiology settings, e.g., Simpson´s method (LVEF) or manual trace (VTI). Eight studies were included, four studying automated-LVEF, three automated-VTI, and one both. When reported, the feasibility of automated measurements ranged from 78.4 to 93.3%. The automated-LVEF had a mean bias ranging from 0 to 2.9% for experienced operators and from 0% to -10.2% for non-experienced ones, but in both cases, with wide limits of agreement (LoA). For the automated-VTI, the mean bias ranged between - 1.7 cm and - 1.9 cm. The correlation between automated and reference methods for automated-LVEF ranged between 0.63 and 0.86 for experienced and between 0.56 and 0.81 for non-experienced operators. Only one study reported a correlation between automated-VTI and manual VTI (0.86 for experienced and 0.79 for non-experienced operators). We found limited studies reporting the interchangeability of automated LVEF or VTI measurements versus a reference approach. The accuracy and precision of these automated methods should be considered within the clinical context and decision-making. Such variability could be acceptable, especially in the hands of trained operators. PROSPERO number CRD42024564868.

To investigate the feasibility of non-invasively estimating the arterial partial pressure of carbon dioxide (PaCO₂) using a computational Adaptive Neuro-Fuzzy Inference System (ANFIS) model fed by noninvasive volumetric capnography (VCap) parameters. In 14 lung-lavaged pigs, we continuously measured PaCO₂ with an optical intravascular catheter and VCap on a breath-by-breath basis. Animals were mechanically ventilated with fixed settings and subjected to 0 to 22 cmH₂O of positive end-expiratory pressure steps. The resultant 8599 pairs of data points - one PaCO₂ value matched with twelve Vcap and ventilatory parameters derived in one breath - fed the ANFIS model. The data was separated into 7370 data points for training the model (85%) and 1229 for testing (15%). The ANFIS analysis was repeated 10 independent times, randomly mixing the total data points. Bland-Altman plot (accuracy and precision), root mean square error (quality of prediction) and four-quadrant and polar plots concordance indexes (trending ability) between reference and estimated PaCO₂ were analyzed. The Bland-Altman plot performed in 10 independent tested ANFIS models showed a mean bias between reference and estimated PaCO₂ of 0.03 ± 0.03 mmHg, with limits of agreement of 2.25 ± 0.42 mmHg, and a root mean square error of 1.15 ± 0.06 mmHg. A good trending ability was confirmed by four quadrant and polar plots concordance indexes of 95.5% and 94.3%, respectively. In an animal lung injury model, the Adaptive Neuro-Fuzzy Inference System model fed by noninvasive volumetric capnography parameters can estimate PaCO₂ with high accuracy, acceptable precision, and good trending ability.

Mitochondrial oxygen tension (MitoPO2) is a promising novel non-invasive bedside marker of circulatory shock and is associated with organ failure. The measurement of mitoPO2 requires the topical application of 5-aminolevulinc acid (ALA) to induce sufficient concentrations of the fluorescent protein protoporphyrin-IX within (epi)dermal cells. Currently, its clinical potential in guiding resuscitation therapies is limited by the long induction time prior to obtaining a reliable measurement signal. We investigated whether microneedle pre-treatment of the skin before ALA application allows for earlier measurement of mitoPO2 in healthy human volunteers. 9 healthy human volunteers were included as part of physiological feasibility study. All participants had two ALA-care plasters administered on the chest after cleaning. One part of the skin was pretreated with microneedling, which perforates the epidermis with a depth of 0.30 mm. The time-to-sufficient signal was recorded for both untreated and microneedled ALA-care application. After induction mitoPO2 was varied using different FiO2 and the agreement between untreated and microneedled skin for mitoPO2 and mitoVO2 was recorded. Pre-treatment with microneedling induced reliable signal at 2 (IQR: 2-2) hours after topical ALA administration compared to 3 (IQR: 3-4) hours without pre-treatment (p = 0.02). The intraclass correlation of mitoPO2 simultaneously measured on microneedling and untreated skin was 0.892 (95%CI 0.821-0.936). MitoVO2 showed poor agreement between untreated and microneedling with an ICC of 0.316 (0.04-0.55). We demonstrate that pre-treatment with microneedling before topical application of 5-aminolevulinic acid enables obtaining a reliable and accurate mitoPO2 signal at least an hour faster than on untreated skin in our population of human volunteers. This potentially increases the applicability of mitoPO2 measurements in acute settings.Trial registration number: R21.106 (01-01-2022).

EEG monitoring during anesthesia or for diagnosing sleep disorders is a common standard. Different approaches for measuring the important information of this biosignal are used. The most often and efficient one for entropic parameters is permutation entropy as it can distinguish the vigilance states in the different settings. Due to high calculation times, it has mostly been used for low orders, although it shows good results even for higher orders. Entropy of difference has a similar way of extracting information from the EEG as permutation entropy. Both parameters and different algorithms for encoding the associated patterns in the signal are described. The runtimes of both entropic measures are compared, not only for the needed encoding but also for calculating the value itself. The mutual information that both parameters extract is measured with the AUC for a linear discriminant analysis classifier. Entropy of difference shows a smaller calculation time than permutation entropy. The reduction is much larger for higher orders, some of them can even only be computed with the entropy of difference. The distinguishing of the vigilance states between both measures is similar as the AUC values for the classification do not differ significantly. As the runtimes for the entropy of difference are smaller than for the permutation entropy, even though the performance stays the same, we state the entropy of difference could be a useful method for analyzing EEG data. Higher orders of entropic features may also be investigated better and more easily.

Perioperative anesthetic, surgical and critical careinterventions can affect brain physiology and overall brain health. The clinical utility of electroencephalogram (EEG) monitoring in anesthesia and intensive care settings is multifaceted, offering critical insights into the level of consciousness and depth of anesthesia, facilitating the titration of anesthetic doses, and enabling the detection of ischemic events and epileptic activity. Additionally, EEG monitoring can aid in predicting perioperative neurocognitive disorders, assessing the impact of systemic insults on cerebral function, and informing neuroprognostication. This review provides a comprehensive overview of the fundamental principles of electroencephalography, including the foundations of processed and quantitative electroencephalography. It further explores the characteristic EEG signatures associated wtih anesthetic drugs, the interpretation of the EEG data during anesthesia, and the broader clinical benefits and applications of EEG monitoring in both anesthetic practice and intensive care environments.

This study aimed to develop an open-source algorithm for the pressure-reactivity index (PRx) to monitor cerebral autoregulation (CA) in pediatric severe traumatic brain injury (sTBI) and compared derived optimal cerebral perfusion pressure (CPPopt) with real-time CPP in relation to long-term outcome. Retrospective study in children (< 18 years) with sTBI admitted to the pediatric intensive care unit (PICU) for intracranial pressure (ICP) monitoring between 2016 and 2023. ICP was analyzed on an insult basis and correlated with outcome. PRx was calculated as Pearson correlation coefficient between ICP and mean arterial pressure. CPPopt was derived as weighted average of CPP-PRx over time. Outcome was determined via Pediatric Cerebral Performance Category (PCPC) scale at one year post-injury. Logistic regression and mixed effect models were developed to associate PRx and CPPopt with outcome. In total 50 children were included, 35 with favorable (PCPC 1-3) and 15 with unfavorable outcome (PCPC 4-6). ICP insults correlated with unfavorable outcome at 20 mmHg for 7 min duration. Mean CPPopt yield was 75.4% of monitoring time. Mean and median PRx and CPPopt yield associated with unfavorable outcome, with odds ratio (OR) 2.49 (1.38-4.50), 1.38 (1.08-1.76) and 0.95 (0.92-0.97) (p < 0.001). PRx thresholds 0.0, 0.20, 0.25 and 0.30 resulted in OR 1.01 (1.00-1.02) (p < 0.006). CPP in optimal range associated with unfavorable outcome on day one (0.018, p = 0.029) and four (-0.026, p = 0.025). Our algorithm can obtain optimal targets for pediatric neuromonitoring that showed association with long-term outcome, and is now available open source.

Given that perioperative normothermia represents a quality parameter in pediatric anesthesia, numerous studies have been conducted on temperature measurement, albeit with heterogeneous measurement intervals, ranging from 30 s to fifteen minutes. We aimed to determine the minimum time interval for reporting of intraoperative core body temperature across commonly used measurement intervals in children. Data were extracted from the records of 65 children who had participated in another clinical study and analyzed using a quasibinomial mixed linear model. Documented artifacts, like probe dislocations or at the end of anesthesia, were removed. Primary outcome was the respective probability of failing to detect a temperature change of 0.2 °C or more at any one measurement point at 30 s, one minute, two minutes, five minutes, ten minutes, and fifteen minutes, considering an expected probability of less than 5% to be acceptable. Secondary outcomes included the probabilities of failing to detect hypothermia (< 36.0 °C) and hyperthermia (> 38.0 °C). Following the removal of 4,909 exclusions, the remaining 222,366 timestamped measurements (representing just over 60 h of monitoring) were analyzed. The median measurement time was 45 min. The expected probabilities of failing to detect a temperature change of 0.2 °C or more were 0.2% [95%-CI 0.0-0.7], 0.5% [95%-CI 0.0-1.2], 1.5% [95%-CI 0.2-2.6], 4.8% [95%-CI 2.7-6.9], 22.4% [95%-CI 18.3-26.4], and 31.9% [95%-CI 27.3-36.4], respectively. Probabilities for the detection of hyperthermia (n = 9) were lower and omitted for hypothermia due to low prevalence (n = 1). In conclusion, the core body temperature should be reported at intervals of no more than five minutes to ensure the detection of any temperature change in normothermic ranges. Further studies should focus on hypothermic and hyperthermic ranges.