Journal of Clinical Monitoring and Computing最新文献

Kumagai et al. provided valuable insights into the effects of postoperative peripheral nerve blocks (PNB) on the high-frequency variability index (HFVI), a surrogate for nociception monitoring. However, the analysis excluded the impact of different brachial plexus block techniques, particularly the interscalene brachial plexus block (ISB), and role of laterality in HFVI variability. ISB produces a stellate ganglion block-like effect through local anesthetic diffusion, influencing autonomic function and heart rate variability, independent of nociceptive modulation. Provided that this study included various brachial plexus block approaches, stratifying HFVI changes according to technique and laterality could enhance their clinical relevance. Right-sided ISB may have a more pronounced autonomic effect than left-sided ISB. Further research is needed to clarify these effects and optimize the interpretation of HFVI during perioperative monitoring.

The "Cellular Oxygen METabolism" (COMET) system (Photonics Healthcare, Utrecht, The Netherlands) non-invasively measures mitochondrial oxygen tension (mitoPO₂) in the skin. The effects of general anesthesia and major non-cardiac surgery on mitoPO₂ remain unknown. In this pre-planned pilot substudy of the "Intraoperative blood pressure Management based on the individual blood PRessure profile: impact on postOperatiVE organ function" (IMPROVE) trial, we measured mitoPO₂ from induction of general anesthesia until the end of surgery in 19 major non-cardiac surgery patients (10 assigned to personalized and 9 to routine intraoperative arterial pressure management). In the overall cohort, the median (25th to 75th percentile) preoperative awake mitoPO₂ was 63 (53 to 82) mmHg and mitoPO₂ after induction of general anesthesia was 42 (35 to 59) mmHg. The intraoperative average mitoPO₂ was 39 (30 to 50) mmHg. Thirteen patients (68%) had intraoperative mitoPO₂ values below 20 mmHg and the median percentage of surgical time with mitoPO₂ < 20 mmHg was 17 (0 to 31)%. MitoPO₂ was weakly correlated with mean arterial pressure (repeated measures correlation (r_rm(n); r_rm(984) = 0.26, 95% confidence interval 0.20 to 0.32; P < 0.001), but not meaningfully with heart rate (r_rm(984) = -0.05, 95% confidence interval -0.11 to 0.01; P = 0.117). There was no important difference in intraoperative average mitoPO₂ between patients assigned to personalized or to routine intraoperative arterial pressure management (P = 0.653). MitoPO₂ under general anesthesia was about a quarter lower than preoperative awake mitoPO₂, substantially fluctuated during major non-cardiac surgery, and transiently decreased below 20 mmHg in about two-thirds of the patients. Personalized - compared to routine - intraoperative arterial pressure management did not increase intraoperative mitoPO₂. Whether intraoperative decreases in mitoPO₂ are clinically meaningful warrants further investigation.

Regional cerebral oxygen saturation (rSO₂) is used to monitor cerebral perfusion with emerging evidence that optimization of rSO₂ may improve neurological and non-neurological outcomes. To manipulate rSO₂ an understanding of the variables that drive its behavior is necessary, and this can be accomplished using supervised machine learning. This study aimed to establish a hierarchy by which various hemodynamic and ventilatory variables contribute to intraoperative changes in rSO₂. A post-hoc analysis 146 patients undergoing high risk surgery. rSO₂ was partitioned into segments with a change of at least 3% points over 5 min. Features from hemodynamic and ventilatory variables were used to train a machine learning classification algorithm (XGBoost) for prediction of association with either up or down-sloping rSO₂. The classifier was optimized and validated using five-fold cross validation. Feature importance was quantified based on information gain and permutation feature importance. The optimized classifier demonstrated a mean accuracy of 77.1% (SD 8.0%) and a mean area-under-ROC-curve of 0.86 (SD 0.06). The most important features based on information gain were the slope of the associated ETCO₂ signal, the slope of the SPO₂ signal, and the mean of the MAP signal. CO₂ is a significant mediator of changes in rSO₂ in an intraoperative setting, through its established effects on cerebral blood flow. This study furthers our overall understanding of the complex physiologic process that governs cerebral oxygenation by quantifying the hierarchy by which rSO₂ is affected. Clinical Trial Number NCT01838733 (ClinicalTrials.gov).

This bicentric retrospective cohort study evaluates reversal of muscle relaxation in real life achieved either by neostigmine or sugammadex in two hospitals using different types of neuromuscular monitoring (acceleromyography and kinemyography). The research question concerns compliance with recommendations. Patients who underwent an abdominal surgery under general anesthesia in the period from January 2017 through December 2020 with a neuromuscular block with rocuronium were included in the study. Data were extracted from the Centricity anesthesia information management system. In total, 2242 patients were assessed: 459 in center 1 (61 having received neostigmine and 398 sugammadex) and 1783 in center 2 (531 and 1252, respectively). Patients' characteristics differed between centers, with more high-risk patients in center 1. The mean train-of-four (TOF) ratio after supramaximal current determination (supramaximal threshold) was higher in center 1 (p < 0.001). Most patients received neostigmine while the TOF ratio was < 40% (68.6% in center 1 and 62.4% in center 2), while extubation was performed while the TOF ratio was > 90% in 61.0% in center 1 and in 32.1% in center 2 (p < 0.001). Patients received sugammadex irrespective of the number of responses to TOF before reversal, and extubation was performed while the TOF ratio was > 90% in 85.0% in center 1 and in 53.6% in center 2 (p < 0.001). No side effect was encountered. Despite guidelines for the TOF ratio before extubation, recommendations were not adequately respected and more vigilance is mandatory. The TOF test before use gave values that were 100% far apart with an underestimation with acceleromyography and an overestimation using kinemyography.

Acute kidney injury (AKI) is associated with an increased risk of morbidity, mortality, and healthcare expenditure, posing a major challenge in clinical practice, and affecting about 50% of patients in the intensive care unit (ICU), particularly the elderly and those with pre-existing chronic comorbidities. In health, intra-renal blood flow is maintained and auto-regulated within a wide range of renal perfusion pressures (60-100 mmHg), mediated predominantly through changes in pre-glomerular vascular tone of the afferent arteriole in response to changes of the intratubular NaCl concentration, i.e. tubuloglomerular feedback. Several neurohormonal processes contribute to regulation of the renal microcirculation, including the sympathetic nervous system, vasodilators such as nitric oxide and prostaglandin E2, and vasoconstrictors such as endothelin, angiotensin II and adenosine. The most common risk factors for AKI include volume depletion, haemodynamic instability, inflammation, nephrotoxic exposure and mitochondrial dysfunction. Fluid management is an essential component of AKI prevention and management. While traditional approaches emphasize fluid resuscitation to ensure renal perfusion, recent evidence urges caution against excessive fluid administration, given AKI patients' susceptibility to volume overload. This review examines the main characteristics of AKI in ICU patients and provides guidance on fluid management, use of biomarkers, and pharmacological strategies.

Purpose: Postoperative Delirium (POD) has an incidence of up to 65% in older patients undergoing cardiac surgery. We aimed to develop two dynamic nomograms to predict the risk of POD in older patients undergoing cardiac surgery.

Methods: This was a single-center retrospective cohort study, which included 531 older patients who underwent cardiac surgery from July 2021 to June 2022 at Nanjing First Hospital, China. Univariable and multivariable logistic regression were used to identify the significant predictors used when constructing the models. We evaluated the performances and accuracy, validated, and estimated the clinical utility and net benefit of the models using the receiver operating characteristic (ROC), the 10-fold cross-validation, and decision curve analysis (DCA).

Results: A total of 30% of the patients developed POD, the significant predictors in the preoperative model were ASA ( p < 0.001 OR = 3.220), cerebrovascular disease (p < 0.001 OR = 2.326), Alb (p < 0.037 OR = 0.946), and URE (p < 0.001 OR = 1.137), while for the postoperative model they were ASA (p = 0.044, OR = 1.737), preoperative MMSE score (p = 0.005, OR = 0.782), URE (p = 0.017 OR = 1.092), CPB duration (p < 0.001 OR = 1.010) and APACHE II (p < 0.001, OR = 1.353). The preoperative and postoperative models achieved satisfactory predictive performances, with AUC values of 0.731 and 0.799, respectively. The web calculators can be accessed at https://xxh152.shinyapps.io/Pre-POD/ and https://xxh152.shinyapps.io/Post-POD/ .

Conclusion: We established two nomogram models based on the preoperative and postoperative time points to predict POD risk and guide the flexible implementation of possible interventions at different time points.

Blood pressure is a very important clinical measurement, offering valuable insights into the hemodynamic status of patients. Regular monitoring is crucial for early detection, prevention, and treatment of conditions like hypotension and hypertension, both of which increasing morbidity for a wide variety of reasons. This monitoring can be done either invasively or non-invasively and intermittently vs. continuously. An invasive method is considered the gold standard and provides continuous measurement, but it carries higher risks of complications such as infection, bleeding, and thrombosis. Non-invasive techniques, in contrast, reduce these risks and can provide intermittent or continuous blood pressure readings. This review explores modern machine learning-based non-invasive methods for blood pressure estimation, discussing their advantages, limitations, and clinical relevance.

Critically ill or anesthetized patients commonly receive pump-driven intravenous infusions of potent, fast-acting, short half-life medications for managing hemodynamics. Stepwise dosing, e.g. over 3-5 min, adjusts physiologic responses. Flow rates range from < 0.1 to > 30 ml/h, depending on pump type (large volume, syringe) and drug concentration. Most drugs are formulated in aqueous solutions. Hydrophobic drugs are formulated as lipid emulsions. Do the physical and chemical properties of emulsions impact delivery compared to aqueous solutions? Does stepwise dose titration by the pump correlate with predicted plasma concentrations? Precise, gravimetric, flow rate measurement compared delivery of a 20% lipid emulsion (LE) and 0.9% saline (NS) using different pump types and flow rates. We measured stepwise delivery and then computed predicted plasma concentrations following stepwise dose titration. We measured the pharmacokinetic coefficient of short-term variation, (PK-CV), to assess pump performance. LE and NS had similar mean flow rates in stepwise rate increments and decrements between 0.5 and 32 ml/h and continuous flows 0.5 and 5 ml/h. Pharmacokinetic computation predictions suggest delayed achievement of intended plasma levels following dose titrations. Syringe pumps exhibited smaller variations in PK-CV than large volume pumps. Pump-driven deliveries of lipid emulsion and aqueous solution behave similarly. At low flow rates we observed large flow rate variability differences between pump types showing they may not be interchangeable. PK-CV analysis provides a quantitative tool to assess infusion pump performance. Drug plasma concentrations may lag behind intent of pump dose titration.

Prediction and avoidance of intraoperative hypotension (IOH) can lead to less postoperative morbidity. Machine learning (ML) is increasingly being applied to predict IOH. We hypothesize that incorporating demographic and physiological features in an ML model will improve the performance of IOH prediction. In addition, we added a "dial" feature to alter prediction performance. An ML prediction model was built based on a multivariate random forest (RF) trained algorithm using 13 physiologic time series and patient demographic data (age, sex, and BMI) for adult patients undergoing hepatobiliary surgery. A novel implementation was developed with an adjustable, multi-model voting (MMV) approach to improve performance in the challenging context of a dynamic, sliding window for which the propensity of data is normal (negative for IOH). The study cohort included 85% of subjects exhibiting at least one IOH event. Males constituted 70% of the cohort, median age was 55.8 years, and median BMI was 27.7. The multivariate model yielded average AUC = 0.97 in the static context of a single prediction made up to 8 min before a possible IOH event, and it outperformed a univariate model based on MAP-only (average AUC = 0.83). The MMV model demonstrated AUC = 0.96, PPV = 0.89, and NPV = 0.98 within the challenging context of a dynamic sliding window across 40 min prior to a possible IOH event. We present a novel ML model to predict IOH with a distinctive "dial" on sensitivity and specificity to predict first IOH episode during liver resection surgeries.