Journal of Clinical Monitoring and Computing最新文献

Unobtrusive pulse rate monitoring by continuous video recording, based on remote photoplethysmography (rPPG), might enable early detection of perioperative arrhythmias in general ward patients. However, the accuracy of an rPPG-based machine learning model to monitor the pulse rate during sinus rhythm and arrhythmias is unknown. We conducted a prospective, observational diagnostic study in a cohort with a high prevalence of arrhythmias (patients undergoing elective electrical cardioversion). Pulse rate was assessed with rPPG via a visible light camera and ECG as reference, before and after cardioversion. A cardiologist categorized ECGs into normal sinus rhythm or arrhythmias requiring further investigation. A supervised machine learning model (support vector machine with Gaussian kernel) was trained using rPPG signal features from 60-s intervals and validated via leave-one-subject-out. Pulse rate measurement performance was evaluated with Bland-Altman analysis. Of 72 patients screened, 51 patients were included in the analyses, including 444 60-s intervals with normal sinus rhythm and 1130 60-s intervals of clinically relevant arrhythmias. The model showed robust discrimination (AUC 0.95 [0.93-0.96]) and good calibration. For pulse rate measurement, the bias and limits of agreement for sinus rhythm were 1.21 [- 8.60 to 11.02], while for arrhythmia, they were - 7.45 [- 35.75 to 20.86]. The machine learning model accurately identified sinus rhythm and arrhythmias using rPPG in real-world conditions. Heart rate underestimation during arrhythmias highlights the need for optimization.

Hypotension in perioperative and intensive care settings is a significant risk factor associated with complications such as myocardial infarction and kidney injury thereby increasing perioperative complications and mortality. Continuous blood pressure monitoring is essential, yet challenging due to the invasive nature of current methods. Non-invasive techniques like Electrical Impedance Tomography (EIT) have been explored but face challenges in accurate and consistent blood pressure estimation. A machine learning (ML) approach was used to predict aortic blood pressures from EIT voltage measurements in landrace pigs. A convolutional neural network (CNN) was trained on a dataset of 75 298 heartbeats, to predict systolic (SAP), mean (MAP), and diastolic arterial pressures (DAP) of individuals whose arterial pressures were unknown to the algorithm. The Intraclass Correlation Coefficient (3,1) with absolute agreement (ICC) was calculated and the concordance was estimated, comparing reference blood pressure measurements and ML-derived estimates. A risk classification was estimated for the calculated blood pressure as suggested by Saugel et al. 2018. The ML-model demonstrated moderate correlations with invasive blood pressure measurements (ICC for SAP of 0.530, for MAP of 0.563, and for DAP of 0.521.) with a low risk score for 75.8% of the SAP and 64.2% of MAP estimated blood pressures. ML-techniques using EIT-voltages showed promising preliminary results in non-invasive aortic blood pressure estimation. Despite limitations in the amount of available training data and the experimental setup, this study illustrates the potential of integrating ML in EIT signal processing for real-time, non-invasive blood pressure monitoring.

To assess the efficacy of electrical impedance tomography (EIT)-guided positive end-expiratory pressure (PEEP) titration in improving outcomes for patients with acute respiratory distress syndrome (ARDS). A systematic review and meta-analysis was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Randomized controlled trials and observational studies with a control group comparing EIT-guided PEEP titration to other strategies were included. Endpoints analysed included mortality, days of mechanical ventilation (MV), intensive care unit (ICU) length of stay (LOS), weaning success rate, barotrauma, driving pressure (∆P), mechanical power (MP), Sequential Organ Failure Assessment (SOFA) score and adverse events. Pooled results were presented as Risk Ratio (RR) for dichotomous outcomes and standardized difference in means (SMD) for continuous outcomes. A total of 4 studies were identified (3 randomized controlled trials and one observational study). All studies were single-center studies (N total = 271 patients). The main limitations were related to potential bias in selecting reported outcomes. EIT-guided PEEP titration was associated with a significant reduction in mortality among critically ill patients with ARDS (RR = 0.64, 95% CI: 0.45-0.91). No significant differences were found in other outcomes. Our findings suggest that EIT may be a valuable tool for PEEP titration in critically ill patients with ARDS. By optimizing lung mechanics, EIT-guided PEEP titration may potentially reduce mortality rates. While larger, multicenter studies are needed to definitively establish the clinical role of EIT in ARDS management, our results provide promising evidence for its potential clinical impact.

In 2023, the first German guideline on intraoperative haemodynamic monitoring and management for adults having non-cardiac surgery was published. The aim of this survey was to identify how anaesthetists in Germany managed intraoperative haemodynamics and blood pressure before its publication. In September to October 2023, members of the German Society of Anaesthesiology and Intensive Care Medicine (DGAI) were invited via email to participate in this anonymous online survey. Thirty-one questions covered demographics, clinical experience, approaches to perioperative blood pressure measurement and common thresholds, as well as the use of advanced haemodynamic monitoring and its potential therapeutic implications. 1,079 fully completed questionnaires were included in the analysis. When intermittent oscillometry was used to measure blood pressure, a 3-minute interval was usually applied during induction of anaesthesia (42%; 451/1,079). For invasive blood pressure monitoring, more than half (53%; 574/1,079) inserted an arterial line after induction of anaesthesia. Nearly all (94%; 1,012/1,079) focused on the mean arterial pressure for blood pressure monitoring, with a large majority (77%; 779/1012) considering values below 60-65 mmHg to be critically low. Intraoperative hypotension was managed based on an internal protocol by only 21% (223/1,079). Regarding advanced haemodynamic monitoring, 43% (459/1,079) frequently used pulse contour analysis, while 67% (721/1,079) reported that monitors with finger-cuff technology were not available in their department. 47% (504/1,079) cited a lack of experience as one of the main reasons for the infrequent use of cardiac output monitoring. This survey among DGAI members provides important insights into current practices of haemodynamic monitoring and management prior to the publication of the recent German guideline on 'Intraoperative haemodynamic monitoring and management of adults having non-cardiac surgery'.

Wearables and Internet of Things (IoT) technologies are increasingly incorporated into healthcare, including perioperative settings. These devices offer continuous non-invasive monitoring of vital signs, patient position, and mobilization. Nonetheless, there is currently little information about tolerance and acceptance of wearables in postoperative patients. We therefore assessed opinions and user experience in postoperative patients who used three popular fitness trackers during their entire hospital stay. Specifically, we evaluate the Apple Watch 7, Garmin Fenix 6 Pro, and Withings ScanWatch. We used an investigator-designed patient questionnaire with 11 questions to quantify patient experience and opinions regarding inpatient and home monitoring. Secondarily, we evaluated compliance and the incidence of associated adverse events during daily patient visits. Data were analyzed using descriptive statistics and non-parametric tests. The majority of the answers to the questions (82.1%) were rated positively defined as Likert-Scale Scores 4 or 5 by the 33 analyzed patients, ranging between 72.7 and 97.0% agreement rate. Specific questions related to data sharing for research and overall user experience received high agreement rates (97.0 and 84.8%, respectively). Women reported slightly higher satisfaction with device comfort, as compared to men (LS-Score 4.8 vs. 4.0). No significant differences were found based on the device model or length of hospitalization. The use of wearable devices in healthcare is rated positively by postoperative inpatients, paving the way for future implementation of these devices in healthcare. However, besides validating the measurement accuracy and demonstrating clinical benefits, several regulatory hurdles must be overcome before implementing wearables in routine clinical care.

Purpose: There are different techniques for ultrasound-guided central venous catheter (CVC) insertion. When using the conventional syringe-on-needle technique, the syringe needs to be removed from the needle after venous puncture to pass the guidewire through the needle into the vein. When, alternatively, using the wire-in-needle technique, the needle is preloaded with the guidewire, and the guidewire-after venous puncture-is advanced into the vein under real-time ultrasound guidance. We tested the hypothesis that the wire-in-needle technique reduces the time to successful guidewire insertion in the internal jugular vein compared with the syringe-on-needle technique in adults.

Methods: We randomized 250 patients to the wire-in-needle or syringe-on-needle technique. Our primary endpoint was the time to successful guidewire insertion in the internal jugular vein.

Results: Two hundred and thirty eight patients were analyzed. The median (25th percentile, 75th percentile) time to successful guidewire insertion was 22 (16, 38) s in patients assigned to the wire-in-needle technique and 25 (19, 34) s in patients assigned to the syringe-on-needle technique (estimated location shift: 2 s; 95%-confidence-interval: - 1 to 5 s, p = 0.165). CVC insertion was successful on the first attempt in 103/116 patients (89%) assigned to the wire-in-needle technique and in 113/122 patients (93%) assigned to the syringe-on-needle technique. CVC insertion-related complications occurred in 8/116 patients (7%) assigned to the wire-in-needle technique and 19/122 patients (16%) assigned to the syringe-on-needle technique.

Conclusion: The wire-in-needle technique-compared with the syringe-on-needle technique-did not reduce the time to successful guidewire insertion in the internal jugular vein. Clinicians can consider either technique for ultrasound-guided CVC insertion in adults.

The effects of pneumoperitoneum on dynamic predictors of fluid responsiveness such as pulse pressure variation (PPV) remain uncertain. This uncertainty arises from potentially opposing physiological mechanisms that affect cardiovascular dynamics during conditions with increased intra-abdominal pressure (IAP). Deriving PPV with high precision during induction of pneumoperitoneum may provide new insights into the complex relationship between intra-abdominal pressure changes and PPV. The hypothesis was that PPV derived from a generalised additive model (PPV_GAM) would increase with the induction of pneumoperitoneum and the associacted increase in IAP. This was a prospective, observational study in patients undergoing oesophagectomy. Before and after induction of pneumoperitoneum, haemodynamic variables including PPV and stroke volume variation (SVV) were recorded with the Hemosphere monitor. PPV_GAM was estimated offline from the arterial blood pressure curve. A total of 34 patients were included in the final analysis. PPV_GAM increased by a factor of 1.49 (95% CI: 1.25-1.77) as intra-abdominal pressure increased from baseline to 12 mmHg. SVV and PPV from the HemoSphere monitor increased with a factor of 1.25 (95% CI: 1.13-1.39, p < 0.001) and 1.14 (95% CI: 1.00-1.29, p = 0.048), respectively. PPV derived from a generalised additive model increased approximately 50% from the induction of pneumoperitoneum to an IAP of 12 mmHg. PPV and SVV derived from the Hemosphere monitor also increased signicantly.

Purpose: Pupillary examination is a central part of the neurological assessment. While quantitative pupillometry (QP) improves reliability, the impact of ambient light, particularly on the Neurological Pupil index (NPi), remains unclear. This study aimed to clarify the effects of ambient light on QP parameters in a critical care setting.

Methods: We performed a prospective crossover study, including 20 adult patients requiring invasive ventilation. Pupillometry was performed during bright condition (BC1), then dark condition (DC), then bright condition again (BC2). In our primary analysis we compared NPi values across conditions (DC1 vs. BC, BC vs. DC2, DC1 vs. DC2). In the secondary analysis, we compared all other QP parameters.

Results: All QP values except constriction velocity and dilation velocity were non-normal. The median NPi was significantly lower in BC compared to dark conditions DC1 in both eyes. In 25% of participants the NPi decreased by 0.6 or more. Conversely, a significant increase in median NPi of both eyes was observed when switching from bright conditions back to dark (BC vs. DC2). No significant difference was found between the two dark condition measurements (DC1 and DC2). The secondary analysis showed that the differences in NPi were driven by differences in most, but not all, QP parameters included in NPi.

Conclusions: We corroborate previous findings that the level of ambient light affects QP parameters in critically ill patients. This needs to be considered for accurate interpretation of QP parameters. Future studies may explore potential automated light correction methods for wider clinical applicability.