What most captures the physician's interest when evaluating a multiparameter monitor in a Neonatal ICU? - A simulation study.

This study aimed to identify early clinical predictors of non-invasive ventilation (NIV) failure in children with Pediatric Acute Respiratory Distress Syndrome (PARDS) and to develop a model to predict NIV outcomes. A prospective observational study including children (0-15 years) who were supported by NIV and met PARDS criteria was performed in two public PICUs in Chile. Demographic, physiological variables, and clinical outcomes were recorded. Logistic regression was used to identify risk factors for NIV failure and develop a predictive model. Seventy-six patients were enrolled (median age 22 months [IQR 11-48]; weight 12.1 kg [9.6-18.3]). Eighteen patients (24%) failed NIV (Failure group), with a median NIV duration of 12.5 h (IQR 8-23.8). The Failure group showed a significant reduction in the SpO2/FiO2 ratio after 2 h of NIV (-59 [-124 to 5] vs -2 [-49 to 30], p = 0.03). In multivariate analysis, an increase in ΔSpO2/FiO2 (improvement in oxygenation) was associated with lower odds of NIV failure (OR 0.98; 95% CI 0.97-0.99; p = 0.006), indicating that worsening oxygenation was associated with a higher risk. The final prediction model included age and early changes in SpO2/FiO2, respiratory rate, and heart rate, demonstrating good discrimination (AUROC 0.764; 95% CI 0.641-0.888). In children with PARDS treated with NIV, a model including age and early changes in oxygenation, heart rate, and respiratory rate within the first 2 h predicts the need for intubation with good discrimination.

The Society for Obstetric Anesthesia and Perinatology (SOAP) recommends monitoring for respiratory depression after cesarean delivery according to intrathecal morphine (ITM) dose. The aim of the study was to compare respiratory metrics after ITM administration, to determine evidence for the SOAP recommendations. Prospective observational cohort study design. Following institutional review board approval, women undergoing non-emergency cesarean delivery with spinal anesthesia in a single center were enrolled. Women were offered 50 µg, 150 µg or transversus abdominis plane (TAP) block without ITM, for postoperative analgesia. Respiratory monitoring, continuous acoustic respiratory rate (RR) and pulse oximetry oxygen saturation (SpO 2 ) monitor Masimo Radius-7, was used after surgery up to 12 hours postoperatively. The primary outcome was bradypnea, number of women with RR ≤ 8 breaths per minute according to dose. Among 80 recruits, 33 (41%) chose 50 µg, 46 (57%) chose 150 µg, and one chose TAP block. Bradypnea events were detected 564 times among 27 (81.8%) women receiving 50 µg and 529 times among 40 (87.0%) women receiving 150 µg. Median [interquartile range (IQR)] duration of bradypnea per patient was 1.58 [1.15-2.42] in 50 µg group and 1.09 [0.83-1.70] in 150 µg group, P < .001. The event rate of bradypnea was 1.89 (95% confidence intervals [CIs], 1.73-2.05) events per person-hour among women receiving 50 µg versus 1.23 (95% CI, 1.12-1.34) events per person-hour among women receiving 150 µg, P = .624. Most, 62% of bradypnea occurred by 6 hours, and 86% of events by 12 hours. Apnea and hypoxemia event rates were similar, but hypoxemia duration was significantly prolonged after 150 µg, median 48 [IQR, 36-90] versus 56 sec [IQR, 40-106]; P = .001. Nursing assessments done per institutional protocol did not identify any respiratory events. No woman required oxygen or naloxone administration. We found no statistically significant differences in RR comparing 50 and 150 µg ITM, using continuous monitoring. Although the 150 µg group experienced a higher incidence of hypoxemia events, these were not clinically significant.

Respiratory heart rate variability: Insights into mechanisms, measurements and interpretations.

Changes in systemic carbon dioxide levels during simulated avalanche burial using a one-way valve device: A randomized, double-blind, crossover trial.

Stress is a widespread concern that impacts human health with its silent progression, causing significant public health burdens and economic loss globally. Non-invasive wearable technology empowered by physiological signal monitoring can enable early warning systems for stress, alleviating some of the burdens, allowing on-time interventions, and thus significantly improving quality of life. This study used the heart rate and respiratory rate data from 34 participants. It evaluated the performance of the hybrid deep learning CNN-Transformer model and benchmarked it against deep learning convolutional neural networks (CNNs) and Transformer models, extreme gradient boosting (XGBoost) and random forest (RF) machine learning models, comprising a total of five AI models. To mitigate data imbalance and observe the efficacy of deep learning data augmentation techniques in physiological signals for stress monitoring, two generative adversarial network (GAN) models: conditional tabular GAN (CTGAN), copula GAN (CopGAN) and variational autoencoder (VAE) based model tabular VAE synthesiser (TVAES) had been employed. The modelling performance significantly improved when applying CTGAN and CopGAN, demonstrating the usefulness of synthetic data. The CNN-Transformer achieved an average accuracy of 77%, a precision of 87% and an AUC of 83%. The study applied leave-one- subject- out (LOSO CV) to prove the CNN-Transformer hybrid’s robustness for generalizability to perform well for unseen subjects. The study integrated explainable AI models, Shapley values (SHAP), and local interpretable model-agnostic explanations (LIME), as well as Monte Carlo Dropout for uncertainty quantification to bring confidence, trust and transparency to AI systems, taking a step closer to real-world deployment. Similar studies can also help in the detection of other disorders, such as anxiety and depression. • Comprehensive multi-domain feature extraction from heart rate and respiratory rate using wearable non-invasive sensor technology. • Comparative evaluation of three deep learning data augmentation techniques (CTGAN, TVAES and CopulaGAN) to mitigate data imbalance. • Comparative performance analysis of the hybrid DL model, CNN-Transformer model against benchmarking DL and ML models. • Implementing uncertainty quantification using Monte Carlo Dropout for confident and reliable model prediction assessment. • Application of xAI models LIME and SHAP for interpretability and feature importance.

Predictive value of early blood glucose trajectory for poor prognosis in ARDS patients.

A Retrospective Study of the Respiratory Rate Oxygenation and Respiratory Rate Oxygenation-Heart Rate Indexes in Predicting the Results of Conventional Oxygen Therapy in Patients with Acute Respiratory Failure.

Stress management is vital for safe avian anaesthesia. Intranasal (IN) midazolam-butorphanol may reduce handling stress and anaesthetic requirements in buzzards (Buteo spp.) OBJECTIVES: To evaluate the clinical efficacy and cardiopulmonary effects of IN midazolam-butorphanol premedication compared to mask induction with sevoflurane in buzzards. Sixteen juvenile buzzards of undetermined sex were randomly allocated into two equal groups. The experimental group received IN midazolam (2mg/kg) and butorphanol (1mg/kg). The control group received sevoflurane mask induction without premedication. Heart rate (HR), respiratory rate (fR), ECG, SpO2, Pe'CO2, body temperature and sevoflurane vaporiser setting (SVS) were monitored at 0, 5, 15, 30 and 60 min. Aortic peak velocity (APV) was measured via echocardiography. Data (mean ± SD) were compared using Student's t-test, and repeated-measures ANOVA (p < 0.05). Statistical analysis revealed no significant differences between groups regarding peri-anaesthetic, fR, body temperature, Pe'CO2 and APV. However, SpO2 was significantly higher in the premedicated group at 15 min (p = 0.045). Notably, the premedicated group required significantly lower SVS at 0 and 5 min compared to the control group (p = 0.001). Repeated-measures analysis indicated significant differences in SVS (p = 0.042) and HR (p = 0.033) with the control group showing higher SVS and lower HR; regarding ECG parameters, both QT (p = 0.001) and ST intervals (p = 0.011) were significantly prolonged in the control group. All physiological variables remained within acceptable limits, and all birds recovered uneventfully from anaesthesia. The IN midazolam-butorphanol provided superior induction quality, significantly reduced inhalant anaesthetic requirements and maintained stable cardiopulmonary function. This protocol is a practical alternative for the anaesthetic management of traumatised buzzards.

Variability in 24-h and visit-to-visit blood pressure (BP) has been reported to be predictive of adverse cardiovascular outcomes. Although recent studies have used beat-to-beat blood pressure variability (BPV) as a predictor of cardiovascular risk, the reliability of beat-to-beat BPV remains equivocal. The purpose of this study was to examine the intraindividual reliability of BPV and to compare that reliability to more widely documented heart rate variability (HRV) in young healthy adults. Continuous heart rate (HR, electrocardiogram), beat-to-beat blood pressure (BP, finger plethysmography), and respiratory rate (pneumobelt) were recorded during 10 min of rest in 77 participants [38 males and 39 females; age: 23 ± 5 yr; body mass index (BMI): 25 ± 4 kg·m-2] on two occasions separated by ≥2 days (25 ± 19 days; 2-98 days). Reliabilities of beat-to-beat BPV, HRV (time- and frequency-domain), and additional cardiovascular (heart rate, blood pressure) assessments were quantified as relative and absolute reliability using the intraclass correlation coefficient (ICC) and the coefficient of variation (CoV), respectively. Beat-to-beat BPV demonstrated moderate relative and good absolute reliability (10-min duration: ICC = 0.601-0.684, CoV = 14%-16%). In contrast, relative reliability of time-domain HRV was good-to-excellent, whereas absolute reliability was poor-to-good (10-min duration: ICC = 0.844-0.904, CoV = 13%-34%). Frequency-domain HRV demonstrated moderate to excellent relative reliability and poor absolute reliability (10-min duration: ICC = 0.626-0.903, CoV = 34%-37%). In summary, the rigor and reliability of continuous BPV and HRV are different when parsed out by relative versus absolute reliability, with relative reliability being stronger with HRV and absolute reliability being stronger with BPV. These findings suggest both caution and specificity when using continuous BPV or HRV for cardiovascular disease (CVD) risk-stratification purposes.NEW & NOTEWORTHY Beat-to-beat blood pressure variability (BPV) has gained attention as a noninvasive predictor of cardiovascular disease. However, the reliability of BPV has not been adequately characterized. We investigated the intraindividual reliability of beat-to-beat BPV and compared that reliability to more traditional heart rate variability (HRV). BPV demonstrated higher absolute, but lower relative, reliability compared with HRV. These findings suggest a need for caution and specific reporting when using continuous BPV or HRV for cardiovascular risk-stratification purposes.

Remote assessment of patients after same-day discharge metabolic and bariatric surgery (MBS) might be improved by continuous remote monitoring (CRM), because it provides heart and respiratory rate (HR, RR), alongside patient complaints. This study aimed to evaluate the added value of CRM on clinical decision making in the remote assessment of patients after same-day discharge MBS. A nationwide survey among MBS healthcare professionals (n = 23) evaluated the clinical applicability of remote monitoring with general questions and its influence on clinical decision making using fictitious patient cases. Additionally, in a retrospective cohort study (n = 213), current remote patient assessment, HR and RR, technical sensor performance and patient satisfaction in (non-)readmitted patients who underwent same-day discharge MBS was evaluated. The survey showed that HR and RR along with complaints does not lead to uniform treatment policies, and only 54% of healthcare professionals always use CRM vital signs in patient assessment. The cohort study shows that CRM data was used in only 20% of remote patient assessments, however when CRM data was used, all patients with normal data but with complaints were still referred to the emergency department. Additionally, no statistically significant differences were found in HR and RR between readmitted (n = 14) and non-readmitted patients (n = 199), 49% of HR and 43% of RR data were missing and 72% of patients would have undergone same-day discharge MBS without CRM. For this specific low-risk population, CRM did not appear to influence clinical decision making after same-day discharge MBS, under the current implementation conditions.

To reveal the effectiveness of the Benson's relaxation technique on the anxiety and hemodynamic parameters of patients with cardiovascular disease. A systematic review and meta-analysis of randomized controlled studies. Eight electronic databases were searched. The Cochrane Handbook 2023 and the preferred reporting items for systematic reviews and meta-analysis 2020 guideline were used to perform the study. A random effects model was used to calculate the combined effects of the interventions, and the robustness of the pooled estimates was assessed through sensitivity analyses. The methodological quality of the included studies was assessed using the Cochrane Risk of Bias Tool version 2. The Grading of Recommendations, Development, and Evaluation approach was used to evaluate the certainty of the evidence. This systematic review and meta-analysis included 8 trials with 538 patients. The Benson's relaxation technique had moderate-quality evidence for anxiety (Hedge's g = -0.662, p < .001) and saturation (Hedge's g = 0.657, p = .001), low-quality evidence for heart rate (Hedge's g = -0.507, p = .006), and diastolic blood pressure (Hedge's g = -0.280, p = .036) and very-low-quality evidence for systolic blood pressure (Hedge's g = -0.687, p = .002). No significant effect was observed on respiratory rate (Hedge's g = -0.578, p = .051) with very low-quality evidence. Sensitivity analyses showed variable robustness. An additional subgroup analysis of the Benson's relaxation intervention's effect on anxiety in surgical and nonsurgical patient groups revealed that this intervention significantly reduced anxiety in nonsurgical patient groups (Hedge's g = -0.603, p < .001) and systolic blood pressure (Hedge's g = -0.822, p < .001), but it was not effective in the surgical patient population (p = .072 and p = .097, respectively). This systematic review and meta-analysis indicate that, although the certainty of the evidence varies, Benson's relaxation techniques may offer potential benefits for certain outcomes. The current evidence supports cautious clinical interpretation and emphasizes the need for high-quality randomized controlled trials.

We evaluated bidirectional long short-term memory models for predicting inpatient mortality using different approaches to processing vital signs data collected during the initial 24h of intensive care unit (ICU) admissions. We compared 3 vital-sign representations: (1) raw data recorded every 5min, (2) preprocessed data averaged hourly, and (3) preprocessed data using biomarker representations that extends a digital oximetry biomarker toolbox of PhysioZoo software, applied to blood pressure, heart rate, temperature, respiratory rate, and SpO2. Across 2 large ICU datasets, HiRID and eICU, models trained on the frequency-normalized representation achieved higher discrimination and lower Brier scores than those trained on raw 5-min and hourly averaged data. The use of biomarker representations of vital signs yielded the largest improvements in discrimination and overall probabilistic performance reflected by lower Brier scores for predicting inpatient mortality by deep learning. Thus, we recommend using a similar approach to vital signs preprocessing for time-series predictive models.

Evaluating the effects of A-803467, a selective NaV1.8 blocker, on cough response in guinea pigs with allergic rhinitis.

Routine chest X-ray in atraumatic chest pain with normal vital signs: A propensity-matched TriNetX analysis.

Continuous vital sign monitoring with wearable biosensors enables earlier detection of clinical deterioration and improves patient safety. This study validated an integrated wireless photoplethysmography-based chest patch biosensor (Biobeat technology Ltd) capable of measuring five vital signs relevant for Early Warning Score calculation, by comparison with standard continuous bedside monitoring in the intensive care unit. Agreement between the wearable biosensor and the reference monitor was assessed using Bland-Altman analysis for repeated measurements. Bias and limits of agreement were calculated for all vital signs. Measurement differences were compared with predefined accuracy limits. Data were aligned at 5- and 15-min intervals using median reference values. Deming regression, Pearson correlation, and Clarke Error Grid analyses were performed. A total of 1931.8 monitoring hours were collected from 32 intensive care patients (mean 60.4 ± 28.5 h). Heart rate showed a bias of 0.16 beats per minute (LoA -17.06 to 17.38), with 18.8% exceeding the 10% threshold and 0.42% in Clarke Error Grid zones D-E. Respiratory rate showed a bias of 3.77 breaths per minute (LoA -6.78 to 14.31), with 61.4% exceeding the 10% threshold and 18.6% in zones D-E. Systolic blood pressure, oxygen saturation, and temperature showed biases of -4.69 mmHg, -1.34%, and -0.29°C, respectively. Agreement between a wearable chest-patch biosensor and standard bedside monitoring was evaluated in intensive care patients. Overall agreement was variable, with wide limits of agreement and a substantial proportion of measurements outside predefined thresholds. Further optimisation and validation are required before broader clinical application. In this article, limitations in agreement between signals from wearable wireless sensors for vital signs and usual vital signs monitor values in intensive care cases are described for data collected from a practical and pragmatic setting where the study participants were in early phase of their illness with some expected clinical deterioration. There were enough test device values outside of predefined limits of agreement based on clinically relevant differences, that the authors interpret this as a need for further improvement in device performance.

Relative contribution of driving pressure and respiratory rate to mortality in ARDS depends on age.

Itaconate delays senescence and browning in litchi fruit via enhanced antioxidant capacity and energy metabolism

Lack of repeatability in red muscle mitochondrial function over time and across oxygen conditions in a teleost fish.

Wearable sensors are crucial for non-invasive and continuous health monitoring. The performance of these devices has been improved by recent advances in materials, device design and system integration. This review focuses on wearable sensors for physiological monitoring with special highlight on biophysical and biochemical sensing approaches. It discusses the ability of biochemical wearable sensors for biofluid biomarker analysis to provide molecular information to supplement physical measurements. Biophysical wearable sensors such as strain, pressure, temperature and bioelectrical sensors are also summarized. Their sensing mechanism and application in monitoring vital signs such as heart rate, respiratory rate, body temperature, blood oxygen saturation and cuffless blood pressure are discussed. The main challenges of wearable sensing including signal interference, interface stability, calibration and individual differences are also discussed. Finally, future directions of wearable sensor systems are highlighted with a focus on multimodal integration and translation towards clinically relevant and personalized health care applications.