Search

Stroke is a leading cause of disability and death worldwide, with acute ischemic stroke (AIS) accounting for the majority of cases. Early and accurate diagnosis of AIS is crucial for improving patient outcomes. Non-invasive monitoring techniques, such as time domain near-infrared spectroscopy (tdNIRS), have shown potential for real-time monitoring of AIS patients at the bedside. However, there is a need for further research to evaluate the effectiveness of tdNIRS in the acute phase of stroke. In this study, we present the results of a case report using tdNIRS to monitor AIS patients without any additional stimulation. The tdNIRS technique allows for non-invasively assessing cerebral oxygenation in absolute units, enabling accurate measurement of changes in oxygenated and deoxygenated hemoglobin concentrations in the brain. Our aim was to determine the feasibility of tdNIRS in monitoring AIS patients. [ABSTRACT FROM AUTHOR]

For infants with congenital heart disease (CHD) requiring surgery, prolonged hospital stays, intermittent caregiver visitation, and constrained unit staffing ratios present barriers to adequately address post-operative stressors and associated need to retain cognitive and physiological reserves. Similar patients requiring high-engagement interventions, such as hospitalized infants with neonatal abstinence syndrome, have found success in utilizing responsive bassinets to soothe infants and save floor nurses' time. However, it remains unclear if such technology can be leveraged in the CHD population given their complex hemodynamics, feeding intolerance, and monitoring requirements. This multidisciplinary feasibility study evaluated responsive bassinet use in a cohort of infants with CHD <6 months of age in a medical-surgical unit at a midwestern children's hospital. Specifically assessing 1) implementation requirements, challenges, and potential of utilizing the device, together with 2) ability to perform bedside monitoring (monitoring) and 3) measuring physiologic trends during use. Between 11/2020–1/2022, nine infants utilized a responsive bassinet over 599 h (mean 13, range 4–26 days per infant). No increase in monitoring alarms and accurate vital signs monitoring during bassinet activity were noted with appropriate physiologic responses for infants with single ventricle and biventricular surgeries. Feasibility of introducing new technology into care, and successful use of its functionality for soothing was found to be plausible for infants with CHD. After cardiac surgery, infants with CHD have need for interventions to reduce stress. Use of a soothing bassinet has the potential to aid in doing so without interference with monitoring requirements. • Soothing bassinets can aid in post-operative care of infants with heart disease. • Research on sympathetic response while in bassinet can be accomplished. • Steps for implementation of nursing-led research discussed. [ABSTRACT FROM AUTHOR]

Around the turn of the century, a better understanding of the complex physiology of cerebral blood flow (CBF) regulation has emerged. It is now acknowledged that cerebral autoregulation is much more complicated than we previously thought it was, with the shape of the autoregulation curve and limits of autoregulation that may vary enormously and unpredictably, both within and between patients. The consequence is that to safeguard the cerebral circulation, the dogma that an empirically chosen blood pressure guarantees adequate CBF in any individual patient has to be abandoned. Integration of cerebral autoregulation monitoring in daily perioperative patient care offers the opportunity to guide blood pressure management to the individual patient's need. The most common approach tests the effect of changes in blood pressure on an estimate of CBF. However, a "gold standard" to assess cerebral autoregulation is not yet available, and the literature shows considerable disparity of methods and criteria. [ABSTRACT FROM AUTHOR]

• A real-time and interpretable machine learning model for predicting cardiac arrest in critically ill patients is proposed. • 43 features are extracted based on four vital signs which are commonly used in bedside vital signs monitoring. • SHAP value is used to capture the overall and real-time interpretability of the model. • Compared with previous studies, this model is more suitable for bedside vital signs monitoring to predict cardiac arrest. The sensitivity and specificity were 0.86 and 0.85, respectively. Cardiac arrest (CA) is the most serious death-related event in critically ill patients and the early detection of CA is beneficial to reduce mortality according to clinical research. This study aims to develop and verify a real-time, interpretable machine learning model, namely cardiac arrest prediction index (CAPI), to predict CA of critically ill patients based on bedside vital signs monitoring. A total of 1,860 patients were analyzed retrospectively from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Based on vital signs, we extracted a total of 43 features for building machine learning model. Extreme Gradient Boosting (XGBoost) was used to develop a real-time prediction model. Three-fold cross validation determined the consistency of model accuracy. SHAP value was used to capture the overall and real-time interpretability of the model. On the test set, CAPI predicted 95% of CA events, 80% of which were identified more than 25 min in advance, resulting in an area under the receiver operating characteristic curve (AUROC) of 0.94. The sensitivity, specificity, area under the precision-recall curve (AUPRC) and F1-score were 0.86, 0.85, 0.12 and 0.05, respectively. CAPI can help predict patients with CA in the vital signs monitoring at bedside. Compared with previous studies, CAPI can give more timely notifications to doctors for CA events. However, current performance was at the cost of alarm fatigue. Future research is still needed to achieve better clinical application. [ABSTRACT FROM AUTHOR]