To evaluate a minute-by-minute monitoring algorithm against a periodic early warning score (EWS) in detecting clinical deterioration and workload. Periodic EWSs suffer from large measurement intervals, causing late detection of deterioration. This might be prevented by continuous vital sign monitoring with a real-time algorithm such as the Visensia Safety Index (VSI). This prospective comparative data modeling cohort study (NCT04189653) compares continuous algorithmic alerts against periodic EWS in continuous monitored medical and surgical inpatients. We evaluated sensitivity, frequency, number of warnings needed to evaluate (NNE) and time of initial alert till escalation of care (EOC): Rapid Response Team activation, unplanned ICU admission, emergency surgery, or death. Also, the percentage of VSI alerting minutes was compared between patients with or without EOC. In 1529 admissions continuous VSI warned for 55% of EOC (95% CI: 45-64%) versus 51% (95% CI: 41-61%) by periodic EWS. NNE for VSI was 152 alerts per detected EOC (95% CI: 114-190) compared to 21 (95% CI: 17-28). It generated 0.99 warnings per day per patient compared to 0.13. Time from detection score till escalation was 8.3 hours (IQR: 2.6-24.8) with VSI versus 5.2 (IQR: 2.7-12.3) hours with EWS (P=0.074). The percentage of warning VSI minutes was higher in patients with EOC than in stable patients (2.36% vs 0.81%, P<0.001). Although sensitivity of detection was not significantly improved continuous vital sign monitoring shows potential for earlier alerts for deterioration compared to periodic EWS. A higher percentage of alerting minutes may indicate risk for deterioration.
Sesame (Sesamum indicum L.) is an important oilseed crop, and its seeds are a source of edible oil and widely used as a nutritious food that is beneficial to health in oriental countries. Phytochemical and biological investigations of the seeds have been well reported; however, those of the leaves have been limited.
Background: Delirium is common in hospitalized patients and correlated with increased morbidity and mortality. Despite this, delirium is underdiagnosed, and many institutions do not have sufficient resources to consistently apply effective screening and prevention.
Objective: To develop a machine learning algorithm to identify patients at highest risk of delirium in the hospital each day in an automated fashion based on data available in the electronic medical record, reducing the barrier to large-scale delirium screening.
Not harming others is widely regarded as a fundamental tenet of human morality. Harm aversion based on the consequences of an action is called utilitarianism while focusing on the action itself is associated with deontology. This study investigated how interoceptive processing affects the neural processing of utilitarian and deontological moral decision-making.
Vital signs such as heart rate (HR) and respiration rate (RR) are essential physiological parameters that are routinely used to monitor human health and bodily functions. They can be continuously monitored through contact or contactless measurements performed in the home or a hospital. In this study, a contactless Doppler radar W-band sensing system was used for short-range, contactless vital sign estimation.
Background: Systemic lupus erythematosus is a multi-organ autoimmune disorder that is treated by immunosuppressive agents that weaken the immune defense against opportunistic pathogens and latent infections such as strongyloidiasis. Herein, we report the case of a 43-year-old woman known to have systemic lupus erythematosus who presented with gastrointestinal symptoms, edema, and bone pain 2 months after receiving immunosuppressive treatment.
Case Presentation: A 43-year-old Iranian female known to have systemic lupus erythematosus and antiphospholipid syndrome presented with abdominal pain, nausea, vomiting, and generalized edema.
