A mixed-methods study to investigate feasibility and acceptability of an early warning score for preterm infants in neonatal units in Kenya: results of the NEWS-K study : Neonatal early warning scores in Kenya.

Preterm birth (< 37 weeks gestation) complications are the leading cause of neonatal mortality. Early-warning scores (EWS) are charts where vital signs (e.g., temperature, heart rate, respiratory rate) are recorded, triggering action. To evaluate whether a neonatal EWS improves clinical outcomes in low-middle income countries, a randomised trial is needed. Determining whether the use of a neonatal EWS is feasible and acceptable in newborn units, is a prerequisite to conducting a trial. We implemented a neonatal EWS in three newborn units in Kenya. Staff were asked to record infants' vital signs on the EWS during the study, triggering additional interventions as per existing local guidelines. No other aspects of care were altered. Feasibility criteria were pre-specified. We also interviewed health professionals (n = 28) and parents/family members (n = 42) to hear their opinions of the EWS. Data were collected on 465 preterm and/or low birthweight (< 2.5 kg) infants. In addition to qualitative study participants, 45 health professionals in participating hospitals also completed an online survey to share their views on the EWS. 94% of infants had the EWS completed at least once during their newborn unit admission. EWS completion was highest on the day of admission (93%). Completion rates were similar across shifts. 15% of vital signs triggered escalation to a more senior member of staff. Health professionals reported liking the EWS, though recognised the biggest barrier to implementation was poor staffing. Newborn unit infant to staff ratios varied between 10 and 53 staff per 1 infant, depending upon time of shift and staff type. A randomised trial of neonatal EWS in Kenya is possible and acceptable, though adaptations are required to the form before implementation.

Wearable wireless continuous vital signs monitoring on the general ward.

PURPOSE OF REVIEW: Wearable wireless sensors for continuous vital signs monitoring (CVSM) offer the potential for early identification of patient deterioration, especially in low-intensity care settings like general wards. This study aims to review advances in wearable CVSM - with a focus on the general ward - highlighting the technological characteristics of CVSM systems, user perspectives and impact on patient outcomes by exploring recent evidence. RECENT FINDINGS: The accuracy of wearable sensors measuring vital signs exhibits variability, especially notable in ambulatory patients within hospital settings, and standard validation protocols are lacking. Usability of CMVS systems is critical for nurses and patients, highlighting the need for easy-to-use wearable sensors, and expansion of the number of measured vital signs. Current software systems lack integration with hospital IT infrastructures and workflow automation. Imperative enhancements involve nurse-friendly, less intrusive alarm strategies, and advanced decision support systems. Despite observed reductions in ICU admissions and Rapid Response Team calls, the impact on patient outcomes lacks robust statistical significance. SUMMARY: Widespread implementation of CVSM systems on the general ward and potentially outside the hospital seems inevitable. Despite the theoretical benefits of CVSM systems in improving clinical outcomes, and supporting nursing care by optimizing clinical workflow efficiency, the demonstrated effects in clinical practice are mixed. This review highlights the existing challenges related to data quality, usability, implementation, integration, interpretation, and user perspectives, as well as the need for robust evidence to support their impact on patient outcomes, workflow and cost-effectiveness.

DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing.

Optical fiber sensors are extensively employed for their unique merits, such as small size, being lightweight, and having strong robustness to electronic interference. The above-mentioned sensors apply to more applications, especially the detection and monitoring of vital signs in medical or clinical. However, it is inconvenient for daily long-term human vital sign monitoring with conventional monitoring methods under the uncomfortable feelings generated since the skin and devices come into direct contact. This study introduces a non-invasive surveillance system that employs an optical fiber sensor and advanced deep-learning methodologies for precise vital sign readings. This system integrates a monitor based on the MZI (Mach-Zehnder interferometer) with LSTM networks, surpassing conventional approaches and providing potential uses in medical diagnostics. This could be potentially utilized in non-invasive health surveillance, evaluation, and intelligent health care.

Ballistocardial Signal-Based Personal Identification Using Deep Learning for the Non-Invasive and Non-Restrictive Monitoring of Vital Signs.

Owing to accelerated societal aging, the prevalence of elderly individuals experiencing solitary or sudden death at home has increased. Therefore, herein, we aimed to develop a monitoring system that utilizes piezoelectric sensors for the non-invasive and non-restrictive monitoring of vital signs, including the heart rate and respiration, to detect changes in the health status of several elderly individuals. A ballistocardiogram with a piezoelectric sensor was tested using seven individuals. The frequency spectra of the biosignals acquired from the piezoelectric sensors exhibited multiple peaks corresponding to the harmonics originating from the heartbeat. We aimed for individual identification based on the shapes of these peaks as the recognition criteria. The results of individual identification using deep learning techniques revealed good identification proficiency. Altogether, the monitoring system integrated with piezoelectric sensors showed good potential as a personal identification system for identifying individuals with abnormal biological signals.

Predictive health monitoring: Leveraging artificial intelligence for early detection of infectious diseases in nursing home residents through discontinuous vital signs analysis.

This research addresses the problem of detecting acute respiratory, urinary tract, and other infectious diseases in elderly nursing home residents using machine learning algorithms. The study analyzes data extracted from multiple vital signs and other contextual information for diagnostic purposes. The daily data collection process encounters sampling constraints due to weekends, holidays, shift changes, staff turnover, and equipment breakdowns, resulting in numerous nulls, repeated readings, outliers, and meaningless values. The short time series generated also pose a challenge to analysis, preventing the extraction of seasonal information or consistent trends. Blind data collection results in most of the data coming from periods when residents are healthy, resulting in excessively imbalanced data. This study proposes a data cleaning process and then builds a mechanism that reproduces the basal activity of the residents to improve the classification of the disease. The results show that the proposed basal module-assisted machine learning techniques allow anticipating diagnostics 2, 3 or 4 days before doctors decide to start treatment with antibiotics, achieving a performance measured by the area-under-the-curve metric of 0.857. The contributions of this work are: (1) a new data cleaning process; (2) the analysis of contextual information to improve data quality; (3) the generation of a baseline measure for relative comparison; and (4) the use of either binary (disease/no disease) or multiclass classification, differentiating among types of infections and showing the advantages of multiclass versus binary classification. From a medical point of view, the anticipated detection of infectious diseases in institutionalized individuals is brand new.

Non-invasive biomarkers for detecting progression toward hypovolemic cardiovascular instability in a lower body negative pressure model.

Occult hemorrhages after trauma can be present insidiously, and if not detected early enough can result in patient death. This study evaluated a hemorrhage model on 18 human subjects, comparing the performance of traditional vital signs to multiple off-the-shelf non-invasive biomarkers. A validated lower body negative pressure (LBNP) model was used to induce progression towards hypovolemic cardiovascular instability. Traditional vital signs included mean arterial pressure (MAP), electrocardiography (ECG), plethysmography (Pleth), and the test systems utilized electrical impedance via commercial electrical impedance tomography (EIT) and multifrequency electrical impedance spectroscopy (EIS) devices. Absolute and relative metrics were used to evaluate the performance in addition to machine learning-based modeling. Relative EIT-based metrics measured on the thorax outperformed vital sign metrics (MAP, ECG, and Pleth) achieving an area-under-the-curve (AUC) of 0.99 (CI 0.95-1.00, 100% sensitivity, 87.5% specificity) at the smallest LBNP change (0-15 mmHg). The best vital sign metric (MAP) at this LBNP change yielded an AUC of 0.6 (CI 0.38-0.79, 100% sensitivity, 25% specificity). Out-of-sample predictive performance from machine learning models were strong, especially when combining signals from multiple technologies simultaneously. EIT, alone or in machine learning-based combination, appears promising as a technology for early detection of progression toward hemodynamic instability.

Early Vital Sign Thresholds Associated with 24-Hour Mortality among Trauma Patients: A Trauma Quality Improvement Program (TQIP) Study.

BACKGROUND: Identifying patients at imminent risk of death is critical in the management of trauma patients. This study measures the vital sign thresholds associated with death among trauma patients. METHODS: This study included data from patients ≥15 years of age in the American College of Surgeons Trauma Quality Improvement Program (TQIP) database. Patients with vital signs of zero were excluded. Documented prehospital and emergency department (ED) vital signs included systolic pressure, heart rate, respiratory rate, and calculated shock index (SI). The area under the receiver operator curves (AUROC) was used to assess the accuracy of these variables for predicting 24-hour survival. Optimal thresholds to predict mortality were identified using Youden's Index, 90% specificity, and 90% sensitivity. Additional analyses examined patients 70+ years of age. RESULTS: There were 1,439,221 subjects in the 2019-2020 datasets that met inclusion for this analysis with <0.1% (10,270) who died within 24 hours. The optimal threshold for prehospital systolic pressure was 110, pulse rate was 110, SI was 0.9, and respiratory rate was 15. The optimal threshold for the ED systolic was 112, pulse rate was 107, SI was 0.9, and respiratory rate was 21. Among the elderly sub-analysis, the optimal threshold for prehospital systolic was 116, pulse rate was 100, SI was 0.8, and respiratory rate was 21. The optimal threshold for ED systolic was 121, pulse rate was 95, SI was 0.8, and respiratory rate was 0.8. CONCLUSIONS: Systolic blood pressure (SBP) and SI offered the best predictor of mortality among trauma patients. The SBP values predictive of mortality were significantly higher than the traditional 90mmHg threshold. This dataset highlights the need for better methods to guide resuscitation as initial vital signs have limited accuracy in predicting subsequent mortality.

Distinct patterns of vital sign and inflammatory marker responses in adults with suspected bloodstream infection.

OBJECTIVES: To identify patterns in inflammatory marker and vital sign responses in adult with suspected bloodstream infection (BSI) and define expected trends in normal recovery. METHODS: We included patients ≥16 y from Oxford University Hospitals with a blood culture taken between 1-January-2016 and 28-June-2021. We used linear and latent class mixed models to estimate trajectories in C-reactive protein (CRP), white blood count, heart rate, respiratory rate and temperature and identify CRP response subgroups. Centile charts for expected CRP responses were constructed via the lambda-mu-sigma method. RESULTS: In 88,348 suspected BSI episodes; 6908 (7.8%) were culture-positive with a probable pathogen, 4309 (4.9%) contained potential contaminants, and 77,131(87.3%) were culture-negative. CRP levels generally peaked 1-2 days after blood culture collection, with varying responses for different pathogens and infection sources (p < 0.0001). We identified five CRP trajectory subgroups: peak on day 1 (36,091; 46.3%) or 2 (4529; 5.8%), slow recovery (10,666; 13.7%), peak on day 6 (743; 1.0%), and low response (25,928; 33.3%). Centile reference charts tracking normal responses were constructed from those peaking on day 1/2. CONCLUSIONS: CRP and other infection response markers rise and recover differently depending on clinical syndrome and pathogen involved. However, centile reference charts, that account for these differences, can be used to track if patients are recovering line as expected and to help personalise infection.

The effect of virtual rainforest and a white noise mobile application on satisfaction, tolerance, comfort, and vital signs during arthroscopy.

BACKGROUND AND PURPOSE: This study was conducted to investigate the effect of Virtual Rainforest (VRF) and a White Noise (WN) mobile applications on patient satisfaction, tolerance, comfort, and vital signs during arthroscopic knee surgery. METHODS: This is a randomized, controlled, interventional study. The study was completed with a total of 93 participants, 31 in the VRF group, 31 in the WN group, and 31 in the control group. Data were collected using a Patient Information Form and a Visual Analog Scale for satisfaction, tolerance, and comfort. RESULTS: The results of study showed that there were significant increases in tolerance, satisfaction, comfort, respiratory rate, and oxygen saturation levels and significant decreases in heart rate, systolic and diastolic blood pressures in both VRF and WN groups (p < .05). In the control group, no significant difference was found between the means of the variables before and after the procedure (p > .05). CONCLUSION: According to the results of the study, VRF and WN applied during the arthroscopy procedure increased satisfaction, tolerance, and comfort in patients and had a positive effect on vital signs. TRIAL AND PROTOCOL REGISTRATION: ClinicalTrials.gov, NCT05992714.

State-trait anxiety levels and vital signs of pregnant women following intervention with virtual reality during the nonstress test: A randomized controlled trial.

BACKGROUND: Pregnant women often experience anxiety due to pregnancy, negatively impacting their and their fetus' health. Non-pharmacological interventions, such as virtual reality (VR), could reduce anxiety levels, potentially impacting non-stress tests or the physiological responses of the pregnant woman and the fetus. METHODS: A randomized clinical trial conducted between February and December 2022 involved 286 term pregnant women. They were divided into a VR intervention group (146 women) and a control group (140 women). The intervention consisted of 20 min of 3D glasses with images and sounds during a third-trimester nonstress test. Anxiety was measured using the Spielberg State-Trait Anxiety Inventory (STAI), alongside physiological parameters. RESULTS: The VR group exhibited lower anxiety levels compared to controls (STAI score: Rosenthal's r: -0.54, p = 0.01; state anxiety: Rosenthal's r: -0.40, p = 0.001; trait anxiety: Rosenthal's r: -0.41, p = 0.001). Within the VR group, there was a significant reduction in trait anxiety (Rosenthal's r, 1.27; p < 0.001) and total anxiety (Rosenthal's r, 1.63; p < 0.001) post-intervention, along with decreased systolic blood pressure (p < 0.001), diastolic blood pressure (p < 0.001), and maternal heart rate (p = 0.02). LIMITATIONS: Future research could explore additional pregnancy-related variables, such as postpartum anxiety. CONCLUSIONS: The results confirm that the use of VR is beneficial for pregnant women and their fetuses, as it decreases anxiety levels, and improves physiological parameters such as blood pressure and maternal heart rate during the nonstress test. VR is a technique that is easy to integrate into the healthcare system due to its non-invasive and non-pharmacological nature.

Case of tizanidine withdrawal showing hallucination, decorticate posture and tremor, with hypersympathetic vital signs.

Tizanidine, an α2-adrenergic receptor agonist commonly prescribed as a muscle relaxant, has been associated with limited cases of acute intoxication or withdrawal. Here, we present a case of tizanidine withdrawal in a woman in her 40s who presented with an unusual combination of systemic and neurological symptoms. These included hallucinations, decorticate posture, limb and eyelid tremors, along with hypertension, tachycardia and tachypnoea. The diagnosis of tizanidine withdrawal was established by a comprehensive assessment of the patient's medical history and the systematic exclusion of other potential diseases. Our approach to managing the withdrawal symptoms was to initiate symptomatic treatment with a combination of a beta-blocker and a calcium channel blocker. Remarkably, this intervention successfully resolved both vital signs and neurological manifestations by the following day. In conclusion, tizanidine withdrawal is associated with a distinct and diagnostically significant neurological syndrome characterised by hallucinations, decorticate posture, tremors and hypersympathetic vital signs.