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.

Safer and more efficient vital signs monitoring protocols to identify the deteriorating patients in the general hospital ward: an observational study.

Background: The frequency at which patients should have their vital signs (e.g. blood pressure, pulse, oxygen saturation) measured on hospital wards is currently unknown. Current National Health Service monitoring protocols are based on expert opinion but supported by little empirical evidence. The challenge is finding the balance between insufficient monitoring (risking missing early signs of deterioration and delays in treatment) and over-observation of stable patients (wasting resources needed in other aspects of care). Objective: Provide an evidence-based approach to creating monitoring protocols based on a patient's risk of deterioration and link these to nursing workload and economic impact. Design: Our study consisted of two parts: (1) an observational study of nursing staff to ascertain the time to perform vital sign observations; and (2) a retrospective study of historic data on patient admissions exploring the relationships between National Early Warning Score and risk of outcome over time. These were underpinned by opinions and experiences from stakeholders. Setting and participants: Observational study: observed nursing staff on 16 randomly selected adult general wards at four acute National Health Service hospitals. Retrospective study: extracted, linked and analysed routinely collected data from two large National Health Service acute trusts; data from over 400,000 patient admissions and 9,000,000 vital sign observations. Results: Observational study found a variety of practices, with two hospitals having registered nurses take the majority of vital sign observations and two favouring healthcare assistants or student nurses. However, whoever took the observations spent roughly the same length of time. The average was 5:01 minutes per observation over a 'round', including time to locate and prepare the equipment and travel to the patient area. Retrospective study created survival models predicting the risk of outcomes over time since the patient was last observed. For low-risk patients, there was little difference in risk between 4 hours and 24 hours post observation. Conclusions: We explored several different scenarios with our stakeholders (clinicians and patients), based on how 'risk' could be managed in different ways. Vital sign observations are often done more frequently than necessary from a bald assessment of the patient's risk, and we show that a maximum threshold of risk could theoretically be achieved with less resource. Existing resources could therefore be redeployed within a changed protocol to achieve better outcomes for some patients without compromising the safety of the rest. Our work supports the approach of the current monitoring protocol, whereby patients' National Early Warning Score 2 guides observation frequency. Existing practice is to observe higher-risk patients more frequently and our findings have shown that this is objectively justified. It is worth noting that important nurse-patient interactions take place during vital sign monitoring and should not be eliminated under new monitoring processes. Our study contributes to the existing evidence on how vital sign observations should be scheduled. However, ultimately, it is for the relevant professionals to decide how our work should be used. Study registration: This study is registered as ISRCTN10863045. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 17/05/03) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 6. See the NIHR Funding and Awards website for further award information.

Patient recovery in hospital is tracked by measuring heart rate, blood pressure and other 'vital signs' and converting them into a score. These are 'observed' regularly by nursing staff so that deterioration can be spotted early. However, taking observations can disturb patients, and taking them too often causes extra work for staff. More frequent monitoring is recommended for higher scores, but evidence is lacking. To work out how often patients should be monitored, we needed to know how likely it is for patients to become more unwell between observations. We analysed over 400,000 patient records from two hospitals to understand how scores change with time. We looked at three of the most serious risks for patients in hospital. These risks are dying, needing intensive care or having a cardiac arrest. We also looked at the risk that a patient's condition would deteriorate significantly before their measurements were taken again. We identified early signs of deterioration and how changes in vital signs affected the risk of a patient's condition becoming worse. From this we calculated a maximum risk of deterioration. We then calculated different monitoring schedules that keep individual patients below this risk level. Some of those would consume less staff time than current National Health Service guidelines suggest. We also watched staff record patients' vital signs. We learnt it takes about 5 minutes to take these measurements from each patient. This information helped us calculate how costs would change if patients' vital signs were taken more or less often. We found that patients with a low overall score could have their vital signs monitored less often without being in danger of serious harm. This frees up nursing time so that patients with a higher score can be monitored more often. Importantly, this can be achieved without employing more staff.

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.

Remote Emotion Recognition Using Continuous-Wave Bio-Radar System.

The Bio-Radar is herein presented as a non-contact radar system able to capture vital signs remotely without requiring any physical contact with the subject. In this work, the ability to use the proposed system for emotion recognition is verified by comparing its performance on identifying fear, happiness and a neutral condition, with certified measuring equipment. For this purpose, machine learning algorithms were applied to the respiratory and cardiac signals captured simultaneously by the radar and the referenced contact-based system. Following a multiclass identification strategy, one could conclude that both systems present a comparable performance, where the radar might even outperform under specific conditions. Emotion recognition is possible using a radar system, with an accuracy equal to 99.7% and an F1-score of 99.9%. Thus, we demonstrated that it is perfectly possible to use the Bio-Radar system for this purpose, which is able to be operated remotely, avoiding the subject awareness of being monitored and thus providing more authentic reactions.

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.

Quality of care in patients with hypertension: a retrospective cohort study of primary care routine data in Germany.

BACKGROUND: Hypertension is a leading cause of morbidity and mortality if not properly managed. Primary care has a major impact on these outcomes if its strengths, such as continuity of care, are deployed wisely. The analysis aimed to evaluate the quality of care for newly diagnosed hypertension in routine primary care data. METHODS: In the retrospective cohort study, routine data (from 2016 to 2022) from eight primary care practices in Germany were exported in anonymized form directly from the electronic health record (EHR) systems and processed for this analysis. The analysis focused on five established quality indicators for the care of patients who have been recently diagnosed with hypertension. RESULTS: A total of 30,691 patients were treated in the participating practices, 2,507 of whom have recently been diagnosed with hypertension. Prior to the pandemic outbreak, 19% of hypertensive patients had blood pressure above 140/90 mmHg and 68% received drug therapy (n = 1,372). After the pandemic outbreak, the proportion of patients with measured blood pressure increased from 63 to 87%, while the other four indicators remained relatively stable. Up to 80% of the total variation of the quality indicators could be explained by individual practices. CONCLUSION: For the majority of patients, diagnostic procedures are not used to the extent recommended by guidelines. The analysis showed that quality indicators for outpatient care could be mapped onto the basis of routine data. The results could easily be reported to the practices in order to optimize the quality of care.

The value of C-reactive protein, leucocytes and vital signs in detecting major complications after oncological colorectal surgery.

PURPOSE: To assess the association of postoperative C-reactive protein (CRP), leucocytes and vital signs in the first three postoperative days (PODs) with major complications after oncological colorectal resections in a tertiary referral centre for colorectal cancer in The Netherlands. METHODS: A retrospective cohort study, including 594 consecutive patients who underwent an oncological colorectal resection at Maastricht University Medical Centre between January 2016 and December 2020. Descriptive analyses of patient characteristics were performed. Logistic regression models were used to assess associations of leucocytes, CRP and Modified Early Warning Score (MEWS) at PODs 1-3 with major complications. Receiver operating characteristic curve analyses were used to establish cut-off values for CRP. RESULTS: A total of 364 (61.3%) patients have recovered without any postoperative complications, 134 (22.6%) patients have encountered minor complications and 96 (16.2%) developed major complications. CRP levels reached their peak on POD 2, with a mean value of 155 mg/L. This peak was significantly higher in patients with more advanced stages of disease and patients undergoing open procedures, regardless of complications. A cut-off value of 170 mg/L was established for CRP on POD 2 and 152 mg/L on POD 3. Leucocytes and MEWS also demonstrated a peak on POD 2 for patients with major complications. CONCLUSIONS: Statistically significant associations were found for CRP, Δ CRP, Δ leucocytes and MEWS with major complications on POD 2. Patients with CRP levels ≥ 170 mg/L on POD 2 should be carefully evaluated, as this may indicate an increased risk of developing major complications.

A Comparison of Normalization Techniques for Individual Baseline-Free Estimation of Absolute Hypovolemic Status Using a Porcine Model.

Hypovolemic shock is one of the leading causes of death in the military. The current methods of assessing hypovolemia in field settings rely on a clinician assessment of vital signs, which is an unreliable assessment of hypovolemia severity. These methods often detect hypovolemia when interventional methods are ineffective. Therefore, there is a need to develop real-time sensing methods for the early detection of hypovolemia. Previously, our group developed a random-forest model that successfully estimated absolute blood-volume status (ABVS) from noninvasive wearable sensor data for a porcine model (n = 6). However, this model required normalizing ABVS data using individual baseline data, which may not be present in crisis situations where a wearable sensor might be placed on a patient by the attending clinician. We address this barrier by examining seven individual baseline-free normalization techniques. Using a feature-specific global mean from the ABVS and an external dataset for normalization demonstrated similar performance metrics compared to no normalization (normalization: R2 = 0.82 ± 0.025|0.80 ± 0.032, AUC = 0.86 ± 5.5 × 10-3|0.86 ± 0.013, RMSE = 28.30 ± 0.63%|27.68 ± 0.80%; no normalization: R2 = 0.81 ± 0.045, AUC = 0.86 ± 8.9 × 10-3, RMSE = 28.89 ± 0.84%). This demonstrates that normalization may not be required and develops a foundation for individual baseline-free ABVS prediction.

Using a wearable patch to develop a digital monitoring biomarker of inflammation in response to LPS challenge.

Remote inflammation monitoring with digital health technologies (DHTs) would provide valuable information for both clinical research and care. Controlled perturbations of the immune system may reveal physiological signatures which could be used to develop a digital biomarker of inflammatory state. In this study, molecular and physiological profiling was performed following an in vivo lipopolysaccharide (LPS) challenge to develop a digital biomarker of inflammation. Ten healthy volunteers received an intravenous LPS challenge and were monitored for 24 h using the VitalConnect VitalPatch (VitalPatch). VitalPatch measurements included heart rate (HR), heart rate variability (HRV), respiratory rate (RR), and skin temperature (TEMP). Conventional episodic inpatient vital signs and serum proteins were measured pre- and post-LPS challenge. The VitalPatch provided vital signs that were comparable to conventional methods for assessing HR, RR, and TEMP. A pronounced increase was observed in HR, RR, and TEMP as well as a decrease in HRV 1-4 h post-LPS challenge. The ordering of participants by magnitude of inflammatory cytokine response 2 h post-LPS challenge was consistent with ordering of participants by change from baseline in vital signs when measured by VitalPatch (r = 0.73) but not when measured by conventional methods (r = -0.04). A machine learning model trained on VitalPatch data predicted change from baseline in inflammatory protein response (R2 = 0.67). DHTs, such as VitalPatch, can improve upon existing episodic measurements of vital signs by enabling continuous sensing and have the potential for future use as tools to remotely monitor inflammation.