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.

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.

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.

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.

Systematic Literature Review Regarding Heart Rate and Respiratory Rate Measurement by Means of Radar Technology.

The use of radar technology for non-contact measurement of vital parameters is increasingly being examined in scientific studies. Based on a systematic literature search in the PubMed, German National Library, Austrian Library Network (Union Catalog), Swiss National Library and Common Library Network databases, the accuracy of heart rate and/or respiratory rate measurements by means of radar technology was analyzed. In 37% of the included studies on the measurement of the respiratory rate and in 48% of those on the measurement of the heart rate, the maximum deviation was 5%. For a tolerated deviation of 10%, the corresponding percentages were 85% and 87%, respectively. However, the quantitative comparability of the results available in the current literature is very limited due to a variety of variables. The elimination of the problem of confounding variables and the continuation of the tendency to focus on the algorithm applied will continue to constitute a central topic of radar-based vital parameter measurement. Promising fields of application of research can be found in particular in areas that require non-contact measurements. This includes infection events, emergency medicine, disaster situations and major catastrophic incidents.

A transformer-based diffusion probabilistic model for heart rate and blood pressure forecasting in Intensive Care Unit.

BACKGROUND AND OBJECTIVE: Vital sign monitoring in the Intensive Care Unit (ICU) is crucial for enabling prompt interventions for patients. This underscores the need for an accurate predictive system. Therefore, this study proposes a novel deep learning approach for forecasting Heart Rate (HR), Systolic Blood Pressure (SBP), and Diastolic Blood Pressure (DBP) in the ICU. METHODS: We extracted 24,886 ICU stays from the MIMIC-III database which contains data from over 46 thousand patients, to train and test the model. The model proposed in this study, Transformer-based Diffusion Probabilistic Model for Sparse Time Series Forecasting (TDSTF), merges Transformer and diffusion models to forecast vital signs. The TDSTF model showed state-of-the-art performance in predicting vital signs in the ICU, outperforming other models' ability to predict distributions of vital signs and being more computationally efficient. The code is available at https://github.com/PingChang818/TDSTF. RESULTS: The results of the study showed that TDSTF achieved a Standardized Average Continuous Ranked Probability Score (SACRPS) of 0.4438 and a Mean Squared Error (MSE) of 0.4168, an improvement of 18.9% and 34.3% over the best baseline model, respectively. The inference speed of TDSTF is more than 17 times faster than the best baseline model. CONCLUSION: TDSTF is an effective and efficient solution for forecasting vital signs in the ICU, and it shows a significant improvement compared to other models in the field.

Comparison of Vital Sign Cutoffs to Identify Children With Major Trauma.

Importance: Vital signs are essential components in the triage of injured children. The Advanced Trauma Life Support (ATLS) and Pediatric Advanced Life Support (PALS) physiologic criteria are frequently used for trauma assessments. Objective: To evaluate the performance of ATLS and PALS criteria vs empirically derived criteria for identifying major trauma in children. Design, Setting, and Participants: This retrospective cohort study used 2021 American College of Surgeons Trauma Quality Improvement Program (TQIP) data contributed by US trauma centers. Included encounters involved pediatric patients (aged <18 years) with severe injury, excluding those who experienced out-of-hospital cardiac arrest, were receiving mechanical ventilation, or were transferred from another facility. Data were analyzed between April 9 and December 21, 2023. Exposure: Initial hospital vital signs, including heart rate, respiratory rate, and systolic blood pressure (SBP). Main Outcome and Measures: Major trauma, determined by the Standard Triage Assessment Tool, a composite measure of injury severity and interventions performed. Multivariable models developed from PALS and ATLS vital sign criteria were compared with models developed from the empirically derived criteria using the area under the receiver operating characteristic curve. Validation of the findings was performed using the 2019 TQIP dataset. Results: A total of 70 748 patients (median [IQR] age, 11 [5-15] years; 63.4% male) were included, of whom 3223 (4.6%) had major trauma. The PALS criteria classified 31.0% of heart rates, 25.7% of respiratory rates, and 57.4% of SBPs as abnormal. The ATLS criteria classified 25.3% of heart rates, 4.3% of respiratory rates, and 1.1% of SBPs as abnormal. Among children with all 3 vital signs documented (64 326 [90.9%]), PALS had a sensitivity of 88.4% (95% CI, 87.1%-89.3%) and specificity of 25.1% (95% CI, 24.7%-25.4%) for identifying major trauma, and ATLS had a sensitivity of 54.5% (95% CI, 52.7%-56.2%) and specificity of 72.9% (95% CI, 72.6%-73.3%). The empirically derived cutoff vital sign z scores had a sensitivity of 80.0% (95% CI, 78.5%-81.3%) and specificity of 48.7% (95% CI, 48.3%-49.1%) and area under the receiver operating characteristic curve of 70.9% (95% CI, 69.9%-71.8%), which was similar to PALS criteria (69.6%; 95% CI, 68.6%-70.6%) and greater than ATLS criteria (65.4%; 95% CI, 64.4%-66.3%). Validation using the 2019 TQIP database showed similar performance to the derivation sample. Conclusions and Relevance: These findings suggest that empirically derived vital sign criteria strike a balance between the sensitivity of PALS criteria and the specificity of ATLS criteria in identifying major trauma in children. These criteria may help to identify children at greatest risk of trauma-related morbidity and mortality.

Adopting the New Norms of Monitoring Postoperative Patients with Wearable Devices in Ireland - Advantages and Challenges.

There has been a growing interest in wearable devices to monitor postoperative patients, providing the healthcare professionals with real-time information on vital signs to detect potential complications and hence, take timely actions to prevent them. Several studies and pilot programme in Ireland and worldwide indicated the effectiveness of wearable devices in monitoring patients, which could result in better patient outcomes and more efficient healthcare system. As more healthcare providers adopt this new technology, better patient outcomes and a more efficient healthcare system can be anticipated. Key Words: Wearable devices, Continuous monitoring, Patient safety.

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.

A customised down-sampling machine learning approach for sepsis prediction.

OBJECTIVE: Sepsis is a life-threatening condition in the ICU and requires treatment in time. Despite the accuracy of existing sepsis prediction models, insufficient focus on reducing alarms could worsen alarm fatigue and desensitisation in ICUs, potentially compromising patient safety. In this retrospective study, we aim to develop an accurate, robust, and readily deployable method in ICUs, only based on the vital signs and laboratory tests. METHODS: Our method consists of a customised down-sampling process and a specific dynamic sliding window and XGBoost to offer sepsis prediction. The down-sampling process was applied to the retrospective data for training the XGBoost model. During the testing stage, the dynamic sliding window and the trained XGBoost were used to predict sepsis on the retrospective datasets, PhysioNet and FHC. RESULTS: With the filtered data from PhysioNet, our method achieved 80.74% accuracy (77.90% sensitivity and 84.42% specificity) and 83.95% (84.82% sensitivity and 82.00% specificity) on the test set of PhysioNet-A and PhysioNet-B, respectively. The AUC score was 0.89 for both datasets. On the FHC dataset, our method achieved 92.38% accuracy (88.37% sensitivity and 95.16% specificity) and 0.98 AUC score on the test set of FHC. CONCLUSION: Our results indicate that the down-sampling process and the dynamic sliding window with XGBoost brought robust and accurate performance to give sepsis prediction under various hospital settings. The localisation and robustness of our method can assist in sepsis diagnosis in different ICU settings.

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.

A Built-In Guidance System to Monitor Vital Signs in Space and on Earth.

INTRODUCTION: Different types of remote expeditions often require an expedition crew to conduct medical emergency assessments without prior medical training. Modern technology offers new devices that support diagnosis with a simple guided user instructions interface. It is not yet clear how quickly medically untrained individuals can acquire the required skills with such a device. This study investigated the time and quality of obtained outcomes, as well as the mental workload when using a vital signs monitor and its guided procedure interface during a simulation of a medical emergency event.METHODS: There were 50 individuals (25 medically inexperienced, 25 medically trained) who participated in this study. In a randomized order subjects measured electrocardiography, noninvasive blood pressure, pulse oximetry, and body temperature. The procedure was repeated after a 20-min break. Completion time, data validity, and mental workload were analyzed.RESULTS: Average times to obtain stable and reliable signals of all recorded vital signs were significantly shorter for both groups during the second attempt and for medically experienced individuals in comparison to medically inexperienced individuals. The number of errors did not change between attempts for both groups. The mental workload was higher during the first attempt in both groups for most vital sign acquisitions.DISCUSSION: Automated devices could be easily and quickly used by members of a given expedition, even if the crew lacks advanced medical training. With relatively little training provided by a built-in guidance system, medically untrained individuals can achieve a basic level of proficiency in reliably obtaining valid vital signs.Huerta R, Kaduk SI, Fatai L, Rusch H, Weber T, Sammito S. A built-in guidance system to monitor vital signs in space and on Earth. Aerosp Med Hum Perform. 2024; 95(3):139-146.

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.