Clinical impact of multi-parameter continuous non-invasive monitoring in hospital wards: a systematic review and meta-analysis

Effectiveness of Remote Patient Monitoring Equipped With an Early Warning System in Tertiary Care Hospital Wards: Retrospective Cohort Study

BACKGROUND

Monitoring vital signs in hospitalized patients is crucial for evaluating their clinical condition. While early warning scores like the modified early warning score (MEWS) are typically calculated 3 to 4 times daily through spot checks, they might not promptly identify early deterioration. Leveraging technologies that provide continuous monitoring of vital signs, combined with an early warning system, has the potential to identify clinical deterioration sooner. This approach empowers health care providers to intervene promptly and effectively.

OBJECTIVE

This study aimed to assess the impact of a Remote Patient Monitoring System (RPMS) with an automated early warning system (R-EWS) on patient safety in noncritical care at a tertiary hospital. R-EWS performance was compared with a simulated Modified Early Warning System (S-MEWS) and a simulated threshold-based alert system (S-Threshold).

METHODS

Patient outcomes, including intensive care unit (ICU) transfers due to deterioration and discharges for nondeteriorating cases, were analyzed in Ramaiah Memorial Hospital's general wards with RPMS. Sensitivity, specificity, chi-square test for alert frequency distribution equality, and the average time from the first alert to ICU transfer in the last 24 hours was determined. Alert and patient distribution by tiers and vitals in R-EWS groups were examined.

RESULTS

Analyzing 905 patients, including 38 with deteriorations, R-EWS, S-Threshold, and S-MEWS generated more alerts for deteriorating cases. R-EWS showed high sensitivity (97.37%) and low specificity (23.41%), S-Threshold had perfect sensitivity (100%) but low specificity (0.46%), and S-MEWS demonstrated moderate sensitivity (47.37%) and high specificity (81.31%). The average time from initial alert to clinical deterioration was at least 18 hours for RPMS and S-Threshold in deteriorating participants. R-EWS had increased alert frequency and a higher proportion of critical alerts for deteriorating cases.

CONCLUSIONS

This study underscores R-EWS role in early deterioration detection, emphasizing timely interventions for improved patient outcomes. Continuous monitoring enhances patient safety and optimizes care quality.

Time to detection of serious adverse events by continuous vital sign monitoring versus clinical practice

BACKGROUND

Continuous vital sign monitoring detects far more severe vital sign deviations (SVDs) than intermittent clinical rounds, and deviations are to some extent related to subsequent serious adverse events (SAEs). Early detection of SAEs is pivotal to allow for effective interventions but the time relationship between detection of SAEs by continuous vital sign monitoring versus clinical practice is not well-described at the general ward.

AIM

To quantify the time difference between detection of SAEs by continuous vital sign monitoring and clinical suspicion of deterioration (CSD) in major abdominal surgery patients.

METHODS

Five hundred and five patients had their vital signs continuously monitored in combination with usual clinical practice consisting of National Early Warning Score assessments at least every 8'th hour, assessments during rounds, and other kinds of staff-patient interactions. The primary outcome was the time difference between the first chart note of CSD versus the first SVD, detected by continuous vital sign monitoring, in patients with a subsequent confirmed SAE during or up to 48 h after end of continuous vital sign monitoring.

RESULTS

Out of the 505 continuously monitored patients, 142 patients had a combination of both postoperative SAE, CSD and SVD, and thus were included in the primary analysis. The median time from the first SVD to SAE was 42.8 h (interquartile range 19.8-72.1 h) compared to 13 minutes (interquartile range - 4.8 to 3.5 h) for CSD with a median difference of 48.1 h (95% confidence interval 43.0-54.8 h), p-value < .001.

CONCLUSION

Continuous vital sign monitoring detects signs of oncoming SAEs in the form of SVD hours before CSD, potentially allowing for earlier and more effective treatments to reduce the extent of SAEs.

Worry as Important "Feelers" in Clinical Anesthesia Practice: A Mixed-Methods Study

PURPOSE

Worry is an intuitive sense that goes beyond logical reasoning and is valuable in situations where patients' conditions are rapidly changing or when objective data may not fully capture the complexity of a patient's situation. Nurse anesthetists' subjective reasons for worry are quite vague as they are valued inconsistently and not accurately expressed. This study aimed to identify factors playing a role in the emergence of worry during anesthesia practice to clarify its concept.

DESIGN

Mixed-methods design consisting of quantitative online surveys followed by qualitative focus group interviews including Dutch nurse anesthetists.

METHODS

Both quantitative and qualitative thematic analyses were performed, followed by data and methodological triangulation to enhance the validity and credibility of findings and mitigate the presence of bias.

FINDINGS

Surveys (N = 102) were analyzed, and 14 nurse anesthetists participated in the focus group interviews. A total of 89% of the survey respondents reported that at least once have had the feeling of worry, of which 92% use worry during clinical anesthesia practice. Worry was mentioned to be a vital element during anesthesia practice that makes it possible to take precautionary actions to change the anesthetic care plan in a changing situation or patient deterioration.

CONCLUSIONS

While a clear definition of worry could not be given, it is a valuable element of anesthesia practice as it serves as a catalyst for critical thinking, problem-solving, clinical reasoning, and decision-making. Use of the feeling of worry alongside technological systems to make an informed decision is crucial. Technology has significantly improved the ability of health care providers to detect and respond to patient deterioration promptly, but it is crucial for nurse anesthetists to use their feeling of worry or intuition alongside technological systems and evidence-based practice to ensure quick assessments or judgments based on experience, knowledge, and observations in clinical practice.

Prognostic value of heart rate variability for risk of serious adverse events in continuously monitored hospital patients

Technological advances allow continuous vital sign monitoring at the general ward, but traditional vital signs alone may not predict serious adverse events (SAE). This study investigated continuous heart rate variability (HRV) monitoring's predictive value for SAEs in acute medical and major surgical patients. Data was collected from four prospective observational studies and two randomized controlled trials using a single-lead ECG. The primary outcome was any SAE, secondary outcomes included all-cause mortality and specific non-fatal SAE groups, all within 30 days. Subgroup analyses of medical and surgical patients were performed. The primary analysis compared the last 24 h preceding an SAE with the last 24 h of measurements in patients without an SAE. The area under a receiver operating characteristics curve (AUROC) quantified predictive performance, interpretated as low prognostic ability (0.5-0.7), moderate prognostic ability (0.7-0.9), or high prognostic ability (> 0.9). Of 1402 assessed patients, 923 were analysed, with 297 (32%) experiencing at least one SAE. The best performing threshold had an AUROC of 0.67 (95% confidence interval (CI) 0.63-0.71) for predicting cardiovascular SAEs. In the surgical subgroup, the best performing threshold had an AUROC of 0.70 (95% CI 0.60-0.81) for neurologic SAE prediction. In the medical subgroup, thresholds for all-cause mortality, cardiovascular, infectious, and neurologic SAEs had moderate prognostic ability, and the best performing threshold had an AUROC of 0.85 (95% CI 0.76-0.95) for predicting neurologic SAEs. Predicting SAEs based on the accumulated time below thresholds for individual continuously measured HRV parameters demonstrated overall low prognostic ability in high-risk hospitalized patients. Certain HRV thresholds had moderate prognostic ability for prediction of specific SAEs in the medical subgroup.

Contactless and continuous monitoring of respiratory rate in a hospital ward: a clinical validation study

Introduction

Continuous monitoring of respiratory rate in hospital wards can provide early detection of clinical deterioration, thereby reducing mortality, reducing transfers to intensive care units, and reducing the hospital length of stay. Despite the advantages of continuous monitoring, manually counting every 1-12 h remains the standard of care in most hospital wards. The objective of this study was to validate continuous respiratory rate measurements from a radar-based contactless patient monitor [Vitalthings Guardian M10 (Vitalthings AS, Norway)] in a hospital ward.

Methods

An observational study (clinicaltrials.gov: NCT06083272) was conducted at the emergency ward of a university hospital. Adult patients were monitored during rest with Vitalthings Guardian M10 in both a stationary and mobile configuration simultaneously with a reference device [Nox T3s (Nox Medical, Alpharetta, GA, United States)]. The agreement was assessed using Bland-Altman 95% limits of agreement. The sensitivity and specificity of clinical alarms were evaluated using a Clarke Error grid modified for continuous monitoring of respiratory rate. Clinical aspects were further evaluated in terms of trend analysis and examination of gaps between valid measurements.

Results

32 patients were monitored for a median duration of 42 min [IQR (range) 35-46 (30-59 min)]. The bias was 0.1 and 0.0 breaths min-1 and the 95% limits of agreement ranged from -1.1 to 1.2 and -1.1 to 1.1 breaths min-1 for the stationary and mobile configuration, respectively. The concordances for trends were 96%. No clinical alarms were missed, and no false alarms or technical alarms were generated. No interval without a valid measurement was longer than 5 min.

Conclusion

Vitalthings Guardian M10 measured respiratory rate accurately and continuously in resting patients in a hospital ward.

The Optimized Use of a Contact-Free Continuous Monitoring System on Clinical Outcomes During COVID-19

OBJECTIVES

The purpose of this study was to examine the impact of a contact-free continuous monitoring system on clinical outcomes including unplanned intensive care unit (ICU) transfer (primary), length of stay (LOS), code blue, and mortality. A secondary aim was to evaluate the return on investment associated with implementing the contact-free continuous monitoring program during the COVID public health emergency.

METHODS

An interrupted time series evaluation was conducted to examine the association between the use of contact-free continuous monitoring and clinical outcomes. A cost-benefit analysis was planned to evaluate the return on investment.

RESULTS

Use of contact-free continuous monitoring was not significantly associated with unplanned ICU transfers, deaths, ICU LOS, and or rapid response team calls. However, there were significant increases in code blue events (P = 0.02) and mean hospital LOS (P = 0.01) in the postimplementation period when compared with the preimplementation period. Due to the lack of improvement, costs were calculated but a cost-benefit analysis was not conducted.

CONCLUSIONS

Contact-free continuous monitoring bed use during the COVID-19 public health emergency was not associated with improvements in clinical outcomes, although there was substantial confounding. Future studies should include large randomized controlled trials to control for factors not under direct experimental control including unit staffing, staff turnover, and differences in the patient population related to surges in the COVID-19 pandemic.

Discrepancies between Promised and Actual AI Capabilities in the Continuous Vital Sign Monitoring of In-Hospital Patients: A Review of the Current Evidence

Continuous vital sign monitoring (CVSM) with wireless sensors in general hospital wards can enhance patient care. An artificial intelligence (AI) layer is crucial to allow sensor data to be managed by clinical staff without over alerting from the sensors. With the aim of summarizing peer-reviewed evidence for AI support in CVSM sensors, we searched PubMed and Embase for studies on adult patients monitored with CVSM sensors in general wards. Peer-reviewed evidence and white papers on the official websites of CVSM solutions were also included. AI classification was based on standard definitions of simple AI, as systems with no memory or learning capabilities, and advanced AI, as systems with the ability to learn from past data to make decisions. Only studies evaluating CVSM algorithms for improving or predicting clinical outcomes (e.g., adverse events, intensive care unit admission, mortality) or optimizing alarm thresholds were included. We assessed the promised level of AI for each CVSM solution based on statements from the official product websites. In total, 467 studies were assessed; 113 were retrieved for full-text review, and 26 studies on four different CVSM solutions were included. Advanced AI levels were indicated on the websites of all four CVSM solutions. Five studies assessed algorithms with potential for applications as advanced AI algorithms in two of the CVSM solutions (50%), while 21 studies assessed algorithms with potential as simple AI in all four CVSM solutions (100%). Evidence on algorithms for advanced AI in CVSM is limited, revealing a discrepancy between promised AI levels and current algorithm capabilities.

Enhancing Early Detection of Sepsis in Neonates through Multimodal Biosignal Integration: A Study of Pulse Oximetry, Near-Infrared Spectroscopy (NIRS), and Skin Temperature Monitoring

Sepsis continues to be challenging to diagnose due to its non-specific clinical signs and symptoms, emphasizing the importance of early detection. Our study aimed to enhance the accuracy of sepsis diagnosis by integrating multimodal monitoring technologies with conventional diagnostic methods. The research included a total of 121 newborns, with 39 cases of late-onset sepsis, 35 cases of early-onset sepsis, and 47 control subjects. Continuous monitoring of biosignals, including pulse oximetry (PO), near-infrared spectroscopy (NIRS), and skin temperature (ST), was conducted. An algorithm was then developed in Python to identify early signs of sepsis. The model demonstrated the capability to detect sepsis 6 to 48 h in advance with an accuracy rate of 87.67 ± 7.42%. Sensitivity and specificity were recorded at 76% and 90%, respectively, with NIRS and ST having the most significant impact on predictive accuracy. Despite the promising results, limitations such as sample size, data variability, and potential biases were noted. These findings highlight the critical role of non-invasive biosensing methods in conjunction with conventional biomarkers and cultures, offering a strong foundation for early sepsis detection and improved neonatal care. Further research should be conducted to validate these results across different clinical settings.

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.

Nurse productivity: using evidence to enhance nurses' use of time

The UK is experiencing a nursing shortage, making it challenging to maintain the staffing levels required to deliver effective patient care. One way of enhancing the care delivered by the existing workforce could be to optimise nurse productivity; however, previous efforts to do this have been largely ineffective, due in part to a focus on the processes of care delivery rather than the nursing activities within these processes. In this article, the author explores the concept of nurse productivity and suggests that enhancing productivity requires the identification of nursing activities and consideration of how these may be undertaken in a more time-efficient manner - or removed altogether. The author discusses two such activities: intentional (hourly) rounding, and fixed-time manual vital signs for patients on general wards. The author also considers the potential of using automatic continuous remote monitoring on general hospital wards to free up nurses' time for other care activities.

Pro-Con Debate: Universal Versus Selective Continuous Monitoring of Postoperative Patients

In this Pro-Con commentary article, we discuss use of continuous physiologic monitoring for clinical deterioration, specifically respiratory depression in the postoperative population. The Pro position advocates for 24/7 continuous surveillance monitoring of all patients starting in the postanesthesia care unit until discharge from the hospital. The strongest arguments for universal monitoring relate to inadequate assessment and algorithms for patient risk. We argue that the need for hospitalization in and of itself is a sufficient predictor of an individual's risk for unexpected respiratory deterioration. In addition, general care units carry the added risk that even the most severe respiratory events will not be recognized in a timely fashion, largely due to higher patient to nurse staffing ratios and limited intermittent vital signs assessments (e.g., every 4 hours). Continuous monitoring configured properly using a "surveillance model" can adequately detect patients' respiratory deterioration while minimizing alarm fatigue and the costs of the surveillance systems. The Con position advocates for a mixed approach of time-limited continuous pulse oximetry monitoring for all patients receiving opioids, with additional remote pulse oximetry monitoring for patients identified as having a high risk of respiratory depression. Alarm fatigue, clinical resource limitations, and cost are the strongest arguments for selective monitoring, which is a more targeted approach. The proponents of the con position acknowledge that postoperative respiratory monitoring is certainly indicated for all patients, but not all patients need the same level of monitoring. The analysis and discussion of each point of view describes who, when, where, and how continuous monitoring should be implemented. Consideration of various system-level factors are addressed, including clinical resource availability, alarm design, system costs, patient and staff acceptance, risk-assessment algorithms, and respiratory event detection. Literature is reviewed, findings are described, and recommendations for design of monitoring systems and implementation of monitoring are described for the pro and con positions.

COVID-19 Detection From Respiratory Sounds With Hierarchical Spectrogram Transformers

Monitoring of prevalent airborne diseases such as COVID-19 characteristically involves respiratory assessments. While auscultation is a mainstream method for preliminary screening of disease symptoms, its utility is hampered by the need for dedicated hospital visits. Remote monitoring based on recordings of respiratory sounds on portable devices is a promising alternative, which can assist in early assessment of COVID-19 that primarily affects the lower respiratory tract. In this study, we introduce a novel deep learning approach to distinguish patients with COVID-19 from healthy controls given audio recordings of cough or breathing sounds. The proposed approach leverages a novel hierarchical spectrogram transformer (HST) on spectrogram representations of respiratory sounds. HST embodies self-attention mechanisms over local windows in spectrograms, and window size is progressively grown over model stages to capture local to global context. HST is compared against state-of-the-art conventional and deep-learning baselines. Demonstrations on crowd-sourced multi-national datasets indicate that HST outperforms competing methods, achieving over 90% area under the receiver operating characteristic curve (AUC) in detecting COVID-19 cases.

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.

Workload associated with manual assessment of vital signs as compared with continuous wireless monitoring

BACKGROUND

Vital sign monitoring is considered an essential aspect of clinical care in hospitals. In general wards, this relies on intermittent manual assessments performed by clinical staff at intervals of up to 12 h. In recent years, continuous monitoring of vital signs has been introduced to the clinic, with improved patient outcomes being one of several potential benefits. The aim of this study was to determine the workload difference between continuous monitoring and manual monitoring of vital signs as part of the National Early Warning Score (NEWS).

METHODS

Three wireless sensors continuously monitored blood pressure, heart rate, respiratory rate, and peripheral oxygen saturation in 20 patients admitted to the general hospital ward. The duration needed for equipment set-up and maintenance for continuous monitoring in a 24-h period was recorded and compared with the time spent on manual assessments and documentation of vital signs performed by clinical staff according to the NEWS.

RESULTS

The time used for continuous monitoring was 6.0 (IQR 3.2; 7.2) min per patient per day vs. 14 (9.7; 32) min per patient per day for the NEWS. Median difference in duration for monitoring of vital signs was 9.9 (95% CI 5.6; 21) min per patient per day between NEWS and continuous monitoring (p < .001). Time used for continuous monitoring in isolated patients was 6.6 (4.6; 12) min per patient per day as compared with 22 (9.7; 94) min per patient per day for NEWS.

CONCLUSION

The use of continuous monitoring was associated with a significant reduction in workload in terms of time for monitoring as compared with manual assessment of vital signs.

Measurement accuracy of a microwave doppler sensor beneath the mattress as a continuous respiratory rate monitor: a method comparison study

PURPOSE

Non-contact continuous respiratory rate monitoring is preferred for early detection of patient deterioration. However, this technique is under development; a gold standard respiratory monitor has not been established. Therefore, this prospective observational method comparison study aimed to compare the measurement accuracy of a non-contact continuous respiratory rate monitor, a microwave Doppler sensor positioned beneath the mattress, with that of other monitors.

METHODS

The respiratory rate of intensive care unit patients was simultaneously measured using a microwave Doppler sensor, capnography, thoracic impedance pneumography, and a piezoelectric sensor beneath the mattress. Bias and 95% limits of agreement between the respiratory rate measured using capnography (standard reference) and that measured using the other three methods were calculated using Bland-Altman analysis for repeated measures. Clarke error grid (CEG) analysis evaluated the sensor's ability to assist in correct clinical decision-making.

RESULTS

Eighteen participants were included, and 2,307 data points were analyzed. The bias values (95% limits of agreement) of the microwave Doppler sensor, thoracic impedance pneumography, and piezoelectric sensor were 0.2 (- 4.8 to 5.2), 1.5 (- 4.4 to 7.4), and 0.4 (- 4.0 to 4.8) breaths per minute, respectively. Clinical decisions evaluated using CEG analyses were correct 98.1% of the time for the microwave Doppler sensor, which was similar to the performance of the other devices.

CONCLUSION

The microwave Doppler sensor had a small bias but relatively low precision, similar to other devices. In CEG analyses, the risk of each monitor leading to inadequate clinical decision-making was low.

TRIAL REGISTRATION NUMBER

UMIN000038900, February 1, 2020.

Outcomes in patients receiving continuous monitoring of vital signs on general wards: A systematic review and meta-analysis of randomised controlled trials

Objective

The timely identification of deterioration on general wards is crucial to patient care with each hour of delay independently associated with increased risk of death. The introduction of continuous monitoring of patient vital signs on general wards, currently not standard care, may improve patient outcomes. Our aim was to investigate whether patients on general wards receiving continuous vital signs monitoring have better outcomes than patients receiving usual care.

Methods

Meta-analysis of randomised controlled trials comparing non-critical care patients receiving continuous monitoring of vital signs to usual care. We searched Medline, Embase, and Web of Science, and assessed risk of bias with version 2 of the Cochrane risk-of-bias tool for randomised trials. In addition to measures related to the early detection of deterioration, we planned to present all patient outcomes reported by the clinical trials included.

Results

We included seven trials involving 1284 participants. There were no statistically significant differences in the four outcomes pooled. Comparing continuously monitored to normal care, the pooled odds for hospital mortality, major event/complication, and HDU/ICU admission was 0.95 (95% CI 0.59-1.53, p = 0.84; 660 participants, 3 studies), 0.71 (95% CI 0.38-1.31, p = 0.27; 948 participants, 4 studies) and 0.82 (95% CI 0.25-2.67, p = 0.74; 655 participants, 4 studies), respectively. The mean difference for length of stay was 2.12 days lower (95% CI -5.56 to 1.32, p = 0.23; 1034 participants, 6 studies).

Conclusion

We found no significant improvements in outcomes for patients continuously monitored compared to usual care. Further research is needed to understand what modalities of continuous monitoring may influence outcomes and investigate the implications of a telepresence service and multi-parameter scoring system.

Registration

PROSPERO CRD42023458656.

Patient Characteristics and Clinical and Economic Outcomes Associated with Unplanned Medical and Surgical Intensive Care Unit Admissions: A Retrospective Analysis

Purpose

To characterize medical and surgical patient characteristics, as well as clinical and economic outcomes, associated with unplanned intensive care unit (ICU) admissions.

Patients and Methods

This was a retrospective matched cohort analysis that utilized the PINC AITM Healthcare Database, which collects deidentified data from 25% of United States (US) hospital admissions. Discharge records were assessed for medical and surgical admissions in 2021. An unplanned ICU admission was defined as direct transfer from a medical, surgical, or telemetry unit to the ICU. Patients with and without an unplanned ICU admission were 1:1 propensity score matched. Differences between patients with and without unplanned ICU admissions were assessed using two-sample t-tests for continuous measures and Chi-square tests for categorical measures.

Results

A total of 3,807,124 qualifying admissions were identified. Medical admissions with unplanned ICU transfers were more likely to be urgent/emergent (odds ratio [OR] 2.9, 95% confidence interval [CI 2.7-3.0], p<0.0001), with patient characteristics including male sex (1.4, [1.4-1.4], p<0.0001), obesity (1.7, [1.6-1.7], p<0.0001), and increased Charlson Comorbidity Index (CCI=1: 1.8, [1.8-1.9], p<0.0001; CCI≥5: 3.2, [3.1-3.3], p<0.0001). Surgical admissions with unplanned ICU transfers were more likely to be urgent/emergent (3.1, [2.9-3.2], p<0.0001) and with patients of higher CCI (2.5, [2.3-2.6], p<0.0001 to a CCI of≥5 (7.9, [7.4-8.4], p<0.0001). Between matched medical patients, mean differences in length of stay, cost, and mortality were 4.1 days (p<0.0001), $13,424 (p<0.0001), and 21% (p<0.0001), respectively. Between matched surgical patients, mean differences in these outcomes were 6.4 days (p<0.0001), $21,448 (p<0.0001), and 14% (p<0.0001), respectively.

Conclusion

Emergency care in patients with a higher co-morbid burden is more likely to lead to unplanned ICU admission, putting patients at a significantly increased chance of mortality, longer length of stay, and increased costs. Improving care and monitoring of patients outside the ICU may help detect early changes in pathophysiology and enable early intervention.

Development and validation of the early warning system scores ontology

BACKGROUND

Clinical early warning scoring systems, have improved patient outcomes in a range of specializations and global contexts. These systems are used to predict patient deterioration. A multitude of patient-level physiological decompensation data has been made available through the widespread integration of early warning scoring systems within EHRs across national and international health care organizations. These data can be used to promote secondary research. The diversity of early warning scoring systems and various EHR systems is one barrier to secondary analysis of early warning score data. Given that early warning score parameters are varied, this makes it difficult to query across providers and EHR systems. Moreover, mapping and merging the parameters is challenging. We develop and validate the Early Warning System Scores Ontology (EWSSO), representing three commonly used early warning scores: the National Early Warning Score (NEWS), the six-item modified Early Warning Score (MEWS), and the quick Sequential Organ Failure Assessment (qSOFA) to overcome these problems.

METHODS

We apply the Software Development Lifecycle Framework-conceived by Winston Boyce in 1970-to model the activities involved in organizing, producing, and evaluating the EWSSO. We also follow OBO Foundry Principles and the principles of best practice for domain ontology design, terms, definitions, and classifications to meet BFO requirements for ontology building.

RESULTS

We developed twenty-nine new classes, reused four classes and four object properties to create the EWSSO. When we queried the data our ontology-based process could differentiate between necessary and unnecessary features for score calculation 100% of the time. Further, our process applied the proper temperature conversions for the early warning score calculator 100% of the time.

CONCLUSIONS

Using synthetic datasets, we demonstrate the EWSSO can be used to generate and query health system data on vital signs and provide input to calculate the NEWS, six-item MEWS, and qSOFA. Future work includes extending the EWSSO by introducing additional early warning scores for adult and pediatric patient populations and creating patient profiles that contain clinical, demographic, and outcomes data regarding the patient.

Predicting ICU admission and death in the Emergency Department: A comparison of six early warning scores

AIM

To compare the ability of the most used Early Warning Scores (EWS) to identify adult patients at risk of poor outcomes in the emergency department (ED).

METHODS

Single-center, retrospective observational study. We evaluated the digital records of consecutive ED admissions in patients ≥ 18 years from 2010 to 2019 and calculated NEWS, NEWS2, MEWS, RAPS, REMS, and SEWS based on parameters measured on ED arrival. We assessed the discrimination and calibration performance of each EWS in predicting death/ICU admission within 24 hours using ROC analysis and visual calibration. We also measured the relative weight of clinical and physiological derangements that identified patients missed by EWS risk stratification using neural network analysis.

RESULTS

Among 225,369 patients assessed in the ED during the study period, 1941 (0.9%) were admitted to ICU or died within 24 hours. NEWS was the most accurate predictor (area under the receiver operating characteristic [AUROC] curve 0.904 [95% CI 0.805-0.913]), followed by NEWS2 (AUROC 0.901). NEWS was also well calibrated. In patients judged at low risk (NEWS < 2), 359 events occurred (18.5% of the total). Neural network analysis revealed that age, systolic BP, and temperature had the highest relative weight for these NEWS-unpredicted events.

CONCLUSIONS

NEWS is the most accurate EWS for predicting the risk of death/ICU admission within 24 h from ED arrival. The score also had a fair calibration with few events occurring in patients classified at low risk. Neural network analysis suggests the need for further improvements by focusing on the prompt diagnosis of sepsis and the development of practical tools for the measurement of the respiratory rate.

Systems-Based Physical Assessments: Earlier Detection of Clinical Deterioration and Reduced Mortality

BACKGROUND

Despite efforts to improve early detection of deterioration in a patient's condition, delays in activating the rapid response team remain common.

OBJECTIVES

To evaluate delays in activating the rapid response team and the occurrence of serious adverse events before and after implementation of a quality improvement initiative aimed at nurses' performing systems-based physical assessments.

METHODS

A retrospective observational cohort design was used to evaluate all patients who had a rapid response team activation during the study period.

RESULTS

A total of 1080 patients were included in the analysis: 536 patients before the quality improvement initiative and 544 patients after the quality improvement initiative. The delay in activating the rapid response team decreased from 11.7 hours in the before group to 9.6 hours in the after group (P < .001). In the after group, fewer patients were transferred to the intensive care unit (36% vs 41%, P = .02) and those who were transferred had 3.58 times greater odds of death than those who stayed at the same level of care. The after group had a 44% reduction in the odds of mortality compared with the before group.

CONCLUSIONS

When nurses focus on conducting a systems-based physical assessment early in their shift, delays in recognizing a patient's deteriorating condition are reduced, fewer patients are admitted to the intensive care unit, and mortality is significantly reduced.

Continuous monitoring of patients in and after the acute admission ward to improve clinical pathways: study protocol for a randomized controlled trial (Optimal-AAW)

BACKGROUND

Because of high demand on hospital beds, hospitals seek to reduce patients' length of stay (LOS) while preserving the quality of care. In addition to usual intermittent vital sign monitoring, continuous monitoring might help to assess the patient's risk of deterioration, in order to improve the discharge process and reduce LOS. The primary aim of this monocenter randomized controlled trial is to assess the effect of continuous monitoring in an acute admission ward (AAW) on the percentage of patients who are discharged safely.

METHODS

A total of 800 patients admitted to the AAW, for whom it is equivocal whether they can be discharged directly after their AAW stay, will be randomized to either receive usual care without (control group) or with additional continuous monitoring of heart rate, respiratory rate, posture, and activity, using a wearable sensor (sensor group). Continuous monitoring data are provided to healthcare professionals and used in the discharge decision. The wearable sensor keeps collecting data for 14 days. After 14 days, all patients fill in a questionnaire to assess healthcare use after discharge and, if applicable, their experience with the wearable sensor. The primary outcome is the difference in the percentage of patients who are safely discharged home directly from the AAW between the control and sensor group. Secondary outcomes include hospital LOS, AAW LOS, intensive care unit (ICU) admissions, Rapid Response Team calls, and unplanned readmissions within 30 days. Furthermore, facilitators and barriers for implementing continuous monitoring in the AAW and at home will be investigated.

DISCUSSION

Clinical effects of continuous monitoring have already been investigated in specific patient populations for multiple purposes, e.g., in reducing the number of ICU admissions. However, to our knowledge, this is the first Randomized Controlled Trial to investigate effects of continuous monitoring in a broad patient population in the AAW.

TRIAL REGISTRATION

https://clinicaltrials.gov/ct2/show/NCT05181111 . Registered on 6 January 2022. Start of recruitment: 7 December 2021.

Comparing Continuous with Periodic Vital Sign Scoring for Clinical Deterioration Using a Patient Data Model

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.

Remote Home Monitoring of Continuous Vital Sign Measurements by Wearables in Patients Discharged After Colorectal Surgery: Observational Feasibility Study

BACKGROUND

Hospital stays after colorectal surgery are increasingly being reduced by enhanced recovery and early discharge protocols. As a result, postoperative complications may frequently manifest after discharge in the home setting, potentially leading to emergency room presentations and readmissions. Virtual care interventions after hospital discharge may capture clinical deterioration at an early stage and hold promise for the prevention of readmissions and overall better outcomes. Recent technological advances have enabled continuous vital sign monitoring by wearable wireless sensor devices. However, the potential of these devices for virtual care interventions for patients discharged after colorectal surgery is currently unknown.

OBJECTIVE

We aimed to determine the feasibility of a virtual care intervention consisting of continuous vital sign monitoring with wearable wireless sensors and teleconsultations for patients discharged after colorectal surgery.

METHODS

In a single-center observational cohort study, patients were monitored at home for 5 consecutive days after discharge. Daily vital sign trend assessments and telephone consultations were performed by a remote patient-monitoring department. Intervention performance was evaluated by analyzing vital sign trend assessments and telephone consultation reports. Outcomes were categorized as "no concern," "slight concern," or "serious concern." Serious concern prompted contact with the surgeon on call. In addition, the quality of the vital sign data was determined, and the patient experience was evaluated.

RESULTS

Among 21 patients who participated in this study, 104 of 105 (99%) measurements of vital sign trends were successful. Of these 104 vital sign trend assessments, 68% (n=71) did not raise any concern, 16% (n=17) were unable to be assessed because of data loss, and none led to contacting the surgeon. Of 62 of 63 (98%) successfully performed telephone consultations, 53 (86%) did not raise any concerns and only 1 resulted in contacting the surgeon. A 68% agreement was found between vital sign trend assessments and telephone consultations. Overall completeness of the 2347 hours of vital sign trend data was 46.3% (range 5%-100%). Patient satisfaction score was 8 (IQR 7-9) of 10.

CONCLUSIONS

A home monitoring intervention of patients discharged after colorectal surgery was found to be feasible, given its high performance and high patient acceptability. However, the intervention design needs further optimization before the true value of remote monitoring for early discharge protocols, prevention of readmissions, and overall patient outcomes can be adequately determined.

The Effect of Continuous Versus Periodic Vital Sign Monitoring on Disease Severity of Patients with an Unplanned ICU Transfer

Continuous vital sign monitoring (CM) may detect ward patient's deterioration earlier than periodic monitoring. This could result in timely ICU transfers or in a transfer delay due to misperceived higher level of care on the ward. The primary objective of this study was to compare patient's disease severity upon unplanned ICU transfer, before and after CM implementation. We included a one-year period before and after CM implementation between August 1, 2017 - July 31, 2019. Before implementation, surgical and internal medicine patients' vital signs were periodically monitored, compared to continuous monitoring with wireless linkage to hospital systems after implementation. In both periods the same early warning score (EWS) protocol was in place. Primary outcome was disease severity scores upon ICU transfer. Secondary outcomes were ICU and hospital length of stay, incidence of mechanical ventilation and ICU mortality. In the two one-year periods 93 and 59 unplanned ICU transfer episodes were included, respectively. Median SOFA (3 (2-6) vs 4 (2-7), p = .574), APACHE II (17 (14-20) vs 16 (14-21), p = .824) and APACHE IV (59 (46-67) vs 50 (36-65), p = .187) were comparable between both periods, as were the median ICU LOS (3.0 (1.7-5.8) vs 3.1 (1.6-6.1), p.962), hospital LOS (23.6 (11.5-38.0) vs 19 (13.9-39.2), p = .880), incidence of mechanical ventilation (28 (47%) vs 22 (54%), p.490), and ICU mortality (11 (13%) vs 10 (19%), p.420). This study shows no difference in disease severity upon unplanned ICU transfer after CM implementation for patients who have deteriorated on the ward.

Continuous vital sign monitoring in patients after elective abdominal surgery: a retrospective study on clinical outcomes and costs

Aim: To assess changes in outcomes and costs upon implementation of continuous vital sign monitoring in postsurgical patients. Materials & methods: Retrospective analysis of clinical outcomes and in-hospital costs compared with a control period. Results: During the intervention period patients were less frequently admitted to the intensive care unit (ICU) (p = 0.004), had shorter length of stay (p < 0.001) and lower costs (p < 0.001). The intervention was associated with a lower odds of ICU admission (odds ratio: 0.422; p = 0.007) and ICU related costs (odds ratio: -662.4; p = 0.083). Conclusion: Continuous vital sign monitoring may have contributed to fewer ICU admissions and lower ICU costs in postsurgical patients.

The impact of continuous wireless monitoring on adverse device effects in medical and surgical wards: a review of current evidence

Novel technologies allow continuous wireless monitoring systems (CWMS) to measure vital signs and these systems might be favorable compared to intermittent monitoring regarding improving outcomes. However, device safety needs to be validated because uncertain evidence challenges the clinical implementation of CWMS. This review investigates the frequency of device-related adverse events in patients monitored with CWMS in general hospital wards. Systematic literature searches were conducted in PubMed and Embase. We included trials of adult patients in general hospital wards monitored with CWMS. Our primary outcome was the frequency of unanticipated serious adverse device effects (USADEs). Secondary outcomes were adverse device effects (ADEs) and serious adverse device effects (SADE). Data were extracted from eligible studies and descriptive statistics were applied to analyze the data. Seven studies were eligible for inclusion with a total of 1485 patients monitored by CWMS. Of these patients, 54 patients experienced ADEs (3.6%, 95% CI 2.8-4.7%) and no USADEs or SADEs were reported (0%, 95% CI 0-0.31%). The studies of the SensiumVitals® patch, the iThermonitor, and the ViSi Mobile® device reported 28 (9%), 25 (5%), and 1 (3%) ADEs, respectively. No ADEs were reported using the HealthPatch, WARD 24/7 system, or Coviden Alarm Management. Current evidence suggests that CWMS are safe to use but systematic reporting of all adverse device effects is warranted.

Continuous wireless pre- and postoperative vital sign monitoring reveal new, severe desaturations after vascular surgery

OBJECTIVES

Postoperative deviating physiologic values (vital signs) may represent postoperative stress or emerging complications. But they can also reflect chronic preoperative values. Distinguishing between the two circumstances may influence the utility of using vital signs in patient monitoring. Thus, we aimed to describe the occurrence of vital sign deviations before and after major vascular surgery, hypothesising that preoperative vital sign deviations were longer in duration postoperatively.

METHODS

In this prospective observational study, arterial vascular patients were continuously monitored wirelessly - from the day before until 5 days after surgery. Recorded values were: heart rate, respiration rate, peripheral arterial oxygen saturation (SpO₂ ) and blood pressure. The outcomes were 1. cumulative duration of SpO₂  < 85% / 24 h, and 2. cumulative duration per 24 h of vital sign deviations.

RESULTS

Forty patients were included with a median monitoring time of 21 h preoperatively and 42 h postoperatively. The median duration of SpO₂  < 85% preoperatively was 14.4 min/24 h whereas it was 28.0 min/24 h during day 0 in the ward (p = .09), and 16.8 min/24 h on day 1 in the ward (p = 0.61). Cumulative duration of SpO₂  < 80% was significantly longer on day 0 in the ward 2.4 min/24 h (IQR 0.0-4.6) versus 6.7 min/24 h (IQR 1.8-16.2) p = 0.01.

CONCLUSION

Deviating physiology is common in patients before and after vascular surgery. A longer duration of severe desaturation was found on the first postoperative day in the ward compared to preoperatively, whereas moderate desaturations were reflected in postoperative desaturations. Cumulative duration outside thresholds is, in some cases, exacerbated after surgery.

Advantage of Vital Sign Monitoring Using a Wireless Wearable Device for Predicting Septic Shock in Febrile Patients in the Emergency Department: A Machine Learning-Based Analysis

Intermittent manual measurement of vital signs may not rapidly predict sepsis development in febrile patients admitted to the emergency department (ED). We aimed to evaluate the predictive performance of a wireless monitoring device that continuously measures heart rate (HR) and respiratory rate (RR) and a machine learning analysis in febrile but stable patients in the ED. We analysed 468 patients (age, ≥18 years; training set, n = 277; validation set, n = 93; test set, n = 98) having fever (temperature >38 °C) and admitted to the isolation care unit of the ED. The AUROC of the fragmented model with device data was 0.858 (95% confidence interval [CI], 0.809−0.908), and that with manual data was 0.841 (95% CI, 0.789−0.893). The AUROC of the accumulated model with device data was 0.861 (95% CI, 0.811−0.910), and that with manual data was 0.853 (95% CI, 0.803−0.903). Fragmented and accumulated models with device data detected clinical deterioration in febrile patients at risk of septic shock 9 h and 5 h 30 min earlier, respectively, than those with manual data. Continuous vital sign monitoring using a wearable device could accurately predict clinical deterioration and reduce the time to recognise potential clinical deterioration in stable ED patients with fever.

The Use of Wearable Pulse Oximeters in the Prompt Detection of Hypoxemia and During Movement: Diagnostic Accuracy Study

Background

Commercially available wearable (ambulatory) pulse oximeters have been recommended as a method for managing patients at risk of physiological deterioration, such as active patients with COVID-19 disease receiving care in hospital isolation rooms; however, their reliability in usual hospital settings is not known.

Objective

We report the performance of wearable pulse oximeters in a simulated clinical setting when challenged by motion and low levels of arterial blood oxygen saturation (SaO₂).

Methods

The performance of 1 wrist-worn (Wavelet) and 3 finger-worn (CheckMe O2+, AP-20, and WristOx2 3150) wearable, wireless transmission–mode pulse oximeters was evaluated. For this, 7 motion tasks were performed: at rest, sit-to-stand, tapping, rubbing, drinking, turning pages, and using a tablet. Hypoxia exposure followed, in which inspired gases were adjusted to achieve decreasing SaO₂ levels at 100%, 95%, 90%, 87%, 85%, 83%, and 80%. Peripheral oxygen saturation (SpO₂) estimates were compared with simultaneous SaO₂ samples to calculate the root-mean-square error (RMSE). The area under the receiver operating characteristic curve was used to analyze the detection of hypoxemia (ie, SaO₂<90%).

Results

SpO₂ estimates matching 215 SaO₂ samples in both study phases, from 33 participants, were analyzed. Tapping, rubbing, turning pages, and using a tablet degraded SpO₂ estimation (RMSE>4% for at least 1 device). All finger-worn pulse oximeters detected hypoxemia, with an overall sensitivity of ≥0.87 and specificity of ≥0.80, comparable to that of the Philips MX450 pulse oximeter.

Conclusions

The SpO₂ accuracy of wearable finger-worn pulse oximeters was within that required by the International Organization for Standardization guidelines. Performance was degraded by motion, but all pulse oximeters could detect hypoxemia. Our findings support the use of wearable, wireless transmission–mode pulse oximeters to detect the onset of clinical deterioration in hospital settings.

Trial Registration

ISRCTN Registry 61535692; http://www.isrctn.com/ISRCTN61535692

International Registered Report Identifier (IRRID)

RR2-10.1136/bmjopen-2019-034404

Discovery of signatures of fatal neonatal illness in vital signs using highly comparative time-series analysis

To seek new signatures of illness in heart rate and oxygen saturation vital signs from Neonatal Intensive Care Unit (NICU) patients, we implemented highly comparative time-series analysis to discover features of all-cause mortality in the next 7 days. We collected 0.5 Hz heart rate and oxygen saturation vital signs of infants in the University of Virginia NICU from 2009 to 2019. We applied 4998 algorithmic operations from 11 mathematical families to random daily 10 min segments from 5957 NICU infants, 205 of whom died. We clustered the results and selected a representative from each, and examined multivariable logistic regression models. 3555 operations were usable; 20 cluster medoids held more than 81% of the information, and a multivariable model had AUC 0.83. New algorithms outperformed others: moving threshold, successive increases, surprise, and random walk. We computed provenance of the computations and constructed a software library with links to the data. We conclude that highly comparative time-series analysis revealed new vital sign measures to identify NICU patients at the highest risk of death in the next week.

Accuracy and reliability of a wireless vital signs monitor for hospitalized patients in a low-resource setting

Objective

The purpose of this study was to evaluate the accuracy and reliability of neoGuard in comparison to a conventional bedside monitor on patients in a low-resource clinical setting.

Design

This was a single-arm methods comparison study involving the use of a wearable vital signs monitor (neoGuardTM) versus a conventional bedside monitor (Edan iM8).

Setting

The study was conducted at Jinja Regional Referral Hospital, a tertiary care hospital situated in Eastern Uganda.

Participants

Thirty patients (10 male, 20 female) were enrolled from the adult recovery ward at JRRH. Participants were eligible for the study if they were at least 18 years of age, had 2 sets of normal vital sign measurements obtained 1 h apart, and were able and willing to provide informed consent.

Main Outcome and Measures

The primary outcome measures were (i) bias (mean deviation) and (ii) limits of agreement [95% CI]. Bland-Altman plots were generated to illustrate the level of agreement between the neoGuardTM technology and the Edan iM8 monitor.

Results

Bland-Altman analysis was performed for 24 participants; datasets from six participants were excluded due to missing or invalid measurements. Findings showed a moderate level of agreement for measurement of SpO2, PR, and RR, with >80% of subject means falling within the predefined acceptability limits. However, there was also notable variation in accuracy between subjects, with large standard deviations observed for measurement of all four parameters. While the level of agreement for measurement of temperature was low, this is partly explained by limitations in the comparison method.

Continuous monitoring of vital signs with the Everion biosensor on the surgical ward: a clinical validation study

BACKGROUND

Wearable sensors enable continuous vital sign monitoring, although information about their performance on nursing wards is scarce. Vital signs measured by telemonitoring and nurse measurements on a surgical ward were compared to assess validity and reliability.

METHODS

In a prospective observational study, surgical patients wore a wearable sensor (Everion, Biovotion AG, Zürich, Switzerland) that continuously measured heart rate (HR), respiratory rate (RR), oxygen saturation (SpO₂), and temperature during their admittance on the ward. Validity was evaluated using repeated-measures correlation and reliability using Bland-Altman plots, mean difference, and 95% limits of agreement (LoA).

RESULTS

Validity analyses of 19 patients (median age, 68; interquartile range, 62.5-72.5 years) showed a moderate relationship between telemonitoring and nurse measurements for HR (r = 0.53; 95% confidence interval, 0.44-0.61) and a poor relationship for RR, SpO₂, and temperature. Reliability analyses showed that Everion measured HR close to nurse measurements (mean difference, 1 bpm; LoA, -16.7 to 18.7 bpm). Everion overestimated RR at higher values, whereas SpO₂ and temperature were underestimated.

CONCLUSIONS

A moderate relationship was determined between Everion and nurse measurements at a surgical ward in this study. Validity and reliability of telemonitoring should also be assessed with gold standard devices in future clinical trials.

Experiences of current vital signs monitoring practices and views of wearable monitoring: A qualitative study in patients and nurses

Aims

To understand current experiences of vital signs monitoring of patients and clinical staff on a surgical ward, and views on the introduction of wearable ambulatory monitoring into the general ward environment.

Design

Qualitative study.

Methods

Semi‐structured interviews using topic guides were conducted with 15 patients and 15 nurses on a surgical ward between July 2018 and August 2019. The concept of ambulatory wearable devices for clinical monitoring was introduced at the end of the interview.

Results

Three interconnected themes were identified. Vital sign data as evidence for escalation, examined nurses' use of data to support escalation of care and the implications for patients perceived to be deteriorating who have not reached the threshold for escalation. The second theme, Trustworthiness of vital sign data, described nurses’ practice of using manual measurements to recheck or confirm automated vital signs readings when concerned. The final theme, finding a balance between continuous and intermittent monitoring, both patients and nurses agreed that although continuous monitoring may improve safety and reassurance, these needed to be balanced with multiple limitations. Factors to be considered included noise pollution, comfort, and impact on patient mobility and independence. Introduction of the concept of ambulatory wearable devices was viewed positively by both groups as offering solutions to some of the issues identified with traditional monitoring. However, most agreed that this would not be suitable for all patients and should not replace direct nurse/patient contact.

Conclusion

Both patients and staff identified the benefits of continuous monitoring to improve patient safety but, due to limitations, use should be carefully considered and patient‐centred.

Impact

Feedback from nurses and patients suggests there is scope for ambulatory monitoring systems to be integrated into the hospital environment; however, both groups emphasized these should not add more noise to the ward nor replace direct nursing contact.

The impact of wearable continuous vital sign monitoring on deterioration detection and clinical outcomes in hospitalised patients: a systematic review and meta-analysis

BACKGROUND

Timely recognition of the deteriorating inpatient remains challenging. Wearable monitoring systems (WMS) may augment current monitoring practices. However, there are many barriers to implementation in the hospital environment, and evidence describing the clinical impact of WMS on deterioration detection and patient outcome remains unclear.

OBJECTIVE

To assess the impact of vital-sign monitoring on detection of deterioration and related clinical outcomes in hospitalised patients using WMS, in comparison with standard care.

METHODS

A systematic search was conducted in August 2020 using MEDLINE, Embase, CINAHL, Cochrane Database of Systematic Reviews, CENTRAL, Health Technology Assessment databases and grey literature. Studies comparing the use of WMS against standard care for deterioration detection and related clinical outcomes in hospitalised patients were included. Deterioration related outcomes (primary) included unplanned intensive care admissions, rapid response team or cardiac arrest activation, total and major complications rate. Other clinical outcomes (secondary) included in-hospital mortality and hospital length of stay. Exploratory outcomes included alerting system parameters and clinical trial registry information.

RESULTS

Of 8706 citations, 10 studies with different designs met the inclusion criteria, of which 7 were included in the meta-analyses. Overall study quality was moderate. The meta-analysis indicated that the WMS, when compared with standard care, was not associated with significant reductions in intensive care transfers (risk ratio, RR 0.87; 95% confidence interval, CI 0.66-1.15), rapid response or cardiac arrest team activation (RR 0.84; 95% CI 0.69-1.01), total (RR 0.77; 95% CI 0.44-1.32) and major (RR 0.55; 95% CI 0.24-1.30) complications prevalence. There was also no statistically significant association with reduced mortality (RR 0.48; 95% CI 0.18-1.29) and hospital length of stay (mean difference, MD - 0.09; 95% CI - 0.43 to 0.44).

CONCLUSION

This systematic review indicates that there is no current evidence that implementation of WMS impacts early deterioration detection and associated clinical outcomes, as differing design/quality of available studies and diversity of outcome measures make it difficult to reach a definite conclusion. Our narrative findings suggested that alarms should be adjusted to minimise false alarms and promote rapid clinical action in response to deterioration. PROSPERO Registration number: CRD42020188633 .

A Chest Patch for Continuous Vital Sign Monitoring: Clinical Validation Study During Movement and Controlled Hypoxia

BACKGROUND

The standard of care in general wards includes periodic manual measurements, with the data entered into track-and-trigger charts, either on paper or electronically. Wearable devices may support health care staff, improve patient safety, and promote early deterioration detection in the interval between periodic measurements. However, regulatory standards for ambulatory cardiac monitors estimating heart rate (HR) and respiratory rate (RR) do not specify performance criteria during patient movement or clinical conditions in which the patient's oxygen saturation varies. Therefore, further validation is required before clinical implementation and deployment of any wearable system that provides continuous vital sign measurements.

OBJECTIVE

The objective of this study is to determine the agreement between a chest-worn patch (VitalPatch) and a gold standard reference device for HR and RR measurements during movement and gradual desaturation (modeling a hypoxic episode) in a controlled environment.

METHODS

After the VitalPatch and gold standard devices (Philips MX450) were applied, participants performed different movements in seven consecutive stages: at rest, sit-to-stand, tapping, rubbing, drinking, turning pages, and using a tablet. Hypoxia was then induced, and the participants' oxygen saturation gradually reduced to 80% in a controlled environment. The primary outcome measure was accuracy, defined as the mean absolute error (MAE) of the VitalPatch estimates when compared with HR and RR gold standards (3-lead electrocardiography and capnography, respectively). We defined these as clinically acceptable if the rates were within 5 beats per minute for HR and 3 respirations per minute (rpm) for RR.

RESULTS

Complete data sets were acquired for 29 participants. In the movement phase, the HR estimates were within prespecified limits for all movements. For RR, estimates were also within the acceptable range, with the exception of the sit-to-stand and turning page movements, showing an MAE of 3.05 (95% CI 2.48-3.58) rpm and 3.45 (95% CI 2.71-4.11) rpm, respectively. For the hypoxia phase, both HR and RR estimates were within limits, with an overall MAE of 0.72 (95% CI 0.66-0.78) beats per minute and 1.89 (95% CI 1.75-2.03) rpm, respectively. There were no significant differences in the accuracy of HR and RR estimations between normoxia (≥90%), mild (89.9%-85%), and severe hypoxia (<85%).

CONCLUSIONS

The VitalPatch was highly accurate throughout both the movement and hypoxia phases of the study, except for RR estimation during the two types of movements. This study demonstrated that VitalPatch can be safely tested in clinical environments to support earlier detection of cardiorespiratory deterioration.

TRIAL REGISTRATION

ISRCTN Registry ISRCTN61535692; https://www.isrctn.com/ISRCTN61535692.

Protocol for a systematic review assessing ambulatory vital sign monitoring impact on deterioration detection and related clinical outcomes in hospitalised patients

INTRODUCTION

Ambulatory monitoring systems (AMS) can facilitate early detection of clinical deterioration, and have the potential to improve hospitalised patient outcomes. The objective of this systematic review is to assess the impact of vital signs monitoring on detection of deterioration and related outcomes in hospitalised patients using AMS, in comparison with standard care.

METHODS AND ANALYSIS

A systematic search was conducted on 27 August 2020 in MEDLINE, Embase, CINAHL, Cochrane Database of Systematic Reviews, CENTRAL and Health Technology Assessment databases, as well as grey literature. Search results will be reviewed in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis checklist for systematic reviews. Studies comparing the use of ambulatory monitoring devices against standard care for deterioration detection and related clinical outcomes in hospitalised patients will be included and further clinical and other outcomes will also be explored. Deterioration-related outcomes may include (but not limited to) unplanned intensive care admissions, rapid response team activation and unscheduled emergency interventions, as defined by the included studies. Two reviewers will independently extract study data and assess the quality and risk of bias of included studies. Where possible, a meta-analysis will be conducted and quantitative results presented. Alternatively, a narrative synthesis will be reported.

ETHICS AND DISSEMINATION

Ethical approval is not required for this study as no primary data will be collected. This study is part of our virtual High Dependency Unit project and will be disseminated through peer-reviewed publications, public and scientific conference presentations.

PROSPERO REGISTRATION NUMBER

CRD42020188633.

How long do nursing staff take to measure and record patients' vital signs observations in hospital? A time-and-motion study

Introduction

Monitoring vital signs in hospital is an important part of safe patient care. However, there are no robust estimates of the workload it generates for nursing staff. This makes it difficult to plan adequate staffing to ensure current monitoring protocols can be delivered.

Objective

To estimate the time taken to measure and record one set of patient's vital signs; and to identify factors associated with the time required to measure and record one set of patient's vital signs.

Methods

We undertook a time-and-motion study of 16 acute medical or surgical wards across four hospitals in England. Two trained observers followed a standard operating procedure to record the time taken to measure and record vital signs. We used mixed-effects models to estimate the mean time using whole vital signs rounds, which included equipment preparation, time spent taking vital signs at the bedside, vital signs documentation, and equipment storing. We tested whether our estimates were influenced by nurse, ward and hospital factors.

Results

After excluding non-vital signs related interruptions, dividing the length of a vital signs round by the number of vital signs assessments in that round yielded an estimated time per vital signs set of 5 min and 1 second (95% Confidence Interval (CI) = 4:39–5:24). If interruptions within the round were included, the estimated time was 6:26 (95% CI = 6:01–6:50). If only time taking each patient's vital signs at the bedside was considered, after excluding non-vital signs related interruptions, the estimated time was 3:45 (95% CI = 3:32–3:58). We found no substantial differences by hospital, ward or nurse characteristics, despite different systems for recording vital signs being used across the hospitals.

Discussion

The time taken to observe and record a patient's vital signs is considerable, so changes to recommended assessment frequency could have major workload implications. Variation in estimates derived from previous studies may, in part, arise from a lack of clarity about what was included in the reported times. We found no evidence that nurses save time when using electronic vital signs recording, or that the grade of staff measuring the vital signs influenced the time taken.

Conclusions

Measuring and recording vital signs is time consuming and the impact of interruptions and preparation away from the bedside is considerable. When considering the nursing workload around vital signs assessment, no assumption of relative efficiency should be made if different technologies or staff groups are deployed.