Automated continuous noninvasive ward monitoring: future directions and challenges

The future of artificial intelligence in cardiovascular monitoring

PURPOSE OF REVIEW

Cardiovascular monitoring is essential for managing hemodynamic instability and preventing complications in critically ill patients. Conventional monitoring approaches are limited by predefined thresholds, dependence on clinician expertise, and a lack of adaptability to individual patients. The aim of this review is to explore recent findings about the use of artificial intelligence (AI) in cardiovascular monitoring.

RECENT FINDINGS

AI has the potential to transform monitoring in critical care through the automated real-time analysis of extensive, high-resolution datasets, and can facilitate early detection of patient deterioration, minimize false alarms, and support patient clustering for tailored therapeutic strategies. These innovations facilitate a shift toward precision medicine, tailoring treatments based on physiological and temporal data patterns. Moreover, wearable devices can further enhance real-time patient surveillance and risk stratification, extending intensivist monitoring beyond the ICU. Despite advantages, challenges persist, including algorithm generalizability, issues with patient consent and data privacy, and the current lack of external validation. Overcoming these barriers is essential for realizing the full potential of AI in critical care and hemodynamic monitoring.

SUMMARY

The integration of continuous high-resolution monitoring with AI real-time applications has the potential to transform hemodynamic assessment, enhance clinical decision-making, and improve safety and clinical outcomes.

Impact on Patient Outcomes of Continuous Vital Sign Monitoring on Medical Wards: Propensity-Matched Analysis

BACKGROUND

Continuous and wireless vital sign (VS) monitoring on hospital wards is superior to intermittent VS monitoring at detecting VS abnormalities; however, the impact on clinical outcomes remains to be confirmed. A recent propensity-matched study of primary surgical patients found decreased odds of intensive care unit (ICU) admission and mortality in patients receiving continuous monitoring. Primary surgical patients are inherently different from their medical counterparts who typically have high morbidity, including frailty. Continuous monitoring research has been limited in primary medical patients.

OBJECTIVE

This study aims to evaluate the clinical outcomes of primary medical patients who received either continuous or, as a contemporaneous control, intermittent vital monitoring as the standard of care using propensity matching.

METHODS

Propensity-matched analysis of a population-based sample of 7971 patients admitted to the medical wards between January 2018 and December 2019 at a single, tertiary United States medical center. The continuous monitoring device measures oxygen saturation, heart rate, respiratory rate, continuous noninvasive blood pressure, and either 3-lead or 5-lead electrocardiogram. Patients received either 12 hours or more of continuous and wireless VS monitoring (n=1450) or intermittent VS monitoring (n=6521). The primary outcome was the odds of a composite of in-hospital mortality or ICU transfer during hospitalization. Secondary outcomes were the odds of individual components of the primary outcome, as well as heart failure (HF), myocardial infarction (MI), acute kidney injury (AKI), and rapid response team (RRT) activations.

RESULTS

Those who received intermittent VS monitoring had greater odds of a composite of in-hospital mortality or ICU admission (odds ratio [OR] 2.79, 95% CI 1.89-4.25; P<.001) compared with those who had continuous and wireless VS monitoring. The odds of HF (OR 1.03, 95% CI 0.83-1.28; P=.77), MI (OR 1.58, 95% CI 0.77-3.47; P=.23), AKI (OR 0.74, 95% CI 0.62-1.02; P=.06), and RRT activation (OR 0.94, 95% CI 0.75-1.19; P=.62) were similar in both groups.

CONCLUSIONS

In this propensity-matched study, medical ward patients who received standard of care intermittent VS monitoring were at nearly 3 times greater odds of transfer to the ICU or death compared with those who received continuous VS monitoring. Our study was primarily limited by the inability to match patients on admission diagnosis due to limitations in electronic health record data. Other limitations included the number of and reasons for false alarms, which can be challenging with continuous monitoring strategies. Given the limitations of this work, these observations need to be confirmed with prospective interventional trials.

Continuous vital sign monitoring of patients recovering from surgery on general wards: a narrative review

Most postoperative deaths occur on general wards, often linked to complications associated with untreated changes in vital signs. Monitoring in these units is typically intermittent checks each shift or maximally every 4-6 h, which misses prolonged periods of subtle changes in physiology that can herald a critical downstream event. Continuous monitoring of vital signs is therefore intuitively necessary for patient safety. The past five decades have seen monitoring systems evolve rapidly, and today entirely wireless, wearable, and portable continuous surveillance of vital signs is possible on general wards. Introduction of this technology has the potential to modify both the sensing (afferent) and response (efferent) limbs of monitoring, and will allow earlier detection of vital signs perturbations. But this comes with challenges, including but not limited to issues with connectivity, data handling, alarm fatigue, information overload, and lack of meaningful clinical interventions. Evidence from before and after studies and retrospective propensity-matched data suggests that continuous ward monitoring decreases the risk of intensive care unit (ICU) admissions, rapid response calls, and in some instances, mortality. This review summarises the history of general ward monitoring and describes future directions, including opportunities to implement these devices using artificial intelligence, pattern detection, and user-friendly interfaces. Pragmatic, well designed and appropriately powered trials, and real-world implementation data are necessary to make continuous monitoring standard practice at every hospital bed.

Exploring the Relationship Between Continuously Monitored Vital Signs, Clinical Deterioration, and Clinical Actions

Continuous monitoring on the general ward leads to more and earlier interventions to prevent clinical deterioration. These clinical actions influence outcomes and may serve as an indicator of impending deterioration. This study aims to correlate clinical actions with clinical endpoints and deviating vital signs. Methods: This cohort study prospectively charted all patients undergoing continuous vital sign monitoring on a gastro-intestinal and oncological surgery, and an internal ward of an academic hospital in The Netherlands from 1 August 2018 till 31 July 2019 (METC 2018-4330, NCT04189653). Clinical actions recorded in electronic medical records were analyzed to assess correlations with patient outcomes, hospital length of stay, and alarming monitoring minutes. Results: A total of 1529 patients were included, of which 68 patients had a negative clinical endpoint. There were 2749 clinical actions recorded. Clinical actions correlated to negative clinical endpoints (ρ = 0.259; p < 0.001, OR: 3.4 to 79.5) and to the length of stay (ρ = 0.560; p < 0.001). Vital sign deviations correlated with clinical actions (ρ = 0.025-0.056; p < 0.001-p = 0.018). In the last 72 h before a clinical endpoint, for alarming minutes, this correlation with clinical actions was more pronounced (ρ = 0.340, p < 0.001). Conclusions: Predefined clinical actions performed on admitted general ward patients correlated with negative endpoints, an increased length of stay, and with deviating vital signs, especially in the period directly preceding severe deterioration. Clinical actions have potential as an intermediate measurement of deterioration.

Enhancing Thoracic Surgery with AI: A Review of Current Practices and Emerging Trends

Artificial intelligence (AI) is increasingly becoming integral to medical practice, potentially enhancing outcomes in thoracic surgery. AI-driven models have shown significant accuracy in diagnosing non-small-cell lung cancer (NSCLC), predicting lymph node metastasis, and aiding in the efficient extraction of electronic medical record (EMR) data. Moreover, AI applications in robotic-assisted thoracic surgery (RATS) and perioperative management reveal the potential to improve surgical precision, patient safety, and overall care efficiency. Despite these advancements, challenges such as data privacy, biases, and ethical concerns remain. This manuscript explores AI applications, particularly machine learning (ML) and natural language processing (NLP), in thoracic surgery, emphasizing their role in diagnosis and perioperative management. It also provides a comprehensive overview of the current state, benefits, and limitations of AI in thoracic surgery, highlighting future directions in the field.

Use of an Integrated Pulmonary Index pathway decreased unplanned ICU admissions in elderly patients with rib fractures

Unplanned intensive care unit (ICU) admission (UIA) is a Trauma Quality Improvement Program benchmark that is associated with increased morbidity, mortality, and length of stay (LOS). Elderly patients with multiple rib fractures are at increased risk of respiratory failure. The Integrated Pulmonary Index (IPI) assesses respiratory compromise by incorporating SpO2, respiratory rate, pulse, and end-tidal CO2 to yield an integer between 1 and 10 (worst and best). We hypothesized that IPI monitoring would decrease UIA for respiratory failure in elderly trauma patients with rib fractures.

Methods

Elderly (≥65 years old) trauma inpatients admitted to a level 1 trauma center from February 2020 to February 2023 were retrospectively studied during the introduction of IPI monitoring on the trauma floor. Patients with ≥4 rib fractures (or ≥2 with history of chronic obstructive pulmonary disease) were eligible for IPI monitoring and were compared with a group of chest Abbreviated Injury Scale score of 3 (≥3 rib fractures) patients who received usual care. Nurses contacted the surgeon for IPI ≤7. Patient intervention was left to the discretion of the provider. The primary endpoint was UIA for respiratory failure. Secondary endpoints were overall UIA, mortality, and LOS. Statistical analysis was performed using χ2 test and Student's t-test, with p<0.05 considered significant.

Results

A total of 110 patients received IPI monitoring and were compared with 207 patients who did not. The IPI cohort was comparable to the non-IPI cohort in terms of gender, Injury Severity Score, Abbreviated Injury Scale, mortality, and LOS. There were 16 UIAs in the non-IPI cohort and two in the IPI cohort (p=0.039). There were no UIAs for respiratory failure in the IPI group compared with nine in the non-IPI group (p=0.03).

Conclusion

IPI monitoring is an easy-to-set up tool with minimal risk and was associated with a significant decrease in UIA in elderly patients with rib fracture.

Level of evidence

Level III, therapeutic/care management.

Variation in nurses' compliance with an Early Warning Score protocol: A retrospective cohort study

Introduction

Early Warning Score (EWS) protocols are based on intermittent vital sign measurements, and aim to detect clinical deterioration in a timely manner. Despite its predictive value, its effectiveness remains suboptimal. An important limitation appears to be poor compliance with the EWS protocol and its variation between general wards. The current research does not yet provide an understanding of EWS compliance and variation in different nursing wards.

Aim

To explore the variation in nurses' compliance with the EWS protocol among patients with and without complications and between different nursing wards.

Methods

In a retrospective single-center cohort study, all patient files from three nursing wards of a tertiary teaching hospital in the Netherlands were reviewed over a 1-month period. Compliance was divided into three categories:1) calculation accuracy, 2) monitoring frequency end 3) clinical response.

Results

The cohort of 210 patients contained 5864 measurements, of which 4125 (70.6 %) included EWS. Significant differences in the measured vital signs within incomplete measurements were found among nursing wards. Compliance to monitoring frequency was higher within EWSs of 0-1 (78.4 %) than within EWSs of ≥2 (26.1 %). The proportion of correct follow-up was significantly higher in patients with complications, as was the correct clinical response to an EWS of ≥3 (84.8 % vs. 55.0; p = .011).

Conclusion

Our results suggest suboptimal compliance with the EWS protocol, with large variations between patients with and without complications and between different general care wards. Nurses tended to be more compliant with the EWS protocol for patients with complications.

Early detection of deteriorating patients in general wards through continuous contactless vital signs monitoring

Objective

To assess the efficacy of continuous contactless vital signs monitoring with an automated Early Warning System (EWS) in detecting clinical deterioration among patients in general wards.

Methods

A prospective observational cohort study was conducted in the medical unit of a tertiary care hospital in India, involving 706 patients over 84,448 monitoring hours. The study used a contactless ballistocardiography system (Dozee system) to continuously monitor heart rate, respiratory rate, and blood pressure. The study assessed total, mean, and median alerts at 24, 48, 72, 96, 120 h, and length of stay (LOS) before patient deterioration or discharge. It analyzed alert sensitivity and specificity, average time from initial alert to deterioration, and healthcare practitioners (HCP) activity. Study was registered with the Clinical Trials Registry-India CTRI/2022/10/046404.

Results

Out of 706 patients, 33 (5%) experienced clinical deterioration, while 673 (95%) did not. The deterioration group consistently had a higher number of alerts compared to those who were discharged normally, across all time-points. On average, the time between the initial alert and clinical deterioration was 16 h within the last 24 h preceding the event. The sensitivity of the Dozee-EWS varied between 67% and 94%. HCP spend 10% of their time on vital signs check and documentation.

Conclusions

This study suggests that utilizing contactless continuous vital signs monitoring with Dozee-EWS in general ward holds promise for enhancing the early detection of clinical deterioration. Further research is essential to evaluate the effectiveness across a wider range of clinical settings.

A retrospective study of the predictors of mortality among patients in intensive care units at North West-Bank hospitals in Palestine

OBJECTIVES

The intensive care unit (ICU) mortality rate remains high, especially in developing countries, regardless of the advances in critical management. There is a lack of studies about mortality causes in hospitals and particularly ICUs in Palestine.This study evaluated the demographic and clinical characteristics of critically ill patients and determined the predictors of mortality among patients in the ICU.

METHODS

A retrospective study assessed all patients who stayed in the ICU for more than 24 h from January 2017 to January 2019. Data were collected from the patient's files. Patient characteristics (background, clinical variables, and comorbidities) were recorded.

RESULTS

The study included 227 eligible ICU patients. The cases' mean age was 55.5 (SD ± 18.2) years. The overall ICU mortality rate was 31.7%. The following factors were associated with high adjusted mortality odds: admission from inside the hospital (adjusted odds ratio (aOR), 2.1, 95% CI: 1.1-3.9, p < 0.05), creatinine level ≥2 mg/dl on admission (aOR, 2.7, 95% CI: 1.3-5.8, p < 0.01), hematology malignancy patients (aOR, 3.4, 95% CI: 1.6-6.7, p = 0.001), immune-compromised (aOR, 2.5, 95% CI: 1.3-4.7, p < 0.01), septic shock (aOR, 27.1, 95% CI: 7.9-88.3, p < 0.001), hospital-acquired infections (aOR: 13.4, 95% CI: 4.1-57.1, p < 0.001), and patients with multiple-source infection (aOR: 16.3, 95% CI: 6.4-57.1, p < 0.001). Also, high SOFA and APACHE scores predicted morality (p < 0.001).

CONCLUSION

The mortality rate among ICU patients was high. It was higher among those admitted from the hospital wards, septic shock, hospital-acquired infection, multiple infection sources, and multi-drug resistance infections. Thus, strategies should be developed to enhance the ICU environment and provide sufficient resources to minimize the effects of these predictors.

Noncontact Sensors for Vital Signs Measurement: A Narrative Review

Vital signs are crucial for monitoring changes in patient health status. This review compared the performance of noncontact sensors with traditional methods for measuring vital signs and investigated the clinical feasibility of noncontact sensors for medical use. We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE) database for articles published through September 30, 2023, and used the key search terms "vital sign," "monitoring," and "sensor" to identify relevant articles. We included studies that measured vital signs using traditional methods and noncontact sensors and excluded articles not written in English, case reports, reviews, and conference presentations. In total, 129 studies were identified, and eligible articles were selected based on their titles, abstracts, and full texts. Three articles were finally included in the review, and the types of noncontact sensors used in each selected study were an impulse radio ultrawideband radar, a microbend fiber-optic sensor, and a mat-type air pressure sensor. Participants included neonates in the neonatal intensive care unit, patients with sleep apnea, and patients with coronavirus disease. Their heart rate, respiratory rate, blood pressure, body temperature, and arterial oxygen saturation were measured. Studies have demonstrated that the performance of noncontact sensors is comparable to that of traditional methods of vital signs measurement. Noncontact sensors have the potential to alleviate concerns related to skin disorders associated with traditional skin-contact vital signs measurement methods, reduce the workload for healthcare providers, and enhance patient comfort. This article reviews the medical use of noncontact sensors for measuring vital signs and aimed to determine their potential clinical applicability.

Health in a Virtual Environment (HIVE): A Novel Continuous Remote Monitoring Service for Inpatient Management

The aim of this study was to describe the implementation of a novel 50-bed continuous remote monitoring service for high-risk acute inpatients treated in non-critical wards, known as Health in a Virtual Environment (HIVE). We report the initial results, presenting the number and type of patients connected to the service, and assess key outcomes from this cohort. This was a prospective, observational study of characteristics and outcomes of patients connected to the HIVE continuous monitoring service at a major tertiary hospital and a smaller public hospital in Western Australia between January 2021 and June 2023. In the first two and a half years following implementation, 7541 patients were connected to HIVE for a total of 331,118 h. Overall, these patients had a median length of stay of 5 days (IQR 2, 10), 11.0% (n = 833) had an intensive care unit admission, 22.4% (n = 1691) had an all-cause emergency readmission within 28 days from hospital discharge, and 2.2% (n = 167) died in hospital. Conclusions: Our initial results show promise, demonstrating that this innovative approach to inpatient care can be successfully implemented to monitor high-risk patients in medical and surgical wards. Future studies will investigate the effectiveness of the program by comparing patients receiving HIVE supported care to comparable patients receiving routine care.

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.

Perioperative Patients With Hemodynamic Instability: Consensus Recommendations of the Anesthesia Patient Safety Foundation

In November of 2022, the Anesthesia Patient Safety Foundation held a Consensus Conference on Hemodynamic Instability with invited experts. The objective was to review the science and use expert consensus to produce best practice recommendations to address the issue of perioperative hemodynamic instability. After expert presentations, a modified Delphi process using discussions, voting, and feedback resulted in 17 recommendations regarding advancing the perioperative care of the patient at risk of, or with, hemodynamic instability. There were 17 high-level recommendations. These recommendations related to the following 7 domains: Current Knowledge (5 statements); Preventing Hemodynamic Instability-Related Harm During All Phases of Care (4 statements); Data-Driven Quality Improvement (3 statements); Informing Patients (2 statements); The Importance of Technology (1 statement); Launch a National Campaign (1 statement); and Advancing the Science (1 statement). A summary of the recommendations is presented in Table 1 .

Impact of continuous and wireless monitoring of vital signs on clinical outcomes: a propensity-matched observational study of surgical ward patients

BACKGROUND

Continuous and wireless vital sign monitoring is superior to intermittent monitoring in detecting vital sign abnormalities; however, the impact on clinical outcomes has not been established.

METHODS

We performed a propensity-matched analysis of data describing patients admitted to general surgical wards between January 2018 and December 2019 at a single, tertiary medical centre in the USA. The primary outcome was a composite of in-hospital mortality or ICU transfer during hospitalisation. Secondary outcomes were the odds of individual components of the primary outcome, and heart failure, myocardial infarction, acute kidney injury, and rapid response team activations. Data are presented as odds ratios (ORs) with 95% confidence intervals (CIs) and n (%).

RESULTS

We initially screened a population of 34,636 patients (mean age 58.3 (Range 18-101) yr, 16,456 (47.5%) women. After propensity matching, intermittent monitoring (n=12 345) was associated with increased risk of a composite of mortality or ICU admission (OR 3.42, 95% CI 3.19-3.67; P<0.001), and heart failure (OR 1.48, 95% CI 1.21-1.81; P<0.001), myocardial infarction (OR 3.87, 95% CI 2.71-5.71; P<0.001), and acute kidney injury (OR 1.32, 95% CI 1.09-1.57; P<0.001) compared with continuous wireless monitoring (n=7955). The odds of rapid response team intervention were similar in both groups (OR 0.86, 95% CI 0.79-1.06; P=0.726).

CONCLUSIONS

Patients who received continuous ward monitoring were less likely to die or be admitted to ICU than those who received intermittent monitoring. These findings should be confirmed in prospective randomised trials.

Postoperative arterial hypotension: the unnoticed enemy

Postoperative hypotension is a frequently underestimated health problem associated with high morbidity and mortality and increased use of health care resources. It also poses significant clinical, technological, and human challenges for healthcare. As it is a modifiable and avoidable risk factor, this document aims to increase its visibility, defining its clinical impact and the technological challenges involved in optimizing its management, taking clinical-technological, humanistic, and economic aspects into account.

Alarm burden and the nursing care environment: a 213-hospital cross-sectional study

BACKGROUND

High rates of medical device alarms in hospitals are a well-documented threat to patient safety. Little is known about organisational features that may be associated with nurses' experience of alarm burden.

AIMS

To evaluate the association between nurse-reported alarm burden, appraisals of patient safety, quality of care and hospital characteristics.

METHODS

Secondary analysis of cross-sectional survey data from 3986 hospital-based direct-care registered nurses in 213 acute care hospitals in New York and Illinois, USA. We evaluated associations of alarm burden with appraisals of patient safety and quality of care and hospital characteristics (work environment, staffing adequacy, size, teaching status) using χ2 tests.

RESULTS

The majority of respondents reported feeling overwhelmed by alarms (83%), delaying their response to alarms because they were unable to step away from another patient/task (76%), and experiencing situations where a patient needed urgent attention but no one responded to an alarm (55%). Nurses on medical-surgical units reported these experiences at higher rates than nurses working in intensive care units (p<0.001). Alarm burden items were significantly associated with poorer nurse-reported patient safety, quality of care, staffing and work environment. Findings were most pronounced for situations where a patient needed urgent attention but no one responded to the alarm, which was frequently/occasionally experienced by 72% of those who rated their hospital's safety as poor versus 38% good, p<0.001; 80% who rated overall quality of care poor/fair versus 46% good/excellent, p<0.001 and 65% from poor work environments versus 42% from good work environments, p<0.001.

CONCLUSION

Most nurses reported feeling overwhelmed by medical device alarms, and our findings suggest that alarm burden may be more pronounced in hospitals with unfavourable working conditions and suboptimal quality and safety. Because this was a cross-sectional study, further research is needed to explore causal relationships and the role of modifiable systems factors in reducing alarm burden.

Early Warning Scores to Support Continuous Wireless Vital Sign Monitoring for Complication Prediction in Patients on Surgical Wards: Retrospective Observational Study

BACKGROUND

Wireless vital sign sensors are increasingly being used to monitor patients on surgical wards. Although early warning scores (EWSs) are the current standard for the identification of patient deterioration in a ward setting, their usefulness for continuous monitoring is unknown.

OBJECTIVE

This study aimed to explore the usability and predictive value of high-rate EWSs obtained from continuous vital sign recordings for early identification of postoperative complications and compares the performance of a sensor-based EWS alarm system with manual intermittent EWS measurements and threshold alarms applied to individual vital sign recordings (single-parameter alarms).

METHODS

Continuous vital sign measurements (heart rate, respiratory rate, blood oxygen saturation, and axillary temperature) collected with wireless sensors in patients on surgical wards were used for retrospective simulation of EWSs (sensor EWSs) for different time windows (1-240 min), adopting criteria similar to EWSs based on manual vital signs measurements (nurse EWSs). Hourly sensor EWS measurements were compared between patients with (event group: 14/46, 30%) and without (control group: 32/46, 70%) postoperative complications. In addition, alarms were simulated for the sensor EWSs using a range of alarm thresholds (1-9) and compared with alarms based on nurse EWSs and single-parameter alarms. Alarm performance was evaluated using the sensitivity to predict complications within 24 hours, daily alarm rate, and false discovery rate (FDR).

RESULTS

The hourly sensor EWSs of the event group (median 3.4, IQR 3.1-4.1) was significantly higher (P<.004) compared with the control group (median 2.8, IQR 2.4-3.2). The alarm sensitivity of the hourly sensor EWSs was the highest (80%-67%) for thresholds of 3 to 5, which was associated with alarm rates of 2 (FDR=85%) to 1.2 (FDR=83%) alarms per patient per day respectively. The sensitivity of sensor EWS-based alarms was higher than that of nurse EWS-based alarms (maximum=40%) but lower than that of single-parameter alarms (87%) for all thresholds. In contrast, the (false) alarm rates of sensor EWS-based alarms were higher than that of nurse EWS-based alarms (maximum=0.6 alarm/patient/d; FDR=80%) but lower than that of single-parameter alarms (2 alarms/patient/d; FDR=84%) for most thresholds. Alarm rates for sensor EWSs increased for shorter time windows, reaching 70 alarms per patient per day when calculated every minute.

CONCLUSIONS

EWSs obtained using wireless vital sign sensors may contribute to the early recognition of postoperative complications in a ward setting, with higher alarm sensitivity compared with manual EWS measurements. Although hourly sensor EWSs provide fewer alarms compared with single-parameter alarms, high false alarm rates can be expected when calculated over shorter time spans. Further studies are recommended to optimize care escalation criteria for continuous monitoring of vital signs in a ward setting and to evaluate the effects on patient outcomes.

Continuous Blood Pressure Monitoring in Patients Having Surgery: A Narrative Review

Hypotension can occur before, during, and after surgery and is associated with postoperative complications. Anesthesiologists should thus avoid profound and prolonged hypotension. A crucial part of avoiding hypotension is accurate and tight blood pressure monitoring. In this narrative review, we briefly describe methods for continuous blood pressure monitoring, discuss current evidence for continuous blood pressure monitoring in patients having surgery to reduce perioperative hypotension, and expand on future directions and innovations in this field. In summary, continuous blood pressure monitoring with arterial catheters or noninvasive sensors enables clinicians to detect and treat hypotension immediately. Furthermore, advanced hemodynamic monitoring technologies and artificial intelligence-in combination with continuous blood pressure monitoring-may help clinicians identify underlying causes of hypotension or even predict hypotension before it occurs.

Continuous Vital Signs Monitoring with a Wireless Device on a General Ward: A Survey to Explore Nurses' Experiences in a Post-Implementation Period

BACKGROUND

Nurse engagement, perceived need and usefulness affect healthcare technology use, acceptance and improvements in quality, safety and accessibility of healthcare. Nurses' opinions regarding continuous monitoring appear to be positive. However, facilitators and barriers were little studied. This study explored nurses' post-implementation experiences of the facilitators and barriers to continuously monitoring patients' vital signs using a wireless device on general hospital wards.

METHODS

This study employed a cross-sectional survey. Vocational and registered nurses from three general wards in a Dutch tertiary university hospital participated in a survey comprising open and closed questions. The data were analysed using thematic analysis and descriptive statistics.

RESULTS

Fifty-eight nurses (51.3%) completed the survey. Barriers and facilitators were identified under four key themes: (1) timely signalling and early action, (2) time savings and time consumption, (3) patient comfort and satisfaction and (4) preconditions.

CONCLUSIONS

According to nurses, early detection and intervention for deteriorating patients facilitate the use and acceptance of continuously monitoring vital signs. Barriers primarily concern difficulties connecting patients correctly to the devices and system.

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.

Wireless monitoring devices in hospitalized children: a scoping review

The purpose of this study is to provide a structured overview of existing wireless monitoring technologies for hospitalized children. A systematic search of the literature published after 2010 was conducted in Medline, Embase, Scielo, Cochrane, and Web of Science. Two investigators independently reviewed articles to determine eligibility for inclusion. Information on study type, hospital setting, number of participants, use of a reference sensor, type and number of vital signs monitored, duration of monitoring, type of wireless information transfer, and outcomes of the wireless devices was extracted. A descriptive analysis was applied. Of the 1130 studies identified from our search, 42 met eligibility for subsequent analysis. Most included studies were observational studies with sample sizes of 50 or less published between 2019 and 2022. Common problems pertaining to study methodology and outcomes observed were short duration of monitoring, single focus on validity, and lack information on wireless transfer and data management.  Conclusion: Research on the use of wireless monitoring for children in hospitals has been increasing in recent years but often limited by methodological problems. More rigorous studies are necessary to establish the safety and accuracy of novel wireless monitoring devices in hospitalized children. What is Known: • Continuous monitoring of vital signs using wired sensors is the standard of care for hospitalized pediatric patients. However, the use of wires may pose significant challenges to optimal care. What is New: • Interest in wireless monitoring for hospitalized pediatric patients has been rapidly growing in recent years. • However, most devices are in early stages of clinical testing and are limited by inconsistent clinical and technological reporting.

Artificial Intelligence for Perioperative Medicine: Perioperative Intelligence

The anesthesiologist's role has expanded beyond the operating room, and anesthesiologist-led care teams can deliver coordinated care that spans the entire surgical experience, from preoperative optimization to long-term recovery of surgical patients. This expanded role can help reduce postoperative morbidity and mortality, which are regrettably common, unlike rare intraoperative mortality. Postoperative mortality, if considered a disease category, will be the third leading cause of death just after heart disease and cancer. Rapid advances in technologies like artificial intelligence provide an opportunity to build safe perioperative practices. Artificial intelligence helps by analyzing complex data across disparate systems and producing actionable information. Using artificial intelligence technologies, we can critically examine every aspect of perioperative medicine and devise innovative value-based solutions that can potentially improve patient safety and care delivery, while optimizing cost of care. In this narrative review, we discuss specific applications of artificial intelligence that may help advance all aspects of perioperative medicine, including clinical care, education, quality improvement, and research. We also discuss potential limitations of technology and provide our recommendations for successful adoption.

Free-living core body temperature monitoring using a wrist-worn sensor after COVID-19 booster vaccination: a pilot study

Core body temperature (CBT) is a key vital sign and fever is an important indicator of disease. In the past decade, there has been growing interest for vital sign monitoring technology that may be embedded in wearable devices, and the COVID-19 pandemic has highlighted the need for remote patient monitoring systems. While wrist-worn sensors allow continuous assessment of heart rate and oxygen saturation, reliable measurement of CBT at the wrist remains challenging. In this study, CBT was measured continuously in a free-living setting using a novel technology worn at the wrist and compared to reference core body temperature measurements, i.e., CBT values acquired with an ingestible temperature-sensing pill. Fifty individuals who received the COVID-19 booster vaccination were included. The datasets of 33 individuals were used to develop the CBT prediction algorithm, and the algorithm was then validated on the datasets of 17 participants. Mean observation time was 26.4 h and CBT > 38.0 °C occurred in 66% of the participants. CBT predicted by the wrist-worn sensor showed good correlation to the reference CBT (r = 0.72). Bland-Altman statistics showed an average bias of 0.11 °C of CBT predicted by the wrist-worn device compared to reference CBT, and limits of agreement were - 0.67 to + 0.93 °C, which is comparable to the bias and limits of agreement of commonly used tympanic membrane thermometers. The small size of the components needed for this technology would allow its integration into a variety of wearable monitoring systems assessing other vital signs and at the same time allowing maximal freedom of movement to the user.

Performance of digital early warning score (NEWS2) in a cardiac specialist setting: retrospective cohort study

INTRODUCTION

Patients with cardiovascular diseases (CVD) are at significant risk of developing critical events. Early warning scores (EWS) are recommended for early recognition of deteriorating patients, yet their performance has been poorly studied in cardiac care settings. Standardisation and integrated National Early Warning Score 2 (NEWS2) in electronic health records (EHRs) are recommended yet have not been evaluated in specialist settings.

OBJECTIVE

To investigate the performance of digital NEWS2 in predicting critical events: death, intensive care unit (ICU) admission, cardiac arrest and medical emergencies.

METHODS

Retrospective cohort analysis.

STUDY COHORT

Individuals admitted with CVD diagnoses in 2020; including patients with COVID-19 due to conducting the study during the COVID-19 pandemic.

MEASURES

We tested the ability of NEWS2 in predicting the three critical outcomes from admission and within 24 hours before the event. NEWS2 was supplemented with age and cardiac rhythm and investigated. We used logistic regression analysis with the area under the receiver operating characteristic curve (AUC) to measure discrimination.

RESULTS

In 6143 patients admitted under cardiac specialties, NEWS2 showed moderate to low predictive accuracy of traditionally examined outcomes: death, ICU admission, cardiac arrest and medical emergency (AUC: 0.63, 0.56, 0.70 and 0.63, respectively). Supplemented NEWS2 with age showed no improvement while age and cardiac rhythm improved discrimination (AUC: 0.75, 0.84, 0.95 and 0.94, respectively). Improved performance was found of NEWS2 with age for COVID-19 cases (AUC: 0.96, 0.70, 0.87 and 0.88, respectively).

CONCLUSION

The performance of NEWS2 in patients with CVD is suboptimal, and fair for patients with CVD with COVID-19 to predict deterioration. Adjustment with variables that strongly correlate with critical cardiovascular outcomes, that is, cardiac rhythm, can improve the model. There is a need to define critical endpoints, engagement with clinical experts in development and further validation and implementation studies of EHR-integrated EWS in cardiac specialist settings.

Implementation of a digital early warning score (NEWS2) in a cardiac specialist and general hospital settings in the COVID-19 pandemic: a qualitative study

OBJECTIVES

To evaluate implementation of digital National Early Warning Score 2 (NEWS2) in a cardiac care setting and a general hospital setting in the COVID-19 pandemic.

DESIGN

Thematic analysis of qualitative semistructured interviews using the non-adoption, abandonment, scale-up, spread, sustainability framework with purposefully sampled nurses and managers, as well as online surveys from March to December 2021.

SETTINGS

Specialist cardiac hospital (St Bartholomew's Hospital) and general teaching hospital (University College London Hospital, UCLH).

PARTICIPANTS

Eleven nurses and managers from cardiology, cardiac surgery, oncology and intensive care wards (St Bartholomew's) and medical, haematology and intensive care wards (UCLH) were interviewed and 67 were surveyed online.

RESULTS

Three main themes emerged: (1) implementing NEWS2 challenges and supports; (2) value of NEWS2 to alarm, escalate and during the pandemic; and (3) digitalisation: electronic health record (EHR) integration and automation. The value of NEWS2 was partly positive in escalation, yet there were concerns by nurses who undervalued NEWS2 particularly in cardiac care. Challenges, like clinicians' behaviours, lack of resources and training and the perception of NEWS2 value, limit the success of this implementation. Changes in guidelines in the pandemic have led to overlooking NEWS2. EHR integration and automated monitoring are improvement solutions that are not fully employed yet.

CONCLUSION

Whether in specialist or general medical settings, the health professionals implementing early warning score in healthcare face cultural and system-related challenges to adopting NEWS2 and digital solutions. The validity of NEWS2 in specialised settings and complex conditions is not yet apparent and requires comprehensive validation. EHR integration and automation are powerful tools to facilitate NEWS2 if its principles are reviewed and rectified, and resources and training are accessible. Further examination of implementation from the cultural and automation domains is needed.

Nighttime Continuous Contactless Smartphone-Based Cough Monitoring for the Ward: Validation Study

BACKGROUND

Clinical deterioration can go unnoticed in hospital wards for hours. Mobile technologies such as wearables and smartphones enable automated, continuous, noninvasive ward monitoring and allow the detection of subtle changes in vital signs. Cough can be effectively monitored through mobile technologies in the ward, as it is not only a symptom of prevalent respiratory diseases such as asthma, lung cancer, and COVID-19 but also a predictor of acute health deterioration. In past decades, many efforts have been made to develop an automatic cough counting tool. To date, however, there is neither a standardized, sufficiently validated method nor a scalable cough monitor that can be deployed on a consumer-centric device that reports cough counts continuously. These shortcomings limit the tracking of coughing and, consequently, hinder the monitoring of disease progression in prevalent respiratory diseases such as asthma, chronic obstructive pulmonary disease, and COVID-19 in the ward.

OBJECTIVE

This exploratory study involved the validation of an automated smartphone-based monitoring system for continuous cough counting in 2 different modes in the ward. Unlike previous studies that focused on evaluating cough detection models on unseen data, the focus of this work is to validate a holistic smartphone-based cough detection system operating in near real time.

METHODS

Automated cough counts were measured consistently on devices and on computers and compared with cough and noncough sounds counted manually over 8-hour long nocturnal recordings in 9 patients with pneumonia in the ward. The proposed cough detection system consists primarily of an Android app running on a smartphone that detects coughs and records sounds and secondarily of a backend that continuously receives the cough detection information and displays the hourly cough counts. Cough detection is based on an ensemble convolutional neural network developed and trained on asthmatic cough data.

RESULTS

In this validation study, a total of 72 hours of recordings from 9 participants with pneumonia, 4 of whom were infected with SARS-CoV-2, were analyzed. All the recordings were subjected to manual analysis by 2 blinded raters. The proposed system yielded a sensitivity and specificity of 72% and 99% on the device and 82% and 99% on the computer, respectively, for detecting coughs. The mean differences between the automated and human rater cough counts were -1.0 (95% CI -12.3 to 10.2) and -0.9 (95% CI -6.5 to 4.8) coughs per hour within subject for the on-device and on-computer modes, respectively.

CONCLUSIONS

The proposed system thus represents a smartphone cough counter that can be used for continuous hourly assessment of cough frequency in the ward.

ROMI: A Real-Time Optical Digit Recognition Embedded System for Monitoring Patients in Intensive Care Units

With advances in the Internet of Things, patients in intensive care units are constantly monitored to expedite emergencies. Due to the COVID-19 pandemic, non-face-to-face monitoring has been required for the safety of patients and medical staff. A control center monitors the vital signs of patients in ICUs. However, some medical devices, such as ventilators and infusion pumps, operate in a standalone fashion without communication capabilities, requiring medical staff to check them manually. One promising solution is to use a robotic system with a camera. We propose a real-time optical digit recognition embedded system called ROMI. ROMI is a mobile robot that monitors patients by recognizing digits displayed on LCD screens of medical devices in real time. ROMI consists of three main functions for recognizing digits: digit localization, digit classification, and digit annotation. We developed ROMI by using Matlab Simulink, and the maximum digit recognition performance was 0.989 mAP on alexnet. The developed system was deployed on NVIDIA GPU embedded platforms: Jetson Nano, Jetson Xavier NX, and Jetson AGX Xavier. We also created a benchmark by evaluating the runtime performance by considering ten pre-trained CNN models and three NVIDIA GPU platforms. We expect that ROMI will support medical staff with non-face-to-face monitoring in ICUs, enabling more effective and prompt patient care.

Accuracy of non-invasive cuffless blood pressure in the intensive care unit: Promises and challenges

Objective

Continuous non-invasive cuffless blood pressure (BP) monitoring may reduce adverse outcomes in hospitalized patients if accuracy is approved. We aimed to investigate accuracy of two different BP prediction models in critically ill intensive care unit (ICU) patients, using a prototype cuffless BP device based on electrocardiogram and photoplethysmography signals. We compared a pulse arrival time (PAT)-based BP model (generalized PAT-based model) derived from a general population cohort to more complex and individualized models (complex individualized models) utilizing other features of the BP sensor signals.

Methods

Patients admitted to an ICU with indication of invasive BP monitoring were included. The first half of each patient's data was used to train a subject-specific machine learning model (complex individualized models). The second half was used to estimate BP and test accuracy of both the generalized PAT-based model and the complex individualized models. A total of 7,327 measurements of 15 s epochs were included in pairwise comparisons across 25 patients.

Results

The generalized PAT-based model achieved a mean absolute error (SD of errors) of 7.6 (7.2) mmHg, 3.3 (3.1) mmHg and 4.6 (4.4) mmHg for systolic BP, diastolic BP and mean arterial pressure (MAP) respectively. Corresponding results for the complex individualized model were 6.5 (6.7) mmHg, 3.1 (3.0) mmHg and 4.0 (4.0) mmHg. Percentage of absolute errors within 10 mmHg for the generalized model were 77.6, 96.2, and 89.6% for systolic BP, diastolic BP and MAP, respectively. Corresponding results for the individualized model were 83.8, 96.2, and 94.2%. Accuracy was significantly improved when comparing the complex individualized models to the generalized PAT-based model in systolic BP and MAP, but not diastolic BP.

Conclusion

A generalized PAT-based model, developed from a different population was not able to accurately track BP changes in critically ill ICU patients. Individually fitted models utilizing other cuffless BP sensor signals significantly improved accuracy, indicating that cuffless BP can be measured non-invasively, but the challenge toward generalizable models remains for future research to resolve.

The transcultural adaptation and validation of the Chinese version of the Attitudes Toward Recognizing Early and Noticeable Deterioration scale

Background

In China, clinical deterioration events present a real problem for every clinical nurse. Patient deterioration is determined in part by nurses' attitudes toward early recognition of clinical deterioration. However, research on attitudes toward the early identification of clinical deterioration is still in its infancy, and even less research has been done on ward nurses' attitudes toward the early identification of clinical deterioration. To drive behavioral change and improve the care of deteriorating patients, nurses need comprehensive, valid, and reliable tools to assess their attitudes toward early identification of deterioration.

Objective

In this study, we aimed to translate the Attitudes Toward Recognizing Early and Noticeable Deterioration (ATREND) scale into Chinese and to assess its validity and reliability tests.

Methods

From March 2022 to July 2022, the ATREND scale was translated, back-translated, and cross-culturally adapted into the Chinese version using a modified Brislin translation model. Then, 460 ward nurses were recruited from tertiary Grade A general hospitals in two cities: Shenyang and Jinzhou in Liaoning Province, China. Reliability analyses were conducted using internal consistency, split-half, and test-retest reliability. We convened a committee of experts to determine the validity of the content. Tests of the structural validity of the scale were conducted using exploratory and validation factor analyses.

Results

The Cronbach's α value of the Chinese version of the ATREND scale was 0.804, and the Cronbach's α value of the dimensions ranged from 0.782 to 0.863. The split-half reliability and test-retest reliability were 0.846 and 0.711, respectively. Furthermore, the scale has an index of content validity of 0.922, indicating a high level of content validity. In exploratory factor analysis, eigenvalues, total variance explained, and scree plot supported a three-factor structure. The three-factor model supported by this study was confirmed by confirmatory factor analysis (CFA). Moreover, the model fitting indexes (e.g., χ ²/DF = 1.498, GFI = 0.954, RMSEA = 0.047) were all within acceptable limits based on the CFA.

Conclusion

The Chinese version of the scale is reliable and valid among ward nurses. Nursing educators and clinicians will be able to develop targeted educational programs to enhance the competence and behaviors of Chinese ward nurses in recognizing clinical deterioration. It will be based on the developed scale to assess Chinese nurses' attitudes and practices regarding early recognition of clinical deterioration. As a result, it is necessary to consider the Chinese scale's three-factor structure. The developed three-factor structured scale will assess Chinese ward nurses' attitudes and practices toward patient observation and vital sign-monitoring empowerment, enlightening them on the importance of patient observation, encouraging ward nurses to use a wider range of patient assessment techniques to capture early signs of clinical deterioration, and helping ward nurses to develop clinical confidence to monitor clinical deterioration.

Evaluating a novel, integrative dashboard for health professionals' performance in managing deteriorating patients: a quality improvement project

BACKGROUND

The quality of recording and documentation of deteriorating patient management by health professionals has been challenged during the COVID-19 pandemic. Non-adherence to escalation and documentation guidelines increases risk of serious adverse events. Electronic health record (EHR)-integrated dashboards are auditing tools of patients' status and clinicians' performance, but neither the views nor the performance of health professionals have been assessed, relating to management of deteriorating patients.

OBJECTIVE

To develop and evaluate a real-time dashboard of deteriorating patients' assessment, referral and therapy.

SETTINGS

Five academic hospitals in the largest National Health Service (NHS) trust in the UK (Barts Health NHS Trust).

INTERVENTION

The dashboard was developed from EHR data to investigate patients with National Early Warning Score (NEWS2)>5, assessment, and escalation of deteriorating patients. We adopted the Plan, Do, Study, Act model and Standards for Quality Improvement Reporting Excellence framework to evaluate the dashboard.

DESIGN

Mixed methods: (1) virtual, face-to-face, interviews and (2) retrospective descriptive EHR data analysis.

RESULTS

We interviewed three nurses (two quality and safety and one informatics specialists). Participants perceived the dashboard as a facilitator for auditing NEWS2 recording and escalation of care to improve practice; (2) there is a need for guiding clinicians and adjusting data sources and metrics to enhance the functionality and usability. Data analysis (2019-2022) showed: (1) NEWS2 recording has gradually improved (May 2021-April 2022) from 64% to 83%;(2) referral and assessment completion increased (n: 170-6800 and 23-540, respectively).

CONCLUSION

The dashboard is an effective real-time data-driven method for improving the quality of managing deteriorating patients. Integrating health systems, a wider analysis NEWS2 and escalation of care metrics, and clinicians' learning digital solutions will enhance functionality and experience to boost its value. There is a need to examine the generalisability of the dashboard through further validation and quality improvement studies.

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.

Clinical impact of vital sign abnormalities in patients admitted with acute exacerbation of chronic obstructive pulmonary disease: an observational study using continuous wireless monitoring

Early detection of abnormal vital signs is critical for timely management of acute hospitalised patients and continuous monitoring may improve this. We aimed to assess the association between preceding vital sign abnormalities and serious adverse events (SAE) in patients hospitalised with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Two hundred patients' vital signs were wirelessly and continuously monitored with peripheral oxygen saturation, heart rate, and respiratory rate during the first 4 days after admission for AECOPD. Non-invasive blood pressure was also measured every 30-60 min. The primary outcome was occurrence of SAE according to international definitions within 30 days and physiological data were analysed for preceding vital sign abnormalities. Data were presented as the mean cumulative duration of vital sign abnormalities per 24 h and analysed using Wilcoxon rank sum test. SAE during ongoing continuous monitoring occurred in 50 patients (25%). Patients suffering SAE during the monitoring period had on average 455 min (SD 413) per 24 h of any preceding vital sign abnormality versus 292 min (SD 246) in patients without SAE, p = 0.08, mean difference 163 min [95% CI 61-265]. Mean duration of bradypnea (respiratory rate < 11 min^-1) was 48 min (SD 173) compared with 30 min (SD 84) in patients without SAE, p = 0.01. In conclusion, the duration of physiological abnormalities was substantial in patients with AECOPD. There were no statistically significant differences between patients with and without SAE in the overall duration of preceding physiological abnormalities.Study registration: http://ClinicalTrials.gov (NCT03660501). Date of registration: Sept 6 2018.

Respiratory monitoring of nonintubated patients in nonoperating room settings: old and new technologies

PURPOSE OF REVIEW

Postoperative mortality in the 30 days after surgery remains disturbingly high. Inadequate, intermittent and incomplete monitoring of vital signs in the nonoperating room environment is common practice. The rise of nonoperating room anaesthesia and sedation outside the operating room has highlighted the need to develop new and robust methods of portable continuous respiratory monitoring. This review provides a summary of old and new technologies in this environment.

RECENT FINDINGS

Technical advances have made possible the utilization of established monitoring to extrapolate respiratory rate, the increased availability and user friendliness of side stream capnography and the advent of other innovative systems. The use of aggregate signals wherein different modalities compensate for individual shortcomings seem to provide a reliable and artefact-free system.

SUMMARY

Respiratory monitoring is required in several situations and patient categories outside the operating room. The chosen modality must be able to detect respiratory compromise in a timely and accurate manner. Combing several modalities in a nonobtrusive, nontethered system and having an integrated output seems to give a reliable and responsive signal.

Provisional Decision-Making for Perioperative Blood Pressure Management: A Narrative Review

Blood pressure (BP) is a basic determinant for organ blood flow supply. Insufficient blood supply will cause tissue hypoxia, provoke cellular oxidative stress, and to some extent lead to organ injury. Perioperative BP is labile and dynamic, and intraoperative hypotension is common. It is unclear whether there is a causal relationship between intraoperative hypotension and organ injury. However, hypotension surely compromises perfusion and causes harm to some extent. Because the harm threshold remains unknown, various guidelines for intraoperative BP management have been proposed. With the pending definitions from robust randomized trials, it is reasonable to consider observational analyses suggesting that mean arterial pressures below 65 mmHg sustained for more than 15 minutes are associated with myocardial and renal injury. Advances in machine learning and artificial intelligence may facilitate the management of hemodynamics globally, including fluid administration, rather than BP alone. The previous mounting studies concentrated on associations between BP targets and adverse complications, whereas few studies were concerned about how to treat and multiple factors for decision-making. Hence, in this narrative review, we discussed the way of BP measurement and current knowledge about baseline BP extracting for surgical patients, highlighted the decision-making process for BP management with a view to providing pragmatic guidance for BP treatment in the clinical settings, and evaluated the merits of an automated blood control system in predicting hypotension.

DeepCNAP: A Deep Learning Approach for Continuous Noninvasive Arterial Blood Pressure Monitoring Using Photoplethysmography

Arterial blood pressure (ABP) monitoring may permit the early diagnosis and management of cardiovascular disease (CVD); however, existing methods for measuring ABP outside the clinic use inconvenient cuff sphygmomanometry, or do not estimate continuous ABP waveforms. This study proposes a novel deep learning model DeepCNAP for estimating continuous BP waveform from a noninvasively measured photoplethysmography (PPG) signal in real time. DeepCNAP was designed through the combination of deep convolutional networks and self-attention. The proposed method was constructed via 10-fold cross-validation based on the MIMIC database (the number of subjects = 942, recording time = 374.43 hours). The performance of DeepCNAP was evaluated from two perspectives: estimating ABP from PPG and classifying hemodynamically unstable events (i.e., hypertension, prehypertension, hypotension, and the normal state). The mean absolute errors of the BP estimates were 3.40 4.36 mmHg for systolic BP, 1.75 2.25 mmHg for diastolic BP, and 3.23 2.21 mmHg for the BP waveform, indicating that DeepCNAP satisfies the standards of both the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). From the estimated BP, hypertension, prehypertension, hypotension, and the normal state were classified with 99.44, 97.58, 92.23, and 94.64% accuracy, respectively. DeepCNAP enables feasible real-time estimation of invasively measured ABP from noninvasive PPG. With its noninvasive nature, high accuracy, and clinical relevance, the proposed model could be valuable in general wards at hospitals and for wearable devices in daily life.

[Hemodynamic Monitoring 2.0 - What is Possible on Normal Wards?]

Life threatening events after surgery often occur on the ward. These events could be prevented by early detection of clinical deterioration of patients' health status during ward care. Therefore, an adequate monitoring could help to identify patients at risk, since there is an imbalance of monitoring intensity and the occurrence of life-threatening events during hospital stay.Additional monitoring on the general ward could lead to more patient safety. The practicability of additional monitoring needs to be considered, and therefore the use of available monitoring systems on the ward is limited. Capillary refill time (CRT) and the passive leg raise test (PLR) seem to be usable intermittent monitoring techniques.Continuous monitoring systems ensure a better detection of unwanted events and hemodynamic trends. However, the increased workload for the nursing staff and tethered monitors are unfavorable. Future trends of developing wireless monitoring systems are of paramount importance in this respect. Controlling artefacts is crucial for the successful balance between false alarms and "missed events". An adequate reaction is needed when detecting adverse events to avoid a "failure to rescue".

Creation of an Evidence-Based Implementation Framework for Digital Health Technology in the Intensive Care Unit: Qualitative Study

Background

Digital health technologies such as continuous remote monitoring and artificial intelligence–driven clinical decision support systems could improve clinical outcomes in intensive care medicine. However, comprehensive evidence and guidelines for the successful implementation of digital health technologies into specific clinical settings such as the intensive care unit (ICU) are scarce. We evaluated the implementation of a remote patient monitoring platform and derived a framework proposal for the implementation of digital health technology in an ICU.

Objective

This study aims to investigate barriers and facilitators to the implementation of a remote patient monitoring technology and to develop a proposal for an implementation framework for digital health technology in the ICU.

Methods

This study was conducted from May 2018 to March 2020 during the implementation of a tablet computer–based remote patient monitoring system. The system was installed in the ICU of a large German university hospital as a supplementary monitoring device. Following a hybrid qualitative approach with inductive and deductive elements, we used the Consolidated Framework for Implementation Research and the Expert Recommendations for Implementing Change to analyze the transcripts of 7 semistructured interviews with clinical ICU stakeholders and descriptive questionnaire data. The results of the qualitative analysis, together with the findings from informal meetings, field observations, and previous explorations, provided the basis for the derivation of the proposed framework.

Results

This study revealed an insufficient implementation process due to lack of staff engagement and few perceived benefits from the novel solution. Further implementation barriers were the high staff presence and monitoring coverage in the ICU. The implementation framework includes strategies to be applied before and during implementation, targeting the implementation setting by involving all ICU stakeholders, assessing the intervention’s adaptability, facilitating the implementation process, and maintaining a vital feedback culture. Setting up a unit responsible for implementation, considering the guidance of an implementation advisor, and building on existing institutional capacities could improve the institutional context of implementation projects in the ICU.

Conclusions

Implementation of digital health in the ICU should involve a thorough preimplementation assessment of the ICU’s need for innovation and its readiness to change, as well as an ongoing evaluation of the implementation conditions. Involvement of all stakeholders, transparent communication, and continuous feedback in an equal atmosphere are essential, but leadership roles must be clearly defined and competently filled. Our proposed framework may guide health care providers with concrete, evidence-based, and step-by-step recommendations for implementation practice, facilitating the introduction of digital health in intensive care.

Trial Registration

ClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173

Wireless wearables for postoperative surveillance on surgical wards: a survey of 1158 anaesthesiologists in Western Europe and the USA

Background

Several continuous monitoring solutions, including wireless wearable sensors, are available or being developed to improve patient surveillance on surgical wards. We designed a survey to understand the current perception and expectations of anaesthesiologists who, as perioperative physicians, are increasingly involved in postoperative care.

Methods

The survey was shared in 40 university hospitals from Western Europe and the USA.

Results

From 5744 anaesthesiologists who received the survey link, there were 1158 valid questionnaires available for analysis. Current postoperative surveillance was mainly based on intermittent spot-checks of vital signs every 4-6 h in the USA (72%) and every 8-12 h in Europe (53%). A majority of respondents (91%) considered that continuous monitoring of vital signs should be available on surgical wards and that wireless sensors are preferable to tethered systems (86%). Most respondents indicated that oxygen saturation (93%), heart rate (80%), and blood pressure (71%) should be continuously monitored with wrist devices (71%) or skin adhesive patches (54%). They believed it may help detect clinical deterioration earlier (90%), decrease rescue interventions (59%), and decrease hospital mortality (54%). Opinions diverged regarding the impact on nurse workload (increase 46%, decrease 39%), and most respondents considered that the biggest implementation challenges are economic (79%) and connectivity issues (64%).

Conclusion

Continuous monitoring of vital signs with wireless sensors is wanted by most anaesthesiologists from university hospitals in Western Europe and in the USA. They believe it may improve patient safety and outcome, but may also be challenging to implement because of cost and connectivity issues.

Continuous non-invasive remote automated blood pressure monitoring with novel wearable technology: A Validation Study

Background

Wearable continuous monitoring biosensor technologies have the potential to transform postoperative care with early detection of impending clinical deterioration.

Objective

Our aim was to validate the accuracy of Cloud DX Vitaliti continuous vital signs monitor (CVSM) continuous noninvasive blood pressure (cNIBP) measurements in postsurgical patients. A secondary aim was to examine user acceptance of the Vitaliti CVSM with respect to comfort, ease of application, sustainability of positioning, and aesthetics.

Methods

Included participants were ≥18 years old and recovering from surgery in a cardiac intensive care unit (ICU). We targeted a maximum recruitment of 80 participants for verification and acceptance testing. We also oversampled to minimize the effect of unforeseen interruptions and other challenges to the study. Validation procedures were according to the International Standards Organization (ISO) 81060-2:2018 standards for wearable, cuffless blood pressure (BP) measuring devices. Baseline BP was determined from the gold-standard ICU arterial catheter. The Vitaliti CVSM was calibrated against the reference arterial catheter. In static (seated in bed) and supine positions, 3 cNIBP measurements, each 30 seconds, were taken for each patient with the Vitaliti CVSM and an invasive arterial catheter. At the conclusion of each test session, captured cNIBP measurements were extracted using MediCollector BEDSIDE data extraction software, and Vitaliti CVSM measurements were extracted to a secure laptop through a cable connection. The errors of these determinations were calculated. Participants were interviewed about device acceptability.

Results

The validation analysis included data for 20 patients. The average times from calibration to first measurement in the static position and to first measurement in the supine position were 133.85 seconds (2 minutes 14 seconds) and 535.15 seconds (8 minutes 55 seconds), respectively. The overall mean errors of determination for the static position were –0.621 (SD 4.640) mm Hg for systolic blood pressure (SBP) and 0.457 (SD 1.675) mm Hg for diastolic blood pressure (DBP). Errors of determination were slightly higher for the supine position, at 2.722 (SD 5.207) mm Hg for SBP and 2.650 (SD 3.221) mm Hg for DBP. The majority rated the Vitaliti CVSM as comfortable. This study was limited to evaluation of the device during a very short validation period after calibration (ie, that commenced within 2 minutes after calibration and lasted for a short duration of time).

Conclusions

We found that the Cloud DX’s Vitaliti CVSM demonstrated cNIBP measurement in compliance with ISO 81060-2:2018 standards in the context of evaluation that commenced within 2 minutes of device calibration; this device was also well-received by patients in a postsurgical ICU setting. Future studies will examine the accuracy of the Vitaliti CVSM in ambulatory contexts, with attention to assessment over a longer duration and the impact of excessive patient motion on data artifacts and signal quality.

Trial Registration

ClinicalTrials.gov NCT03493867; https://clinicaltrials.gov/ct2/show/NCT03493867

Algorithms for Prediction of Clinical Deterioration on the General Wards: A Scoping Review

OBJECTIVE

The primary objective of this scoping review was to identify and describe state-of-the-art models that use vital sign monitoring to predict clinical deterioration on the general ward. The secondary objective was to identify facilitators, barriers, and effects of implementing these models.

DATA SOURCES

PubMed, Embase, and CINAHL databases until November 2020.

STUDY SELECTION

We selected studies that compared vital signs-based automated real-time predictive algorithms to current track-and-trace protocols in regard to the outcome of clinical deterioration in a general ward population.

DATA EXTRACTION

Study characteristics, predictive characteristics and barriers, facilitators, and effects.

RESULTS

We identified 1741 publications, 21 of which were included in our review. Two of the these were clinical trials, 2 were prospective observational studies, and the remaining 17 were retrospective studies. All of the studies focused on hospitalized adult patients. The reported area under the receiver operating characteristic curves ranged between 0.65 and 0.95 for the outcome of clinical deterioration. Positive predictive value and sensitivity ranged between 0.223 and 0.773 and from 7.2% to 84.0%, respectively. Input variables differed widely, and predicted endpoints were inconsistently defined. We identified 57 facilitators and 48 barriers to the implementation of these models. We found 68 reported effects, 57 of which were positive.

CONCLUSION

Predictive algorithms can detect clinical deterioration on the general ward earlier and more accurately than conventional protocols, which in one recent study led to lower mortality. Consensus is needed on input variables, predictive time horizons, and definitions of endpoints to better facilitate comparative research.

Clinical evaluation of a wearable sensor for mobile monitoring of respiratory rate on hospital wards

A wireless and wearable system was recently developed for mobile monitoring of respiratory rate (RR). The present study was designed to compare RR mobile measurements with reference capnographic measurements on a medical-surgical ward. The wearable sensor measures impedance variations of the chest from two thoracic and one abdominal electrode. Simultaneous measurements of RR from the wearable sensor and from the capnographic sensor (1 measure/minute) were compared in 36 ward patients. Patients were monitored for a period of 182 ± 56 min (range 68–331). Artifact-free RR measurements were available 81% of the monitoring time for capnography and 92% for the wearable monitoring system (p < 0.001). A total of 4836 pairs of simultaneous measurements were available for analysis. The average reference RR was 19 ± 5 breaths/min (range 6–36). The average difference between the wearable and capnography RR measurements was − 0.6 ± 2.5 breaths/min. Error grid analysis showed that the proportions of RR measurements done with the wearable system were 89.7% in zone A (no risk), 9.6% in zone B (low risk) and < 1% in zones C, D and E (moderate, significant and dangerous risk). The wearable method detected RR values > 20 (tachypnea) with a sensitivity of 81% and a specificity of 93%. In ward patients, the wearable sensor enabled accurate and precise measurements of RR within a relatively broad range (6–36 b/min) and the detection of tachypnea with high sensitivity and specificity.

Opioid-induced respiratory depression increases hospital costs and length of stay in patients recovering on the general care floor

BACKGROUND

Opioid-induced respiratory depression is common on the general care floor. However, the clinical and economic burden of respiratory depression is not well-described. The PRediction of Opioid-induced respiratory Depression In patients monitored by capnoGraphY (PRODIGY) trial created a prediction tool to identify patients at risk of respiratory depression. The purpose of this retrospective sub-analysis was to examine healthcare utilization and hospital cost associated with respiratory depression.

METHODS

One thousand three hundred thirty-five patients (N = 769 United States patients) enrolled in the PRODIGY trial received parenteral opioids and underwent continuous capnography and pulse oximetry monitoring. Cost data was retrospectively collected for 420 United States patients. Differences in healthcare utilization and costs between patients with and without ≥1 respiratory depression episode were determined. The impact of respiratory depression on hospital cost per patient was evaluated using a propensity weighted generalized linear model.

RESULTS

Patients with ≥1 respiratory depression episode had a longer length of stay (6.4 ± 7.8 days vs 5.0 ± 4.3 days, p = 0.009) and higher hospital cost ($21,892 ± $11,540 vs $18,206 ± $10,864, p = 0.002) compared to patients without respiratory depression. Patients at high risk for respiratory depression, determined using the PRODIGY risk prediction tool, who had ≥1 respiratory depression episode had higher hospital costs compared to high risk patients without respiratory depression ($21,948 ± $9128 vs $18,474 ± $9767, p = 0.0495). Propensity weighted analysis identified 17% higher costs for patients with ≥1 respiratory depression episode (p = 0.007). Length of stay significantly increased total cost, with cost increasing exponentially for patients with ≥1 respiratory depression episode as length of stay increased.

CONCLUSIONS

Respiratory depression on the general care floor is associated with a significantly longer length of stay and increased hospital costs. Early identification of patients at risk for respiratory depression, along with early proactive intervention, may reduce the incidence of respiratory depression and its associated clinical and economic burden.

TRIAL REGISTRATION

ClinicalTrials.gov , NCT02811302 .