Review: Continuous Monitoring to Detect Failure to Rescue in Adult Postoperative Inpatients

2018 ◽  
Vol 52 (4) ◽  
pp. 281-287 ◽  
Author(s):  
Sue Carol Verrillo ◽  
Bradford D. Winters
Keyword(s):  
Vital Sign ◽  
Sleep Disordered Breathing ◽  
Continuous Monitoring ◽  
Vital Signs ◽  
Clinical Deterioration ◽  
Failure To Rescue ◽  
Contributing Factors ◽  
Unexpected Death ◽  
Practice Model ◽  
Preventable Complication

Abstract Failure to rescue, or the unexpected death of a patient due to a preventable complication, is a nationally documented problem with numerous and multifaceted contributing factors. These factors include the frequency and method of collecting vital sign data, response to abnormal vital signs, and delays in the escalation of care for general ward patients who are showing signs of clinical deterioration. Patients' clinical deterioration can be complicated by concurrent secondary factors, including opioid abuse/dependence, being uninsured, or having sleep-disordered breathing. Using the Johns Hopkins Nursing Evidence-Based Practice Model, this integrative review synthesizes 43 research and nonresearch sources of evidence. Published between 2001 and 2017, these sources of evidence focus on failure to rescue, the multifaceted contributing factors to failure to rescue, and how continuous vital sign monitoring could ameliorate failure to rescue and its causes. Recommendations from the sources of evidence have been divided into system, structural, or technological categories.

scholarly journals Feasibility of continuous monitoring of vital signs in surgical patients on a general ward: an observational cohort study

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e042735
Author(s):  
Jobbe P L Leenen ◽  
Eline M Dijkman ◽  
Joris D van Dijk ◽  
Henderik L van Westreenen ◽  
Cor Kalkman ◽  
...  
Keyword(s):  
Heart Rate ◽  
Cohort Study ◽  
Respiratory Rate ◽  
Vital Sign ◽  
Continuous Monitoring ◽  
Vital Signs ◽  
Observational Cohort Study ◽  
General Ward ◽  
Surgical Patients ◽  
Observational Cohort

ObjectiveTo determine feasibility, in terms of acceptability and system fidelity, of continuous vital signs monitoring in abdominal surgery patients on a general ward.DesignObservational cohort study.SettingTertiary teaching hospital.ParticipantsPostoperative abdominal surgical patients (n=30) and nurses (n=23).InterventionsPatients were continuously monitored with the SensiumVitals wearable device until discharge in addition to usual care, which is intermittent Modified Early Warning Score measurements. Heart rate, respiratory rate and axillary temperature were monitored every 2 min. Values and trends were visualised and alerts sent to the nurses.OutcomesSystem fidelity was measured by analysis of the monitoring data. Acceptability by patients and nurses was assessed using questionnaires.ResultsThirty patients were monitored for a median duration of 81 hours (IQR 47–143) per patient, resulting in 115 217 measurements per parameter. In total, 19% (n=21 311) of heart rate, 51% (n=59 184) of respiratory rate and 9% of temperature measurements showed artefacts (n=10 269). The system algorithm sent 972 alerts (median alert rate of 4.5 per patient per day), of which 90.3% (n=878) were system alerts and 9.7% (n=94) were vital sign alerts. 35% (n=33) of vital sign alerts were true positives. 93% (n=25) of patients rated the patch as comfortable, 67% (n=18) felt safer and 89% (n=24) would like to wear it next time in the hospital. Nurses were neutral about usefulness, with a median score of 3.5 (IQR 3.1–4) on a 7-point Likert scale, ease of use 3.7 (IQR 3.2–4.8) and satisfaction 3.7 (IQR 3.2–4.8), but agreed on ease of learning at 5.0 (IQR 4.0–5.8). Neutral scores were mostly related to the perceived limited fidelity of the system.ConclusionsContinuous monitoring of vital signs with a wearable device was well accepted by patients. Nurses’ ratings were highly variable, resulting in on average neutral attitude towards remote monitoring. Our results suggest it is feasible to monitor vital signs continuously on general wards, although acceptability of the device among nurses needs further improvement.

Clinical deterioration and perception of cues among medical surgical nurses

2018 ◽  
Author(s):  
◽  
Sabrina B. Orique
Keyword(s):  
Signal Detection ◽  
Situation Awareness ◽  
Vital Signs ◽  
Individual Characteristics ◽  
Clinical Deterioration ◽  
Future Research ◽  
Skills Acquisition ◽  
Failure To Rescue ◽  
Cross Sectional ◽  
Surgical Nurses

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] In the medical-surgical setting, failure to rescue events continue to remain prevalent. While failure to rescue events are often proceeded by changes in patient cues (i.e. vital signs), prior research suggests that both subtle and overt patient cues are sometimes missed or misinterpreted. Prompt recognition and management of failure to rescue events is dependent on nurses' capacity and tendency to perceive changes in patient cues that indicate clinical deterioration. To better understand the cognitive nature of medical-surgical nurses' deterioration cue perception, a cross-sectional non-experimental study was conducted. The Situation Awareness Model and Signal Detection Theory were used as a framework to examine nurses' capacity to perceive deterioration cues and associated nurse characteristics. Findings showed that as nurses' capacity to perceive deterioration cues increased, nurses were more likely to classify patient cues as indicators of deterioration. Though fatigue, education, and certification were not predictors of nurses' capacity to perceive deterioration cues, experience was observed to be a predictor based on levels of skills acquisition. Future research should aim to examine whether other individual characteristics such as information processing mechanisms and signal detection training affect nurses' ability to perceive deterioration cues.

The Arkansas Active Kids Study: Identifying contributing factors to metabolic health and obesity status in prepubertal school-age children

2020 ◽  
pp. 026010602097557
Author(s):  
Shasha Bai ◽  
Anthony Goudie ◽  
Elisabet Børsheim ◽  
Judith L Weber
Keyword(s):  
Physical Activity ◽  
Vital Signs ◽  
Statistical Analysis Plan ◽  
Metabolic Health ◽  
Environmental Data ◽  
School Age Children ◽  
School Age ◽  
Contributing Factors ◽  
Activity Data ◽  
Obesity Status

Background: We report the design, protocol and statistical analysis plan for the Arkansas Active Kids (AAK) Study. The study investigates the complex relationships between factors that contribute to metabolic health and obesity status in prepubertal school-age children in the state of Arkansas. Aim: We aim to identify modifiable behavioral and environmental factors and phenotypes related to metabolic health that are associated with obesity status that, if addressed effectively, can aid in designing effective intervention strategies to improve fitness and reduce obesity in children. Methods: We analyzed dietary and physical activity data from two national surveys (National Survey of Children’s Health and Youth Risk Behavior Surveillance System). We then conducted detailed surveys to collect dietary, physical activity, socio-demographic, and environmental data from a sample of 226 prepubertal Arkansas children. In the same sample of prepubertal children, we also collected extensive physiologic data to further study associations between physical activity and metabolic health. Results: All study visits included detailed measures of vital signs, energy expenditure, components of physical fitness, body composition and the collection of biological samples for determination of metabolic analytes. Conclusion: The observational, environmental and physiological results will be used to craft multivariate statistical models to identify which variables define ‘phenotype signatures’ that associate with fitness level and obesity status.

scholarly journals Multi-Parameter Vital Sign Telemedicine System Using Web Socket for COVID-19 Pandemics

Healthcare ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 285
Author(s):  
Chuchart Pintavirooj ◽  
Tanapon Keatsamarn ◽  
Treesukon Treebupachatsakul
Keyword(s):  
Real Time ◽  
Vital Sign ◽  
Vital Signs ◽  
Mobile Network ◽  
Cloud Platform ◽  
Telemedicine System ◽  
Web Based ◽  
The Real ◽  
Real Time Application ◽  
Commercial Platform

Telemedicine has become an increasingly important part of the modern healthcare infrastructure, especially in the present situation with the COVID-19 pandemics. Many cloud platforms have been used intensively for Telemedicine. The most popular ones include PubNub, Amazon Web Service, Google Cloud Platform and Microsoft Azure. One of the crucial challenges of telemedicine is the real-time application monitoring for the vital sign. The commercial platform is, by far, not suitable for real-time applications. The alternative is to design a web-based application exploiting Web Socket. This research paper concerns the real-time six-parameter vital-sign monitoring using a web-based application. The six vital-sign parameters are electrocardiogram, temperature, plethysmogram, percent saturation oxygen, blood pressure and heart rate. The six vital-sign parameters were encoded in a web server site and sent to a client site upon logging on. The encoded parameters were then decoded into six vital sign signals. Our proposed multi-parameter vital-sign telemedicine system using Web Socket has successfully remotely monitored the six-parameter vital signs on 4G mobile network with a latency of less than 5 milliseconds.

RF Vital Sign Sensing under Free Body Movement

2021 ◽  
Vol 5 (3) ◽  
pp. 1-22
Author(s):  
Jian Gong ◽  
Xinyu Zhang ◽  
Kaixin Lin ◽  
Ju Ren ◽  
Yaoxue Zhang ◽  
...  
Keyword(s):  
Vital Sign ◽  
Estimation Error ◽  
Body Movement ◽  
Vital Signs ◽  
Motion Artifacts ◽  
Body Parts ◽  
Training Scheme ◽  
Pattern Movement ◽  
Free Body ◽  
Average Estimation Error

Radio frequency (RF) sensors such as radar are instrumental for continuous, contactless sensing of vital signs, especially heart rate (HR) and respiration rate (RR). However, decades of related research mainly focused on static subjects, because the motion artifacts from other body parts may easily overwhelm the weak reflections from vital signs. This paper marks a first step in enabling RF vital sign sensing under ambulant daily living conditions. Our solution is inspired by existing physiological research that revealed the correlation between vital signs and body movement. Specifically, we propose to combine direct RF sensing for static instances and indirect vital sign prediction based on movement power estimation. We design customized machine learning models to capture the sophisticated correlation between RF signal pattern, movement power, and vital signs. We further design an instant calibration and adaptive training scheme to enable cross-subjects generalization, without any explicit data labeling from unknown subjects. We prototype and evaluate the framework using a commodity radar sensor. Under a variety of moving conditions, our solution demonstrates an average estimation error of 5.57 bpm for HR and 3.32 bpm for RR across multiple subjects, which largely outperforms state-of-the-art systems.

Abstract 18150: Prediction of Imminent Deterioration of Children after Stage I Palliation Using Real-Time Processing of Physiological Data

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Craig G Rusin ◽  
Sebastian I Acosta ◽  
Eric L Vu ◽  
Risa B Myers ◽  
Kenneth M Brady ◽  
...  
Keyword(s):  
Early Life ◽  
Physiological State ◽  
Vital Signs ◽  
Clinical Deterioration ◽  
Physiological Data ◽  
Real Time Processing ◽  
Life Saving ◽  
St Segment ◽  
The Hours ◽  
Roc Area

Patients after stage 1 palliation (S1P) for hypoplastic left heart syndrome (HLHS) and related lesions are at risk of life threatening deterioration resulting in shock, cardiac arrest, & hypoxemia. We hypothesize that these sudden deteriorations may be forecast by subtle, previously unidentified changes in cardiorespiratory dynamics. Identification of these precursors may provide an opportunity for early, life-saving intervention. We created complete high-resolution physiological recordings for all patients who had a primary admission of S1P after Jan. 1, 2013. We used the SickbayTM system (Medical Informatics Corp, Houston, TX) to collect high frequency physiological waveforms including EKG, ABP, LAP, SpO2 and Chest Impedance (60Hz - 240Hz), as well as HR, RR, Temp. and ST segment vital signs (0.5 Hz) during the patient’s interstage hospitalization. A logistic regression model was constructed to discriminate between physiological characteristics observed in the hours prior to deterioration from the characteristics observed >24 hours prior to or >96 hours after a clinical deterioration. Model validation was done using a standard bagging approach with a REPtree classifier and 10 fold cross validation. Twenty five patients were included in the study. Of these, 15 (60%) were found to have one or more deterioration events (arrest, CPR, unplanned intubation), with 24 total events observed during the interstage period. Characteristics associated with imminent deterioration were low SpO2 and depressed ST segment. Changes in physiological dynamics could be detected 1-2 hours before overt deterioration occurs (ROC area = 0.89) (Figure 1). This altered physiological state remains for ~96 hours after deterioration. In conclusion, it is possible to identify clinical deterioration in HLHS patients during their interstage period ~1-2 hours before overt deterioration occurs, providing an opportunity for early, life-saving intervention to be administered.

scholarly journals A Prospective Study of Nighttime Vital Sign Monitoring Frequency and Risk of Clinical Deterioration

2013 ◽  
Vol 173 (16) ◽  
pp. 1554 ◽  
Author(s):  
Jordan C. Yoder ◽  
Trevor C. Yuen ◽  
Matthew M. Churpek ◽  
Vineet M. Arora ◽  
Dana P. Edelson
Keyword(s):  
Prospective Study ◽  
Vital Sign ◽  
Clinical Deterioration ◽  
Monitoring Frequency ◽  
A Prospective Study

scholarly journals The association between abnormal vital sign groups and undesirable patient outcomes

2018 ◽  
Vol 25 (3) ◽  
pp. 137-145
Author(s):  
Marina Lee ◽  
David McD Taylor ◽  
Antony Ugoni
Keyword(s):  
Emergency Department ◽  
Confidence Interval ◽  
Patient Outcomes ◽  
Vital Sign ◽  
Odds Ratio ◽  
Medical Emergency Team ◽  
Vital Signs ◽  
Medical Emergency ◽  
Emergency Team ◽  
Abnormal Vital Sign

Introduction: To determine the association between both abnormal individual vital signs and abnormal vital sign groups in the emergency department, and undesirable patient outcomes: hospital admission, medical emergency team calls and death. Method: We undertook a prospective cohort study in a tertiary referral emergency department (February–May 2015). Vital signs were collected prospectively in the emergency department and undesirable outcomes from the medical records. The primary outcomes were undesirable outcomes for individual vital signs (multivariate logistic regression) and vital sign groups (univariate analyses). Results: Data from 1438 patients were analysed. Admission was associated with tachycardia, tachypnoea, fever, ≥1 abnormal vital sign on admission to the emergency department, ≥1 abnormal vital sign at any time in the emergency department, a persistently abnormal vital sign, and vital signs consistent with both sepsis (tachycardia/hypotension/abnormal temperature) and pneumonia (tachypnoea/fever) (p < 0.05). Medical emergency team calls were associated with tachycardia, tachypnoea, ≥1 abnormal vital sign on admission (odds ratio: 2.3, 95% confidence interval: 1.4–3.8), ≥2 abnormal vital signs at any time (odds ratio: 2.4, 95% confidence interval: 1.2–4.7), and a persistently abnormal vital sign (odds ratio: 2.7, 95% confidence interval: 1.6–4.6). Death was associated with Glasgow Coma Score ≤13 (odds ratio: 6.3, 95% confidence interval: 2.5–16.0), ≥1 abnormal vital sign on admission (odds ratio: 2.6, 95% confidence interval: 1.2–5.6), ≥2 abnormal vital signs at any time (odds ratio: 6.4, 95% confidence interval: 1.4–29.5), a persistently abnormal vital sign (odds ratio: 4.3, 95% confidence interval: 2.0–9.0), and vital signs consistent with pneumonia (odds ratio: 5.3, 95% confidence interval: 1.9–14.8). Conclusion: Abnormal vital sign groups are generally superior to individual vital signs in predicting undesirable outcomes. They could inform best practice management, emergency department disposition, and communication with the patient and family.

Sign in / Sign up

Export Citation Format

Share Document