Monitoring vital signs: development of a modified early warning scoring (MEWS) system for general wards in a developing country

Validating the Performance of Modified Early Obstetrics Warning Score (MEOWS) for Prediction of Obstetrics Morbidity: A Prospective Observational Study in a Tertiary Care Institute in East India

Background

An early warning score can be used to identify worsening in obstetric patients as they are more prone for deterioration. This study was conducted to evaluate and validate the performance of the modified early obstetric warning system (MEOWS) as a screening tool for early prediction of severe obstetric morbidity.

Methods

This prospective observational study was conducted at obstetrics and gynaecology department, Tata Main Hospital, Jamshedpur, Jharkhand, India. A total of 1800 patients were included over a period of 10 month, from December 2021 to September 2022. Study population included all pregnant women admitted in labour room with > 28 weeks of gestation till 6 weeks postpartum. MEOWS charts were plotted for each patient, score calculated and documented at admission. Patients were categorized based on this score for further management, and follow-up was made till discharge. Outcome assessment was done in terms of ICU admission, length of hospital stays, obstetric morbidity, and maternal mortality. Correlation of each outcome with scoring was evaluated.

Results

The sensitivity of MEOWS in predicting maternal morbidity was 72.91%, specificity 91.87%, positive predictive value 85.96%, and negative predictive value 83.24%. The area under receiver operator characteristic of MEOWS for prediction of maternal mortality was 0.79 (95% CI 0.75-0.82).

Conclusion

MEOWS helps in early recognition of obstetric morbidity even before signs, and symptoms become clinically evident. It is a useful tool for predicting adverse maternal outcome in pregnant women.

Development of a Pediatric Behavioral Early Warning Scale (Pedi-BEWS) for Children

Background and Purpose: The prevention of aggressive behavior in child and adolescent inpatient settings is essential. However, it can be difficult to prompt a quick appropriate intervention without an early warning scale for the prevention and management of behavioral emergencies in a psychiatric inpatient adolescent unit. Behavioral emergencies often result in restraint/seclusion and/or administration of psychotropic medications. The objectives are to develop and evaluate the Pediatric Behavioral Early Warning Scale (Pedi-BEWS) as a screening tool to prompt quick appropriate interventions and to decrease incidents of restraint/seclusion and/or administration of psychotropic medications. Methods: A two-phase methodological design was applied. A total of 447 inpatients and 21 nurses were used to test internal consistency reliability and validity. Results: Receiver operating characteristic curve areas for the developed instrument were calculated to be 0.890 (cognition), 0.959 (affect), and 0.951 (behavior). The overall Pedi-BEWS indicated high reliability (Cronbach's alpha = .98). Conclusions: The Pedi-BEWS shows high internal consistency and validity. The use of the newly developed tool may reduce or eliminate episodes of seclusion and restraint for pediatric patients with a behavioral problem. In addition, the tool has the potential to enhance psychiatric nurses' assessment skills and competencies. The use of the newly developed Pedi-BEWS can promote an appropriate and timely nursing assessment and intervention before the deterioration in psychiatric nursing practice.

Predictors of intensive care unit admission in adult cancer patients presenting to the emergency department with COVID-19 infection: A retrospective study

BACKGROUND

Adult cancer patients with COVID-19 were shown to be at higher risk of Intensive Care Unit (ICU) admission. Previously published prediction models showed controversy and enforced the importance of heterogeneity among different populations studied. Therefore, this study aimed to identify predictors of ICU admission (demographic, clinical, and COVID-19 targeted medications) in cancer patients with active COVID-19 infection presenting to the Emergency Department (ED).

METHODS

This is a retrospective cohort study. It was conducted on adult cancer patients older than 18 years who presented to the American University of Beirut Medical Center ED from February 21, 2020, till February 21, 2021, and were found to have COVID-19 infection. Relevant data were extracted from electronic medical records. The association between different variables and ICU admission was tested. Logistic regression was done to adjust for confounding variables. A p-value less than 0.05 was considered significant.

RESULTS

Eighty-nine distinct patients were included. About 37% were admitted to the ICU (n = 33). Higher ICU admission was seen in patients who had received chemotherapy within one month, had a respiratory rate at triage above 22 breaths per minute, oxygen saturation less than 95%, and a higher c-reactive protein upon presentation to the ED. After adjusting for confounding variables, only recent chemotherapy and higher respiratory rate at triage were significantly associated with ICU admission.

CONCLUSION

Physicians need to be vigilant when taking care of COVID-19 infected cancer patients. Patients who are tachypneic at presentation and those who have had chemotherapy within one month are at high risk for ICU admission.

Barriers to Quality Perioperative Care Delivery in Low- and Middle-Income Countries: A Qualitative Rapid Appraisal Study

BACKGROUND

Provision of timely, safe, and affordable surgical care is an essential component of any high-quality health system. Increasingly, it is recognized that poor quality of care in the perioperative period (before, during, and after surgery) may contribute to significant excess mortality and morbidity. Therefore, improving access to surgical procedures alone will not address the disparities in surgical outcomes globally until the quality of perioperative care is addressed. We aimed to identify key barriers to quality perioperative care delivery for 3 "Bellwether" procedures (cesarean delivery, emergency laparotomy, and long-bone fracture fixation) in 5 low- and middle-income countries (LMICs).

METHODS

Ten hospitals representing secondary and tertiary facilities from 5 LMICs were purposefully selected: 2 upper-middle income (Colombia and South Africa); 2 lower-middle income (Sri Lanka and Tanzania); and 1 lower income (Uganda). We used a rapid appraisal design (pathway mapping, ethnography, and interviews) to map out and explore the complexities of the perioperative pathway and care delivery for the Bellwether procedures. The framework approach was used for data analysis, with triangulation across different data sources to identify barriers in the country and pattern matching to identify common barriers across the 5 LMICs.

RESULTS

We developed 25 pathway maps, undertook >30 periods of observation, and held >40 interviews with patients and clinical staff. Although the extent and impact of the barriers varied across the LMIC settings, 4 key common barriers to safe and effective perioperative care were identified: (1) the fragmented nature of the care pathways, (2) the limited human and structural resources available for the provision of care, (3) the direct and indirect costs of care for patients (even in health systems for which care is ostensibly free of charge), and (4) patients' low expectations of care.

CONCLUSIONS

We identified key barriers to effective perioperative care in LMICs. Addressing these barriers is important if LMIC health systems are to provide safe, timely, and affordable provision of the Bellwether procedures.

Technologies used by nursing to predict clinical deterioration in hospitalized adults: a scoping review

OBJECTIVE

to map the early clinical deterioration technologies used in nurses' professional practice in the care of hospitalized adult patients.

METHODS

this is a scoping review, according to Joanna Briggs Institute Reviewer's Manual, which seeks to map the main technologies for detecting early clinical deterioration of hospitalized patients available for use by nurses, summarizing them and indicating gaps in knowledge to be investigated.

RESULTS

twenty-seven studies were found. The most present variables in the technologies were vital signs, urinary output, awareness and risk scales, clinical examination and nurses' judgment. The main outcomes were activation of rapid response teams, death, cardiac arrest and admission to critical care units.

FINAL CONSIDERATIONS

the study emphasizes the most accurate variables in patient clinical assessment, so that indicative signs of potential severity can be prioritized to guide health conducts aiming to intervene early in the face of ongoing clinical deterioration.

Tecnologias utilizadas pela enfermagem para predição de deterioração clínica em adultos hospitalizados: revisão de escopo

RESUMO Objetivo: mapear as tecnologias de deterioração clínica precoce utilizadas na prática profissional do enfermeiro na assistência a pacientes adultos hospitalizados. Métodos: trata-se de scoping review, segundo Joanna Briggs Institute Reviewer’s Manual, que busca o mapeamento das principais tecnologias para detecção de deterioração clínica precoce de pacientes hospitalizados disponíveis de uso do enfermeiro, sumarizando-as e indicando lacunas no conhecimento a serem investigadas. Resultados: foram encontrados 27 estudos. As variáveis mais presentes nas tecnologias foram sinais vitais, débito urinário, escalas de consciência e riscos, exame clínico e julgamento do enfermeiro. Os principais desfechos foram acionamento de times de resposta rápida, morte, parada cardiorrespiratória e admissão em unidades de cuidados críticos. Considerações finais: o estudo enfatiza as variáveis mais acuradas na avaliação clínica do paciente, para que se possam priorizar sinais indicativos de potencial gravidade para guiar condutas em saúde visando intervir precocemente diante da deterioração clínica em curso.

Estimating COVID-19 cases and outbreaks on-stream through phone calls

One of the main problems in controlling COVID-19 epidemic spread is the delay in confirming cases. Having information on changes in the epidemic evolution or outbreaks rise before laboratory-confirmation is crucial in decision making for Public Health policies. We present an algorithm to estimate on-stream the number of COVID-19 cases using the data from telephone calls to a COVID-line. By modelling the calls as background (proportional to population) plus signal (proportional to infected), we fit the calls in Province of Buenos Aires (Argentina) with coefficient of determination R ² > 0.85. This result allows us to estimate the number of cases given the number of calls from a specific district, days before the laboratory results are available. We validate the algorithm with real data. We show how to use the algorithm to track on-stream the epidemic, and present the Early Outbreak Alarm to detect outbreaks in advance of laboratory results. One key point in the developed algorithm is a detailed track of the uncertainties in the estimations, since the alarm uses the significance of the observables as a main indicator to detect an anomaly. We present the details of the explicit example in Villa Azul (Quilmes) where this tool resulted crucial to control an outbreak on time. The presented tools have been designed in urgency with the available data at the time of the development, and therefore have their limitations which we describe and discuss. We consider possible improvements on the tools, many of which are currently under development.

Implementation of a Pediatric Early Warning Score to Improve Communication and Nursing Empowerment in a Rural District Hospital in Rwanda

Implementation of the Pediatric Early Warning Score for Resource-Limited Settings tool improved nurses’ competency and confidence in their triage capabilities. This tool has the potential to improve patient outcomes. However, staff turnover and limited physician buy-in were barriers to sustainability of the tool in low-resource settings.

Background:

Pediatric early warning (PEW) scores represent a “track-and-trigger system” that identifies clinical deterioration in a patient’s condition in the hours preceding a sentinel event. Before implementation, nurses reported feeling unprepared to identify and advocate for acutely ill patients owing to a lack of skills, vocabulary, and agency. We implemented a Pediatric Early Warning Score for Resource-Limited Settings (PEWS-RL) with nurses in a rural district hospital in Rwanda. Although PEW scores can improve clinical outcomes, empowering nurses in resource-limited settings to discuss patient acuity with physicians is a critical first step. Our primary aims were to train nurses to obtain more accurate vital signs and assess their importance as early warning signs of clinical deterioration and use PEW scores to improve communication between nurses and physicians.

Implementation:

The PEWS-RL tool implementation began with a training program that was created through discussions with nurses, physicians, and the medical director of the hospital. The program included lectures and application of learned skills through direct clinical mentorship of nurses, as well as training of physicians regarding PEWS-RL as a communication tool.

Evaluation:

The PEWS-RL protocol was evaluated based on pre- and post-tests to assess improvement in nurses’ knowledge and skill, as well as skills assessments of accurate recognition of clinical deterioration. All 6 nurses passed skill testing with >80% accuracy. Nurses’ feelings of empowerment to advocate for patients and to escalate care were assessed through pre- and post-training interviews. Nurses described increased confidence in calling for physician support.

Discussion:

Implementation of PEW scores increased nurses’ technical skills and feelings of confidence and empowerment; however, the low-resource setting presented major challenges. Barriers to sustainable implementation include the rapid ward staff turnover as well as limited physician buy-in. Nevertheless, the PEWS-RL tool has the potential to empower nurses and improve patient outcomes if fully embraced by staff.

The Promise of Clinical Decision Support Systems Targetting Low-Resource Settings

Low-resource clinical settings are plagued by low physician-to-patient ratios and a shortage of high-quality medical expertise and infrastructure. Together, these phenomena lead to over-burdened healthcare systems that under-serve the needs of the community. Alleviating this burden can be undertaken by the introduction of clinical decision support systems (CDSSs); systems that support stakeholders (ranging from physicians to patients) within the clinical setting in their day-to-day activities. Such systems, which have proven to be effective in the developed world, remain to be under-explored in low-resource settings. This review attempts to summarize the research focused on clinical decision support systems that either target stakeholders within low-resource clinical settings or diseases commonly found in such environments. When categorizing our findings according to disease applications, we find that CDSSs are predominantly focused on dealing with bacterial infections and maternal care, do not leverage deep learning, and have not been evaluated prospectively. Together, these highlight the need for increased research in this domain in order to impact a diverse set of medical conditions and ultimately improve patient outcomes.

Early warning scores for detecting deterioration in adult hospital patients: systematic review and critical appraisal of methodology

OBJECTIVE

To provide an overview and critical appraisal of early warning scores for adult hospital patients.

DESIGN

Systematic review.

DATA SOURCES

Medline, CINAHL, PsycInfo, and Embase until June 2019.

ELIGIBILITY CRITERIA FOR STUDY SELECTION

Studies describing the development or external validation of an early warning score for adult hospital inpatients.

RESULTS

13 171 references were screened and 95 articles were included in the review. 11 studies were development only, 23 were development and external validation, and 61 were external validation only. Most early warning scores were developed for use in the United States (n=13/34, 38%) and the United Kingdom (n=10/34, 29%). Death was the most frequent prediction outcome for development studies (n=10/23, 44%) and validation studies (n=66/84, 79%), with different time horizons (the most frequent was 24 hours). The most common predictors were respiratory rate (n=30/34, 88%), heart rate (n=28/34, 83%), oxygen saturation, temperature, and systolic blood pressure (all n=24/34, 71%). Age (n=13/34, 38%) and sex (n=3/34, 9%) were less frequently included. Key details of the analysis populations were often not reported in development studies (n=12/29, 41%) or validation studies (n=33/84, 39%). Small sample sizes and insufficient numbers of event patients were common in model development and external validation studies. Missing data were often discarded, with just one study using multiple imputation. Only nine of the early warning scores that were developed were presented in sufficient detail to allow individualised risk prediction. Internal validation was carried out in 19 studies, but recommended approaches such as bootstrapping or cross validation were rarely used (n=4/19, 22%). Model performance was frequently assessed using discrimination (development n=18/22, 82%; validation n=69/84, 82%), while calibration was seldom assessed (validation n=13/84, 15%). All included studies were rated at high risk of bias.

CONCLUSIONS

Early warning scores are widely used prediction models that are often mandated in daily clinical practice to identify early clinical deterioration in hospital patients. However, many early warning scores in clinical use were found to have methodological weaknesses. Early warning scores might not perform as well as expected and therefore they could have a detrimental effect on patient care. Future work should focus on following recommended approaches for developing and evaluating early warning scores, and investigating the impact and safety of using these scores in clinical practice.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42017053324.

Implementing the National Early Warning Score 2 into pre-registration nurse education

Recognising signs of deteriorating health in patients and responding to them appropriately are crucial nursing competencies. In acute care, failure to detect and act promptly on deterioration can lead to the patient's death. To achieve clinical competence, nursing students require training in the use of techniques for monitoring physiological observations as well as protocols that enable them to respond to deterioration. The use of early warning scores has been advocated to standardise the methods and frequency of patient monitoring in acute care settings. In 2012, the Royal College of Physicians developed the National Early Warning Score (NEWS), which was updated in 2017 and known as NEWS2. This early warning score is used in acute hospitals in England, Scotland, Wales and Northern Ireland. This article explores the benefits and challenges of using NEWS2 as an educational tool in pre-registration nursing programmes to support nursing students in recognising and responding to deteriorating health.

Vital Signs Data and Probability of Hospitalization, Transfer to Another Facility, or Emergency Department Death Among Adults Presenting for Medical Illnesses to the Emergency Department at a Large Urban Hospital in the United States

BACKGROUND

Vital signs are routinely measured from patients presenting to the emergency department (ED), but how they predict clinical outcomes like hospitalization is unclear.

OBJECTIVES

To evaluate how pulse, respiratory rate, temperature, and mean arterial pressure (MAP) at ED presentation predicted probability of hospitalization, transfer to another center, or death in the ED (as a composite outcome) vs. other ED dispositions (discharged, eloped, and sent to observation or labor and delivery), and to assess the performance of different modeling strategies, specifically, models including flexible forms of vital signs (as restricted cubic splines) vs. linear forms (untransformed numeric variables) vs. binary transformations (vital signs values categorized simply as normal or abnormal).

METHODS

We analyzed the data of 12,660 adults presenting for medical illnesses to the ED at the University of California, San Francisco Medical Center, San Francisco, California, throughout 2014. We used flexible forms of vital signs data at presentation (pulse, temperature, respiratory rate, and MAP) to predict ED disposition (admitted, transferred, or died, vs. other ED dispositions) and to guide binary transformation of vital signs. We compared performance of models including vital signs as flexible terms, binary transformations, or linear terms.

RESULTS

A model including flexible forms of vital signs and age to predict the outcome had good calibration and moderate discrimination (C-statistic = 71.2, 95% confidence interval [CI] 70.0-72.4). Binary transformation of vital signs had minimal impact on performance (C-statistic = 71.3, 95% CI 70.2-72.5). A model with linear forms was less calibrated and had slightly reduced discrimination (C-statistic = 70.3, 95% CI 69.1-71.5).

CONCLUSIONS

Findings suggest that flexible modeling of vital signs may better reflect their association with clinical outcomes. Future studies to evaluate how vital signs could assist clinical decision-making in acute care settings are suggested.

Testing effectiveness of the revised Cape Town modified early warning and SBAR systems: a pilot pragmatic parallel group randomised controlled trial

BACKGROUND

Nurses' recognition of clinical deterioration is crucial for patient survival. Evidence for the effectiveness of modified early warning scores (MEWS) is derived from large observation studies in developed countries.

METHODS

We tested the effectiveness of the paper-based Cape Town (CT) MEWS vital signs observation chart and situation-background-assessment-recommendation (SBAR) communication guide. Outcomes were: proportion of appropriate responses to deterioration, differences in recording of clinical parameters and serious adverse events (SAEs) in intervention and control trial arms. Public teaching hospitals for adult patients in Cape Town were randomised to implementation of the CT MEWS/SBAR guide or usual care (observation chart without track-and-trigger information) for 31 days on general medical and surgical wards. Nurses in intervention wards received training, as they had no prior knowledge of early warning systems. Identification and reporting of patient deterioration in intervention and control wards were compared. In the intervention arm, 24 day-shift and 23 night-shift nurses received training. Clinical records were reviewed retrospectively at trial end. Only records of patients who had given signed consent were reviewed.

RESULTS

We recruited two of six CT general hospitals. We consented 363 patients and analysed 292 (80.4%) patient records (n = 150, 51.4% intervention, n = 142, 48.6% control arm). Assistance was summoned for fewer patients with abnormal vital signs in the intervention arm (2/45, 4.4% versus (vs) 11/81, 13.6%, OR 0.29 (0.06-1.39)), particularly low systolic blood pressure. There was a significant difference in recording between trial arms for parameters listed on the MEWS chart but omitted from the standard observations chart: oxygen saturation, level of consciousness, pallor/cyanosis, pain, sweating, wound oozing, pedal pulses, glucose concentration, haemoglobin concentration, and "looks unwell". SBAR was used twice. There was no statistically significant difference in SAEs (5/150, 3.3% vs 3/143, 2.1% P = 0.72, OR 1.61 (0.38-6.86)).

CONCLUSIONS

The revised CT MEWS observations chart improved recording of certain parameters, but did not improve nurses' ability to identify early signs of clinical deterioration and to summon assistance. Recruitment of only two hospitals and exclusion of patients too ill to consent limits generalisation of results. Further work is needed on educational preparation for the CT MEWS/SBAR and its impact on nurses' reporting behaviour.

TRIAL REGISTRATION

Pan African Clinical Trials Registry, PACTR201406000838118. Registered on 2 June 2014, www.pactr.org.

A Data-Driven Approach to Predicting Septic Shock in the Intensive Care Unit

Early diagnosis of sepsis and septic shock has been unambiguously linked to lower mortality and better patient outcomes. Despite this, there is a strong unmet need for a reliable clinical tool that can be used for large-scale automated screening to identify high-risk patients. We addressed the following questions: Can a novel algorithm to identify patients at high risk of septic shock 24 hours before diagnosis be discovered using available clinical data? What are performance characteristics of this predictive algorithm? Can current metrics for evaluation of sepsis be improved using novel algorithm? Publicly available data from the intensive care unit setting was used to build septic shock and control patient cohorts. Using Bayesian networks, causal relationships between diagnosis groups, procedure groups, laboratory results, and demographic data were inferred. Predictive model for septic shock 24 hours prior to digital diagnosis was built based on inferred causal networks. Sepsis risk scores were augmented by de novo inferred model and performance was evaluated. A novel predictive model to identify high-risk patients 24 hours ahead of time, with area under curve of 0.81, negative predictive value of 0.87, and a positive predictive value as high as 0.65 was built. The specificity of quick sequential organ failure assessment, systemic inflammatory response syndrome, and modified early warning score was improved when augmented with the novel model, whereas no improvements were made to the sequential organ failure assessment score. We used a data-driven, expert knowledge agnostic method to build a screening algorithm for early detection of septic shock. The model demonstrates strong performance in the data set used and provides a basis for expanding this work toward building an algorithm that is used to screen patients based on electronic medical record data in real time.

The use of the Modified Early Obstetric Warning System (MEOWS) in women after pregnancies: a descriptive study

Abstract Objectives: to evaluate the modified early obstetric warning system (MEOWS) in women after pregnancies in a tertiary hospital in Brazil. Methods: a descriptive study was conducted with 705 hospitalized women. Vital signs (systolic and diastolic blood pressure, heart rate, respiratory rate, temperature) and lochia were registered on medical records and transcribed into the MEOWS chart of physiological parameters. On this graphic chart, yellow alerts were used to present moderate abnormalities in vital signs, while severe abnormalities were presented in red. The presence of at least one red alert or two yellow alerts were triggered to indicate the need for medical evaluation. Results: although abnormalities were found in the physiological parameters of 49.8% of the women identified from MEOWS triggers, medical evaluation was only requested for three patients (0.8%). Conclusions: in a retrospective application of the use of MEOWS showed a significant number of patients had triggered in which the nursing team did not recognize 99.2% of cases. This finding could be attributed to the fact that MEOWS has not been yet adopted in this service as part of the nursing care. The application of this tool would result in a better care because critical situations would be recognized and corrected quickly, avoiding unfavorable outcomes.

Inability to Walk Predicts Death among Adult Patients in Hospitals in Malawi

Objective

Vital signs are often used in triage, but some may be difficult to assess in low-resource settings. A patient's ability to walk is a simple and rapid sign that requires no equipment or expertise. This study aimed to determine the predictive performance for death of an inability to walk among hospitalized Malawian adults and to compare its predictive value with the vital signs-based National Early Warning Score (NEWS).

Methods

It is a prospective cohort study of adult in-patients on selected days in two hospitals in Malawi. Patients were asked to walk five steps with close observation and their vital signs were assessed. Sensitivities, specificities, and predictive values for in-patient death of an inability to walk were calculated and an inability to walk was compared with NEWS.

Results

Four-hundred and forty-three of the 1094 participants (40.5%) were unable to walk independently. In this group, 70 (15.8 %) died in-hospital compared to 16 (2.5%) among those who could walk: OR 7.4 (95% CI 4.3-13.0 p<0.001). Inability to walk had a sensitivity for death of 81.4%, specificity of 63.0%, positive predictive value (PPV) of 15.8%, and negative predictive value (NPV) of 97.5%. NEWS>6 had sensitivity 70.9%, specificity 70.6%, PPV 17.1%, and NPV 96.6%. An inability to walk had a fair concordance with NEWS>6 (kappa 0.21).

Conclusion

Inability to walk predicted mortality as well as NEWS among hospitalized adults in Malawi. Patients who were able to walk had a low risk of death. Walking ability could be considered an additional vital sign and may be useful for triage.

Disaster Early Warning Systems: The Potential Role and Limitations of Emerging Text and Data Messaging Mitigation Capabilities

OBJECTIVE

The increased risk of mass accidents or major catastrophes taking place necessitates the organization of remedial measures to help protect against these unusual events and adequate preparation in order to minimize their effects. One such initiative is the early notification of residents within a specific area about the risk of a particular calamity. Nowadays, the prevalence of mobile devices enables the installation of various mobile applications allowing for the communication and receiving of information about potential dangers. In many countries there are variously developed systems of notification in place based specifically on text messages.

METHODS

Currently, new laws introduced in Poland establish that it is the obligation of operators of mobile networks to send text messages to all customers of these networks who are within the area where there is a serious risk of a catastrophe. Such messages are in the form of a short alert, to be sent only in extraordinary situations when there is an immediate threat to health or life. The alert is intended to help in the avoidance of danger or to mitigate its impact.

RESULTS

This article presents the potential implementation of the early warning system based on text message alerts in Poland, and in particular focuses on decreasing the risks associated with natural disasters.

CONCLUSIONS

While early text messaging is essential to disaster communications and mitigation, the article further states that means must be found to ensure equal access to the most vulnerable populations and all those, vulnerable and not, who do not have immediate access to text messaging systems. (Disaster Med Public Health Preparedness. 2019;13:709-712).

Management of abnormal observations in the emergency department: A review

OBJECTIVE

To assess utility and accuracy of general observation modified early warning score charts; and compare sensitivity and specificity of single- and multiple-parameter-based trigger scores on patient outcomes in the ED.

METHODS

Retrospective cohort clinical audit of all adult Modified Early Warning Score charts in the ED of a mixed tertiary hospital over 4 weeks. Data extracted included recorded parameters required to calculate Modified Early Warning Score and evidence of response.

RESULTS

Of 5901 ED presentations, medical records system identified 2482 Modified Early Warning Score; 347 were missing or blank. Of 2135 Modified Early Warning Score charts, 19.5% contained a calculation error, 51.9% had one or more missing parameters and 36.6% did not have usual/target systolic blood pressure recorded; with 25.1% (95% confidence interval [CI] 23.3-27.0) charts correctly completed. Four hundred and forty-three had a single-abnormal parameter of which chart review showed 96.6% (94.5-97.3) were identified as abnormal by nurses with 25.7% (21.9-30.0); only 5.6% (3.9-8.2) had evidence of recognition by medical staff. Modified Early Warning Score sensitivity and specificity for ward admission was 14.7% and 96.1%, respectively. Modelling using the dataset of a single-abnormal parameter suggested sensitivity and specificity of 31.6% and 85.8%, respectively.

CONCLUSIONS

This study highlights serious deficiencies in documentation of abnormal parameters and emergency response. It has also shown poor accuracy of both single- and multiple-parameter-based trigger scores in predicting patient outcomes within the ED. However, single-parameter-based trigger scores are twice as sensitive as total Modified Early Warning Score for admission and reduces documentation error by 23%.

Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards

BACKGROUND

Large amounts of patient data are routinely manually collected in hospitals by using standalone medical devices, including vital signs. Such data is sometimes stored in spreadsheets, not forming part of patients' electronic health records, and is therefore difficult for caregivers to combine and analyze. One possible solution to overcome these limitations is the interconnection of medical devices via the Internet using a distributed platform, namely the Internet of Things. This approach allows data from different sources to be combined in order to better diagnose patient health status and identify possible anticipatory actions.

METHODS

This work introduces the concept of the Internet of Health Things (IoHT), focusing on surveying the different approaches that could be applied to gather and combine data on vital signs in hospitals. Common heuristic approaches are considered, such as weighted early warning scoring systems, and the possibility of employing intelligent algorithms is analyzed.

RESULTS

As a result, this article proposes possible directions for combining patient data in hospital wards to improve efficiency, allow the optimization of resources, and minimize patient health deterioration.

CONCLUSION

It is concluded that a patient-centered approach is critical, and that the IoHT paradigm will continue to provide more optimal solutions for patient management in hospital wards.

Patient attitudes towards remote continuous vital signs monitoring on general surgery wards: An interview study

BACKGROUND

Vital signs monitoring is used to identify deteriorating patients in hospital. The most common tool for vital signs monitoring is an early warning score, although emerging technologies allow for remote, continuous patient monitoring. A number of reviews have examined the impact of continuous monitoring on patient outcomes, but little is known about the patient experience. This study aims to discover what patients think of monitoring in hospital, with a particular emphasis on intermittent early warning scores versus remote continuous monitoring, in order to inform future implementations of continuous monitoring technology.

METHODS

Semi-structured interviews were undertaken with 12 surgical inpatients as part of a study testing a remote continuous monitoring device. All patients were monitored with both an early warning score and the new device. Interviews were audio-recorded, transcribed verbatim and analysed using thematic analysis.

FINDINGS

Patients can see the value in remote, continuous monitoring, particularly overnight. However, patients appreciate the face-to-face aspect of early warning score monitoring as it allows for reassurance, social interaction, and gives them further opportunity to ask questions about their medical care.

CONCLUSION

Early warning score systems are widely used to facilitate detection of the deteriorating patient. Continuous monitoring technologies may provide added reassurance. However, patients value personal contact with their healthcare professionals and remote monitoring should not replace this. We suggest that remote monitoring is best introduced in a phased manner, and initially as an adjunct to usual care, with careful consideration of the patient experience throughout.

Respiratory Rate Variability as a Prognostic Factor in Hospitalized Patients Transferred to the Intensive Care Unit

Introduction

Increasing mortality rates within the intensive care unit (ICU) is an ever growing problem, ultimately leading to increases in the cost of healthcare expenditures. Currently, there are attempts to use guidelines in the hospital setting to predict overall mortality in critically ill patients. However, a predictor of subsequent ICU admissions remains to be explored. Recent data has shown the importance of monitoring respiratory rate variability (RRV) as a useful predictor of the deterioration of patients. Respiratory rate, in comparison to blood pressure or pulse rate, is deemed as the better determinant in identifying high-risk patients.

Aim

Our study aims to assess the role of RRV monitoring as a potential prognostic marker predictive of ICU admission.

Results

There was a significant (p = 0.009) increase in RRV between the third and fourth set of respiratory rates prior to ICU admission, such that coefficient of variation percentage (CV%) increased from 0.3% (95% confidence interval (CI): 0.09 - 0.42) to 0.7% (95% CI: 0.04 - 0.9) about 12 hours before admission to the ICU independent from diagnosis.

Conclusion

Using elevated RRV as a signal may be a useful prognostic tool in providing early intervention, thus reducing the incidence of subsequent morbidity and mortality in patients that might necessitate an ICU admission.

Comparison of Quick Sequential Organ Failure Assessment and Modified Systemic Inflammatory Response Syndrome Criteria in a Lower Middle Income Setting

INTRODUCTION

Quick Sequential Organ Failure Assessment (qSOFA) is potentially feasible tool to identify risk of deteriorating in the context of infection for to use in resource limited settings.

PURPOSE

To compare the discriminative ability of qSOFA and a simplified systemic inflammatory response syndrome (SIRS) score to detect deterioration in patients admitted with infection.

METHODS

Observational study conducted at District General Hospital Monaragala, Sri Lanka, utilising bedside available observations extracted from healthcare records. Discrimination was evaluated using area under the receiver operating curve (AUROC). 15,577 consecutive adult ( ≥ 18 years) admissions were considered. Patients classifi ed as having infection per ICD-10 diagnostic coding were included.

RESULTS

Both scores were evaluated for their ability to discriminate patients at risk of death or a composite adverse outcome (death, cardiac arrest, intensive care unit [ICU], admission or critical care transfer). 1844 admissions (11.8%) were due to infections with 20 deaths (1.1%), 29 ICU admissions (1.6%), 30 cardiac arrests and 9 clinical transfers to a tertiary hospital (0.5%). Sixty-seven (3.6%) patients experienced at least one event. Complete datasets were available for qSOFA in 1238 (67.14%) and for simplified SIRS (mSIRS) in 1628 (88.29%) admissions. Mean (SD) qSOFA score and mSIRS score at admission were 0.58 (0.69) and 0.66 (0.79) respectively. Both demonstrated poor discrimination for predicting adverse outcome AUROC = 0.625; 95% CI, 0.56-0.69 and AUROC = 0.615; 95% CI, 0.55-0.69 respectively) with no significant difference (p value = 0.74). Similarly, both systems had poor discrimination for predicting deaths (AUROC = 0.685; 95% CI, 0.55-0.82 and AUROC = 0.629; 95% CI, 0.50-0.76 respectively) with no statistically signifi cant difference (p value = 0.31).

CONCLUSIONS

qSOFA at admission had poor discrimination and was not superior to the bedside observations featured in SIRS. Availability of observations, especially for mentation, is poor in these settings and requires strategies to improve reporting.

Validation of a modified early warning score-linked Situation-Background-Assessment-Recommendation communication tool: A mixed methods study

AIMS AND OBJECTIVES

To develop and validate a modified Situation-Background-Assessment-Recommendation communication tool incorporating components of the Cape Town modified early warning score vital signs chart for reporting early signs of clinical deterioration.

BACKGROUND

Reporting early signs of physiological and clinical deterioration could prevent "failure to rescue" or unexpected intensive care admission, cardiac arrest or death. A structured communication tool incorporating physiological and clinical parameters allows nurses to provide pertinent information about a deteriorating patient in a logical order.

DESIGN

Mixed methods instrument development and validation.

METHODS

We used a sequential three-phase method: cognitive interviews, content validation and inter-rater reliability testing to validate a self-designed communication tool. Participants were purposively selected expert nurses and doctors in government sector hospitals in Cape Town.

RESULTS

Cognitive interviews with five experts prompted most changes to the communication tool: 15/42 (35.71%) items were modified. Content validation of a revised tool was high by a predetermined ≥70% of 18 experts: 4/49 (8.2%) items were modified. Inter-rater reliability testing by two nurses indicated substantial to full agreement (Cohen's kappa .61-1) on 37/45 (82%) items. The one item achieving slight agreement (Cohen's kappa .20) indicated a difference in clinical judgement. The high overall percentage agreement (82%) suggests that the modified items are sound. Overall, 45 items remained on the validated tool.

CONCLUSION

The first modified early warning score-linked Situation-Background-Assessment-Recommendation communication tool developed in South Africa was found to be valid and reliable in a local context.

RELEVANCE TO CLINICAL PRACTICE

Nurses in South Africa can use the validated tool to provide doctors with pertinent information about a deteriorating patient in a logical order to prevent a serious adverse event. Our findings provide a reference for other African countries to develop and validate communication tools for reporting early signs of clinical deterioration.

Can a Patient's In-Hospital Length of Stay and Mortality Be Explained by Early-Risk Assessments?

Objective

To assess whether a patient’s in-hospital length of stay (LOS) and mortality can be explained by early objective and/or physicians’ subjective-risk assessments.

Data Sources/Study Setting

Analysis of a detailed dataset of 1,021 patients admitted to a large U.S. hospital between January and September 2014.

Study Design

We empirically test the explanatory power of objective and subjective early-risk assessments using various linear and logistic regression models.

Principal Findings

The objective measures of early warning can only weakly explain LOS and mortality. When controlled for various vital signs and demographics, objective signs lose their explanatory power. LOS and death are more associated with physicians’ early subjective risk assessments than the objective measures.

Conclusions

Explaining LOS and mortality require variables beyond patients’ initial medical risk measures. LOS and in-hospital mortality are more associated with the way in which the human element of healthcare service (e.g., physicians) perceives and reacts to the risks.

High incidence of adverse events during intra-hospital transport of critically ill patients and new related risk factors: a prospective, multicenter study in China

BACKGROUND

The aim of the present study was to investigate the incidence of adverse events (AEs) during intra-hospital transport (IHT) of critically ill patients and evaluate the risk factors associated with these events.

METHODS

This prospective multicenter observational study was performed in 34 intensive care units in China during 20 consecutive days from 5 November to 25 November 2012. All consecutive patients who required IHT for diagnostic testing or therapeutic procedures during the study period were included. All AEs that occurred during IHT were recorded. The incidence of AEs was defined as the rate of transports with at least one AE. The statistical analysis included a description of demographic and clinical characteristics of the cohort as well as identification of risk factors for AEs during IHT by univariate and multivariate logistic regression analyses.

RESULTS

In total, 441 IHTs of 369 critically ill patients were analyzed. The overall incidence of AEs was 79.8% (352 IHTs). The proportion of equipment- and staff-related adverse events was 7.9% (35 IHTs). The rate of patient-related adverse events (P-AEs) was 79.4% (349 IHTs). The rates of vital sign-related P-AEs and arterial blood gas analysis-related P-AEs were 57.1% (252 IHTs) and 46.9% (207 IHTs), respectively. The incidence of critical P-AEs was 33.1% (146 IHTs). The rates of vital sign-related critical P-AEs and arterial blood gas analysis-related critical P-AEs were 22.9% (101 IHTs) and 15.0% (66 IHTs), respectively. All data collected in our study were considered potential risk factors. In the multivariate analysis, predictive factors for P-AEs were pH, partial pressure of carbon dioxide in arterial blood, lactate level, glucose level, and heart rate before IHT. Furthermore, the Acute Physiology and Chronic Health Evaluation II score, partial pressure of oxygen in arterial blood, lactate level, glucose level, heart rate, respiratory rate, pulse oximetry, and sedation before transport were independent influential factors for critical P-AEs during IHT.

CONCLUSIONS

The incidence of P-AEs during IHT of critically ill patients was high. Risk factors for P-AEs during IHT were identified. Strategies are needed to reduce their frequency.

TRIAL REGISTRATION

Chinese Clinical Trial Register identifier ChiCTR-OCS-12002661. Registered 5 November 2012.

A protocol for developing early warning score models from vital signs data in hospitals using ensembles of decision trees

INTRODUCTION

Multiple early warning scores (EWS) have been developed and implemented to reduce cardiac arrests on hospital wards. Case-control observational studies that generate an area under the receiver operator curve (AUROC) are the usual validation method, but investigators have also generated EWS with algorithms with no prior clinical knowledge. We present a protocol for the validation and comparison of our local Hamilton Early Warning Score (HEWS) with that generated using decision tree (DT) methods.

METHODS AND ANALYSIS

A database of electronically recorded vital signs from 4 medical and 4 surgical wards will be used to generate DT EWS (DT-HEWS). A third EWS will be generated using ensemble-based methods. Missing data will be multiple imputed. For a relative risk reduction of 50% in our composite outcome (cardiac or respiratory arrest, unanticipated intensive care unit (ICU) admission or hospital death) with a power of 80%, we calculated a sample size of 17,151 patient days based on our cardiac arrest rates in 2012. The performance of the National EWS, DT-HEWS and the ensemble EWS will be compared using AUROC.

ETHICS AND DISSEMINATION

Ethics approval was received from the Hamilton Integrated Research Ethics Board (#13-724-C). The vital signs and associated outcomes are stored in a database on our secure hospital server. Preliminary dissemination of this protocol was presented in abstract form at an international critical care meeting. Final results of this analysis will be used to improve on the existing HEWS and will be shared through publication and presentation at critical care meetings.

Early warning scoring systems versus standard observations charts for wards in South Africa: a cluster randomized controlled trial

BACKGROUND

On South African public hospital wards, observation charts do not incorporate early warning scoring (EWS) systems to inform nurses when to summon assistance. The aim of this trial was to test the impact of a new chart incorporating a modified EWS (MEWS) system and a linked training program on nurses' responses to clinical deterioration (primary outcome). Secondary outcomes were: numbers of patients with vital signs recordings in the first eight postoperative hours; number of times each vital sign was recorded; and nurses' knowledge.

METHODS/DESIGN

A pragmatic, parallel-group, cluster randomized, controlled clinical trial of intervention versus standard care was conducted in three intervention and three control adult surgical wards in an 867-bed public hospital in Cape Town, between March and July 2010; thereafter the MEWS chart was withdrawn. A total of 50 out of 122 nurses in full-time employment participated. From 1,427 case notes, 114 were selected by randomization for assessment. The MEWS chart was implemented in intervention wards. Control wards delivered standard care, without training. Case notes were reviewed two weeks after the trial's completion. Knowledge was assessed in both trial arms by blinded independent marking of written tests before and after training of nurses in intervention wards. Analyses were undertaken with IBM SPSS software on an intention-to-treat basis.

RESULTS

Patients in trial arms were similar. Introduction of the MEWS was not associated with statistically significant changes in responses to clinical deterioration (50 of 57 received no assistance versus 55 of 57, odds ratio (OR): 0.26, 95% confidence interval (CI): 0.05 to 1.31), despite improvement in nurses' knowledge in intervention wards. More patients in intervention than control wards had recordings of respiratory rate (27 of 57 versus 2 of 57, OR: 24.75, 95% CI: 5.5 to 111.3) and recordings of all seven parameters (5 of 57 versus 0 of 57 patients, risk estimate: 1.10, 95% CI: 1.01 to 1.2).

CONCLUSIONS

A MEWS chart and training program enhanced recording of respiratory rate and of all parameters, and nurses' knowledge, but not nurses' responses to patients who triggered the MEWS reporting algorithm.

TRIAL REGISTRATION

This trial was registered with the Pan African Clinical Trials Registry (identifier: PACTR201309000626545 ) on 9 September 2013.

A simple prognostic index based on admission vital signs data among patients with sepsis in a resource-limited setting

Introduction

In sub-Saharan Africa, vital signs are a feasible option for monitoring critically ill patients. We assessed how admission vital signs data predict in-hospital mortality among patients with sepsis. In particular, we assessed whether vital signs data can be incorporated into a prognostic index with reduced segmentation in the values of included variables.

Methods

Subjects were patients with sepsis hospitalized in Uganda, who participated in two cohort studies. Using restricted cubic splines of admission vital signs data, we predicted probability of in-hospital death in the development cohort and used this information to construct a simple prognostic index. We assessed the performance of the index in a validation cohort and compared its performance to that of the Modified Early Warning Score (MEWS).

Results

We included 317 patients (167 in the development cohort and 150 in the validation cohort). Based on how vital signs predicted mortality, we created a prognostic index giving a score of 1 for: respiratory rates ≥30 cycles/minute; pulse rates ≥100 beats/minute; mean arterial pressures ≥110/<70 mmHg; temperatures ≥38.6/<35.6°C; and presence of altered mental state defined as Glasgow coma score ≤14; 0 for all other values. The proposed index (maximum score = 5) predicted mortality comparably to MEWS. Patients scoring ≥3 on the index were 3.4-fold (95% confidence interval (CI) 1.6 to 7.3, P = 0.001) and 2.3-fold (95% CI 1.1 to 4.7, P = 0.031) as likely to die in hospital as those scoring 0 to 2 in the development and validation cohorts respectively; those scoring ≥5 on MEWS were 2.5-fold (95% CI 1.2 to 5.3, P = 0.017) and 1.8-fold (95% CI 0.74 to 4.2, P = 0.204) as likely to die as those scoring 0 to 4 in the development and validation cohorts respectively.

Conclusion

Among patients with sepsis, a prognostic index incorporating admission vital signs data with reduced segmentation in the values of included variables adequately predicted mortality. Such an index may be more easily implemented when triaging acutely-ill patients. Future studies using a similar approach may develop indexes that can be used to monitor treatment among acutely-ill patients, especially in resource-limited settings.