Predictive Value of 3 Clinical Criteria for Sepsis (Quick Sequential Organ Failure Assessment, Systemic Inflammatory Response Syndrome, and National Early Warning Score) With Respect to Short-term Mortality in Allogeneic Hematopoietic Cell Transplant Recipients With Suspected Infections

BACKGROUND

Sepsis disproportionately affects allogeneic hematopoietic cell transplant (HCT) recipients and is challenging to define. Clinical criteria that predict mortality and intensive care unit end-points in patients with suspected infections (SIs) are used in sepsis definitions, but their predictive value among immunocompromised populations is largely unknown. Here, we evaluate 3 criteria among allogeneic HCT recipients with SIs.

METHODS

We evaluated Systemic Inflammatory Response Syndrome (SIRS), quick Sequential Organ Failure Assessment (qSOFA), and National Early Warning Score (NEWS) in relation to short-term mortality among recipients transplanted between September 2010 and July 2017. We used cut-points of ≥ 2 for qSOFA/SIRS and ≥ 7 for NEWS and restricted to first SI per hospital encounter during patients' first 100 days posttransplant.

RESULTS

Of the 880 recipients who experienced ≥ 1 SI, 58 (6.6%) died within 28 days and 22 (2.5%) within 10 days of an SI. In relation to 10-day mortality, SIRS was the most sensitive (91.3% [95% confidence interval {CI}, 72.0%-98.9%]) but least specific (35.0% [95% CI, 32.6%-37.5%]), whereas qSOFA was the most specific (90.5% [95% CI, 88.9%-91.9%]) but least sensitive (47.8% [95% CI, 26.8%-69.4%]). NEWS was moderately sensitive (78.3% [95% CI, 56.3%-92.5%]) and specific (70.2% [95% CI, 67.8%-72.4%]).

CONCLUSIONS

NEWS outperformed qSOFA and SIRS, but each criterion had low to moderate predictive accuracy, and the magnitude of the known limitations of qSOFA and SIRS was at least as large as in the general population. Our data suggest that population-specific criteria are needed for immunocompromised patients.

Performance of universal early warning scores in different patient subgroups and clinical settings: a systematic review

OBJECTIVE

To assess predictive performance of universal early warning scores (EWS) in disease subgroups and clinical settings.

DESIGN

Systematic review.

DATA SOURCES

Medline, CINAHL, Embase and Cochrane database of systematic reviews from 1997 to 2019.

INCLUSION CRITERIA

Randomised trials and observational studies of internal or external validation of EWS to predict deterioration (mortality, intensive care unit (ICU) transfer and cardiac arrest) in disease subgroups or clinical settings.

RESULTS

We identified 770 studies, of which 103 were included. Study designs and methods were inconsistent, with significant risk of bias (high: n=16 and unclear: n=64 and low risk: n=28). There were only two randomised trials. There was a high degree of heterogeneity in all subgroups and in national early warning score (I2=72%-99%). Predictive accuracy (mean area under the curve; 95% CI) was highest in medical (0.74; 0.74 to 0.75) and surgical (0.77; 0.75 to 0.80) settings and respiratory diseases (0.77; 0.75 to 0.80). Few studies evaluated EWS in specific diseases, for example, cardiology (n=1) and respiratory (n=7). Mortality and ICU transfer were most frequently studied outcomes, and cardiac arrest was least examined (n=8). Integration with electronic health records was uncommon (n=9).

CONCLUSION

Methodology and quality of validation studies of EWS are insufficient to recommend their use in all diseases and all clinical settings despite good performance of EWS in some subgroups. There is urgent need for consistency in methods and study design, following consensus guidelines for predictive risk scores. Further research should consider specific diseases and settings, using electronic health record data, prior to large-scale implementation.

PROSPERO REGISTRATION NUMBER

PROSPERO CRD42019143141.

EHealth monitoring testbed with fuzzy based early warning score system

BACKGROUND AND OBJECTIVE

EHealth monitoring systems are able to save the persons' lives and track some vital physiological signs of patients, sportsmen, and soldiers for some purposes. Instant data tracking enables appropriate clinical interventions. The early warning score concept defines that specific vital human body signs that are considered together and gives the persons' health score. The patient's vital signs are periodically recorded with the Early Warning Score (EWS) system and the illness severity score of the patient is decided manually. The aim of the study is to monitor a person's health data continuously and calculate the EWS score thanks to the fuzzy logic. Therefore, the simulation as a testbed is constructed for real-time applications with ISO/IEEE 11073 Health informatics - Medical/health device communication standard.

METHODS

In our paper, a fuzzy-based early warning score system in the EHealth monitoring testbed is proposed. Real-time data are obtained from Riverbed Modeler simulation software with socket programming and stored in the InfluxDB using Node-Red and monitored on the remote desktop with Grafana.

RESULTS

Heart rate, body temperature, systolic blood pressure, respiratory rate, and SPO2 are taken into consideration in the fuzzy-based evaluation system for EWS. The data produced in the Riverbed has been provided in a realistic manner because the real human vital sign values are considered during generating vital signs.

CONCLUSIONS

Using real-time Riverbed Modeler health data with fuzzy-based EWS, a more realistic testbed platform is constructed in this study.

Can capillary lactate improve early warning scores in emergency department? An observational, prospective, multicentre study

AIMS

To determine the prognostic usefulness of the National Early Warning Score-2 (NEWS2) and quick Sepsis-related Organ Failure Assessment (qSOFA) scores, in isolation and combined with capillary lactate (CL), using the new NEWS2-L and qSOFA-L scores to predict the 30-day mortality risk.

METHODS

Prospective, multicentre and observational study in patients across four EDs. We collected sets of vital signs and CL and subsequently calculated NEWS2, qSOFA, NEWS2-L and qSOFA-L scores when patients arrived at the ED. The main outcome measure was all-cause mortality 30 days from the index event.

RESULTS

A total of 941 patients were included. Thirty-six patients (3.8%) died within 30 days of the index event. A high CL level has not been linked to a higher mortality. The NEWS2 presented AUROC of 0.72 (95% CI: 0.62-0.81), qSOFA of 0.66 (95% CI: 0.56-0.77) (P < .001 in both cases) and CL 0.55 (95% CI: 0.42-0.65; P = .229) to predict 30-day mortality. The addition of CL to the scores analysed does not improve the results of the scores used in isolation.

CONCLUSION

NEWS2 and qSOFA scores are a very useful tool for assessing the status of patients who come to the ED in general for all types of patients in triage categories II and III and for detecting the 30-day mortality risk. CL determined systematically in the ED does not seem to provide information on the prognosis of the patients.

Clinical decision-making in older adults following emergency admission to hospital. Derivation and validation of a risk stratification score: OPERA

OBJECTIVES OF THE STUDY

Demographic changes alongside medical advances have resulted in older adults accounting for an increasing proportion of emergency hospital admissions. Current measures of illness severity, limited to physiological parameters, have shortcomings in this cohort, partly due to patient complexity. This study aimed to derive and validate a risk score for acutely unwell older adults which may enhance risk stratification and support clinical decision-making.

METHODS

Data was collected from emergency admissions in patients ≥65 years from two UK general hospitals (April 2017- April 2018). Variables underwent regression analysis for in-hospital mortality and independent predictors were used to create a risk score. Performance was assessed on external validation. Secondary outcomes included seven-day mortality and extended hospital stay.

RESULTS

Derivation (n = 8,974) and validation (n = 8,391) cohorts were analysed. The model included the National Early Warning Score 2 (NEWS2), clinical frailty scale (CFS), acute kidney injury, age, sex, and Malnutrition Universal Screening Tool. For mortality, area under the curve for the model was 0.79 (95% CI 0.78-0.80), superior to NEWS2 0.65 (0.62-0.67) and CFS 0.76 (0.74-0.77) (P<0.0001). Risk groups predicted prolonged hospital stay: the highest risk group had an odds ratio of 9.7 (5.8-16.1) to stay >30 days.

CONCLUSIONS

Our simple validated model (Older Persons' Emergency Risk Assessment [OPERA] score) predicts in-hospital mortality and prolonged length of stay and could be easily integrated into electronic hospital systems, enabling automatic digital generation of risk stratification within hours of admission. Future studies may validate the OPERA score in external populations and consider an impact analysis.

Predictive value of Modified Early Warning Score (MEWS) and Revised Trauma Score (RTS) for the short-term prognosis of emergency trauma patients: a retrospective study

OBJECTIVES

This study aimed to assess the predictive value of the Modified Early Warning Score (MEWS) and Revised Trauma Score (RTS) for emergency trauma patients who died within 24 hours.

DESIGN

A retrospective, single-centred study.

SETTING

This study was conducted at a tertiary hospital in Southern China.

PARTICIPANTS

A total of 1739 patients with acute trauma, aged 16 years or older who presented to the emergency department from 1 November 2016 to 30 November 2019, were included.

INTERVENTIONS NONE

None.

OUTCOME

24-hour mortality was the primary outcome of trauma.

RESULTS

1739 patients were divided into the survival group (1709 patients,98.27%), and the non-survival group (30 patients,1.73%). Crude OR and adjusted OR of MEWS were 1.99, 95% CI (1.73 to 2.29), and 2.00, 95% CI (1.74 to 2.31), p<0.001, respectively. Crude OR and adjusted OR of RTS were 0.62, 95% CI (0.55 to 0.69) and 0.61, 95% CI (0.55 to 0.68), p<0.001, respectively. The area under the curve of MEWS was significantly higher than that of RTS (p=0.005): 0.927, 95% CI (0.914 to 0.939) vs 0.799, 95% CI (0.779 to 0.817).

CONCLUSIONS

Both MEWS and RTS were independent predictors of the short-term prognosis in emergency trauma patients, MEWS had better predictive efficacy.

Performance of the Pandemic Medical Early Warning Score (PMEWS), Simple Triage Scoring System (STSS) and Confusion, Uremia, Respiratory rate, Blood pressure and age ≥ 65 (CURB-65) score among patients with COVID-19 pneumonia in an emergency department triage setting: a retrospective study

BACKGROUND

Healthcare institutions are confronted with large numbers of patient admissions during large-scale or long-term public health emergencies like pandemics. Appropriate and effective triage is needed for effective resource use.

OBJECTIVES

To evaluate the effectiveness of the Pandemic Medical Early Warning Score (PMEWS), Simple Triage Scoring System (STSS) and Confusion, Uremia, Respiratory rate, Blood pressure and age ≥ 65 years (CURB-65) score in an emergency department (ED) triage setting.

DESIGN AND SETTING

Retrospective study in the ED of a tertiary-care university hospital in Düzce, Turkey.

METHODS

PMEWS, STSS and CURB-65 scores of patients diagnosed with COVID-19 pneumonia were calculated. Thirty-day mortality, intensive care unit (ICU) admission, mechanical ventilation (MV) need and outcomes were recorded. The predictive accuracy of the scores was assessed using receiver operating characteristic curve analysis.

RESULTS

One hundred patients with COVID-19 pneumonia were included. The 30-day mortality was 6%. PMEWS, STSS and CURB-65 showed high performance for predicting 30-day mortality (area under the curve: 0.968, 0.962 and 0.942, respectively). Age > 65 years, respiratory rate > 20/minute, oxygen saturation (SpO2) < 90% and ED length of stay > 4 hours showed associations with 30-day mortality (P < 0.05).

CONCLUSIONS

CURB-65, STSS and PMEWS scores are useful for predicting mortality, ICU admission and MV need among patients diagnosed with COVID-19 pneumonia. Advanced age, increased respiratory rate, low SpO2 and prolonged ED length of stay may increase mortality. Further studies are needed for developing the triage scoring systems, to ensure effective long-term use of healthcare service capacity during pandemics.

Pediatric early warning score versus a paediatric triage tool in the emergency department: A reliability study

AIM

In the paediatric emergency department (PED), it is important to correctly prioritize children for physician assessment. The pediatric early warning score (PEWS), although not a triage tool, is often used for PED triage. The scandinavian Rapid Emergency Triage and Treatment System-pediatric (RETTS-p) is a reliability tested triage tool. We aimed to compare PEWS and RETTS-p in a Norwegian PED.

DESIGN

A reliability study.

METHODS

The PED nurse routinely did PEWS observations, while the principal investigator concomitantly made RETTS-p observations. Inter-tool agreement was calculated for the complete PEWS and RETTS-p and for vital signs scores, disregarding the RETTS-p emergency symptoms and signs (ESS).

RESULTS

Rapid Emergency Triage and Treatment System-pediatric assigned a higher urgency than PEWS. The inter-tool agreement between PEWS and RETTS-p was low (weighted kappa [95% confidence interval [CI] = 0.32 [0.24-0.40]]). Weighted kappa (95% CI) was 0.50 (0.41-0.59) for PEWS and RETTS-p without ESS, indicating that PEWS is not equivalent to five-level triage tools.

Comparing associations between frailty and mortality in hospitalised older adults with or without COVID-19 infection: a retrospective observational study using electronic health records

BACKGROUND

The aim of this study was to describe outcomes in hospitalised older people with different levels of frailty and COVID-19 infection.

METHODS

We undertook a single-centre, retrospective cohort study examining COVID-19-related mortality using electronic health records, for older people (65 and over) with frailty, hospitalised with or without COVID-19 infection. Baseline covariates included demographics, early warning scores, Charlson Comorbidity Indices and frailty (Clinical Frailty Scale, CFS), linked to COVID-19 status.

FINDINGS

We analysed outcomes on 1,071 patients with COVID-19 test results (285 (27%) were positive for COVID-19). The mean age at ED arrival was 79.7 and 49.4% were female. All-cause mortality (by 30 days) rose from 9 (not frail) to 33% (severely frail) in the COVID-negative cohort but was around 60% for all frailty categories in the COVID-positive cohort. In adjusted analyses, the hazard ratio for death in those with COVID-19 compared to those without COVID-19 was 7.3 (95% CI: 3.00, 18.0) with age, comorbidities and illness severity making small additional contributions.

INTERPRETATION

In this study, frailty measured using the CFS appeared to make little incremental contribution to the hazard of dying in older people hospitalised with COVID-19 infection; illness severity and comorbidity had a modest association with the overall adjusted hazard of death, whereas confirmed COVID-19 infection dominated, with a sevenfold hazard for death.

Do medical students recognise the deteriorating patient by analysing their vital signs? A monocentric observational study based on the National Early Warning Score 2

OBJECTIVE

Assessment of the expertise of medical students in evaluating vital signs and their implications for the current risk of a patient, an appropriate monitoring frequency, and a proper clinical response.

METHODS

251 second-year and 267 fifth-year medical students in a curriculum consisting of 6 years of medical school at Ulm University, Germany, were interviewed in a paper-based questionnaire. The students were asked to rate their proficiency in interpreting vital signs and to give pathological thresholds of vital signs. Based on the National Early Warning Score 2 (NEWS2), nine vital signs of fictional patients were created and students were asked to comment on their clinical risk, to set an appropriate monitoring frequency as well as a clinical response.

RESULTS

Interviewing medical students regarding each vital sign individually, the students indicated a pathological threshold in accordance with the NEWS2 for respiratory rate, temperature, and heart rate. By contrast, inappropriate pathological limits were given regarding oxygen saturation and systolic blood pressure. Translating the vital signs into nine fictional patients, fifth-year medical students overall chose an appropriate response in 78% (67%-78%, median±IQR). In detail, fifth-year students successfully identified patients at very high or low risk and allocated them accordingly. However, cases on the edge were often stratified inappropriately. For example, a fictional case with vital signs indicating a surging sepsis was frequently underappreciated (48.5%) and allocated to an insufficient clinical response by fifth-year students.

CONCLUSIONS

Recognising the healthy as well as the deteriorating patient is a key ability for future physicians. NEWS2-based education might be a valuable tool to assess and give feedback on student's knowledge in this vital professional activity.

Use of the first National Early Warning Score recorded within 24 hours of admission to estimate the risk of in-hospital mortality in unplanned COVID-19 patients: a retrospective cohort study

OBJECTIVES

Although the National Early Warning Score (NEWS) and its latest version NEWS2 are recommended for monitoring deterioration in patients admitted to hospital, little is known about their performance in COVID-19 patients. We aimed to compare the performance of the NEWS and NEWS2 in patients with COVID-19 versus those without during the first phase of the pandemic.

DESIGN

A retrospective cross-sectional study.

SETTING

Two acute hospitals (Scarborough and York) are combined into a single dataset and analysed collectively.

PARTICIPANTS

Adult (≥18 years) non-elective admissions discharged between 11 March 2020 and 13 June 2020 with an index or on-admission NEWS2 electronically recorded within ±24 hours of admission to predict mortality at four time points (in-hospital, 24 hours, 48 hours and 72 hours) in COVID-19 versus non-COVID-19 admissions.

RESULTS

Out of 6480 non-elective admissions, 620 (9.6%) had a diagnosis of COVID-19. They were older (73.3 vs 67.7 years), more often male (54.7% vs 50.1%), had higher index NEWS (4 vs 2.5) and NEWS2 (4.6 vs 2.8) scores and higher in-hospital mortality (32.1% vs 5.8%). The c-statistics for predicting in-hospital mortality in COVID-19 admissions was significantly lower using NEWS (0.64 vs 0.74) or NEWS2 (0.64 vs 0.74), however, these differences reduced at 72hours (NEWS: 0.75 vs 0.81; NEWS2: 0.71 vs 0.81), 48 hours (NEWS: 0.78 vs 0.81; NEWS2: 0.76 vs 0.82) and 24hours (NEWS: 0.84 vs 0.84; NEWS2: 0.86 vs 0.84). Increasing NEWS2 values reflected increased mortality, but for any given value the absolute risk was on average 24% higher (eg, NEWS2=5: 36% vs 9%).

CONCLUSIONS

The index or on-admission NEWS and NEWS2 offers lower discrimination for COVID-19 admissions versus non-COVID-19 admissions. The index NEWS2 was not proven to be better than the index NEWS. For each value of the index NEWS/NEWS2, COVID-19 admissions had a substantially higher risk of mortality than non-COVID-19 admissions which reflects the increased baseline mortality risk of COVID-19.

Real-time prediction of COVID-19 related mortality using electronic health records

Coronavirus disease 2019 (COVID-19) is a respiratory disease with rapid human-to-human transmission caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to the exponential growth of infections, identifying patients with the highest mortality risk early is critical to enable effective intervention and prioritisation of care. Here, we present the COVID-19 early warning system (CovEWS), a risk scoring system for assessing COVID-19 related mortality risk that we developed using data amounting to a total of over 2863 years of observation time from a cohort of 66 430 patients seen at over 69 healthcare institutions. On an external cohort of 5005 patients, CovEWS predicts mortality from 78.8% (95% confidence interval [CI]: 76.0, 84.7%) to 69.4% (95% CI: 57.6, 75.2%) specificity at sensitivities greater than 95% between, respectively, 1 and 192 h prior to mortality events. CovEWS could enable earlier intervention, and may therefore help in preventing or mitigating COVID-19 related mortality.

A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation

BACKGROUND

Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease.

OBJECTIVE

Our objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department.

METHODS

Data were collected from patients with COVID-19 who were admitted to Northwell Health acute care hospitals and were discharged, died, or spent a minimum of 48 hours in the hospital between March 1 and May 11, 2020. Of 11,525 patients, 933 (8.1%) were placed on invasive mechanical ventilation within 48 hours of admission. Variables used by the models included clinical and laboratory data commonly collected in the emergency department. We trained and validated three predictive models (two based on XGBoost and one that used logistic regression) using cross-hospital validation. We compared model performance among all three models as well as an established early warning score (Modified Early Warning Score) using receiver operating characteristic curves, precision-recall curves, and other metrics.

RESULTS

The XGBoost model had the highest mean accuracy (0.919; area under the curve=0.77), outperforming the other two models as well as the Modified Early Warning Score. Important predictor variables included the type of oxygen delivery used in the emergency department, patient age, Emergency Severity Index level, respiratory rate, serum lactate, and demographic characteristics.

CONCLUSIONS

The XGBoost model had high predictive accuracy, outperforming other early warning scores. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19.

Diagnostic challenge: an atypical presentation of infective endocarditis

It is recognised that infective endocarditis is frequently a challenging diagnosis to make, as it may present with a range of non-specific symptoms. A middle-aged man was admitted with an 8-day history of profuse non-bloody diarrhoea and vomiting. He had no medical history and no identifiable risk factors for infective endocarditis, and so this in combination with the patient's atypical symptoms presented a diagnostic challenge. The patient was eventually diagnosed with a Staphylococcus aureus right-sided infective endocarditis. This case report explores the events which led to this diagnosis and demonstrates a number of unique learning points. It also highlights the importance of maintaining an open mind and being prepared to revise an initial diagnosis in the face of medical uncertainty.

Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study

BACKGROUND

The National Early Warning Score (NEWS2) is currently recommended in the UK for the risk stratification of COVID-19 patients, but little is known about its ability to detect severe cases. We aimed to evaluate NEWS2 for the prediction of severe COVID-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of NEWS2 alone for medium-term risk stratification.

METHODS

Training cohorts comprised 1276 patients admitted to King's College Hospital National Health Service (NHS) Foundation Trust with COVID-19 disease from 1 March to 30 April 2020. External validation cohorts included 6237 patients from five UK NHS Trusts (Guy's and St Thomas' Hospitals, University Hospitals Southampton, University Hospitals Bristol and Weston NHS Foundation Trust, University College London Hospitals, University Hospitals Birmingham), one hospital in Norway (Oslo University Hospital), and two hospitals in Wuhan, China (Wuhan Sixth Hospital and Taikang Tongji Hospital). The outcome was severe COVID-19 disease (transfer to intensive care unit (ICU) or death) at 14 days after hospital admission. Age, physiological measures, blood biomarkers, sex, ethnicity, and comorbidities (hypertension, diabetes, cardiovascular, respiratory and kidney diseases) measured at hospital admission were considered in the models.

RESULTS

A baseline model of 'NEWS2 + age' had poor-to-moderate discrimination for severe COVID-19 infection at 14 days (area under receiver operating characteristic curve (AUC) in training cohort = 0.700, 95% confidence interval (CI) 0.680, 0.722; Brier score = 0.192, 95% CI 0.186, 0.197). A supplemented model adding eight routinely collected blood and physiological parameters (supplemental oxygen flow rate, urea, age, oxygen saturation, C-reactive protein, estimated glomerular filtration rate, neutrophil count, neutrophil/lymphocyte ratio) improved discrimination (AUC = 0.735; 95% CI 0.715, 0.757), and these improvements were replicated across seven UK and non-UK sites. However, there was evidence of miscalibration with the model tending to underestimate risks in most sites.

CONCLUSIONS

NEWS2 score had poor-to-moderate discrimination for medium-term COVID-19 outcome which raises questions about its use as a screening tool at hospital admission. Risk stratification was improved by including readily available blood and physiological parameters measured at hospital admission, but there was evidence of miscalibration in external sites. This highlights the need for a better understanding of the use of early warning scores for COVID.

Update on early warning scores

Early warning scores (EWS) have the objective to provide a preventive approach for detecting those patients in general wards at risk of deterioration before it begins. Well implemented and combined with a tiered response, the EWS expect to be a relevant tool for patient safety. Most of the evidence for their use has been published for the general EWS. Their strengths, such as objectivity and systematic response, health provider training, universal applicability and automatization potential need to be highlighted to counterbalance the weakness and limitations that have also been described. The near future will probably increase availability of EWS, reliability and predictive value through the spread and acceptability of continuous monitoring in general ward, its integration in decision support algorithms with automatic alerts and the elaboration of temporal vital signs patterns that will finally allow to perform a personal modelling depending on individual patient characteristics.

Design and development of an early warning score for covid-19 hospitalized patients

Pandemics pose a major challenge for public health preparedness, requiring a coordinated international response and the development of solid containment plans. Early and accurate identification of high-risk patients in the course of the current COVID-19 pandemic is vital for planning and making proper use of available resources. The purpose of this study was to identify the key variables that account for worse outcomes to create a predictive model that could be used effectively for triage. Through literature review, 44 variables that could be linked to an unfavorable course of COVID-19 disease were obtained, including clinical, laboratory, and X-ray variables. These were used for a 2-round modified Delphi processing with 14 experts to select a final list of variables with the greatest predictive power for the construction of a scoring system, leading to the creation of a new scoring system: the COVID-19 Severity Index. The analysis of the area under the curve for the COVID-19 Severity Index was 0.94 to predict the need for ICU admission in the following 24 hours against 0.80 for NEWS-2. Additionally, the digital medical record of the Hospital Italiano de Buenos Aires was electronically set for an automatic calculation and constant update of the COVID-19 Severity Index. Specifically designed for the current COVID-19 pandemic, COVID-19 Severity Index could be used as a reliable tool for strategic planning, organization, and administration of resources by easily identifying hospitalized patients with a greater need of intensive care.

Research protocol for the validation of a new portable technology for real-time continuous monitoring of Early Warning Score (EWS) in hospital practice and for an early-stage multistakeholder assessment

INTRODUCTION

The real-time continuous monitoring of vital parameters in patients affected by multiple chronic conditions and/or COVID-19 can lead to several benefits to the Italian National Healthcare System (IT-NHS). The UBiquitous Integrated CARE (UBICARE) technology is a novel health digital platform at the validation stage in hospital setting. UBICARE might support the urgent need for digitalisation and early intervention, as well as minimise the face-to-face delivery of care in both hospital and community-based care settings. This research protocol aims to design an early-stage assessment of the multidimensional impact induced by UBICARE within the IT-NHS alongside technology validation in a hospital ward.

METHODS AND ANALYSIS

The targeted patients will be medium/high-risk hypertensive individuals as an illustrative first example of how UBICARE might bring benefits to susceptible patients. A mixed-method study will be applied to incorporate to the validation study a multistakeholder perspective, including perceived patient experiences and preferences, and facilitate technology adoption. First, semistructured interviews will be undertaken with a variety of stakeholders including clinicians, health managers and policy-makers to capture views on the likely technology utility, economic sustainability, impact of adoption in hospital practice and alternative adoption scenarios. Second, a monocentric, non-randomised and non-comparative clinical study, supplemented by the administration of standardised usability questionnaires to patients and health professionals, will validate the use of UBICARE in hospital practice. Finally, the results of the previous stages will be discussed in a multidisciplinary-facilitated workshop with IT-NHS relevant stakeholders to reconcile stakeholders' perspectives. Limitations include a non-random recruitment strategy in the clinical study, small sample size of the key stakeholders and potential stakeholder recruitment bias introduced by the research technique.

ETHICS AND DISSEMINATION

The Ethics Committee for Clinical Experimentation of Tuscany Region approved the protocol. Participation in this study is voluntary. Study results will be disseminated through peer-reviewed publications and academic conferences.

What items should be included in an early warning score for remote assessment of suspected COVID-19? qualitative and Delphi study

BACKGROUND

To develop items for an early warning score (RECAP: REmote COVID-19 Assessment in Primary Care) for patients with suspected COVID-19 who need escalation to next level of care.

METHODS

The study was based in UK primary healthcare. The mixed-methods design included rapid review, Delphi panel, interviews, focus groups and software development. Participants were 112 primary care clinicians and 50 patients recovered from COVID-19, recruited through social media, patient groups and snowballing. Using rapid literature review, we identified signs and symptoms which are commoner in severe COVID-19. Building a preliminary set of items from these, we ran four rounds of an online Delphi panel with 72 clinicians, the last incorporating fictional vignettes, collating data on R software. We refined the items iteratively in response to quantitative and qualitative feedback. Items in the penultimate round were checked against narrative interviews with 50 COVID-19 patients. We required, for each item, at least 80% clinician agreement on relevance, wording and cut-off values, and that the item addressed issues and concerns raised by patients. In focus groups, 40 clinicians suggested further refinements and discussed workability of the instrument in relation to local resources and care pathways. This informed design of an electronic template for primary care systems.

RESULTS

The prevalidation RECAP-V0 comprises a red flag alert box and 10 assessment items: pulse, shortness of breath or respiratory rate, trajectory of breathlessness, pulse oximeter reading (with brief exercise test if appropriate) or symptoms suggestive of hypoxia, temperature or fever symptoms, duration of symptoms, muscle aches, new confusion, shielded list and known risk factors for poor outcome. It is not yet known how sensitive or specific it is.

CONCLUSIONS

Items on RECAP-V0 align strongly with published evidence, clinical judgement and patient experience. The validation phase of this study is ongoing.

TRIAL REGISTRATION NUMBER

NCT04435041.

[Early Warning Scores at the nursing ward: what do you really want to know?]

Early Warning Scores (EWSs) are based on the assumption that critical illness is preceded by physical deterioration. The question is whether measuring 5 vital parameters several times a day can predict changes in a highly complex and dynamic clinical condition. Little evidence has yet been found for clinical superiority of current EWSs over good clinical assessment. If we want to predict better and act structurally proactively, the measurement frequency of vital parameters must increase and much more complex scores are needed to identify specific changes in individual patients at an early stage. It is plausible that the many innovative developments in this area are the stepping stone to an era in which care in regular nursing wards is increasingly directed in the right direction by predictive algorithms. Integration of such super EWSs in new working methods may contribute to continuously adaptive care that is ultimately better and more efficient and relieves the care provider.

Development of a Quality Indicator to Measure Urgent Requests to the Bedside

OBJECTIVE

To develop a quality indicator describing the response time to an urgent request for a physician to the bedside of a pregnant or postpartum woman and to identify opportunities for improvement in care timeliness for women with worsening serious clinical conditions.

DESIGN

Evidence-based quality improvement project using the Iowa Model-Revised framework to develop a maternal care quality indicator.

SETTING

Labor and delivery, antepartum, and mother/baby units in a large urban safety-net hospital preparing for a state level of maternal care designation survey.

PARTICIPANTS

All nurses and physicians caring for hospitalized pregnant and postpartum women participated in implementation.

INTERVENTION/MEASUREMENTS

Physician response time was measured as the elapsed time from a nurse's urgent request for a physician and the presence of a physician at the bedside of a woman in one of the identified units, as recorded in the electronic health record.

RESULTS

Physician response time to an urgent request to the bedside was documented 179 times during the first 3 months after implementation. Physician presence at the bedside within 30 minutes of a request was recorded in more than 99% of these events.

CONCLUSION

Physicians' responses to early warning signs within our facility were timely and within the parameters established by the Texas state-mandated criteria for a Level IV maternal care hospital. Response time as documented in the electronic health record provides an important quality indicator of maternal care in the inpatient setting.

Predicting intensive care unit admission and death for COVID-19 patients in the emergency department using early warning scores

AIMS

To identify the most accurate early warning score (EWS) for predicting an adverse outcome in COVID-19 patients admitted to the emergency department (ED).

METHODS

In adult consecutive patients admitted (March 1-April 15, 2020) to the ED of a major referral centre for COVID-19, we retrospectively calculated NEWS, NEWS2, NEWS-C, MEWS, qSOFA, and REMS from physiological variables measured on arrival. Sensitivity, specificity, positive (PPV) and negative predictive value (NPV), and the area under the receiver operating characteristic (AUROC) curve of each EWS for predicting admission to the intensive care unit (ICU) and death at 48 h and 7 days were calculated.

RESULTS

We included 334 patients (119 [35.6%] females, median age 66 [54-78] years). At 7 days, the rates of ICU admission and death were 56/334 (17%) and 26/334 (7.8%), respectively. NEWS was the most accurate predictor of ICU admission within 7 days (AUROC 0.783 [95% CI, 0.735-0.826]; sensitivity 71.4 [57.8-82.7]%; NPV 93.1 [89.8-95.3]%), while REMS was the most accurate predictor of death within 7 days (AUROC 0.823 [0.778-0.863]; sensitivity 96.1 [80.4-99.9]%; NPV 99.4[96.2-99.9]%). Similar results were observed for ICU admission and death at 48 h. NEWS and REMS were as accurate as the triage system used in our ED. MEWS and qSOFA had the lowest overall accuracy for both outcomes.

CONCLUSION

In our single-centre cohort of COVID-19 patients, NEWS and REMS measured on ED arrival were the most sensitive predictors of 7-day ICU admission or death. EWS could be useful to identify patients with low risk of clinical deterioration.

Derivation and validation of modified early warning score plus SpO2/FiO2 score for predicting acute deterioration of patients with hematological malignancies

BACKGROUND/AIMS

Scoring systems play an important role in predicting intensive care unit (ICU) admission or estimating the risk of death in critically ill patients with hematological malignancies. We evaluated the modified early warning score (MEWS) for predicting ICU admissions and in-hospital mortality among at-risk patients with hematological malignancies and developed an optimized MEWS.

METHODS

We retrospectively analyzed derivation cohort patients with hematological malignancies who were managed by a medical emergency team (MET) in the general ward and prospectively validated the data. We compared the traditional MEWS with the MEWS plus SpO2/FiO2 (MEWS_SF) score, which were calculated at the time of MET contact.

RESULTS

In the derivation cohort, the areas under the receiver-operating characteristic (AUROC) curves were 0.81 for the MEWS (95% confidence interval [CI], 0.76 to 0.87) and 0.87 for the MEWS_SF score (95% CI, 0.87 to 0.92) for predicting ICU admission. The AUROC curves were 0.70 for the MEWS (95% CI, 0.63 to 0.77) and 0.76 for the MEWS_SF score (95% CI, 0.70 to 0.83) for predicting in-hospital mortality. In the validation cohort, the AUROC curves were 0.71 for the MEWS (95% CI, 0.66 to 0.77) and 0.83 for the MEWS_SF score (95% CI, 0.78 to 0.87) for predicting ICU admission. The AUROC curves were 0.64 for the MEWS (95% CI, 0.57 to 0.70) and 0.74 for the MEWS_SF score (95% CI, 0.69 to 0.80) for predicting in-hospital mortality.

CONCLUSION

Compared to the traditional MEWS, the MEWS_SF score may be a useful tool that can be used in the general ward to identify deteriorating patients with hematological malignancies.

Feasibility of using an Early Warning Score for preterm or low birthweight infants in a low-resource setting: results of a mixed-methods study at a national referral hospital in Kenya

INTRODUCTION

Fifteen million babies are born prematurely, before 37 weeks gestational age, globally. More than 80% of these are in sub-Saharan Africa and Asia. 35% of all deaths in the first month of life are due to prematurity and the neonatal mortality rate is eight times higher in low-income and middle-income countries (LMICs) than in Europe. Early Warning Scores (EWS) are a way of recording vital signs using standardised charts to easily identify adverse clinical signs and escalate care appropriately. A range of EWS have been developed for neonates, though none in LMICs. This paper reports the findings of early work to examine if the use of EWS is feasible in LMICs.

METHODS

We conducted an observational study to understand current practices for monitoring of preterm infants at a large national referral hospital in Nairobi, Kenya. Using hospital records, data were collected over an 8-week period in 2019 on all live born infants born at <37 weeks and/or <2500 g (n=294, 255 mothers) in the first week of life. Using a chart adopted from the EWS developed by the British Association of Perinatal Medicine, we plotted infants' vital signs. In addition, we held group discussions with stakeholders in Kenya to examine opinions on use of EWS.

RESULTS

Recording of vital signs was variable; only 63% of infants had at least one temperature recorded and 53% had at least one heart rate and respiratory rate recorded. Stakeholders liked the traffic-light system and simplicity of the chart, though recognised challenges, such as staffing levels and ability to print in colour, to its adoption.

CONCLUSION

EWS may standardise documentation and identify infants who are at higher risk of an adverse outcome. However, human and non-human resource issues would need to be explored further before development of an EWS for LMICs.

Performance of Pediatric Mortality Prediction Models in Low- and Middle-Income Countries: A Systematic Review and Meta-Analysis

OBJECTIVE

To describe the performance of prognostic models for mortality or clinical deterioration events among hospitalized children developed or validated in low- and middle-income countries.

STUDY DESIGN

A medical librarian systematically searched EMBASE, Ovid Medline, Scopus, Cochrane Library, EBSCO Global Health, LILACS, African Index Medicus, African Journals Online, African Healthline, Med-Carib, and Global Index Medicus (from 2000 to October 2019). We included citations that described the development or validation of a pediatric prognostic model for hospital mortality or clinical deterioration events in low- and middle-income countries. In duplicate and independently, we extracted data on included populations and model prognostic performance and evaluated risk of bias using the Prediction model Risk Of Bias Assessment Tool.

RESULTS

Of 41 279 unique citations, we included 15 studies describing 15 prognostic models for mortality and 3 models for clinical deterioration events. Six models were validated in >1 external cohort. The Lambarene Organ Dysfunction Score (0.85 [0.77-0.92]) and Signs of Inflammation in Children that Kill (0.85 [0.82-0.88]) had the highest summary C-statistics (95% CI) for discrimination. Calibration and classification measures were poorly reported. All models were at high risk of bias owing to inappropriate selection of predictor variables and handling of missing data and incomplete performance measure reporting.

CONCLUSIONS

Several prognostic models for mortality and clinical deterioration events have been validated in single cohorts, with good discrimination. Rigorous validation that conforms to current standards for prediction model studies and updating of existing models are needed before clinical implementation.

Implementing the Risk Identification (RI) and Modified Early Obstetric Warning Signs (MEOWS) tool in district hospitals in Rwanda: a cross-sectional study

BACKGROUND

Despite reaching Millennium Development Goal (MDG) 3, the maternal mortality rate (MMR) is still high in Rwanda. Most deaths occur after transfer of patients with obstetric complications from district hospitals (DHs) to referral hospitals; timely detection and management may improve these outcomes. The RI and MEOWS tool has been designed to predict morbidity and decrease delay of transfer. Our study aimed: 1) to determine if the use of the RI and MEOWS tool is feasible in DHs in Rwanda and 2) to determine the role of the RI and MEOWS tool in predicting morbidity.

METHODS

A cross-sectional study enrolled parturient admitted to 4 district hospitals during the study period from April to July 2019. Data was collected on completeness rate (feasibility) to RI and MEOWS tool, and prediction of morbidity (hemorrhage, infection, and pre-eclampsia).

RESULTS

Among 478 RI and MEOWS forms used, 75.9% forms were fully completed suggesting adequate feasibility. In addition, the RI and MEOWS tool showed to predict morbidity with a sensitivity of 28.9%, a specificity of 93.5%, a PPV of 36.1%, a NPV of 91.1%, an accuracy of 86.2%, and a relative risk of 4.1 (95% Confidential Interval (CI), 2.4-7.1). When asked about challenges faced during use of the RI and MEOWS tool, most of the respondents reported that the tool was long, the staff to patient ratio was low, the English language was a barrier, and the printed forms were sometimes unavailable.

CONCLUSION

The RI and MEOWS tool is a feasible in the DHs of Rwanda. In addition, having moderate or high scores on the RI and MEOWS tool predict morbidity. After consideration of local context, this tool can be considered for scale up to other DHs in Rwanda or other low resources settings.

TRIAL REGISTRATION

This is not a clinical trial rather a quality improvement project. It will be registered retrospectively.

Protocol for the DisCoVeRy trial: multicentre, adaptive, randomised trial of the safety and efficacy of treatments for COVID-19 in hospitalised adults

INTRODUCTION

To find effective and safe treatments for COVID-19, the WHO recommended to systemically evaluate experimental therapeutics in collaborative randomised clinical trials. As COVID-19 was spreading in Europe, the French national institute for Health and Medical Research (Inserm) established a transdisciplinary team to develop a multi-arm randomised controlled trial named DisCoVeRy. The objective of the trial is to evaluate the clinical efficacy and safety of different investigational re-purposed therapeutics relative to Standard of Care (SoC) in patients hospitalised with COVID-19.

METHODS AND ANALYSIS

DisCoVeRy is a phase III, open-label, adaptive, controlled, multicentre clinical trial in which hospitalised patients with COVID-19 in need of oxygen therapy are randomised between five arms: (1) a control group managed with SoC and four therapeutic arms with re-purposed antiviral agents: (2) remdesivir + SoC, (3) lopinavir/ritonavir + SoC, (4) lopinavir/ritonavir associated with interferon (IFN)-β-1a + SoC and (5) hydroxychloroquine + SoC. The primary endpoint is the clinical status at Day 15 on the 7-point ordinal scale of the WHO Master Protocol (V.3.0, 3 March 2020). This trial involves patients hospitalised in conventional departments or intensive care units both from academic or non-academic hospitals throughout Europe. A sample size of 3100 patients (620 patients per arm) is targeted. This trial has begun on 22 March 2020. Since 5 April 2020, DisCoVeRy has been an add-on trial of the Solidarity consortium of trials conducted by the WHO in Europe and worldwide. On 8 June 2020, 754 patients have been included.

ETHICS AND DISSEMINATION

Inserm is the sponsor of DisCoVeRy. Ethical approval has been obtained from the institutional review board on 13 March 2020 (20.03.06.51744) and from the French National Agency for Medicines and Health Products (ANSM) on 9 March 2020. Results will be submitted for publication in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT04315948 Eudra-CT 2020-000936-23.

Importance of the National Early Warning Score (NEWS) at the time of discharge from the intensive care unit

Backround/aim

To identify, at an early stage of intensive care, patients who will require readmission to the intensive care unit (ICU) based on their National Early Warning Score (NEWS-d) at discharge.

Materials and methods

Overall, 536 patients aged >18 years who stayed at a tertiary ICU for >24 h were included. Patients who readmitted and not readmitted to the intensive care within 48 h after discharge were compared.

Results

Mean patient age was 64.26 ± 18.50 years and 252 (44.7%) patients were male. Mean Acute Physiology and Chronic Health Evaluation II (APACHE II) score was 21.86 ± 8.74; mean NEWS-d was 4.48 ± 2.53. Forty-nine (9.1%) were readmitted to ICU. The reasons for initial admission, age, and NEWS-d vvalues were significantly different between the 2 groups. The NEWS-d values of the readmitted group were significantly higher (9.16 ± 1.05) than nonreadmitted group (4.01 ± 2.13). Based on receiver operation curve analysis, sensitivity and specificity were 98% and 95%, respectively, considering a NEWS-d cut-off value of 7.5 as the limit value for estimating readmission.

Conclusion

A NEWS-d value of >7.5 demonstrated high sensitivity and specificity in identifying the risk of readmission for patients being discharged from ICU.

Hyperosmolar dehydration: A predictor of kidney injury and outcome in hospitalised older adults

BACKGROUND & AIMS

Hospitalised older adults are vulnerable to dehydration. However, the prevalence of hyperosmolar dehydration (HD) and its impact on outcome is unknown. Serum osmolality is not measured routinely but osmolarity, a validated alternative, can be calculated using routinely measured serum biochemistry. This study aimed to use calculated osmolarity to measure the prevalence of HD (serum osmolarity >300 mOsm/l) and assess its impact on acute kidney injury (AKI) and outcome in hospitalised older adults.

METHODS

This retrospective cohort study used data from a UK teaching hospital retrieved from the electronic database relating to all medical emergency admissions of patients aged ≥ 65 years admitted between 1st May 2011 and 31st October 2013. Using these data, Charlson comorbidity index (CCI), National Early Warning Score (NEWS), length of hospital stay (LOS) and mortality were determined. Osmolarity was calculated using the equation of Krahn and Khajuria.

RESULTS

A total of 6632 patients were identified; 27% had HD, 39% of whom had AKI. HD was associated with a median (Q1, Q3) LOS of 5 (1, 12) days compared with 3 (1, 9) days in the euhydrated group, P < 0.001. Adjusted Cox-regression analysis demonstrated that patients with HD were four-times more likely to develop AKI 12-24 h after admission [Hazards Ratio (95% Confidence Interval) 4.5 (3.5-5.6), P < 0.001], and had 60% greater 30-day mortality [1.6 (1.4-1.9), P < 0.001], compared with those who were euhydrated.

CONCLUSION

HD is common in hospitalised older adults and is associated with increased LOS, risk of AKI and mortality. Further work is required to assess the validity of osmolality or osmolarity as an early predictor of AKI and the impact of HD on outcome prospectively.

Development and validation of a Paediatric Early Warning Score for use in the emergency department: a multicentre study

BACKGROUND

Paediatric Early Warning Scores (PEWSs) are being used increasingly in hospital wards to identify children at risk of clinical deterioration, but few scores exist that were designed for use in emergency care settings. To improve the prioritisation of children in the emergency department (ED), we developed and validated an ED-PEWS.

METHODS

The TrIAGE project is a prospective European observational study based on electronic health record data collected between Jan 1, 2012, and Nov 1, 2015, from five diverse EDs in four European countries (Netherlands, the UK, Austria, and Portugal). This study included data from all consecutive ED visits of children under age 16 years. The main outcome measure was a three-category reference standard (high, intermediate, low urgency) that was developed as part of the TrIAGE project as a proxy for true patient urgency. The ED-PEWS was developed based on an ordinal logistic regression model, with cross-validation by setting. After completing the study, we fully externally validated the ED-PEWS in an independent cohort of febrile children from a different ED (Greece).

FINDINGS

Of 119 209 children, 2007 (1·7%) were of high urgency and 29 127 (24·4%) of intermediate urgency, according to our reference standard. We developed an ED-PEWS consisting of age and the predictors heart rate, respiratory rate, oxygen saturation, consciousness, capillary refill time, and work of breathing. The ED-PEWS showed a cross-validated c-statistic of 0·86 (95% prediction interval 0·82-0·90) for high-urgency patients and 0·67 (0·61-0·73) for high-urgency or intermediate-urgency patients. A cutoff of score of at least 15 was useful for identifying high-urgency patients with a specificity of 0·90 (95% CI 0·87-0·92) while a cutoff score of less than 6 was useful for identifying low-urgency patients with a sensitivity of 0·83 (0·81-0·85).

INTERPRETATION

The proposed ED-PEWS can assist in identifying high-urgency and low-urgency patients in the ED, and improves prioritisation compared with existing PEWSs.

FUNDING

Stichting de Drie Lichten, Stichting Sophia Kinderziekenhuis Fonds, and the European Union's Horizon 2020 research and innovation programme.

Efficacy of prehospital National Early Warning Score to predict outpatient disposition at an emergency department of a Japanese tertiary hospital: a retrospective study

OBJECTIVES

The National Early Warning Score (NEWS) was originally developed to assess hospitalised patients in the UK. We examined whether the NEWS could be applied to patients transported by ambulance in Japan.

DESIGN

This retrospective study assessed patients and calculated the NEWS from paramedic records. Emergency department (ED) disposition data were categorised into the following groups: discharged from the ED, admitted to the ward, admitted to the intensive care unit (ICU) or died in the ED. The predictive performance of NEWS for patient disposition was assessed using receiver operating characteristic curve analysis. Patient dispositions were compared among NEWS-based categories after adjusting for age, sex and presence of traumatic injury.

SETTING

A tertiary hospital in Japan.

PARTICIPANTS

Overall, 2847 patients transported by ambulance between April 2017 and March 2018 were included.

RESULTS

The mean (±SD) NEWS differed significantly among patients discharged from the ED (n=1330, 3.7±2.9), admitted to the ward (n=1263, 60.3±3.8), admitted to the ICU (n=232, 9.4±4.0) and died in the ED (n=22, 110.7±2.9) (p<0.001). The prehospital NEWS C-statistics (95% CI) for admission to the ward, admission to the ICU or death in the ED; admission to the ICU or death in the ED; and death in the ED were 0.73 (0.72-0.75), 0.81 (0.78-0.83) and 0.90 (0.87-0.93), respectively. After adjusting for age, sex and trauma, the OR (95% CI) of admission to the ICU or death in the ED for the high-risk (NEWS ≥7) and medium-risk (NEWS 5-6) categories was 13.8 (8.9-21.6) and 4.2 (2.5-7.1), respectively.

CONCLUSION

The findings from this Japanese tertiary hospital setting showed that prehospital NEWS could be used to identify patients at a risk of adverse outcomes. NEWS stratification was strongly correlated with patient disposition.

STAPLAg: a convenient early warning score for use in infected patients in the intensive care unit

Sepsis is a life-threatening disease in the intensive care unit (ICU). The current diagnostic criteria for sequential organ failure assessment (SOFA) scores do not reflect the current understanding of sepsis. We developed a novel and convenient score to aid early prognosis.Retrospective multivariable regression analysis of 185 infected emergency ICU (EICU) patients was conducted to identify independent variables associated with death, to develop the new "STAPLAg" score; STAPLAg was then validated in an internal cohort (n = 106) and an external cohort (n = 78) and its predictive efficacy was compared with that of the initial SOFA score.Age, and initial serum albumin, sodium, PLR, troponin, and lactate tests in the emergency department were independent predictors of death in infected EICU patients, and were used to establish the STAPLAg score (area under the curve [AUC] 0.865). The initial SOFA score on admission was predictive of death (AUC 0.782). Applying the above categories to the derivation cohort yielded mortality risks of 7.7% for grade I, 56.3% for grade II, and 75.0% for grade III. Internal (AUC 0.884) and external (AUC 0.918) cohort validation indicated that the score had good predictive power.The STAPLAg score can be determined early in infected EICU patients, and exhibited better prognostic capacity than the initial SOFA score on admission in both internal and external cohorts. STAPLAg constitutes a new resource for use in the clinical diagnosis of sepsis and can also predict mortality in infected EICU patients. REGISTRATION NUMBER:: ChinCTR-PNC-16010288.

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.

Predictive powers of the Modified Early Warning Score and the National Early Warning Score in general ward patients who activated the medical emergency team

Background

The current early warning scores may be insufficient for medical emergency teams (METs) to use in assessing the severity and the prognosis of activated patients. We evaluated the predictive powers of the Modified Early Warning Score (MEWS) and the National Early Warning Score (NEWS) for 28-day mortality and to analyze predictors of 28-day mortality in general ward patients who activate the MET.

Methods

Adult general ward inpatients who activated the MET in a tertiary referral teaching hospital between March 2009 and December 2016 were included. The demographic and clinical characteristics and physiologic parameters at the time of MET activation were collected, and MEWS and NEWS were calculated.

Results

A total of 6,729 MET activation events were analyzed. Patients who died within 28 days were younger (mean age 60 vs 62 years), were more likely to have malignancy (72% vs 53%), were more likely to be admitted to the medical department rather than the surgical department (93% vs 80%), had longer intervals from admission to MET activation (median, 7 vs 5 days), and were less likely to activate the MET during nighttime hours (5 PM to 8 AM) (61% vs 66%) compared with those who did not die within 28 days (P < 0.001 for all comparisons). The areas under the receiver operating characteristic curves of MEWS and NEWS for 28-day mortality were 0.58 (95% CI, 0.56–0.59) and 0.60 (95% CI, 0.59–0.62), which were inferior to that of the logistics regression model (0.73; 95% CI, 0.72–0.74; P < 0.001 for both comparisons).

Conclusions

Both the MEWS and NEWS had poor predictive powers for 28-day mortality in patients who activated the MET. A new scoring system is needed to stratify the severity and prognosis of patients who activated the MET.

Comparison of Early Warning Scoring Systems for Hospitalized Patients With and Without Infection at Risk for In-Hospital Mortality and Transfer to the Intensive Care Unit

IMPORTANCE

Risk scores used in early warning systems exist for general inpatients and patients with suspected infection outside the intensive care unit (ICU), but their relative performance is incompletely characterized.

OBJECTIVE

To compare the performance of tools used to determine points-based risk scores among all hospitalized patients, including those with and without suspected infection, for identifying those at risk for death and/or ICU transfer.

DESIGN, SETTING, AND PARTICIPANTS

In a cohort design, a retrospective analysis of prospectively collected data was conducted in 21 California and 7 Illinois hospitals between 2006 and 2018 among adult inpatients outside the ICU using points-based scores from 5 commonly used tools: National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), Between the Flags (BTF), Quick Sequential Sepsis-Related Organ Failure Assessment (qSOFA), and Systemic Inflammatory Response Syndrome (SIRS). Data analysis was conducted from February 2019 to January 2020.

MAIN OUTCOMES AND MEASURES

Risk model discrimination was assessed in each state for predicting in-hospital mortality and the combined outcome of ICU transfer or mortality with area under the receiver operating characteristic curves (AUCs). Stratified analyses were also conducted based on suspected infection.

RESULTS

The study included 773 477 hospitalized patients in California (mean [SD] age, 65.1 [17.6] years; 416 605 women [53.9%]) and 713 786 hospitalized patients in Illinois (mean [SD] age, 61.3 [19.9] years; 384 830 women [53.9%]). The NEWS exhibited the highest discrimination for mortality (AUC, 0.87; 95% CI, 0.87-0.87 in California vs AUC, 0.86; 95% CI, 0.85-0.86 in Illinois), followed by the MEWS (AUC, 0.83; 95% CI, 0.83-0.84 in California vs AUC, 0.84; 95% CI, 0.84-0.85 in Illinois), qSOFA (AUC, 0.78; 95% CI, 0.78-0.79 in California vs AUC, 0.78; 95% CI, 0.77-0.78 in Illinois), SIRS (AUC, 0.76; 95% CI, 0.76-0.76 in California vs AUC, 0.76; 95% CI, 0.75-0.76 in Illinois), and BTF (AUC, 0.73; 95% CI, 0.73-0.73 in California vs AUC, 0.74; 95% CI, 0.73-0.74 in Illinois). At specific decision thresholds, the NEWS outperformed the SIRS and qSOFA at all 28 hospitals either by reducing the percentage of at-risk patients who need to be screened by 5% to 20% or increasing the percentage of adverse outcomes identified by 3% to 25%.

CONCLUSIONS AND RELEVANCE

In all hospitalized patients evaluated in this study, including those meeting criteria for suspected infection, the NEWS appeared to display the highest discrimination. Our results suggest that, among commonly used points-based scoring systems, determining the NEWS for inpatient risk stratification could identify patients with and without infection at high risk of mortality.

Development and validation of early warning score system: A systematic literature review

OBJECTIVES

This review aims to: 1) evaluate the quality of model reporting, 2) provide an overview of methodology for developing and validating Early Warning Score Systems (EWSs) for adult patients in acute care settings, and 3) highlight the strengths and limitations of the methodologies, as well as identify future directions for EWS derivation and validation studies.

METHODOLOGY

A systematic search was conducted in PubMed, Cochrane Library, and CINAHL. Only peer reviewed articles and clinical guidelines regarding developing and validating EWSs for adult patients in acute care settings were included. 615 articles were extracted and reviewed by five of the authors. Selected studies were evaluated based on the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist. The studies were analyzed according to their study design, predictor selection, outcome measurement, methodology of modeling, and validation strategy.

RESULTS

A total of 29 articles were included in the final analysis. Twenty-six articles reported on the development and validation of a new EWS, while three reported on validation and model modification. Only eight studies met more than 75% of the items in the TRIPOD checklist. Three major techniques were utilized among the studies to inform their predictive algorithms: 1) clinical-consensus models (n = 6), 2) regression models (n = 15), and 3) tree models (n = 5). The number of predictors included in the EWSs varied from 3 to 72 with a median of seven. Twenty-eight models included vital signs, while 11 included lab data. Pulse oximetry, mental status, and other variables extracted from electronic health records (EHRs) were among other frequently used predictors. In-hospital mortality, unplanned transfer to the intensive care unit (ICU), and cardiac arrest were commonly used clinical outcomes. Twenty-eight studies conducted a form of model validation either within the study or against other widely-used EWSs. Only three studies validated their model using an external database separate from the derived database.

CONCLUSION

This literature review demonstrates that the characteristics of the cohort, predictors, and outcome selection, as well as the metrics for model validation, vary greatly across EWS studies. There is no consensus on the optimal strategy for developing such algorithms since data-driven models with acceptable predictive accuracy are often site-specific. A standardized checklist for clinical prediction model reporting exists, but few studies have included reporting aligned with it in their publications. Data-driven models are subjected to biases in the use of EHR data, thus it is particularly important to provide detailed study protocols and acknowledge, leverage, or reduce potential biases of the data used for EWS development to improve transparency and generalizability.

An intelligent healthcare system with data priority based on multi vital biosignals

BACKGROUND AND OBJECTIVE

Home-based personal healthcare systems are becoming popular and affordable due to the development of Internet of Things (IoT) devices. However, with an increasing number of users, such healthcare systems are challenged to store and process enormous volumes of data. For instance, multi-biosignal data are collected continuously from patients using IoT device like body sensors and are sent to the server by portable devices for further analysis (e.g., knowledge discovery or the clinical event prediction). These enormous amount of data from large number of patients are causing the transmission overhead and high latency in the network which are responsible for inefficiency issues in clinical event prediction. To address these problems, in this paper, data assessment method is introduced to improve the efficiency in data collection and data prediction.

METHODS

The assessment algorithm is inspired by National Early Warning Score (NEWS) used in Emergency Department. In our method, only the abnormal time-sequence data for analysis are sent to the server. Thus, the waiting time of data before prediction can be optimized because data with higher priority are processed in front of those with lower priority, which helps our system to provide diagnostic decisions in a proper time according to patients' urgency.

RESULTS

Our experiments show that the proposed model ideally can save 20% volume of data in the collection and can reduce 75% waiting time of data with the highest priority before predicting. In addition, the waiting time of data for further analysis is optimized compared to the normal processing flow.

CONCLUSION

The paper introduces an enhanced healthcare system with assessing data priority in order to optimize the data collection and the prediction in terms of data size and waiting time.

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.

Four early warning scores predict mortality in emergency surgical patients at University Teaching Hospital, Lusaka: a prospective observational study

PURPOSE

The value of early warning scoring systems has been established in high-income countries. There is little evidence for their use in low-resource settings. We aimed to compare existing early warning scores to predict 30-day mortality.

METHODS

University Teaching Hospital is a tertiary center in Lusaka, Zambia. Adult surgical patients, excluding obstetrics, admitted for > 24 hr were included in this prospective observational study. On days 1 to 3 of admission, we collected data on patient demographics, heart rate, blood pressure, oxygen saturation, oxygen administration, temperature, consciousness level, and mobility. Two-, three-, and 30-day mortality were recorded with their associated variables analyzed using area under receiver operating curves (AUROC) for the National Early Warning Score (NEWS); the Modified Early Warning Score (MEWS); a modified Hypotension, Oxygen Saturation, Temperature, ECG, Loss of Independence (mHOTEL) score; and the Tachypnea, Oxygen saturation, Temperature, Alertness, Loss of Independence (TOTAL) score.

RESULTS

Data were available for 254 patients from March 2017 to July 2017. Eighteen (7.5%) patients died at 30 days. The four early warning scores were found to be predictive of 30-day mortality: MEWS (AUROC, 0.76; 95% confidence interval [CI], 0.63 to 0.88; P < 0.001), NEWS (AUROC 0.805; 95% CI, 0.688 to 0.92; P < 0.001), mHOTEL (AUROC 0.759; 95% CI, 0.63 to 0.89, P < 0.001), and TOTAL (AUROC 0.782; 95% CI, 0.66 to 0.90; P < 0.001).

CONCLUSIONS

We validated four scoring systems in predicting mortality in a Zambian surgical population. Further work is required to assess if implementation of these scoring systems can improve outcomes.

Adjusting Early Warning Score by clinical assessment: a study protocol for a Danish cluster-randomised, multicentre study of an Individual Early Warning Score (I-EWS)

INTRODUCTION

Track and trigger systems (TTSs) based on vital signs are implemented in hospitals worldwide to identify patients with clinical deterioration. TTSs may provide prognostic information but do not actively include clinical assessment, and their impact on severe adverse events remain uncertain. The demand for prospective, multicentre studies to demonstrate the effectiveness of TTSs has grown the last decade. Individual Early Warning Score (I-EWS) is a newly developed TTS with an aggregated score based on vital signs that can be adjusted according to the clinical assessment of the patient. The objective is to compare I-EWS with the existing National Early Warning Score (NEWS) algorithm regarding clinical outcomes and use of resources.

METHOD AND ANALYSIS

In a prospective, multicentre, cluster-randomised, crossover, non-inferiority study. Eight hospitals are randomised to use either NEWS in combination with the Capital Region of Denmark NEWS Override System (CROS) or implement I-EWS for 6.5 months, followed by a crossover. Based on their clinical assessment, the nursing staff can adjust the aggregated score with a maximum of -4 or +6 points. We expect to include 150 000 unique patients. The primary endpoint is all-cause mortality at 30 days. Coprimary endpoint is the average number of times per day a patient is NEWS/I-EWS-scored, and secondary outcomes are all-cause mortality at 48 hours and at 7 days as well as length of stay.

ETHICS AND DISSEMINATION

The study was presented for the Regional Ethics committee who decided that no formal approval was needed according to Danish law (J.no. 1701733). The I-EWS study is a large prospective, randomised multicentre study that investigates the effect of integrating a clinical assessment performed by the nursing staff in a TTS, in a head-to-head comparison with the internationally used NEWS with the opportunity to use CROS.

TRIAL REGISTRATION NUMBER

NCT03690128.

Low daily MEWS scores as predictors of low-risk hospitalized patients

BACKGROUND

The Modified Early Warning System (MEWS) is a well-validated tool used by hospitals to identify patients at high risk for an adverse event to occur. However, there has been little evaluation into whether a low MEWS score can be predictive of patients with a low likelihood of an adverse event.

AIM

The present study aims to evaluate the MEWS score as a method of identifying patients at low risk for adverse events.

DESIGN

Retrospective cohort study of 5676 patient days and analysis of associated MEWS scores, medical comorbidities and adverse events. The primary outcome was the association of average daily MEWS scores in those who had an adverse event compared with those who did not.

RESULTS

Those with an average MEWS score of >2 were over 9 times more likely to have an adverse event compared with those with an average MEWS score of 1-2, and over 15 times more likely to have an adverse event compared to those with an average MEWS score of <1.

CONCLUSIONS

Our study shows that those with average daily MEWS scores <2 are at a significantly lower likelihood of having an adverse event compared with a score of >2, deeming them 'low-risk patients'. Formal recognition of such patients can have major implications in a hospital setting, including more efficient resource allocation in hospitals and better patient satisfaction and safety by adjusting patient monitoring according to their individual risk profile.

Use of the Modified Early Warning Score in intrahospital transfer of patients

Objective

To verify whether there is an association between the Modified Early Warning Score before the transfer from the emergency room to the ward and death or admission to the intensive care unit within 30 days.

Methods

This is a historical cohort study conducted in a high-complexity hospital in southern Brazil with patients who were transferred from the emergency room to the ward between January and June 2017. The following data were collected: sociodemographic variables; comorbidities, as determined by the Charlson index; reason for hospitalization; Modified Early Warning Score at the time of transfer; admission to the intensive care unit; care by the Rapid Response Team; mortality within 30 days; and hospital mortality.

Results

A total of 278 patients were included in the study. Regarding the Modified Early Warning Score, patients who died within 30 days had a significantly higher score than surviving patients during this period (2.0 [1.0 - 3.0] versus 1.0 [1.0 - 2.0], respectively; p = 0.006). The areas under the receiver operating characteristic curve for death within 30 days and for ICU admission were 0.67 (0.55 - 0.80; p = 0.012) and 0.72 (0.59 - 0.84; p = 0.02), respectively, with a Modified Early Warning Score cutoff of ≥ 2. In the Cox regression, the Modified Early Warning Score was independently associated with mortality within 30 days after multivariate adjustment (hazard ratio 2.91; 95% confidence interval 1.04 - 8.13).

Conclusion

The Modified Early Warning Score before intrahospital transfer from the emergency room to the ward is associated with admission to the intensive care unit and death within 30 days. The Modified Early Warning Score can be an important indicator for monitoring these patients and can prompt the receiving team to take specific actions.

The effectiveness of a national early warning score as a triage tool for activating a rapid response system in an outpatient setting: A retrospective cohort study

Rapid response system (RRS) efficacy and national early warning score (NEWS) performances have largely been reported in inpatient settings, with few such reports undertaken in outpatient settings.This study aimed to investigate NEWS validity in predicting poor clinical outcomes among outpatients who had activated the RRS using single-parameter criteria.A single-center retrospective cohort studyFrom April 1, 2014 to November 30, 2017 in an urban 350-bed referral hospital in JapanWe collected patient characteristics such as activation triggers, interventions, arrival times, dispositions, final diagnoses, and patient outcomes. Poor clinical outcomes were defined as unplanned intensive care unit transfers or deaths within 24 hours. Correlations between the NEWS and clinical outcomes at the time of deterioration and disposition were analyzed.Among 31 outpatients, the NEWS value decreased significantly after a medical emergency team intervention (median, 8 vs 4, P < .001). The difference in the NEWS at the time of deterioration and at disposition was significantly less in patients with poor clinical outcomes (median 3 vs 1.5, P = .03). The area under the curve (AUC) for the NEWS high-risk patient group at the time of deterioration for predicting hospital admission was 0.85 (95% confidence interval [CI], 0.67-1.0), while the AUC for the NEWS high-risk patient group at disposition for predicting poor clinical outcomes was 0.83 (95% CI, 0.62-1.0).The difference between the NEWS at the time of deterioration and at disposition might usefully predict admissions and poor clinical outcomes in RRS outpatient settings.

Early warning score adjusted for age to predict the composite outcome of mortality, cardiac arrest or unplanned intensive care unit admission using observational vital-sign data: a multicentre development and validation

OBJECTIVES

Early warning scores (EWS) alerting for in-hospital deterioration are commonly developed using routinely collected vital-sign data from the whole in-hospital population. As these in-hospital populations are dominated by those over the age of 45 years, resultant scores may perform less well in younger age groups. We developed and validated an age-specific early warning score (ASEWS) derived from statistical distributions of vital signs.

DESIGN

Observational cohort study.

SETTING

Oxford University Hospitals (OUH) July 2013 to March 2018 and Portsmouth Hospitals (PH) NHS Trust January 2010 to March 2017 within the Hospital Alerting Via Electronic Noticeboard database.

PARTICIPANTS

Hospitalised patients with electronically documented vital-sign observations OUTCOME: Composite outcome of unplanned intensive care unit admission, mortality and cardiac arrest.

METHODS AND RESULTS

Statistical distributions of vital signs were used to develop an ASEWS to predict the composite outcome within 24 hours. The OUH development set consisted of 2 538 099 vital-sign observation sets from 142 806 admissions (mean age (SD): 59.8 (20.3)). We compared the performance of ASEWS to the National Early Warning Score (NEWS) and our previous EWS (MCEWS) on an OUH validation set consisting of 581 571 observation sets from 25 407 emergency admissions (mean age (SD): 63.0 (21.4)) and a PH validation set consisting of 5 865 997 observation sets from 233 632 emergency admissions (mean age (SD): 64.3 (21.1)). ASEWS performed better in the 16-45 years age group in the OUH validation set (AUROC 0.820 (95% CI 0.815 to 0.824)) and PH validation set (AUROC 0.840 (95% CI 0.839 to 0.841)) than NEWS (AUROC 0.763 (95% CI 0.758 to 0.768) and AUROC 0.836 (95% CI 0.835 to 0.838) respectively) and MCEWS (AUROC 0.808 (95% CI 0.803 to 0.812) and AUROC 0.833 (95% CI 0.831 to 0.834) respectively). Differences in performance were not consistent in the elder age group.

CONCLUSIONS

Accounting for age-related vital sign changes can more accurately detect deterioration in younger patients.

Performance of externally validated enhanced computer-aided versions of the National Early Warning Score in predicting mortality following an emergency admission to hospital in England: a cross-sectional study

OBJECTIVES

In the English National Health Service, the patient's vital signs are monitored and summarised into a National Early Warning Score (NEWS) to support clinical decision making, but it does not provide an estimate of the patient's risk of death. We examine the extent to which the accuracy of NEWS for predicting mortality could be improved by enhanced computer versions of NEWS (cNEWS).

DESIGN

Logistic regression model development and external validation study.

SETTING

Two acute hospitals (YH-York Hospital for model development; NH-Northern Lincolnshire and Goole Hospital for external model validation).

PARTICIPANTS

Adult (≥16 years) medical admissions discharged over a 24-month period with electronic NEWS (eNEWS) recorded on admission are used to predict mortality at four time points (in-hospital, 24 hours, 48 hours and 72 hours) using the first electronically recorded NEWS (model M0) versus a cNEWS model which included age+sex (model M1) +subcomponents of NEWS (including diastolic blood pressure) (model M2).

RESULTS

The risk of dying in-hospital following emergency medical admission was 5.8% (YH: 2080/35 807) and 5.4% (NH: 1900/35 161). The c-statistics for model M2 in YH for predicting mortality (in-hospital=0.82, 24 hours=0.91, 48 hours=0.88 and 72 hours=0.88) was higher than model M0 (in-hospital=0.74, 24 hours=0.89, 48 hours=0.86 and 72 hours=0.85) with higher Positive Predictive Value (PPVs) for in-hospital mortality (M2 19.3% and M0 16.6%). Similar findings were seen in NH. Model M2 performed better than M0 in almost all major disease subgroups.

CONCLUSIONS

An externally validated enhanced computer-aided NEWS model (cNEWS) incrementally improves on the performance of a NEWS only model. Since cNEWS places no additional data collection burden on clinicians and is readily automated, it may now be carefully introduced and evaluated to determine if it can improve care in hospitals that have eNEWS systems.

Comparing the predictive ability of a commercial artificial intelligence early warning system with physician judgement for clinical deterioration in hospitalised general internal medicine patients: a prospective observational study

OBJECTIVE

Our study compares physician judgement with an automated early warning system (EWS) for predicting clinical deterioration of hospitalised general internal medicine patients.

DESIGN

Prospective observational study of clinical predictions made at the end of the daytime work-shift for an academic general internal medicine floor team compared with the risk assessment from an automated EWS collected at the same time.

SETTING

Internal medicine teaching wards at a single tertiary care academic medical centre in the USA.

PARTICIPANTS

Intern physicians working on the internal medicine wards and an automated EWS (Rothman Index by PeraHealth).

OUTCOME

Clinical deterioration within 24 hours including cardiac or pulmonary arrest, rapid response team activation or unscheduled intensive care unit transfer.

RESULTS

We collected predictions for 1874 patient days and saw 35 clinical deteriorations (1.9%). The area under the receiver operating curve (AUROC) for the EWS was 0.73 vs 0.70 for physicians (p=0.571). A linear regression model combining physician and EWS predictions had an AUROC of 0.75, outperforming physicians (p=0.016) and the EWS (p=0.05).

CONCLUSIONS

There is no significant difference in the performance of the EWS and physicians in predicting clinical deterioration at 24 hours on an inpatient general medicine ward. A combined model outperformed either alone. The EWS and physicians identify partially overlapping sets of at-risk patients suggesting they rely on different cues or decision rules for their predictions.

TRIAL REGISTRATION NUMBER

NCT02648828.

Comparison of Reverse Triage with National Early Warning Score, Sequential Organ Failure Assessment and Charlson Comorbidity Index to classify medical inpatients of an Italian II level hospital according to their resource's need

Resource allocation in our overcrowded hospitals would require classification of inpatients according to the severity of illness, the evolving risk and the clinical complexity. Reverse triage (RT) is a method used in disasters to identify inpatients according to their use of hospital resources. The aim of this observational prospective study is to evaluate the use of RT in medical inpatients of an Italian Hospital and to compare the RT score with National Early Warning Score, Sequential Organ Failure Assessment and Charlson Comorbidity Index. Cluster sampling was performed on high dependency unit (HDU), geriatrics (Ger) and internal medicine (IM) wards. We calculate RT, NEWS, SOFA and CCI from inpatient charts. Length of stay (LOS), transfer to a higher level of care, death and discharge date were collected after 30 days. We obtained demographics, comorbidities, severity and clinical complexity of 260 inpatients. We highlighted differences in NEWS, SOFA and CCI in the three divisions. On the contrary RT score was uniformly high (median 7), with 85% of patients with RT = 8. NEWS, SOFA and CCI were higher in patients with higher RT score. We used the sum of the interventions listed by RT (RT sum) as a proxy of the level of care needed. RT-sum showed moderate correlation with NEWS (r = 0.52 Spearman, p < 0.001). RT-sum was the highest in HDU, related to the evolving severity of HDU patients. Ger patients that showed the highest CCI score (with all patients in the CCI ≥ 3 category) had the second highest RT-sum. RT score showed similar values in the majority of the inpatients regardless of differences in NEWS, SOFA and CCI in different ward subgroups. RT-sum is related both to evolving severity (NEWS) and to clinical complexity (CCI). RT and NEWS could predict inpatient level of care and resource need associated with CCI.

ChroniSense National Early Warning Score Study (CHESS): a wearable wrist device to measure vital signs in hospitalised patients-protocol and study design

INTRODUCTION

The National Early Warning Score is used as standard clinical practice in the UK as a track and trigger system to monitor hospitalised patients. Currently, nurses are tasked to take routine vital signs measurements and manually record these on a clinical chart. Wearable devices could provide an easier, reliable, more convenient and cost-effective method of monitoring. Our aim is to evaluate the clinical validity of Polso (ChroniSense Medical, Yokneam Illit, Israel), a wrist-based device, to provide National Early Warning Scores.

METHODS AND ANALYSIS

We will compare Polso National Early Warning Score measurements to the currently used manual measurements in a UK Teaching District General Hospital. Patients aged 18 years or above who require recordings of observations of vital signs at least every 6 hours will be enrolled after consenting. The sample size for the study was calculated to be 300 participants based on the assumption that the final dataset will include four pairs of measurements per-patient and per-vital sign, resulting in a total of 1200 pairs of data points per vital sign. The primary outcome is the agreement on the individual parameter scores and values of the National Early Warning Score: (1) respiratory rate, (2) oxygen saturation, (3) body temperature, (4) systolic blood pressure and (5) heart rate. Secondary outcomes are the agreement on the aggregate National Early Warning Score. The incidence of adverse events will be recorded. The measurements by the device will not be used for the clinical decision-making in this study.

ETHICS AND DISSEMINATION

We obtained ethical approval, reference number 18/LO/0123 from London-Hampstead Research Ethics Committee, through the Integrated Research Application System, (reference number: 235 034. The study received no objection from the Medicine and Health Regulatory Authority, reference number: CI/20018/005 and has National Institute for Health Research portfolio adoption status CPMS number: 32 532.

TRIAL REGISTRATION NUMBER

NCT03448861; Pre-results.

Deep Interpretable Early Warning System for the Detection of Clinical Deterioration

Assessment of physiological instability preceding adverse events on hospital wards has been previously investigated through clinical early warning score systems. Early warning scores are simple to use yet they consider data as independent and identically distributed random variables. Deep learning applications are able to learn from sequential data, however they lack interpretability and are thus difficult to deploy in clinical settings. We propose the 'Deep Early Warning System' (DEWS), an interpretable end-to-end deep learning model that interpolates temporal data and predicts the probability of an adverse event, defined as the composite outcome of cardiac arrest, mortality or unplanned ICU admission. The model was developed and validated using routinely collected vital signs of patients admitted to the the Oxford University Hospitals between 21st March 2014 and 31st March 2018. We extracted 45 314 vital-sign measurements as a balanced training set and 359 481 vital-sign measurements as an imbalanced testing set to mimic a real-life setting of emergency admissions. DEWS achieved superior accuracy than the state-of-the-art that is currently implemented in clinical settings, the National Early Warning Score, in terms of the overall area under the receiver operating characteristic curve (AUROC) (0.880 vs. 0.866) and when evaluated independently for each of the three outcomes. Our attention-based architecture was able to recognize 'historical' trends in the data that are most correlated with the predicted probability. With high sensitivity, improved clinical utility and increased interpretability, our model can be easily deployed in clinical settings to supplement existing EWS systems.

Multicentre, prospective observational study of the correlation between the Glasgow Admission Prediction Score and adverse outcomes

OBJECTIVES

To assess whether the Glasgow Admission Prediction Score (GAPS) is correlated with hospital length of stay, 6-month hospital readmission and 6-month all-cause mortality. This study represents a 6-month follow-up of patients who were included in an external validation of the GAPS' ability to predict admission at the point of triage.

SETTING

Sampling was conducted between February and May 2016 at two separate emergency departments (EDs) in Sheffield and Glasgow.

PARTICIPANTS

Data were collected prospectively at triage for consecutive adult patients who presented to the ED within sampling times. Any patients who avoided formal triage were excluded from the study. In total, 1420 patients were recruited.

PRIMARY OUTCOMES

GAPS was calculated following triage and did not influence patient management. Length of hospital stay, hospital readmission and mortality against GAPS were modelled using survival analysis at 6 months.

RESULTS

Of the 1420 patients recruited, 39.6% of these patients were initially admitted to hospital. At 6 months, 30.6% of patients had been readmitted and 5.6% of patients had died. For those admitted at first presentation, the chance of being discharged fell by 4.3% (95% CI 3.2% to 5.3%) per GAPS point increase. Cox regression indicated a 9.2% (95% CI 7.3% to 11.1%) increase in the chance of 6-month hospital readmission per point increase in GAPS. An association between GAPS and 6-month mortality was demonstrated, with a hazard increase of 9.0% (95% CI 6.9% to 11.2%) for every point increase in GAPS.

CONCLUSION

A higher GAPS is associated with increased hospital length of stay, 6-month hospital readmission and 6-month all-cause mortality. While GAPS's primary application may be to predict admission and support clinical decision making, GAPS may provide valuable insight into inpatient resource allocation and bed planning.