Showing papers on "Early warning score published in 2015"

A targeted real-time early warning score (TREWScore) for septic shock

Abstract: Sepsis is a leading cause of death in the United States, with mortality highest among patients who develop septic shock. Early aggressive treatment decreases morbidity and mortality. Although automated screening tools can detect patients currently experiencing severe sepsis and septic shock, none predict those at greatest risk of developing shock. We analyzed routinely available physiological and laboratory data from intensive care unit patients and developed “TREWScore,” a targeted real-time early warning score that predicts which patients will develop septic shock. TREWScore identified patients before the onset of septic shock with an area under the ROC (receiver operating characteristic) curve (AUC) of 0.83 [95% confidence interval (CI), 0.81 to 0.85]. At a specificity of 0.67, TREWScore achieved a sensitivity of 0.85 and identified patients a median of 28.2 [interquartile range (IQR), 10.6 to 94.2] hours before onset. Of those identified, two-thirds were identified before any sepsis-related organ dysfunction. In comparison, the Modified Early Warning Score, which has been used clinically for septic shock prediction, achieved a lower AUC of 0.73 (95% CI, 0.71 to 0.76). A routine screening protocol based on the presence of two of the systemic inflammatory response syndrome criteria, suspicion of infection, and either hypotension or hyperlactatemia achieved a lower sensitivity of 0.74 at a comparable specificity of 0.64. Continuous sampling of data from the electronic health records and calculation of TREWScore may allow clinicians to identify patients at risk for septic shock and provide earlier interventions that would prevent or mitigate the associated morbidity and mortality.

A simple tool to predict admission at the time of triage

Abstract: Aim To create and validate a simple clinical score to estimate the probability of admission at the time of triage. Methods This was a multicentre, retrospective, cross-sectional study of triage records for all unscheduled adult attendances in North Glasgow over 2 years. Clinical variables that had significant associations with admission on logistic regression were entered into a mixed-effects multiple logistic model. This provided weightings for the score, which was then simplified and tested on a separate validation group by receiving operator characteristic (ROC) analysis and goodness-of-fit tests. Results 215 231 presentations were used for model derivation and 107 615 for validation. Variables in the final model showing clinically and statistically significant associations with admission were: triage category, age, National Early Warning Score (NEWS), arrival by ambulance, referral source and admission within the last year. The resulting 6-variable score showed excellent admission/discharge discrimination (area under ROC curve 0.8774, 95% CI 0.8752 to 0.8796). Higher scores also predicted early returns for those who were discharged: the odds of subsequent admission within 28 days doubled for every 7-point increase (log odds=+0.0933 per point, p Conclusions This simple, 6-variable score accurately estimates the probability of admission purely from triage information. Most patients could accurately be assigned to ‘admission likely’, ‘admission unlikely’, ‘admission very unlikely’ etc., by setting appropriate cut-offs. This could have uses in patient streaming, bed management and decision support. It also has the potential to control for demographics when comparing performance over time or between departments.

Differences in vital signs between elderly and nonelderly patients prior to ward cardiac arrest.

Abstract: Objective Vital signs and composite scores, such as the Modified Early Warning Score (MEWS), are used to identify high-risk ward patients and trigger rapid response teams. Although age-related vital sign changes are known to occur, little is known about the differences in vital signs between elderly and non-elderly patients prior to ward cardiac arrest (CA). We aimed to compare the accuracy of vital signs for detecting CA between elderly and non-elderly patients.

An Early Warning Scoring System to Identify Septic Patients in the Prehospital Setting: The PRESEP Score

Abstract: Objectives The objective was to develop and evaluate an early sepsis detection score for the prehospital setting. Methods A retrospective analysis of consecutive patients who were admitted by emergency medical services (EMS) to the emergency department of the Jena University Hospital was performed. Because potential predictors for sepsis should be based on consensus criteria, the following parameters were extracted from the EMS protocol for further analysis: temperature, heart rate (HR), respiratory rate (RR), oxygen saturation (SaO2), Glasgow Coma Scale score, blood glucose, and systolic blood pressure (sBP). Potential predictors were stratified based on inspection of Loess graphs. Backward model selection was performed to select risk factors for the final model. The Prehospital Early Sepsis Detection (PRESEP) score was calculated as the sum of simplified regression weights. Its predictive validity was compared to the Modified Early Warning Score (MEWS), the Robson screening tool, and the BAS 90-30-90. Results A total of 375 patients were included in the derivation sample; 93 (24.8%) of these had sepsis, including 60 patients with severe sepsis and 12 patients with septic shock. Backward model selection identified temperature, HR, RR, SaO2, and sBP for inclusion in the PRESEP score. Simplified weights were as follows: temperature > 38°C = 4, temperature 90 beats/min = 2, RR > 22 breaths/min = 1, SaO2 < 92% = 2, and sBP < 90 mm Hg = 2. The cutoff value for a possible existing septic disease based on maximum Youden's index was ≥4 (sensitivity 0.85, specificity 0.86, positive predictive value [PPV] 0.66, and negative predictive value [NPV] 0.95). The area under the receiver operating characteristic curve (AUC) of the PRESEP score was 0.93 (95% confidence interval [CI] = 0.89 to 0.96) and was larger than the AUC of the MEWS (0.93 vs. 0.77, p < 0.001). The PRESEP score surpassed MEWS and BAS 90-60-90 for sensitivity (0.74 and 0.62, respectively), specificity (0.75 and 0.83), PPV (0.45 and 0.51), and NPV (0.91 and 0.89). The Robson screening tool had a higher sensitivity and NPV (0.95 and 0.97), but its specificity and PPV were lower (0.43 and 0.32). Conclusions The PRESEP score could be a valuable tool for identifying septic patients in the prehospital setting in the case of suspected infection. It should be prospectively validated.

'Errors' and omissions in paper-based early warning scores: the association with changes in vital signs--a database analysis.

Abstract: Objectives: To understand factors associated with errors using an established paper-based early warning score (EWS) system. We investigated the types of error, where they are most likely to occur, and whether ‘errors’ can predict subsequent changes in patient vital signs. Methods: Retrospective analysis of prospectively collected early warning system database from a single large UK teaching hospital. Results: 16 795 observation sets, from 200 postsurgical patients, were collected. Incomplete observation sets were more likely to contain observations which should have led to an alert than complete observation sets (15.1% vs 7.6%, p<0.001), but less likely to have an alerting score correctly calculated (38.8% vs 30.0%, p<0.001). Mis-scoring was much more common when leaving a sequence of three or more consecutive observation sets with aggregate scores of 0 (55.3%) than within the sequence (3.0%, p<0.001). Observation sets that ‘incorrectly’ alerted were more frequently followed by a correctly alerting observation set than error-free nonalerting observation sets (14.7% vs 4.2%, p<0.001). Observation sets that ‘incorrectly’ did not alert were more frequently followed by an observation set that did not alert than error-free alerting observation sets (73.2% vs 45.8%, p<0.001). Conclusions: Missed alerts are particularly common in incomplete observation sets and when a patient first becomes unstable. Observation sets that ‘incorrectly’ alert or ‘incorrectly’ do not alert are highly predictive of the next observation set, suggesting that clinical staff detect both deterioration and improvement in advance of the EWS system by using information not currently encoded within it. Work is urgently needed to understand how best to capture this information.

Early Warning Scores: A Sign of Deterioration in Patients and Systems

Abstract: The early warning score is a decision-making tool that has a simple design, yet its implementation in healthcare organisations is proving complex. This article reports the results of a survey that evaluated the nurses’ experiences of using the NEWS (National Early Warning Score) in an acute hospital in Ireland. Staff reported that the NEWS was easy to use, did not increase workload and enhanced their ability to identify deteriorating patients. However, they also identified problems related to doctors’ delayed response times, doctors lack of training in the use of the tool, and a failure by doctors to modify parameters for patients with chronic conditions. NEWS enhances nurses’ role in early detection of patient deterioration but delays in response times by doctors, exposes systematic flaws in healthcare. This suggests that it is not only an indicator of patient deterioration but also of deteriorating healthcare systems.

Validation of pediatric early warning score in pediatric emergency department

Abstract: Background One of the most important functions of the emergency department (ED) is to assess patient status. Only one, the pediatric early warning score (PEWS), has been designed for ED with acceptable validity, but it has never been validated in Thailand. The objective of this study was to validate PEWS in predicting hospitalization in children visiting the ED. Methods During the initial phase, two triage nurses performed blind scoring (in order to determine inter-rater reliability using kappa statistics) for the first 30 patients who presented to the ED at Ramathibodi Hospital between March and May 2014 and who were aged <15 years. The second phase then consisted of validation and involved 1136 patients. Patients who presented with trauma, psychiatric, dental and surgical concerns were excluded. Validity of the scoring system in predicting admission was assessed using area under the receiver operating characteristics (ROC) curve (AUC), sensitivity, and specificity, positive predictive value (PPV) and negative predictive value (NPV). Results Phase I demonstrated good inter-rater reliability (kappa = 0.75). In phase II, of the total group of 1136 patients, 168 patients (14.8%) were admitted: 162 to the general ward and six to the intensive care unit (ICU) during the study period. AUC for predicting overall, ICU, and general ward admission were 0.73 (95%CI: 0.68–0.77), 0.98 (95%CI: 0.96–1) and 0.71 (95%CI: 0.66–0.75), respectively. The sensitivity and specificity in predicting overall admission with a cut-off of PEWS ≥1 was 78% and 60%, respectively (PPV, 28%; NPV, 95%). Sensitivity and specificity in predicting ICU admission with the cut-off PEWS ≥3 was 100% and 91%, respectively (PPV, 5%; NPV, 100%). Using the cut-off PEWS ≥1, sensitivity and specificity in predicting ward admission were 77% and 59%, respectively (PPV, 24%; NPV, 94%). Conclusion PEWS can be helpful in assessing patient status in pediatric ED with acceptable validity and can serve as a potentially excellent screening tool for prediction of ICU admission.

Early Prediction of Cardiac Arrest (Code Blue) using Electronic Medical Records

Abstract: Code Blue is an emergency code used in hospitals to indicate when a patient goes into cardiac arrest and needs resuscitation. When Code Blue is called, an on-call medical team staffed by physicians and nurses is paged and rushes in to try to save the patient's life. It is an intense, chaotic, and resource-intensive process, and despite the considerable effort, survival rates are still less than 20% [4]. Research indicates that patients actually start showing clinical signs of deterioration some time before going into cardiac arrest [1][2[][3], making early prediction, and possibly intervention, feasible. In this paper, we describe our work, in partnership with NorthShore University HealthSystem, that preemptively flags patients who are likely to go into cardiac arrest, using signals extracted from demographic information, hospitalization history, vitals and laboratory measurements in patient-level electronic medical records. We find that early prediction of Code Blue is possible and when compared with state of the art existing method used by hospitals (MEWS - Modified Early Warning Score)[4], our methods perform significantly better. Based on these results, this system is now being considered for deployment in hospital settings.

Validation of a Paediatric Early Warning Score: first results and implications of usage

Abstract: Timely recognition of deterioration of hospitalised children is important to improve mortality. We developed a modified Paediatric Early Warning Score (PEWS) and studied the effects by performing three different cohort studies using different end points. Taking unplanned Paediatric Intensive Care Unit admission as end point and only using data until 2 h prior to end point, we found a sensitivity of 0.67 and specificity of 0.88 to timely recognise patients. This proves that earlier identification is possible without a loss of sensitivity compared to other PEWS systems. When determining the corresponding clinical condition in patients with an elevated PEWS dichotomously as ‘sick’ or ‘well’, this resulted in a total of 27 % false-positive scores. This can cause motivational problems for caregivers to use the system but is a consequence of PEWS design to minimise false-negative rates because of high mortality associated with paediatric resuscitation. Using the need for emergency medical interventions as end point, sensitivity of PEWS is high and it seems, therefore, that it is also fit to alert health-care professionals that urgent interventions may be needed. Conclusion: These data show the effectiveness of a modified PEWS in identifying critically ill patients in an early phase making early interventions possible and hopefully reduce mortality.

SEND: a system for electronic notification and documentation of vital sign observations

Abstract: Recognising the limitations of a paper-based approach to documenting vital sign observations and responding to national clinical guidelines, we have explored the use of an electronic solution that could improve the quality and safety of patient care. We have developed a system for recording vital sign observations at the bedside, automatically calculating an Early Warning Score, and saving data such that it is accessible to all relevant clinicians within a hospital trust. We have studied current clinical practice of using paper observation charts, and attempted to streamline the process. We describe our user-focussed design process, and present the key design decisions prior to describing the system in greater detail.

Internet of Things Enabled In-Home Health Monitoring System Using Early Warning Score

Abstract: Early warning score (EWS) is an approach to detect the deterioration of a patient. It is based on a fact that there are several changes in the physiological parameters prior a clinical deterioration of a patient. Currently, EWS procedure is mostly used for in-hospital clinical cases and is performed in a manual paper-based fashion. In this paper, we propose an automated EWS health monitoring system to intelligently monitor vital signs and prevent health deterioration for in-home patients using Internet-of-Things (IoT) technologies. IoT enables our solution to provide a real-time 24/7 service for health professionals to remotely monitor in-home patients via Internet and receive notifications in case of emergency. We also demonstrate a proof-of-concept EWS system where continuous reading, transferring, recording, and processing of vital signs have been implemented.

Internet of Things Enabled In-Home Health Monitoring System Using Early Warning Score

Abstract: Early warning score (EWS) is an approach to detect the deterioration of a patient. It is based on a fact that there are several changes in the physiological parameters prior a clinical deterioration of a patient. Currently, EWS procedure is mostly used for in-hospital clinical cases and is performed in a manual paper-based fashion. In this paper, we propose an automated EWS health monitoring system to intelligently monitor vital signs and prevent health deterioration for in-home patients using Internet-of-Things (IoT) technologies. IoT enables our solution to provide a real-time 24/7 service for health professionals to remotely monitor in-home patients via Internet and receive notifications in case of emergency. We also demonstrate a proof-of-concept EWS system where continuous reading, transferring, recording, and processing of vital signs have been implemented.

Comparison of Pediatric Early Warning Score to Physician Opinion for Deteriorating Patients.

Abstract: BACKGROUND: This study compares a Pediatric Early Warning Score (PEWS) to physician opinion in identifying patients at risk for deterioration. METHODS: Maximum PEWS recorded during each admission was retrospectively ascertained from electronic medical record data. Physician opinion regarding risk of subsequent deterioration was determined by assignment to an institutional “senior sign-out” (SSO) list that highlights patients whom senior pediatric residents have identified as at risk. Deterioration events were defined as intubation, initiation of high flow nasal cannula, inotropes, noninvasive mechanical ventilation, or aggressive fluid resuscitation within 12 hours of transfer to the PICU. We assessed the relationships of sociodemographic variables, PEWS, and SSO assignment with subsequent deterioration events using multivariate regression analysis to control for a number of covariates. RESULTS: There were 97 patients with nonelective transfers to the PICU who were eligible for placement on the SSO lists before transfer, 51 of whom experienced qualifying deterioration events. Maximum recorded PEWS was significantly higher for patients with a subsequent deterioration event during the first 12 hours after transfer, compared with those who were transferred but did not experience a deterioration event in the first 12 hours (mean [SD]: 3.9 [2.0] vs 2.9 [2.0]; P = .01). This association persisted even after multivariate adjustment. SSO assignment was only marginally associated with risk of deterioration among this patient population, with or without adjustment for covariates. CONCLUSIONS: The PEWS was significantly associated with ICU deterioration, whereas physician opinion was not. Used alone or in conjunction with physician assessment, PEWS is a valuable tool for identifying patients vulnerable to acute deterioration.

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

Abstract: In sub-Saharan Africa, vital signs are a feasible option for monitoring critically ill patients. We assessed how admission vital signs data predict in-hospital mortality among patients with sepsis. In particular, we assessed whether vital signs data can be incorporated into a prognostic index with reduced segmentation in the values of included variables. Subjects were patients with sepsis hospitalized in Uganda, who participated in two cohort studies. Using restricted cubic splines of admission vital signs data, we predicted probability of in-hospital death in the development cohort and used this information to construct a simple prognostic index. We assessed the performance of the index in a validation cohort and compared its performance to that of the Modified Early Warning Score (MEWS). We included 317 patients (167 in the development cohort and 150 in the validation cohort). Based on how vital signs predicted mortality, we created a prognostic index giving a score of 1 for: respiratory rates ≥30 cycles/minute; pulse rates ≥100 beats/minute; mean arterial pressures ≥110/<70 mmHg; temperatures ≥38.6/<35.6°C; and presence of altered mental state defined as Glasgow coma score ≤14; 0 for all other values. The proposed index (maximum score = 5) predicted mortality comparably to MEWS. Patients scoring ≥3 on the index were 3.4-fold (95% confidence interval (CI) 1.6 to 7.3, P = 0.001) and 2.3-fold (95% CI 1.1 to 4.7, P = 0.031) as likely to die in hospital as those scoring 0 to 2 in the development and validation cohorts respectively; those scoring ≥5 on MEWS were 2.5-fold (95% CI 1.2 to 5.3, P = 0.017) and 1.8-fold (95% CI 0.74 to 4.2, P = 0.204) as likely to die as those scoring 0 to 4 in the development and validation cohorts respectively. Among patients with sepsis, a prognostic index incorporating admission vital signs data with reduced segmentation in the values of included variables adequately predicted mortality. Such an index may be more easily implemented when triaging acutely-ill patients. Future studies using a similar approach may develop indexes that can be used to monitor treatment among acutely-ill patients, especially in resource-limited settings.

Early warning scores: unravelling detection and escalation

Abstract: Purpose – The purpose of this paper is to increase understanding of how patient deterioration is detected and how clinical care escalates when early warning score (EWS) systems are used. Design/methodology/approach – The authors critically review a recent National Early Warning Score paper published in IJHCQA using personal experience and EWS-related publications, and debate the difference between detection and escalation. Findings – Incorrect EWS choice or poorly understood EWS escalation may result in unnecessary workloads forward and responding staff. Practical implications – EWS system implementers may need to revisit their guidance materials; medical and nurse educators may need to expand the curriculum to improve EWS system understanding and use. Originality/value – The paper raises the EWS debate and alerts EWS users that scrutiny is required.

Evaluation of the effects of implementing an electronic early warning score system: protocol for a stepped wedge study.

Abstract: An Early Warning Score is a clinical risk score based upon vital signs intended to aid recognition of patients in need of urgent medical attention. The use of an escalation of care policy based upon an Early Warning Score is mandated as the standard of practice in British hospitals. Electronic systems for recording vital sign observations and Early Warning Score calculation offer theoretical benefits over paper-based systems. However, the evidence for their clinical benefit is limited. Previous studies have shown inconsistent results. The majority have employed a “before and after” study design, which may be strongly confounded by simultaneously occurring events. This study aims to examine how the implementation of an electronic early warning score system, System for Notification and Documentation (SEND), affects the recognition of clinical deterioration occurring in hospitalised adult patients. This study is a non-randomised stepped wedge evaluation carried out across the four hospitals of the Oxford University Hospitals NHS Trust, comparing charting on paper and charting using SEND. We assume that more frequent monitoring of acutely ill patients is associated with better recognition of patient deterioration. The primary outcome measure is the time between a patient’s first observations set with an Early Warning Score above the alerting threshold and their subsequent set of observations. Secondary outcome measures are in-hospital mortality, cardiac arrest and Intensive Care admission rates, hospital length of stay and system usability measured using the System Usability Scale. We will also measure Intensive Care length of stay, Intensive Care mortality, Acute Physiology and Chronic Health Evaluation (APACHE) II acute physiology score on admission, to examine whether the introduction of SEND has any effect on Intensive Care-related outcomes. The development of this protocol has been informed by guidance from the Agency for Healthcare Research and Quality (AHRQ) Health Information Technology Evaluation Toolkit and Delone and McLeans’s Model of Information System Success. Our chosen trial design, a stepped wedge study, is well suited to the study of a phased roll out. The choice of primary endpoint is challenging. We have selected the time from the first triggering observation set to the subsequent observation set. This has the benefit of being easy to measure on both paper and electronic charting and having a straightforward interpretation. We have collected qualitative measures of system quality via a user questionnaire and organisational descriptors to help readers understand the context in which SEND has been implemented.

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

Abstract: Introduction Multiple early warning scores (EWS) have been developed and implemented to reduce cardiac arrests on hospital wards. Case–control observational studies that generate an area under the receiver operator curve (AUROC) are the usual validation method, but investigators have also generated EWS with algorithms with no prior clinical knowledge. We present a protocol for the validation and comparison of our local Hamilton Early Warning Score (HEWS) with that generated using decision tree (DT) methods. Methods and analysis A database of electronically recorded vital signs from 4 medical and 4 surgical wards will be used to generate DT EWS (DT-HEWS). A third EWS will be generated using ensemble-based methods. Missing data will be multiple imputed. For a relative risk reduction of 50% in our composite outcome (cardiac or respiratory arrest, unanticipated intensive care unit (ICU) admission or hospital death) with a power of 80%, we calculated a sample size of 17 151 patient days based on our cardiac arrest rates in 2012. The performance of the National EWS, DT-HEWS and the ensemble EWS will be compared using AUROC. Ethics and dissemination Ethics approval was received from the Hamilton Integrated Research Ethics Board (#13-724-C). The vital signs and associated outcomes are stored in a database on our secure hospital server. Preliminary dissemination of this protocol was presented in abstract form at an international critical care meeting. Final results of this analysis will be used to improve on the existing HEWS and will be shared through publication and presentation at critical care meetings.

Relationship between input and output in acute medicine – secondary analysis of the Society for Acute Medicine's benchmarking audit 2013 (SAMBA ‘13)

Abstract: The performance of acute medical units (AMUs) against published quality indicators is variable. We aimed to identify the impact of case-mix and unit resources on timely assessment and discharge of patients admitted to 43 AMUs on a single day in June 2013, as part of the Society for Acute Medicine's benchmarking audit 2013. Performance against quality indicators was at its worst in the early evening hours. Units admitting fewer than 40 patients performed better. Patients who were more frail, as measured by the Clinical Frailty Scale, were also more likely to have significant physiological abnormalities and a higher risk of death, as measured by the National Early Warning Score. Our analysis suggests that resource allocation at the front door is related to quality indicators. Teams will need strengthening in the evening hours and if looking after higher numbers of frail patients.

National early warning score (NEWS) - evaluation in surgery

Abstract: Purpose – The purpose of this paper is to evaluate staff opinion on the impact of the National Early Warning Score (NEWS) system on surgical wards. In 2012, the NEWS system was introduced to Irish hospitals on a phased basis as part of a national clinical programme in acute care. Design/methodology/approach – A modified established questionnaire was given to surgical nursing staff, surgical registrars, surgical senior house officers and surgical interns for completion six months following the introduction of the NEWS system into an Irish university hospital. Findings – Amongst the registrars, 89 per cent were unsure if the NEWS system would improve patient care. Less than half of staff felt consultants and surgical registrars supported the NEWS system. Staff felt the NEWS did not correlate well clinically with patients within the first 24 hours (Day zero) post-operatively. Furthermore, 78-85 per cent of nurses and registrars felt a rapid response team should be part of the escalation protocol. Research li...

Context-Aware Early Warning System for In-Home Healthcare Using Internet-of-Things

Abstract: Early warning score (EWS) is a prediction method to notify caregivers at a hospital about the deterioration of a patient. Deterioration can be identified by detecting abnormalities in patient’s vital signs several hours prior the condition of the patient gets life-threatening. In the existing EWS systems, monitoring of patient’s vital signs and the determining the score is mostly performed in a paper and pen based way. Furthermore, currently it is done solely in a hospital environment. In this paper, we propose to import this system to patients’ home to provide an automated platform which not only monitors patents’ vital signs but also looks over his/her activities and the surrounding environment. Thanks to the Internet-of-Things technology, we present an intelligent early warning method to remotely monitor in-home patients and generate alerts in case of different medical emergencies or radical changes in condition of the patient. We also demonstrate an early warning score analysis system which continuously performs sensing, transferring, and recording vital signs, activity-related data, and environmental parameters.