Dataset Information

Manual centile-based early warning scores derived from statistical distributions of observational vital-sign data.

ABSTRACT:

Aims of study

To develop and validate a centile-based early warning score using manually-recorded data (mCEWS). To compare mCEWS performance with a centile-based early warning score derived from continuously-acquired data (from bedside monitors, cCEWS), and with other published early warning scores.

Materials and methods

We used an unsupervised approach to investigate the statistical properties of vital signs in an in-hospital patient population and construct an early-warning score from a "development" dataset. We evaluated scoring systems on a separate "validation" dataset. We assessed the ability of scores to discriminate patients at risk of cardiac arrest, unanticipated intensive care unit admission, or death, each within 24?h of a given vital-sign observation, using metrics including the area under the receiver-operating characteristic curve (AUC).

Results

The development dataset contained 301,644 vital sign observations from 12,153 admissions (median age (IQR): 63 (49-73); 49.2% females) March 2014-September 2015. The validation dataset contained 1,459,422 vital-sign observations from 53,395 admissions (median age (IQR): 68 (48-81), 51.4% females) October 2015-May 2017. The AUC (95% CI) for the mCEWS was 0.868 (0.864-0.872), comparable with the National EWS, 0.867 (0.863-0.871), and other recently proposed scores. The AUC for cCEWS was 0.808 (95% CI, 0.804-0.812). The improvement in performance in comparison to the continuous CEWS was mainly explained by respiratory rate threshold differences.

Conclusions

Performance of an EWS is highly dependent on the database from which itis derived. Our unsupervised statistical approach provides a straightforward, reproducible method to enable the rapid development of candidate EWS systems.

SUBMITTER: Watkinson PJ

PROVIDER: S-EPMC6062656 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Publications

Manual centile-based early warning scores derived from statistical distributions of observational vital-sign data.

Watkinson Peter J PJ Pimentel Marco A F MAF Clifton David A DA Tarassenko Lionel L

Resuscitation 20180604

<h4>Aims of study</h4>To develop and validate a centile-based early warning score using manually-recorded data (mCEWS). To compare mCEWS performance with a centile-based early warning score derived from continuously-acquired data (from bedside monitors, cCEWS), and with other published early warning scores.<h4>Materials and methods</h4>We used an unsupervised approach to investigate the statistical properties of vital signs in an in-hospital patient population and construct an early-warning scor ...[more]

PMID: 29879432

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Project description:BackgroundWireless vital sign sensors are increasingly being used to monitor patients on surgical wards. Although early warning scores (EWSs) are the current standard for the identification of patient deterioration in a ward setting, their usefulness for continuous monitoring is unknown.ObjectiveThis study aimed to explore the usability and predictive value of high-rate EWSs obtained from continuous vital sign recordings for early identification of postoperative complications and compares the performance of a sensor-based EWS alarm system with manual intermittent EWS measurements and threshold alarms applied to individual vital sign recordings (single-parameter alarms).MethodsContinuous vital sign measurements (heart rate, respiratory rate, blood oxygen saturation, and axillary temperature) collected with wireless sensors in patients on surgical wards were used for retrospective simulation of EWSs (sensor EWSs) for different time windows (1-240 min), adopting criteria similar to EWSs based on manual vital signs measurements (nurse EWSs). Hourly sensor EWS measurements were compared between patients with (event group: 14/46, 30%) and without (control group: 32/46, 70%) postoperative complications. In addition, alarms were simulated for the sensor EWSs using a range of alarm thresholds (1-9) and compared with alarms based on nurse EWSs and single-parameter alarms. Alarm performance was evaluated using the sensitivity to predict complications within 24 hours, daily alarm rate, and false discovery rate (FDR).ResultsThe hourly sensor EWSs of the event group (median 3.4, IQR 3.1-4.1) was significantly higher (P<.004) compared with the control group (median 2.8, IQR 2.4-3.2). The alarm sensitivity of the hourly sensor EWSs was the highest (80%-67%) for thresholds of 3 to 5, which was associated with alarm rates of 2 (FDR=85%) to 1.2 (FDR=83%) alarms per patient per day respectively. The sensitivity of sensor EWS-based alarms was higher than that of nurse EWS-based alarms (maximum=40%) but lower than that of single-parameter alarms (87%) for all thresholds. In contrast, the (false) alarm rates of sensor EWS-based alarms were higher than that of nurse EWS-based alarms (maximum=0.6 alarm/patient/d; FDR=80%) but lower than that of single-parameter alarms (2 alarms/patient/d; FDR=84%) for most thresholds. Alarm rates for sensor EWSs increased for shorter time windows, reaching 70 alarms per patient per day when calculated every minute.ConclusionsEWSs obtained using wireless vital sign sensors may contribute to the early recognition of postoperative complications in a ward setting, with higher alarm sensitivity compared with manual EWS measurements. Although hourly sensor EWSs provide fewer alarms compared with single-parameter alarms, high false alarm rates can be expected when calculated over shorter time spans. Further studies are recommended to optimize care escalation criteria for continuous monitoring of vital signs in a ward setting and to evaluate the effects on patient outcomes.

Project description:IntroductionSuccessive confidential enquiries into maternal deaths in the UK have identified an urgent need to develop a national early warning score (EWS) specifically for pregnant or recently pregnant women to aid more timely recognition, referral and treatment of women who are developing life-threatening complications in pregnancy or the puerperium. Although many local EWS are in use in obstetrics, most have been developed heuristically. No current obstetric EWS has defined the thresholds at which an alert should be triggered using evidence-based normal ranges, nor do they reflect the changing physiology that occurs with gestation during pregnancy.Methods and analysisAn observational cohort study involving 1000 participants across three UK sites in Oxford, London and Newcastle. Pregnant women will be recruited at approximately 14 weeks' gestation and have their vital signs (heart rate, blood pressure, respiratory rate, oxygen saturation and temperature) measured at 4 to 6-week intervals during pregnancy. Vital signs recorded during labour and delivery will be extracted from hospital records. After delivery, participants will measure and record their own vital signs daily for 2 weeks. During the antenatal and postnatal periods, vital signs will be recorded on an Android tablet computer through a custom software application and transferred via mobile internet connection to a secure database. The data collected will be used to define reference ranges of vital signs across normal pregnancy, labour and the immediate postnatal period. This will inform the design of an evidence-based obstetric EWS.Ethics and disseminationThe study has been approved by the NRES committee South East Coast-Brighton and Sussex (14/LO/1312) and is registered with the ISRCTN (10838017). All participants will provide written informed consent and can withdraw from the study at any point. All data collected will be managed anonymously. The findings will be disseminated in international peer-reviewed journals and through research conferences.

Project description:Pediatric early warning systems using expert-derived vital sign parameters demonstrate limited sensitivity and specificity in identifying deterioration. We hypothesized that modified tools using data-driven vital sign parameters would improve the performance of a validated tool.Retrospective case control.Quaternary-care children's hospital.Hospitalized, noncritically ill patients less than 18 years old. Cases were defined as patients who experienced an emergent transfer to an ICU or out-of-ICU cardiac arrest. Controls were patients who never required intensive care. Cases and controls were split into training and testing groups.The Bedside Pediatric Early Warning System was modified by integrating data-driven heart rate and respiratory rate parameters (modified Bedside Pediatric Early Warning System 1 and 2). Modified Bedside Pediatric Early Warning System 1 used the 10th and 90th percentiles as normal parameters, whereas modified Bedside Pediatric Early Warning System 2 used fifth and 95th percentiles.The training set consisted of 358 case events and 1,830 controls; the testing set had 331 case events and 1,215 controls. In the sensitivity analysis, 207 of the 331 testing set cases (62.5%) were predicted by the original tool versus 206 (62.2%; p = 0.54) with modified Bedside Pediatric Early Warning System 1 and 191 (57.7%; p < 0.001) with modified Bedside Pediatric Early Warning System 2. For specificity, 1,005 of the 1,215 testing set control patients (82.7%) were identified by original Bedside Pediatric Early Warning System versus 1,013 (83.1%; p = 0.54) with modified Bedside Pediatric Early Warning System 1 and 1,055 (86.8%; p < 0.001) with modified Bedside Pediatric Early Warning System 2. There was no net gain in sensitivity and specificity using either of the modified Bedside Pediatric Early Warning System tools.Integration of data-driven vital sign parameters into a validated pediatric early warning system did not significantly impact sensitivity or specificity, and all the tools showed lower than desired sensitivity and specificity at a single cutoff point. Future work is needed to develop an objective tool that can more accurately predict pediatric decompensation.

Project description:BackgroundTimely recognition of patient deterioration remains challenging. Ambulatory monitoring systems (AMSs) may provide support to current monitoring practices; however, they need to be thoroughly tested before implementation in the clinical environment for early detection of deterioration.ObjectiveThe objective of this study was to assess the wearability of a selection of commercially available AMSs to inform a future prospective study of ambulatory vital sign monitors in an acute hospital ward.MethodsFive pulse oximeters (4 with finger probes and 1 wrist-worn only, collecting pulse rates and oxygen saturation) and 2 chest patches (collecting heart rates and respiratory rates) were selected to be part of this study: The 2 chest-worn patches were VitalPatch (VitalConnect) and Peerbridge Cor (Peerbridge); the 4 wrist-worn devices with finger probe were Nonin WristOx2 3150 (Nonin), Checkme O2+ (Viatom Technology), PC-68B, and AP-20 (both from Creative Medical); and the 1 solely wrist-worn device was Wavelet (Wavelet Health). Adult participants wore each device for up to 72 hours while performing usual "activities of daily living" and were asked to score the perceived exertion and perception of pain or discomfort by using the Borg CR-10 scale; thoughts and feelings caused by the AMS using the Comfort Rating Scale (CRS); and to provide general free text feedback. Median and IQRs were reported and nonparametric tests were used to assess differences between the devices' CRS scores.ResultsQuantitative scores and feedback were collected in 70 completed questionnaires from 20 healthy volunteers, with each device tested approximately 10 times. The Wavelet seemed to be the most wearable device (P<.001) with an overall median (IQR) CRS score of 1.00 (0.88). There were no statistically significant differences in wearability between the chest patches in the CRS total score; however, the VitalPatch was superior in the Attachment section (P=.04) with a median (IQR) score of 3.00 (1.00). General pain and discomfort scores and total percentage of time worn are also reflective of this.ConclusionsOur results suggest that adult participants prefer to wear wrist-worn pulse oximeters without a probe compressing the fingertip and they prefer to wear a smaller chest patch. A compromise between wearability, reliability, and accuracy should be made for successful and practical integration of AMSs within the hospital environment.

Project description:ObjectivesEarly 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.DesignObservational cohort study.SettingOxford 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.ParticipantsHospitalised patients with electronically documented vital-sign observations OUTCOME: Composite outcome of unplanned intensive care unit admission, mortality and cardiac arrest.Methods and resultsStatistical 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.ConclusionsAccounting for age-related vital sign changes can more accurately detect deterioration in younger patients.

Dataset Information

Manual centile-based early warning scores derived from statistical distributions of observational vital-sign data.

Aims of study

Materials and methods

Results

Conclusions

Publications

Manual centile-based early warning scores derived from statistical distributions of observational vital-sign data.

Similar Datasets

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