Manual centile-based early warning scores derived from statistical distributions of observational vital-sign data
<p>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 warni...
| Main Authors: | , , , |
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| Format: | Journal article |
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Elsevier
2018
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| _version_ | 1826288765270753280 |
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| author | Watkinson, P Pimentel, M Clifton, D Tarassenko, L |
| author_facet | Watkinson, P Pimentel, M Clifton, D Tarassenko, L |
| author_sort | Watkinson, P |
| collection | OXFORD |
| description | <p>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.</p><p> 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).</p><p> 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.</p><p> 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.</p> |
| first_indexed | 2024-03-07T02:18:39Z |
| format | Journal article |
| id | oxford-uuid:a324698b-ba4d-4b30-8da8-93268948977d |
| institution | University of Oxford |
| last_indexed | 2024-03-07T02:18:39Z |
| publishDate | 2018 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:a324698b-ba4d-4b30-8da8-93268948977d2022-03-27T02:24:51ZManual centile-based early warning scores derived from statistical distributions of observational vital-sign dataJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:a324698b-ba4d-4b30-8da8-93268948977dSymplectic Elements at OxfordElsevier2018Watkinson, PPimentel, MClifton, DTarassenko, L<p>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.</p><p> 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).</p><p> 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.</p><p> 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.</p> |
| spellingShingle | Watkinson, P Pimentel, M Clifton, D Tarassenko, L Manual centile-based early warning scores derived from statistical distributions of observational vital-sign data |
| title | Manual centile-based early warning scores derived from statistical distributions of observational vital-sign data |
| title_full | Manual centile-based early warning scores derived from statistical distributions of observational vital-sign data |
| title_fullStr | Manual centile-based early warning scores derived from statistical distributions of observational vital-sign data |
| title_full_unstemmed | Manual centile-based early warning scores derived from statistical distributions of observational vital-sign data |
| title_short | Manual centile-based early warning scores derived from statistical distributions of observational vital-sign data |
| title_sort | manual centile based early warning scores derived from statistical distributions of observational vital sign data |
| work_keys_str_mv | AT watkinsonp manualcentilebasedearlywarningscoresderivedfromstatisticaldistributionsofobservationalvitalsigndata AT pimentelm manualcentilebasedearlywarningscoresderivedfromstatisticaldistributionsofobservationalvitalsigndata AT cliftond manualcentilebasedearlywarningscoresderivedfromstatisticaldistributionsofobservationalvitalsigndata AT tarassenkol manualcentilebasedearlywarningscoresderivedfromstatisticaldistributionsofobservationalvitalsigndata |