Decision-tree early warning score (DTEWS) validates the design of the National Early Warning Score (NEWS)

This data was imported from PubMed:

Authors: Badriyah, T., Briggs, J.S., Meredith, P., Jarvis, S.W., Schmidt, P.E., Featherstone, P.I., Prytherch, D.R. and Smith, G.B.

Journal: Resuscitation

Volume: 85

Issue: 3

Pages: 418-423

eISSN: 1873-1570

DOI: 10.1016/j.resuscitation.2013.12.011

AIM OF STUDY: To compare the performance of a human-generated, trial and error-optimised early warning score (EWS), i.e., National Early Warning Score (NEWS), with one generated entirely algorithmically using Decision Tree (DT) analysis. MATERIALS AND METHODS: We used DT analysis to construct a decision-tree EWS (DTEWS) from a database of 198,755 vital signs observation sets collected from 35,585 consecutive, completed acute medical admissions. We evaluated the ability of DTEWS to discriminate patients at risk of cardiac arrest, unanticipated intensive care unit admission or death, each within 24h of a given vital signs observation. We compared the performance of DTEWS and NEWS using the area under the receiver-operating characteristic (AUROC) curve. RESULTS: The structures of DTEWS and NEWS were very similar. The AUROC (95% CI) for DTEWS for cardiac arrest, unanticipated ICU admission, death, and any of the outcomes, all within 24h, were 0.708 (0.669-0.747), 0.862 (0.852-0.872), 0.899 (0.892-0.907), and 0.877 (0.870-0.883), respectively. Values for NEWS were 0.722 (0.685-0.759) [cardiac arrest], 0.857 (0.847-0.868) [unanticipated ICU admission}, 0.894 (0.887-0.902) [death], and 0.873 (0.866-0.879) [any outcome]. CONCLUSIONS: The decision-tree technique independently validates the composition and weightings of NEWS. The DT approach quickly provided an almost identical EWS to NEWS, although one that admittedly would benefit from fine-tuning using clinical knowledge. We believe that DT analysis could be used to quickly develop candidate models for disease-specific EWSs, which may be required in future.

This source preferred by Gary Smith

This data was imported from Scopus:

Authors: Badriyah, T., Briggs, J.S., Meredith, P., Jarvis, S.W., Schmidt, P.E., Featherstone, P.I., Prytherch, D.R. and Smith, G.B.

Journal: Resuscitation

Volume: 85

Issue: 3

Pages: 418-423

eISSN: 1873-1570

ISSN: 0300-9572

DOI: 10.1016/j.resuscitation.2013.12.011

Aim of study: : To compare the performance of a human-generated, trial and error-optimised early warning score (EWS), i.e., National Early Warning Score (NEWS), with one generated entirely algorithmically using Decision Tree (DT) analysis. Materials and methods: We used DT analysis to construct a decision-tree EWS (DTEWS) from a database of 198,755 vital signs observation sets collected from 35,585 consecutive, completed acute medical admissions. We evaluated the ability of DTEWS to discriminate patients at risk of cardiac arrest, unanticipated intensive care unit admission or death, each within 24. h of a given vital signs observation. We compared the performance of DTEWS and NEWS using the area under the receiver-operating characteristic (AUROC) curve. Results: The structures of DTEWS and NEWS were very similar. The AUROC (95% CI) for DTEWS for cardiac arrest, unanticipated ICU admission, death, and any of the outcomes, all within 24. h, were 0.708 (0.669-0.747), 0.862 (0.852-0.872), 0.899 (0.892-0.907), and 0.877 (0.870-0.883), respectively. Values for NEWS were 0.722 (0.685-0.759) [cardiac arrest], 0.857 (0.847-0.868) [unanticipated ICU admission}, 0.894 (0.887-0.902) [death], and 0.873 (0.866-0.879) [any outcome]. Conclusions: The decision-tree technique independently validates the composition and weightings of NEWS. The DT approach quickly provided an almost identical EWS to NEWS, although one that admittedly would benefit from fine-tuning using clinical knowledge. We believe that DT analysis could be used to quickly develop candidate models for disease-specific EWSs, which may be required in future. © 2013 Elsevier Ireland Ltd.

This data was imported from Web of Science (Lite):

Authors: Badriyah, T., Briggs, J.S., Meredith, P., Jarvis, S.W., Schmidt, P.E., Featherstone, P.I., Prytherch, D.R. and Smith, G.B.

Journal: RESUSCITATION

Volume: 85

Issue: 3

Pages: 418-423

ISSN: 0300-9572

DOI: 10.1016/j.resuscitation.2013.12.011

This data was imported from Europe PubMed Central:

Authors: Badriyah, T., Briggs, J.S., Meredith, P., Jarvis, S.W., Schmidt, P.E., Featherstone, P.I., Prytherch, D.R. and Smith, G.B.

Journal: Resuscitation

Volume: 85

Issue: 3

Pages: 418-423

eISSN: 1873-1570

ISSN: 0300-9572

AIM OF STUDY: To compare the performance of a human-generated, trial and error-optimised early warning score (EWS), i.e., National Early Warning Score (NEWS), with one generated entirely algorithmically using Decision Tree (DT) analysis. MATERIALS AND METHODS: We used DT analysis to construct a decision-tree EWS (DTEWS) from a database of 198,755 vital signs observation sets collected from 35,585 consecutive, completed acute medical admissions. We evaluated the ability of DTEWS to discriminate patients at risk of cardiac arrest, unanticipated intensive care unit admission or death, each within 24h of a given vital signs observation. We compared the performance of DTEWS and NEWS using the area under the receiver-operating characteristic (AUROC) curve. RESULTS: The structures of DTEWS and NEWS were very similar. The AUROC (95% CI) for DTEWS for cardiac arrest, unanticipated ICU admission, death, and any of the outcomes, all within 24h, were 0.708 (0.669-0.747), 0.862 (0.852-0.872), 0.899 (0.892-0.907), and 0.877 (0.870-0.883), respectively. Values for NEWS were 0.722 (0.685-0.759) [cardiac arrest], 0.857 (0.847-0.868) [unanticipated ICU admission}, 0.894 (0.887-0.902) [death], and 0.873 (0.866-0.879) [any outcome]. CONCLUSIONS: The decision-tree technique independently validates the composition and weightings of NEWS. The DT approach quickly provided an almost identical EWS to NEWS, although one that admittedly would benefit from fine-tuning using clinical knowledge. We believe that DT analysis could be used to quickly develop candidate models for disease-specific EWSs, which may be required in future.

The data on this page was last updated at 04:58 on April 25, 2019.