Improving the performance of clinical decision support for early detection of sepsis: a retrospective observational cohort study

Ling Li; Kasun Rathnayake; Malcolm Green; Mary Fullick; Amith Shetty; Scott Walter; Jeffrey Braithwaite; Harvey Lander; Johanna I. Westbrook

doi:10.3233/SHTI190309

Improving the performance of clinical decision support for early detection of sepsis: a retrospective observational cohort study

Ling Li^*, Kasun Rathnayake, Malcolm Green, Mary Fullick, Amith Shetty, Scott Walter, Jeffrey Braithwaite, Harvey Lander, Johanna I. Westbrook

^*Corresponding author for this work

Australian Institute of Health Innovation

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

4 Citations (Scopus)

298 Downloads (Pure)

Abstract

Sepsis remains a significant global health problem. It is a life-threatening, but poorly defined and recognized condition. Early recognition and intervention are essential to optimize patient outcomes. Automated clinical decision support systems (CDS) may be particularly beneficial for early detection of sepsis. The aim of this study was to use retrospective data to develop and evaluate seven revised versions of an electronic sepsis alert rule to assess their performance in detecting sepsis cases and patient deterioration (in-hospital mortality or ICU admission). Four revised options had higher sensitivity but lower specificity than the original rule. After discussion with clinical experts, two revised options with the highest sensitivity were selected. Further analysis on the number of alerts and time intervals between alerts and patient outcomes was conducted to decide the option to be implemented. This study has provided a data-driven approach to improve the CDS on early detection of sepsis.

Original language	English
Title of host publication	MEDINFO 2019
Subtitle of host publication	Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
Editors	Lucila Ohno-Machado, Brigitte Seroussi
Place of Publication	Amsterdam
Publisher	IOS Press
Pages	679-683
Number of pages	5
ISBN (Electronic)	9781643680033
ISBN (Print)	9781643680026
DOIs	https://doi.org/10.3233/SHTI190309
Publication status	Published - 21 Aug 2019
Event	17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France Duration: 25 Aug 2019 → 30 Aug 2019

Publication series

Name	Studies in Health Technology and Informatics
Volume	264
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	17th World Congress on Medical and Health Informatics, MEDINFO 2019
Country/Territory	France
City	Lyon
Period	25/08/19 → 30/08/19

Bibliographical note

Copyright International Medical Informatics Association (IMIA) and IOS Press 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

Clinical Decision Support Systems
Early Diagnosis
Sepsis

Access to Document

10.3233/SHTI190309

Publisher version (open access)Final published version, 533 KBLicence: CC BY-NC

Cite this

Li, L., Rathnayake, K., Green, M., Fullick, M., Shetty, A., Walter, S., Braithwaite, J., Lander, H., & Westbrook, J. I. (2019). Improving the performance of clinical decision support for early detection of sepsis: a retrospective observational cohort study. In L. Ohno-Machado, & B. Seroussi (Eds.), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics (pp. 679-683). (Studies in Health Technology and Informatics; Vol. 264). IOS Press. https://doi.org/10.3233/SHTI190309

Li, Ling ; Rathnayake, Kasun ; Green, Malcolm et al. / Improving the performance of clinical decision support for early detection of sepsis : a retrospective observational cohort study. MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. editor / Lucila Ohno-Machado ; Brigitte Seroussi. Amsterdam : IOS Press, 2019. pp. 679-683 (Studies in Health Technology and Informatics).

@inproceedings{4561335691f74b798eb58248285ecc43,

title = "Improving the performance of clinical decision support for early detection of sepsis: a retrospective observational cohort study",

abstract = "Sepsis remains a significant global health problem. It is a life-threatening, but poorly defined and recognized condition. Early recognition and intervention are essential to optimize patient outcomes. Automated clinical decision support systems (CDS) may be particularly beneficial for early detection of sepsis. The aim of this study was to use retrospective data to develop and evaluate seven revised versions of an electronic sepsis alert rule to assess their performance in detecting sepsis cases and patient deterioration (in-hospital mortality or ICU admission). Four revised options had higher sensitivity but lower specificity than the original rule. After discussion with clinical experts, two revised options with the highest sensitivity were selected. Further analysis on the number of alerts and time intervals between alerts and patient outcomes was conducted to decide the option to be implemented. This study has provided a data-driven approach to improve the CDS on early detection of sepsis.",

keywords = "Clinical Decision Support Systems, Early Diagnosis, Sepsis",

author = "Ling Li and Kasun Rathnayake and Malcolm Green and Mary Fullick and Amith Shetty and Scott Walter and Jeffrey Braithwaite and Harvey Lander and Westbrook, {Johanna I.}",

note = "Copyright International Medical Informatics Association (IMIA) and IOS Press 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.; 17th World Congress on Medical and Health Informatics, MEDINFO 2019 ; Conference date: 25-08-2019 Through 30-08-2019",

year = "2019",

month = aug,

day = "21",

doi = "10.3233/SHTI190309",

language = "English",

isbn = "9781643680026",

series = "Studies in Health Technology and Informatics",

publisher = "IOS Press",

pages = "679--683",

editor = "Lucila Ohno-Machado and Brigitte Seroussi",

booktitle = "MEDINFO 2019",

address = "Netherlands",

}

Li, L, Rathnayake, K, Green, M, Fullick, M, Shetty, A, Walter, S , Braithwaite, J, Lander, H & Westbrook, JI 2019, Improving the performance of clinical decision support for early detection of sepsis: a retrospective observational cohort study. in L Ohno-Machado & B Seroussi (eds), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. Studies in Health Technology and Informatics, vol. 264, IOS Press, Amsterdam, pp. 679-683, 17th World Congress on Medical and Health Informatics, MEDINFO 2019, Lyon, France, 25/08/19. https://doi.org/10.3233/SHTI190309

Improving the performance of clinical decision support for early detection of sepsis: a retrospective observational cohort study. / Li, Ling; Rathnayake, Kasun; Green, Malcolm et al.
MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. ed. / Lucila Ohno-Machado; Brigitte Seroussi. Amsterdam: IOS Press, 2019. p. 679-683 (Studies in Health Technology and Informatics; Vol. 264).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Improving the performance of clinical decision support for early detection of sepsis

T2 - 17th World Congress on Medical and Health Informatics, MEDINFO 2019

AU - Li, Ling

AU - Rathnayake, Kasun

AU - Green, Malcolm

AU - Fullick, Mary

AU - Shetty, Amith

AU - Walter, Scott

AU - Braithwaite, Jeffrey

AU - Lander, Harvey

AU - Westbrook, Johanna I.

N1 - Copyright International Medical Informatics Association (IMIA) and IOS Press 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2019/8/21

Y1 - 2019/8/21

N2 - Sepsis remains a significant global health problem. It is a life-threatening, but poorly defined and recognized condition. Early recognition and intervention are essential to optimize patient outcomes. Automated clinical decision support systems (CDS) may be particularly beneficial for early detection of sepsis. The aim of this study was to use retrospective data to develop and evaluate seven revised versions of an electronic sepsis alert rule to assess their performance in detecting sepsis cases and patient deterioration (in-hospital mortality or ICU admission). Four revised options had higher sensitivity but lower specificity than the original rule. After discussion with clinical experts, two revised options with the highest sensitivity were selected. Further analysis on the number of alerts and time intervals between alerts and patient outcomes was conducted to decide the option to be implemented. This study has provided a data-driven approach to improve the CDS on early detection of sepsis.

AB - Sepsis remains a significant global health problem. It is a life-threatening, but poorly defined and recognized condition. Early recognition and intervention are essential to optimize patient outcomes. Automated clinical decision support systems (CDS) may be particularly beneficial for early detection of sepsis. The aim of this study was to use retrospective data to develop and evaluate seven revised versions of an electronic sepsis alert rule to assess their performance in detecting sepsis cases and patient deterioration (in-hospital mortality or ICU admission). Four revised options had higher sensitivity but lower specificity than the original rule. After discussion with clinical experts, two revised options with the highest sensitivity were selected. Further analysis on the number of alerts and time intervals between alerts and patient outcomes was conducted to decide the option to be implemented. This study has provided a data-driven approach to improve the CDS on early detection of sepsis.

KW - Clinical Decision Support Systems

KW - Early Diagnosis

KW - Sepsis

UR - http://www.scopus.com/inward/record.url?scp=85071454480&partnerID=8YFLogxK

U2 - 10.3233/SHTI190309

DO - 10.3233/SHTI190309

M3 - Conference proceeding contribution

C2 - 31438010

AN - SCOPUS:85071454480

SN - 9781643680026

T3 - Studies in Health Technology and Informatics

SP - 679

EP - 683

BT - MEDINFO 2019

A2 - Ohno-Machado, Lucila

A2 - Seroussi, Brigitte

PB - IOS Press

CY - Amsterdam

Y2 - 25 August 2019 through 30 August 2019

ER -

Li L, Rathnayake K, Green M, Fullick M, Shetty A, Walter S et al. Improving the performance of clinical decision support for early detection of sepsis: a retrospective observational cohort study. In Ohno-Machado L, Seroussi B, editors, MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. Amsterdam: IOS Press. 2019. p. 679-683. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI190309

Improving the performance of clinical decision support for early detection of sepsis: a retrospective observational cohort study

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this