Literature DB >> 31082902

Validating the Electronic Cardiac Arrest Risk Triage (eCART) Score for Risk Stratification of Surgical Inpatients in the Postoperative Setting: Retrospective Cohort Study.

Bartlomiej Bartkowiak1, Ashley M Snyder1, Andrew Benjamin2, Andrew Schneider2, Nicole M Twu1, Matthew M Churpek1, Kevin K Roggin2, Dana P Edelson1.   

Abstract

OBJECTIVE: Assess the accuracy of 3 early warning scores for predicting severe adverse events in postoperative inpatients. SUMMARY OF BACKGROUND DATA: Postoperative clinical deterioration on inpatient hospital services is associated with increased morbidity, mortality, and cost. Early warning scores have been developed to detect inpatient clinical deterioration and trigger rapid response activation, but knowledge regarding the application of early warning scores to postoperative inpatients is limited.
METHODS: This was a retrospective cohort study of adult patients hospitalized on the wards after surgical procedures at an urban academic medical center from November, 2008 to January, 2016. The accuracies of the Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), and the electronic cardiac arrest risk triage (eCART) score were compared in predicting severe adverse events (ICU transfer, ward cardiac arrest, or ward death) in the postoperative period using the area under the receiver operating characteristic curve (AUC).
RESULTS: Of the 32,537 patient admissions included in the study, 3.8% (n = 1243) experienced a severe adverse outcome after the procedure. The accuracy for predicting the composite outcome was highest for eCART [AUC 0.79 (95% CI: 0.78-0.81)], followed by NEWS [AUC 0.76 (95% CI: 0.75-0.78)], and MEWS [AUC 0.75 (95% CI: 0.73-0.76)]. Of the individual vital signs and labs, maximum respiratory rate was the most predictive (AUC 0.67) and maximum temperature was an inverse predictor (AUC 0.46).
CONCLUSION: Early warning scores are predictive of severe adverse events in postoperative patients. eCART is significantly more accurate in this patient population than both NEWS and MEWS.

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Mesh:

Year:  2019        PMID: 31082902      PMCID: PMC6610875          DOI: 10.1097/SLA.0000000000002665

Source DB:  PubMed          Journal:  Ann Surg        ISSN: 0003-4932            Impact factor:   12.969


  16 in total

1.  Predicting cardiac arrest on the wards: a nested case-control study.

Authors:  Matthew M Churpek; Trevor C Yuen; Michael T Huber; Seo Young Park; Jesse B Hall; Dana P Edelson
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2.  A Role for the Early Warning Score in Early Identification of Critical Postoperative Complications.

Authors:  Robert H Hollis; Laura A Graham; John P Lazenby; Daran M Brown; Benjamin B Taylor; Martin J Heslin; Loring W Rue; Mary T Hawn
Journal:  Ann Surg       Date:  2016-05       Impact factor: 12.969

3.  Can physiological variables and early warning scoring systems allow early recognition of the deteriorating surgical patient?

Authors:  Brian H Cuthbertson; Massoud Boroujerdi; Laurin McKie; Lorna Aucott; Gordon Prescott
Journal:  Crit Care Med       Date:  2007-02       Impact factor: 7.598

4.  An Apgar score for surgery.

Authors:  Atul A Gawande; Mary R Kwaan; Scott E Regenbogen; Stuart A Lipsitz; Michael J Zinner
Journal:  J Am Coll Surg       Date:  2006-12-27       Impact factor: 6.113

5.  Comparison of the National Early Warning Score in non-elective medical and surgical patients.

Authors:  C Kovacs; S W Jarvis; D R Prytherch; P Meredith; P E Schmidt; J S Briggs; G B Smith
Journal:  Br J Surg       Date:  2016-08-03       Impact factor: 6.939

6.  Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons.

Authors:  Karl Y Bilimoria; Yaoming Liu; Jennifer L Paruch; Lynn Zhou; Thomas E Kmiecik; Clifford Y Ko; Mark E Cohen
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7.  Multicenter development and validation of a risk stratification tool for ward patients.

Authors:  Matthew M Churpek; Trevor C Yuen; Christopher Winslow; Ari A Robicsek; David O Meltzer; Robert D Gibbons; Dana P Edelson
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8.  Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards.

Authors:  Matthew M Churpek; Trevor C Yuen; Christopher Winslow; David O Meltzer; Michael W Kattan; Dana P Edelson
Journal:  Crit Care Med       Date:  2016-02       Impact factor: 7.598

9.  Using electronic health record data to develop and validate a prediction model for adverse outcomes in the wards*.

Authors:  Matthew M Churpek; Trevor C Yuen; Seo Young Park; Robert Gibbons; Dana P Edelson
Journal:  Crit Care Med       Date:  2014-04       Impact factor: 7.598

Review 10.  Clinical review: Can we predict which patients are at risk of complications following surgery?

Authors:  Nirav Shah; Mark Hamilton
Journal:  Crit Care       Date:  2013-05-07       Impact factor: 9.097

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  17 in total

Review 1.  Opportunities for machine learning to improve surgical ward safety.

Authors:  Tyler J Loftus; Patrick J Tighe; Amanda C Filiberto; Jeremy Balch; Gilbert R Upchurch; Parisa Rashidi; Azra Bihorac
Journal:  Am J Surg       Date:  2020-02-26       Impact factor: 2.565

2.  Improved inpatient deterioration detection in general wards by using time-series vital signs.

Authors:  Chang-Fu Su; Shu-I Chiu; Jyh-Shing Roger Jang; Feipei Lai
Journal:  Sci Rep       Date:  2022-07-13       Impact factor: 4.996

3.  Early Detection of In-Patient Deterioration: One Prediction Model Does Not Fit All.

Authors:  Jacob N Blackwell; Jessica Keim-Malpass; Matthew T Clark; Rebecca L Kowalski; Salim N Najjar; Jamieson M Bourque; Douglas E Lake; J Randall Moorman
Journal:  Crit Care Explor       Date:  2020-05-11

4.  Performance of universal early warning scores in different patient subgroups and clinical settings: a systematic review.

Authors:  Baneen Alhmoud; Timothy Bonnici; Riyaz Patel; Daniel Melley; Bryan Williams; Amitava Banerjee
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5.  Detecting Deteriorating Patients in the Hospital: Development and Validation of a Novel Scoring System.

Authors:  Marco A F Pimentel; Oliver C Redfern; James Malycha; Paul Meredith; David Prytherch; Jim Briggs; J Duncan Young; David A Clifton; Lionel Tarassenko; Peter J Watkinson
Journal:  Am J Respir Crit Care Med       Date:  2021-07-01       Impact factor: 21.405

6.  Validation of National Early Warning Score for predicting 30-day mortality after rapid response system activation in Japan.

Authors:  Takaki Naito; Kuniyoshi Hayashi; Hsiang-Chin Hsu; Kazuhiro Aoki; Kazuma Nagata; Masayasu Arai; Taka-Aki Nakada; Shinichiro Suzaki; Yoshiro Hayashi; Shigeki Fujitani
Journal:  Acute Med Surg       Date:  2021-05-15

7.  Early warning scores for detecting deterioration in adult hospital patients: systematic review and critical appraisal of methodology.

Authors:  Stephen Gerry; Timothy Bonnici; Jacqueline Birks; Shona Kirtley; Pradeep S Virdee; Peter J Watkinson; Gary S Collins
Journal:  BMJ       Date:  2020-05-20

8.  Logistic early warning scores to predict death, cardiac arrest or unplanned intensive care unit re-admission after cardiac surgery.

Authors:  Y-D Chiu; S S Villar; J W Brand; M V Patteril; D J Morrice; J Clayton; J H Mackay
Journal:  Anaesthesia       Date:  2019-07-03       Impact factor: 6.955

9.  Dynamic individual vital sign trajectory early warning score (DyniEWS) versus snapshot national early warning score (NEWS) for predicting postoperative deterioration.

Authors:  Yajing Zhu; Yi-Da Chiu; Sofia S Villar; Jonathan W Brand; Mathew V Patteril; David J Morrice; James Clayton; Jonathan H Mackay
Journal:  Resuscitation       Date:  2020-11-09       Impact factor: 5.262

Review 10.  Aligning Patient Acuity With Resource Intensity After Major Surgery: A Scoping Review.

Authors:  Tyler J Loftus; Jeremy A Balch; Matthew M Ruppert; Patrick J Tighe; William R Hogan; Parisa Rashidi; Gilbert R Upchurch; Azra Bihorac
Journal:  Ann Surg       Date:  2022-02-01       Impact factor: 13.787

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