TY - JOUR
T1 - The use of an electronic medical record based automatic calculation tool to quantify risk of unplanned readmission to the intensive care unit
T2 - A validation study
AU - Chandra, Subhash
AU - Agarwal, Dipti
AU - Hanson, Andrew
AU - Farmer, Joseph C.
AU - Pickering, Brian W.
AU - Gajic, Ognjen
AU - Herasevich, Vitaly
N1 - Funding Information:
Financial support: This publication was made possible by grant 1 KL2 RR024151 from the National Center for Research Resources (NCRR) , a component of the National Institutes of Health (NIH), the NIH Roadmap for Medical Research, and Mayo Foundation. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the NCRR or NIH. Information on NCRR is available at http://www.ncrr.nih.gov/ . Information on Reengineering the Clinical Research Enterprise can be obtained from http://nihroamap.nih.gov/clinicalresearch/overviewtranslational.asp . This study was supported in part by National Heart, Lung and Blood Institute grant K23 HL78743-01A1 and NIH grant KL2 RR024151 .
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2011/12
Y1 - 2011/12
N2 - Objective: The aim of this study was to refine and validate an automatic risk of unplanned readmission (Stability and Workload Index for Transfer, or SWIFT) calculator in a prospective cohort of consecutive medical intensive care unit (ICU) patients in a teaching hospital with comprehensive electronic medical records (EMRs). Design: A 2-phase (derivation and validation) prospective cohort study was conducted. Settings: The study was conducted in an academic medical ICU. Subjects: A consecutive cohort of adult (age >18 years) patients with research authorization were analyzed. Intervention: The EMR-based automatic SWIFT calculator was used for this study. Measurement: Agreement between the manual ("gold standard") and automatic SWIFT calculation tool was obtained. Main results: During the derivation phase, we enrolled 191 consecutive medical ICU patients. Scores of SWIFT for these patients calculated manually by the 2 reviewers had strong positive correlation (r = 0.97), and the mean (SD) difference was 0.43 (3.5). The first iteration of the automatic SWIFT calculator in the derivation cohort demonstrated excellent agreement with manual calculation, partial pressure of carbon dioxide in arterial blood (κ = 0.95), partial pressure of oxygen in arterial blood/fraction of inspired oxygen ratio (κ = 0.69), length of ICU stay (κ = 0.91), and Glasgow comma scale (κ = 0.90) and no agreement for source of ICU admission (κ = -0.15). After adjustment in rules, the κ value for hospital admission source improved to 1.0. Automatic calculation demonstrated strong correlation with manual (r = 0.92), and mean (SD) difference was -2.3 (5.9). During validation phase, 100 subjects were enrolled at 5 days. The automatic tool retained excellent correlation with gold-standard calculation for SWIFT (r = 0.92), and the mean (SD) difference was -2.2 (5.5). Conclusion: The EMR-based automatic tool accurately calculates SWIFT score and can facilitate ICU discharge decisions without the need for manual data collection.
AB - Objective: The aim of this study was to refine and validate an automatic risk of unplanned readmission (Stability and Workload Index for Transfer, or SWIFT) calculator in a prospective cohort of consecutive medical intensive care unit (ICU) patients in a teaching hospital with comprehensive electronic medical records (EMRs). Design: A 2-phase (derivation and validation) prospective cohort study was conducted. Settings: The study was conducted in an academic medical ICU. Subjects: A consecutive cohort of adult (age >18 years) patients with research authorization were analyzed. Intervention: The EMR-based automatic SWIFT calculator was used for this study. Measurement: Agreement between the manual ("gold standard") and automatic SWIFT calculation tool was obtained. Main results: During the derivation phase, we enrolled 191 consecutive medical ICU patients. Scores of SWIFT for these patients calculated manually by the 2 reviewers had strong positive correlation (r = 0.97), and the mean (SD) difference was 0.43 (3.5). The first iteration of the automatic SWIFT calculator in the derivation cohort demonstrated excellent agreement with manual calculation, partial pressure of carbon dioxide in arterial blood (κ = 0.95), partial pressure of oxygen in arterial blood/fraction of inspired oxygen ratio (κ = 0.69), length of ICU stay (κ = 0.91), and Glasgow comma scale (κ = 0.90) and no agreement for source of ICU admission (κ = -0.15). After adjustment in rules, the κ value for hospital admission source improved to 1.0. Automatic calculation demonstrated strong correlation with manual (r = 0.92), and mean (SD) difference was -2.3 (5.9). During validation phase, 100 subjects were enrolled at 5 days. The automatic tool retained excellent correlation with gold-standard calculation for SWIFT (r = 0.92), and the mean (SD) difference was -2.2 (5.5). Conclusion: The EMR-based automatic tool accurately calculates SWIFT score and can facilitate ICU discharge decisions without the need for manual data collection.
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U2 - 10.1016/j.jcrc.2011.05.003
DO - 10.1016/j.jcrc.2011.05.003
M3 - Article
C2 - 21715140
AN - SCOPUS:81955161199
VL - 26
SP - 634.e9-634.e15
JO - Journal of Critical Care
JF - Journal of Critical Care
SN - 0883-9441
IS - 6
ER -