The use of an electronic medical record based automatic calculation tool to quantify risk of unplanned readmission to the intensive care unit: A validation study

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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).


A 2-phase (derivation and validation) prospective cohort study was conducted.


The study was conducted in an academic medical ICU.


A consecutive cohort of adult (age >18 years) patients with research authorization were analyzed.


The EMR-based automatic SWIFT calculator was used for this study.


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).


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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