Performance of Electronic Prediction Rules for Prevalent Delirium at Hospital Admission
Authors: Christopher W. Halladay, Andrea Yevchak Sillner, James L. Rudolph
Journal: JAMA Network Open (2018)

<h3>Importance</h3> Delirium at admission is associated with increased hospital morbidity and mortality, but it may be missed in up to 70% of cases. Use of a predictive algorithm in an electronic medical record (EMR) system could provide critical information to target assessment of those with delirium at admission. <h3>Objectives</h3> To develop and assess a prediction rule for delirium using 2 populations of veterans and compare this rule with previously confirmed rules. <h3>Design, Setting, and Participants</h3> In a diagnostic study, randomly selected EMRs of hospitalized veterans from the Veterans Affairs (VA) External Peer Review Program at 118 VA medical centers with inpatient facilities were reviewed for delirium risk factors associated with the National Institute for Health and Clinical Excellence (NICE) delirium rule in a derivation cohort (October 1, 2012, to September 30, 2013) and a confirmation cohort (October 1, 2013, to March 31, 2014). Delirium within 24 hours of admission was identified using key word terms. A total of 39 377 veterans 65 years or older who were admitted to a VA medical center for congestive heart failure, acute coronary syndrome, community-acquired pneumonia, and chronic obstructive pulmonary disease were included in the study. <h3>Exposure</h3> The EMR calculated delirium risk. <h3>Main Outcomes and Measures</h3> Delirium at admission as identified by trained nurse reviewers was the main outcome measure. Random forest methods were used to identify accurate risk factors for prevalent delirium. A prediction rule for prevalent delirium was developed, and its diagnostic accuracy was tested in the confirmation cohort. This consolidated NICE rule was compared with previously confirmed scoring algorithms (electronic NICE and Pendlebury NICE). <h3>Results</h3> A total of 27 625 patients were included in the derivation cohort (28 118 [92.2%] male; mean [SD] age, 75.95 [8.61] years) and 11 752 in the confirmation cohort (11 536 [98.2%] male; mean [SD] age, 75.43 [8.55] years). Delirium at admission was identified in 2343 patients (8.5%) in the derivation cohort and 882 patients (7.0%) in the confirmation cohort. Modeling techniques identified cognitive impairment, infection, sodium level, and age of 80 years or older as the dominant risk factors. The consolidated NICE rule (area under the receiver operating characteristic [AUROC] curve, 0.91; 95% CI, 0.91-0.92;<i>P</i> &lt; .001) had significantly higher discriminatory function than the eNICE rule (AUROC curve, 0.81; 95% CI, 0.80-0.82;<i>P</i> &lt; .001) or Pendlebury NICE rule (AUROC curve, 0.87; 95% CI, 0.86-0.88;<i>P</i> &lt; .001). These findings were confirmed in the confirmation cohort. <h3>Conclusions and Relevance</h3> This analysis identified preexisting cognitive impairment, infection, sodium level, and age of 80 years or older as delirium screening targets. Use of this algorithm in an EMR system could direct clinical assessment efforts to patients with delirium at admission.