Leveraging EHR Data to Evaluate the Association of Late Recognition of Deterioration With Outcomes

Abstract

OBJECTIVES: Emergency transfers (ETs), deterioration events with late recognition requiring ICU interventions within 1 hour of transfer, are associated with adverse outcomes. We leveraged electronic health record (EHR) data to assess the association between ETs and outcomes. We also evaluated the association between intervention timing (urgency) and outcomes. METHODS: We conducted a propensity-score-matched study of hospitalized children requiring ICU transfer between 2015 and 2019 at a single institution. The primary exposure was ET, automatically classified using Epic Clarity Data stored in our enterprise data warehouse endotracheal tube in lines/drains/airway flowsheet, vasopressor in medication administration record, and/or ≥60 ml/kg intravenous fluids in intake/output flowsheets recorded within 1 hour of transfer. Urgent intervention was defined as interventions within 12 hours of transfer. RESULTS: Of 2037 index transfers, 129 (6.3%) met ET criteria. In the propensity-score-matched cohort (127 ET, 374 matched controls), ET was associated with higher in-hospital mortality (13% vs 6.1%; odds ratio, 2.47; 95% confidence interval [95% CI], 1.24–4.9, P = .01), longer ICU length of stay (subdistribution hazard ratio of ICU discharge 0.74; 95% CI, 0.61–0.91, P < .01), and longer posttransfer length of stay (SHR of hospital discharge 0.71; 95% CI, 0.56–0.90, P < .01). Increased intervention urgency was associated with increased mortality risk: 4.1% no intervention, 6.4% urgent intervention, and 10% emergent intervention. CONCLUSIONS: An EHR measure of deterioration with late recognition is associated with increased mortality and length of stay. Mortality risk increased with intervention urgency. Leveraging EHR automation facilitates generalizability, multicenter collaboratives, and metric consistency.

Opportunities to improve diagnosis in emergency transfers to the pediatric intensive care unit

“…We compared ET cases with and without MOID using descriptive statistics, including Fisher's exact test for categorical variables. Multivariable regression was used to compare mortality and posttransfer LOS in days among cases with and without MOID with a priori defined confounders of age category, originating service, deterioration category, and pretransfer LOS, 3 which have been associated with risk of mortality in ET and were hypothesized to impact the diagnostic process in deteriorating patients. 2,24 Mortality was modeled with logistic regression.…”

“…Unplanned transfers that met ET criteria were identified using an automated query of the institutional Enterprise Data Warehouse (EDW). 3 Variables Patient data including demographic information, originating service (the primary service caring for the patient before transfer to the ICU), hospital outcome data, and primary ICU diagnoses were obtained from the VPS database. Primary ICU diagnoses were grouped based on the VPS provided diagnosis category into one of four deterioration categories: circulatory, neurologic, respiratory, or other.…”

“…Primary ICU diagnoses were grouped based on the VPS provided diagnosis category into one of four deterioration categories: circulatory, neurologic, respiratory, or other. 3 Pertinent ET related intervention data and additional demographic characteristics which might impact risk of deterioration including the presence of a Complex Chronic Condition 20 and Nationally Normed Child Opportunity Index Level 21 were extracted from the EDW. 3 Information about risk identification or assessment before ET including whether there was code blue activation at the time of the ET, any additional MET evaluation that occurred in the 48 h before the ET (beyond the MET evaluation that occurred at the time of ET), or a recent transfer out of the PICU in the 48 h before ET was obtained through chart review.…”

“…2,3 ETs, in-hospital deterioration events requiring intensive care unit (ICU) interventions within 1 h of ICU transfer, are associated with increased mortality and length of stay (LOS). 2,3 To date, efforts to promote early recognition of clinical deterioration and reduce ETs have focused on improving automated detection systems, situation awareness (SA), and rapid response system infrastructure with variable success. 1,[4][5][6][7][8][9] Incorporating alternative methodologic frameworks to study and mitigate deterioration events could provide new directions for improvement.…”

“…ETs, which are trackable in the electronic health record (EHR) and involve late recognition of a potentially unexpected clinical change, could provide an enriched cohort to identify and study MOID in clinical deterioration. 3,16 While diagnostic improvement frameworks have demonstrated reliability and validity in acute pediatric settings, the incidence of MOID and potential improvement opportunities associated with ETs have not been characterized. 16 We utilized diagnostic process improvement tools to evaluate for MOID in an existing cohort of ETs, with the aims of (1) identifying and describing the incidence of MOID among ETs; (2) identifying diagnostic process opportunities to inform future improvements in ET detection and prevention; and (3) evaluating the association of MOID with outcomes in the ET cohort.…”