Discovering Hidden Patterns in Anesthesia Data Associated with Unanticipated Intensive Care Unit Admissions

Abstract

Unanticipated intensive care unit admissions (UIA) are a metric of quality anesthesia care since they have been associated with intraoperative incidents and nearly four times as likely to die within 30 days of surgery compared to patients that were not admitted to the intensive care unit unexpectedly. Patient age, American Society of Anesthesiology Classification, type of procedure, tachycardia, hypotension, and cardiovascular and neuromuscular blocking drugs administered in the operating room have all been associated with patient UIA. Intraoperative anesthesia data is generated in real-time and can be used to identify patterns in patient care associated with UIA. Knowledge about patterns in intraoperative medication administration and hemodynamic data is important to develop interventions that can be used to prevent intraoperative deterioration. Patterns were defined as two or more characteristics in the line graphs. This data visualization study discovered, labeled, and tested patterns in intraoperative hemodynamic management for association with patient UIA. Data from 68 adult, inpatient, elective surgical patients were matched to 34 patients with UIA in the University of Minnesota, Academic Health Center, Clinical Data Repository. A prototype line graph was evaluated to identify salient (obvious) patterns in intraoperative hemodynamic management for the data set. Line graphs for patients with and without UIA were created and visualized. Patterns in intraoperative hemodynamic management were discovered using data visualization with line graphs and operationally defined. Odds ratios were used to test categorical patterns and one-way analysis of variance was used to test continuous numeric patterns for association with patient UIA. Seven patterns were significantly associated with patient UIA (p < .05).