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Integrating monitor alarms with laboratory test results to enhance patient deterioration prediction
Journal of Biomedical Informatics (2015)
  • Yong Bai, Department of Bioengineering, University of California, Los Angeles
  • Duc H. Do, UCLA Cardiac Arrhythmia Center, David Geffen School of Medicine, University of California
  • Patricia Harris, Department of Physiological Nursing, University of California, San Francisco
  • Daniel Schindler, Department of Physiological Nursing, University of California, San Francisco
  • Noel G. Boyle, UCLA Cardiac Arrhythmia Center, David Geffen School of Medicine, University of California
  • Barbara J. Drew, Department of Physiological Nursing, University of California, San Francisco
  • Xiao Hu, Department of Physiological Nursing, University of California, San Francisco
Abstract
Patient monitors in modern hospitals have become ubiquitous but they generate an excessive number of false alarms causing alarm fatigue. Our previous work showed that combinations of frequently co-occurring monitor alarms, called SuperAlarm patterns, were capable of predicting in-hospital code blue events at a lower alarm frequency. In the present study, we extend the conceptual domain of a SuperAlarm to incorporate laboratory test results along with monitor alarms so as to build an integrated data set to mine SuperAlarm patterns. We propose two approaches to integrate monitor alarms with laboratory test results and use a maximal frequent itemsets mining algorithm to find SuperAlarm patterns. Under an acceptable false positive rate FPRmax, optimal parameters including the minimum support threshold and the length of time window for the algorithm to find the combinations of monitor alarms and laboratory test results are determined based on a 10-fold cross-validation set. SuperAlarm candidates are generated under these optimal parameters. The final SuperAlarm patterns are obtained by further removing the candidates with false positive rate>FPRmax. The performance of SuperAlarm patterns are assessed using an independent test data set. First, we calculate the sensitivity with respect to prediction window and the sensitivity with respect to lead time. Second, we calculate the false SuperAlarm ratio (ratio of the hourly number of SuperAlarm triggers for control patients to that of the monitor alarms, or that of regular monitor alarms plus laboratory test results if the SuperAlarm patterns contain laboratory test results) and the work-up to detection ratio, WDR (ratio of the number of patients triggering any SuperAlarm patterns to that of code blue patients triggering any SuperAlarm patterns). The experiment results demonstrate that when varying FPRmax between 0.02 and 0.15, the SuperAlarm patterns composed of monitor alarms along with the last two laboratory test results are triggered at least once for [56.7-93.3%] of code blue patients within an 1-h prediction window before code blue events and for [43.3-90.0%] of code blue patients at least 1-h ahead of code blue events. However, the hourly number of these SuperAlarm patterns occurring in control patients is only [2.0-14.8%] of that of regular monitor alarms with WDR varying between 2.1 and 6.5 in a 12-h window. For a given FPRmax threshold, the SuperAlarm set generated from the integrated data set has higher sensitivity and lower WDR than the SuperAlarm set generated from the regular monitor alarm data set. In addition, the McNemar's test also shows that the performance of the SuperAlarm set from the integrated data set is significantly different from that of the SuperAlarm set from the regular monitor alarm data set. We therefore conclude that the SuperAlarm patterns generated from the integrated data set are better at predicting code blue events.
Keywords
  • Alarm fatigue; Clinical deterioration; Code blue; Event prediction; Maximal frequent itemsets mining; Monitor alarm
Publication Date
February, 2015
Citation Information
Yong Bai, Duc H. Do, Patricia Harris, Daniel Schindler, et al.. "Integrating monitor alarms with laboratory test results to enhance patient deterioration prediction" Journal of Biomedical Informatics Vol. 53 (2015) p. 81 - 92 ISSN: 1532-0464
Available at: http://works.bepress.com/patricia_harris/10/