Predictive combinations of monitor alarms preceding in-hospital code blue events

  • Authors:

  • Xiao Hu;Monica Sapo;Val Nenov;Tod Barry;Sunghan Kim;Duc H. Do;Noel Boyle;Neil Martin

  • Affiliations:

  • Neural Systems and Dynamics Laboratory, Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, United States and Biomedical Engineering Graduate Progra ...;Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, United States;Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, United States;Quality Management Service, UCLA Ronald Reagan University Medical Center, Los Angeles, United States;Neural Systems and Dynamics Laboratory, Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, United States;UCLA Cardiac Arrhythmia Center, David Geffen School of Medicine, University of California, Los Angeles, United States;UCLA Cardiac Arrhythmia Center, David Geffen School of Medicine, University of California, Los Angeles, United States;Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, United States

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2012

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Abstract

Bedside monitors are ubiquitous in acute care units of modern healthcare enterprises. However, they have been criticized for generating an excessive number of false positive alarms causing alarm fatigue among care givers and potentially compromising patient safety. We hypothesize that combinations of regular monitor alarms denoted as SuperAlarm set may be more indicative of ongoing patient deteriorations and hence predictive of in-hospital code blue events. The present work develops and assesses an alarm mining approach based on finding frequent combinations of single alarms that are also specific to code blue events to compose a SuperAlarm set. We use 4-way analysis of variance (ANOVA) to investigate the influence of four algorithm parameters on the performance of the data mining approach. The results are obtained from millions of monitor alarms from a cohort of 223 adult code blue and 1768 control patients using a multiple 10-fold cross-validation experiment setup. Using the optimal setting of parameters determined in the cross-validation experiment, final SuperAlarm sets are mined from the training data and used on an independent test data set to simulate running a SuperAlarm set against live regular monitor alarms. The ANOVA shows that the content of a SuperAlarm set is influenced by a subset of key algorithm parameters. Simulation of the extracted SuperAlarm set shows that it can predict code blue events one hour ahead with sensitivity between 66.7% and 90.9% while producing false SuperAlarms for control patients that account for between 2.2% and 11.2% of regular monitor alarms depending on user-supplied acceptable false positive rate. We conclude that even though the present work is still preliminary due to the usage of a moderately-sized database to test our hypothesis it represents an effort to develop algorithms to alleviate the alarm fatigue issue in a unique way.