J Biomed Inform - Predictive combinations of monitor alarms preceding in-hospital code blue events.

Tópicos

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Resumo

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.

Resumo Limpo

bedsid monitor ubiquit acut care unit modern healthcar enterpris howev critic generat excess number fals posit alarm caus alarm fatigu among care giver potenti compromis patient safeti hypothes combin regular monitor alarm denot superalarm set may indic ongo patient deterior henc predict inhospit code blue event present work develop assess alarm mine approach base find frequent combin singl alarm also specif code blue event compos superalarm set use way analysi varianc anova investig influenc four algorithm paramet perform data mine approach result obtain million monitor alarm cohort adult code blue control patient use multipl fold crossvalid experi setup use optim set paramet determin crossvalid experi final superalarm set mine train data use independ test data set simul run superalarm set live regular monitor alarm anova show content superalarm set influenc subset key algorithm paramet simul extract superalarm set show can predict code blue event one hour ahead sensit produc fals superalarm control patient account regular monitor alarm depend usersuppli accept fals posit rate conclud even though present work still preliminari due usag moderatelys databas test hypothesi repres effort develop algorithm allevi alarm fatigu issu uniqu way

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