Alarm limit settings for early warning systems to identify at‐risk patients

Burgess, Lawrence P. A.; Herdman, T. Heather; Berg, Benjamin W.; Feaster, William; Hebsur, Shashidhar

doi:10.1111/j.1365-2648.2009.05048.x

Cited by 36 publications

(21 citation statements)

References 23 publications

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“…[10][11][12][13][14][15] One such solution is to change alarm limit settings, an intervention shown to be efficacious in the literature. 5,6,16,17 Although no adverse patient outcomes are reported in these studies, none of them included a formal safety evaluation to evaluate whether alarm rate reduction occurred at the expense of clinically significant alarms.…”

Section: Discussionmentioning

confidence: 99%

“…We believe it is crucial to perform a safety analysis prior to implementation due to the role vital signs play in clinical assessment and detection of patient deterioration. 30,[34][35][36][37] Though a few studies have shown that modification of alarm parameters decreases alarm frequency, 5,6,10,16,17 to our knowledge no formal safety evaluations have ever been published. This study provides the first published safety evaluation of data-driven HR and RR parameters.…”

Section: Discussionmentioning

confidence: 99%

“…Studies have quantified the frequency and types of medical device alarms, and some proposed solutions to decrease excess alarms . One such solution is to change alarm limit settings, an intervention shown to be efficacious in the literature . Although no adverse patient outcomes are reported in these studies, none of them included a formal safety evaluation to evaluate whether alarm rate reduction occurred at the expense of clinically significant alarms.…”

mentioning

confidence: 99%

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Safety analysis of proposed data‐driven physiologic alarm parameters for hospitalized children

Goel

Poole

Longhurst

et al. 2016

Journal of Hospital Medicine

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INTRODUCTION Modification of alarm limits is one approach to mitigating alarm fatigue. We aimed to create and validate heart rate (HR) and respiratory rate (RR) percentiles for hospitalized children, and analyze the safety of replacing current vital sign reference ranges with proposed data‐driven, age‐stratified 5th and 95th percentile values. METHODS In this retrospective cross‐sectional study, nurse‐charted HR and RR data from a training set of 7202 hospitalized children were used to develop percentile tables. We compared 5th and 95th percentile values with currently accepted reference ranges in a validation set of 2287 patients. We analyzed 148 rapid response team (RRT) and cardiorespiratory arrest (CRA) events over a 12‐month period, using HR and RR values in the 12 hours prior to the event, to determine the proportion of patients with out‐of‐range vitals based upon reference versus data‐driven limits. RESULTS There were 24,045 (55.6%) fewer out‐of‐range measurements using data‐driven vital sign limits. Overall, 144/148 RRT and CRA patients had out‐of‐range HR or RR values preceding the event using current limits, and 138/148 were abnormal using data‐driven limits. Chart review of RRT and CRA patients with abnormal HR and RR per current limits considered normal by data‐driven limits revealed that clinical status change was identified by other vital sign abnormalities or clinical context. CONCLUSIONS A large proportion of vital signs in hospitalized children are outside presently used norms. Safety evaluation of data‐driven limits suggests they are as safe as those currently used. Implementation of these parameters in physiologic monitors may mitigate alarm fatigue. Journal of Hospital Medicine 2015;11:817–823. © 2015 Society of Hospital Medicine

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Safety analysis of proposed data‐driven physiologic alarm parameters for hospitalized children

Goel

Poole

Longhurst

et al. 2016

Journal of Hospital Medicine

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“…Recently, work has been done to develop evidence-based methods for determining alarm limits. Burgess et al [11] established a database of HR and respiration rate (RR) measurements from 317 patients (18,737 h) in a general care unit, with no adverse events. Modeling was done with this database to predict the alarm rate for different alarm limits, with the goal of reducing the number of false positive alarms.…”

Section: Introductionmentioning

confidence: 99%

Multi-parameter vital sign database to assist in alarm optimization for general care units

Welch

Kanter

Skora

et al. 2015

J Clin Monit Comput

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Continual vital sign assessment on the general care, medical-surgical floor is expected to provide early indication of patient deterioration and increase the effectiveness of rapid response teams. However, there is concern that continual, multi-parameter vital sign monitoring will produce alarm fatigue. The objective of this study was the development of a methodology to help care teams optimize alarm settings. An on-body wireless monitoring system was used to continually assess heart rate, respiratory rate, SpO2 and noninvasive blood pressure in the general ward of ten hospitals between April 1, 2014 and January 19, 2015. These data, 94,575 h for 3430 patients are contained in a large database, accessible with cloud computing tools. Simulation scenarios assessed the total alarm rate as a function of threshold and annunciation delay (s). The total alarm rate of ten alarms/patient/day predicted from the cloud-hosted database was the same as the total alarm rate for a 10 day evaluation (1550 h for 36 patients) in an independent hospital. Plots of vital sign distributions in the cloud-hosted database were similar to other large databases published by different authors. The cloud-hosted database can be used to run simulations for various alarm thresholds and annunciation delays to predict the total alarm burden experienced by nursing staff. This methodology might, in the future, be used to help reduce alarm fatigue without sacrificing the ability to continually monitor all vital signs.Electronic supplementary materialThe online version of this article (doi:10.1007/s10877-015-9790-8) contains supplementary material, which is available to authorized users.

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“…Simply improving skin preparation with fine sandpaper and daily electrode replacement reduced alarms by 46% (Clochesy et al, 1991;Cvach et al, 2012;Pettersen et al, 2014). Empiric adjustments of the default heart rate settings (upper limit 130-135 bpm and lower limit of 40-45 bpm) safely reduced clinical alarms in a general medical surgical ward (Burgess, 2009). …”

Section: Alarm Fatiguementioning

confidence: 99%

2017 ISHNE-HRS expert consensus statement on ambulatory ECG and external cardiac monitoring/telemetry

Steinberg

Varma

Cygankiewicz

et al. 2017

Ann Noninvasive Electrocardiol

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Alarm limit settings for early warning systems to identify at‐risk patients

Abstract: Alarm frequency data will assist with evidence-based configuration of alarm limits for early warning systems.

Cited by 36 publications

References 23 publications

Safety analysis of proposed data‐driven physiologic alarm parameters for hospitalized children

Safety analysis of proposed data‐driven physiologic alarm parameters for hospitalized children

Multi-parameter vital sign database to assist in alarm optimization for general care units

2017 ISHNE-HRS expert consensus statement on ambulatory ECG and external cardiac monitoring/telemetry

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