Dataset Information

The proportion of clinically relevant alarms decreases as patient clinical severity decreases in intensive care units: a pilot study.

ABSTRACT:

Objectives

To determine (1) the proportion and number of clinically relevant alarms based on the type of monitoring device; (2) whether patient clinical severity, based on the sequential organ failure assessment (SOFA) score, affects the proportion of clinically relevant alarms and to suggest; (3) methods for reducing clinically irrelevant alarms in an intensive care unit (ICU).

Design

A prospective, observational clinical study.

Setting

A medical ICU at the University of Tokyo Hospital in Tokyo, Japan.

Participants

All patients who were admitted directly to the ICU, aged ?18 years, and not refused active treatment were registered between January and February 2012.

Methods

The alarms, alarm settings, alarm messages, waveforms and video recordings were acquired in real time and saved continuously. All alarms were annotated with respect to technical and clinical validity.

Results

18 ICU patients were monitored. During 2697 patient-monitored hours, 11 591 alarms were annotated. Only 740 (6.4%) alarms were considered to be clinically relevant. The monitoring devices that triggered alarms the most often were the direct measurement of arterial pressure (33.5%), oxygen saturation (24.2%), and electrocardiogram (22.9%). The numbers of relevant alarms were 12.4% (direct measurement of arterial pressure), 2.4% (oxygen saturation) and 5.3% (electrocardiogram). Positive correlations were established between patient clinical severities and the proportion of relevant alarms. The total number of irrelevant alarms could be reduced by 21.4% by evaluating their technical relevance.

Conclusions

We demonstrated that (1) the types of devices that alarm the most frequently were direct measurements of arterial pressure, oxygen saturation and ECG, and most of those alarms were not clinically relevant; (2) the proportion of clinically relevant alarms decreased as the patients' status improved and (3) the irrelevance alarms can be considerably reduced by evaluating their technical relevance.

SUBMITTER: Inokuchi R

PROVIDER: S-EPMC3773633 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

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Publications

The proportion of clinically relevant alarms decreases as patient clinical severity decreases in intensive care units: a pilot study.

Inokuchi Ryota R Sato Hajime H Nanjo Yuko Y Echigo Masahiro M Tanaka Aoi A Ishii Takeshi T Matsubara Takehiro T Doi Kent K Gunshin Masataka M Hiruma Takahiro T Nakamura Kensuke K Shinohara Kazuaki K Kitsuta Yoichi Y Nakajima Susumu S Umezu Mitsuo M Yahagi Naoki N

BMJ open 20130910 9

<h4>Objectives</h4>To determine (1) the proportion and number of clinically relevant alarms based on the type of monitoring device; (2) whether patient clinical severity, based on the sequential organ failure assessment (SOFA) score, affects the proportion of clinically relevant alarms and to suggest; (3) methods for reducing clinically irrelevant alarms in an intensive care unit (ICU).<h4>Design</h4>A prospective, observational clinical study.<h4>Setting</h4>A medical ICU at the University of T ...[more]

PMID: 24022391

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Project description:BackgroundAs one of the most essential technical components of the intensive care unit (ICU), continuous monitoring of patients' vital parameters has significantly improved patient safety by alerting staff through an alarm when a parameter deviates from the normal range. However, the vast number of alarms regularly overwhelms staff and may induce alarm fatigue, a condition recently exacerbated by COVID-19 and potentially endangering patients.ObjectiveThis study focused on providing a complete and repeatable analysis of the alarm data of an ICU's patient monitoring system. We aimed to develop do-it-yourself (DIY) instructions for technically versed ICU staff to analyze their monitoring data themselves, which is an essential element for developing efficient and effective alarm optimization strategies.MethodsThis observational study was conducted using alarm log data extracted from the patient monitoring system of a 21-bed surgical ICU in 2019. DIY instructions were iteratively developed in informal interdisciplinary team meetings. The data analysis was grounded in a framework consisting of 5 dimensions, each with specific metrics: alarm load (eg, alarms per bed per day, alarm flood conditions, alarm per device and per criticality), avoidable alarms, (eg, the number of technical alarms), responsiveness and alarm handling (eg alarm duration), sensing (eg, usage of the alarm pause function), and exposure (eg, alarms per room type). Results were visualized using the R package ggplot2 to provide detailed insights into the ICU's alarm situation.ResultsWe developed 6 DIY instructions that should be followed iteratively step by step. Alarm load metrics should be (re)defined before alarm log data are collected and analyzed. Intuitive visualizations of the alarm metrics should be created next and presented to staff in order to help identify patterns in the alarm data for designing and implementing effective alarm management interventions. We provide the script we used for the data preparation and an R-Markdown file to create comprehensive alarm reports. The alarm load in the respective ICU was quantified by 152.5 (SD 42.2) alarms per bed per day on average and alarm flood conditions with, on average, 69.55 (SD 31.12) per day that both occurred mostly in the morning shifts. Most alarms were issued by the ventilator, invasive blood pressure device, and electrocardiogram (ie, high and low blood pressure, high respiratory rate, low heart rate). The exposure to alarms per bed per day was higher in single rooms (26%, mean 172.9/137.2 alarms per day per bed).ConclusionsAnalyzing ICU alarm log data provides valuable insights into the current alarm situation. Our results call for alarm management interventions that effectively reduce the number of alarms in order to ensure patient safety and ICU staff's work satisfaction. We hope our DIY instructions encourage others to follow suit in analyzing and publishing their ICU alarm data.

Dataset Information

The proportion of clinically relevant alarms decreases as patient clinical severity decreases in intensive care units: a pilot study.

Objectives

Design

Setting

Participants

Methods

Results

Conclusions

Publications

The proportion of clinically relevant alarms decreases as patient clinical severity decreases in intensive care units: a pilot study.

Similar Datasets

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