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A Behavioral Approach Describing Parameter Adjustments in Alarm Fatigue Mitigation
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ISCE Poster Session I / Journal of Electrocardiology 49 (2016) 925–931
A Behavioral Approach Describing Parameter Adjustments in Alarm Fatigue Mitigation
The Relationship Between ST Depression in Standard 12-leads and the ST Elevation in Extended Leads
Jorge Arroyo-Palacios, Xiao Hu, Yong Bai, Andrea Villaroman, Michele Pelter, Richard Fidler University of California San Francisco, San Francisco, CA, USA
Simon C. Chiena,b, Richard Gregga, Ming-Shien Wenc,d a Advanced Algorithm Research Center, Philips Healthcare, Andover, MA, USA b Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA c Second Section of Cardiology, Chang Gung Memorial Hospital, Linko, Taiwan d School of Medicine, Chang Gung University, Taoyuan, Taiwan
Research aimed at improving monitoring alarms management has focused on the equipment side, not the clinician side. Lacking in the literature is quantitative research describing clinicians' motivation to change parameters alarms. This study aimed to describe clinicians' interactions with heart rate (HR) alarm parameters. Retrospective analysis of automatically recorded HR alarm reports collected over 31 days from 5 intensive care units (ICUs) at a tertiary hospital was conducted. Monitoring alarm data was archived using BedMasterEx (Excel Medical) and reports were generated. Clinician behavior was inferred by analyzing alarm adjustment metrics. Data were analyzed with SPSS, and due to their non-normal distribution, nonparametric statistical tests, median (mdn) and inter quartile range (IQR) are reported. A total of 23,624 HR alarms from 337 patients were analyzed, with 65.4% of HR alarms for upper HR limit violations. Only 51% of patients had HR parameters adjusted for 811 total adjustments (411 in the upper HR limit). The magnitude of HR parameter adjustments had a median of 5 beats/min for the upper (IQR = 10, range −55 to +60), and the lower HR median of -1, (IQR = 5, range −50 to +40). Time to the first HR alarm adjustment was mdn = 17.9 hours, IQR = 45.05. There were 8 HR alarms prior to the first HR parameter adjustment (IQR 24). A binary logistic regression reveals that increasing HR alarms frequency effects the likelihood of clinicians making an adjustment of parameters (χ2(1) = 32.62, p b 0.001). Bivariate correlation indicated that increased alarms frequency shortened the delay to adjust the alarm parameters (r = −0.67, p b 0.001). Finally we examined the efficacy of the first alarm adjustment in the reduction of the alarm frequency. Wilcoxon signed rank test showed that there was no significant difference on the frequency of alarms/hour before (mdn = 0.46, IQR = 1.66) and after (mdn = 0.65, IQR = 1.21) the alarm adjustment (p = 0.57). The goals of HR alarms management should include strategies to reduce the total number of alarms, decrease false alarms, and make the alarms more meaningful. Although 51% of patients had HR alarm adjustments, there was no reduction in the frequency of HR alarms. High HR alarms frequency leads clinicians to make parameter adjustments and make them sooner. Trending and diagnosis-specific information may improve clinicians' optimization of HR alarm limits, while improving the precision of HR monitoring. This study shows that automatically generated alarm reports can provide useful objective information about the behavioral aspects of alarm fatigue and alarms management.
http://dx.doi:10.1016/j.jelectrocard.2016.09.014
Background: Transmural ischemia in right ventricular and posterior walls can cause ST elevation (STe) in right precordial and posterior leads and can subsequently cause possible ST depression (STd) in standard 12-leads. Regarding the existence of STd in 12-leads (12L-STd) or STe in extended leads (extL-STe) as a way to rule out STe confounders for STEMI diagnosis, the relationship between STd in 12-leads and the STe in extended leads and their diagnostic accuracy of STEMI needs to be further investigated. Methods: Out of total 49,500 de-identified non-paced ECGs with accompanying ICD-9 discharge codes from patients admitted at the UCLA Long Beach Hospital from 1993 to 2006, total 502 15-leads ECGs with at least one lead of STe ≥0.1 mV in V8R, V4R and V8 excluding acute pericarditis, sinus tachycardia and LBBB were found. extL-STe and 12L-STd were analyzed for their correlation, voltage level and the diagnostic accuracy in STEMI. Results: In this extended lead STe dataset, 58.5% (36.2% STEMI) shows maximal STe voltage in V4R, 25.4% (31.7% STEMI) in V8R, and 16.1% (68.42% STEMI) in V8. Reciprocal leads of 12L-STd to extL-STe for both STEMI and not-STEMI are as expected: 1) maximal STe in V4R mostly shows minimal STd in aVF, II and V6; 2) maximal STe in V8R mostly shows minimal STd in aVF and V6; 3) maximal STe in V8 mostly showed minimal STd in V3, aVF and aVL. Further, we examined the correlation between the maximal extL-STe and the corresponding minimal 12L-STd. As shown in the table below, correlation coefficients were calculated separately for each of the three groups and by STEMI partitioning. For V8R, maximal extL-STe and minimal 12L-STd are uncorrelated. For V4R, the correlation is higher in the not-STEMI group, whereas the correlation is higher in the STEMI part for V8. In terms of the diagnostic ability of using extL-STe or 12L-STd alone for STEMI diagnosis, the receiver-operating-curves of the two methods are both close to diagonal. Correlation coefficients between extL-STe and 12L-STd
Maximal STe in V4R
V8R
V8
STEMI partition Not-STEMI remainder
0.64 0.86
−0.01 0.02
0.95 0.33
Conclusion: The result suggests that STe in V8 can mostly be found through STd in V3, aVF or aVL, and the deviation voltages caused by acute MI are correlated. STe in V8 highly suggests STEMI, which improves the specificity compared to using only 12-leads. On the other hand, STe in V8R and V4R does not suggest STEMI as accurately as V8. http://dx.doi:10.1016/j.jelectrocard.2016.09.015
Table 1 Additional comparisons between patients with alarm parameters changed and not changed.
Stay in ICU (days) Patients with setting:
# Alarms per patient Test
Changed Not Changed Median IQR Mean SD Minimum Maximum N
3.73 7.77 6.62 7.14 0.19 30 172
1 1.35 2.37 3.66 0.04 30 164
Patients with settings:
Alarms per patient per day Test
Changed Not Changed Mann–Whitney p b 0.001, r = 0.45
64 117.75 121.74 175.21 1 1070 172
IQR = inter quartile range, SD = standard deviation, N = number of patients.
4 15 16.27 38.23 1 363 165
Patients with settings:
Test
Changed Not Changed Mann–Whitney p b 0.001, r = 0.61
15.21 25.49 23.71 26.6 0.57 146.44 172
2.98 8.13 9.7 25.53 0.21 276.92 165
Mann–Whitney p b 0.001, r = 0.47
ISCE Poster Session I / Journal of Electrocardiology 49 (2016) 925–931
A Behavioral Approach Describing Parameter Adjustments in Alarm Fatigue Mitigation
The Relationship Between ST Depression in Standard 12-leads and the ST Elevation in Extended Leads
Jorge Arroyo-Palacios, Xiao Hu, Yong Bai, Andrea Villaroman, Michele Pelter, Richard Fidler University of California San Francisco, San Francisco, CA, USA
Simon C. Chiena,b, Richard Gregga, Ming-Shien Wenc,d a Advanced Algorithm Research Center, Philips Healthcare, Andover, MA, USA b Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA c Second Section of Cardiology, Chang Gung Memorial Hospital, Linko, Taiwan d School of Medicine, Chang Gung University, Taoyuan, Taiwan
Research aimed at improving monitoring alarms management has focused on the equipment side, not the clinician side. Lacking in the literature is quantitative research describing clinicians' motivation to change parameters alarms. This study aimed to describe clinicians' interactions with heart rate (HR) alarm parameters. Retrospective analysis of automatically recorded HR alarm reports collected over 31 days from 5 intensive care units (ICUs) at a tertiary hospital was conducted. Monitoring alarm data was archived using BedMasterEx (Excel Medical) and reports were generated. Clinician behavior was inferred by analyzing alarm adjustment metrics. Data were analyzed with SPSS, and due to their non-normal distribution, nonparametric statistical tests, median (mdn) and inter quartile range (IQR) are reported. A total of 23,624 HR alarms from 337 patients were analyzed, with 65.4% of HR alarms for upper HR limit violations. Only 51% of patients had HR parameters adjusted for 811 total adjustments (411 in the upper HR limit). The magnitude of HR parameter adjustments had a median of 5 beats/min for the upper (IQR = 10, range −55 to +60), and the lower HR median of -1, (IQR = 5, range −50 to +40). Time to the first HR alarm adjustment was mdn = 17.9 hours, IQR = 45.05. There were 8 HR alarms prior to the first HR parameter adjustment (IQR 24). A binary logistic regression reveals that increasing HR alarms frequency effects the likelihood of clinicians making an adjustment of parameters (χ2(1) = 32.62, p b 0.001). Bivariate correlation indicated that increased alarms frequency shortened the delay to adjust the alarm parameters (r = −0.67, p b 0.001). Finally we examined the efficacy of the first alarm adjustment in the reduction of the alarm frequency. Wilcoxon signed rank test showed that there was no significant difference on the frequency of alarms/hour before (mdn = 0.46, IQR = 1.66) and after (mdn = 0.65, IQR = 1.21) the alarm adjustment (p = 0.57). The goals of HR alarms management should include strategies to reduce the total number of alarms, decrease false alarms, and make the alarms more meaningful. Although 51% of patients had HR alarm adjustments, there was no reduction in the frequency of HR alarms. High HR alarms frequency leads clinicians to make parameter adjustments and make them sooner. Trending and diagnosis-specific information may improve clinicians' optimization of HR alarm limits, while improving the precision of HR monitoring. This study shows that automatically generated alarm reports can provide useful objective information about the behavioral aspects of alarm fatigue and alarms management.
http://dx.doi:10.1016/j.jelectrocard.2016.09.014
Background: Transmural ischemia in right ventricular and posterior walls can cause ST elevation (STe) in right precordial and posterior leads and can subsequently cause possible ST depression (STd) in standard 12-leads. Regarding the existence of STd in 12-leads (12L-STd) or STe in extended leads (extL-STe) as a way to rule out STe confounders for STEMI diagnosis, the relationship between STd in 12-leads and the STe in extended leads and their diagnostic accuracy of STEMI needs to be further investigated. Methods: Out of total 49,500 de-identified non-paced ECGs with accompanying ICD-9 discharge codes from patients admitted at the UCLA Long Beach Hospital from 1993 to 2006, total 502 15-leads ECGs with at least one lead of STe ≥0.1 mV in V8R, V4R and V8 excluding acute pericarditis, sinus tachycardia and LBBB were found. extL-STe and 12L-STd were analyzed for their correlation, voltage level and the diagnostic accuracy in STEMI. Results: In this extended lead STe dataset, 58.5% (36.2% STEMI) shows maximal STe voltage in V4R, 25.4% (31.7% STEMI) in V8R, and 16.1% (68.42% STEMI) in V8. Reciprocal leads of 12L-STd to extL-STe for both STEMI and not-STEMI are as expected: 1) maximal STe in V4R mostly shows minimal STd in aVF, II and V6; 2) maximal STe in V8R mostly shows minimal STd in aVF and V6; 3) maximal STe in V8 mostly showed minimal STd in V3, aVF and aVL. Further, we examined the correlation between the maximal extL-STe and the corresponding minimal 12L-STd. As shown in the table below, correlation coefficients were calculated separately for each of the three groups and by STEMI partitioning. For V8R, maximal extL-STe and minimal 12L-STd are uncorrelated. For V4R, the correlation is higher in the not-STEMI group, whereas the correlation is higher in the STEMI part for V8. In terms of the diagnostic ability of using extL-STe or 12L-STd alone for STEMI diagnosis, the receiver-operating-curves of the two methods are both close to diagonal. Correlation coefficients between extL-STe and 12L-STd
Maximal STe in V4R
V8R
V8
STEMI partition Not-STEMI remainder
0.64 0.86
−0.01 0.02
0.95 0.33
Conclusion: The result suggests that STe in V8 can mostly be found through STd in V3, aVF or aVL, and the deviation voltages caused by acute MI are correlated. STe in V8 highly suggests STEMI, which improves the specificity compared to using only 12-leads. On the other hand, STe in V8R and V4R does not suggest STEMI as accurately as V8. http://dx.doi:10.1016/j.jelectrocard.2016.09.015
Table 1 Additional comparisons between patients with alarm parameters changed and not changed.
Stay in ICU (days) Patients with setting:
# Alarms per patient Test
Changed Not Changed Median IQR Mean SD Minimum Maximum N
3.73 7.77 6.62 7.14 0.19 30 172
1 1.35 2.37 3.66 0.04 30 164
Patients with settings:
Alarms per patient per day Test
Changed Not Changed Mann–Whitney p b 0.001, r = 0.45
64 117.75 121.74 175.21 1 1070 172
IQR = inter quartile range, SD = standard deviation, N = number of patients.
4 15 16.27 38.23 1 363 165
Patients with settings:
Test
Changed Not Changed Mann–Whitney p b 0.001, r = 0.61
15.21 25.49 23.71 26.6 0.57 146.44 172
2.98 8.13 9.7 25.53 0.21 276.92 165
Mann–Whitney p b 0.001, r = 0.47