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Pelter MM, Prasad PA, Mortara DW, Badilini F. Technical article: Overview of hospital-based data capture systems that acquire continuous ECG and physiologic data. J Electrocardiol 2024; 86:153777. [PMID: 39178814 DOI: 10.1016/j.jelectrocard.2024.153777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 07/16/2024] [Accepted: 08/08/2024] [Indexed: 08/26/2024]
Abstract
Data capture systems that acquire continuous hospital-based electrocardiographic (ECG) and physiologic (vital signs) data can foster robust research (i.e., large sample sizes from consecutive patients). However, the application of these systems and the data generated are complex and requires careful human oversight to ensure that accurate and high quality data are procured. This technical article will describe two different data capture systems created by our research group designed to examine false alarms associated with alarm fatigue in nurses. The following aspects regarding these data capture systems will be discussed: (1) history of development; (2) summary of advantages, challenges, and important considerations; (3) their use in research; (4) their use in clinical care; and (5) future developments.
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Affiliation(s)
- Michele M Pelter
- Department of Physiological Nursing, University of California San Francisco School of Nursing, California, USA.
| | - Priya A Prasad
- Department of Medicine, Division of Hospital Medicine, University of California San Francisco School of Medicine, California, USA.
| | - David W Mortara
- Department of Physiological Nursing, University of California San Francisco School of Nursing, California, USA; Division of Cardiology, University of California San Francisco School of Medicine, California, USA.
| | - Fabio Badilini
- Department of Physiological Nursing, University of California San Francisco School of Nursing, California, USA; Division of Cardiology, University of California San Francisco School of Medicine, California, USA.
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Prasad PA, Isaksen JL, Abe-Jones Y, Zègre-Hemsey JK, Sommargren CE, Al-Zaiti SS, Carey MG, Badilini F, Mortara D, Kanters JK, Pelter MM. Ventricular tachycardia and in-hospital mortality in the intensive care unit. Heart Rhythm O2 2023; 4:715-722. [PMID: 38034889 PMCID: PMC10685163 DOI: 10.1016/j.hroo.2023.09.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2023] Open
Abstract
Background Continuous electrocardiographic (ECG) monitoring is used to identify ventricular tachycardia (VT), but false alarms occur frequently. Objective The purpose of this study was to assess the rate of 30-day in-hospital mortality associated with VT alerts generated from bedside ECG monitors to those from a new algorithm among intensive care unit (ICU) patients. Methods We conducted a retrospective cohort study in consecutive adult ICU patients at an urban academic medical center and compared current bedside monitor VT alerts, VT alerts from a new-unannotated algorithm, and true-annotated VT. We used survival analysis to explore the association between VT alerts and mortality. Results We included 5679 ICU admissions (mean age 58 ± 17 years; 48% women), 503 (8.9%) experienced 30-day in-hospital mortality. A total of 30.1% had at least 1 current bedside monitor VT alert, 14.3% had a new-unannotated algorithm VT alert, and 11.6% had true-annotated VT. Bedside monitor VT alert was not associated with increased rate of 30-day mortality (adjusted hazard ratio [aHR] 1.06; 95% confidence interval [CI] 0.88-1.27), but there was an association for VT alerts from our new-unannotated algorithm (aHR 1.38; 95% CI 1.12-1.69) and true-annotated VT(aHR 1.39; 95% CI 1.12-1.73). Conclusion Unannotated and annotated-true VT were associated with increased rate of 30-day in-hospital mortality, whereas current bedside monitor VT was not. Our new algorithm may accurately identify high-risk VT; however, prospective validation is needed.
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Affiliation(s)
- Priya A. Prasad
- Department of Medicine, Division of Hospital Medicine, School of Medicine, University of California, San Francisco, San Francisco, California
- Center for Physiologic Research, University of California San Francisco School of Nursing, San Francisco, California
| | - Jonas L. Isaksen
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Yumiko Abe-Jones
- Department of Medicine, Division of Hospital Medicine, School of Medicine, University of California, San Francisco, San Francisco, California
| | | | - Claire E. Sommargren
- Department of Physiological Nursing, University of California School of Nursing, San Francisco, California
| | - Salah S. Al-Zaiti
- Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Mary G. Carey
- School of Nursing, University of Rochester, Rochester, New York
| | - Fabio Badilini
- Center for Physiologic Research, University of California San Francisco School of Nursing, San Francisco, California
- Department of Physiological Nursing, University of California School of Nursing, San Francisco, California
- Department of Medicine, Division of Cardiology, School of Medicine, University of California, San Francisco, San Francisco, California
| | - David Mortara
- Center for Physiologic Research, University of California San Francisco School of Nursing, San Francisco, California
- Department of Physiological Nursing, University of California School of Nursing, San Francisco, California
- Department of Medicine, Division of Cardiology, School of Medicine, University of California, San Francisco, San Francisco, California
| | - Jørgen K. Kanters
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michele M. Pelter
- Center for Physiologic Research, University of California San Francisco School of Nursing, San Francisco, California
- Department of Physiological Nursing, University of California School of Nursing, San Francisco, California
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Pelter MM, Carey MG, Al-Zaiti S, Zegre-Hemsey J, Sommargren C, Isola L, Prasad P, Mortara D, Badilini F. An annotated ventricular tachycardia (VT) alarm database: Toward a uniform standard for optimizing automated VT identification in hospitalized patients. Ann Noninvasive Electrocardiol 2023:e13054. [PMID: 36892130 DOI: 10.1111/anec.13054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/12/2023] [Accepted: 02/01/2023] [Indexed: 03/10/2023] Open
Abstract
BACKGROUND False ventricular tachycardia (VT) alarms are common during in-hospital electrocardiographic (ECG) monitoring. Prior research shows that the majority of false VT can be attributed to algorithm deficiencies. PURPOSE The purpose of this study was: (1) to describe the creation of a VT database annotated by ECG experts and (2) to determine true vs. false VT using a new VT algorithm created by our group. METHODS The VT algorithm was processed in 5320 consecutive ICU patients with 572,574 h of ECG and physiologic monitoring. A search algorithm identified potential VT, defined as: heart rate >100 beats/min, QRSs > 120 ms, and change in QRS morphology in >6 consecutive beats compared to the preceding native rhythm. Seven ECG channels, SpO2 , and arterial blood pressure waveforms were processed and loaded into a web-based annotation software program. Five PhD-prepared nurse scientists performed the annotations. RESULTS Of the 5320 ICU patients, 858 (16.13%) had 22,325 VTs. After three levels of iterative annotations, a total of 11,970 (53.62%) were adjudicated as true, 6485 (29.05%) as false, and 3870 (17.33%) were unresolved. The unresolved VTs were concentrated in 17 patients (1.98%). Of the 3870 unresolved VTs, 85.7% (n = 3281) were confounded by ventricular paced rhythm, 10.8% (n = 414) by underlying BBB, and 3.5% (n = 133) had a combination of both. CONCLUSIONS The database described here represents the single largest human-annotated database to date. The database includes consecutive ICU patients, with true, false, and challenging VTs (unresolved) and could serve as a gold standard database to develop and test new VT algorithms.
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Affiliation(s)
- Michele M Pelter
- Department of Physiological Nursing, University of California San Francisco School of Nursing, San Francisco, California, USA
| | - Mary G Carey
- School of Nursing, University of Rochester, Rochester, New York, USA
| | - Salah Al-Zaiti
- Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Jessica Zegre-Hemsey
- School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Claire Sommargren
- Department of Physiological Nursing, University of California San Francisco School of Nursing, San Francisco, California, USA
| | | | - Priya Prasad
- Department of Medicine, Division of Hospital Medicine, School of Medicine, University of California, San Francisco, California, USA
| | - David Mortara
- Department of Physiological Nursing, University of California San Francisco School of Nursing, San Francisco, California, USA
| | - Fabio Badilini
- Department of Physiological Nursing, University of California San Francisco School of Nursing, San Francisco, California, USA
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Bawua LK, Miaskowski C, Suba S, Badilini F, Rodway GW, Hu X, Pelter MM. Thoracic Impedance Pneumography-Derived Respiratory Alarms and Associated Patient Characteristics. Am J Crit Care 2022; 31:355-365. [PMID: 36045046 DOI: 10.4037/ajcc2022295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
BACKGROUND Respiratory rate (RR) alarms alert clinicians to a change in a patient's condition. However, RR alarms are common occurrences. To date, no study has examined RR alarm types and associated patient characteristics, which could guide alarm management strategies. OBJECTIVES To characterize RR alarms by type, frequency, duration, and associated patient demographic and clinical characteristics. METHODS A secondary data analysis of alarms generated with impedance pneumography in 461 adult patients admitted to either a cardiac, a medical/surgical, or a neurological intensive care unit (ICU). The RR alarms included high parameter limit (≥30 breaths/min), low parameter limit (≤5 breaths/min), and apnea (no breathing ≥20 s). The ICU type; total time monitored; and alarm type, frequency, and duration were evaluated. RESULTS Of 159 771 RR alarms, parameter limit alarms (n = 140 975; 88.2%) were more frequent than apnea alarms (n = 18 796; 11.8%). High parameter limit alarms were most frequent (n = 131 827; 82.5%). After ICU monitoring time was controlled for, multivariate analysis showed that alarm rates were higher in patients in the cardiac and neurological ICUs (P = .001), patients undergoing mechanical ventilation (P = .005), and patients without a ventricular assist device or pacemaker (P = .02). Male sex was associated with low parameter limit (P = .01) and apnea (P = .005) alarms. CONCLUSION High parameter limit RR alarms were most frequent. Factors associated with RR alarms included monitoring time, ICU type, male sex, and mechanical ventilation. Although these factors are not modifiable, these data could be used to guide management strategies.
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Affiliation(s)
- Linda K Bawua
- Linda K. Bawua is a former PhD student, School of Nursing, University of California, San Francisco, California
| | - Christine Miaskowski
- Christine Miaskowski is a professor, School of Nursing, University of California, San Francisco, California
| | - Sukardi Suba
- Sukardi Suba is a postdoctoral associate, School of Nursing, University of Rochester Medical Center, Rochester, New York
| | - Fabio Badilini
- Fabio Badilini is director of the Center for Physiological Research, School of Nursing, University of California, San Francisco, California
| | - George W Rodway
- George W. Rodway is an assistant professor, School of Medicine, University of Nevada, Reno, Nevada
| | - Xiao Hu
- Xiao Hu is a professor, School of Nursing, Duke University, Durham, North Carolina
| | - Michele M Pelter
- Michele M. Pelter is an associate professor, School of Nursing, University of California, San Francisco, California
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Computer Assisted Patient Monitoring: Associated Patient, Clinical and ECG Characteristics and Strategy to Minimize False Alarms. HEARTS 2021. [DOI: 10.3390/hearts2040036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
This chapter is a review of studies that have examined false arrhythmia alarms during in-hospital electrocardiographic (ECG) monitoring in the intensive care unit. In addition, we describe an annotation effort being conducted at the UCSF School of Nursing, Center for Physiologic Research designed to improve algorithms for lethal arrhythmias (i.e., asystole, ventricular fibrillation, and ventricular tachycardia). Background: Alarm fatigue is a serious patient safety hazard among hospitalized patients. Data from the past five years, showed that alarm fatigue was responsible for over 650 deaths, which is likely lower than the actual number due to under-reporting. Arrhythmia alarms are a common source of false alarms and 90% are false. While clinical scientists have implemented a number of interventions to reduce these types of alarms (e.g., customized alarm settings; daily skin electrode changes; disposable vs. non-disposable lead wires; and education), only minor improvements have been made. This is likely as these interventions do not address the primary problem of false arrhythmia alarms, namely deficient and outdated arrhythmia algorithms. In this chapter we will describe a number of ECG features associated with false arrhythmia alarms. In addition, we briefly discuss an annotation effort our group has undertaken to improve lethal arrhythmia algorithms.
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The Current Situation and Influencing Factors of the Alarm Fatigue of Nurses' Medical Equipment in the Intensive Care Unit Based on Intelligent Medical Care. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:9994303. [PMID: 34285785 PMCID: PMC8275382 DOI: 10.1155/2021/9994303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 06/10/2021] [Accepted: 06/24/2021] [Indexed: 11/17/2022]
Abstract
After rapid development and reform, the health level and medical diagnosis and treatment capabilities of Chinese residents have been significantly improved, and high-quality medical resources have significantly improved the life safety and health of the masses. As the most concentrated area of medical equipment in the hospital, the intensive care unit produces the most alarms during the operation of the equipment. The intensive care unit nurses are responsible for heavier nursing work, and the problem of alarming in other departments is more prominent. Therefore, this paper presents an analysis and research on the current situation and influencing factors of the alarm fatigue of nurse medical equipment in the intensive care unit based on intelligent medicine. This article uses a variety of related methods such as literature data method and questionnaire survey method to deeply study the theoretical knowledge of intelligent medical treatment, medical equipment alarm fatigue device, and so on. The logistic regression analysis method is introduced to classify its influencing factors, and the analysis experiment on the influencing factors of the medical equipment alarm fatigue of nurses in the intensive care unit is designed. The nurses' cognition of clinical alarms and the analysis of clinical alarm fatigue questionnaire data are studied. The alarm fatigue of nurses in the intensive care unit is at a severe level, which needs to be taken seriously in the intensive care unit. Unmarried, high-level positions, long working years, high professional titles, and high education are negatively correlated with alarm fatigue (P < 0.05), and those without an alarm habit are positively correlated with alarm fatigue (P < 0.05), and the number of night shifts per month is related to alarm fatigue. There is no correlation between them (P > 0.05).
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Watanakeeree K, Suba S, Mackin LA, Badilini F, Pelter MM. ECG alarms during left ventricular assist device (LVAD) therapy in the ICU. Heart Lung 2021; 50:763-769. [PMID: 34225087 DOI: 10.1016/j.hrtlng.2021.03.080] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 03/22/2021] [Accepted: 03/25/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND In hospitalized patients with left ventricular assist device (LVAD), electrical interference and low amplitude QRS complexes are common, which could impact the accuracy of electrocardiographic (ECG) arrhythmia detection and create technical alarms. This could contribute to provider alarm fatigue and threaten patient safety. OBJECTIVES We examined three LVAD patients in the cardiac intensive care unit (ICU) to determine: 1) the frequency and accuracy of audible arrhythmia alarms; 2) occurrence rates of technical alarms; and 3) alarm burden (# alarms/hour of monitoring) METHODS: Secondary analysis. RESULTS During 593 h, there were 549 audible arrhythmia alarms and 98% were false. There were 25,232 technical alarms and 93% were for artifact, which was configured as an inaudible text alert. CONCLUSION False-arrhythmia and technical alarms are frequent in LVAD patients. Future studies are needed to identify both clinical and algorithm-based strategies to improve arrhythmia detection and reduce technical alarms in LVAD patients.
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Affiliation(s)
- Kevin Watanakeeree
- Assistant Unit Director, Emergency Department, UCSF Medical Center, United States
| | - Sukardi Suba
- PhD Graduate, ECG Monitoring Research Lab, Department of Physiological Nursing, United States.
| | - Lynda A Mackin
- Clinical Professor, Department of Physiological Nursing, United States
| | - Fabio Badilini
- Director, Center for Physiologic Research, Department of Physiological Nursing, United States
| | - Michele M Pelter
- Associate Professor, Director, ECG Monitoring Research Lab, and Associate Translational Scientist, Center for Physiologic Research, Department of Physiological Nursing, United States.
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Nguyen SC, Suba S, Hu X, Pelter MM. Double Trouble: Patients With Both True and False Arrhythmia Alarms. Crit Care Nurse 2021; 40:14-23. [PMID: 32236427 DOI: 10.4037/ccn2020363] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
BACKGROUND Patients with both true and false arrhythmia alarms pose a challenge because true alarms might be buried among a large number of false alarms, leading to missed true events. OBJECTIVE To determine (1) the frequency of patients with both true and false arrhythmia alarms; (2) patient, clinical, and electrocardiographic characteristics associated with both true and false alarms; and (3) the frequency and types of true and false arrhythmia alarms. METHODS This was a secondary analysis using data from an alarm study conducted at a tertiary academic medical center. RESULTS Of 461 intensive care unit patients, 211 (46%) had no arrhythmia alarms, 12 (3%) had only true alarms, 167 (36%) had only false alarms, and 71 (15%) had both true and false alarms. Ventricular pacemaker, altered mental status, mechanical ventilation, and cardiac intensive care unit admission were present more often in patients with both true and false alarms than among other patients (P < .001). Intensive care unit stays were longer in patients with only false alarms (mean [SD], 106 [162] hours) and those with both true and false alarms (mean [SD], 208 [333] hours) than in other patients. Accelerated ventricular rhythm was the most common alarm type (37%). CONCLUSIONS An awareness of factors associated with arrhythmia alarms might aid in developing solutions to decrease alarm fatigue. To improve detection of true alarms, further research is needed to build and test electrocardiographic algorithms that adjust for clinical and electrocardiographic characteristics associated with false alarms.
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Affiliation(s)
- Stella Chiu Nguyen
- Stella Chiu Nguyen is a registered nurse in the radiology department at Stanford Healthcare, Palo Alto, California. At the time of writing this article, Ms Nguyen was a registered nurse in the emergency department and a Master's student at University of California San Francisco (UCSF) Health, San Francisco, California. Sukardi Suba is a doctoral student and an ECG monitoring predoctoral fellow in the Department of Physiological Nursing, UCSF School of Nursing. Xiao Hu is a biomedical engineer in the UCSF School of Nursing and the Institute for Computational Health Sciences, UCSF-UC Berkeley Graduate Program in Bioengineering, San Francisco. Michele M. Pelter is an assistant professor and the Director of the ECG Monitoring Research Lab, UCSF School of Nursing
| | - Sukardi Suba
- Stella Chiu Nguyen is a registered nurse in the radiology department at Stanford Healthcare, Palo Alto, California. At the time of writing this article, Ms Nguyen was a registered nurse in the emergency department and a Master's student at University of California San Francisco (UCSF) Health, San Francisco, California. Sukardi Suba is a doctoral student and an ECG monitoring predoctoral fellow in the Department of Physiological Nursing, UCSF School of Nursing. Xiao Hu is a biomedical engineer in the UCSF School of Nursing and the Institute for Computational Health Sciences, UCSF-UC Berkeley Graduate Program in Bioengineering, San Francisco. Michele M. Pelter is an assistant professor and the Director of the ECG Monitoring Research Lab, UCSF School of Nursing
| | - Xiao Hu
- Stella Chiu Nguyen is a registered nurse in the radiology department at Stanford Healthcare, Palo Alto, California. At the time of writing this article, Ms Nguyen was a registered nurse in the emergency department and a Master's student at University of California San Francisco (UCSF) Health, San Francisco, California. Sukardi Suba is a doctoral student and an ECG monitoring predoctoral fellow in the Department of Physiological Nursing, UCSF School of Nursing. Xiao Hu is a biomedical engineer in the UCSF School of Nursing and the Institute for Computational Health Sciences, UCSF-UC Berkeley Graduate Program in Bioengineering, San Francisco. Michele M. Pelter is an assistant professor and the Director of the ECG Monitoring Research Lab, UCSF School of Nursing
| | - Michele M Pelter
- Stella Chiu Nguyen is a registered nurse in the radiology department at Stanford Healthcare, Palo Alto, California. At the time of writing this article, Ms Nguyen was a registered nurse in the emergency department and a Master's student at University of California San Francisco (UCSF) Health, San Francisco, California. Sukardi Suba is a doctoral student and an ECG monitoring predoctoral fellow in the Department of Physiological Nursing, UCSF School of Nursing. Xiao Hu is a biomedical engineer in the UCSF School of Nursing and the Institute for Computational Health Sciences, UCSF-UC Berkeley Graduate Program in Bioengineering, San Francisco. Michele M. Pelter is an assistant professor and the Director of the ECG Monitoring Research Lab, UCSF School of Nursing
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ECG Monitoring during End of Life Care: Implications on Alarm Fatigue. MULTIMODAL TECHNOLOGIES AND INTERACTION 2019. [DOI: 10.3390/mti3010018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Excessive numbers of clinical alarms in the intensive care unit (ICU) contribute to alarm fatigue. Efforts to eliminate unnecessary alarms, including during end of life (EOL) care, are pivotal. This study describes electrocardiographic (ECG) arrhythmia alarm usage following the decision for comfort care. We conducted a review of electronic health records (EHR) in patients who died and had comfort care orders that were in place during our study. The occurrences of ECG arrhythmia alarms among these patients were examined. We found 151 arrhythmia alarms that were generated in 11 patients after comfort care was initiated: 72% were audible, 21% were manually muted, and 7% had an unknown audio label. Level of alarm: 33% crisis, 58% warning, 1% message, and 8% were labeled unknown. Our report shows that ECG monitoring was commonly maintained during the EOL care. Since the goal of care during this phase is for both patient and family comfort, it is important for the clinicians to weigh the benefits versus harms of the continuous ECG monitoring.
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