Continuous electrocardiographic (ECG) monitoring was first introduced into hospitals in the 1960s, initially into critical care, as bedside monitors, and eventually into step-down units with telemetry capabilities. Although the initial use was rather simplistic (ie, heart rate and rhythm assessment), the capabilities of these devices and associated physiologic (vital sign) monitors have expanded considerably. Current bedside monitors now include sophisticated ECG software designed to identify myocardial ischemia (ie, ST-segment monitoring), QT-interval prolongation, and a myriad of other cardiac arrhythmia types. Physiologic monitoring has had similar advances from noninvasive assessment of core vital signs (blood pressure, respiratory rate, oxygen saturation) to invasive monitoring including arterial blood pressure, temperature, central venous pressure, intracranial pressure, carbon dioxide, and many others. The benefit of these monitoring devices is that continuous and real-time information is displayed and can be configured to alarm to alert nurses to a change in a patient’s condition. I think it is fair to say that critical and high-acuity care nurses see these devices as having a positive impact in patient care. However, this enthusiasm has been somewhat dampened in the past decade by research highlighting the shortcomings and unanticipated consequences of these devices, namely alarm and alert fatigue. In this article, which is associated with the American Association of Critical-Care Nurses’ Distinguished Research Lecture, I describe my 36-year journey from a clinical nurse to nurse scientist and the trajectory of my program of research focused primarily on ECG and physiologic monitoring. Specifically, I discuss the good, the not so good, and the untapped potential of these monitoring systems in clinical care. I also describe my experiences with community-based research in patients with acute coronary syndrome and/or heart failure.

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Hospital-Based ECG Monitoring

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Hospital-Based ECG Monitoring

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My initial introduction to electrocardiographic (ECG) monitoring began in 1990, when I was working as a clinical/bedside nurse in a cardiac telemetry unit in my hometown of Reno, Nevada. I was immediately fascinated—my love of anatomy and physiology came to life. I could actually see the action and reaction of a patient’s heart recorded on an ECG in real time.

This is not to say that ECG monitoring was my sole clinical focus; I absolutely loved everything about direct patient care. I found caring for cardiac patients to be fulfilling: this was a specialty in which a nurse could see a patient improve from a critical disease state to often walking out of the hospital to live a full life, even with myocardial damage, due in large part to treatment advances in cardiology. I began to submerge myself in learning everything I could about ECG monitoring and interpretation and subsequently developed a strong perspective that ECG monitoring was an important nursing domain in hospital-based care; thus, nurses must be the drivers of scientific knowledge development in this area.

In 1991, I read a research paper by Barbara J. Drew, PhD, RN, and her colleagues1  at the University of California San Francisco (UCSF) describing the accuracy of ECG lead placement during bedside ECG monitoring. I was captivated by the topic and the thoughtful research design. I recall thinking—who knew nurses could be researchers? I continued to closely read Dr Drew’s work and was determined to meet her and one day study under her tutelage. Two years later, in 1993, this goal was realized when I was accepted into UCSF’s master’s program in the cardiovascular clinical nurse specialty track. A few short months later, I interviewed for a research nurse position in Dr Drew’s ECG Monitoring Research Lab. A major hurdle I had to overcome to secure the position was convincing Dr Drew that I could do the work, which was a challenge because I was living in Reno and commuting to UCSF to attend classes, a 220-mile trek. I am quite certain that Dr Drew thought long and hard about my ability to pull it off; in fact, I was worried too. Only my 25-year-old self would have had the audacity to make such a proposal. Ultimately Dr Drew took a chance and hired me, and thus began my research journey. This experience paved my path toward becoming a nurse scientist and led to many ongoing friendships and collaborations.

Nurses must be the drivers of scientific knowledge in hospital-based ECG monitoring.

Becoming a Researcher

In my role as a research nurse in the “Drew Lab,” I was tasked with collecting ECG and patient data for the ST Analysis Trial (STAT) (NR03436, funded by a National Institutes of Health Research Project Grant [R01]). The project director was Mary G. Carey (formerly Adams), PhD, RN. Mary became an instant colleague and eventually my dearest friend. She taught me everything there was to know about research, including recruitment, informed consent, data collection, and analysis in hospitalized adults in both the cardiac catheterization laboratory and the cardiac intensive care unit (CICU). The research protocol in the catheterization lab was rather intense because we had to quickly apply both a traditional 12-lead ECG and a 5-lead–derived 12-lead ECG26  before the procedure. Monitoring was maintained throughout the entire procedure, which could last for hours. The purpose of the STAT study was to compare the accuracy of the two 12-lead methods for detecting ST-segment changes associated with myocardial ischemia during percutaneous coronary intervention. It is interesting to note that at this time (1995), coronary stents had not been introduced to treat atherosclerotic plaque; rather balloon angioplasty was used—that is, a balloon was inflated at the site of coronary occlusion, typically for 90 seconds, and then deflated. This 90-second or more time period allowed our team to compare the accuracy of the two 12-lead methods in real time. Dr Drew, whom I at that point comfortably called Barbara, had developed a rigorous research design that was so characteristic of her scientific work, and I was thrilled to be a part of it.

In addition, we maintained the derived 12-lead ECG device in the CICU. Here, the goal was to examine and compare the derived 12-lead with the bedside ECG monitor (6 leads) for detection of transient myocardial ischemia (TMI). An important lesson I learned was that our team had to collaborate intensely and intentionally with the cardiologists, nurses, and nursing leadership in both settings given the research design. While I developed expertise in data collection, I also substantially enhanced my ECG interpretation skills. Given that we were collecting prospective data, I was also able to observe patient responses to TMI (eg, changes in vital signs or symptoms or no symptoms), which helped me appreciate important aspects of patient care.

Being a research nurse in the Drew Lab was instrumental in my development. I learned firsthand the most fundamental aspects of robust research (ie, informed consent, precise data collection, working with a team). In addition, members of the STAT team had the opportunity to coauthor or be the first author of manuscripts and submit and present abstracts at professional conferences. I went on to be the first author of 3 papers, including a study that examined whether there were ST-segment amplitude differences by sex during angioplasty-induced ischemia,7  a study that compared ST-segment measurements between computerized methods and humans,8  and a study of the peak occurrence time of TMI in the CICU.9  All of these experiences inspired me to apply to UCSF’s PhD program and relocate to the San Francisco Bay Area in 1995.

At the completion of the STAT study, we had collected ECG data in 490 patients in 2 high-acuity hospital settings and in critically ill patients. The overarching findings of the STAT study showed that there was more than 99% agreement (150 of 151 patients) for the presence and absence of ischemia between the 2 ECG methods5 ; 67% of TMI events (311 of 463) were not detected in leads II or V1, the commonly monitored ECG leads10 ; and 80% of the TMI events (370 of 463) were asymptomatic.10  These findings had important implications for clinical practice and showed that most TMI events would be undetected using current methods. Our group would publish a number of peer-reviewed papers from these data.46,1017  Electrocardiographic monitoring manufacturers also took notice of our work and that of others, and many subsequently developed and introduced myocardial ischemia detection software (ST-segment monitoring) in commercially available devices. Although our group saw the introduction of this software as a win for clinical care, we would later discover the shortcomings of the developed algorithms, as discussed below.

During my PhD program (1996-2001), Dr Drew was awarded 2 more R01 grants (NR03436 and HL69753) to further explore TMI among hospitalized patients. In the STAMPEDE Study (ST Analysis and Monitoring of Patients and Evaluation of a Derived Electrocardiogram), we monitored patients with both a derived 12-lead ECG and a traditional 12-lead ECG (Mason-Likar lead configuration) and expanded our data collection to the emergency department and the high-acuity and step-down unit. As was always the case, Dr Drew pushed the envelope on data collection, as shown in Figure 1. While I continued to collect data for both studies, I advanced from a research nurse to coproject director, a role I shared with Mary Carey, who was also my classmate in the PhD program. This position allowed me to learn important administrative aspects of research.

It was during this time that I identified my PhD dissertation topic, having observed a shift in the care of patients admitted for suspected acute coronary syndrome (ACS). By the end of the 1990s, it was becoming standard practice to admit patients with suspected ACS to the step-down telemetry unit, rather than the CICU, due to cost constraints and the fact that a high number of patients eventually were ruled out for ACS. From our prior studies conducted exclusively in the CICU, we knew that TMI was not uncommon, yet no one had studied TMI in telemetry unit patients. I had the unique opportunity to use our large R01 databases to compare TMI occurrence rates and untoward patient outcomes in 186 patients initially admitted to the CICU (1994-1996) and 186 patients initially admitted in the step-down telemetry unit (1997-2000).18  We found that the frequency of TMI did not differ by initial admission unit (15% telemetry [28 of 186] vs 19% CICU [36 of 186]; P = .27),18  and similar to prior studies, only 26% of patients (10 of 39) experienced symptoms during TMI.19  An interesting finding was that TMI often preceded unplanned transfer from the telemetry unit to the CICU (Figure 2).19 

At the time of this study, telemetry unit ECG devices did not include ST-segment software. Hence, my research not only informed clinical care but supported the addition of ST-segment software in newer telemetry ECG devices. As noted above, although the new software development was a positive advancement in telemetry monitoring (the good), the software would ultimately prove to be problematic due to high rates of false alarms (the not so good), which I will address below.

In 2001, just after completing my PhD, I had the good fortune of working as the project director for Kathy Dracup, PhD, FNP, RN, and her R01-funded study (NR07952) after her appointment as the dean of the UCSF School of Nursing. The PROMOTION (Patient Response to Myocardial Infarction Following a Teaching Intervention Offered by Nurses) study was a community-based multisite randomized clinical trial that examined an educational intervention designed to reduce prehospital delay in patients with suspected ACS.20  This was an entirely new research experience for me. I was tasked with overseeing data collection at each of the 5 sites, including the University of Kentucky with Debra K. Moser, PhD, RN, as the site principal investigator [PI]); the University of San Diego with Barbara Riegel, PhD, RN; the University of Washington with Hendrika Meischke, PhD, MPH; the University of California Los Angeles with Lynn V. Doering, PhD, RN; and the University of Technology Sydney, Australia, with Sharon McKinley, PhD, RN. My decision to take this position, rather than jump into a new faculty role after obtaining my PhD or a postdoctoral fellowship, was one of the best decisions I could have made at the time because I developed new expertise in data management and analysis and was working with a team of outstanding nurse scientists from around the world.

Our team ultimately enrolled 3522 community-based patients with documented coronary heart disease who were randomized to experimental (n = 1777 [50%]; educational intervention) or control (n = 1745 [50%]; usual care) groups. During 2 years of follow-up, 565 patients (16%) were admitted to an emergency department with ACS symptoms. We found that the median prehospital delay time and the use of emergency medical services was not different between groups. However, patients in the experimental group were more likely than patients in the control group to call an ambulance if the symptoms occurred within the first 6 months after the intervention and more likely to take aspirin after symptom onset. Our group went on to publish several peer-reviewed papers from this rich dataset.2129 

Becoming an Independent Researcher

In 2005, after being at UCSF for 12 years, I returned to Reno and became the director of nursing research and outcomes at Renown Regional Medical Center. This was an interesting nonacademic position in which I learned valuable insights about how hospitals and health care systems operate and how research fits within these systems. This position was also helpful for making connections with nurses and physicians for future research projects. At the time, I was somewhat of a pioneer in Reno because large-scale nursing research was uncommon. It was truly a point of pride to bring a robust nursing research program to my community and introduce nurses to a new role—that of a research nurse.

A year later, I was coinvestigator on Dr Dracup’s R01-funded REMOTE-HF (Rural Education to Improve Outcomes in Heart Failure) study (HL083176).30  My living in Northern Nevada at the time, which has a large rural population, was ideal for this multisite randomized clinical trial, and this was the first time I advanced from the role of project director to coinvestigator. In this study, we compared 2 different intensity levels of heart failure: (1) patient education focused on volume-overload prevention and self-care enhancement and (2) usual care on the composite end point of cardiac death or heart failure hospitalization. Enrolled patients were followed up for 2 years. Because a nurse was required to deliver the educational intervention, I recruited the most trusted and knowledgeable nurse I knew, Denise Loranger, RN. As a side note, Denise was my preceptor when I graduated from nursing school in 1988 and eventually became my sister-in-law. The other 2 sites included the University of California Davis with Tom Nesbitt, MD, MPH, as the site PI and the University of Kentucky with Dr Moser as the site PI. Denise, and sometimes I, traveled more than 20 000 miles throughout Northern Nevada and California, and remarkably only 4 of 200 patients (2%) were lost to follow-up, a testament to Denise’s ability as a nurse to connect with patients.

Nearly 20% of patients admitted for suspected ACS will experience transient myocardial ischemia, yet 80% will have no symptoms.

During the 2-year follow-up period, we found that of 602 total patients, 35% (211) experienced cardiac death or hospitalization for heart failure, with no differences in the combined clinical outcome (P = .06) among the 3 groups.30  This was an incredible experience and was key in my development as an independent researcher. This unique dataset would lead to a number of publications among our immediate team and several pre doctoral and postdoctoral nursing students.3136 

In 2008, I joined the Orvis School of Nursing at the University of Nevada, Reno, as a tenure-track assistant professor. Denise remained my research nurse. In addition, Teri Kozik, PhD, RN, a cardiac clinical nurse specialist, and Richard Ganchan, MD, and Anita Kedia, MD, both cardiologists, joined our team. At the time (2010), there was debate about which treatment pathway, early invasive vs selectively invasive, was most effective in patients presenting for non-ST elevation ACS (NSTE-ACS).3741  I saw this clinical debate (best treatment pathway) as a real opportunity to contribute to this area of research, given our prior ischemia research. I designed the COMPARE (Electrocardiographic Evaluation of Ischemia Comparing Invasive to Pharmacological Treatment) study, which was funded by the National Institutes of Health (R21 NR011202), to examine the frequency and consequences of TMI in patients with NSTE-ACS by treatment pathway (ie, early invasive vs selectively invasive).42 

This study was observational; therefore, physicians or advanced practice clinicians determined treatments while we prospectively collected ECG data using 12-lead Holter recorders so as to not influence or interrupt patient care. This work was relevant in that there had been a paucity of TMI research for more than a decade, and newer medications and stent treatments had been introduced. Overall, we enrolled 488 patients, of whom 291 (60%) eventually were ruled in for NSTE-ACS.43  The treatment pathway was early invasive in 123 (42%) and selective in 168 (58%). Of the 49 patients (17%) who had TMI, 19 (39%) had early invasive treatment and 30 (61%) had selective treatment (P = .64). Acute myocardial infarction after admission was higher in patients with TMI regardless of treatment strategy, and only 16 (33%) experienced symptoms during TMI.44  At the outset of this study, we hypothesized that TMI would be higher in the selectively invasive group, thinking reperfusion would not be as complete with medications alone, but we found that there was no difference in the rate of TMI between the groups. We also showed that the rate of TMI was similar to the rate reported for work done more than a decade earlier despite contemporary treatment options. An important observation we made was that despite ST-segment software being available in the telemetry and CICU devices during the study, it was rarely turned on. This was not surprising as several research studies showed that ST-segment software was underused owing to multiple factors, including lack of physician interest,45  lack of nursing knowledge on how to use the software,46  and a high rate of false-positive alarms.47  This finding would be informative for my future work in algorithm development and alarm fatigue.

ECG device–related alarm fatigue cannot be solved by nursing interventions alone.

Despite my success at the University of Nevada, Reno, including a promotion to associate professor with tenure, after Dr Drew’s retirement in 2015, I was recruited back to UCSF to join the tenure-track faculty in the School of Nursing and became the director of the ECG Monitoring Research Lab. Data collection in the lab had advanced considerably since my departure 10 years prior. Under the expertise of Xiao Hu, PhD, a biomedical engineer, the UCSF team had constructed a data capture system that acquired continuous ECG and physiologic data from all of the hospital’s 72 adult ICU monitors.47  This was a substantial advancement because the data were collected in consecutive patients; thus, it minimized selection bias and was done in the background so that clinical care was not interrupted, and data collection did not require a large team of research nurses. Dr Hu was also a member of the faculty in the School of Nursing, which was a huge plus for the next phase of my research. The collaboration between nurse scientists and engineers creates synergy and opens up enormous possibilities in ECG research. I had returned to UCSF at an opportune time—all of the needed pieces to conduct robust and meaningful research had come together.

It was at this time that I transitioned my research focus from primarily working in myocardial ischemia research to exploring alarm fatigue. I had access to 2 valuable data assets: the COMPARE study database and the UCSF alarm study that I had inherited from Dr Drew. The latter database came from a seminal paper Dr Drew published just before her retirement illustrating the magnitude of the alarm fatigue problem.47  Her group found that there were 2 558 760 unique alarms from the bedside ICU monitors during the 31-day study period: arrhythmia, 1 154 201 (45%); parameter (too high or low), 612 927 (24%); and technical (artifact, leads off), 791 632 (31%). A total of 381 560 (15%) were audible alarms, or an alarm burden of 187 alarms per bed per day. A group of nurse scientists annotated the 12 671 audible arrhythmia alarms and found that 89% (11 251) were false positive.47  These data were used for several secondary data analyses (described below), and because the data capture system was still in place, prospective studies also were conducted.

It had become clear to me at this point that adding new software capabilities (eg, ST-segment monitoring), which were developed with scientific evidence and sincere intentions of enhancing monitoring systems and, thus, patient care, were actually impeding nursing care and setting the stage for an unintended patient safety hazard, namely alarm fatigue. It is thought that the assimilation of alarm noise into nurses’ workflow can lead to inadvertently ignoring alarms, alarm response delays, lowering the volume, and silencing alarms.4854  These responses have been associated with more than 640 in-hospital alarm-related deaths.5557  A number of federal agencies and national organizations, including the American Association of Critical-Care Nurses (AACN), have issued alerts about this problem, and a Joint Commission National Patient Safety Goal specific to using alarms safely has been in place since 2016.5861 

Interestingly, although a strong desire to solve the problem of alarm fatigue exists, nearly 15 years have passed since this problem was first described, and little substantive progress toward a general solution has occurred.62  I began to approach this research topic by focusing on ECG device–related false alarms by asking 3 central questions, which are outlined below.

1. What Alarms Should be Enabled as Audible, and Can We Identify Ideal Alarm Settings?

Early alarm fatigue research focused on identifying the number and types of alarms to establish the magnitude of this problem.47,54,6370  Subsequent studies showed that a low proportion (<1% to 26%) of true alarms generated for heart rate and respiratory rate (RR), oxygen saturation, and ventilators actually required a clinical action and thus were nuisance alarms that contributed to alarm fatigue.48,49,54,63,64,71,72  Few studies had examined nuisance alarms by arrhythmia type, which was achievable given our annotated (true vs false) database. Although one might argue that we should simply turn off certain alarms because most are false, these decisions are much easier to justify when driven by patient outcomes.

We hypothesized that a possible nuisance alarm was accelerated ventricular rhythm (AVR), also referred to as slow ventricular tachycardia (VT). Importantly, AVR alarms (>6 consecutive wide QRS complexes at a heart rate between 50 and 100 beats per minute) accounted for 34% (4361 of 12 671) of the annotated arrhythmia alarms in the UCSF alarm study, and only 5% (223 of 4361) were true.47  Using funding from an AACN Impact Research Grant, we examined true AVR alarms to determine if they were clinically actionable (eg, new or changed drug, pacemaker, cardioversion) or associated with a hospital cardiac arrest or death. Overall, we found that none of the 223 true AVR alarms was clinically actionable or led to a cardiac arrest or death. One interesting finding was that only 1 true AVR alarm was acknowledged in the electronic health records (ie, nursing progress notes). This suggested that true AVR alarms were either not identified (buried among alarms)68  or were silenced without assessing the patient.73  Our recommendation was that hospitals should carefully and thoughtfully reevaluate the need to closely monitor for AVR and consider configuring this alarm type to an inaudible text message. We stopped short of making a recommendation to turn this alarm off given our small sample size—a larger study might support this idea.

In a separate study, Sukardi Suba, PhD, RN, then my doctoral student, examined the association of premature ventricular complexes (PVCs) with VT. We hypothesized that PVC-type alarms (with 6 different types) were also nuisance alarms. Importantly, PVCs are exceedingly common. For example, in the UCSF alarm study, of the more than 2.5 million total alarms, 34% (854 901) were PVC alarms, or 358 PVCs per bed per day.47  Surprisingly, no contemporary hospital-based studies had examined the association of PVCs with VT.74  We examined 446 ICU patients with 797 072 PVC alarms and found that isolated PVCs were by far the most common (81%), whereas R-on-T type were the least common (0.29%).75  In a separate study with both unadjusted and adjusted logistic regression models, we found that none of the 6 PVC-type alarms were associated with VT.76  We found that 42 of 446 patients (9.4% of sample) generated 40% of all of the PVC alarms (320 342 of 797 072).74  These so-called outlier patients have been identified among other alarm types (eg, arrhythmias, vital sign alarms).47,67  As with AVR alarm settings, we recommended that hospitals should carefully and thoughtfully reevaluate the need to closely monitor for PVCs, especially in the small number of select patients who generate most of these types of alarms.

2. What Are the Sources of Alarms (Nursing Practices, Algorithm Deficiencies, Patient-Level Factors)?

A widely held belief is that poor skin electrode contact or default monitor settings cause false ECG alarms. As a result, clinical scientists, many of whom are nurses, have tried a variety of interventions (eg, customized alarm settings,58,66,69,72,77,78  daily skin electrode changes,54,79  disposable lead wires,80  education54,66,72 ) to decrease the number of false alarms. However, our research group showed that most false arrhythmia alarms are due to suboptimally designed ECG algorithms. For example, false arrhythmia alarms were more common in patients with abnormal ECG features, including bundle branch block (right or left), ventricular-paced rhythms, low-amplitude QRS complexes and co-occurring sinus tachycardia, or atrial fibrillation, and were responsible for as many as 60% of the false alarms.47,67,81,82  Importantly, 32% to 60% of hospitalized patients older than 60 years of age have these ECG abnormalities.14,8385  We also found that patient characteristics (>60 years of age; male sex; cognitive impairment), a cardiac history, longer ICU length of stay and use of mechanical ventilation, or having a left ventricular assist device were associated with higher rates of arrhythmia, RR, and PVC alarms.67,75,86  Based on these studies, it became clear to our group that nursing interventions alone would have minimal implications on reducing alarms. Rather, algorithms used in many current bedside monitors were a primary source. This finding highlights the importance of collaborations between nurse scientists and engineers who together can design and test new algorithms to address these issues.62,81,87 

Solving alarm fatigue requires the collaboration of both nurse scientists and engineers.

3. Can We Develop Novel Solutions to Improve Bedside Monitoring Systems?

As mentioned above, it was discovered that ST-segment monitoring algorithms were too sensitive and resulted in frequent false alarms. As a result, many hospitals made the decision to turn off this feature, and that decision was supported in the updated American Heart Association practice standards for electrocardiographic monitoring in hospital settings.88  This is unfortunate because we knew that TMI was relatively common (approximately 20% of patients with ACS), often asymptomatic (70% of patients), and associated with untoward patient outcomes. Using the COMPARE study database, our group tested 4 algorithms that incorporated ST magnitude, duration, and contiguous ECG leads.87  Of the algorithms tested, there was moderate sensitivity and specificity (70% and 68%) applying an algorithm that used a 100-µV ST-segment threshold, integrated contiguous ECG leads, and a 5-minute ST-segment duration. We also examined novel algorithms using machine learning approaches that showed some promise.89,90  Our goal is to further refine our algorithms and test and validate them in a new dataset.

Our most recent work in the area of ECG device–associated alarm fatigue has been an ambitious study using 20 months (> 572 000 hours) of ECG and physiologic (ie, vital signs) data from 5320 consecutive ICU patients.81  The goal of this effort was to test a VT algorithm created by my engineering colleagues Fabio Badilini, PhD, and David Mortara, PhD, from the UCSF Center for Physiologic Research. Much of our prior work that had identified ECG features associated with false alarms guided the design of our algorithms. We also chose to examine VT because among the lethal arrhythmia types (ie, VT, asystole, and ventricular fibrillation) VT has the highest rate of false alarms (90%) as compared with asystole (67%) and ventricular fibrillation (32%).47,68,81,91  A key aspect of the study design was that potential VT alerts needed to be carefully annotated as true or false by clinical experts. To accomplish this, I recruited the top 4 ECG nurse scientists—Salah Al-Zaiti, PhD, ANP-BC, RN (University of Pittsburgh); Mary Carey, PhD, RN (University of Rochester); Claire Sommargren, PhD, RN (UCSF), and Jessica Zègre-Hemsey, PhD, RN (University of North Carolina at Chapel Hill)—with whom I have worked for decades.

Potential VT alerts (n = 22 325) generated by our VT algorithm were annotated by the nurse scientist team, who found that 69% were true VTs. Although we have improvements to make, our nearly 70% true-positive rate is substantially better than the rate for the current bedside monitor (10%).81  The UCSF VT database represents the single largest human-annotated database in existence and was designed to serve as a criterion standard database for developing and testing new VT algorithms by both monitoring manufacturers and other scientific teams. This database will be used for a recently funded R01 grant (HL167975, PI Pelter), in which we will compare our improved VT algorithm (using signal processing), a data-driven artificial intelligence VT algorithm, and the hospital-based ECG monitors. We will expand our sample to include both ICU and non-ICU patients.

Untapped Potential of ECG Waveform Data From Hospital-Based Monitors

Although the work described above leads me to rethink the idea of introducing novel new ECG algorithms that might result in more alarms, our group is always seeking to maximize ECG waveform data to identify other relevant assessments, namely respirations and sleep-disordered breathing. My former PhD student, Linda Bawua, PhD, RN, compared the RR between the current impedance pneumography method and our group’s ECG-derived method. This work is relevant because false RR alarms are ubiquitous when using the impedance pneumography–derived method. Our results are promising and showed that our ECG-derived RR method had good agreement with the impedance pneumography method for normal RR. However, the ECG-derived RR was consistently higher than the impedance pneumography method for low RR (<5 breaths per minute) and was almost always lower for high RR (> 30 breaths per minute), which could reduce the number of RR alarms. However, replication in a larger sample, including confirmation with visual RR assessment, is warranted.92 

In a separate investigation, we tested a novel ECG-derived respiratory disturbance index (ECG-DRDI) algorithm that was designed to identify obstructive sleep apnea, a common pathology in patients with cardiovascular disease. Figure 3 illustrates our ECG-DRDI method that was tested in hospitalized patients with NSTE-ACS. The motivation for this study was based on prior investigations that showed that obstructive sleep apnea may have a cardioprotective effect (ie, lower troponin levels), via an ischemic preconditioning mechanism, in patients with ACS when measured using a polygraph device.94,95  We had the unique opportunity to corroborate the findings of these studies by applying our novel ECG-DRDI algorithm using the COMPARE study database. We found that NSTE-ACS patients with a moderate ECG-DRDI (eg, moderate obstructive sleep apnea) had lower troponin levels (less cardiac injury) than patients without moderate ECG-DRDI.93  Furthermore, although there was a trend for fewer TMI events in patients with moderate ECG-DRDI, there was no statistically significant difference, most likely due to our small sample size (n = 110). Our study was distinctive in that we used an ECG method, which is interesting given that ECG monitoring is standard practice in hospitals, meaning that another device would not have to be added to the patient. However, future research is needed to explore the underlying physiologic mechanisms of this finding and further validate our ECG-DRDI against the criterion standard—polysomnography, which our group plans to do.

Closing

I want to thank AACN for selecting me as the 2024 Distinguished Research Lecturer—it is truly an honor. I must say, writing this paper has been incredibly meaningful. This activity has allowed me the opportunity to reflect back on my 36-year nursing journey, identify lessons learned, and remember all of the outstanding and talented people I have collaborated with along the way and the contributions my research has made to critical and high-acuity care nursing (see Table, Figure 4). Most especially, however, this process has allowed me to reflect on the patients and their families and loved ones involved in my work. A poignant moment for me came in 2010 (the midpoint of my career) when I was consenting a patient. I was discussing the consent form in great detail when he and his wife stopped me and said, “I, we, WANT to participate, honey, and don’t need all of that information.” They shared with me that their teenage daughter had died from congenital heart disease, and they had vowed to do anything they could to move cardiac science forward on her behalf. In that moment, I grasped the profound impact that my research and that of others can have on patients and society. We do not always see the impact of our research as we make our way through the many challenges inherent in this work—it is hard, lengthy, costly, and filled with high and (many) low points, and its tangible impact on clinical care is not always observable. I have often reflected back on this touching and emotional moment and have used this as fuel when faced with challenges.

Thank you to my husband, Bill Pelter, for encouraging me to take so many leaps, many filled with uncertainty, and supporting my journey every step of the way—1, 4, 3. To my sons, Will and Elliott, thank you for always cheering me on. To my in-laws, Dr Bill and Andrea Pelter, you are dearly missed, but your love and support remain deeply rooted in all that I do. Last, to my parents, Harold (miss you) and Sally (now 94 years of age) Loranger, thank you for blessing me with love, laughter, support, encouragement, 6 brothers and for taking the time (often) to tell me how proud you are of me.

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Footnotes

Presented May 20, 2024, at the AACN National Teaching Institute in Denver, Colorado.

 

FUNDING DISCLOSURES

The research presented in this paper was supported by grants from the American Association of Critical-Care Nurses (Pelter PI); the National Institutes of Health National Heart, Lung, and Blood Institute (HL69753 [Drew PI], HL083176 [Dracup PI, Pelter co-investigator], HL167975 [Pelter PI]) and National Institute of Nursing Research (NR03436 [Drew PI], NR07952 [Dracup PI], NR011202 [Pelter PI]). Dr Pelter collaborated with and received funding from industry partners, including GE HealthCare and Philips Healthcare.

 

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