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In-hospital electrocardiographic (ECG) monitors are typically configured to alarm for premature ventricular complexes (PVCs) due to the potential association of PVCs with ventricular tachycardia (VT). However, no contemporary hospital-based studies have examined the association of PVCs with VT. Hence, the benefit of PVC monitoring in hospitalized patients is largely unknown. This secondary analysis used a large PVC alarm data set to determine whether PVCs identified during continuous ECG monitoring were associated with VT, in-hospital cardiac arrest (IHCA), and/or death in a cohort of adult intensive care unit patients. Six PVC types were examined (i.e., isolated, bigeminy, trigeminy, couplets, R-on-T, and run PVCs) and were compared between patients with and without VT, IHCA, and/or death. Of 445 patients, 48 (10.8%) had VT; 11 (2.5%) had IHCA; and 49 (11%) died. Isolated and run PVC counts were higher in the VT group (p = 0.03 both), but group differences were not seen for the other four PVC types. The regression models showed no significant associations between any of the six PVC types and VT or death, although confidence intervals were wide. Due to the small number of cases, we were unable to test for associations between PVCs and IHCA. Our findings suggest that we should question the clinical relevance of activating PVC alarms as a forewarning of VT, and more work should be done with larger sample sizes. A more precise characterization of clinically relevant PVCs that might be associated with VT is warranted.

BackgroundFalse 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.PurposeThe 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.MethodsThe 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.ResultsOf 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.ConclusionsThe 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.

BackgroundThe impact of depressive symptoms and anxiety on rural patients' decisions to seek care for worsening heart failure (HF) symptoms remains unknown. The purposes of this study were (1) to describe rural patients' reasons for delay in seeking care for HF, and (2) to determine whether depressive symptoms or anxiety was associated with patients' reasons for delay in seeking medical care for worsening symptoms.MethodsA total of 611 rural HF patients were included. Data on reasons for patient delay in seeking medical care (The Reasons for Delay Questionnaire), depressive symptoms (PHQ-9), and anxiety (BSI-ANX) were collected. Statistical analyses included chi-square and multiple regression.ResultsA total of 85.4% of patients reported at least 1 reason for delay. Patients with higher levels of depressive symptoms were more likely to cite embarrassment, problems with transportation, and financial concerns as a reason for delay. Patients with anxiety not only cited nonsymptom-related reasons but also reported symptom-related reasons for delay in seeking care (ie, symptoms seemed vague, not sure of symptoms, symptoms didn't seem to be serious enough, and symptoms were different from the last episode). In multiple regression, patients with greater depressive symptoms and anxiety had a greater number of reasons for delay in seeking care (P = .003 and P = .023, respectively).ConclusionsOur findings suggest that enhancement of patients' symptom appraisal abilities and improvement in psychological distress may result in a reduction in delay in seeking medical care for worsening symptoms in rural patients with HF.

BackgroundPrehospital electrocardiogram(s) (ECG) can improve early detection of acute coronary syndrome (ST-segment elevation myocardial infarction [STEMI], non-STEMI, and unstable angina) and inform prehospital activation of cardiac catheterization lab; thus, reducing total ischemic time and improving patient outcomes. Less is known, however, about the association of prehospital ECG ischemic findings and long term adverse clinical events. With this in mind, this study was designed to examine the: 1) frequency of prehospital ECGs for acute myocardial ischemia (ST-elevation, ST-depression, and/or T-wave inversion); and, 2) whether any of these specific ECG features are associated with adverse clinical events within 30 day of initial presentation to the emergency department (ED).MethodsWe included consecutive patients ≥ 21 years during a five-year period (2013-2017), who were transported by ambulance to the ED with non-traumatic chest pain and/or anginal equivalent(s) and had a prehospital 12‑lead ECG. Two cardiologists (LG, EC), blinded to clinical data, interpreted the 12‑lead ECGs applying current guideline based ischemia criteria. Adverse clinical events, return to ED, and rehospitalization evaluated at 30-days.ResultsWe identified 3646 patients (mean age, 59.7 years ±15.7; 45% female) with ECGs, of which N = 3587 had data on the three ischemic markers of interest. Of these, 1762 (49.1%) had ECG evidence of ischemia. In adjusted logistic regression models, those with T-wave inversion had a higher odds (OR = 1.59) of new onset heart failure, while ST-elevation was associated with lower odds (OR = 0.69). Patients with ST-depression had higher odds of new onset heart failure and death within 30 days (OR = 1.29, 1.49 respectively), but this association attenuated after controlling for other ECG features.ConclusionsST-depression and/or T-wave inversion are independent predictors of new onset heart failure, within 30 days of initial ED presentation. Our study in a large cohort of patients, suggests that using ECG ST-elevation alone may not capture patients with ischemia who may benefit from aggressive anti-ischemic therapies to reduce myocardial damage with resultant heart failure.

BackgroundRespiratory rate (RR) is one of the most important indicators of a patient's health. In critically ill patients, unrecognized changes in RR are associated with poorer outcomes. Visual assessment (VA), impedance pneumography (IP), and electrocardiographic-derived respiration (EDR) are the three most commonly used methods to assess RR. While VA and IP are widely used in hospitals, the EDR method has not been validated for use in hospitalized patients. Additionally, little is known about their accuracy compared with one another. The purpose of this systematic review was to compare the accuracy, strengths, and limitations of VA of RR to two methods that use physiologic data, namely IP and EDR.MethodsA systematic review of the literature was undertaken using prespecified inclusion and exclusion criteria. Each of the studies was evaluated using standardized criteria.ResultsFull manuscripts for 23 studies were reviewed, and four studies were included in this review. Three studies compared VA to IP and one study compared VA to EDR. In terms of accuracy, when Bland-Altman analyses were performed, the upper and lower levels of agreement were extremely poor for both the VA and IP and VA and EDR comparisons.ConclusionGiven the paucity of research and the fact that no studies have compared all three methods, no definitive conclusions can be drawn about the accuracy of these three methods. The clinical importance of accurate assessment of RR warrants new research with rigorous designs to determine the accuracy, and clinically meaningful levels of agreement of these methods.

Artificial intelligence (AI)-based solutions have revolutionized our world, using extensive datasets and computational resources to create automatic tools for complex tasks that, until now, have been performed by humans. Massive data is a fundamental aspect of the most powerful AI-based algorithms. However, for AI-based healthcare solutions, there are several socioeconomic, technical/infrastructural, and most importantly, legal restrictions, which limit the large collection and access of biomedical data, especially medical imaging. To overcome this important limitation, several alternative solutions have been suggested, including transfer learning approaches, generation of artificial data, adoption of blockchain technology, and creation of an infrastructure composed of anonymous and abstract data. However, none of these strategies is currently able to completely solve this challenge. The need to build large datasets that can be used to develop healthcare solutions deserves special attention from the scientific community, clinicians, all the healthcare players, engineers, ethicists, legislators, and society in general. This paper offers an overview of the data limitation in medical predictive models; its impact on the development of healthcare solutions; benefits and barriers of sharing data; and finally, suggests future directions to overcome data limitations in the medical field and enable AI to enhance healthcare. This perspective is dedicated to the technical requirements of the learning models, and it explains the limitation that comes from poor and small datasets in the medical domain and the technical options that try or can solve the problem related to the lack of massive healthcare data.

BackgroundWhile there are published studies that have examined premature ventricular complexes (PVCs) among patients with and without cardiac disease, there has not been a comprehensive review of the literature examining the diagnostic and prognostic significance of PVCs. This could help guide both community and hospital-based research and clinical practice.MethodsScoping review frameworks by Arksey and O'Malley and the Joanna Briggs Institute (JBI) were used. A systematic search of the literature using four databases (CINAHL, Embase, PubMed, and Web of Science) was conducted. The review was prepared adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Review (PRISMA-ScR).ResultsA total of 71 relevant articles were identified, 66 (93%) were observational, and five (7%) were secondary analyses from randomized clinical trials. Three studies (4%) examined the diagnostic importance of PVC origin (left/right ventricle) and QRS morphology in the diagnosis of acute myocardial ischemia (MI). The majority of the studies examined prognostic outcomes including left ventricular dysfunction, heart failure, arrhythmias, ischemic heart diseases, and mortality by PVCs frequency, burden, and QRS morphology.ConclusionsVery few studies have evaluated the diagnostic significance of PVCs and all are decades old. No hospital setting only studies were identified. Community-based longitudinal studies, which make up most of the literature, show that PVCs are associated with structural and coronary heart disease, lethal arrhythmias, atrial fibrillation, stroke, all-cause and cardiac mortality. However, a causal association between PVCs and these outcomes cannot be established due to the purely observational study designs employed.

Background: The American Heart Association recommends acquiring and interpreting prehospital electrocardiograms (ECG) for patients transported by Emergency Medical Services (EMS) to the emergency department with symptoms highly suspicious of acute coronary syndrome. If interpreted correctly, prehospital ECGs have the potential to improve early detection of ST-elevation myocardial infarction (STEMI) and inform prehospital activation of the cardiac catheterization laboratory, thus reducing total ischemic time and improving patient outcomes. Standardized protocols for prehospital ECG interpretation methods are lacking due to variations in EMS system design, training, and procedures. Objectives: We aimed to describe approaches for prehospital ECG interpretation in EMS systems across North Carolina (NC), and examine potential differences among systems. Methods: A 35-item internet survey was sent to all NC EMS systems (n = 99). Questions pertaining to prehospital ECG interpretation methods included: paramedic, computerized algorithm (i.e., software interpretation), combined approaches, and/or transmission for physician interpretation, transmission capability, cardiac catheterization laboratory activation, and EMS system characteristics (e.g. rural versus urban). Data were summarized and compared. Results: A total of 96 EMS systems across NC responded to the survey (97% response rate); of these, 69% were rural. EMS medical directors (53%) or EMS administrative directors (42%) completed the majority of surveys. While 91% of EMS systems had a prehospital ECG interpretation protocol in place, only 61% had a written cardiac catheterization laboratory activation policy. More than half (55%) of systems reported paramedic interpretation of prehospital ECGs, followed by a combined paramedic and software interpretation approach (39%), physician interpretation (4%), or software interpretation only approach (2%). Nearly 80% of EMS systems transmitted prehospital ECGs to receiving hospitals (always or sometimes), regardless of interpretation method. All EMS systems had some paid versus non-paid EMS personnel and the majority (86%) had both basic and advanced life support capabilities. Conclusions: Most NC EMS systems had a paramedic only ECG interpretation or paramedic in combination with a computerized algorithm approach. Very few used a physician read approach following transmission, even in rural service areas.

ObjectivesTo test effects of an educational intervention on patient-reported outcomes among rural heart failure (HF) patients and to examine whether effects differed between patients with and without depression.MethodsPatients (N = 614) were randomized to usual care (UC) or 1 of 2 intervention groups. Both intervention groups received face-to-face education, followed by either 2 phone calls (LITE) or biweekly calls until they demonstrated content competency (PLUS). Follow-up lasted 24 months. Statistical analyses included linear mixed models and subgroup analyses by depression status.ResultsBoth intervention groups showed improvement in HF knowledge at 3 months (LITE-UC, p = 0.003; PLUS-UC, p

BackgroundVentricular tachycardia (V-tach) is the most common lethal arrhythmia, yet 90% of alarms are false and contribute to alarm fatigue. We hypothesize that some true V-tach also causes alarm fatigue because current criteria are too sensitive (i.e., ≥6 beats ≥100 beats/min [bpm]).PurposeThis study was designed to determine (1) the proportion of clinically actionable true V-tach events; (2) whether true actionable versus nonactionable V-tach differs in terms of heart rate and/or duration (seconds); and (3) if actionable V-tach is associated with adverse outcomes.MethodsThis was a secondary analysis in 460 intensive care unit (ICU) patients. Electronic health records were examined to determine if a V-tach event was actionable or nonactionable. Actionable V-tach was defined if a clinical action(s) was taken within 15 minutes of its occurrence (i.e., new and/or change of medication, defibrillation, and/or laboratory test). Maximal heart rate and duration for each V-tach event were measured from bedside monitor electrocardiography. Adverse patient outcomes included a code blue event and/or death.ResultsIn 460 ICU patients, 50 (11%) had 151 true V-tach events (range 1-20). Of the 50 patients, 40 (80%) had only nonactionable V-tach (97 events); 3 (6%) had both actionable and nonactionable V-tach (32 events); and 7 patients (14%) had only actionable V-tach (23 events). There were differences in duration comparing actionable versus nonactionable V-tach (mean 56.19 ± 116.87 seconds vs. 4.28 ± 4.09 seconds; P = 0.001) and maximal heart rate (188.81 ± 116.83 bpm vs. 150.79 ± 28.26 bpm; P = 0.001). Of the 50 patients, 3 (6%) had a code blue, 2 died, and all were in the actionable V-tach group.ConclusionsIn our sample,

BackgroundExternal ventricular drain (EVD) is a standard approach for both monitoring intracranial pressure (ICP) and draining cerebrospinal fluid (CSF) for patients with subarachnoid hemorrhage. Documenting an accurate ICP value is important to assess the status of the brain, which would require the EVD system to be leveled properly and closed to CSF drainage for an adequate period. It is suggested that a minimum period of 5-minute EVD closure is needed before documenting a true ICP; however, there is no commonly agreed upon standard for documenting ICP. To obtain an insight into how well the intermittent EVD clamping procedure is performed for ICP documentation, we conducted a retrospective analysis of ICP recordings obtained through EVD from 107 patients with subarachnoid hemorrhage.MethodsThe EVD was kept open for continuous CSF drainage and then intermittently closed for ICP documentation. For each EVD closure, mean ICP, standard deviation of ICP, duration of EVD closure, and time interval between 2 adjacent EVD closures were studied. The total number of EVD closures was calculated for each patient. We developed an algorithm to evaluate whether ICP reached a new equilibrium before the EVD was reopened to drainage. The percentage of EVD closures that reach the equilibrium was calculated.ResultsThe 107 patients had 32 755 EVD closures in total, among which 65.9% instances lasted less than 1 minute and only 16.3% of all the EVD closure episodes lasted longer than 5 minutes. The median duration of each EVD closure was 25 seconds (interquartile range, 10.2 seconds to 2.33 minutes). Only 22.9% of the EVD closures reached ICP equilibrium before EVD reopening.ConclusionA standard guideline and proper training are needed for bedside nurses, and a potential tool that can render ICP trend at a proper scale at bedside would help clinicians correctly document ICP.

Atrial fibrillation (AF) is a cardiac rhythm disorder associated with increased morbidity and mortality. It is the leading risk factor for cardioembolic stroke and its early detection is crucial in both primary and secondary stroke prevention. Continuous monitoring of cardiac rhythm is today possible thanks to consumer-grade wearable devices, enabling transformative diagnostic and patient management tools. Such monitoring is possible using low-cost easy-to-implement optical sensors that today equip the majority of wearables. These sensors record blood volume variations-a technology known as photoplethysmography (PPG)-from which the heart rate and other physiological parameters can be extracted to inform about user activity, fitness, sleep, and health. Recently, new wearable devices were introduced as being capable of AF detection, evidenced by large prospective trials in some cases. Such devices would allow for early screening of AF and initiation of therapy to prevent stroke. This review is a summary of a body of work on AF detection using PPG. A thorough account of the signal processing, machine learning, and deep learning approaches used in these studies is presented, followed by a discussion of their limitations and challenges towards clinical applications.

BackgroundThe common reality of not following a recommended course of treatment is a major cause of poor health outcomes in patients with heart failure (HF). The purpose of this study was to identify predictors of adherence to HF self-care recommendations in rural HF patients who received an intervention to promote symptom management and self-care.MethodsData from 349 rural HF patients (42% female, 90% Caucasian) randomized to the intervention arms of the study were used. Adherence was measured using the European Heart Failure Self-Care Scale questionnaire, a brief measure that asks patients to report their adherence to a variety of recommended HF symptom management behaviors (ie, daily weight monitoring, when to call the physician, medications, diet, and exercise). The following predictors were tested: age, gender, marital status, education level, depression score (measured using PHQ-9), anxiety score (measured with the Brief Symptom Inventory), and level of perceived control (measured using Control Attitudes Scale-R). Multivariate linear regression was used to test the model.ResultsThe model to predict adherence was significant (P < .0001). Of the covariates tested in the regression model, being a male (P = .009), having less anxiety (P = .018), not being depressed (P = .017), and having higher perceived control (P = .003) were predictors of improved self-care score at 3 months.ConclusionAdherence is a multifaceted and a challenging behavior based on the assumption that the patient agrees with self-care recommendations. These data suggest interventions designed to promote adherence behaviors should include an assessment of gender, anxiety, depression, and perceived control for optimal outcomes.

BackgroundExcessive electrocardiographic alarms contribute to "alarm fatigue," which can lead to patient harm. In a prior study, one-third of audible electrocardiographic alarms were for accelerated ventricular rhythm (AVR), and most of these alarms were false. It is uncertain whether true AVR alarms are clinically relevant.ObjectivesTo determine from bedside electrocardiographic monitoring data (1) how often true AVR alarms are acknowledged by clinicians, (2) whether such alarms are actionable, and (3) whether such alarms are associated with adverse outcomes ("code blue," death).MethodsSecondary analysis using data from a study conducted in an academic medical center involving 5 adult intensive care units with 77 beds. Electronic health records of 23 patients with 223 true alarms for AVR were examined.ResultsThe mean age of the patients was 62.9 years, and 61% were white and male. All 223 of the true alarms were configured at the warning level (ie, 2 continuous beeps), and 215 (96.4%) lasted less than 30 seconds. Only 1 alarm was acknowledged in the electronic health record. None of the alarms were clinically actionable or led to a code blue or death.ConclusionsTrue AVR alarms may contribute to alarm fatigue. Hospitals should reevaluate the need for close monitoring of AVR and consider configuring this alarm to an inaudible message setting to reduce the risk of patient harm due to alarm fatigue. Prospective studies involving larger patient samples and varied monitors are warranted.

BackgroundCheyne-Stokes respiration and periodic breathing (CSRPB) have not been studied sufficiently in the intensive care unit setting (ICU).ObjectivesTo determine whether CSRPB is associated with adverse outcomes in ICU patients.MethodsThe ICU group was divided into quartiles by CSRPB (86 patients in quartile 1 had the least CSRPB and 85 patients in quartile 4 had the most CSRPB). Adverse outcomes (emergent intubation, cardiorespiratory arrest, inpatient mortality and the composite of all) were compared between patients with most CSRPB (quartile 4) and those with least CSRPB (quartile 1).ResultsICU patients in quartile 4 had a higher proportion of cardiorespiratory arrests (5% versus 0%, (p=.042), and more adverse events over all (19% versus 8%, p=.041) as compared to patients in quartile 1.ConclusionsCSRPB can be measured in the ICU and it's severity is associated with adverse outcomes in critically ill patients.

AimsHeart failure (HF) patients are taught to identify a rapid 5 lb body-weight gain for early detection of cardiac decompensation. Few data support this common advice. The study aim was to determine whether a 5 lb weight gain in 1 week and signs and symptoms of HF increased risk for unplanned physician or emergency department (ED) visits or hospital admission in rural HF patients.Methods and resultsThis was a secondary analysis of a randomized trial. Patients tracked body weight and HF symptoms using diaries. We included patients adherent to daily diaries >50% over 24 months (N = 119). Mean age was 69 ± 11 years; 77% (65) were male, and 67% completed diaries. A weight gain of 5 lb over 7 days was associated with a greater risk for ED visits but not hospital admission [hazard ratio (HR) 1.06, 95% confidence interval (CI) 1.04, 1.08; P

BackgroundAccurate and timely detection of atrial fibrillation (AF) episodes is important in primarily and secondary prevention of ischemic stroke and heart-related problems. In this work, heart rate regularity of ECG inter-beat intervals was investigated in episodes of AF and other rhythms using a wavelet leader based multifractal analysis. Our aim was to improve the detectability of AF episodes.MethodsInter-beat intervals from 25 ECG recordings available in the MIT-BIH atrial fibrillation database were analysed. Four types of annotated rhythms (atrial fibrillation, atrial flutter, AV junctional rhythm, and other rhythms) were available. A wavelet leader based multifractal analysis was applied to 5 min non-overlapping windows of each recording to estimate the multifractal spectrum in each window. The width of the multifractal spectrum was analysed for its discrimination power between rhythm episodes.ResultsIn 10 of 25 recordings, the width of multifractal spectrum was significantly lower in episodes of AF than in other rhythms indicating increased regularity during AF. High classification accuracy (95%) of AF episodes was achieved using a combination of features derived from the multifractal analysis and statistical central moment features.ConclusionsAn increase in the regularity of inter-beat intervals was observed during AF episodes by means of multifractal analysis. Multifractal features may be used to improve AF detection accuracy.

ObjectiveWe developed an image-based electrocardiographic (ECG) quality assessment technique that mimics how clinicians annotate ECG signal quality.MethodsWe adopted the structural similarity measure (SSIM) to compare images of two ECG records that are obtained from displaying ECGs in a standard scale. Then, a subset of representative ECG images from the training set was selected as templates through a clustering method. SSIM between each image and all the templates were used as the feature vector for the linear discriminant analysis classifier. We also employed three commonly used ECG signal quality index (SQI) measures: baseSQI, kSQI, and sSQI to compare with the proposed image quality index (IQI) approach. We used 1926 annotated ECGs, recorded from patient monitors, and associated with six different ECG arrhythmia alarm types which were obtained previously from an ECG alarm study at the University of California, San Francisco (UCSF). In addition, we applied the templates from the UCSF database to test the SSIM approach on the publicly available PhysioNet Challenge 2011 data.ResultsFor the UCSF database, the proposed IQI algorithm achieved an accuracy of 93.1% and outperformed all the SQI metrics, baseSQI, kSQI, and sSQI, with accuracies of 85.7%, 63.7%, and 73.8% respectively. Moreover, evaluation of our algorithm on the PhysioNet data showed an accuracy of 82.5%.ConclusionThe proposed algorithm showed better performance for assessing ECG signal quality than traditional signal processing methods.SignificanceA more accurate assessment of ECG signal quality can lead to a more robust ECG-based diagnosis of cardiovascular conditions.

BackgroundPatients hospitalized for suspected acute coronary syndrome (ACS) are at risk for transient myocardial ischemia. During the "rule-out" phase, continuous ECG ST-segment monitoring can identify transient myocardial ischemia, even when asymptomatic. However, current ST-segment monitoring software is vastly underutilized due to false positive alarms, with resultant alarm fatigue. Current ST algorithms may contribute to alarm fatigue because; (1) they are not designed with a delay (minutes), rather alarm to brief spikes (i.e., turning, heart rate changes), and (2) alarm to changes in a single ECG lead, rather than contiguous leads.PurposeThis study was designed to determine sensitivity, and specificity, of ST algorithms when accounting for; ST magnitude (100μV vs 200μV), duration, and changes in contiguous ECG leads (i.e., aVL, I, - aVR, II, aVF, III; V1, V2, V3, V4, V5, V6, V6, I).MethodsThis was a secondary analysis from the COMPARE Study, which assessed occurrence rates for transient myocardial ischemia in hospitalized patients with suspected ACS using 12-lead Holter. Transient myocardial ischemia was identified from Holter using >100μV ST-segment ↑ or ↓, in >1 ECG lead, >1min. Algorithms tested against Holter transient myocardial ischemia were done using the University of California San Francisco (UCSF) ECG algorithm and included: (1)100μV vs 200μV any lead during a 5-min ST average; (2)100μV vs 200μV any lead >5min, (3) 100μV vs 200μV any lead during a 5-min ST average in contiguous leads, and (4) 100μV vs 200μV>5min in contiguous leads (Table below).ResultsIn 361 patients; mean age 63+12years, 63% male, 56% prior CAD, 43 (11%) had transient myocardial ischemia. Of the 43 patients with transient myocardial ischemia, 17 (40%) had ST-segment elevation events, and 26 (60%) ST-segment depression events. A higher proportion of patients with ST segment depression has missed ischemic events. Table shows sensitivity and specificity for the four algorithms tested.ConclusionsSensitivity was highly variable, due to the ST threshold selected, with the 100μV measurement point being superior to the 200μV amplitude threshold. Of all the algorithms tested, there was moderate sensitivity and specificity (70% and 68%) using the 100μV ST-segment threshold, integrated ST-segment changes in contiguous leads during a 5-min average.

Patients with suspected acute coronary syndrome (ACS) are at risk of transient myocardial ischemia (TMI), which could lead to serious morbidity or even mortality. Early detection of myocardial ischemia can reduce damage to heart tissues and improve patient condition. Significant ST change in the electrocardiogram (ECG) is an important marker for detecting myocardial ischemia during the rule-out phase of potential ACS. However, current ECG monitoring software is vastly underused due to excessive false alarms. The present study aims to tackle this problem by combining a novel image-based approach with deep learning techniques to improve the detection accuracy of significant ST depression change. The obtained convolutional neural network (CNN) model yields an average area under the curve (AUC) at 89.6% from an independent testing set. At selected optimal cutoff thresholds, the proposed model yields a mean sensitivity at 84.4% while maintaining specificity at 84.9%.

BackgroundAttention to symptoms of weight gain and dyspnea are central tenets of patient education in heart failure (HF). However, it is not known whether diary use improves patient outcomes. The aims of this study were to compare mortality among rural patients with HF who completed versus did not complete a daily diary of weight and symptom self-assessment and to identify predictors of diary use.Methods and resultsThis is a secondary analysis of a 3-arm randomized controlled trial on HF education of self-care with 2 intervention groups versus control who were given diaries for 24 months to track daily weight, HF symptoms, and response to symptom changes. Mean age was 66±13, 58% were men, and 67% completed diaries (n=393). We formed 5 groups (no use, low, medium, high, and very high) based on the first 3 months of diary use and then analyzed time to event (cardiac mortality, all-cause mortality, and HF-related readmission) starting at 3 months. Compared with patients with no diary use, high and very high diary users were less likely to experience all-cause mortality (P=0.02 and P=0.01, respectively). Self-reported sedentary lifestyle was associated with less diary use in an adjusted model (odds ratio, 0.66; 95% confidence interval, 0.46-0.95; P=0.03). Depression and sex were not significant predictors of diary use in the adjusted model.ConclusionsIn this study of 393 rural patients with HF, we found that greater diary use was associated with longer survival. These findings suggest that greater engagement in self-care behaviors is associated with better HF outcomes.Clinical trial registrationURL: https://www.clinicaltrials.gov. Unique Identifier: NCT00415545.

Cheyne-Stokes respiration (CSR) has been investigated primarily in outpatients with heart failure. In this study we compare CSR and periodic breathing (PB) between healthy and cardiac groups. We compared CSR and PB, measured during 24 hr of continuous 12-lead electrocardiographic (ECG) Holter recording, in a group of 90 hospitalized patients presenting to the emergency department with symptoms suggestive of acute coronary syndrome (ACS) to a group of 100 healthy ambulatory participants. We also examined CSR and PB in the 90 patients presenting with ACS symptoms, divided into a group of 39 (43%) with confirmed ACS, and 51 (57%) with a cardiac diagnosis but non-ACS. SuperECG software was used to derive respiration and then calculate CSR and PB episodes from the ECG Holter data. Regression analyses were used to analyze the data. We hypothesized SuperECG software would differentiate between the groups by detecting less CSR and PB in the healthy group than the group of patients presenting to the emergency department with ACS symptoms. Hospitalized patients with suspected ACS had 7.3 times more CSR episodes and 1.6 times more PB episodes than healthy ambulatory participants. Patients with confirmed ACS had 6.0 times more CSR episodes and 1.3 times more PB episodes than cardiac non-ACS patients. Continuous 12-lead ECG derived CSR and PB appear to differentiate between healthy participants and hospitalized patients.

ObjectiveOur previous studies have shown that "code blue" events can be predicted by SuperAlarm patterns that are multivariate combinations of monitor alarms and laboratory test results cooccurring frequently preceding the events but rarely among control patients. Deploying these patterns to the monitor data streams can generate SuperAlarm sequences. The objective of this study is to test the hypothesis that SuperAlarm sequences may contain more predictive sequential patterns than monitor alarms sequences.MethodsMonitor alarms and laboratory test results are extracted from a total of 254 adult coded and 2213 control patients. The training dataset is composed of subsequences that are sampled from complete sequences and then further represented as fixed-dimensional vectors by the term frequency inverse document frequency method. The information gain technique and weighted support vector machine are adopted to select the most relevant features and train a classifier to differentiate sequences between coded patients and control patients. Performances are assessed based on an independent dataset using three metrics: sensitivity of lead time (Sen L @T), alarm frequency reduction rate (AFRR), and work-up to detection ratio (WDR).ResultsThe performance of 12-h-long sequences of SuperAlarm can yield a Sen L@2 of 93.33%, an AFRR of 87.28%, and a WDR of 3.01. At an AFRR = 87.28%, Sen L@2 for raw alarm sequences and discretized alarm sequences are 73.33% and 70.19%, respectively. At a WDR = 3.01, Sen L@2 are 49.88% and 43.33%.Conclusion and significanceThe results demonstrate that SuperAlarm sequences indeed outperform monitor alarm sequences and suggest that one can focus on sequential patterns from SuperAlarm sequences to develop more precise patient monitoring solutions.

IntroductionA high rate of false arrhythmia alarms in the intensive care unit (ICU) leads to alarm fatigue, the condition of desensitization and potentially inappropriate silencing of alarms due to frequent invalid and nonactionable alarms, often referred to as false alarms.ObjectiveThe aim of this study was to identify patient characteristics, such as gender, age, body mass index, and diagnosis associated with frequent false arrhythmia alarms in the ICU.MethodsThis descriptive, observational study prospectively enrolled patients who were consecutively admitted to one of five adult ICUs (77 beds) at an urban medical center over a period of 31 days in 2013. All monitor alarms and continuous waveforms were stored on a secure server. Nurse scientists with expertise in cardiac monitoring used a standardized protocol to annotate six clinically important types of arrhythmia alarms (asystole, pause, ventricular fibrillation, ventricular tachycardia, accelerated ventricular rhythm, and ventricular bradycardia) as true or false. Total monitoring time for each patient was measured, and the number of false alarms per hour was calculated for these six alarm types. Medical records were examined to acquire data on patient characteristics.ResultsA total of 461 unique patients (mean age =60±17 years) were enrolled, generating a total of 2,558,760 alarms, including all levels of arrhythmia, parameter, and technical alarms. There were 48,404 hours of patient monitoring time, and an average overall alarm rate of 52 alarms/hour. Investigators annotated 12,671 arrhythmia alarms; 11,345 (89.5%) were determined to be false. Two hundred and fifty patients (54%) generated at least one of the six annotated alarm types. Two patients generated 6,940 arrhythmia alarms (55%). The number of false alarms per monitored hour for patients' annotated arrhythmia alarms ranged from 0.0 to 7.7, and the duration of these false alarms per hour ranged from 0.0 to 158.8 seconds. Patient characteristics were compared in relation to 1) the number and 2) the duration of false arrhythmia alarms per 24-hour period, using nonparametric statistics to minimize the influence of outliers. Among the significant associations were the following: age ≥60 years (P=0.013; P=0.034), confused mental status (P

BackgroundMost patients presenting with suspected acute coronary syndrome (ACS) are admitted to telemetry units. While telemetry is an appropriate level of care, acute complications requiring a higher level of care in the intensive care unit (ICU) occur.PurposeAmong patients admitted to telemetry for suspected ACS, we determine the frequency of unplanned ICU transfer, and examine whether ECG changes indicative of myocardial ischemia, and/or symptoms preceded unplanned transfer.MethodThis was a secondary analysis from a study assessing occurrence rates for transient myocardial ischemia (TMI) using a 12-lead Holter. Clinicians were blinded to Holter data as it was used in the context research; off-line analysis was performed post discharge. Hospital telemetry monitoring was maintained as per hospital protocol. TMI was defined as >1mm ST-segment ↑ or ↓, in >1 ECG lead, >1minute. Symptoms were assessed by chart review.ResultsIn 409 patients (64±13years), most were men (60%), Caucasian (93%), and had a history of coronary artery disease (47%). Unplanned transfer to the ICU occurred in 9 (2.2%), was equivalent by gender, and age (no transfer 64±13years vs transfer 67±11years). Four patients were transferred following unsuccessful percutaneous coronary intervention (PCI) attempt, four due to recurrent angina, and one due to renal and hepatic failure. Mean time from admission to transfer was 13±6hours, mean time to ECG detected ischemia was 6±5hours, and 8.8±5hours for symptoms prompting transfer. In two patients ECG detected ischemia and acute symptoms prompting transfer were simultaneous. In five patients, ECG detected ischemia was clinically silent. All patients eventually had symptoms that prompted transfer to the ICU. In all nine patients, there was no documentation or nursing notes regarding bedside ECG monitor changes prior to unplanned transfer. Hospital length of stay was longer in the unplanned transfer group (2days ± 2 versus 6days ± 4; p=0.018).ConclusionsIn patients with suspected ACS, while unplanned transfer from telemetry to ICU is uncommon, it is associated with prolonged hospitalization. Two primary scenarios were identified; (1) following unsuccessful PCI, and (2) recurrent angina. Symptoms prompting unplanned transfer occurred, but happened on average 8.8 hours after hospital admission; whereas ECG detected ischemia preceding unplanned transfer occurred on average 6 hours after hospital admission.

IntroductionThe American Heart Association recommends individuals with symptoms suggestive of acute coronary syndrome (ACS) activate the Emergency Medical Services' (EMS) 911 system for ambulance transport to the emergency department (ED), which enables treatment to begin prior to hospital arrival. Despite this recommendation, the majority of patients with symptoms suspicious of ACS continue to self-transport to the ED. The IMMEDIATE AIM study was a prospective study that enrolled individuals who presented to the ED with ischemic symptoms.ObjectivesThe purpose of this secondary analysis was to determine differences in patients presenting the ED for possible ACS who arrive by ambulance versus self-transport on: 1) time-to-initial hospital electrocardiogram (ECG), 2) presence of ischemic ECG changes, and 3) patient characteristics.MethodsInitial 12-lead ECGs acquired upon patient arrival to the ED were evaluated for ST-elevation, ST-depression, and T-wave inversion. ECG signs of ischemia were analyzed both individually and collapsed into an independent dichotomous variable (ED ECG ischemia yes/no) for statistical analysis. Patient characteristics tested included: gender, age, race, ethnicity, English speaking, living alone, mode of transport, and presenting symptoms (chest pain, jaw pain, shortness of breath, nausea/vomiting, syncope, and clinical history).ResultsIn 1299 patients (mean age 63.9, 46.7% male), 384 (29.6%) patients arrived by ambulance to the ED. The mean time-to-initial ECG was 47minutes for ambulance patients versus 53minutes for self-transport patients (p

BackgroundTreatment for unstable angina (UA) or non-ST-elevation myocardial infarction (NSTEMI) is aimed at plaque stabilization to prevent infarction. Two treatment strategies are (1) invasive (ie, cardiac catheterization laboratory 24 hours for recurrent symptoms). However, it is not known if the frequency of transient myocardial ischemia (TMI) or complications during hospitalization varies by treatment.PurposeWe aimed to (1) examine occurrence of TMI in UA/NSTEMI, (2) compare frequency of TMI by treatment pathway, and (3) determine predictors of in-hospital complications (ie, death, myocardial infarction [MI], pulmonary edema, shock, dysrhythmia with intervention).MethodsHospitalized patients with coronary artery disease (ie, history of MI, percutaneous coronary intervention/stent, coronary artery bypass graft, >50% lesion via angiogram, or positive troponin) were recruited, and 12-lead electrocardiogram Holter initiated. Clinicians, blinded to Holter data, decided treatment strategy; offline analysis was done after discharge. Transient myocardial ischemia was defined as more than 1-mm ST segment ↑ or ↓, in more than 1 electrocardiographic lead, more than 1 minute.ResultsOf 291 patients, 91% were white, 66% were male, 44% had prior MI, and 59% had prior percutaneous coronary intervention/stent or coronary artery bypass graft. Treatment pathway was early in 123 (42%) and selective in 168 (58%). Forty-nine (17%) had TMI: 19 (15%) early invasive, 30 (18%) selective (P = .637). Acute MI after admission was higher in patients with TMI regardless of treatment strategy (early: no TMI 4% vs yes TMI 21%; P = .020; selective: no TMI 1% vs yes TMI 13%; P = .0004). Predictors of major in-hospital complication were TMI (odds ratio, 9.9; 95% confidence interval, 3.84-25.78) and early invasive treatment (odds ratio 3.5; 95% confidence interval, 1.23-10.20).ConclusionsIn UA/NSTEMI patients treated with contemporary therapies, TMI is not uncommon. The presence of TMI and early invasive treatment are predictors of major in-hospital complications.

BackgroundThe etiology of cognitive impairment in heart failure (HF) is controversial and likely multifactorial. Physicians may hesitate to prescribe evidence-based HF medication because of concerns related to potential negative changes in cognition among a population that is already frequently impaired. We conducted a study to determine if prescription of evidence-based HF medications (specifically, β-blockers, angiotensin-converting enzyme inhibitors, angiotensin-receptor blocking agents, diuretics, and aldosterone inhibitors) was associated with cognition in a large HF sample.MethodsA total of 612 patients completed baseline data collection for the Rural Education to Improve Outcomes in Heart Failure clinical trial, including information about medications. Global cognition was evaluated using the Mini-Cog.ResultsThe sample mean (SD) age was 66 (13) years, 58% were men, and 89% were white. Global cognitive impairment was identified in 206 (34%) of the 612 patients. Prescription of evidence-based HF medications was not related to global cognitive impairment in this sample. This relationship was maintained even after adjusting for potential confounders (eg, age, education, and comorbid burden).ConclusionPrescription of evidence-based HF medications is not related to low scores on a measure of global cognitive function in rural patients with HF.

We investigated 17 metrics derived from four leads of electrocardiographic (ECG) signals from hospital patient monitors to develop new ECG alarms for predicting adult bradyasystolic cardiac arrest events.A retrospective case-control study was designed to analyze 17 ECG metrics from 27 adult bradyasystolic and 304 control patients. The 17 metrics consisted of PR interval (PR), P-wave duration (Pdur), QRS duration (QRSdur), RR interval (RR), QT interval (QT), estimate of serum K  +  using only frontal leads (SerumK2), T-wave complexity (T Complex), ST segment levels for leads I, II, V (ST I, ST II, ST V), and 7 heart rate variability (HRV) metrics. These 7 HRV metrics were standard deviation of normal to normal intervals (SDNN), total power, very low frequency power, low frequency power, high frequency power, normalized low frequency power, and normalized high frequency power. Controls were matched by gender, age (±5 years), admission to the same hospital unit within the same month, and the same major diagnostic category. A research ECG analysis software program developed by co-author D M was used to automatically extract the metrics. The absolute value for each ECG metric, and the duration, terminal value, and slope of the dominant trend for each ECG metric, were derived and tested as the alarm conditions. The maximal true positive rate (TPR) of detecting cardiac arrest at a prescribed maximal false positive rate (FPR) based on the trending conditions was reported. Lead time was also recorded as the time between the first time alarm condition was triggered and the event of cardiac arrest.While conditions based on the absolute values of ECG metrics do not provide discriminative information to predict bradyasystolic cardiac arrest, the trending conditions can be useful. For example, with a max FPR  =  5.0%, some derived alarms conditions are: trend duration of PR  >  2.8 h (TPR  =  48.2%, lead time  =  10.0  ±  6.6 h), trend duration of QRSdur  >  2.7 h (TPR  =  40.7%, lead time  =  8.8  ±  6.2 h), trend duration of RR  >  3.5 h (TPR  =  51.9%, lead time  =  6.4  ±  5.5 h), trend duration of T Complex  >  2.9 h (TPR  =  40.7%, lead time  =  6.8  ±  5.5 h), trend duration of ST I  >  3.0 h (TPR of 51.9%, lead time  =  8.4  ±  8.0 h), trend duration of SDNN  >  3.6 h (TPR of 40.7%, lead time  =  11.0  ±  8.6 h), trend duration of HRV total power  >  3.0 h (TPR of 25.9%, lead time  =  7.5  ±  8.1 h), terminal value of ST I    19.4 ms h(-1) (TPR  =  25.9%, lead time  =  6.7  ±  6.9 h). Eleven trend duration alarms, eight terminal value alarms, and ten slope alarms, achieved a positive TPR with zero FPR. Furthermore, these alarms conditions with zero PFR can be combined by the 'OR'logic could further improve the TPR without increasing the FPR.The trend duration, terminal value, and slope of the dominant trend of the ECG metrics considered in this study are able to predict a subset of patients with bradyasystolic cardiac arrests with low or even zero FPR, which can be used for developing new ECG alarms.

Practice standards for electrocardiographic monitoring recommend continuous ST-segment monitoring (C-STM) in patients presenting to the emergency department (ED) with signs and/or symptoms of acute coronary syndrome (ACS), but few studies have evaluated its use in the ED. We compared time to diagnosis and 30-day adverse events before and after implementation of C-STM. We also evaluated the diagnostic accuracy of C-STM in detecting ischemia and infarction. We prospectively studied 163 adults (preintervention: n = 78; intervention: n = 85) in a single ED and stratified them into low (n = 51), intermediate (n = 100), or high (n = 12) risk using History, ECG, Age, Risk factors, and Troponin (HEART) scores. The principal investigator monitored participants, activating C-STM on bedside monitors in the intervention phase. We used likelihood ratios (LRs) as the measure of diagnostic accuracy. Overall, 9% of participants were diagnosed with ACS. Median time to diagnosis did not differ before and after implementation of C-STM (5.55 vs. 5.98 hr; p = 0.43). In risk-stratified analyses, no significant pre-/postdifference in time to diagnosis was found in low-, intermediate-, or high-risk participants. There was no difference in the rate of 30-day adverse events before versus after C-STM implementation (11.5% vs. 10.6%; p = 0.85). The +LR and -LR of C-STM for ischemia were 24.0 (95% confidence interval [CI]: 1.4, 412.0) and 0.3 (95% CI: 0.02, 2.9), respectively, and for infarction were 13.7 (95% CI: 1.7, 112.3) and 0.7 (95% CI: 0.3, 1.5), respectively. Use of C-STM did not provide added diagnostic benefit for patients with signs and/or symptoms of myocardial ischemia in the ED.

ObjectivesDepression is an independent predictor of adverse outcomes in patients with heart failure (HF). However, the effect of changes in cognitive-affective and somatic symptoms on mortality of HF patients is not known. The purpose of this study was to examine whether changes in cognitive-affective and somatic depressive symptoms over time were associated with mortality in HF.MethodsIn this secondary analysis of data from the Rural Education to Improve Outcomes in Heart Failure clinical trial, we analyzed data from 457 HF patients (39% female, mean [standard deviation] age = 65.6 [12.8] years) who survived at least 1 year and repeated the Patient Health Questionnaire at 1 year. Cognitive-affective and somatic depression scores were calculated, respectively, based on published Patient Health Questionnaire factor models. Using Cox proportional hazards regression analyses, we evaluated the effect of changes in cognitive-affective and somatic symptoms from baseline to 1 year on cardiac and all-cause deaths.ResultsControlling for baseline depression scores and other patient characteristics, the change in somatic symptoms was associated with increased risk of cardiac death during the subsequent 1-year period (hazard ratio = 1.24, 95% confidence interval = 1.07-1.44, p = .005), but the change in cognitive-affective symptoms was not (hazard ratio = 0.94, 95% confidence interval = 0.81-1.08, p = .38). Similar results were found for all-cause mortality.ConclusionsWorsening somatic depressive symptoms, not cognitive-affective symptoms, are independently associated with increased mortality of HF patients. The findings suggest that routine and ongoing assessment of somatic depressive symptoms in HF patients may help clinicians identify patients at increased risk for adverse outcomes.Trial registrationClinicalTrials.gov NCT00415545.

BackgroundPatients hospitalized with heart failure are often readmitted. Health literacy may play a substantial role in the high rate of readmissions. The purpose of this study was to examine the association of health literacy with the composite end point of heart failure readmission rates and all-cause mortality in patients with heart failure living in rural areas.Methods and resultsRural adults (n = 575), hospitalized for heart failure within the past 6 months, completed the Short Test of Functional Health Literacy in Adults (STOFHLA) to measure health literacy and were followed for ≥2 years. The percentage of patients with the end point of heart failure readmission or all-cause death was different (P = .001) among the 3 STOFHLA score levels. Unadjusted analysis revealed that patients with inadequate and marginal health literacy were 1.94 (95% confidence interval [CI] 1.43-2.63; P < .001) times, and 1.91 (95% CI 1.36-2.67; P < .001) times, respectively, more likely to experience the outcome. After adjustment for covariates, health literacy remained a predictor of outcomes. Of the other covariates, worse functional class, higher comorbidity burden, and higher depression score predicted worse outcomes.ConclusionsInadequate or marginal health literacy is a risk factor for heart failure rehospitalization or all-cause mortality among rural patients with heart failure.

BackgroundA specific electrocardiographic (ECG) marker of ischemia would greatly improve the speed and accuracy of detecting and treating non-ST elevation myocardial infarction (NSTEMI). We hypothesize that ischemia induces ventricular repolarization dispersion (VRD), altering the T-wave before any ST segment deviation. We sought to evaluate the clinical utility of VRD to (1) detect NSTEMI cases in the emergency department (ED) and (2) identify NSTEMI cases at high risk for in-hospital major adverse cardiac events (MACEs).Methods and resultsWe continuously recorded 12-lead Holter ECGs from chest pain patients upon their arrival to the ED. VRD was quantified using principal component analysis of the 12-lead ECG to compute a T-wave complexity ratio (ie, ratio of second to first eigenvectors of repolarization). Clinical outcomes were obtained from hospital records. The sample was composed mainly of older males (n=369; ages 63±12 years; 63% males), and 92 (25%) had NSTEMI and 26 (7%) had MACEs. Baseline T-wave complexity ratio modestly correlated with peak troponin levels (r=0.41; P

BackgroundHeart failure (HF) is a potentially disabling condition requiring significant patient knowledge to manage the requirements of self-care. The need for self-care is important for all patients but particularly for those living in rural areas that are geographically remote from healthcare services.ObjectiveThe aim of this study was to identify the level of knowledge of rural patients with HF and the clinical and demographic characteristics associated with low levels of HF knowledge.MethodsBaseline data from 612 patients with HF enrolled in the Rural Education to Improve Outcomes in Heart Failure trial were analyzed using the Heart Failure Knowledge Scale, the Short Test of Functional Health Literacy in Adults, and the anxiety subscale of the Brief Symptom Inventory. Multiple linear regression was used to explore the contribution of sociodemographic and clinical variables to levels of HF knowledge.ResultsThe mean (SD) age was 66 (13) years; 59% were men, and 50.5% had an ejection fraction of less than 40%. The mean (SD) percent correct on the Heart Failure Knowledge Scale was 69.5% (13%; range, 25%-100%), with the most frequent incorrect items related to symptoms of HF and the need for daily weights. The men and the older patients scored significantly lower in HF knowledge than did the women and the younger patients (P = 0.002 and 0.011, respectively). The patients with preserved systolic function also scored significantly lower than those with systolic HF (P = 0.030).ConclusionsPatients who are at risk for poor self-care because of low levels of HF knowledge can be identified. Older patients, men, and, patients with HF with preserved systolic function may require special educational strategies to gain the knowledge required for effective self-care.