Multicomponent Pharmacist Intervention Did Not Reduce Clinically Important Medication Errors for Ambulatory Patients Initiating Direct Oral Anticoagulants.

BACKGROUND: Anticoagulants including direct oral anticoagulants (DOACs) are among the highest-risk medications in the United States. We postulated that routine consultation and follow-up from a clinical pharmacist would reduce clinically important medication errors (CIMEs) among patients beginning or resuming a DOAC in the ambulatory care setting. OBJECTIVE: To evaluate the effectiveness of a multicomponent intervention for reducing CIMEs. DESIGN: Randomized controlled trial. PARTICIPANTS: Ambulatory patients initiating a DOAC or resuming one after a complication. INTERVENTION: Pharmacist evaluation and monitoring based on the implementation of a recently published checklist. Key elements included evaluation of the appropriateness of DOAC, need for DOAC affordability assistance, three pharmacist-initiated telephone consultations, access to a DOAC hotline, documented hand-off to the patient's continuity provider, and monitoring of follow-up laboratory tests. CONTROL: Coupons and assistance to increase the affordability of DOACs. MAIN MEASURE: Anticoagulant-related CIMEs (Anticoagulant-CIMEs) and non-anticoagulant-related CIMEs over 90 days from DOAC initiation; CIMEs identified through masked assessment process including two physician adjudication of events presented by a pharmacist distinct from intervention pharmacist who reviewed participant electronic medical records and interview data. ANALYSIS: Incidence and incidence rate ratio (IRR) of CIMEs (intervention vs. control) using multivariable Poisson regression modeling. KEY RESULTS: A total of 561 patients (281 intervention and 280 control patients) contributed 479 anticoagulant-CIMEs including 31 preventable and ameliorable ADEs and 448 significant anticoagulant medication errors without subsequent documented ADEs (0.95 per 100 person-days). Failure to perform required blood tests and concurrent, inappropriate usage of a DOAC with aspirin or NSAIDs were the most common anticoagulant-related CIMEs despite pharmacist documentation systematically identifying these issues when present. There was no reduction in anticoagulant-related CIMEs among intervention patients (IRR 1.17; 95% CI 0.98-1.42) or non-anticoagulant-related CIMEs (IRR 1.05; 95% CI 0.80-1.37). CONCLUSION: A multi-component intervention in which clinical pharmacists implemented an evidence-based DOAC Checklist did not reduce CIMEs. NIH TRIAL NUMBER: NCT04068727.

Deep cross-modal feature learning applied to predict acutely decompensated heart failure using in-home collected electrocardiography and transthoracic bioimpedance.

BACKGROUND: Deep learning has been successfully applied to ECG data to aid in the accurate and more rapid diagnosis of acutely decompensated heart failure (ADHF). Previous applications focused primarily on classifying known ECG patterns in well-controlled clinical settings. However, this approach does not fully capitalize on the potential of deep learning, which directly learns important features without relying on a priori knowledge. In addition, deep learning applications to ECG data obtained from wearable devices have not been well studied, especially in the field of ADHF prediction. METHODS: We used ECG and transthoracic bioimpedance data from the SENTINEL-HF study, which enrolled patients (≥21 years) who were hospitalized with a primary diagnosis of heart failure or with ADHF symptoms. To build an ECG-based prediction model of ADHF, we developed a deep cross-modal feature learning pipeline, termed ECGX-Net, that utilizes raw ECG time series and transthoracic bioimpedance data from wearable devices. To extract rich features from ECG time series data, we first adopted a transfer learning approach in which ECG time series were transformed into 2D images, followed by feature extraction using ImageNet-pretrained DenseNet121/VGG19 models. After data filtering, we applied cross-modal feature learning in which a regressor was trained with ECG and transthoracic bioimpedance. Then, we concatenated the DenseNet121/VGG19 features with the regression features and used them to train a support vector machine (SVM) without bioimpedance information. RESULTS: The high-precision classifier using ECGX-Net predicted ADHF with a precision of 94 %, a recall of 79 %, and an F1-score of 0.85. The high-recall classifier with only DenseNet121 had a precision of 80 %, a recall of 98 %, and an F1-score of 0.88. We found that ECGX-Net was effective for high-precision classification, while DenseNet121 was effective for high-recall classification. CONCLUSION: We show the potential for predicting ADHF from single-channel ECG recordings obtained from outpatients, enabling timely warning signs of heart failure. Our cross-modal feature learning pipeline is expected to improve ECG-based heart failure prediction by handling the unique requirements of medical scenarios and resource limitations.

An Emergency Department Delirium Screening and Management Initiative: The Development and Refinement of the SCREENED-ED Intervention.

The current article describes an intervention aimed at emergency department (ED) nurses and physicians that was designed to address the challenges of managing delirium in the ED environment. The intervention development process followed the Medical Research Council principles paired with a user-centered design perspective. Expert clinicians and nursing staff were involved in the development process. As a result, the SCREENED-ED intervention includes four major components: screening for delirium, informing providers, an acronym (ALTERED), and documentation in the electronic health record. The acronym "ALTERED" includes seven key elements of delirium management that were considered the most evidence-based, relevant, and practical for the ED. Nurses are at the frontline of delirium recognition and management and the SCREENED-ED intervention with the ALTERED acronym holds the potential to improve nursing care in this complex clinical setting. [Journal of Gerontological Nursing, 47(12), 13-17.].

Machine Learning Model Based on Transthoracic Bioimpedance and Heart Rate Variability for Lung Fluid Accumulation Detection: Prospective Clinical Study.

BACKGROUND: Accumulation of excess body fluid and autonomic dysregulation are clinically important characteristics of acute decompensated heart failure. We hypothesized that transthoracic bioimpedance, a noninvasive, simple method for measuring fluid retention in lungs, and heart rate variability, an assessment of autonomic function, can be used for detection of fluid accumulation in patients with acute decompensated heart failure. OBJECTIVE: We aimed to evaluate the performance of transthoracic bioimpedance and heart rate variability parameters obtained using a fluid accumulation vest with carbon black-polydimethylsiloxane dry electrodes in a prospective clinical study (System for Heart Failure Identification Using an External Lung Fluid Device; SHIELD). METHODS: We computed 15 parameters: 8 were calculated from the model to fit Cole-Cole plots from transthoracic bioimpedance measurements (extracellular, intracellular, intracellular-extracellular difference, and intracellular-extracellular parallel circuit resistances as well as fitting error, resonance frequency, tissue heterogeneity, and cellular membrane capacitance), and 7 were based on linear (mean heart rate, low-frequency components of heart rate variability, high-frequency components of heart rate variability, normalized low-frequency components of heart rate variability, normalized high-frequency components of heart rate variability) and nonlinear (principal dynamic mode index of sympathetic function, and principal dynamic mode index of parasympathetic function) analysis of heart rate variability. We compared the values of these parameters between 3 participant data sets: control (n=32, patients who did not have heart failure), baseline (n=23, patients with acute decompensated heart failure taken at the time of admittance to the hospital), and discharge (n=17, patients with acute decompensated heart failure taken at the time of discharge from hospital). We used several machine learning approaches to classify participants with fluid accumulation (baseline) and without fluid accumulation (control and discharge), termed with fluid and without fluid groups, respectively. RESULTS: Among the 15 parameters, 3 transthoracic bioimpedance (extracellular resistance, R0; difference in extracellular-intracellular resistance, R0 - R∞, and tissue heterogeneity, α) and 3 heart rate variability (high-frequency, normalized low-frequency, and normalized high-frequency components) parameters were found to be the most discriminatory between groups (patients with and patients without heart failure). R0 and R0 - R∞ had significantly lower values for patients with heart failure than for those without heart failure (R0: P=.006; R0 - R∞: P=.001), indicating that a higher volume of fluids accumulated in the lungs of patients with heart failure. A cubic support vector machine model using the 5 parameters achieved an accuracy of 92% for with fluid and without fluid group classification. The transthoracic bioimpedance parameters were related to intra- and extracellular fluid, whereas the heart rate variability parameters were mostly related to sympathetic activation. CONCLUSIONS: This is useful, for instance, for an in-home diagnostic wearable to detect fluid accumulation. Results suggest that fluid accumulation, and subsequently acute decompensated heart failure detection, could be performed using transthoracic bioimpedance and heart rate variability measurements acquired with a wearable vest.

Family Identification of Delirium in the Emergency Department in Patients With and Without Dementia: Validity of the Family Confusion Assessment Method (FAM-CAM).

OBJECTIVE: To examine the ability of the family-rated Family Confusion Assessment Method (FAM-CAM) to identify delirium in the emergency department (ED) among patients with and without dementia, as compared to the reference-standard Confusion Assessment Method (CAM). DESIGN: Validation study. SETTING: Urban academic ED. PARTICIPANTS: Dyads of ED patients, aged 70 years and older, and their family caregivers (N = 108 dyads). MEASUREMENTS: A trained reference standard interviewer performed a cognitive screen, delirium symptom assessment, and scored the CAM. The caregiver self-administered the FAM-CAM. Dementia was assessed using the Informant Questionnaire on Cognitive Decline in the Elderly and the medical record. For concurrent validity, performance of the FAM-CAM was compared to the CAM. For predictive validity, clinical outcomes (ED visits, hospitalization, and mortality) over 6 months were compared in FAM-CAM positive and negative patients, controlling for age, sex, comorbidity, and cognitive status. RESULTS: Among the 108 patients, 30 (28%) were CAM positive for delirium and 58 (54%) presented with dementia. The FAM-CAM had a specificity of 83% and a negative predictive value of 83%. Most false negatives (n = 9 of 13, 69%) were due to caregivers not identifying the inattention criteria for delirium on the FAM-CAM. In patients with dementia, sensitivity was higher than in patients without (61% vs 43%). In adjusted models, a hospitalization in the following 6 months was more than three times as likely in FAM-CAM positive compared to negative patients (odds ratio = 3.4; 95% confidence interval = 1.2-9.3). CONCLUSIONS: Among patients with and without dementia, the FAM-CAM shows qualities that are important in the ED setting for identification of delirium. Using the FAM-CAM as part of a systematic screening strategy for the ED, in which families' assessments could supplement healthcare professionals' assessments, is promising. J Am Geriatr Soc 68:983-990, 2020.

An Emergency Medicine-focused Summary of the HFSA/SAEM/ISHLT Clinical Consensus Document on the Emergency Management of Patients With Ventricular Assist Devices.

Mechanical circulatory support is increasingly used as a long-term treatment option for patients with end-stage heart failure. Patients with implanted ventricular assist devices are at high risk for a range of diverse medical urgencies and emergencies. Given the increasing prevalence of mechanical circulatory support devices, this expert clinical consensus document seeks to help inform emergency medicine and prehospital providers regarding the approach to acute medical and surgical conditions encountered in these complex patients.

Reducing Hospitalizations and Emergency Department Visits in Patients With Venous Thromboembolism Using a Multicomponent Care Transition Intervention.

Preventing utilization of hospital and emergency department after diagnosis of venous thromboembolism is a complex problem. The objective of this study is to assess the impact of a care transition intervention on hospitalizations and emergency department visits after venous thromboembolism. We randomized adults diagnosed with a new episode of venous thromboembolism to usual care or a multicomponent intervention that included a home pharmacist visit in the week after randomization (typically occurring at time of discharge), illustrated medication instructions distributed during home visit, and a follow-up phone call with an anticoagulation expert scheduled for 8 to 30 days from time of randomization. Through physician chart review of the 90 days following randomization, we measured the incidence rate of hospital and emergency department visits for each group and their ratio. We also determined which visits were related to recurrent venous thromboembolism, bleeding, or anticoagulation and which where preventable. We enrolled 77 intervention and 85 control patients. The incidence rate was 4.50 versus 6.01 visits per 1000 patient days in the intervention versus control group (incidence rate ratio = 0.71; 95% confidence interval = 0.40-1.27). Most visits in the control group were not related to venous thromboembolism or bleeding (21%) and of those that were, most were not preventable (25%). The adjusted incidence rate ratio for the intervention was 1.05 (95% confidence interval = 0.57-1.91). Our patients had a significant number of hospital and emergency department visits after diagnosis. Most visits were not related to recurrent venous thromboembolism or bleeding and of those that were, most were not preventable. Our multicomponent intervention did not decrease hospitalizations and emergency department visits.

Supplying Pharmacist Home Visit and Anticoagulation Professional Consultation During Transition of Care for Patients With Venous Thromboembolism.

OBJECTIVE: The aim of the study was to assess the feasibility, satisfaction, and effectiveness of a care transition intervention with pharmacist home visit and subsequent anticoagulation expert consultation for patients with new episode of venous thromboembolism within a not-for-profit health care network. METHODS: We randomized patients to the intervention or control. During the home visit, a clinical pharmacist assessed medication management proficiency, asked open-ended questions to discuss knowledge gaps, and distributed illustrated medication instructions. Subsequent consultation with anticoagulation expert further filled knowledge gaps. At 30 days, we assessed satisfaction with the intervention and also measured the quality of care transition, knowledge of anticoagulation and venous thromboembolism, and anticoagulant beliefs (level of agreement that anticoagulant is beneficial, is worrisome, and is confusing/difficult to take). RESULTS: The mean ± SD time required to conduct home visits was 52.4 ± 20.5 minutes and most patients agreed that the intervention was helpful. In general, patients reported a high-quality care transition including having been advised of safety issues related to medications. Despite that, the mean percentage of knowledge items answered correctly among patients was low (51.5 versus 50.7 for intervention and controls, respectively). We did not find any significant difference between intervention and control patients for care transition quality, knowledge, or anticoagulant beliefs. CONCLUSIONS: We executed a multicomponent intervention that was feasible and rated highly. Nevertheless, the intervention did not improve care transition quality, knowledge, or beliefs. Future research should examine whether alternate strategies potentially including some but not all components of our intervention would be more impactful.

HFSA/SAEM/ISHLT clinical expert consensus document on the emergency management of patients with ventricular assist devices.

Mechanical circulatory support is now widely accepted as a viable long-term treatment option for patients with end-stage heart failure (HF). As the range of indications for the implantation of ventricular assist devices grows, so does the number of patients living in the community with durable support. Because of their underlying disease and comorbidities, in addition to the presence of mechanical support, these patients are at a high risk for medical urgencies and emergencies (Table 1). Thus, it is the responsibility of clinicians to understand the basics of their emergency care. This consensus document represents a collaborative effort by the Heart Failure Society of America, the Society for Academic Emergency Medicine, and the International Society for Heart and Lung Transplantation (ISHLT) to educate practicing clinicians about the emergency management of patients with ventricular assist devices. The target audience includes HF specialists and emergency medicine physicians, as well as general cardiologists and community-based providers.

HFSA/SAEM/ISHLT Clinical Expert Consensus Document on the Emergency Management of Patients with Ventricular Assist Devices.

Mechanical circulatory support is now widely accepted as a viable long-term treatment option for patients with end-stage heart failure (HF). As the range of indications for the implantation of ventricular assist devices grows, so does the number of patients living in the community with durable support. Because of their underlying disease and comorbidities, in addition to the presence of mechanical support, these patients are at a high risk for medical urgencies and emergencies (Table 1). Thus, it is the responsibility of clinicians to understand the basics of their emergency care. This consensus document represents a collaborative effort by the Heart Failure Society of America, the Society for Academic Emergency Medicine, and the International Society for Heart and Lung Transplantation (ISHLT) to educate practicing clinicians about the emergency management of patients with ventricular assist devices. The target audience includes HF specialists and emergency medicine physicians, as well as general cardiologists and community-based providers.

Clinically significant ventricular arrhythmias and progression of depression and anxiety following an acute coronary syndrome.

BACKGROUND: Depression and anxiety are common and associated with worse clinical outcomes in patients who experience an acute coronary syndrome (ACS). We investigated the association between major ventricular arrhythmias (VAs) with the progression of depression and anxiety among hospital survivors of an ACS. METHODS: Patients were interviewed in hospital and by telephone up to 12 months after hospital discharge. The primary outcome was the presence of moderate/severe symptoms of depression and anxiety defined as a Patient Health Questionnaire (PHQ)-9 score ≥ 10 and a Generalized Anxiety Disorder (GAD)-7 score ≥ 10 at baseline and 1 month and PHQ-2 ≥ 3 and GAD-2 ≥ 3 at 3, 6, and 12 months. We used marginal models to examine the association between major VAs and the symptoms of depression or anxiety over time. RESULTS: The average age of the study population (n = 2074) was 61.1 years, 33.5% were women, and 78.3% were white. VAs developed in 105 patients (5.1%). Symptoms of depression and anxiety were present in 22.2% and 23.5% of patients at baseline, respectively, and declined to 14.1% and 12.6%, respectively, at 1-month post-discharge. VAs were not significantly associated with the progression of symptoms of depression (adjusted relative risk [aRR] = 1.29, 95% confidence interval [CI] = 0.94-1.77) and anxiety (aRR = 1.22, 95% CI = 0.86-1.72), or with change in average scores of PHQ-2 and GAD-2 over time, both before and after risk adjustment. CONCLUSION: The prevalence of symptoms of depression and anxiety was high after an ACS but declined thereafter and may not be associated with the occurrence of major in-hospital VAs.

Twenty-five year trends (1986-2011) in hospital incidence and case-fatality rates of ventricular tachycardia and ventricular fibrillation complicating acute myocardial infarction.

BACKGROUND: Long-term trends in the incidence rates (IRs) and hospital case-fatality rates (CFRs) of ventricular tachycardia (VT) and ventricular fibrillation (VF) among patients hospitalized with acute myocardial infarction (AMI) have not been recently examined. METHODS: We used data from 11,825 patients hospitalized with AMI at all 11 medical centers in central Massachusetts on a biennial basis between 1986 and 2011. Multivariable adjusted logistic regression modeling was used to examine trends in hospital IRs and CFRs of VT and VF complicating AMI. RESULTS: The median age of the study population was 71 years, 57.9% were men, and 94.7% were white. The hospital IRs declined from 14.3% in 1986/1988 to 10.5% in 2009/2011 for VT and from 8.2% to 1.7% for VF. The in-hospital CFRs declined from 27.7% to 6.9% for VT and from 49.6% to 36.0% for VF between 1986/1988 and 2009/2011, respectively. The IRs of both early (<48 hours) and late VT and VF declined over time, with greater declines in those of late VT and VF. The incidence rates of VT declined similarly for patients with either an ST-segment elevation myocardial infarction (STEMI) or non-STEMI, whereas they only declined in those with VF and a STEMI. CONCLUSIONS: The hospital IRs and CHRs of VT and VF complicating AMI have declined over time, likely because of changes in acute monitoring and treatment practices. Despite these encouraging trends, efforts remain needed to identify patients at risk for these serious ventricular arrhythmias so that preventive and treatment strategies might be implemented as necessary.

Online health information seeking by adults hospitalized for acute coronary syndromes: Who looks for information, and who discusses it with healthcare providers?

OBJECTIVE: To describe characteristics associated with online health information-seeking and discussing resulting information with healthcare providers among adults with acute coronary syndromes (ACS). METHODS: Consecutive patients hospitalized with ACS in 6 hospitals in Massachusetts and Georgia who reported Internet use in the past 4 weeks (online patients) were asked about online health information-seeking and whether they discussed information with healthcare providers. Participants reported demographic and psychosocial characteristics; clinical characteristics were abstracted from medical records. Logistic regression models estimated associations with information-seeking and provider communication. RESULTS: Online patients (N = 1142) were on average aged 58.8 (SD: 10.6) years, 30.3% female, and 82.8% non-Hispanic white; 56.7% reported online health information-seeking. Patients with higher education and difficulty accessing medical care were more likely to report information-seeking; patients hospitalized with myocardial infarction, and those with impaired health numeracy and limited social networks were less likely. Among information-seekers, 33.9% discussed information with healthcare providers. More education and more frequent online information-seeking were associated with provider discussions. CONCLUSION: Over half of online patients with ACS seek health information online, but only 1 in 3 of these discuss information with healthcare providers. PRACTICE IMPLICATIONS: Clinician awareness of patient information-seeking may enhance communication including referral to evidence-based online resources.

Hyperglycemia and risk of ventricular tachycardia among patients hospitalized with acute myocardial infarction.

BACKGROUND: Little is known about the association of hyperglycemia with the development of ventricular tachycardia (VT) in patients hospitalized with acute myocardial infarction (AMI) which we examined in the present study. The objectives of this community-wide observational study were to examine the relation between elevated serum glucose levels at the time of hospital admission for AMI and occurrence of VT, and time of occurrence of VT, during the patient's acute hospitalization. METHODS: We used data from a population-based study of patients hospitalized with AMI at all central Massachusetts medical centers between 2001 and 2011. Hyperglycemia was defined as a serum glucose level ≥ 140 mg/dl at the time of hospital admission. The development of VT was identified from physicians notes and electrocardiographic findings by our trained team of data abstractors. RESULTS: The average age of the study population was 70 years, 58.0% were men, and 92.7% were non-Hispanic whites. The mean and median serum glucose levels at the time of hospital admission were 171.4 mg/dl and 143.0, respectively. Hyperglycemia was present in 51.9% of patients at the time of hospital admission; VT occurred in 652 patients (15.8%), and two-thirds of these episodes occurred during the first 48 h after hospital admission (early VT). After multivariable adjustment, patients with hyperglycemia were at increased risk for developing VT (adjusted OR = 1.48, 95% CI = 1.23-1.78). The presence of hyperglycemia was significantly associated with early (multivariable adjusted OR = 1.39, 95% CI = 1.11-1.73) but not with late VT. Similar associations were observed in patients with and without diabetes and in patients with and without ST-segment elevation AMI. CONCLUSIONS: Efforts should be made to closely monitor and treat patients who develop hyperglycemia, especially early after hospital admission, to reduce their risk of VT.

Using support vector machines on photoplethysmographic signals to discriminate between hypovolemia and euvolemia.

Identifying trauma patients at risk of imminent hemorrhagic shock is a challenging task in intraoperative and battlefield settings given the variability of traditional vital signs, such as heart rate and blood pressure, and their inability to detect blood loss at an early stage. To this end, we acquired N = 58 photoplethysmographic (PPG) recordings from both trauma patients with suspected hemorrhage admitted to the hospital, and healthy volunteers subjected to blood withdrawal of 0.9 L. We propose four features to characterize each recording: goodness of fit (r2), the slope of the trend line, percentage change, and the absolute change between amplitude estimates in the heart rate frequency range at the first and last time points. Also, we propose a machine learning algorithm to distinguish between blood loss and no blood loss. The optimal overall accuracy of discriminating between hypovolemia and euvolemia was 88.38%, while sensitivity and specificity were 88.86% and 87.90%, respectively. In addition, the proposed features and algorithm performed well even when moderate blood volume was withdrawn. The results suggest that the proposed features and algorithm are suitable for the automatic discrimination between hypovolemia and euvolemia, and can be beneficial and applicable in both intraoperative/emergency and combat casualty care.

A Historical Perspective on Presentations of Hypertensive Acute Heart Failure.

BACKGROUND: The initial systolic blood pressure (SBP) in patients presenting to the hospital with acute heart failure (AHF) informs prognosis, diagnosis, and guides initial treatment. However, over time AHF presentations with elevated SBP appear to have declined. The present study examined whether the frequency of AHF presentations with systolic hypertension (SBP >160 mmHg) declined over a nearly two-decade time interval. METHODS: This study compares four historical, cross-sectional cohorts with AHF who were admitted to tertiary care medical centres in the North-eastern USA in 1995, 2000, 2006, and 2011-13. The main outcome was the proportion of AHF patients presenting with an initial SBP >160 mmHg. RESULTS: 2,366 patients comprised the study sample. The average age was 77 years, 55% were female, 94% white, and 75% had prior heart failure. In 1995, 34% of AHF patients presented with an initial SBP >160 mmHg compared to 20% in 2011-2013 (p<0.01). Multivariate logistic regression demonstrated reduced odds of presenting with a SBP >160 mmHg in 2006 (0.64, 95% CI 0.42-0.96) and 2011-13 (0.46, 95% CI 0.28-0.74) compared with patients in 1995. CONCLUSION: The proportion of patients with AHF and initial SBP >160 mmHg significantly declined over the study time period. There are several potential reasons for this observation and these findings highlight the need for ongoing surveillance of patients with AHF as changing clinical characteristics can impact early treatment decisions.

A Robust Motion Artifact Detection Algorithm for Accurate Detection of Heart Rates From Photoplethysmographic Signals Using Time-Frequency Spectral Features.

Motion and noise artifacts (MNAs) impose limits on the usability of the photoplethysmogram (PPG), particularly in the context of ambulatory monitoring. MNAs can distort PPG, causing erroneous estimation of physiological parameters such as heart rate (HR) and arterial oxygen saturation (SpO2). In this study, we present a novel approach, "TifMA," based on using the time-frequency spectrum of PPG to first detect the MNA-corrupted data and next discard the nonusable part of the corrupted data. The term "nonusable" refers to segments of PPG data from which the HR signal cannot be recovered accurately. Two sequential classification procedures were included in the TifMA algorithm. The first classifier distinguishes between MNA-corrupted and MNA-free PPG data. Once a segment of data is deemed MNA-corrupted, the next classifier determines whether the HR can be recovered from the corrupted segment or not. A support vector machine (SVM) classifier was used to build a decision boundary for the first classification task using data segments from a training dataset. Features from time-frequency spectra of PPG were extracted to build the detection model. Five datasets were considered for evaluating TifMA performance: (1) and (2) were laboratory-controlled PPG recordings from forehead and finger pulse oximeter sensors with subjects making random movements, (3) and (4) were actual patient PPG recordings from UMass Memorial Medical Center with random free movements and (5) was a laboratory-controlled PPG recording dataset measured at the forehead while the subjects ran on a treadmill. The first dataset was used to analyze the noise sensitivity of the algorithm. Datasets 2-4 were used to evaluate the MNA detection phase of the algorithm. The results from the first phase of the algorithm (MNA detection) were compared to results from three existing MNA detection algorithms: the Hjorth, kurtosis-Shannon entropy, and time-domain variability-SVM approaches. This last is an approach recently developed in our laboratory. The proposed TifMA algorithm consistently provided higher detection rates than the other three methods, with accuracies greater than 95% for all data. Moreover, our algorithm was able to pinpoint the start and end times of the MNA with an error of less than 1 s in duration, whereas the next-best algorithm had a detection error of more than 2.2 s. The final, most challenging, dataset was collected to verify the performance of the algorithm in discriminating between corrupted data that were usable for accurate HR estimations and data that were nonusable. It was found that on average 48% of the data segments were found to have MNA, and of these, 38% could be used to provide reliable HR estimation.

Bioimpedance-Based Heart Failure Deterioration Prediction Using a Prototype Fluid Accumulation Vest-Mobile Phone Dyad: An Observational Study.

BACKGROUND: Recurrent heart failure (HF) events are common in patients discharged after acute decompensated heart failure (ADHF). New patient-centered technologies are needed to aid in detecting HF decompensation. Transthoracic bioimpedance noninvasively measures pulmonary fluid retention. OBJECTIVE: The objectives of our study were to (1) determine whether transthoracic bioimpedance can be measured daily with a novel, noninvasive, wearable fluid accumulation vest (FAV) and transmitted using a mobile phone and (2) establish whether an automated algorithm analyzing daily thoracic bioimpedance values would predict recurrent HF events. METHODS: We prospectively enrolled patients admitted for ADHF. Participants were trained to use a FAV-mobile phone dyad and asked to transmit bioimpedance measurements for 45 consecutive days. We examined the performance of an algorithm analyzing changes in transthoracic bioimpedance as a predictor of HF events (HF readmission, diuretic uptitration) over a 75-day follow-up. RESULTS: We observed 64 HF events (18 HF readmissions and 46 diuretic uptitrations) in the 106 participants (67 years; 63.2%, 67/106, male; 48.1%, 51/106, with prior HF) who completed follow-up. History of HF was the only clinical or laboratory factor related to recurrent HF events (P=.04). Among study participants with sufficient FAV data (n=57), an algorithm analyzing thoracic bioimpedance showed 87% sensitivity (95% CI 82-92), 70% specificity (95% CI 68-72), and 72% accuracy (95% CI 70-74) for identifying recurrent HF events. CONCLUSIONS: Patients discharged after ADHF can measure and transmit daily transthoracic bioimpedance using a FAV-mobile phone dyad. Algorithms analyzing thoracic bioimpedance may help identify patients at risk for recurrent HF events after hospital discharge.

Relation of Atrial Fibrillation in Acute Myocardial Infarction to In-Hospital Complications and Early Hospital Readmission.

Atrial fibrillation (AF) is a common complication of acute myocardial infarction (AMI) and contributes to high rates of in-hospital adverse events. However, there are few contemporary studies examining rates of AF in the contemporary era of AMI or the impact of new-onset AF on key in-hospital and postdischarge outcomes. We examined trends in AF in 6,384 residents of Worcester, Massachusetts, who were hospitalized with confirmed AMI during 7 biennial periods between 1999 and 2011. Multivariate logistic regression analysis was used to examine associations between occurrence of AF and various in-hospital and postdischarge complications. The overall incidence of AF complicating AMI was 10.8%. Rates of new-onset AF increased from 1999 to 2003 (9.8% to 13.2%), and decreased thereafter. In multivariable adjusted models, patients developing new-onset AF after AMI were at a higher risk for in-hospital stroke (odds ratio [OR] 2.5, 95% confidence interval [CI] 1.6 to 4.1), heart failure (OR 2.0, 95% CI 1.7 to 2.4), cardiogenic shock (OR 3.7, 95% CI 2.8 to 4.9), and death (OR 2.3, 95% CI 1.9 to 3.0) than patients without AF. Development of AF during hospitalization for AMI was associated with higher rates of readmission within 30 days after discharge (21.7% vs 16.0%), but no significant difference was noted in early postdischarge 30-day all-cause mortality rates (8.3% vs 5.1%). In conclusion, new-onset AF after AMI is strongly related to in-hospital complications of AMI and higher short-term readmission rates.

PULSE-SMART: Pulse-Based Arrhythmia Discrimination Using a Novel Smartphone Application.

BACKGROUND: Atrial fibrillation (AF) is a common and dangerous rhythm abnormality. Smartphones are increasingly used for mobile health applications by older patients at risk for AF and may be useful for AF screening. OBJECTIVES: To test whether an enhanced smartphone app for AF detection can discriminate between sinus rhythm (SR), AF, premature atrial contractions (PACs), and premature ventricular contractions (PVCs). METHODS: We analyzed two hundred and nineteen 2-minute pulse recordings from 121 participants with AF (n = 98), PACs (n = 15), or PVCs (n = 15) using an iPhone 4S. We obtained pulsatile time series recordings in 91 participants after successful cardioversion to sinus rhythm from preexisting AF. The PULSE-SMART app conducted pulse analysis using 3 methods (Root Mean Square of Successive RR Differences; Shannon Entropy; Poincare plot). We examined the sensitivity, specificity, and predictive accuracy of the app for AF, PAC, and PVC discrimination from sinus rhythm using the 12-lead EKG or 3-lead telemetry as the gold standard. We also administered a brief usability questionnaire to a subgroup (n = 65) of app users. RESULTS: The smartphone-based app demonstrated excellent sensitivity (0.970), specificity (0.935), and accuracy (0.951) for real-time identification of an irregular pulse during AF. The app also showed good accuracy for PAC (0.955) and PVC discrimination (0.960). The vast majority of surveyed app users (83%) reported that it was "useful" and "not complex" to use. CONCLUSION: A smartphone app can accurately discriminate pulse recordings during AF from sinus rhythm, PACs, and PVCs.