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Article ; Online: Beyond duty hours

Amit Kaushal / Laurence Katznelson / Robert A. Harrington

npj Digital Medicine, Vol 2, Iss 1, Pp 1-

leveraging large-scale paging data to monitor resident workload

2019 Volume 6

Abstract: Abstract Monitoring and managing resident workload is a cornerstone of policy in graduate medical education, and the duty hours metric is the backbone of current regulations. While the duty hours metric measures hours worked, it does not capture ... ...

Abstract	Abstract Monitoring and managing resident workload is a cornerstone of policy in graduate medical education, and the duty hours metric is the backbone of current regulations. While the duty hours metric measures hours worked, it does not capture differences in intensity of work completed during those hours, which may independently contribute to fatigue and burnout. Few such metrics exist. Digital data streams generated during the usual course of hospital operations can serve as a novel source of insight into workload intensity by providing high-resolution, minute-by-minute data at the individual level; however, study and use of these data streams for workload monitoring has been limited to date. Paging data is one such data stream. In this work, we analyze over 500,000 pages—two full years of pages in an academic internal medicine residency program—to characterize paging patterns among housestaff. We demonstrate technical feasibility, validity, and utility of paging burden as a metric to provide insight into resident workload beyond duty hours alone, and illustrate a general framework for evaluation and incorporation of novel digital data streams into resident workload monitoring.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	005
Language	English
Publishing date	2019-09-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Taking a Stand Against Air Pollution – The Impact on Cardiovascular Disease

Michael Brauer / Barbara Casadei / Robert A. Harrington / Richard Kovacs / Karen Sliwa / the WHF Air Pollution Expert Group

Global Heart, Vol 16, Iss

A Joint Opinion from the World Heart Federation, American College of Cardiology, American Heart Association, and the European Society of Cardiology

2021 Volume 1

Abstract: Although the attention of the world and the global health community specifically is deservedly focused on the COVID-19 pandemic, other determinants of health continue to have large impacts and may also interact with COVID-19. Air pollution is one crucial ...

Abstract	Although the attention of the world and the global health community specifically is deservedly focused on the COVID-19 pandemic, other determinants of health continue to have large impacts and may also interact with COVID-19. Air pollution is one crucial example. Established evidence from other respiratory viruses and emerging evidence for COVID-19 specifically indicates that air pollution alters respiratory defense mechanisms leading to worsened infection severity. Air pollution also contributes to co-morbidities that are known to worsen outcomes amongst those infected with COVID-19, and air pollution may also enhance infection transmission due to its impact on more frequent coughing. Yet despite the massive disruption of the COVID-19 pandemic, there are reasons for optimism: broad societal lockdowns have shown us a glimpse of what a future with strong air pollution measures could yield. Thus, the urgency to combat air pollution is not diminished, but instead heightened in the context of the pandemic.
Keywords	air pollution ; cardiovascular disease ; cvd ; environmental health impacts ; climate ; Diseases of the circulatory (Cardiovascular) system ; RC666-701 ; Public aspects of medicine ; RA1-1270
Subject code	333
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Ubiquity Press
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Deep learning interpretation of echocardiograms

Amirata Ghorbani / David Ouyang / Abubakar Abid / Bryan He / Jonathan H. Chen / Robert A. Harrington / David H. Liang / Euan A. Ashley / James Y. Zou

npj Digital Medicine, Vol 3, Iss 1, Pp 1-

2020 Volume 10

Abstract: Abstract Echocardiography uses ultrasound technology to capture high temporal and spatial resolution images of the heart and surrounding structures, and is the most common imaging modality in cardiovascular medicine. Using convolutional neural networks ... ...

Abstract	Abstract Echocardiography uses ultrasound technology to capture high temporal and spatial resolution images of the heart and surrounding structures, and is the most common imaging modality in cardiovascular medicine. Using convolutional neural networks on a large new dataset, we show that deep learning applied to echocardiography can identify local cardiac structures, estimate cardiac function, and predict systemic phenotypes that modify cardiovascular risk but not readily identifiable to human interpretation. Our deep learning model, EchoNet, accurately identified the presence of pacemaker leads (AUC = 0.89), enlarged left atrium (AUC = 0.86), left ventricular hypertrophy (AUC = 0.75), left ventricular end systolic and diastolic volumes ($${R}^{2}$$ R2 = 0.74 and $${R}^{2}$$ R2 = 0.70), and ejection fraction ($${R}^{2}$$ R2 = 0.50), as well as predicted systemic phenotypes of age ($${R}^{2}$$ R2 = 0.46), sex (AUC = 0.88), weight ($${R}^{2}$$ R2 = 0.56), and height ($${R}^{2}$$ R2 = 0.33). Interpretation analysis validates that EchoNet shows appropriate attention to key cardiac structures when performing human-explainable tasks and highlights hypothesis-generating regions of interest when predicting systemic phenotypes difficult for human interpretation. Machine learning on echocardiography images can streamline repetitive tasks in the clinical workflow, provide preliminary interpretation in areas with insufficient qualified cardiologists, and predict phenotypes challenging for human evaluation.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Machine learning to predict venous thrombosis in acutely ill medical patients

Tarek Nafee / C. Michael Gibson / Ryan Travis / Megan K. Yee / Mathieu Kerneis / Gerald Chi / Fahad AlKhalfan / Adrian F. Hernandez / Russell D. Hull / Ander T. Cohen / Robert A. Harrington / Samuel Z. Goldhaber

Research and Practice in Thrombosis and Haemostasis, Vol 4, Iss 2, Pp 230-

2020 Volume 237

Abstract: Abstract Background The identification of acutely ill patients at high risk for venous thromboembolism (VTE) may be determined clinically or by use of integer‐based scoring systems. These scores demonstrated modest performance in external data sets. ... ...

Abstract	Abstract Background The identification of acutely ill patients at high risk for venous thromboembolism (VTE) may be determined clinically or by use of integer‐based scoring systems. These scores demonstrated modest performance in external data sets. Objectives To evaluate the performance of machine learning models compared to the IMPROVE score. Methods The APEX trial randomized 7513 acutely medically ill patients to extended duration betrixaban vs. enoxaparin. Including 68 variables, a super learner model (ML) was built to predict VTE by combining estimates from 5 families of candidate models. A “reduced” model (rML) was also developed using 16 variables that were thought, a priori, to be associated with VTE. The IMPROVE score was calculated for each patient. Model performance was assessed by discrimination and calibration to predict a composite VTE end point. The frequency of predicted risks of VTE were plotted and divided into tertiles. VTE risks were compared across tertiles. Results The ML and rML algorithms outperformed the IMPROVE score in predicting VTE (c‐statistic: 0.69, 0.68 and 0.59, respectively). The Hosmer‐Lemeshow goodness‐of‐fit P‐value was 0.06 for ML, 0.44 for rML, and <0.001 for the IMPROVE score. The observed event rate in the lowest tertile was 2.5%, 4.8% in tertile 2, and 11.4% in the highest tertile. Patients in the highest tertile of VTE risk had a 5‐fold increase in odds of VTE compared to the lowest tertile. Conclusion The super learner algorithms improved discrimination and calibration compared to the IMPROVE score for predicting VTE in acute medically ill patients.
Keywords	acute medically ill ; machine learning ; personalized medicine ; super learner ; venous thromboembolism ; Diseases of the blood and blood-forming organs ; RC633-647.5
Subject code	610
Language	English
Publishing date	2020-02-01T00:00:00Z
Publisher	Wiley
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Deep learning interpretation of echocardiograms

Amirata Ghorbani / David Ouyang / Abubakar Abid / Bryan He / Jonathan H. Chen / Robert A. Harrington / David H. Liang / Euan A. Ashley / James Y. Zou

npj Digital Medicine, Vol 3, Iss 1, Pp 1-

2020 Volume 10

Abstract	Abstract Echocardiography uses ultrasound technology to capture high temporal and spatial resolution images of the heart and surrounding structures, and is the most common imaging modality in cardiovascular medicine. Using convolutional neural networks on a large new dataset, we show that deep learning applied to echocardiography can identify local cardiac structures, estimate cardiac function, and predict systemic phenotypes that modify cardiovascular risk but not readily identifiable to human interpretation. Our deep learning model, EchoNet, accurately identified the presence of pacemaker leads (AUC = 0.89), enlarged left atrium (AUC = 0.86), left ventricular hypertrophy (AUC = 0.75), left ventricular end systolic and diastolic volumes ( $${R}^{2}$$ R 2 = 0.74 and $${R}^{2}$$ R 2 = 0.70), and ejection fraction ( $${R}^{2}$$ R 2 = 0.50), as well as predicted systemic phenotypes of age ( $${R}^{2}$$ R 2 = 0.46), sex (AUC = 0.88), weight ( $${R}^{2}$$ R 2 = 0.56), and height ( $${R}^{2}$$ R 2 = 0.33). Interpretation analysis validates that EchoNet shows appropriate attention to key cardiac structures when performing human-explainable tasks and highlights hypothesis-generating regions of interest when predicting systemic phenotypes difficult for human interpretation. Machine learning on echocardiography images can streamline repetitive tasks in the clinical workflow, provide preliminary interpretation in areas with insufficient qualified cardiologists, and predict phenotypes challenging for human evaluation.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The Systematic Evaluation of Identifying the Infarct Related Artery Utilizing Cardiac Magnetic Resonance in Patients Presenting with ST-Elevation Myocardial Infarction.

Carine E Hamo / Igor Klem / Sunil V Rao / Vincent Songco / Samer Najjar / Edward G Lakatta / Subha V Raman / Robert A Harrington / John F Heitner

PLoS ONE, Vol 12, Iss 1, p e

2017 Volume 0169108

Abstract: Identification of the infarct-related artery (IRA) in patients with STEMI using coronary angiography (CA) is often based on the ECG and can be challenging in patients with severe multi-vessel disease. The current study aimed to determine how often ... ...

Abstract	Identification of the infarct-related artery (IRA) in patients with STEMI using coronary angiography (CA) is often based on the ECG and can be challenging in patients with severe multi-vessel disease. The current study aimed to determine how often percutaneous intervention (PCI) is performed in a coronary artery different from the artery supplying the territory of acute infarction on cardiac magnetic resonance imaging (CMR).We evaluated 113 patients from the Reduction of infarct Expansion and Ventricular remodeling with Erythropoetin After Large myocardial infarction (REVEAL) trial, who underwent CMR within 4±2 days of revascularization. Blinded reviewers interpreted CA to determine the IRA and CMR to determine the location of infarction on a 17-segment model. In patients with multiple infarcts on CMR, acuity was determined with T2-weighted imaging and/or evidence of microvascular obstruction.A total of 5 (4%) patients were found to have a mismatch between the IRA identified on CMR and CA. In 4/5 cases, there were multiple infarcts noted on CMR. Thirteen patients (11.5%) had multiple infarcts in separate territories on CMR with 4 patients (3.5%) having multiple acute infarcts and 9 patients (8%) having both acute and chronic infarcts.In this select population of patients, the identification of the IRA by CA was incorrect in 4% of patients presenting with STEMI. Four patients with a mismatch had an acute infarction in more than one coronary artery territory on CMR. The role of CMR in patients presenting with STEMI with multi-vessel disease on CA deserves further investigation.
Keywords	Medicine ; R ; Science ; Q
Subject code	610 ; 616
Language	English
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Physical activity, sleep and cardiovascular health data for 50,000 individuals from the MyHeart Counts Study

Scientific Data, Vol 6, Iss 1, Pp 1-

2019 Volume 10

Abstract: Design Type(s)observation design · source-based data analysis objective · data collection and processing objectiveMeasurement Type(s)physical activity · sleepTechnology Type(s)crowd-sourced data generationFactor Type(s)sex · height · weight · age · ... ...

Abstract	Design Type(s)observation design · source-based data analysis objective · data collection and processing objectiveMeasurement Type(s)physical activity · sleepTechnology Type(s)crowd-sourced data generationFactor Type(s)sex · height · weight · age · smoking status measurement · employment statusSample Characteristic(s)Homo sapiens · United States of America Machine-accessible metadata file describing the reported data (ISA-Tab format)
Keywords	Science ; Q
Language	English
Publishing date	2019-04-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The effect of digital physical activity interventions on daily step count

Anna Shcherbina, MS / Steven G Hershman, PhD / Laura Lazzeroni, ProfPhD / Abby C King, ProfPhD / Jack W O'Sullivan, MBBS / Eric Hekler, PhD / Yasbanoo Moayedi, MD / Aleksandra Pavlovic, BS / Daryl Waggott, MSc / Abhinav Sharma, MD / Alan Yeung, MD / Jeffrey W Christle, PhD / Matthew T Wheeler, MD / Michael V McConnell, MD / Robert A Harrington, ProfMD / Euan A Ashley, ProfMBChB

The Lancet: Digital Health, Vol 1, Iss 7, Pp e344-e

a randomised controlled crossover substudy of the MyHeart Counts Cardiovascular Health Study

2019 Volume 352

Abstract: Summary: Background: Smartphone apps might enable interventions to increase physical activity, but few randomised trials testing this hypothesis have been done. The MyHeart Counts Cardiovascular Health Study is a longitudinal smartphone-based study with ... ...

Abstract	Summary: Background: Smartphone apps might enable interventions to increase physical activity, but few randomised trials testing this hypothesis have been done. The MyHeart Counts Cardiovascular Health Study is a longitudinal smartphone-based study with the aim of elucidating the determinants of cardiovascular health. We aimed to investigate the effect of four different physical activity coaching interventions on daily step count in a substudy of the MyHeart Counts Study. Methods: In this randomised, controlled crossover trial, we recruited adults (aged ≥18 years) in the USA with access to an iPhone smartphone (Apple, Cupertino, CA, USA; version 5S or newer) who had downloaded the MyHeart Counts app (version 2.0). After completion of a 1 week baseline period of interaction with the MyHeart Counts app, participants were randomly assigned to receive one of 24 permutations (four combinations of four 7 day interventions) in a crossover design using a random number generator built into the app. Interventions consisted of either daily prompts to complete 10 000 steps, hourly prompts to stand following 1 h of sitting, instructions to read the guidelines from the American Heart Association website, or e-coaching based upon the individual's personal activity patterns from the baseline week of data collection. Participants completed the trial in a free-living setting. Due to the nature of the interventions, participants could not be masked from the intervention. Investigators were not masked to intervention allocation. The primary outcome was change in mean daily step count from baseline for each of the four interventions, assessed in the modified intention-to-treat analysis set, which included all participants who had completed 7 days of baseline monitoring and at least 1 day of one of the four interventions. This trial is registered with ClinicalTrials.gov, NCT03090321. Findings: Between Dec 12, 2016, and June 6, 2018, 2783 participants consented to enrol in the coaching study, of whom 1075 completed 7 days of baseline ...
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	796
Language	English
Publishing date	2019-11-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Autologous CD34 Cell Therapy for Refractory Angina

Cell Transplantation, Vol

2-Year Outcomes from the ACT34-CMI Study

2016 Volume 25

Abstract: An increasing number of patients have refractory angina despite optimal medical therapy and are without further revascularization options. Preclinical studies indicate that human CD34 + stem cells can stimulate new blood vessel formation in ischemic ... ...

Abstract	An increasing number of patients have refractory angina despite optimal medical therapy and are without further revascularization options. Preclinical studies indicate that human CD34 + stem cells can stimulate new blood vessel formation in ischemic myocardium, improving perfusion and function. In ACT34-CMI ( N = 167), patients treated with autologous CD34 + stem cells had improvements in angina and exercise time at 6 and 12 months compared to placebo; however, the longer-term effects of this treatment are unknown. ACT34 was a phase II randomized, double-blind, placebo-controlled clinical trial comparing placebo, low dose (1 × 10 5 CD34/kg body weight), and high dose (5 × 10 5 CD34/kg) using intramyocardial delivery into the ischemic zone following NOGA ® mapping. To obtain longer-term safety and efficacy in these patients, we compiled data of major adverse cardiac events (MACE; death, myocardial infarction, acute coronary syndrome, or heart failure hospitalization) up to 24 months as well as angina and quality of life assessments in patients who consented for 24-month follow-up. A total of 167 patients with class III–IV refractory angina were randomized and completed the injection procedure. The low-dose-treated patients had a significant reduction in angina frequency ( p = 0.02, 0.035) and improvements in exercise tolerance testing (ETT) time ( p = 0.014, 0.017) compared to the placebo group at 6 and 12 months. At 24 months, patients treated with both low-and high-dose CD34 + cells had significant reduction in angina frequency ( p = 0.03). At 24 months, there were a total of seven deaths (12.5%) in the control group versus one (1.8%) in the low-dose and two (3.6%) in the high-dose ( p = 0.08) groups. At 2 years, MACE occurred at a rate of 33.9%, 21.8%, and 16.2% in control, low-, and high-dose patients, respectively ( p = 0.08). Autologous CD34 + cell therapy was associated with persistent improvement in angina at 2 years and a trend for reduction in mortality in no-option patients with refractory angina.
Keywords	Medicine ; R
Subject code	610 ; 616
Language	English
Publishing date	2016-09-01T00:00:00Z
Publisher	SAGE Publishing
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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