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2. Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation

Author

Perez, Marco V, Mahaffey, Kenneth W, Hedlin, Haley, Rumsfeld, John S, Garcia, Ariadna, Ferris, Todd, Balasubramanian, Vidhya, Russo, Andrea M, Rajmane, Amol, Cheung, Lauren, Hung, Grace, Lee, Justin, Kowey, Peter, Talati, Nisha, Nag, Divya, Gummidipundi, Santosh E, Beatty, Alexis, Hills, Mellanie True, Desai, Sumbul, Granger, Christopher B, Desai, Manisha, and Turakhia, Mintu P

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Clinical Research, Cardiovascular, Clinical Trials and Supportive Activities, Heart Disease, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Good Health and Well Being, Adult, Aged, Algorithms, Atrial Fibrillation, Confidentiality, Electrocardiography, Female, Humans, Male, Middle Aged, Mobile Applications, Predictive Value of Tests, Prospective Studies, Telemedicine, Wearable Electronic Devices, Apple Heart Study Investigators, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundOptical sensors on wearable devices can detect irregular pulses. The ability of a smartwatch application (app) to identify atrial fibrillation during typical use is unknown.MethodsParticipants without atrial fibrillation (as reported by the participants themselves) used a smartphone (Apple iPhone) app to consent to monitoring. If a smartwatch-based irregular pulse notification algorithm identified possible atrial fibrillation, a telemedicine visit was initiated and an electrocardiography (ECG) patch was mailed to the participant, to be worn for up to 7 days. Surveys were administered 90 days after notification of the irregular pulse and at the end of the study. The main objectives were to estimate the proportion of notified participants with atrial fibrillation shown on an ECG patch and the positive predictive value of irregular pulse intervals with a targeted confidence interval width of 0.10.ResultsWe recruited 419,297 participants over 8 months. Over a median of 117 days of monitoring, 2161 participants (0.52%) received notifications of irregular pulse. Among the 450 participants who returned ECG patches containing data that could be analyzed - which had been applied, on average, 13 days after notification - atrial fibrillation was present in 34% (97.5% confidence interval [CI], 29 to 39) overall and in 35% (97.5% CI, 27 to 43) of participants 65 years of age or older. Among participants who were notified of an irregular pulse, the positive predictive value was 0.84 (95% CI, 0.76 to 0.92) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular pulse notification and 0.71 (97.5% CI, 0.69 to 0.74) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular tachogram. Of 1376 notified participants who returned a 90-day survey, 57% contacted health care providers outside the study. There were no reports of serious app-related adverse events.ConclusionsThe probability of receiving an irregular pulse notification was low. Among participants who received notification of an irregular pulse, 34% had atrial fibrillation on subsequent ECG patch readings and 84% of notifications were concordant with atrial fibrillation. This siteless (no on-site visits were required for the participants), pragmatic study design provides a foundation for large-scale pragmatic studies in which outcomes or adherence can be reliably assessed with user-owned devices. (Funded by Apple; Apple Heart Study ClinicalTrials.gov number, NCT03335800.).

Published
2019

5. International Collaborative Partnership for the Study of Atrial Fibrillation (INTERAF): Rationale, Design, and Initial Descriptives.

Author

Hsu, Jonathan C, Akao, Masaharu, Abe, Mitsuru, Anderson, Karen L, Avezum, Alvaro, Glusenkamp, Nathan, Kohsaka, Shun, Lane, Deirdre A, Lip, Gregory YH, Ma, Chang-Sheng, Masoudi, Frederick A, Potpara, Tatjana S, Siong, Teo Wee, Turakhia, Mintu P, Tse, Hung-Fat, Rumsfeld, John S, and Maddox, Thomas M

Subjects
Humans, Atrial Fibrillation, Myocardial Revascularization, Registries, Research Design, International Cooperation, Disease Management, atrial fibrillation, clinical registries, global health, quality of care and outcomes, Cardiorespiratory Medicine and Haematology
Published
2016

6. Medication Initiation Burden Required to Comply With Heart Failure Guideline Recommendations and Hospital Quality Measures

Author

Allen, Larry A, Fonarow, Gregg C, Liang, Li, Schulte, Phillip J, Masoudi, Frederick A, Rumsfeld, John S, Ho, P Michael, Eapen, Zubin J, Hernandez, Adrian F, Heidenreich, Paul A, Bhatt, Deepak L, Peterson, Eric D, and Krumholz, Harlan M

Subjects
Heart Disease, Cardiovascular, Clinical Research, Aged, Aged, 80 and over, Body Mass Index, Cardiovascular Agents, Comorbidity, Contraindications, Cross-Sectional Studies, Drug Hypersensitivity, Drug Prescriptions, Drug Substitution, Drug Therapy, Combination, Drug Utilization, Evidence-Based Medicine, Female, Guideline Adherence, Heart Failure, Hospitals, Humans, Male, Medication Adherence, Middle Aged, Patient Admission, Patient Discharge, Polypharmacy, Practice Guidelines as Topic, Quality Assurance, Health Care, heart failure, medication adherence, medication therapy management, prescribing patterns, physician, quality of health care, American Heart Association’s Get With The Guidelines Heart Failure (GWTG-HF) Investigators, prescribing patterns, physician, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Public Health and Health Services, Cardiovascular System & Hematology
Abstract

BackgroundGuidelines for heart failure (HF) recommend prescription of guideline-directed medical therapy before hospital discharge; some of these therapies are included in publicly reported performance measures. The burden of new medications for individual patients has not been described.Methods and resultsWe used Get With The Guidelines-HF registry data from 2008 to 2013 to characterize prescribing, indications, and contraindications for angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, β-blockers, aldosterone antagonists, hydralazine/isosorbide dinitrate, and anticoagulants. The difference between a patient's medication regimen at hospital admission and that recommended by HF quality measures at discharge was calculated. Among 158 922 patients from 271 hospitals with a primary discharge diagnosis of HF, initiation of angiotensin-converting enzyme inhibitors or angiotensin receptor blockers was indicated in 18.1% of all patients (55.5% of those eligible at discharge were not receiving angiotensin-converting enzyme inhibitors or angiotensin receptor blockers at admission), β-blockers in 20.3% (50.5% of eligible), aldosterone antagonists in 24.1% (87.4% of eligible), hydralazine/isosorbide dinitrate in 8.6% (93.1% of eligible), and anticoagulants in 18.0% (58.0% of eligible). Cumulatively, 0.4% of patients were eligible for 5 new medication groups, 4.1% for 4 new medication groups, 9.4% for 3 new medication groups, 10.1% for 2 new medication groups, and 22.7% for 1 new medication group; 15.0% were not eligible for new medications because of adequate prescribing at admission; and 38.4% were not eligible for any medications recommended by HF quality measures. Compared with newly indicated medications (mean, 1.45 ± 1.23), actual new prescriptions were lower (mean, 1.16 ± 1.00).ConclusionsA quarter of patients hospitalized with HF need to start >1 medication to meet HF quality measures. Systems for addressing medication initiation and managing polypharmacy are central to HF transitional care.

Published
2015

9. Arrhythmias Other Than Atrial Fibrillation in Those With an Irregular Pulse Detected With a Smartwatch: Findings From the Apple Heart Study

Author

Perino, Alexander C., Gummidipundi, Santosh E., Lee, Justin, Hedlin, Haley, Garcia, Ariadna, Ferris, Todd, Balasubramanian, Vidhya, Gardner, Rebecca M., Cheung, Lauren, Hung, Grace, Granger, Christopher B., Kowey, Peter, Rumsfeld, John S., Russo, Andrea M., True Hills, Mellanie, Talati, Nisha, Nag, Divya, Tsay, David, Desai, Sumbul, Desai, Manisha, Mahaffey, Kenneth W., Turakhia, Mintu P., and Perez, Marco V.

Published
2021
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10. ACC/AHA/SCAI/AMA-Convened PCPI/NCQA 2013 performance measures for adults undergoing percutaneous coronary intervention: a report of the American College of Cardiology/American Heart Association Task Force on Performance Measures, the Society for Cardiovascular Angiography and Interventions, the American Medical Association-Convened Physician Consortium for Performance Improvement, and the National Committee for Quality Assurance.

Author

Nallamothu, Brahmajee K, Tommaso, Carl L, Anderson, H Vernon, Anderson, Jeffrey L, Cleveland, Joseph C, Dudley, R Adams, Duffy, Peter Louis, Faxon, David P, Gurm, Hitinder S, Hamilton, Lawrence A, Jensen, Neil C, Josephson, Richard A, Malenka, David J, Maniu, Calin V, McCabe, Kevin W, Mortimer, James D, Patel, Manesh R, Persell, Stephen D, Rumsfeld, John S, Shunk, Kendrick A, Smith, Sidney C, Stanko, Stephen J, and Watts, Brook

Subjects
Humans, Coronary Angiography, Cardiology, Clinical Competence, Adult, American Heart Association, Societies, Medical, American Medical Association, Advisory Committees, Quality Assurance, Health Care, United States, Research Report, Percutaneous Coronary Intervention, ACC/AHA/SCAI/AMA-PCPI/NCQA Performance Measures, ambulatory-level quality, health policy and outcome research, hospital quality, percutaneous coronary intervention, quality indicators, Cardiorespiratory Medicine and Haematology, Public Health and Health Services, Cardiovascular System & Hematology
Published
2014

25. Cardiovascular Care Facts: A Report From the National Cardiovascular Data Registry: 2011

Author

Masoudi, Frederick A., Ponirakis, Angelo, Yeh, Robert W., Maddox, Thomas M., Beachy, Jim, Casale, Paul N., Curtis, Jeptha P., De Lemos, James, Fonarow, Gregg, Heidenreich, Paul, Koutras, Christina, Kremers, Mark, Messenger, John, Moussa, Issam, Oetgen, William J., Roe, Matthew T., Rosenfield, Kenneth, Shields, Thomas P., Jr., Spertus, John A., Wei, Jessica, White, Christopher, Young, Christopher H., and Rumsfeld, John S.

Published
2013
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29. The development of a mobile app‐focused deduplication strategy for the Apple Heart Study that informs recommendations for future digital trials.

Author

Garcia, Ariadna, Lee, Justin, Balasubramanian, Vidhya, Gardner, Rebecca, Gummidipundi, Santosh E., Hung, Grace, Ferris, Todd, Cheung, Lauren, Desai, Sumbul, Granger, Christopher B., Hills, Mellanie True, Kowey, Peter, Nag, Divya, Rumsfeld, John S., Russo, Andrea M., Stein, Jeffrey W., Talati, Nisha, Tsay, David, Mahaffey, Kenneth W., and Perez, Marco V.

Abstract

An app‐based clinical trial enrolment process can contribute to duplicated records, carrying data management implications. Our objective was to identify duplicated records in real time in the Apple Heart Study (AHS). We leveraged personal identifiable information (PII) to develop a dissimilarity score (DS) using the Damerau–Levenshtein distance. For computational efficiency, we focused on four types of records at the highest risk of duplication. We used the receiver operating curve (ROC) and resampling methods to derive and validate a decision rule to classify duplicated records. We identified 16,398 (4%) duplicated participants, resulting in 419,297 unique participants out of a total of 438,435 possible. Our decision rule yielded a high positive predictive value (96%) with negligible impact on the trial's original findings. Our findings provide principled solutions for future digital trials. When establishing deduplication procedures for digital trials, we recommend collecting device identifiers in addition to participant identifiers; collecting and ensuring secure access to PII; conducting a pilot study to identify reasons for duplicated records; establishing an initial deduplication algorithm that can be refined; creating a data quality plan that informs refinement; and embedding the initial deduplication algorithm in the enrolment platform to ensure unique enrolment and linkage to previous records. [ABSTRACT FROM AUTHOR]

Published
2022
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31. Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study

Author

Huang, Chenxi, Murugiah, Karthik, Mahajan, Shiwani, Li, Shu-Xia, Dhruva, Sanket S., Haimovich, Julian S., Wang, Yongfei, Schulz, Wade L., Testani, Jeffrey M., Wilson, Francis P., Mena, Carlos I., Masoudi, Frederick A., Rumsfeld, John S., Spertus, John A., Mortazavi, Bobak J., and Krumholz, Harlan M.

Subjects
Machine learning -- Usage, Acute kidney failure -- Complications and side effects -- Care and treatment, Postoperative complications -- Risk factors, Cardiac patients, Cardiology, Balloon angioplasty, Biological sciences
Abstract

Background The current acute kidney injury (AKI) risk prediction model for patients undergoing percutaneous coronary intervention (PCI) from the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) employed regression techniques. This study aimed to evaluate whether models using machine learning techniques could significantly improve AKI risk prediction after PCI. Methods and findings We used the same cohort and candidate variables used to develop the current NCDR CathPCI Registry AKI model, including 947,091 patients who underwent PCI procedures between June 1, 2009, and June 30, 2011. The mean age of these patients was 64.8 years, and 32.8% were women, with a total of 69,826 (7.4%) AKI events. We replicated the current AKI model as the baseline model and compared it with a series of new models. Temporal validation was performed using data from 970,869 patients undergoing PCIs between July 1, 2016, and March 31, 2017, with a mean age of 65.7 years; 31.9% were women, and 72,954 (7.5%) had AKI events. Each model was derived by implementing one of two strategies for preprocessing candidate variables (preselecting and transforming candidate variables or using all candidate variables in their original forms), one of three variable-selection methods (stepwise backward selection, lasso regularization, or permutation-based selection), and one of two methods to model the relationship between variables and outcome (logistic regression or gradient descent boosting). The cohort was divided into different training (70%) and test (30%) sets using 100 different random splits, and the performance of the models was evaluated internally in the test sets. The best model, according to the internal evaluation, was derived by using all available candidate variables in their original form, permutation-based variable selection, and gradient descent boosting. Compared with the baseline model that uses 11 variables, the best model used 13 variables and achieved a significantly better area under the receiver operating characteristic curve (AUC) of 0.752 (95% confidence interval [CI] 0.749-0.754) versus 0.711 (95% CI 0.708-0.714), a significantly better Brier score of 0.0617 (95% CI 0.0615-0.0618) versus 0.0636 (95% CI 0.0634-0.0638), and a better calibration slope of observed versus predicted rate of 1.008 (95% CI 0.988-1.028) versus 1.036 (95% CI 1.015-1.056). The best model also had a significantly wider predictive range (25.3% versus 21.6%, p < 0.001) and was more accurate in stratifying AKI risk for patients. Evaluated on a more contemporary CathPCI cohort (July 1, 2015-March 31, 2017), the best model consistently achieved significantly better performance than the baseline model in AUC (0.785 versus 0.753), Brier score (0.0610 versus 0.0627), calibration slope (1.003 versus 1.062), and predictive range (29.4% versus 26.2%). The current study does not address implementation for risk calculation at the point of care, and potential challenges include the availability and accessibility of the predictors. Conclusions Machine learning techniques and data-driven approaches resulted in improved prediction of AKI risk after PCI. The results support the potential of these techniques for improving risk prediction models and identification of patients who may benefit from risk-mitigation strategies., Author(s): Chenxi Huang 1, Karthik Murugiah 2, Shiwani Mahajan 1, Shu-Xia Li 1, Sanket S. Dhruva 3,4, Julian S. Haimovich 5, Yongfei Wang 1, Wade L. Schulz 1,6, Jeffrey M. [...]

Published
2018
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35. Risk adjustment for in-hospital mortality of contemporary patients with acute myocardial infarction: The Acute Coronary Treatment and Intervention Outcomes Network (ACTION) Registry ®–Get With The Guidelines (GWTG)™ acute myocardial infarction mortality model and risk score

Author

Chin, Chee Tang, Chen, Anita Y., Wang, Tracy Y., Alexander, Karen P., Mathews, Robin, Rumsfeld, John S., Cannon, Christopher P., Fonarow, Gregg C., Peterson, Eric D., and Roe, Matthew T.

Published
2011
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38. Comparison of performance on Hospital Compare process measures and patient outcomes between hospitals that do and do not participate in Acute Coronary Treatment and Intervention Outcomes Network Registry–Get With The Guidelines

Author

Mathews, Robin, Fonarow, Gregg C., Li, Shuang, Peterson, Eric D., Rumsfeld, John S., Heidenreich, Paul A., Roe, Matthew T., Oetgen, William J., Jollis, James G., Cannon, Christopher P., de Lemos, James A., and Wang, Tracy Y.

Published
2016
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41. Association of prior coronary artery bypass graft surgery with quality of care of patients with non–ST-segment elevation myocardial infarction: A report from the National Cardiovascular Data Registry Acute Coronary Treatment and Intervention Outcomes Network Registry-Get With the Guidelines

Author

Kim, Michael S., Wang, Tracy Y., Ou, Fang-Shu, Klein, Andrew J., Hudson, Paul A., Messenger, John C., Masoudi, Frederick A., Rumsfeld, John S., and Ho, P. Michael

Published
2010
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43. Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction

Author

Khera, Rohan, primary, Haimovich, Julian, additional, Hurley, Nathan C., additional, McNamara, Robert, additional, Spertus, John A., additional, Desai, Nihar, additional, Rumsfeld, John S., additional, Masoudi, Frederick A., additional, Huang, Chenxi, additional, Normand, Sharon-Lise, additional, Mortazavi, Bobak J., additional, and Krumholz, Harlan M., additional

Published
2021
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50. Utilization and Impact of Pre-Hospital Electrocardiograms for Patients With Acute ST-Segment Elevation Myocardial Infarction: Data From the NCDR (National Cardiovascular Data Registry) ACTION (Acute Coronary Treatment and Intervention Outcomes Network) Registry

Author

Diercks, Deborah B., Kontos, Michael C., Chen, Anita Y., Pollack, Charles V., Jr, Wiviott, Stephen D., Rumsfeld, John S., Magid, David J., Gibler, W. Brian, Cannon, Christopher P., Peterson, Eric D., and Roe, Matthew T.

Published
2009
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