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594 results on '"Rumsfeld, John S."'

11. 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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18. The Association of Transcatheter Aortic Valve Replacement Availability and Hospital Aortic Valve Replacement Volume and Mortality in the United States

Author

Brennan, J. Matthew, Holmes, David R., Sherwood, Matthew W., Edwards, Fred H., Carroll, John D., Grover, Fred L., Tuzcu, E. Murat, Thourani, Vinod, Brindis, Ralph G., Shahian, David M., Svensson, Lars G., O’Brien, Sean M., Shewan, Cynthia M., Hewitt, Kathleen, Gammie, James S., Rumsfeld, John S., Peterson, Eric D., and Mack, Michael J.

Published
2014
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20. Invasive coronary procedure use and outcomes among veterans with posttraumatic stress disorder: Insights from the Veterans Affairs Clinical Assessment, Reporting, and Tracking Program

Author

Bradley, Steven M., Stanislawski, Maggie A., Bekelman, David B., Monteith, Lindsey L., Cohen, Beth E., Schilling, John H., Hunt, Stephen C., Milek, Debra, Maddox, Thomas M., Ho, P. Michael, Shore, Supriya, Varosy, Paul D., Matthieu, Monica M., and Rumsfeld, John S.

Published
2014
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21. Addressing barriers to optimal oral anticoagulation use and persistence among patients with atrial fibrillation: Proceedings, Washington, DC, December 3-4, 2012

Author

Hess, Paul L., Mirro, Michael J., Diener, Hans-Christoph, Eikelboom, John W., Al-Khatib, Sana M., Hylek, Elaine M., Bosworth, Hayden B., Gersh, Bernard J., Singer, Daniel E., Flaker, Greg, Mega, Jessica L., Peterson, Eric D., Rumsfeld, John S., Steinberg, Benjamin A., Kakkar, Ajay K., Califf, Robert M., and Granger, Christopher B.

Published
2014
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33. 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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36. 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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37. 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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47. 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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