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4. Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study

Author

Kamran, Fahad, Tang, Shengpu, Otles, Erkin, McEvoy, Dustin S, Saleh, Sameh N, Gong, Jen, Li, Benjamin Y, Dutta, Sayon, Liu, Xinran, Medford, Richard J, Valley, Thomas S, West, Lauren R, Singh, Karandeep, Blumberg, Seth, Donnelly, John P, Shenoy, Erica S, Ayanian, John Z, Nallamothu, Brahmajee K, Sjoding, Michael W, and Wiens, Jenna

Subjects
Health Services and Systems, Biomedical and Clinical Sciences, Clinical Sciences, Health Sciences, Clinical Research, Patient Safety, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, Good Health and Well Being, Adolescent, Adult, Aged, Aged, 80 and over, Area Under Curve, COVID-19, Clinical Decision Rules, Clinical Deterioration, Electronic Health Records, Female, Hospitalization, Hospitals, Humans, Linear Models, Machine Learning, Male, Middle Aged, Predictive Value of Tests, Prognosis, ROC Curve, Retrospective Studies, Risk Assessment, SARS-CoV-2, Young Adult, Public Health and Health Services, General & Internal Medicine, Biomedical and clinical sciences, Health sciences, Psychology
Abstract

ObjectiveTo create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical deterioration in patients with covid-19 across institutions, through use of a novel paradigm for model development and code sharing.DesignRetrospective cohort study.SettingOne US hospital during 2015-21 was used for model training and internal validation. External validation was conducted on patients admitted to hospital with covid-19 at 12 other US medical centers during 2020-21.Participants33 119 adults (≥18 years) admitted to hospital with respiratory distress or covid-19.Main outcome measuresAn ensemble of linear models was trained on the development cohort to predict a composite outcome of clinical deterioration within the first five days of hospital admission, defined as in-hospital mortality or any of three treatments indicating severe illness: mechanical ventilation, heated high flow nasal cannula, or intravenous vasopressors. The model was based on nine clinical and personal characteristic variables selected from 2686 variables available in the electronic health record. Internal and external validation performance was measured using the area under the receiver operating characteristic curve (AUROC) and the expected calibration error-the difference between predicted risk and actual risk. Potential bed day savings were estimated by calculating how many bed days hospitals could save per patient if low risk patients identified by the model were discharged early.Results9291 covid-19 related hospital admissions at 13 medical centers were used for model validation, of which 1510 (16.3%) were related to the primary outcome. When the model was applied to the internal validation cohort, it achieved an AUROC of 0.80 (95% confidence interval 0.77 to 0.84) and an expected calibration error of 0.01 (95% confidence interval 0.00 to 0.02). Performance was consistent when validated in the 12 external medical centers (AUROC range 0.77-0.84), across subgroups of sex, age, race, and ethnicity (AUROC range 0.78-0.84), and across quarters (AUROC range 0.73-0.83). Using the model to triage low risk patients could potentially save up to 7.8 bed days per patient resulting from early discharge.ConclusionA model to predict clinical deterioration was developed rapidly in response to the covid-19 pandemic at a single hospital, was applied externally without the sharing of data, and performed well across multiple medical centers, patient subgroups, and time periods, showing its potential as a tool for use in optimizing healthcare resources.

Published
2022

8. Anatomical Variations in Pulmonary Arterial Branches in Patients Undergoing Evaluation for Chronic Thromboembolic Pulmonary Hypertension

Author

Michaud, Erinleigh, Pan, Michael, Lakhter, Vladimir, Reddy Maligireddy, Anand, Hyder, Syed N., Patel, Nimai, Moles, Victor M., McLaughlin, Vallerie V., Agarwal, Prachi P., Visovatti, Scott H., Cascino, Thomas M., Haft, Jonathan, Rosenfield, Kenneth, Nallamothu, Brahmajee K., Giri, Jay S., Bashir, Riyaz, and Aggarwal, Vikas

Published
2023
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12. Vessel Segmentation and Catheter Detection in X-Ray Angiograms Using Superpixels

Author

Fazlali, Hamid R., Karimi, Nader, Soroushmehr, S. M. Reza, Shirani, Shahram, Nallamothu, Brahmajee. K., Ward, Kevin R., Samavi, Shadrokh, and Najarian, Kayvan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Coronary artery disease (CAD) is the leading causes of death around the world. One of the most common imaging methods for diagnosing this disease is X-ray angiography. Diagnosing using these images is usually challenging due to non-uniform illumination, low contrast, presence of other body tissues, presence of catheter etc. These challenges make the diagnoses task of cardiologists tougher and more prone to misdiagnosis. In this paper we propose a new automated framework for coronary arteries segmentation, catheter detection and center-line extraction in x-ray angiography images. Our proposed segmentation method is based on superpixels. In this method at first three different superpixel scales are exploited and a measure for vesselness probability of each superpixel is determined. A majority voting is used for obtaining an initial segmentation map from these three superpixel scales. This initial segmentation is refined by finding the orthogonal line on each ridge pixel of vessel region. In this framework we use our catheter detection and tracking method which detects the catheter by finding its ridge in the first frame and traces in other frames by fitting a second order polynomial on it. Also we use the image ridges for extracting the coronary arteries centerlines. We evaluated our method qualitatively and quantitatively on two different challenging datasets and compared it with one of the previous well-known coronary arteries segmentation methods. Our method could detect the catheter and reduced the false positive rate in addition to achieving better segmentation results. The evaluation results prove that our method performs better in a much shorter time., Comment: 14 Pages, 19 figures, 2 tables

Published
2017

16. Hospital-cardiologist integration often occurs without a practice acquisition

Author

Post, Brady, Nallamothu, Brahmajee K., and Hollenbeck, Brent

Subjects
Medicine -- Practice, Cardiologists -- Employment -- Research, Vertical integration -- Research, Hospitals -- Human resource management -- Research -- United States, Company personnel management, Business, Health care industry
Abstract

Objective: To describe how much of the recent increase in hospital-cardiologist integration has come from acquisitions of physician practices compared to individual employment decisions. While the role of physician practice acquisitions has received considerable attention in the news, integration may also be driven by individual physicians accepting employment at hospital-based practices. Data Sources: American Medical Association Physician Masterfile and Medicare data. Study Design: Analysis of changes in hospital-cardiologist integration from 2011 to 2018. We measured increases in integration and changes in the number of independent and hospital-owned practices. Data Collection/Extraction Methods: Not applicable. Principal Findings: In 2011, 18% of cardiologists were integrated, rising to 25% in 2016. Of this rise, 48% occurred with no acquisitions. Physicians who had completed residencies in the past 5 years (early career physicians) had higher rates of integration that also increased over time: the percentage of early career physicians joining hospital systems rose from 25% to 32%, indicating rapid growth in the number of physicians who began their careers working in hospital-based sites. Conclusions: A large and growing portion of hospital-cardiologist integration came from hospital employment at the individual physician level. Future policies focused on preserving competition and affordability may benefit from better understanding this form of consolidation. KEYWORDS delivery system organization, hospital workforce, hospital-physician integration, outpatient care delivery, physician employment, physician practice organization, vertical integration, 1 | INTRODUCTION Hospitals and physicians have become increasingly integrated in recent years. (1-3) This trend, in which hospitals employ physicians either directly or indirectly through ownership of physician practices, [...]

Published
2022
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17. Resource Requirements for Participant Enrollment From a University Health System and a Federally Qualified Health Center Network in a Mobile Health Study: The myBPmyLife Trial

Author

Skolarus, Lesli E., primary, Farhan, Zahera, additional, Mishra, Sonali R., additional, Rao, Nikhila, additional, Bowie, Kaitlyn, additional, Bailey, Sarah, additional, Dorsch, Michael P., additional, Newman, Mark W., additional, Nallamothu, Brahmajee K., additional, and Golbus, Jessica R., additional

Published
2024
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18. Challenge of Optimizing Medical Therapy in Heart Failure: Unlocking the Potential of Digital Health and Patient Engagement

Author

Azizi, Zahra, primary, Golbus, Jessica R., additional, Spaulding, Erin M., additional, Hwang, Phillip H., additional, Ciminelli, Ana L. A., additional, Lacar, Kathleen, additional, Hernandez, Mario Funes, additional, Gilotra, Nisha A., additional, Din, Natasha, additional, Brant, Luisa C. C., additional, Au, Rhoda, additional, Beaton, Andrea, additional, Nallamothu, Brahmajee K., additional, Longenecker, Chris T., additional, Martin, Seth S., additional, Dorsch, Michael P., additional, and Sandhu, Alexander T., additional

Published
2024
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20. A Physical Activity and Diet Just‐in‐Time Adaptive Intervention to Reduce Blood Pressure: The myBPmyLife Study Rationale and Design

Author

Golbus, Jessica R., primary, Jeganathan, V. Swetha E., additional, Stevens, Rachel, additional, Ekechukwu, Weena, additional, Farhan, Zahera, additional, Contreras, Rocio, additional, Rao, Nikhila, additional, Trumpower, Brad, additional, Basu, Tanima, additional, Luff, Evan, additional, Skolarus, Lesli E., additional, Newman, Mark W., additional, Nallamothu, Brahmajee K., additional, and Dorsch, Michael P., additional

Published
2024
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31. 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

37. Association Between Wearable Device Measured Activity and Patient-Reported Outcomes for Heart Failure

Author

Golbus, Jessica R., primary, Gosch, Kensey, additional, Birmingham, Mary C., additional, Butler, Javed, additional, Lingvay, Ildiko, additional, Lanfear, David E., additional, Abbate, Antonio, additional, Kosiborod, Mikhail L., additional, Damaraju, C.V., additional, Januzzi, James L., additional, Spertus, John, additional, and Nallamothu, Brahmajee K., additional

Published
2023
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