Search

Your search keyword '"Bray, Bruce E."' showing total 216 results
Start Over Author "Bray, Bruce E."
216 results on '"Bray, Bruce E."'

1. 2022 ACC/AHA guideline for the diagnosis and management of aortic disease: A report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines

Author

Isselbacher, Eric M., Preventza, Ourania, Hamilton Black, James, III, Augoustides, John G., Beck, Adam W., Bolen, Michael A., Braverman, Alan C., Bray, Bruce E., Brown-Zimmerman, Maya M., Chen, Edward P., Collins, Tyrone J., DeAnda, Abe, Jr., Fanola, Christina L., Girardi, Leonard N., Hicks, Caitlin W., Hui, Dawn S., Schuyler Jones, William, Kalahasti, Vidyasagar, Kim, Karen M., Milewicz, Dianna M., Oderich, Gustavo S., Ogbechie, Laura, Promes, Susan B., Ross, Elsie Gyang, Schermerhorn, Marc L., Singleton Times, Sabrina, Tseng, Elaine E., Wang, Grace J., Woo, Y. Joseph, Faxon, David P., Upchurch, Gilbert R., Jr, Aday, Aaron W., Azizzadeh, Ali, Boisen, Michael, Hawkins, Beau, Kramer, Christopher M., Luc, Jessica G.Y., MacGillivray, Thomas E., Malaisrie, S. Christopher, Osteen, Kathryn, Patel, Himanshu J., Patel, Parag J., Popescu, Wanda M., Rodriguez, Evelio, Sorber, Rebecca, Tsao, Philip S., Santos Volgman, Annabelle, Beckman, Joshua A., Otto, Catherine M., O'Gara, Patrick T., Armbruster, Anastasia, Birtcher, Kim K., de las Fuentes, Lisa, Deswal, Anita, Dixon, Dave L., Gorenek, Bulent, Haynes, Norrisa, Hernandez, Adrian F., Joglar, José A., Jones, W. Schuyler, Mark, Daniel, Mukherjee, Debabrata, Palaniappan, Latha, Piano, Mariann R., Rab, Tanveer, Spatz, Erica S., and Tamis-Holland, Jacqueline E.

Published
2023
Full Text
View/download PDF

2. Summary of third annual MCBK public meeting: Mobilizing computable biomedical knowledge—Accelerating the second knowledge revolution

Author

Williams, Michelle, Richesson, Rachel L, Bray, Bruce E, Greenes, Robert A, McIntosh, Leslie D, Middleton, Blackford, Perry, Gerald, Platt, Jodyn, and Shaffer, Christopher

Subjects
Information and Computing Sciences, Health Services and Systems, Health Sciences, computable biomedical knowledge, dissemination, metadata, standards, clinical decision support, HIT policy
Abstract

The volume of biomedical knowledge is growing exponentially and much of this knowledge is represented in computer executable formats, such as models, algorithms, and programmatic code. There is a growing need to apply this knowledge to improve health in Learning Health Systems, health delivery organizations, and other settings. However, most organizations do not yet have the infrastructure required to consume and apply computable knowledge, and national policies and standards adoption are not sufficient to ensure that it is discoverable and used safely and fairly, nor is there widespread experience in the process of knowledge implementation as clinical decision support. The Mobilizing Computable Biomedical Knowledge (MCBK) community was formed in 2016 to address these needs. This report summarizes the main outputs of the third annual MCBK public meeting, which was held virtually from June 30 to July 1, 2020 and brought together over 200 participants from various domains to frame and address important dimensions for mobilizing CBK.

Published
2021

3. Summary of second annual MCBK public meeting: Mobilizing Computable Biomedical Knowledge—A movement to accelerate translation of knowledge into action

Author

Richesson, Rachel L, Bray, Bruce E, Dymek, Christine, Greenes, Robert A, McIntosh, Leslie D, Middleton, Blackford, Perry, Gerald, Platt, Jodyn, and Shaffer, Christopher

Subjects
Information and Computing Sciences, Health Services and Systems, Health Sciences, Rare Diseases, Good Health and Well Being, clinical decision support, computable biomedical knowledge, dissemination, HIT policy, knowledge, metadata, standards
Abstract

The volume of biomedical knowledge is growing exponentially and much of this knowledge is represented in computer executable formats, such as models, algorithms and programmatic code. There is a growing need to apply this knowledge to improve health in Learning Health Systems, health delivery organizations, and other settings. However, most organizations do not yet have the infrastructure required to consume and apply computable knowledge, and national policies and standards adoption are not sufficient to ensure that it is discoverable and used safely and fairly, nor is there widespread experience in the process of knowledge implementation as clinical decision support. The Mobilizing Computable Biomedical Knowledge (MCBK) community formed in 2016 to address these needs. This report summarizes the main outputs of the Second Annual MCBK public meeting, which was held at the National Institutes of Health on July 18-19, 2019 and brought together over 150 participants from various domains to frame and address important dimensions for mobilizing CBK.

Published
2020

4. Topic Modeling Based on ICD Codes for Clinical Documents

Author

Shao, Yijun, Morris, Rebecca S., Bray, Bruce E., Zeng-Treitler, Qing, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Arai, Kohei, editor

Published
2022
Full Text
View/download PDF

5. 2022 ACC/AHA Guideline for the Diagnosis and Management of Aortic Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines

Author

Isselbacher, Eric M., Preventza, Ourania, Hamilton Black III, James, Augoustides, John G., Beck, Adam W., Bolen, Michael A., Braverman, Alan C., Bray, Bruce E., Brown-Zimmerman, Maya M., Chen, Edward P., Collins, Tyrone J., DeAnda, Abe, Jr., Fanola, Christina L., Girardi, Leonard N., Hicks, Caitlin W., Hui, Dawn S., Jones, William Schuyler, Kalahasti, Vidyasagar, Kim, Karen M., Milewicz, Dianna M., Oderich, Gustavo S., Ogbechie, Laura, Promes, Susan B., Ross, Elsie Gyang, Schermerhorn, Marc L., Times, Sabrina Singleton, Tseng, Elaine E., Wang, Grace J., and Woo, Y. Joseph

Published
2022
Full Text
View/download PDF

16. 2022 ACC/AHA Guideline for the Diagnosis and Management of Aortic Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines

Author

Isselbacher, Eric M., primary, Preventza, Ourania, additional, Hamilton Black, James, additional, Augoustides, John G., additional, Beck, Adam W., additional, Bolen, Michael A., additional, Braverman, Alan C., additional, Bray, Bruce E., additional, Brown-Zimmerman, Maya M., additional, Chen, Edward P., additional, Collins, Tyrone J., additional, DeAnda, Abe, additional, Fanola, Christina L., additional, Girardi, Leonard N., additional, Hicks, Caitlin W., additional, Hui, Dawn S., additional, Schuyler Jones, William, additional, Kalahasti, Vidyasagar, additional, Kim, Karen M., additional, Milewicz, Dianna M., additional, Oderich, Gustavo S., additional, Ogbechie, Laura, additional, Promes, Susan B., additional, Gyang Ross, Elsie, additional, Schermerhorn, Marc L., additional, Singleton Times, Sabrina, additional, Tseng, Elaine E., additional, Wang, Grace J., additional, and Woo, Y. Joseph, additional

Published
2022
Full Text
View/download PDF

18. Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?

Author

Brown, Jeremiah R., primary, Ricket, Iben M., additional, Reeves, Ruth M., additional, Shah, Rashmee U., additional, Goodrich, Christine A., additional, Gobbel, Glen, additional, Stabler, Meagan E., additional, Perkins, Amy M., additional, Minter, Freneka, additional, Cox, Kevin C., additional, Dorn, Chad, additional, Denton, Jason, additional, Bray, Bruce E., additional, Gouripeddi, Ramkiran, additional, Higgins, John, additional, Chapman, Wendy W., additional, MacKenzie, Todd, additional, and Matheny, Michael E., additional

Published
2022
Full Text
View/download PDF

20. An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records

Author

Wesołowski, Sergiusz, primary, Lemmon, Gordon, additional, Hernandez, Edgar J., additional, Henrie, Alex, additional, Miller, Thomas A., additional, Weyhrauch, Derek, additional, Puchalski, Michael D., additional, Bray, Bruce E., additional, Shah, Rashmee U., additional, Deshmukh, Vikrant G., additional, Delaney, Rebecca, additional, Yost, H. Joseph, additional, Eilbeck, Karen, additional, Tristani-Firouzi, Martin, additional, and Yandell, Mark, additional

Published
2022
Full Text
View/download PDF

22. Value Driven Outcomes (VDO): a pragmatic, modular, and extensible software framework for understanding and improving health care costs and outcomes

Author

Kawamoto, Kensaku, Martin, Cary J, Williams, Kip, Tu, Ming-Chieh, Park, Charlton G, Hunter, Cheri, Staes, Catherine J, Bray, Bruce E, Deshmukh, Vikrant G, Holbrook, Reid A, Morris, Scott J, Fedderson, Matthew B, Sletta, Amy, Turnbull, James, Mulvihill, Sean J, Crabtree, Gordon L, Entwistle, David E, McKenna, Quinn L, Strong, Michael B, Pendleton, Robert C, and Lee, Vivian S

Published
2015
Full Text
View/download PDF

23. An Explainable Artificial Intelligence Approach for Predicting Cardiovascular Outcomes using Electronic Health Records

Author

Wesołowski, Sergiusz, primary, Lemmon, Gordon, additional, Hernandez, Edgar J., additional, Henrie, Alex, additional, Miller, Thomas A., additional, Weyhrauch, Derek, additional, Puchalski, Michael D., additional, Bray, Bruce E., additional, Shah, Rashmee U., additional, Deshmukh, Vikrant G., additional, Delaney, Rebecca, additional, Yost, H. Joseph, additional, Eilbeck, Karen, additional, Tristani-Firouzi, Martin, additional, and Yandell, Mark, additional

Published
2021
Full Text
View/download PDF

25. Categorizing metadata to help mobilize computable biomedical knowledge

Author

Alper, Brian S., primary, Flynn, Allen, additional, Bray, Bruce E., additional, Conte, Marisa L., additional, Eldredge, Christina, additional, Gold, Sigfried, additional, Greenes, Robert A., additional, Haug, Peter, additional, Jacoby, Kim, additional, Koru, Gunes, additional, McClay, James, additional, Sainvil, Marc L., additional, Sottara, Davide, additional, Tuttle, Mark, additional, Visweswaran, Shyam, additional, and Yurk, Robin Ann, additional

Published
2021
Full Text
View/download PDF

26. Development of Electronic Health Record–Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction

Author

Matheny, Michael E., primary, Ricket, Iben, additional, Goodrich, Christine A., additional, Shah, Rashmee U., additional, Stabler, Meagan E., additional, Perkins, Amy M., additional, Dorn, Chad, additional, Denton, Jason, additional, Bray, Bruce E., additional, Gouripeddi, Ram, additional, Higgins, John, additional, Chapman, Wendy W., additional, MacKenzie, Todd A., additional, and Brown, Jeremiah R., additional

Published
2021
Full Text
View/download PDF

31. Predicting Adverse Outcomes in Heart Failure Patients Using Different Frailty Status Measures

Author

Cheng, Yan, Shao, Yijun, Weir, Charlene R., Shah, Rashmee U., Bray, Bruce E., Garvin, Jennifer H., and Zeng-Treitler, Qing

Subjects
Heart Failure, Mental Health, Frailty, Frail Elderly, Prevalence, Humans, Geriatric Assessment, Article, Medical Informatics, Aged
Abstract

Frailty is an important outcome predictor in older patients. We randomly sampled 12,000 veterans with heart failure diagnosed in 2010. The topic modeling method was applied to identify frailty-related topics from the clinical notes in the electronic medical records. The frailty topics were classified into five deficit areas including physical functioning (PF), role-physical (RP), general health (GH), social functioning (SF), and mental health (MH). We experimented with different covariates and four different frailty measures: individual frailty topics, number of distinct frailty topics, a dichotomous deficit category, and the number of distinct deficits, respectively. A total of 8,531 (71.1%) patients had at least one frailty topic. The prevalence of GH, PF, MH, SF, and RP deficits were 89.0%, 61.3%, 56.9%, 40.6%, and 9.5%, respectively. PF deficits (yes/no) and the number of distinct deficits were the most consistent, significant predictors of adverse outcomes of rehosptalization or death.

Published
2017

33. Summary of fourth annual MCBK public meeting: Mobilizing computable biomedical knowledge—metadata and trust.

Author

Williams, Michelle, Bray, Bruce E., Greenes, Robert A., McCusker, Jamie, Middleton, Blackford, Perry, Gerald, Platt, Jodyn, Richesson, Rachel L., Rubin, Joshua C., and Wheeler, Terrie

Subjects
*PUBLIC meetings, *METADATA, *GOVERNMENT policy, *INSTRUCTIONAL systems
Abstract

The exponential growth of biomedical knowledge in computable formats challenges organizations to consider mobilizing artifacts in findable, accessible, interoperable, reusable, and trustable (FAIR+T) ways1. There is a growing need to apply biomedical knowledge artifacts to improve health in Learning Health Systems, health delivery organizations, and other settings. However, most organizations lack the infrastructure required to consume and apply computable knowledge, and national policies and standards adoption are insufficient to ensure that it is discoverable and used safely and fairly, nor is there widespread experience in the process of knowledge implementation as clinical decision support. The Mobilizing Computable Biomedical Knowledge (MCBK) community formed in 2016 to address these needs. This report summarizes the main outputs of the Fourth Annual MCBK public meeting, which was held virtually July 20 to July 21, 2021 and convened over 100 participants spanning diverse domains to frame and address important dimensions for mobilizing CBK. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

34. Categorizing metadata to help mobilize computable biomedical knowledge.

Author

Alper, Brian S., Flynn, Allen, Bray, Bruce E., Conte, Marisa L., Eldredge, Christina, Gold, Sigfried, Greenes, Robert A., Haug, Peter, Jacoby, Kim, Koru, Gunes, McClay, James, Sainvil, Marc L., Sottara, Davide, Tuttle, Mark, Visweswaran, Shyam, and Yurk, Robin Ann

Subjects
METADATA, INTERNET stores
Abstract

Introduction: Computable biomedical knowledge artifacts (CBKs) are digital objects conveying biomedical knowledge in machine‐interpretable structures. As more CBKs are produced and their complexity increases, the value obtained from sharing CBKs grows. Mobilizing CBKs and sharing them widely can only be achieved if the CBKs are findable, accessible, interoperable, reusable, and trustable (FAIR+T). To help mobilize CBKs, we describe our efforts to outline metadata categories to make CBKs FAIR+T. Methods: We examined the literature regarding metadata with the potential to make digital artifacts FAIR+T. We also examined metadata available online today for actual CBKs of 12 different types. With iterative refinement, we came to a consensus on key categories of metadata that, when taken together, can make CBKs FAIR+T. We use subject‐predicate‐object triples to more clearly differentiate metadata categories. Results: We defined 13 categories of CBK metadata most relevant to making CBKs FAIR+T. Eleven of these categories (type, domain, purpose, identification, location, CBK‐to‐CBK relationships, technical, authorization and rights management, provenance, evidential basis, and evidence from use metadata) are evident today where CBKs are stored online. Two additional categories (preservation and integrity metadata) were not evident in our examples. We provide a research agenda to guide further study and development of these and other metadata categories. Conclusion: A wide variety of metadata elements in various categories is needed to make CBKs FAIR+T. More work is needed to develop a common framework for CBK metadata that can make CBKs FAIR+T for all stakeholders. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

37. Automating Quality Measures for Heart Failure Using Natural Language Processing: A Descriptive Study in the Department of Veterans Affairs

Author

Garvin, Jennifer Hornung, primary, Kim, Youngjun, additional, Gobbel, Glenn Temple, additional, Matheny, Michael E, additional, Redd, Andrew, additional, Bray, Bruce E, additional, Heidenreich, Paul, additional, Bolton, Dan, additional, Heavirland, Julia, additional, Kelly, Natalie, additional, Reeves, Ruth, additional, Kalsy, Megha, additional, Goldstein, Mary Kane, additional, and Meystre, Stephane M, additional

Published
2018
Full Text
View/download PDF

38. Automating Quality Measures for Heart Failure Using Natural Language Processing: A Descriptive Study in the Department of Veterans Affairs (Preprint)

Author

Garvin, Jennifer Hornung, primary, Kim, Youngjun, additional, Gobbel, Glenn Temple, additional, Matheny, Michael E, additional, Redd, Andrew, additional, Bray, Bruce E, additional, Heidenreich, Paul, additional, Bolton, Dan, additional, Heavirland, Julia, additional, Kelly, Natalie, additional, Reeves, Ruth, additional, Kalsy, Megha, additional, Goldstein, Mary Kane, additional, and Meystre, Stephane M, additional

Published
2017
Full Text
View/download PDF

40. Representation of Functional Status Concepts from Clinical Documents and Social Media Sources by Standard Terminologies

Author

Kuang, Jinqiu, Mohanty, April F., Rashmi, V.H., Weir, Charlene R., Bray, Bruce E., and Zeng-Treitler, Qing

Subjects
Patient Outcome Assessment, Vocabulary, Controlled, Cardiovascular Diseases, Health Status, Electronic Health Records, Humans, Articles, Social Media, Unified Medical Language System, Semantics
Abstract

Patient-reported functional status is widely recognized as an important patient-centered outcome that adds value to medical care, research, and quality improvement. Functional status outcomes are, however, not routinely or uniformly collected in the medical record, except in certain small patient populations (e.g. geriatrics, nursing home residents). To utilize patient reported functional status for clinical research and practice, we manually collected 2,763 terms from clinical records and social media sites and modeled them on the widely used Short Form-36 Health Survey. We then examined the coverage of the Unified Medical Language System (UMLS) for these functional status terms through automated mapping. Most terms (85.9%) did not have exact matches in the UMLS. The partial matches were prevalent, however, they typically did not capture the terms' exact semantics. Our study suggests that there is a need to extend existing standard terminologies to incorporate functional status terms used by patients and clinicians.

Published
2015

42. Heart Failure Medications Detection and Prescription Status Classification in Clinical Narrative Documents

Author

Meystre, Stéphane M., Kim, Youngjun, Heavirland, Julia, Williams, Jenifer, Bray, Bruce E., and Garvin, Jennifer

Subjects
Heart Failure, Machine Learning, Angiotensin Receptor Antagonists, Narration, Vocabulary, Controlled, Data Mining, Electronic Health Records, Humans, Angiotensin-Converting Enzyme Inhibitors, Drug Prescriptions, Article, Natural Language Processing
Abstract

Angiotensin Converting Enzyme Inhibitors (ACEI) and Angiotensin II Receptor Blockers (ARB) are two common medication classes used for heart failure treatment. The ADAHF (Automated Data Acquisition for Heart Failure) project aimed at automatically extracting heart failure treatment performance metrics from clinical narrative documents, and these medications are an important component of the performance metrics. We developed two different systems to detect these medications, rule-based and machine learning-based. The rule-based system used dictionary lookups with fuzzy string searching and showed successful performance even if our corpus contains various misspelled medications. The machine learning-based system uses lexical and morphological features and produced similar results. The best performance was achieved when combining the two methods, reaching 99.3% recall and 98.8% precision. To determine the prescription status of each medication (i.e., active, discontinued, or negative), we implemented a SVM classifier with lexical features and achieved good performance, reaching 95.49% accuracy, in a five-fold cross-validation evaluation.

Published
2015

43. EXTRACTING FRAILTY STATUS FOR POST SURGICAL MORTALITY PREDICTION.

Author

Qing Zeng-Treitler, Yijun Shao, Yan Cheng, Doing-Harris, Kristina, Shah, Rashmee U., Weir, Charlene R., and Bray, Bruce E.

Subjects
FRAGILITY (Psychology), DEATH, ONTOLOGIES (Information retrieval), MORTALITY
Abstract

Frailty is increasingly recognized as a leading indicator of worsening health outcomes, even death. Frailty is also used implicitly by many clinicians for decision-making, but rarely in a systematic way. We developed a novel ontology-based natural language processing approach to extract frailty status from clinical notes and support retrospective clinical studies and prospective clinical decision making. The natural language processing classification of frailty description achieved an accuracy of 80.3%. We used the extracted frailty information along with other clinical variables to predict mortality after major cardiovascular procedures. The area under the curve for mortality prediction was 78.3% on the test data for a deep neural network model, compared to 74.9% using a support vector machine. [ABSTRACT FROM AUTHOR]

Published
2018

44. Clinical Decision Support-based Quality Measurement (CDS-QM) Framework: Prototype Implementation, Evaluation, and Future Directions

Author

Kukhareva, Polina V, Kawamoto, Kensaku, Shields, David E, Barfuss, Darryl T, Halley, Anne M, Tippetts, Tyler J, Warner, Phillip B, Bray, Bruce E, and Staes, Catherine J

Subjects
Academic Medical Centers, Surgical Procedures, Operative, Utah, Electronic Health Records, Feasibility Studies, Humans, Articles, Decision Support Systems, Clinical, Quality Improvement
Abstract

Electronic quality measurement (QM) and clinical decision support (CDS) are closely related but are typically implemented independently, resulting in significant duplication of effort. While it seems intuitive that technical approaches could be re-used across these two related use cases, such reuse is seldom reported in the literature, especially for standards-based approaches. Therefore, we evaluated the feasibility of using a standards-based CDS framework aligned with anticipated EHR certification criteria to implement electronic QM. The CDS-QM framework was used to automate a complex national quality measure (SCIP-VTE-2) at an academic healthcare system which had previously relied on time-consuming manual chart abstractions. Compared with 305 manually-reviewed reference cases, the recall of automated measurement was 100%. The precision was 96.3% (CI:92.6%-98.5%) for ascertaining the denominator and 96.2% (CI:92.3%-98.4%) for the numerator. We therefore validated that a standards-based CDS-QM framework can successfully enable automated QM, and we identified benefits and challenges with this approach.

Published
2014

45. Automated Heart Failure Quality Measurement with Natural Language Processing

Author

Garvin, Jennifer H., primary, Kim, Youngjun, additional, Gobbel, Glenn T., additional, Matheny, Michael E., additional, Redd, Andrew, additional, Bray, Bruce E., additional, Heidenreich, Paul, additional, Kalsy, Megha, additional, Goldstein, Mary K., additional, Bolton, Dan, additional, Nativi-Nicolau, Jose, additional, and Meystre, Stephane M., additional

Published
2016
Full Text
View/download PDF

48. Merging Electronic Health Record Data and Genomics for Cardiovascular Research

Author

Hall, Jennifer L., primary, Ryan, John J., additional, Bray, Bruce E., additional, Brown, Candice, additional, Lanfear, David, additional, Newby, L. Kristin, additional, Relling, Mary V., additional, Risch, Neil J., additional, Roden, Dan M., additional, Shaw, Stanley Y., additional, Tcheng, James E., additional, Tenenbaum, Jessica, additional, Wang, Thomas N., additional, and Weintraub, William S., additional

Published
2016
Full Text
View/download PDF

50. Predicting Adverse Outcomes in Heart Failure Patients Using Different Frailty Status Measures.

Author

Yan Cheng, Yijun Shao, Weir, Charlene R., Shah, Rashmee U., Bray, Bruce E., Garvin, Jennifer H., and Qing Zeng-Treitler

Subjects
HEART failure, ELECTRONIC health records, COMPUTERS in the health care industry, MEDICAL databases, INFORMATION storage & retrieval systems, MEDICAL informatics, CONFERENCES & conventions
Abstract

Frailty is an important outcome predictor in older patients. We randomly sampled 12,000 veterans with heart failure diagnosed in 2010. The topic modeling method was applied to identify frailty-related topics from the clinical notes in the electronic medical records. The frailty topics were classified into five deficit areas including physical functioning (PF), role-physical (RP), general health (GH), social functioning (SF), and mental health (MH). We experimented with different covariates and four different frailty measures: individual frailty topics, number of distinct frailty topics, a dichotomous deficit category and the number of distinct deficits, respectively. A total of 8,531 (71.1%) patients had at least one frailty topic. The prevalence of GH, PF, MH, SF, and RP deficits were 89.0%, 61.3%, 56.9%, 40.6%, and 9.5%, respectively. PF deficits (yes/no) and the number of distinct deficits were the most consistent, significant predictors of adverse outcomes of rehosptalization or death. [ABSTRACT FROM AUTHOR]

Published
2017
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results