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Archive for the ‘Aortic Valve: TAVR’ Category


 

Application of Natural Language Processing (NLP) on ~1MM cases of semi-structured echocardiogram reports: Identification of aortic stenosis (AS) cases – Accuracy comparison to administrative diagnosis codes (IDC 9/10 codes)

Reporter: Aviva Lev-Ari, PhD, RN

Large-Scale Identification of Aortic Stenosis and its Severity Using Natural Language Processing on Electronic Health Records

Background Systematic case identification is critical to improving population health, but widely used diagnosis code-based approaches for conditions like valvular heart disease are inaccurate and lack specificity. Objective To develop and validate natural language processing (NLP) algorithms to identify aortic stenosis (AS) cases and associated parameters from semi-structured echocardiogram reports and compare its accuracy to administrative diagnosis codes. Methods Using 1,003 physician-adjudicated echocardiogram reports from Kaiser Permanente Northern California, a large, integrated healthcare system (>4.5 million members), NLP algorithms were developed and validated to achieve positive and negative predictive values >95% for identifying AS and associated echocardiographic parameters. Final NLP algorithms were applied to all adult echocardiography reports performed between 2008-2018, and compared to ICD-9/10 diagnosis code-based definitions for AS found from 14 days before to six months after the procedure date. Results A total of 927,884 eligible echocardiograms were identified during the study period among 519,967 patients. Application of the final NLP algorithm classified 104,090 (11.2%) echocardiograms with any AS (mean age 75.2 years, 52% women), with only 67,297 (64.6%) having a diagnosis code for AS between 14 days before and up to six months after the associated echocardiogram. Among those without associated diagnosis codes, 19% of patients had hemodynamically significant AS (i.e., greater than mild disease). Conclusion A validated NLP algorithm applied to a systemwide echocardiography database was substantially more accurate than diagnosis codes for identifying AS. Leveraging machine learning-based approaches on unstructured EHR data can facilitate more effective individual and population management than using administrative data alone.

Large-scale identification of aortic stenosis and its severity using natural language processing on electronic health records

Author links open overlay panel

Matthew D.SolomonMD, PhD∗†GraceTabadaMPH∗AmandaAllen∗Sue HeeSungMPH∗Alan S.GoMD∗‡§‖

Division of Research, Kaiser Permanente Northern California, Oakland, California

Department of Cardiology, Kaiser Oakland Medical Center, Oakland, California

Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, California

§

Departments of Epidemiology, Biostatistics and Medicine, University of California, San Francisco, San Francisco, California

Department of Medicine, Stanford University, Stanford, California

Available online 18 March 2021.

https://www.sciencedirect.com/science/article/pii/S2666693621000256

Background

Systematic case identification is critical to improving population health, but widely used diagnosis code–based approaches for conditions like valvular heart disease are inaccurate and lack specificity.

Objective

To develop and validate natural language processing (NLP) algorithms to identify aortic stenosis (AS) cases and associated parameters from semi-structured echocardiogram reports and compare their accuracy to administrative diagnosis codes.

Methods

Using 1003 physician-adjudicated echocardiogram reports from Kaiser Permanente Northern California, a large, integrated healthcare system (>4.5 million members), NLP algorithms were developed and validated to achieve positive and negative predictive values > 95% for identifying AS and associated echocardiographic parameters. Final NLP algorithms were applied to all adult echocardiography reports performed between 2008 and 2018 and compared to ICD-9/10 diagnosis code–based definitions for AS found from 14 days before to 6 months after the procedure date.

Results

A total of 927,884 eligible echocardiograms were identified during the study period among 519,967 patients. Application of the final NLP algorithm classified 104,090 (11.2%) echocardiograms with any AS (mean age 75.2 years, 52% women), with only 67,297 (64.6%) having a diagnosis code for AS between 14 days before and up to 6 months after the associated echocardiogram. Among those without associated diagnosis codes, 19% of patients had hemodynamically significant AS (ie, greater than mild disease).

Conclusion

A validated NLP algorithm applied to a systemwide echocardiography database was substantially more accurate than diagnosis codes for identifying AS. Leveraging machine learning–based approaches on unstructured electronic health record data can facilitate more effective individual and population management than using administrative data alone.

Keywords

Aortic stenosis Echocardiography Machine learning Population health Quality and outcomes Valvular heart disease

SOURCE

https://www.sciencedirect.com/science/article/pii/S2666693621000256

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Bioprosthetic Aortic Valves: The risk for Leaflet Thrombosis

Reporters: Aviva Lev-Ari, PhD, RN

Possible Subclinical Leaflet Thrombosis in Bioprosthetic Aortic Valves

Raj R. Makkar, M.D., Gregory Fontana, M.D., Hasan Jilaihawi, M.D., Tarun Chakravarty, M.D., Klaus F. Kofoed, M.D., D.M.Sc., Ole de Backer, M.D., Ph.D., Federico M. Asch, M.D., Carlos E. Ruiz, M.D., Niels T. Olsen, M.D., Ph.D., Alfredo Trento, M.D., John Friedman, M.D., Daniel Berman, M.D., Wen Cheng, M.D., Mohammad Kashif, M.D., Vladimir Jelnin, M.D., Chad A. Kliger, M.D., Hongfei Guo, Ph.D., Augusto D. Pichard, M.D., Neil J. Weissman, M.D., Samir Kapadia, M.D., Eric Manasse, M.D., Deepak L. Bhatt, M.D., M.P.H., Martin B. Leon, M.D., and Lars Søndergaard, M.D.

October 5, 2015DOI: 10.1056/NEJMoa1509233

https://clinicaltrials.gov/ct2/show/NCT02426307

Video

Leaflet Thrombosis in Bioprosthetic Valves?

http://www.nejm.org/action/showMediaPlayer?doi=10.1056%2FNEJMoa1509233&aid=NEJMoa1509233_attach_1&area=aop

Video

Possible Subclinical Leaflet Thrombosis in Bioprosthetic Aortic Valves.

http://www.nejm.org/action/showMediaPlayer?doi=10.1056%2FNEJMoa1509233&aid=NEJMoa1509233_attach_2&area=aop

Transcatheter aortic-valve replacement (TAVR) is a recent innovation in the management of aortic stenosis. The efficacy and safety of this therapeutic intervention have been studied in several randomized clinical trials.1-6 The Portico Re-sheathable Transcatheter Aortic Valve System U.S. Investigational Device Exemption (PORTICO IDE) study is an ongoing, prospective clinical trial to evaluate TAVR with either a Portico valve (St. Jude Medical) or a commercially available valve.

As specified in the PORTICO IDE protocol, computed tomography (CT) was performed in a subgroup of patients to assess the stent frame of the implanted valve. A finding of reduced leaflet motion on CT in a patient who had had a stroke after TAVR and similar findings in an asymptomatic patient at one clinical site led to closer scrutiny of this observation. Additional CT review by the core laboratory revealed that this finding was not isolated, which prompted a more extensive investigation that involved analysis of all available CT and echocardiographic data.

These events also led to the establishment of two physician-initiated registries to evaluate bioprosthetic leaflet function after TAVR or surgical aortic-valve replacement: the Assessment of Transcatheter and Surgical Aortic Bioprosthetic Valve Thrombosis and Its Treatment with Anticoagulation (RESOLVE) registry and the Subclinical Aortic Valve Bioprosthesis Thrombosis Assessed with Four-Dimensional Computed Tomography (SAVORY) registry. We report the findings of these investigations from the randomized, controlled PORTICO IDE study as well as from the two registries. The major objective of our analyses was to examine the prevalence of reduced leaflet motion in bioprosthetic aortic valves, as assessed on four-dimensional, volume-rendered CT; the association between reduced leaflet motion and strokes and transient ischemic attacks (TIAs); and the influence of anticoagulation on reduced leaflet motion.

Methods

Study Populations, Funding, and Oversight

This study was conducted in patients who were enrolled in the PORTICO IDE randomized trial and in the RESOLVE and SAVORY registries. The PORTICO IDE trial, which is sponsored by St. Jude Medical, has a target enrollment of 1206 patients. The trial protocol is available with the full text of this article at NEJM.org.

The RESOLVE and SAVORY registries are also ongoing. RESOLVE is a single-center registry study that is being conducted at Cedars–Sinai Medical Center in Los Angeles and that includes patients with previously implanted valves (retrospective enrollment) with a target enrollment of 200 patients; it is funded by the Cedars–Sinai Heart Institute. SAVORY is a single-center registry study that is being conducted at Rigshospitalet in Copenhagen and that includes patients with newly implanted valves (prospective enrollment) with a target enrollment of 75 patients; it is funded by St. Jude Medical.

For the PORTICO IDE trial, the sponsor contributed to the study design, supervised data collection and analysis, and reviewed the manuscript to verify the accuracy of data with respect to the PORTICO IDE patients. For the two registries, study design and data collection were conducted by the respective institutions. For the trial and the two registries, approval by the institutional review board at each participating site was obtained before study initiation, and all patients provided written informed consent. The coprincipal investigators had unrestricted access to the data from all three data sets for the purpose of this analysis. They also made the decision to submit the manuscript for publication, prepared all drafts of the manuscript, and attest to the completeness and accuracy of the reported data and for the fidelity of the study to the protocol.

SOURCE

NEJM

DOI: 10.1056/NEJMoa1509233

https://clinicaltrials.gov/ct2/show/NCT02426307

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Metabolic Genomics and Pharmaceutics, Vol. 1 of BioMed Series D available on Amazon Kindle


Metabolic Genomics and Pharmaceutics, Vol. 1 of BioMed Series D available on Amazon Kindle

Reporter: Stephen S Williams, PhD

 

Leaders in Pharmaceutical Business Intelligence would like to announce the First volume of their BioMedical E-Book Series D:

Metabolic Genomics & Pharmaceutics, Vol. I

SACHS FLYER 2014 Metabolomics SeriesDindividualred-page2

which is now available on Amazon Kindle at

http://www.amazon.com/dp/B012BB0ZF0.

This e-Book is a comprehensive review of recent Original Research on  METABOLOMICS and related opportunities for Targeted Therapy written by Experts, Authors, Writers. This is the first volume of the Series D: e-Books on BioMedicine – Metabolomics, Immunology, Infectious Diseases.  It is written for comprehension at the third year medical student level, or as a reference for licensing board exams, but it is also written for the education of a first time baccalaureate degree reader in the biological sciences.  Hopefully, it can be read with great interest by the undergraduate student who is undecided in the choice of a career. The results of Original Research are gaining value added for the e-Reader by the Methodology of Curation. The e-Book’s articles have been published on the Open Access Online Scientific Journal, since April 2012.  All new articles on this subject, will continue to be incorporated, as published with periodical updates.

We invite e-Readers to write an Article Reviews on Amazon for this e-Book on Amazon.

All forthcoming BioMed e-Book Titles can be viewed at:

https://pharmaceuticalintelligence.com/biomed-e-books/

Leaders in Pharmaceutical Business Intelligence, launched in April 2012 an Open Access Online Scientific Journal is a scientific, medical and business multi expert authoring environment in several domains of  life sciences, pharmaceutical, healthcare & medicine industries. The venture operates as an online scientific intellectual exchange at their website http://pharmaceuticalintelligence.com and for curation and reporting on frontiers in biomedical, biological sciences, healthcare economics, pharmacology, pharmaceuticals & medicine. In addition the venture publishes a Medical E-book Series available on Amazon’s Kindle platform.

Analyzing and sharing the vast and rapidly expanding volume of scientific knowledge has never been so crucial to innovation in the medical field. WE are addressing need of overcoming this scientific information overload by:

  • delivering curation and summary interpretations of latest findings and innovations on an open-access, Web 2.0 platform with future goals of providing primarily concept-driven search in the near future
  • providing a social platform for scientists and clinicians to enter into discussion using social media
  • compiling recent discoveries and issues in yearly-updated Medical E-book Series on Amazon’s mobile Kindle platform

This curation offers better organization and visibility to the critical information useful for the next innovations in academic, clinical, and industrial research by providing these hybrid networks.

Table of Contents for Metabolic Genomics & Pharmaceutics, Vol. I

Chapter 1: Metabolic Pathways

Chapter 2: Lipid Metabolism

Chapter 3: Cell Signaling

Chapter 4: Protein Synthesis and Degradation

Chapter 5: Sub-cellular Structure

Chapter 6: Proteomics

Chapter 7: Metabolomics

Chapter 8:  Impairments in Pathological States: Endocrine Disorders; Stress

                   Hypermetabolism and Cancer

Chapter 9: Genomic Expression in Health and Disease 

 

Summary 

Epilogue

 

 

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SACHS FLYER 2014 CVD Title

Please see Further Titles at

https://pharmaceuticalintelligence.com/biomed-e-books/

Please see Further Information on the Sachs Associates 14th Annual Biotech in Europe Forum for Global Investing & Partnering at:

https://pharmaceuticalintelligence.com/2014/03/25/14th-annual-biotech-in-europe-forum-for-global-partnering-investment-930-1012014-%E2%80%A2-congress-center-basel-sachs-associates-london/

AND

http://www.sachsforum.com/basel14/index.html

why-is-twitter-s-logo-named-after-larry-bird--b8d70319daON TWITTER Follow at

@SachsAssociates

#Sachs14thBEF

@pharma_BI

@AVIVA1950 

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