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Record W3035359438 · doi:10.7326/l20-0269

Preoperative <i>N</i>-Terminal Pro–B-Type Natriuretic Peptide and Cardiovascular Events After Noncardiac Surgery

2020· letter· en· W3035359438 on OpenAlexaffabout
Emmanuelle Duceppe, Diane Heels‐Ansdell, P.J. Devereaux

Bibliographic record

VenueAnnals of Internal Medicine · 2020
Typeletter
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineConfoundingNatriuretic peptideContinuous variableInternal medicineCohortCardiologyHeart failure

Abstract

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Letters16 June 2020Preoperative N-Terminal Pro–B-Type Natriuretic Peptide and Cardiovascular Events After Noncardiac SurgeryEmmanuelle Duceppe, MD, Diane Heels-Ansdell, MSc, and P.J. Devereaux, MD, PhDEmmanuelle Duceppe, MDMcMaster University, Hamilton, Ontario, Canada (E.D., D.H., P.D.), Diane Heels-Ansdell, MScMcMaster University, Hamilton, Ontario, Canada (E.D., D.H., P.D.), and P.J. Devereaux, MD, PhDMcMaster University, Hamilton, Ontario, Canada (E.D., D.H., P.D.)Author, Article, and Disclosure Informationhttps://doi.org/10.7326/L20-0269 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE:Dr. Gupta and colleagues commented on the relationship between BMI and NT-proBNP levels. In post hoc analysis, 10 176 of 10 404 patients in our cohort had an available BMI; 1214 (11.9%) of these patients met our primary composite outcome. The interaction term between BMI as a continuous variable and NT-proBNP was not significant (P = 0.051). We used BMI as a continuous variable, because exploring an association between a continuous variable that has been categorized a priori using arbitrary thresholds without first showing the association using the continuous data has been shown to result in type I error inflation and ...References1. Barnwell-Ménard JL, Li Q, Cohen AA. Effects of categorization method, regression type, and variable distribution on the inflation of type-I error rate when categorizing a confounding variable. Stat Med. 2015;34:936-949. [PMID: 25504513] doi:10.1002/sim.6387 CrossrefMedlineGoogle Scholar2. Royston P, Altman DG, Sauerbrei W. Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med. 2006;25:127-141. [PMID: 16217841] CrossrefMedlineGoogle Scholar3. Das SR, Drazner MH, Dries DL, et al. Impact of body mass and body composition on circulating levels of natriuretic peptides: results from the Dallas Heart Study. Circulation. 2005;112:2163-2168. [PMID: 16203929] CrossrefMedlineGoogle Scholar4. Costello-Boerrigter LC, Boerrigter G, Redfield MM, et al. Amino-terminal pro-B-type natriuretic peptide and B-type natriuretic peptide in the general community: determinants and detection of left ventricular dysfunction. J Am Coll Cardiol. 2006;47:345-53. [PMID: 16412859] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: McMaster University, Hamilton, Ontario, Canada (E.D., D.H., P.D.)Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M19-2501. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoPreoperative N-Terminal Pro–B-Type Natriuretic Peptide and Cardiovascular Events After Noncardiac Surgery Emmanuelle Duceppe , Ameen Patel , Matthew T.V. Chan , Otavio Berwanger , Gareth Ackland , Peter A. Kavsak , Reitze Rodseth , Bruce Biccard , Clara K. Chow , Flavia K. Borges , Gordon Guyatt , Rupert Pearse , Daniel I. Sessler , Diane Heels-Ansdell , Andrea Kurz , Chew Yin Wang , Wojciech Szczeklik , Sadeesh Srinathan , Amit X. Garg , Shirley Pettit , Erin N. Sloan , James L. Januzzi Jr. , Matthew McQueen , Giovanna Lurati Buse , Nicholas L. Mills , Lin Zhang , Robert Sapsford , Guillaume Paré , Michael Walsh , Richard Whitlock , Andre Lamy , Stephen Hill , Lehana Thabane , Salim Yusuf , and P.J. Devereaux Preoperative N-Terminal Pro–B-Type Natriuretic Peptide and Cardiovascular Events After Noncardiac Surgery Nisha Bhudia , Ameet Bakhai , and Rachel Baumber Preoperative N-Terminal Pro–B-Type Natriuretic Peptide and Cardiovascular Events After Noncardiac Surgery Neelesh Gupta , Rajkumar Doshi , and Vineet Meghrajani Metrics Cited byAssessment and Correction of the Cardiac Complications Risk in Non-cardiac Operations – What's New?Preventing Myocardial Injury Following Non-Cardiac Surgery: A Potential Role for Preoperative Antioxidant Therapy with Ubiquinone 16 June 2020Volume 172, Issue 12Page: 843KeywordsBody mass indexCardiac surgeryCohort studiesDisclosureHeartHeart diseasesLongitudinal studiesN terminal pro brain natriuretic peptidesNatriuretic peptidesSurgery ePublished: 16 June 2020 Issue Published: 16 June 2020 Copyright & PermissionsCopyright © 2020 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.302
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations19
Published2020
Admission routes2
Has abstractyes

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