Literature DB >> 25579640

Transparent reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.

Gary S Collins1, Johannes B Reitsma2, Douglas G Altman3, Karel G M Moons2.   

Abstract

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
Copyright © 2015 Elsevier Inc. All rights reserved.

Mesh:

Year:  2015        PMID: 25579640     DOI: 10.1016/j.jclinepi.2014.11.010

Source DB:  PubMed          Journal:  J Clin Epidemiol        ISSN: 0895-4356            Impact factor:   6.437


  68 in total

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Authors:  Donald H Arnold; Marion R Sills; Colin G Walsh
Journal:  Curr Opin Allergy Clin Immunol       Date:  2016-06

2.  Prediction models need appropriate internal, internal-external, and external validation.

Authors:  Ewout W Steyerberg; Frank E Harrell
Journal:  J Clin Epidemiol       Date:  2015-04-18       Impact factor: 6.437

3.  Clinical Prediction Rules: Challenges, Barriers, and Promise.

Authors:  Emma Wallace; Michael E Johansen
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Review 4.  Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review.

Authors:  Benjamin A Goldstein; Ann Marie Navar; Michael J Pencina; John P A Ioannidis
Journal:  J Am Med Inform Assoc       Date:  2016-05-17       Impact factor: 4.497

5.  Training Load and Its Role in Injury Prevention, Part 2: Conceptual and Methodologic Pitfalls.

Authors:  Franco M Impellizzeri; Alan McCall; Patrick Ward; Luke Bornn; Aaron J Coutts
Journal:  J Athl Train       Date:  2020-09-01       Impact factor: 2.860

6.  Diabetes: Predicting severe hypoglycaemia - a step forward.

Authors:  Francesco Zaccardi; Kamlesh Khunti
Journal:  Nat Rev Endocrinol       Date:  2017-10-23       Impact factor: 43.330

7.  The LAS VEGAS risk score for prediction of postoperative pulmonary complications: An observational study.

Authors:  Ary Serpa Neto; Luiz Guilherme V da Costa; Sabrine N T Hemmes; Jaume Canet; Göran Hedenstierna; Samir Jaber; Michael Hiesmayr; Markus W Hollmann; Gary H Mills; Marcos F Vidal Melo; Rupert Pearse; Christian Putensen; Werner Schmid; Paolo Severgnini; Hermann Wrigge; Marcelo Gama de Abreu; Paolo Pelosi; Marcus J Schultz
Journal:  Eur J Anaesthesiol       Date:  2018-09       Impact factor: 4.330

8.  Development and Validation of a Predictive Model of the Risk of Pediatric Septic Shock Using Data Known at the Time of Hospital Arrival.

Authors:  Halden F Scott; Kathryn L Colborn; Carter J Sevick; Lalit Bajaj; Niranjan Kissoon; Sara J Deakyne Davies; Allison Kempe
Journal:  J Pediatr       Date:  2019-11-13       Impact factor: 4.406

Review 9.  Predicting urinary incontinence after surgery for pelvic organ prolapse.

Authors:  John E Jelovsek
Journal:  Curr Opin Obstet Gynecol       Date:  2016-10       Impact factor: 1.927

10.  Predictive Factors for Insufficient Weight Loss After Bariatric Surgery: Does Obstructive Sleep Apnea Influence Weight Loss?

Authors:  Christel A L de Raaff; Usha K Coblijn; Nico de Vries; Martijn W Heymans; Bob T J van den Berg; Willem F van Tets; Bart A van Wagensveld
Journal:  Obes Surg       Date:  2016-05       Impact factor: 4.129

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