Literature DB >> 29406366

Lack of Prognostic Model Validation in Low Back Pain Prediction Studies: A Systematic Review.

Greg McIntosh1, Ivan Steenstra2, Sheilah Hogg-Johnson3,4,5, Tom Carter1, Hamilton Hall1.   

Abstract

OBJECTIVE: The objective of this study was to investigate the frequency with which prediction studies for low back pain outcomes utilize prospective methods of prognostic model validation.
METHOD: Searches of Medline and Embase for terms "predict/predictor," "prognosis," or "prognostic factor." The search was limited to studies conducted in humans and reported in the English language. Included articles were all those published in 2 Spine specialty journals (Spine and The Spine Journal) over a 13-month period, January 2013 to January 2014. Conference papers, reviews, and letters were excluded. The initial screen identified 55 potential studies (44 in Spine, 11 in The Spine Journal); 34 were excluded because they were not primary data collection prediction studies; 23 were not prediction studies and 11 were review articles. This left 21 prognosis papers for review, 19 in Spine, 2 in The Spine Journal.
RESULTS: None of the 21 studies provided validation for the predictors that they documented (neither internal or external validation). On the basis of the study designs and lack of validation, only 2 studies used the correct terminology for describing associations/relationships between independent and dependent variables. DISCUSSION: Unless researchers and clinicians consider sophisticated and rigorous methods of statistical/external validity for prediction/prognostic findings they will make incorrect assumptions and draw invalid conclusions regarding treatment effects and outcomes. Without proper validation methods, studies that claim to present prediction models actually describe only traits or characteristics of the studied sample.

Entities:  

Mesh:

Year:  2018        PMID: 29406366     DOI: 10.1097/AJP.0000000000000591

Source DB:  PubMed          Journal:  Clin J Pain        ISSN: 0749-8047            Impact factor:   3.442


  4 in total

1.  Developing clinical prediction models for nonrecovery in older patients seeking care for back pain: the back complaints in the elders prospective cohort study.

Authors:  Wendelien H van der Gaag; Alessandro Chiarotto; Martijn W Heymans; Wendy T M Enthoven; Jantine van Rijckevorsel-Scheele; Sita M A Bierma-Zeinstra; Arthur M Bohnen; Bart W Koes
Journal:  Pain       Date:  2021-06-01       Impact factor: 6.961

2.  Predicting Physician Consultations for Low Back Pain Using Claims Data and Population-Based Cohort Data-An Interpretable Machine Learning Approach.

Authors:  Adrian Richter; Julia Truthmann; Jean-François Chenot; Carsten Oliver Schmidt
Journal:  Int J Environ Res Public Health       Date:  2021-11-16       Impact factor: 3.390

3.  Development and external validation of a prediction model for patient-relevant outcomes in patients with chronic widespread pain and fibromyalgia.

Authors:  V P Moen; A T Tveter; R D Herbert; K B Hagen
Journal:  Eur J Pain       Date:  2022-03-15       Impact factor: 3.651

4.  A prediction model of low back pain risk: a population based cohort study in Korea.

Authors:  David Mukasa; Joohon Sung
Journal:  Korean J Pain       Date:  2020-04-01
  4 in total

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