Literature DB >> 36268503

Predictive models for musculoskeletal injury risk: why statistical approach makes all the difference.

Daniel I Rhon1,2, Deydre S Teyhen3, Gary S Collins4,5, Garrett S Bullock6,7.   

Abstract

Objective: Compare performance between an injury prediction model categorising predictors and one that did not and compare a selection of predictors based on univariate significance versus assessing non-linear relationships.
Methods: Validation and replication of a previously developed injury prediction model in a cohort of 1466 service members followed for 1 year after physical performance, medical history and sociodemographic variables were collected. The original model dichotomised 11 predictors. The second model (M2) kept predictors continuous but assumed linearity and the third model (M3) conducted non-linear transformations. The fourth model (M4) chose predictors the proper way (clinical reasoning and supporting evidence). Model performance was assessed with R2, calibration in the large, calibration slope and discrimination. Decision curve analyses were performed with risk thresholds from 0.25 to 0.50.
Results: 478 personnel sustained an injury. The original model demonstrated poorer R2 (original:0.07; M2:0.63; M3:0.64; M4:0.08), calibration in the large (original:-0.11 (95% CI -0.22 to 0.00); M2: -0.02 (95% CI -0.17 to 0.13); M3:0.03 (95% CI -0.13 to 0.19); M4: -0.13 (95% CI -0.25 to -0.01)), calibration slope (original:0.84 (95% CI 0.61 to 1.07); M2:0.97 (95% CI 0.86 to 1.08); M3:0.90 (95% CI 0.75 to 1.05); M4: 081 (95% CI 0.59 to 1.03) and discrimination (original:0.63 (95% CI 0.60 to 0.66); M2:0.90 (95% CI 0.88 to 0.92); M3:0.90 (95% CI 0.88 to 0.92); M4: 0.63 (95% CI 0.60 to 0.66)). At 0.25 injury risk, M2 and M3 demonstrated a 0.43 net benefit improvement. At 0.50 injury risk, M2 and M3 demonstrated a 0.33 net benefit improvement compared with the original model.
Conclusion: Model performance was substantially worse in the models with dichotomised variables. This highlights the need to follow established recommendations when developing prediction models. © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  Injury; Neuromuscular; Prevention; Risk factor

Year:  2022        PMID: 36268503      PMCID: PMC9577931          DOI: 10.1136/bmjsem-2022-001388

Source DB:  PubMed          Journal:  BMJ Open Sport Exerc Med        ISSN: 2055-7647


  35 in total

Review 1.  Multiple imputation: a primer.

Authors:  J L Schafer
Journal:  Stat Methods Med Res       Date:  1999-03       Impact factor: 3.021

2.  Complex systems approach for sports injuries: moving from risk factor identification to injury pattern recognition-narrative review and new concept.

Authors:  N F N Bittencourt; W H Meeuwisse; L D Mendonça; A Nettel-Aguirre; J M Ocarino; S T Fonseca
Journal:  Br J Sports Med       Date:  2016-07-21       Impact factor: 13.800

3.  Injury prediction as a non-linear system.

Authors:  Benjamin D Stern; Eric J Hegedus; Ying-Cheng Lai
Journal:  Phys Ther Sport       Date:  2019-11-08       Impact factor: 2.365

4.  Association of Physical Inactivity, Weight, Smoking, and Prior Injury on Physical Performance in a Military Setting.

Authors:  Deydre S Teyhen; Daniel I Rhon; Robert J Butler; Scott W Shaffer; Stephen L Goffar; Danny J McMillian; Robert E Boyles; Kyle B Kiesel; Phillip J Plisky
Journal:  J Athl Train       Date:  2016-10-03       Impact factor: 2.860

5.  Triple-hop distance as a valid predictor of lower limb strength and power.

Authors:  R Tyler Hamilton; Sandra J Shultz; Randy J Schmitz; David H Perrin
Journal:  J Athl Train       Date:  2008 Apr-Jun       Impact factor: 2.860

6.  Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls.

Authors:  Jonathan A C Sterne; Ian R White; John B Carlin; Michael Spratt; Patrick Royston; Michael G Kenward; Angela M Wood; James R Carpenter
Journal:  BMJ       Date:  2009-06-29

7.  PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies.

Authors:  Robert F Wolff; Karel G M Moons; Richard D Riley; Penny F Whiting; Marie Westwood; Gary S Collins; Johannes B Reitsma; Jos Kleijnen; Sue Mallett
Journal:  Ann Intern Med       Date:  2019-01-01       Impact factor: 25.391

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

Authors:  Gary S Collins; Johannes B Reitsma; Douglas G Altman; Karel G M Moons
Journal:  Ann Intern Med       Date:  2015-01-06       Impact factor: 25.391

9.  Developing predictive models for return to work using the Military Power, Performance and Prevention (MP3) musculoskeletal injury risk algorithm: a study protocol for an injury risk assessment programme.

Authors:  Daniel I Rhon; Deydre S Teyhen; Scott W Shaffer; Stephen L Goffar; Kyle Kiesel; Phil P Plisky
Journal:  Inj Prev       Date:  2016-11-24       Impact factor: 2.399

10.  Visualising statistical models using dynamic nomograms.

Authors:  Amirhossein Jalali; Alberto Alvarez-Iglesias; Davood Roshan; John Newell
Journal:  PLoS One       Date:  2019-11-15       Impact factor: 3.240

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