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

Publication ,  Journal Article
Rhon, DI; Teyhen, DS; Collins, GS; Bullock, GS
Published in: BMJ Open Sport Exerc Med
2022

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.

Duke Scholars

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Published In

BMJ Open Sport Exerc Med

DOI

ISSN

2055-7647

Publication Date

2022

Volume

8

Issue

4

Start / End Page

e001388

Location

England

Related Subject Headings

  • 4207 Sports science and exercise
  • 3202 Clinical sciences
  • 1106 Human Movement and Sports Sciences
 

Citation

APA
Chicago
ICMJE
MLA
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Rhon, D. I., Teyhen, D. S., Collins, G. S., & Bullock, G. S. (2022). Predictive models for musculoskeletal injury risk: why statistical approach makes all the difference. BMJ Open Sport Exerc Med, 8(4), e001388. https://doi.org/10.1136/bmjsem-2022-001388
Rhon, Daniel I., Deydre S. Teyhen, Gary S. Collins, and Garrett S. Bullock. “Predictive models for musculoskeletal injury risk: why statistical approach makes all the difference.BMJ Open Sport Exerc Med 8, no. 4 (2022): e001388. https://doi.org/10.1136/bmjsem-2022-001388.
Rhon DI, Teyhen DS, Collins GS, Bullock GS. Predictive models for musculoskeletal injury risk: why statistical approach makes all the difference. BMJ Open Sport Exerc Med. 2022;8(4):e001388.
Rhon, Daniel I., et al. “Predictive models for musculoskeletal injury risk: why statistical approach makes all the difference.BMJ Open Sport Exerc Med, vol. 8, no. 4, 2022, p. e001388. Pubmed, doi:10.1136/bmjsem-2022-001388.
Rhon DI, Teyhen DS, Collins GS, Bullock GS. Predictive models for musculoskeletal injury risk: why statistical approach makes all the difference. BMJ Open Sport Exerc Med. 2022;8(4):e001388.

Published In

BMJ Open Sport Exerc Med

DOI

ISSN

2055-7647

Publication Date

2022

Volume

8

Issue

4

Start / End Page

e001388

Location

England

Related Subject Headings

  • 4207 Sports science and exercise
  • 3202 Clinical sciences
  • 1106 Human Movement and Sports Sciences