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May 31, 2026BMC Musculoskeletal Disorders1 citationsOpen Access

Performance of prediction models for delayed union and nonunion after fracture: a systematic review and meta-analysis

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FYFu Cheng YinXWX L WangGLGan Luo

Key Points

  • To systematically review and assess multivariable prediction models for delayed union and nonunion after fractures.
  • Conducted a systematic review using MEDLINE, EMBASE, CINAHL, SinoMed, and CNKI up to November 2025.
  • Included studies that developed or validated models predicting delayed union or nonunion outcomes.
  • Assessed risk of bias with PROBAST and summarized discrimination using the area under the receiver operating characteristic curve (AUC).
  • Pooled AUC was 0.88 (95% CI 0.86–0.90) for apparent performance and 0.81 (95% CI 0.73–0.86) for external validation.
  • 56 models reported only apparent performance, while 13 models underwent external validation.
  • Discrimination performance was optimistic and insufficient for clinical implementation due to high risk of bias and limited validation.

Abstract

Abstract Background Delayed union and nonunion are common and costly complications after fractures, yet early risk stratification remains challenging. We systematically reviewed and meta-analyzed multivariable prediction models for compromised fracture healing. Methods MEDLINE, EMBASE, CINAHL, SinoMed, and CNKI were searched from inception to 30 November 2025. Studies developing or validating models predicting delayed union, nonunion, or related healing outcomes after fractures were included. Risk of bias was assessed with PROBAST and certainty of evidence with GRADE. Discrimination was summarized using the area under the receiver operating characteristic curve (AUC), pooled with random-effects models by validation tier (apparent performance, internal validation, external validation), and further described by anatomical subgroup. The protocol was registered in PROSPERO (CRD420251252244). Results Seventy-seven studies reporting 97 model entries were included across appendicular, proximal femoral, multisite, and axial skeletal fracture settings; 56 models reported apparent performance only, 28 had internal validation, and 13 underwent external validation. Pooled AUC was 0.88 (95% CI 0.86–0.90) for apparent performance, 0.72 (95% CI 0.41–0.90) for internal validation, and 0.81 (95% CI 0.73–0.86) for external validation, with substantial heterogeneity. Apparent performance exceeded validated performance (mean optimism 0.052). Conclusion Existing models often show optimistic apparent discrimination; however, discrimination alone is insufficient to justify clinical implementation, especially given limited external validation, sparse calibration reporting, major clinical and anatomical heterogeneity, and high risk of bias. Future studies should prioritize clearer outcome definitions, robust methodology, transparent reporting, independent external validation with calibration assessment, and evaluation of clinical utility before these models are used to guide care.

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Cite This Study

Yin et al. (2026) studied this question.

synapsesocial.com/papers/6a1bd12d5783ba022b6fcc96https://doi.org/10.1186/s12891-026-09941-4
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