Objectives: The purpose of this study was to develop a prediction model to assess the risk of adjacent vertebral compression fractures (AVCFs) after percutaneous kyphoplasty (PKP) surgery.
Design: A retrospective chart review.
Setting and participants: Patients were collected from the Quzhou People's Hospital, from March 2017 to May 2019. Patients were included if they suffered from osteoporotic vertebral compression fractures (OVCFs), underwent PKP surgery and were followed up for 2 years.
Interventions: None.
Methods: This was a retrospective cohort study of all PKP surgery procedures of the thoracic, lumbar and thoracolumbar (TL) spine that have been performed for OVCF from 1 March 2017 up to 1 May 2019. The least absolute shrinkage and selection operator (LASSO) regression model was used to optimise feature selection for the AVCF risk model. Multivariable logistic regression analysis was applied to build a predicting model incorporating the feature selected in the LASSO regression model. The C-index, calibration plot and decision curve analysis were applied to assess this model.
Results: Gender, age, the number of surgical vertebrae, cement volume, bone mineral density, diabetes, hypertension, bone cement leakage, duration of anti-osteoporosis treatment after surgery and TL junction were identified as predictors. The model displayed good discrimination with a C-index of 0.886 (95% CI 0.828-0.944) and good calibration. High C-index value of 0.833 could still be reached in the interval validation. Decision curve analysis showed that the AVCF nomogram was clinically useful when intervention was decided at the AVCF possibility threshold of 1%.
Conclusions: This study developed a clinical prediction model to identify the risk factors for AVCF after PKP surgery, and this tool is of great value in sharing surgical decision-making among patients consulted before surgery.
Trial registration number: researchregistry7716.
Keywords: back pain; neurosurgery; spine.
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