Risk Factors Analysis of Phantom Limb Pain in Amputees with Malignant Tumors

Abstract

Purpose

Postamputation neuropathic pain is a common disease in patients with malignant tumor amputation, seriously affecting amputees’ quality of life and mental health. The objective of this study was to identify independent risk factors for phantom limb pain in patients with tumor amputation and to construct a risk prediction model.

Methods

Patients who underwent amputation due to malignant tumors from 2013 to 2023 were retrospectively analyzed and divided into phantom limb pain group and non-phantom limb pain group. To determine which preoperative factors would affect the occurrence of phantom limb pain, we searched for candidate factors by univariate analysis and used multivariate logistic regression analysis to identify independent factors and construct a predictive model. The receiver operating characteristic curve (ROC) was drawn to further evaluate the accuracy of the prediction model in evaluating the phantom limb pain after amputation of bone and soft tissue tumors.

Results

Multivariate analysis showed that age (OR, 1.054; 95% CI, 1.027 to 1.080), preoperative pain (OR, 5.773; 95% CI, 2.362 to 14.104), number of surgeries (OR, 3.425; 95% CI, 1.505 to 7.795), amputation site (OR, 5.848; 95% CI, 1.837 to 18.620), amputation level (OR, 8.031; 95% CI, 2.491 to 25.888) were independent risk factors for phantom limb pain for bone and soft tissue tumors. The the area under the curve (AUC) of this model was 0.834.

Conclusion

Risk factors for postoperative phantom limb pain were the site of amputation, proximal amputation, preoperative pain, multiple amputations, and older age. These factors will help surgeons to individualize and stratify phantom limb pain and help patients with risk counseling. In particular, an informed clinical decision targeting those modifiable factors can be considered when needed.

Keywords:

• neuropathic pain
• phantom limb pain
• bone and soft tissue tumors
• amputation
• risk factors
• prediction model

Data Sharing Statement

The data sets used and analyzed during the current study are available from the corresponding authors upon reasonable request.

Acknowledgments

All authors are thanked for their participation and dedication. Thanks to Zekun Li and Huanhuan Li for their help in the data collection process. We thank Cunhui Zhang for help in statistical analysis.

Disclosure

The authors declare that there is no conflict of interest in the publication of this article.

Additional information

Funding

This research was supported by the Medical Science Research Project of Hebei Province, China in 2023 (20230753) and the provincial medical excellent talents project funded by the government in 2022 (30320222704).