Development and internal validation of machine-learning models for predicting survival in patients who underwent surgery for spinal metastases
- Author:
Borriwat SANTIPAS
1
;
Kanyakorn VEERAKANJANA
;
Piyalitt ITTICHAIWONG
;
Piya CHAVALPARIT
;
Sirichai WILARTRATSAMI
;
Panya LUKSANAPRUKSA
Author Information
- Publication Type:Clinical Study
- From:Asian Spine Journal 2024;18(3):325-335
- CountryRepublic of Korea
- Language:EN
-
Abstract:
Methods:A registry of patients who underwent surgery (instrumentation, decompression, or fusion) for spinal metastases between 2004 and 2018 was used. The outcome measure was survival at postoperative days 90, 180, and 365. Preoperative variables were used to develop machine-learning algorithms to predict survival chance in each period. The performance of the algorithms was measured using the area under the receiver operating characteristic curve (AUC).
Results:A total of 389 patients were identified, with 90-, 180-, and 365-day mortality rates of 18%, 41%, and 45% postoperatively, respectively. The XGBoost algorithm showed the best performance for predicting 180-day and 365-day survival (AUCs of 0.744 and 0.693, respectively). The CatBoost algorithm demonstrated the best performance for predicting 90-day survival (AUC of 0.758). Serum albumin had the highest positive correlation with survival after surgery.
Conclusions:These machine-learning algorithms showed promising results in predicting survival in patients who underwent spinal palliative surgery for spinal metastasis, which may assist surgeons in choosing appropriate treatment and increasing awareness of mortality-related factors before surgery.
