Development and internal validation of machine-learning models for predicting survival in patients who underwent surgery for spinal metastases
1
Issued Date
2024-01-01
Resource Type
ISSN
19761902
eISSN
19767846
Scopus ID
2-s2.0-85199537524
Journal Title
Asian Spine Journal
Volume
18
Issue
3
Start Page
325
End Page
335
Rights Holder(s)
SCOPUS
Bibliographic Citation
Asian Spine Journal Vol.18 No.3 (2024) , 325-335
Suggested Citation
Santipas B., Veerakanjana K., Ittichaiwong P., Chavalparit P., Wilartratsami S., Luksanapruksa P. Development and internal validation of machine-learning models for predicting survival in patients who underwent surgery for spinal metastases. Asian Spine Journal Vol.18 No.3 (2024) , 325-335. 335. doi:10.31616/asj.2023.0314 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/100198
Title
Development and internal validation of machine-learning models for predicting survival in patients who underwent surgery for spinal metastases
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Purpose: This study aimed to develop machine-learning algorithms for predicting survival in patients who underwent surgery for spinal metastasis. Overview of Literature: This study develops machine-learning models to predict postoperative survival in spinal metastasis patients, filling the gaps of traditional prognostic systems. Utilizing data from 389 patients, the study highlights XGBoost and CatBoost algorithms̓ effectiveness for 90, 180, and 365-day survival predictions, with preoperative serum albumin as a key predictor. These models offer a promising approach for enhancing clinical decision-making and personalized patient care. 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.