Machine learning-based algorithms to predict severe psychological distress among cancer patients with spinal metastatic disease

Gao，Le1; Cao，Yuncen2; Cao，Xuyong2; Shi，Xiaolin3; Lei，Mingxing4,5; Su，Xiuyun6; Liu，Yaosheng2,5

2023-09-01

10.1016/j.spinee.2023.05.009

Spine Journal   Impact Factor & Quartile

1529-9430

1878-1632

BACKGROUND CONTEXT: Metastatic spinal disease is an advanced stage of cancer patients and often suffer from terrible psychological health status; however, the ability to estimate the risk probability of this adverse outcome using current available data is very limited. PURPOSE: The goal of this study was to propose a precise model based on machine learning techniques to predict psychological status among cancer patients with spinal metastatic disease. STUDY DESIGN/SETTING: A prospective cohort study. PATIENT SAMPLE: A total of 1043 cancer patients with spinal metastatic disease were included. OUTCOME MEASURES: The main outcome was severe psychological distress. METHODS: The total of patients was randomly divided into a training dataset and a testing dataset on a ratio of 9:1. Patients' demographics, lifestyle choices, cancer-related features, clinical manifestations, and treatments were collected as potential model predictors in the study. Five machine learning algorithms, including XGBoosting machine, random forest, gradient boosting machine, support vector machine, and ensemble prediction model, as well as a logistic regression model were employed to train and optimize models in the training set, and their predictive performance was assessed in the testing set. RESULTS: Up to 21.48% of all patients who were recruited had severe psychological distress. Elderly patients (p<0.001), female (p =0.045), current smoking (p=0.002) or drinking (p=0.003), a lower level of education (p<0.001), a stronger spiritual desire (p<0.001), visceral metastasis (p=0.005), and a higher Eastern Cooperative Oncology Group (ECOG) score (p<0.001) were significantly associated with worse psychological health. With an area under the curve (AUC) of 0.865 (95% CI: 0.788–0.941) and an accuracy of up to 0.843, the gradient boosting machine algorithm performed best in the prediction of the outcome, followed by the XGBooting machine algorithm (AUC: 0.851, 95% CI: 0.768–0.934; Accuracy: 0.826) and ensemble prediction (AUC: 0.851, 95% CI: 0.770–0.932; Accuracy: 0.809) in the testing set. In contrast, the AUC of the logistic regression model was only 0.836 (95% CI: 0.756–0.916; Accuracy: 0.783). CONCLUSIONS: Machine learning models have greater predictive power and can offer useful tools to identify individuals with spinal metastatic disease who are experiencing severe psychological distress.

Machine learning Mental health Model explainability Prediction model Psychological distress Spinal metastatic disease

SCI

English

Corresponding

Neurosciences & Neurology ; Orthopedics

Clinical Neurology ; Orthopedics

WOS:001066430900001

ELSEVIER SCIENCE INC

2-s2.0-85162256579

Scopus

1.Department of Oncology,Senior Department of Oncology,The Fifth Medical Center of PLA General Hospital,Beijing,No. 8 Dongdajie Street, Fengtai District,China
2.Senior Department of Orthopedics,The Fourth Medical Center of PLA General Hospital,Beijing,No. 51 Fucheng Road, Haidian District,100048,China
3.Department of Orthopedic Surgery,The Second Affiliated Hospital of Zhejiang Chinese Medical University,Hangzhou,No. 318 Chaowang Road,310005,China
4.Department of Orthopedic Surgery,Hainan Hospital of PLA General Hospital,Sanya,No. 80 Jianglin Road, Haitang District,572022,China
5.National Clinical Research Center for Orthopedics,Sports Medicine & Rehabilitation,Beijing,No. 28 Fuxing Road, Haidian District,100039,China
6.Intelligent Medical Innovation Institute,Southern University of Science and Technology Hospital,Shenzhen,No. 6019 Xili Liuxian Avenue, Nanshan District,518071,China

Gao，Le,Cao，Yuncen,Cao，Xuyong,et al. Machine learning-based algorithms to predict severe psychological distress among cancer patients with spinal metastatic disease[J]. Spine Journal,2023,23(9):1255-1269.

Gao，Le.,Cao，Yuncen.,Cao，Xuyong.,Shi，Xiaolin.,Lei，Mingxing.,...&Liu，Yaosheng.(2023).Machine learning-based algorithms to predict severe psychological distress among cancer patients with spinal metastatic disease.Spine Journal,23(9),1255-1269.

Gao，Le,et al."Machine learning-based algorithms to predict severe psychological distress among cancer patients with spinal metastatic disease".Spine Journal 23.9(2023):1255-1269.