Does the Presence of Missing Data Affect the Performance of the SORG Machine-learning Algorithm for Patients With Spinal Metastasis? Development of an Internet Application Algorithm

• Published in: Clinical orthopaedics and related research 2024-01, Vol.482 (1), p.143-157
• Main Authors: Huang, Chi-Ching, Peng, Kuang-Ping, Hsieh, Hsiang-Chieh, Groot, Olivier Q., Yen, Hung-Kuan, Tsai, Cheng-Chen, Karhade, Aditya V., Lin, Yen-Po, Kao, Yin-Tien, Yang, Jiun-Jen, Dai, Shih-Hsiang, Huang, Chuan-Ching, Chen, Chih-Wei, Yen, Mao-Hsu, Xiao, Fu-Ren, Lin, Wei-Hsin, Verlaan, Jorrit-Jan, Schwab, Joseph H., Hsu, Feng-Ming, Wong, Tzehong, Yang, Rong-Sen, Yang, Shu-Hua, Hu, Ming-Hsiao
• Format: Article
• Language: English
• Subjects: Aged; Albumin; Algorithms; Alkaline phosphatase; Calibration; Cell number; Clinical Research; Comorbidity; Creatinine; Decision making; Decision Support Techniques; Female; Humans; Internet; Learning algorithms; Leukocytes (neutrophilic); Lymphocytes; Machine Learning; Male; Medical prognosis; Medical treatment; Metastases; Metastasis; Middle Aged; Missing data; Patients; Prediction models; Predictive Value of Tests; Prognosis; Radiation therapy; Retrospective Studies; Risk Factors; Spinal Neoplasms - mortality; Spinal Neoplasms - secondary; Spinal Neoplasms - surgery; Spinal Neoplasms - therapy; Validation studies
• ISSN: 0009-921X; 1528-1132; 1528-1132
• DOI: 10.1097/CORR.0000000000002706

The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA) was developed to predict the survival of patients with spinal metastasis. The algorithm was successfully tested in five international institutions using 1101 patients from different continents. The incorporation of 18 prognostic factors strengthens its predictive ability but limits its clinical utility because some prognostic factors might not be clinically available when a clinician wishes to make a prediction. We performed this study to (1) evaluate the SORG-MLA's performance with data and (2) develop an internet-based application to impute the missing data. A total of 2768 patients were included in this study. The data of 617 patients who were treated surgically were intentionally erased, and the data of the other 2151 patients who were treated with radiotherapy and medical treatment were used to impute the artificially missing data. Compared with those who were treated nonsurgically, patients undergoing surgery were younger (median 59 years [IQR 51 to 67 years] versus median 62 years [IQR 53 to 71 years]) and had a higher proportion of patients with at least three spinal metastatic levels (77% [474 of 617] versus 72% [1547 of 2151]), more neurologic deficit (normal American Spinal Injury Association [E] 68% [301 of 443] versus 79% [1227 of 1561]), higher BMI (23 kg/m 2 [IQR 20 to 25 kg/m 2 ] versus 22 kg/m 2 [IQR 20 to 25 kg/m 2 ]), higher platelet count (240 × 10 3 /µL [IQR 173 to 327 × 10 3 /µL] versus 227 × 10 3 /µL [IQR 165 to 302 × 10 3 /µL], higher lymphocyte count (15 × 10 3 /µL [IQR 9 to 21× 10 3 /µL] versus 14 × 10 3 /µL [IQR 8 to 21 × 10 3 /µL]), lower serum creatinine level (0.7 mg/dL [IQR 0.6 to 0.9 mg/dL] versus 0.8 mg/dL [IQR 0.6 to 1.0 mg/dL]), less previous systemic therapy (19% [115 of 617] versus 24% [526 of 2151]), fewer Charlson comorbidities other than cancer (28% [170 of 617] versus 36% [770 of 2151]), and longer median survival. The two patient groups did not differ in other regards. These findings aligned with our institutional philosophy of selecting patients for surgical intervention based on their level of favorable prognostic factors such as BMI or lymphocyte counts and lower levels of unfavorable prognostic factors such as white blood cell counts or serum creatinine level, as well as the degree of spinal instability and severity of neurologic deficits. This approach aims to identify patients with better survival outcomes and prioritize their surgical