Hung-Kuan Yen, Wei-Hsin Lin, Olivier Quinten Groot, Chih-Wei Chen, Jiun-Jen Yang, Michiel Erik Reinier Bongers, Aditya Karhade, Akash Shah, Tse-Chuan Yang, Bas Jj Bindels, Shih-Hsiang Dai, Jorrit-Jan Verlaan, Joseph Schwab, Shu-Hua Yang, Francis J Hornicek, Ming-Hsiao Hu
{"title":"脊柱转移患者的经典生存预测模型与机器学习生成的生存预测模型的比较--对最近开发的两种算法的元分析。","authors":"Hung-Kuan Yen, Wei-Hsin Lin, Olivier Quinten Groot, Chih-Wei Chen, Jiun-Jen Yang, Michiel Erik Reinier Bongers, Aditya Karhade, Akash Shah, Tse-Chuan Yang, Bas Jj Bindels, Shih-Hsiang Dai, Jorrit-Jan Verlaan, Joseph Schwab, Shu-Hua Yang, Francis J Hornicek, Ming-Hsiao Hu","doi":"10.1177/21925682231162817","DOIUrl":null,"url":null,"abstract":"<p><strong>Study design: </strong>A systemic review and a meta-analysis. We also provided a retrospective cohort for validation in this study.</p><p><strong>Objective: </strong>(1) Using a meta-analysis to determine the pooled discriminatory ability of The Skeletal Oncology Research Group (SORG) classical algorithm (CA) and machine learning algorithms (MLA); and (2) test the hypothesis that SORG-CA has less variability in performance than SORG-MLA in non-American validation cohorts as SORG-CA does not incorporates regional-specific variables such as body mass index as input.</p><p><strong>Methods: </strong>After data extraction from the included studies, logit-transformation was applied for extracted AUCs for further analysis. The discriminatory abilities of both algorithms were directly compared by their logit (AUC)s. Further subgroup analysis by region (America vs non-America) was also conducted by comparing the corresponding logit (AUC).</p><p><strong>Results: </strong>The pooled logit (AUC)s of 90-day SORG-CA was .82 (95% confidence interval [CI], .53-.11), 1-year SORG-CA was 1.11 (95% CI, .74-1.48), 90-day SORG-MLA was 1.36 (95% CI, 1.09-1.63), and 1-year SORG-MLA was 1.57 (95% CI, 1.17-1.98). All the algorithms performed better in United States than in Taiwan (<i>P</i> < .001). The performance of SORG-CA was less influenced by a non-American cohort than SORG-MLA.</p><p><strong>Conclusion: </strong>These observations might highlight the importance of incorporating region-specific variables into existing models to make them generalizable to racially or geographically distinct regions.</p>","PeriodicalId":12680,"journal":{"name":"Global Spine Journal","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparison of Classically and Machine Learning Generated Survival Prediction Models for Patients With Spinal Metastasis - A meta-Analysis of Two Recently Developed Algorithms.\",\"authors\":\"Hung-Kuan Yen, Wei-Hsin Lin, Olivier Quinten Groot, Chih-Wei Chen, Jiun-Jen Yang, Michiel Erik Reinier Bongers, Aditya Karhade, Akash Shah, Tse-Chuan Yang, Bas Jj Bindels, Shih-Hsiang Dai, Jorrit-Jan Verlaan, Joseph Schwab, Shu-Hua Yang, Francis J Hornicek, Ming-Hsiao Hu\",\"doi\":\"10.1177/21925682231162817\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Study design: </strong>A systemic review and a meta-analysis. We also provided a retrospective cohort for validation in this study.</p><p><strong>Objective: </strong>(1) Using a meta-analysis to determine the pooled discriminatory ability of The Skeletal Oncology Research Group (SORG) classical algorithm (CA) and machine learning algorithms (MLA); and (2) test the hypothesis that SORG-CA has less variability in performance than SORG-MLA in non-American validation cohorts as SORG-CA does not incorporates regional-specific variables such as body mass index as input.</p><p><strong>Methods: </strong>After data extraction from the included studies, logit-transformation was applied for extracted AUCs for further analysis. The discriminatory abilities of both algorithms were directly compared by their logit (AUC)s. Further subgroup analysis by region (America vs non-America) was also conducted by comparing the corresponding logit (AUC).</p><p><strong>Results: </strong>The pooled logit (AUC)s of 90-day SORG-CA was .82 (95% confidence interval [CI], .53-.11), 1-year SORG-CA was 1.11 (95% CI, .74-1.48), 90-day SORG-MLA was 1.36 (95% CI, 1.09-1.63), and 1-year SORG-MLA was 1.57 (95% CI, 1.17-1.98). All the algorithms performed better in United States than in Taiwan (<i>P</i> < .001). The performance of SORG-CA was less influenced by a non-American cohort than SORG-MLA.</p><p><strong>Conclusion: </strong>These observations might highlight the importance of incorporating region-specific variables into existing models to make them generalizable to racially or geographically distinct regions.</p>\",\"PeriodicalId\":12680,\"journal\":{\"name\":\"Global Spine Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-07-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Global Spine Journal\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/21925682231162817\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Spine Journal","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/21925682231162817","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Comparison of Classically and Machine Learning Generated Survival Prediction Models for Patients With Spinal Metastasis - A meta-Analysis of Two Recently Developed Algorithms.
Study design: A systemic review and a meta-analysis. We also provided a retrospective cohort for validation in this study.
Objective: (1) Using a meta-analysis to determine the pooled discriminatory ability of The Skeletal Oncology Research Group (SORG) classical algorithm (CA) and machine learning algorithms (MLA); and (2) test the hypothesis that SORG-CA has less variability in performance than SORG-MLA in non-American validation cohorts as SORG-CA does not incorporates regional-specific variables such as body mass index as input.
Methods: After data extraction from the included studies, logit-transformation was applied for extracted AUCs for further analysis. The discriminatory abilities of both algorithms were directly compared by their logit (AUC)s. Further subgroup analysis by region (America vs non-America) was also conducted by comparing the corresponding logit (AUC).
Results: The pooled logit (AUC)s of 90-day SORG-CA was .82 (95% confidence interval [CI], .53-.11), 1-year SORG-CA was 1.11 (95% CI, .74-1.48), 90-day SORG-MLA was 1.36 (95% CI, 1.09-1.63), and 1-year SORG-MLA was 1.57 (95% CI, 1.17-1.98). All the algorithms performed better in United States than in Taiwan (P < .001). The performance of SORG-CA was less influenced by a non-American cohort than SORG-MLA.
Conclusion: These observations might highlight the importance of incorporating region-specific variables into existing models to make them generalizable to racially or geographically distinct regions.
期刊介绍:
Global Spine Journal (GSJ) is the official scientific publication of AOSpine. A peer-reviewed, open access journal, devoted to the study and treatment of spinal disorders, including diagnosis, operative and non-operative treatment options, surgical techniques, and emerging research and clinical developments.GSJ is indexed in PubMedCentral, SCOPUS, and Emerging Sources Citation Index (ESCI).