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.
CONCLUSIONS: These observations might highlight the importance of incorporating region-specific variables into existing models to make them generalizable to racially or geographically distinct regions.
目的:(1)使用荟萃分析来确定骨骼肿瘤学研究小组(SORG)经典算法(CA)和机器学习算法(MLA)的合并辨别能力;(2)检验以下假设:在非美国验证队列中,SORG-CA的性能变异性小于SORG-MLA,因为SORG-MLA不包含特定的体重指数,例如CA输入。
方法:从纳入的研究中提取数据后,将logit-transformation应用于提取的AUC以进行进一步分析。通过两种算法的logit(AUC)s直接比较了两种算法的判别能力。通过比较相应的logit(AUC),还进行了按地区(美国与非美国)的进一步亚组分析。
结果:90天SORG-CA的合并logit(AUC)为.82(95%置信区间[CI],.53-.11),1年期SORG-CA为1.11(95%CI,0.74-1.48),90天SORG-MLA为1.36(95%CI,1.09-1.63),1年期SORG-MLA为1.57(95%CI,1.17-1.98)。所有算法在美国的表现都优于台湾(P<.001)。与SORG-MLA相比,SORG-CA的性能受非美国队列的影响较小。
结论:这些观察结果可能突出了将特定地区变量纳入现有模型的重要性,使其可推广到种族或地理上不同的地区。