Abstract：
OBJECTIVE: The scientific literature contains an abundance of prediction models for hospital readmissions. However, no review has yet synthesized their predictors across various patient populations. Therefore, our aim was to examine predictors of hospital readmissions across 13 patient populations.
METHODS: An overview of systematic reviews was combined with a meta-analytical approach. Two thousand five hundred four different predictors were categorized using common ontologies to pool and examine their odds ratios and frequencies of use in prediction models across and within different patient populations.
RESULTS: Twenty-eight systematic reviews with 440 primary studies were included. Numerous predictors related to prior use of healthcare services (odds ratio; 95% confidence interval: 1.64; 1.42-1.89), diagnoses (1.41; 1.31-1.51), health status (1.35; 1.20-1.52), medications (1.28; 1.13-1.44), administrative information about the index hospitalization (1.23; 1.14-1.33), clinical procedures (1.20; 1.07-1.35), laboratory results (1.18; 1.11-1.25), demographic information (1.10; 1.06-1.14), and socioeconomic status (1.07; 1.02-1.11) were analyzed. Diagnoses were frequently used (in 37.38%) and displayed large effect sizes across all populations. Prior use of healthcare services showed the largest effect sizes but were seldomly used (in 2.57%), whereas demographic information (in 13.18%) was frequently used but displayed small effect sizes.
CONCLUSIONS: Diagnoses and patients\' prior use of healthcare services showed large effects both across and within different populations. These results can serve as a foundation for future prediction modeling.
METHODS: An overview of systematic reviews was combined with a meta-analytical approach. Two thousand five hundred four different predictors were categorized using common ontologies to pool and examine their odds ratios and frequencies of use in prediction models across and within different patient populations.
RESULTS: Twenty-eight systematic reviews with 440 primary studies were included. Numerous predictors related to prior use of healthcare services (odds ratio; 95% confidence interval: 1.64; 1.42-1.89), diagnoses (1.41; 1.31-1.51), health status (1.35; 1.20-1.52), medications (1.28; 1.13-1.44), administrative information about the index hospitalization (1.23; 1.14-1.33), clinical procedures (1.20; 1.07-1.35), laboratory results (1.18; 1.11-1.25), demographic information (1.10; 1.06-1.14), and socioeconomic status (1.07; 1.02-1.11) were analyzed. Diagnoses were frequently used (in 37.38%) and displayed large effect sizes across all populations. Prior use of healthcare services showed the largest effect sizes but were seldomly used (in 2.57%), whereas demographic information (in 13.18%) was frequently used but displayed small effect sizes.
CONCLUSIONS: Diagnoses and patients\' prior use of healthcare services showed large effects both across and within different populations. These results can serve as a foundation for future prediction modeling.
摘要：
目的：科学文献包含了大量的再入院预测模型。然而,目前尚无综述在不同患者人群中综合预测因素.因此,我们的目的是在13例患者人群中检查再入院的预测因子.
方法：和设置：系统综述与荟萃分析方法相结合。使用共同的本体对2,504种不同的预测因子进行了分类，以汇总并检查其在不同患者人群中和在预测模型中使用的比值比和频率。
结果：共纳入了28篇系统评价和440项主要研究。与先前使用医疗保健服务相关的众多预测因素（OR；95CI：1.64；1.42-1.89），诊断（1.41;1.31-1.51），健康状况（1.35;1.20-1.52），药物(1.28;1.13-1.44)，录取信息（1.23;1.14-1.33），临床程序（1.20；1.07-1.35），实验室结果（1.18；1.11-1.25），人口统计信息(1.10;1.06-1.14)，和社会经济地位(1.07；1.02-1.11)进行了分析。诊断经常使用（37.38％），并在所有人群中显示出较大的效果。先前使用医疗保健服务的效果最大，但很少使用（占2.57％），而人口统计信息（13.18％）经常使用，但效果较小。
结论：诊断和患者先前使用医疗保健服务在不同人群中和人群中都显示出巨大的影响。这些结果可以作为未来预测建模的基础。
方法：和设置：系统综述与荟萃分析方法相结合。使用共同的本体对2,504种不同的预测因子进行了分类，以汇总并检查其在不同患者人群中和在预测模型中使用的比值比和频率。
结果：共纳入了28篇系统评价和440项主要研究。与先前使用医疗保健服务相关的众多预测因素（OR；95CI：1.64；1.42-1.89），诊断（1.41;1.31-1.51），健康状况（1.35;1.20-1.52），药物(1.28;1.13-1.44)，录取信息（1.23;1.14-1.33），临床程序（1.20；1.07-1.35），实验室结果（1.18；1.11-1.25），人口统计信息(1.10;1.06-1.14)，和社会经济地位(1.07；1.02-1.11)进行了分析。诊断经常使用（37.38％），并在所有人群中显示出较大的效果。先前使用医疗保健服务的效果最大，但很少使用（占2.57％），而人口统计信息（13.18％）经常使用，但效果较小。
结论：诊断和患者先前使用医疗保健服务在不同人群中和人群中都显示出巨大的影响。这些结果可以作为未来预测建模的基础。
