Hogan Julien, Arenson Michael D, Adhikary Sandesh M, Li Kevin, Zhang Xingyu, Zhang Rebecca, Valdez Jeffrey N, Lynch Raymond J, Sun Jimeng, Adams Andrew B, Patzer Rachel E
Department of Surgery, Emory Transplant Center, Emory University School of Medicine, Atlanta, GA.
College of computing, Georgia Institute of Technology, Atlanta, GA.
Transplant Direct. 2019 Jul 29;5(8):e479. doi: 10.1097/TXD.0000000000000918. eCollection 2019 Aug.
A better understanding of the risk factors of posttransplant hospital readmission is needed to develop accurate predictive models.
We included 40 461 kidney transplant recipients from United States renal data system (USRDS) between 2005 and 2014. We used Prentice, Williams and Peterson Total time model to compare the importance of various risk factors in predicting posttransplant readmission based on the number of the readmissions (first vs subsequent) and a random forest model to compare risk factors based on the timing of readmission (early vs late).
Twelve thousand nine hundred eighty-five (31.8%) and 25 444 (62.9%) were readmitted within 30 days and 1 year postdischarge, respectively. Fifteen thousand eight hundred (39.0%) had multiple readmissions. Predictive accuracies of our models ranged from 0.61 to 0.63. Transplant factors remained the main predictors for early and late readmission but decreased with time. Although recipients' demographics and socioeconomic factors only accounted for 2.5% and 11% of the prediction at 30 days, respectively, their contribution to the prediction of later readmission increased to 7% and 14%, respectively. Donor characteristics remained poor predictors at all times. The association between recipient characteristics and posttransplant readmission was consistent between the first and subsequent readmissions. Donor and transplant characteristics presented a stronger association with the first readmission compared with subsequent readmissions.
These results may inform the development of future predictive models of hospital readmission that could be used to identify kidney transplant recipients at high risk for posttransplant hospitalization and design interventions to prevent readmission.
为了开发准确的预测模型，需要更好地了解移植后医院再入院的风险因素。
我们纳入了2005年至2014年间来自美国肾脏数据系统（USRDS）的40461名肾移植受者。我们使用普伦蒂斯、威廉姆斯和彼得森总时间模型，根据再入院次数（首次与后续）比较各种风险因素在预测移植后再入院中的重要性，并使用随机森林模型根据再入院时间（早期与晚期）比较风险因素。
分别有12985名（31.8%）和25444名（62.9%）在出院后30天内和1年内再次入院。15800名（39.0%）有多次再入院。我们模型的预测准确率在0.61至0.63之间。移植因素仍然是早期和晚期再入院的主要预测因素，但随着时间的推移而降低。尽管受者的人口统计学和社会经济因素在30天时分别仅占预测的2.5%和11%，但它们对后期再入院预测的贡献分别增加到7%和14%。供体特征在所有时间都是较差的预测因素。受者特征与移植后再入院之间的关联在首次和后续再入院之间是一致的。与后续再入院相比，供体和移植特征与首次再入院的关联更强。
这些结果可能为未来医院再入院预测模型的开发提供信息，该模型可用于识别肾移植受者移植后住院的高风险人群，并设计预防再入院的干预措施。
