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Abstract：
BACKGROUND: Prosthetic joint infections (PJIs) are a serious negative outcome of arthroplasty with incidence of about 1%. Risk of PJI could depend on local treatment policies and guidelines; no UK-specific risk scoring is currently available.
OBJECTIVE: To determine a risk quantification model for the development of PJI using electronic health records.
METHODS: Records in Clinical Practice Research Datalink (CPRD) GOLD and AURUM of patients undergoing hip or knee arthroplasty between January 2007 and December 2014, with linkage to Hospital Episode Statistics and Office of National Statistics, were obtained. Cohorts\' characteristics and risk equations through parametric models were developed and compared between the two databases. Pooled cohort risk equations were determined for the UK population and simplified through stepwise selection.
RESULTS: After applying the inclusion/exclusion criteria, 174 905 joints (1021 developed PJI) were identified in CPRD AURUM and 48 419 joints (228 developed PJI) in CPRD GOLD. Patients undergoing hip or knee arthroplasty in both databases exhibited different sociodemographic characteristics and medical/drug history. However, the quantification of the impact of such covariates (coefficients of parametric models fitted to the survival curves) on the risk of PJI between the two cohorts was not statistically significant. The log-normal model fitted to the pooled cohorts after stepwise selection had a C-statistic >0.7.
CONCLUSIONS: The risk prediction tool developed here could help prevent PJI through identifying modifiable risk factors pre-surgery and identifying the patients most likely to benefit from close monitoring/preventive actions. As derived from the UK population, such tool will help the National Health Service reduce the impact of PJI on its resources and patient lives.
OBJECTIVE: To determine a risk quantification model for the development of PJI using electronic health records.
METHODS: Records in Clinical Practice Research Datalink (CPRD) GOLD and AURUM of patients undergoing hip or knee arthroplasty between January 2007 and December 2014, with linkage to Hospital Episode Statistics and Office of National Statistics, were obtained. Cohorts\' characteristics and risk equations through parametric models were developed and compared between the two databases. Pooled cohort risk equations were determined for the UK population and simplified through stepwise selection.
RESULTS: After applying the inclusion/exclusion criteria, 174 905 joints (1021 developed PJI) were identified in CPRD AURUM and 48 419 joints (228 developed PJI) in CPRD GOLD. Patients undergoing hip or knee arthroplasty in both databases exhibited different sociodemographic characteristics and medical/drug history. However, the quantification of the impact of such covariates (coefficients of parametric models fitted to the survival curves) on the risk of PJI between the two cohorts was not statistically significant. The log-normal model fitted to the pooled cohorts after stepwise selection had a C-statistic >0.7.
CONCLUSIONS: The risk prediction tool developed here could help prevent PJI through identifying modifiable risk factors pre-surgery and identifying the patients most likely to benefit from close monitoring/preventive actions. As derived from the UK population, such tool will help the National Health Service reduce the impact of PJI on its resources and patient lives.
摘要：
背景：人工关节感染（PJIs）是关节成形术的严重负面结果，发生率约为1％。PJI的风险可能取决于当地的治疗政策和指南；目前尚无英国特有的风险评分。
目的：确定使用电子健康记录开发PJI的风险量化模型。
方法：2007年1月至2014年12月期间接受髋关节或膝关节置换术的患者的临床实践研究数据链（CPRD）GOLD和AURUM记录，与医院事件统计和国家统计局联系，已获得。通过参数模型开发了队列特征和风险方程，并在两个数据库之间进行了比较。确定了英国人群的汇总队列风险方程，并通过逐步选择进行了简化。
结果：应用纳入/排除标准后，在CPRDAURUM中确定了174905个关节（1021个发达的PJI），在CPRDGOLD中确定了48419个关节（228个发达的PJI）。在两个数据库中接受髋关节或膝关节置换术的患者表现出不同的社会人口统计学特征和医疗/药物史。然而,此类协变量(生存曲线拟合的参数模型的系数)对两个队列之间PJI风险的影响的量化无统计学意义.在逐步选择后，拟合到合并队列的对数正态模型的C统计量>0.7。
结论：这里开发的风险预测工具可以通过确定手术前可改变的风险因素和确定最有可能从密切监测/预防措施中受益的患者来帮助预防PJI。从英国人口中得出，这种工具将有助于国家卫生服务减少PJI对其资源和患者生活的影响。
目的：确定使用电子健康记录开发PJI的风险量化模型。
方法：2007年1月至2014年12月期间接受髋关节或膝关节置换术的患者的临床实践研究数据链（CPRD）GOLD和AURUM记录，与医院事件统计和国家统计局联系，已获得。通过参数模型开发了队列特征和风险方程，并在两个数据库之间进行了比较。确定了英国人群的汇总队列风险方程，并通过逐步选择进行了简化。
结果：应用纳入/排除标准后，在CPRDAURUM中确定了174905个关节（1021个发达的PJI），在CPRDGOLD中确定了48419个关节（228个发达的PJI）。在两个数据库中接受髋关节或膝关节置换术的患者表现出不同的社会人口统计学特征和医疗/药物史。然而,此类协变量(生存曲线拟合的参数模型的系数)对两个队列之间PJI风险的影响的量化无统计学意义.在逐步选择后，拟合到合并队列的对数正态模型的C统计量>0.7。
结论：这里开发的风险预测工具可以通过确定手术前可改变的风险因素和确定最有可能从密切监测/预防措施中受益的患者来帮助预防PJI。从英国人口中得出，这种工具将有助于国家卫生服务减少PJI对其资源和患者生活的影响。
