...
【24h】

A validated preoperative score for predicting 30-day readmission after 1-2 level elective posterior lumbar fusion

机译:在1-2级选修后腰椎融合后预测30天读取的验证术前分数

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

PurposeTo develop a model to predict 30-day readmission rates in elective 1-2 level posterior lumbar spine fusion (PSF) patients.MethodsIn this retrospective case control study, patients were identified in the State Inpatient Database using ICD-9 codes. Data were queried for 30-day readmission, as well as demographic and surgical data. Patients were randomly assigned to either the derivation or the validation cohort. Stepwise multivariate analysis was conducted on the derivation cohort to predict 30-day readmission. Readmission after posterior spinal fusion (RAPSF) score was created by including variables with odds ratio (OR)>1.1 and p<0.01; value assigned to each variable was based on the OR and calibrated to 100. Linear regression was performed between readmission rate and RAPSF score to test correlation in both cohorts.ResultsThere were 92,262 and 90,257 patients in the derivation and validation cohorts. Thirty-day readmission rates were 10.9% and 11.1%, respectively. Variables in RAPSF included: age, female gender, race, insurance, anterior approach, cerebrovascular disease, chronic pulmonary disease, congestive heart failure, diabetes, hemiplegia/paraplegia, rheumatic disease, drug abuse, electrolyte disorder, osteoporosis, depression, obesity, and morbid obesity. Linear regression between readmission rate and RAPSF fits the derivation cohort and validation cohort with an adjusted r(2) of 0.92 and 0.94, respectively, and a coefficient of 0.011 (p<0.001) in both cohorts.ConclusionThe RAPSF can accurately predict readmission rates in PSF patients and may be used to guide an evidence-based approach to preoperative optimization and risk adjustment within alternative payment models for elective spine surgery.Level of evidence3.Graphical abstractThese slides can be retrieved under Electronic Supplementary Material.
机译:Purposeto开发了一种模型来预测选修1-2级后腰椎脊柱融合(PSF)患者的30天即将入院率。此次回顾性案例控制研究,患者在状态住院数据库中使用ICD-9代码识别。询问数据30天的阅览,以及人口统计和手术数据。患者随机分配给推导或验证队列。逐步多变量分析在推导队队列上进行以预测30天的入院。通过包括差异比(或)> 1.1和P <0.01的变量,创建后脊柱融合(RAPSF)评分后的阅览。分配给每个变量的值基于或校准到100.在再次入院率和RAPSF评分之间进行线性回归,以测试群体中的相关性。结果是衍生和验证队列中的92,262和90,257名患者。 30天的入院率分别为10.9%和11.1%。 Rapsf中的变量包括:年龄,女性性别,种族,保险,前方法,脑血管病,慢性肺病,充血性心力衰竭,糖尿病,偏瘫/截瘫,风湿病,药物滥用,电解质障碍,骨质疏松症,抑郁,肥胖,和病态肥胖。入院率和RAPSF之间的线性回归适合推导群组和验证队员,分别具有0.92和0.94的调节R(2),以及两个队列中的0.011(P <0.001)的系数。结论RAPSF可以准确地预测入住率PSF患者,可用于指导基于证据的术语优化和风险调整,以便在选修脊柱外科的替代支付模型中进行术前优化和风险调整。有证据3的级别。图形摘要可以在电子补充材料下取回幻灯片。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号