Disease-Specific Trends of Comorbidity Coding and Implications for Risk Adjustment in Hospital Administrative Data
Disease-Specific Trends of Comorbidity Coding and Implications for Risk Adjustment in Hospital Administrative Data
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DOI：
10.1111/1475-6773.12398
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发表时间：
2016-06-01
影响因子：
3.4
通讯作者：
Nimptsch, Ulrike
中科院分区：
文献类型：
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作者：
Nimptsch, Ulrike
Objective.研究引入基于诊断相关组（DRGs）的前瞻性支付后合并症编码的变化，以及特定合并症的趋势是否不同。2005年至2012年德国急性护理医院的全国管理数据（DRG统计）。研究设计。观察性研究，分析因常见原发病住院患者的合并症编码趋势以及对合并症相关住院死亡风险的影响。提取方法。共病编码由Elixhauser诊断组操作。分析集中于因原发性疾病心力衰竭、中风、肺炎以及髋部骨折而住院的成人患者。当关注每个记录的诊断组的总频率时，观察到编码深度的增加。在整个观察期内，医院间编码深度存在差异。在31个诊断组中的15个组中观察到特异性共病性增加，在11个组中观察到共病性减少。在因心力衰竭住院的患者中，共病相关的院内死亡风险在9个诊断组中发生了变化，其中8个诊断组直接朝向零。在纵向管理数据分析中，合并症调整的结果可能会因随时间推移的非恒定风险、编码完整性的变化和医院间编码差异而产生偏倚。当相应的观察期与报销系统的变化或可能改变临床编码实践的其他条件相一致时，考虑这些问题非常重要。
Objective. To investigate changes in comorbidity coding after the introduction of diagnosis related groups (DRGs) based prospective payment and whether trends differ regarding specific comorbidities.Data Sources. Nationwide administrative data (DRG statistics) from German acute care hospitals from 2005 to 2012.Study Design. Observational study to analyze trends in comorbidity coding in patients hospitalized for common primary diseases and the effects on comorbidity-related risk of in-hospital death.Extraction Methods. Comorbidity coding was operationalized by Elixhauser diagnosis groups. The analyses focused on adult patients hospitalized for the primary diseases of heart failure, stroke, and pneumonia, as well as hip fracture.Principal Findings. When focusing the total frequency of diagnosis groups per record, an increase in depth of coding was observed. Between-hospital variations in depth of coding were present throughout the observation period. Specific comorbidity increases were observed in 15 of the 31 diagnosis groups, and decreases in comorbidity were observed for 11 groups. In patients hospitalized for heart failure, shifts of comorbidity-related risk of in-hospital death occurred in nine diagnosis groups, in which eight groups were directed toward the null.Conclusions. Comorbidity-adjusted outcomes in longitudinal administrative data analyses may be biased by nonconstant risk over time, changes in completeness of coding, and between-hospital variations in coding. Accounting for such issues is important when the respective observation period coincides with changes in the reimbursement system or other conditions that are likely to alter clinical coding practice.