Vesterager Jeppe Damgren, Madsen Morten, Hjelholt Thomas Johannesson, Kristensen Pia Kjær, Pedersen Alma Becic
Department of Clinical Epidemiology, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Clin Epidemiol. 2022 Mar 8;14:275-287. doi: 10.2147/CLEP.S346745. eCollection 2022.
Comorbidity has an important role in risk prediction and risk adjustment modelling in observational studies. However, it is unknown which comorbidity index is most accurate to predict mortality in hip fracture patients. We aimed to evaluate the prediction ability, including discrimination and calibration of Charlson comorbidity index (CCI), Elixhauser comorbidity index (ECI) and Rx-risk index for 30 day- and 1 year mortality in a population-based cohort of hip fracture surgery patients.
Using the Danish Multidisciplinary Hip Fracture Registry in the period 2014-2018, 31,443 patients were included. CCI and ECI were based on discharge diagnoses, while Rx-Risk index was based on pharmacy dispensings. We used logistic regression to assess discrimination of the different indices, individually and in combinations, by calculating c-statistics and the contrast in c-statistic to a base model including only age and gender with 95% confidence intervals (CI).
The study cohort were primarily female (69%) and older than 85 years (42%). The 30-day mortality was 10.1% and the 1-year mortality was 26.6%. Age and gender alone had a good discrimination ability for 30-day and 1-year mortality (c-statistic=0.70, CI: 0.69-0.71 and c-statistic=0.68, CI: 0.67 -0.69, respectively). By adding indices individually to the base model, Rx-risk index had the best 30-day and 1-year mortality discrimination ability (c-statistic=0.73, CI: 0.72-0.74 and 0.71 CI: 0.71-0.72, respectively). By adding combination of indices to the base model, a combination of CCI and the Rx-risk index had a 30-day and 1-year mortality discrimination ability of c-statistic=0.74, CI: 0.73-0.75 and c-statistic=0.73, CI: 0.73-0.74, respectively. Calibration of indices was similar.
The highest discrimination ability was achieved by combining CCI and Rx-risk index in addition to age and gender. However, age and gender alone had a fair mortality discrimination ability.
在观察性研究中，合并症在风险预测和风险调整模型中起着重要作用。然而，尚不清楚哪种合并症指数在预测髋部骨折患者死亡率方面最为准确。我们旨在评估基于人群的髋部骨折手术患者队列中，Charlson合并症指数（CCI）、Elixhauser合并症指数（ECI）和Rx风险指数对30天和1年死亡率的预测能力，包括区分度和校准度。
利用丹麦多学科髋部骨折登记处2014 - 2018年期间的数据，纳入了31443例患者。CCI和ECI基于出院诊断，而Rx风险指数基于药房配药记录。我们使用逻辑回归，通过计算c统计量以及与仅包含年龄和性别的基础模型相比c统计量的差异（95%置信区间），来单独和联合评估不同指数的区分度。
研究队列主要为女性（69%），年龄超过85岁（42%）。30天死亡率为10.1%，1年死亡率为26.6%。仅年龄和性别对30天和1年死亡率就有较好的区分能力（c统计量分别为0.70，置信区间：0.69 - 0.71和c统计量为0.68，置信区间：0.67 - 0.69）。通过将各指数单独添加到基础模型中，Rx风险指数对30天和1年死亡率的区分能力最佳（c统计量分别为0.73，置信区间：0.72 - 0.74和0.71，置信区间：0.71 - 0.72）。通过将指数组合添加到基础模型中，CCI和Rx风险指数的组合对30天和1年死亡率的区分能力的c统计量分别为0.74，置信区间：0.73 - 0.75和c统计量为0.73，置信区间：0.73 - 0.74。各指数的校准情况相似。
除年龄和性别外，将CCI和Rx风险指数相结合可实现最高的区分能力。然而，仅年龄和性别就有一定的死亡率区分能力。
