Chlebicki Andrzej Z, Kozioł Milena
Analyses and Strategy Department, Ministry of Health, Warsaw, Poland.
Arch Med Sci. 2019 Aug 1;16(2):321-336. doi: 10.5114/aoms.2019.87017. eCollection 2020.
The purpose of this study was to introduce a measure of patient's burden based on Elixhauser's comorbidity index. The mentioned measure needed to be based solely on administrative data and be applicable to all specialisations of hospital treatment. Moreover, the intention was to validate the estimation power of the models based on the groups of hospitalisations which were similar with respect to the primary diagnosis.
In the study, we considered all hospitalisations in Poland from 2014 and 2015. Overall, 22 045 267 hospitalisation records of 11 566 525 patients were retrieved. An important element of this research was to validate the estimation power of the models based on the groups of patients who were similar with respect to the main reason for hospitalisation. Therefore, the population was split into 21 Homogeneous Groups based on the changed primary diagnosis. As explanatory variables we used demographic variables and 31 comorbidities defined by Elixhauser. The outcome variable was patient's mortality - in-hospital or up to 365 days after discharge.
Out of the 21 created models, 9 had a very good estimation power (C-statistic over 0.85), the other 9 had satysfying results (C-statistic between 0.75 and 0.85) and only 3 performed poorly (C-statistic below 0.75). The odds ratio of variables varied widely between the groups.
Our results support the hypothesis that comorbidity properly describes mortality in homogeneous groups of patients. Our models could be condensed into one, uniform, single-number comorbidity scale that summarizes all of the patient's burden. It was found that the odds ratio of some variables differed between homogeneous groups.
本研究的目的是引入一种基于埃利克斯豪泽共病指数的患者负担衡量方法。上述衡量方法需要仅基于行政数据,并且适用于医院治疗的所有专科。此外,目的是基于在主要诊断方面相似的住院分组来验证模型的估计能力。
在本研究中,我们考虑了2014年和2015年波兰的所有住院病例。总体而言,检索到了11566525名患者的22045267条住院记录。本研究的一个重要因素是基于在住院主要原因方面相似的患者分组来验证模型的估计能力。因此,根据主要诊断的变化将总体分为21个同质组。作为解释变量,我们使用了人口统计学变量和埃利克斯豪泽定义的31种共病。结果变量是患者的死亡率——住院期间或出院后365天内。
在创建的21个模型中,9个具有非常好的估计能力(C统计量超过0.85),另外9个结果令人满意(C统计量在0.75至0.85之间),只有3个表现不佳(C统计量低于0.75)。各分组之间变量的比值比差异很大。
我们的结果支持这样的假设,即共病能够恰当地描述同质患者组中的死亡率。我们的模型可以浓缩为一个统一的、单一数字的共病量表,该量表总结了患者的所有负担。研究发现,一些变量的比值比在同质组之间存在差异。