Tessier Annie, Finch Lois, Daskalopoulou Stella S, Mayo Nancy E
School of Physical and Occupational Therapy, Faculty of Medicine, McGill University, Montreal, QC, Canada.
Arch Phys Med Rehabil. 2008 Jul;89(7):1276-83. doi: 10.1016/j.apmr.2007.11.049.
To determine whether a separate comorbidity index is needed to predict functional outcome after stroke, we compared the predictability of the Charlson Comorbidity Index (CMI) and the Functional Comorbidity Index (FCI) to that of a stroke-specific comorbidity index with function quantified with a measure developed with a Rasch model as outcome.
Two prospective inception cohort studies, in 1996 through 1998 and in 2002 through 2005, with up to 9 months of follow-up.
Participants enrolled in 2 studies were recruited from acute care hospitals in the Montreal area.
For study one, 1027 persons with a first stroke discharged into the community were eligible; the 437 who were interviewed a second time at 6 months were included in the analysis. In study two, 235 of 262 patients with stroke were enrolled.
Not applicable.
To predict recovery, we developed 3 stroke-specific comorbidity algorithms based on the estimated strength of association between comorbidities and stroke function. The various indices were compared on the basis of their predictive ability with a c statistic.
In study 1, the c statistics were .758, .763, .766, and .763 for the stroke-specific algorithms 1, 2, and 3 and the CMI, respectively. In study 2, the c statistics were .680, .700, .704, .714, and .714 for the algorithms 1, 2, and 3, the CMI, and the FCI, respectively.
For purposes of case-mix adjustment, the CMI seems to be more than adequate.
为了确定预测卒中后功能结局是否需要一个单独的合并症指数,我们将查尔森合并症指数(CMI)和功能合并症指数(FCI)的预测能力与一个以Rasch模型开发的测量方法量化功能的卒中特异性合并症指数的预测能力进行了比较。
两项前瞻性起始队列研究,分别在1996年至1998年以及2002年至2005年进行,随访时间长达9个月。
参与两项研究的参与者均从蒙特利尔地区的急性护理医院招募。
在第一项研究中,1027名首次卒中后出院进入社区的患者符合条件;其中437名在6个月时接受第二次访谈的患者纳入分析。在第二项研究中,262名卒中患者中有235名被纳入。
不适用。
为了预测恢复情况,我们基于合并症与卒中功能之间估计的关联强度开发了3种卒中特异性合并症算法。根据各指数的预测能力,用c统计量对它们进行比较。
在研究1中,卒中特异性算法1、2、3以及CMI的c统计量分别为0.758、0.763、0.766和0.763。在研究2中,算法1、2、3、CMI以及FCI的c统计量分别为0.680、0.700、0.704、0.714和0.714。
出于病例组合调整的目的,CMI似乎就足够了。