OBJECTIVE: 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. DESIGN: Two prospective inception cohort studies, in 1996 through 1998 and in 2002 through 2005, with up to 9 months of follow-up. SETTING: Participants enrolled in 2 studies were recruited from acute care hospitals in the Montreal area. PARTICIPANTS: 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. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: 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. RESULTS: 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. CONCLUSIONS: For purposes of case-mix adjustment, the CMI seems to be more than adequate.
OBJECTIVE: 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. DESIGN: Two prospective inception cohort studies, in 1996 through 1998 and in 2002 through 2005, with up to 9 months of follow-up. SETTING:Participants enrolled in 2 studies were recruited from acute care hospitals in the Montreal area. PARTICIPANTS: 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. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: 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. RESULTS: 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. CONCLUSIONS: For purposes of case-mix adjustment, the CMI seems to be more than adequate.
Authors: Anne G Rosenfeld; Elizabeth P Knight; Alana Steffen; Larisa Burke; Mohamud Daya; Holli A DeVon Journal: Heart Lung Date: 2015-06-26 Impact factor: 2.210
Authors: Amit Kumar; James E Graham; Linda Resnik; Amol M Karmarkar; Alai Tan; Anne Deutsch; Kenneth J Ottenbacher Journal: Am J Phys Med Rehabil Date: 2016-12 Impact factor: 2.159
Authors: Larisa A Burke; Anne G Rosenfeld; Mohamud R Daya; Karen M Vuckovic; Jessica K Zegre-Hemsey; Maria Felix Diaz; Josemare Tosta Daiube Santos; Sahereh Mirzaei; Holli A DeVon Journal: Eur J Cardiovasc Nurs Date: 2017-02-15 Impact factor: 3.908
Authors: Pieter H van Baal; Peter M Engelfriet; Hendriek C Boshuizen; Jan van de Kassteele; Francois G Schellevis; Rudolf T Hoogenveen Journal: Popul Health Metr Date: 2011-09-01
Authors: Xiaqing Jiang; Lu Wang; Lewis B Morgenstern; Christine T Cigolle; Edward S Claflin; Lynda D Lisabeth Journal: Neurology Date: 2020-10-06 Impact factor: 9.910