Oxford case complexity assessment measure (OCCAM), a new scale to measure complexity in patients affected by stroke and its concordance with other scales of disability

• Published in: Annals of physical and rehabilitation medicine Vol. 61; p. e201
• Main Authors: Martin Castillo, E., Marquez Rodriguez, P., Báez Suárez, A., Lopez Fernandez, J.C., Deniz Caceres, A., Saavedra Santana, P., Rodriguez Molina, D.
• Format: Journal Article
• Language: English
• Published: Elsevier Masson SAS 01.07.2018
• Subjects: OCCAM; Quality of life; Stroke; Stroke; OCCAM; Quality of life
• ISSN: 1877-0657; 1877-0665
• DOI: 10.1016/j.rehab.2018.05.462

Stroke is considered the most common cause of complex disability in our society. There are only few scales evaluating complexity. The aim of our study is to evaluate the correlation of the Spanish version of OCCAM score, which includes biopsychosocial aspects, with other scales which measure disability and quality of life, in patients affected by stroke: National Institutes Health Stroke Scale (NIHSS), Barthel Index (BI), Modified Rankin Scale (MRS) and the Short-Form 12 Questionnaire (SF-12). A prospective study was conducted, 74 patients admitted to hospital diagnosed with stroke and subsidiary to rehabilitation programs. The concordance between the OCCAM scale with the other scales were evaluated by means of the Spearman correlation coefficient. Statistical significance was set at P<0.05. A total of 74 patients were analysed, 62% men against 38% women, mean age 74 years. Previous history of: high blood pressure (60%), diabetes mellitus (36%), dyslipidemia (39%), previous stroke (12%), arrythmia (33%). The correlation coefficients with OCCAM were: NIHSS (ρ=0.697), BI (ρ=0.905), MRS (ρ=0.829), SF-12 (ρ=0.331). The OCCAM scale has a strong correlation with other measures of disability, less with the quality of life measures; and is a quick and easy way to evaluate complexity in patients affected by stroke. It is based in a biopsychosocial model taking into considerations all factors that influence patients, so resources can be used more efficiently and predict prognosis/outcomes.