Multimorbidity in Stroke

The prevalence of long-term conditions is increasing across the world because of improved treatments for acute conditions and an increase in life expectancy. Importantly, people are now commonly living with multiple long-term conditions, and this has important implications for the design and cost of health services. Those with higher numbers of long-term conditions have worse health-related outcomes and use health services more frequently. The presence of multiple conditions adds complexity to disease management; for example, disease-disease and disease-drug interactions are more likely to occur. Co-occurring conditions can be concordant, that is, similar in origin and treatment requirements or discordant, that is, apparently unrelated or requiring different treatments. The terms multimorbidity and comorbidity are used frequently in the medical literature, the first commonly defined as the presence of ≥2 long-term conditions within an individual and the second as the presence of at least 1 long-term condition alongside an index condition. However, it is widely accepted that there is great variation in definitions and this makes comparison of populations difficult. A recent systematic review of systematic reviews examined definitions and methods of measurement of multimorbidity. The authors reported that multimorbidity is most commonly defined as the presence of ≥2 conditions within an individual, but there is variation in whether duration, severity, or clustering of conditions are considered. Various options for the measurement of multimorbidity, including weighted and unweighted options, were also examined, and the authors concluded that the optimum measure depends on the purpose of the study. So, at the present time, there remains no consensus on the optimal approach to measuring multimorbidity. Common measures include the Charlson Comorbidity Index (CCI), Elixhauser Index, and the Cumulative Illness Rating Scale. All are weighted measures, but Elixhauser can also be used in an unweighted form. The CCI was designed to predict mortality and includes 16 conditions (including cerebrovascular disease and hemiplegia). Elixhauser is a more extensive measure, it includes 30 acute and chronic conditions and was developed to predict length of hospital stay, hospital charges, and in-hospital death. The Cumulative Illness Rating Scale was developed to assess the medical burden of chronic illness; it includes 14 body system categories. By contrast, the Liu comorbidity index was specifically constructed from a cohort in Japan to be used in stroke outcomes research. It includes 41 conditions and is a weighted measure. When choosing a measure, a weighted one that has been validated for the outcome being considered may be best, but where evidence is weak or multiple outcomes are being examined, then a simple count of conditions may be appropriate. Importantly, a clear reason for choosing a definition and measure should be provided, and it may even be appropriate to include acute conditions, biopsychosocial factors, and risk factors in the measure if appropriate for the aim of the study. It is currently unclear what data source is best suited to the measurement of multimorbidity; for example, whether routinely collected or self-reported data are more helpful. Traditionally, routinely collected data has been considered to be more valid, but this relies on accurate coding by clinicians and risks missing conditions that are poorly coded such as depression or chronic pain. However, 1 risk with self-reported data is that it may omit conditions less visible to patients, such as chronic kidney disease. This article focuses on multimorbidity in stroke. Stroke survivors commonly have comorbid conditions, and these may arise due to a variety of biopsychosocial reasons. Many concomitant conditions may be concordant, that is, share risk factors and pathological disease processes with stroke, for example heart disease, or may predispose an individual to stroke, for example atrial fibrillation (AF). Other coexisting conditions may occur secondary to stroke, for example vascular dementia, or may be entirely unrelated or discordant, for example breast cancer. When measuring multimorbidity in stroke, how we define comorbidity will affect results; for example inclusion of conditions that are risk factors for stroke will make prevalence of comorbidity high. The recent Academy of Medical Sciences document makes it clear that even if someone only has a cluster of concordant conditions then the person should still be considered multimorbid. There are several reasons why multimorbidity in stroke merits attention. The additive effect of illness burdens from multiple conditions may contribute to poorer health status,