Profiling illness perceptions to identify patients at-risk for decline in health status after heart valve replacement

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• 作者：Sebastian Kohlmann^a ; ^{s.kohlmann@uke.de} ; Helen Rimington^b ; John Weinman^c
• 关键词：Cardiac surgery ; Cluster analysis ; Heart valve disease ; Illness perceptions ; NYHA ; Quality of life
• 刊名：Journal of Psychosomatic Research
• 出版年：2012
• 期刊代码：170_00223999
• 类别：med
• 出版时间：June, 2012
• 卷：72
• 期：6
• 页码：427-433
• 文件大小：401 K

摘要

Objective

Identification of risk factors for decline in health status by profiling illness perceptions before and one year after heart valve replacement surgery.

Methods

Prospective data from N=225 consecutively admitted first time valve replacement patients was assessed before and one year after surgery. Patients were asked about their illness perceptions (Illness Perception Questionnaire-Revised) and mood state (Hospital Anxiety and Depression Scale). Health status was defined by quality of life (Short-Form 36) and New York Heart Association (NYHA) class. Cluster analyses were conducted to identify illness perception profiles over time. Predictors of health status after surgery were analyzed with multivariate methods.

Results

Patients were grouped according to the stability and nature (positive, negative) of their illness perception profile over one year. One year after surgery patients holding a negative illness perception profile showed a lower physical quality of life and were diagnosed in a higher New York Heart Association class than patients changing to positive and patients with stable positive illness perceptions (P<.001). Over and above biological determinants, post-surgery physical quality of life and NYHA class were both predicted by pre-surgery illness perception profiles (P<.05).

Conclusion

Patients going for heart valve replacement surgery can be easily categorized into illness perception profiles that predict health status one year after surgery. These patients could benefit from early screening as negative illness perceptions are modifiable risk factors.