Ganz P A, Hirji K, Sim M S, Schag C A, Fred C, Polinsky M L
Department of Medicine, UCLA School of Medicine.
Med Care. 1993 May;31(5):419-31. doi: 10.1097/00005650-199305000-00004.
Breast cancer is the most common neoplasm in North American women. The psychosocial impact of breast cancer has been extensively studied, and a number of investigators have attempted to characterize women who are at high risk for increased psychosocial morbidity. Although a detailed interview performed by a professional is the clinical standard for psychosocial assessment, such interviews are usually time-consuming and expensive, and thus are rarely performed. This study was designed to develop a strategy for the rapid identification of newly-diagnosed breast cancer patients at risk for psychosocial morbidity. A sample of 227 newly diagnosed breast cancer patients were interviewed systematically by a clinical social worker and were subsequently classified for risk of psychosocial distress in the year after diagnosis. In addition, these women completed a battery of standardized instruments designed to assess quality of life, rehabilitation needs and psychological distress. A logistic regression procedure was used to examine a wide range of variables for their ability to correctly classify the risk of psychosocial distress in this sample. The final model included the Cancer Rehabilitation Evaluation System (CARES) Psychosocial Summary Scale, the Karnofsky Performance Status score and age as the best predictors of psychosocial risk. Subsequently these three variables were used to construct a clinically usable risk prediction model. Additional research should be performed to validate this predictive model.
乳腺癌是北美女性中最常见的肿瘤。乳腺癌的社会心理影响已得到广泛研究,许多研究人员试图对社会心理发病率增加风险较高的女性进行特征描述。尽管由专业人员进行详细访谈是社会心理评估的临床标准,但此类访谈通常耗时且昂贵,因此很少进行。本研究旨在制定一种策略,用于快速识别有社会心理发病风险的新诊断乳腺癌患者。一名临床社会工作者对227名新诊断的乳腺癌患者进行了系统访谈,随后对这些患者在诊断后一年内的社会心理困扰风险进行了分类。此外,这些女性完成了一系列标准化测评工具,旨在评估生活质量、康复需求和心理困扰。采用逻辑回归程序来检验一系列变量正确分类该样本中社会心理困扰风险的能力。最终模型包括癌症康复评估系统(CARES)社会心理总结量表、卡诺夫斯基功能状态评分和年龄,作为社会心理风险的最佳预测指标。随后,利用这三个变量构建了一个临床可用的风险预测模型。应开展更多研究以验证该预测模型。