Rosenthal G E, Halloran E J, Kiley M, Pinkley C, Landefeld C S
Department of Medicine, Cleveland Veterans Administration Medical Center, OH 44106.
Med Care. 1992 Dec;30(12):1127-41. doi: 10.1097/00005650-199212000-00005.
The purpose of this study was to develop and validate the Nursing Severity Index, a new method used to measure the admission severity of illness of hospital patients using nursing diagnoses, which categorize biologic, functional, cognitive, and psychosocial abnormalities. This retrospective cohort study with independent development and testing phases was conducted at a U.S. academic medical center. In the development phase, data regarding 14,183 adult medical-surgical patients admitted to the medical center in 1985 and 1986 was used. In the testing phase, data regarding 7,302 patients admitted in 1987 and 1988 was used. Primary nurses prospectively recorded the presence or absence of 61 nursing diagnoses on admission. Demographic and clinical data were obtained from hospital data bases. In the development phase, the number of admission nursing diagnoses was highly related (P < 0.001) to in-hospital mortality. Using multiple logistic regression, 34 nursing diagnoses were identified as independent predictors of mortality; the Nursing Severity Index equals the number of these 34 diagnoses. In the testing phase of 7,302 patients, the Nursing Severity Index was related (P < 0.001) to mortality rates, which were 0.5%, 1%, 2%, 6%, 13%, 22%, and 31% in seven hierarchical strata defined by the Index. The Index was as accurate in predicting mortality as MedisGroups (receiver-operating-characteristic curve areas, 0.814 +/- 0.016 vs. 0.845 +/- 0.015, respectively, P = 0.12). Furthermore, the Nursing Severity Index and MedisGroups together (receiver operating characteristic curve area 0.880 +/- 0.014), were more accurate (P < 0.01) than either measure alone. The Nursing Severity Index assesses multiple dimensions of illness, can be easily measured during routine patient care, accurately predicts the risk of in-hospital death, and has similar prognostic accuracy as MedisGroups. Its usefulness in outcomes assessment, quality assurance, and case management merits further study.
本研究的目的是开发并验证护理严重程度指数，这是一种利用护理诊断来衡量医院患者入院时疾病严重程度的新方法，护理诊断可对生物、功能、认知和心理社会异常进行分类。这项具有独立开发和测试阶段的回顾性队列研究在美国一家学术医疗中心开展。在开发阶段，使用了1985年和1986年入住该医疗中心的14183名成年内科-外科患者的数据。在测试阶段，使用了1987年和1988年入院的7302名患者的数据。责任护士前瞻性地记录了入院时61项护理诊断的存在与否。人口统计学和临床数据从医院数据库中获取。在开发阶段，入院护理诊断的数量与住院死亡率高度相关（P<0.001）。通过多元逻辑回归，确定了34项护理诊断为死亡率的独立预测因素；护理严重程度指数等于这34项诊断的数量。在7302名患者的测试阶段，护理严重程度指数与死亡率相关（P<0.001），在由该指数定义的七个分层中，死亡率分别为0.5%、1%、2%、6%、13%、22%和31%。该指数在预测死亡率方面与MedisGroups一样准确（受试者工作特征曲线面积分别为0.814±0.016和0.845±0.015，P=0.12）。此外，护理严重程度指数和MedisGroups联合使用（受试者工作特征曲线面积为0.880±0.014），比单独使用任何一种方法都更准确（P<0.01）。护理严重程度指数评估疾病的多个维度，可在常规患者护理期间轻松测量，准确预测住院死亡风险，并且与MedisGroups具有相似的预后准确性。其在结局评估、质量保证和病例管理中的实用性值得进一步研究。
