Brailer D J, Kroch E, Pauly M V, Huang J
Health Care Systems Department, The Wharton School, University of Pennsylvania, Philadelphia, 19104-6218, USA.
Med Care. 1996 May;34(5):490-505. doi: 10.1097/00005650-199605000-00010.
The measurement of inpatient complications his received substantial attention in recent years because mortality rates and other outcome measures often appear unable to discriminate superior from inferior hospital care. Complication measurement holds out the promise of being more sensitive to variations in patient care because complications occur more frequently than do mortalities, and because complications are more direct consequences of the process of care. The authors developed a new measure of complications that seeks to give insight into the patient care given by different hospitals or physicians by using commonly available data. Specifically, this measure is based on a decision-theoretic model that estimates the probability of a complication for combinations of admitting and secondary International Classification of Diseases, 9th Revision, Clinical Modification diagnoses. The measure can be evaluated at the patient level, or aggregated and risk-adjusted for the population of a given care provider (eg, physician or hospital). When applied to a set of patient-level UB- 82/92 data, this measure estimates the risk of complication for any member of a population, controlling for comorbidity, and hence is designated comorbidity-adjusted complication risk (CACR). The authors describe the development of CACR and its testing and validation using data acquired from the states of Pennsylvania, California, and Florida, as well as facility data obtained directly from hospitals. The data set includes 480,000 patients from 50 Pennsylvania hospitals, 300,000 patients from 33 Florida hospitals, 370,000 patients from 35 California hospitals, and 37,000 patients from six validation hospitals. Comorbidity-adjusted complication risk is constructed from widely available data common to most patient cases. Comorbidity-adjusted complication risk can be adjusted for its case mix, but such risk adjustment has much less effect on CACR than on other adverse outcomes such as mortality and morbidity. Comorbidity-adjusted complication risk varies widely across the hospitals in this sample, yet it is stable across time and is correlated with other known quality outcomes, including such accepted "gold standards" as hospital-documented adverse event rates and chart review determinations of complications.
近年来，住院并发症的测量受到了广泛关注，因为死亡率和其他结果指标似乎常常无法区分优质与劣质的医院护理。并发症测量有望对患者护理的差异更加敏感，这是因为并发症的发生频率高于死亡，而且并发症是护理过程更直接的后果。作者开发了一种新的并发症测量方法，旨在通过使用常见数据来深入了解不同医院或医生提供的患者护理情况。具体而言，该测量方法基于一个决策理论模型，该模型可估算国际疾病分类第九版临床修订本（ICD-9-CM）的入院诊断和二级诊断组合出现并发症的概率。该测量方法可在患者层面进行评估，也可汇总并针对特定护理提供者（如医生或医院）的人群进行风险调整。当应用于一组患者层面的UB-82/92数据时，该测量方法可估算人群中任何成员发生并发症的风险，并控制合并症，因此被称为合并症调整并发症风险（CACR）。作者描述了CACR的开发过程，以及使用从宾夕法尼亚州、加利福尼亚州和佛罗里达州获取的数据以及直接从医院获得的机构数据对其进行的测试和验证。数据集包括来自宾夕法尼亚州50家医院的480,000名患者、来自佛罗里达州33家医院的300,000名患者、来自加利福尼亚州35家医院的370,000名患者以及来自6家验证医院的37,000名患者。合并症调整并发症风险是根据大多数患者病例普遍可用的数据构建的。合并症调整并发症风险可针对其病例组合进行调整，但这种风险调整对CACR的影响远小于对死亡率和发病率等其他不良结果的影响。在这个样本中，合并症调整并发症风险在不同医院之间差异很大，但随时间稳定，并且与其他已知的质量结果相关，包括医院记录的不良事件发生率和并发症图表审查判定等公认的“黄金标准”。
