Clinical Epidemiology Program, Ottawa Hospital Research Institute, 1053 Carling Avenue, Ottawa, K1Y 4E9, Canada.
BMC Health Serv Res. 2011 Oct 7;11:258. doi: 10.1186/1472-6963-11-258.
Surgeries and other procedures can influence the risk of death in hospital. All published scales that predict post-operative death risk require clinical data and cannot be measured using administrative data alone. This study derived and internally validated an index that can be calculated using administrative data to quantify the independent risk of hospital death after a procedure.
For all patients admitted to a single academic centre between 2004 and 2009, we estimated the risk of all-cause death using the Kaiser Permanente Inpatient Risk Adjustment Methodology (KP-IRAM). We determined whether each patient underwent one of 503 commonly performed therapeutic procedures using Canadian Classification of Interventions codes and whether each procedure was emergent or elective. Multivariate logistic regression modeling was used to measure the association of each procedure-urgency combination with death in hospital independent of the KP-IRAM risk of death. The final model was modified into a scoring system to quantify the independent influence each procedure had on the risk of death in hospital.
275 460 hospitalizations were included (137,730 derivation, 137,730 validation). In the derivation group, the median expected risk of death was 0.1% (IQR 0.01%-1.4%) with 4013 (2.9%) dying during the hospitalization. 56 distinct procedure-urgency combinations entered our final model resulting in a Procedural Index for Mortality Rating (PIMR) score values ranging from -7 to +11. In the validation group, the PIMR score significantly predicted the risk of death by itself (c-statistic 67.3%, 95% CI 66.6-68.0%) and when added to the KP-IRAM model (c-index improved significantly from 0.929 to 0.938).
We derived and internally validated an index that uses administrative data to quantify the independent association of a broad range of therapeutic procedures with risk of death in hospital. This scale will improve risk adjustment when administrative data are used for analyses.
手术和其他程序会影响住院期间的死亡风险。所有已发表的预测术后死亡风险的量表都需要临床数据，并且不能仅使用行政数据进行测量。本研究得出并内部验证了一个指数，该指数可以使用行政数据计算，以量化手术后医院死亡的独立风险。
对于 2004 年至 2009 年间在一家学术中心住院的所有患者，我们使用 Kaiser Permanente 住院风险调整方法 (KP-IRAM) 估计全因死亡风险。我们使用加拿大干预分类代码确定每位患者是否进行了 503 种常见治疗程序中的一种，以及每种程序是紧急程序还是择期程序。使用多变量逻辑回归模型来衡量每个程序紧急程度组合与医院死亡的关联，而不考虑 KP-IRAM 的死亡风险。最终模型被修改为评分系统，以量化每个程序对医院死亡风险的独立影响。
共纳入 275460 例住院患者（137730 例用于推导，137730 例用于验证）。在推导组中，中位预期死亡风险为 0.1%（IQR 0.01%-1.4%），住院期间有 4013 例（2.9%）死亡。56 种不同的程序紧急程度组合进入我们的最终模型，导致死亡率评分（PIMR）的分值范围从-7 到+11。在验证组中，PIMR 评分本身显著预测死亡风险（C 统计量为 67.3%，95%CI 66.6-68.0%），并且当添加到 KP-IRAM 模型中时（C 指数从 0.929 显著提高到 0.938）。
我们得出并内部验证了一个指数，该指数使用行政数据来量化广泛的治疗程序与医院死亡风险的独立关联。当使用行政数据进行分析时，该量表将提高风险调整。
