Prytherch D R, Sirl J S, Schmidt P, Featherstone P I, Weaver P C, Smith G B
Department of Information Systems and Computer Applications, University of Portsmouth, Portsmouth, UK.
Resuscitation. 2005 Aug;66(2):203-7. doi: 10.1016/j.resuscitation.2005.02.011.
The ability to predict clinical outcomes in the early phase of a patient's hospital admission could facilitate the optimal use of resources, might allow focused surveillance of high-risk patients and might permit early therapy. We investigated the hypothesis that the risk of in-hospital death of general medical patients can be modelled using a small number of commonly used laboratory and administrative items available within the first few hours of hospital admission. Matched administrative and laboratory data from 9497 adult hospital discharges, with a hospital discharge specialty of general medicine, were divided into two subsets. The dataset was split into a single development set, Q(1) (n=2257), and three validation sets, Q(2), Q(3) and Q(4) (n(1)=2335, n(2)=2361, n(3)=2544). Hospital outcome (survival/non-survival) was obtained for all discharges. An outcome model was constructed from binary logistic regression of the development set data. The goodness-of-fit of the model for the validation sets was tested using receiver-operating characteristics curves (c-index) and Hosmer-Lemeshow statistics. Application of the model to the validation sets produced c-indices of 0.779 (Q(2)), 0.764 (Q(3)) and 0.757 (Q(4)), respectively, indicating good discrimination. Hosmer-Lemeshow analysis gave chi(2)=9.43 (Q(2)), chi(2)=7.39 (Q(3)) and chi(2)=8.00 (Q(4)) (p-values of 0.307, 0.495 and 0.433) for 8 degrees of freedom, indicating good calibration. The finding that the risk of hospital death can be predicted with routinely available data very early on after hospital admission has several potential uses. It raises the possibility that the surveillance and treatment of patients might be categorised by risk assessment means. Such a system might also be used to assess clinical performance, to evaluate the benefits of introducing acute care interventions or to investigate differences between acute care systems.
在患者入院早期预测临床结局的能力有助于优化资源利用，可能允许对高危患者进行重点监测，并可能允许早期治疗。我们研究了这样一个假设，即可以使用患者入院后头几个小时内常用的少量实验室检查项目和管理数据来构建普通内科患者的院内死亡风险模型。从9497例成人出院病例中匹配得到行政和实验室数据，这些病例的出院科室为普通内科，将其分为两个子集。数据集被分为一个单一的开发集Q(1)（n = 2257）和三个验证集Q(2)、Q(3)和Q(4)（n(1)=2335，n(2)=2361，n(3)=2544）。获取所有出院病例的医院结局（存活/未存活）。根据开发集数据的二元逻辑回归构建结局模型。使用受试者工作特征曲线（c指数）和Hosmer-Lemeshow统计量检验该模型对验证集的拟合优度。将该模型应用于验证集，c指数分别为0.779（Q(2)）、0.764（Q(3)）和0.757（Q(4)），表明具有良好的区分度。Hosmer-Lemeshow分析在8个自由度下得到的卡方值分别为：Q(2)的卡方值为9.43，Q(3)的卡方值为7.39，Q(4)的卡方值为8.00（p值分别为0.307、0.495和0.433），表明具有良好的校准度。入院后很早就能用常规可得数据预测院内死亡风险这一发现有几个潜在用途。它增加了通过风险评估手段对患者进行监测和治疗分类的可能性。这样一个系统还可用于评估临床绩效、评估引入急性护理干预措施的益处或调查急性护理系统之间的差异。
