Zhu B P, Lemeshow S, Hosmer D W, Klar J, Avrunin J, Teres D
School of Public Health, University of Massachusetts, Amherst, USA.
Crit Care Med. 1996 Jan;24(1):57-63. doi: 10.1097/00003246-199601000-00011.
To examine the impact of hospital mortality and intensive care unit (ICU) size on the performance of the Mortality Probability Model II system for use in quality assessment, and to examine the ability of model customization to produce accurate estimates of hospital mortality to characterize patients by severity of illness for clinical trials.
Prospective evaluation of model performance, using retrospective data.
Data for the simulation were assembled from six adult medical and surgical ICUs in Massachusetts and New York.
Consecutive admissions (n = 4,224) to the Massachusetts and New York ICUs were studied. The mortality rate in the database was 18.7%.
A computer simulation of several different hospital mortality rates and ICU sample sizes, using a multicenter database of consecutive ICU admissions, was utilized. We simulated 20 different mortality rates by randomly changing the outcomes at hospital discharge from "survived" to "deceased" and from "deceased" to "survived". Four sample size simulations used 75%, 50%, 25%, and 10% of the database. Ten replications of each mortality rate and samples size were constructed, and model calibration and discrimination were assessed for each replication. Model coefficients were customized, using logistic regression.
Vital status at hospital discharge was the outcome measure among the ICU patient population. Model performance was assessed using the Hosmer-Lemeshow C statistic for calibration, and the area under the receiver operating characteristic curve for discrimination. Goodness-of-fit tests and receiver operating characteristic curve areas demonstrated that the models were sensitive to differences in hospital mortality, indicating that they are useful quality assurance tools. Goodness-of-fit tests were more sensitive than the receiver operating characteristic curve areas. The further the hospital mortality rate diverged from the original rate, the worse the performance of the model. Sample size had an impact on these results. The smaller the sample size, the less likely the model was to perform poorly. Model coefficients were successfully customized to demonstrate that improved model performance can be achieved when necessary for clinical trial stratification.
Mortality Probability Model II models can be used to assess quality of care in ICUs, but the size of the sample should be considered when assessing calibration and discrimination.
探讨医院死亡率和重症监护病房（ICU）规模对用于质量评估的死亡率概率模型II系统性能的影响，并研究模型定制在临床试验中根据疾病严重程度准确估计医院死亡率以对患者进行特征描述的能力。
使用回顾性数据对模型性能进行前瞻性评估。
模拟数据来自马萨诸塞州和纽约州的6个成人内科和外科ICU。
对马萨诸塞州和纽约州ICU的连续入院患者（n = 4,224）进行研究。数据库中的死亡率为18.7%。
利用一个多中心连续ICU入院数据库对几种不同的医院死亡率和ICU样本量进行计算机模拟。我们通过随机改变出院结局从“存活”变为“死亡”以及从“死亡”变为“存活”来模拟20种不同的死亡率。四个样本量模拟分别使用数据库的75%、50%、25%和10%。对每种死亡率和样本量进行10次重复，并对每次重复评估模型校准和区分度。使用逻辑回归对模型系数进行定制。
出院时的生命状态是ICU患者群体的结局指标。使用Hosmer-Lemeshow C统计量评估校准的模型性能，并使用受试者工作特征曲线下面积评估区分度。拟合优度检验和受试者工作特征曲线面积表明模型对医院死亡率差异敏感，表明它们是有用的质量保证工具。拟合优度检验比受试者工作特征曲线面积更敏感。医院死亡率与原始率差异越大，模型性能越差。样本量对这些结果有影响。样本量越小，模型表现不佳的可能性越小。成功定制模型系数以表明在临床试验分层必要时可实现模型性能的改善。
死亡率概率模型II可用于评估ICU的护理质量，但在评估校准时应考虑样本量。
