Velayati Ali A, Mehrabi Yadollah, Radmand Golnar, Maboudi Ali A Khadem, Jamaati Hamid R, Shahbazi A, Mohajerani Seyed A, Hashemian Seyed M R
Clinical Tuberculosis and Epidemiology Research Center, National Research Institute of Tuberculosis and Lung Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Int J Crit Illn Inj Sci. 2013 Jan;3(1):40-5. doi: 10.4103/2229-5151.109419.
Several models have been developed to measure the severity of illness in intensive care unit (ICU) patients, It is suggested that the models should be customized depending on the characteristics of different population of patients. This study is aimed to assess and modify the performance of Acute Physiology and Chronic Health Evaluation II (APACHE-II) model in a respiratory diseases referral center.
A total of 730 patients, admitted to an intensive care unit during one year, were divided into two sets (71% training and 29% test). Our modified APACHE-II model was developed and calibrated on training set. Then, the integrity of the customized model was checked and compared to the original APACHE-II, on the test set. Logistic regression was used to develop ROC analysis, F-measure and kappa coefficient and were employed to calibrate the model.
Both Original and Our modified APACHE-II scores performed acceptable discriminative power (AUC = 0.908: 95%CI 0.861-0.854; and AUC = 0.856: 95%CI 0.789-0.923, respectively); the difference was not significant (P = 0.132). Our modified APACHE-II showed improved accuracy (87.9% vs. 84.1%) and sensitivity (56.4% vs. 16.3%) compared to the original model. F-measure and Kappa also gave the impression of improvement for our modified APACHE-II system.
The results demonstrated that a modified APACHE-II system in a local ICU of respiratory disease could have similar discrimination and comparable calibration to the original model.
已经开发了几种模型来衡量重症监护病房(ICU)患者的疾病严重程度,建议根据不同患者群体的特征对模型进行定制。本研究旨在评估和改进急性生理与慢性健康状况评估II(APACHE-II)模型在一家呼吸系统疾病转诊中心的性能。
共有730例在一年内入住重症监护病房的患者被分为两组(71%为训练组,29%为测试组)。我们改进的APACHE-II模型在训练组上进行开发和校准。然后,在测试组上检查定制模型的完整性并与原始APACHE-II进行比较。使用逻辑回归进行ROC分析、F测量和kappa系数计算,并用于校准模型。
原始APACHE-II评分和我们改进后的APACHE-II评分均具有可接受的判别力(AUC分别为0.908:95%CI 0.861-0.854;和AUC为0.856:95%CI 0.789-0.923);差异不显著(P = 0.132)。与原始模型相比,我们改进后的APACHE-II显示出更高的准确性(87.9%对84.1%)和敏感性(56.4%对16.3%)。F测量和kappa系数也表明我们改进后的APACHE-II系统有所改进。
结果表明,在当地呼吸系统疾病ICU中改进的APACHE-II系统与原始模型具有相似的判别力和可比的校准。