Groeger J S, Lemeshow S, Price K, Nierman D M, White P, Klar J, Granovsky S, Horak D, Kish S K
Memorial Sloan-Kettering Cancer Center, New York, NY, USA.
J Clin Oncol. 1998 Feb;16(2):761-70. doi: 10.1200/JCO.1998.16.2.761.
To develop prospectively and validate a model for probability of hospital survival at admission to the intensive care unit (ICU) of patients with malignancy.
This was an inception cohort study in the setting of four ICUs of academic medical centers in the United States. Defined continuous and categorical variables were collected on consecutive patients with cancer admitted to the ICU. A preliminary model was developed from 1,483 patients and then validated on an additional 230 patients. Multiple logistic regression modeling was used to develop the models and subsequently evaluated by goodness-of-fit and receiver operating characteristic (ROC) analysis. The main outcome measure was hospital survival after ICU admission.
The observed hospital mortality rate was 42%. Continuous variables used in the ICU admission model are PaO2/FiO2 ratio, platelet count, respiratory rate, systolic blood pressure, and days of hospitalization pre-ICU. Categorical entries include presence of intracranial mass effect, allogeneic bone marrow transplantation, recurrent or progressive cancer, albumin less than 2.5 g/dL, bilirubin > or = 2 mg/dL, Glasgow Coma Score less than 6, prothrombin time greater than 15 seconds, blood urea nitrogen (BUN) greater than 50 mg/dL, intubation, performance status before hospitalization, and cardiopulmonary resuscitation (CPR). The P values for the fit of the preliminary and validation models are .939 and .314, respectively, and the areas under the ROC curves are .812 and .802.
We report a disease-specific multivariable logistic regression model to estimate the probability of hospital mortality in a cohort of critically ill cancer patients admitted to the ICU. The model consists of 16 unambiguous and readily available variables. This model should move the discussion regarding appropriate use of ICU resources forward. Additional validation in a community hospital setting is warranted.
前瞻性地建立并验证一个用于预测恶性肿瘤患者入住重症监护病房(ICU)时医院生存概率的模型。
这是一项在美国学术医疗中心的四个ICU开展的起始队列研究。对连续入住ICU的癌症患者收集定义好的连续变量和分类变量。从1483例患者中建立一个初步模型,然后在另外230例患者中进行验证。使用多因素逻辑回归建模来建立模型,随后通过拟合优度和受试者工作特征(ROC)分析进行评估。主要结局指标是入住ICU后的医院生存情况。
观察到的医院死亡率为42%。ICU入院模型中使用的连续变量有动脉血氧分压/吸入氧浓度比值、血小板计数、呼吸频率、收缩压以及入住ICU前的住院天数。分类变量包括颅内占位效应、异基因骨髓移植、复发或进展性癌症、白蛋白低于2.5g/dL、胆红素≥2mg/dL、格拉斯哥昏迷评分低于6分、凝血酶原时间大于15秒、血尿素氮(BUN)大于50mg/dL、插管、住院前的体能状态以及心肺复苏(CPR)。初步模型和验证模型的拟合P值分别为0.939和0.314,ROC曲线下面积分别为0.812和0.802。
我们报告了一个针对特定疾病的多因素逻辑回归模型,用于估计入住ICU的重症癌症患者队列中的医院死亡概率。该模型由16个明确且易于获取的变量组成。此模型应推动关于ICU资源合理使用的讨论。有必要在社区医院环境中进行进一步验证。