Department of Neuroanaesthesia, National Institute of Mental Health and Neurosciences, Bangalore, India.
J Neurosurg Anesthesiol. 2011 Jul;23(3):183-7. doi: 10.1097/ANA.0b013e31821cb9ec.
Models for prediction of outcome of intensive care patients greatly help the physician to make decisions and are also important for risk stratification in clinical research and quality improvement. At present, there are no major predictive models for neurosurgical intensive care unit (NSICU) patients. This study aimed to develop a predictive model for survival in NSICU patients.
This is a prospective observational study in the NSICU at a tertiary-care university hospital. The data were collected within 24 hours of admission in all patients admitted to the NSICU. The parameters collected were demographic variables, systolic blood pressure, arterial oxygen tension after resuscitation (PaO2), Glasgow coma score (GCS) and pupillary signs, blood urea, creatinine, albumin, glucose, sodium, potassium, serum glutamic oxaloacetic transaminase, serum glutamic pyruvic transaminase, alkaline phosphatase, bilirubin, hemoglobin concentration, leukocyte count, platelet count, temperature, and evidence of infection. Mortality or discharge from NSICU was the primary outcome variable. All patients were provided full care until death or discharge from the ICU. Life support was not withdrawn in any of the patient based on the perception of outcome by the treating physician. All variables were compared between survivors and nonsurvivors. Significant variables were analyzed by multivariate logistic regression and a prediction model was developed.
Four hundred six patients were included in the study. Three hundred two patients survived and 104 died (mortality of 25.6%). Significant variables on univariate analysis include primary reason for admission, GCS, pupillary reaction, systolic blood pressure, serum albumin, glucose, serum sodium concentration, hypothermia, and infection at the time of admission. Multivariate analysis showed that the significant independent factors for predicting outcome in NSICU patients are age, diagnosis, GCS, pupillary status, albumin, and serum sodium concentration. The predictive model has good discrimination (receiver operating characteristic curve=0.796) and good calibration (P=0.937). The overall accuracy of the model was 81%.
In the current model of prediction of survival in a neurosurgical ICU, age, diagnosis, GCS, pupillary status, serum albumin, and serum sodium are independent predictors of survival in NSICU patients.
预测重症监护患者结局的模型有助于医生做出决策，对于临床研究和质量改进中的风险分层也很重要。目前，尚无针对神经外科重症监护病房（NSICU）患者的主要预测模型。本研究旨在开发一种用于 NSICU 患者生存预测的模型。
这是一项在三级大学医院 NSICU 进行的前瞻性观察性研究。所有入住 NSICU 的患者在入院 24 小时内采集数据。收集的参数包括人口统计学变量、复苏后收缩压、动脉氧分压（PaO2）、格拉斯哥昏迷评分（GCS）和瞳孔体征、血尿素、肌酐、白蛋白、血糖、钠、钾、血清谷草转氨酶、血清谷丙转氨酶、碱性磷酸酶、胆红素、血红蛋白浓度、白细胞计数、血小板计数、体温和感染证据。死亡率或从 NSICU 出院是主要的结局变量。所有患者均接受充分的护理，直至死亡或从 ICU 出院。根据主治医生对结局的看法，并未基于对结局的感知而从任何患者中撤出生命支持。对幸存者和非幸存者进行所有变量比较。通过多变量逻辑回归分析有显著意义的变量，并开发预测模型。
本研究共纳入 406 例患者。302 例患者存活，104 例死亡（死亡率为 25.6%）。单因素分析有显著意义的变量包括入院的主要原因、GCS、瞳孔反应、收缩压、血清白蛋白、血糖、血清钠浓度、低体温和入院时的感染。多变量分析显示，预测 NSICU 患者结局的独立显著因素是年龄、诊断、GCS、瞳孔状态、白蛋白和血清钠浓度。预测模型具有良好的区分度（受试者工作特征曲线=0.796）和良好的校准度（P=0.937）。该模型的总体准确率为 81%。
在当前的神经外科重症监护病房生存预测模型中，年龄、诊断、GCS、瞳孔状态、血清白蛋白和血清钠是 NSICU 患者生存的独立预测因素。
