Division of Pulmonary and Critical Care Medicine, Cecil B. Sheps Center for Health Services Research, University of North Carolina, Chapel Hill, NC, USA.
Crit Care Med. 2012 Apr;40(4):1171-6. doi: 10.1097/CCM.0b013e3182387d43.
Significant deficiencies exist in the communication of prognosis for patients requiring prolonged mechanical ventilation after acute illness, in part because of clinician uncertainty about long-term outcomes. We sought to refine a mortality prediction model for patients requiring prolonged ventilation using a multicentered study design.
Cohort study.
Five geographically diverse tertiary care medical centers in the United States (California, Colorado, North Carolina, Pennsylvania, and Washington).
Two hundred sixty adult patients who received at least 21 days of mechanical ventilation after acute illness.
None.
For the probability model, we included age, platelet count, and requirement for vasopressors and/or hemodialysis, each measured on day 21 of mechanical ventilation, in a logistic regression model with 1-yr mortality as the outcome variable. We subsequently modified a simplified prognostic scoring rule (ProVent score) by categorizing the risk variables (age 18-49, 50-64, and ≥65 yrs; platelet count 0-150 and >150; vasopressors; hemodialysis) in another logistic regression model and assigning points to variables according to β coefficient values. Overall mortality at 1 yr was 48%. The area under the curve of the receiver operator characteristic curve for the primary ProVent probability model was 0.79 (95% confidence interval 0.75-0.81), and the p value for the Hosmer-Lemeshow goodness-of-fit statistic was .89. The area under the curve for the categorical model was 0.77, and the p value for the goodness-of-fit statistic was .34. The area under the curve for the ProVent score was 0.76, and the p value for the Hosmer-Lemeshow goodness-of-fit statistic was .60. For the 50 patients with a ProVent score >2, only one patient was able to be discharged directly home, and 1-yr mortality was 86%.
The ProVent probability model is a simple and reproducible model that can accurately identify patients requiring prolonged mechanical ventilation who are at high risk of 1-yr mortality.
在需要长时间机械通气的急性疾病患者的预后沟通方面存在显著缺陷，部分原因是临床医生对长期结果存在不确定性。我们试图使用多中心研究设计来完善一个用于预测需要长时间通气的患者死亡率的模型。
队列研究。
美国五个地理位置不同的三级保健医疗中心（加利福尼亚州、科罗拉多州、北卡罗来纳州、宾夕法尼亚州和华盛顿州）。
260 名接受至少 21 天机械通气的成年患者，急性疾病后。
无。
对于概率模型，我们在一个以 1 年死亡率为因变量的逻辑回归模型中纳入了第 21 天的年龄、血小板计数以及血管加压素和/或血液透析的需求，包括年龄、血小板计数和需要血管加压素和/或血液透析，作为预后变量。随后，我们通过将风险变量（年龄 18-49、50-64 和 ≥65 岁；血小板计数 0-150 和 >150；血管加压素；血液透析）分类到另一个逻辑回归模型中，并根据β系数值为变量分配分数，对简化的预后评分规则（ProVent 评分）进行了修改。1 年总死亡率为 48%。主要 ProVent 概率模型的接收者操作特征曲线下面积为 0.79（95%置信区间 0.75-0.81），Hosmer-Lemeshow 拟合优度统计量的 p 值为 0.89。分类模型的曲线下面积为 0.77，拟合优度统计量的 p 值为 0.34。ProVent 评分的曲线下面积为 0.76，Hosmer-Lemeshow 拟合优度统计量的 p 值为 0.60。对于 ProVent 评分>2 的 50 名患者，只有 1 名患者能够直接出院，1 年死亡率为 86%。
ProVent 概率模型是一种简单且可重复的模型，可以准确识别需要长时间机械通气且 1 年死亡率高的患者。
