Hoen B, Viel J F, Maignan M, Hennequin L, May T, Amiel C, Kures L, Canton P
Département de Maladies infectieuses et tropicales, CHU de Nancy.
Presse Med. 1996 Mar 16;25(9):443-8.
Identify prognosis factors in Pneumocystis carinii pneumonia at diagnosis and construct a model to predict mortality according to these prognosis factors.
Seventy-seven consecutive cases of proven AIDS-related Pneumocystis carinii pneumonia (67 men, 10 women, mean age 37.2 years) were reviewed to determine the most accurate initial prognostic factors and estimate an individual prediction of death. A stepwise logistic regression analysis was performed. Three kinds of data were entered into the logistic model: historical data, clinical and laboratory data obtained within the first 24 hours of diagnosis, and specific data related to chest X-ray and bronchoalveolar lavage results.
The sum of arterial partial pressure of oxygen and carbon dioxide (PaO2 + PaCO2) and serum albumin level best predicted a fatal outcome in multivariate analysis.
The logistic equation provided by the model might be used to accurately and quickly identify the patients with severe Pneumocystis carinii pneumonia who might benefit from supportive intensive care.
确定卡氏肺孢子虫肺炎诊断时的预后因素，并根据这些预后因素构建一个预测死亡率的模型。
回顾了77例连续确诊的艾滋病相关卡氏肺孢子虫肺炎病例（67例男性，10例女性，平均年龄37.2岁），以确定最准确的初始预后因素并估计个体死亡预测。进行了逐步逻辑回归分析。三种数据被输入逻辑模型：历史数据、诊断后24小时内获得的临床和实验室数据，以及与胸部X线和支气管肺泡灌洗结果相关的特定数据。
在多变量分析中，动脉血氧分压与二氧化碳分压之和（PaO2 + PaCO2）和血清白蛋白水平最能预测致命结局。
该模型提供的逻辑方程可用于准确快速地识别可能从支持性重症监护中获益的重症卡氏肺孢子虫肺炎患者。
