Department of Statistics and Operational Research, University of Jaén, Spain.
Value Health. 2010 Jun-Jul;13(4):431-9. doi: 10.1111/j.1524-4733.2009.00680.x. Epub 2010 Jan 8.
Nosocomial infection is one of the main causes of morbidity and mortality in patients admitted to hospital. One aim of this study is to determine its intrinsic and extrinsic risk factors. Nosocomial infection also increases the duration of hospital stay. We quantify, in relative terms, the increased duration of the hospital stay when a patient has the infection.
We propose the use of logistic regression models with an asymmetric link to estimate the probability of a patient suffering a nosocomial infection. We use Poisson-Gamma regression models as a multivariate technique to detect the factors that really influence the average hospital stay of infected and noninfected patients. For both models, frequentist and Bayesian estimations were carried out and compared.
The models are applied to data from 1039 patients operated on in a Spanish hospital. Length of stay, the existance of a preoperative stay and obesity were found the main risk factors for a nosomial infection. The existence of a nosocomial infection multiplies the length of stay in the hospital by a factor of 2.87.
The results show that the asymmetric logit improves the predictive capacity of conventional logistic regressions.
医院感染是导致住院患者发病率和死亡率的主要原因之一。本研究旨在确定其内在和外在的风险因素。医院感染还会延长住院时间。我们定量地衡量了患者感染后住院时间延长的相对程度。
我们提出使用具有非对称链接的逻辑回归模型来估计患者发生医院感染的概率。我们使用泊松-伽马回归模型作为一种多变量技术来检测真正影响感染和未感染患者平均住院时间的因素。对于这两种模型，我们进行了频率论和贝叶斯估计，并进行了比较。
该模型应用于西班牙一家医院 1039 名手术患者的数据。住院时间、术前停留时间和肥胖是医院感染的主要危险因素。医院感染的存在使住院时间延长了 2.87 倍。
结果表明，非对称逻辑提高了传统逻辑回归的预测能力。
