title: Predicting hospital-acquired infections by scoring system with simple parameters.
abstract: BACKGROUND
  Hospital-acquired infections (HAI) are associated with increased attributable morbidity, mortality, prolonged hospitalization, and economic costs. A simple, reliable prediction model for HAI has great clinical relevance. The objective of this study is to develop a scoring system to predict HAI that was derived from Logistic Regression (LR) and validated by Artificial Neural Networks (ANN) simultaneously.
  
  
  RESULTS
  A total of 476 patients from all the 806 HAI inpatients were included for the study between 2004 and 2005. A sample of 1,376 non-HAI inpatients was randomly drawn from all the admitted patients in the same period of time as the control group. External validation of 2,500 patients was abstracted from another academic teaching center. Sixteen variables were extracted from the Electronic Health Records (EHR) and fed into ANN and LR models. With stepwise selection, the following seven variables were identified by LR models as statistically significant: Foley catheterization, central venous catheterization, arterial line, nasogastric tube, hemodialysis, stress ulcer prophylaxes and systemic glucocorticosteroids. Both ANN and LR models displayed excellent discrimination (area under the receiver operating characteristic curve [AUC]: 0.964 versus 0.969, p = 0.507) to identify infection in internal validation. During external validation, high AUC was obtained from both models (AUC: 0.850 versus 0.870, p = 0.447). The scoring system also performed extremely well in the internal (AUC: 0.965) and external (AUC: 0.871) validations.
  
  
  CONCLUSIONS
  We developed a scoring system to predict HAI with simple parameters validated with ANN and LR models. Armed with this scoring system, infectious disease specialists can more efficiently identify patients at high risk for HAI during hospitalization. Further, using parameters either by observation of medical devices used or data obtained from EHR also provided good prediction outcome that can be utilized in different clinical settings.
authors: Graduate Institute of Medical Science, College of Medicine, Taipei Medical University, Taipei, Taiwan.
authors: Ying-Jui
authors: YJ
authors: Chang
authors: Min-Li
authors: ML
authors: Yeh
authors: Yu-Chuan
authors: YC
authors: Li
authors: Chien-Yeh
authors: CY
authors: Hsu
authors: Chao-Cheng
authors: CC
authors: Lin
authors: Meng-Shiuan
authors: MS
authors: Hsu
authors: Wen-Ta
authors: WT
authors: Chiu
mesh_terms: D000368:Aged; D003428:Cross Infection / Q000175:diagnosis*; D003710:Demography; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012372:ROC Curve; D015203:Reproducibility of Results; D012107:Research Design