VAP gave the highest risk, followed by CRBSI and CAUTI , while NNIS report of US ICUs reported overall rate of CVC was 4.0 per 1,000 CVC-day, 5.4 per 1,000 ventilator-day for VAP and 3.9 per 1,000 catheter-day for CAUTI in ICUs of teaching hospitals. ICU is not the only place where DAI is reported, many CVCs are also used outside the ICU, and the rates of CRBSI in these settings appear to be similar to that of the infections in ICUs. A German study revealed that in non- ICU patients, the device-associated HAI rates were 4.3 per 1,000 CVC-days for CVC-associated bloodstream infections and 6.8 infections per 1,000 urinary catheter�Cdays for catheter-associated urinary tract infections. The DAIs attribute to HAI and cause high morbidity and mortality, nerveless prolonged stay and high expense is consequential. Another German study conveyed by Kamp-Hopmans et al. found that the risk factors contributing HAI in surgical wards were: RR of enteral tube feeding over 48 hours was 6.6 followed by ventilation used over 24 hours of 5.0 and used of steroids of 3.4 for respiratory infection; urinary catheter used for UTI was 3.9. The current reimbursement system fails to penalize hospitals for largely preventable conditions due to medical negligence. The system rewards them in the form of special reimbursement. As the CMS wishes, hospitals should additionally enhance their efficiency in preventing the preventable adverse events and reduce the supposed expenses to be reimbursed priory in the future. On the other hand as our results indicated, to monitor and predict the possibility of HAIs before infection would contribute to reduce the unintended consequences and expenses for such complications. As more information becomes available electronically in the healthcare setups, the use of highly reliable electronic PI-103 surveillance for HAIs has become effective in daily usage, some significant progress is being made for surveillance of CRBSI, VAP, and other HAIs. Our results show the high accuracy of prediction with scoring and both models. From the analyses of LR, we found 7 risk factors relevant to HAI, in which Foley and CVC were included. As we anticipated, the results are quite compatible to that of previous studies and, explore new insights of factors. Medical devices are examples for us to review the role in predicting HAI. The study revealed the differences, with or without presence of these devices as main PLX-4720 parameters. No matter how much information is available, we can accurately predict HAI with simple parameters. We have also found the factors that proved to be significant than the HAI by medical devices alone.