The Risk Prediction Model for Healthcare-Associated Infections among Elderly Patients Underwent Pancreaticoduodenectomy

Abstract

Background: The elderly are a high-risk group of healthcare-associated infections (HAIs) after pancreaticoduodenectomy (PD), and the effective prediction model may be beneficial to HAIs control. Methods: The data were obtained from Hospital Infection Surveillance System. The BP-ANN model was conducted according to univariate analysis. The receiver operating characteristic curve (ROC), the prediction accuracy, sensitivity and specificity were used to estimate the predicted performance. The final weight coefficients were calculated to illustrate the relative importance of indicators. Results: Of 688 elderly patients underwent PD, 83 (12.06%) were diagnosed with HAIs. 9 significant factors (P<0.05) including weight, fever, continuous fever for more than three days, blood routine abnormal percentage, ever livered in intensive care unit (ICU), antibacterial combination, postoperative antibacterial use days, ventilator use and urinary catheter use days were included into prediction model. The prediction accuracy in testing sets was 93.79%, and the sensitivity and specificity were 0.67 and 0.97. The contribution level of 9 significant factors were 10.65%, 8.54%, 10.17%, 9.64%, 9.26%, 10.02%, 12.53%, 11.90% and 17.29%, respectively. Conclusions: The 9-9-1 BP-ANN prediction model underpinned by complex factors in this study has relatively excellent performance for HAIs prediction among elderly patients after PD. Urinary catheter use days, postoperative antibacterial use days, ventilator use, weight and continuous fever for more than three days are the top five contribution indicators for HAIs prediction, which should be fully taken into consideration when developing HAIs prediction for the PD patients.