| Objective To construct a nomogram model for predicting the risk of ventilator pneumonia in mechanically ventilated patients in the Intensive Care Unit (ICU). Methods Atotal of 198 patients who underwent mechanical ventilation treatment in the ICU of Ma'anshan Seventeenth Metallurgical Hospital from March 2016 to July 2021wereselectedas the research objects. LASSO analysis and Logistic regression analysis were used to screen the independent risk factors for ventilator pneumonia in mechanically ventilated patients in ICU. R (R3.5.3) was used to establish a nomogram model for predicting the risk of ventilator pneumonia in ICU mechanically ventilated patients.Results Age ≥ 60 years, combined use of antibacterial drugs, serum albumin <40 g/L, mechanical ventilation time ≥ 7 d, hospitalization time ≥ 14 d, use of acid inhibitors, tracheotomy, and diabetes were independent risk factorsfor ventilator pneumonia in patients withmechanical ventilation in ICU (P<0.05). Based on this, the nomogram model wasestablished. The model consistency index was 0.836, the predicted probability of the model was basically the same as the actual probability, the area under the ROC curve was 0.815, and the decision curve showed that the threshold probability was within the range of 5% to 84%, which brought net benefit value.Conclusions There are many risk factors for ventilator pneumonia in ICU mechanically ventilated patients. The predictive ability of the nomogram established in this study is relatively accurate, which can provide reference for clinical identification of high-risk patients with ventilator pneumonia and improve the prognosis of ICU mechanically ventilated patients. |
