The risk factors and prediction model for postoperative pneumonia after craniotomy

Background Craniotomy is highly susceptible to postoperative pneumonia, which significantly impacts the outcomes of patients undergoing such procedures. Our study aims to examine the risk factors associated with postoperative pneumonia and establish a predictive model with a nomogram to assess this risk. Methods We conducted a matched 1:1 case-control study involving 831 adult patients undergoing craniotomy at our hospital. Cases consisted of patients who developed postoperative pneumonia within 30 days after surgery, as defined by consensus criteria. Controls were randomly selected from a pool of eligible patients. Results The overall incidence rate of postoperative pneumonia is 12.39% in a total of 831 surgeries, which associated with unfavorable outcomes. Gram-negative bacteria were found to be the most common causative agents and approximately 27.94% of cases attributed to multi-drug resistant strains. The logistic regression analysis revealed five independent risk factors, as follows: smoking history, surgical duration, postoperative albumin, unplanned re-operation, and deep vein catheterization. A risk prediction model was derived and a nomogram was constructed. The Hosmer-Lemeshow test yielded X 2 = 3.871 (P=0.869), and the receiver operator characteristic curve analysis demonstrated an area under the curve of 0.898 (P<0.05), with a sensitivity of 79.6% and a specificity of 85.4%, indicating excellent model fit and predictive performance. In addition, the C-index of the nomogram model was 0.898(95%CI, 0.853~0.941). The calibration curves of the nomogram model showed p-values of 0.797 and the Brier scores were 0.127. The analysis of the clinical decision curve showed that the nomograph model had high clinical application value. Conclusions Postoperative pneumonia patients after craniotomy exhibits distinct pathogen distribution and is strongly associated with unfavorable outcomes. The risk prediction model developed in this study demonstrates a good fitting degree and predictive performance. The constructed nomogram model is objective, specific, and easily applicable in clinical practice.