Constrction and validation of a nomogram model for pulmonary infection after aneurysm embolization

Blackground and objective: In order to analyze the risk factors associated with pulmonary infection following aneurysm embolization, we developed a nomogram model and validated its predictive accuracy. Methods: Clinical data pertaining to patients who underwent aneurysm embolization procedures at Huizhou Central People’s Hospital during the period from 2019 to 2023 were retrieved utilizing the hospital's electronic medical record system.Variables including age, Platelet-to-Lymphocyte Ratio (PLR), Systemic Immune-Inflammation Index (SIRI), Red Blood Cell Distribution Width-to-Albumin/Globulin Ratio (RDWAGR), and creatinine (CREA) levels were calculated. The variables identified were subsequently incorporated into a logistic regression model to evaluate their associations with the incidence of pulmonary infections subsequent to aneurysm embolization surgery.A nomogram model was subsequently developed and validated, with its predictive performance assessed via the Receiver Operating Characteristic (ROC) curve. Results: Logistic regression analysis identified that age, PLR, SIRI, RDWAGR, d-NLR, and FIB were significantly associated with postoperative pulmonary infection (P < 0.05). Drawing upon these significant variables, the constructed nomogram model demonstrated robust discriminatory power in forecasting the risk of pulmonary infection, with an area under the receiver operating characteristic curve (AUC) of 0.769, signifying a high level of predictive precision.The calibration plot exhibited satisfactory alignment between the model's predicted probabilities and the observed outcomes. Additionally, internal validation assessments further underscored the nomogram model's stability and reproducibility. Conclusions: The nomogram model established in this research accurately forecasts the risk of pulmonary infection in patients post-aneurysm embolization surgery, showcasing robust predictive capabilities and significant clinical utility. This tool can be a pivotal asset for clinicians in postoperative care, facilitating early identification of patients at high risk and guiding the customization of intervention strategies.