Construction of logistic regression model and ROC curve analysis on influencing factors of urogenic infection after benign prostatic hyperplasia surgery

Background: This study aimed to construct a logistic regression model to identify factors influencing urogenic infection following benign prostatic hyperplasia (BPH) surgery and to analyze the Receiver Operating Characteristic (ROC) curve for predictive evaluation. Methods: The clinical data from 205 patients with simple BPH admitted to our hospital between January 2019 and June 2024 were retrospectively analyzed after categorizing them into an infection group (n = 35) and an uninfected group (n = 170) based on the occurrence of urogenic infection. Their general clinical data, relevant medical history, and surgical details, including indwelling urinary catheter use, operation duration, prophylactic use of antibacterial drugs, intraoperative blood loss and other factors, were compared between the two groups. Results: Among the 205 patients, 35 were identified as having a urogenic infection, defined by a urine culture colony count of ≥105 CFU/mL. Factors such as advanced age, diabetes, absence of preoperative prophylactic antibacterial drug use, prostate size >55 g, and postoperative indwelling urinary catheter were found to significantly influence the likelihood of urogenic infection. The binary logistic multivariate regression model was established as follows: Logit(P) = ln[P/(1 − P)] = 18.428 + 0.185X1 + 2.378X2 + 1.999X3 + 1.298X4 + 2.176X5. The model’s goodness-of-fit was confirmed through the Hosmer and Lemeshow test (χ2 = 3.612, p = 0.890). The ROC curve analysis demonstrated a high area under the curve (AUC = 0.935), with a 95% confidence interval (CI) ranging from 0.895 to 0.975. In conclusion, advanced age, diabetes mellitus, lack of preoperative antibiotic prophylaxis, prostate size exceeding 55 g, and postoperative indwelling urinary catheter use were identified as key factors influencing the occurrence of urogenic infection after BPH surgery in patients with simple BPH. Conclusions: The constructed logistic regression model offers a high predictive value and provides valuable guidance for clinical practice.

Simple benign prostatic hyperplasia surgery; Urogenic infection; Factors; Regression model

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