Development and Validation of a Disaster Risk Prediction Model for Postoperative Anal Pain in Patients

Background Anorectal surgery frequently induces significant postoperative pain, which can precipitate pain catastrophizing, a maladaptive cognitive pattern that exacerbates suffering and delays recovery. The absence of validated tools for early identification of at-risk patients impedes targeted interventions. This study aimed to develop and internally validate a clinical prediction model for postoperative pain catastrophizing in this patient group. Methods We conducted a single-center study of 412 patients assessed 3–4 days after anorectal surgery (October–December 2025). Participants were randomly divided into a modeling group (n = 288) for model development and a validation group (n = 124) for internal validation. Data collected via structured questionnaires (demographics, Pain Catastrophizing Scale, Visual Analogue Scale for pain, Positive and Negative Affect Schedule). This study employed a multivariate logistic regression approach to analyze independent factors influencing catastrophic pain in patients following anorectal surgery. Using R software, we constructed a risk prediction model and a nomogram model for catastrophic pain in patients after anorectal surgery. The predictive efficacy of these models was validated through the area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow goodness-of-fit test. Result In the modeling group (n = 288), 127 patients (44.1%) experienced pain catastrophizing. Logistic regression identified Visual Analogue Scale (VAS) score, negative emotions, lack of preoperative pain cognition, chronic comorbidities, and postoperative constipation as predictors (P < 0.05). The model demonstrated excellent discriminative ability in the modeling group [area under the curve (AUC) = 0.957, 95% CI: 0.937–0.977; Youden index = 0.763], with a sensitivity of 82.6%, specificity of 93.7%, optimal cutoff value of 0.277, and good fit (Hosmer-Lemeshow test, P = 0.790). In the validation group (n = 124, 47.7% experiencing pain catastrophizing), model performance remained outstanding (AUC = 0.985, 95% CI: 0.970–1.000; Youden index = 0.871; sensitivity 92.3%, specificity 94.9%; optimal cutoff value 0.315; Hosmer-Lemeshow test P = 0.991). Calibration curves for both groups indicated good agreement between predicted and observed outcomes. Conclusion We developed and validated a practical nomogram using five clinical variables to effectively identify patients at high risk for post-anorectal surgery pain catastrophizing. This tool facilitates early targeted intervention. The main limitation is the single-center design.