Impact of preoperative anxiety on postoperative pain in patients undergoing guided bone regeneration surgery: a state anxiety inventory-based assessment

Guided bone regeneration (GBR) involves significant surgical trauma and notable postoperative pain, and patients often experience preoperative anxiety due to an insufficient understanding of the procedure. Current research focuses primarily on optimizing materials and surgical techniques; however, few studies have quantitatively assessed the association between preoperative anxiety and postoperative pain. Preoperative anxiety can lower pain thresholds and exacerbate postoperative pain by activating the neuroendocrine system; nevertheless, the predictive threshold of this association in oral GBR surgery remains unclear. This study is the first in which the State Anxiety Inventory (S-AI) is used to evaluate preoperative anxiety in patients undergoing GBR and is the first to explore the correlation between preoperative anxiety and postoperative pain and determine the optimal threshold for predicting moderate-to-severe pain. Thus, this study provides a quantitative basis for personalized perioperative analgesia. The aim of this study was to explore the correlation between preoperative anxiety and postoperative pain in patients undergoing oral GBR surgery, to identify the optimal preoperative S-AI score threshold for predicting moderate-to-severe postoperative pain, and to screen independent risk factors using multivariate regression analysis. The findings provide a theoretical reference for the optimization of perioperative analgesia strategies for oral GBR surgery. This prospective cohort study included 129 patients who underwent GBR for single maxillary anterior tooth loss, grouped by preoperative S-AI scores. Baseline data, postoperative visual analogue scale (VAS) for pain/VAS for anxiety (VAS-A) scores, analgesic consumption and preoperative Pittsburgh Sleep Quality Index (PSQI) scores were obtained, and Spearman correlation analysis was used to analyse the associations between variables. Bootstrap-corrected receiver operating characteristic (ROC) curve analysis revealed the optimal S-AI score threshold for moderate-to-severe postoperative pain (6-hour VAS score ≥ 4). Two independent multivariate logistic models were constructed to avoid multicollinearity, adjusting for gender, age, body mass index (BMI) and operation time; 1000-sample bootstrap internal validation was used to assess model discrimination, calibration and overfitting. Decision curve analysis (DCA) was performed to compare the clinical net benefit of the two models. Significant intergroup differences in VAS pain scores were observed between the high-anxiety group and low-anxiety group at 6 and 24 h postoperatively ( P < 0.05), whereas no between-group difference was detected at 2 h postoperatively ( P > 0.05). Preoperative S-AI scores were positively correlated with postoperative VAS pain scores at all time points, preoperative and postoperative VAS-A scores, and preoperative PSQI scores (all P < 0.001). Patients exhibited markedly lower anxiety levels after surgery than before surgery ( P < 0.001). Patients with high preoperative anxiety experienced poor preoperative sleep quality, consumed more postoperative analgesics, and required first-rescue analgesia earlier ( P < 0.05). ROC curve analysis with 1000-sample bootstrap bias correction revealed an optimal S-AI score threshold of 35 for predicting moderate-to-severe postoperative pain, with an area under the curve (AUC) of 0.831, a corrected sensitivity of 84.4% and a specificity of 75.0%. Two separate multivariate logistic regression models revealed that an elevated preoperative S-AI score was an independent risk factor for moderate-to-severe postoperative pain (odds ratio [OR] = 1.125, P < 0.001), whereas better preoperative sleep quality served as an independent protective factor. Gender, age and surgical duration were not independent predictors of moderate-to-severe postoperative pain. Bootstrap internal validation, calibration tests and DCA further verified that the S-AI-based model outperformed the PSQI-based model in terms of discrimination, calibration and clinical net benefit, with no obvious overfitting observed. High preoperative anxiety levels can exacerbate postoperative pain. The S-AI score acts as an independent predictor of moderate-to-severe postoperative pain with satisfactory predictive efficacy. A preoperative S-AI score of ≥ 35 can be adopted as the risk threshold for identifying patients at high risk of moderate-to-severe postoperative pain; therefore, a more individualized and comprehensive perioperative analgesia regimen should be developed for this population. These findings and the S-AI score ≥ 35 threshold are particularly applicable to Chinese adult patients with American Society of Anesthesiologists (ASA) I-II who undergo horizontal GBR surgery due to a single missing maxillary anterior tooth; direct extrapolation to other ethnic groups or other GBR indications requires further research.