Sydney Dental School, Faculty of Medicine and Health, The University of Sydney, Level 2-3 Westmead Centre for Oral Health, Westmead, NSW, 2145, Australia.
Division of Advanced Prosthetic Dentistry, Tohoku University Graduate School of Dentistry, 4-1 Seiryo-Machi, Aoba-Ku, Sendai, 980-8575, Japan.
Clin Oral Investig. 2023 Nov;27(11):6461-6470. doi: 10.1007/s00784-023-05251-4. Epub 2023 Sep 20.
This prospective cohort study aimed to assess the association between dental disease burden and postoperative infective complications (POICs) in patients undergoing major surgical procedures under general anaesthesia.
Pre-surgical dental assessment was undertaken on patients planned for major surgery. Demographic and surgical variables including putative risk factors for POICs and POIC status were documented. The univariable association between POIC status and each factor was examined. Those variables associated at P value ≤ 0.2 were candidates for inclusion in multiple logistic regression models. Backward stepwise variable selection was used to identify the independent predictors for POIC in the best fitting logistic regression model. The area under the receiver operating curve (AUC) was used to quantify the model's global classification performance.
Among the 285 patients, 49 patients (17.2%) had POICs. The independent predictors for POIC were expected length of hospital stay (4-6 days; odds ratio [OR] = 4.80, 95% confidence internal [CI]: 1.30-17.70, P = 0.018, 7-9 days; OR = 5.42, 95% CI: 1.51-19.41, P = 0.009, ≥ 10 days; OR = 28.80, 95% CI: 4.12-201.18, P < 0.001), four or more decayed teeth (OR = 6.03, 95% CI: 2.28-15.94, P < 0.001) and visible tongue plaque (OR = 3.21, 95% CI: 1.54-6.70, P = 0.002). The AUC was 0.78 (95% CI: 0.71-0.85) indicating good discrimination. A simple screening tool for POIC was developed.
CONCLUSIONS/CLINICAL RELEVANCE: In addition to systemic/surgical factors, this study identified clinically detected decayed teeth and visible tongue plaque as independent predictors for POICs. Preoperative dental assessment/care might be beneficial to assess risk for POICs and improve postoperative outcomes.
本前瞻性队列研究旨在评估接受全身麻醉下大手术的患者的口腔疾病负担与术后感染性并发症（POIC）之间的关系。
对计划进行大手术的患者进行术前口腔评估。记录人口统计学和手术变量，包括 POIC 的潜在危险因素和 POIC 状态。检查 POIC 状态与每个因素之间的单变量关联。那些在 P 值≤0.2 时相关的因素被认为是纳入多因素逻辑回归模型的候选因素。使用向后逐步变量选择法，从最佳拟合逻辑回归模型中确定 POIC 的独立预测因素。接收者操作特征曲线下的面积（AUC）用于量化模型的整体分类性能。
在 285 名患者中，有 49 名（17.2%）发生了 POIC。POIC 的独立预测因素是预期住院时间（4-6 天；比值比[OR] = 4.80，95%置信区间[CI]：1.30-17.70，P = 0.018，7-9 天；OR = 5.42，95% CI：1.51-19.41，P = 0.009，≥10 天；OR = 28.80，95% CI：4.12-201.18，P<0.001），四颗或更多龋齿（OR = 6.03，95% CI：2.28-15.94，P<0.001）和可见的舌菌斑（OR = 3.21，95% CI：1.54-6.70，P = 0.002）。AUC 为 0.78（95% CI：0.71-0.85），表明具有良好的区分度。开发了一种用于 POIC 的简单筛查工具。
结论/临床相关性：除了全身/手术因素外，本研究还确定了临床上发现的龋齿和可见的舌菌斑是 POIC 的独立预测因素。术前口腔评估/护理可能有助于评估 POIC 的风险并改善术后结局。
