Haartman Institute, University of Helsinki, Helsinki, Finland.
Nordic Healthcare Group, Helsinki, Finland.
PLoS One. 2022 Apr 29;17(4):e0267146. doi: 10.1371/journal.pone.0267146. eCollection 2022.
Revision endoscopic sinus surgery (ESS) is often considered for chronic rhinosinusitis (CRS) if maximal conservative treatment and baseline ESS prove insufficient. Emerging research outlines the risk factors of revision ESS. However, accurately predicting revision ESS at the individual level remains uncertain. This study aims to examine the prediction accuracy of revision ESS and to identify the effects of risk factors at the individual level.
We collected demographic and clinical variables from the electronic health records of 767 surgical CRS patients ≥16 years of age. Revision ESS was performed on 111 (14.5%) patients. The prediction accuracy of revision ESS was examined by training and validating different machine learning models, while the effects of variables were analysed using the Shapley values and partial dependence plots.
The logistic regression, gradient boosting and random forest classifiers performed similarly in predicting revision ESS. Area under the receiving operating characteristic curve (AUROC) values were 0.744, 0.741 and 0.730, respectively, using data collected from the baseline visit until six months after baseline ESS. The length of time during which data were collected improved the prediction performance. For data collection times of 0, 3, 6 and 12 months after baseline ESS, AUROC values for the logistic regression were 0.682, 0.715, 0.744 and 0.784, respectively. The number of visits before or after baseline ESS, the number of days from the baseline visit to the baseline ESS, patient age, CRS with nasal polyps (CRSwNP), asthma, non-steroidal anti-inflammatory drug exacerbated respiratory disease and immunodeficiency or suspicion of it all associated with revision ESS. Patient age and number of visits before baseline ESS carried non-linear effects for predictions.
Intelligent data analysis identified important predictors of revision ESS at the individual level, such as the frequency of clinical visits, patient age, Type 2 high diseases and immunodeficiency or a suspicion of it.
如果最大程度的保守治疗和基线内镜鼻窦手术（ESS）都证明不足，慢性鼻-鼻窦炎（CRS）患者通常会接受翻修 ESS。新兴研究概述了翻修 ESS 的危险因素。然而，在个体水平上准确预测翻修 ESS 仍然不确定。本研究旨在检验翻修 ESS 的预测准确性，并确定个体水平危险因素的影响。
我们从 767 名年龄≥16 岁的接受手术治疗的 CRS 患者的电子健康记录中收集了人口统计学和临床变量。对 111 例（14.5%）患者进行了翻修 ESS。通过训练和验证不同的机器学习模型来检验翻修 ESS 的预测准确性，同时使用 Shapley 值和部分依赖图分析变量的影响。
逻辑回归、梯度提升和随机森林分类器在预测翻修 ESS 方面表现相似。使用基线 ESS 前至基线 ESS 后 6 个月期间的数据，ROC 曲线下面积（AUROC）值分别为 0.744、0.741 和 0.730。收集数据的时间长短提高了预测性能。对于基线 ESS 后 0、3、6 和 12 个月的数据收集时间，逻辑回归的 AUROC 值分别为 0.682、0.715、0.744 和 0.784。基线 ESS 前或后就诊次数、从基线就诊到基线 ESS 的天数、患者年龄、伴有鼻息肉的 CRS（CRSwNP）、哮喘、非甾体抗炎药加重的呼吸道疾病和免疫缺陷或怀疑有免疫缺陷与翻修 ESS 相关。患者年龄和基线 ESS 前就诊次数存在非线性影响。
智能数据分析确定了个体水平上翻修 ESS 的重要预测因素，如就诊频率、患者年龄、2 型高疾病和免疫缺陷或怀疑有免疫缺陷。
