BACKGROUND & AIMS:
PURPOSE:To assess the performance of statistical modeling in predicting follow-up adherence of incidentally detected pulmonary nodules (IPN) on CT, based on patient variables (PV), radiology report related variables (RRRV) and physician-patient communication variables (PPCV).
METHODS:200 patients with IPN on CT were retrospectively identified and randomly selected. PV (age, gender, smoking status, ethnicity), RRRV (nodule size, patient context, whether follow-up recommendations were provided) and PPCV (whether referring physician documented IPN and ordered follow-up on the electronic medical record) were recorded. Primary outcome was whether patients received appropriate follow-up within +/- 1 month of the recommended time frame. Statistical methods included logistic regression and machine learning (K-nearest neighbors and support vector machine).
RESULTS:Adherence was low, with or without recommendations provided in the radiology report (23.4 %-27.4 %). Whether the referring physician ordered follow-up was the dominant predictor of adherence in all models. The following variables were statistically significant predictors of whether referring physician ordered follow-up: recommendations provided in the radiology report, smoking status, patient context and nodule size (FDR logworth of respectively 21.18, 11.66, 2.35, 1.63, p < 0.05). Prediction accuracy varied from 72 % (PV) to 93 % (PPCV, all variables).
CONCLUSION:PPCV are the most important predictors of adherence. Amongst all variables, patient context, smoking status, nodule size, and whether the radiologist provided follow-up recommendations in the report were all statistically significant predictors of patient follow-up adherence, supporting the utility of statistical modeling for analytics, quality assurance and optimization of outcomes related to IPN.
背景与目标:
目的:基于患者变量(PV),放射学报告相关变量(RRRV)和医患沟通变量(PPCV),评估统计模型在预测偶然发现的肺结节(IPN)在CT上的随访依从性方面的性能。
方法:对200例CT上IPN患者进行回顾性鉴定并随机选择。记录PV(年龄,性别,吸烟状况,种族),RRRV(结节大小,患者情况,是否提供随访建议)和PPCV(是否由主治医师记录IPN并在电子病历上下令进行随访)。主要结局是患者是否在建议的时间范围内/ -1个月内接受了适当的随访。统计方法包括逻辑回归和机器学习(K近邻和支持向量机)。
结果:坚持率很低,放射学报告中有或没有建议(23.4%-27.4%)。在所有模型中,主治医生是否下令进行随访都是依从性的主要预测指标。以下变量是主治医生是否下令进行随访的统计学上显着的预测指标:放射学报告,吸烟状况,患者情况和结节大小提供的建议(FDR logworth分别为21.18、11.66、2.35、1.63,p <0.05)。预测准确度从72%(PV)到93%(PPCV,所有变量)不等。
结论:PPCV是最重要的依从性预测指标。在所有变量中,患者背景,吸烟状况,结节大小以及放射科医生是否在报告中提供了随访建议,这些都是患者随访依从性的统计学显着预测因素,从而支持统计模型在分析,质量保证和优化方面的实用性与IPN相关的结果。