Assessment of the reporting of quality and outcome measures in hepatic resections: a call for 90-day reporting in all hepatectomy series

BackgroundThe aim of this paper is to assess the current state of quality and outcomes measures being reported for hepatic resections in the recent literature.MethodsMedline and PubMed databases were searched for English language articles published between 1 January 2002 and 30 April 2013. Two examiners reviewed each article and relevant citations for appropriateness of inclusion, which excluded papers of liver donor hepatic resections, repeat hepatectomies or meta-analyses. Data were extracted and summarized by two examiners for analysis.ResultsFifty-five studies were identified with suitable reporting to assess peri-operative mortality in hepatic resections. In only 35% (19/55) of the studies was the follow-up time explicitly stated, and in 47% (26/55) of studies peri-operative mortality was limited to in-hospital or 30 days. The time period in which complications were captured was not explicitly stated in 19 out of 28 studies. The remaining studies only captured complications within 30 days of the index operation (8/28). There was a paucity of quality literature addressing truly patient-centred outcomes.ConclusionQuality outcomes after a hepatic resection are inconsistently reported in the literature. Quality outcome studies for a hepatectomy should report mortality and morbidity at a minimum of 90 days after surgery.     

基于剂量组学预测肺癌患者放射性肺炎发生的研究

目的 探讨剂量组学在预测肺癌根治性放疗患者放射性肺炎发生中的应用潜能。方法 回顾性收集行根治性放疗的314例肺癌患者的临床资料、放疗剂量文件、定位及随访CT图像,根据临床资料及影像学随访资料对放射性肺炎进行分级,提取全肺的剂量组学特征,构建机器学习模型。应用1000次自助抽样法（bootstrap）的最小绝对值收敛和选择算子嵌套逻辑回归（LASSO‐LR）及1000次bootstrap的赤池信息量准则（AIC）向后法筛选与放射性肺炎相关的剂量组学特征,随机按照7∶3划分为训练集及验证集,应用逻辑回归建立预测模型,并应用ROC曲线及校正曲线评价模型的性能。结果 共提取120个剂量组学特征,经LASSO‐LR降维筛选得到12个特征进入“特征池”,再经过AIC向后法筛选,最终筛选出6个剂量组学特征进行模型构建,训练集AUC为0.77（95%CI为0.65~0.87）,独立验证集AUC为0.72（95%CI为0.64~0.81）。结论 利用剂量组学建立的预测模型具有预测放射性肺炎发生的潜力,但仍需继续纳入多中心数据及前瞻性数据进一步挖掘剂量组学的应用潜能。