The database used for the NHS Bowel Cancer Screening Programme (BCSP) derives participant information from primary care records. Combining predictors with FOBTs has shown to improve referral decisions and accuracy. The richer data available from GP databases could be used to complement screening referral decisions by identifying those at greatest risk of colorectal cancer. We determined the availability of data for key predictors and whether this information could be used to inform more accurate screening referral decisions. An English BCSP cohort was derived using the electronic notifications received from the BCSP database to GP records. The cohort covered a period between 13th May 2009 to 17th January 2017. Completeness of variables and univariable associations were assessed. Risk prediction models were developed using Cox regression and multivariable fractional polynomials with backwards elimination. Optimism adjusted performance metrics were reported. The sensitivity and specificity of a combined approach using the negative FOBT model plus FOBT positive patients was determined using a probability equivalent to a 3% PPV NICE guidelines level. 292,059 participants aged 60–74 were derived for the BCSP screening cohort. A model including the screening test result had a C-statistic of 0.860, c-slope of 0.997, and R2 of 0.597. A model developed for negative screening results only had a C-statistic of 0.597, c-slope of 0.940, and R2 of 0.062. Risk predictors included in the models included; age, sex, alcohol consumption, IBS diagnosis, family history of gastrointestinal cancer, smoking status, previous negatives and whether a GP had ordered a blood test. For the combined screening approach, sensitivity increased slightly from 53.90% (FOBT only) to 58.82% but at the expense of an increased referral rate. This research has identified several potential predictors for CRC in a BCSP population. A risk prediction model developed for BCSP FOBT negative patients was not clinically useful due to a low sensitivity and increased referral rate. The predictors identified in this study should be investigated in a refined algorithm combining the quantitative FIT result. Combining data from multiple sources enables fuller patient profiles using the primary care and screening database interface.

中文翻译：

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电子医疗记录在大肠癌筛查转诊决策和风险预测模型开发中的使用

用于NHS肠癌筛查计划（BCSP）的数据库从初级保健记录中获取参与者信息。将预测因子与FOBT相结合已显示出可以改善推荐决策和准确性。GP数据库提供的丰富数据可以通过识别那些结肠直肠癌高风险人群来辅助筛查转诊决策。我们确定了关键预测指标的数据可用性，以及是否可以使用此信息来指导更准确的筛查转诊决策。使用从BCSP数据库接收到的GP记录的电子通知得出了一个英语BCSP队列。该研究对象涵盖了2009年5月13日至2017年1月17日之间的时间段。评估了变量和单变量关联的完整性。使用Cox回归和具有向后消除功能的多元分数多项式开发了风险预测模型。报告了乐观调整的绩效指标。使用等于3％PPV NICE指南水平的概率确定使用阴性FOBT模型加上FOBT阳性患者的联合方法的敏感性和特异性。年龄为60-74岁的292,059名参与者来自BCSP筛查队列。包含筛选测试结果的模型的C统计值为0.860，C斜率为0.997，R2为0.597。为阴性筛查结果开发的模型仅具有0.597的C统计量，0.940的c斜率和0.062的R2。包含在模型中的风险预测因子；年龄，性别，饮酒，IBS诊断，胃肠道癌家族史，吸烟状况，以前的阴性结果以及GP是否已下令进行血液检查。对于联合筛查方法，敏感性从53.90％（仅FOBT）略微增加到58.82％，但以增加的转诊率为代价。这项研究确定了BCSP人群中CRC的几种潜在预测因子。为BCSP FOBT阴性患者开发的风险预测模型由于敏感性低和转诊率增加而在临床上无用。在这项研究中确定的预测因素应结合定量FIT结果的改进算法进行研究。结合来自多个来源的数据，可以使用初级保健和筛查数据库界面实现更全面的患者资料。82％，但以增加推荐率为代价。这项研究确定了BCSP人群中CRC的几种潜在预测因子。为BCSP FOBT阴性患者开发的风险预测模型由于敏感性低和转诊率增加而在临床上无用。在这项研究中确定的预测因素应结合定量FIT结果的改进算法进行研究。结合来自多个来源的数据，可以使用初级保健和筛查数据库界面实现更全面的患者资料。82％，但以增加推荐率为代价。这项研究确定了BCSP人群中CRC的几种潜在预测因子。为BCSP FOBT阴性患者开发的风险预测模型由于敏感性低和转诊率增加而在临床上无用。在这项研究中确定的预测因素应结合定量FIT结果的改进算法进行研究。结合来自多个来源的数据，可以使用初级保健和筛查数据库界面实现更全面的患者资料。在这项研究中确定的预测因素应结合定量FIT结果的改进算法进行研究。结合来自多个来源的数据，可以使用初级保健和筛查数据库界面实现更全面的患者资料。在这项研究中确定的预测因素应结合定量FIT结果的改进算法进行研究。结合来自多个来源的数据，可以使用初级保健和筛查数据库界面实现更全面的患者资料。