Jarbøl Dorte E, Hyldig Nana, Möller Sören, Wehberg Sonja, Rasmussen Sanne, Balasubramaniam Kirubakaran, Haastrup Peter F, Søndergaard Jens, Rubin Katrine H
Research Unit of General Practice, Department of Public Health, University of Southern Denmark, 5000 Odense, Denmark.
OPEN-Open Patient Data Explorative Network, Odense University Hospital, 5000 Odense, Denmark.
Cancers (Basel). 2022 Aug 6;14(15):3823. doi: 10.3390/cancers14153823.
To develop a predictive model based on Danish administrative registers to facilitate automated identification of individuals at risk of any type of cancer.
A nationwide register-based cohort study covering all individuals in Denmark aged +20 years. The outcome was all-type cancer during 2017 excluding nonmelanoma skin cancer. Diagnoses, medication, and contact with general practitioners in the exposure period (2007-2016) were considered for the predictive model. We applied backward selection to all variables by logistic regression to develop a risk model for cancer. We applied the models to the validation cohort, calculated the receiver operating characteristic curves, and estimated the corresponding areas under the curve (AUC).
The study population consisted of 4.2 million persons; 32,447 (0.76%) were diagnosed with cancer in 2017. We identified 39 predictive risk factors in women and 42 in men, with age above 30 as the strongest predictor for cancer. Testing the model for cancer risk showed modest accuracy, with an AUC of 0.82 (95% CI 0.81-0.82) for men and 0.75 (95% CI 0.74-0.75) for women.
We have developed and tested a model for identifying the individual risk of cancer through the use of administrative data. The models need to be further investigated before being applied to clinical practice.
基于丹麦行政登记数据开发一种预测模型，以促进对有患任何类型癌症风险个体的自动识别。
一项基于全国登记数据的队列研究，涵盖丹麦所有20岁及以上的个体。结局为2017年期间除非黑色素瘤皮肤癌外的所有类型癌症。预测模型考虑暴露期（2007 - 2016年）的诊断、用药情况以及与全科医生的接触情况。我们通过逻辑回归对所有变量进行向后选择，以建立癌症风险模型。我们将模型应用于验证队列，计算受试者工作特征曲线，并估计相应的曲线下面积（AUC）。
研究人群包括420万人；2017年有32447人（0.76%）被诊断患有癌症。我们在女性中识别出39个预测风险因素，在男性中识别出42个，年龄超过30岁是癌症最强的预测因素。对癌症风险模型进行测试显示准确性一般，男性的AUC为0.82（95%CI 0.81 - 0.82），女性为0.75（95%CI 0.74 - 0.75）。
我们开发并测试了一种通过使用行政数据识别个体癌症风险的模型。在应用于临床实践之前，该模型需要进一步研究。
