Department of General Internal Medicine and Psychosomatics, Heidelberg University, Heidelberg, Germany.
Clinical Psychology and Psychotherapy, Ulm University, Ulm, Baden-Württemberg, Germany.
Cancer Med. 2021 Jul;10(14):5001-5016. doi: 10.1002/cam4.4048. Epub 2021 Jun 2.
The purpose of this study was to explore predictors for anxiety as the most common form of psychological distress in cancer survivors while accounting for physical comorbidity.
We conducted a secondary data analysis of a large study within the German National Cancer Plan which enrolled primary care cancer survivors diagnosed with colon, prostatic, or breast cancer. We selected candidate predictors based on a systematic MEDLINE search. Using supervised machine learning, we developed a prediction model for anxiety by splitting the data into a 70% training set and a 30% test set and further split the training set into 10-folds for cross-validating the hyperparameter tuning step during model selection. We fit six different regression models, selected the model that maximized the root mean square error (RMSE) and fit the selected model to the entire training set. Finally, we evaluated the model performance on the holdout test set.
In total, data from 496 cancer survivors were analyzed. The LASSO model (α = 1.0) with weakly penalized model complexity (λ = 0.015) slightly outperformed all other models (RMSE = 0.370). Physical symptoms, namely, fatigue/weakness (β = 0.18), insomnia (β = 0.12), and pain (β = 0.04), were the most important predictors, while the degree of physical comorbidity was negligible.
Prediction of clinically significant anxiety in cancer survivors using readily available predictors is feasible. The findings highlight the need for considering cancer survivors' physical functioning regardless of the degree of comorbidity when assessing their psychological well-being. The generalizability of the model to other populations should be investigated in future external validations.
本研究旨在探讨癌症幸存者中最常见的心理困扰形式——焦虑的预测因素，同时考虑到身体合并症。
我们对德国国家癌症计划中的一项大型研究进行了二次数据分析，该研究纳入了被诊断患有结肠癌、前列腺癌或乳腺癌的初级保健癌症幸存者。我们根据系统的 MEDLINE 搜索选择候选预测因素。使用有监督的机器学习，我们通过将数据分为 70%的训练集和 30%的测试集，并进一步将训练集分为 10 折，在模型选择过程中对超参数调优步骤进行交叉验证，为焦虑开发了一个预测模型。我们拟合了六种不同的回归模型，选择了最大化均方根误差 (RMSE) 的模型，并将选定的模型拟合到整个训练集。最后，我们在保留的测试集上评估模型性能。
共分析了 496 名癌症幸存者的数据。具有较弱惩罚模型复杂度的 LASSO 模型 (α = 1.0) 略优于所有其他模型 (RMSE = 0.370)。身体症状，即疲劳/虚弱 (β = 0.18)、失眠 (β = 0.12) 和疼痛 (β = 0.04) 是最重要的预测因素，而身体合并症的程度可以忽略不计。
使用现成的预测因素预测癌症幸存者的临床显著焦虑是可行的。研究结果强调，在评估癌症幸存者的心理幸福感时，无论合并症的程度如何，都需要考虑他们的身体功能。该模型在其他人群中的泛化能力应在未来的外部验证中进行研究。
