Predicting anxiety in cancer survivors presenting to primary care – A machine learning approach accounting for physical comorbidity

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.