Construction and Validation of a Risk Prediction Model for Cancer-Related Cognitive Impairment in Lung Cancer Patients

Abstract

Background

Cancer-related cognitive impairment (CRCI) is a major clinical challenge faced by lung cancer patients during or after treatment. Early identification of at-risk populations by healthcare professionals is inadequate, and little is known about measures that can be taken to enhance their prevention. Existing systematic reviews and meta-analyses have summarized common risk factors for CRCI in lung cancer patients, but integrated predictive models based on holistic theoretical frameworks remain scarce.

Aim

To construct a visual assessment tool for the identification of CRCI in lung cancer survivors based on the theory of unpleasant symptoms (TOUS), complementing existing predictive models with a multidimensional theoretical perspective.

Design

A prospective, observational, single-centre study.

Methods

The present study was conducted in a major hospital in Urumqi, China, between October 2023 and July 2024. A total of 350 lung cancer survivors participated in this survey, which was divided into a training and validation group in a 7:3 ratio. Lasso regression and logistic regression analyses were employed to identify the risk factors for CRCI, construct a nomogram prediction model and test the prediction effect in the validation set. Model performance was evaluated using the area under the curve (AUC) and goodness-of-fit statistics, and the model was internally validated.

Results

A total of 350 lung cancer patients, comprising 245 in the training and 105 in validation groups, were included. Of these, 117 (33.4%) experienced CRCI. The predictive model identified significant predictors, including age, pathological stage, chemotherapy, post-traumatic stress disorder (PTSD), depression and social support scores. At the 32.3% optimal cut-off, the model had AUC values of 0.863 and 0.818 in the training and validation groups. Calibration plots demonstrated a strong correlation between predicted and observed rates, and decision curve analysis revealed optimal net benefit at threshold probabilities ranging from 10% to 80%.

Conclusions

The risk prediction model constructed in this study, based on TOUS, demonstrates satisfactory predictive ability superior to some existing models. It integrates physiological, psychological and environmental factors, serving as a valuable complementary tool for healthcare professionals in identifying high-risk groups, particularly in clinical settings emphasizing holistic symptom management.