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. 2026 Aug 6;32(4):e70178. doi: 10.1111/ijn.70178

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

Mengyuan Qiao 1,✉, Li Luo 2, Hui Zhang 2
PMCID: PMC13445996  PMID: 42560001

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.

Keywords: cancer‐related cognitive impairment, lung cancer survivors, nomogram, predictive model, the theory of unpleasant symptoms

Summary

What is already known about this topic?

  • Cancer‐related cognitive impairment is a significant symptom experienced by lung cancer survivors during and after cancer treatment, severely impairing their mental health and overall quality of life.

  • Predictors of cancer‐related cognitive impairment in lung cancer survivors have not been well established.

  • The theory of unpleasant symptoms offers a more effective framework for examining patients' symptom experiences, analysing the factors influencing these symptoms and exploring comprehensive nursing interventions.

What is the contribution of this paper?

  • This study developed and validated a predictive model for cancer‐related cognitive dysfunction in lung cancer survivors based on the unpleasant symptom theory.

  • The nomogram assessment tool constructed in this study facilitates prediction by assigning values to each predictor and calculating a total score, thereby alleviating the computational burden on users and significantly reducing assessment time for healthcare professionals.

What are the implications of this paper?

  • This nomogram prediction model for cancer‐related cognitive impairment in lung cancer survivors can help healthcare professionals to identify at‐risk populations at an early stage in order to provide timely interventions.

1. Introduction

Lung cancer ranks among the most prevalent malignant tumours in China (Zheng et al. 2022) and holds the highest morbidity and mortality rates globally (Sung et al. 2021). Despite significant advancements in cancer treatment extending survival, treatment‐related sequelae such as cancer‐related cognitive impairment (CRCI) have become increasingly prominent (Wefel et al. 2014). CRCI refers to cognitive impairment associated with structural and functional brain changes caused by non–central nervous system malignancies and their treatments (Janelsins et al. 2014), manifesting as diminished memory, attention and executive function (Vizer et al. 2022). Several studies have established CRCI as a prevalent and clinically significant symptom among lung cancer survivors, with reported incidence rates varying widely from 11.70% to 96.80% (Ye et al. 2024). Notably, CRCI can manifest at any phase of the cancer trajectory. Although early research primarily focused on chemotherapy‐induced cognitive decline, evidence indicates that 30%–40% of patients exhibit CRCI symptoms even before initiating treatment (Janelsins et al. 2014), with incidence rising to as high as 75% during active therapy. Furthermore, a prospective longitudinal study has found that cognitive deficits in cancer patients may persist long term, continuing or persisting for months to years following the conclusion of treatment (Wefel et al. 2010). This high prevalence underscores the importance of recognizing and addressing CRCI in both clinical management and patient care.

CRCI imposes a multifaceted burden on patients with lung cancer, extending beyond core cognitive deficits to impair work capacity, daily functioning, mental health and overall quality of life (Isenberg‐Grzeda and Ellis 2017). Even minor cognitive declines can disrupt social and occupational engagement while diminishing subjective well‐being. Notably, within lung cancer populations, cognitive dysfunction is associated with poorer clinical outcomes, including reduced post‐hospitalization survival (Vardy et al. 2018). Furthermore, patients experiencing cognitive decline frequently exhibit lower adherence to complex treatment regimens such as chemotherapy, targeted therapy and supportive medications, which may compromise treatment efficacy and potentially influence disease progression (Olson and Marks 2019; Alatawi et al. 2021). Consequently, the early identification of CRCI risk factors is crucial to enable timely intervention, optimize treatment adherence and ultimately improve both prognostic outcomes and quality of life in this patient group. Research into factors influencing CRCI has advanced substantially in recent years, yielding a clearer understanding of its multifactorial aetiology. Early study by Brezden et al. (2000) highlighted chemotherapy as a key contributor, coining the term ‘chemo brain’ to describe associated cognitive deficits. Subsequent studies have identified additional treatment‐related factors, including tumour biology (Ahles et al. 2007), surgery (Chen et al. 2011) and other adjuvant therapies (Lange et al. 2016; Jung et al. 2020).

Beyond treatment‐specific effects, psychosocial factors and systemic pathophysiology have gained recognition. For instance, blood–brain barrier (BBB) dysfunction has been linked to early cognitive decline and may represent a shared pathway underlying both post‐traumatic stress disorder (PTSD) and CRCI (Ni et al. 2022). A recent systematic review further established a strong association between psychological distress, particularly anxiety and depression, and cognitive deterioration in this population (Hou et al. 2024; Ye et al. 2024). Translational research in CRCI is emerging. For instance, Ye et al. (2025) developed a nomogram from a nursing precision health perspective, and Simó et al. (2025) investigated mechanistic and predictive factors in lung cancer–related CRCI. However, these existing models often focus on isolated clinical or biological dimensions. There remains a notable gap in prediction tools that systematically integrate physiological, psychological and environmental determinants under a unifying theoretical framework, such as the theory of unpleasant symptoms (TOUS).

The TOUS was first proposed in 1995 by a team led by Lenz et al. (1995), a member of the American Academy of Nursing, and revised in 1997 (Lenz et al. 1997). This theory consists of three main concepts: symptoms, influencing factors and symptom presentation outcomes. Individual symptoms represent manifestations that deviate from normal functioning and serve as indicators of potential health threats, reflecting the patient's subjective experience. The influencing factors mainly include physiological, psychological and environmental aspects, which are interconnected and mutually influential. Physiological factors mainly include physiological indicators and physiological status, such as age and gender. Psychological factors relate to mental or emotional states, including feelings of anxiety and depression. Environmental factors pertain to the social and physical contexts that can shape the perception of disease symptoms, such as family and social support. The symptom presentation outcomes refer to the outcomes or prognosis associated with a patient's experience of symptoms, contingent upon their severity, quantity and associated influencing factors, ultimately affecting quality of life. It is theorized that physiological, psychological and environmental factors interact and influence each other, collectively impacting disease symptoms and outcomes (Kim et al. 2015). TOUS is now increasingly being applied to the study of symptoms in a wide range of diseases. A previous study in mainland China reported that a comprehensive understanding of how somatic symptoms and associated factors are interrelated in patients with chronic heart failure, based on TOUS, is potentially valuable for improving overall health (Wang et al. 2024). In the present study, the theoretical framework was constructed on the basis of literature review and clinical practice, and the influencing factors of CRCI in lung cancer survivors were classified into general demographic factors, physiological factors, psychological factors and environmental factors, which most closely matched the dimensional division of TOUS (Figure 1). Therefore, TOUS was chosen as the theoretical foundation to provide support for research pertaining to CRCI in lung cancer patients.

FIGURE 1.

FIGURE 1

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Theoretical framework diagram of CRCI prediction model for lung cancer survivors.

Considering that current evidence on CRCI in lung cancer survivors often focuses on isolated factors or lacks theoretical integration (Hou et al. 2024; Ye et al. 2024), this study was designed to develop and validate a TOUS‐based risk prediction model with three primary objectives: (a) to construct a multidimensional model integrating physiological, psychological and environmental factors; (b) to generate a user‐friendly nomogram with enhanced predictive performance; and (c) to provide a practical tool for holistic symptom management. The successful implementation of this model is expected to enable early identification of high‐risk patients in nursing practice, serve as a theory‐grounded teaching case in nursing education, support the integration of routine CRCI screening into standard clinical protocols from a policy perspective and offer a validated framework for future multicentre external validation and further refinement.

2. Methods

2.1. Aim and Design

The aim of this study was to develop and validate a risk prediction model for CRCI with lung cancer, which was expected to direct personalized decision‐making for early prevention. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies checklist was used.

2.2. Participants

In this cross‐sectional study, lung cancer patients in the cardiothoracic surgery and oncology departments of a tertiary hospital in the Xinjiang Uygur Autonomous Region, China, were recruited from October 2023 to July 2024 using the convenience sampling method. Inclusion criteria were as follows: (1) age above 18 years; (2) meet the definition of malignant tumour originating from respiratory epithelial cells (bronchial, fine bronchial and alveolar) as set out by the WHO and clearly diagnosed as lung cancer by pathological examination (Force et al. 2011); and (3) patients who gave informed consent and voluntarily participated in this investigation. Exclusion criteria were (1) history of serious mental or psychological illness and (2) inability to cooperate with researchers due to other critical illnesses. A total of 380 questionnaires were distributed in this study, and 350 valid questionnaires were recovered, resulting in an effective response rate of 92.1%. The patients were divided into a training set (n = 245) and a validation set (n = 105) using the principle of random allocation according to the ratio of 7:3.

The study was approved by the Medical Research Ethics Committee of the study hospital (approval number: KY2024070301).

2.3. Measurements

2.3.1. General Information Questionnaire

The researcher designed a general information questionnaire based on the TOUS model and a comprehensive review of relevant literature. It included four types of information: (1) socio‐demographic information, including age, gender, ethnicity, marital status, education, work status, medical payment method and per capita monthly household income (in RMB); (2) physiological and disease‐related factors, including smoking, drinking, time since diagnosis (months), TNM stage, chronic diseases, targeted therapy, immunotherapy, chemotherapy and anaemia (venous blood specimen collected in the early morning on an empty stomach, haemoglobin < 120 g/L for men and < 110 g/L for women) and Nutritional Risk Screening 2002 (NRS2002); (3) psychological factors, including anxiety, depression and PTSD; and (4) environmental factors: social support.

2.3.2. Hospital Anxiety and Depression Scale (HADS)

The Hospital Anxiety and Depression Scale (HADS), developed by Zigmond and Snaith (1983), is a widely used instrument for screening anxiety and depressive symptoms in general hospital inpatients. It comprises two seven‐item subscales (14 items total), one for anxiety and one for depression. Items are rated on a 4‐point Likert scale from 0 to 3, yielding subscale scores ranging from 0 to 21, with higher scores indicating more severe symptoms. The Chinese version of HADS used in this study demonstrated good internal consistency, with a Cronbach's α coefficient of 0.88 (Tong et al. 2022).

2.3.3. Post‐Traumatic Stress Disorder Checklist for DSM‐5 (PCL‐5)

The Post‐Traumatic Stress Disorder Checklist for DSM‐5 (PCL‐5) was used to assess PTSD symptoms. Originally developed in accordance with DSM‐IV criteria (Conybeare et al. 2012), this 17‐item scale comprises three symptom clusters: re‐experiencing, avoidance/numbing and hyperarousal. Each item is rated on a 5‐point Likert scale from 1 (not at all) to 5 (extremely). Total scores range from 17 to 85, with higher scores indicating greater PTSD symptom severity. The Chinese version employed in this study showed acceptable internal consistency, with a Cronbach's α of 0.84 (Tong et al. 2022).

2.3.4. Social Support Rating Scale (SSRS)

The Social Support Rating Scale (SSRS), developed and refined by Xiao (1993) between 1986 and 1993, is a widely utilized instrument for assessing social support. It contains 10 items organized into three subscales related to objective support, subjective support and support utilization. Total scores range from 12 to 66, with higher scores indicating greater overall social support. The scale has demonstrated sound psychometric properties in Chinese populations, with reported Cronbach's α values ranging from 0.89 to 0.94 and a test–retest reliability of 0.92 (Bao et al. 2023).

2.3.5. Montreal Cognitive Assessment (MoCA)

The Montreal Cognitive Assessment (MoCA) was developed by Nasreddine et al. (2005) as a brief screening tool for mild cognitive impairment. It assesses eight cognitive domains: visuospatial/executive function, naming, attention, language, abstraction, delayed recall and orientation. Total scores range from 0 to 30, with higher scores indicating better cognitive performance. In this study, a score of 26 or above was classified as normal cognition, whereas a score below 26 indicated cognitive impairment. The Chinese version of MoCA demonstrated good internal consistency, with a reported Cronbach's α of 0.807 (Chen et al. 2016).

2.4. Data Collection

Following approval from relevant hospital departments, paper‐based questionnaires were prepared and administered. To accommodate clinical routines and minimize disruption to patient care, data collection was scheduled daily after 7:00 PM within the participating departments. Using a standardized script, trained researchers explained the study's purpose, significance and questionnaire completion instructions to all participants and obtained their informed consent. For patients who were older, had physical limitations or were illiterate, researchers provided assistance in completing the questionnaires. All questionnaires were distributed and collected on‐site. Immediately following completion, researchers checked each questionnaire for omissions or errors and reconfirmed responses with participants as needed to ensure data reliability. Participants were informed of their right to voluntary participation and to withdraw from the study at any time.

2.5. Statistical Analysis

The statistical analyses were performed using SPSS 27.0 and R (4.2.1) software. Measurement information was described by M (P 25, P 75) if it did not conform to normal distribution, and Wilcoxon rank‐sum test was chosen. Count data were described by frequency and percentage, and the χ 2 test was chosen for between‐group comparison; the Wilcoxon rank sum test was used for rank data. The independent risk factors of cognitive impairment were identified by multifactorial logistic regression analysis, and the predictive model was constructed by using the ‘rms’ package of R software, and the area under the curve (AUC) of the operational characteristics was used to evaluate the predictive value of the predictive model of cognitive impairment. The Hosmer–Lemeshow goodness‐of‐fit test was used to reflect the calibration of the model, and p > 0.05 was used to indicate that the model had a good calibration. Decision curves were used to test the clinical utility.

3. Results

3.1. General Information and Univariate Analysis of Cognitive Function

A total of 350 patients were enrolled, with a CRCI incidence of 33.4% (n = 117). They were randomized into training and validation sets according to a 7:3 ratio, and the clinical characteristics of the study sample in the training set are shown in Table 1. The results of univariate analysis showed statistically significant differences (p < 0.05) when comparing the two groups in terms of age, education level, time since diagnosis (months), TNM stage, anaemia, chemotherapy, depression, PTSD and social support.

TABLE 1.

General information of the subjects and the risk of cognitive impairment were analysed by univariate analysis (n = 245).

Variables Total (n = 245) Cognitive impairment (n = 82) Non‐cognitive impairment (n = 163) Statistic values p
Age (years) 56 (47.00, 66.00) 64.50 (53.00, 68.00) 54.00 (42.00, 61.00) −4.974 a < 0.001**
Gender 0.764 b 0.382
Male 141 (57.6) 44 (53.7) 97 (59.5)
Female 104 (42.4) 38 (46.3) 66 (40.5)
Marital status 0.184 b 0.668
Single/divorced/widowed 94 (38.4) 33 (40.2) 61 (37.4)
Married 151 (61.6) 49 (59.8) 102 (62.6)
Education level −2.004 a 0.045
Primary school 88 (35.9) 36 (43.9) 52 (31.9)
Middle school 91 (37.1) 29 (35.4) 62 (38.0)
Middle school and above 66 (26.9) 17 (20.7) 49 (30.1)
Work status 0.263 b 0.608
Retired/unemployed 165 (67.3) 57 (69.5) 108 (66.3)
Employed 80 (32.7) 25 (30.5) 55 (33.7)
Medical insurance 1.156 b 0.764
UEBMI 72 (29.4) 27 (32.9) 45 (27.6)
URBMI 106 (43.3) 33 (40.2) 73 (44.8)
NCMS 40 (16.3) 12 (14.6) 28 (17.2)
Other 27 (11.0) 10 (12.2) 17 (10.4)
Monthly income (RMB) −1.369 a 0.171
< 1000 80 (32.7) 31 (37.8) 49 (30.1)
1000–3000 114 (46.5) 37 (45.1) 77 (47.2)
≥ 3000 51 (20.8) 14 (17.1) 37 (22.7)
Smoking 0.990 b 0.320
No 180 (73.5) 57 (69.5) 123 (75.5)
Yes 65 (26.5) 25 (30.5) 40 (24.5)
Drinking 0.229 b 0.632
No 178 (72.7) 58 (70.7) 120 (73.6)
Yes 67 (27.3) 24 (29.3) 43 (26.4)
Time since diagnosis (months) −2.347 a 0.019*
< 6 78 (31.8) 19 (23.2) 59 (36.2)
6–12 86 (35.1) 29 (35.4) 57 (35.0)
≥ 12 81 (33.1) 34 (41.5) 47 (28.8)
TNM stage −5.278 a < 0.001**
I 15 (6.1) 2 (2.4) 13 (8.0)
II 40 (16.3) 6 (7.3) 34 (20.9)
III 84 (34.3) 19 (23.2) 65 (39.9)
IV 106 (43.3) 55 (67.1) 51 (31.3)
Chronic disease 0.050 b 0.822
No 119 (48.6) 39 (47.6) 80 (49.1)
Yes 126 (51.4) 43 (52.4) 83 (50.9)
Targeted therapy 0.050 b 0.823
No 165 (67.3) 56 (68.3) 109 (66.9)
Yes 80 (32.7) 26 (31.7) 54 (33.1)
Immunotherapy 2.010 b 0.156
No 211 (86.1) 67 (81.7) 144 (88.3)
Yes 34 (13.9) 15 (18.3) 19 (11.7)
Anaemia 9.819 b 0.002**
No 153 (62.4) 40 (48.8) 113 (69.3)
Yes 92 (37.6) 42 (51.2) 50 (30.7)
Chemotherapy 7.629 b 0.006**
No 76 (31.0) 16 (19.5) 60 (36.8)
Yes 169 (69.0) 40 (80.5) 103 (63.2)
Score of NRS2002 2.442 b 0.118
≥ 3 71 (29.0) 29 (35.4) 42 (25.8)
< 3 174 (71.0) 53 (64.6) 121 (74.2)
Anxiety 3.00 (2.00, 4.00) 3.00 (2.00, 4.00) 3.00 (2.00, 4.00) −0.627 a 0.531
Depression 5.00 (3.00, 6.00) 5.00 (4.00, 7.00) 4.00 (3.00, 5.00) −5.672 a < 0.001**
PTSD 42.00 (39.00, 46.00) 44.00 (41.75, 46.00) 40.00 (38.00, 45.00) −4.662 a < 0.001**
Social support 26.00 (22.00, 29.00) 24.00 (20.00, 26.25) 27.00 (23.00, 30.00) −4.205 a < 0.001**
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Abbreviations: NCMS, New Cooperative Medical Scheme; NRS, Nutritional Risk Screening; PTSD, post‐traumatic stress disorder; UEBMI, Urban Employees Basic Medical Insurance; URBMI, Urban and Rural Residents Basic Medical Insurance.

*

p < 0.05.

**

p < 0.01.

a

Mann–Whitney U test.

b

χ2 test.

3.2. Lasso Regression and Multifactor Logistic Regression Analyses

Lasso regression analysis was performed with whether CRCI occurred in lung cancer survivors as the dependent variable and all variables as independent variables, and the results showed that age, TNM stage, chemotherapy, depression, PTSD and social support may be the influencing factors for the occurrence of cognitive impairment in lung cancer survivors (Figures 2 and 3) using whether cognitive impairment occurred in lung cancer survivors as the dependent variable (no = 0 and yes = 1) and age (original value entry), TNM stage (I = 1; II = 2; III = 3; IV = 4), chemotherapy (No = 0, Yes = 1), depression (original value entry), PTSD (original value entry) and social support (original value entry) were used as independent variables for logistic regression analyses, and the results showed that age, TNM stage, chemotherapy, depression, PTSD and social support were independent influences on the occurrence of cognitive impairment in lung cancer survivors (p < 0.05), as shown in Table 2.

FIGURE 2.

FIGURE 2

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Variable selection path of Lasso regression analysis.

FIGURE 3.

FIGURE 3

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Cross‐validation plot of Lasso regression analysis.

TABLE 2.

Logistic regression analysis of cognitive impairment in lung cancer survivors.

Variables β SE Wald p OR 95% CI
Age 0.054 0.017 10.304 0.001 1.056 1.021 1.092
TNM stage 27.799 < 0.001
II 0.825 1.081 0.582 0.445 2.282 0.274 18.997
III 1.433 1.009 2.017 0.156 4.190 0.580 30.254
IV 3.045 1.008 9.127 0.003 21.012 2.914 151.515
Chemotherapy 0.867 0.371 5.453 0.020 2.381 1.150 4.930
Depression 0.205 0.091 5.114 0.024 1.227 1.028 1.465
PTSD 0.146 0.042 12.032 0.001 1.157 1.065 1.256
Social support −0.104 0.037 7.831 0.005 0.902 0.839 0.969
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3.3. The Nomogram of the CRCI Risk Prediction Model

A nomogram prediction model for cognitive impairment in lung cancer survivors was constructed based on six independent predictors identified by multifactorial logistic regression (Figure 4) The area under the ROC was 0.863 (95% CI, 0.817–0.909) (Figure 5). The optimal critical value of this model was calculated based on the Youden index to be 0.323, with a sensitivity of 84.1% and a specificity of 76.7%. The calibration curve was built on the basis of bootstrap method repeated 1000 times sampling (Figure 6) shows that the calibration curve of the model has small deviation from the standard curve, and the Hosmer–Lemeshow test χ2 = 6.816, p = 0.557, the model is well‐fitted goodness of fit. Most of the decision curves were located between the two extremes, indicating that the model has some clinical utility (Figure 7).

FIGURE 4.

FIGURE 4

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Nomogram prediction model for the risk of cognitive impairment in lung cancer survivors.

FIGURE 5.

FIGURE 5

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ROC curve of the nomogram in the training set.

FIGURE 6.

FIGURE 6

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Calibration plots for the training set.

FIGURE 7.

FIGURE 7

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DCA curves for the training set.

3.4. The Evaluation of the Cognitive Impairment Risk Prediction Model

A total of 105 lung cancer survivors admitted to this hospital were randomly assigned as study subjects for internal validation of the model. The area under the ROC curve in the validation group was 0.818 (95% CI, 0.735–0.900), and the maximum value of the Youden index was 0.406, which corresponded to a sensitivity of 77.1% and a specificity of 81.4% (Figure 8). The calibration curve of the model showed a relatively small deviation from the standard curve, with a Hosmer–Lemeshow test of χ2 = 6.862, p = 0.552, showed good model fit superiority (Figure 9). The decision curve was mostly located between the two extremes, showing good clinical utility (Figure 10).

FIGURE 8.

FIGURE 8

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ROC curve of the nomogram in the validation set.

FIGURE 9.

FIGURE 9

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Calibration plot for the validation set.

FIGURE 10.

FIGURE 10

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DCA curves for the validation set.

4. Discussion

CRCI represents a serious adverse effect in lung cancer survivors, occurring both during and after treatment (Isenberg‐Grzeda and Ellis 2017; De Rosa et al. 2021; Oppegaard et al. 2023). Early assessment and identification of CRCI are therefore essential for timely intervention in high‐risk patients. In this study, the incidence of CRCI among lung cancer patients was 33.4%, consistent with the 30%–40% range reported in a prior review (Janelsins et al. 2014). The prediction model demonstrated excellent discriminative ability, with AUC values of 0.863 in the training set and 0.818 in the validation set. Furthermore, the Hosmer–Lemeshow goodness‐of‐fit test indicated adequate calibration (p > 0.05). These results suggest that the model performs reliably in stratifying lung cancer survivors into high‐ and low‐risk groups for CRCI. Additionally, the developed nomogram provides a practical clinical tool by assigning weighted scores to predictors and generating a total risk estimate, thereby reducing computational burden and streamlining assessment for healthcare providers.

To our knowledge, this study is among the first to construct a CRCI prediction model for lung cancer patients explicitly grounded in the TOUS, which emphasizes the interactive effects of physiological, psychological and environmental factors on symptom presentation. The predictors retained in our final model, spanning physiological (age, TNM stage and chemotherapy), psychological (depression and PTSD) and environmental (social support) domains, provide a concrete instantiation of the TOUS framework in the context of CRCI. The independent predictors identified in this study are consistent with core factors in recent meta‐analyses (Ye et al. 2024; Hou et al. 2024), but we further link them to emerging mechanistic research. The findings of this study suggest that age and TNM staging are independent risk factors for the occurrence of CRCI in lung cancer survivors. Older age and advanced stage are associated with increased systemic inflammation (García‐Mesa et al. 2015; Vardy et al. 2018) and tumour load, which activate microglia and impair the BBB (Ni et al. 2022). Simó et al. (2025) recently confirmed that BBB dysfunction mediates CRCI in lung cancer patients, providing a biological basis for the high OR value of TNM IV stage in our model. Consistent with previous studies (Cauli 2021; Brezden et al. 2000), our study confirms chemotherapy as an independent factor influencing CRCI in lung cancer survivors. Chemotherapeutic agents commonly used in lung cancer treatment, such as paclitaxel and cyclophosphamide, are known to induce oxidative stress and elevate levels of pro‐inflammatory cytokines in the brain, including tumour necrosis factor‐alpha and interleukin‐6 (Kankılıç et al. 2024; Khan et al. 2025). These processes can disrupt synaptic function and contribute to cognitive decline. Our study extends this mechanistic understanding by demonstrating that the detrimental effect of chemotherapy on cognitive outcomes operates independently of patient age and disease stage. This underscores the need for neuroprotective strategies in patients receiving chemotherapy, regardless of age or disease stage. Furthermore, depression, PTSD and social support were identified as independent predictors of CRCI in lung cancer survivors. These psychological and environmental factors are mechanistically linked through their convergence on the hypothalamic–pituitary–adrenal axis (Sarkar et al. 2018). Chronic psychological distress can lead to persistent HPA axis activation and elevated cortisol, which may contribute to hippocampal neuronal damage (Yang et al. 2023). In contrast, robust social support is thought to mitigate this pathway by downregulating HPA axis activity (Li et al. 2021). Our study provides important empirical support for this mechanism by quantitatively demonstrating the independent protective effect of social support, a dimension that was not fully explored in prior predictive models (Ye et al. 2025).

Unlike existing models focused on single‐dimensional mechanisms (Simó et al. 2025) or nursing precision health (Ye et al. 2025), our work operationalizes TOUS to reveal bidirectional interactions between predictors. Physiological factors (age, TNM stage and chemotherapy) act as ‘initiators’ of CRCI. Chemotherapy‐induced neuroinflammation and oxidative stress contribute directly to neuronal damage and synaptic dysfunction (McElroy et al. 2020). Simultaneously, these physiological insults can exacerbate psychological distress, including depression and PTSD, through their detrimental impact on patients' functional status and quality of life (De Rosa et al. 2021). Psychological factors (depression and PTSD) serve as ‘amplifiers’. Elevated cortisol levels associated with PTSD can exacerbate damage to hippocampal neurons, a region critical for memory (Ni et al. 2022). Concurrently, depression is linked to imbalances in key neurotransmitters such as serotonin and norepinephrine (La Salvia et al. 2021), which may impair neural plasticity and weaken cognitive reserve. Together, these psychosocial factors can interact synergistically with cancer‐ and treatment‐related physiological insults, establishing a self‐perpetuating cycle that amplifies cognitive vulnerability. Environmental factors (social support) function as ‘buffers’. High social support reduces psychological distress by enhancing patients' sense of security (Geels et al. 2024) and may mitigate systemic inflammation (Li et al. 2021), thereby offsetting the adverse effects of physiological and psychological risk factors. This theoretical integration reflects a contemporary shift in CRCI research from single‐factor analyses toward multifactorial interaction models (Oppegaard et al. 2023). By explicitly modelling the synergistic relationships among predictors as conceptualized by the TOUS, our approach more comprehensively captures the complex aetiology of CRCI, which may account for its enhanced predictive performance compared to models that treat risk factors in isolation (Ye et al. 2025).

4.1. Implications for Practice and Research

This nomogram holds promise as a practical tool with direct implications for clinical care and the broader healthcare system. At the bedside, it offers nurses a standardized, multidimensional instrument to systematically assess CRCI risk, facilitating timely and individualized interventions, including cognitive support strategies, simplified care instructions and enhanced family involvement, especially for patients with limited social support, particularly those with significant psychological distress. In educational contexts, the model provides a concrete, theory‐driven illustration of how the TOUS framework can be operationalized into clinical decision support, allowing educators to demonstrate the complex interaction of physiological, psychological and environmental factors in symptom management. From a policy standpoint, the model's strong predictive performance provides empirical support for incorporating routine CRCI screening into standard oncology nursing protocols, which may contribute to more efficient resource allocation and reduced healthcare costs related to unrecognized cognitive decline.

4.2. Strengths and Limitations of the Study

This study had several strengths. First, the study contributes to the application of the TOUS to develop a CRCI prediction model, systematically integrating physiological, psychological and environmental predictors. Second, the resulting nomogram provides a rapid, user‐friendly clinical tool, with decision curve analysis confirming its utility across a broad threshold range. Finally, it uniquely quantifies the protective role of social support, translating a known correlate into a measurable intervention target within the predictive framework.

The study has several potential limitations. First, only lung cancer patients were included in this study, and the results may not be applicable to other populations. Future studies should consider a more diverse study population. Second, considering that our study was a single‐centre study, although the most valuable known early warning factors have been identified and validated, it is possible that some unmeasured confounders may still be present in the actual clinical setting, and their impact on the study results cannot yet be judged. Third, the sample size of this study only meets the needs of the study design, and it is a single‐centre study; the prediction effect still needs to be further verified. Future research endeavours should conduct prospective, multicentre, large‐sample clinical trials and continuously add, remove or modify risk factors in the prediction model.

5. Conclusion

In conclusion, this study constructed a TOUS‐based CRCI prediction model that integrates physiological, psychological and environmental factors. The resulting nomogram offers healthcare professionals a practical and validated tool for early risk stratification. Ultimately, this theory‐driven approach may contribute to improvements in nursing practice, education, policy and future research, with the potential to enhance holistic care and quality of life for lung cancer survivors.

Author Contributions

Mengyuan Qiao: writing – review and editing, writing – original draft, software, methodology, formal analysis, data curation. Li Luo: writing – review and editing, software, methodology. Hui Zhang: writing – review and editing, validation, supervision, conceptualization.

Funding

The authors have nothing to report.

Ethics Statement

This study adheres to the Declaration of Helsinki and received approval from the Ethics Committee of the People's Hospital of Xinjiang Uygur Autonomous Region (KY2024070301).

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgements

The authors would like to thank all the patients who participated in the survey and the health care workers who assisted in the data collection process.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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