ABSTRACT
Objective
To investigate the current status of decisional conflict in lung cancer patients receiving systemic therapy and to analyze its influencing factors, with the aim of providing a basis for developing decision support strategies.
Methods
From August to September 2024, a convenience sample of 500 patients receiving systemic therapy for lung cancer at the Department of Medical Oncology, Cancer Hospital, Chinese Academy of Medical Sciences, was surveyed. Data were collected using a general information questionnaire, the Decisional Conflict Scale (DCS), Cancer Patient's Involvement in Treatment Decision‐Making Scale (CPITDM), Preparation for Decision‐Making Scale (PreDM), and Decisional Regret Scale (DRS).
Result
The mean DCS score was 47.28 ± 15.83, with subscale scores ranking from highest to lowest as decision support/effectiveness, decision uncertainty, and information/values. The mean CPITDM, PreDM, and DRS scores were 28.56 ± 3.91, 63.02 ± 11.65, and 9.46 ± 2.62, respectively. DCS was negatively correlated with CPITDM (r = −0.188, p < 0.001) and PreDM (r = −0.303, p < 0.001) but positively correlated with DRS (r = 0.342, p < 0.001). Multiple regression identified occupation, medical payment, treatment line, pathology, medication type, patient involvement, and preparedness as significant influencing factors (p < 0.05), explaining 59.9% of variance.
Conclusion
Lung cancer patients receiving systemic therapy experience a relatively high level of decisional conflict, with many exhibiting delayed decision‐making. Healthcare providers should identify high‐risk patients early based on key influencing factors and explore practical clinical decision support interventions. Enhancing decision readiness and reducing decision regret may help to improve quality of life and reduce decisional conflict.
Keywords: decisional conflict, decisional readiness, influencing factors, lung cancer patients receiving systemic therapy, oncology nursing
Multivariate analysis revealed that occupation status, medical payment method, treatment line, pathological type, types of other medication taken, decision readiness, and decision regret were significant predictors of decisional conflict in lung cancer patients.
1. Introduction
According to data released by the National Cancer Center in 2022, there were 1.0606 million newly diagnosed lung cancer cases and 733 300 deaths, ranking first among all malignancies in both incidence and mortality [1, 2]. Lung cancer poses a serious threat to human life and has become a major public health challenge that hinders social development [3].
Medical therapy remains a cornerstone in the treatment of lung cancer. With rapid advances in medical technology, treatment options have expanded beyond conventional chemotherapy to include targeted therapies and immunotherapies. While these newer strategies may offer improved clinical outcomes, they differ significantly in efficacy, side effects, and cost, often forcing patients to weigh survival benefits against quality of life [4, 5].
However, the increasing diversity of treatment regimens has also led to rising medical expenses, imposing a substantial financial burden on patients, families, and the healthcare system [6]. Evidence suggests that higher treatment‐related costs are closely linked to increased decisional conflict [7]. In addition, many treatment choices in lung cancer depend on genetic testing, yet regional disparities in medical resources and economic conditions can limit access to accurate diagnostics [8]. Further complicating decision‐making, lung cancer is characterized by rapid progression, poor prognosis, and limited survival time. Any delay in treatment can result in missed therapeutic windows and worse outcomes [9]. This urgency places patients under significant pressure to make swift decisions—often amid uncertainty and fear of making the wrong choice. These combined factors—therapeutic complexity, financial strain, diagnostic limitations, and time‐sensitive decision‐making—make the treatment decision process in lung cancer uniquely challenging and emotionally burdensome.
The concept of decisional conflict was first introduced by Janis et al. in 1977 [10], referring to a state of uncertainty experienced by individuals when faced with multiple healthcare choices. It is now recognized as a key factor influencing both the decision‐making process and clinical outcomes. In China, traditional cultural values often play a role in shaping medical decisions, especially those involving life‐and‐death situations. As a result, patients and their surrogate decision‐makers frequently experience decisional conflict in such contexts [7]. Studies have shown that high levels of decisional conflict are associated with increased psychological distress, including anxiety and depression, which may in turn lead to decision regret, treatment delays, reduced quality of life, and strained physician–patient relationships [11, 12]. Other research has highlighted the role of decision‐making self‐efficacy in helping patients cope more effectively with decisional conflict and improving the overall quality of healthcare decisions [13].
To date, decisional conflict has been well documented in patients with brain tumors [14], bladder cancer [13] and breast cancer [15], revealing that key determinants encompass patient‐related characteristics, disease‐ and treatment‐related attributes, and features of the healthcare system and clinician–patient communication. Yet evidence specific to lung‐cancer patients receiving systemic therapy remains scarce. Against this gap, we asked: (1) What is the current level of decisional conflict among lung cancer patients receiving systemic therapy? (2) Which potentially modifiable factors contribute most strongly to this conflict? Addressing these questions will clarify where and how medical staff can intervene to reduce conflict and improve treatment adherence.
2. Methods
2.1. Study Design
For this cross‐sectional study, we utilized the STROBE checklist provided by the EQUATOR network.
2.2. Sample and Setting
A convenience sampling method was used to recruit patients undergoing treatment for lung cancer at the Department of Medical Oncology, Cancer Hospital, Chinese Academy of Medical Sciences, from August to December 2024. A convenience sampling method was used to recruit patients receiving systemic therapy for lung cancer in the Department of Internal Medicine at the Cancer Hospital, Chinese Academy of Medical Sciences, from August to December 2024. The inclusion criteria were as follows: (1) patients with a pathological diagnosis of lung cancer; (2) age ≧ 18 years; (3) adequate ability to speak and write in Chinese; (4) awareness of their diagnosis; and (5) voluntary participation with informed consent. The exclusion criteria were as follows: (1) critically ill patients unable to cooperate; (2) patients unaware of their diagnosis; (3) patients whose treating physicians did not permit participation; (4) participation in other similar studies; and (5) history of psychiatric disorders. The study was approved by the Ethics Committee of the Cancer Hospital, Chinese Academy of Medical Sciences (Approval No. 24/439‐4719) and conducted in accordance with the principles of the Declaration of Helsinki.
This study employed a rough sample size estimation method, determining the sample size as 5 to 10 times the number of variables [16]. With a total of 25 variables in this study and considering a 20% invalid questionnaire rate, the estimated sample size ranged from 157 to 313. Ultimately, a sample size of 500 was selected.
2.3. Data Collection
This study used a cross‐sectional survey design. Before data collection began, the project leader trained the nurses responsible for data collection to ensure consistency. Data were collected using electronic questionnaires. Upon admission, patients receiving systemic therapy for lung cancer were informed about the study's purpose, significance, methodology, and confidentiality measures, and provided informed consent. Each section of the questionnaire included instructions for completion. To ensure data integrity, each electronic device was limited to a single submission. The questionnaire took approximately 15 min to complete, and participants could contact the data collection nurses for assistance. Questionnaires were considered invalid if completed in under 8 min, if all responses were identical, or if there were significant logical inconsistencies. A total of 538 questionnaires were collected, with 500 valid responses, resulting in an effective response rate of 92.94%. There were no significant differences in age, gender, or other basic demographics between participants with valid and invalid questionnaires (p > 0.05).
2.4. Instrument
2.4.1. General Information Questionnaire
Based on an extensive literature review and disease‐specific consideration, a self‐designed questionnaire was developed to collect general patient information, including gender, age, ethnicity, educational level, marital status, number of children, primary family caregiver, economic status, occupational status, residence, and medical payment method. Additionally, treatment‐related information was collected, such as treatment cycle, disease stage, pathological type, treatment line, treatment regimen, history of lung‐related surgery, and types of other medication taken.
2.5. Scale Evaluation
The Decisional Conflict Scale (DCS), developed by O'Connor et al. [17] and adapted into Chinese by Li [18], consists of 16 items across three dimensions: information and values, decision support and effectiveness, and decisional uncertainty. The scale uses a 5‐point Likert system (0 to 4), where 0 represents “yes,” 1 means “no,” 2 indicates “uncertain,” 3 stands for “probably not” and 4 means “no.” Higher total scores reflect greater decisional conflict. On a 100‐point scale, a score ≥ 25 indicates decisional conflict, and ≥ 37.5 signifies high‐level decisional conflict (decisional delay). The scale had a Cronbach's α coefficient of 0.937 in this study.
Cancer Patient's Involvement in Treatment Decision‐Making Scale (CPITDM) was originally developed by Sainio et al. in Finland [19] and was translated into Chinese by Ma in 2004 [20]. It consists of 12 items, each rated on a 3‐point scale: “very important” (3 points), “somewhat important” (2 points), and “not important” (1 point). The total score is calculated by summing the responses to all 12 items, with higher scores indicating a more positive attitude toward participating in treatment decision‐making and a greater degree of actual involvement. Lower scores suggest less engagement. The scale has demonstrated good reliability and validity, with a Cronbach's alpha coefficient of 0.830 in this study.
The Preparation Decision Making Scale (PrepDM), developed by Canadian nursing scholars and later revised by Bennett et al. [21], was used to assess patients' decision readiness. It was translated into Chinese by Li [18] as the Chinese version (C‐PrepDM) and serves as a tool for evaluating decision‐support interventions and patients' readiness to participate in decision‐making. The scale consists of 10 items rated on a five‐point Likert scale: 1 = “not at all,” 2 = “a little,” 3 = “somewhat,” 4 = “quite a lot,” and 5 = “very much.” The score is calculated by averaging the item scores and multiplying by 20 to convert the range to 20–100 points. Higher scores reflect greater decision readiness and more effective decision support. The scale has a high reliability, with a Cronbach's α coefficient of 0.895 in this study.
The Decision Regret Scale (DRS), developed by Brehaut et al. [22] and translated into Chinese by Chen et al. [23], consists of 5 items rated on a five‐point Likert scale: 1 = “strongly agree,” 2 = “agree,” 3 = “neutral,” 4 = “disagree,” and 5 = “strongly disagree.” Items 2 and 4 are reverse‐scored. The total score is calculated using the formula: total score = (average score of all items—1) × 5, resulting in a range of 0 to 20. A higher score indicates greater decision regret. The scale has good reliability, with a Cronbach's α coefficient of 0.830 in this study.
2.6. Statistical Analysis
After data collection, a database was established using Excel, and data were processed and analyzed using SPSS 26.0. Categorical variables were described using frequencies and percentages, while continuous variables following a normal distribution were presented as mean ± standard deviation. Differences in decisional conflict among patients with different characteristics were analyzed using the t‐test or one‐way analysis of variance (ANOVA). Pearson correlation analysis was conducted to examine the relationships between decisional conflict, involvement in treatment decision‐making, decision readiness, and decision regret. Multiple linear regression analysis was performed to identify factors influencing decisional conflict in lung cancer patients. A p < 0.05 was considered statistically significant.
3. Result
3.1. The Current Status of Decisional Conflict in Lung Cancer Patients Receiving Systemic Therapy
The total decisional conflict score among lung cancer patients receiving systemic therapy was 47.28 ± 15.83. A total of 445 (89%) patients experienced some level of decisional conflict (≥ 27 points); 400 (80%) patients experienced a high level of decisional conflict (decisional delay, ≥ 37.5 points). The mean item score for each dimension, ranked from highest to lowest were: decision support and effectiveness, decision uncertainty, and information and values. The scores for each dimension were presented in Table 1.
TABLE 1.
The total decisional conflict score and dimension scores in lung cancer patients receiving systemic therapy (mean ± SD, points).
| Dimensions | Items | Raw score ( ± s) | Standard score ( ± s) | Average score ( ± s) |
|---|---|---|---|---|
| Total score | 16 | 30.26 ± 10.13 | 47.28 ± 15.83 | 1.89 ± 0.63 |
| Decision support and effectiveness | 8 | 15.54 ± 5.81 | 48.55 ± 18.14 | 1.94 ± 0.73 |
| Decision uncertainty | 2 | 3.77 ± 1.57 | 47.10 ± 19.62 | 1.89 ± 0.79 |
| Information and values | 6 | 10.96 ± 4.09 | 45.65 ± 17.04 | 1.83 ± 0.68 |
A score ≥ 25 indicates decisional conflict, and ≥ 37.5 signifies high‐level decisional conflict (decisional delay).
3.2. DCS Among Lung Cancer Patients Receiving Systemic Therapy With Different Characteristics
Among lung cancer patients receiving systemic therapy, factors such as gender, age, ethnicity, education level, number of children, residence, treatment cycle, disease stage, and history of lung surgery showed no statistically significant association with decisional conflict (p > 0.05). However, significant differences in DCS were observed based on marital status, primary family caregiver, economic status, occupation status, medical payment method, treatment line, pathological type, treatment regimen, and types of other medication taken (p < 0.05). Detailed information was presented in Table 2.
TABLE 2.
Univariate analysis of DCS among lung cancer patients receiving systemic therapy with different characteristics (n = 500).
| Variables | n (%) | DCS, mean ± SD | Statistic | p |
|---|---|---|---|---|
| Total | 500 (100) | 47.28 ± 15.83 | ||
| Gender | t = −0.57 | 0.570 | ||
| Man | 371 (74.20) | 47.04 ± 16.08 | ||
| Female | 129 (25.80) | 47.97 ± 15.13 | ||
| Age | t = 0.89 | 0.372 | ||
| < 60 | 175 (35.00) | 48.14 ± 16.21 | ||
| ≥ 60 | 325 (65.00) | 46.82 ± 15.63 | ||
| Ethnicity | t = −1.04 | 0.301 | ||
| Han | 465 (93.00) | 47.08 ± 16.00 | ||
| Others | 35 (7.00) | 49.96 ± 13.34 | ||
| Education level | F = 0.76 | 0.468 | ||
| Primary | 57 (11.40) | 49.64 ± 13.16 | ||
| Secondary | 287 (57.40) | 46.81 ± 16.58 | ||
| College | 156 (31.20) | 47.29 ± 15.31 | ||
| Marital status | F = 12.12 | < 0.001 | ||
| Unmarried | 6 (1.20) | 36.46 ± 15.83 | ||
| Divorced or widowed | 98 (19.60) | 53.87 ± 9.59 | ||
| Married | 396 (79.20) | 45.81 ± 16.62 | ||
| Number of children | F = 1.44 | 0.231 | ||
| No kid | 8 (1.60) | 46.88 ± 8.05 | ||
| 1 | 201 (40.20) | 45.62 ± 17.04 | ||
| 2 | 234 (46.80) | 48.12 ± 15.22 | ||
| ≥ 3 | 57 (11.40) | 49.78 ± 14.34 | ||
| Primary family caregiver | F = 3.42 | 0.017 | ||
| Alone | 18 (3.60) | 50.87 ± 13.75 | ||
| Spouse | 281 (56.20) | 46.64 ± 15.37 | ||
| Kids | 139 (27.80) | 45.69 ± 18.63 | ||
| Others | 62 (12.40) | 52.72 ± 9.33 | ||
| Economic status | F = 3.12 | 0.026 | ||
| < 2000 | 26 (5.20) | 47.66 ± 21.53 | ||
| 2000–4000 | 183 (36.60) | 48.10 ± 14.77 | ||
| 4000–6000 | 186 (37.20) | 48.75 ± 13.78 | ||
| > 6000 | 105 (21.00) | 43.15 ± 18.70 | ||
| Occupation status | t = −9.13 | < 0.001 | ||
| No occupation | 37 (7.40) | 26.10 ± 15.91 | ||
| Occupational | 463 (92.60) | 48.97 ± 14.57 | ||
| Residence | F = 0.29 | 0.750 | ||
| Rural | 42 (8.40) | 49.03 ± 17.83 | ||
| Town | 174 (34.80) | 47.00 ± 16.56 | ||
| City | 284 (56.80) | 47.19 ± 15.10 | ||
| Medical payment method | t = −2.54 | 0.023 | ||
| Self pay | 15 (3.00) | 32.60 ± 22.94 | ||
| Non‐self pay | 485 (97.00) | 47.74 ± 15.37 | ||
| Treatment cycles | F = 0.53 | 0.592 | ||
| 1 | 78 (15.60) | 46.11 ± 17.98 | ||
| 2–3 | 143 (28.60) | 48.30 ± 15.14 | ||
| ≥ 4 | 279 (55.80) | 47.09 ± 15.57 | ||
| Treatment lines | t = −2.26 | 0.024 | ||
| First line | 334 (66.80) | 48.40 ± 15.81 | ||
| Non‐first line | 166 (33.20) | 45.02 ± 15.69 | ||
| Disease stage | F = 0.42 | 0.740 | ||
| I | 27 (5.40) | 47.40 ± 19.29 | ||
| II | 35 (7.00) | 44.42 ± 15.56 | ||
| III | 139 (27.80) | 47.70 ± 16.80 | ||
| IV | 299 (59.80) | 47.41 ± 15.10 | ||
| Pathology type | t = −3.55 | < 0.001 | ||
| Non‐small | 316 (63.20) | 45.51 ± 17.04 | ||
| Small | 184 (36.80) | 50.32 ± 13.01 | ||
| Treatment regimen | F = 7.81 | < 0.001 | ||
| Chemotherapy | 49 (9.80) | 48.25 ± 18.40 | ||
| Chemotherapy + immunotherapy | 179 (35.80) | 42.42 ± 17.35 | ||
| Chemotherapy_immunotherapy + targetedtherapy | 45 (9.00) | 46.88 ± 17.61 | ||
| Chemotherapy + targetedtherapy | 134 (26.80) | 50.65 ± 13.49 | ||
| Others | 93 (18.60) | 51.46 ± 10.41 | ||
| History of lung surgery | t = 1.45 | 0.147 | ||
| No | 318 (63.60) | 48.06 ± 15.71 | ||
| Yes | 182 (36.40) | 45.92 ± 16.01 | ||
| Types of other medication taken | F = 10.56 | < 0.001 | ||
| None | 51 (10.20) | 42.00 ± 20.21 | ||
| 1–2 | 311 (62.20) | 46.02 ± 15.40 | ||
| > 3 | 138 (27.60) | 52.08 ± 13.79 |
Note: Among lung cancer patients receiving systemic therapy, factors such as gender, age, ethnicity, education level, number of children, residence, treatment cycle, disease stage, and history of lung surgery showed no statistically significant association with decisional conflict (P > 0.05). However, significant differences in DCS were observed based on marital status (P < 0.001), primary family caregiver (P = 0.017), economic status (P = 0.026), occupation status (P < 0.001), medical payment method (P = 0.023), treatment line (P = 0.024), pathological type (P < 0.001), treatment regimen (P < 0.001), and types of other medication taken (P < 0.001).
Abbreviations: F: ANOVA; SD: standard deviation; t: t‐test.
3.3. Correlation Analysis of Decisional Conflict With Involvement in Treatment Decision‐Making, Decision Readiness, and Decision Regret in Lung Cancer Patients Receiving Systemic Therapy
The scores of involvement in treatment decision‐making, decision readiness, and decision regret among lung cancer patients receiving systemic therapy were 28.56 ± 3.91, 63.02 ± 11.65, and 9.46 ± 2.62, respectively, as shown in Table 3. Pearson correlation analysis showed that the total decisional conflict score was negatively correlated with the involvement in treatment decision‐making (r = −0.188, p < 0.001) and decision readiness (r = −0.303, p < 0.001), and positively correlated with decision regret (r = 0.342, p < 0.001), as shown in Figure 1.
TABLE 3.
The scores of CPITDM, PrepDM, DRS in lung cancer patients receiving systemic therapy.
| Variable | Items | Score range | Total score ( ± s) | Average score ( ± s) |
|---|---|---|---|---|
| CPITDM | 12 | 12 ~ 36 | 28.56 ± 3.91 | 2.38 ± 0.33 |
| PrepDM | 10 | 20 ~ 100 | 63.02 ± 11.65 | 6.30 ± 1.17 |
| DRS | 5 | 0 ~ 20 | 9.46 ± 2.62 | 1.89 ± 0.52 |
FIGURE 1.
Correlation between decisional conflict and decision readiness, involvement of treatment decision‐making and decision regret in lung cancer patients receiving systemic therapy.
3.4. Multiple Analysis of Decisional Conflict in Lung Cancer Patients Receiving Systemic Therapy
A stepwise multiple linear regression analysis was conducted with the decisional conflict scores as the dependent variable. Independent variables included those with statistical significance in the univariate analysis, as well as the scores of decision readiness, involvement in treatment decision‐making, and decision regret. Categorical variables were coded, and dummy variables were set for multi‐category variables with one category as the reference group. Continuous variables were entered using their original values. The coding of independent variables was shown in Table A1.
The results indicated that occupational status, medical payment method, treatment line, pathological type, types of other medication taken, decision readiness, and decision regret were significant influencing factors of decisional conflict in lung cancer patients receiving systemic therapy (p < 0.05). These variables explained 59.9% of variance in decisional conflict scores. Details of the regression analysis and the forest plot were presented in Table 4 and Figure 2, respectively.
TABLE 4.
Multiple analysis of decisional conflict in lung cancer patients receiving systemic therapy.
| Variables | β | SE | t | p | β (95% CI) |
|---|---|---|---|---|---|
| Intercept | 31.80 | 4.93 | 6.45 | < 0.001 | 31.80 (22.13 ~ 41.47) |
| Occupation status | |||||
| No | 0.00 (reference) | ||||
| Yes | 17.00 | 2.27 | 7.50 | < 0.001 | 17.00 (12.56 ~ 21.44) |
| Medical payment method | |||||
| Non‐self‐paying | 0.00 (reference) | ||||
| Self‐paying | −10.13 | 3.41 | −2.97 | 0.003 | −10.13 (−16.82 ~ −3.45) |
| Treatment line | |||||
| First‐line | 0.00 (reference) | ||||
| Second‐line and above | −3.41 | 1.22 | −2.79 | 0.006 | −3.41 (−5.81 ~ −1.01) |
| Pathological type | |||||
| Non‐small | 0.00 (reference) | ||||
| Small | 3.06 | 1.20 | 2.55 | 0.011 | 3.06 (0.70 ~ 5.41) |
| Types of other medication taken | |||||
| None | 0.00 (reference) | ||||
| 1–2 | 2.49 | 1.96 | 1.27 | 0.204 | 2.49 (−1.35 ~ 6.33) |
| > 3 | 6.77 | 2.13 | 3.18 | 0.002 | 6.77 (2.59 ~ 10.95) |
| Decision readiness | −0.33 | 0.05 | −6.57 | < 0.001 | −0.33 (−0.43 ~ −0.23) |
| Decision regret | 1.86 | 0.22 | 8.40 | < 0.001 | 1.86 (1.43 ~ 2.30) |
Note: R 2 = 0.599, adjusted R 2 = 0.590, F = 7.653, p < 0.001.
Abbreviation: CI = confidence interval.
FIGURE 2.
Forest plot of multivariate regression analysis of decisional conflict in lung cancer patients receiving systemic therapy.
4. Discussion
This study surveyed 500 lung cancer patients to assess the status of decisional conflict and its influencing factors. The large sample size and comprehensive data provide a foundation for developing culturally appropriate decision aids in China.
The mean decisional conflict score was 47.28 ± 15.83, with 89% of patients experiencing some level of conflict and 80% at a high level. These results indicate a generally high degree of decisional conflict among lung cancer patients. Similarly, Wang et al. [13] reported that 92.5% of bladder cancer patients experienced decisional conflict, with 77.5% at a high level. One possible explanation is that, like bladder cancer, lung cancer has a relatively poor prognosis. Regardless of treatment choice, patients face side effects and uncertain outcomes, contributing to high decisional conflict.
Further analysis revealed that patients scored highest in the “decision support and effectiveness” dimension of the DCS, suggesting a lack of external support and limited confidence in decision‐making. Lung cancer—related stigma [24, 25] and the predominance of male participants—who may be less likely to express emotional needs—could contribute to reduced support. Additionally, the majority of patients had stage IV disease, making it more difficult to comprehend disease progression and evaluate treatment options effectively.
Several sociodemographic and clinical factors were independently associated with decisional conflict. Employed patients reported higher levels of conflict than unemployed ones, possibly due to concerns about job security and treatment‐related role disruption [15]. In contrast, unemployed individuals may have more flexible schedules and fewer external constraints. Similarly, patients covered by insurance showed higher decisional conflict than those who self‐paid. While insured patients may have access to a broader range of treatment options, this could inadvertently increase complexity and uncertainty [26]. However, the number of self‐paying patients in this study was small, and further investigation is warranted.
Notably, treatment line emerged as a novel influencing factor. Patients receiving first‐line therapy reported higher conflict than those on second‐ or third‐line regimens. This may reflect their initial exposure to treatment, coupled with limited disease understanding and heightened anxiety. In contrast, those receiving later‐line therapies may have gained experiential knowledge and tend to follow medical recommendations more readily, reducing internal conflict.
Our study also found that patients with non‐small cell lung cancer (NSCLC) experienced less decisional conflict than those with small cell lung cancer (SCLC). This may be due to the availability of individualized, biomarker‐driven treatment strategies for NSCLC [27], which offer patients a clearer path forward. In contrast, SCLC's aggressive course and limited therapeutic options contribute to greater uncertainty, emotional distress, and reduced self‐efficacy; these factors are shown to correlate negatively with decision confidence [28, 29].
Patients taking more non‐systemic therapy medications also reported greater conflict, possibly reflecting the burden of comorbidities. These individuals must navigate additional risks and treatment trade‐offs, which may compromise decision clarity and increase psychological burden [30].
Psychological factors were also important. Decision regret was an independent predictor of decisional conflict, supporting earlier evidence of their interrelationship [31, 32]. Regret may prompt patients to second‐guess prior decisions, fueling indecision in future choices. In contrast, higher decision‐making preparedness was associated with significantly lower conflict. Patients who are well prepared—armed with evidence‐based information and a clear understanding of options—are more likely to feel confident and less conflicted when facing complex treatment decisions [33].
Although the identified factors, such as medical payment method, pathological type, treatment line, are immutable, they can be reframed as early, actionable signals. By embedding these signals into routine workflows, clinicians can deliver precisely timed, risk‐stratified decision support. Specifically, (1) at diagnosis, a one‐minute electronic flag can auto‐trigger a tailored consultation for patients with SCLC or high out‐of‐pocket costs; (2) a brief employment/support screener can identify working adults who would benefit from flexible scheduling or tele‐consults; and (3) culturally adapted decision aids, delivered immediately, can mitigate decisional conflict before first‐line therapy is selected. Integrating these steps into the electronic health records allows us to convert static patient characteristics into dynamic, point‐of‐care interventions that improve adherence and, ultimately, clinical outcomes.
5. Limitations
This study has several limitations. First, it was conducted at a single medical center and examined only a limited set of demographic and clinical variables, which may not capture the full range of factors influencing decisional conflict. Second, data collection focused solely on the patient perspective; potential influences from healthcare providers were not assessed. Third, although all participants provided informed consent and voluntarily completed the questionnaires, the data were entirely self‐reported, which may have introduced reporting bias and affected data accuracy. Additionally, this study assessed decisional conflict in patients who had already initiated lung cancer treatment, rather than those actively making treatment‐related decisions. Future research should focus on this latter group, employing prospective study designs to track changes in decisional conflict over time. Such data could support the development of individualized decision‐support systems tailored to patients' evolving needs.
6. Conclusion
This study investigated the current status and influencing factors of decisional conflict among lung cancer patients. The findings revealed that patients undergoing treatment experienced a high level of decisional conflict, with inadequate perceived external support. This highlights the need for both material and psychological support during the decision‐making process.
Notably, this study identified several independent predictors of decisional conflict for the first time, including clinical characteristics such as pathological type and line of treatment. Additional influencing factors included occupational status, method of medical payment, and the type of medications taken. Higher levels of decision regret were associated with increased decisional conflict, while greater decision preparedness was linked to lower conflict.
Author Contributions
All authors had full access to the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Weicai Su: conceptualization, resource, supervision, formal analysis. Yan Wang: data curation, formal analysis, funding acquisition, methodology, writing – original draft preparation, writing – reviewing and editing. Jinping Li: investigation, methodology, writing – original draft preparation, writing – reviewing and editing. Panrong Wang: investigation. Minfeng Zhai: investigation. Yang Zhao: investigation. Xuenan Hu: investigation.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgments
We would like to express our heartfelt gratitude to all the patients who participated in this study for their time and invaluable contributions. We also thank the healthcare staff at the Department of Medical Oncology, Cancer Hospital of the Chinese Academy of Medical Sciences, for their assistance in patient recruitment and data collection. This study was funded by the General Program of the NSFC general program of China (82272796, 82241229), Medical Oncology Innovation Team of Cancer Hospital Chinese Academy of Medical Sciences; Y&M Cultivation project of Cancer Hospital Chinese Academy of Medical Sciences (CICAMS‐MOY&M‐202404).
Appendix A.
TABLE A1.
The coding of independent variables.
| Independent variables | Coding |
|---|---|
| Marital status | “unmarried” = 0, “Divorced or widowed” = 1, “Married” = 2 |
| Primary family caregiver | “Alone” = 0, “Spous” = 1, “Kids” = 2, “Others” = 3 |
| Economic status | “< 2000” = 0, “2000–4000” = 1, “4000–6000” = 2, “> 6000” = 3 |
| Occupation status | “No” = 0, “Yes” = 1 |
| Medical payment method | “Non‐self‐paying” = 0, “Self‐paying” = 1 |
| Treatment line | “First‐line” =0, “Second‐line and above” = 1 |
| Pathology type | “Non‐small” = 0, “Small” = 1 |
| Treatment regimen | “Chemotherapy” = 0, “Chemotherapy + immunotherapy” = 1, “Chemotherapy_immunotherapy + targetedtherapy” = 2, “Chemotherapy + targetedtherapy” = 3, “Others” = 4 |
| Types of other medication taken | “None” = 0, “1–2” = 1, “> 3” = 2 |
Su W., Li J., Zhai M., et al., “Current Status and Influencing Factors of Decisional Conflict in Lung Cancer Patients Receiving Systemic Therapy: A Cross‐Sectional Analysis,” Thoracic Cancer 16, no. 16 (2025): e70150, 10.1111/1759-7714.70150.
Funding: This work was supported by the General Program of the NSFC General Program of China (Grant/Award Number: 82241229, 82272796), and the Medical Oncology Innovation Team of Cancer Hospital Chinese Academy of Medical Sciences; Y&M Cultivation Project of Cancer Hospital Chinese Academy of Medical Sciences (Grant/Award Number: CICAMS‐MOY&M‐202404).
Weicai Su is the first author. Jinping Li and Minfeng Zhai are co‐first authors.
References
- 1. Zheng R. S., Chen R., Han B. F., et al., “Cancer Incidence and Mortality in China, 2022,” Zhonghua Zhong Liu Za Zhi 46, no. 3 (2024): 221–231, 10.3760/cma.j.cn112152-20240119-00035. [DOI] [PubMed] [Google Scholar]
- 2. Han B., Zheng R., Zeng H., et al., “Cancer Incidence and Mortality in China, 2022,” Journal of the National Cancer Center 4, no. 1 (2024): 47–53, 10.1016/j.jncc.2024.01.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Song Z. Q., Guo Q., Wang J. H., et al., “Development of a Prognostic Model for Lung Adenocarcinoma Based on a Large Sample Multicenter Study,” Chinese Journal of Cancer Prevention and Treatment 32, no. 9 (2025): 532–539, 10.16073/j.cnki.cjcpt.2025.09.03. [DOI] [Google Scholar]
- 4. Liu S. Y., Gan B., and Wu Y. L., “An Overview and Interpretation of Important Clinical Studies on Non‐Small Cell Lung Cancer in 2022,” Journal of Evidence‐Based Medicine 23, no. 5 (2023): 311–320, 10.12019/j.issn.1671-5144.2023.05.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Wu C. J., Li W. T., Tao H. Y., et al., “Review of Research on Pharmacoeconomics of First‐Line Immunotherapy for Treating Non‐Small Cell Lung Cance,” Medicine and Society 36, no. 11 (2023): 94–100, 10.13723/j.yxysh.2023.11.016. [DOI] [Google Scholar]
- 6. Henderson R., Keeling P., French D., Smart D., Sullivan R., and Lawler M., “Cost ‐Effectiveness of Precision Diagnostic Testing for Precision Medicine Approaches Against Non‐Small‐Cell Lung Cancer: A Systematic Review,” Molecular Oncology 15, no. 10 (2021): 2672–2687, 10.1002/1878-0261.13038. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Zhao L. H., Qu H., Li J. J., et al., “Analysis on Status and Influencing Factors of Palliative Care Decisions Conflict Among Substitutive Decision‐Makers of Advanced Cancer Patient,” Chinese Journal of Nursing 57, no. 14 (2022): 1683–1690, 10.3761/j.issn.0254-1769.2022.14.003. [DOI] [Google Scholar]
- 8. Mamti H., “Correlation Between Serum Tumor Markers and Genemutation Types in Patients With Lung Adenocarcinom,” 2021, Xinjiang, China: Xinjiang Medical Uni, 10.27433/d.cnki.gxyku.2021.001025. [DOI]
- 9. Duan H., “Retrospective Study on Survival of 227 Patients With Stage IIIb/lV Non‐Small Cell Lung Cancer,” 2018, Beijing, China: Beijing University of Chinese Medicine.
- 10. Janis I. L. and Maan L., Decision Making: A Psychological Analysis of Conflict, Choice and Commitment (Free Press, 1977), 15–89. [Google Scholar]
- 11. October T. W., Jones A. H., Michals H. G., Hebert L. M., Jiang J., and Wang J., “Parental Conflict, Regret, and Short‐Term Impact on Quality of Life in Tracheostomy Decision‐Making,” Pediatric Critical Care Medicine 21, no. 2 (2020): 136–142, 10.1097/PCC.0000000000002109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Heirman A. N., Arends C. R., de Jel D. V. C., et al., “Decisional Conflict and Decision Regret in Head and Neck Oncology: A Systematic Review and Meta‐Analysis,” JAMA Otolaryngology. Head & Neck Surgery 150, no. 5 (2024): 393–404, 10.1001/jamaoto.2024.0214. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Wang H. J., Wang Y., Wu M. L. Y., He M., Lu Z. H., and Qin Q. X., “Status Quo of Decision Conflicts Among Bladder Cancer Patients With Urinary Diversion and Its Influencing Factors,” Military Medicine 40, no. 9 (2023): 60–64, 10.3969/j.issn.2097-1826.2023.09.015. [DOI] [Google Scholar]
- 14. Ge S., Zhai Q. H., Li X. J., and Qin G., “Relationship of Health Literacy and Decision‐Making Conflict Among Patients With Colorectal Cancer in a Grade‐A Tertiary Hospital in Zhengzhou,” Medicine and Society 35, no. 6 (2022): 116–120, 10.13723/j.yxysh.2022.06.022. [DOI] [Google Scholar]
- 15. Guo Y., “Study on the Construction and Application of Decision Support Scheme for Operation Mode of Breast Cancer Patients,” 2023, Henan, China: Henan University of Science and Technology, 10.27115/d.cnki.glygc.2023.000069. [DOI]
- 16. Ni P., Chen J. L., and Liu N., “Sample Size Estimation in Quantitative Research in Nursing Studies,” Chinese Journal of Nursing 45, no. 4 (2010): 378–380, 10.3761/j.issn.0254-1769.2010.04.037. [DOI] [Google Scholar]
- 17. O'Connor A. M., “Validation of a Decisional Conflict Scale,” Medical Decision Making 15, no. 1 (1995): 25–30, 10.1177/0272989X9501500105. [DOI] [PubMed] [Google Scholar]
- 18. Li Y., “Construction and Application of Treatment Decision Aids for Early‐Stage Primary Liver Cancer Patients,” 2017, Naval Medical University Second Military Medical University, 10.7666/d.Y3247328. [DOI]
- 19. Sainio C. and Lauri S., “Cancer Patients' Decision‐Making Regarding Treatment and Nursing Care,” Journal of Advanced Nursing 41, no. 3 (2003): 250–260, 10.1046/j.1365-2648.2003.02525.x. [DOI] [PubMed] [Google Scholar]
- 20. Ma L. L., “A Study on the Current Status and Influencing Factors of Ancer Patients' Participation in Treatment and Nursing Decision‐Making,” 2004, Peking Union Medical College.
- 21. Bennett C., Graham I. D., Kristjansson E., Kearing S. A., Clay K. F., and O'Connor A. M., “Validation of a Preparation for Decision Making Scale,” Patient Education and Counseling 78, no. 1 (2010): 130–133, 10.1016/j.pec.2009.05.012. [DOI] [PubMed] [Google Scholar]
- 22. Brehaut J. C., O'Connor A. M., Wood T. J., et al., “Validation of a Decision Regret Scale,” Medical Decision Making 23, no. 4 (2003): 281–292, 10.1177/0272989X03256005. [DOI] [PubMed] [Google Scholar]
- 23. Chen F. and Cheng X. J., “Reliability and Validity Evaluation of the Chinese Version of the Decision Regret Scale in Facial Aesthetic Surgery Patients,” Journal of Nursing (China) 25, no. 7 (2018): 42–44, 10.16460/j.issn1008-9969.2018.07.042. [DOI] [Google Scholar]
- 24. Wang R., Sui H. P., Li T. T., Jiang T. T., Xia Y. L., and Shi T. Y., “The Stigma Experience of Lung Cancer Patients:A Meta‐Synthesis of Qualitative Studies,” Chinese Journal of Nursing Education 21, no. 2 (2024): 231–237, 10.3761/j.issn.1672-9234.2024.02.017. [DOI] [Google Scholar]
- 25. Du B. L., Meng G., and Dong C. L., “Correlation and Influencing Factors of Stigma,Psychological Resilience,and Medical Coping Ability in Patients With Lung Cancer,” Chinese General Practice Nursing 18, no. 4 (2020): 385–390, 10.12104/j.issn.1674-4748.2020.04.001. [DOI] [Google Scholar]
- 26. Zhao L. H., “Analysis on Conflict Status and Influencing Factors of Palliative Treatment Decisions Among Substitutive Decision‐Makers of Advanced Cancer Patients,” 2023, Liaoning, China: Dalian Medical University, 10.26994/d.cnki.gdlyu.2022.001061. [DOI]
- 27. Miao D., Zhao J., Han Y., et al., “Management of Locally Advanced Non‐Small Cell Lung Cancer: State of the Art and Future Directions,” Cancer Communications (London) 44, no. 1 (2024): 23–46, 10.1002/cac2.12505. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Kleiman E. M., Rizvi S. l., and Duberstein P. R., “Initial Validation of an Ecological Momentary Assessment Measure of Self‐Efficacy to Manage Negative Emotions,” Assessment 30, no. 7 (2023): 2223, 10.1177/10731911221146341. [DOI] [PubMed] [Google Scholar]
- 29. Wang L. P., Wang H., Hou M., Guo J. M., Zu B. F., and Li P., “Current Status of Decisional Conflict of Family Members of Patients With Intracranial Tumors and Its Influencing Factors: A 222‐Case Study,” Journal of Nursing (China) 29, no. 20 (2022): 11–16, 10.16460/j.issn1008-9969.2022.20.011. [DOI] [Google Scholar]
- 30. Shen W. J. and Yang Q. F., “The Current Status and Influencing Factors of Decisional Conflict in Anticoagulant Therapy for Patients With Atrial Fibrillation,” Journal of Nursing Science 35, no. 6 (2020): 18–21, 10.3870/j.issn.1001-4152.2020.06.018. [DOI] [Google Scholar]
- 31. Wang J. Q., Shen P. H., Hua Y. T., et al., “Research Progress of Decision Regret in Breast Cancer Patients,” Journal of Nursing Science 39, no. 4 (2024): 21–24, 10.3870/j.issn.1001-4152.2024.04.021. [DOI] [Google Scholar]
- 32. Hoefel L., O'connor A. M., Lewis K. B., et al., “20th Anniversary Update of the Ottawa Decision Support Framework Part 1: A Systematic Review of the Decisional Needs of People Making Health or Social Decisions,” Medical Decision Making 40, no. 5 (2020): 555–581, 10.1177/0272989X20936209. [DOI] [PubMed] [Google Scholar]
- 33. Flitcroft K., Brennan M., and Spillane A., “Making Decisions About Breast Reconstruction: A Systematic Review of Patient‐Reported Factors Influencing Choice,” Quality of Life Research 26, no. 9 (2017): 2287–2319, 10.1007/s11136-017-1555-z. [DOI] [PubMed] [Google Scholar]