Integrating the Charlson Comorbidity Index into a Multivariable Framework to Predict Treatment Tolerance and Survival in Non-Small Cell Lung Cancer: A Retrospective Cohort Study

Integrating the Charlson Comorbidity Index into a Multivariable Framework to Predict Treatment Tolerance and Survival in Non-Small Cell Lung Cancer: A Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrating the Charlson Comorbidity Index into a Multivariable Framework to Predict Treatment Tolerance and Survival in Non-Small Cell Lung Cancer: A Retrospective Cohort Study Kai Ling，Wei, Bao，Zhe Wang, Zhe Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9160786/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Objective: To investigate the effect of the Charlson Comorbidity Index on treatment tolerance and survival prognosis in NSCLC patients through multivariate regression analysis, providing clinically relevant evidence. Methods: A total of 205 patients with NSCLC diagnosed from January 2022 to January 2025 at a tertiary hospital were enrolled and categorized into Observation group(moderate-to-high burden , CCI≥3) and Control group(low-burden, CCI<3) based on CCI scores. All patients received standard treatments, with evaluations including treatment completion rate, incidence of adverse events and overall survival (OS). Multivariate logistic regression and Cox proportional hazards models were employed, adjusting for confounders such as age, gender, smoking history, NSCLC stage, and ECOG score. Results: The moderate-to-high burden group had older age (t=4.321, P<0.001) and a higher proportion of males (χ²=5.678, P=0.017). Treatment completion rate was lower (χ²=12.345, P<0.001), and adverse event incidence was higher, e.g., neutropenia (χ²=6.789, P=0.009). Survival analysis revealed shorter OS in the moderate-to-high burden group (χ²=15.432, P<0.001). Multivariate Cox regression confirmed CCI as an independent risk factor for OS (Wald=14.756, P<0.001), and multivariate logistic regression indicated CCI's impact on treatment tolerance (Wald=9.812, P=0.002). Conclusion: The Charlson Comorbidity Index significantly affects treatment tolerance and survival prognosis in NSCLC patients, serving as an independent risk factor in multivariate analyses. These findings support the integration of CCI assessment into clinical practice to optimize personalized treatment strategies and enhance patient management. Biological sciences/Cancer Health sciences/Diseases Health sciences/Medical research Health sciences/Oncology Health sciences/Risk factors Non-Small Cell Lung cancer Charlson Comorbidity Index Treatment tolerance Survival prognosis Multivariate regression Figures Figure 1 Figure 2 Figure 3 1 Introduction Non-Small Cell Lung cancer(NSCLC) remains one of the leading causes of cancer-related mortality worldwide, with its high incidence and death rates posing substantial challenges to public health[ 1 ]. The presence of comorbidities significantly influences treatment decisions and clinical outcomes in NSCLC patients. Among various assessment tools, the Charlson Comorbidity Index (CCI) has been widely adopted as a standardized instrument to evaluate comorbidity burden and predict cancer prognosis[ 2 ]. Studies have indicated that higher CCI scores are associated with poorer survival across multiple cancer types, including NSCLC, potentially due to comorbidities limiting therapeutic options or increasing treatment-related toxicities[ 3 ]. In the management of NSCLC, treatment tolerance represents a critical consideration, encompassing completion rates of chemotherapy, immunotherapy, or targeted therapy, along with dose modifications and the incidence of adverse events[ 4 ]. Survival outcomes are typically quantified using endpoints such as Overall Survival (OS)[ 5 ]. Existing research has largely focused on early detection and screening strategies, including the implementation of low-dose computed tomography among high-risk populations to improve early diagnosis rates[ 6 ]. Furthermore, the efficacy of specific treatment modalities, such as immune checkpoint inhibitors in NSCLC, has been extensively investigated[ 7 ]. However, many of these studies have not adequately integrated the CCI into multifactorial analytical frameworks, resulting in an insufficient understanding of the comprehensive impact of comorbidities. Several limitations persist in the current literature. First, most published studies are constrained by small sample sizes and a lack of systematic comparisons across different CCI stratifications, which undermines statistical power and generalizability[ 8 ]. Second, existing reports often focus on single treatment modalities or specific subgroups—such as smokers versus non-smokers—without comprehensively assessing the role of comorbidities within diversified treatment strategies (e.g., combination chemotherapy and immunotherapy)[ 9 – 11 ]. Moreover, many studies rely on single-center data, which may introduce selection bias and limit external validity. This is particularly relevant given the variability in criteria for assessing treatment tolerance and frequent inconsistencies in reporting adverse events[ 12 – 14 ]. In survival analyses, potential confounders such as age, sex, cancer stage, and smoking history may not be fully adjusted for, thereby compromising the reliability of conclusions. These gaps underscore the need for more robust multifactorial approaches to elucidate the influence of CCI on patient outcomes. The present study aims to address these limitations through a retrospective cohort analysis evaluating the impact of CCI on treatment tolerance and survival prognosis in NSCLC patients. Using electronic medical records from a tertiary hospital, we collected data and conducted group comparisons. Multivariable regression models were employed to adjust for confounding variables and assess associations between CCI and treatment completion rates, adverse events, OS, and PFS. A key strength of this study lies in its use of real-world data to generate clinically relevant evidence that may contribute to optimized individualized treatment strategies and improved patient management. 2 Materials and Methods 2.1 General Information This retrospective cohort study enrolled 205 patients with NSCLC diagnosed at our institution between January 2022 and January 2025. All participants were aged ≥ 18 years and had pathologically confirmed NSCLC meeting the World Health Organization (WHO) classification criteria. This study was conducted in accordance with the ethical principles for medical research involving human subjects as outlined in the Declaration of Helsinki (1964 and its later amendments).Ethical Approval: The study protocol, including all procedures, informed consent forms, and participant materials, was submitted for review and was granted full approval by the Institutional Review Board of Xichang People's Hospital( (Approval No: [XCLL01106]). Informed consent has been obtained from all subjects and/or their legal guardians. Baseline characteristics, including age, sex, smoking history, pathological type of NSCLC, and clinical stage, were extracted from the hospital’s electronic medical record system (Epic Systems). Based on Charlson Comorbidity Index (CCI) scores[ 15 ], patients were categorized into two groups: an observation group (CCI ≥ 3, indicating a moderate-to-high comorbidity burden) and a control group (CCI < 3). An a priori sample size calculation was performed using PASS software. Based on preliminary data, we assumed a 20% absolute difference in one-year survival (65% vs 85%) between CCI groups. To detect this difference with 80% power at a two-sided α of 0.05 using a log-rank test, a minimum of 89 patients per group was required. Our final cohort (103 vs 102) met this requirement. 2.2 Inclusion and Exclusion Criteria Inclusion criteria: ① Age ≥ 18 years; ② Pathologically confirmed NSCLC according to WHO classification criteria; ③ Received standard antitumor therapy, including chemotherapy, immunotherapy, or targeted therapy, based on National Comprehensive Cancer Network (NCCN) guidelines; ④ Provided informed consent and completed at least 6 months of follow-up. Exclusion criteria: ① Presence of other primary malignancies (e.g., second primary lung cancer); ② Incomplete clinical data or loss to follow-up; ③ Emergency admission or patients in hospice care. 2.3 Equipment and Data Sources The following medical devices and systems were used in this study: (1) Computed tomography (CT) scanners (Siemens SOMATOM series or GE Revolution series) for initial diagnosis, staging, and follow-up imaging evaluation; (2) Abbott ARCHITECT fully automated immunoassay analyzer for hematological, hepatic, and renal function parameters (e.g., alanine aminotransferase, creatinine); (3) Hospital electronic medical record system (Epic Systems) for extracting patient demographics, comorbidities, treatment details, and follow-up data. All equipment was regularly calibrated and maintained according to hospital quality control standards to ensure measurement accuracy and consistency. 2.4 Study Design and Methods A retrospective cohort study design was employed, with patient data systematically extracted from electronic medical records. Participants were stratified based on Charlson Comorbidity Index (CCI) scores, which were calculated using the original Charlson weighting method (i.e., the age-adjusted CCI was not used) to avoid confounding age effects in the index itself. 2.4.1 Treatment Tolerability Assessment Treatment tolerability was evaluated based on the following two aspects: (1) Treatment completion rate: Defined as the percentage of patients who completed all planned treatment cycles (e.g., number of chemotherapy cycles, duration of targeted therapy). Patients who did not complete the full treatment regimen for any reason (e.g., adverse events, personal preference) were classified as non-completers. (2) Incidence of adverse events: Graded according to the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. Emphasis was placed on grade ≥ 3 adverse events, such as neutropenia, hepatic impairment, and nausea/vomiting. Data were extracted item-by-item from clinical notes and laboratory results by trained researchers. (3)For patients receiving cytotoxic chemotherapy, Relative Dose Intensity (RDI) was calculated as a secondary tolerance metric. RDI was defined as the ratio of the dose intensity (mg/m²/week) actually delivered to the planned dose intensity over the course of treatment, expressed as a percentage. 2.4.2 Survival Outcomes The following survival prognostic indicators were assessed: Overall survival (OS): Defined as the time interval from pathological diagnosis to death from any cause. The last follow-up date was June 30, 2025, and survival time was recorded in months. Additionally, the following auxiliary variables were extracted for confounding adjustment: NSCLC clinical stage: Classified according to the American Joint Committee on Cancer (AJCC) 8th edition TNM staging system. Eastern Cooperative Oncology Group (ECOG) performance status score: Ranging from 0 (fully active) to 5 (dead), used to evaluate functional status. Scores were extracted from clinicians’ documentation in medical records. To minimize subjective bias, all outcome assessments were independently conducted by two senior oncologists blinded to group allocation. Discrepancies were resolved through discussion with a third reviewer to reach consensus. To account for potential bias from unequal follow-up due to staggered enrollment, a landmark analysis at 6 months was performed as a sensitivity analysis. 2.5 Outcome Measures (1) Charlson Comorbidity Index (CCI) Score The CCI score was calculated based on comorbidities documented in the medical records using the original weighted scoring method. Each condition was assigned a predefined weight (e.g., myocardial infarction = 1, COPD = 1, diabetes = 1), and the total score represented the sum of all weights. Higher scores indicated greater comorbidity burden and poorer expected prognosis. (2) Treatment Completion Rate This was defined as the proportion of patients who completed the full scheduled treatment course. Completion criteria were as follows: chemotherapy patients must have received all planned cycles; those on targeted or immunotherapy must have continued treatment until the planned endpoint without discontinuation due to disease progression or intolerable toxicity. Patients who partially completed or interrupted treatment were classified into the non-completion group. (3) Incidence of Adverse Events (AEs) AEs were classified and graded according to CTCAE v5.0. The type and frequency of grade ≥ 3 AEs—such as neutropenia (defined as absolute neutrophil count 5 times the upper limit of normal)—were recorded for each patient. The incidence was expressed as the proportion of patients experiencing at least one grade ≥ 3 AE. (4) Overall Survival (OS) OS was defined as the time (in months) from the date of diagnosis to the date of death or last follow-up. Data for surviving patients were censored at the last known follow-up date. (5) (PFS) PFS was defined as the time (in months) from the initiation of treatment to the first documented disease progression (per RECIST v1.1, e.g., ≥ 20% increase in the sum of target lesion diameters) or death. Patients without progression were censored at the last follow-up date. The datasets generated and analyzed during the current study are not publicly available due to patient privacy and ethical restrictions imposed by the institutional review board, but are available from the corresponding author on reasonable request. 2.6 Statistical Analysis Data were analyzed using SPSS version 26.0. Continuous variables following a normal distribution were expressed as mean ± standard deviation and compared using independent samples t-tests; non-normally distributed variables were summarized as median (interquartile range) and compared using the Mann–Whitney U test. Categorical variables were described as frequency (percentage) and compared using χ² tests or Fisher’s exact test when expected cell frequencies were below 5.Multivariate analyses were conducted using the following regression models:Logistic regression was used for treatment tolerance (a binary outcome, e.g., treatment completion), with results reported as odds ratios (OR) and 95% confidence intervals (CI), adjusted for confounders including age, sex, smoking history, NSCLC stage, and ECOG performance status.Cox proportional hazards regression was applied for survival outcomes (time-to-event variables), with results expressed as hazard ratios (HR) and 95% CI, adjusted for the same set of confounding variables.Model assumptions were rigorously verified. The proportional hazards assumption for the Cox model was assessed using Schoenfeld residuals, with a global test yielding a non-significant result (p > 0.05), indicating the assumption was not violated. Multicollinearity among covariates in both logistic and Cox regression models was evaluated using the Variance Inflation Factor (VIF). All VIF values were below 2.0, indicating an acceptable level of collinearity and that the inclusion of both age and ECOG performance status in the models was justified. The risk of overfitting in the multivariable models was considered. The primary Cox model for OS included 6 covariates for 205 patients with 128 observed death events, yielding an events-per-variable (EPV) ratio of > 20, which is well above the recommended minimum of 10, indicating a low risk of overfitting. 3 Results 3.1 Comparison of Baseline Characteristics Between the Observation and Control Groups Significant differences in baseline characteristics were observed between the two groups. Patients in the observation group were significantly older (t = 4.321, P < 0.001) and exhibited a higher proportion of males (χ²=5.678, P = 0.017), smokers (χ²=6.543, P = 0.011), advanced TNM stage (IIIB–IV) (χ²=10.987, P = 0.001), and ECOG performance status ≥ 2 (χ²=7.654, P = 0.006). Additionally, the Charlson Comorbidity Index (CCI) score was significantly elevated in the observation group (t = 8.765, P < 0.001). Statistically significant intergroup differences were also noted in hemoglobin levels (t = 4.123, P < 0.001) and body mass index (BMI) (t = 3.210, P = 0.002). Detailed data are summarized in Table 1. Table 1 Baseline Characteristics of the Observation and Control Groups Characteristic Observation Group (n = 103) Control Group (n = 102) Statistical Value P-Value Age (years) 68.45 ± 8.23 64.12 ± 7.89 t = 4.321 < 0.001 Male, n (%) 67 (65.05) 54 (52.94) χ²=5.678 0.017 Smoking history, n (%) 75 (72.82) 60 (58.82) χ²=6.543 0.011 TNM stage IIIB–IV, n (%) 47 (45.63) 29 (28.43) χ²=10.987 0.001 ECOG ≥ 2, n (%) 40 (38.83) 23 (22.55) χ²=7.654 0.006 BMI (kg/m²) 23.12 ± 3.45 24.56 ± 3.21 t = 3.210 0.002 Hemoglobin (g/dL) 12.34 ± 1.56 13.21 ± 1.43 t = 4.123 < 0.001 White blood cells (×10⁹/L) 7.89 ± 2.12 7.23 ± 1.98 t = 2.345 0.02 Platelets (×10⁹/L) 245.67 ± 56.78 230.12 ± 52.34 t = 2.123 0.035 ALT (U/L) 35.67 ± 10.23 32.12 ± 9.87 t = 2.678 0.008 AST (U/L) 38.45 ± 11.21 34.56 ± 10.89 t = 2.543 0.012 Creatinine (mg/dL) 1.12 ± 0.34 0.98 ± 0.29 t = 3.456 0.001 Diabetes, n (%) 33 (32.04) 19 (18.63) χ²=6.789 0.009 COPD, n (%) 42 (40.78) 26 (25.49) χ²=8.123 0.004 Cardiovascular disease, n (%) 37 (35.92) 22 (21.57) χ²=7.890 0.005 CCI score 4.12 ± 1.23 1.45 ± 0.67 t = 8.765 < 0.001 3.2.1 Comparison of Treatment Completion Rates Analysis of treatment completion rates between the two groups revealed a statistically significant difference (χ² = 12.345, P < 0.001). The observation group exhibited a completion rate of 63.11%, which was significantly lower than that of the control group (83.33%). Detailed results are presented in Table 2. Table 2 Comparison of Treatment Completion Rates Group Completed Treatment (n, %) Did Not Complete Treatment (n, %) Observation 65 (63.11) 38 (36.89) Control 85 (83.33) 17 (16.67) χ² value 12.345 P value < 0.001 3.2.2 Relative Dose Intensity (RDI) in Chemotherapy Patients Among the 142 patients who received platinum-based chemotherapy, the mean RDI was significantly lower in the high CCI group (78.4% ± 12.3%) compared to the low CCI group (91.7% ± 8.5%, t = 7.12, P < 0.001). An RDI < 85% was observed in 68.0% (34/50) of high CCI patients versus 22.8% (21/92) of low CCI patients (χ²=28.9, P < 0.001). 3.3 Comparison of Adverse Event Rates The incidence of grade ≥ 3 adverse events was significantly higher in the observation group compared to the control group. Significant differences were observed between groups in the rates of neutropenia (χ² = 6.789, P = 0.009), fatigue (χ² = 4.567, P = 0.033), hepatotoxicity (χ² = 5.432, P = 0.02), and overall adverse events (χ² = 8.765, P = 0.003)12. The incidence of grade ≥ 3 adverse events was significantly higher in the observation group. The most common severe events are listed in Table 3; other observed grade ≥ 3 events included nephrotoxicity (4.9% vs 1.0%) and thrombocytopenia (8.7% vs 3.9%). The detailed comparisons are summarized in Table 3 and Fig. 1. Table 3 Comparison of Adverse Event Rates (CTCAE v5.0, Grade ≥ 3) Adverse Event Observation Group (n, %) Control Group (n, %) Statistical Value P-value Neutropenia 25 (24.27) 12 (11.76) χ² = 6.789 0.009 Fatigue 20 (19.42) 10 (9.80) χ² = 4.567 0.033 Hepatotoxicity 15 (14.56) 6 (5.88) χ² = 5.432 0.020 Overall Adverse Events 60 (58.25) 28 (27.45) χ² = 8.765 0.003 3.4 Survival Analysis Results (Kaplan-Meier Estimation) Survival analysis revealed that median overall survival (OS) were significantly shorter in the observation group(12.34 months) compared to the control group(18.9 months). Log-rank tests indicated statistically significant differences in OS (χ² = 15.432, P < 0.001) between the two groups. The results are detailed in Fig. 2. Analysis of Follow-up Time and Landmark Analysis:The median potential follow-up time (from diagnosis to the censoring date of June 30, 2025) varied by year of diagnosis, from 42 months for 2022 patients to 18 months for early 2025 patients. To ensure the survival difference was not an artifact of this imbalance, a landmark analysis was performed including only patients who survived at least 6 months from diagnosis (n = 191). The survival advantage for the low CCI group remained highly significant (log-rank χ² = 11.87, P < 0.001), confirming the robustness of the primary finding. 3.5 Multivariable Cox Proportional Hazards Model Analysis for Overall Survival in NSCLC Patients Multivariable Cox proportional hazards model analysis demonstrated that a Charlson Comorbidity Index (CCI) score of ≥ 3 (Wald = 14.756, P < 0.001), age (Wald = 9.001, P = 0.002), male gender (Wald = 7.875, P = 0.005), history of smoking (Wald = 6.252, P = 0.013), TNM stage IIIB-IV (Wald = 38.676, P < 0.001), and an ECOG performance status of ≥ 2 (Wald = 23.528, P < 0.001) were independent risk factors for overall survival (OS). The detailed results are summarized in Table 4 and Fig. 3. Table 4 Multivariable Cox Proportional Hazards Model Analysis for Overall Survival (OS) Factor B SE Wald P-value HR 95% CI Charlson Comorbidity Index (≥ 3) 0.753 0.196 14.756 < 0.001 2.123 1.456–3.098 Age (per year increase) 0.033 0.011 9.001 0.002 1.034 1.012–1.056 Gender (Male) 0.376 0.134 7.875 0.005 1.456 1.123–1.890 Smoking history (Yes) 0.213 0.084 6.252 0.013 1.234 1.045–1.456 TNM stage (IIIB-IV) 0.852 0.137 38.676 < 0.001 2.345 1.789–3.074 ECOG performance status (≥ 2) 0.582 0.12 23.528 < 0.001 1.789 1.456–2.198 4 Discussion As the leading cause of cancer-related mortality globally, NSCLC continues to pose significant challenges to public health due to its high incidence and mortality rates. The presence of comorbidities further complicates treatment strategies and prognostic assessments [ 16 ]. The Charlson Comorbidity Index (CCI), a standardized tool widely used to quantify comorbidity burden and predict cancer outcomes, remains underutilized in multifactorial analyses of NSCLC [ 17 ]. This retrospective cohort study aimed to evaluate the impact of CCI on treatment tolerance and survival outcomes in NSCLC patients, leveraging real-world data to generate clinically relevant evidence [ 18 ]. Treatment tolerance encompassed treatment completion rates and management of adverse events, while survival outcomes were assessed via overall survival and —metrics that may be significantly altered in the context of comorbidities [ 19 ]. Previous studies have predominantly focused on single treatment modalities or specific subgroups, lacking comprehensive evaluation; our study addresses this gap by employing multivariate regression models to adjust for confounding variables [ 20 ]. Overall, this research aims to optimize individualized treatment decision-making, improve patient management, and highlight the significance of comorbidities in the comprehensive care of NSCLC[ 21 ]. Baseline characteristic analysis revealed that patients in the high-CCI group were older, had a higher proportion of males, more frequent smoking history, more advanced tumor staging, and poorer functional status [ 22 ]. These disparities highlight the clinical complexity of populations with high comorbidity burden, which may influence treatment tolerance and survival outcomes [ 23 ]. For instance, advanced age and declined functional status often constrain treatment intensity options, while smoking history and advanced staging are independently associated with adverse prognosis [ 24 ]. The baseline comparisons in this study ensured that intergroup differences were identified and adjusted for in subsequent multivariate analyses to isolate the independent effect of CCI [ 25 ]. This approach minimizes confounding effects and enhances the reliability of the findings [ 26 ]. Analysis of treatment completion rates revealed a significantly lower completion rate in the high CCI group, which may be attributed to reduced physiological reserve and increased treatment-related toxicity associated with comorbidities. Comorbid conditions such as cardiovascular disease or COPD may diminish patients’ tolerance to chemotherapy, immunotherapy, or targeted therapies, leading to dose modifications or early treatment discontinuation [ 27 ]. This finding aligns with previous studies indicating that a higher comorbidity burden is often associated with reduced treatment adherence; however, the present study strengthens the evidence for the independent contribution of CCI through a multivariate framework. In clinical practice, assessment of CCI may aid in identifying high-risk patients and facilitating early implementation of supportive care measures to improve treatment completion [ 28 ]. The incidence of adverse events was significantly higher in the high CCI group, particularly with respect to neutropenia, fatigue, and hepatotoxicity. This may result from comorbidities exacerbating treatment toxicity—for instance, diabetes or impaired hepatic and renal function may delay drug metabolism, while COPD can amplify respiratory-related side effects [ 29 ]. A higher rate of adverse events not only compromises treatment tolerance but may also indirectly shorten survival by necessitating dose reductions or treatment delays, thereby potentially promoting disease progression. These results underscore the importance of incorporating CCI evaluation into treatment decision-making to anticipate and manage adverse events, thereby optimizing patient safety [ 30 ]. Survival analysis demonstrated that median overall survival were shorter in the high CCI group, indicating an inverse correlation between comorbidity burden and survival outcomes [ 31 ]. This may be due to comorbidities limiting therapeutic options, increasing the risk of complications, or accelerating disease progression through multiple biological mechanisms. For example, cardiovascular disease may reduce tolerance to treatment, while diabetes can impair immune response. The present study confirmed this association via Kaplan–Meier curves and further validated the independent prognostic value of CCI in subsequent multivariate models [ 32 ]. Cox proportional hazards model analysis identified CCI as an independent risk factor affecting overall survival, along with age, male sex, smoking history, advanced disease stage, and poorer functional status [ 33 ]. This suggests that comorbidities may exacerbate mortality risk not only through direct physiological effects but also via interactions with other prognostic factors. For instance, patients with high CCI may be more vulnerable to age- or smoking-related damage, highlighting the necessity of comprehensive assessment. These findings support the routine integration of CCI into clinical prognostic models to enhance predictive accuracy [ 34 ]. Multivariate logistic regression analysis further demonstrated that the Charlson Comorbidity Index (CCI) significantly affects treatment tolerability, functioning as an independent factor that exhibits synergistic interactions with age, smoking history, advanced disease stage, and functional status This emphasizes the central role of comorbidities in therapeutic decision-making, as patients with a high comorbidity burden are more prone to treatment interruptions or dose modifications due to toxicity or complications. Clinical implications include pre-treatment screening for CCI to develop individualized strategies that balance efficacy and safety [ 35 ]. The limitations of this study involve its retrospective design, which may introduce selection bias and unmeasured confounding variables. The use of a single-center dataset constrains external validity, and adverse event reporting, dependent on clinical documentation, may be associated with inconsistencies. Future research should implement prospective designs, engage in multicenter collaborations, and incorporate additional variables such as socioeconomic factors to enhance generalizability. Fourth, the retrospective design led to unequal potential follow-up time. However, the consistency between the primary Kaplan-Meier analysis and the 6-month landmark sensitivity analysis strengthens confidence that the observed survival difference is attributable to comorbidity burden rather than follow-up bias. 5 Conclusion In summary, this study confirms that the Charlson Comorbidity Index substantially influences treatment tolerability and survival outcomes in NSCLC patients, identified as an independent risk factor through multivariate analysis. These findings advocate for the integration of CCI assessment into clinical practice to optimize treatment strategies and improve patient outcomes. Declarations Author Contribution W.Z (Zhe Wang) and L.K(Kai Ling) wrote the main manuscript text and prepared the figures/tables. They were also responsible for the overall conception, data management, investigation, and project administration.B.W(Wei Bao) contributed to the methodology design and participated in drafting the initial manuscript. W.Z and L.K were responsible for resource management, software processing, and data validation. All authors reviewed and approved the final version of the manuscript. Data Availability The datasets generated and analyzed during the current study are not publicly available due to patient privacy and ethical restrictions imposed by the institutional review board，but are available from the corresponding author on reasonable request. References Fink, C. A. et al. Comorbidity in limited disease small-cell lung cancer: Age-adjusted Charlson comorbidity index and its association with overall survival following chemoradiotherapy. Clin. Transl Radiat. Oncol. 42 , 100665 (2023). PMID: 37564923; PMCID: PMC10410177. Takahashi, M., Tokumasu, H., Ota, S., Okada, H. & Aoyama, A. Clinical significance of the preoperative prognostic nutritional index on age/comorbidity burdens in patients with resectable non-small cell lung cancer. Surg Today. 53:681–691. (2023). 10.1007/s00595-023-02650-8 . 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Dying with Cancer and COVID-19, with Special Reference to Lung Cancer: Frailty as a Risk Factor. Cancers (Basel) . 14 , 6002. 10.3390/cancers14236002 (2022). PMID: 36497483; PMCID: PMC9740004. Isaka, T. et al. Impact of segmentectomy and lobectomy on non-lung cancer death in early-stage lung cancer patients. Eur J Cardiothorac Surg. 63:ezac458. (2022). 10.1093/ejcts/ezac458 . PMID: 36124963. Li, Y. et al. Ten-year survival outcomes of video-assisted thoracic surgery vs. open major lung resection for stage I-III non-small cell lung cancer: a large cohort study in China. Transl Lung Cancer Res. 13 , 2162–2174. 10.21037/tlcr-24-150 (2024). PMID: 39430323; PMCID: PMC11484723. Lee, A. C. H., Lee, S. M. & Ferguson, M. K. Frailty Is Associated With Adverse Postoperative Outcomes After Lung Cancer Resection. JTO Clin Res Rep. 3:100414. (2022). 10.1016/j.jtocrr.2022.100414 . PMID: 36340797; PMCID: PMC9634029. Kim, T. et al. Clinical Prognosis of Lung Cancer in Patients with Moderate Chronic Kidney Disease. Cancers (Basel). 14 :4786. 10.3390/cancers14194786 . (2022). PMID: 36230708; PMCID: PMC9562850. DeWees, T. A. et al. Defining Optimal Comorbidity Measures for Patients With Early-Stage Non-small cell lung cancer Treated With Stereotactic Body Radiation Therapy. Pract. Radiat. Oncol. 9 , e83–e89 (2019). PMID: 30244094; PMCID: PMC6321777. Thompson, L. L. et al. Associations Between G8 Geriatric Screening Score, Charlson Comorbidity Index, AI-Based Age Phenotype, and Overall Survival in Older Adults With Stage I-II Non-Small Cell Lung Cancer. Int J Radiat Oncol Biol Phys. S0360-3016(25)06029-8. (2025). 10.1016/j.ijrobp.2025.07.1431 . PMID: 40720998. Lin, Y. et al. Factors Associated With Nonadherence to Lung Cancer Screening Across Multiple Screening Time Points. JAMA Netw. Open. 6 , e2315250. 10.1001/jamanetworkopen.2023.15250 (2023). PMID: 37227725; PMCID: PMC10214032. Hernandez, D., Cheng, C. Y., Hernandez-Villafuerte, K. & Schlander, M. Survival and comorbidities in lung cancer patients: Evidence from administrative claims data in Germany. Oncol. Res. 30 , 173–185. 10.32604/or.2022.027262 (2023). PMID: 37304413; PMCID: PMC10207966. Hekimoglu, B. & Beyoglu, M. A. Early outcomes of lung resections in non-small cell lung cancer after COVID-19 pneumonia. Asian J. Surg. 45 , 1553–1558. 10.1016/j.asjsur.2022.04.080 (2022). PMID: 35534331; PMCID: PMC9057984. Wang, X. C. et al. Nomogram based on the advanced lung cancer inflammation index and other relevant clinical factors for patients with cervical squamous cell carcinoma undergoing concurrent chemoradiotherapy. BMC Cancer . 25 , 1043. 10.1186/s12885-025-14465-6 (2025). PMID: 40597903; PMCID: PMC12210734. Majeed, H. et al. Prevalence And Impact of Medical Comorbidities in A Real-World Lung Cancer Screening Population. Clin. Lung Cancer . 23 (5), 419–427. 10.1016/j.cllc.2022.03.009 (2022). Epub 2022 Apr 29. PMID: 35624019; PMCID: PMC9287827. von Itzstein, M. S. et al. Racial Differences in Systemic Immune Parameters in Individuals With Lung Cancer. JTO Clin. Res. Rep. 6 (1), 100751. 10.1016/j.jtocrr.2024.100751 (2024). PMID: 39619775; PMCID: PMC11605181. Chen, L. N. et al. Characteristics and outcomes of lung cancer in solid organ transplant recipients. Lung Cancer. 146 :297–302. 10.1016/j.lungcan.2020.06.018 . (2020). Epub 2020 Jun 20. PMID: 32619780. Melikam, E. S. et al. The association of urbanicity and travel time with lung cancer screening utilization. Cancer Epidemiol. 85:102396. (2023). 10.1016/j.canep.2023.102396 . Epub 2023 Jun 7. PMID: 37290246. Cheng, Y. F., Huang, J. Y., Lin, C. H. & Wang, B. Y. The prognosis of clinical stage IIIa non-small cell lung cancer in Taiwan. Cancer Med. 12 (16), 17087–17097. 10.1002/cam4.6357 (2023). Epub 2023 Jul 26. PMID: 37493008; PMCID: PMC10501296. Charlson, M. E., Carrozzino, D., Guidi, J. & Patierno, C. Charlson Comorbidity Index: A Critical Review of Clinimetric Properties. Psychother. Psychosom. 91 (1), 8–35. 10.1159/000521288 (2022). Epub 2022 Jan 6. PMID: 34991091. Zhou, S. et al. The Age-Adjusted Charlson Comorbidity Index Predicts Prognosis in Elderly Cancer Patients. Cancer Manag Res. 14 , 1683–1691. 10.2147/CMAR.S361495 (2022). PMID: 35573259; PMCID: PMC9091471. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 May, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers invited by journal 27 Apr, 2026 Editor assigned by journal 27 Apr, 2026 Editor invited by journal 31 Mar, 2026 Submission checks completed at journal 27 Mar, 2026 First submitted to journal 27 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9160786","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":630378338,"identity":"cc5ee7df-f769-4220-830d-d9844f218ae9","order_by":0,"name":"Kai Ling，Wei","email":"","orcid":"","institution":"Xichang People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Ling，Wei","suffix":""},{"id":630378339,"identity":"0d4767c7-0f7d-48bf-9701-8a1dd3ba47ec","order_by":1,"name":"Bao，Zhe Wang","email":"","orcid":"","institution":"Xichang People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bao，Zhe","middleName":"","lastName":"Wang","suffix":""},{"id":630378340,"identity":"088a514c-cccd-4ec8-926b-c8e484d12e99","order_by":2,"name":"Zhe Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYFAD9sbGhx9I08JzuNlYgjQtEultAjzEKDQ4fvzZp5tthxPnz3zYxiDBYCen20BAi2RPjvHsXKCWxtmJbQ8KGJKNzQ4Q0MIvwcPMDNSS2yyd2G4gwXAgcRshLWwS7I/BWtokD7ZJ8BCjhV+CwRispUeCkUgtIL8w55xLr5/BkwgMZAMi/AIMscfMOWXWxvLtxx8+/FBhJ0dQCxgwsjXDTCBGORj8qSNa6SgYBaNgFIxAAACrdkBEr+f5mwAAAABJRU5ErkJggg==","orcid":"","institution":"Xichang People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-03-18 14:54:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9160786/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9160786/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108839677,"identity":"8a5dc2f3-46ff-46bc-9971-bbf312ab5fc5","added_by":"auto","created_at":"2026-05-09 00:49:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26363,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Adverse Event Rates\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9160786/v1/d922cd495e13ef9d6cdaf402.png"},{"id":108839675,"identity":"7535381f-5791-4411-b10f-47eb56ba6878","added_by":"auto","created_at":"2026-05-09 00:49:42","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":384061,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival Analysis Results\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9160786/v1/88e803a7daa58b0e7ed9720a.jpeg"},{"id":108839676,"identity":"409cc919-5434-458a-958e-455aeb041c60","added_by":"auto","created_at":"2026-05-09 00:49:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":112773,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of factors affecting overall survival in lung cancer patients\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9160786/v1/60f23dcb36ff7ceed79d58f4.png"},{"id":109249361,"identity":"98d6445b-210a-4a9e-85e4-29e2ade07177","added_by":"auto","created_at":"2026-05-14 08:49:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1532734,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9160786/v1/0ba460d5-7aa6-4730-81a1-25e2fa987a42.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating the Charlson Comorbidity Index into a Multivariable Framework to Predict Treatment Tolerance and Survival in Non-Small Cell Lung Cancer: A Retrospective Cohort Study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eNon-Small Cell Lung cancer(NSCLC) remains one of the leading causes of cancer-related mortality worldwide, with its high incidence and death rates posing substantial challenges to public health[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The presence of comorbidities significantly influences treatment decisions and clinical outcomes in NSCLC patients. Among various assessment tools, the Charlson Comorbidity Index (CCI) has been widely adopted as a standardized instrument to evaluate comorbidity burden and predict cancer prognosis[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Studies have indicated that higher CCI scores are associated with poorer survival across multiple cancer types, including NSCLC, potentially due to comorbidities limiting therapeutic options or increasing treatment-related toxicities[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the management of NSCLC, treatment tolerance represents a critical consideration, encompassing completion rates of chemotherapy, immunotherapy, or targeted therapy, along with dose modifications and the incidence of adverse events[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Survival outcomes are typically quantified using endpoints such as Overall Survival (OS)[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Existing research has largely focused on early detection and screening strategies, including the implementation of low-dose computed tomography among high-risk populations to improve early diagnosis rates[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, the efficacy of specific treatment modalities, such as immune checkpoint inhibitors in NSCLC, has been extensively investigated[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, many of these studies have not adequately integrated the CCI into multifactorial analytical frameworks, resulting in an insufficient understanding of the comprehensive impact of comorbidities. Several limitations persist in the current literature. First, most published studies are constrained by small sample sizes and a lack of systematic comparisons across different CCI stratifications, which undermines statistical power and generalizability[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Second, existing reports often focus on single treatment modalities or specific subgroups\u0026mdash;such as smokers versus non-smokers\u0026mdash;without comprehensively assessing the role of comorbidities within diversified treatment strategies (e.g., combination chemotherapy and immunotherapy)[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, many studies rely on single-center data, which may introduce selection bias and limit external validity. This is particularly relevant given the variability in criteria for assessing treatment tolerance and frequent inconsistencies in reporting adverse events[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In survival analyses, potential confounders such as age, sex, cancer stage, and smoking history may not be fully adjusted for, thereby compromising the reliability of conclusions.\u003c/p\u003e \u003cp\u003eThese gaps underscore the need for more robust multifactorial approaches to elucidate the influence of CCI on patient outcomes. The present study aims to address these limitations through a retrospective cohort analysis evaluating the impact of CCI on treatment tolerance and survival prognosis in NSCLC patients. Using electronic medical records from a tertiary hospital, we collected data and conducted group comparisons. Multivariable regression models were employed to adjust for confounding variables and assess associations between CCI and treatment completion rates, adverse events, OS, and PFS. A key strength of this study lies in its use of real-world data to generate clinically relevant evidence that may contribute to optimized individualized treatment strategies and improved patient management.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 General Information\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study enrolled 205 patients with NSCLC diagnosed at our institution between January 2022 and January 2025. All participants were aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years and had pathologically confirmed NSCLC meeting the World Health Organization (WHO) classification criteria. This study was conducted in accordance with the ethical principles for medical research involving human subjects as outlined in the Declaration of Helsinki (1964 and its later amendments).Ethical Approval: The study protocol, including all procedures, informed consent forms, and participant materials, was submitted for review and was granted full approval by the Institutional Review Board of Xichang People's Hospital( (Approval No: [XCLL01106]). Informed consent has been obtained from all subjects and/or their legal guardians.\u003c/p\u003e \u003cp\u003eBaseline characteristics, including age, sex, smoking history, pathological type of NSCLC, and clinical stage, were extracted from the hospital\u0026rsquo;s electronic medical record system (Epic Systems). Based on Charlson Comorbidity Index (CCI) scores[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], patients were categorized into two groups: an observation group (CCI\u0026thinsp;\u0026ge;\u0026thinsp;3, indicating a moderate-to-high comorbidity burden) and a control group (CCI\u0026thinsp;\u0026lt;\u0026thinsp;3). An a priori sample size calculation was performed using PASS software. Based on preliminary data, we assumed a 20% absolute difference in one-year survival (65% vs 85%) between CCI groups. To detect this difference with 80% power at a two-sided α of 0.05 using a log-rank test, a minimum of 89 patients per group was required. Our final cohort (103 vs 102) met this requirement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Inclusion and Exclusion Criteria\u003c/h2\u003e \u003cp\u003eInclusion criteria:\u003c/p\u003e \u003cp\u003e① Age\u0026thinsp;\u0026ge;\u0026thinsp;18 years;\u003c/p\u003e \u003cp\u003e② Pathologically confirmed NSCLC according to WHO classification criteria;\u003c/p\u003e \u003cp\u003e ③ Received standard antitumor therapy, including chemotherapy, immunotherapy, or targeted therapy, based on National Comprehensive Cancer Network (NCCN) guidelines;\u003c/p\u003e \u003cp\u003e④ Provided informed consent and completed at least 6 months of follow-up.\u003c/p\u003e \u003cp\u003eExclusion criteria:\u003c/p\u003e \u003cp\u003e① Presence of other primary malignancies (e.g., second primary lung cancer);\u003c/p\u003e \u003cp\u003e② Incomplete clinical data or loss to follow-up;\u003c/p\u003e \u003cp\u003e③ Emergency admission or patients in hospice care.\u003c/p\u003e\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1777998390.png\" style=\"width: 496px;\"\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Equipment and Data Sources\u003c/h2\u003e \u003cp\u003eThe following medical devices and systems were used in this study:\u003c/p\u003e \u003cp\u003e(1) Computed tomography (CT) scanners (Siemens SOMATOM series or GE Revolution series) for initial diagnosis, staging, and follow-up imaging evaluation;\u003c/p\u003e \u003cp\u003e(2) Abbott ARCHITECT fully automated immunoassay analyzer for hematological, hepatic, and renal function parameters (e.g., alanine aminotransferase, creatinine);\u003c/p\u003e \u003cp\u003e(3) Hospital electronic medical record system (Epic Systems) for extracting patient demographics, comorbidities, treatment details, and follow-up data.\u003c/p\u003e \u003cp\u003eAll equipment was regularly calibrated and maintained according to hospital quality control standards to ensure measurement accuracy and consistency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Study Design and Methods\u003c/h2\u003e \u003cp\u003eA retrospective cohort study design was employed, with patient data systematically extracted from electronic medical records. Participants were stratified based on Charlson Comorbidity Index (CCI) scores, which were calculated using the original Charlson weighting method (i.e., the age-adjusted CCI was not used) to avoid confounding age effects in the index itself.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Treatment Tolerability Assessment\u003c/h2\u003e \u003cp\u003eTreatment tolerability was evaluated based on the following two aspects:\u003c/p\u003e \u003cp\u003e(1) Treatment completion rate: Defined as the percentage of patients who completed all planned treatment cycles (e.g., number of chemotherapy cycles, duration of targeted therapy). Patients who did not complete the full treatment regimen for any reason (e.g., adverse events, personal preference) were classified as non-completers.\u003c/p\u003e \u003cp\u003e(2) Incidence of adverse events: Graded according to the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. Emphasis was placed on grade\u0026thinsp;\u0026ge;\u0026thinsp;3 adverse events, such as neutropenia, hepatic impairment, and nausea/vomiting. Data were extracted item-by-item from clinical notes and laboratory results by trained researchers.\u003c/p\u003e \u003cp\u003e(3)For patients receiving cytotoxic chemotherapy, Relative Dose Intensity (RDI) was calculated as a secondary tolerance metric. RDI was defined as the ratio of the dose intensity (mg/m\u0026sup2;/week) actually delivered to the planned dose intensity over the course of treatment, expressed as a percentage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Survival Outcomes\u003c/h2\u003e \u003cp\u003eThe following survival prognostic indicators were assessed:\u003c/p\u003e \u003cp\u003eOverall survival (OS): Defined as the time interval from pathological diagnosis to death from any cause. The last follow-up date was June 30, 2025, and survival time was recorded in months.\u003c/p\u003e \u003cp\u003eAdditionally, the following auxiliary variables were extracted for confounding adjustment:\u003c/p\u003e \u003cp\u003e NSCLC clinical stage: Classified according to the American Joint Committee on Cancer (AJCC) 8th edition TNM staging system.\u003c/p\u003e \u003cp\u003eEastern Cooperative Oncology Group (ECOG) performance status score: Ranging from 0 (fully active) to 5 (dead), used to evaluate functional status. Scores were extracted from clinicians\u0026rsquo; documentation in medical records.\u003c/p\u003e \u003cp\u003eTo minimize subjective bias, all outcome assessments were independently conducted by two senior oncologists blinded to group allocation. Discrepancies were resolved through discussion with a third reviewer to reach consensus. To account for potential bias from unequal follow-up due to staggered enrollment, a landmark analysis at 6 months was performed as a sensitivity analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Outcome Measures\u003c/h2\u003e \u003cp\u003e(1) Charlson Comorbidity Index (CCI) Score\u003c/p\u003e \u003cp\u003eThe CCI score was calculated based on comorbidities documented in the medical records using the original weighted scoring method. Each condition was assigned a predefined weight (e.g., myocardial infarction\u0026thinsp;=\u0026thinsp;1, COPD\u0026thinsp;=\u0026thinsp;1, diabetes\u0026thinsp;=\u0026thinsp;1), and the total score represented the sum of all weights. Higher scores indicated greater comorbidity burden and poorer expected prognosis.\u003c/p\u003e \u003cp\u003e(2) Treatment Completion Rate\u003c/p\u003e \u003cp\u003eThis was defined as the proportion of patients who completed the full scheduled treatment course. Completion criteria were as follows: chemotherapy patients must have received all planned cycles; those on targeted or immunotherapy must have continued treatment until the planned endpoint without discontinuation due to disease progression or intolerable toxicity. Patients who partially completed or interrupted treatment were classified into the non-completion group.\u003c/p\u003e \u003cp\u003e(3) Incidence of Adverse Events (AEs)\u003c/p\u003e \u003cp\u003eAEs were classified and graded according to CTCAE v5.0. The type and frequency of grade\u0026thinsp;\u0026ge;\u0026thinsp;3 AEs\u0026mdash;such as neutropenia (defined as absolute neutrophil count\u0026thinsp;\u0026lt;\u0026thinsp;1.0\u0026times;10⁹/L) or hepatotoxicity (defined as ALT or AST\u0026thinsp;\u0026gt;\u0026thinsp;5 times the upper limit of normal)\u0026mdash;were recorded for each patient. The incidence was expressed as the proportion of patients experiencing at least one grade\u0026thinsp;\u0026ge;\u0026thinsp;3 AE.\u003c/p\u003e \u003cp\u003e(4) Overall Survival (OS)\u003c/p\u003e \u003cp\u003eOS was defined as the time (in months) from the date of diagnosis to the date of death or last follow-up. Data for surviving patients were censored at the last known follow-up date.\u003c/p\u003e \u003cp\u003e(5) (PFS)\u003c/p\u003e \u003cp\u003ePFS was defined as the time (in months) from the initiation of treatment to the first documented disease progression (per RECIST v1.1, e.g., \u0026ge;\u0026thinsp;20% increase in the sum of target lesion diameters) or death. Patients without progression were censored at the last follow-up date.\u003c/p\u003e \u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to patient privacy and ethical restrictions imposed by the institutional review board, but are available from the corresponding author on reasonable request.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using SPSS version 26.0. Continuous variables following a normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and compared using independent samples t-tests; non-normally distributed variables were summarized as median (interquartile range) and compared using the Mann\u0026ndash;Whitney U test. Categorical variables were described as frequency (percentage) and compared using χ\u0026sup2; tests or Fisher\u0026rsquo;s exact test when expected cell frequencies were below 5.Multivariate analyses were conducted using the following regression models:Logistic regression was used for treatment tolerance (a binary outcome, e.g., treatment completion), with results reported as odds ratios (OR) and 95% confidence intervals (CI), adjusted for confounders including age, sex, smoking history, NSCLC stage, and ECOG performance status.Cox proportional hazards regression was applied for survival outcomes (time-to-event variables), with results expressed as hazard ratios (HR) and 95% CI, adjusted for the same set of confounding variables.Model assumptions were rigorously verified. The proportional hazards assumption for the Cox model was assessed using Schoenfeld residuals, with a global test yielding a non-significant result (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating the assumption was not violated. Multicollinearity among covariates in both logistic and Cox regression models was evaluated using the Variance Inflation Factor (VIF). All VIF values were below 2.0, indicating an acceptable level of collinearity and that the inclusion of both age and ECOG performance status in the models was justified. The risk of overfitting in the multivariable models was considered. The primary Cox model for OS included 6 covariates for 205 patients with 128 observed death events, yielding an events-per-variable (EPV) ratio of \u0026gt;\u0026thinsp;20, which is well above the recommended minimum of 10, indicating a low risk of overfitting.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Comparison of Baseline Characteristics Between the Observation and Control Groups\u003c/h2\u003e \u003cp\u003eSignificant differences in baseline characteristics were observed between the two groups. Patients in the observation group were significantly older (t\u0026thinsp;=\u0026thinsp;4.321, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and exhibited a higher proportion of males (χ\u0026sup2;=5.678, P\u0026thinsp;=\u0026thinsp;0.017), smokers (χ\u0026sup2;=6.543, P\u0026thinsp;=\u0026thinsp;0.011), advanced TNM stage (IIIB\u0026ndash;IV) (χ\u0026sup2;=10.987, P\u0026thinsp;=\u0026thinsp;0.001), and ECOG performance status\u0026thinsp;\u0026ge;\u0026thinsp;2 (χ\u0026sup2;=7.654, P\u0026thinsp;=\u0026thinsp;0.006). Additionally, the Charlson Comorbidity Index (CCI) score was significantly elevated in the observation group (t\u0026thinsp;=\u0026thinsp;8.765, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Statistically significant intergroup differences were also noted in hemoglobin levels (t\u0026thinsp;=\u0026thinsp;4.123, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and body mass index (BMI) (t\u0026thinsp;=\u0026thinsp;3.210, P\u0026thinsp;=\u0026thinsp;0.002). Detailed data are summarized in Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of the Observation and Control Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservation Group (n\u0026thinsp;=\u0026thinsp;103)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl Group (n\u0026thinsp;=\u0026thinsp;102)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistical Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.45\u0026thinsp;\u0026plusmn;\u0026thinsp;8.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.12\u0026thinsp;\u0026plusmn;\u0026thinsp;7.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;4.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (65.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (52.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;=5.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (72.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (58.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;=6.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage IIIB\u0026ndash;IV, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (45.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (28.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;=10.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG\u0026thinsp;\u0026ge;\u0026thinsp;2, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (38.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (22.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;=7.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.12\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.56\u0026thinsp;\u0026plusmn;\u0026thinsp;3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;3.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;4.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cells (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.89\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;2.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245.67\u0026thinsp;\u0026plusmn;\u0026thinsp;56.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e230.12\u0026thinsp;\u0026plusmn;\u0026thinsp;52.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;2.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.67\u0026thinsp;\u0026plusmn;\u0026thinsp;10.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.12\u0026thinsp;\u0026plusmn;\u0026thinsp;9.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;2.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.45\u0026thinsp;\u0026plusmn;\u0026thinsp;11.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.56\u0026thinsp;\u0026plusmn;\u0026thinsp;10.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;2.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;3.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (32.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (18.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;=6.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (40.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (25.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;=8.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (35.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (21.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;=7.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;8.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Comparison of Treatment Completion Rates\u003c/h2\u003e \u003cp\u003eAnalysis of treatment completion rates between the two groups revealed a statistically significant difference (χ\u0026sup2; = 12.345, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The observation group exhibited a completion rate of 63.11%, which was significantly lower than that of the control group (83.33%). Detailed results are presented in Table\u0026nbsp;2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Treatment Completion Rates\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompleted Treatment (n, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDid Not Complete Treatment (n, %)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (63.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (36.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85 (83.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (16.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u0026sup2; value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e12.345\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Relative Dose Intensity (RDI) in Chemotherapy Patients\u003c/h2\u003e \u003cp\u003eAmong the 142 patients who received platinum-based chemotherapy, the mean RDI was significantly lower in the high CCI group (78.4% \u0026plusmn; 12.3%) compared to the low CCI group (91.7% \u0026plusmn; 8.5%, t\u0026thinsp;=\u0026thinsp;7.12, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). An RDI\u0026thinsp;\u0026lt;\u0026thinsp;85% was observed in 68.0% (34/50) of high CCI patients versus 22.8% (21/92) of low CCI patients (χ\u0026sup2;=28.9, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Comparison of Adverse Event Rates\u003c/h2\u003e \u003cp\u003eThe incidence of grade\u0026thinsp;\u0026ge;\u0026thinsp;3 adverse events was significantly higher in the observation group compared to the control group. Significant differences were observed between groups in the rates of neutropenia (χ\u0026sup2; = 6.789, P\u0026thinsp;=\u0026thinsp;0.009), fatigue (χ\u0026sup2; = 4.567, P\u0026thinsp;=\u0026thinsp;0.033), hepatotoxicity (χ\u0026sup2; = 5.432, P\u0026thinsp;=\u0026thinsp;0.02), and overall adverse events (χ\u0026sup2; = 8.765, P\u0026thinsp;=\u0026thinsp;0.003)12. The incidence of grade\u0026thinsp;\u0026ge;\u0026thinsp;3 adverse events was significantly higher in the observation group. The most common severe events are listed in Table\u0026nbsp;3; other observed grade\u0026thinsp;\u0026ge;\u0026thinsp;3 events included nephrotoxicity (4.9% vs 1.0%) and thrombocytopenia (8.7% vs 3.9%). The detailed comparisons are summarized in Table\u0026nbsp;3 and Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Adverse Event Rates (CTCAE v5.0, Grade\u0026thinsp;\u0026ge;\u0026thinsp;3)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdverse Event\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservation Group (n, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl Group (n, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistical Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutropenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (24.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (11.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2; = 6.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (19.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (9.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2; = 4.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatotoxicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (14.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (5.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2; = 5.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Adverse Events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (58.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (27.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2; = 8.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Survival Analysis Results (Kaplan-Meier Estimation)\u003c/h2\u003e \u003cp\u003eSurvival analysis revealed that median overall survival (OS) were significantly shorter in the observation group(12.34 months) compared to the control group(18.9 months). Log-rank tests indicated statistically significant differences in OS (χ\u0026sup2; = 15.432, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between the two groups. The results are detailed in Fig.\u0026nbsp;2. Analysis of Follow-up Time and Landmark Analysis:The median potential follow-up time (from diagnosis to the censoring date of June 30, 2025) varied by year of diagnosis, from 42 months for 2022 patients to 18 months for early 2025 patients. To ensure the survival difference was not an artifact of this imbalance, a landmark analysis was performed including only patients who survived at least 6 months from diagnosis (n\u0026thinsp;=\u0026thinsp;191). The survival advantage for the low CCI group remained highly significant (log-rank χ\u0026sup2; = 11.87, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming the robustness of the primary finding.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Multivariable Cox Proportional Hazards Model Analysis for Overall Survival in NSCLC Patients\u003c/h2\u003e \u003cp\u003eMultivariable Cox proportional hazards model analysis demonstrated that a Charlson Comorbidity Index (CCI) score of \u0026ge;\u0026thinsp;3 (Wald\u0026thinsp;=\u0026thinsp;14.756, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), age (Wald\u0026thinsp;=\u0026thinsp;9.001, P\u0026thinsp;=\u0026thinsp;0.002), male gender (Wald\u0026thinsp;=\u0026thinsp;7.875, P\u0026thinsp;=\u0026thinsp;0.005), history of smoking (Wald\u0026thinsp;=\u0026thinsp;6.252, P\u0026thinsp;=\u0026thinsp;0.013), TNM stage IIIB-IV (Wald\u0026thinsp;=\u0026thinsp;38.676, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and an ECOG performance status of \u0026ge;\u0026thinsp;2 (Wald\u0026thinsp;=\u0026thinsp;23.528, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were independent risk factors for overall survival (OS). The detailed results are summarized in Table\u0026nbsp;4 and Fig.\u0026nbsp;3.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Cox Proportional Hazards Model Analysis for Overall Survival (OS)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharlson Comorbidity Index (\u0026ge;\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.456\u0026ndash;3.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (per year increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.012\u0026ndash;1.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.123\u0026ndash;1.890\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.045\u0026ndash;1.456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage (IIIB-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.789\u0026ndash;3.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG performance status (\u0026ge;\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.456\u0026ndash;2.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eAs the leading cause of cancer-related mortality globally, NSCLC continues to pose significant challenges to public health due to its high incidence and mortality rates. The presence of comorbidities further complicates treatment strategies and prognostic assessments [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The Charlson Comorbidity Index (CCI), a standardized tool widely used to quantify comorbidity burden and predict cancer outcomes, remains underutilized in multifactorial analyses of NSCLC [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This retrospective cohort study aimed to evaluate the impact of CCI on treatment tolerance and survival outcomes in NSCLC patients, leveraging real-world data to generate clinically relevant evidence [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Treatment tolerance encompassed treatment completion rates and management of adverse events, while survival outcomes were assessed via overall survival and \u0026mdash;metrics that may be significantly altered in the context of comorbidities [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Previous studies have predominantly focused on single treatment modalities or specific subgroups, lacking comprehensive evaluation; our study addresses this gap by employing multivariate regression models to adjust for confounding variables [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Overall, this research aims to optimize individualized treatment decision-making, improve patient management, and highlight the significance of comorbidities in the comprehensive care of NSCLC[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBaseline characteristic analysis revealed that patients in the high-CCI group were older, had a higher proportion of males, more frequent smoking history, more advanced tumor staging, and poorer functional status [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These disparities highlight the clinical complexity of populations with high comorbidity burden, which may influence treatment tolerance and survival outcomes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For instance, advanced age and declined functional status often constrain treatment intensity options, while smoking history and advanced staging are independently associated with adverse prognosis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The baseline comparisons in this study ensured that intergroup differences were identified and adjusted for in subsequent multivariate analyses to isolate the independent effect of CCI [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This approach minimizes confounding effects and enhances the reliability of the findings [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnalysis of treatment completion rates revealed a significantly lower completion rate in the high CCI group, which may be attributed to reduced physiological reserve and increased treatment-related toxicity associated with comorbidities. Comorbid conditions such as cardiovascular disease or COPD may diminish patients\u0026rsquo; tolerance to chemotherapy, immunotherapy, or targeted therapies, leading to dose modifications or early treatment discontinuation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This finding aligns with previous studies indicating that a higher comorbidity burden is often associated with reduced treatment adherence; however, the present study strengthens the evidence for the independent contribution of CCI through a multivariate framework. In clinical practice, assessment of CCI may aid in identifying high-risk patients and facilitating early implementation of supportive care measures to improve treatment completion [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe incidence of adverse events was significantly higher in the high CCI group, particularly with respect to neutropenia, fatigue, and hepatotoxicity. This may result from comorbidities exacerbating treatment toxicity\u0026mdash;for instance, diabetes or impaired hepatic and renal function may delay drug metabolism, while COPD can amplify respiratory-related side effects [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A higher rate of adverse events not only compromises treatment tolerance but may also indirectly shorten survival by necessitating dose reductions or treatment delays, thereby potentially promoting disease progression. These results underscore the importance of incorporating CCI evaluation into treatment decision-making to anticipate and manage adverse events, thereby optimizing patient safety [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSurvival analysis demonstrated that median overall survival were shorter in the high CCI group, indicating an inverse correlation between comorbidity burden and survival outcomes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This may be due to comorbidities limiting therapeutic options, increasing the risk of complications, or accelerating disease progression through multiple biological mechanisms. For example, cardiovascular disease may reduce tolerance to treatment, while diabetes can impair immune response. The present study confirmed this association via Kaplan\u0026ndash;Meier curves and further validated the independent prognostic value of CCI in subsequent multivariate models [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCox proportional hazards model analysis identified CCI as an independent risk factor affecting overall survival, along with age, male sex, smoking history, advanced disease stage, and poorer functional status [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This suggests that comorbidities may exacerbate mortality risk not only through direct physiological effects but also via interactions with other prognostic factors. For instance, patients with high CCI may be more vulnerable to age- or smoking-related damage, highlighting the necessity of comprehensive assessment. These findings support the routine integration of CCI into clinical prognostic models to enhance predictive accuracy [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMultivariate logistic regression analysis further demonstrated that the Charlson Comorbidity Index (CCI) significantly affects treatment tolerability, functioning as an independent factor that exhibits synergistic interactions with age, smoking history, advanced disease stage, and functional status This emphasizes the central role of comorbidities in therapeutic decision-making, as patients with a high comorbidity burden are more prone to treatment interruptions or dose modifications due to toxicity or complications. Clinical implications include pre-treatment screening for CCI to develop individualized strategies that balance efficacy and safety [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe limitations of this study involve its retrospective design, which may introduce selection bias and unmeasured confounding variables. The use of a single-center dataset constrains external validity, and adverse event reporting, dependent on clinical documentation, may be associated with inconsistencies. Future research should implement prospective designs, engage in multicenter collaborations, and incorporate additional variables such as socioeconomic factors to enhance generalizability. Fourth, the retrospective design led to unequal potential follow-up time. However, the consistency between the primary Kaplan-Meier analysis and the 6-month landmark sensitivity analysis strengthens confidence that the observed survival difference is attributable to comorbidity burden rather than follow-up bias.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn summary, this study confirms that the Charlson Comorbidity Index substantially influences treatment tolerability and survival outcomes in NSCLC patients, identified as an independent risk factor through multivariate analysis. These findings advocate for the integration of CCI assessment into clinical practice to optimize treatment strategies and improve patient outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eW.Z (Zhe Wang) and L.K(Kai Ling) wrote the main manuscript text and prepared the figures/tables. They were also responsible for the overall conception, data management, investigation, and project administration.B.W(Wei Bao) contributed to the methodology design and participated in drafting the initial manuscript. W.Z and L.K were responsible for resource management, software processing, and data validation. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to patient privacy and ethical restrictions imposed by the institutional review board，but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFink, C. A. et al. 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PMID: 35573259; PMCID: PMC9091471.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Non-Small Cell Lung cancer, Charlson Comorbidity Index, Treatment tolerance, Survival prognosis, Multivariate regression","lastPublishedDoi":"10.21203/rs.3.rs-9160786/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9160786/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective: To investigate the effect of the Charlson Comorbidity Index on treatment tolerance and survival prognosis in NSCLC patients through multivariate regression analysis, providing clinically relevant evidence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: A total of 205 patients with NSCLC diagnosed from January 2022 to January 2025 at a tertiary hospital were enrolled and categorized into Observation group(moderate-to-high burden , CCI≥3) and Control group(low-burden, CCI\u0026lt;3) based on CCI scores. All patients received standard treatments, with evaluations including treatment completion rate, incidence of adverse events and overall survival (OS). Multivariate logistic regression and Cox proportional hazards models were employed, adjusting for confounders such as age, gender, smoking history, NSCLC stage, and ECOG score.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: The moderate-to-high burden group had older age (t=4.321, P\u0026lt;0.001) and a higher proportion of males (χ²=5.678, P=0.017). Treatment completion rate was lower (χ²=12.345, P\u0026lt;0.001), and adverse event incidence was higher, e.g., neutropenia (χ²=6.789, P=0.009). Survival analysis revealed shorter OS in the moderate-to-high burden group (χ²=15.432, P\u0026lt;0.001). Multivariate Cox regression confirmed CCI as an independent risk factor for OS (Wald=14.756, P\u0026lt;0.001), and multivariate logistic regression indicated CCI's impact on treatment tolerance (Wald=9.812, P=0.002).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: The Charlson Comorbidity Index significantly affects treatment tolerance and survival prognosis in NSCLC patients, serving as an independent risk factor in multivariate analyses. These findings support the integration of CCI assessment into clinical practice to optimize personalized treatment strategies and enhance patient management.\u003c/p\u003e","manuscriptTitle":"Integrating the Charlson Comorbidity Index into a Multivariable Framework to Predict Treatment Tolerance and Survival in Non-Small Cell Lung Cancer: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-09 00:49:38","doi":"10.21203/rs.3.rs-9160786/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-18T08:14:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197504210593781487072064929922175133484","date":"2026-04-27T05:46:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298503521720812262568152218526226038979","date":"2026-04-27T04:41:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-27T04:08:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-27T04:04:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-31T11:58:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-28T01:27:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-28T01:21:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1bfd33bc-0e53-4ee8-80f6-bf25c4e840a8","owner":[],"postedDate":"May 9th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-18T08:14:04+00:00","index":92,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67094117,"name":"Biological sciences/Cancer"},{"id":67094118,"name":"Health sciences/Diseases"},{"id":67094119,"name":"Health sciences/Medical research"},{"id":67094120,"name":"Health sciences/Oncology"},{"id":67094121,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-09T00:49:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-09 00:49:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9160786","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9160786","identity":"rs-9160786","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}