Chronological Age Is Not an Independent Determinant of Survival or Treatment Access in Metastatic Non–Small Cell Lung Cancer: A Real-World Cohort Study

Chronological Age Is Not an Independent Determinant of Survival or Treatment Access in Metastatic Non–Small Cell Lung Cancer: A Real-World 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 Research Article Chronological Age Is Not an Independent Determinant of Survival or Treatment Access in Metastatic Non–Small Cell Lung Cancer: A Real-World Cohort Study Güzide Kofalı Ayakdaş, Kübra Canaslan, İlhan Öztop, Elif Atağ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8751117/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Older adults constitute a substantial proportion of patients with metastatic non–small cell lung cancer (NSCLC); however, the independent impact of chronological age on survival, treatment allocation, and toxicity remains controversial. This study aimed to evaluate whether age ≥ 70 years independently influences outcomes in patients with metastatic NSCLC in a real-world setting. Methods We conducted a single-center, retrospective cohort study including 268 patients with metastatic NSCLC diagnosed between January 2018 and December 2022. Patients were stratified into two age groups (< 70 vs ≥ 70 years). Clinical characteristics, treatment patterns, and outcomes were analyzed. Overall survival (OS) was assessed using Kaplan–Meier methods and Cox proportional hazards models. Logistic regression was used to identify determinants of treatment omission and toxicity-related hospitalization. Results The median OS for the entire cohort was 8.8 months. Patients aged ≥ 70 years had shorter unadjusted OS compared with younger patients (6.6 vs 10.3 months; p = 0.020); however, age was not an independent predictor of OS in multivariable analysis. Poor performance status, smoking exposure, and higher metastatic burden were independently associated with worse survival, whereas receipt of systemic therapy and the presence of any driver mutation were associated with improved outcomes. Chronological age did not independently predict access to systemic therapy; instead, poor performance status and cognitive impairment were the primary determinants of treatment omission. Among treated patients, older age was not associated with increased severe toxicity; notably, patients aged ≥ 70 years had lower odds of hospitalization due to treatment-related toxicity. Conclusions Chronological age alone should not guide treatment decisions in metastatic NSCLC. Functional status, disease burden, smoking exposure and access to effective systemic therapy are the principal determinants of survival and treatment outcomes. Appropriately selected older adults can safely receive systemic therapy without excess severe toxicity, underscoring the importance of individualized, geriatric-informed treatment strategies. Metastatic non–small cell lung cancer Geriatric oncology Age-related outcomes Overall survival Systemic therapy Treatment-related toxicity Real-world data Figures Figure 1 Figure 2 Introduction Lung cancer is the leading cause of cancer-related deaths worldwide, affecting both genders [ 1 , 2 ]. Lung cancer predominantly affects the older population, a substantial proportion of all patients being 70 years of age or older at the time of diagnosis, with this proportion expected to increase over the next two decades [ 1 , 3 , 4 ].In older adults, non–small cell lung cancer (NSCLC) represents the most frequent histologic subtype [ 4 ] and more than 50% are diagnosed in advanced stage with 5-year survival rate of less than 20% [ 4 – 6 ]. Over the past decade, systemic therapy options for metastatic NSCLC have expanded substantially, including molecularly targeted therapies and immune checkpoint inhibitors (ICIs) [ 7 , 8 ]. Yet the evidence base guiding treatment selection in older adults remains imperfect because patients with advanced age, multimorbidity, or frailty are commonly underrepresented in practice-defining phase III trials [ 9 ]. In real-world settings, older individuals frequently present with higher comorbidity burden, functional limitations, cognitive impairment, and polypharmacy—factors that can influence staging completeness, treatment selection, adherence, toxicity risk, and survival independently of chronological age [ 4 , 10 , 11 ]. However, chronological age alone may not accurately reflect functional reserve or treatment tolerance. While there is no universal biologic threshold that defines “old,” geriatric oncology has commonly operationalized older age using pragmatic cut-offs, most frequently ≥ 70 years in oncologic studies evaluating or implementing geriatric assessment [ 12 , 13 ]. Due to heterogeneity of this population, factors such as performance status, comorbidity burden, cognitive function, and disease extent may be more relevant determinants of outcome than age itself [ 4 , 10 , 11 , 14 ]. This study aimed to evaluate the independent association of chronological age with overall survival, systemic therapy patterns, and treatment-related toxicity in real-world metastatic NSCLC. Materials and Methods Study Design This was a single-center, retrospective cohort study conducted in the Department of Medical Oncology, Dokuz Eylül University Faculty of Medicine. Consecutive patients were identified from institutional electronic medical records between January 2018 and December 2022. Study Population All adult patients (≥ 18 years) with histologically confirmed non-small cell lung cancer (NSCLC) who were treated and/or followed in the medical oncology department during the study period were screened. Metastatic disease was confirmed pathologically or radiologically. Patients with de novo metastatic disease, available baseline clinical, pathological and staging data, and sufficient follow-up data to assess overall survival included. Exclusion criteria were mixed histology with small cell lung cancer, synchronous solid or hematologic malignancy, missing key data. Patients were a priori stratified into two age groups for analysis: <70 years and ≥ 70 years at the time of metastatic diagnosis. Detailed flowchart is presented in Fig. 1 . Data Collection Data were collected retrospectively from electronic medical records and digital archives using a standardized case report form. Demographic and clinical characteristics (age, sex, smoking history, comorbidities, ECOG PS), tumor characteristics (histologic subtype, moleculer and immunohistochemical data, metastatic sites), and treatment-related variables (receipt of any systemic therapy, type of first-, second- and later-line systemic regimens, use of immunotherapy and tyrosine kinase inhibitors (TKIs), dose reductions, and hospitalizations due to treatment-related toxicity) were extracted. PD-L1 expression was assessed by immunohistochemistry using the SP263 monoclonal antibody, according to the manufacturer’s recommendations. EGFR, ALK and ROS1 status were evaluated by fluorescence in situ hybridization (FISH). Cognitive impairment was defined by explicit documentation in baseline oncology and/or geriatric consultation notes within the diagnostic work-up period and coded as present/absent. Outcomes Overall survival (OS) was defined as the time from metastatic diagnosis to death from any cause. Patients who were alive at last contact were censored at the date of last follow-up. Statistical Analysis All statistical analyses were performed using IBM SPSS Statistics version 25.0 (IBM Corp., Armonk, NY, USA) and R 4.5.2. Comparisons between age groups (< 70 vs ≥ 70 years) were made using the chi-square test or Fisher’s exact test for categorical variables and the Mann–Whitney U test or Student’s t-test for continuous variables, based on distribution. Age was analyzed as a binary variable (< 70 vs ≥ 70 years) in accordance with the study hypothesis. To explore determinants of treatment allocation and toxicity, multivariable logistic regression models were constructed to identify factors associated with not receiving any systemic therapy in the metastatic setting (binary outcome: treated vs not treated), and to identify predictors of hospitalization due to treatment-related toxicity among patients who received systemic therapy. Logistic regression results are presented as odds ratios (ORs) with 95% CIs. A two-sided p < 0.05 was considered statistically significant for all analyses. Overall survival was estimated using the Kaplan–Meier method, and survival curves were compared with the log-rank test. Variables associated with OS in univariate analysis (p < 0.10) or deemed clinically relevant were entered into Cox proportional hazards regression models to identify independent prognostic factors. Results are presented as hazard ratios (HRs) with 95% confidence intervals (CIs). Ethical Approval: The study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Dokuz Eylül University Non-Interventional Research Ethics Committee (Approval No: 2023/05-28, Date: 22.02.2023). The requirement for informed consent was waived due to the retrospective nature of the study and anonymization of patient data. Results Patient Characteristics A total of 268 patients with metastatic NSCLC were included (median age 65 years, range 28–89); 185 (69.0%) were <70 years and 83 (31.0%) were ≥70 years at diagnosis. Smoking exposure was substantial and similar between age groups, and sex, histologic subtype distribution and smoking status (never vs ever) did not differ significantly. In contrast, patients ≥70 years had a markedly higher comorbidity burden (any comorbidity 94.0% vs 69.2%, p<0.001), more frequent cognitive impairment (16.9% vs 5.4%, p=0.002), and worse ECOG performance status (ECOG 2–4: 57.8% vs 27.6%, p<0.001). Patterns of metastatic involvement, number of metastatic sites and the distribution of EGFR, ALK, ROS1 and PD-L1 status were broadly similar across age groups (Table 1). Table 1. Baseline characteristics of metastatic NSCLC patients according to age group (<70 vs ≥70 years) Variable All patients (n=268) <70 years (n=185) ≥70 years (n=83) p value Age at diagnosis, years 65 (28–89) 62 (28–69) 75 (70–89) <0.001 Smoking exposure (pack-years) 40 (1–190) 40 (1–160) 49 (3–190) 0.237 Sex 0.333 Male 216 (80.6%) 152 (82.2%) 64 (77.1%) Female 52 (19.4%) 33 (17.8%) 19 (22.9%) Histology 0.163 Adenocarcinoma 177 (66.0%) 123 (66.5%) 54 (65.1%) Squamous cell carcinoma 79 (29.5%) 51 (27.6%) 28 (33.7%) NOS 12 (4.5%) 11 (5.9%) 1 (1.2%) Smoking status 0.665 Never smoker 29 (10.8%) 19 (9.8%) 10 (12.0%) Current or Ex-smoker 239 (89.2%) 166 (89.7%) 73 (88.0%) Any comorbidity present 206 (76.9%) 128 (69.2%) 78 (94.0%) <0.001 Hypertension 115 (42.9%) 67 (36.2%) 48 (57.8%) <0.001 Diabetes mellitus 65 (24.3%) 38 (20.5%) 27 (32.5%) 0.034 Cardiac disease 94 (35.1%) 46 (24.9%) 48 (57.8%) <0.001 Chronic kidney disease (GFR <60 mL/min) 39 (14.6%) 18 (9.7%) 21 (25.3%) <0.001 COPD 94 (35.1%) 59 (31.9%) 35 (42.2%) 0.103 Cognitive impairment 24 (9.0%) 10 (5.4%) 14 (16.9%) 0.002 ECOG performance status <0.001 0–1 169 (63.1%) 134 (72.4%) 35 (42.2%) 2–4 99 (36.9%) 51 (27.6%) 48 (57.8%) Metastatic involvement Liver metastasis 52 (19.4%) 36 (19.5%) 16 (19.3%) 0.972 Bone metastasis 143 (53.4%) 101 (54.6%) 42 (50.6%) 0.545 Adrenal metastasis 60 (22.4%) 46 (24.9%) 14 (16.9%) 0.146 Brain metastasis 85 (31.7%) 65 (35.1%) 20 (24.1%) 0.073 Distant lymph node metastasis 112 (41.8%) 82 (44.3%) 30 (36.1%) 0.209 Number of metastatic sites 2 (0–7) 2 (0–7) 2 (0–6) 0.166 Molecular testing performed 170 (63.4%) 118 (63.8%) 52 (62.7%) 0.859 EGFR mutation (n=170) 28/170 (16.5%) 17/118 (14.4%) 11/52 (21.2%) 0.274 ALK rearrangement (n=149) 11/149 (7.4%) 8/105 (7.6%) 3/44 (6.8%) 0.865 ROS1 rearrangement (n=99) 3/99 (3.0%) 3/74 (4.1%) 0/25 (0.0%) 0.307 PD-L1 expression (n=46) (n=33) (n=13) 0.630 <1% 26 (56.5%) 18 (54.5%) 8 (61.5%) 1–49% 5 (10.9%) 3 (9.1%) 2 (15.4%) ≥50% 15 (32.6%) 12 (36.4%) 3 (23.1%) Treatment characteristics Overall, 78.0% of patients received at least one line of systemic therapy, with younger patients more likely to be treated than those ≥70 years (82.2% vs 68.7%, p=0.014). First-line regimens differed significantly by age (p<0.001): older patients were less often treated with cisplatin-based chemotherapy and more frequently received carboplatin-based or other regimens. Second-line systemic therapy was also less commonly used in patients ≥70 years, and the total number of treatment lines in the metastatic setting was lower in this group (p=0.004). Use of immunotherapy or tyrosine kinase inhibitors (TKIs) at any line was comparable between age groups. Among treated patients, chemotherapy dose reductions were frequent in both groups, whereas hospitalization due to treatment-related toxicity was paradoxically more common in younger patients (46.7% vs 28.2%, p=0.008) (Table 2). Table 2. Treatment characteristics of metastatic NSCLC patients (<70 vs ≥70 years) Variable All patients (n=268) <70 years (n=185) ≥70 years (n=83) p value Received any systemic therapy 209 (78.0%) 152 (82.2%) 57 (68.7%) 0.014 First-line treatment <0.001 No systemic therapy 59 (22.0%) 33 (17.8%) 26 (31.3%) Cisplatin-based chemotherapy 61 (22.8%) 54 (29.2%) 7 (8.4%) Carboplatin-based chemotherapy 112 (41.8%) 77 (41.6%) 35 (42.2%) Immunotherapy alone 4 (1.5%) 4 (2.2%) 0 (0.0%) Other regimens 32 (12.0%) 17 (9.1%) 15 (18.1%) Second-line treatment 0.034 No second-line therapy 156 (58.2%) 96 (51.9%) 60 (72.3%) Taxane-based therapy 44 (16.4%) 34 (18.4%) 10 (12.0%) Immunotherapy 9 (3.4%) 7 (3.8%) 2 (2.4%) Gemcitabine 16 (6.0%) 12 (6.5%) 4 (4.8%) Other regimens 43 (16.0%) 36 (19.5%) 7 (8.4%) Third or more -line therapy 57 (21.3%) 43 (23.2%) 14 (16.9%) 0.238 Number of systemic treatment lines in metastatic stage (median, IQR) 1 (1–2) 1 (1-2) 1 (0–2) 0.004 Any immunotherapy (at any line) 21 (7.8%) 17 (9.2%) 4 (4.8%) 0.218 Atezolizumab 2 (0.7%) 1 (0.5%) 1 (1.2%) Nivolumab 14 (5.2%) 11 (5.9%) 3 (3.6%) Cemiplimab 5 (1.9%) 5 (2.7%) 0 (0.0%) TKI therapy (any TKI) 32 (11.9%) 21 (11.4%) 11 (13.3%) 0.657 EGFR inhibitor 22 (8.2%) 14 (7.6%) 8 (9.6%) ALK inhibitor 8 (3.0%) 5 (2.7%) 3 (3.6%) ROS1 inhibitor 2 (0.7%) 2 (1.1%) 0 (0.0%) Immune-related adverse event present, any grade 21 (7.8%) 17 (9.2%) 4 (4.8%) 0.218 Hospitalization due to treatment-related toxicity (n=240) 99/240 (41.3%) 79/169 (46.7%) 20/71 (28.2%) 0.008 Chemotherapy dose reduction (n=209) 72/209 (34.4%) 52/153 (34.0%) 20/56 (35.7%) 0.816 Determinants of not receiving systemic therapy In multivariable logistic regression, chronological age ≥70 years was not associated with withholding systemic treatment (OR 0.92, 95% CI 0.41–2.06, p =0.838). Instead, the main determinants of no systemic therapy were poor performance status (ECOG 2–4 vs 0–1; OR 28.61, 95% CI 10.49–78.06, p <0.001) and cognitive impairment (OR 4.55, 95% CI 1.48–13.92, p =0.008). The number of metastatic sites showed a nonsignificant trend toward higher odds of no treatment, while comorbidity burden and the presence of any driver mutation were not independently associated with treatment omission (Table 3). Table 3: Multivariable logistic regression analysis for not receiving systemic treatment Variable OR for no treatment 95% CI for OR p-value Age group (≥70 vs <70 years) 0.92 0.41 – 2.06 0.838 ECOG (2–4 vs 0–1) 28.61 10.49 – 78.06 <0.001 Comorbidities (any vs none) 0.64 0.21 – 1.93 0.431 Cognitive impairment ( Present vs absent ) 4.55 1.48 – 13.92 0.008 Number of metastatic sites (per site) 1.27 0.97 – 1.64 0.080 Driver mutation (any, positive vs negative) 0.65 0.21 – 2.02 0.459 Treatment-related toxicity and hospitalization Among patients who received systemic therapy, older age was not associated with a higher risk of hospitalization due to treatment-related toxicity; notably, patients aged ≥70 years had significantly lower odds of toxicity-related hospitalization (Model 1 OR 0.43, p=0.018; Model 2 OR 0.36, p=0.007). ECOG performance status, cognitive impairment, metastatic burden and major comorbidities (cardiac disease, chronic kidney disease, COPD) were not independently associated with toxicity-related hospitalization. In the clinical model, the presence of any driver mutation was linked to a lower risk of toxicity-related hospitalization (OR 0.36, p=0.014), although this association was attenuated after additional adjustment for treatment-related variables (Table 4). Table 4: Multivariable models evaluating factors associated with hospitalization due to treatment-related toxicity among patients receiving systemic therapy Variable OR for tox-hospitalization p-value Age group (≥70 vs <70) 0.43 0.018 ECOG (2–4 vs 0–1) 1.00 0.989 Cognitive impairment (Present vs absent) 1.74 0.483 Number of metastatic sites (Per additional site) 1.02 0.847 Driver mutation (any vs negative) 0.36 0.014 Cardiac disease (Present vs absent) 1.00 0.998 Chronic kidney disease (Present vs absent) 1.11 0.818 COPD (Present vs absent) 1.09 0.781 Survival Analyses Median follow-up time, estimated using the reverse Kaplan–Meier method, was 48.7 months with 247 deaths. Median OS in whole cohort was 8.8 months (95% CI 6.97 – 10.63). The median overall survival was 10.3 months (95% CI, 8.2–12.3) in patients <70 years and 6.6 months (95% CI, 4.9–8.3) in those ≥70 years (p=0.020) (Figure 2). Figure 2: Overall survival according to age groups In univariate Cox analysis, age ≥70 years, history of smoking exposure, ECOG 2–4, bone and brain metastases, and increasing number of metastatic sites were all associated with worse OS, whereas the presence of any driver mutation, receipt of systemic treatment, use of immunotherapy and use of TKIs were associated with improved survival (all p ≤0.040). Histologic subtype, comorbidity status, liver or adrenal metastases, individual EGFR/ALK/ROS1 alterations and PD-L1 expression were not significantly associated with OS (Table 5). Table 5: Univariate Cox regression analysis for mortality Variable HR 95% CI for HR p-value Age group ( ≥70 vs <70 years) 1.37 1.05 – 1.80 0.021 Sex ( Female vs male ) 0.69 0.50 – 0.95 0.024 Histologic subtype ( Adenocarcinoma vs SCC) 1.00 0.76 – 1.32 0.980 Smoking status (Any exposure vs never) 2.29 1.45 – 3.61 <0.001 Comorbidities ( Any vs none) 1.18 0.87 – 1.58 0.282 ECOG ( 2–4 vs 0–1 ) 2.60 2.00 – 3.38 <0.001 Liver metastasis ( Present vs absent ) 1.20 0.87 – 1.64 0.268 Bone metastasis ( Present vs absent ) 1.55 1.21 – 2.00 <0.001 Adrenal metastasis ( Present vs absent ) 1.18 0.88 – 1.59 0.279 Brain metastasis ( Present vs absent ) 1.32 1.01 – 1.73 0.040 Number of metastatic sites ( Per additional site ) 1.17 1.07 – 1.27 <0.001 EGFR mutation * (Positive vs negative) 0.65 0.42 – 1.01 0.057 ALK rearrangement * (Positive vs negative) 0.65 0.33 – 1.28 0.212 ROS1 rearrangement * (Positive vs negative) 0.37 0.09 – 1.51 0.165 Driver mutation (any) (Positive vs negative) 0.55 0.38 – 0.79 0.001 PD-L1 expression (>1% vs <1%) 1.10 0.57 – 2.13 0.763 Any systemic treatment ( Treated vs no treatment) 0.31 0.23 – 0.42 <0.001 Immunotherapy (any IO) ( yes vs no ) 0.24 0.13 – 0.45 <0.001 Tyrosine kinase inhibitor (TKI) ( yes vs no) 0.47 0.31 – 0.71 <0.001 In multivariable analysis, age ≥70 years was not an independent predictor of OS in either model (Model 1 HR 1.27, 95% CI 0.95–1.69, p =0.107; Model 2 HR 1.23, 95% CI 0.92–1.64, p =0.162). Across both models, ever-smoking, poorer ECOG performance and higher number of metastatic sites consistently predicted higher mortality, while the presence of any driver mutation remained associated with better survival (borderline in Model 2). Receipt of systemic treatment was strongly and independently associated with improved OS (HR 0.39, 95% CI 0.26–0.56, p <0.001) (Table 6). Table 6: Multivariate Cox regression for mortality Variable HR 95% CI p-value Model 1 Age group (≥70 vs <70 years) 1.27 0.95 – 1.69 0.107 Sex (Female vs male) 0.87 0.60 – 1.25 0.444 Histology (Adeno vs SCC) 0.84 0.63 – 1.13 0.246 Smoking (Any exposure vs never) 1.75 1.07 – 2.87 0.026 Comorbidities (any vs none) 0.90 0.65 – 1.25 0.527 ECOG (2–4 vs 0–1) 2.34 1.76 – 3.12 <0.001 Number of metastatic sites (per site) 1.23 1.12 – 1.34 <0.001 Driver mutation (any, positive vs negative) 0.66 0.44 – 0.99 0.045 Model 2 Age group (≥70 vs <70 years) 1.23 0.92 – 1.64 0.162 Sex (Female vs male) 0.92 0.64 – 1.32 0.641 Histology (Adeno vs SCC) 0.85 0.63 – 1.13 0.262 Smoking (Any exposure vs never) 1.96 1.18 – 3.26 0.009 Comorbidities (any vs none) 0.87 0.63 – 1.22 0.429 ECOG (2–4 vs 0–1) 1.67 1.19 – 2.35 0.003 Number of metastatic sites (per site) 1.26 1.15 – 1.38 <0.001 Driver mutation (any, positive vs negative) 0.66 0.44 – 1.00 0.048 Any systemic treatment (yes vs no) 0.39 0.26 – 0.56 <0.001 Discussion In this single-center real-world cohort of 268 patients with de novo metastatic NSCLC, chronological age ≥70 years was not an independent determinant of overall survival (OS) or access to systemic therapy after adjustment for key clinical and disease-related factors. Although older patients had shorter crude OS and were treated less often, they also presented with greater comorbidity burden, worse ECOG performance status, and more frequent cognitive impairment—features that plausibly explain unadjusted age differences and are consistent with prior geriatric oncology literature [15-18]. After multivariable adjustment, smoking exposure, ECOG performance status, metastatic burden, and receipt of systemic therapy remained the principal drivers of OS, supporting the concept that functional reserve and disease extent outweigh chronological age in prognostic assessment [13, 19, 20]. A key observation was that age ≥70 years was not independently associated with withholding systemic treatment, whereas poor performance status and cognitive impairment were the predominant determinants of treatment omission. This aligns with prior evidence that impaired ECOG status and geriatric vulnerabilities limit treatment receipt in advanced NSCLC and older cancer populations, and underscores the need to anchor decision-making in functional and cognitive domains rather than age alone [15, 21, 22]. Notably, the lack of an independent effect of comorbidity burden on treatment allocation is compatible with the view that comorbidities often influence outcomes indirectly through functional impairment and treatment deliverability rather than acting as stand-alone determinants [16, 17]. Consistent with real-world reports, older patients were less likely to proceed to later lines of therapy [16, 23]. Regarding regimen selection, the higher use of carboplatin-based or non-platinum approaches in older adults reflects routine tailoring and is concordant with data showing broadly comparable efficacy between cisplatin- and carboplatin-based regimens but higher morbidity and hospitalization risk with cisplatin, particularly in vulnerable patients [24, 25]. Despite lower overall treatment intensity, age-group differences in ICIs and TKI use were not prominent; when appropriately selected, older adults can achieve acceptable tolerability with ICIs, consistent with contemporary real-world and comparative data [26-28]. The absence of major age-related differences in driver alterations or PD-L1 expression should be interpreted cautiously: while prior studies suggest molecular profile may differ by age [29, 30], molecular testing and PD-L1 assessment were incomplete in our cohort, limiting inference. Among treated patients, older age was not associated with higher hospitalization due to treatment-related toxicity; paradoxically, toxicity-related hospitalization was less frequent in patients ≥70 years. A plausible explanation for this “toxicity paradox” is selection and treatment tailoring in routine practice (selection bias), whereby treated older patients likely represent a fitter subgroup, while younger patients may receive more intensive strategies and more subsequent lines, increasing the probability of inpatient complications [4, 31]. In addition, our toxicity endpoint captured only events requiring hospitalization; outpatient-managed or lower-grade adverse events could not be reliably ascertained retrospectively and were therefore not captured, potentially underestimating the overall toxicity burden. In univariate analyses, age ≥70 years was associated with poorer OS; however, this association was largely attenuated after adjustment, consistent with confounding by clustered adverse clinical features in older patients. Poor ECOG status, metastatic burden, and the presence of bone and brain metastases remained key adverse prognostic factors, whereas exposure to effective systemic therapy, particularly ICIs and TKIs, and driver mutation positivity were associated with improved survival, supporting a treatment-access–centered interpretation of age disparities [28, 31-33]. Overall, our findings are concordant with established prognostic index work showing that the apparent impact of age is largely mediated through function, disease burden, and treatment-related variables, and we extend this literature by demonstrating that receipt of systemic therapy is a dominant, independent determinant of survival after accounting for host and tumor characteristics [19, 20]. Interpretation of biomarker and treatment-pattern findings must also acknowledge healthcare-system constraints. During 2018–2022, ICIs were not reimbursed by the national health insurance system in Türkiye; consequently, PD-L1 testing was performed in only a minority of patients and ICI exposure was largely limited to clinical trials or out-of-pocket access, implying that ICI use may have been shaped by socioeconomic and structural factors rather than tumor biology alone. As reimbursement and biomarker testing availability evolve, future cohorts may show different treatment allocation patterns. Strengths include restriction to a relatively homogeneous de novo metastatic population and the single-center design, supporting consistency in diagnostic and supportive-care practices and enhancing interpretability in routine care. Limitations include the retrospective design with potential residual confounding, incomplete biomarker testing, and toxicity ascertainment limited to hospitalization-requiring events. PD-L1 and molecular testing were incomplete, therefore between-group comparisons are underpowered and prone to selection bias. Despite these limitations, the present analysis provides clinically relevant real-world evidence supporting an individualized, geriatric-informed and treatment-centered approach to metastatic NSCLC, emphasizing functional reserve and access to effective systemic therapy rather than chronological age alone [13, 15, 21, 22]. Conclusion In this real-world, single-center cohort of patients with metastatic denovo NSCLC, chronological age ≥70 years was not an independent determinant of overall survival, access to systemic therapy, or treatment-related toxicity requiring hospitalization after adjustment for key clinical and disease-related factors. Although older patients demonstrated shorter unadjusted survival and were less likely to receive systemic treatment, these differences were largely explained by poorer performance status, higher metastatic burden, smoking exposure, and cognitive impairment rather than age itself. Importantly, receipt of systemic therapy emerged as the strongest predictor of improved survival, and appropriately selected older patients did not experience excess severe toxicity, supporting the safety of treatment in this population. Collectively, these findings reinforce that prognosis and treatment decisions in metastatic NSCLC should be guided by functional reserve, disease extent, and treatment feasibility rather than chronological age alone. Incorporation of geriatric-informed assessment into routine clinical practice may help optimize treatment allocation and outcomes for older adults with advanced NSCLC, avoiding both undertreatment and unnecessary toxicity. Declarations Acknowledgments: None Declaration of interest: The authors have no conflict of interest to disclose. Funding: No funding was received for this study. Author contributions: GKA: Conceptualization, Methodology, Data Curation, Formal analysis, Writing - Original Draft, KC: Formal analysis, Writing - Original Draft İÖ: Methodology, Writing - Review & Editing EA: Conceptualization, Methodology, Writing - Review & Editing, Supervision References Siegel RL, Kratzer TB, Wagle NS, Sung H, Jemal A. Cancer statistics, 2026. CA Cancer J Clin. 2026;76(1):e70043. 10.3322/caac.70043 . Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63. 10.3322/caac.21834 . Torre LA, Siegel RL, Ward EM, Jemal A. Global Cancer Incidence and Mortality Rates and Trends–An Update. Cancer Epidemiol Biomarkers Prev. 2016;25(1):16–27. 10.1158/1055-9965.EPI-15-0578 . 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ESMO Handbook of Cancer in the Senior Patient. 2 ed. 2025. Wildiers H, Heeren P, Puts M, et al. International Society of Geriatric Oncology Consensus on Geriatric Assessment in Older Patients With Cancer. J Clin Oncol. 2014;32(24):2595–603. 10.1200/JCO.2013.54.8347 . Bergerot CD, Temin S, Verduzco-Aguirre HC, et al. Geriatric Assessment: ASCO Global Guideline. JCO Global Oncol. 2025;11e2500276. 10.1200/GO-25-00276 . Wong ML, Miaskowski C, Smith AK, et al. Prognostic factors among older adults with advanced non-small cell lung cancer (NSCLC): A multisite cohort study. J Clin Oncol. 2019;37(15suppl):11540–11540. 10.1200/JCO.2019.37.15_suppl.11540 . Ozturk A, Oruc O, Igde M, et al. Survival Evaluation in Patients Over 70 Years of Age With Advanced Stage Non-small Cell Lung Cancer. Eurasian J Med Invest. 2023;7:501–8. 10.14744/ejmi.2023.29049 . Tammemagi CM, Neslund-Dudas C, Simoff M, Kvale P. Impact of comorbidity on lung cancer survival. Int J Cancer. 2003;103(6):792–802. 10.1002/ijc.10882 . Radzikowska E, Roszkowski K, Głaz P. Lung cancer in patients under 50 years old. Lung Cancer. 2001;33(2–3):203–11. 10.1016/s0169-5002(01)00199-4 . Blanchon F, Grivaux M, Asselain B, et al. 4-year mortality in patients with non-small-cell lung cancer: development and validation of a prognostic index. Lancet Oncol. 2006;7(10):829–36. 10.1016/s1470-2045(06)70868-3 . Alexander M, Wolfe R, Ball D, et al. Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer. Br J Cancer. 2017;117(5):744–51. 10.1038/bjc.2017.232 . Su Q, Sun YP, Liu YH, et al. Prognostic factors in older patients with advanced non-small cell lung cancer in China. Tumori. 2014;100(1):69–74. 10.1700/1430.15818 . Loh KP, Liposits G, Arora SP, et al. Adequate assessment yields appropriate care-the role of geriatric assessment and management in older adults with cancer: a position paper from the ESMO/SIOG Cancer in the Elderly Working Group. ESMO Open. 2024;9(8):103657. 10.1016/j.esmoop.2024.103657 . Carroll NM, Eisenstein J, Burnett-Hartman AN, et al. Uptake of novel systemic therapy: Real world patterns among adults with advanced non-small cell lung cancer. Cancer Treat Res Commun. 2023;36:100730. 10.1016/j.ctarc.2023.100730 . Santana-Davila R, Szabo A, Arce-Lara C, et al. Cisplatin versus carboplatin-based regimens for the treatment of patients with metastatic lung cancer. An analysis of Veterans Health Administration data. J Thorac Oncol. 2014;9(5):702–9. 10.1097/jto.0000000000000146 . Pallis AG, Gridelli C, van Meerbeeck JP, et al. EORTC Elderly Task Force and Lung Cancer Group and International Society for Geriatric Oncology (SIOG) experts’opinion for the treatment of non-small-cell lung cancer in an elderly population.Annals of Oncology. 2010;21(4):692–706. doi:10.1093/annonc/mdp360. del Corral-Morales J, Ayala-de Miguel C, Quintana-Cortés L, et al. Real-World Data on Immune-Checkpoint Inhibitors in Elderly Patients with Advanced Non-Small Cell Lung Cancer: A Retrospective Study. Cancers. 2025;17(13):2194. 10.3390/cancers17132194 . Tsukita Y, Tozuka T, Kushiro K, et al. Immunotherapy or Chemoimmunotherapy in Older Adults With Advanced Non-Small Cell Lung Cancer. JAMA Oncol. 2024;10(4):439–47. 10.1001/jamaoncol.2023.6277 . Leung B, Shokoohi A, Al-Hashami Z, et al. Improved uptake and survival with systemic treatments for metastatic non-small cell lung cancer: younger versus older adults. BMC Cancer. 2023;23(1):360. 10.1186/s12885-023-10800-x . Zhong W, Zhao J, Huang K, Zhang J, Chen Z. Comparison of clinicopathological and molecular features between young and old patients with lung cancer. Int J Clin Exp Pathol. 2018;11(2):1031–5. Hu M, Tan J, Liu Z, et al. Comprehensive Comparative Molecular Characterization of Young and Old Lung Cancer Patients. Front Oncol. 2022;11–2021. 10.3389/fonc.2021.806845 . Li F, Li F, Lu H, Zhao D. Analysis of age-related survival differences in advanced non-small cell lung cancer patients based on real-world data. BMC Cancer. 2025;25(1):1244. 10.1186/s12885-025-14687-8 . Maione P, Rossi A, Sacco PC, et al. Treating advanced non-small cell lung cancer in the elderly. Ther Adv Med Oncol. 2010;2(4):251–60. 10.1177/1758834010366707 . D'Antonio C, Passaro A, Gori B, et al. Bone and brain metastasis in lung cancer: recent advances in therapeutic strategies. Ther Adv Med Oncol. 2014;6(3):101–14. 10.1177/1758834014521110 . Additional Declarations No competing interests reported. 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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-8751117","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596332336,"identity":"7bf2d441-0d61-4669-869a-a0c5c76b8528","order_by":0,"name":"Güzide Kofalı Ayakdaş","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYJCCAzwMCQzsDcwNBz4AeWzsxGrhOcDYcHAGSAszMdbAtDDzgHiEtMhHpD888KYiTY6H/WDjYZtf2+T5mBkYP3zMwa3F8EZCwsE5Z3KMeXgSGw7n9t02bGNmYJacuQ2PlhkJBw7ztlUk7mcAaem5zQjUwsbMi1cLUCXvv4rEHv6HDYcte27bE9QiL5HMcJi3ISexRwKol+HH7USCWgx4njEcnHMszZhH4mHDwd6G28ltzIzNeP0i357++MObmmQ5Hv7kwx9+/LltO7+9+eCHj/hsOYDMY2wDkw241YNsQZX+g1fxKBgFo2AUjFAAAMjmWLq8ZxW5AAAAAElFTkSuQmCC","orcid":"","institution":"Izmir City Hospital","correspondingAuthor":true,"prefix":"","firstName":"Güzide","middleName":"Kofalı","lastName":"Ayakdaş","suffix":""},{"id":596332337,"identity":"8cabaf9d-8919-4a60-bc94-9aefb7442481","order_by":1,"name":"Kübra Canaslan","email":"","orcid":"","institution":"Dokuz Eylül University","correspondingAuthor":false,"prefix":"","firstName":"Kübra","middleName":"","lastName":"Canaslan","suffix":""},{"id":596332338,"identity":"d3e2dfab-2442-45ba-9a31-9456fdddfae7","order_by":2,"name":"İlhan Öztop","email":"","orcid":"","institution":"Dokuz Eylül University","correspondingAuthor":false,"prefix":"","firstName":"İlhan","middleName":"","lastName":"Öztop","suffix":""},{"id":596332339,"identity":"9ee3d19c-0ba3-48d5-8af5-4a357070d466","order_by":3,"name":"Elif Atağ","email":"","orcid":"","institution":"Dokuz Eylül University","correspondingAuthor":false,"prefix":"","firstName":"Elif","middleName":"","lastName":"Atağ","suffix":""}],"badges":[],"createdAt":"2026-01-31 15:53:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8751117/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8751117/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103577558,"identity":"9b1a68e3-fab5-4814-b441-3993a9376eb0","added_by":"auto","created_at":"2026-02-27 09:28:28","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":418503,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient selection\u003c/p\u003e","description":"","filename":"Figure12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8751117/v1/3b97ee641848d1203f1557a3.jpeg"},{"id":103577611,"identity":"47a4cc10-8548-46b3-8364-522fafcd2ea5","added_by":"auto","created_at":"2026-02-27 09:28:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":158948,"visible":true,"origin":"","legend":"\u003cp\u003eOverall survival according to age groups\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8751117/v1/61112a629ae44ffea4f821a7.png"},{"id":103577638,"identity":"8fac2ce5-c612-4baf-9b3b-729c4286564e","added_by":"auto","created_at":"2026-02-27 09:28:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1902096,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8751117/v1/f36f4018-d898-4f79-91a8-edca45dc51e7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Chronological Age Is Not an Independent Determinant of Survival or Treatment Access in Metastatic Non–Small Cell Lung Cancer: A Real-World Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer is the leading cause of cancer-related deaths worldwide, affecting both genders [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Lung cancer predominantly affects the older population, a substantial proportion of all patients being 70 years of age or older at the time of diagnosis, with this proportion expected to increase over the next two decades [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].In older adults, non\u0026ndash;small cell lung cancer (NSCLC) represents the most frequent histologic subtype [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and more than 50% are diagnosed in advanced stage with 5-year survival rate of less than 20% [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOver the past decade, systemic therapy options for metastatic NSCLC have expanded substantially, including molecularly targeted therapies and immune checkpoint inhibitors (ICIs) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Yet the evidence base guiding treatment selection in older adults remains imperfect because patients with advanced age, multimorbidity, or frailty are commonly underrepresented in practice-defining phase III trials [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In real-world settings, older individuals frequently present with higher comorbidity burden, functional limitations, cognitive impairment, and polypharmacy\u0026mdash;factors that can influence staging completeness, treatment selection, adherence, toxicity risk, and survival independently of chronological age [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, chronological age alone may not accurately reflect functional reserve or treatment tolerance. While there is no universal biologic threshold that defines \u0026ldquo;old,\u0026rdquo; geriatric oncology has commonly operationalized older age using pragmatic cut-offs, most frequently\u0026thinsp;\u0026ge;\u0026thinsp;70 years in oncologic studies evaluating or implementing geriatric assessment [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Due to heterogeneity of this population, factors such as performance status, comorbidity burden, cognitive function, and disease extent may be more relevant determinants of outcome than age itself [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aimed to evaluate the independent association of chronological age with overall survival, systemic therapy patterns, and treatment-related toxicity in real-world metastatic NSCLC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis was a single-center, retrospective cohort study conducted in the Department of Medical Oncology, Dokuz Eyl\u0026uuml;l University Faculty of Medicine. Consecutive patients were identified from institutional electronic medical records between January 2018 and December 2022.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eAll adult patients (\u0026ge;\u0026thinsp;18 years) with histologically confirmed non-small cell lung cancer (NSCLC) who were treated and/or followed in the medical oncology department during the study period were screened. Metastatic disease was confirmed pathologically or radiologically. Patients with de novo metastatic disease, available baseline clinical, pathological and staging data, and sufficient follow-up data to assess overall survival included. Exclusion criteria were mixed histology with small cell lung cancer, synchronous solid or hematologic malignancy, missing key data. Patients were a priori stratified into two age groups for analysis: \u0026lt;70 years and \u0026ge;\u0026thinsp;70 years at the time of metastatic diagnosis. Detailed flowchart is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eData were collected retrospectively from electronic medical records and digital archives using a standardized case report form. Demographic and clinical characteristics (age, sex, smoking history, comorbidities, ECOG PS), tumor characteristics (histologic subtype, moleculer and immunohistochemical data, metastatic sites), and treatment-related variables (receipt of any systemic therapy, type of first-, second- and later-line systemic regimens, use of immunotherapy and tyrosine kinase inhibitors (TKIs), dose reductions, and hospitalizations due to treatment-related toxicity) were extracted.\u003c/p\u003e \u003cp\u003ePD-L1 expression was assessed by immunohistochemistry using the SP263 monoclonal antibody, according to the manufacturer\u0026rsquo;s recommendations. EGFR, ALK and ROS1 status were evaluated by fluorescence in situ hybridization (FISH). Cognitive impairment was defined by explicit documentation in baseline oncology and/or geriatric consultation notes within the diagnostic work-up period and coded as present/absent.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eOverall survival (OS) was defined as the time from metastatic diagnosis to death from any cause. Patients who were alive at last contact were censored at the date of last follow-up.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAll statistical analyses were performed using IBM SPSS Statistics version 25.0 (IBM Corp., Armonk, NY, USA) and R 4.5.2. Comparisons between age groups (\u0026lt;\u0026thinsp;70 vs\u0026thinsp;\u0026ge;\u0026thinsp;70 years) were made using the chi-square test or Fisher\u0026rsquo;s exact test for categorical variables and the Mann\u0026ndash;Whitney U test or Student\u0026rsquo;s t-test for continuous variables, based on distribution. Age was analyzed as a binary variable (\u0026lt;\u0026thinsp;70 vs\u0026thinsp;\u0026ge;\u0026thinsp;70 years) in accordance with the study hypothesis. To explore determinants of treatment allocation and toxicity, multivariable logistic regression models were constructed to identify factors associated with not receiving any systemic therapy in the metastatic setting (binary outcome: treated vs not treated), and to identify predictors of hospitalization due to treatment-related toxicity among patients who received systemic therapy. Logistic regression results are presented as odds ratios (ORs) with 95% CIs. A two-sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for all analyses. Overall survival was estimated using the Kaplan\u0026ndash;Meier method, and survival curves were compared with the log-rank test. Variables associated with OS in univariate analysis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.10) or deemed clinically relevant were entered into Cox proportional hazards regression models to identify independent prognostic factors. Results are presented as hazard ratios (HRs) with 95% confidence intervals (CIs).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical Approval:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Dokuz Eylül University Non-Interventional Research Ethics Committee (Approval No: 2023/05-28, Date: 22.02.2023). The requirement for informed consent was waived due to the retrospective nature of the study and anonymization of patient data.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePatient Characteristics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 268 patients with metastatic NSCLC were included (median age 65 years, range 28\u0026ndash;89); 185 (69.0%) were \u0026lt;70 years and 83 (31.0%) were \u0026ge;70 years at diagnosis. Smoking exposure was substantial and similar between age groups, and sex, histologic subtype distribution and smoking status (never vs ever) did not differ significantly. In contrast, patients \u0026ge;70 years had a markedly higher comorbidity burden (any comorbidity 94.0% vs 69.2%, p\u0026lt;0.001), more frequent cognitive impairment (16.9% vs 5.4%, p=0.002), and worse ECOG performance status (ECOG 2\u0026ndash;4: 57.8% vs 27.6%, p\u0026lt;0.001). Patterns of metastatic involvement, number of metastatic sites and the distribution of EGFR, ALK, ROS1 and PD-L1 status were broadly similar across age groups (Table 1).\u003c/p\u003e\n\u003cp\u003eTable 1. Baseline characteristics of metastatic NSCLC patients according to age group (\u0026lt;70 vs \u0026ge;70 years)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll patients (n=268)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;70 years (n=185)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;70 years (n=83)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at diagnosis, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65 (28\u0026ndash;89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62 (28\u0026ndash;69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75 (70\u0026ndash;89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking exposure (pack-years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40 (1\u0026ndash;190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40 (1\u0026ndash;160)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49 (3\u0026ndash;190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e216 (80.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e152 (82.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64 (77.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33 (17.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (22.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e177 (66.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e123 (66.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54 (65.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Squamous cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79 (29.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51 (27.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28 (33.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Never smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Current or Ex-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e239 (89.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e166 (89.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73 (88.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAny comorbidity present\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e206 (76.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e128 (69.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78 (94.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e115 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67 (36.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48 (57.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Diabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65 (24.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38 (20.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27 (32.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Cardiac disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e94 (35.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46 (24.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48 (57.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Chronic kidney disease (GFR \u0026lt;60 mL/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39 (14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (25.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;COPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e94 (35.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59 (31.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35 (42.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Cognitive impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24 (9.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (16.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eECOG performance status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;0\u0026ndash;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e169 (63.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e134 (72.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35 (42.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;2\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99 (36.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51 (27.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48 (57.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetastatic involvement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Liver metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36 (19.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (19.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Bone metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e143 (53.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e101 (54.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42 (50.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Adrenal metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60 (22.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46 (24.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (16.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Brain metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85 (31.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65 (35.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 (24.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Distant lymph node metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e112 (41.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82 (44.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30 (36.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of metastatic sites\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (0\u0026ndash;7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (0\u0026ndash;7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (0\u0026ndash;6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular testing performed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e170 (63.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e118 (63.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52 (62.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEGFR mutation\u003c/strong\u003e (n=170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28/170 (16.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17/118 (14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11/52 (21.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eALK rearrangement\u003c/strong\u003e (n=149)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11/149 (7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8/105 (7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3/44 (6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eROS1 rearrangement\u003c/strong\u003e (n=99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3/99 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3/74 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0/25 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePD-L1 expression\u0026nbsp;\u003c/strong\u003e(n=46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(n=33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(n=13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;\u0026lt;1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26 (56.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (54.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (61.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;1\u0026ndash;49%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (10.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (15.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;\u0026ge;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (32.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (23.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTreatment characteristics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, 78.0% of patients received at least one line of systemic therapy, with younger patients more likely to be treated than those \u0026ge;70 years (82.2% vs 68.7%, p=0.014). First-line regimens differed significantly by age (p\u0026lt;0.001): older patients were less often treated with cisplatin-based chemotherapy and more frequently received carboplatin-based or other regimens. Second-line systemic therapy was also less commonly used in patients \u0026ge;70 years, and the total number of treatment lines in the metastatic setting was lower in this group (p=0.004). Use of immunotherapy or tyrosine kinase inhibitors (TKIs) at any line was comparable between age groups. Among treated patients, chemotherapy dose reductions were frequent in both groups, whereas hospitalization due to treatment-related toxicity was paradoxically more common in younger patients (46.7% vs 28.2%, p=0.008) (Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2. Treatment characteristics of metastatic NSCLC patients (\u0026lt;70 vs \u0026ge;70 years)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll patients (n=268)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;70 years (n=185)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;70 years (n=83)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReceived any systemic therapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e209 (78.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e152 (82.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57 (68.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFirst-line treatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;No systemic therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59 (22.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33 (17.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26 (31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Cisplatin-based chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61 (22.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54 (29.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (8.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Carboplatin-based chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e112 (41.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e77 (41.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35 (42.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Immunotherapy alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Other regimens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17 (9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (18.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSecond-line treatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;No second-line therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e156 (58.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e96 (51.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60 (72.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Taxane-based therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44 (16.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34 (18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Immunotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Gemcitabine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (6.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Other regimens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43 (16.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36 (19.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (8.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eThird or more -line therapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43 (23.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (16.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of systemic treatment lines in metastatic stage (median, IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (1\u0026ndash;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (1-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAny immunotherapy (at any line)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Atezolizumab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Nivolumab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;Cemiplimab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTKI therapy (any TKI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 (11.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;EGFR inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;ALK inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;ROS1 inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eImmune-related adverse event present, any grade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospitalization due to treatment-related toxicity (n=240)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99/240 (41.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79/169 (46.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20/71 (28.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemotherapy dose reduction (n=209)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72/209 (34.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52/153 (34.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20/56 (35.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDeterminants of not receiving systemic therapy\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn multivariable logistic regression, chronological age \u0026ge;70 years was\u0026nbsp;\u003cstrong\u003enot\u003c/strong\u003e associated with withholding systemic treatment (OR 0.92, 95% CI 0.41\u0026ndash;2.06,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e=0.838). Instead, the main determinants of no systemic therapy were poor performance status (ECOG 2\u0026ndash;4 vs 0\u0026ndash;1; OR 28.61, 95% CI 10.49\u0026ndash;78.06,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) and cognitive impairment (OR 4.55, 95% CI 1.48\u0026ndash;13.92,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e=0.008). The number of metastatic sites showed a nonsignificant trend toward higher odds of no treatment, while comorbidity burden and the presence of any driver mutation were not independently associated with treatment omission (Table 3).\u003c/p\u003e\n\u003cp\u003eTable 3: Multivariable logistic regression analysis for not receiving systemic treatment\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR for no treatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI for OR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge group (\u0026ge;70 vs \u0026lt;70 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.41 \u0026ndash; 2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eECOG (2\u0026ndash;4 vs 0\u0026ndash;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.49 \u0026ndash; 78.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComorbidities (any vs none)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.21 \u0026ndash; 1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.431\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCognitive impairment \u003cstrong\u003e(\u003c/strong\u003ePresent vs absent\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.48 \u0026ndash; 13.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of metastatic sites (per site)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.97 \u0026ndash; 1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDriver mutation (any, positive vs negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.21 \u0026ndash; 2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTreatment-related toxicity and hospitalization\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong patients who received systemic therapy, older age was not associated with a higher risk of hospitalization due to treatment-related toxicity; notably, patients aged \u0026ge;70 years had significantly lower odds of toxicity-related hospitalization (Model 1 OR 0.43, p=0.018; Model 2 OR 0.36, p=0.007). ECOG performance status, cognitive impairment, metastatic burden and major comorbidities (cardiac disease, chronic kidney disease, COPD) were not independently associated with toxicity-related hospitalization. In the clinical model, the presence of any driver mutation was linked to a lower risk of toxicity-related hospitalization (OR 0.36, p=0.014), although this association was attenuated after additional adjustment for treatment-related variables (Table 4).\u003c/p\u003e\n\u003cp\u003eTable 4: Multivariable models evaluating factors associated with hospitalization due to treatment-related toxicity among patients receiving systemic therapy\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR for tox-hospitalization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge group (\u0026ge;70 vs \u0026lt;70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.43\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eECOG (2\u0026ndash;4 vs 0\u0026ndash;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCognitive impairment (Present vs absent)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of metastatic sites (Per additional site)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDriver mutation (any vs negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.36\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCardiac disease (Present vs absent)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChronic kidney disease (Present vs absent)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCOPD (Present vs absent)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSurvival Analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMedian follow-up time, estimated using the reverse Kaplan\u0026ndash;Meier method, was 48.7 months with 247 deaths. Median OS in whole cohort was 8.8 months (95% CI 6.97 \u0026ndash; 10.63). The median overall survival was 10.3 months (95% CI, 8.2\u0026ndash;12.3) in patients \u0026lt;70 years and 6.6 months (95% CI, 4.9\u0026ndash;8.3) in those \u0026ge;70 years (p=0.020) (Figure 2).\u003c/p\u003e\n\u003cp\u003eFigure 2: Overall survival according to age groups\u003c/p\u003e\n\u003cp\u003eIn univariate Cox analysis, age \u0026ge;70 years, history of smoking exposure, ECOG 2\u0026ndash;4, bone and brain metastases, and increasing number of metastatic sites were all associated with worse OS, whereas the presence of any driver mutation, receipt of systemic treatment, use of immunotherapy and use of TKIs were associated with improved survival (all\u0026nbsp;\u003cem\u003ep\u003c/em\u003e\u0026le;0.040). Histologic subtype, comorbidity status, liver or adrenal metastases, individual EGFR/ALK/ROS1 alterations and PD-L1 expression were not significantly associated with OS (Table 5).\u003c/p\u003e\n\u003cp\u003eTable 5: Univariate Cox regression analysis for mortality\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI for HR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group (\u003c/strong\u003e\u0026ge;70 vs \u0026lt;70 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.05 \u0026ndash; 1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex (\u003c/strong\u003eFemale vs male\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.50 \u0026ndash; 0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistologic subtype (\u003c/strong\u003eAdenocarcinoma vs SCC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.76 \u0026ndash; 1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status\u0026nbsp;\u003c/strong\u003e(Any exposure vs never)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.45 \u0026ndash; 3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities (\u003c/strong\u003eAny vs none)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.87 \u0026ndash; 1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eECOG (\u003c/strong\u003e2\u0026ndash;4 vs 0\u0026ndash;1\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.00 \u0026ndash; 3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiver metastasis (\u003c/strong\u003ePresent vs absent\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.87 \u0026ndash; 1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBone metastasis (\u003c/strong\u003ePresent vs absent\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.21 \u0026ndash; 2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdrenal metastasis (\u003c/strong\u003ePresent vs absent\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.88 \u0026ndash; 1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBrain metastasis (\u003c/strong\u003ePresent vs absent\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.01 \u0026ndash; 1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of metastatic sites (\u003c/strong\u003ePer additional site\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.07 \u0026ndash; 1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEGFR mutation\u003c/strong\u003e* (Positive vs negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.42 \u0026ndash; 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eALK rearrangement\u003c/strong\u003e* (Positive vs negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.33 \u0026ndash; 1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eROS1 rearrangement\u003c/strong\u003e* (Positive vs negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09 \u0026ndash; 1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDriver mutation (any)\u0026nbsp;\u003c/strong\u003e(Positive vs negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.38 \u0026ndash; 0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePD-L1 expression (\u0026gt;1% vs \u0026lt;1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.57 \u0026ndash; 2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.763\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAny systemic treatment (\u003c/strong\u003eTreated vs no treatment)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.23 \u0026ndash; 0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eImmunotherapy (any IO) (\u003c/strong\u003eyes vs no\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.13 \u0026ndash; 0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTyrosine kinase inhibitor (TKI) (\u003c/strong\u003eyes vs no)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.31 \u0026ndash; 0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn multivariable analysis, age \u0026ge;70 years was\u0026nbsp;\u003cstrong\u003enot\u003c/strong\u003e an independent predictor of OS in either model (Model 1 HR 1.27, 95% CI 0.95\u0026ndash;1.69,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e=0.107; Model 2 HR 1.23, 95% CI 0.92\u0026ndash;1.64,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e=0.162). Across both models, ever-smoking, poorer ECOG performance and higher number of metastatic sites consistently predicted higher mortality, while the presence of any driver mutation remained associated with better survival (borderline in Model 2). Receipt of systemic treatment was strongly and independently associated with improved OS (HR 0.39, 95% CI 0.26\u0026ndash;0.56,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) (Table 6).\u003c/p\u003e\n\u003cp\u003eTable 6: Multivariate Cox regression for mortality\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge group (\u0026ge;70 vs \u0026lt;70 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.27\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.95 \u0026ndash; 1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex (Female vs male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.60 \u0026ndash; 1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHistology (Adeno vs SCC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.63 \u0026ndash; 1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmoking (Any exposure vs never)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.07 \u0026ndash; 2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComorbidities (any vs none)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.65 \u0026ndash; 1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eECOG (2\u0026ndash;4 vs 0\u0026ndash;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.76 \u0026ndash; 3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of metastatic sites (per site)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.12 \u0026ndash; 1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDriver mutation (any, positive vs negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.66\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.44 \u0026ndash; 0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge group (\u0026ge;70 vs \u0026lt;70 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.92 \u0026ndash; 1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex (Female vs male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.64 \u0026ndash; 1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHistology (Adeno vs SCC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.63 \u0026ndash; 1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmoking (Any exposure vs never)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.96\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.18 \u0026ndash; 3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComorbidities (any vs none)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.63 \u0026ndash; 1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eECOG (2\u0026ndash;4 vs 0\u0026ndash;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.67\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.19 \u0026ndash; 2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of metastatic sites (per site)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.15 \u0026ndash; 1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDriver mutation (any, positive vs negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.66\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.44 \u0026ndash; 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAny systemic treatment (yes vs no)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.26 \u0026ndash; 0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this single-center real-world cohort of 268 patients with de novo metastatic NSCLC, chronological age ≥70 years was not an independent determinant of overall survival (OS) or access to systemic therapy after adjustment for key clinical and disease-related factors. Although older patients had shorter crude OS and were treated less often, they also presented with greater comorbidity burden, worse ECOG performance status, and more frequent cognitive impairment—features that plausibly explain unadjusted age differences and are consistent with prior geriatric oncology literature [15-18]. After multivariable adjustment, smoking exposure, ECOG performance status, metastatic burden, and receipt of systemic therapy remained the principal drivers of OS, supporting the concept that functional reserve and disease extent outweigh chronological age in prognostic assessment [13, 19, 20].\u003c/p\u003e\n\u003cp\u003eA key observation was that age ≥70 years was not independently associated with withholding systemic treatment, whereas poor performance status and cognitive impairment were the predominant determinants of treatment omission. This aligns with prior evidence that impaired ECOG status and geriatric vulnerabilities limit treatment receipt in advanced NSCLC and older cancer populations, and underscores the need to anchor decision-making in functional and cognitive domains rather than age alone [15, 21, 22]. Notably, the lack of an independent effect of comorbidity burden on treatment allocation is compatible with the view that comorbidities often influence outcomes indirectly through functional impairment and treatment deliverability rather than acting as stand-alone determinants [16, 17].\u003c/p\u003e\n\u003cp\u003eConsistent with real-world reports, older patients were less likely to proceed to later lines of therapy [16, 23]. Regarding regimen selection, the higher use of carboplatin-based or non-platinum approaches in older adults reflects routine tailoring and is concordant with data showing broadly comparable efficacy between cisplatin- and carboplatin-based regimens but higher morbidity and hospitalization risk with cisplatin, particularly in vulnerable patients [24, 25]. Despite lower overall treatment intensity, age-group differences in ICIs and TKI use were not prominent; when appropriately selected, older adults can achieve acceptable tolerability with ICIs, consistent with contemporary real-world and comparative data [26-28]. The absence of major age-related differences in driver alterations or PD-L1 expression should be interpreted cautiously: while prior studies suggest molecular profile may differ by age [29, 30], molecular testing and PD-L1 assessment were incomplete in our cohort, limiting inference.\u003c/p\u003e\n\u003cp\u003eAmong treated patients, older age was not associated with higher hospitalization due to treatment-related toxicity; paradoxically, toxicity-related hospitalization was less frequent in patients ≥70 years. A plausible explanation for this “toxicity paradox” is selection and treatment tailoring in routine practice (selection bias), whereby treated older patients likely represent a fitter subgroup, while younger patients may receive more intensive strategies and more subsequent lines, increasing the probability of inpatient complications [4, 31]. In addition, our toxicity endpoint captured only events requiring hospitalization; outpatient-managed or lower-grade adverse events could not be reliably ascertained retrospectively and were therefore not captured, potentially underestimating the overall toxicity burden.\u003c/p\u003e\n\u003cp\u003eIn univariate analyses, age ≥70 years was associated with poorer OS; however, this association was largely attenuated after adjustment, consistent with confounding by clustered adverse clinical features in older patients. Poor ECOG status, metastatic burden, and the presence of bone and brain metastases remained key adverse prognostic factors, whereas exposure to effective systemic therapy, particularly ICIs and TKIs, and driver mutation positivity were associated with improved survival, supporting a treatment-access–centered interpretation of age disparities [28, 31-33]. Overall, our findings are concordant with established prognostic index work showing that the apparent impact of age is largely mediated through function, disease burden, and treatment-related variables, and we extend this literature by demonstrating that receipt of systemic therapy is a dominant, independent determinant of survival after accounting for host and tumor characteristics [19, 20].\u003c/p\u003e\n\u003cp\u003eInterpretation of biomarker and treatment-pattern findings must also acknowledge healthcare-system constraints. During 2018–2022, ICIs were not reimbursed by the national health insurance system in Türkiye; consequently, PD-L1 testing was performed in only a minority of patients and ICI exposure was largely limited to clinical trials or out-of-pocket access, implying that ICI use may have been shaped by socioeconomic and structural factors rather than tumor biology alone. As reimbursement and biomarker testing availability evolve, future cohorts may show different treatment allocation patterns.\u003c/p\u003e\n\u003cp\u003eStrengths include restriction to a relatively homogeneous de novo metastatic population and the single-center design, supporting consistency in diagnostic and supportive-care practices and enhancing interpretability in routine care. Limitations include the retrospective design with potential residual confounding, incomplete biomarker testing, and toxicity ascertainment limited to hospitalization-requiring events. PD-L1 and molecular testing were incomplete, therefore between-group comparisons are underpowered and prone to selection bias. Despite these limitations, the present analysis provides clinically relevant real-world evidence supporting an individualized, geriatric-informed and treatment-centered approach to metastatic NSCLC, emphasizing functional reserve and access to effective systemic therapy rather than chronological age alone [13, 15, 21, 22].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this real-world, single-center cohort of patients with metastatic denovo NSCLC, chronological age ≥70 years was not an independent determinant of overall survival, access to systemic therapy, or treatment-related toxicity requiring hospitalization after adjustment for key clinical and disease-related factors. Although older patients demonstrated shorter unadjusted survival and were less likely to receive systemic treatment, these differences were largely explained by poorer performance status, higher metastatic burden, smoking exposure, and cognitive impairment rather than age itself. Importantly, receipt of systemic therapy emerged as the strongest predictor of improved survival, and appropriately selected older patients did not experience excess severe toxicity, supporting the safety of treatment in this population.\u003c/p\u003e\n\u003cp\u003eCollectively, these findings reinforce that prognosis and treatment decisions in metastatic NSCLC should be guided by functional reserve, disease extent, and treatment feasibility rather than chronological age alone. Incorporation of geriatric-informed assessment into routine clinical practice may help optimize treatment allocation and outcomes for older adults with advanced NSCLC, avoiding both undertreatment and unnecessary toxicity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e None\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interest:\u003c/strong\u003e The authors have no conflict of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e No funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions: GKA:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Data Curation, Formal analysis, Writing - Original Draft,\u0026nbsp;\u003cstrong\u003eKC:\u0026nbsp;\u003c/strong\u003eFormal analysis, Writing - Original Draft\u003cstrong\u003e\u0026nbsp;İÖ:\u0026nbsp;\u003c/strong\u003eMethodology, Writing - Review \u0026amp; Editing\u003cstrong\u003e\u0026nbsp;EA:\u003c/strong\u003e Conceptualization, Methodology, Writing - Review \u0026amp; Editing, Supervision\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Kratzer TB, Wagle NS, Sung H, Jemal A. 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Ther Adv Med Oncol. 2014;6(3):101\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/1758834014521110\u003c/span\u003e\u003cspan address=\"10.1177/1758834014521110\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metastatic non–small cell lung cancer, Geriatric oncology, Age-related outcomes, Overall survival, Systemic therapy, Treatment-related toxicity, Real-world data","lastPublishedDoi":"10.21203/rs.3.rs-8751117/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8751117/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOlder adults constitute a substantial proportion of patients with metastatic non\u0026ndash;small cell lung cancer (NSCLC); however, the independent impact of chronological age on survival, treatment allocation, and toxicity remains controversial. This study aimed to evaluate whether age\u0026thinsp;\u0026ge;\u0026thinsp;70 years independently influences outcomes in patients with metastatic NSCLC in a real-world setting.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a single-center, retrospective cohort study including 268 patients with metastatic NSCLC diagnosed between January 2018 and December 2022. Patients were stratified into two age groups (\u0026lt;\u0026thinsp;70 vs\u0026thinsp;\u0026ge;\u0026thinsp;70 years). Clinical characteristics, treatment patterns, and outcomes were analyzed. Overall survival (OS) was assessed using Kaplan\u0026ndash;Meier methods and Cox proportional hazards models. Logistic regression was used to identify determinants of treatment omission and toxicity-related hospitalization.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe median OS for the entire cohort was 8.8 months. Patients aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years had shorter unadjusted OS compared with younger patients (6.6 vs 10.3 months; p\u0026thinsp;=\u0026thinsp;0.020); however, age was not an independent predictor of OS in multivariable analysis. Poor performance status, smoking exposure, and higher metastatic burden were independently associated with worse survival, whereas receipt of systemic therapy and the presence of any driver mutation were associated with improved outcomes. Chronological age did not independently predict access to systemic therapy; instead, poor performance status and cognitive impairment were the primary determinants of treatment omission. Among treated patients, older age was not associated with increased severe toxicity; notably, patients aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years had lower odds of hospitalization due to treatment-related toxicity.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eChronological age alone should not guide treatment decisions in metastatic NSCLC. Functional status, disease burden, smoking exposure and access to effective systemic therapy are the principal determinants of survival and treatment outcomes. Appropriately selected older adults can safely receive systemic therapy without excess severe toxicity, underscoring the importance of individualized, geriatric-informed treatment strategies.\u003c/p\u003e","manuscriptTitle":"Chronological Age Is Not an Independent Determinant of Survival or Treatment Access in Metastatic Non–Small Cell Lung Cancer: A Real-World Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-27 09:28:01","doi":"10.21203/rs.3.rs-8751117/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-02T15:39:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22084848709582583525905682505278432709","date":"2026-03-02T07:41:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-24T05:05:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-02T06:48:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-02T04:35:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-02T04:34:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-01-31T15:36:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a3413246-5417-42da-b4bd-ecca0c0f945e","owner":[],"postedDate":"February 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-27T09:28:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-27 09:28:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8751117","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8751117","identity":"rs-8751117","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}