Effects of Concomitant Medications on the Therapeutic Effectiveness of PD-1/PD-L1 Inhibitors in Advanced Non-Small Cell Lung Cancer: A Retrospective Cohort Study

Effects of Concomitant Medications on the Therapeutic Effectiveness of PD-1/PD-L1 Inhibitors in Advanced Non-Small Cell Lung Cancer: A Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of Concomitant Medications on the Therapeutic Effectiveness of PD-1/PD-L1 Inhibitors in Advanced Non-Small Cell Lung Cancer: A Retrospective Cohort Study Yue Shen, Jie-Qi Chen, Zhen Yu, Zhi-Lan Lin, Lei Cao, Hui-Zhen Li, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6615560/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background: Patients with advanced non-small cell lung cancer (NSCLC) often require concurrent administration of other medications during immunotherapy to manage cancer-related complications or symptoms. However, these concomitant medications may interact with immune checkpoint inhibitors (ICIs), potentially impacting their therapeutic effectiveness. Aim: The study aims to systematically investigate the effect of concomitant medications for anti-tumor effectiveness of PD-1/PD-L1 inhibitors, providing suggestions for the selection of concomitant medications during ICIs treatment in NSCLC patients. Methods: This retrospective study collected and analyzed clinical characteristics and concomitant medication information of 650 advanced NSCLC patients treated with PD-1/PD-L1 inhibitors at Xiangya Hospital. The impact of commonly used concomitant medications on patients' progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR) was analyzed using SPSS 28.0. Propensity score matching (PSM) was employed to mitigate the influence of other confounding factors. The Log-rank test and Cox proportional hazards model were used to identify independent predictors of PFS. Results: Univariate COX analysis showed that antimicrobial agents, proton pump inhibitors (PPIs), calcium channel blockers (CCBs), glucocorticoids (GCs), non-steroidal anti-inflammatory drugs (NASIDs), antihistamines, anticoagulants, and opioids reduced PFS and lowered ORR and DCR ( P <0.01), while antihypertensive drugs (excluding CCBs), sedative-hypnotics, antidiabetic drugs, statins, and bronchodilators had no effect on antitumor efficacy. Additionally, the use of aspirin is significantly correlated with better PFS, ORR, and DCR ( P <0.05). Moreover, Multivariate Cox regression analysis indicated that the use of antimicrobial agents, PPIs, GCs and opioids are independent risk factors that adversely affect the effectiveness of PD-1/PD-L1 inhibitor therapy ( P <0.001). After PSM, their use remained linked to worse PFS, ORR, and DCR. Conclusion: Concomitant use of antimicrobial agents, PPIs, GCs, and opioids may diminish the antitumor efficacy of PD-1/PD-L1 inhibitors. Therefore, clinicians should consider these factors before prescribing ICIs to patients with NSCLC. Figures Figure 1 Figure 2 Figure 3 Impact of findings on practice statements 1. This study comprehensively analyzes the impact of various concomitant medications on the effectiveness of immune checkpoint inhibitors (ICIs) in patients with advanced non-small cell lung cancer (NSCLC), which better reflects the real-world scenario of polypharmacy in cancer patients undergoing clinical treatment. 2. By employing PSM to reduce confounding factors, it was demonstrated that the negative effects of antimicrobial drugs, PPIs, GCs, and opioids on ICIs were independent of patients' baseline characteristics (particularly ECOG scores). 3. A more in-depth investigation into the specific types of antimicrobial drugs and their effects on ICIs revealed that the use of penicillins, cephalosporins, quinolones, carbapenems, and antifungal drugs can weaken the effectiveness of PD-1/L1 inhibitors. 4. As a notable exception among adjuvant drugs, aspirin was shown to enhance the antitumor efficacy of PD-1/L1 inhibitors, highlighting its potential as a beneficial addition to immunotherapy regimens. INTRODUCTION Lung cancer is the leading cause of cancer-related mortality worldwide [ 1 ] , with non-small cell lung cancer (NSCLC) representing 80% of cases, making it the most prevalent subtype [ 2 ] . In recent years, the advent of immune checkpoint inhibitors (ICIs) has introduced novel opportunities for NSCLC treatment [ 3 ] . However, the effectiveness of ICIs is not universal, with only 20–40% of patients experiencing significant benefit [ 4 ] . This variability has prompted investigations into factors modulating ICI efficacy, including the emerging role of concomitant medications. In the management of NSCLC, patients often require concomitant medications to address comorbid conditions or secondary symptoms related to the cancer, such as hypertension, diabetes, pain, and anxiety. Growing evidence suggests that concomitant medications may significantly influence ICIs outcomes through diverse mechanisms [ 5 – 7 ] .Antimicrobial agents have been consistently associated with reduced survival, potentially via gut microbiome disruption. For example, Ochi et al. found that patients who used antimicrobial agents before and after ICIs treatment had a median overall survival (OS) of 11.7 months, compared to 16.1 months in those who did not use antibiotics [ 8 ] . Similarly, proton pump inhibitors (PPIs) have shown negative associations with survival outcomes, possibly through gastric pH alteration and subsequent microbiome changes [ 9 ] . Glucocorticoids (GCs), frequently used for cancer-related symptoms or immune-related adverse events, have been linked to poorer progression-free survival (PFS) and OS, likely via immunosuppressive effects on the tumor microenvironment [ 10 ] . However, the potential mechanisms through which these concomitant medications adversely affect the effectiveness of ICIs remain inadequately explored. Furthermore, most studies have focused exclusively on individual concomitant medications, with limited exploration of the effects when drug categories like antihypertensives, antidiabetics, statins, anticoagulants, and bronchodilators are used in combination with ICIs. Previous studies have not distinguished between specific cancer types [ 11 – 13 ] , although the impact of concomitant medication on the effectiveness of ICIs may vary across different cancers [ 14 ] . A growing body of evidence suggests that antibiotic use is associated with poorer outcomes of ICIs therapy in most solid tumors. However, this association appears to be tumor-specific, as demonstrated by Wang et al. [ 15 ] who reported no significant adverse effects of antibiotic use (within ± 30 days of ICI treatment) on PFS, DCR, or OS in patients with primary liver cancer. Additionally, a study investigating patients with solid tumors receiving ICIs treatment found that only in patients with urothelial carcinoma (UC) was the use of β-blockers significantly associated with improved OS [ 16 ] . This result may be attributed to the higher number of β-receptors in UC [ 17 ] . In this study, in order to reveal the impact of concomitant medications on ICIs treatment in lung cancer, we conducted a multicenter retrospective study to explore the effects of commonly used concomitant drugs on the effectiveness of PD-1/PD-L1 inhibitors in patients with advanced NSCLC, aiming to provide insights for more informed clinical decision-making regarding concomitant medication use. METHODS Study Population We retrospectively collected advanced NSCLC patients treated with ICIs between January 2017 and January 2023 at Xiangya Hospital of Central South University. Based on preliminary study data (antibacterial agents HR = 1.375), sample size estimation was performed using PASS 2025 software (one-sample log-rank test, Weibull model, α = 0.05, β = 0.1). The calculation demonstrated that a minimum of 63 patients per group was required. To account for the needs of multifactorial analysis and subgroup analysis, a total of 650 patients were ultimately enrolled to ensure sufficient statistical power for the study. Inclusion and Exclusion Criteria: The inclusion criteria include: (1) Patients with pathologically confirmed stage IIIB/IV NSCLC who are ineligible for surgical treatment. (2) Patients who have received systematic PD-1 or PD-L1 inhibitor therapy, either as monotherapy or in combination. (3) Availability of detailed records on concomitant medications. (4) Complete clinical data, including baseline information, treatment regimen, follow-up records, and PFS. (5) Patients aged between 18 and 80 years. Exclusion criteria include: (1) Presence of other malignant tumors. (2) Severe organ failure. (3) Pregnant or lactating women. Data collection Through the electronic medical record system, we collected the following data: Demographic Characteristics including gender, age, body mass index (BMI), smoking index, pathological classification, Eastern Cooperative Oncology Group (ECOG) performance status, genetic mutation status, PD-L1 expression, brain metastasis, distant metastasis, and concurrent medication information. Concomitant medication records : (1) Systemic antimicrobial treatment involves selecting antimicrobial drugs used within 30 days before and after ICIs, including penicillins, cephalosporins, carbapenems, fluoroquinolones, and antifungal drugs; antimicrobial drugs usage duration categorized as either ≥ 7 days or < 7 days. (2) Baseline GCs usage is divided based on dosage into two categories: ≥10 mg (prednisone equivalent) and < 10 mg. (3) Antihypertensive drugs include angiotensin-converting enzyme inhibitors (ACEI), angiotensin II receptor blockers (ARB), beta-blockers, calcium channel blockers (CCBs), and diuretics. (4) Antidiabetic drugs comprise insulin, metformin, sulfonylureas, alpha-glucosidase inhibitors, SGLT-2 inhibitors, and DPP-4 inhibitors. (5) Bronchodilators include β2-adrenergic receptor agonists and muscarinic receptor agonists. (6) Antiplatelet drugs consist of clopidogrel and aspirin (75mg-300mg). (7) Other baseline medications include PPIs, non-steroidal anti-inflammatory drugs (NASIDs), statins, sedative-hypnotics, antihistamines, anticoagulants, and opioids. The follow-up terminate date was December 1, 2024. During this period, patient survival status, disease progression, and medication usage were monitored through telephone interviews, outpatient visits, and electronic medical records. Study Endpoints The primary effectiveness endpoint included real-world progression-free survival (rwPFS), objective response rate (ORR), and disease control rate (DCR). rwPFS is defined as the time from the first administration of ICIs to the time of disease progression (PD) or death from any cause, confirmed by radiology reports or clinical assessments at the last major evaluation. ORR was defined as the proportion of patients achieving complete response (CR) or partial response (PR) by the end of follow-up. DCR was defined as the proportion of patients achieving CR, PR, or stable disease (SD) by the end of follow-up. Clinicians systematically evaluated the effectiveness of immunotherapy every 4 to 12 weeks based on patients' clinical responses and imaging findings. Statistical analysis Patient baseline characteristics were analyzed using descriptive statistics. Chi-square tests or Fisher's exact tests were utilized for inter-group comparisons of ORR and DCR. Log-rank tests and Cox proportional hazards models were applied to conduct univariate/multivariate analyses on rwPFS. For concomitant medication groups that significantly affected treatment efficacy, 1:1 propensity score matching was performed with a caliper value of 0.02 to control for confounding factors. Matching variables included demographic characteristics such as age, sex, and PD-L1 expression level. The Kaplan-Meier method was employed to estimate median rwPFS, using R 4.3.2 to plot the K-M survival curves. All statistical analyses were performed with SPSS 28.0, adopting a significance level of α = 0.05 (two-tailed), with P < 0.05 considered statistically significant. RESULTS 1.Patient characteristics The study enrolled a total of 650 patients. The baseline characteristics and concomitant medication usage of the patients are presented in Table 1 . The median age was 63 years (ranging from 35 to 80). The median rwPFS was 5 months, with 317 (48.77%) patients experiencing PD at the end of the follow-up period. ORR and DCR were 18.31% and 51.23%, respectively, with 6 patients (0.9%) achieving CR, 113 patients (17.4%) achieving PR, and 214 patients (32.9%) achieving SD. Regarding treatment regimens, the most frequently administered PD-1/PD-L1 inhibitors were pembrolizumab (n = 191, 29.4%), camrelizumab (n = 180, 27.7%), and tislelizumab (n = 107, 16.5%). The majority of patients received PD-1/PD-L1 inhibitors combined with chemotherapy (n = 416, 64.0%), while 122 patients (18.8%) underwent monotherapy. A subset received triple therapy combining PD-1/PD-L1 inhibitors, chemotherapy, and targeted agents (n = 69, 10.6%), and 43 patients (6.6%) received dual therapy with PD-1/PD-L1 inhibitors and targeted drugs. Table 1 Patient Baseline Characteristics Baseline Characteristics N (%) Gender Male 508(78.2) Female 142(21.8) Age < 65 393(60.5) ≥ 65 257(39.5) ECOG-PS 0–1 488(75.1) ≥ 2 162(24.9) Pathological Type Adenocarcinoma 338(52) Non-adenocarcinoma 312(48) Smoking Index < 400 254(39.1) ≥ 400 396(60.1) BMI(Kg/M^2) 23.9 225(34.6) Brain Metastasis No 529(81.4) Yes 121(18.6) Distant Metastasis ≤ 1 site 502(77.2) ≥ 2 site 148(22.8) TP53 Mutation No 590(90.8) Yes 60(9.2) EGFR Mutation No 604(92.9) Yes 46(7.1) KRAS Mutation No 585(90) Yes 65(10) PD-L1 Expression <1% 207(31.9) 1–49% 220(33.8) ≥ 50% 223(34.3) Treatment Modality PD-1/PD-L1 Monotherapy 122 PD-1/PD-L1 Combination Therapy 528 Concomitant Medication Type Antimicrobial Agents 244(37.5) Penicillin 104 Cephalosporins 88 Quinolones 134 Carbapenems 42 Antifungal Agents 10 Others 45 PPIs 420(64.6) Antihypertensive Agents 153(23.54) ACEI/ARB 21 Beta-Blockers 32 CCBs 120 Diuretics 34 Antidiabetic Drugs 63(9.7) Insulin 37 Metformin 35 Sulfonylureas 4 Alpha-glucosidase Inhibitors 32 SGLT-2 Inhibitors 7 DPP-4 Inhibitors 12 GCs 452(69.5) NASIDs 116(17.8) Statins 99(15.2) Sedative-hypnotics 236(36.3) Anticoagulants 198(30.5) Antiplatelet Drugs 183(28.2) Aspirin 92 Clopidogrel 103 Antihistamines 256(39.4) β2- adrenergic receptor agonists 65(10) Muscarinic receptor agonists 64(9.8) Opioids 240(36.9) Note: Proton pump inhibitors (PPIs), Angiotensin-converting enzyme inhibitors (ACEI), Angiotensin II receptor blockers (ARB), Calcium channel blockers (CCBs), Glucocorticoids (GCs), Non-steroidal anti-inflammatory drugs (NASIDs). 2.The association of Concomitant Medication with Effectiveness of Immune Checkpoint Inhibitors Univariate Cox regression analysis of patients' rwPFS revealed that the use of antimicrobial agents, PPIs, CCBs, GCs, NSAIDs, anticoagulants, antihistamines, and opioids were identified as risk factors for reduced effectiveness of ICIs ( P 0.05) (Table 2 ). However, no significant differences were observed in rwPFS, ORR, or DCR regarding antihypertensives, sedative-hypnotics, bronchodilators, antidiabetics, statins and clopidogrel. (Supplementary Material-Table 1). Table 2 Univariate Cox Regression Analysis of the Impact of Concomitant Medication on rwPFS Concomitant Medication HR 95%CL P Antimicrobial Agents 3.044 2.431–3.811 < 0.001 Penicillin 2.319 1.790–3.005 < 0.001 Cephalosporins 2.155 1.635–2.839 < 0.001 Quinolones 2.035 1.602–2.586 < 0.001 Carbapenems 2.155 1.431–3.247 < 0.001 Antifungal Agents 3.123 1.542–6.327 0.002 Others 1.846 1.280–2.661 0.001 PPIs 2.887 2.219–3.755 < 0.001 Antihypertensive Agents 1.525 1.198–1.942 < 0.001 ACEI/ARB 1.559 0.927–2.622 0.094 Beta-Blockers 1.359 0.889–2.079 0.157 CCBs 1.516 1.166–1.970 0.002 Diuretics 1.35 0.883–2.081 0.164 Antidiabetic Drugs 1.228 0.867–1.737 0.147 Insulin 1.158 0.743–1.803 0.517 Metformin 0.837 0.513–1.364 0.474 Sulfonylureas 0.330 0.046–2.349 0.268 Alpha-glucosidase Inhibitors 0.689 0.403–1.179 0.174 SGLT-2 Inhibitors 0.154 0.022–1.099 0.062 DPP-4 Inhibitors 2.140 1.102–4.158 0.025 GCs 2.719 2.055–3.597 < 0.001 NASIDs 2.277 1.775–2.920 < 0.001 Statins 0.996 0.734–1.352 0.981 Sedative-hypnotics 1.234 0.984–1.548 0.069 Anticoagulants 1.907 1.511–2.406 < 0.001 Antiplatelet Drugs 0.847 0.658–1.090 0.197 Aspirin 0.596 0.417–0.851 0.004 Clopidogrel 1.177 0.873–1.586 0.285 Antihistamines 1.596 1.273–2.002 < 0.001 β2-adrenergic receptor agonists 1.371 0.977–1.925 0.068 Muscarinic receptor agonists 0.871 0.587–1.292 0.491 Opioids 2.397 1.916-3.000 < 0.001 3. The Impact of Various Concomitant Medications on Effectiveness Outcomes 3.1 Antimicrobial agents Among the 244 patients receiving antimicrobial treatment, 42 were administered orally, 141 via intravenous injection, and 61 through a combined route. The most frequently used categories antimicrobial agents were beta-lactams (n = 235) and quinolones (n = 134). Univariate Cox regression analysis indicated that all categories of antimicrobial agents reduced rwPFS (Table 2 ). Compared to patients not treated with antimicrobials, those receiving antimicrobial treatment had a significant reduction in median rwPFS (4.8 vs 11.6 months; hazard ratio [HR], 3.044; 95% CI, 2.431–3.811; P < 0.001; Fig. 1 A), especially those on penicillins (5.1 vs 8.5 months, P < 0.001), cephalosporins (4.5 vs 8.0 months, P < 0.001), quinolones (5.3 vs 8.3 months, P < 0.001), carbapenems (4.1 vs 7.7 months, P < 0.001), and antifungals (4.8 vs 7.7 months, P < 0.001) (Supplementary Material-Figure 1).Similarly, the ORR and DCR were lower in the group receiving antimicrobial agents (ORR, 12.3% vs 21.9%, P = 0.002; DCR, 28.7% vs 64.8%, P < 0.001). Furthermore, when patients used antimicrobial drugs for more than 7 days, there was a notable decrease in PFS (4.2 vs 5.1 months; P = 0.016, Fig. 1 B). 3.2 Glucocorticoids In the cohort of 452 patients receiving GCs, dosage varied according to specific indications, leading to a categorization based on prednisone equivalence: ≥10mg/day (n = 220) and < 10mg/day (n = 140). Compared to untreated counterparts, patients on GCs exhibited a marked decrease in median rwPFS (6.0 vs 15.0 months; [HR], 2.219; 95% CI, 2.055–3.597; P < 0.001; Fig. 1 C). A similar negative effect was observed in DCR (50% vs 67.2%, P = 0.001), while ORR showed no significant difference (16.6% vs 22.2%, P = 0.088). Moreover, patients with a GCs dosage ≥ 10mg/day of prednisone equivalent saw even shorter median rwPFS (4.9 vs 7.0 months; P < 0.001; Fig. 1 D). 3.3 Proton Pump Inhibitors Among the 420 patients treated with PPIs, pantoprazole was the most frequently used (n = 239, 56.9%) with a general dosage of 40mg. Following were omeprazole (n = 87), esomeprazole (n = 46), lansoprazole (n = 31), and rabeprazole (n = 8). Most patients received PPIs via intravenous administration (n = 330). Compared to those not receiving PPIs therapy, patients on PPIs had a significantly shorter median rwPFS (5.7 vs 13.4 months; [HR], 2.887; 95% CI, 2.219–3.755; P < 0.001; Fig. 1 E), and fewer achieved SD (42.9% vs 66.5%, P < 0.001). Additionally, patients receiving combined oral and intravenous PPIs administration had a shorter rwPFS ( P = 0.026, Fig. 1 F). (A) Antimicrobial agents, (B) Duration of antimicrobial therapy (≥ 7 days), (C) Glucocorticoids, (D) Glucocorticoid dosage ≥ 10mg/day, (E) Proton pump inhibitors, (F) Administration route of proton pump inhibitors (oral/intravenous/oral + intravenous) 3.4 Non-Steroidal Anti-Inflammatory Drugs Among the 116 patients treated with NSAIDs, 52 received ibuprofen, 40 diclofenac sodium, 10 etodolac, and 7 celecoxib. Compared to patients not receiving NSAIDs, those treated with NSAIDs had a shorter median rwPFS (4.5 vs 8.5 months; [HR], 2.277; 95% CI, 1.775–2.920; P < 0.001; Fig. 2 A), and lower ORR (9.1% vs 20.4%, P = 0.003) and DCR (25.9% vs 56.7%, P < 0.001). 3.5 Antihistamines 256 patients were treated with antihistamines, of which diphenhydramine accounted for approximately 70%. Compared to patients who did not receive antihistamine treatment, those treated with antihistamines had a shorter median rwPFS (6.1 vs 8.1 months; [HR], 1.596; 95% CI, 1.273–2.002; P < 0.001; Fig. 2 B). However, both ORR and DCR showed no significant difference (ORR,19.5% vs 17.5%; P = 0.516; DCR; 48.0% vs 53.3%; P = 0.191, respectively). 3.6 Anticoagulants In a cohort of 198 patients treated with anticoagulants, heparins were administered to 163 patients and factor Xa inhibitors to 76. Compared to patients who did not receive anticoagulation therapy, those who did had a shorter median rwPFS (5.6 vs 8.5 months; [HR], 1.907;95% CI,1.511–2.406; P < 0.001; Fig. 2 C). Similarly, the DCR was significantly reduced (43% vs 54.9%; P = 0.005), while the ORR showed no statistical difference (18.2% vs 18.4%; P = 0.184). 3.7 Opioid Approximately 37% of patients used opioids to control cancer pain during immunotherapy, with most receiving tramadol (n = 117) and/or oxycodone (n = 121). Patients receiving opioid treatment had a shorter median rwPFS (5.6 vs 10.0 months; [HR], 2.397; 95% CI, 1.916-3.000; P < 0.001; Fig. 2 D), and significantly lower ORR (14.2% vs 20.7%, P < 0.001) and DCR (34.6% vs 56.8%, P < 0.001) compared to those not on opioids. 3.8 Calcium channel blockers Among 153 patients receiving antihypertensive treatment, CCBs were the predominant medication (n = 120). Univariate Cox regression analysis showed that, except for CCBs ([HR], 1.516; 95% CI, 1.166–1.970; P = 0.002), other categories of antihypertensive drugs had no significant impact on patients' rwPFS. Compared to patients not treated with CCBs, those treated with CCBs had a shorter median rwPFS (5.6 vs 8.0 months; [HR], 2.397; 95% CI, 1.916-3.000; P = 0.002; Fig. 2 E), a lower DCR (39.2% vs 54.0%; P = 0.003), while no significant difference in ORR (18.3% vs 18.3%; P = 0.994). 3.9 Aspirin Aspirin, a widely utilized antiplatelet medication in clinical practice, is typically administered at doses ranging from 75 to 300 mg. Compared to patients who did not receive aspirin, those treated with it (n = 92) had a significantly prolonged median rwPFS (11.2 vs 7.0 months; P = 0.004; Fig. 2 F), elevated ORR (29.3% vs 16.5%; P = 0.003), and higher DCR (63% vs 49.3%; P = 0.014). 4. Analysis of clinical characteristics and concomitant medications Considering the interactions among various concomitant medications in patients, we performed multivariate Cox regression on those with a univariate P < 0.05, aiming to precisely evaluate each drug's independent impact on patient outcome. The results indicated that the use of antimicrobial agents ([HR], 2.603; 95% CI, 2.070–3.274), PPIs ([HR], 1.786; 95% CI, 1.786–1.336), GCs ([HR], 1.982; 95% CI, 1.459–2.691), and opioids ([HR], 1.847; 95% CI, 1.470–2.321) were independent risk factors for patients' rwPFS (P < 0.001). Additionally, we observed that the use of ATBs, PPIs, GCs, and opioids was significantly associated with worse ECOG PS scores, lower BMI and higher rates of distant metastasis (Table 3 ). Therefore, when analyzing the effectiveness of ICIs, it is essential to consider the relationship between these medications and ECOG PS scores. Furthermore, as the expression of PD-L1 increased, the negative impact of these drugs on rwPFS was attenuated (Supplementary Material - Fig. 2 ).Due to the multitude of factors affecting the effectiveness of ICIs, we employed PSM to match patients in each group at a 1:1 ratio, thereby reducing the impact of confounding variables such as gender, age, body mass index, smoking index, pathological subtype, ECOG score, gene mutation status, PD-L1 expression, brain metastasis, and distant metastasis on the antitumor effectiveness of ICIs. The matched results indicated that even after controlling for these confounders, patients using antimicrobials, PPIs, GCs, and opioids continued to exhibit lower rwPFS (Fig. 3). Table 3 Association Between Concomitant Medications (Antimicrobial Agents, GCs, PPIs, Opioids) and ECOG PS scores, BMI and Distant Metastasis. Concomitant Medication ECOG PS (%) P(χ²) BMI (%) P(χ²) Distant metastasis (%) P(χ²) ≤ 1 ≥ 2 23.9 ≤ 1 site ≥ 2 site Antimicrobial Agents < 0.001 0.001 < 0.001 Yes 160 (65.57) 84 (34.43) 38 (15.57) 138 (56.56) 68 (56.57) 181 (74.18) 63 (25.82) No 328 (80.79) 222 (54.68) 31 (7.64) 218 (53.69) 157 (38.67) 321 (79.06) 85 (20.94) GCs 0.033 0.007 0.001 Yes 328 (72.57) 124 (27.43) 52 (11.50) 261 (57.74) 139 (30.75) 332 (73.45) 120 (26.55) No 160 (80.81) 38 (19.19) 17 (8.59) 95 (47.98) 86 (43.43) 170 (85.86) 28 (14.14) Proton Pump Inhibitors < 0.001 < 0.001 0.007 Yes 290 (69.05) 130 (30.95) 55 (13.10) 244 (58.10) 121 (28.81) 310 (73.81) 110 (26.19) No 198 (86.09) 32 (13.91) 14 (6.09) 112 (48.70) 104 (45.22) 192 (83.48) 38 (16.52) Opioids 0.028 < 0.001 < 0.001 Yes 168 (70.00) 72 (30.00) 40 (16.67) 137 (57.08) 63 (26.25) 159 (66.25) 81 (33.75) No 320 (78.05) 90 (21.95) 29 (7.07) 219 (53.41) 162 (39.51) 343 (83.66) 67 (16.34) DISCUSSION We observed that the use of antimicrobial drugs, PPIs, GCs, NSAIDs, antihistamines, and opioids adversely affected the effectiveness of immune checkpoint inhibitors in patients. The use of antihypertensive drugs (except CCBs), antidiabetic drugs, statins, anticoagulants, antiplatelet drugs, sedative-hypnotics, and bronchodilators had no impact, whereas aspirin use was associated with a better antitumor prognosis. Compared to other similar studies, our comprehensive analysis of various concomitant medication types more accurately reflects the real-world scenario of polypharmacy in patients with advanced cancer. Numerous reports highlight the adverse effects of antimicrobial drugs on immunotherapy [ 18 , 19 ] , yet few studies have examined whether different types of antimicrobial drugs have consistent effects on ICIs. Given the variability in antimicrobial spectrum and activity, the detrimental effects of antimicrobial drugs cannot be generalized. Our study indicates that penicillins, cephalosporins, quinolones, carbapenems, and antifungal drugs all weaken the effectiveness of PD-1/PD-L1 inhibitors, with antifungals having the most significant negative impact. PPIs reduce the absorption of drugs and alter the microbiota by decreasing gastric acid secretion [ 20 ] , thus affecting the antitumor effectiveness of immunotherapy [ 9 , 21 ] .We found that patients receiving both oral and intravenous PPIs had worse prognoses. The rationale for combining these drugs with the same mechanism warrants further consideration. Due to varying indications for GCs (tumor or non-tumor reasons), the dosage used varies significantly. Our study found that patients receiving more than 10mg of prednisone equivalents had further reduced rwPFS, consistent with previous research [ 10 ] , suggesting that physicians should minimize GCs overuse, especially during ICI treatment. Nevertheless, in the event of immune-related adverse reactions (irAEs) during PD-1/PD-L1 therapy, GCs use can improve patient outcomes [ 22 ] . While the use of opioid analgesics is common in cancer patients, research on the impact of opioids on the effectiveness of ICIs is limited [ 13 ] . Studies suggest that opioids can cause significant changes in the intestinal microbiota, thus exerting a mechanism similar to antibiotics [ 23 ] .Studies suggest that opioids can cause significant changes in the intestinal microbiota, thus exerting a mechanism similar to antibiotics. Interestingly, NSAIDs have shown immunomodulatory synergies in certain preclinical studies, with research by Hussain et al. indicating that NASIDs can facilitate the infiltration of CD4 + and CD8 + T cells into tumors [ 24 ] . Similarly, Aboelella et al. found that indomethacin can enhance death receptor 5 signaling, sensitizing tumor cells to adoptive T cell therapy [ 25 ] . However, a study by Kostine et al. reported that the use of NSAIDs resulted in similar mPFS to non-use in advanced cancer patients ( P = 0.4) [ 11 ] , consistent with our multivariate analysis findings. Preclinical studies suggest that antihistamines and anticoagulants can enhance responses to ICIs [ 20 , 26 , 27 ] , yet in our study, patients receiving these medications had worse antitumor prognoses. A significant reason may be that the use of antihistamines and anticoagulants is often closely associated with higher frequency use of PPIs and GCs, thus necessitating further research to elucidate the interaction between these drugs and ICIs. In general, it is reasonable for patients with underlying conditions such as hypertension, diabetes, hyperlipidemia, and COPD(Chronic Obstructive Pulmonary Disease) to continue their regular medication during anti-tumor treatments. However, it is noteworthy that among antihypertensive drugs, CCBs have exhibited significant detrimental effects. Previous studies have found that exposure to CCBs is associated with an increased risk of lung cancer [ 28 ] , however, the specific mechanism remains unclear. The choice of antihypertensive drugs in cancer patients, specifically whether to avoid CCBs, necessitates further research evidence. Additionally, patients with advanced cancer often use sedative-hypnotic drugs due to anxiety and insomnia and this study's results indicate that these drugs do not affect the effectiveness of PD-1/PD-L1 inhibitors. Moreover, the judicious use of sedative-hypnotic drugs is necessary to improve the mental state of the patients. Although most adjuvant drugs negatively impact patients' anti-tumor treatment, some have been found in preclinical studies to exhibit synergistic effects with ICIs' anti-tumor efficacy. Among these, aspirin is particularly noteworthy. As early as 2015, Sousa and colleagues identified that aspirin could inhibit the production of PGE2 and restore the levels of specific dendritic cells (cDC1) [ 29 ] . Recently, new mechanisms by which aspirin enhances the effectiveness of PD-L1 immunotherapy have been continually identified, such as by downregulating FGL1 and CD8 + T cell PD-1 expression [ 30 , 31 ] . Furthermore, studies have found that aspirin can reduce the risk of distant tumor metastasis and decrease the mortality rate due to adenocarcinoma [ 32 ] . The above studies correspond with the findings of this research, which show that patients taking aspirin have a better clinical prognosis. Furthermore, the interest in metabolic reprogramming of the tumor microenvironment has brought significant attention to the synergistic anti-tumor effects of metformin and statins. Numerous in vitro and animal studies have demonstrated that inhibiting glucose/lipid metabolism in the tumor microenvironment can enhance the anti-tumor activity of ICIs [ 33 – 35 ] . This indicates that metformin and statins may have potential benefits for patients undergoing immunotherapy, yet the desired synergistic effect has not been observed in clinical phases. Afzal et al. found that patients with metastatic melanoma, treatment combining metformin and ICIs did not significantly change ORR, DCR, mPFS, or median OS compared to the control group [ 36 ] . The study by Ondřej Fiala et al. also indicates that the use of statins or metformin is not associated with the response to immunotherapy or survival [ 37 ] , similar to the findings of our study. This may be because tumor patients control their blood glucose and lipid levels with conventional drug doses during immunotherapy, whereas higher doses are required to exert an anti-tumor effect. Our findings offer beneficial guidance for rational pharmacotherapy in patients with NSCLC. This includes stringent management of the indications, dosage, route of administration, and purpose of antimicrobial drugs. For analgesics, the implementation of the 'three-step analgesic ladder' principle is essential. Similar to antimicrobials, PPIs are often overprescribed in the clinical treatment of cancer patients, frequently issued unjustifiably for the prevention of nausea and vomiting. Additionally, with the increasing variety of concomitant medications, the negative impact on anti-tumor efficacy continues to accumulate. We should also be aware that an increase in the variety and quantity of drugs can lead to a higher incidence of adverse reactions. Concomitant medication use often occurs in patients with underlying conditions or compromised immunity. For instance, antimicrobial agents may indicate infection or immunosuppression, while PPIs, GCs, and opioids might be related to gastrointestinal symptoms, chronic inflammation, or pain management. Because a decline in performance status can impact both a patient’s tolerance to ICIs and overall treatment efficacy, we used PSM to control for various confounding factors, including ECOG scores. The results revealed that the negative effects of antimicrobial agents, PPIs, GCs, and opioids on ICIs' efficacy were independent of the patients' baseline characteristics. After analyzing factors such as the ECOG PS score, we found that antimicrobial drugs, PPIs, GCs, and opioids had no significant correlation with the effectiveness of ICIs, independent of the patient's baseline characteristics. However, the complexity between concomitant medication use and tumor efficacy necessitates further prospective studies for validation. For instance, opioids are frequently employed to manage severe cancer pain associated with extensive bone metastasis. Despite the application of the PSM method in this study, it is not possible to entirely eliminate the potential impacts of the underlying disease. Furthermore, immunotherapy for specific populations, including those with hypertension, hyperglycemia, hyperlipidemia, and COPD, warrants particular attention to formulate individualized treatment strategies. We acknowledge that the retrospective design of this study may lead to some analytical bias. Additionally, the absence of data on adverse reaction rates in patients using ICIs with concomitant medications precludes discussion on the impact of these drugs on the safety of ICI therapy. In conclusion, concomitant medications play a significant role in the treatment with ICIs, often exerting negative effects. Therefore, a comprehensive evaluation of the clinical use of antimicrobial agents, PPIs, GCs, and opioids should be conducted during the development of anti-tumor protocols to ensure rational drug use. Declarations Ethics approval and consent to participate: This retrospective study was approved by the Ethics Committee of Xiangya Hospital, Central South University (Approval No.: 2024111428; Date: November 6, 2024). The requirement for informed consent was waived by the committee due to the retrospective nature of the study. Clinical trial number: Not applicable. Consent for publication ： This study did not use personally identifiable data, therefore the requirement for publication consent is not applicable. Availability of data and materials ： The data that support the findings of this study are available on request from the corresponding author upon reasonable request. Competing interests ： All authors declare that there are no conflicts of interest related to this work. Funding: This work was supported by the National Natural Science Foundation of China (82474012) and the National Natural Science Foundation of China (82204531). Author Contribution: Conceptualization, Xiang-Ping Li; Data curation, Jie-Qi Chen; Investigation, Yu Zhen, Zhi-Lan Lin , Lei Cao, Hui-Zhen Li ,Ying-Cai Meng and Bin Li; Funding acquisition, Yue-Qin Li; Writing – original draft, Yue Shen; Writing – review & editing, Jie-Qi Chen, Juan Chen. Acknowledgements ： Not applicable References SIEGEL RL, MILLER K D WAGLENS, et al. Cancer statistics, 2023 [J]. CA Cancer J Clin. 2023;73(1):17–48. https://doi.org/10.3322/caac.21763 . TRAVIS W D BRAMBILLAE, NICHOLSON A G, et al. The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification [J]. J Thorac Oncol. 2015;10(9):1243–60. https://doi.org/10.1097/jto.0000000000000630 . CLARKE JM, GEORGE D J, LISI S, et al. Immune Checkpoint Blockade: The New Frontier in Cancer Treatment [J]. Target Oncol. 2018;13(1):1–20. https://doi.org/10.1007/s11523-017-0549-7 . DOROSHOW D B, BHALLA S, BEASLEY M B, et al. 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Liver Res. 2022;6(3):175–80. https://doi.org/10.1016/j.livres.2022.05.004 . MELLGARD G, PATEL V G, ZHONG X, et al. Effect of concurrent beta-blocker use in patients receiving immune checkpoint inhibitors for advanced solid tumors [J]. J Cancer Res Clin Oncol. 2023;149(7):2833–41. https://doi.org/10.1007/s00432-022-04159-y . RAINS SL, AMAYA C N, BRYAN BA. Beta-adrenergic receptors are expressed across diverse cancers [J]. Oncoscience. 2017;4(7–8):95–105. https://doi.org/10.18632/oncoscience.357 . TINSLEY N, ZHOU C, TAN G, et al. Cumulative Antibiotic Use Significantly Decreases Efficacy of Checkpoint Inhibitors in Patients with Advanced Cancer [J]. Oncologist. 2020;25(1):55–63. https://doi.org/10.1634/theoncologist.2019-0160 . DEROSA L, HELLMANN M D, SPAZIANO M, et al. Negative association of antibiotics on clinical activity of immune checkpoint inhibitors in patients with advanced renal cell and non-small-cell lung cancer [J]. Ann Oncol. 2018;29(6):1437–44. https://doi.org/10.1093/annonc/mdy103 . RAOUL JL, MOREAU-BACHELARD C, GILABERT M, et al. Drug-drug interactions with proton pump inhibitors in cancer patients: an underrecognized cause of treatment failure [J]. ESMO Open. 2023;8(1):100880. https://doi.org/10.1016/j.esmoop.2023.100880 . GIORDAN Q, SALLERON J, VALLANCE C, et al. Impact of Antibiotics and Proton Pump Inhibitors on Efficacy and Tolerance of Anti-PD-1 Immune Checkpoint Inhibitors [J]. Front Immunol. 2021;12:716317. https://doi.org/10.3389/fimmu.2021.716317 . SHAFQAT H, GOURDIN T. Immune-related adverse events are linked with improved progression-free survival in patients receiving anti-PD-1/PD-L1 therapy [J]. Semin Oncol. 2018;45(3):156–63. https://doi.org/10.1053/j.seminoncol.2018.07.003 . VERSCHUEREN M V, VAN DER WELLE C M C, TONN M, et al. The association between gut microbiome affecting concomitant medication and the effectiveness of immunotherapy in patients with stage IV NSCLC [J]. Sci Rep. 2021;11(1):23331. https://doi.org/10.1038/s41598-021-02598-0 . HUSSAIN M, JAVEED A, ASHRAF M, et al. Non-steroidal anti-inflammatory drugs, tumour immunity and immunotherapy [J]. Pharmacol Res. 2012;66(1):7–18. https://doi.org/10.1016/j.phrs.2012.02.003 . ABOELELLA NS, BRANDLE C, OKOKO O, et al. Indomethacin-induced oxidative stress enhances death receptor 5 signaling and sensitizes tumor cells to adoptive T-cell therapy [J]. J Immunother Cancer. 2022;10(7). https://doi.org/10.1136/jitc-2022-004938 . CHOI J U, LEE N K, SEO H, et al. Anticoagulation therapy promotes the tumor immune-microenvironment and potentiates the efficacy of immunotherapy by alleviating hypoxia [J]. J Immunother Cancer. 2021;9(8). https://doi.org/10.1136/jitc-2021-002332 . WEI F, SU Y, QUAN Y, et al. Anticoagulants Enhance Molecular and Cellular Immunotherapy of Cancer by Improving Tumor Microcirculation Structure and Function and Redistributing Tumor Infiltrates [J]. Clin Cancer Res. 2023;29(13):2525–39. https://doi.org/10.1158/1078-0432.Ccr-22-2757 . ROTSHILD V, AZOULAY L, ZARIFEH M, et al. The Risk for Lung Cancer Incidence with Calcium Channel Blockers: A Systematic Review and Meta-Analysis of Observational Studies [J]. Drug Saf. 2018;41(6):555–64. https://doi.org/10.1007/s40264-018-0644-4 . ZELENAY S, VAN DER VEEN A G, BöTTCHER JP, et al. Cyclooxygenase-Dependent Tumor Growth through Evasion of Immunity [J]. Cell. 2015;162(6):1257–70. https://doi.org/10.1016/j.cell.2015.08.015 . LIN M, HE J, ZHANG X, et al. Targeting fibrinogen-like protein 1 enhances immunotherapy in hepatocellular carcinoma [J]. J Clin Invest. 2023;133(9). https://doi.org/10.1172/jci164528 . DE MATTEIS R, FLAK M B, GONZALEZ-NUNEZ M, et al. Aspirin activates resolution pathways to reprogram T cell and macrophage responses in colitis-associated colorectal cancer [J]. Sci Adv. 2022;8(5):eabl5420. https://doi.org/10.1126/sciadv.abl5420 . ROTHWELL P M, WILSON M, PRICE JF, et al. Effect of daily aspirin on risk of cancer metastasis: a study of incident cancers during randomised controlled trials [J]. Lancet. 2012;379(9826):1591–601. https://doi.org/10.1016/s0140-6736(12)60209-8 . MAO W, CAI Y, CHEN D, et al. Statin shapes inflamed tumor microenvironment and enhances immune checkpoint blockade in non-small cell lung cancer [J]. JCI Insight. 2022;7(18). https://doi.org/10.1172/jci.insight.161940 . NI W, MO H, LIU Y, et al. Targeting cholesterol biosynthesis promotes anti-tumor immunity by inhibiting long noncoding RNA SNHG29-mediated YAP activation [J]. Mol Ther. 2021;29(10):2995–3010. https://doi.org/10.1016/j.ymthe.2021.05.012 . WU L, JIN Y, ZHAO X, et al. Tumor aerobic glycolysis confers immune evasion through modulating sensitivity to T cell-mediated bystander killing via TNF-α [J]. Cell Metab. 2023;35(9):1580–e15961589. https://doi.org/10.1016/j.cmet.2023.07.001 . AFZAL M Z, MERCADO R R, SHIRAI K. Efficacy of metformin in combination with immune checkpoint inhibitors (anti-PD-1/anti-CTLA-4) in metastatic malignant melanoma [J]. J Immunother Cancer. 2018;6(1):64. https://doi.org/10.1186/s40425-018-0375-1 . FIALA O. Use of concomitant proton pump inhibitors, statins or metformin in patients treated with pembrolizumab for metastatic urothelial carcinoma: data from the ARON-2 retrospective study [J]. Cancer Immunol Immunother. 2023;72(11):3665–82. https://doi.org/10.1007/s00262-023-03518-z . Additional Declarations No competing interests reported. Supplementary Files SupplementalMaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 27 May, 2025 Reviews received at journal 26 May, 2025 Reviewers agreed at journal 21 May, 2025 Reviewers agreed at journal 20 May, 2025 Reviewers invited by journal 20 May, 2025 Editor invited by journal 19 May, 2025 Editor assigned by journal 17 May, 2025 Submission checks completed at journal 17 May, 2025 First submitted to journal 07 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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01:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6615560/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6615560/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83335273,"identity":"241f35bb-46f5-443c-9868-81a7ea0634b2","added_by":"auto","created_at":"2025-05-23 08:54:09","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":810613,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier survival curves of rwPFS in patients treated with antimicrobial agents, glucocorticoids, and proton pump inhibitors;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Antimicrobial agents, (B) Duration of antimicrobial therapy (≥7 days), (C) Glucocorticoids, (D) Glucocorticoid dosage≥10mg/day, (E) Proton pump inhibitors, (F) Administration route of proton pump inhibitors (oral/intravenous/oral + intravenous)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6615560/v1/302f6dfd108831e8c66150a9.jpeg"},{"id":83334284,"identity":"c56f90c0-03ea-4670-84eb-6360397e11a3","added_by":"auto","created_at":"2025-05-23 08:46:09","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":691342,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier survival curves of rwPFS in patients treated with Non-steroidal anti-inflammatory drugs,\u003c/strong\u003e \u003cstrong\u003eAntihistamines, Anticoagulants，Opioids and Aspirin； \u003c/strong\u003e(A) Non-steroidal anti-inflammatory drugs, (B) Antihistamines, (C) Anticoagulants, (D) Opioids, (E)\u003cstrong\u003e \u003c/strong\u003eCalcium channel blockers(CCBs), (F) Aspirin.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6615560/v1/2b01593bdcb368a83c42a8fb.jpeg"},{"id":83335272,"identity":"340fa57e-90c0-4e1f-8442-5312d8ced865","added_by":"auto","created_at":"2025-05-23 08:54:09","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":872119,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier survival curves for rwPFS in patients treated with antimicrobial agents, GCs, PPIs, and opioids after PSM; \u003c/strong\u003e(A) Antimicrobial agents: 5.2 vs 8.3 months; [HR], 2.185; 95% CI, 1.674-2.851; (B) GCs: 6.0 vs 15.0 months; [HR], 2.219; 95% CI, 2.055-3.597; (C) PPIs: 7.8 vs 12.1 months; [HR], 1.757; 95% CI, 1.300-2.375; (D) Opioids: 5.6 vs 10 months; [HR], 2.397; 95% CI, 1.916-3.000.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6615560/v1/af536645c2f9511456a2cd79.jpeg"},{"id":83335443,"identity":"c6fafb36-029f-4b0a-a321-6bc0fbd5e573","added_by":"auto","created_at":"2025-05-23 09:02:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3529342,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6615560/v1/a33c4956-7727-4d0f-b87d-a76c770c119c.pdf"},{"id":83334277,"identity":"fa46b740-7a8d-4424-93a4-93b3d229a8b1","added_by":"auto","created_at":"2025-05-23 08:46:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1787259,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6615560/v1/a5616ec82a5f4e8f951f6a9d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of Concomitant Medications on the Therapeutic Effectiveness of PD-1/PD-L1 Inhibitors in Advanced Non-Small Cell Lung Cancer: A Retrospective Cohort Study","fulltext":[{"header":"Impact of findings on practice statements","content":"\u003cp\u003e1. This study comprehensively analyzes the impact of various concomitant medications on the effectiveness of immune checkpoint inhibitors (ICIs) in patients with advanced non-small cell lung cancer (NSCLC), which better reflects the real-world scenario of polypharmacy in cancer patients undergoing clinical treatment.\u003c/p\u003e\n\u003cp\u003e2. By employing PSM to reduce confounding factors, it was demonstrated that the negative effects of antimicrobial drugs, PPIs, GCs, and opioids on ICIs were independent of patients' baseline characteristics (particularly ECOG scores).\u003c/p\u003e\n\u003cp\u003e3. A more in-depth investigation into the specific types of antimicrobial drugs and their effects on ICIs revealed that the use of penicillins, cephalosporins, quinolones, carbapenems, and antifungal drugs can weaken the effectiveness of PD-1/L1 inhibitors.\u003c/p\u003e\n\u003cp\u003e4. As a notable exception among adjuvant drugs, aspirin was shown to enhance the antitumor efficacy of PD-1/L1 inhibitors, highlighting its potential as a beneficial addition to immunotherapy regimens.\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eLung cancer is the leading cause of cancer-related mortality worldwide \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, with non-small cell lung cancer (NSCLC) representing 80% of cases, making it the most prevalent subtype \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. In recent years, the advent of immune checkpoint inhibitors (ICIs) has introduced novel opportunities for NSCLC treatment \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. However, the effectiveness of ICIs is not universal, with only 20\u0026ndash;40% of patients experiencing significant benefit\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. This variability has prompted investigations into factors modulating ICI efficacy, including the emerging role of concomitant medications.\u003c/p\u003e \u003cp\u003eIn the management of NSCLC, patients often require concomitant medications to address comorbid conditions or secondary symptoms related to the cancer, such as hypertension, diabetes, pain, and anxiety. Growing evidence suggests that concomitant medications may significantly influence ICIs outcomes through diverse mechanisms \u003csup\u003e[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.Antimicrobial agents have been consistently associated with reduced survival, potentially via gut microbiome disruption. For example, Ochi et al. found that patients who used antimicrobial agents before and after ICIs treatment had a median overall survival (OS) of 11.7 months, compared to 16.1 months in those who did not use antibiotics \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Similarly, proton pump inhibitors (PPIs) have shown negative associations with survival outcomes, possibly through gastric pH alteration and subsequent microbiome changes \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Glucocorticoids (GCs), frequently used for cancer-related symptoms or immune-related adverse events, have been linked to poorer progression-free survival (PFS) and OS, likely via immunosuppressive effects on the tumor microenvironment \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, the potential mechanisms through which these concomitant medications adversely affect the effectiveness of ICIs remain inadequately explored. Furthermore, most studies have focused exclusively on individual concomitant medications, with limited exploration of the effects when drug categories like antihypertensives, antidiabetics, statins, anticoagulants, and bronchodilators are used in combination with ICIs. Previous studies have not distinguished between specific cancer types \u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, although the impact of concomitant medication on the effectiveness of ICIs may vary across different cancers \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. A growing body of evidence suggests that antibiotic use is associated with poorer outcomes of ICIs therapy in most solid tumors. However, this association appears to be tumor-specific, as demonstrated by Wang et al.\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e who reported no significant adverse effects of antibiotic use (within \u0026plusmn;\u0026thinsp;30 days of ICI treatment) on PFS, DCR, or OS in patients with primary liver cancer.\u003c/p\u003e \u003cp\u003eAdditionally, a study investigating patients with solid tumors receiving ICIs treatment found that only in patients with urothelial carcinoma (UC) was the use of β-blockers significantly associated with improved OS \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. This result may be attributed to the higher number of β-receptors in UC\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, in order to reveal the impact of concomitant medications on ICIs treatment in lung cancer, we conducted a multicenter retrospective study to explore the effects of commonly used concomitant drugs on the effectiveness of PD-1/PD-L1 inhibitors in patients with advanced NSCLC, aiming to provide insights for more informed clinical decision-making regarding concomitant medication use.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eWe retrospectively collected advanced NSCLC patients treated with ICIs between January 2017 and January 2023 at Xiangya Hospital of Central South University. Based on preliminary study data (antibacterial agents HR\u0026thinsp;=\u0026thinsp;1.375), sample size estimation was performed using PASS 2025 software (one-sample log-rank test, Weibull model, α\u0026thinsp;=\u0026thinsp;0.05, β\u0026thinsp;=\u0026thinsp;0.1). The calculation demonstrated that a minimum of 63 patients per group was required. To account for the needs of multifactorial analysis and subgroup analysis, a total of 650 patients were ultimately enrolled to ensure sufficient statistical power for the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion and Exclusion Criteria:\u003c/h3\u003e\n\u003cp\u003eThe inclusion criteria include: (1) Patients with pathologically confirmed stage IIIB/IV NSCLC who are ineligible for surgical treatment. (2) Patients who have received systematic PD-1 or PD-L1 inhibitor therapy, either as monotherapy or in combination. (3) Availability of detailed records on concomitant medications. (4) Complete clinical data, including baseline information, treatment regimen, follow-up records, and PFS. (5) Patients aged between 18 and 80 years. Exclusion criteria include: (1) Presence of other malignant tumors. (2) Severe organ failure. (3) Pregnant or lactating women.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eThrough the electronic medical record system, we collected the following data:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDemographic Characteristics\u003c/strong\u003e \u003cp\u003eincluding gender, age, body mass index (BMI), smoking index, pathological classification, Eastern Cooperative Oncology Group (ECOG) performance status, genetic mutation status, PD-L1 expression, brain metastasis, distant metastasis, and concurrent medication information.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eConcomitant medication records\u003c/b\u003e: (1) Systemic antimicrobial treatment involves selecting antimicrobial drugs used within 30 days before and after ICIs, including penicillins, cephalosporins, carbapenems, fluoroquinolones, and antifungal drugs; antimicrobial drugs usage duration categorized as either \u0026ge;\u0026thinsp;7 days or \u0026lt;\u0026thinsp;7 days. (2) Baseline GCs usage is divided based on dosage into two categories: \u0026ge;10 mg (prednisone equivalent) and \u0026lt;\u0026thinsp;10 mg. (3) Antihypertensive drugs include angiotensin-converting enzyme inhibitors (ACEI), angiotensin II receptor blockers (ARB), beta-blockers, calcium channel blockers (CCBs), and diuretics. (4) Antidiabetic drugs comprise insulin, metformin, sulfonylureas, alpha-glucosidase inhibitors, SGLT-2 inhibitors, and DPP-4 inhibitors. (5) Bronchodilators include β2-adrenergic receptor agonists and muscarinic receptor agonists. (6) Antiplatelet drugs consist of clopidogrel and aspirin (75mg-300mg). (7) Other baseline medications include PPIs, non-steroidal anti-inflammatory drugs (NASIDs), statins, sedative-hypnotics, antihistamines, anticoagulants, and opioids.\u003c/p\u003e \u003cp\u003eThe follow-up terminate date was December 1, 2024. During this period, patient survival status, disease progression, and medication usage were monitored through telephone interviews, outpatient visits, and electronic medical records.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStudy Endpoints\u003c/strong\u003e \u003cp\u003eThe primary effectiveness endpoint included real-world progression-free survival (rwPFS), objective response rate (ORR), and disease control rate (DCR). rwPFS is defined as the time from the first administration of ICIs to the time of disease progression (PD) or death from any cause, confirmed by radiology reports or clinical assessments at the last major evaluation. ORR was defined as the proportion of patients achieving complete response (CR) or partial response (PR) by the end of follow-up. DCR was defined as the proportion of patients achieving CR, PR, or stable disease (SD) by the end of follow-up. Clinicians systematically evaluated the effectiveness of immunotherapy every 4 to 12 weeks based on patients' clinical responses and imaging findings.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003ePatient baseline characteristics were analyzed using descriptive statistics. Chi-square tests or Fisher's exact tests were utilized for inter-group comparisons of ORR and DCR. Log-rank tests and Cox proportional hazards models were applied to conduct univariate/multivariate analyses on rwPFS. For concomitant medication groups that significantly affected treatment efficacy, 1:1 propensity score matching was performed with a caliper value of 0.02 to control for confounding factors. Matching variables included demographic characteristics such as age, sex, and PD-L1 expression level. The Kaplan-Meier method was employed to estimate median rwPFS, using R 4.3.2 to plot the K-M survival curves. All statistical analyses were performed with SPSS 28.0, adopting a significance level of α\u0026thinsp;=\u0026thinsp;0.05 (two-tailed), with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e1.Patient characteristics\u003c/h2\u003e \u003cp\u003eThe study enrolled a total of 650 patients. The baseline characteristics and concomitant medication usage of the patients are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age was 63 years (ranging from 35 to 80). The median rwPFS was 5 months, with 317 (48.77%) patients experiencing PD at the end of the follow-up period. ORR and DCR were 18.31% and 51.23%, respectively, with 6 patients (0.9%) achieving CR, 113 patients (17.4%) achieving PR, and 214 patients (32.9%) achieving SD.\u003c/p\u003e \u003cp\u003eRegarding treatment regimens, the most frequently administered PD-1/PD-L1 inhibitors were pembrolizumab (n\u0026thinsp;=\u0026thinsp;191, 29.4%), camrelizumab (n\u0026thinsp;=\u0026thinsp;180, 27.7%), and tislelizumab (n\u0026thinsp;=\u0026thinsp;107, 16.5%). The majority of patients received PD-1/PD-L1 inhibitors combined with chemotherapy (n\u0026thinsp;=\u0026thinsp;416, 64.0%), while 122 patients (18.8%) underwent monotherapy. A subset received triple therapy combining PD-1/PD-L1 inhibitors, chemotherapy, and targeted agents (n\u0026thinsp;=\u0026thinsp;69, 10.6%), and 43 patients (6.6%) received dual therapy with PD-1/PD-L1 inhibitors and targeted drugs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient Baseline Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e508(78.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142(21.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e393(60.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e257(39.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eECOG-PS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e488(75.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162(24.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePathological Type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e338(52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e312(48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254(39.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e396(60.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI(Kg/M^2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69(10.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5\u0026ndash;23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e356(54.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225(34.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBrain Metastasis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e529(81.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121(18.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistant Metastasis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1 site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e502(77.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2 site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148(22.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTP53 Mutation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e590(90.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60(9.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEGFR Mutation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e604(92.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(7.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKRAS Mutation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e585(90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65(10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePD-L1 Expression\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e207(31.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220(33.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e223(34.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment Modality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-1/PD-L1 Monotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-1/PD-L1 Combination Therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConcomitant Medication Type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntimicrobial Agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e244(37.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePenicillin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCephalosporins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuinolones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbapenems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntifungal Agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPIs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e420(64.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihypertensive Agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153(23.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEI/ARB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta-Blockers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCBs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiuretics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntidiabetic Drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63(9.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfonylureas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlpha-glucosidase Inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSGLT-2 Inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDPP-4 Inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e452(69.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNASIDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116(17.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99(15.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSedative-hypnotics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e236(36.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnticoagulants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198(30.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiplatelet Drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183(28.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspirin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClopidogrel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihistamines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e256(39.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ2- adrenergic receptor agonists\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65(10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscarinic receptor agonists\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64(9.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpioids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e240(36.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eNote: Proton pump inhibitors (PPIs), Angiotensin-converting enzyme inhibitors (ACEI), Angiotensin II receptor blockers (ARB), Calcium channel blockers (CCBs), Glucocorticoids (GCs), Non-steroidal anti-inflammatory drugs (NASIDs).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.The association of Concomitant Medication with Effectiveness of Immune Checkpoint Inhibitors\u003c/h3\u003e\n\u003cp\u003eUnivariate Cox regression analysis of patients' rwPFS revealed that the use of antimicrobial agents, PPIs, CCBs, GCs, NSAIDs, anticoagulants, antihistamines, and opioids were identified as risk factors for reduced effectiveness of ICIs (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), whereas antidiabetic drugs, statins, sedative-hypnotics, β2-adrenergic receptor agonists, and muscarinic receptor agonists do not affect the effectiveness of ICI therapy (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, no significant differences were observed in rwPFS, ORR, or DCR regarding antihypertensives, sedative-hypnotics, bronchodilators, antidiabetics, statins and clopidogrel. (Supplementary Material-Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate Cox Regression Analysis of the Impact of Concomitant Medication on rwPFS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcomitant Medication\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntimicrobial Agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.431\u0026ndash;3.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePenicillin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.790\u0026ndash;3.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCephalosporins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.635\u0026ndash;2.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuinolones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.602\u0026ndash;2.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbapenems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.431\u0026ndash;3.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntifungal Agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.542\u0026ndash;6.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.280\u0026ndash;2.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPIs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.219\u0026ndash;3.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihypertensive Agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.198\u0026ndash;1.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEI/ARB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.927\u0026ndash;2.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta-Blockers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.889\u0026ndash;2.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCBs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.166\u0026ndash;1.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiuretics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.883\u0026ndash;2.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntidiabetic Drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.867\u0026ndash;1.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.743\u0026ndash;1.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.513\u0026ndash;1.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfonylureas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.046\u0026ndash;2.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlpha-glucosidase Inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.403\u0026ndash;1.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSGLT-2 Inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u0026ndash;1.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDPP-4 Inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.102\u0026ndash;4.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.055\u0026ndash;3.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNASIDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.775\u0026ndash;2.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.734\u0026ndash;1.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSedative-hypnotics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.984\u0026ndash;1.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnticoagulants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.511\u0026ndash;2.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiplatelet Drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.658\u0026ndash;1.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspirin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.417\u0026ndash;0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClopidogrel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.873\u0026ndash;1.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihistamines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.273\u0026ndash;2.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ2-adrenergic receptor agonists\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.977\u0026ndash;1.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscarinic receptor agonists\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.587\u0026ndash;1.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpioids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.916-3.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cb\u003e3. The Impact of Various Concomitant Medications on Effectiveness Outcomes\u003c/b\u003e\u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Antimicrobial agents\u003c/h2\u003e \u003cp\u003eAmong the 244 patients receiving antimicrobial treatment, 42 were administered orally, 141 via intravenous injection, and 61 through a combined route. The most frequently used categories antimicrobial agents were beta-lactams (n\u0026thinsp;=\u0026thinsp;235) and quinolones (n\u0026thinsp;=\u0026thinsp;134). Univariate Cox regression analysis indicated that all categories of antimicrobial agents reduced rwPFS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared to patients not treated with antimicrobials, those receiving antimicrobial treatment had a significant reduction in median rwPFS (4.8 vs 11.6 months; hazard ratio [HR], 3.044; 95% CI, 2.431\u0026ndash;3.811; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), especially those on penicillins (5.1 vs 8.5 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), cephalosporins (4.5 vs 8.0 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), quinolones (5.3 vs 8.3 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), carbapenems (4.1 vs 7.7 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and antifungals (4.8 vs 7.7 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Supplementary Material-Figure 1).Similarly, the ORR and DCR were lower in the group receiving antimicrobial agents (ORR, 12.3% vs 21.9%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002; DCR, 28.7% vs 64.8%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, when patients used antimicrobial drugs for more than 7 days, there was a notable decrease in PFS (4.2 vs 5.1 months; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Glucocorticoids\u003c/h2\u003e \u003cp\u003eIn the cohort of 452 patients receiving GCs, dosage varied according to specific indications, leading to a categorization based on prednisone equivalence: \u0026ge;10mg/day (n\u0026thinsp;=\u0026thinsp;220) and \u0026lt;\u0026thinsp;10mg/day (n\u0026thinsp;=\u0026thinsp;140). Compared to untreated counterparts, patients on GCs exhibited a marked decrease in median rwPFS (6.0 vs 15.0 months; [HR], 2.219; 95% CI, 2.055\u0026ndash;3.597; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). A similar negative effect was observed in DCR (50% vs 67.2%, P\u0026thinsp;=\u0026thinsp;0.001), while ORR showed no significant difference (16.6% vs 22.2%, P\u0026thinsp;=\u0026thinsp;0.088). Moreover, patients with a GCs dosage\u0026thinsp;\u0026ge;\u0026thinsp;10mg/day of prednisone equivalent saw even shorter median rwPFS (4.9 vs 7.0 months; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Proton Pump Inhibitors\u003c/h2\u003e \u003cp\u003eAmong the 420 patients treated with PPIs, pantoprazole was the most frequently used (n\u0026thinsp;=\u0026thinsp;239, 56.9%) with a general dosage of 40mg. Following were omeprazole (n\u0026thinsp;=\u0026thinsp;87), esomeprazole (n\u0026thinsp;=\u0026thinsp;46), lansoprazole (n\u0026thinsp;=\u0026thinsp;31), and rabeprazole (n\u0026thinsp;=\u0026thinsp;8). Most patients received PPIs via intravenous administration (n\u0026thinsp;=\u0026thinsp;330). Compared to those not receiving PPIs therapy, patients on PPIs had a significantly shorter median rwPFS (5.7 vs 13.4 months; [HR], 2.887; 95% CI, 2.219\u0026ndash;3.755; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE), and fewer achieved SD (42.9% vs 66.5%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, patients receiving combined oral and intravenous PPIs administration had a shorter rwPFS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(A) Antimicrobial agents, (B) Duration of antimicrobial therapy (\u0026ge;\u0026thinsp;7 days), (C) Glucocorticoids, (D) Glucocorticoid dosage\u0026thinsp;\u0026ge;\u0026thinsp;10mg/day, (E) Proton pump inhibitors, (F) Administration route of proton pump inhibitors (oral/intravenous/oral\u0026thinsp;+\u0026thinsp;intravenous)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Non-Steroidal Anti-Inflammatory Drugs\u003c/h2\u003e \u003cp\u003eAmong the 116 patients treated with NSAIDs, 52 received ibuprofen, 40 diclofenac sodium, 10 etodolac, and 7 celecoxib. Compared to patients not receiving NSAIDs, those treated with NSAIDs had a shorter median rwPFS (4.5 vs 8.5 months; [HR], 2.277; 95% CI, 1.775\u0026ndash;2.920; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), and lower ORR (9.1% vs 20.4%, P\u0026thinsp;=\u0026thinsp;0.003) and DCR (25.9% vs 56.7%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Antihistamines\u003c/h2\u003e \u003cp\u003e256 patients were treated with antihistamines, of which diphenhydramine accounted for approximately 70%. Compared to patients who did not receive antihistamine treatment, those treated with antihistamines had a shorter median rwPFS (6.1 vs 8.1 months; [HR], 1.596; 95% CI, 1.273\u0026ndash;2.002; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). However, both ORR and DCR showed no significant difference (ORR,19.5% vs 17.5%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.516; DCR; 48.0% vs 53.3%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.191, respectively).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Anticoagulants\u003c/h2\u003e \u003cp\u003eIn a cohort of 198 patients treated with anticoagulants, heparins were administered to 163 patients and factor Xa inhibitors to 76. Compared to patients who did not receive anticoagulation therapy, those who did had a shorter median rwPFS (5.6 vs 8.5 months; [HR], 1.907;95% CI,1.511\u0026ndash;2.406; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Similarly, the DCR was significantly reduced (43% vs 54.9%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), while the ORR showed no statistical difference (18.2% vs 18.4%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.184).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Opioid\u003c/h2\u003e \u003cp\u003eApproximately 37% of patients used opioids to control cancer pain during immunotherapy, with most receiving tramadol (n\u0026thinsp;=\u0026thinsp;117) and/or oxycodone (n\u0026thinsp;=\u0026thinsp;121). Patients receiving opioid treatment had a shorter median rwPFS (5.6 vs 10.0 months; [HR], 2.397; 95% CI, 1.916-3.000; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), and significantly lower ORR (14.2% vs 20.7%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and DCR (34.6% vs 56.8%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to those not on opioids.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Calcium channel blockers\u003c/h2\u003e \u003cp\u003eAmong 153 patients receiving antihypertensive treatment, CCBs were the predominant medication (n\u0026thinsp;=\u0026thinsp;120). Univariate Cox regression analysis showed that, except for CCBs ([HR], 1.516; 95% CI, 1.166\u0026ndash;1.970; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), other categories of antihypertensive drugs had no significant impact on patients' rwPFS. Compared to patients not treated with CCBs, those treated with CCBs had a shorter median rwPFS (5.6 vs 8.0 months; [HR], 2.397; 95% CI, 1.916-3.000; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE), a lower DCR (39.2% vs 54.0%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), while no significant difference in ORR (18.3% vs 18.3%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.994).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Aspirin\u003c/h2\u003e \u003cp\u003eAspirin, a widely utilized antiplatelet medication in clinical practice, is typically administered at doses ranging from 75 to 300 mg. Compared to patients who did not receive aspirin, those treated with it (n\u0026thinsp;=\u0026thinsp;92) had a significantly prolonged median rwPFS (11.2 vs 7.0 months; P\u0026thinsp;=\u0026thinsp;0.004; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF), elevated ORR (29.3% vs 16.5%; P\u0026thinsp;=\u0026thinsp;0.003), and higher DCR (63% vs 49.3%; P\u0026thinsp;=\u0026thinsp;0.014).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4. Analysis of clinical characteristics and concomitant medications\u003c/h2\u003e \u003cp\u003eConsidering the interactions among various concomitant medications in patients, we performed multivariate Cox regression on those with a univariate \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, aiming to precisely evaluate each drug's independent impact on patient outcome. The results indicated that the use of antimicrobial agents ([HR], 2.603; 95% CI, 2.070\u0026ndash;3.274), PPIs ([HR], 1.786; 95% CI, 1.786\u0026ndash;1.336), GCs ([HR], 1.982; 95% CI, 1.459\u0026ndash;2.691), and opioids ([HR], 1.847; 95% CI, 1.470\u0026ndash;2.321) were independent risk factors for patients' rwPFS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, we observed that the use of ATBs, PPIs, GCs, and opioids was significantly associated with worse ECOG PS scores, lower BMI and higher rates of distant metastasis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Therefore, when analyzing the effectiveness of ICIs, it is essential to consider the relationship between these medications and ECOG PS scores. Furthermore, as the expression of PD-L1 increased, the negative impact of these drugs on rwPFS was attenuated (Supplementary Material - Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).Due to the multitude of factors affecting the effectiveness of ICIs, we employed PSM to match patients in each group at a 1:1 ratio, thereby reducing the impact of confounding variables such as gender, age, body mass index, smoking index, pathological subtype, ECOG score, gene mutation status, PD-L1 expression, brain metastasis, and distant metastasis on the antitumor effectiveness of ICIs. The matched results indicated that even after controlling for these confounders, patients using antimicrobials, PPIs, GCs, and opioids continued to exhibit lower rwPFS (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation Between Concomitant Medications (Antimicrobial Agents, GCs, PPIs, Opioids) and ECOG PS scores, BMI and Distant Metastasis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConcomitant Medication\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eECOG PS (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP(χ\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eBMI (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP(χ\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eDistant metastasis (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP(χ\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;18.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.5\u0026ndash;23.9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;23.9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1 site\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2 site\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntimicrobial Agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160\u003c/p\u003e \u003cp\u003e(65.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003cp\u003e(34.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003cp\u003e(15.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003cp\u003e(56.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e68 (56.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e181\u003c/p\u003e \u003cp\u003e(74.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e63\u003c/p\u003e \u003cp\u003e(25.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e328\u003c/p\u003e \u003cp\u003e(80.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222 (54.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31\u003c/p\u003e \u003cp\u003e(7.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e218\u003c/p\u003e \u003cp\u003e(53.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e157 (38.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e321\u003c/p\u003e \u003cp\u003e(79.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e85\u003c/p\u003e \u003cp\u003e(20.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e328\u003c/p\u003e \u003cp\u003e(72.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (27.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52\u003c/p\u003e \u003cp\u003e(11.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e261\u003c/p\u003e \u003cp\u003e(57.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e139 (30.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e332\u003c/p\u003e \u003cp\u003e(73.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e120\u003c/p\u003e \u003cp\u003e(26.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160\u003c/p\u003e \u003cp\u003e(80.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003cp\u003e(19.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003cp\u003e(8.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95\u003c/p\u003e \u003cp\u003e(47.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86 (43.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e170\u003c/p\u003e \u003cp\u003e(85.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e28\u003c/p\u003e \u003cp\u003e(14.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProton Pump Inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e290\u003c/p\u003e \u003cp\u003e(69.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130 (30.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55\u003c/p\u003e \u003cp\u003e(13.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e244\u003c/p\u003e \u003cp\u003e(58.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e121 (28.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e310\u003c/p\u003e \u003cp\u003e(73.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e110\u003c/p\u003e \u003cp\u003e(26.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198\u003c/p\u003e \u003cp\u003e(86.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003cp\u003e(13.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e(6.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e112\u003c/p\u003e \u003cp\u003e(48.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e104 (45.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e192\u003c/p\u003e \u003cp\u003e(83.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e38\u003c/p\u003e \u003cp\u003e(16.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpioids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168\u003c/p\u003e \u003cp\u003e(70.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003cp\u003e(30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003cp\u003e(16.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e137\u003c/p\u003e \u003cp\u003e(57.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63 (26.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e159\u003c/p\u003e \u003cp\u003e(66.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e81\u003c/p\u003e \u003cp\u003e(33.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e320\u003c/p\u003e \u003cp\u003e(78.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003cp\u003e(21.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003cp\u003e(7.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e219\u003c/p\u003e \u003cp\u003e(53.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e162\u003c/p\u003e \u003cp\u003e(39.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e343\u003c/p\u003e \u003cp\u003e(83.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e67\u003c/p\u003e \u003cp\u003e(16.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eWe observed that the use of antimicrobial drugs, PPIs, GCs, NSAIDs, antihistamines, and opioids adversely affected the effectiveness of immune checkpoint inhibitors in patients. The use of antihypertensive drugs (except CCBs), antidiabetic drugs, statins, anticoagulants, antiplatelet drugs, sedative-hypnotics, and bronchodilators had no impact, whereas aspirin use was associated with a better antitumor prognosis. Compared to other similar studies, our comprehensive analysis of various concomitant medication types more accurately reflects the real-world scenario of polypharmacy in patients with advanced cancer. Numerous reports highlight the adverse effects of antimicrobial drugs on immunotherapy \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, yet few studies have examined whether different types of antimicrobial drugs have consistent effects on ICIs. Given the variability in antimicrobial spectrum and activity, the detrimental effects of antimicrobial drugs cannot be generalized. Our study indicates that penicillins, cephalosporins, quinolones, carbapenems, and antifungal drugs all weaken the effectiveness of PD-1/PD-L1 inhibitors, with antifungals having the most significant negative impact. PPIs reduce the absorption of drugs and alter the microbiota by decreasing gastric acid secretion \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, thus affecting the antitumor effectiveness of immunotherapy \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.We found that patients receiving both oral and intravenous PPIs had worse prognoses. The rationale for combining these drugs with the same mechanism warrants further consideration. Due to varying indications for GCs (tumor or non-tumor reasons), the dosage used varies significantly. Our study found that patients receiving more than 10mg of prednisone equivalents had further reduced rwPFS, consistent with previous research\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, suggesting that physicians should minimize GCs overuse, especially during ICI treatment. Nevertheless, in the event of immune-related adverse reactions (irAEs) during PD-1/PD-L1 therapy, GCs use can improve patient outcomes \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. While the use of opioid analgesics is common in cancer patients, research on the impact of opioids on the effectiveness of ICIs is limited \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Studies suggest that opioids can cause significant changes in the intestinal microbiota, thus exerting a mechanism similar to antibiotics \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e .Studies suggest that opioids can cause significant changes in the intestinal microbiota, thus exerting a mechanism similar to antibiotics.\u003c/p\u003e \u003cp\u003eInterestingly, NSAIDs have shown immunomodulatory synergies in certain preclinical studies, with research by Hussain et al. indicating that NASIDs can facilitate the infiltration of CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells into tumors\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Similarly, Aboelella et al. found that indomethacin can enhance death receptor 5 signaling, sensitizing tumor cells to adoptive T cell therapy \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. However, a study by Kostine et al. reported that the use of NSAIDs resulted in similar mPFS to non-use in advanced cancer patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4)\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, consistent with our multivariate analysis findings. Preclinical studies suggest that antihistamines and anticoagulants can enhance responses to ICIs \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, yet in our study, patients receiving these medications had worse antitumor prognoses. A significant reason may be that the use of antihistamines and anticoagulants is often closely associated with higher frequency use of PPIs and GCs, thus necessitating further research to elucidate the interaction between these drugs and ICIs.\u003c/p\u003e \u003cp\u003eIn general, it is reasonable for patients with underlying conditions such as hypertension, diabetes, hyperlipidemia, and COPD(Chronic Obstructive Pulmonary Disease) to continue their regular medication during anti-tumor treatments. However, it is noteworthy that among antihypertensive drugs, CCBs have exhibited significant detrimental effects. Previous studies have found that exposure to CCBs is associated with an increased risk of lung cancer \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, however, the specific mechanism remains unclear. The choice of antihypertensive drugs in cancer patients, specifically whether to avoid CCBs, necessitates further research evidence. Additionally, patients with advanced cancer often use sedative-hypnotic drugs due to anxiety and insomnia and this study's results indicate that these drugs do not affect the effectiveness of PD-1/PD-L1 inhibitors. Moreover, the judicious use of sedative-hypnotic drugs is necessary to improve the mental state of the patients.\u003c/p\u003e \u003cp\u003eAlthough most adjuvant drugs negatively impact patients' anti-tumor treatment, some have been found in preclinical studies to exhibit synergistic effects with ICIs' anti-tumor efficacy. Among these, aspirin is particularly noteworthy. As early as 2015, Sousa and colleagues identified that aspirin could inhibit the production of PGE2 and restore the levels of specific dendritic cells (cDC1)\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Recently, new mechanisms by which aspirin enhances the effectiveness of PD-L1 immunotherapy have been continually identified, such as by downregulating FGL1 and CD8\u0026thinsp;+\u0026thinsp;T cell PD-1 expression \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Furthermore, studies have found that aspirin can reduce the risk of distant tumor metastasis and decrease the mortality rate due to adenocarcinoma \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. The above studies correspond with the findings of this research, which show that patients taking aspirin have a better clinical prognosis.\u003c/p\u003e \u003cp\u003eFurthermore, the interest in metabolic reprogramming of the tumor microenvironment has brought significant attention to the synergistic anti-tumor effects of metformin and statins. Numerous in vitro and animal studies have demonstrated that inhibiting glucose/lipid metabolism in the tumor microenvironment can enhance the anti-tumor activity of ICIs \u003csup\u003e[\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. This indicates that metformin and statins may have potential benefits for patients undergoing immunotherapy, yet the desired synergistic effect has not been observed in clinical phases. Afzal et al. found that patients with metastatic melanoma, treatment combining metformin and ICIs did not significantly change ORR, DCR, mPFS, or median OS compared to the control group \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. The study by Ondřej Fiala et al. also indicates that the use of statins or metformin is not associated with the response to immunotherapy or survival \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e, similar to the findings of our study. This may be because tumor patients control their blood glucose and lipid levels with conventional drug doses during immunotherapy, whereas higher doses are required to exert an anti-tumor effect.\u003c/p\u003e \u003cp\u003eOur findings offer beneficial guidance for rational pharmacotherapy in patients with NSCLC. This includes stringent management of the indications, dosage, route of administration, and purpose of antimicrobial drugs. For analgesics, the implementation of the 'three-step analgesic ladder' principle is essential. Similar to antimicrobials, PPIs are often overprescribed in the clinical treatment of cancer patients, frequently issued unjustifiably for the prevention of nausea and vomiting. Additionally, with the increasing variety of concomitant medications, the negative impact on anti-tumor efficacy continues to accumulate. We should also be aware that an increase in the variety and quantity of drugs can lead to a higher incidence of adverse reactions. Concomitant medication use often occurs in patients with underlying conditions or compromised immunity. For instance, antimicrobial agents may indicate infection or immunosuppression, while PPIs, GCs, and opioids might be related to gastrointestinal symptoms, chronic inflammation, or pain management. Because a decline in performance status can impact both a patient\u0026rsquo;s tolerance to ICIs and overall treatment efficacy, we used PSM to control for various confounding factors, including ECOG scores. The results revealed that the negative effects of antimicrobial agents, PPIs, GCs, and opioids on ICIs' efficacy were independent of the patients' baseline characteristics.\u003c/p\u003e \u003cp\u003eAfter analyzing factors such as the ECOG PS score, we found that antimicrobial drugs, PPIs, GCs, and opioids had no significant correlation with the effectiveness of ICIs, independent of the patient's baseline characteristics. However, the complexity between concomitant medication use and tumor efficacy necessitates further prospective studies for validation. For instance, opioids are frequently employed to manage severe cancer pain associated with extensive bone metastasis. Despite the application of the PSM method in this study, it is not possible to entirely eliminate the potential impacts of the underlying disease. Furthermore, immunotherapy for specific populations, including those with hypertension, hyperglycemia, hyperlipidemia, and COPD, warrants particular attention to formulate individualized treatment strategies.\u003c/p\u003e \u003cp\u003eWe acknowledge that the retrospective design of this study may lead to some analytical bias. Additionally, the absence of data on adverse reaction rates in patients using ICIs with concomitant medications precludes discussion on the impact of these drugs on the safety of ICI therapy.\u003c/p\u003e \u003cp\u003eIn conclusion, concomitant medications play a significant role in the treatment with ICIs, often exerting negative effects. Therefore, a comprehensive evaluation of the clinical use of antimicrobial agents, PPIs, GCs, and opioids should be conducted during the development of anti-tumor protocols to ensure rational drug use.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThis retrospective study was approved by the Ethics Committee of Xiangya Hospital, Central South University (Approval No.: 2024111428; Date: November 6, 2024). The requirement for informed consent was waived by the committee due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThis study did not use personally identifiable data, therefore the requirement for publication consent is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe data that support the findings of this study are available on request from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eAll authors declare that there are no conflicts of interest related to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the National Natural Science Foundation of China (82474012) and the National Natural Science Foundation of China (82204531).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Xiang-Ping Li; Data curation, Jie-Qi Chen; Investigation, Yu Zhen, Zhi-Lan Lin , Lei Cao, Hui-Zhen Li ,Ying-Cai Meng and Bin Li; Funding acquisition, Yue-Qin Li; Writing\u0026nbsp;\u0026ndash;\u0026nbsp;original draft, Yue Shen; Writing\u0026nbsp;\u0026ndash;\u0026nbsp;review \u0026amp; editing, Jie-Qi Chen, Juan Chen.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSIEGEL RL, MILLER K D WAGLENS, et al. 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Use of concomitant proton pump inhibitors, statins or metformin in patients treated with pembrolizumab for metastatic urothelial carcinoma: data from the ARON-2 retrospective study [J]. Cancer Immunol Immunother. 2023;72(11):3665\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00262-023-03518-z\u003c/span\u003e\u003cspan address=\"10.1007/s00262-023-03518-z\" 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-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6615560/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6615560/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients with advanced non-small cell lung cancer (NSCLC) often require concurrent administration of other medications during immunotherapy to manage cancer-related complications or symptoms. However, these concomitant medications may interact with immune checkpoint inhibitors (ICIs), potentially impacting their therapeutic effectiveness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study aims to systematically investigate the effect of concomitant medications for anti-tumor effectiveness of PD-1/PD-L1 inhibitors, providing suggestions for the selection of concomitant medications during ICIs treatment in NSCLC patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study collected and analyzed clinical characteristics and concomitant medication information of 650 advanced NSCLC patients treated with PD-1/PD-L1 inhibitors at Xiangya Hospital. The impact of commonly used concomitant medications on patients' progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR) was analyzed using SPSS 28.0. Propensity score matching (PSM) was employed to mitigate the influence of other confounding factors. The Log-rank test and Cox proportional hazards model were used to identify independent predictors of PFS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate COX analysis showed that antimicrobial agents, proton pump inhibitors (PPIs), calcium channel blockers (CCBs), glucocorticoids (GCs), non-steroidal anti-inflammatory drugs (NASIDs), antihistamines, anticoagulants, and opioids reduced PFS and lowered ORR and DCR (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01), while antihypertensive drugs (excluding CCBs), sedative-hypnotics, antidiabetic drugs, statins, and bronchodilators had no effect on antitumor efficacy. Additionally, the use of aspirin is significantly correlated with better PFS, ORR, and DCR (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Moreover, Multivariate Cox regression analysis indicated that the use of antimicrobial agents, PPIs, GCs and opioids are independent risk factors that adversely affect the effectiveness of PD-1/PD-L1 inhibitor therapy (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). After PSM, their use remained linked to worse PFS, ORR, and DCR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcomitant use of antimicrobial agents, PPIs, GCs, and opioids may diminish the antitumor efficacy of PD-1/PD-L1 inhibitors. Therefore, clinicians should consider these factors before prescribing ICIs to patients with NSCLC.\u003c/p\u003e","manuscriptTitle":"Effects of Concomitant Medications on the Therapeutic Effectiveness of PD-1/PD-L1 Inhibitors in Advanced Non-Small Cell Lung Cancer: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-23 08:46:04","doi":"10.21203/rs.3.rs-6615560/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-05-27T14:26:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-26T08:32:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23824353258280393850976907071898454146","date":"2025-05-21T08:47:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34742279231742877466320728721375793892","date":"2025-05-20T23:58:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-20T08:35:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-19T05:04:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-17T17:00:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-17T16:56:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2025-05-08T01:09:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"86122528-25e1-460d-b986-cfe2806b9480","owner":[],"postedDate":"May 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-23T08:46:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-23 08:46:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6615560","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6615560","identity":"rs-6615560","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}