Real-World Study on the Safety of Sintilimab in Clinical Use and Establishment of a Risk Prediction Model

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Methods Medical records of patients who received Sintilimab treatment from January 2021 and December 2022 were reviewed. Clinical data, including demographic characteristics, medication details, and ADRs. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for Sintilimab-induced ADRs, considering variables such as gender, age, comorbidities, and treatment regimens. Receiver operating characteristic (ROC) curve analysis was performed to assess the predictive accuracy of individual and combined risk factors. And the safety of Sintilimab also was evaluated. Results Among 337 included cases, 208 (61.72%) experienced ADRs. Multivariate analysis identified combination therapy (OR = 25.670, 95% CI: 11.319–58.218, P < 0.001) and baseline assessment (OR = 0.388, 95% CI: 0.191–0.789, P = 0.009) as two independent risk factors. The logistic model indicated that the combined prediction of these factors achieved an area under the curve (AUC) of 0.800 ( P < 0.001) and a Youden index of 0.583, which outperformed single-factor predictions. Cross-validation using 115 cases demonstrated an accuracy rate of 80.87% for this model. Conclusion Combination therapy and baseline assessment are independent risk factors for ADRs in Sintilimab-treated patients. The combined predictive model exhibits high accuracy and may serve as a valuable clinical tool to anticipate risks and guide personalized treatment decisions. Sintilimab Adverse drug reactions Risk factor analysis Logistic regression model ROC curve Figures Figure 1 Introduction Immune checkpoint inhibitors (ICIs) are a class of monoclonal antibody drugs that exert antitumor effects by blocking the interaction between tumor cells expressing immune checkpoints and immune cells, thereby reversing the immunosuppressive effects of tumor cells on immune cells. In recent years, ICIs, particularly programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors, have become a major focus of global drug development. Sintilimab, a domestically developed PD-1 inhibitor in China, blocks the binding of PD-1 to its ligands (PD-L1 and PD-L2), thereby releasing T cell inhibition and restoring endogenous antitumor T cell responses to eliminate tumor cells [1-2]. It has demonstrated significant clinical efficacy in the treatment of various malignancies and has been included in the National Reimbursement Drug List (NRDL), enabling widespread clinical use [3] . Sintilimab was first approved in China in December 2018 for the treatment of relapsed or refractory classical Hodgkin lymphoma (cHL). By 2023, its approved indications had expanded to multiple high-incidence malignancies, including cHL, non-small cell lung cancer (first-line), hepatocellular carcinoma (first-line), gastric cancer (first-line), and esophageal squamous cell carcinoma (first-line), making it the first PD-1 inhibitor in China approved for first-line treatment across five major cancer types. However, as its indications broaden and its user population expands, safety concerns have garnered increasing attention. The unique mechanism of ICIs, while activating antitumor immunity, may also lead to immune-related adverse events (irAEs), such as pneumonitis, colitis, hepatitis, and endocrine disorders, some of which can be life-threatening [4] . Although Sintilimab has shown a relatively manageable safety profile in clinical trials, real-world patient heterogeneity (e.g., comorbidities, concomitant medications, and varying tumor types) may influence its safety characteristics, and existing clinical trial data may not fully reflect real-world risk profiles. Currently, several reports on Sintilimab-related adverse events have been published [5-7] , but most consist of case reports or literature reviews, leaving the real-world incidence, severity, and risk factors of adverse events insufficiently characterized. Additionally, risk variations among different populations (e.g., elderly patients or those with chronic diseases) have not been systematically evaluated. Furthermore, tumor type and combination therapies (e.g., chemotherapy or antiangiogenic agents) may further influence the risk of adverse events [8]. Regarding adverse event prediction, existing studies suggest that certain clinical factors (e.g., baseline inflammatory markers, history of autoimmune disease, specific genetic polymorphisms) may correlate with irAE occurrence [9,10] . However, systematic research on Sintilimab safety risk prediction remains limited, particularly in developing integrated models incorporating clinical features, patient characteristics, and drug usage patterns. Establishing such models could facilitate early identification of high-risk patients, enabling personalized monitoring and intervention to optimize the benefit-risk ratio. This study aims to comprehensively analyze the adverse event profile of Sintilimab using real-world data, identify risk factors, and construct a risk prediction model. The findings are expected to provide a scientific basis for the safe and rational clinical use of Sintilimab, supporting precision medicine practices. Immune checkpoint inhibitors (ICIs) are monoclonal antibody drugs that enhance antitumor immunity by blocking the interaction between immune checkpoints on tumor cells and their ligands on immune cells, thereby reversing tumor-mediated immunosuppression. Among the various types of ICIs, programmed death-1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitors have taken center-stage in oncology research. Sintilimab, a PD-1 inhibitor, exerts its antitumor effects by disrupting PD-1/PD-L1/PD-L2 signaling, thereby restoring T-cell activity and promoting tumor cell elimination [1-2] . Sintilimab was initial approval in December 2018 for relapsed or refractory classical Hodgkin’s lymphoma. Subsequently, it has been approved in China for multiple malignancies, such as lymphoma, hepatocellular carcinoma, gastric cancer, non-small cell lung cancer, and esophageal cancer. Notably, it remains the only PD-1 inhibitor approved for first-line treatment across all five major high-incidence cancers. However, with expanding clinical use, safety concerns have gained prominence. Although Sintilimab-associated adverse drug reactions (ADRs) have been documented [3-5] , the existing evidence has certain limitations. Primarily, it comes from case reports and literature reviews. There has been limited systematic investigation into the risk factors and predictive models for these ADRs. This study employs a retrospective analysis to explore the risk factors associated with Sintilimab-induced ADRs and establishes a logistic regression model for prediction. The receiver operating characteristic (ROC) curve was used to evaluate the model's accuracy, aiming to provide a reference for the safe and rational clinical application of Sintilimab. 1 Materials and Methods 1.1 Data Sources and Study Population We conducted a retrospective study using anonymized data from the Hospital Information System (HIS) of our institution. Patients treated with Sintilimab Injection (Innovent Biologics; NMPA Approval No.: S20180016; 10 mL: 100 mg/vial) between January 2021 and December 2022 were randomly selected. Inclusion criteria: ①Age ≥ 18 years; ②Non-pregnant/non-lactating status; ③Complete clinical records (verified by dual independent review). Exclusion criteria: ①Age < 18 years; ②Incomplete medical documentation. A total of 337 cases meeting the inclusion/exclusion criteria were enrolled. Following relevant reporting guidelines [ 11 ] , the cases were allocated in a 3:1 ratio for model development and validation, an additional 115 patients treated between January–December 2023 were included for external validation of the predictive model. Consent to Participate: The requirement for written informed consent was waived by the Institutional Review Board of Hefei BOE Hospital because this retrospective study used only anonymized clinical data without any additional interventional procedures. 1.2 Data Collection and Variables Clinical data were extracted from the HIS and systematically recorded, including: ①Demographics: Patient ID, sex, age, comorbidities, smoking/alcohol history; ②Treatment details: Sintilimab dosage, treatment cycles, off-label use, infusion duration, baseline assessments, administration sequence, premedication protocols; ③Concomitant therapies: Chemotherapy, radiotherapy, or immunosuppressants; ④ Underlying diseases; ⑤ Concomitant anti-infective or glucocorticoid therapy. 1.3 Causality Assessment An ADR evaluation team (Comprising oncologists and Pharmacists) assessed the association between adverse reactions and Sintilimab based on the causality criteria from China’s Adverse Drug Reaction Reporting and Monitoring Manual [ 12 ] . Only reactions classified as "Definite," "Probable," or "Possible" were included. ADR severity grading followed the Chinese Society of Clinical Oncology (CSCO) Guidelines for Immune Checkpoint Inhibitor-Related Toxicity Management (2019) [ 13 ] : G1(Mild): Asymptomatic or mild symptoms, self-resolving without intervention. G2 (Moderate): Mild limitations in daily activities, requiring local treatment. G3(Severe): Disabling or significant activity limitations (non-life-threatening), requiring hospitalization. G4 (Life-threatening). G5 (Death related to toxicity). 1.4 Statistical Analysis Data were analyzed using SPSS 23.0. Normally distributed continuous variables: Expressed as mean ± standard deviation, compared via independent t-test. Non-normally distributed variables: Expressed as median (P25, P75), compared via Wilcoxon rank-sum test. Categorical variables: Expressed as percentages (%), compared via χ² test. Binary logistic regression was performed to identify independent risk factors for Sintilimab-induced ADRs and establish a predictive model. Receiver operating characteristic (ROC) curves were plotted for individual and combined risk factors, with Youden index calculated based on sensitivity and specificity. A P-value < 0.05 was considered statistically significant. 2 Results 2.1 Baseline Characteristics of Patients The study cohort comprised 337 patients aged 29–84 years. The most common malignancies were Lung cancer (30.27%) and Gastric cancer (21.07%), followed by Esophageal cancer (16.32%) and Hepatocellular carcinoma (11.57%). In addition, Comorbid conditions were considered. These were documented in 116 patients (34.4%). Among them, the majority (n = 78, 67.2%) presenting with one comorbidity, while 25 (21.6%) and 13 (11.2%) patients had two or three and more comorbidities, respectively. Complete demographic and clinical characteristics are summarized in Table 1 . Table 1 Clinical Characteristics and Distribution of Cancer Patients Treated with Sintilimab Variables Categories Cases (n) Percentage (%) Department Medical Oncology 313 92.88 Thoracic Surgery 13 3.86 Respiratory Medicine 7 2.08 Radiation Oncology 2 0.59 General Surgery 2 0.59 Gender Male 269 79.82 Female 68 20.18 Age(years) ≤ 50 24 7.12 51–60 119 35.31 61–70 68 20.18 71–80 117 34.72 >80 9 2.67 Tumor Type Lung Cancer 102 30.27 Esophageal carcinoma 55 16.32 Gastric cancer 71 21.07 Colorectal cancer 14 4.15 Hepatocellular carcinoma 39 11.57 Other malignancies 56 16.62 Comorbidities 0 221 65.58 1 78 23.14 2 25 7.42 ≥ 3 13 3.86 2.2 Incidence of Adverse Drug Reactions 2.2.1 Spectrum and Severity of Adverse Events Of the 337 enrolled patients, 208 (61.7%) developed at least one adverse drug reaction (ADR), with 270 distinct ADRs were documented, which reflected multi - organ involvement in some cases. The severity distribution was as follows: Grade 1–2: 219 events (81.1%); Grade 3: 42 events (15.6%); Grade 4: 9 events (3.3%). Hematologic toxicities predominated (19.88%), followed by Gastrointestinal adverse events (15.13%) and systemic manifestations (14.80%). All the ADRs are detailed in Table 2 . Table 2 Distribution and Incidence of Adverse Reactions Induced by Sintilimab Adverse Reaction Type Number of adverse events by grade total Incidence(%) G1 G2 G3 G4 Hematologic toxicity 4 42 12 9 67 19.88 Gastrointestinal toxicity 21 25 5 0 51 15.13 Systemic manifestations 22 13 14 0 49 14.80 Endocrine toxicity 9 25 11 0 45 13.35 Dermatologic and mucosal toxicity 11 17 0 0 28 8.31 Hepatotoxicity 13 3 0 0 16 4.83 Cardiotoxicity 2 12 14 4.15 2.3.2 Hematological Toxicity A total of 67 hematological adverse events were observed, primarily manifesting as myelosuppression including thrombocytopenia, leukopenia, hypoproteinemia, and Anemia. Grade 4 toxicities were characterized by progressive Leukocytopenia (WBC < 1.0×10⁹/L) and Thrombocytopenia (platelet count < 25×10⁹/L), or severe (Grade IV) bone marrow suppression. All severe cases showed clinical improvement following aggressive treatment but required permanent discontinuation of Sintilimab therapy. 2.3.3 Gastrointestinal Toxicity The 51 gastrointestinal adverse events included hepatobiliary disorders (elevated liver enzymes (AST/ALT > 3×ULN) and hepatitis), upper gastrointestinal symptoms (Nausea/Vomiting) and vnorexia-Lower gastrointestinal symptoms (diarrhea). 2.3.4 Systemic Symptoms Among 49 systemic adverse events, the predominant manifestations were Neurological symptoms (dizziness and lower extremity weakness) and constitutional symptoms (fatigue/malaise). 2.3 Risk Factor Analysis for Adverse Drug Reactions 2.3.1 Univariate Analysis Univariate analysis of patients with ADRs revealed that gender, age, smoking history, alcohol consumption history, comorbidities, infusion duration, off-label use, and dosage showed no significant correlation with Sintilimab-induced adverse reactions (all P ≥ 0.05). However, combination therapy and baseline assessment were significantly associated with adverse reaction occurrence ( P < 0.05) (Table 3 ). Table 3 Univariate analysis of risk factors for Sintilimab-associated adverse events Variable Category/Definition Cases (n) Percentage n(%) χ 2 P Gender 2.835 0.092 Male 269 160(59.48) Female 68 48(70.59) Age 0.155 0.693 ≤ 60 143 90(62.94) >60 194 118(60.82) Smoking history 2.201 0.138 YES 48 35(72.92) NO 289 173(59.86) Alcohol consumption history 1.427 0.232 YES 77 52(67.53) NO 260 156(60) Baseline assessment 9.434 0.002 YES 59 26(44.07) NO 278 182(65.46) Comorbidities 3.050 0.081 YES 116 79(68.10) NO 221 129(58.37) Appropriate infusion duration 0.769 0.380 YES 327 200(61.16) NO 10 8(80) Medication use 0.166 0.684 YES 95 57(60) NO 242 151(62.40) Administered dose 0.037 0.847 100mg 38 24(63.16) 200mg 299 184(61.54) Drug sequence Correct 0.451 0.502 YES 312 192(61.54) NO 25 16(64) Glucocorticoids Administered 2.982 0.084 YES 51 16(31.37) NO 286 192(67.13) Anti-infectives 1.090 0.296 YES 42 22(52.38) NO 295 186(63.05) Combination therapy 96.928 <0.01 YES 130 112(86.15) NO 207 96(46.38) Catheter flushing 0.163 0.686 YES 292 181(61.99) NO 45 27(60) 2.3.2 Multivariate Logistic Regression Analysis To further identify independent risk factors for adverse reactions, variables demonstrating statistical significance (P < 0.05) in univariate analysis were included in a multivariate binary logistic regression model. The multivariate analysis revealed that combination therapy (OR = 25.67, 95% CI: 11.319–58.218) and baseline assessment (OR = 0.825, 95% CI: 0.191–0.789) were independent risk factors for Sintilimab-associated adverse reactions (Table 4 ). Table 4 Binary logistic regression analysis of risk factors for Sintilimab-associated adverse events Variable Β SD Wald P OR 95%CI Combination Therapy 3.245 0.418 60.3351 <0.001 25.670 11.319–58.218 Baseline Assessment -0.946 0.362 6.840 0.009 0.388 0.191–0.789 Intercept -0.192 0.154 1.559 0.212 0.825 2.4 Establishment of the Logistic Model and ROC Curve for Predicting and Validating Adverse Events Based on the multivariate logistic regression results, a predictive model was established. In this model, the occurrence of ADRs as the dependent variable. Meanwhile, combination therapy and baseline assessment as independent variables, The Logistic Model is as follows: logit(P) = -0.192–0.946X (Baseline Assessment) + 3.245X(Combination Therapy). Transformed Equation for Combined Predictors: Y(combined = X(Baseline Assessment)- 3.430X (Combination Therapy). Model Validation - Goodness-of-fit test (Hosmer-Lemeshow): χ² = 3.575, df = 2, P = 0.167 (indicating good model fit), Overall accuracy 72.7% Additionally, a receiver operating characteristic (ROC) curve was constructed to evaluate the predictive performance of combination therapy and baseline assessment (Fig. 1 ). The area under the curve (AUC), sensitivity, specificity, and Youden index were calculated. The results demonstrated that the AUC values for combination therapy, baseline assessment, and their combined prediction were all > 0.5. Notably, the AUC of the combined prediction was higher than that of either individual predictor. It indicates that the combination of baseline assessment and combination therapy had superior predictive value for ADRs associated with Sintilimab.(Table 5 for details.). Among the 115 patients included in the analysis, only 22 cases were mispredicted, yielding an overall accuracy of 80.87%. Table 5 Predictive Value of Various Risk Factors for Adverse Reaction Risk Associated with Sintilimab Variable Sensitivity Specificity Youden index AUC 95%CI P Combination Therapy 0.591 0.946 0.537 0.769 0.718–0.819 <0.001 Baseline Assessment 0.125 0.744 -0.131 -0.435 0..370-0.499 0.044 Combination Predictor 0.038 0.744 -0.217 0.200 0.153–0.247 <0.001 3 Discussion Analysis of the 337 medical records incorporated in this study revealed a significantly elevated proportion of male patients (79.82%). This phenomenon could be ascribed to the higher incidence of malignancies for which Sintilimab is indicated (such as lymphoma, hepatocellular carcinoma, gastric cancer, lung cancer, and esophageal cancer) within the male population. Current clinical investigations report that PD-1/PD-L1 inhibitors generally induce immune-related adverse events (irAEs) at an overall incidence rate of approximately 70% [ 14 , 15 ] . In contrast, our study documented a lower ADR incidence rate of 61.72% in patients receiving Sintilimab treatmen. This disparity might be elucidated by the potential under-detection and incomplete documentation of grade 1–2 mild adverse reactions in routine clinical practice, especially during the initial treatment phase. In this study, hematologic toxicity emerged with the highest incidence, standing at 19.88%. The clinical manifestations were predominantly characterized by myelosuppression, thrombocytopenia, leukopenia, and neutropenia. Significantly, grade III - IV hematologic toxicity accounted for 31.34% of all hematologic adverse events, which is in alignment with previous reports in the literature [ 16 ] . Current research indicates that the overall incidence of PD-1/PD-L1 inhibitor-related hematologic adverse reactions ranges from 10–15%, with thrombocytopenia and anemia being the most frequently observed manifestations [ 17 ] . The underlying mechanism may be associated with the immunomodulatory function of the PD-1 pathway within the hematopoietic stem cell microenvironment. PD-1 inhibition may disrupt immune tolerance, potentially triggering autoimmune myelosuppression. According to the Management Guidelines for Immunotherapy-Related Toxicities, grade III or higher toxicity warrants immediate treatment discontinuation, while grade IV toxicity typically requires permanent cessation of therapy. The incidence of gastrointestinal toxicity was 15.13%, primarily manifesting as hepatotoxicity (elevated ALT/AST, immune-mediated hepatitis) and gastrointestinal reactions (nausea/vomiting, decreased appetite). Relevant literature [ 18 ] reports that immune - mediated liver injury has a characteristic. That is, the levels of ALT and AST are significantly elevated, but there are no obvious clinical symptoms. This characteristic aligns with the hepatotoxicity observed in this study. Some studies have identified gender, age, and combination therapy as risk factors for immune-mediated hepatotoxicity. This study also found that combination therapy is an independent risk factor for higher incidence of adverse reactions to Sintilimab. The incidence of hepatotoxicity is higher in patients receiving ICIs combined with targeted therapy than in those receiving monotherapy or combination chemotherapy [ 19 , 20 ] . Immune-mediated hepatotoxicity occurs in approximately 9.5% of patients receiving ICIs treatment [ 21 ] , with CD8 + T-cell infiltration and lymphocyte activation by ICIs being the primary causes of liver injury [ 22 ] . Sintilimab-induced liver function abnormalities are also common ADRs [ 23 , 24 ] , and their relatively high incidence warrants attention.. According to guideline recommendations, grade 1 hepatotoxicity may allow continued treatment, while for grade 2, the drug should be discontinued until liver function recovers, and then resumption can be considered [ 25 ] . The incidence of systemic symptoms was 14.80%, primarily including fatigue and neurological symptoms (dizziness, lower limb weakness). Current evidence shows that PD − 1 inhibitor - related fatigue occurs in 30–50% of cases [ 26 ] , The potential mechanisms involve two aspects. One is cytokine release, such as elevated levels of IL − 6 and TNF - α. The other is endocrine abnormalities, like thyroid/adrenal dysfunction.. Although systemic symptoms have a relatively high incidence, their severity is typically low (mostly grade 1–2) and often do not require intervention. This study employed univariate analysis to delve into the risk factors associated with Sintilimab - related ADRs. The results indicated that combination therapy and baseline assessment were identified as independent risk factors for ADRs. In contrast, factors including gender, age, lifestyle habits (such as smoking and alcohol consumption), comorbidities, medication administration modalities (such as infusion duration, line flushing, off - label use, and dosage), as well as glucocorticoid/anti - infective therapy, did not show a significant correlation. Combination therapy (e.g., chemotherapy, targeted therapy, or other immunotherapies) was the strongest risk factor for ADRs, consistent with existing studies [ 27 , 28 ] . When PD-1/PD-L1 inhibitors were combined with chemotherapy or CTLA-4 inhibitors, the incidence of grade 3–4 ADRs significantly increased. Two plausible explanations can be proposed: ①Immunological synergy: Chemotherapy enhances T-cell activation, increasing the risk of immune-related adverse events (irAEs) such as colitis, pneumonitis, and hepatitis [ 29 ] ; ②Additive toxicity: Myelosuppression or hepatotoxicity from chemotherapeutic agents (e.g., paclitaxel, platinum-based drugs) may exacerbate immunotherapy-related side effects. In response, intensified monitoring (e.g., blood tests, liver/kidney function, pulmonary imaging), dose adjustment of combination regimens, or sequential therapy is recommended to mitigate risks. Baseline assessment helped identify potential risks (e.g., abnormal liver function, autoimmune disease history), and pretreatment measures (e.g., hepatoprotective therapy) could reduce ADR occurrence. Factors showing no significant correlation in this study, such as demographic characteristics (gender, age), differed from some prior findings, possibly due to sample size or population heterogeneity. Lifestyle factors (smoking, alcohol consumption) were also unrelated to ADRs in this study—while smoking may upregulate PD-L1, it did not affect ADR incidence. Medication administration methods (infusion duration, line flushing, off-label use) showed no correlation with ADRs, suggesting that ADRs primarily stem from systemic immune activation rather than local factors. Glucocorticoid/anti-infective therapy did not exhibit a protective effect against ADRs, possibly due to suboptimal timing or insufficient dosing. Multivariate logistic regression analysis revealed that pre-treatment baseline assessment (OR = 0.825, 95% CI: 0.191–0.789) and combination therapy (OR = 25.67, 95% CI: 11.319–58.218) were independent risk factors for Sintilimab-related adverse drug reactions (both P < 0.01). The joint predictive model constructed based on these factors demonstrated strong discriminative performance, suggesting high clinical predictive value. In the model equation, the regression coefficient for combination therapy (3.245) was significantly higher than that for baseline assessment (− 0.946). This implies that combination therapy could substantially elevate the risk of ADRs, which is consistent with the findings of most immunotherapy studies. On the other hand, the negative coefficient (− 0.946) of baseline assessment indicates that a comprehensive pre - treatment evaluation, such as assessing organ function and inflammatory markers, may help mitigate the risk, highlighting the significance of pre - treatment assessment. The joint predictive model achieved a higher AUC than individual predictors, consistent with the expected performance of a multivariate model. This model serves as a preliminary tool for predicting Sintilimab-related ADRs, aiding in clinical decision-making and patient management. It effectively stratified high- and low-risk patients, providing robust support for clinical decisions and enabling proactive interventions. The logistic model demonstrated strong utility in predicting Sintilimab-related ADRs, with advantages including interpretability of risk factors, adjustment for confounders, and synergistic analysis with ROC curves. It assists clinicians in identifying high-risk patients and optimizing treatment strategies, thereby reducing ADRs and improving therapeutic safety. Future directions may involve model refinement through nonlinear extensions, dynamic prediction, and multi-omics data integration to explore additional risk factors and develop more precise predictive tools, ultimately advancing personalized precision medicine. 4 Conclusion This study systematically examined the risk factors for adverse drug reactions (ADRs) associated with Sintilimab treatment and constructed a risk prediction model to provide clinicians with a pretreatment risk assessment tool. The purpose of this model is to offer clinicians a pre - treatment risk assessment instrument. It can forecast the potential risk of ADRs in patients undergoing Sintilimab treatment, thus helping physicians take individual patient variations and risk profiles into more comprehensive account when devising treatment plans. Based on the model's evaluation results, clinicians can implement personalized treatment strategies, including but not limited to: enhanced baseline assessments, optimized combination therapy regimens, or preventive interventions to reduce ADR incidence, improve treatment safety, and enhance patient outcomes. Although this study is a single-center retrospective analysis with a limited sample size, its findings preliminarily reflect the real-world clinical application of Sintilimab. Future multicenter, large - scale prospective studies are necessary to further verify the model’s predictive performance and more comprehensively assess the efficacy and safety of Sintilimab. Consequently, a more solid foundation can be provided for individualized treatment strategies in clinical practice. Declarations Author Contributions: RCT: Conceptualization, Methodology, Formal analysis, Writing - Original Draft. ZH: Investigation, Data Curation, Visualization. HL: Resources, Supervision, Grammar proofreading. LJJ, LYN, XJJ：Clinical data collection and curation. WXA: Proofreading - Review & Editing. Conflict of interests : The authors have no relevant financial or non-financial interests to disclose. Funding statement : This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors Acknowledgements : We thank all the authors for their contributions to this paper. Data Sharing Statement : The clinical datasets generated and analyzed during the current study are not publicly available due to patient confidentiality obligations under the GDPR but are available from the corresponding author on reasonable request, subject to approval from the institutional ethics committee. References ZHU D, LI Y Y, SONG Y Q et al (2020) Clinical research progress of PD-1 inhibitor sintilimab[J]. 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Nat Rev Clin Oncol 19(4):254–267 Martins F, Sofiya L, Sykiotis GP et al (2019) Adverse effects of immune-checkpoint inhibitors: epidemiology, management and surveillance. Nat Rev Clin Oncol 16(9):563–580 Sullivan RJ, Weber JS (2022) Immune-related toxicities of checkpoint inhibitors: mechanisms and mitigation strategies. Nat Rev Drug Discov 21(7):495–508 NCCN (2023) (National Comprehensive Cancer Network). NCCN Guidelines®. Management of Immunotherapy-Related Toxicities. Version 1 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7495701","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":529472780,"identity":"bbe16c3f-1d7f-40b2-9481-adccd7c1382e","order_by":0,"name":"RONG Chengting","email":"","orcid":"","institution":"Hefei BOE Hospital","correspondingAuthor":false,"prefix":"","firstName":"RONG","middleName":"","lastName":"Chengting","suffix":""},{"id":529472783,"identity":"24d876fa-c0ac-44f8-87a7-66e51eee178e","order_by":1,"name":"Hong ZHANG","email":"","orcid":"","institution":"Fuyang People's 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02:06:26","extension":"html","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":112156,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7495701/v1/d71f38432a515eb4775cacfc.html"},{"id":93638546,"identity":"65a0aab8-4fba-47d5-a21f-e2b009a66fec","added_by":"auto","created_at":"2025-10-16 01:58:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83531,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of risk factors and combined predictors for Sintilimab-associated adverse events\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7495701/v1/81c241055adedd1e50d0e46b.png"},{"id":102449263,"identity":"a8e19af8-a3f3-455c-a644-d4ee977b3ebf","added_by":"auto","created_at":"2026-02-11 18:25:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1103576,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7495701/v1/630de580-ae67-4d28-914a-65f92e2c0e35.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Real-World Study on the Safety of Sintilimab in Clinical Use and Establishment of a Risk Prediction Model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eImmune checkpoint inhibitors (ICIs) are a class of monoclonal antibody drugs that exert antitumor effects by blocking the interaction between tumor cells expressing immune checkpoints and immune cells, thereby reversing the immunosuppressive effects of tumor cells on immune cells. In recent years, ICIs, particularly programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors, have become a major focus of global drug development. Sintilimab, a domestically developed PD-1 inhibitor in China, blocks the binding of PD-1 to its ligands (PD-L1 and PD-L2), thereby releasing T cell inhibition and restoring endogenous antitumor T cell responses to eliminate tumor cells \u003csup\u003e[1-2].\u003c/sup\u003e It has demonstrated significant clinical efficacy in the treatment of various malignancies and has been included in the National Reimbursement Drug List (NRDL), enabling widespread clinical use \u003csup\u003e[3]\u003c/sup\u003e. Sintilimab was first approved in China in December 2018 for the treatment of relapsed or refractory classical Hodgkin lymphoma (cHL). By 2023, its approved indications had expanded to multiple high-incidence malignancies, including cHL, non-small cell lung cancer (first-line), hepatocellular carcinoma (first-line), gastric cancer (first-line), and esophageal squamous cell carcinoma (first-line), making it the first PD-1 inhibitor in China approved for first-line treatment across five major cancer types. However, as its indications broaden and its user population expands, safety concerns have garnered increasing attention. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe unique mechanism of ICIs, while activating antitumor immunity, may also lead to immune-related adverse events (irAEs), such as pneumonitis, colitis, hepatitis, and endocrine disorders, some of which can be life-threatening \u003csup\u003e[4]\u003c/sup\u003e. Although Sintilimab has shown a relatively manageable safety profile in clinical trials, real-world patient heterogeneity (e.g., comorbidities, concomitant medications, and varying tumor types) may influence its safety characteristics, and existing clinical trial data may not fully reflect real-world risk profiles. Currently, several reports on Sintilimab-related adverse events have been published \u003csup\u003e[5-7]\u003c/sup\u003e, but most consist of case reports or literature reviews, leaving the real-world incidence, severity, and risk factors of adverse events insufficiently characterized. Additionally, risk variations among different populations (e.g., elderly patients or those with chronic diseases) have not been systematically evaluated. Furthermore, tumor type and combination therapies (e.g., chemotherapy or antiangiogenic agents) may further influence the risk of adverse events \u003csup\u003e[8].\u003c/sup\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding adverse event prediction, existing studies suggest that certain clinical factors (e.g., baseline inflammatory markers, history of autoimmune disease, specific genetic polymorphisms) may correlate with irAE occurrence \u003csup\u003e[9,10]\u003c/sup\u003e. However, systematic research on Sintilimab safety risk prediction remains limited, particularly in developing integrated models incorporating clinical features, patient characteristics, and drug usage patterns. Establishing such models could facilitate early identification of high-risk patients, enabling personalized monitoring and intervention to optimize the benefit-risk ratio. This study aims to comprehensively analyze the adverse event profile of Sintilimab using real-world data, identify risk factors, and construct a risk prediction model. The findings are expected to provide a scientific basis for the safe and rational clinical use of Sintilimab, supporting precision medicine practices.\u003c/p\u003e\n\u003cp\u003eImmune checkpoint inhibitors (ICIs) are monoclonal antibody drugs that enhance antitumor immunity by blocking the interaction between immune checkpoints on tumor cells and their ligands on immune cells, thereby reversing tumor-mediated immunosuppression. Among the various types of ICIs, programmed death-1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitors have taken center-stage in oncology research. Sintilimab, a PD-1 inhibitor, exerts its antitumor effects by disrupting PD-1/PD-L1/PD-L2 signaling, thereby restoring T-cell activity and promoting tumor cell elimination\u003csup\u003e\u0026nbsp;[1-2]\u003c/sup\u003e. Sintilimab was initial approval in December 2018 for relapsed or refractory classical Hodgkin\u0026rsquo;s lymphoma. Subsequently, it has been approved in China for multiple malignancies, such as lymphoma, hepatocellular carcinoma, gastric cancer, non-small cell lung cancer, and esophageal cancer. Notably, it remains the only PD-1 inhibitor approved for first-line treatment across all five major high-incidence cancers. However, with expanding clinical use, safety concerns have gained prominence. Although Sintilimab-associated adverse drug reactions (ADRs) have been documented\u003csup\u003e\u0026nbsp;[3-5]\u003c/sup\u003e, the existing evidence has certain limitations. Primarily, it comes from case reports and literature reviews. There has been limited systematic investigation into the risk factors and predictive models for these ADRs. This study employs a retrospective analysis to explore the risk factors associated with Sintilimab-induced ADRs and establishes a logistic regression model for prediction. The receiver operating characteristic (ROC) curve was used to evaluate the model\u0026apos;s accuracy, aiming to provide a reference for the safe and rational clinical application of Sintilimab.\u003c/p\u003e"},{"header":"1 Materials and Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Data Sources and Study Population\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective study using anonymized data from the Hospital Information System (HIS) of our institution. Patients treated with Sintilimab Injection (Innovent Biologics; NMPA Approval No.: S20180016; 10 mL: 100 mg/vial) between January 2021 and December 2022 were randomly selected. Inclusion criteria: ①Age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; ②Non-pregnant/non-lactating status; ③Complete clinical records (verified by dual independent review). Exclusion criteria: ①Age\u0026thinsp;\u0026lt;\u0026thinsp;18 years; ②Incomplete medical documentation. A total of 337 cases meeting the inclusion/exclusion criteria were enrolled. Following relevant reporting guidelines \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, the cases were allocated in a 3:1 ratio for model development and validation, an additional 115 patients treated between January\u0026ndash;December 2023 were included for external validation of the predictive model. Consent to Participate: The requirement for written informed consent was waived by the Institutional Review Board of Hefei BOE Hospital because this retrospective study used only anonymized clinical data without any additional interventional procedures.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Data Collection and Variables\u003c/h2\u003e\u003cp\u003eClinical data were extracted from the HIS and systematically recorded, including: ①Demographics: Patient ID, sex, age, comorbidities, smoking/alcohol history; ②Treatment details: Sintilimab dosage, treatment cycles, off-label use, infusion duration, baseline assessments, administration sequence, premedication protocols; ③Concomitant therapies: Chemotherapy, radiotherapy, or immunosuppressants; ④ Underlying diseases; ⑤ Concomitant anti-infective or glucocorticoid therapy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.3 Causality Assessment\u003c/h2\u003e\u003cp\u003eAn ADR evaluation team (Comprising oncologists and Pharmacists) assessed the association between adverse reactions and Sintilimab based on the causality criteria from China\u0026rsquo;s Adverse Drug Reaction Reporting and Monitoring Manual \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Only reactions classified as \"Definite,\" \"Probable,\" or \"Possible\" were included.\u003c/p\u003e\u003cp\u003eADR severity grading followed the Chinese Society of Clinical Oncology (CSCO) Guidelines for Immune Checkpoint Inhibitor-Related Toxicity Management (2019) \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e: G1(Mild): Asymptomatic or mild symptoms, self-resolving without intervention. G2 (Moderate): Mild limitations in daily activities, requiring local treatment. G3(Severe): Disabling or significant activity limitations (non-life-threatening), requiring hospitalization. G4 (Life-threatening). G5 (Death related to toxicity).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e1.4 Statistical Analysis\u003c/h2\u003e\u003cp\u003eData were analyzed using SPSS 23.0. Normally distributed continuous variables: Expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, compared via independent t-test. Non-normally distributed variables: Expressed as median (P25, P75), compared via Wilcoxon rank-sum test. Categorical variables: Expressed as percentages (%), compared via χ\u0026sup2; test. Binary logistic regression was performed to identify independent risk factors for Sintilimab-induced ADRs and establish a predictive model. Receiver operating characteristic (ROC) curves were plotted for individual and combined risk factors, with Youden index calculated based on sensitivity and specificity. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"2 Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Baseline Characteristics of Patients\u003c/h2\u003e\u003cp\u003eThe study cohort comprised 337 patients aged 29\u0026ndash;84 years. The most common malignancies were Lung cancer (30.27%) and Gastric cancer (21.07%), followed by Esophageal cancer (16.32%) and Hepatocellular carcinoma (11.57%). In addition, Comorbid conditions were considered. These were documented in 116 patients (34.4%). Among them, the majority (n\u0026thinsp;=\u0026thinsp;78, 67.2%) presenting with one comorbidity, while 25 (21.6%) and 13 (11.2%) patients had two or three and more comorbidities, respectively. Complete demographic and clinical characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eClinical Characteristics and Distribution of Cancer Patients Treated with Sintilimab\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=\"left\" 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\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCases (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eDepartment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedical Oncology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e92.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThoracic Surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRespiratory Medicine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRadiation Oncology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGeneral Surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e79.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eAge(years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51\u0026ndash;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61\u0026ndash;70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71\u0026ndash;80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eTumor Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLung Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEsophageal carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGastric cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eColorectal cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHepatocellular carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther malignancies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eComorbidities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Incidence of Adverse Drug Reactions\u003c/h2\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Spectrum and Severity of Adverse Events\u003c/h2\u003e\u003cp\u003eOf the 337 enrolled patients, 208 (61.7%) developed at least one adverse drug reaction (ADR), with 270 distinct ADRs were documented, which reflected multi - organ involvement in some cases. The severity distribution was as follows: Grade 1\u0026ndash;2: 219 events (81.1%); Grade 3: 42 events (15.6%); Grade 4: 9 events (3.3%). Hematologic toxicities predominated (19.88%), followed by Gastrointestinal adverse events (15.13%) and systemic manifestations (14.80%). All the ADRs are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eDistribution and Incidence of Adverse Reactions Induced by Sintilimab\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAdverse Reaction Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eNumber of adverse events by grade\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003etotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIncidence(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eG3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eG4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHematologic toxicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e19.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastrointestinal toxicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystemic manifestations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e14.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEndocrine toxicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e13.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDermatologic and mucosal toxicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatotoxicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiotoxicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2 Hematological Toxicity\u003c/h2\u003e\u003cp\u003eA total of 67 hematological adverse events were observed, primarily manifesting as myelosuppression including thrombocytopenia, leukopenia, hypoproteinemia, and Anemia. Grade 4 toxicities were characterized by progressive Leukocytopenia (WBC\u0026thinsp;\u0026lt;\u0026thinsp;1.0\u0026times;10⁹/L) and Thrombocytopenia (platelet count\u0026thinsp;\u0026lt;\u0026thinsp;25\u0026times;10⁹/L), or severe (Grade IV) bone marrow suppression. All severe cases showed clinical improvement following aggressive treatment but required permanent discontinuation of Sintilimab therapy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.3.3 Gastrointestinal Toxicity\u003c/h2\u003e\u003cp\u003eThe 51 gastrointestinal adverse events included hepatobiliary disorders (elevated liver enzymes (AST/ALT\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026times;ULN) and hepatitis), upper gastrointestinal symptoms (Nausea/Vomiting) and vnorexia-Lower gastrointestinal symptoms (diarrhea).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.3.4 Systemic Symptoms\u003c/h2\u003e\u003cp\u003eAmong 49 systemic adverse events, the predominant manifestations were Neurological symptoms (dizziness and lower extremity weakness) and constitutional symptoms (fatigue/malaise).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Risk Factor Analysis for Adverse Drug Reactions\u003c/h2\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 Univariate Analysis\u003c/h2\u003e\u003cp\u003eUnivariate analysis of patients with ADRs revealed that gender, age, smoking history, alcohol consumption history, comorbidities, infusion duration, off-label use, and dosage showed no significant correlation with Sintilimab-induced adverse reactions (all P\u0026thinsp;\u0026ge;\u0026thinsp;0.05). However, combination therapy and baseline assessment were significantly associated with adverse reaction occurrence (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eUnivariate analysis of risk factors for Sintilimab-associated adverse events\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory/Definition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCases (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage n(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\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\u003eGender\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e160(59.48)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48(70.59)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.693\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90(62.94)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e118(60.82)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking history\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35(72.92)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e173(59.86)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol consumption history\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.232\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52(67.53)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e156(60)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline assessment\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26(44.07)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e182(65.46)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComorbidities\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79(68.10)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e129(58.37)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAppropriate infusion duration\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.380\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e200(61.16)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8(80)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedication use\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.684\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57(60)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e151(62.40)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdministered dose\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.847\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24(63.16)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e200mg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e184(61.54)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug sequence Correct\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.502\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e192(61.54)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16(64)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucocorticoids Administered\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16(31.37)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e192(67.13)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnti-infectives\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.296\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22(52.38)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e186(63.05)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombination therapy\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e96.928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e112(86.15)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96(46.38)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCatheter flushing\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.686\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e181(61.99)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27(60)\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\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2 Multivariate Logistic Regression Analysis\u003c/h2\u003e\u003cp\u003eTo further identify independent risk factors for adverse reactions, variables demonstrating statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in univariate analysis were included in a multivariate binary logistic regression model. The multivariate analysis revealed that combination therapy (OR\u0026thinsp;=\u0026thinsp;25.67, 95% CI: 11.319\u0026ndash;58.218) and baseline assessment (OR\u0026thinsp;=\u0026thinsp;0.825, 95% CI: 0.191\u0026ndash;0.789) were independent risk factors for Sintilimab-associated adverse reactions (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBinary logistic regression analysis of risk factors for Sintilimab-associated adverse events\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eΒ\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWald\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e95%CI\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\u003eCombination Therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.3351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e25.670\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11.319\u0026ndash;58.218\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline Assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.191\u0026ndash;0.789\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Establishment of the Logistic Model and ROC Curve for Predicting and Validating Adverse Events\u003c/h2\u003e\u003cp\u003eBased on the multivariate logistic regression results, a predictive model was established. In this model, the occurrence of ADRs as the dependent variable. Meanwhile, combination therapy and baseline assessment as independent variables, The Logistic Model is as follows: logit(P) = -0.192\u0026ndash;0.946X (Baseline Assessment)\u0026thinsp;+\u0026thinsp;3.245X(Combination Therapy). Transformed Equation for Combined Predictors: Y(combined\u0026thinsp;=\u0026thinsp;X(Baseline Assessment)- 3.430X (Combination Therapy). Model Validation - Goodness-of-fit test (Hosmer-Lemeshow): χ\u0026sup2; = 3.575, df\u0026thinsp;=\u0026thinsp;2, P\u0026thinsp;=\u0026thinsp;0.167 (indicating good model fit), Overall accuracy 72.7%\u003c/p\u003e\u003cp\u003eAdditionally, a receiver operating characteristic (ROC) curve was constructed to evaluate the predictive performance of combination therapy and baseline assessment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The area under the curve (AUC), sensitivity, specificity, and Youden index were calculated. The results demonstrated that the AUC values for combination therapy, baseline assessment, and their combined prediction were all \u0026gt;\u0026thinsp;0.5. Notably, the AUC of the combined prediction was higher than that of either individual predictor. It indicates that the combination of baseline assessment and combination therapy had superior predictive value for ADRs associated with Sintilimab.(Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e for details.). Among the 115 patients included in the analysis, only 22 cases were mispredicted, yielding an overall accuracy of 80.87%.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePredictive Value of Various Risk Factors for Adverse Reaction Risk Associated with Sintilimab\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYouden index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\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\u003eCombination Therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.718\u0026ndash;0.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline Assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0..370-0.499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombination Predictor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.153\u0026ndash;0.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;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\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Discussion","content":"\u003cp\u003eAnalysis of the 337 medical records incorporated in this study revealed a significantly elevated proportion of male patients (79.82%). This phenomenon could be ascribed to the higher incidence of malignancies for which Sintilimab is indicated (such as lymphoma, hepatocellular carcinoma, gastric cancer, lung cancer, and esophageal cancer) within the male population. Current clinical investigations report that PD-1/PD-L1 inhibitors generally induce immune-related adverse events (irAEs) at an overall incidence rate of approximately 70% \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. In contrast, our study documented a lower ADR incidence rate of 61.72% in patients receiving Sintilimab treatmen. This disparity might be elucidated by the potential under-detection and incomplete documentation of grade 1\u0026ndash;2 mild adverse reactions in routine clinical practice, especially during the initial treatment phase.\u003c/p\u003e\u003cp\u003eIn this study, hematologic toxicity emerged with the highest incidence, standing at 19.88%. The clinical manifestations were predominantly characterized by myelosuppression, thrombocytopenia, leukopenia, and neutropenia. Significantly, grade III - IV hematologic toxicity accounted for 31.34% of all hematologic adverse events, which is in alignment with previous reports in the literature\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Current research indicates that the overall incidence of PD-1/PD-L1 inhibitor-related hematologic adverse reactions ranges from 10\u0026ndash;15%, with thrombocytopenia and anemia being the most frequently observed manifestations\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. The underlying mechanism may be associated with the immunomodulatory function of the PD-1 pathway within the hematopoietic stem cell microenvironment. PD-1 inhibition may disrupt immune tolerance, potentially triggering autoimmune myelosuppression. According to the Management Guidelines for Immunotherapy-Related Toxicities, grade III or higher toxicity warrants immediate treatment discontinuation, while grade IV toxicity typically requires permanent cessation of therapy.\u003c/p\u003e\u003cp\u003eThe incidence of gastrointestinal toxicity was 15.13%, primarily manifesting as hepatotoxicity (elevated ALT/AST, immune-mediated hepatitis) and gastrointestinal reactions (nausea/vomiting, decreased appetite). Relevant literature\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e reports that immune - mediated liver injury has a characteristic. That is, the levels of ALT and AST are significantly elevated, but there are no obvious clinical symptoms. This characteristic aligns with the hepatotoxicity observed in this study. Some studies have identified gender, age, and combination therapy as risk factors for immune-mediated hepatotoxicity. This study also found that combination therapy is an independent risk factor for higher incidence of adverse reactions to Sintilimab. The incidence of hepatotoxicity is higher in patients receiving ICIs combined with targeted therapy than in those receiving monotherapy or combination chemotherapy\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Immune-mediated hepatotoxicity occurs in approximately 9.5% of patients receiving ICIs treatment \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, with CD8\u003csup\u003e+\u003c/sup\u003e T-cell infiltration and lymphocyte activation by ICIs being the primary causes of liver injury\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Sintilimab-induced liver function abnormalities are also common ADRs\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, and their relatively high incidence warrants attention.. According to guideline recommendations, grade 1 hepatotoxicity may allow continued treatment, while for grade 2, the drug should be discontinued until liver function recovers, and then resumption can be considered \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. The incidence of systemic symptoms was 14.80%, primarily including fatigue and neurological symptoms (dizziness, lower limb weakness). Current evidence shows that PD \u0026minus;\u0026thinsp;1 inhibitor - related fatigue occurs in 30\u0026ndash;50% of cases\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e, The potential mechanisms involve two aspects. One is cytokine release, such as elevated levels of IL \u0026minus;\u0026thinsp;6 and TNF - α. The other is endocrine abnormalities, like thyroid/adrenal dysfunction.. Although systemic symptoms have a relatively high incidence, their severity is typically low (mostly grade 1\u0026ndash;2) and often do not require intervention.\u003c/p\u003e\u003cp\u003eThis study employed univariate analysis to delve into the risk factors associated with Sintilimab - related ADRs. The results indicated that combination therapy and baseline assessment were identified as independent risk factors for ADRs. In contrast, factors including gender, age, lifestyle habits (such as smoking and alcohol consumption), comorbidities, medication administration modalities (such as infusion duration, line flushing, off - label use, and dosage), as well as glucocorticoid/anti - infective therapy, did not show a significant correlation. Combination therapy (e.g., chemotherapy, targeted therapy, or other immunotherapies) was the strongest risk factor for ADRs, consistent with existing studies \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. When PD-1/PD-L1 inhibitors were combined with chemotherapy or CTLA-4 inhibitors, the incidence of grade 3\u0026ndash;4 ADRs significantly increased. Two plausible explanations can be proposed: ①Immunological synergy: Chemotherapy enhances T-cell activation, increasing the risk of immune-related adverse events (irAEs) such as colitis, pneumonitis, and hepatitis\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e; ②Additive toxicity: Myelosuppression or hepatotoxicity from chemotherapeutic agents (e.g., paclitaxel, platinum-based drugs) may exacerbate immunotherapy-related side effects. In response, intensified monitoring (e.g., blood tests, liver/kidney function, pulmonary imaging), dose adjustment of combination regimens, or sequential therapy is recommended to mitigate risks. Baseline assessment helped identify potential risks (e.g., abnormal liver function, autoimmune disease history), and pretreatment measures (e.g., hepatoprotective therapy) could reduce ADR occurrence. Factors showing no significant correlation in this study, such as demographic characteristics (gender, age), differed from some prior findings, possibly due to sample size or population heterogeneity. Lifestyle factors (smoking, alcohol consumption) were also unrelated to ADRs in this study\u0026mdash;while smoking may upregulate PD-L1, it did not affect ADR incidence. Medication administration methods (infusion duration, line flushing, off-label use) showed no correlation with ADRs, suggesting that ADRs primarily stem from systemic immune activation rather than local factors. Glucocorticoid/anti-infective therapy did not exhibit a protective effect against ADRs, possibly due to suboptimal timing or insufficient dosing.\u003c/p\u003e\u003cp\u003eMultivariate logistic regression analysis revealed that pre-treatment baseline assessment (OR\u0026thinsp;=\u0026thinsp;0.825, 95% CI: 0.191\u0026ndash;0.789) and combination therapy (OR\u0026thinsp;=\u0026thinsp;25.67, 95% CI: 11.319\u0026ndash;58.218) were independent risk factors for Sintilimab-related adverse drug reactions (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The joint predictive model constructed based on these factors demonstrated strong discriminative performance, suggesting high clinical predictive value. In the model equation, the regression coefficient for combination therapy (3.245) was significantly higher than that for baseline assessment (\u0026minus;\u0026thinsp;0.946). This implies that combination therapy could substantially elevate the risk of ADRs, which is consistent with the findings of most immunotherapy studies. On the other hand, the negative coefficient (\u0026minus;\u0026thinsp;0.946) of baseline assessment indicates that a comprehensive pre - treatment evaluation, such as assessing organ function and inflammatory markers, may help mitigate the risk, highlighting the significance of pre - treatment assessment. The joint predictive model achieved a higher AUC than individual predictors, consistent with the expected performance of a multivariate model. This model serves as a preliminary tool for predicting Sintilimab-related ADRs, aiding in clinical decision-making and patient management. It effectively stratified high- and low-risk patients, providing robust support for clinical decisions and enabling proactive interventions. The logistic model demonstrated strong utility in predicting Sintilimab-related ADRs, with advantages including interpretability of risk factors, adjustment for confounders, and synergistic analysis with ROC curves. It assists clinicians in identifying high-risk patients and optimizing treatment strategies, thereby reducing ADRs and improving therapeutic safety. Future directions may involve model refinement through nonlinear extensions, dynamic prediction, and multi-omics data integration to explore additional risk factors and develop more precise predictive tools, ultimately advancing personalized precision medicine.\u003c/p\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eThis study systematically examined the risk factors for adverse drug reactions (ADRs) associated with Sintilimab treatment and constructed a risk prediction model to provide clinicians with a pretreatment risk assessment tool. The purpose of this model is to offer clinicians a pre - treatment risk assessment instrument. It can forecast the potential risk of ADRs in patients undergoing Sintilimab treatment, thus helping physicians take individual patient variations and risk profiles into more comprehensive account when devising treatment plans. Based on the model's evaluation results, clinicians can implement personalized treatment strategies, including but not limited to: enhanced baseline assessments, optimized combination therapy regimens, or preventive interventions to reduce ADR incidence, improve treatment safety, and enhance patient outcomes. Although this study is a single-center retrospective analysis with a limited sample size, its findings preliminarily reflect the real-world clinical application of Sintilimab. Future multicenter, large - scale prospective studies are necessary to further verify the model\u0026rsquo;s predictive performance and more comprehensively assess the efficacy and safety of Sintilimab. Consequently, a more solid foundation can be provided for individualized treatment strategies in clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eRCT: Conceptualization, Methodology, Formal analysis, Writing - Original Draft.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZH: Investigation, Data Curation, Visualization.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHL: Resources, Supervision, Grammar proofreading.\u003c/li\u003e\n \u003cli\u003eLJJ, LYN, XJJ：Clinical data collection and curation.\u003c/li\u003e\n \u003cli\u003eWXA: \u0026nbsp;Proofreading - Review \u0026amp; Editing.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interests\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eWe thank all the authors for their contributions to this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Sharing Statement\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThe clinical datasets generated and analyzed during the current study are not publicly available due to patient confidentiality obligations under the GDPR but are available from the corresponding author on reasonable request, subject to approval from the institutional ethics committee.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZHU D, LI Y Y, SONG Y Q et al (2020) Clinical research progress of PD-1 inhibitor sintilimab[J]. 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Chin J Lung Cancer 22(10):661\u0026ndash;665\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohnson DB, Nebhan CA, Moslehi JJ et al (2022) Immune-checkpoint inhibitors: long-term implications of toxicity. Nat Rev Clin Oncol 19(4):254\u0026ndash;267\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartins F, Sofiya L, Sykiotis GP et al (2019) Adverse effects of immune-checkpoint inhibitors: epidemiology, management and surveillance. Nat Rev Clin Oncol 16(9):563\u0026ndash;580\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSullivan RJ, Weber JS (2022) Immune-related toxicities of checkpoint inhibitors: mechanisms and mitigation strategies. Nat Rev Drug Discov 21(7):495\u0026ndash;508\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNCCN (2023) (National Comprehensive Cancer Network). NCCN Guidelines\u0026reg;. Management of Immunotherapy-Related Toxicities. Version 1\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sintilimab, Adverse drug reactions, Risk factor analysis, Logistic regression model, ROC curve","lastPublishedDoi":"10.21203/rs.3.rs-7495701/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7495701/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo retrospectively analyze the clinical safety and adverse drug reactions (ADRs) of Sintilimab, evaluate risk factors for ADRs occurrence, and develop a predictive model to support individualized treatment strategies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eMedical records of patients who received Sintilimab treatment from January 2021 and December 2022 were reviewed. Clinical data, including demographic characteristics, medication details, and ADRs. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for Sintilimab-induced ADRs, considering variables such as gender, age, comorbidities, and treatment regimens. Receiver operating characteristic (ROC) curve analysis was performed to assess the predictive accuracy of individual and combined risk factors. And the safety of Sintilimab also was evaluated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 337 included cases, 208 (61.72%) experienced ADRs. Multivariate analysis identified combination therapy (OR\u0026thinsp;=\u0026thinsp;25.670, 95% CI: 11.319\u0026ndash;58.218, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and baseline assessment (OR\u0026thinsp;=\u0026thinsp;0.388, 95% CI: 0.191\u0026ndash;0.789, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) as two independent risk factors. The logistic model indicated that the combined prediction of these factors achieved an area under the curve (AUC) of 0.800 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a Youden index of 0.583, which outperformed single-factor predictions. Cross-validation using 115 cases demonstrated an accuracy rate of 80.87% for this model.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eCombination therapy and baseline assessment are independent risk factors for ADRs in Sintilimab-treated patients. The combined predictive model exhibits high accuracy and may serve as a valuable clinical tool to anticipate risks and guide personalized treatment decisions.\u003c/p\u003e","manuscriptTitle":"Real-World Study on the Safety of Sintilimab in Clinical Use and Establishment of a Risk Prediction Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 01:58:21","doi":"10.21203/rs.3.rs-7495701/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d092c0e9-2ea1-4b5e-b6af-87e93fa881d8","owner":[],"postedDate":"October 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T18:24:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-16 01:58:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7495701","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7495701","identity":"rs-7495701","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}