The Link Between Malignancy and Arterial Thrombotic Events: A Systematic Review Across Cancer Types

The Link Between Malignancy and Arterial Thrombotic Events: A Systematic Review Across Cancer Types | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review The Link Between Malignancy and Arterial Thrombotic Events: A Systematic Review Across Cancer Types Moontasir Ahmed, Shadman Newaz, Jannatara Tina, Ananya Sen, Lamia Ashraf, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9242659/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background A diagnosis of cancer is associated with an elevated risk of arterial thrombotic events (ATEs), including myocardial infarction (MI) and ischemic stroke. This systematic review synthesizes the current evidence on the epidemiology, risk factors, time-dependent risks, and outcomes of ATEs across a spectrum of malignancies to guide clinical practice and future research. Methods We systematically searched PubMed and Science Direct from inception to January, 2026 for studies reporting on ATEs in cancer patients. Data on patient demographics, cancer types, treatment modalities, ATE outcomes, and risk estimates were extracted. The risk of bias was assessed using appropriate tools. Results 43 studies were included. The evidence demonstrates a clear association between cancer and an increased risk of ATEs (HR/OR range: 1.5-3.0). High-risk malignancies included lung, pancreatic, gastrointestinal, and brain cancers. The risk was most pronounced in the peri-diagnostic and first 6–12 months after diagnosis. Key contributing factors included advanced cancer stage, specific chemotherapies (e.g., platinum-based agents), radiotherapy, and the perioperative period. Traditional cardiovascular risk factors compounded this risk. Despite the established link, evidence for optimal prophylactic strategies is lacking. Conclusion Cancer confers a significant and time-dependent increased risk of ATEs, necessitating increased clinical vigilance. A proactive, multidisciplinary approach involving cardio-oncology is essential for risk stratification, aggressive management of traditional risk factors, and patient education. Future research must focus on mechanistic studies, predictive biomarker development, and randomized controlled trials to establish effective prevention and treatment strategies. Arterial Thrombotic Events Cancer Myocardial Infarction Ischemic Stroke Thromboembolism Cardio-Oncology Systematic Review Figures Figure 1 Figure 2 1. Introduction Advances in cancer diagnosis and treatment have significantly improved survival rates, shifting clinical focus towards managing long-term complications. Among these, cardiovascular disease represents a major cause of morbidity and mortality in cancer patients and survivors. While the association between cancer and venous thromboembolism (VTE) is well-established, the link between malignancy and arterial thrombotic events (ATEs)—such as myocardial infarction (MI) and ischemic stroke—has gained substantial recognition more recently ( 1 , 2 ). The pathogenesis of cancer-associated ATEs is multifactorial, involving a cancer-induced hypercoagulable state, systemic inflammation, endothelial injury, and direct atherogenic effects of anticancer therapies ( 3 , 4 ). The risk is not uniform; it varies significantly by cancer type, stage, treatment modality, and time since diagnosis. Understanding this complex interplay is crucial for risk prediction, prevention, and optimal management ( 5 – 7 , 9 ). Over the past decade, a growing body of evidence from large cohort studies, registries, and meta-analyses has characterized the burden and determinants of ATEs in oncologic populations. However, a comprehensive synthesis of this evidence is needed to consolidate our understanding and inform clinical decision-making across different cancer types and treatment phases. This systematic review aims to provide a detailed analysis of the global research landscape, risk estimates, time-dependent patterns, treatment-related factors, and outcomes of ATEs in cancer patients, integrating data from a wide range of published studies to offer a definitive overview for clinicians and researchers. 2. Methods This systematic review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. 2.1. Search Strategy and Selection Criteria A systematic search was performed in PubMed and Science Direct from database inception to January, 2026. The search strategy combined terms related to ("cancer" OR "neoplasm" OR "malignancy" OR "oncology") AND ("arterial thrombotic event" OR "myocardial infarction" OR "ischemic stroke" OR "acute coronary syndrome" OR "cardiovascular disease"). Studies were included if they: ( 1 ) reported on human cancer patients and the incidence or risk of ATEs; ( 2 ) provided original data on epidemiology, risk factors, or outcomes; and ( 3 ) were published in English. Cohort studies, case-control studies, registries, and systematic reviews/meta-analyses were eligible. 2.2. Data Extraction and Quality Assessment Two reviewers independently screened titles, abstracts, and full-text articles. Data were extracted using a standardized form, capturing information on study design, patient demographics, cancer types, treatments, ATE outcomes, risk estimates, and key findings. The risk of bias for RCTs was assessed using the Cochrane Risk of Bias 2 (RoB 2) tool. 2.3. Data Synthesis Given the heterogeneity in study designs and reporting, a narrative synthesis was conducted. Data are presented in summary tables and descriptive text. 3. Results 3.1. Study Selection and Characteristics The initial search yielded 2599 records. After removing duplicates and screening titles and abstracts, 62 full-text articles were assessed for eligibility. Ultimately, 43 studies were included in the final synthesis (Fig. 1 ) 3.2. Risk of Bias Assessment The methodological quality of the included studies was assessed. The overall risk of bias was low to moderate. Common limitations included the retrospective nature of most studies and potential for residual confounding. The risk of bias summary and graph are presented in Figs. 2 a and 2 b. 3.3. Geographical Distribution and Research Output The 43 included studies originated from a range of countries, with the United States (n = 11), Denmark (n = 3), Canada (n = 3), South Korea (n = 3), and France (n = 3) being the largest contributors (Table 1 ). The presence of multi-national collaborations (n = 7) strengthened the generalizability of findings. However, significant geographical gaps were noted, with limited representation from South America, Africa, and parts of Asia. Table 1 Geographical Distribution of Included Studies Country / Region Number of Studies References (Study Numbers) United States 11 2, 5, 8, 12, 17–18, 25, 28, 34–35, 40 Denmark 3 9, 10, 40 Canada 3 1, 7, 12 South Korea 3 24, 27, 30 France 3 13, 31, 39 Taiwan 2 22–23 Japan 2 29, 42 Multi-National* 7 3 (Australia/US), 7 (Asia/US/Europe), 11, 15 (Israel/Int.), 33 (Germany), 40 (Global), 43 Other Single Countries 9 4 (Hong Kong), 6 (Israel), 14 (Spain), 19 (Austria), 20 (Israel), 26 (Romania), 32 (Greece), 36 (Austria), 37 (Argentina), 38 (Netherlands), 41 (Switzerland) The global distribution of the 43 included studies reflects a widespread and concerted research effort to understand the link between cancer and arterial thrombotic events (ATEs). The United States contributed the largest number of studies (n = 11), a dominance largely facilitated by the availability of extensive, high-quality national databases such as the Surveillance, Epidemiology, and End Results (SEER) program ( 5 ), the National Inpatient Sample (NIS) ( 17 ), and the National Health and Nutrition Examination Survey (NHANES) ( 2 ). These databases enable large-scale, population-level analyses that are critical for establishing overall risk estimates. Europe and East Asia are also major contributors, with significant outputs from Denmark ( 9 , 10 ), South Korea ( 24 , 27 ), Canada ( 1 , 12 ), and France ( 13 , 31 ). The presence of multinational collaborations and meta-analyses ( 7 , 40 ) significantly strengthens the generalizability of the findings, suggesting that the cancer-ATE relationship is a universal phenomenon and not confined to specific healthcare systems or genetic populations. However, the relative scarcity of studies from Africa, South America, and parts of Asia indicates a geographical gap in the literature where the interplay of different cancer profiles, comorbidities, and healthcare access might yield unique insights. 3.4. Study Design and Scale The methodological landscape was predominantly built upon observational study designs (Table 2 ). Retrospective cohort studies (n = 21) formed the backbone of the evidence, efficiently leveraging pre-existing data. The inclusion of prospective cohort studies (n = 4) and systematic reviews/meta-analyses (n = 5) provided higher-quality evidence and synthesized summary estimates. The studies exhibited a striking dichotomy in scale (Table 3 ), with large-scale population studies (n > 100,000) providing statistical power and generalizability, while smaller, focused studies (n ≤ 1,000) offered invaluable depth and granularity on specific mechanisms and high-risk scenarios. Table 2 Study Design Characteristics Study Design Number of Studies References (Study Numbers) Retrospective Cohort Study 21 1, 4–5, 8, 10, 12–13, 16–19, 20, 24–28, 31, 34–35, 37 Prospective Cohort Study 4 3, 36, 40, 42 Systematic Review and/or Meta-Analysis 5 7, 11, 21 (Protocol), 40–41 Matched Cohort Study 4 1, 28, 30, 34 Cross-sectional Study 2 2, 26 Review Article (Narrative) 1 6 Secondary Analysis of a Clinical Trial 1 3 Observational / Other* 5 14 (Case-control), 15 (Historical cohort), 29 (Observational), 32 (Observational cohort), 38 (Prospective) The methodological landscape of this field is predominantly built upon observational study designs, which are well-suited for investigating associations where randomized controlled trials are often impractical or unethical. Retrospective cohort studies (n = 21) form the backbone of the evidence, efficiently leveraging pre-existing data from cancer registries and administrative health records to track ATE outcomes over time ( 1 , 5 , 12 ). This design is powerful for studying rare outcomes and establishing temporal sequence. The inclusion of several prospective cohort studies ( 3 , 36 , 42 ) provides higher-quality evidence by design, as they predefine outcomes and can collect data more systematically, minimizing certain biases. The five systematic reviews and meta-analyses ( 7 , 11 , 21 , 40 , 41 ) are pivotal, as they synthesize data from millions of individuals, offering the most precise summary estimates and formally assessing heterogeneity across studies. The reliance on observational data, while necessary, universally introduces the challenge of residual confounding, a limitation explicitly acknowledged across many studies and detailed in Table 10 . Table 3 Sample Size of Included Studies Sample Size Category Number of Studies Example References (Study Numbers) > 1,000,000 5 5, 10, 12, 16, 40 100,001–1,000,000 5 9, 18, 27, 31, 35 10,001–100,000 12 1, 3, 8, 13, 19, 20, 22, 24, 28–29, 34, 39 1,001–10,000 12 4, 7, 14–15, 17, 23, 25–26, 30, 32, 36, 38 ≤ 1,000 8 2, 14, 19, 26, 32–33, 37, 43 Not Applicable 1 6 (Review) The reviewed studies exhibited a striking dichotomy in scale, which serves complementary purposes. Large-scale population studies (n > 100,000), including several with cohorts exceeding one million participants ( 5 , 10 , 40 ), provide the statistical power needed to detect overall associations, study rare cancer types, and generate robust, generalizable risk estimates. These "big data" approaches are instrumental in confirming that the increased ATE risk is a pervasive issue across the oncologic population. Conversely, smaller, focused studies (n ≤ 1,000) ( 19 , 32 , 43 ), often from single institutions, offer invaluable depth. They allow for detailed phenotyping of strokes, precise documentation of chemotherapy regimens and doses, and exploration of novel biomarkers—granularity that is typically lost in registry-based studies. This combination of breadth and depth is essential; the large studies map the epidemiology of the problem, while the smaller studies delve into the specific mechanisms and high-risk scenarios, such as the impact of cisplatin in testicular cancer survivors ( 19 ) or stroke in pediatric oncology ( 43 ). 3.5. Spectrum of Cancer Types and ATE Outcomes The research scope revealed a two-pronged approach (Table 4 ). Nearly half of the studies (n = 23) took a "Pan-Cancer" approach, establishing the fundamental principle that a cancer diagnosis itself is a significant ATE risk factor. Another 16 studies focused on "Specific Solid Tumors," delineating a hierarchy of risk, with cancers of the lung, pancreas, brain, and gastrointestinal tract consistently emerging as high-risk entities. Ischemic stroke and myocardial infarction (MI) were the most frequently investigated individual endpoints, each being the focus of over 20 studies (Table 5 ). A significant number of studies (n = 13) employed a "Composite ATE" endpoint to increase statistical power and acknowledge the systemic nature of the prothrombotic state. Table 4 Spectrum of Cancer Types Studied Cancer Focus Category Number of Studies Example References (Study Numbers) Pan-Cancer (All/Multiple) 23 1–2, 5–7, 9–12, 15–16, 18, 26–29, 31, 35–38, 40–41 Specific Solid Tumors 16 4 (Lung), 8 (HNSCC), 19 (Testicular), 20 (NSCLC), 22 (Pancreatic), 23 (HCC), 24 (Kidney), 25 (Colon), 30 (HNC), 32 (Urinary), 33 (Lung, Pancreatic, Colorectal), 34 (Male Breast), 39 (Breast), 42 (Lung) Hematological Malignancies 4 13 (Lymphoma), 17 (Hematopoietic), 36 (Lymphoma, Leukemia), 43 (Leukemia, Lymphoma) The research scope reveals a two-pronged approach: investigating universal risk and defining cancer-specific vulnerabilities. The "Pan-Cancer" category (n = 23 studies) establishes the fundamental principle that a diagnosis of cancer, in and of itself, is a significant risk factor for ATEs, independent of traditional cardiovascular risk factors ( 1 , 28 ). This suggests common underlying pathways, such as a cancer-associated hypercoagulable state and systemic inflammation. The substantial body of literature focusing on "Specific Solid Tumors" (n = 16) then delineates the hierarchy of risk. Cancers of the lung ( 4 , 42 ), pancreas ( 22 ), brain ( 5 , 38 ), and gastrointestinal tract ( 25 ) consistently emerge as high-risk entities, often linked to their particularly aggressive biology and potent prothrombotic potential. The focus on "Hematological Malignancies" ( 13 , 43 ), though smaller, highlights that liquid tumors also confer a substantial risk, potentially through different mechanisms involving blood cell dyscrasias and specific chemotherapeutic agents like L-asparaginase. This table underscores that while the risk is widespread, it is not uniform, and prevention strategies must be tailored to the specific malignancy. Table 5 Primary Arterial Thrombotic Outcomes Reported Outcome Measure Number of Studies Example References (Study Numbers) Stroke (Ischemic, Hemorrhagic, or unspecified) 26 1, 5, 7–8, 12–18, 22–29, 33, 35, 38, 40–43 Myocardial Infarction (MI) / Acute Coronary Syndrome (ACS) 21 3, 8, 11–13, 15–16, 18, 25, 28, 30, 31, 34–37, 39–42 Composite ATE (e.g., MI + Stroke + Peripheral Arterial Event) 13 6, 10, 15, 19, 28, 32, 34–37, 40–42 Other (e.g., Heart Failure, CVD Mortality, MACE, Peripheral Arterial Occlusion) 13 1 (Bleeding), 3 (Composite CVD), 4 (MACE), 9 (HF, VTE), 12 (CV Mortality, HF, PE), 24 (Composite CVD), 29 (Ischemic Stroke), 31 (MI, Stroke), 39 (HF, Bleeding) Ischemic stroke and myocardial infarction (MI) were the most frequently investigated individual endpoints, each being the focus of over 20 studies. This reflects their clinical salience as major, disabling, and often fatal cardiovascular events. The high prevalence of stroke as an outcome ( 7 , 22 , 27 ) may indicate a particular susceptibility of the cerebral vasculature to cancer-related hypercoagulability or tumor embolization. A significant number of studies (n = 13) employed a "Composite ATE" endpoint, which combines stroke, MI, and sometimes peripheral arterial events ( 10 , 28 , 36 ). This approach increases the statistical power to detect an overall signal of arterial toxicity and acknowledges that the prothrombotic state in cancer patients is a systemic condition that can manifest in any arterial bed. The inclusion of other outcomes like heart failure ( 3 , 12 , 39 ) and cardiovascular mortality ( 12 , 31 ) broadens the perspective to include not only acute thrombotic events but also longer-term, treatment-related cardiovascular sequelae. 3.6. Overall Risk Estimates and Key Influencing Factors The collective data presents a compelling and consistent picture of elevated risk (Table 6 ). Hazard Ratios (HR) and Odds Ratios (OR) predominantly ranged from 1.5 to 3.0, indicating a 50% to 200% increase in the relative risk of ATEs for cancer patients. Certain contexts revealed a dramatically higher risk, such as the perioperative period (OR 8.81 for MI) ( 16 ). The risk of ATE is modulated by a complex interplay of factors (Table 7 ). Cancer-related factors are paramount, including cancer type, advanced stage, and time since diagnosis. Treatment-related factors are major iatrogenic drivers, including chemotherapy (especially platinum-based), radiotherapy, and the perioperative period. Finally, traditional patient-related cardiovascular risk factors act as potent effect modifiers. Table 6 Reported Risk Estimates for Arterial Thrombotic Events Risk Estimate Type Reported Risk Value (Range or Example) Example References (Study Numbers) Hazard Ratio (HR) 1.01–5.8 (e.g., HR 1.45 for bleeding ( 1 ); HR 5.8 for 30-day ATE risk ( 28 )) 1, 3, 9, 10, 12, 16, 20, 24, 27–28, 34–35, 39, 41–42 Odds Ratio (OR) 1.15–43.64 (e.g., OR 1.15 for all-cancer risk post-CAD ( 11 ); OR 43.64 for age 80 + vs < 39 ( 5 )) 2, 5, 11, 16–17, 25–26 Standardized Incidence/Mortality Ratio (SIR/SMR) 1.2–2.17 (e.g., SMR 2.17 for fatal stroke ( 5 ); SPR 1.2 for any cancer in stroke patients ( 38 )) 5, 6, 38 Subdistribution Hazard Ratio (SHR) 0.592–5.55 (e.g., SHR 5.55 for ATE in urinary cancer ( 32 ); SHR 0.592 for lower MI risk in cancer ( 31 )) 10, 13, 22–24, 27, 29, 31–32, 36, 41 Cumulative Incidence 0.42% − 12.5% (e.g., 1.4% stroke in first year post-diagnosis ( 7 ); 12.5% 10-year stroke risk in HNSCC ( 8 )) 7–8, 19, 22–23, 29 The collective data presents a compelling and consistent picture of elevated risk. Hazard Ratios (HR) and Odds Ratios (OR) predominantly ranged from 1.5 to 3.0, indicating a 50% to 200% increase in the relative risk of ATEs for cancer patients compared to non-cancer controls. However, certain contexts reveal a dramatically higher risk. The peri-diagnostic and perioperative periods are particularly hazardous, with one study reporting an OR of 8.81 for MI during hospitalization for cancer surgery ( 16 ) and another an HR of 5.8 for ATEs in the first 30 days after cancer diagnosis ( 28 ). The evolution of statistical methodology is also evident. While early studies often reported standard HRs, more recent investigations increasingly use Subdistribution Hazard Ratios (SHR) ( 10 , 23 , 29 ), which are more appropriate in cancer populations where the high competing risk of death from the malignancy itself can otherwise obscure the true incidence of non-fatal cardiovascular outcomes. The reported cumulative incidences, such as a 1.4% stroke rate in the first-year post-diagnosis ( 7 ), translate these relative risks into tangible, absolute risks that are highly relevant for clinical communication and planning. Table 7 Key Influencing Factors for ATE Risk in Cancer Patients Factor Category Specific Factors Example References (Study Numbers) Cancer-Related • Cancer Type (e.g., Lung, Pancreatic, Brain, GI, Hematological) ( 5 , 7 , 22 , 28 , 40 ) • Advanced Stage / Metastatic Disease ( 3 , 16 , 23 , 28 , 41 ) • Time Since Diagnosis (Highest risk near diagnosis) ( 7 , 12 , 22 , 27 , 28 ) 3, 5, 7, 12, 16, 22–23, 27–29, 33, 40–41 Treatment-Related • Chemotherapy (especially Platinum-based, Cytotoxic) ( 3 , 19 , 27 , 32 ) • Radiotherapy (e.g., for HNSCC) ( 8 , 30 ) • Cancer Surgery (perioperative period) ( 16 , 35 ) • Specific Therapies (e.g., Cisplatin ( 19 ), Perioperative chemo ( 32 )) 3, 8, 16, 19, 27, 30, 32, 35 Patient-Related • Traditional CV Risk Factors (Hypertension, Diabetes, Atrial Fibrillation, Smoking) ( 8 , 29 , 36 ) • Older Age ( 5 , 29 , 36 ) • Male Sex ( 10 , 36 ) • Pre-existing Cardiovascular Disease ( 11 , 37 ) 5, 8, 10, 11, 15, 29, 36–37 Laboratory/Biomarkers • Elevated Leukocytes, Platelets, D-dimer, CRP ( 26 , 29 , 33 , 36 , 42 ) • Anemia / Low Hemoglobin ( 14 , 26 ) • Hypercoagulability Markers 14, 26, 29, 33, 36, 42 The risk of ATE in cancer patients is not a monolithic entity but is modulated by a complex interplay of factors. Cancer-related factors are paramount; the type of cancer is a primary determinant, with lung, pancreatic, and gastrointestinal cancers carrying the highest risk profiles ( 5 , 22 , 28 ). Furthermore, advanced or metastatic disease consistently portends a greater risk than localized cancer ( 3 , 23 , 28 ), likely due to a higher tumor burden and more pronounced systemic effects. The temporal pattern is critical, with the highest risk concentrated in the initial months following diagnosis ( 7 , 12 , 28 ), a period marked by diagnostic stress, surgical interventions, and the initiation of chemotherapy. Treatment-related factors are major iatrogenic drivers; chemotherapy (especially platinum-based agents) ( 19 , 32 ), radiotherapy ( 8 ), and the perioperative period ( 16 ) are all established high-risk windows. Finally, the baseline cardiovascular health of the patient remains crucial; traditional risk factors like hypertension, diabetes, atrial fibrillation, and smoking ( 8 , 29 , 36 ) act as potent effect modifiers, compounding the risk imposed by the cancer itself. 3.7. Time-Dependent Risk and Impact of Treatments A cornerstone finding of this review is the profoundly time-dependent nature of ATE risk (Table 8 ). The trajectory is characterized by a sharp "spike" immediately after diagnosis (first 30 days), a period of exceptional vulnerability, followed by a persistently elevated risk during the first 6–12 months, and a gradual decline thereafter. Modern cancer therapies are significant contributors (Table 9 ). Chemotherapy (e.g., cisplatin), radiotherapy (with site-specific risks), and the perioperative period are all established high-risk windows. The perioperative period stands out as a time of extreme risk, with studies showing an 8–9 fold increase in the odds of MI and stroke ( 16 ). Table 8 Time-Dependent Risk of Arterial Thrombotic Events Following Cancer Diagnosis Time Period Post-Diagnosis Risk Trend & Key Findings Example References (Study Numbers) Peri-Diagnosis & First 30 Days Extremely High Risk. The immediate period surrounding diagnosis carries the highest relative risk, often driven by diagnostic procedures, initial treatment, and the cancer's hypercoagulable state. 28 (HR 5.8 for 30-day risk), 35 (Increased perioperative risk) First 6–12 Months Persistently Elevated Risk. Risk remains significantly high, attributed to intensive treatments (surgery, chemotherapy) and the initial biological impact of the tumor. 7 (1.4% cumulative stroke incidence in 1st year), 12 (Highest risk in 1st year), 22 (46.6 per 1000 person-years in 1st 6 months for pancreatic cancer), 27 (Significant risk in first 3 years), 34 (60% increased risk in first 6 months for male breast cancer) 1–5 Years Post-Diagnosis Gradually Declining but Elevated Risk. The risk decreases from its initial peak but remains higher than in the non-cancer population, especially for certain cancers and treatments. 12 (Risk declined but remained elevated for CV mortality, HF, and PE beyond 10 years), 24 (HR 1.77 at 1 year, 1.10 at 5 years for kidney cancer) Long-Term (> 5 Years) Variable Risk. For many survivors, risk approaches baseline, but certain groups (e.g., those treated with cardiotoxic therapies or with persistent risk factors) remain at elevated long-term risk. 12 (Persistent elevation for some outcomes), 40 (Risk remained elevated in meta-analysis, varying by cancer type) A cornerstone finding of this review is the profoundly time-dependent nature of ATE risk. The trajectory is characterized by a sharp "spike" immediately after diagnosis, followed by a gradual decline. The first 30 days represent a period of exceptional vulnerability, with one study reporting a near-sixfold increase in risk ( 28 ). This acute phase is likely driven by a "perfect storm" of factors: the intrinsic hypercoagulability of the newly diagnosed, often untreated tumor; the profound physiological stress of major cancer surgery ( 16 , 35 ); and the pro-thrombotic effects of initiating cytotoxic chemotherapy ( 3 ). The risk remains substantially elevated throughout the first year ( 7 , 12 , 22 ), a period encompassing the most intensive phase of treatment. While the risk attenuates over subsequent years, it often remains above baseline for a decade or more, particularly for specific outcomes like heart failure and in survivors of certain cancers ( 12 , 40 ). This temporal pattern mandates a dynamic and phase-specific approach to risk assessment and prevention, with the most intensive monitoring and prophylactic strategies reserved for the high-risk initial period. Table 9 Impact of Specific Cancer Treatments on ATE Risk Treatment Modality Associated ATE Risk & Key Findings Example References (Study Numbers) Chemotherapy Significantly Increased Risk. Cytotoxic agents, particularly platinum-based regimens, are strongly associated with ATEs. The risk is often short-term but can have long-term consequences. 3 (HR 2.19 for CVD with cytotoxic chemo), 19 (Cisplatin increases short-term risk in testicular cancer), 27 (Chemotherapy is a risk factor for ischemic stroke), 32 (Perioperative chemotherapy is an independent risk factor for ATE) Radiotherapy Increased Risk, Often Site-Specific. Radiation to the chest (e.g., for breast cancer, lymphoma) increases coronary risk, while neck irradiation accelerates carotid atherosclerosis and stroke risk. 8 (Radiotherapy is a noted risk factor for stroke in HNSCC), 30 (Suggests increased CV risk in HNC is likely due to treatments like radiation) Cancer Surgery Very High Perioperative Risk. The immediate postoperative period carries a dramatically elevated risk for MI and stroke, likely due to surgical stress, inflammation, and hypercoagulability. 16 (OR 8.81 for MI and 6.71 for ischemic stroke during hospitalization for cancer surgery), 35 (Cancer is an independent risk factor for perioperative arterial ischemic events) Targeted Therapy / Immunotherapy Emerging and Variable Risk. Certain targeted agents (e.g., VEGF inhibitors) are known to increase ATE risk. The risk with newer immunotherapies is still being defined. 4 (Found no significant difference in MACE between PD-1 inhibitors and chemo-immunotherapy in lung cancer, indicating a need for further study) Modern cancer therapies, while life-saving, are significant contributors to cardiovascular morbidity. The table delineates the arterial toxicities associated with major treatment modalities. Chemotherapy, particularly regimens containing cisplatin, is strongly implicated in increasing ATE risk, both in the short term (e.g., during treatment for testicular cancer ( 19 )) and as a long-term legacy effect ( 27 ). Radiotherapy induces vascular injury through mechanisms like endothelial dysfunction and accelerated atherosclerosis, with the risk profile being highly anatomy-specific (e.g., chest irradiation for breast cancer increasing coronary risk, and neck irradiation for head and neck cancer increasing carotid and stroke risk ( 8 , 30 )). The perioperative period stands out as a time of extreme risk, with studies showing an 8–9 fold increase in the odds of MI and stroke during the initial hospitalization for cancer surgery ( 16 ). This is attributed to surgical stress, inflammation, immobilization, and potential interruptions in chronic antithrombotic medications. The vascular safety profile of newer targeted and immunotherapies is an area of active investigation, with current evidence for agents like PD-1 inhibitors showing no significant difference in risk compared to chemotherapy in some studies ( 4 ), underscoring the need for ongoing vigilance. 3.8. Methodological Considerations and Clinical Recommendations Interpreting the collective evidence requires a careful consideration of its methodological constraints (Table 10 ). The overwhelming reliance on observational designs is the primary limitation, preventing causal inference and leaving studies vulnerable to residual confounding, surveillance bias, and the competing risk of death from cancer. The synthesized evidence culminates in a clear call for a paradigm shift in the care of cancer patients (Table 11 ). Proposed clinical actions include increased awareness and risk stratification, implementation of multidisciplinary cardio-oncology care, and aggressive management of traditional cardiovascular risk factors. The research agenda is clear, emphasizing the need for mechanistic studies, randomized controlled trials for prophylactic strategies, and the development of validated risk prediction tools. Table 10 Methodological Considerations and Common Limitations in Included Studies Methodological Aspect Common Challenges & Limitations Example References (Study Numbers) Study Design • Residual Confounding: Inability to fully account for all variables (e.g., smoking, detailed lifestyle factors). • Observational Nature: Precludes causal inference. 1–2, 4, 11–12, 24, 28–29, 31, 35, 36 Data Sources • Coding Inaccuracies: Reliance on ICD codes from administrative databases without adjudication. • Lack of Granular Data: Missing information on cancer stage, treatment details (dose, duration), and lab values. 4–5, 8, 13, 17, 22, 25–26, 30–31 Bias • Surveillance Bias: Cancer patients may have more frequent medical contact, leading to higher detection of ATEs. • Healthy Survivor Bias: Clinical trial participants (e.g., ASPREE ( 3 )) may be healthier than the general cancer population. • Immortal Time Bias: Misclassification of time-at-risk in some cohort designs. 3, 10, 15, 31, 35, 38 Outcome Ascertainment • Competing Risk of Death: High mortality in cancer cohorts can mask the true incidence of ATEs if not accounted for statistically. • Lack of Adjudication: Many studies used unvalidated code-based definitions for ATEs. 13, 23, 28–29, 31, 36, 41 Interpreting the collective evidence requires a careful consideration of its methodological constraints. The overwhelming reliance on observational designs is the primary limitation, as it inherently prevents the establishment of causality and leaves studies vulnerable to residual confounding. The frequent lack of data on key confounders like smoking status, detailed body mass index, and physical activity ( 12 , 25 ) means that the estimated risk could be partially attributed to these unmeasured factors. The widespread use of administrative data and ICD codes for outcome identification, while enabling large sample sizes, introduces the potential for misclassification bias, as codes may not always reflect clinically adjudicated events ( 4 , 8 ). Furthermore, bias is a recurring concern; surveillance bias may lead to over-estimation of risk if cancer patients have more contact with the healthcare system ( 38 ), while the competing risk of death from cancer can lead to under-estimation if not handled with appropriate statistical methods ( 29 , 36 ). These limitations do not invalidate the findings but emphasize that the reported risk estimates should be viewed as associations within a complex clinical landscape and highlight the critical need for prospective studies designed a priori to address these specific challenges. Table 11 Clinical Recommendations and Future Directions from Included Studies Category Key Recommendations and Future Directions Example References (Study Numbers) Clinical Practice • Awareness & Risk Stratification: Increase clinician awareness of the link. Develop risk prediction models to identify high-risk patients. ( 10 , 29 , 32 ) • Multidisciplinary Care: Implement collaborative cardio-oncology care models. ( 12 , 24 , 37 ) • Optimize CV Risk Factors: Aggressively manage hypertension, diabetes, and dyslipidemia in cancer patients. ( 6 , 8 , 24 ) • Personalized Anticoagulation: Do not lower the threshold for anticoagulation in AF based on cancer alone; consider cancer-specific bleeding risk. ( 1 ) 1, 6, 8, 10, 12, 24, 29, 32, 37 Patient Management • Education: Educate patients about stroke/MI symptoms, especially in the high-risk period after diagnosis. ( 7 , 22 ) • Survivorship Care: Incorporate cardiovascular risk screening and management into long-term survivorship plans. ( 5 , 24 , 40 ) 5, 7, 22, 24, 40 Research Priorities • Mechanistic Studies: Investigate the biological pathways linking cancer, its treatments, and ATEs. ( 2 , 6 , 28 ) • Prospective Trials: Conduct randomized controlled trials to establish optimal prophylactic and treatment strategies (e.g., role of DOACs, antiplatelets). ( 4 , 17 , 28 , 32 , 35 , 42 ) • Risk Prediction Tools: Develop and validate tools to identify high-risk patients for targeted interventions. ( 10 , 29 , 33 ) • Long-Term Follow-up: Study the long-term cardiovascular outcomes in cancer survivors, especially with newer therapies. ( 12 , 40 ) 2, 4, 6, 10, 12, 17, 28–29, 32–33, 35, 40, 42 The synthesized evidence culminates in a clear call for a paradigm shift in the care of cancer patients, moving from a reactive to a proactive and preventive model. The proposed clinical actions are multi-faceted: 1) Awareness and Risk Stratification: Clinicians must be educated about this link, and there is a pressing need to develop and validate risk prediction tools to identify high-risk patients who would benefit most from interventions ( 10 , 29 ). 2) Multidisciplinary Care: The integration of cardiology expertise into oncology care through formal cardio-oncology programs is repeatedly advocated as the optimal framework for managing these complex patients ( 12 , 24 , 37 ). 3) Aggressive Risk Factor Management: Optimizing control of hypertension, diabetes, and dyslipidemia is considered a foundational element of risk reduction ( 6 , 8 ). The research agenda is equally clear. There is a stark evidence gap regarding effective interventions; while observational data clearly identifies the problem, a near-universal recommendation is for randomized controlled trials to determine the efficacy and safety of antithrombotic agents (e.g., DOACs, antiplatelets) for primary and secondary prevention in cancer patients ( 28 , 32 , 42 ). Furthermore, a deeper understanding of the underlying biological mechanisms ( 2 , 6 ) is needed to identify novel therapeutic targets and biomarkers for risk prediction. 4. Discussion 4.1. Summary of Evidence This systematic review of 43 studies provides a comprehensive synthesis of the evidence linking malignancy to an increased risk of arterial thrombotic events (ATEs). The collective data paints a consistent and compelling picture: a cancer diagnosis confers a significant, though variable, increase in the risk of myocardial infarction and ischemic stroke. The reported hazard and odds ratios, predominantly ranging from 1.5 to 3.0, translate to a 50% to 200% elevation in relative risk compared to the non-cancer population. This risk is not a monolithic entity but is dynamically shaped by a triad of factors: (1) cancer-specific characteristics, such as primary site (with lung, pancreatic, and GI cancers carrying the highest burden) and stage (advanced disease being a key driver); (2) treatment-related exposures, including chemotherapy, radiotherapy, and the profound stress of surgery; and (3) patient-specific vulnerabilities, where traditional cardiovascular risk factors act as potent effect multipliers. Crucially, the temporal pattern of risk is a cornerstone finding, characterized by a dramatic spike immediately following diagnosis that gradually attenuates but often remains elevated for years, fundamentally shaping the window for clinical intervention. 4.2. Interpretation in the Context of Existing Literature and Proposed Pathophysiology Our findings consolidate a paradigm shift in oncology and cardiology, moving the cancer-ATE link from a peripheral observation to a central tenet of patient management. The evidence strongly supports a pathophysiological model where the "perfect storm" of cancer-associated ATE risk arises from the confluence of several mechanisms, many of which are most active in the high-risk initial phase following diagnosis. The Hypercoagulable State and Systemic Inflammation: Cancer cells can directly activate the coagulation cascade through tissue factor expression and release of procoagulant microparticles. Concurrently, tumors create a state of systemic inflammation, with elevated levels of cytokines like IL-6 and TNF-α, which promote endothelial dysfunction, platelet activation, and plaque instability (3, 4). This underlying pro-thrombotic milieu is the substrate upon which other risk factors act. Treatment-Induced Endothelial Injury: Our review highlights the significant iatrogenic risk. Chemotherapeutic agents, particularly platinum-based drugs, are directly toxic to the vascular endothelium, disrupting its natural anti-thrombotic properties (19, 27). Radiotherapy induces accelerated atherosclerosis and vascular fibrosis through direct DNA damage and chronic inflammation in the irradiated field, explaining the site-specific risks (e.g., carotid disease after neck irradiation, coronary disease after chest irradiation) (8, 30). The Peri-Diagnostic "Spike": The exceptionally high risk in the first 30 days post-diagnosis, as evidenced by hazard ratios exceeding 5.0 (28), can be attributed to multiple converging factors. The physiological stress of a new cancer diagnosis, the pro-inflammatory and pro-thrombotic impact of major surgical interventions (16, 35), and the immediate initiation of cytotoxic therapies create a perfect storm. This period likely represents the clinical manifestation of the most intense hypercoagulable and inflammatory state. The Role of Traditional Risk Factors: The data unequivocally shows that traditional cardiovascular risk factors are not supplanted by the cancer diagnosis but are compounded. Hypertension, diabetes, dyslipidemia, and smoking (8, 29, 36) continue to be major determinants of ATE risk, suggesting that the baseline health of the vascular system is a critical modifier of the cancer-specific insult. 4.3. Clinical and Research Implications: From Recognition to Action The synthesized evidence mandates a proactive and structured approach to cardiovascular care in oncology. Towards Dynamic Risk Stratification: The current one-size-fits-all approach is inadequate. The field urgently needs validated, dynamic risk prediction tools that integrate cancer type, stage, planned treatment regimen, and traditional CV risk factors to identify patients who would benefit most from intensified monitoring and prophylactic strategies (10, 29). Risk is not static; it must be re-evaluated at diagnosis, before initiating high-risk therapies, and during survivorship. The Central Role of Multidisciplinary Cardio-Oncology: The management of these complex, competing risks requires seamless collaboration. Formal cardio-oncology programs are no longer a luxury but a necessity (12, 24, 37). These teams are best positioned to make high-stakes decisions, such as the timing of surgery in a patient with recent coronary stents, or the management of anticoagulation in a thrombocytopenic patient with atrial fibrillation (1). The Stark Interventional Evidence Gap: A critical and consistent finding across this review is the almost complete absence of evidence from randomized controlled trials (RCTs) guiding the prevention and treatment of ATEs in cancer patients. While observational data clearly identifies the problem, it cannot define the solution. It remains unknown whether prophylactic antiplatelet or anticoagulant therapy is effective and safe in high-risk cancer patients, and if so, in whom, with which agent, and for how long (4, 17, 28). This represents the single most important gap in the literature and a clear mandate for future research. 4.4. Limitations The conclusions of this review must be interpreted within the context of the limitations inherent in the source literature. The overwhelming reliance on observational, predominantly retrospective, study designs precludes definitive causal inference and leaves the findings vulnerable to residual confounding. The inability to fully adjust for lifestyle factors like smoking, diet, and physical activity may lead to overestimation of the independent effect of cancer. The widespread use of administrative data and ICD codes for outcome identification, while enabling large-scale analysis, introduces the potential for misclassification bias. Furthermore, methodological challenges such as surveillance bias (increased ATE detection due to more frequent medical contact) and the competing risk of death from cancer (which can obscure the true incidence of non-fatal ATEs if not properly accounted for) are recurring concerns. Finally, the geographical concentration of research in high-income countries limits the generalizability of findings to regions with different cancer profiles, genetic backgrounds, and healthcare systems. 4.5. Future Directions This review illuminates a clear path forward for both research and clinical practice: Mechanistic Research: Deepen the understanding of the biological pathways linking specific cancers and treatments to endothelial dysfunction and platelet hyperreactivity (2, 6). Interventional Trials: Prioritize RCTs to test the efficacy and safety of preventive strategies (e.g., low-dose DOACs, antiplatelets) in high-risk cancer populations, particularly in the peri-diagnostic and treatment phases (28, 32, 42). Precision Medicine: Develop and validate integrated risk prediction models that combine clinical data with novel biomarkers (e.g., circulating tumor-derived microparticles, specific inflammatory markers) to enable personalized prophylaxis (10, 33). Survivorship Care: Establish long-term follow-up protocols for cancer survivors, especially those exposed to cardiotoxic therapies, to monitor and manage delayed cardiovascular sequelae (12, 40). 5. Conclusion Malignancy is a significant and independent risk factor for arterial thrombotic events, with a risk profile that is dynamic and multifactorial. A structured approach involving awareness, risk stratification, multidisciplinary collaboration, and aggressive management of modifiable risk factors is essential to mitigate this threat. Future research must focus on elucidating underlying mechanisms, validating predictive biomarkers, and most importantly, conducting prospective randomized trials to establish evidence-based strategies for the prevention and management of ATEs in cancer patients. Declarations Funding Resource This research did not receive any external funding or support from external entities. All aspects of this work were conducted independently, and there are no financial or material conflicts of interest to disclose. Author's Contribution MA developed the methodology and wrote the methodology section. SN also conducted data extraction using a predesigned Excel spreadsheet, capturing key study details. Additionally, MA oversaw the entire review process and coordinated the writing of the manuscript. SN independently verified 50% of the extracted data to ensure accuracy and consistency. SN also wrote the results section, contributed to the final review of the manuscript, played a role in developing the study design, and assisted in refining the methodology section. JT contributed to refining the search strategy, participated in the full-text review process, and assisted in synthesizing the extracted data. JT also built the tables and diagrams for the manuscript and helped review the methodology section. AS independently conducted the title and abstract screening using Rayyan software, ensuring the initial selection of studies. AS also conducted the full-text review for studies meeting the inclusion criteria and wrote the discussion section. LA independently verified 50% of the extracted data alongside SN to enhance data accuracy. LA also contributed to refining the study methodology and participated in manuscript revisions. HA wrote the introduction section and assisted in optimizing the search strategy. HA also played a role in screening fulltext articles and contributed to drafting and reviewing the discussion section. KN independently conducted the title and abstract screening using Rayyan software, ensuring the initial selection of studies. KN also wrote the conclusion section and participated in discussions regarding study inclusion and exclusion criteria. SH contributed to writing the discussion section and provided critical revisions to improve clarity and coherence. SH also participated in reviewing the final manuscript to ensure consistency and accuracy. TD played a role in the quality assessment of included studies and assisted in synthesizing the extracted data. TD also contributed to reviewing the discussion and conclusion sections to ensure alignment with the study objectives. All authors contributed to the conception and design of the study, provided input on data interpretation, and participated in manuscript revisions. All authors approved the final version before submission. Conflict of Interest No conflicts of interest were reported among the authors involved in this systematic review. References El-Rayes M, Adam M, Fang J, Wang X, Jeong I, Austin PC, et al. The Association of Malignancy With Stroke and Bleeding in Atrial Fibrillation: A Population-Based Cohort Study. JACC CardioOncol. 2025;7(2):157–67. Bai T, Wu C. Association of cardiovascular disease on cancer: observational and mendelian randomization analyses. Sci Rep. 2024;14:28465. Muhandiramge J, Zalcberg JR, Warner ET, Polekhina G, Gibbs P, van Londen GJ, et al. Cardiovascular disease and stroke following cancer and cancer treatment in older adults. 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Bravo-Anguiano Y, Echavarría-Iñiguez A, Madrigal-Lkhou E, Muñoz-Martín A. Ictus asociado a cáncer: estudio de prevalencia y factores predictores entre pacientes con ictus isquémico. Rev Neurol. 2023;76(6):189–95. Leader A, Dagan N, Barda N, Goldberg I, Raanani P, Spectre G, et al. Previously undiagnosed cancer in patients with arterial thrombotic events – A population-based cohort study. J Thromb Haemost. 2022;20:635–47. Rautiola J, Björklund J, Zelic R, Edgren G, Bottai M, Nilsson M, et al. Risk of Postoperative Ischemic Stroke and Myocardial Infarction in Patients Operated for Cancer. Ann Surg Oncol. 2024;31:1739–48. Vazquez S, Das A, Spirollari E, Brabant P, Nolan B, Clare K, et al. Inpatient Outcomes of Cerebral Venous Thrombosis in Patients With Malignancy Throughout the United States. J Stroke. 2024;26(3):425–33. Masson R, Titievsky L, Corley DA, Zhao W, Lopez AR, Schneider J, Zaroff JG. Incidence rates of cardiovascular outcomes in a community-based population of cancer patients. Cancer Med. 2019;8(18):7913–23. Moik F, Terbuch A, Sprakel A, Pichler G, Barth DA, Pichler R, et al. Arterial thromboembolic events in testicular cancer patients: short- and long-term incidence, risk factors, and impact on mortality. J Thromb Haemost. 2025;23:2796–806. Ichil O, Leader A, Batat E, Yosef L, Shochat T, Goldstein DA, et al. Arterial and venous thromboembolism in ALK-rearrangement-positive non-small cell lung cancer: a population-based cohort study. Oncologist. 2023;28:e391–6. Lun R, Roy DC, Ramsay T, Siegal D, Shorr R, Fergusson D, et al. Incidence of stroke in the first year after diagnosis of cancer—A protocol for systematic review and meta-analysis. PLoS ONE. 2021;16(9):e0256825. Chan P-C, Chang W-L, Hsu M-H, Yeh C-H, Muo C-H, Chang K-S, et al. Higher stroke incidence in the patients with pancreatic cancer: A nation-based cohort study in Taiwan. Med (Baltim). 2018;97(11):e10133. Hsu JY, Liu PPS, Liu AB, Huang HK, Loh CH. High 1-year risk of stroke in patients with hepatocellular carcinoma: a nationwide registry-based cohort study. Sci Rep. 2021;11:10444. Jung M, Choo E, Li S, Deng Z, Li J, Li M, et al. Increased risk of cardiovascular disease among kidney cancer survivors: a nationwide population-based cohort study. Front Oncol. 2024;14:1420333. Desai R, Mondal A, Patel V, Singh S, Chauhan S, Jain A. Elevated cardiovascular risk and acute events in hospitalized colon cancer survivors: A decade-apart study of two nationwide cohorts. World J Clin Oncol. 2024;15(4):548–53. Motataianu A, Maier S, Andone S, Barcutean L, Serban G, Bajko Z, et al. Ischemic Stroke in Patients with Cancer: A Retrospective Cross-Sectional Study. J Crit Care Med (Targu Mures). 2021;7(1):54–61. Jang HS, Choi J, Shin J, Chung JW, Bang OY, Kim GM, et al. The Long-Term Effect of Cancer on Incident Stroke: A Nationwide Population-Based Cohort Study in Korea. Front Neurol. 2019;10:52. Navi BB, Howard G, Howard VJ, Zhao H, Judd SE, Elkind MSV, et al. The risk of arterial thromboembolic events after cancer diagnosis. Res Pract Thromb Haemost. 2019;3(4):639–51. Terada H, Nakamura K, Fujita S, Gon Y, Kawano T, Kitano T, et al. Incidence and risk factors for ischemic stroke in patients with cancer: A retrospective observational study. Thromb Res. 2025;254:109455. Kim DK. Exploring the Link between Head and Neck Cancer and the Elevated Risk of Acute Myocardial Infarction: A National Population-Based Cohort Study. Cancers. 2024;16(10):1930. Boyer J, Deharo P, Angoulvant D, Ivanes F, Ferrara J, Vaillier A, et al. Cardiovascular outcomes in patients with cancer during a 5-year follow-up: Results from a French administrative database. Arch Cardiovasc Dis. 2023;116(2):88–97. Bamias A, Tzannis K, Zakopoulou R, Sakellakis M, Dimitriadis J, Papatheodoridi A, et al. Risk for Arterial Thromboembolic Events (ATEs) in Patients with Advanced Urinary Tract Cancer (aUTC) Treated with First-Line Chemotherapy: Single-Center, Observational Study. Curr Oncol. 2022;29(9):6077–90. Kassubek R, Winter M-AGR, Dreyhaupt J, Laible M, Kassubek J, Ludolph AC, et al. Development of an algorithm for identifying paraneoplastic ischemic stroke in association with lung, pancreatic, and colorectal cancer. Ther Adv Neurol Disord. 2024;17:1–22. Reiner AS, Navi BB, DeAngelis LM, Panageas KS. Increased Risk of Arterial Thromboembolism in Older Men with Breast Cancer. Breast Cancer Res Treat. 2017;166(3):903–10. Navi BB, Zhang C, Kaiser JH, Liao V, Cushman M, Kasner SE, et al. Cancer and the risk of perioperative arterial ischaemic events. Eur Heart J Qual Care Clin Outcomes. 2024;10:345–56. Grilz E, Königsbrügge O, Posch F, Schmidinger M, Pirker R, Lang IM, et al. Frequency, risk factors, and impact on mortality of arterial thromboembolism in patients with cancer. Haematologica. 2018;103(9):1549–56. Melchiori R, Diaz Saravia S, Rubio PM, Szlaien L, Mouriño R, O’Flaherty M, et al. Cancer as a novel risk factor for major cardiovascular adverse events in secondary prevention. Int J Cardiol Cardiovasc Risk Prev. 2025;27:200501. Wilbers J, Sondag L, Mulder DS, Siegerink B, van Dijk EJ. Cancer prevalence higher in stroke patients than in the general population: the Dutch String-of-Pearls Institute (PSI) Stroke study. Eur J Neurol. 2020;27:85–91. Gue YX, Bisson A, Bodin A, Herbert J, Lip GYH, Fauchier L. Breast cancer and incident cardiovascular events: A systematic analysis at the nationwide level. Eur J Clin Invest. 2022;52(5):e13754. Li Q, Zhang G, Li X, Xu S, Wang H, Deng J, et al. Risk of cardiovascular disease among cancer survivors: systematic review and meta-analysis. Clin Med. 2025;84:103274. Costamagna G, Hottinger AF, Millonis H, Salerno A, Strambo D, Livio F, et al. Acute ischaemic stroke in active cancer versus non-cancer patients: stroke characteristics, mechanisms and clinical outcomes. Eur J Neurol. 2024;31:e16200. Furuya N, Tsubata Y, Hotta T, Yokoyama T, Yamasaki M, Ishikawa N, et al. Arterial Thromboembolism in Patients With Advanced Lung Cancer: Secondary Analyses of the Rising-VTE/NEJ037 Study. Cancer Med. 2025;14:e70568. Zadeh C, AlArab N, Muwakkit S, Atweh LA, Tamim H, Makki M, et al. Stroke in Middle Eastern children with cancer: prevalence and risk factors. BMC Neurol. 2022;22:31. 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-9242659","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":613210078,"identity":"6d8bd13c-b51b-48bc-872c-c3f9cf5e1f65","order_by":0,"name":"Moontasir Ahmed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBADHgb25oMPQAw+otQfAGnhOZZsANLCRqwWBgaJHDMJEE1Qi25787HHH2rqZMwbEswqv+bYybAxMD98dAOPFrMzx9INDhw7zCNz4EDabdltyUCHsRkb5+DTcgPongNsB3gkGBuO3ZbcxgzUwsMmTVjLvzoeCWbGtmLJbfVEajnYxswjwcbMxvhx22EitJw5liZxtu8wjwQPG7M047bjQIqQX443H5Oo+FZnLyH//uPHn9uq7fnZmx8+xqcFBTDzgElilYMA4w9SVI+CUTAKRsGIAQCgo0RknPA0FgAAAABJRU5ErkJggg==","orcid":"","institution":"Tangail Medical College Hospital","correspondingAuthor":true,"prefix":"","firstName":"Moontasir","middleName":"","lastName":"Ahmed","suffix":""},{"id":613210080,"identity":"8ce1298a-a79e-494d-b5ab-dfd47db88ecd","order_by":1,"name":"Shadman Newaz","email":"","orcid":"","institution":"Tangail Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shadman","middleName":"","lastName":"Newaz","suffix":""},{"id":613210081,"identity":"1cf97b16-6e21-4b10-a725-757d2525557d","order_by":2,"name":"Jannatara Tina","email":"","orcid":"","institution":"Tangail Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jannatara","middleName":"","lastName":"Tina","suffix":""},{"id":613210082,"identity":"99b363e4-1fcc-4914-b8ef-3ff1cddb86ae","order_by":3,"name":"Ananya Sen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ananya","middleName":"","lastName":"Sen","suffix":""},{"id":613210084,"identity":"c9ae490f-3d77-4f40-a9f5-a2ed599f5844","order_by":4,"name":"Lamia Ashraf","email":"","orcid":"","institution":"Tangail Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lamia","middleName":"","lastName":"Ashraf","suffix":""},{"id":613210085,"identity":"29d77f78-8057-415a-b310-d5d5191bea67","order_by":5,"name":"Kumari Preity Rani Neogie","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kumari","middleName":"Preity Rani","lastName":"Neogie","suffix":""},{"id":613210087,"identity":"e089a8cc-0c57-4ab8-a41c-17129daa67a9","order_by":6,"name":"Hafsa Akter Ava","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hafsa","middleName":"Akter","lastName":"Ava","suffix":""},{"id":613210088,"identity":"e1397394-4e6a-4ee8-979e-a8ac43db1ac6","order_by":7,"name":"Snigdho Hritom Sil","email":"","orcid":"","institution":"Tangail Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Snigdho","middleName":"Hritom","lastName":"Sil","suffix":""},{"id":613210089,"identity":"d7edc13d-9b7f-45b2-abce-6131b4121b58","order_by":8,"name":"Tahea Zaman Deena","email":"","orcid":"","institution":"Tangail Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tahea","middleName":"Zaman","lastName":"Deena","suffix":""}],"badges":[],"createdAt":"2026-03-27 09:09:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9242659/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9242659/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105906037,"identity":"a6172e18-3e1c-439f-acf8-3b7e48444900","added_by":"auto","created_at":"2026-04-01 10:16:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49466,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA Flow Diagram\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9242659/v1/ecfe9b2f458dd4e3520ce71b.png"},{"id":105878751,"identity":"cc841c95-1249-4fc3-bc54-144a5e20e2c2","added_by":"auto","created_at":"2026-04-01 06:21:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":474270,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e2a: Risk of Bias Summary plot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2b: Risk of Bias Graph\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk of bias assessment across included studies. Figure 2a shows the proportion of studies assessed for various domains of bias, including: selection of participants, confounding variables, measurement of exposure, blinding of outcome assessment, incomplete outcome data, and selective outcome reporting. Each domain is color-coded to represent the assessed level of bias: Low risk (green), Unclear risk (yellow), High risk (red), Critical risk (dark red), and No information (blue). Figure 2b provides a study-wise breakdown of risk of bias assessments, allowing a granular comparison across individual studies.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9242659/v1/aaf068ca0692f566ea8c40cd.png"},{"id":106724722,"identity":"5e46b3d2-34ee-4d7d-bf25-7ff389af5ba1","added_by":"auto","created_at":"2026-04-12 18:29:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2067714,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9242659/v1/1697d269-a086-40e3-994e-8e24f4c5d834.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Link Between Malignancy and Arterial Thrombotic Events: A Systematic Review Across Cancer Types\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAdvances in cancer diagnosis and treatment have significantly improved survival rates, shifting clinical focus towards managing long-term complications. Among these, cardiovascular disease represents a major cause of morbidity and mortality in cancer patients and survivors. While the association between cancer and venous thromboembolism (VTE) is well-established, the link between malignancy and arterial thrombotic events (ATEs)\u0026mdash;such as myocardial infarction (MI) and ischemic stroke\u0026mdash;has gained substantial recognition more recently (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe pathogenesis of cancer-associated ATEs is multifactorial, involving a cancer-induced hypercoagulable state, systemic inflammation, endothelial injury, and direct atherogenic effects of anticancer therapies (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The risk is not uniform; it varies significantly by cancer type, stage, treatment modality, and time since diagnosis. Understanding this complex interplay is crucial for risk prediction, prevention, and optimal management (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOver the past decade, a growing body of evidence from large cohort studies, registries, and meta-analyses has characterized the burden and determinants of ATEs in oncologic populations. However, a comprehensive synthesis of this evidence is needed to consolidate our understanding and inform clinical decision-making across different cancer types and treatment phases. This systematic review aims to provide a detailed analysis of the global research landscape, risk estimates, time-dependent patterns, treatment-related factors, and outcomes of ATEs in cancer patients, integrating data from a wide range of published studies to offer a definitive overview for clinicians and researchers.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThis systematic review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Search Strategy and Selection Criteria\u003c/h2\u003e \u003cp\u003eA systematic search was performed in PubMed and Science Direct from database inception to January, 2026. The search strategy combined terms related to (\"cancer\" OR \"neoplasm\" OR \"malignancy\" OR \"oncology\") AND (\"arterial thrombotic event\" OR \"myocardial infarction\" OR \"ischemic stroke\" OR \"acute coronary syndrome\" OR \"cardiovascular disease\").\u003c/p\u003e \u003cp\u003eStudies were included if they: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) reported on human cancer patients and the incidence or risk of ATEs; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) provided original data on epidemiology, risk factors, or outcomes; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) were published in English. Cohort studies, case-control studies, registries, and systematic reviews/meta-analyses were eligible.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data Extraction and Quality Assessment\u003c/h2\u003e \u003cp\u003eTwo reviewers independently screened titles, abstracts, and full-text articles. Data were extracted using a standardized form, capturing information on study design, patient demographics, cancer types, treatments, ATE outcomes, risk estimates, and key findings. The risk of bias for RCTs was assessed using the Cochrane Risk of Bias 2 (RoB 2) tool.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data Synthesis\u003c/h2\u003e \u003cp\u003eGiven the heterogeneity in study designs and reporting, a narrative synthesis was conducted. Data are presented in summary tables and descriptive text.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Study Selection and Characteristics\u003c/h2\u003e \u003cp\u003eThe initial search yielded 2599 records. After removing duplicates and screening titles and abstracts, 62 full-text articles were assessed for eligibility. Ultimately, 43 studies were included in the final synthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Risk of Bias Assessment\u003c/h2\u003e \u003cp\u003eThe methodological quality of the included studies was assessed. The overall risk of bias was low to moderate. Common limitations included the retrospective nature of most studies and potential for residual confounding. The risk of bias summary and graph are presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Geographical Distribution and Research Output\u003c/h2\u003e \u003cp\u003eThe 43 included studies originated from a range of countries, with the United States (n\u0026thinsp;=\u0026thinsp;11), Denmark (n\u0026thinsp;=\u0026thinsp;3), Canada (n\u0026thinsp;=\u0026thinsp;3), South Korea (n\u0026thinsp;=\u0026thinsp;3), and France (n\u0026thinsp;=\u0026thinsp;3) being the largest contributors (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The presence of multi-national collaborations (n\u0026thinsp;=\u0026thinsp;7) strengthened the generalizability of findings. However, significant geographical gaps were noted, with limited representation from South America, Africa, and parts of Asia.\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\u003eGeographical Distribution of Included Studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry / Region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Studies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReferences (Study Numbers)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2, 5, 8, 12, 17\u0026ndash;18, 25, 28, 34\u0026ndash;35, 40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9, 10, 40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 7, 12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24, 27, 30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13, 31, 39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaiwan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29, 42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulti-National*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (Australia/US), 7 (Asia/US/Europe), 11, 15 (Israel/Int.), 33 (Germany), 40 (Global), 43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Single Countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (Hong Kong), 6 (Israel), 14 (Spain), 19 (Austria), 20 (Israel), 26 (Romania), 32 (Greece), 36 (Austria), 37 (Argentina), 38 (Netherlands), 41 (Switzerland)\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\u003eThe global distribution of the 43 included studies reflects a widespread and concerted research effort to understand the link between cancer and arterial thrombotic events (ATEs). The United States contributed the largest number of studies (n\u0026thinsp;=\u0026thinsp;11), a dominance largely facilitated by the availability of extensive, high-quality national databases such as the Surveillance, Epidemiology, and End Results (SEER) program (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), the National Inpatient Sample (NIS) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and the National Health and Nutrition Examination Survey (NHANES) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). These databases enable large-scale, population-level analyses that are critical for establishing overall risk estimates. Europe and East Asia are also major contributors, with significant outputs from Denmark (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), South Korea (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), Canada (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), and France (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The presence of multinational collaborations and meta-analyses (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) significantly strengthens the generalizability of the findings, suggesting that the cancer-ATE relationship is a universal phenomenon and not confined to specific healthcare systems or genetic populations. However, the relative scarcity of studies from Africa, South America, and parts of Asia indicates a geographical gap in the literature where the interplay of different cancer profiles, comorbidities, and healthcare access might yield unique insights.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Study Design and Scale\u003c/h2\u003e \u003cp\u003eThe methodological landscape was predominantly built upon observational study designs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Retrospective cohort studies (n\u0026thinsp;=\u0026thinsp;21) formed the backbone of the evidence, efficiently leveraging pre-existing data. The inclusion of prospective cohort studies (n\u0026thinsp;=\u0026thinsp;4) and systematic reviews/meta-analyses (n\u0026thinsp;=\u0026thinsp;5) provided higher-quality evidence and synthesized summary estimates. The studies exhibited a striking dichotomy in scale (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), with large-scale population studies (n\u0026thinsp;\u0026gt;\u0026thinsp;100,000) providing statistical power and generalizability, while smaller, focused studies (n\u0026thinsp;\u0026le;\u0026thinsp;1,000) offered invaluable depth and granularity on specific mechanisms and high-risk scenarios.\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\u003eStudy Design Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy Design\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Studies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReferences (Study Numbers)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetrospective Cohort Study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 4\u0026ndash;5, 8, 10, 12\u0026ndash;13, 16\u0026ndash;19, 20, 24\u0026ndash;28, 31, 34\u0026ndash;35, 37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProspective Cohort Study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3, 36, 40, 42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystematic Review and/or Meta-Analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7, 11, 21 (Protocol), 40\u0026ndash;41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatched Cohort Study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 28, 30, 34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCross-sectional Study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2, 26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReview Article (Narrative)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary Analysis of a Clinical Trial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservational / Other*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (Case-control), 15 (Historical cohort), 29 (Observational), 32 (Observational cohort), 38 (Prospective)\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\u003eThe methodological landscape of this field is predominantly built upon observational study designs, which are well-suited for investigating associations where randomized controlled trials are often impractical or unethical. Retrospective cohort studies (n\u0026thinsp;=\u0026thinsp;21) form the backbone of the evidence, efficiently leveraging pre-existing data from cancer registries and administrative health records to track ATE outcomes over time (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). This design is powerful for studying rare outcomes and establishing temporal sequence. The inclusion of several prospective cohort studies (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) provides higher-quality evidence by design, as they predefine outcomes and can collect data more systematically, minimizing certain biases. The five systematic reviews and meta-analyses (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) are pivotal, as they synthesize data from millions of individuals, offering the most precise summary estimates and formally assessing heterogeneity across studies. The reliance on observational data, while necessary, universally introduces the challenge of residual confounding, a limitation explicitly acknowledged across many studies and detailed in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\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\u003eSample Size of Included Studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample Size Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Studies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExample References (Study Numbers)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5, 10, 12, 16, 40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100,001\u0026ndash;1,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9, 18, 27, 31, 35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10,001\u0026ndash;100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 3, 8, 13, 19, 20, 22, 24, 28\u0026ndash;29, 34, 39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1,001\u0026ndash;10,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4, 7, 14\u0026ndash;15, 17, 23, 25\u0026ndash;26, 30, 32, 36, 38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2, 14, 19, 26, 32\u0026ndash;33, 37, 43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (Review)\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\u003eThe reviewed studies exhibited a striking dichotomy in scale, which serves complementary purposes. Large-scale population studies (n\u0026thinsp;\u0026gt;\u0026thinsp;100,000), including several with cohorts exceeding one million participants (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), provide the statistical power needed to detect overall associations, study rare cancer types, and generate robust, generalizable risk estimates. These \"big data\" approaches are instrumental in confirming that the increased ATE risk is a pervasive issue across the oncologic population. Conversely, smaller, focused studies (n\u0026thinsp;\u0026le;\u0026thinsp;1,000) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), often from single institutions, offer invaluable depth. They allow for detailed phenotyping of strokes, precise documentation of chemotherapy regimens and doses, and exploration of novel biomarkers\u0026mdash;granularity that is typically lost in registry-based studies. This combination of breadth and depth is essential; the large studies map the epidemiology of the problem, while the smaller studies delve into the specific mechanisms and high-risk scenarios, such as the impact of cisplatin in testicular cancer survivors (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) or stroke in pediatric oncology (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Spectrum of Cancer Types and ATE Outcomes\u003c/h2\u003e \u003cp\u003eThe research scope revealed a two-pronged approach (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Nearly half of the studies (n\u0026thinsp;=\u0026thinsp;23) took a \"Pan-Cancer\" approach, establishing the fundamental principle that a cancer diagnosis itself is a significant ATE risk factor. Another 16 studies focused on \"Specific Solid Tumors,\" delineating a hierarchy of risk, with cancers of the lung, pancreas, brain, and gastrointestinal tract consistently emerging as high-risk entities. Ischemic stroke and myocardial infarction (MI) were the most frequently investigated individual endpoints, each being the focus of over 20 studies (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). A significant number of studies (n\u0026thinsp;=\u0026thinsp;13) employed a \"Composite ATE\" endpoint to increase statistical power and acknowledge the systemic nature of the prothrombotic state.\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\u003eSpectrum of Cancer Types Studied\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer Focus Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Studies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExample References (Study Numbers)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePan-Cancer (All/Multiple)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;2, 5\u0026ndash;7, 9\u0026ndash;12, 15\u0026ndash;16, 18, 26\u0026ndash;29, 31, 35\u0026ndash;38, 40\u0026ndash;41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecific Solid Tumors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (Lung),\u003c/p\u003e \u003cp\u003e8 (HNSCC),\u003c/p\u003e \u003cp\u003e19 (Testicular),\u003c/p\u003e \u003cp\u003e20 (NSCLC),\u003c/p\u003e \u003cp\u003e22 (Pancreatic),\u003c/p\u003e \u003cp\u003e23 (HCC),\u003c/p\u003e \u003cp\u003e24 (Kidney),\u003c/p\u003e \u003cp\u003e25 (Colon),\u003c/p\u003e \u003cp\u003e30 (HNC),\u003c/p\u003e \u003cp\u003e32 (Urinary),\u003c/p\u003e \u003cp\u003e33 (Lung, Pancreatic, Colorectal),\u003c/p\u003e \u003cp\u003e34 (Male Breast),\u003c/p\u003e \u003cp\u003e39 (Breast),\u003c/p\u003e \u003cp\u003e42 (Lung)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematological Malignancies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (Lymphoma),\u003c/p\u003e \u003cp\u003e17 (Hematopoietic),\u003c/p\u003e \u003cp\u003e36 (Lymphoma, Leukemia),\u003c/p\u003e \u003cp\u003e43 (Leukemia, Lymphoma)\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\u003eThe research scope reveals a two-pronged approach: investigating universal risk and defining cancer-specific vulnerabilities. The \"Pan-Cancer\" category (n\u0026thinsp;=\u0026thinsp;23 studies) establishes the fundamental principle that a diagnosis of cancer, in and of itself, is a significant risk factor for ATEs, independent of traditional cardiovascular risk factors (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This suggests common underlying pathways, such as a cancer-associated hypercoagulable state and systemic inflammation. The substantial body of literature focusing on \"Specific Solid Tumors\" (n\u0026thinsp;=\u0026thinsp;16) then delineates the hierarchy of risk. Cancers of the lung (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), pancreas (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), brain (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), and gastrointestinal tract (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) consistently emerge as high-risk entities, often linked to their particularly aggressive biology and potent prothrombotic potential. The focus on \"Hematological Malignancies\" (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), though smaller, highlights that liquid tumors also confer a substantial risk, potentially through different mechanisms involving blood cell dyscrasias and specific chemotherapeutic agents like L-asparaginase. This table underscores that while the risk is widespread, it is not uniform, and prevention strategies must be tailored to the specific malignancy.\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\u003ePrimary Arterial Thrombotic Outcomes Reported\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome Measure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Studies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExample References (Study Numbers)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke (Ischemic, Hemorrhagic, or unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 5, 7\u0026ndash;8, 12\u0026ndash;18, 22\u0026ndash;29, 33, 35, 38, 40\u0026ndash;43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial Infarction (MI) / Acute Coronary Syndrome (ACS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3, 8, 11\u0026ndash;13, 15\u0026ndash;16, 18, 25, 28, 30, 31, 34\u0026ndash;37, 39\u0026ndash;42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComposite ATE (e.g., MI\u0026thinsp;+\u0026thinsp;Stroke\u0026thinsp;+\u0026thinsp;Peripheral Arterial Event)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6, 10, 15, 19, 28, 32, 34\u0026ndash;37, 40\u0026ndash;42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther (e.g., Heart Failure, CVD Mortality, MACE, Peripheral Arterial Occlusion)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (Bleeding),\u003c/p\u003e \u003cp\u003e3 (Composite CVD),\u003c/p\u003e \u003cp\u003e4 (MACE),\u003c/p\u003e \u003cp\u003e9 (HF, VTE),\u003c/p\u003e \u003cp\u003e12 (CV Mortality, HF, PE),\u003c/p\u003e \u003cp\u003e24 (Composite CVD),\u003c/p\u003e \u003cp\u003e29 (Ischemic Stroke),\u003c/p\u003e \u003cp\u003e31 (MI, Stroke),\u003c/p\u003e \u003cp\u003e39 (HF, Bleeding)\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\u003eIschemic stroke and myocardial infarction (MI) were the most frequently investigated individual endpoints, each being the focus of over 20 studies. This reflects their clinical salience as major, disabling, and often fatal cardiovascular events. The high prevalence of stroke as an outcome (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) may indicate a particular susceptibility of the cerebral vasculature to cancer-related hypercoagulability or tumor embolization. A significant number of studies (n\u0026thinsp;=\u0026thinsp;13) employed a \"Composite ATE\" endpoint, which combines stroke, MI, and sometimes peripheral arterial events (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). This approach increases the statistical power to detect an overall signal of arterial toxicity and acknowledges that the prothrombotic state in cancer patients is a systemic condition that can manifest in any arterial bed. The inclusion of other outcomes like heart failure (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) and cardiovascular mortality (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) broadens the perspective to include not only acute thrombotic events but also longer-term, treatment-related cardiovascular sequelae.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Overall Risk Estimates and Key Influencing Factors\u003c/h2\u003e \u003cp\u003eThe collective data presents a compelling and consistent picture of elevated risk (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Hazard Ratios (HR) and Odds Ratios (OR) predominantly ranged from 1.5 to 3.0, indicating a 50% to 200% increase in the relative risk of ATEs for cancer patients. Certain contexts revealed a dramatically higher risk, such as the perioperative period (OR 8.81 for MI) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The risk of ATE is modulated by a complex interplay of factors (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Cancer-related factors are paramount, including cancer type, advanced stage, and time since diagnosis. Treatment-related factors are major iatrogenic drivers, including chemotherapy (especially platinum-based), radiotherapy, and the perioperative period. Finally, traditional patient-related cardiovascular risk factors act as potent effect modifiers.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReported Risk Estimates for Arterial Thrombotic Events\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk Estimate Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReported Risk Value (Range or Example)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExample References (Study Numbers)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHazard Ratio (HR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01\u0026ndash;5.8 (e.g., HR 1.45 for bleeding (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e); HR 5.8 for 30-day ATE risk (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 3, 9, 10, 12, 16, 20, 24, 27\u0026ndash;28, 34\u0026ndash;35, 39, 41\u0026ndash;42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15\u0026ndash;43.64 (e.g., OR 1.15 for all-cancer risk post-CAD (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e); OR 43.64 for age 80\u0026thinsp;+\u0026thinsp;vs\u0026thinsp;\u0026lt;\u0026thinsp;39 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2, 5, 11, 16\u0026ndash;17, 25\u0026ndash;26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandardized Incidence/Mortality Ratio (SIR/SMR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2\u0026ndash;2.17 (e.g., SMR 2.17 for fatal stroke (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e); SPR 1.2 for any cancer in stroke patients (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5, 6, 38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubdistribution Hazard Ratio (SHR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.592\u0026ndash;5.55 (e.g., SHR 5.55 for ATE in urinary cancer (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e); SHR 0.592 for lower MI risk in cancer (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10, 13, 22\u0026ndash;24, 27, 29, 31\u0026ndash;32, 36, 41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative Incidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.42% \u0026minus;\u0026thinsp;12.5% (e.g., 1.4% stroke in first year post-diagnosis (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e); 12.5% 10-year stroke risk in HNSCC (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u0026ndash;8, 19, 22\u0026ndash;23, 29\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\u003eThe collective data presents a compelling and consistent picture of elevated risk. Hazard Ratios (HR) and Odds Ratios (OR) predominantly ranged from 1.5 to 3.0, indicating a 50% to 200% increase in the relative risk of ATEs for cancer patients compared to non-cancer controls. However, certain contexts reveal a dramatically higher risk. The peri-diagnostic and perioperative periods are particularly hazardous, with one study reporting an OR of 8.81 for MI during hospitalization for cancer surgery (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) and another an HR of 5.8 for ATEs in the first 30 days after cancer diagnosis (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The evolution of statistical methodology is also evident. While early studies often reported standard HRs, more recent investigations increasingly use Subdistribution Hazard Ratios (SHR) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), which are more appropriate in cancer populations where the high competing risk of death from the malignancy itself can otherwise obscure the true incidence of non-fatal cardiovascular outcomes. The reported cumulative incidences, such as a 1.4% stroke rate in the first-year post-diagnosis (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), translate these relative risks into tangible, absolute risks that are highly relevant for clinical communication and planning.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKey Influencing Factors for ATE Risk in Cancer Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecific Factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExample References (Study Numbers)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer-Related\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Cancer Type (e.g., Lung, Pancreatic, Brain, GI, Hematological) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Advanced Stage / Metastatic Disease (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Time Since Diagnosis (Highest risk near diagnosis) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3, 5, 7, 12, 16, 22\u0026ndash;23, 27\u0026ndash;29, 33, 40\u0026ndash;41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment-Related\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Chemotherapy (especially Platinum-based, Cytotoxic) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Radiotherapy (e.g., for HNSCC) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Cancer Surgery (perioperative period) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Specific Therapies (e.g., Cisplatin (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), Perioperative chemo (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3, 8, 16, 19, 27, 30, 32, 35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient-Related\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Traditional CV Risk Factors (Hypertension, Diabetes, Atrial Fibrillation, Smoking) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Older Age (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Male Sex (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Pre-existing Cardiovascular Disease (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5, 8, 10, 11, 15, 29, 36\u0026ndash;37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory/Biomarkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Elevated Leukocytes, Platelets, D-dimer, CRP (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Anemia / Low Hemoglobin (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Hypercoagulability Markers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14, 26, 29, 33, 36, 42\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\u003eThe risk of ATE in cancer patients is not a monolithic entity but is modulated by a complex interplay of factors. Cancer-related factors are paramount; the type of cancer is a primary determinant, with lung, pancreatic, and gastrointestinal cancers carrying the highest risk profiles (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Furthermore, advanced or metastatic disease consistently portends a greater risk than localized cancer (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), likely due to a higher tumor burden and more pronounced systemic effects. The temporal pattern is critical, with the highest risk concentrated in the initial months following diagnosis (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), a period marked by diagnostic stress, surgical interventions, and the initiation of chemotherapy. Treatment-related factors are major iatrogenic drivers; chemotherapy (especially platinum-based agents) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), radiotherapy (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), and the perioperative period (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) are all established high-risk windows. Finally, the baseline cardiovascular health of the patient remains crucial; traditional risk factors like hypertension, diabetes, atrial fibrillation, and smoking (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) act as potent effect modifiers, compounding the risk imposed by the cancer itself.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Time-Dependent Risk and Impact of Treatments\u003c/h2\u003e \u003cp\u003eA cornerstone finding of this review is the profoundly time-dependent nature of ATE risk (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The trajectory is characterized by a sharp \"spike\" immediately after diagnosis (first 30 days), a period of exceptional vulnerability, followed by a persistently elevated risk during the first 6\u0026ndash;12 months, and a gradual decline thereafter. Modern cancer therapies are significant contributors (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Chemotherapy (e.g., cisplatin), radiotherapy (with site-specific risks), and the perioperative period are all established high-risk windows. The perioperative period stands out as a time of extreme risk, with studies showing an 8\u0026ndash;9 fold increase in the odds of MI and stroke (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTime-Dependent Risk of Arterial Thrombotic Events Following Cancer Diagnosis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime Period Post-Diagnosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk Trend \u0026amp; Key Findings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExample References (Study Numbers)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeri-Diagnosis \u0026amp; First 30 Days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtremely High Risk. The immediate period surrounding diagnosis carries the highest relative risk, often driven by diagnostic procedures, initial treatment, and the cancer's hypercoagulable state.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (HR 5.8 for 30-day risk),\u003c/p\u003e \u003cp\u003e35 (Increased perioperative risk)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst 6\u0026ndash;12 Months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePersistently Elevated Risk. Risk remains significantly high, attributed to intensive treatments (surgery, chemotherapy) and the initial biological impact of the tumor.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (1.4% cumulative stroke incidence in 1st year),\u003c/p\u003e \u003cp\u003e12 (Highest risk in 1st year),\u003c/p\u003e \u003cp\u003e22 (46.6 per 1000 person-years in 1st 6 months for pancreatic cancer),\u003c/p\u003e \u003cp\u003e27 (Significant risk in first 3 years),\u003c/p\u003e \u003cp\u003e34 (60% increased risk in first 6 months for male breast cancer)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;5 Years Post-Diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGradually Declining but Elevated Risk. The risk decreases from its initial peak but remains higher than in the non-cancer population, especially for certain cancers and treatments.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (Risk declined but remained elevated for CV mortality, HF, and PE beyond 10 years),\u003c/p\u003e \u003cp\u003e24 (HR 1.77 at 1 year, 1.10 at 5 years for kidney cancer)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong-Term (\u0026gt;\u0026thinsp;5 Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable Risk. For many survivors, risk approaches baseline, but certain groups (e.g., those treated with cardiotoxic therapies or with persistent risk factors) remain at elevated long-term risk.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (Persistent elevation for some outcomes),\u003c/p\u003e \u003cp\u003e40 (Risk remained elevated in meta-analysis, varying by cancer type)\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\u003eA cornerstone finding of this review is the profoundly time-dependent nature of ATE risk. The trajectory is characterized by a sharp \"spike\" immediately after diagnosis, followed by a gradual decline. The first 30 days represent a period of exceptional vulnerability, with one study reporting a near-sixfold increase in risk (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This acute phase is likely driven by a \"perfect storm\" of factors: the intrinsic hypercoagulability of the newly diagnosed, often untreated tumor; the profound physiological stress of major cancer surgery (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e); and the pro-thrombotic effects of initiating cytotoxic chemotherapy (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The risk remains substantially elevated throughout the first year (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), a period encompassing the most intensive phase of treatment. While the risk attenuates over subsequent years, it often remains above baseline for a decade or more, particularly for specific outcomes like heart failure and in survivors of certain cancers (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). This temporal pattern mandates a dynamic and phase-specific approach to risk assessment and prevention, with the most intensive monitoring and prophylactic strategies reserved for the high-risk initial period.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImpact of Specific Cancer Treatments on ATE Risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment Modality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociated ATE Risk \u0026amp; Key Findings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExample References (Study Numbers)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificantly Increased Risk. Cytotoxic agents, particularly platinum-based regimens, are strongly associated with ATEs. The risk is often short-term but can have long-term consequences.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (HR 2.19 for CVD with cytotoxic chemo),\u003c/p\u003e \u003cp\u003e19 (Cisplatin increases short-term risk in testicular cancer),\u003c/p\u003e \u003cp\u003e27 (Chemotherapy is a risk factor for ischemic stroke),\u003c/p\u003e \u003cp\u003e32 (Perioperative chemotherapy is an independent risk factor for ATE)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncreased Risk, Often Site-Specific. Radiation to the chest (e.g., for breast cancer, lymphoma) increases coronary risk, while neck irradiation accelerates carotid atherosclerosis and stroke risk.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (Radiotherapy is a noted risk factor for stroke in HNSCC),\u003c/p\u003e \u003cp\u003e30 (Suggests increased CV risk in HNC is likely due to treatments like radiation)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer Surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery High Perioperative Risk. The immediate postoperative period carries a dramatically elevated risk for MI and stroke, likely due to surgical stress, inflammation, and hypercoagulability.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (OR 8.81 for MI and 6.71 for ischemic stroke during hospitalization for cancer surgery),\u003c/p\u003e \u003cp\u003e35 (Cancer is an independent risk factor for perioperative arterial ischemic events)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTargeted Therapy / Immunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmerging and Variable Risk. Certain targeted agents (e.g., VEGF inhibitors) are known to increase ATE risk. The risk with newer immunotherapies is still being defined.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (Found no significant difference in MACE between PD-1 inhibitors and chemo-immunotherapy in lung cancer, indicating a need for further study)\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\u003eModern cancer therapies, while life-saving, are significant contributors to cardiovascular morbidity. The table delineates the arterial toxicities associated with major treatment modalities. Chemotherapy, particularly regimens containing cisplatin, is strongly implicated in increasing ATE risk, both in the short term (e.g., during treatment for testicular cancer (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)) and as a long-term legacy effect (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Radiotherapy induces vascular injury through mechanisms like endothelial dysfunction and accelerated atherosclerosis, with the risk profile being highly anatomy-specific (e.g., chest irradiation for breast cancer increasing coronary risk, and neck irradiation for head and neck cancer increasing carotid and stroke risk (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)). The perioperative period stands out as a time of extreme risk, with studies showing an 8\u0026ndash;9 fold increase in the odds of MI and stroke during the initial hospitalization for cancer surgery (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). This is attributed to surgical stress, inflammation, immobilization, and potential interruptions in chronic antithrombotic medications. The vascular safety profile of newer targeted and immunotherapies is an area of active investigation, with current evidence for agents like PD-1 inhibitors showing no significant difference in risk compared to chemotherapy in some studies (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), underscoring the need for ongoing vigilance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Methodological Considerations and Clinical Recommendations\u003c/h2\u003e \u003cp\u003eInterpreting the collective evidence requires a careful consideration of its methodological constraints (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The overwhelming reliance on observational designs is the primary limitation, preventing causal inference and leaving studies vulnerable to residual confounding, surveillance bias, and the competing risk of death from cancer. The synthesized evidence culminates in a clear call for a paradigm shift in the care of cancer patients (Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Proposed clinical actions include increased awareness and risk stratification, implementation of multidisciplinary cardio-oncology care, and aggressive management of traditional cardiovascular risk factors. The research agenda is clear, emphasizing the need for mechanistic studies, randomized controlled trials for prophylactic strategies, and the development of validated risk prediction tools.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMethodological Considerations and Common Limitations in Included Studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethodological Aspect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommon Challenges \u0026amp; Limitations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExample References (Study Numbers)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy Design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Residual Confounding: Inability to fully account for all variables (e.g., smoking, detailed lifestyle factors).\u003c/p\u003e \u003cp\u003e\u0026bull; Observational Nature: Precludes causal inference.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;2, 4, 11\u0026ndash;12, 24, 28\u0026ndash;29, 31, 35, 36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData Sources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Coding Inaccuracies: Reliance on ICD codes from administrative databases without adjudication.\u003c/p\u003e \u003cp\u003e\u0026bull; Lack of Granular Data: Missing information on cancer stage, treatment details (dose, duration), and lab values.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u0026ndash;5, 8, 13, 17, 22, 25\u0026ndash;26, 30\u0026ndash;31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBias\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Surveillance Bias: Cancer patients may have more frequent medical contact, leading to higher detection of ATEs.\u003c/p\u003e \u003cp\u003e\u0026bull; Healthy Survivor Bias: Clinical trial participants (e.g., ASPREE (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)) may be healthier than the general cancer population.\u003c/p\u003e \u003cp\u003e\u0026bull; Immortal Time Bias: Misclassification of time-at-risk in some cohort designs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3, 10, 15, 31, 35, 38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome Ascertainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Competing Risk of Death: High mortality in cancer cohorts can mask the true incidence of ATEs if not accounted for statistically.\u003c/p\u003e \u003cp\u003e\u0026bull; Lack of Adjudication: Many studies used unvalidated code-based definitions for ATEs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13, 23, 28\u0026ndash;29, 31, 36, 41\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\u003eInterpreting the collective evidence requires a careful consideration of its methodological constraints. The overwhelming reliance on observational designs is the primary limitation, as it inherently prevents the establishment of causality and leaves studies vulnerable to residual confounding. The frequent lack of data on key confounders like smoking status, detailed body mass index, and physical activity (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) means that the estimated risk could be partially attributed to these unmeasured factors. The widespread use of administrative data and ICD codes for outcome identification, while enabling large sample sizes, introduces the potential for misclassification bias, as codes may not always reflect clinically adjudicated events (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Furthermore, bias is a recurring concern; \u003cem\u003esurveillance bias\u003c/em\u003e may lead to over-estimation of risk if cancer patients have more contact with the healthcare system (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), while the \u003cem\u003ecompeting risk of death\u003c/em\u003e from cancer can lead to under-estimation if not handled with appropriate statistical methods (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). These limitations do not invalidate the findings but emphasize that the reported risk estimates should be viewed as associations within a complex clinical landscape and highlight the critical need for prospective studies designed a priori to address these specific challenges.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical Recommendations and Future Directions from Included Studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKey Recommendations and Future Directions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExample References (Study Numbers)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Awareness \u0026amp; Risk Stratification: Increase clinician awareness of the link. Develop risk prediction models to identify high-risk patients. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Multidisciplinary Care: Implement collaborative cardio-oncology care models. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Optimize CV Risk Factors: Aggressively manage hypertension, diabetes, and dyslipidemia in cancer patients. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Personalized Anticoagulation: Do not lower the threshold for anticoagulation in AF based on cancer alone; consider cancer-specific bleeding risk. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 6, 8, 10, 12, 24, 29, 32, 37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient Management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Education: Educate patients about stroke/MI symptoms, especially in the high-risk period after diagnosis. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Survivorship Care: Incorporate cardiovascular risk screening and management into long-term survivorship plans. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5, 7, 22, 24, 40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearch Priorities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Mechanistic Studies: Investigate the biological pathways linking cancer, its treatments, and ATEs. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Prospective Trials: Conduct randomized controlled trials to establish optimal prophylactic and treatment strategies (e.g., role of DOACs, antiplatelets). (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Risk Prediction Tools: Develop and validate tools to identify high-risk patients for targeted interventions. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Long-Term Follow-up: Study the long-term cardiovascular outcomes in cancer survivors, especially with newer therapies. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2, 4, 6, 10, 12, 17, 28\u0026ndash;29, 32\u0026ndash;33, 35, 40, 42\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\u003eThe synthesized evidence culminates in a clear call for a paradigm shift in the care of cancer patients, moving from a reactive to a proactive and preventive model. The proposed clinical actions are multi-faceted: 1) Awareness and Risk Stratification: Clinicians must be educated about this link, and there is a pressing need to develop and validate risk prediction tools to identify high-risk patients who would benefit most from interventions (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). 2) Multidisciplinary Care: The integration of cardiology expertise into oncology care through formal cardio-oncology programs is repeatedly advocated as the optimal framework for managing these complex patients (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). 3) Aggressive Risk Factor Management: Optimizing control of hypertension, diabetes, and dyslipidemia is considered a foundational element of risk reduction (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The research agenda is equally clear. There is a stark evidence gap regarding effective interventions; while observational data clearly identifies the problem, a near-universal recommendation is for randomized controlled trials to determine the efficacy and safety of antithrombotic agents (e.g., DOACs, antiplatelets) for primary and secondary prevention in cancer patients (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Furthermore, a deeper understanding of the underlying biological mechanisms (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) is needed to identify novel therapeutic targets and biomarkers for risk prediction.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cstrong\u003e4.1. Summary of Evidence\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This systematic review of 43 studies provides a comprehensive synthesis of the evidence linking malignancy to an increased risk of arterial thrombotic events (ATEs). The collective data paints a consistent and compelling picture: a cancer diagnosis confers a significant, though variable, increase in the risk of myocardial infarction and ischemic stroke. The reported hazard and odds ratios, predominantly ranging from 1.5 to 3.0, translate to a 50% to 200% elevation in relative risk compared to the non-cancer population. This risk is not a monolithic entity but is dynamically shaped by a triad of factors: (1) cancer-specific characteristics, such as primary site (with lung, pancreatic, and GI cancers carrying the highest burden) and stage (advanced disease being a key driver); (2) treatment-related exposures, including chemotherapy, radiotherapy, and the profound stress of surgery; and (3) patient-specific vulnerabilities, where traditional cardiovascular risk factors act as potent effect multipliers. Crucially, the temporal pattern of risk is a cornerstone finding, characterized by a dramatic spike immediately following diagnosis that gradually attenuates but often remains elevated for years, fundamentally shaping the window for clinical intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2. Interpretation in the Context of Existing Literature and Proposed Pathophysiology\u003cbr\u003e\u003c/strong\u003eOur findings consolidate a paradigm shift in oncology and cardiology, moving the cancer-ATE link from a peripheral observation to a central tenet of patient management. The evidence strongly supports a pathophysiological model where the \u0026quot;perfect storm\u0026quot; of cancer-associated ATE risk arises from the confluence of several mechanisms, many of which are most active in the high-risk initial phase following diagnosis.\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eThe Hypercoagulable State and Systemic Inflammation: Cancer cells can directly activate the coagulation cascade through tissue factor expression and release of procoagulant microparticles. Concurrently, tumors create a state of systemic inflammation, with elevated levels of cytokines like IL-6 and TNF-\u0026alpha;, which promote endothelial dysfunction, platelet activation, and plaque instability (3, 4). This underlying pro-thrombotic milieu is the substrate upon which other risk factors act.\u003c/li\u003e\n \u003cli\u003eTreatment-Induced Endothelial Injury: Our review highlights the significant iatrogenic risk. Chemotherapeutic agents, particularly platinum-based drugs, are directly toxic to the vascular endothelium, disrupting its natural anti-thrombotic properties (19, 27). Radiotherapy induces accelerated atherosclerosis and vascular fibrosis through direct DNA damage and chronic inflammation in the irradiated field, explaining the site-specific risks (e.g., carotid disease after neck irradiation, coronary disease after chest irradiation) (8, 30).\u003c/li\u003e\n \u003cli\u003eThe Peri-Diagnostic \u0026quot;Spike\u0026quot;: The exceptionally high risk in the first 30 days post-diagnosis, as evidenced by hazard ratios exceeding 5.0 (28), can be attributed to multiple converging factors. The physiological stress of a new cancer diagnosis, the pro-inflammatory and pro-thrombotic impact of major surgical interventions (16, 35), and the immediate initiation of cytotoxic therapies create a perfect storm. This period likely represents the clinical manifestation of the most intense hypercoagulable and inflammatory state.\u003c/li\u003e\n \u003cli\u003eThe Role of Traditional Risk Factors: The data unequivocally shows that traditional cardiovascular risk factors are not supplanted by the cancer diagnosis but are compounded. Hypertension, diabetes, dyslipidemia, and smoking (8, 29, 36) continue to be major determinants of ATE risk, suggesting that the baseline health of the vascular system is a critical modifier of the cancer-specific insult.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e4.3. Clinical and Research Implications: From Recognition to Action\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The synthesized evidence mandates a proactive and structured approach to cardiovascular care in oncology.\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eTowards Dynamic Risk Stratification: The current one-size-fits-all approach is inadequate. The field urgently needs validated, dynamic risk prediction tools that integrate cancer type, stage, planned treatment regimen, and traditional CV risk factors to identify patients who would benefit most from intensified monitoring and prophylactic strategies (10, 29). Risk is not static; it must be re-evaluated at diagnosis, before initiating high-risk therapies, and during survivorship.\u003c/li\u003e\n \u003cli\u003eThe Central Role of Multidisciplinary Cardio-Oncology: The management of these complex, competing risks requires seamless collaboration. Formal cardio-oncology programs are no longer a luxury but a necessity (12, 24, 37). These teams are best positioned to make high-stakes decisions, such as the timing of surgery in a patient with recent coronary stents, or the management of anticoagulation in a thrombocytopenic patient with atrial fibrillation (1).\u003c/li\u003e\n \u003cli\u003eThe Stark Interventional Evidence Gap: A critical and consistent finding across this review is the almost complete absence of evidence from randomized controlled trials (RCTs) guiding the prevention and treatment of ATEs in cancer patients. While observational data clearly identifies the problem, it cannot define the solution. It remains unknown whether prophylactic antiplatelet or anticoagulant therapy is effective and safe in high-risk cancer patients, and if so, in whom, with which agent, and for how long (4, 17, 28). This represents the single most important gap in the literature and a clear mandate for future research.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e4.4. Limitations\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The conclusions of this review must be interpreted within the context of the limitations inherent in the source literature. The overwhelming reliance on observational, predominantly retrospective, study designs precludes definitive causal inference and leaves the findings vulnerable to residual confounding. The inability to fully adjust for lifestyle factors like smoking, diet, and physical activity may lead to overestimation of the independent effect of cancer. The widespread use of administrative data and ICD codes for outcome identification, while enabling large-scale analysis, introduces the potential for misclassification bias. Furthermore, methodological challenges such as surveillance bias (increased ATE detection due to more frequent medical contact) and the competing risk of death from cancer (which can obscure the true incidence of non-fatal ATEs if not properly accounted for) are recurring concerns. Finally, the geographical concentration of research in high-income countries limits the generalizability of findings to regions with different cancer profiles, genetic backgrounds, and healthcare systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5. Future Directions\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This review illuminates a clear path forward for both research and clinical practice:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eMechanistic Research: Deepen the understanding of the biological pathways linking specific cancers and treatments to endothelial dysfunction and platelet hyperreactivity (2, 6).\u003c/li\u003e\n \u003cli\u003eInterventional Trials: Prioritize RCTs to test the efficacy and safety of preventive strategies (e.g., low-dose DOACs, antiplatelets) in high-risk cancer populations, particularly in the peri-diagnostic and treatment phases (28, 32, 42).\u003c/li\u003e\n \u003cli\u003ePrecision Medicine: Develop and validate integrated risk prediction models that combine clinical data with novel biomarkers (e.g., circulating tumor-derived microparticles, specific inflammatory markers) to enable personalized prophylaxis (10, 33).\u003c/li\u003e\n \u003cli\u003eSurvivorship Care: Establish long-term follow-up protocols for cancer survivors, especially those exposed to cardiotoxic therapies, to monitor and manage delayed cardiovascular sequelae (12, 40).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eMalignancy is a significant and independent risk factor for arterial thrombotic events, with a risk profile that is dynamic and multifactorial. A structured approach involving awareness, risk stratification, multidisciplinary collaboration, and aggressive management of modifiable risk factors is essential to mitigate this threat. Future research must focus on elucidating underlying mechanisms, validating predictive biomarkers, and most importantly, conducting prospective randomized trials to establish evidence-based strategies for the prevention and management of ATEs in cancer patients.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Resource\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any external funding or support from external entities. All aspects of this work were conducted independently, and there are no financial or material conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026apos;s Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMA\u003c/strong\u003e developed the methodology and wrote the methodology section. SN also conducted data extraction using a predesigned Excel spreadsheet, capturing key study details. Additionally, MA oversaw the entire review process and coordinated the writing of the manuscript. \u003cstrong\u003eSN\u003c/strong\u003e independently verified 50% of the extracted data to ensure accuracy and consistency. SN also wrote the results section, contributed to the final review of the manuscript, played a role in developing the study design, and assisted in refining the methodology section. \u003cstrong\u003eJT\u003c/strong\u003e contributed to refining the search strategy, participated in the full-text review process, and assisted in synthesizing the extracted data. JT also built the tables and diagrams for the manuscript and helped review the methodology section. \u003cstrong\u003eAS\u003c/strong\u003e independently conducted the title and abstract screening using Rayyan software, ensuring the initial selection of studies. AS also conducted the full-text review for studies meeting the inclusion criteria and wrote the discussion section. \u003cstrong\u003eLA\u003c/strong\u003e independently verified 50% of the extracted data alongside SN to enhance data accuracy. LA also contributed to refining the study methodology and participated in manuscript revisions. \u003cstrong\u003eHA\u003c/strong\u003e wrote the introduction section and assisted in optimizing the search strategy. HA also played a role in screening fulltext articles and contributed to drafting and reviewing the discussion section. \u003cstrong\u003eKN\u003c/strong\u003e independently conducted the title and abstract screening using Rayyan software, ensuring the initial selection of studies. KN also wrote the conclusion section and participated in discussions regarding study inclusion and exclusion criteria. \u003cstrong\u003eSH\u003c/strong\u003e contributed to writing the discussion section and provided critical revisions to improve clarity and coherence. SH also participated in reviewing the final manuscript to ensure consistency and accuracy. \u003cstrong\u003eTD\u003c/strong\u003e played a role in the quality assessment of included studies and assisted in synthesizing the extracted data. TD also contributed to reviewing the discussion and conclusion sections to ensure alignment with the study objectives. All authors contributed to the conception and design of the study, provided input on data interpretation, and participated in manuscript revisions. All authors approved the final version before submission.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflicts of interest were reported among the authors involved in this systematic review.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEl-Rayes M, Adam M, Fang J, Wang X, Jeong I, Austin PC, et al. 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Front Neurol. 2019;10:52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNavi BB, Howard G, Howard VJ, Zhao H, Judd SE, Elkind MSV, et al. The risk of arterial thromboembolic events after cancer diagnosis. Res Pract Thromb Haemost. 2019;3(4):639\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerada H, Nakamura K, Fujita S, Gon Y, Kawano T, Kitano T, et al. Incidence and risk factors for ischemic stroke in patients with cancer: A retrospective observational study. Thromb Res. 2025;254:109455.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim DK. Exploring the Link between Head and Neck Cancer and the Elevated Risk of Acute Myocardial Infarction: A National Population-Based Cohort Study. Cancers. 2024;16(10):1930.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoyer J, Deharo P, Angoulvant D, Ivanes F, Ferrara J, Vaillier A, et al. Cardiovascular outcomes in patients with cancer during a 5-year follow-up: Results from a French administrative database. Arch Cardiovasc Dis. 2023;116(2):88\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBamias A, Tzannis K, Zakopoulou R, Sakellakis M, Dimitriadis J, Papatheodoridi A, et al. Risk for Arterial Thromboembolic Events (ATEs) in Patients with Advanced Urinary Tract Cancer (aUTC) Treated with First-Line Chemotherapy: Single-Center, Observational Study. Curr Oncol. 2022;29(9):6077\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKassubek R, Winter M-AGR, Dreyhaupt J, Laible M, Kassubek J, Ludolph AC, et al. Development of an algorithm for identifying paraneoplastic ischemic stroke in association with lung, pancreatic, and colorectal cancer. Ther Adv Neurol Disord. 2024;17:1\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReiner AS, Navi BB, DeAngelis LM, Panageas KS. 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BMC Neurol. 2022;22:31.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Arterial Thrombotic Events, Cancer, Myocardial Infarction, Ischemic Stroke, Thromboembolism, Cardio-Oncology, Systematic Review","lastPublishedDoi":"10.21203/rs.3.rs-9242659/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9242659/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eA diagnosis of cancer is associated with an elevated risk of arterial thrombotic events (ATEs), including myocardial infarction (MI) and ischemic stroke. This systematic review synthesizes the current evidence on the epidemiology, risk factors, time-dependent risks, and outcomes of ATEs across a spectrum of malignancies to guide clinical practice and future research.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe systematically searched PubMed and Science Direct from inception to January, 2026 for studies reporting on ATEs in cancer patients. Data on patient demographics, cancer types, treatment modalities, ATE outcomes, and risk estimates were extracted. The risk of bias was assessed using appropriate tools.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e43 studies were included. The evidence demonstrates a clear association between cancer and an increased risk of ATEs (HR/OR range: 1.5-3.0). High-risk malignancies included lung, pancreatic, gastrointestinal, and brain cancers. The risk was most pronounced in the peri-diagnostic and first 6\u0026ndash;12 months after diagnosis. Key contributing factors included advanced cancer stage, specific chemotherapies (e.g., platinum-based agents), radiotherapy, and the perioperative period. Traditional cardiovascular risk factors compounded this risk. Despite the established link, evidence for optimal prophylactic strategies is lacking.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCancer confers a significant and time-dependent increased risk of ATEs, necessitating increased clinical vigilance. A proactive, multidisciplinary approach involving cardio-oncology is essential for risk stratification, aggressive management of traditional risk factors, and patient education. Future research must focus on mechanistic studies, predictive biomarker development, and randomized controlled trials to establish effective prevention and treatment strategies.\u003c/p\u003e","manuscriptTitle":"The Link Between Malignancy and Arterial Thrombotic Events: A Systematic Review Across Cancer Types","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 06:21:30","doi":"10.21203/rs.3.rs-9242659/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":"f3ed58b4-e9b2-49ba-9f8f-d5c86486d086","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T15:12:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 06:21:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9242659","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9242659","identity":"rs-9242659","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}