Skip to main content
Download PDF

Use of computed tomography coronary calcium score for prediction of cardiovascular events in cancer patients: a retrospective cohort analysis

Cardio-Oncology volume 10, Article number: 1 (2024) Cite this article

Abstract

Background

CT- coronary calcium score, is one of the most studied and widely available modalities in cardiovascular medicine. Coronary artery calcium score (CACS) is an established predictor of coronary artery disease. The ‘standard of care’ diagnostic modality to measure CACS is ECG-gated Cardiac Multi-Detector Computed Tomography. There is convincing evidence of a strong association between CACS and major cardiovascular (CV) events in asymptomatic individuals. Cancer patients (C) may have a higher risk for CV disease than non-cancer patients (NC) related not only to cancer treatments but also to shared biological factors and pathways. Thus, identifying tools for early detection of CV disease in this population is of utmost importance.

Methods

A retrospective cohort analysis was performed with patients from Cleveland Clinic Florida and Ohio who had CACS from 2017 to 2021. Patients who had cancer diagnosis prior to CACS were matched to NC for age and sex. CV events after their index CACS events were compared between C and NC, and matched control and propensity analysis were conducted.

Results

Ten thousand seven hundred forty-two patients had CACS; 703 cancer patients had CACS and were eligible. Extensive CACS (> 400) were significantly higher in cancer, 94 (13.37%) vs non-cancer patients, 76 (10.83%), P = 0.011. Furthermore, after propensity matched analysis, CACS > 400 was 14.8% in C vs 9.6% in NC, P =  < 0.05. CV events were similar in both cohorts (p = NS), despite less CV risk factors in cancer patients (P =  < 0.05). For the combined moderate (101–400) & extensive (> 400) CACS, the prevalence of stroke and peripheral arterial disease, a marker of systemic atherosclerosis, was significantly higher in patients with cancer (P < 0.01).

Conclusions

Despite having fewer CV risk factors in our study, similar CACS in cancer patients are suggestive of a higher prevalence of CV disease independent of traditional risk factors. High CACS and the overall prevalence of vascular events were more frequent in patients with cancer. Higher prevalence of peripheral arterial disease and cerebrovascular accident further suggests the increased atherosclerotic burden in C.

Background

Cardiovascular disease (CVD) is the leading cause of mortality worldwide. Approximately 17.9 million people die from CVD every year, accounting for 31% of all global deaths [1]. Behind CVD, cancer is responsible for the second-most deaths in the global population, with the most recent data attributing over 10 million deaths to cancer annually worldwide [2]. It is increasingly understood that these two disease processes not only pose a mortality threat individually but often behave synergistically to promote an inflammatory environment that hastens mortality and worsens numerous systemic comorbidities [3, 4]. Cancer patients (C) are estimated to, on average, have a 42% greater relative risk for cardiovascular disease and death when compared to non-cancer patients (NC) in the general population [5]. Furthermore, with advances in cancer care and with the introduction of new treatment modalities that prolong patients’ longevity, underlying comorbidities and cancer treatment-related toxicities’ impact on morbidity and mortality are more pertinent than ever [3, 6]. For these reasons, tools for the early detection of developing CVD in cancer patients are needed.

One such stratification tool is the coronary artery calcium score (CACS). Typically performed via ECG-gated cardiac multi-detector computed tomography (MDCT) or derived from routine computed tomography (CT) of the chest, the CACS is one of the most thoroughly studied and widely available tests in cardiovascular medicine [7,8,9]. Multiple large, long-term, population-based observational studies out of the United States, Germany, and the Netherlands have built a large body of evidence demonstrating a strong association between CACS and major cardiovascular outcomes in asymptomatic patients [10,11,12,13]. As a result, current clinical practice guidelines in the United States and Europe recommend CACS as a useful way to predict the risk of subclinical cardiovascular disease (CVD) in asymptomatic patients, allowing for risk stratification and guidance of follow-up testing and management [14,15,16,17]. Thus, low CACS can limit the need for follow-up examinations, unnecessary testing, and excessive intervention while, conversely, a high or increasing CACS can presage an increasing risk for cardiovascular events and the need for more aggressive preventive measures.

Despite the firm establishment of CACS as a powerful risk stratification tool in the general population, specific recommendations regarding its application to the cancer patient population have not yet been developed. The physiologic effects of malignancy extend far beyond a focal tumor. It is proposed that through mechanisms such as pro-inflammatory cytokine release, increased oxidative stress, and promotion of a pro-thrombotic state, cancer can independently alter the body’s chemistry on a systemic scale [18]. These mechanisms, coupled with the noxious chemo-, radio-, immuno- and hormonal therapies that cancer patients are regularly exposed to, are consistently demonstrated to result in a statistically higher burden of CVD with earlier onset of clinically significant disease on average [19, 20]. Moreover, increasingly used immune checkpoint inhibitors, while highly effective, have been shown to have a direct role in development of accelerated atherosclerosis [20,21,22].

This study sought to evaluate the burden of atherosclerosis and CV events on patients with cancer using the CACS as a screening tool to detect CVD most effectively in this vulnerable population. We examined the association of CACS with CVD events in C vs. NC.

Methods

A retrospective cohort analysis from Cleveland Clinic Florida and Cleveland Clinic Ohio was performed on patients from 2017 to 2021 (5 years). Patients who had CACS during that period were identified and cancer patients (C) who had a CT CACS after the cancer diagnosis were selected (C) and compared to an age- and a sex-matched cohort of non-cancer patients (NC).

CACS reported by staff radiologists at the time of test performance were utilized for analysis.

The Student T-test of unequal variances was used for continuous variables and Chi-square for categorical variables.

Five cardiovascular events that occurred after CACS date were individually evaluated by retrospective chart review of the electronic medical record: coronary artery disease, heart failure, myocardial infarction, cerebrovascular accident (CVA) and peripheral arterial disease (PAD). Events were adjudicated from the electronic records diagnoses.

Primary goals were to compare the CV risk factors and CACS in C vs. NC patients and the prevalence of CVD events in these two groups according to their CACS.

Propensity-score matching was performed by using the nearest neighbor method with a caliper of 0.05 standard deviation of the logit. Variables utilized for propensity matching included hypertension, obesity, diabetes, dyslipidemia, and history of cigarette smoking. A standardized mean difference < 0.1 (10%) was used for each of the matching variables. A subgroup analysis was performed to evaluate any differences in primary endpoint in patients with CACS with or without cancer. Univariate analysis was conducted to compare and summarize characteristics and outcomes between groups. A multivariate regression model was performed to assess the differences between groups and adjust for unbalanced characteristics. We established a statistical significance level of 0.05 Fig. 1.

Fig. 1
figure 1

Central Illustration: Patients with C may have high CACS and increased incidence of atherosclerotic/vascular events, even when having less CV risk factors

Full size image

Results

A total of 10,742 patients had CACS during the study period. 1016 patients had cancer. From that group, 703 had complete data at the Cleveland Clinic electronic health care records (EHR) and were the subject group of this study: they were analyzed (C) and compared to 703 NC. Mean age was 61.4 ± 8.6 (C) and 61.3 ± 8.5 years (NC). Melanoma and nonmelanoma skin (37.6%), genitourinary (21.1%), and breast (16.1%) were the most prevalent cancer types (Fig. 2). Mean CACS was 199.2 in C and 169.3 in NC (P = 0.51). CACS were higher in C, in the > 400 category, 94 (13.37%) vs 76 in NC (10.83%), (p = 0.011). The prevalence of CVD events was similar in both cohorts despite a lower prevalence of hypertension, obesity, diabetes, and dyslipidemia in C  (Table 1). CVA (p = 0.001) and PAD (p = 0.010) were more frequent in C than NC (Table 1). For the subgroup with the combined 101–400 and > 400 CACS, C had a higher incidence of PAD, a marker of systemic atherosclerosis, p = 0.009 (Central Illustration).

Fig. 2
figure 2

Prevalence of different C types in CACS patients' population

Full size image
Table 1 The prevalence of CVD events was similar in both cohorts despite a lower prevalence of CV risk factors in C subjects
Full size table

A summary of the means and percentages of the baseline variables for each group can be found in Table 1. In this cohort of patients who underwent CACS, C had a lower prevalence of hypertension (P =  < 0.001), obesity (P =  < 0.001), Diabetes (P = 0.047) and dyslipidemia (P = 0.023) compared to NC. It should be noticed that, even without correction by CV risk factors, there was a higher prevalence of CVA: 75 (10.67%) vs 42 (5.97%), P = 0.001 and PAD: 84 (11.95%) vs 55 (7.82%), P = 0.01 in C vs NC (Table 1 and Central Illustration).

Propensity scores matched analysis correcting by CV risk factors was conducted to minimize potential confounding results from the primary matched controlled analysis. A summary of the means and percentages of the variables for each group can be found in Table 2. In this propensity analysis, after correcting for CV risk factors, lower CACS (0, 1–10, and 11–100) were slightly higher in NC than in C: 428 (74.69%) vs 397 (69.28%), P = NS. There were no differences between both groups for the intermediate CACS 101–400: NC 89 (15.6%) vs C 91 (15.9%). However, the highest CACS > 400 was more frequent among C, 85 (14.8%) vs NC, 55 (9.4%), P =  < 0.01. After adjustment by propensity scores, there was a higher incidence of CVA, PAD and in this analysis also CAD in C.

Table 2 Calcium score corrected by propensity matched analysis for patients with CACS > 400: It was significantly higher for C vs NC. PAD, Stroke and CAD were more prevalent in C vs NC for this subgroup
Full size table

Discussion

This study sought to assess the CACS as a targeted cardiovascular risk stratification tool in an at-risk population. Though CACS is a well-established screening tool in the general population [7, 23, 24], in this retrospective analysis of over 700 cancer patients, we observed that cancer patients showed significant elevations in levels of coronary calcium scores despite having a lower prevalence of traditional CV risk factors, and when cross-matched with non-cancer counterparts also exhibited an excess of atherosclerotic cardiovascular disease (ASCVD) by quantifiable calcific burden (Table 1). With statistically lower rates of common CVD risk factors including hypertension, obesity, diabetes mellitus type 2, and dyslipidemia, cancer patients were found to have equivalent rates of CVD events (defined as coronary artery disease, heart failure, myocardial infarction, cerebrovascular accident, or peripheral arterial disease). This highlights the possibility and likelihood of cancer diagnosis and treatment being independent risk factors in the development of CVD and suggests that clinical risk stratification models centered on common risk factor profiles and clinical characteristics may underestimate risk in this population.

Furthermore, cancer patients with moderate (101–400) and extensive (> 400) CACS were found to have significantly increased prevalenceof atherosclerotic calcific burden, including both PAD and CVA, in comparison to their non-cancer counterparts (Table 2). This is in keeping with prior studies exemplified by Whitlock, et al. [24], which showed the association between cancer diagnosis and treatment with the development of coronary calcifications. Recognizing the incremental value of these findings is critical. Our findings, in building on those of prior studies, suggest that CACS may serve as a risk stratification tool for the identification of an at-risk for CV disease cancer patients’ population. Early identification of CV risk may lead to implementation of preventive therapies.

The proposed mechanisms underlying the link between cancer and CVD center primarily on the role of cancer in promoting local and systemic inflammatory cascades. Inflammasomes, interleukins, and pro-inflammatory cytokines that are commonly elevated in cancer patients have been linked to accelerated atherogenesis [25,26,27,28]. Furthermore, proposed secondary mechanisms such as biomarker-mediated alterations in metabolism [13, 29] suggest that mitigation of risk through dietary, lifestyle, and risk factors modification alone is likely insufficient.

Beyond the direct pathophysiological impacts of cancer itself, the effects of the many therapies used to treat cancer cannot be ignored. Chemotherapeutic agents such as tyrosine kinase inhibitors, platinum agents, taxanes, 5-fluorouricil, and hormonal therapies have all been implicated in the development of CVD through mechanisms such as direct endothelial cell injury, oxidative stress, myocardial ischemia through vasospasm, arterial thrombosis, and promoting CVD risk factors such as relevant systemic hypertension [3, 30,31,32,33]. These impacts are only further compounded by exposures to other noxious therapies such as radiation therapy and androgen deprivation therapy, both commonly implicated in the development of CVD [18, 34,35,36].

Therapies well-established for ASCVD risk reduction in the general population are not without risk, especially in cancer patients who are prone to several iatrogenic and intrinsic comorbidities such as myelosuppression and end-organ failure [37, 38]. Therefore, risk stratification tools in this population must not only assess for the presence of ASCVD, but also help guiding the need for safe and early intervention [39].

Though beyond the scope of this study, our data on increased incidence of atherosclerotic disease by relative calcific burden may also suggest the need for future establishment of adjusted CACS thresholds that are specific to this at-risk population and highlights a need for further dedicated investigations. Most prior studies on this topic have been limited to small, cross-sectional cohorts, often focused on one specific malignancy. To our knowledge, our cohort of over 700 patients, representing a wide variety of cancer types, is one of the largest of its kind.

Study limitations

Potential for referral bias could exist, but this retrospective analysis is not equipped to evaluate for this bias.

Like previous investigations into this topic, limitations of this study include the lack of data regarding the specific therapy received by each patient undergoing cancer treatment, representing a possible confounding contributor to outcomes and CVD burden. This highlights a need for a dedicated study into CACS profiles and risk stratification according to the type of therapy received. Furthermore, the single-center and retrospective nature of the analysis limits the statistical power of the study, precluding stratification by individual cancer type to better determine specific screening and intervention needs by type of malignancy. Randomized, prospective investigation into the topic will be required.

Short-term follow-up was deemed a limitation in correlating CACS with CVD events such as myocardial infarction and heart failure. Future analyses are needed with a focus on CACS profiles by malignancy sub-type and treatment modality to better assess the CVD impact on each individual scenario. Considering the increasingly understood risk of CVD faced by cancer patients across all sub-types, studies like these may yield new applications of widely available CACS to improve detection, stratification, and intervention of CVD in this vulnerable population.

Conclusions

Comparing an age- & sex-matched cohort of patients with cancer vs. non-cancer, we found similar CACS scores and prevalence of CVD despite a lower relative burden of CV risk factors. Ranges of CACS scoring and the prevalence of CVD events in cancer patients were no different from non-cancer group, suggesting higher prevalence after adjusting for risk factors. A propensity analysis was made to confirm this finding. Cancer patients with high CACS scores had a higher incidence of PAD. Given the retrospective single-center nature of this study, we propose additional prospective multicenter studies to better assess CACS in patients with cancer.

We acknowledge that this comparison is limited by the database, hence further prospective studies are needed to confirm, expand, and better understand the mechanisms behind our results.

Clinical perspective

Patients with cancer may have high calcium scores and increased risk of atherosclerotic /vascular events, even when they have less CV risk factors, suggesting an independent increased risk of atherosclerosis/CVD present in the cancer population.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

CACS:

Coronary artery calcium score

CV:

Cardiovascular

CT:

Computed tomography

CAD:

Coronary artery disease

MDCT:

Multi-detector computed tomography

HER:

Electronic health care records

C:

Cancer patients

NC:

Non-cancer patients

CVD:

Cardiovascular disease

PAD:

Peripheral arterial disease

CVA:

Cerebrovascular accident

ASCVD:

Atherosclerotic cardiovascular disease

References

  1. WHO, World Health Organization, Keyfacts Cardiovascular Diseases (CVDs), 2017. Accessed March 18, 2019.

  2. Murphy SL, Kochanek KD, Xu J, Arias E. Mortality in the United States, 2020. NCHS Data Brief. 2021;427:1–8.

    Google Scholar 

  3. Wang FM, Reiter-Brennan C, Dardari Z, Marshall CH, Nasir K, Miedema MD, Berman DS, Rozanski A, Rumberger JA, Budoff MJ, Dzaye O, Blaha MJ. Association between coronary artery calcium and cardiovascular disease as a supporting cause in cancer: The CAC consortium. Am J Prev Cardiol. 2020;12(4): 100119. https://doi.org/10.1016/j.ajpc.2020.100119.

    Article  Google Scholar 

  4. Mladosievicova B, Petrikova L, Valaskova Z, Bernadic M Jr, Chovanec M, Mego M, Bernadic M Sr. Atherosclerosis in cancer patients. Bratisl Lek Listy. 2019;120(9):636–40. https://doi.org/10.4149/BLL_2019_105.

    Article  PubMed  CAS  Google Scholar 

  5. Florido R, Daya NR, Ndumele CE, Koton S, Russell SD, Prizment A, Blumenthal RS, Matsushita K, Mok Y, Felix AS, Chores J, Joshu CE, Platz EA, Selvin E. Cardiovascular disease risk among cancer survivors: the Atherosclerosis Risk In Communities (ARIC) Study. J Am Coll Cardiol. 2022;80(1):22–32. https://doi.org/10.1016/j.jacc.2022.04.042.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Polonsky TS, Mcclelland RL, Jorgensen NW, et al. Coronary artery calcium score and risk classification for coronary heart disease prediction. JAMA. 2010;303(16):1610–6.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Nasir K, Clouse M. Role of non-enhanced multidetector Ct coronary artery calcium testing in asymptomatic and symptomatic individuals. Radiology. 2012;264(3):637–49.

    Article  PubMed  Google Scholar 

  8. Yeboah J, McClelland RL, Polonsky TS, et al. Comparison of novel risk markers for improvement in cardiovascular risk assessment in intermediate-risk individuals. JAMA. 2012;308(8):788–95.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Karady J, Ferencik M. Coronary artery calcium for cardiovascular risk estimation in patients with cancer. Circ Cardiovasc Imaging. 2023;16(2):e015172. https://doi.org/10.1161/CIRCIMAGING.123.015172.

  10. Oudkerk M, Stillman AE, Halliburton SS, et al. Coronary artery calcium screening: current status and recommendations from the European Society of Cardiac Radiology and North American Society for Cardiovascular Imaging. Eur Radiol. 2008;24:645–71.

    Google Scholar 

  11. Demer LL, Tintut Y. Vascular calcification: pathobiology of a multifaceted disease. Circulation. 2008;117:2938–48.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Bild DE, Bluemke DA, Burke GL, et al. Multi-Ethnic Study of Atherosclerosis: objectives and design. Am J Epidemiol. 2002;156:871–81.

    Article  PubMed  Google Scholar 

  13. Schmermund A, Möhlenkamp S, Stang A, et al. Heinz Nixdorf RECALL Study Investigative Group. Assessment of clinically silent atherosclerotic disease and established and novel risk factors for predicting myocardial infarction and cardiac death in healthy middle-aged subjects: rationale and design of the Heinz Nixdorf RECALL Study. Am Heart J. 2002;144:212–8.

  14. Goff DC, Jr, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines [Published correction appears in: J Am Coll Cardiol 2014;63:3026] J Am Coll Cardiol. 2014;63:2935–59.

  15. Authors/Task Force Members. Piepoli MF, Hoes AW, et al. 2016 European Guidelines on cardiovascular disease prevention in clinical practice: the sixth joint task force of the European society of cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of 10 societies and by invited experts): developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR) Eur J Prev Cardiol. 2016;23:NP1–NP96.

  16. Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, et al. 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: a report of the American college of cardiology/American heart association task force on clinical practice guidelines. Circulation. 2019;140:e596-646.

    PubMed  PubMed Central  Google Scholar 

  17. Lopez-Mattei J, Yang EH, Baldassarre LA, Agha A, Blankstein R, Choi AD, et al. Cardiac computed tomographic imaging in cardio-oncology: an expert consensus document of the society of cardiovascular computed tomography (SCCT). Endorsed by the international cardio-oncology society (ICOS). J Cardiovasc Comput Tomogr. (2021) S1934–5925:277–275.

  18. Masoudkabir F, Sarrafzadegan N, Gotay C, Ignaszewski A, Krahn AD, Davis MK, Franco C, Mani A. Cardiovascular disease and cancer: evidence for shared disease pathways and pharmacologic prevention. Atherosclerosis. 2017;263:343–351. https://doi.org/10.1016/j.atherosclerosis.2017.06.001.

  19. Strongman H, Gadd S, Matthews A. Medium and long-term risks of specific cardiovascular diseases in survivors of 20 adult cancers: a population-based cohort study using multiple linked UK electronic health records databases. Lancet. 2019;394(10203):1041–54. https://doi.org/10.1016/S0140-6736(19)31674-5.09.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Drobni ZD, Alvi RM, Taron J, Zafar A, Murphy SP, Rambarat PK, Mosarla RC, Lee C, Zlotoff DA, Raghu VK, Hartmann SE, Gilman HK, Gong J, Zubiri L, Sullivan RJ, Reynolds KL, Mayrhofer T, Zhang L, Hoffmann U, Neilan TG. Association between immune checkpoint inhibitors with cardiovascular events and atherosclerotic plaque. Circulation. 2020;142(24):2299–2311. https://doi.org/10.1161/CIRCULATIONAHA.120.049981.

  21. Inno A, Chiampan A, Lanzoni L, Verzè M, Molon G, Gori S. Immune checkpoint inhibitors and atherosclerotic vascular events in cancer patients. Front Cardiovasc Med. 2021;28(8): 652186. https://doi.org/10.3389/fcvm.2021.652186.

    Article  Google Scholar 

  22. Suero-Abreu G, Zanni M, Neilan T, et al. Atherosclerosis with immune checkpoint inhibitor therapy. J Am Coll Cardiol CardioOnc. 2022; 4(5):598–615. https://doi.org/10.1016/j.jaccao.2022.11.011

  23. O’Leary DH, Szklo M, Wong ND, et al. Coronary calcium as a predictor of coronary events in four racial or ethnic groups. N Engl J Med. 2008;358(13):1336–45.

    Article  PubMed  Google Scholar 

  24. Whitlock MC, Yeboah J, Burke GL, Chen H, Klepin HD, Hundley WG. Cancer and its association with the development of coronary artery calcification: an assessment from the multi-ethnic study of atherosclerosis. J Am Heart Assoc. 2015;4(11): e002533. https://doi.org/10.1161/JAHA.115.002533.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Leiva O, AbdelHameid D, Connors J, et al. Common pathophysiology in cancer, atrial fibrillation, atherosclerosis, and thrombosis. J Am Coll Cardiol CardioOnc. 2021;3(5):619–34.

    Google Scholar 

  26. Il'yasova D, Colbert LH, Harris TB, Newman AB, Bauer DC, Satterfield S, Kritchevsky SB. Circulating levels of inflammatory markers and cancer risk in the health aging and body composition cohort. Cancer Epidemiol Biomarkers Prev. 2005; 14 (10): 2413–2418.

  27. Ridker PM, Hennekens CH, Buring JE, Rifai N. C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N Engl J Med. 2000;342:836–43.

    Article  PubMed  CAS  Google Scholar 

  28. Yao C, Veleva T, Scott L Jr, et al. Enhanced cardiomyocyte NLRP3 inflammasome signaling promotes atrial fibrillation. Circulation. 2018;138:2227–42.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Liberale L, Holy EW, Akhmedov A, et al. Interleukin-1b mediates arterial thrombus formation via NET-associated tissue factor. J Clin Med. 2019;8:2072.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Freisling H, Viallon V, Lennon H, et al. Lifestyle factors and risk of multimorbidity of cancer and cardiometabolic diseases: a multinational cohort study. BMC Med. 2020;18:5.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Leiva O, Newcomb R, Connors JM, Al-Samkari H. Cancer and thrombosis: new insights to an old problem. J Med Vasc. 2020;45:6S8–6S16.

  32. Lee MS, Finch W, Mahmud E. Cardiovascular complications of radiotherapy. Am J Cardiol. 2013;112:1688–96.

    Article  PubMed  Google Scholar 

  33. Armenian SH, Xu L, Ky B, et al. Cardiovascular disease among survivors of adult onset cancer: a community-based retrospective cohort study. J Clin Oncol. 2016;34(10):1122–30.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Lyon AR, López-Fernández T, Couch LS, et al. 2022 ESC Guidelines on cardio-oncology developed in collaboration with the European Hematology Association (EHA), the European Society for Therapeutic Radiology and Oncology (ESTRO) and the International Cardio-Oncology Society (IC-OS): Developed by the task force on cardio-oncology of the European Society of Cardiology (ESC). Eur Heart J. 2022;43(41):4229–361.

    Article  PubMed  Google Scholar 

  35. Hufnagle JJ, Andersen SN, Maani EV. Radiation Therapy Induced Cardiac Toxicity. [Updated 2022 Oct 2]. In: StatPearls [Internet]. Treasure Island: StatPearls Publishing; 2022 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK554453/

  36. Li Y, Ong WS, Shwe MTT, et al. Cardiovascular toxicities of androgen deprivation therapy in Asian men with localized prostate cancer after curative radiotherapy: a registry-based observational study. Cardio-Oncology. 2022;8:4. https://doi.org/10.1186/s40959-022-00131-4.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Heidenreich PA, Bozkurt B, Aguilar D, et al. 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines [published correction appears in Circulation. 2022 May 3;145(18):e1033] [published correction appears in Circulation. 2022;146(13):e185]. Circulation. 2022;145(18):e895-e1032. https://doi.org/10.1161/CIR.0000000000001063.

  38. Smith AW, Reeve BB, Bellizzi KM, Harlan LC, Klabunde CN, Amsellem M, Bierman AS, Hays RD. Cancer, comorbidities, and health-related quality of life of older adults. Health Care Financ Rev. 2008;29(4):41–56.

  39. Bellizzi KM, Rowland JH. The Role of comorbidity, symptoms and age in the health of older survivors following treatment for cancer. Aging Health. 2007;3(5):625–35.

    Article  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

There was no funding for the present study.

Author information

Authors and Affiliations

  1. Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA

    Sinal Patel, Francisco X. Franco, Malcolm McDonald, Carlos Rivera, Bernardo Perez-Villa, Patrick Collier, Rohit Moudgil, Neha Gupta & Diego B. Sadler

  2. Cardio Oncology, Robert and Suzanne Tomsich Department of Cardiovascular Medicine. Heart, Vascular and Thoracic Institute, Cleveland Clinic Florida, 2950 Cleveland Clinic Blvd, Weston, FL, 33331, USA

    Sinal Patel, Francisco X. Franco, Malcolm McDonald, Carlos Rivera, Bernardo Perez-Villa, Patrick Collier, Rohit Moudgil, Neha Gupta & Diego B. Sadler

Authors
  1. Sinal Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francisco X. Franco
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Malcolm McDonald
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carlos Rivera
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bernardo Perez-Villa
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Patrick Collier
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Rohit Moudgil
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Neha Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Diego B. Sadler
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conception and design: Sadler, Perez-Villa, Collier; Data collection: Patel, Sadler; Analysis and interpretation of data: Sadler, Patel, Rivera; Drafting of the manuscript: Franco, McDonald, Sadler; Manuscript revision: Sadler, Franco, Moudgil, Gupta; Approval of the manuscript submitted: Sadler et al.

Corresponding author

Correspondence to Diego B. Sadler.

Ethics declarations

Ethics approval and consent to participate

Approval from our Institutional Review Board and ethics committee was granted. The study abides by all the rules and regulations set aside by Helsinki declaration and conforms to all regional and national boards.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Patel, S., Franco, F.X., McDonald, M. et al. Use of computed tomography coronary calcium score for prediction of cardiovascular events in cancer patients: a retrospective cohort analysis. Cardio-Oncology 10, 1 (2024). https://doi.org/10.1186/s40959-023-00196-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40959-023-00196-9

Cardio-Oncology

ISSN: 2057-3804

Contact us