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Coronary artery disease
Original research
Impact of cancer on short-term in-hospital mortality after primary acute myocardial infarction
  1. Robert Zheng1,
  2. Kenya Kusunose1,
  3. Yuichiro Okushi1,
  4. Yoshihiro Okayama1,
  5. Michikazu Nakai2,
  6. Yoko Sumita2,
  7. Takayuki Ise1,
  8. Koji Yamaguchi1,
  9. Shusuke Yagi1,
  10. Daiju Fukuda1,
  11. Hirotsugu Yamada3,
  12. Takeshi Soeki1,
  13. Tetsuzo Wakatsuki1 and
  14. Masataka Sata1
  1. 1Department of Cardiovascular Medicine, Tokushima University Hospital, Tokushima, Japan
  2. 2Center for Cerebral and Cardiovascular Disease Information, National Cerebral and Cardiovascular Center, Osaka, Japan
  3. 3Department of Community Medicine for Cardiology, Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan
  1. Correspondence to Dr Kenya Kusunose; kusunosek{at}tokushima-u.ac.jp

Abstract

Background Cardiovascular diseases are the second most common cause of mortality among cancer survivors, after death from cancer. We sought to assess the impact of cancer on the short-term outcomes of acute myocardial infarction (AMI), by analysing data obtained from a large-scale database.

Methods This study was based on the Diagnosis Procedure Combination database in the Japanese Registry of All Cardiac and Vascular Diseases and the Diagnosis Procedure Combination. We identified patients who were hospitalised for primary AMI between April 2012 and March 2017. Propensity Score (PS) was estimated with logistic regression model, with cancer as the dependent variable and 21 clinically relevant covariates. The main outcome was in-hospital mortality.

Results We split 1 52 208 patients into two groups with or without cancer. Patients with cancer tended to be older (cancer group 73±11 years vs non-cancer group 68±13 years) and had smaller body mass index (cancer group 22.8±3.6 vs non-cancer 23.9±4.3). More patients in the non-cancer group had hypertension or dyslipidaemia than their cancer group counterparts. The non-cancer group also had a higher rate of percutaneous coronary intervention (cancer 92.6% vs non-cancer 95.2%). Patients with cancer had a higher 30-day mortality (cancer 6.0% vs non-cancer 5.3%) and total mortality (cancer 8.1% vs non-cancer 6.1%) rate, but this was statistically insignificant after PS matching.

Conclusion Cancer did not significantly impact short-term in-hospital mortality rates after hospitalisation for primary AMI.

  • myocardial infarction
  • risk factors
  • acute coronary syndrome

Data availability statement

Data are available upon reasonable request. Individual anonymsed data supporting the analyses contained in the manuscript will be made available upon reasonable written request from researchers whose proposed use of the data for a specific purpose has been approved.

https://creativecommons.org/licenses/by/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

https://doi.org/10.1136/openhrt-2021-001860

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    Key questions

    What is already known about this subject?

    • Some reports have identified cardiovascular diseases as being the second most common cause of mortality among cancer survivors, after death from cancer.

    What does this study add?

    • Cancer did not significantly impact short-term in-hospital mortality rates after hospitalisation for primary acute myocardial infarction (AMI).

    How might this impact on clinical practice?

    • It is of great importance to know the risk factors of deaths in AMI and cancers.

    Introduction

    With advances in detection and treatment methods, survival rates and overall survival have greatly improved in patients with cancer. As a result, cardiovascular diseases (CVDs) have become more prevalent in patients with cancer and cancer survivors.1 2 Some reports have identified CVDs as being the second most common cause of mortality among cancer survivors, after death from cancer. Many factors are thought to be attributed to this trend, such as the presence of common risk factors, inadequate management of these factors, attenuation of atherosclerosis arising from chemotherapy, radiation or the malignancy itself.3–18

    Among the multiple CVDs observed in patients with cancer, ischaemic heart disease is relatively easy to prevent with proper intervention. However, with the large variety of malignancies and an even larger variety of treatments, patients with cancer are an extremely heterogenous group, with each individual cancer type potentially having differing risks and outcomes. In this study, we sought to assess the impact of cancer on the short-term outcomes of acute myocardial infarction (AMI), by analysing data obtained from a nationwide insurance claims database based on electronic health records in Japan.

    Methods

    Study population

    Data from the Japanese Registry of All Cardiac and Vascular Diseases and the Diagnosis Procedure Combination (JROAD-DPC) database was used for this study. JROAD-DPC is a nationwide registry, with information of admission and discharge for CVDs, clinical examinations and treatment status, patient status and hospital overview. JROAD-DPC database integrates the information composed by JROAD-DPC data, with analysis data sets covering 5.1 million hospitalisations from 1022 facilities, between April 2012 and March 2017.19 The identification of AMI and cancer type was based on the International Classification of Diseases (ICD)-10 diagnosis codes related to AMI (I210, I211, I212, I213, I214, I219) and cancers of oesophagus (C15), stomach (C16), colon (C18-20), liver (C22), biliary tract (C23, 24), pancreas (C25), lung (C34), breast (C50), cervix (C53), uterine (C54), ovary (C56), prostate (C61), kidney and urinary tract (C64–66, 68), bladder (C67) and leukaemia (C91–95) based on our previous paper.20 Hospitalisation for AMI would be identified when AMI was registered as the main diagnosis, admission-precipitating diagnosis or being the most resource-consuming diagnosis. The most resource-consuming diagnosis is defined as the diagnosis for which the most medical resources are used, such as examinations, medications and treatments. Data regarding patient age and sex, main diagnosis, documented comorbidity at admission, length of hospitalisation and treatment content were extracted from the database.

    Out of a total of 5 106 151 hospitalisations extracted from the database, 2 10 940 patients were hospitalised for AMI. After exclusion of readmissions, 2 04 930 patients remained for calculation of cancer type proportion. A further 52 722 patients were excluded due to young age (<20 years) or incomplete data. The remaining 1 52 208 patients were split into a group with cancer (6995 patients) and those without cancer (1 45 213 patients) and were analysed with propensity matching (figure 1).

    Figure 1
    Figure 1

    Flowchart of this study. AMI, acute myocardial infarction; JROAD-DPC, Japanese Registry of All Cardiac and Vascular Diseases and the Diagnosis Procedure Combination.

    Clinical outcomes

    The main outcome was in-hospital mortality (total number of deaths during hospitalisation and death at 14 and 30 days after admission). Patients were censored on discharge and were not followed beyond that point.

    Sample matching

    Propensity Score (PS) matching was used to reduce confounding effects related to differences in patient background. PS was estimated with a logistic regression model, with the following 21 clinically relevant covariates: age, sex, body mass index (BMI), smoking, Killip class, comorbidities (hypertension (HT), diabetes mellitus (DM), dyslipidaemia (DL), hyperuricaemia, stroke, peripheral vascular disease, renal disease, liver failure, chronic obstructive pulmonary disease, dementia) and treatment (percutaneous coronary intervention (PCI), coronary artery bypass graft, catecholamine use, intra-aortic balloon pumping, percutaneous cardiopulmonary support, chemotherapy). These covariates were chosen for their potential association as risk factors of AMI and in-hospital mortality in general. Matching was performed with greedy-matching algorithm (ratio=1:1 without replacement), with a calliper of width 0.2 SD of the logistic of the estimated PS. Absolute value of standardised differences less than 10% was considered to be statistically insignificant.

    Statistical analysis

    The Shapiro-Wilk test was used to assess the normal distribution of continuous data. Continuous variables were expressed as mean±SD for parameters with normal distribution, as median (IQR) for parameters with skewed distribution and categorical variables as proportion (%). After PS matching, 6995 patients each in the cancer and non-cancer groups were included in the final analysis. Concordance Index was 0.667 and the consistency of PS densities was matched after PS matching. The balance of each covariate before and after the matching between the two groups was evaluated by standardised differences. Absolute value of standardised differences less than 10% was considered as a relatively small imbalance. We estimated the OR with cancer for in-hospital mortality (total, within 14 days and 30 days) major adverse cardiovascular events and major bleeding by matched logistic regression analysis adjusted for hospitalisation days. We also analysed subgroups by type of cancer in the PS-matched cohort. The OR for each type of cancer was calculated using matched non-cancer patients as controls. All statistical tests were two sided and p values less than 0.05 were considered statistically significant. Statistical analysis was performed using SAS V.9.4 and JMP V.14.0 (SAS Institute).

    Results

    Patient characteristics

    The clinical characteristics of the cancer group and non-cancer group are shown in table 1. Patients with cancer tended to be older (cancer group 73±11 years vs non-cancer group 68±13 years) and had smaller BMI (cancer group 22.8±3.6 vs non-cancer 23.9±4.3). More patients in the non-cancer group had HT or DL than their cancer group counterparts. The non-cancer group also had a higher rate of PCI (cancer 92.6% vs non-cancer 95.2%). All the other clinical characteristics, including Killip class, showed no statistical difference between the two groups. The proportion of cancer types after the initial exclusion is shown in figure 2. Colon (17.0%), stomach (16.9%) and prostate (13.1%) cancers were the most numerous one. The cancer proportions found in our study were similar to the national trend of Japan, with slight differences in the percentage of prostate and breast cancer.

    Figure 2
    Figure 2

    Proportion of cancer type of first hospitalised patients with acute myocardial infarction and comparison with national statistics (each cancer proportion of national statistics (%) and this study (%)).

    View this table:
    Table 1

    Baseline characteristics before and after Propensity Score matching

    Outcomes

    Patients with cancer had a higher 30-day mortality (cancer 6.0% vs non-cancer 5.3%) and total mortality (cancer 8.1% vs non-cancer 6.1%) rate (table 2), but this was statistically insignificant after PS matching. Bleeding events were significantly more frequent in the cancer group, with more patients requiring blood transfusion than the non-cancer group. The cancer group also had a higher rate of recurrent myocardial infarction (MI) (cancer 2.3% vs non-cancer 1.0%, p<0.001), while incidence rates of cerebral haemorrhages and infarctions showed no difference between the two groups. Further analysis of patients with recurrent MI (table 3) showed that patients with a cancer history were less likely to be selected for PCI (cancer 85.7% vs non-cancer 90.8%, p=0.041). Patients with cancer were less likely to underwent stenting (cancer 68.9% vs non-cancer 82.5%, p<0.001) or dual antiplatelet therapy (cancer 78.9% vs non-cancer 86.7%, p=0.007).

    View this table:
    Table 2

    Odds ratio of in-hospital mortality and MACE/bleeding incidence in patients before and after Propensity Score matching

    View this table:
    Table 3

    Characteristics of patients with recurrent myocardial infarction after initial admission

    Cancer types

    The cancer types were generally spread out in multiple groups, though small in number. Regarding mortality rates in each cancer subtype, patients with pancreatic cancer (OR: 2.95, 95% CI: 1.26 to 6.95), liver cancer (OR: 1.94, 95% CI: 1.02 to 3.68) and lung cancer (OR: 1.48, 95% CI: 1.08 to 2.04) had higher mortality rates compared with non-cancer cohort (figure 3).

    Figure 3
    Figure 3

    OR of in-hospital mortality in patients with each cancer compared with matched patients without cancer. Dots and lines mean OR and 95% CI, respectively.

    Discussion

    This analysis of data obtained from the J-ROAD DPC database revealed the following facts: (1) AMI patients with cancer were older and had lower BMI compared with their non-cancer counterparts, while having a lower prevalence of HT and DL, (2) patients with cancer had higher in-hospital mortality rates, but this difference was insignificant after PS matching and (3) patients with cancer had a higher rate of recurrent MI after initial hospitalisation.

    Cancer on mortality

    Patients with cancer tended to be older (cancer group 73±11 years vs non-cancer group 68±13 years) and had smaller BMI (cancer group 22.8±3.6 vs non-cancer 23.9±4.3). Old age and lower BMI itself could be is generally related with higher overall mortality.21 It is worth noting that although there were many factors, older age, lower BMI and lower PCI rates, that could potentially affect overall mortality, cancer itself did not influence short-term in-hospital mortality after PS matching. PCI rates in the cancer group were not as low as past reports.22–24 Along with HT and DL, other conventional coronary risk factors were also less prevalent in patients with cancer, though not to an extent of statistical significance. There are several possibilities behind this trend. As mentioned in the previous sections, J-ROAD DPC is a database centred on cardiovascular centres, with these centres not necessarily having the capacity for cancer treatment. One possible reason for the increased prevalence of CVDs in cancer survivors is the fact that the management of occult atherosclerotic risk factors, such as HT, DL and DM, is not as sufficient as their non-cancer counterparts.25–27 In the case of this study, there is a possibility that some patients in the cancer group had previously undocumented comorbidities, not necessarily receiving proper treatment for such risk factors prior to hospitalisation. Another possible explanation for this trend is the existence of other atherosclerotic factors, such as prolonged inflammation from cancer, radiation therapy and chemotherapy. There is also a possibility that these factors contribute to accelerated atherosclerosis, even with the absence of contemporary coronary risk factors.

    In this study, cancer did not show a significant increase in in-hospital mortality after PS matching. Being based on data obtained from a CVD -centred database compiled from data obtained from cardiovascular centres, cancer-oriented data, such as chemotherapy regimen, cancer staging and metastasis, are not included in the database. Data from this study at least indicated that the short-term outcome did not differ significantly with cancer.

    Cancer and recurrent MI

    Analysis of major adverse cadiovascular events (MACE) after initial hospitalisation suggested that the cancer group had a higher incidence of recurrent MI. Secondary analysis of patients with recurrent MI showed a smaller rate of PCIs and antiplatelet usage in the cancer group. One possible explanation for this trend is higher bleeding risk in patients with cancer. Anaemia is more profound in patients with cancer, partly due to bleeding and myelosuppressive effects of chemoradiation therapies, further compounding the use of antiplatelet therapy. The hypercoagulative state in active cancers may also be another explanation.28 29

    Limitations

    First of all, given the insurance claim-like nature of the database, detailed data, such as culprit lesion numbers/regions, stent types and dimensions and specific medication usage and doses, are not included in the original database, this would make it difficult to determine whether there were major differences in the management of AMI in both cancer/non-cancer groups. Particularly lacking information is detailed data on the use (and cessation) of antiplatelets/anticoagulants. The use of such medication may play a role in secondary bleeding/thrombotic events. Using a heterogenous group for analysis of the effects of cancer may diminish profound differences between cancer types, as some cancers may have higher bleeding risk than others. Second, J-ROAD DPC is a database based on data centred on CVDs, data specific to cancer, such as cancer stage (active or non-active), radiation treatment and chemotherapy regimens, were not included. We were unable to assess the differences among the types of AMI (type 1 or type 2, ST elevation or not). This database is large in scale but does not necessarily encompass the entire Japanese nation. Since this is a CVD database, medical centres solely purposed for cancer treatment and patients with cancer without documented CVDs may not be included, limiting the number and stages of patients with cancer. In this aspect, the result of this study may potentially underestimate the scope of which cancer affects the course of AMI. In our analysis, the number of patients for each cancer type is extremely small, with an even smaller number of mortalities. We attempted to analyse the mortality rates in each cancer type, but the statistical power of such an analysis weakens greatly with such minute numbers. Data are limited to in-hospital mortality rates, with each case censured on discharge. Therefore, status related to follow-up is not included in our study. Also, the accuracy of diagnosis is not complete in the JROAD-DPC database because of the low validity of these studies compared with planned prospective studies. However, the original JROAD-DPC dataset was validated.30 31 The registration of an ICD code for a cancer disease indicates that the cancer was associated with the treatment, which would at least suggest that it was a problematic condition of a history of or active cancer in the patient. However, as this database is based on data from cardiovascular centres, specific data on the state of each malignancy (active or past) were unavailable.

    Conclusions

    Patients with cancer had a smaller proportion of HT and DL at admission for primary AMI. Cancer did not significantly impact short-term in-hospital mortality rates after hospitalisation for primary AMI.

    Data availability statement

    Data are available upon reasonable request. Individual anonymsed data supporting the analyses contained in the manuscript will be made available upon reasonable written request from researchers whose proposed use of the data for a specific purpose has been approved.

    Ethics statements

    Patient consent for publication

    Ethics approval

    The Institutional Review Board of the Tokushima University Hospital approved the study protocol (3059-2).

    References

    1. ↵
      1. Armenian SH,
      2. Xu L,
      3. Ky B, et al
      . Cardiovascular disease among survivors of adult-onset cancer: a community-based retrospective cohort study. J Clin Oncol 2016;34:1122–30.doi:10.1200/JCO.2015.64.0409pmid:http://www.ncbi.nlm.nih.gov/pubmed/26834065
      OpenUrlAbstract/FREE Full Text
    2. ↵
      1. Yandrapalli S,
      2. Malik AH,
      3. Pemmasani G, et al
      . Risk factors and outcomes during a first acute myocardial infarction in breast cancer survivors compared with females without breast cancer. Am J Med 2020;133:444–51.doi:10.1016/j.amjmed.2019.10.018pmid:http://www.ncbi.nlm.nih.gov/pubmed/31715170
      OpenUrlPubMed
    3. ↵
      1. Kero AE,
      2. Järvelä LS,
      3. Arola M, et al
      . Cardiovascular morbidity in long-term survivors of early-onset cancer: a population-based study. Int J Cancer 2014;134:664–73.doi:10.1002/ijc.28385pmid:http://www.ncbi.nlm.nih.gov/pubmed/23852751
      OpenUrlCrossRefPubMedWeb of Science
    4. ↵
      1. Hooning MJ,
      2. Botma A,
      3. Aleman BMP, et al
      . Long-term risk of cardiovascular disease in 10-year survivors of breast cancer. J Natl Cancer Inst 2007;99:365–75.doi:10.1093/jnci/djk064pmid:http://www.ncbi.nlm.nih.gov/pubmed/17341728
      OpenUrlCrossRefPubMedWeb of Science
    5. ↵
      1. Inohara T,
      2. Endo A,
      3. Melloni C
      . Unmet needs in managing myocardial infarction in patients with malignancy. Front Cardiovasc Med 2019;6:57. doi:10.3389/fcvm.2019.00057pmid:http://www.ncbi.nlm.nih.gov/pubmed/31157239
      OpenUrlPubMed
    6. ↵
      1. Velders MA,
      2. Hagström E,
      3. James SK
      . Temporal trends in the prevalence of cancer and its impact on outcome in patients with first myocardial infarction: a nationwide study. J Am Heart Assoc 2020;9:e014383. doi:10.1161/JAHA.119.014383pmid:http://www.ncbi.nlm.nih.gov/pubmed/32067596
      OpenUrlPubMed
    7. ↵
      1. Gong IY,
      2. Yan AT,
      3. Ko DT, et al
      . Temporal changes in treatments and outcomes after acute myocardial infarction among cancer survivors and patients without cancer, 1995 to 2013. Cancer 2018;124:1269–78.doi:10.1002/cncr.31174pmid:http://www.ncbi.nlm.nih.gov/pubmed/29211307
      OpenUrlPubMed
    8. ↵
      1. Boekel NB,
      2. Boekel LY,
      3. Buddeke J, et al
      . Prognosis of acute coronary syndromes after radiotherapy for breast cancer. Radiother Oncol 2020;146:110–7.doi:10.1016/j.radonc.2020.02.007pmid:http://www.ncbi.nlm.nih.gov/pubmed/32146256
      OpenUrlPubMed
    9. ↵
      1. Han X-J,
      2. Li J-Q,
      3. Khannanova Z, et al
      . Optimal management of coronary artery disease in cancer patients. Chronic Dis Transl Med 2019;5:221–33.doi:10.1016/j.cdtm.2019.12.007pmid:http://www.ncbi.nlm.nih.gov/pubmed/32055781
      OpenUrlPubMed
    10. ↵
      1. Rugbjerg K,
      2. Mellemkjaer L,
      3. Boice JD, et al
      . Cardiovascular disease in survivors of adolescent and young adult cancer: a Danish cohort study, 1943-2009. J Natl Cancer Inst 2014;106:dju110. doi:10.1093/jnci/dju110pmid:http://www.ncbi.nlm.nih.gov/pubmed/24848622
      OpenUrlCrossRefPubMed
    11. ↵
      1. Swerdlow AJ,
      2. Higgins CD,
      3. Smith P, et al
      . Myocardial infarction mortality risk after treatment for Hodgkin disease: a collaborative British cohort study. J Natl Cancer Inst 2007;99:206–14.doi:10.1093/jnci/djk029pmid:http://www.ncbi.nlm.nih.gov/pubmed/17284715
      OpenUrlCrossRefPubMedWeb of Science
    12. ↵
      1. Dieckmann K-P,
      2. Gerl A,
      3. Witt J, et al
      . Myocardial infarction and other major vascular events during chemotherapy for testicular cancer. Ann Oncol 2010;21:1607–11.doi:10.1093/annonc/mdp597pmid:http://www.ncbi.nlm.nih.gov/pubmed/20067918
      OpenUrlCrossRefPubMed
    13. ↵
      1. Sueta D,
      2. Tabata N,
      3. Ikeda S, et al
      . Differential predictive factors for cardiovascular events in patients with or without cancer history. Medicine 2019;98:e17602. doi:10.1097/MD.0000000000017602pmid:http://www.ncbi.nlm.nih.gov/pubmed/31689764
      OpenUrlPubMed
    14. ↵
      1. Driver JA,
      2. Djoussé L,
      3. Logroscino G, et al
      . Incidence of cardiovascular disease and cancer in advanced age: prospective cohort study. BMJ 2008;337:a2467. doi:10.1136/bmj.a2467pmid:http://www.ncbi.nlm.nih.gov/pubmed/19066258
      OpenUrlAbstract/FREE Full Text
    15. ↵
      1. Moore RA,
      2. Adel N,
      3. Riedel E, et al
      . High incidence of thromboembolic events in patients treated with cisplatin-based chemotherapy: a large retrospective analysis. J Clin Oncol 2011;29:3466–73.doi:10.1200/JCO.2011.35.5669pmid:http://www.ncbi.nlm.nih.gov/pubmed/21810688
      OpenUrlAbstract/FREE Full Text
    16. ↵
      1. Kero AE,
      2. Madanat-Harjuoja LM,
      3. Järvelä LS, et al
      . Health conditions associated with metabolic syndrome after cancer at a young age: a nationwide register-based study. Cancer Epidemiol 2016;41:42–9.doi:10.1016/j.canep.2016.01.009pmid:http://www.ncbi.nlm.nih.gov/pubmed/26816350
      OpenUrlPubMed
    17. ↵
      1. Sardar P,
      2. Kundu A,
      3. Chatterjee S, et al
      . Long-term cardiovascular mortality after radiotherapy for breast cancer: a systematic review and meta-analysis. Clin Cardiol 2017;40:73–81.doi:10.1002/clc.22631pmid:http://www.ncbi.nlm.nih.gov/pubmed/28244595
      OpenUrlPubMed
    18. ↵
      1. Armstrong GT,
      2. Liu Q,
      3. Yasui Y, et al
      . Late mortality among 5-year survivors of childhood cancer: a summary from the childhood cancer Survivor study. J Clin Oncol 2009;27:2328–38.doi:10.1200/JCO.2008.21.1425pmid:http://www.ncbi.nlm.nih.gov/pubmed/19332714
      OpenUrlAbstract/FREE Full Text
    19. ↵
      1. Yasuda S,
      2. Nakao K,
      3. Nishimura K, et al
      . The Current Status of Cardiovascular Medicine in Japan　- Analysis of a Large Number of Health Records From a Nationwide Claim-Based Database, JROAD-DPC. Circ J 2016;80:2327–35.doi:10.1253/circj.CJ-16-0196pmid:http://www.ncbi.nlm.nih.gov/pubmed/27725417
      OpenUrlPubMed
    20. ↵
      1. Okushi Y,
      2. Kusunose K,
      3. Okayama Y, et al
      . Acute hospital mortality of venous thromboembolism in patients with cancer from registry data. J Am Heart Assoc 2021;10:e019373. doi:10.1161/JAHA.120.019373pmid:http://www.ncbi.nlm.nih.gov/pubmed/34027673
      OpenUrlPubMed
    21. ↵
      1. Bhaskaran K,
      2. Dos-Santos-Silva I,
      3. Leon DA, et al
      . Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3·6 million adults in the UK. Lancet Diabetes Endocrinol 2018;6:944–53.doi:10.1016/S2213-8587(18)30288-2pmid:http://www.ncbi.nlm.nih.gov/pubmed/30389323
      OpenUrlPubMed
    22. ↵
      1. Mohamed MO,
      2. Lopez-Mattei JC,
      3. Parwani P, et al
      . Management strategies and clinical outcomes of acute myocardial infarction in leukaemia patients: Nationwide insights from United States hospitalisations. Int J Clin Pract 2020;74:e13476. doi:10.1111/ijcp.13476pmid:http://www.ncbi.nlm.nih.gov/pubmed/31922635
      OpenUrlPubMed
    23. ↵
      1. Bharadwaj A,
      2. Potts J,
      3. Mohamed MO, et al
      . Acute myocardial infarction treatments and outcomes in 6.5 million patients with a current or historical diagnosis of cancer in the USA. Eur Heart J 2020;41:2183–93.doi:10.1093/eurheartj/ehz851pmid:http://www.ncbi.nlm.nih.gov/pubmed/31800032
      OpenUrlPubMed
    24. ↵
      1. Pothineni NV,
      2. Shah NN,
      3. Rochlani Y, et al
      . Temporal trends and outcomes of acute myocardial infarction in patients with cancer. Ann Transl Med 2017;5:482.doi:10.21037/atm.2017.11.29pmid:http://www.ncbi.nlm.nih.gov/pubmed/29299444
      OpenUrlPubMed
    25. ↵
      1. Wong KF,
      2. Reulen RC,
      3. Winter DL, et al
      . Risk of adverse health and social outcomes up to 50 years after Wilms tumor: the British childhood cancer Survivor study. J Clin Oncol 2016;34:1772–9.doi:10.1200/JCO.2015.64.4344pmid:http://www.ncbi.nlm.nih.gov/pubmed/27022116
      OpenUrlAbstract/FREE Full Text
    26. ↵
      1. Bhakta N,
      2. Liu Q,
      3. Yeo F, et al
      . Cumulative burden of cardiovascular morbidity in paediatric, adolescent, and young adult survivors of Hodgkin's lymphoma: an analysis from the ST Jude lifetime cohort study. Lancet Oncol 2016;17:1325–34.doi:10.1016/S1470-2045(16)30215-7pmid:http://www.ncbi.nlm.nih.gov/pubmed/27470081
      OpenUrlPubMed
    27. ↵
      1. Nathan PC,
      2. Amir E,
      3. Abdel-Qadir H
      . Cardiac outcomes in survivors of pediatric and adult cancers. Can J Cardiol 2016;32:871–80.doi:10.1016/j.cjca.2016.02.065pmid:http://www.ncbi.nlm.nih.gov/pubmed/27179545
      OpenUrlPubMed
    28. ↵
      1. Franco AT,
      2. Corken A,
      3. Ware J
      . Platelets at the interface of thrombosis, inflammation, and cancer. Blood 2015;126:582–8.doi:10.1182/blood-2014-08-531582pmid:http://www.ncbi.nlm.nih.gov/pubmed/26109205
      OpenUrlAbstract/FREE Full Text
    29. ↵
      1. Gross CM,
      2. Posch MG,
      3. Geier C, et al
      . Subacute coronary stent thrombosis in cancer patients. J Am Coll Cardiol 2008;51:1232–3.doi:10.1016/j.jacc.2007.11.061pmid:http://www.ncbi.nlm.nih.gov/pubmed/18355663
      OpenUrlFREE Full Text
    30. ↵
      1. Yamana H,
      2. Moriwaki M,
      3. Horiguchi H, et al
      . Validity of diagnoses, procedures, and laboratory data in Japanese administrative data. J Epidemiol 2017;27:476–82.doi:10.1016/j.je.2016.09.009pmid:http://www.ncbi.nlm.nih.gov/pubmed/28142051
      OpenUrlCrossRefPubMed
    31. ↵
      1. Nakai M,
      2. Iwanaga Y,
      3. Sumita Y, et al
      . Validation of acute myocardial infarction and heart failure diagnoses in hospitalized patients with the nationwide Claim-Based JROAD-DPC database. Circ Rep 2021;3:131–6.doi:10.1253/circrep.CR-21-0004pmid:http://www.ncbi.nlm.nih.gov/pubmed/33738345
      OpenUrlPubMed

    Footnotes

    • Contributors RZ and KK conceived the idea for this study and produced the initial draft of the manuscript. YOkushi and YOkayama conducted the data analyses. All authors were involved in interpreting the results and writing of the manuscript and read and approved the final manuscript. KK is responsible for the overall content as guarantor.

    • Funding This work was partially supported by JSPS Kakenhi Grants (20K17084 to YOkushi and 19H03654 to MS) and the Takeda Science Foundation (to KK).

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.