Racial Disparities in Cardiovascular and Cerebrovascular Adverse Events in Patients with Non-Hodgkin Lymphoma: A Nationwide Analysis
Abstract
:1. Introduction
2. Methods
2.1. Study Design and Data Source
2.2. Study Population and Variables
2.3. Outcomes
2.4. Statistical Analyses
2.5. Ethical Considerations
3. Results
4. Discussion
Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- de Leval, L.; Jaffe, E.S. Lymphoma Classification. Cancer J. 2020, 26, 176–185. [Google Scholar] [CrossRef] [PubMed]
- NHL Subtypes. Available online: https://www.lls.org/lymphoma/non-hodgkin-lymphoma/nhl-subtypes (accessed on 12 April 2024).
- Thandra, K.C.; Barsouk, A.; Saginala, K.; Padala, S.A.; Barsouk, A.; Rawla, P. Epidemiology of Non-Hodgkin’s Lymphoma. Med. Sci. 2021, 9, 5. [Google Scholar] [CrossRef] [PubMed]
- Lymphoma-Non-Hodgkin: Statistics. Available online: https://www.cancer.net/cancer-types/lymphoma-non-hodgkin/statistics#:~:text=The%205%2Dyear%20survival%20rate,well%20the%20treatment%20plan%20works (accessed on 12 April 2024).
- Cancer Stat Facts: Non-Hodgkin Lymphoma. Available online: https://seer.cancer.gov/statfacts/html/nhl.html (accessed on 12 April 2024).
- Jia, G.; Aroor, A.R.; Jia, C.; Sowers, J.R. Endothelial cell senescence in aging-related vascular dysfunction. Biochim. Biophys. Acta Mol. Basis Dis. 2019, 1865, 1802–1809. [Google Scholar] [CrossRef]
- Ailawadhi, S.; Aldoss, I.T.; Yang, D.; Razavi, P.; Cozen, W.; Sher, T.; Chanan-Khan, A. Outcome disparities in multiple myeloma: A SEER-based comparative analysis of ethnic subgroups. Br. J. Haematol. 2012, 158, 91–98. [Google Scholar] [CrossRef]
- Nabhan, C.; Aschebrook-Kilfoy, B.; Chiu, B.C.H.; Kruczek, K.; Smith, S.M.; Evens, A.M. The impact of race, age, and sex in follicular lymphoma: A comprehensive SEER analysis across consecutive treatment eras. Am. J. Hematol. 2014, 89, 633–638. [Google Scholar] [CrossRef]
- Zhang, L.; Rothman, N.; Li, G.; Guo, W.; Yang, W.; Hubbard, A.E.; Hayes, R.B.; Yin, S.; Lu, W.; Smith, M.T. Aberrations in chromosomes associated with lymphoma and therapy-related leukemia in benzene-exposed workers. Environ. Mol. Mutagen. 2007, 48, 467–474. [Google Scholar] [CrossRef]
- Ilieva, M.; Panella, R.; Uchida, S. MicroRNAs in Cancer and Cardiovascular Disease. Cells 2022, 11, 3551. [Google Scholar] [CrossRef] [PubMed]
- Karlstaedt, A.; Moslehi, J.; de Boer, R.A. Cardio-onco-metabolism: Metabolic remodelling in cardiovascular disease and cancer. Nat. Rev. Cardiol. 2022, 19, 414–425. [Google Scholar] [CrossRef] [PubMed]
- Avraham, S.; Abu-Sharki, S.; Shofti, R.; Haas, T.; Korin, B.; Kalfon, R.; Friedman, T.; Shiran, A.; Saliba, W.; Shaked, Y.; et al. Early Cardiac Remodeling Promotes Tumor Growth and Metastasis. Circulation 2020, 142, 670–683. [Google Scholar] [CrossRef]
- Meijers, W.C.; Maglione, M.; Bakker, S.J.L.; Oberhuber, R.; Kieneker, L.M.; de Jong, S.; Haubner, B.J.; Nagengast, W.B.; Lyon, A.R.; van der Vegt, B.; et al. Heart Failure Stimulates Tumor Growth by Circulating Factors. Circulation 2018, 138, 678–691. [Google Scholar] [CrossRef]
- Mestas, J.; Hughes, C.C.W. Of Mice and Not Men: Differences between Mouse and Human Immunology. J. Immunol. 2004, 172, 2731–2738. [Google Scholar] [CrossRef] [PubMed]
- Bellinger, A.M.; Arteaga, C.L.; Force, T.; Humphreys, B.D.; Demetri, G.D.; Druker, B.J.; Moslehi, J.J. Cardio-Oncology: How New Targeted Cancer Therapies and Precision Medicine Can Inform Cardiovascular Discovery. Circulation 2015, 132, 2248–2258. [Google Scholar] [CrossRef] [PubMed]
- Asnani, A.; Moslehi, J.J.; Adhikari, B.B.; Baik, A.H.; Beyer, A.M.; de Boer, R.A.; Ghigo, A.; Grumbach, I.M.; Jain, S.; Zhu, H.; et al. Preclinical Models of Cancer Therapy–Associated Cardiovascular Toxicity: A Scientific Statement from the American Heart Association. Circ. Res. 2021, 129, e21–e34. [Google Scholar] [CrossRef] [PubMed]
- Sturgeon, K.M.; Deng, L.; Bluethmann, S.M.; Zhou, S.; Trifiletti, D.M.; Jiang, C.; Kelly, S.P.; Zaorsky, N.G. A population-based study of cardiovascular disease mortality risk in US cancer patients. Eur. Heart J. 2019, 40, 3889–3897. [Google Scholar] [CrossRef] [PubMed]
- Stouffer, J.A.; Hendrickson, M.J.; Arora, S.; Vavalle, J.P. Contemporary Trends in Acute Myocardial Infarction in the American Indian/Alaska Native U.S. Population, 2000 to 2018. Am. J. Cardiol. 2023, 194, 34–39. [Google Scholar] [CrossRef]
- Gonuguntla, K.; Sattar, Y.; Iqbal, K.; Sharma, A.; Yadav, R.; Alharbi, A.; Chobufo, M.D.; Naeem, M.; Shaik, A.; Balla, S. Trends in Premature Mortality from Acute Myocardial Infarction in American Indians/Alaska Natives in the United States from 1999 to 2020. Am. J. Cardiol. 2024, 213, 72–75. [Google Scholar] [CrossRef] [PubMed]
- Crozier, J.A.; Sher, T.; Yang, D.; Swaika, A.; Foran, J.; Ghosh, R.; Tun, H.; Colon-Otero, G.; Kelly, K.; Chanan-Khan, A.; et al. Persistent Disparities Among Patients With T-Cell Non-Hodgkin Lymphomas and B-Cell Diffuse Large Cell Lymphomas Over 40 Years: A SEER Database Review. Clin. Lymphoma Myeloma Leuk. 2015, 15, 578–585. [Google Scholar] [CrossRef] [PubMed]
- Hasan, S.; Dinh, K.; Lombardo, F.; Kark, J. Doxorubicin cardiotoxicity in African Americans. J. Natl. Med. Assoc. 2004, 96, 196–199. [Google Scholar] [PubMed]
- Kommalapati, A.; Tella, S.H.; Ganti, A.K.; Armitage, J.O. Natural Killer/T-cell Neoplasms: Analysis of Incidence, Patient Characteristics, and Survival Outcomes in the United States. Clin. Lymphoma Myeloma Leuk. 2018, 18, 475–479. [Google Scholar] [CrossRef]
- Guadamuz, J.S.; Ozenberger, K.; Qato, D.M.; Ko, N.Y.; Saffore, C.D.; Adimadhyam, S.; Cha, A.S.; Moran, K.M.; Sweiss, K.; Patel, P.R.; et al. Mediation analyses of socioeconomic factors determining racial differences in the treatment of diffuse large B-cell lymphoma in a cohort of older adults. Medicine 2019, 98, e17960. [Google Scholar] [CrossRef]
- Hall, A.G.; Winestone, L.E.; Sullivan, E.M.; Wu, Q.; Lamble, A.J.; Walters, M.C.; Aguayo-Hiraldo, P.; Conde, L.B.; Coker, T.R.; Dornsife, D.; et al. Access to Chimeric Antigen Receptor T Cell Clinical Trials in Underrepresented Populations: A Multicenter Cohort Study of Pediatric and Young Adult Acute Lymphobastic Leukemia Patients. Transplant. Cell Ther. 2023, 29, 356.e1–356.e7. [Google Scholar] [CrossRef]
- Emole, J.; Lawal, O.; Lupak, O.; Dias, A.; Shune, L.; Yusuf, K. Demographic differences among patients treated with chimeric antigen receptor T-cell therapy in the United States. Cancer Med. 2022, 11, 4440–4448. [Google Scholar] [CrossRef] [PubMed]
- Christidi, E.; Brunham, L.R. Regulated cell death pathways in doxorubicin-induced cardiotoxicity. Cell Death Dis. 2021, 12, 339. [Google Scholar] [CrossRef] [PubMed]
- Burns, E.A.; Gentille, C.; Trachtenberg, B.; Pingali, S.R.; Anand, K. Cardiotoxicity Associated with Anti-CD19 Chimeric Antigen Receptor T-Cell (CAR-T) Therapy: Recognition, Risk Factors, and Management. Diseases 2021, 9, 20. [Google Scholar] [CrossRef] [PubMed]
- Hanna, K.S.; Kaur, H.; Alazzeh, M.S.; Thandavaram, A.; Channar, A.; Purohit, A.; Shrestha, B.; Patel, D.; Shah, H.; Mohammed, L. Cardiotoxicity Associated with Chimeric Antigen Receptor (CAR)-T Cell Therapy for Hematologic Malignancies: A Systematic Review. Cureus 2022, 14, e28162. [Google Scholar] [CrossRef]
- Kirtane, K.; Lee, S.J. Racial and ethnic disparities in hematologic malignancies. Blood 2017, 130, 1699–1705. [Google Scholar] [CrossRef]
Characteristics (Total = 777,740) | White (577,215) | Black (71,180) | Hispanic (73,000) | Asian/Pacific Islander (25,935) | Native American (2855) | Other (27,555) | p-Value |
---|---|---|---|---|---|---|---|
Characteristics | |||||||
Male | 57.55% | 55.59% | 56.95% | 55.78% | 56.39% | 57.71% | 0.01 |
Female | 42.45% | 44.41% | 43.05% | 44.22% | 43.61% | 42.29% | 0.01 |
Age (years) | 67.75418 | 58.08492 | 60.22411 | 62.87527 | 62.96844 | 61.08111 | <0.0001 |
Non-elective admissions | 75.17% | 79.11% | 74.24% | 70.16% | 77.15% | 70.26% | <0.0001 |
Primary expected payer (uniform) | <0.0001 | ||||||
Medicare | 63.40% | 43.49% | 41.80% | 45.49% | 55.01% | 44.45% | |
Medicaid | 5.75% | 21.78% | 22.79% | 14.14% | 18.45% | 12.81% | |
Private insurances | 27.31% | 27.55% | 26.75% | 35.69% | 21.44% | 34.57% | |
Self-pay | 1.21% | 3.74% | 5.12% | 2.41% | 2.11% | 5.17% | |
No charges | 0.10% | 0.36% | 0.95% | 0.33% | 0.00% | 0.45% | |
Others | 2.24% | 3.08% | 2.58% | 1.95% | 2.99% | 2.54% | |
Median household income national quartile for patient zip code (percentiles) # | <0.0001 | ||||||
0–25th | 19.77% | 48.18% | 34.48% | 10.49% | 41.82% | 22.31% | |
26–50th | 25.59% | 21.48% | 25.03% | 15.72% | 27.14% | 19.88% | |
51–75th | 27.02% | 18.29% | 23.48% | 25.54% | 18.40% | 25.06% | |
76–100th | 27.62% | 12.05% | 17.01% | 48.25% | 12.64% | 32.76% | |
Bed size of the hospital § | <0.0001 | ||||||
Small | 16.11% | 13.84% | 12.66% | 11.20% | 18.04% | 9.69% | |
Medium | 25.07% | 24.35% | 25.17% | 22.54% | 19.09% | 18.64% | |
Large | 58.82% | 61.81% | 62.16% | 66.26% | 62.87% | 71.67% | |
Location/teaching status of the hospital ~ | <0.0001 | ||||||
Rural | 6.32% | 2.66% | 1.24% | 0.98% | 11.73% | 1.14% | |
Urban non-teaching | 16.81% | 11.46% | 15.68% | 13.50% | 12.96% | 10.36% | |
Urban teaching | 76.86% | 85.89% | 83.08% | 85.52% | 75.31% | 88.50% | |
Region of hospital | <0.0001 | ||||||
Northeast | 22.98% | 18.88% | 17.27% | 19.39% | 9.28% | 27.36% | |
Midwest | 25.95% | 19.08% | 8.08% | 9.22% | 17.16% | 7.95% | |
South | 33.12% | 52.94% | 37.73% | 15.15% | 29.60% | 48.78% | |
West | 17.96% | 9.10% | 36.92% | 56.24% | 43.96% | 15.91% | |
Disposition of patients | |||||||
Routine | 56.75% | 60.07% | 66.99% | 64.47% | 60.07% | 68.23% | |
Transfer to short-term hospitals | 2.67% | 2.81% | 2.23% | 2.83% | 3.85% | 1.98% | |
Other transfers include SNF, ICF, etc. | 16.74% | 12.82% | 9.33% | 10.22% | 15.59% | 10.42% | |
Home healthcare | 18.51% | 17.17% | 16.08% | 16.62% | 15.06% | 13.92% | |
Length of stay (days) | 6.52 | 8.09 | 7.467397 | 7.524489 | 6.922949 | 7.861343 | <0.0001 |
Hospitalization charges (USD) | 81,533.52 | 95,395.64 | 111,004.6 | 114,352.8 | 83,761.54 | 110,831.2 | <0.0001 |
Comorbidities (Total = 777,740) | White (577,215) | Black (71,180) | Hispanic (73,000) | Asian/Pacific Islander (25,935) | Native American (2855) | Other (27,555) | p-Value |
---|---|---|---|---|---|---|---|
Hypertension | 36.75% | 36.68% | 34.35% | 34.37% | 35.73% | 35.73% | <0.0001 |
Diabetes mellitus | 22.32% | 26.43% | 29.32% | 26.89% | 33.80% | 23.15% | <0.0001 |
Smoking | 6.96% | 10.64% | 4.41% | 2.93% | 12.43% | 4.66% | <0.0001 |
Dyslipidemia | 36.52% | 25.44% | 27.29% | 32.64% | 29.77% | 26.84% | <0.0001 |
Obesity | 9.95% | 11.46% | 10.40% | 3.30% | 11.73% | 6.75% | <0.0001 |
Renal failure | 20.45% | 24.89% | 18.00% | 17.22% | 20.49% | 17.53% | <0.0001 |
Congestive heart failure | 17.10% | 16.82% | 11.23% | 11.20% | 15.24% | 10.56% | <0.0001 |
Chronic obstructive pulmonary disease | 15.59% | 10.96% | 6.86% | 6.11% | 14.71% | 7.78% | <0.0001 |
Pulmonary circulation disease | 5.53% | 6.31% | 3.96% | 3.49% | 7.88% | 3.90% | <0.0001 |
Coagulopathy | 0.93% | 1.26% | 1.23% | 1.39% | 1.05% | 1.00% | <0.0001 |
Depression | 12.45% | 7.76% | 8.16% | 4.51% | 12.78% | 8.13% | <0.0001 |
Outcomes (Total = 777,740) | White (577,215) | Black (71,180) | Hispanic (73,000) | Asian/Pacific Islander (25,935) | Native American (2855) | Other (27,555) | p-Value |
---|---|---|---|---|---|---|---|
Acute myocardial infarction | 2.45% | 1.79% | 1.42% | 1.83% | 2.63% | 1.07% | <0.0001 |
Atrial fibrillation | 20.84% | 10.18% | 10.07% | 12.92% | 11.91% | 12.52% | <0.0001 |
Cerebral events | 4.29% | 4.87% | 3.64% | 4.38% | 4.55% | 3.30% | <0.0001 |
Died during hospitalization | 4.77% | 5.64% | 4.45% | 5.51% | 4.90% | 4.81% | 0.0002 |
Sudden cardiac death | 0.67% | 1.22% | 0.77% | 0.77% | 0.88% | 0.83% | <0.0001 |
Race | Adjusted OR | 95% Confidence Interval | Significance Value (p) | |
---|---|---|---|---|
All-cause mortality | ||||
White | Reference | |||
Black | 1.276216 | 1.174679 | 1.38653 | <0.001 |
Hispanic | 1.069395 | 0.9814615 | 1.165207 | 0.125 |
Asian/Pacific Islander | 1.277725 | 1.125053 | 1.451114 | <0.001 |
Native American | 1.134511 | 0.7686936 | 1.674418 | 0.525 |
Other | 1.185762 | 1.014941 | 1.385333 | 0.032 |
Acute myocardial infarction | ||||
White | Reference | |||
Black | 0.7018127 | 0.6072979 | 0.8110371 | <0.001 |
Hispanic | 0.6900896 | 0.5908637 | 0.8059789 | <0.001 |
Asian/Pacific Islander | 0.880522 | 0.7105613 | 1.091136 | 0.245 |
Native American | 1.14092 | 0.6940258 | 1.875577 | 0.603 |
Other | 0.5749502 | 0.43748 | 0.7556178 | <0.001 |
Atrial fibrillation | ||||
White | Reference | |||
Black | 0.6158417 | 0.5765031 | 0.6578647 | <0.001 |
Hispanic | 0.5728468 | 0.53643 | 0.6117359 | <0.001 |
Asian/Pacific Islander | 0.6982262 | 0.6289933 | 0.7750795 | <0.001 |
Native American | 0.6121325 | 0.4497586 | 0.8331272 | 0.002 |
Other | 0.7705061 | 0.7047596 | 0.8423861 | <0.001 |
Cerebrovascular accident | ||||
White | Reference | |||
Black | 1.000388 | 0.9084044 | 1.101686 | 0.994 |
Hispanic | 0.9343409 | 0.8461065 | 1.031777 | 0.18 |
Asian/Pacific Islander | 1.143281 | 0.9902107 | 1.320014 | 0.068 |
Native American | 1.131307 | 0.7323143 | 1.747687 | 0.578 |
Other | 0.9243223 | 0.7692416 | 1.110668 | 0.401 |
Sudden cardiac death | ||||
White | Reference | |||
Black | 1.817808 | 1.524071 | 2.168156 | <0.001 |
Hispanic | 1.220238 | 0.9926749 | 1.499967 | 0.059 |
Asian/Pacific Islander | 1.232986 | 0.9005987 | 1.688049 | 0.191 |
Native American | 1.358769 | 0.5607135 | 3.292687 | 0.497 |
Other | 1.413797 | 0.9845441 | 2.030202 | 0.061 |
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Uttam Chandani, K.; Agrawal, S.P.; Raval, M.; Siddiq, S.; Nadeem, A.; Chintakuntlawar, A.V.; Hashmi, S.K. Racial Disparities in Cardiovascular and Cerebrovascular Adverse Events in Patients with Non-Hodgkin Lymphoma: A Nationwide Analysis. Medicina 2024, 60, 800. https://doi.org/10.3390/medicina60050800
Uttam Chandani K, Agrawal SP, Raval M, Siddiq S, Nadeem A, Chintakuntlawar AV, Hashmi SK. Racial Disparities in Cardiovascular and Cerebrovascular Adverse Events in Patients with Non-Hodgkin Lymphoma: A Nationwide Analysis. Medicina. 2024; 60(5):800. https://doi.org/10.3390/medicina60050800
Chicago/Turabian StyleUttam Chandani, Kanishka, Siddharth Pravin Agrawal, Maharshi Raval, Sajid Siddiq, Ahmed Nadeem, Ashish V. Chintakuntlawar, and Shahrukh K. Hashmi. 2024. "Racial Disparities in Cardiovascular and Cerebrovascular Adverse Events in Patients with Non-Hodgkin Lymphoma: A Nationwide Analysis" Medicina 60, no. 5: 800. https://doi.org/10.3390/medicina60050800