Next Article in Journal
A Device Strategy-Matched Comparison Analysis among Different Intermacs Profiles: A Single Center Experience
Previous Article in Journal
The Incidence of Severe Hypercalcaemia-Induced Mental Status Changes in Patients Treated with Antibiotic-Loaded Calcium Sulphate Depot for Orthopaedic Infections
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 11
Issue 16
10.3390/jcm11164899
jcm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Outcomes of Octogenarians with Primary Malignant Cardiac Tumors: National Cancer Database Analysis

by
Mohamed Rahouma
1,2,*,
Massimo Baudo
1,3,
Anas Dabsha
1,2,
Arnaldo Dimagli
1,
Abdelrahman Mohamed
2,
Stephanie L. Mick
1,
Leonard Girardi
1,
Mario Gaudino
1 and
Roberto Lorusso
4,5
1
Cardiothoracic Surgery Departments, Weill Cornell Medicine/New York Presbyterian Hospital, 525 East 68th St, Box 110, New York, NY 10065, USA
2
Surgical Oncology Department, National Cancer Institute, Cairo University, Cairo 11796, Egypt
3
Cardiac Surgery Department, SpedaliCivili di Brescia, University of Brescia, 25123 Brescia, Italy
4
Department of Cardio-Thoracic Surgery, Maastricht University Medical Centre, Maastricht University, P. Debyelaan, 25, 6202 AZ Maastricht, The Netherlands
5
Cardiovascular Research Institute Maastricht, 6202 AZ Maastricht, The Netherlands
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2022, 11(16), 4899; https://doi.org/10.3390/jcm11164899
Submission received: 24 July 2022 / Revised: 9 August 2022 / Accepted: 17 August 2022 / Published: 20 August 2022
(This article belongs to the Section Oncology)
Browse Figure
Review Reports Versions Notes

Abstract

:
Data concerning age-related populations affected with primary malignant cardiac tumors (PMCTs) are still scarce. The aim of the current study was to analyze mortality differences amongst different age groups of patients with PMCTs, as reported by the National Cancer Database (NCDB). The NCDB was retrospectively reviewed for PMCTs from 2004 to 2017. The primary outcome was late mortality differences amongst different age categories (octogenarian, septuagenarian, younger age), while secondary outcomes included differences in treatment patterns and perioperative (30-day) mortality. A total of 736 patients were included, including 72 (9.8%) septuagenarians and 44 (5.98%) octogenarians. Angiosarcoma was the most prevalent PMCT. Surgery was performed in 432 (58.7%) patients (60.3%, 55.6%, and 40.9% in younger age, septuagenarian, and octogenarian, respectively, p = 0.04), with a corresponding 30-day mortality of 9.0% (7.0, 15.0, and 38.9% respectively, p < 0.001) and a median overall survival of 15.7 months (18.1, 8.7, and 4.5 months respectively). Using multivariable Cox regression, independent predictors of late mortality included octogenarian, governmental insurance, CDCC grade II/III, earlier year of diagnosis, angiosarcoma, stage III/IV, and absence of surgery/chemotherapy. With increasing age, patients presented a more significant comorbidity burden compared to younger ones and were treated more conservatively. Early and late survival outcomes progressively declined with advanced age.

1. Introduction

Cardiac tumors represent a heterogeneous group of masses that can potentially involve any part of the heart. The classification of cardiac neoplasms was updated in 2015 by the World Health Organization (WHO), subdividing them into benign tumors, tumor-like lesions, malignant tumors, and pericardial tumors [1].
Approximately 10% of primary cardiac tumors are malignant [2,3]. Cardiac malignancies can manifest as either primary malignant cardiac tumors (PMCTs) or as metastases from an extra-cardiac location [4,5]. Atrial myxoma is the most frequent primary cardiac tumor in adults, while rhabdomyosarcoma predominates in children [6].
PMCTs generally display aggressive biological behavior [7], and even if they are detected at an early stage and an aggressive surgical resection is performed, the survival outcome remains poor [8,9]. If surgery is not performed, results show an even more dismal prognosis, with a 10% survival rate up to 1 year following diagnosis [10,11]. The best treatment for malignant cardiac tumors consists of the combination of surgery with systemic chemotherapy [11]. Concerning perioperative chemotherapy and radiotherapy, no unanimous consensus exists regarding its efficacy [12,13,14]. Finally, for inoperable or metastatic disease, palliative chemotherapy should be offered [11].
Global life expectancy and health care have improved over the recent decades and do not seem to stop in recent projections [15]. This has led to a progressively increasing number of elderlies requiring health care and cardiac surgery in particular, including octogenarians. It is well-recognized that elderly patients undergoing cardiac surgery have a higher risk of mortality and morbidity compared to younger patients [16,17]. As a matter of fact, the Society of Thoracic Surgeons (STS) [18] score and the European System for Cardiac Operative Risk Evaluation (EuroSCORE) II [19] still include age as an incremental risk factor.
Cardiac surgery mortality and morbidities in the elderly are mostly due to the adverse cardiovascular effects of aging. In general, anemia and a suboptimal nutritional status are common [20]. Vascular dysfunction is triggered by oxidative stress and inflammation [21]. Postoperative respiratory complications are favored by reduced lung compliance, chronic lung disease, and respiratory muscle strength [22]. The glomerular filtration rate progressively falls independently of an overt pathology, and other age-associated disorders, such as diabetes and hypertension, have an additional deleterious effect on the kidney [23]. Furthermore, other chronic illnesses, such as peripheral vascular disease [24] and diabetes [25], may further negatively characterize the overall comorbidity-based profile of elderly patients [26].
Tumors may represent an additional burden of the increasingly aging population requiring health care [27,28]. However, data on the elderly population affected with PMCT are still scarce and frequently unspecified. The aim of the current study was to analyze late-mortality differences amongst different age groups of patients with PMCTs and to assess the difference in treatment pattern and perioperative mortality from a national patient population, as reported by the National Cancer Database (NCDB).

2. Materials and Methods

2.1. Study Population

The NCDB was retrospectively reviewed for PMCTs from 2004 to 2017. The NCDB is a joint project of the Commission on Cancer (CoC) of the American College of Surgeons and the American Cancer Society that includes only patients treated in the United States of America. The CoC’s NCDB and the hospitals participating in the CoC’s NCDB are the source of the de-identified data used herein; they are not responsible for the statistical validity of the data analysis or the conclusions derived by the authors. Tumors with a sequence of malignant neoplasms of more than 1 over the patient’s lifetime and those with missing survival status or time were excluded. As this study was an epidemiological study using de-identified data from the NCDB database, consent for patient participation and study publication was not required.

2.2. Variables, Outcomes, and Follow-Up

The following patient variables were assessed: age, sex, race, insurance status, median income quartile, metropolitan/urban/rural counties, education, great circle distance (the distance in miles between the patient’s residence and the hospital that reported the case), year of diagnosis, Charlson–Deyo comorbidity condition index (CDCC, categorized as (0 or 1 vs 2 or 3)), histology, tumor size, analytic stage, facility type, different treatment patterns, such as surgery, radiotherapy, and chemotherapy, and 30-day mortality and late mortality. Facility type was categorized as 1. academic/integrated facilities (including academic/research programs and integrated network cancer programs) or 2. community facilities (including community cancer programs and comprehensive community cancer program).
The primary outcome of this study was late mortality differences amongst different age categories (octogenarian, septuagenarian, and younger-age group), while secondary outcomes included differences in treatment patterns and early (30-day) mortality, as well as its annual proportion among different age categories throughout the study period. Furthermore, sensitivity analysis was conducted on surgical patients.

2.3. Statistical Analysis

Categorical variables were presented as frequency counts and percentages and compared between groups using a Chi-square test. After checking normality of continuous variables, they were presented as means and standard deviations if normally distributed and were compared between groups using analysis of variance (ANOVA). If they were not normally distributed, median and interquartile ranges were presented and compared between groups using the Kruskal–Wallis test. All tests were 2-sided, and the alpha level was set at 0.05 for statistical significance.
Early (30-day) mortality predictors were identified through univariate and multivariable logistic regression among the surgical subgroups. Late all-cause mortality was assessed using Kaplan–Meier curves and compared between the groups using the log-rank test. Median follow-up time was assessed among different age categories utilizing reversed Kaplan–Meier curves. A univariate Cox proportional hazards model was used to assess hazard ratios of late all-cause mortality amongst different age categories (octogenarian, septuagenarian and younger-age group). The proportional hazards assumption was tested using Schoenfeld residuals. Independent predictors of late mortality were assessed using multivariate Cox regression.
All statistical analyses were performed using R version 4.1.1 within RStudio with the help of the following packages “tableone”, “Publish”, “Hmisc”, and “survminer” [29,30,31].

3. Results

3.1. Demographics

Out of the 110,991 malignant soft-tissue tumors present in the NCDB, 907 malignant cardiac tumors were identified. Due to the presence of a sequence of malignant neoplasms of more than one over the lifetime of the patient, 107 patients were excluded. In addition, 64 additional patients were excluded due to missing survival time or status. Therefore, a total of 736 patients were finally included in the study analysis.
Median age was 52 years (interquartile range (IQR): 37 to 65 years) and included 72 septuagenarians and 44 octogenarians. Overall, there were 352 females (47.8%). Almost 80% of the study population was white, followed by black (13.6%) and other (6.5%). Less than 10% were uninsured/unknown, while 56% had private insurance and 34.2% governmental insurance (Table 1).
The three age groups showed some significant differences: among the different age groups, white was the most prevalent race, while black was the second most prevalent for younger patients and the least prevalent in septuagenarians and octogenarians (p = 0.007). Younger patients were more likely to have private insurance compared to elderly patients, who showed a greater predominance of governmental insurance (p < 0.001). Finally, elderly patients showed a significantly higher CDCC degree when compared to the younger group (p = 0.002).

3.2. Histology

Among all patients, angiosarcoma was the most common histology (43.8%), followed by fibrosarcoma and leiomyosarcoma (5.2% each), while Ewing sarcoma (0.3%) was the least prevalent one. This trend was maintained in the younger group. Although angiosarcoma was still the most prevalent cardiac tumor in the elderly, the second most common was different between septuagenarians and octogenarians (Table 1).
There were no significant differences between the age groups as far as grade and tumor stage were concerned (p = 0.732 and p = 0.34, respectively).

3.3. Treatment

A total of 432 patients (58.7%) underwent surgery; 374 (50.8%) received chemotherapy, and 130 (17.7%) received radiotherapy. Treatment analysis showed significant differences between age groups as far as surgery and chemotherapy were concerned (p = 0.037 and p < 0.001, respectively): with increasing age, surgery and chemotherapy were performed less frequently. Amongst the surgically treated patients, 219 (50.7%) patients had chemotherapy and 73 (16.9%) had radiation therapy (Table 1). Also in this subgroup, septuagenarians and octogenarians received less chemotherapy than younger patients (p < 0.001) (Supplementary Table S1).

3.4. Survival Analysis

Overall, the 30-day mortality was 8.7% (6.7%, 14.0%, and 36.8% for younger, septuagenarians, and octogenarians, respectively, p < 0.001) (Table 1). Over the years, the direct relation between age and mortality was preserved (Supplementary Figure S2). In the surgically treated patients, 30-day mortality was 9.0% (7.0%, 15.0%, and 38.9% for younger, septuagenarians, and octogenarians, respectively, p < 0.001) (Supplementary Table S1).
Median follow-up was 42.3 months in the octogenarian category vs. 61.4 and 67 months in the septuagenarian and younger-age categories, respectively. Median overall survival was 12.98 (95%CI: 11.50–14.65), 6.03 (95%CI: 2.60–9.30), and 1.33 (95%CI: 0.49–4.14) for younger, septuagenarians, and octogenarians respectively.
Two-year overall survival was 31.40%, 20.48%, and 11.36% for younger, septuagenarians, and octogenarians, respectively. Five-year overall survival was 14.10%, 9.78%, and 6.82% for younger, septuagenarians, and octogenarians, respectively (p < 0.001, Figure 1).
Using multivariable Cox regression, independent predictors of late mortality among the entire cohort included octogenarian (hazard ratio (HR): 1.810, 95% confidence interval (CI): [1.277;2.565], p < 0.001, younger set as reference), governmental insurance (HR: 1.460, 95%CI: [1.071;1.992], p = 0.017, no/unknown insurance set as reference), CDCC grade II/III (HR: 2.000, 95%CI: [1.514;2.642], p < 0.001, CDCC grade 0/I set as reference), year of diagnosis (HR: 0.960, 95%CI: [0.939;0.982], p < 0.001), angiosarcoma (HR: 1.295, 95%CI: [1.092;1.536], p = 0.003, other histology set as reference), stage III/IV (HR: 1.298, 95%CI: [1.082;1.556], p = 0.005, stage 0/I/x set as reference), surgery (HR: 0.478, 95%CI: [0.400;0.572], p < 0.001), and chemotherapy (HR: 0.601, 95%CI: [0.501;0.721], p < 0.001) (Table 2).
Using multivariable logistic regression, independent predictors of 30-day mortality among the surgical cohort included octogenarian (odds ratio (OR): 7.31, 95%CI: [2.55;20.94], p < 0.01, younger set as reference) and CDCC grade II/III (HR: 2.46, 95%CI: [1.03;5.86], p = 0.042, CDCC grade 0/I set as reference) (Supplementary Table S2).

4. Discussion

Age is currently one of the most commonly reported risk factors for adverse outcomes in cardiac surgery [16,17] and the most frequently cited reason for not operating on otherwise eligible patients [32,33]. Nevertheless, a clear definition of the “elderly” patient in cardiac surgery has never been established. The cut-off used in published articles ranges from 65 to 80 years [34]. The present study evaluated the outcome of 736 patients with PMCTs from the NCDB based on three age groups (younger, septuagenarians, and octogenarians).
The baseline characteristics significantly differed between the age groups for race, insurance status, comorbidity index (CDCC), histology, and cancer stage. As far as patients’ sex was concerned, a trend of higher female prevalence was noted for increasing age (p = 0.064). Previous studies on PMCTs showed that females were significantly older and had a lower cancer stage in comparison to males [35].
Treatment strategies significantly differed among the age groups: with increasing age, less surgery (p = 0.037) and less chemotherapy (p < 0.001) were chosen. Early survival outcomes were significantly impacted by age. Higher 30-day mortality increased with age in the whole population (p < 0.001) and in the surgical group (p < 0.001). The same trend was noted for long-term death from any cause in the overall cohort (log-rank p < 0.001) and in the surgically treated (log-rank p = 0.004). Independent predictors of late mortality among the entire cohort included octogenarian age group, governmental insurance, advanced CDCC grade, angiosarcoma histology, and stage III/IV. Surgery and chemotherapy were associated with longer survival benefits. Notably, radiotherapy was not an independent predictor of survival.
Generally, in cardiac surgery, the chronological age may not be per se a risk factor for higher perioperative and postoperative morbidity and mortality. The more crucial factor is the biological age, particularly the associated comorbidities and the timing of the surgical procedure in the course of the disease. In a meta-analysis by Wiegmann et al. [36], the authors reported how elderly patients, even those over 80 years old, can benefit in all aspects of cardiac surgery, as long as individually adapted operative techniques are considered. However, for PMCTs, the neoplastic nature of the disease further complicates the clinical picture, and such statements may not be completely applicable. In the present analysis, despite surgery and chemotherapy being independent predictors of longer survival, elderly patients were at higher risk of early and late mortality. As a result, with these premises and the presence of limited benefit in the face of high early mortality among octogenarians, it is appropriate to convey such data to the patients and their families in order to obtain a shared decision on the treatment plan among the available therapeutic options.
Previous studies on PMCTs showed that a survival disparity among patients with different insurance status exists [37]. This was confirmed in the current analysis: governmental insurance was associated with a significant increase in late mortality with multivariable regression (HR: 1.460, 95%CI: [1.071;1.992], p = 0.017, no/unknown insurance set as reference). Furthermore, uninsured and governmental-insured patients were shown to be associated with higher early-mortality rates after cardiac valve surgery in a recent paper by Hoyler [38]. While the current analysis showed that patients of advanced age had a lower prevalence of advanced cancer stages (p = 0.037), it also confirmed that advanced age was associated with higher comorbidities (CDCC, p = 0.002). The prior literature [39] has consistently shown that the mortality rate is higher in elderly patients undergoing cardiac surgery, and that is reiterated in this study. Nevertheless, multiple studies [40,41,42] reported that better perioperative mortality and morbidity can be achieved in this subset of patients than is expected. This is likely due to multiple reasons, including better preoperative selection of patients, improvement in perioperative care, and the ability to rescue patients from postoperative complications. Moreover, currently, less invasive treatment options may recruit patients referred for cardiac surgery who could not have been considered in the past [43]. In general, older patients often require longer postoperative stays and therefore an increase in resources compared to younger patients [44]. Despite the high rates of early re-hospitalization, octogenarians with prolonged intensive care unit length of stay have been reported to have acceptable functional survival at 1 year [45].
Multidisciplinary discussions are of paramount importance for older patients, who often have issues of frailty and multiple comorbidities to consider, in order to establish the best treatment option for each individual.

Study Limitations

The retrospective nature of the present study may have introduced important biases and confounding factors that cannot be avoided even after different types of statistical adjustments. In addition, classifying some categorical variables as “unknown” is a probable source of bias. It was not possible to perform a complete subgroup analysis of multimodal therapy due to the small sample size of some groups.
The data obtained from the NCDB are subject to human error in both coding and data input due to the multiple registrars. Furthermore, no genetic data are provided in the NCDB. Moreover, specific chemotherapy regimens were not included, and the diagnostic imaging modalities, clinical presentation, and tumor location within the heart were not available. However, diagnoses were confirmed with histological examinations after resection or biopsy.

5. Conclusions

The most frequent PMCT is angiosarcoma at all considered ages. With increasing age, patients presented a more significant comorbidity burden compared to younger ones and were treated more conservatively. Early and late survival outcomes progressively declined with advanced age. However, most recent data show that acceptable results might be achieved with the use of surgery and chemotherapy, although elderly patients are often not considered appropriate candidates for such therapies. Finally, independent predictors of late mortality among the entire cohort included octogenarian age group, governmental insurance, advanced CDCC grade, angiosarcoma histology, and stage III/IV. On the contrary, surgery and chemotherapy were associated with longer survival benefits.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jcm11164899/s1, Figure S1: Cumulative death from any cause among different age categories in patients who underwent surgery; Figure S2: Annual 30-day mortality percentage among different age categories in the surgical cohort. Categories with 100% mortality had ≤2 cases/year; Table S1: Criteria and outcomes among surgically treated patients; Table S2: Independent predictors of 30-day mortality among the surgical cohort (n = 39/432).

Author Contributions

Conceptualization ideas: M.R.; Software Programming, Software development: M.R.; Validation: M.R., M.G. and R.L.; Formal analysis: M.R.; Investigation: M.R. and M.B.; Resources: M.R. and M.G.; Data Curation: M.R.; Writing: M.R. and M.B.; Writing—Review and Editing: M.R., M.B., A.D. (Anas Dabsha), A.D. (Arnaldo Dimagli), A.M., S.L.M., L.G., M.G. and R.L.; Visualization: M.R. and M.B.; Supervision: M.R., M.B., M.G. and R.L.; Project administration: M.R., M.B. and M.G.; All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

As this study was an epidemiological study using de-identified data from the NCDB database, consent for patient participation and study publication was not required.

Informed Consent Statement

As this study was an epidemiological study using de-identified data from the NCDB database, consent for patient participation and study publication was not required.

Data Availability Statement

This is a national cancer database that we got approval to use, but it is not publically available.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Travis, W.D.; Brambilla, E.; Burke, A.P.; Marx, A.; Nicholson, A.G. Introduction to The 2015 World Health Organization Classification of Tumors of the Lung, Pleura, Thymus, and Heart. J. Thorac. Oncol. 2015, 10, 1240–1242. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Rahouma, M.; Arisha, M.J.; Elmously, A.; El-Sayed Ahmed, M.M.; Spadaccio, C.; Mehta, K.; Baudo, M.; Kamel, M.; Mansor, E.; Ruan, Y.; et al. Cardiac tumors prevalence and mortality: A systematic review and meta-analysis. Int. J. Surg. Lond. Engl. 2020, 76, 178–189. [Google Scholar] [CrossRef] [PubMed]
  3. Basso, C.; Valente, M.; Poletti, A.; Casarotto, D.; Thiene, G. Surgical pathology of primary cardiac and pericardial tumors. Eur. J. Cardio-Thorac. Surg. 1997, 12, 730–737; discussion 737–738. [Google Scholar] [CrossRef]
  4. Hoffmeier, A.; Sindermann, J.R.; Scheld, H.H.; Martens, S. Cardiac tumors--diagnosis and surgical treatment. Dtsch. Arzteblatt Int. 2014, 111, 205–211. [Google Scholar] [CrossRef] [Green Version]
  5. Taguchi, S. Comprehensive review of the epidemiology and treatments for malignant adult cardiac tumors. Gen Thorac. Cardiovasc. Surg. 2018, 66, 257–262. [Google Scholar] [CrossRef]
  6. Reynen, K. Frequency of primary tumors of the heart. Am. J. Cardiol. 1996, 77, 107. [Google Scholar] [CrossRef]
  7. Oliveira, G.H.; Al-Kindi, S.G.; Hoimes, C.; Park, S.J. Characteristics and survival of malignant cardiac tumors: A 40-year analysis of> 500 patients. Circulation 2015, 132, 2395–2402. [Google Scholar] [CrossRef]
  8. Catton, C. The management of malignant cardiac tumors: Clinical considerations. Semin. Diagn. Pathol. 2008, 25, 69–75. [Google Scholar] [CrossRef]
  9. Ramlawi, B.; Leja, M.J.; Abu Saleh, W.K.; Al Jabbari, O.; Benjamin, R.; Ravi, V.; Shapira, O.M.; Blackmon, S.H.; Bruckner, B.A.; Reardon, M.J. Surgical Treatment of Primary Cardiac Sarcomas: Review of a Single-Institution Experience. Ann. Thorac. Surg. 2016, 101, 698–702. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  10. Yanagawa, B.; Mazine, A.; Chan, E.Y.; Barker, C.M.; Gritti, M.; Reul, R.M.; Ravi, V.; Ibarra, S.; Shapira, O.M.; Cusimano, R.J.; et al. Surgery for Tumors of the Heart. Semin. Thorac. Cardiovasc. Surg. 2018, 30, 385–397. [Google Scholar] [CrossRef]
  11. Leja, M.J.; Shah, D.J.; Reardon, M.J. Primary cardiac tumors. Tex. Heart Inst. J. 2011, 38, 261–262. [Google Scholar]
  12. Habertheuer, A.; Laufer, G.; Wiedemann, D.; Andreas, M.; Ehrlich, M.; Rath, C.; Kocher, A. Primary cardiac tumors on the verge of oblivion: A European experience over 15years. J. Cardiothorac. Surg. 2015, 10, 56. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Yu, L.; Gu, T.; Shi, E.; Xiu, Z.; Fang, Q.; Wang, C.; Wang, X.; Cheng, Y. Primary malignant cardiac tumors. J. Cancer Res. Clin. Oncol. 2014, 140, 1047–1055. [Google Scholar] [CrossRef] [PubMed]
  14. Isambert, N.; Ray-Coquard, I.; Italiano, A.; Rios, M.; Kerbrat, P.; Gauthier, M.; Blouet, A.; Chaigneau, L.; Duffaud, F.; Piperno-Neumann, S.; et al. Primary cardiac sarcomas: A retrospective study of the French Sarcoma Group. Eur. J. Cancer 2014, 50, 128–136. [Google Scholar] [CrossRef]
  15. World Population Prospects 2019, Highlights|Multimedia Library—United Nations Department of Economic and Social Affairs n.d. Available online: https://www.un.org/development/desa/publications/world-population-prospects-2019-highlights.html (accessed on 1 March 2022).
  16. Alexander, K.P.; Anstrom, K.J.; Muhlbaier, L.H.; Grosswald, R.D.; Smith, P.K.; Jones, R.H.; Peterson, E.D. Outcomes of cardiac surgery in patients > or = 80 years: Results from the National Cardiovascular Network. J. Am. Coll. Cardiol. 2000, 35, 731–738. [Google Scholar] [CrossRef] [Green Version]
  17. Thorsteinsson, K.; Fonager, K.; Mérie, C.; Gislason, G.; Køber, L.; Torp-Pedersen, C.; Mortensen, R.N.; Andreasen, J.J. Age-dependent trends in postoperative mortality and preoperative comorbidity in isolated coronary artery bypass surgery: A nationwide study. Eur. J. Cardio-Thorac. Surg. 2016, 49, 391–397. [Google Scholar] [CrossRef] [PubMed]
  18. Online STS Risk Calculator, n.d. Available online: https://riskcalc.sts.org/stswebriskcalc/calculate (accessed on 1 March 2022).
  19. Nashef, S.A.M.; Roques, F.; Sharples, L.D.; Nilsson, J.; Smith, C.; Goldstone, A.R.; Lockowandt, U. EuroSCORE II. Eur. J. Cardio-Thorac. Surg. 2012, 41, 734–744; discussion 744–745. [Google Scholar] [CrossRef] [Green Version]
  20. Price, E.A.; Mehra, R.; Holmes, T.H.; Schrier, S.L. Anemia in older persons: Etiology and evaluation. Blood Cells Mol. Dis. 2011, 46, 159–165. [Google Scholar] [CrossRef]
  21. Wadley, A.J.; Veldhuijzen van Zanten, J.J.C.S.; Aldred, S. The interactions of oxidative stress and inflammation with vascular dysfunction in ageing: The vascular health triad. Age 2013, 35, 705–718. [Google Scholar] [CrossRef]
  22. Dyer, C. The interaction of ageing and lung disease. Chron. Respir. Dis. 2012, 9, 63–67. [Google Scholar] [CrossRef]
  23. Martin, J.E.; Sheaff, M.T. Renal ageing. J. Pathol. 2007, 211, 198–205. [Google Scholar] [CrossRef] [PubMed]
  24. Safer, U.; Naharci, I.; Tasci, I.; Safer, V.B. Peripheral Vascular Disease is Associated with Impaired Activities of Daily Living in the Elderly with Dementia: A Prospective Study. CNS Neurol. Disord. Drug Targets, 2022; E-pub Ahead of Print. [Google Scholar] [CrossRef] [PubMed]
  25. Khan, M.S.; Samman Tahhan, A.; Vaduganathan, M.; Greene, S.J.; Alrohaibani, A.; Anker, S.D.; Vardeny, O.; Fonarow, G.C.; Butler, J. Trends in prevalence of comorbidities in heart failure clinical trials. Eur. J. Heart Fail 2020, 22, 1032–1042. [Google Scholar] [CrossRef] [PubMed]
  26. Varkevisser, R.R.B.; O’Donnell, T.F.X.; Swerdlow, N.J.; Liang, P.; Li, C.; Ultee, K.H.J.; Patel, V.I.; Scali, S.T.; Verhagen, H.J.; Schermerhorn, M.L. Factors associated with in-hospital complications and long-term implications of these complications in elderly patients undergoing endovascular aneurysm repair. J. Vasc. Surg. 2020, 71, 470–480.e1. [Google Scholar] [CrossRef] [PubMed]
  27. Kadambi, S.; Soto-Perez-de-Celis, E.; Garg, T.; Loh, K.P.; Krok-Schoen, J.L.; Battisti, N.M.L.; Moffat, G.T.; Gil, L.A., Jr.; Mohile, S.; Hsu, T. Social support for older adults with cancer: Young International Society of Geriatric Oncology review paper. J. Geriatr. Oncol. 2020, 11, 217–224. [Google Scholar] [CrossRef] [Green Version]
  28. Pilleron, S.; Sarfati, D.; Janssen-Heijnen, M.; Vignat, J.; Ferlay, J.; Bray, F.; Soerjomataram, I. Global cancer incidence in older adults, 2012 and 2035, A population-based study. Int. J. Cancer 2019, 144, 49–58. [Google Scholar] [CrossRef]
  29. Kassambara, A.; Kosinski, M.; Biecek, P. Survminer: Drawing Survival Curves Using ‘ggplot2’. R Package Version 0.4.9. 2022. Available online: https://CRAN.R-project.org/package=survminer (accessed on 1 July 2022).
  30. Yoshida, K.; Bohn, J.; Yoshida, M.K. tableone: Create ‘Table 1’ to Describe Baseline Characteristics with or without Propensity Score Weights. R Package Version 0.13.2. 2022. Available online: https://CRAN.R-project.org/package=tableone (accessed on 1 July 2022).
  31. Harrell, F.E., Jr.; Harrell, M.F.E., Jr. Hmisc: Harrell Miscellaneous. R Package Version 0.4.7-0. 2022. Available online: https://CRAN.R-project.org/package=Hmisc (accessed on 1 July 2022).
  32. Iung, B.; Baron, G.; Butchart, E.G.; Delahaye, F.; Gohlke-Bärwolf, C.; Levang, O.W.; Tornos, P.; Vanoverschelde, J.-L.; Vermeer, F.; Boersma, E.; et al. A prospective survey of patients with valvular heart disease in Europe: The Euro Heart Survey on Valvular Heart Disease. Eur. Heart J. 2003, 24, 1231–1243. [Google Scholar] [CrossRef] [Green Version]
  33. Piérard, S.; Seldrum, S.; de Meester, C.; Pasquet, A.; Gerber, B.; Vancraeynest, D.; El Khoury, G.; Noirhomme, P.; Robert, A.; Vanoverschelde, J.-L. Incidence, determinants, and prognostic impact of operative refusal or denial in octogenarians with severe aortic stenosis. Ann. Thorac. Surg. 2011, 91, 1107–1112. [Google Scholar] [CrossRef]
  34. Afilalo, J.; Steele, R.; Manning, W.J.; Khabbaz, K.R.; Rudski, L.G.; Langlois, Y.; Morin, J.-F.; Picard, M.H. Derivation and Validation of Prognosis-Based Age Cutoffs to Define Elderly in Cardiac Surgery. Circ. Cardiovasc. Qual. Outcomes 2016, 9, 424–431. [Google Scholar] [CrossRef]
  35. Rahouma, M.; Baudo, M.; Shmushkevich, S.; Chadow, D.; Mohamed, A.; Girardi, L.; Gaudino, M.; Lorusso, R. Sex differences in primary malignant cardiac tumors: A multi-institutional cohort study from National Cancer Database. J. Card. Surg. 2022, 37, 1275–1286. [Google Scholar] [CrossRef]
  36. Wiegmann, B.; Ismail, I.; Haverich, A. Cardiac surgery in the elderly. Chir. Z. Alle Geb. Oper. Medizen 2017, 88, 110–115. [Google Scholar] [CrossRef] [PubMed]
  37. Rahouma, M.; Baudo, M.; Shmushkevich, S.; Chadow, D.; Mohamed, A.; Gaudino, M.; Lorusso, R. Association between insurance status and survival among patients with malignant cardiac tumours. Br. J. Surg. 2022, 109, e24–e25. [Google Scholar] [CrossRef] [PubMed]
  38. Hoyler, M.M.; Feng, T.R.; Ma, X.; Rong, L.Q.; Avgerinos, D.V.; Tam, C.W.; White, R.S. Insurance Status and Socioeconomic Factors Affect Early Mortality After Cardiac Valve Surgery. J. Cardiothorac. Vasc. Anesth. 2020, 34, 3234–3242. [Google Scholar] [CrossRef] [PubMed]
  39. Volk, L.; Chao, J.; Dombrovskiy, V.; Ikegami, H.; Russo, M.J.; Lemaire, A.; Lee, L.Y.; Volk, M.L.; Chao, J.J.; Dombrovskiy, V. Impact of risk factors on in-hospital mortality for octogenarians undergoing cardiac surgery. J. Card. Surg. 2021, 36, 2400–2406. [Google Scholar] [CrossRef] [PubMed]
  40. Chen, J.-W.; Sainbayar, N.; Hsu, R.-B. Outcome of emergency surgery for acute type A aortic dissection in octogenarians. J. Card. Surg. 2022, 37, 610–615. [Google Scholar] [CrossRef]
  41. Tsubota, H.; Sakaguchi, G.; Arakaki, R.; Marui, A. Contemporary outcomes of mitral valve replacement in octogenarians. J. Card. Surg. 2022, 37, 581–587. [Google Scholar] [CrossRef]
  42. Jones, J.M.; Loubani, M.; Grant, S.W.; Goodwin, A.T.; Trivedi, U.; Kendall, S.; Jenkins, D.P. Cardiac surgery in older patients: Hospital outcomes during a 15-year period from a complete national series. Interact. Cardiovasc. Thorac. Surg. 2021, 34, 532–539. [Google Scholar] [CrossRef]
  43. Booth, K.; Beattie, R.; McBride, M.; Manoharan, G.; Spence, M.; Jones, J.M. High Risk Aortic Valve Replacement—The Challenges of Multiple Treatment Strategies with an Evolving Technology. Ulst. Med. J. 2016, 85, 18–22. [Google Scholar]
  44. Habib, A.M.; Hussain, A.; Jarvis, M.; Cowen, M.E.; Chaudhry, M.A.; Loubani, M.; Cale, A.; Ngaage, D.L. Changing clinical profiles and in-hospital outcomes of octogenarians undergoing cardiac surgery over 18 years: A single-centre experience. Interact. Cardiovasc. Thorac. Surg. 2019, 28, 602–606. [Google Scholar] [CrossRef] [Green Version]
  45. Arora, R.C.; Manji, R.A.; Singal, R.K.; Hiebert, B.; Menkis, A.H. Outcomes of octogenarians discharged from the hospital after prolonged intensive care unit length of stay after cardiac surgery. J. Thorac. Cardiovasc. Surg. 2017, 154, 1668–1678.e2. [Google Scholar] [CrossRef]
Jcm 11 04899 g001 550
Figure 1. Cumulative death from any cause among different age categories (the younger category was used as reference for the obtained hazard ratios).
Figure 1. Cumulative death from any cause among different age categories (the younger category was used as reference for the obtained hazard ratios).
Jcm 11 04899 g001
Table
Table 1. Criteria of the included patients.
Table 1. Criteria of the included patients.
OverallYoungerSeptuagenarianOctogenarianp
n7366207244
Age (median [IQR])52.00 [37.00, 65.00]47.00 [35.00, 58.00]74.00 [72.00, 77.00]84.00 [82.00, 86.00]<0.001
Female (%)352 (47.8)285 (46.0)41 (56.9)26 (59.1)0.064
Race (%) 0.007
   • White575 (79.9)474 (78.1)64 (90.1)37 (88.1)
   • Black98 (13.6)95 (15.7)2 (2.8)1 (2.4)
   • Others47 (6.5)38 (6.3)(7.0)4 (9.5)
Insurance status <0.001
   • No insurance/Unknown72 (9.8)69 (11.1)3 (4.2)0 (0.0)
   • Private412 (56.0)396 (63.9)9 (12.5)7 (15.9)
   • Governmental252 (34.2)155 (25.0)60 (83.3)37 (84.1)
Median income quartile (%) 0.136
   • Less than USD 40,227124 (17.7)110 (18.7)8 (11.1)6 (14.0)
   • USD 40,227– USD 50,353145 (20.7)116 (19.8)14 (19.4)15 (34.9)
   • USD 50,354– USD 63,332170 (24.2)143 (24.4)21 (29.2)6 (14.0)
   • USD 63,333 or more263 (37.5)218 (37.1)40.316 (37.2)
Urban/Rural counties (%) 0.62
   • Metropolitan591 (84.3)495 (84.0)59 (84.3)37 (88.1)
   • Urban98 (14.0)82 (13.9)11 (15.7)5 (11.9)
   • Rural12 (1.7)12 (2.0)0 (0.0)0 (0.0)
No high school graduate quartile (%) ¶ 0.053
   • 17.6% or more149 (21.1)134 (22.8)10 (13.9)5 (11.4)
   • 10.9–17.5%174 (24.7)141 (23.9)15 (20.8)18 (40.9)
   • 6.3–10.8%194 (27.5)162 (27.5)23 (31.9)9 (20.5)
   • Less than 6.3%188 (26.7)152 (25.8)24 (33.3)12 (27.3)
Great circle distance (miles; median [IQR]) ¶¶17.00 [6.20, 45.80]17.80 [6.40, 48.85]15.00 [7.27, 29.80]10.30 [3.98, 33.22]0.069
Facility type (%) <0.001
   • Academic/integrated344 (46.7)282 (45.5)39 (54.2)23 (52.3)
   • Community184 (25.0)130 (21.0)33 (45.8)21 (47.7)
   • Unknown208 (28.3)208 (33.5)0 (0.0)0 (0.0)
CDCC (0 or 1/2 or 3) (%) *671/65 (91.2/8.8)575/45 (92.7/7.3)59/13 (81.9/18.1)37/7 (84.1/15.9)0.002
Year of diagnosis (median [IQR])2011.00 [2007.00, 2014.00]2011.00 [2007.00, 2014.00]2012.00 [2008.75, 2014.00]2012.00 [2008.00, 2014.25]0.125
Histology (%) <0.001
   • Angiosarcoma322 (43.8)289 (46.6)24 (33.3)9 (20.5)
   • Ewing sarcoma2 (0.3)2 (0.3)0 (0.0)0 (0.0)
   • Fibrosarcoma38 (5.2)33 (5.3)2 (2.8)3 (6.8)
   • Giant cell sarcoma34 (4.6)25 (4.0)7 (9.7)2 (4.5)
   • Leiomyosarcoma38 (5.2)31 (5.0)4 (5.6)3 (6.8)
   • Liposarcoma6 (0.8)3 (0.5)3 (4.2)0 (0.0)
   • Malignant fibrous histiocytoma (MFH)13 (1.8)13 (2.1)0 (0.0)0 (0.0)
   • MPNST *4 (0.5)4 (0.6)0 (0.0)0 (0.0)
   • Myxosarcoma23 (3.1)22 (3.5)1 (1.4)0 (0.0)
   • Osteosarcoma18 (2.4)18 (2.9)0 (0.0)0 (0.0)
   • Others/Unclassified186 (25.3)140 (22.6)22 (30.6)24 (54.5)
   • Rhabdomyosarcoma (RMS)21 (2.9)12 (1.9)6 (8.3)3 (6.8)
   • Synovial sarcoma31 (4.2)28 (4.5)3 (4.2)0 (0.0)
Grade (poorly differentiated/anaplastic) (%)362 (49.2)304 (49.0)38 (52.8)20 (45.5)0.732
Tumor size (in mm; median [IQR])60.00 [44.00, 80.00]60.00 [45.00, 80.00]60.00 [41.25, 73.75]50.00 [40.00, 70.50]0.34
Analytic stage group (%) 0.037
   • Stage 0/I/x355 (48.2)293 (47.3)37 (51.4)25 (56.8)
   • Stage II89 (12.1)70 (11.3)9 (12.5)10 (22.7)
   • Stage III/IV292 (39.7)257 (41.5)36.19 (20.5)
Surgery (%) 0.037
   • No301 (40.9)244 (39.4)32 (44.4)25 (56.8)
   • Yes432 (58.7)374 (60.3)40 (55.6)18 (40.9)
   • Unknown3 (0.4)2 (0.3)(0.0)1 (2.3)
Radiation (%) 0.148
   • No581 (78.9)485 (78.2)57 (79.2)39 (88.6)
   • Yes130 (17.7)115 (18.5)13 (18.1)2 (4.5)
   • Unknown25 (3.4)20 (3.2)(2.8)3 (6.8)
Chemotherapy (%) <0.001
   • No308 (41.8)229 (36.9)44 (61.1)35 (79.5)
   • Yes374 (50.8)353 (56.9)20 (27.8)1 (2.3)
   • Unknown54 (7.3)38 (6.1)8 (11.1)8 (18.2)
30-day mortality (Alive/Dead) (%)410/39 (91.3/8.7)361/26 (93.3/6.7)37/6 (86.0/14.0)12/7 (63.2/36.8)<0.001
¶ This item provides a measure of the number of adults aged 25 or older in the zip code of patients who did not graduate from high school, and is categorized as equally proportioned quartiles among all US zip codes. ¶¶ The “great circle” distance in miles between the patient’s residence and the hospital that reported the case. * CDCC: Charlson–Deyo comorbidity condition, MPNST: malignant peripheral nerve sheath tumors, USD: United States Dollar.
Table
Table 2. Independent predictors of late mortality among the entire cohort.
Table 2. Independent predictors of late mortality among the entire cohort.
VariableUnitsUnivariate Analysis
Hazard Ratio (95% CI) p-Value		Multivariable Analysis
Hazard Ratio (95% CI) p-Value
AgeYoungerRefRef
Septuagenarian1.507 [1.159;1.958] 0.0021.139 [0.858;1.512] 0.367
Octogenarian2.569 [1.872;3.526] <0.0011.810 [1.277;2.565] <0.001
SexMaleRef
Female1.031 [0.880;1.207] 0.708
RaceWhiteRef
Black1.071 [0.848;1.352] 0.565
Others1.305 [0.933;1.825] 0.120
InsuranceNo/UnknownRefRef
Private0.862 [0.649;1.144] 0.30370.962 [0.721;1.283] 0.790
Governmental1.451 [1.081;1.947] 0.0131.460 [1.071;1.992] 0.017
Median income quartileLess than USD 40,227Ref
Higher than USD 40,2271.093 [0.882;1.353] 0.417
Urban/MetropolitanUrbanRef
Metropolitan0.965 [0.763;1.220] 0.764
Rural1.077 [0.592;1.958] 0.808
CDCC0/IRefRef
II/III1.614 [1.236;2.108] <0.0012.000 [1.514;2.642] <0.001
Year of diagnosis 0.989 [0.968;1.010] 0.3080.960 [0.939;0.982] <0.001
HistologyOthersRefRef
Angiosarcoma1.312 [1.118;1.539] <0.0011.295 [1.092;1.536] 0.003
Analytic stageStage 0/I/xRefRef
Stage II0.862 [0.666;1.116] 0.2610.936 [0.715;1.224] 0.628
Stage III/IV1.164 [0.983;1.379] 0.0781.298 [1.082;1.556] 0.005
Facility typeCommunityRef
Academic/Integrated1.092 [0.900;1.326] 0.373
SurgeryNoRefRef
Yes0.494 [0.419;0.581] <0.0010.478 [0.400;0.572] <0.001
RadiationNoRef
Yes0.833 [0.677;1.025] 0.085
ChemotherapyNoRefRef
Yes0.682 [0.577;0.806] <0.0010.601 [0.501;0.721] <0.001
Variables with p < 0.05 in univariate analysis were included in multivariable analysis.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Rahouma, M.; Baudo, M.; Dabsha, A.; Dimagli, A.; Mohamed, A.; Mick, S.L.; Girardi, L.; Gaudino, M.; Lorusso, R. Outcomes of Octogenarians with Primary Malignant Cardiac Tumors: National Cancer Database Analysis. J. Clin. Med. 2022, 11, 4899. https://doi.org/10.3390/jcm11164899

AMA Style

Rahouma M, Baudo M, Dabsha A, Dimagli A, Mohamed A, Mick SL, Girardi L, Gaudino M, Lorusso R. Outcomes of Octogenarians with Primary Malignant Cardiac Tumors: National Cancer Database Analysis. Journal of Clinical Medicine. 2022; 11(16):4899. https://doi.org/10.3390/jcm11164899

Chicago/Turabian Style

Rahouma, Mohamed, Massimo Baudo, Anas Dabsha, Arnaldo Dimagli, Abdelrahman Mohamed, Stephanie L. Mick, Leonard Girardi, Mario Gaudino, and Roberto Lorusso. 2022. "Outcomes of Octogenarians with Primary Malignant Cardiac Tumors: National Cancer Database Analysis" Journal of Clinical Medicine 11, no. 16: 4899. https://doi.org/10.3390/jcm11164899

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop