Abstract
Aims
Whether a history of malignancy affects heart failure (HF) management and prognosis is unclear. In HF randomized controlled trials, enrolment has been limited to patients with a cancer diagnosis at least 2 years before screening. We investigated clinical profile, HF treatment patterns, and outcomes of patients with a history of cancer of >2 years.
Methods and results
Among 53 314 patients included in the Swedish HF Registry between 2000 and 2020, 9066 (17%) had previous cancer (diagnosed >2 years prior to index date), most frequently of prostate (26%), breast (15%), colon (11%), and haematologic system (11%). Previous cancer was associated with older age, female sex, comorbidity burden, and less likely specialized follow‐up. Over a median follow‐up of 2.4 (0.0–5.0) years, mortality rates were 24 (95% confidence interval [CI] 23–25) and 18 (95% CI 18–19) per 100 subject‐years in patients with versus without previous cancer, respectively. Cancer accounted for 16% of deaths in the previous cancer group and for 5.6% in the no‐cancer group. Previous cancer was independently associated with higher risk of all‐cause death (adjusted hazard ratio [HR] 1.14, 95% CI 1.11–1.18), non‐cardiovascular death (adjusted HR 1.38, 95% CI 1.31–1.44), and first all‐cause hospitalization (adjusted HR 1.11, 95% CI 1.09–1.14). The risk of non‐cardiovascular death declined with increasing time from cancer diagnosis. In patients with HF and reduced ejection fraction (HFrEF), previous cancer was associated with less frequent use of mineralocorticoid receptor antagonists, triple pharmacotherapy, and HF devices.
Conclusions
Previous cancer was common among patients with HF, and it was associated with comorbidity burden, non‐cardiovascular outcomes and, in HFrEF, with lower use of guideline‐recommended therapies.
Keywords: Cardio‐oncology, Heart failure, Ejection fraction, Comorbidity, Outcomes, Prognosis
Introduction
Cancer and heart failure (HF) are the two leading causes of death worldwide and often coexist, representing individually and together a burden for public health systems. 1 Although HF age‐adjusted incidence is decreasing due to better control of risk factors, the prevalence is increasing, partly due to an ageing population and better HF treatment and management. 2 , 3
Improvements in cancer therapies have increased survival following the diagnosis of a malignancy, and more patients with a history of cancer now develop HF as a consequence of the prolonged exposure to cardiovascular (CV) risk factors, heart disease and, possibly, cancer treatment‐related CV toxicity (CTR‐CVT). 4
Real‐world data on patients with HF and previous cancer are limited, leading to uncertainty regarding the current implementation of HF treatments in this subgroup and whether prognosis varies depending on when malignancy was diagnosed. 5 Answering these questions may inform the design of future randomized controlled trials (RCTs) in the field. So far, enrolment of patients with previous cancer in HF RCTs has been limited to those in whom cancer was found >2 years, and often >5 years before screening, 6 , 7 in order to exclude subjects with malignancies that could still have an impact on major outcomes and, therefore, shadow the effects of the tested intervention. However, this assumption is yet to be verified. 8
By analysing a large, nationwide, registry‐based cohort, we aimed to provide a full characterization of patients with HF and previous cancer in terms of (i) clinical profiles, (ii) HF treatment and management, and (iii) clinical outcomes.
Methods
Data sources
The study population was derived from the Swedish HF Registry (SwedeHF), which has previously been described. 9 Briefly, it is a nationwide health quality and research registry started in 2000, including in‐ and outpatients with HF regardless of ejection fraction (EF) and previous history of HF. Around 80 variables are recorded at hospital discharge or the outpatient visit prompting entry into SwedeHF, that is the index date. Until April 2017, the only inclusion criterion was a clinical diagnosis of HF; thereafter, HF was defined according to the International Statistical Classification of Diseases, Tenth Revision (ICD‐10) codes I50.0, I50.1, I50.9, I42.0, I42.6, I42.7, I25.5, I11.0, I13.0, I13.2. In 2021, SwedeHF had a 32% coverage of prevalent HF in Sweden. Through the unique personal identification number held by all Swedish residents, SwedeHF was linked with the Swedish Cancer Register and other national administrative registers. 10
The Swedish Cancer Register provided information on formerly diagnosed cancers (date and site). It was established in 1958 and has nationwide coverage, since the registration of newly detected tumours in Swedish residents is compulsory. 11
Statistics Sweden provided socioeconomic data (income, level of education, living environment). The National Patient Register provided additional comorbidities and cause‐specific hospitalization outcomes.
The Cause of Death Register provided data on cause‐specific mortality (online supplementary Table Appendix S1 ).
Patient selection, definitions, and outcomes
We included patients ≥18 years old with chronic HF (≥6 months after an HF diagnosis), available data on EF and an index visit in SwedeHF between 2000 and 2020 (online supplementary Figure Appendix S1 ). For patients with >1 registration in SwedeHF, the last one was selected to better represent contemporary care. After excluding patients with any malignancy within the past 2 years in the National Cancer Registry, we identified those with the latest diagnosis recorded in the National Cancer Registry >2 years prior to index date in SwedeHF. 12 This criterion, corresponding to the shortest time interval allowing enrolment of patients with a history of malignancy in HF RCTs, 13 , 14 defined the group of subjects with previous cancer in this analysis. We considered all malignancy sites, except for non‐melanoma skin cancer, while benign tumours were excluded (online supplementary Table S2 ).
Patients with previous cancer were compared with those without any history of cancer. Time from last malignancy diagnosis was further categorized as 2–3, 4–5, 6–10, 11–15 and ≥15 years before index date in SwedeHF.
Patients were also categorized as with HF with preserved EF (HFpEF) if EF was ≥50%, HF with mildly reduced EF (HFmrEF) if EF was 40–49%, and HF with reduced EF (HFrEF) if EF was <40%, since in SwedeHF EF was collected as a categorical variable (i.e. <40%, 40–49%, ≥50%) in most patients.
Triple therapy for HFrEF was defined as a combination of a beta‐blocker, a mineralocorticoid receptor antagonist (MRA), and an angiotensin‐converting enzyme inhibitor (ACEi) or angiotensin receptor blocker (ARB) or angiotensin receptor–neprilysin inhibitor (ARNi). Sodium–glucose co‐transporter 2 inhibitors were not considered since not having indication for HF during the study period. Doses were reported as percentage of achieved target dose according to the 2021 European Society of Cardiology (ESC) HF guidelines (online supplementary Table S3 ).
Follow‐up time was censored at 5 years from index date in SwedeHF, death, or emigration from Sweden, or on 31 December 2021.
Outcomes were all‐cause death, CV death, non‐CV death, time to first HF hospitalization, time to first any‐cause hospitalization, and total HF hospitalizations (defined according to ICD‐10 codes as reported in online supplementary Table Appendix S1 ).
Statistical analysis
Patient characteristics at index date were presented as frequencies (percentages) if categorical, and medians (interquartile range [IQR]) if continuous, and compared by χ 2 test and Kruskal–Wallis test, respectively, in patients with versus without previous cancer in the overall HF population and within EF phenotypes, according to time from malignancy diagnosis categories, and cancer sites.
Multivariable logistic regression models, including as covariates the variables labelled with ‘a’ in Table 1 , were fitted to identify patient characteristics independently associated with previous cancer, and results were reported as odds ratios (OR) and 95% confidence intervals (CI). Separate analyses were performed stratifying by EF category. In patients with HFrEF and previous cancer, additional multivariable models were performed to identify cancer types and time from malignancy diagnosis independently associated with likelihood of receiving HFrEF guideline‐directed medical therapies, including triple therapy.
Table 1.
Baseline characteristics of the study population according to previous cancer diagnosis
| No previous cancer | Previous cancer | p‐value | |
|---|---|---|---|
| n (%) | 44 248 (83) | 9066 (17) | |
| Time since last cancer (years) | – | 9.6 [5.2–12.62] | – |
| Time since last cancer (years) | – | ||
| 2–3 | – | 1179 (13) | |
| 4–5 | – | 1262 (14) | |
| 6–10 | – | 2491 (27) | |
| 11–15 | – | 1670 (18) | |
| >15 | – | 2464 (27) | |
| Number of previous cancers | – | ||
| 1 | – | 7966 (88) | |
| 2 | – | 970 (11) | |
| 3 | – | 108 (1) | |
| ≥4 | – | 22 (0) | |
| Index year a | <0.001 | ||
| 2000–2010 | 12 449 (28) | 2164 (24) | |
| 2011–2015 | 11 981 (27) | 2595 (29) | |
| 2016–2018 | 10 613 (24) | 2309 (25) | |
| 2019–2020 | 9205 (21) | 1998 (22) | |
| Sex a | <0.001 | ||
| Female | 15 397 (35) | 3504 (39) | |
| Male | 28 851 (65) | 5562 (61) | |
| Age (years) | 77 [68–83] | 80 [74–85] | <0.001 |
| Age strata (years) a | <0.001 | ||
| <70 | 12 639 (29) | 1214 (13) | |
| 70–80 | 15 936 (36) | 3553 (39) | |
| >80 | 15 673 (35) | 4299 (47) | |
| Setting a | 0.004 | ||
| Outpatient | 28 700 (65) | 5734 (63) | |
| Inpatient | 15 548 (35) | 3332 (37) | |
| Follow‐up in referral HF nurse clinic a | 22 041 (53) | 4484 (53) | 0.475 |
| Follow‐up setting a | <0.001 | ||
| Primary care/other | 17 325 (41) | 3916 (45) | |
| Hospital | 24 833 (59) | 4731 (55) | |
| Family type a | 0.023 | ||
| Cohabitating | 22 089 (50) | 4648 (51) | |
| Living alone | 22 121 (50) | 4416 (49) | |
| Children a | 36 808 (83) | 7894 (87) | <0.001 |
| Education a | <0.001 | ||
| Compulsory school | 19 127 (44) | 3947 (44) | |
| Secondary school | 17 026 (39) | 3373 (38) | |
| University | 7038 (16) | 1640 (18) | |
| Income a | <0.001 | ||
| 1st tertile within year (lowest) | 14 788 (33) | 2771 (31) | |
| 2nd tertile within year | 14 358 (32) | 3228 (36) | |
| 3rd tertile within year (highest) | 15 064 (34) | 3065 (34) | |
| Medical history | |||
| Smoking a | 3482 (10) | 474 (7) | <0.001 |
| Diabetes a | 13 601 (31) | 2634 (29) | 0.002 |
| Hypertension a | 30 580 (69) | 6530 (72) | <0.001 |
| Ischaemic heart disease a | 26 722 (60) | 5468 (60) | 0.899 |
| Revascularization | 13 669 (32) | 2556 (29) | <0.001 |
| Stroke a | 7430 (17) | 1607 (18) | 0.032 |
| Atrial fibrillation/flutter a | 27 347 (62) | 6088 (67) | <0.001 |
| Anaemia a | 14 958 (37) | 3736 (45) | <0.001 |
| Valvular heart disease a | 10 834 (24) | 2361 (26) | 0.002 |
| Liver disease | 1169 (3) | 189 (2) | 0.002 |
| COPD a | 6968 (16) | 1567 (17) | <0.001 |
| Clinical characteristics | |||
| EF (%) a | <0.001 | ||
| HFrEF | 22 059 (50) | 4217 (47) | |
| HFmrEF | 10 698 (24) | 2277 (25) | |
| HFpEF | 11 491 (26) | 2572 (28) | |
| NYHA class | <0.001 | ||
| I | 3690 (11) | 544 (8) | |
| II | 14 627 (44) | 2843 (43) | |
| III | 13 253 (40) | 2910 (44) | |
| IV | 1456 (4) | 308 (5) | |
| NYHA class a | <0.001 | ||
| I–II | 18 317 (55) | 3387 (51) | |
| III–IV | 14 709 (45) | 3218 (49) | |
| BMI (kg/m2) | 27 [24–31] | 26 [23–29] | <0.001 |
| BMI (kg/m2) a | <0.001 | ||
| <30 | 20 030 (71) | 4614 (78) | |
| ≥30 | 8224 (29) | 1338 (22) | |
| Systolic blood pressure (mmHg) | 123 [110–140] | 124 [110–140] | 0.825 |
| Diastolic blood pressure (mmHg) | 70 [64–80] | 70 [60–80] | <0.001 |
| Mean arterial pressure (mmHg) | 90 [80–98] | 89 [80–97] | <0.001 |
| Mean arterial pressure (mmHg) a | 0.005 | ||
| ≤90 | 23 411 (55) | 4943 (56) | |
| >90 | 19 318 (45) | 3814 (44) | |
| Heart rate (bpm) | 70 [62–80] | 71 [63–80] | 0.004 |
| Heart rate (bpm) a | 0.012 | ||
| ≤70 | 21 167 (51) | 4234 (50) | |
| >70 | 20 243 (49) | 4300 (50) | |
| NT‐proBNP (pg/ml) | 1930 [730–4710] | 2442 [1068–5783] | <0.001 |
| NT‐proBNP (pg/ml) a | <0.001 | ||
| 1st tertile within EF | 8487 (34) | 1356 (26) | |
| 2nd tertile within EF | 8061 (33) | 1793 (34) | |
| 3rd tertile within EF | 8086 (33) | 2064 (40) | |
| Laboratory | |||
| eGFR (ml/min/1.73 m2) | 60 [43–80] | 55 [40–73] | <0.001 |
| eGFR (ml/min/1.73 m2) a | <0.001 | ||
| ≥60 | 21 610 (50) | 3699 (42) | |
| <60 | 21 279 (50) | 5113 (58) | |
| Potassium (mmol/L) | 4.2 [4.0–4.5] | 4.2 [3.9–4.5] | 0.118 |
| Potassium (mmol/L) a | 0.009 | ||
| Normokalaemia | 32 476 (92) | 6730 (91) | |
| Hypokalaemia | 1304 (4) | 321 (4) | |
| Hyperkalaemia | 1483 (4) | 338 (5) | |
| Haemoglobin (g/L) | 132 [119–144] | 127 [116–140] | <0.001 |
| Medical therapy | |||
| ACEi/ARB/ARNi a | 36 494 (83) | 7195 (80) | <0.001 |
| Target dose ACEi/ARB/ARNi (%) | <0.001 | ||
| No/Missing ACEi/ARB/ARNi | 7754 (18) | 1871 (21) | |
| 1–49 | 10 659 (24) | 2469 (28) | |
| 50–99 | 10 353 (24) | 2099 (23) | |
| ≥100 | 15 072 (34) | 2534 (28) | |
| Beta‐blocker a | 39 048 (89) | 7857 (87) | <0.001 |
| Target dose beta‐blocker (%) | <0.001 | ||
| No/missing beta‐blocker | 5200 (12) | 1209 (14) | |
| 1–49 | 11 807 (27) | 2595 (29) | |
| 50–99 | 13 111 (30) | 2624 (30) | |
| ≥100 | 12 981 (30) | 2412 (27) | |
| MRA a | 18 652 (42) | 3462 (38) | <0.001 |
| Target dose MRA (%) | <0.001 | ||
| No/missing MRA | 25 596 (68) | 5604 (71) | |
| 1–49 | 1506 (4) | 353 (4) | |
| 50–99 | 8053 (21) | 1539 (20) | |
| ≥100 | 2359 (6) | 390 (5) | |
| Triple therapy | 14 741 (33) | 2583 (28) | <0.001 |
| Diuretic a | 35 072 (80) | 7449 (83) | <0.001 |
| Nitrate a | 7182 (16) | 1566 (17) | 0.014 |
| Digoxin a | 6310 (14) | 1269 (14) | 0.523 |
| Oral anticoagulant a | 22 191 (50) | 4672 (52) | 0.013 |
| Platelet inhibitor a | 17 148 (39) | 3272 (36) | <0.001 |
| Statin a | 22 431 (51) | 4226 (47) | <0.001 |
| CRT/ICD a | 5772 (13) | 937 (10) | <0.001 |
ACEi, angiotensin‐converting enzyme inhibitor; ARB, angiotensin receptor blocker; ARNi, angiotensin receptor–neprilysin inhibitor; BMI, body mass index; COPD, chronic obstructive pulmonary disease; CRT, cardiac resynchronization therapy; EF, ejection fraction; eGFR, estimated glomerular filtration rate; HF, heart failure; HFmrEF, heart failure with mildly reduced ejection fraction; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction; ICD, implantable cardioverter‐defibrillator; MRA, mineralocorticoid receptor antagonist; NT‐proBNP, N‐terminal pro‐B‐type natriuretic peptide; NYHA, New York Heart Association.
eGFR was calculated by using the Chronic Kidney Disease Epidemiology Collaboration formula. Anaemia was defined as haemoglobin <120 g/L in females and <130 g/L in males.
Included in multiple imputation and regression models.
The associations between previous cancer and time‐to‐event outcomes were assessed by univariable and multivariable Cox proportional hazard regressions (adjusted for variables labelled with ‘a’ in Table 1 ) in the overall cohort and across the EF spectrum, and results were presented as hazard ratio (HR) with 95% CI. The proportional hazards assumption was investigated using the scaled Schoenfeld residuals for the primary outcome and the model was stratified for ACEi/ARB//ARNi/MRA and type of follow‐up referral. Recurrent events were modelled using a negative binomial regression including the log of time as an offset in the model.
In further multivariable Cox proportional hazard models, previous cancer was modelled as time from malignancy to the index date by using restricted cubic spline with five knots placed at the quintiles, in order to display crude and adjusted HRs for CV and non‐CV death, with 2–3 years from index date as reference.
Recurrent events were modelled using negative binomial regression including the log of time as an offset in the model.
Survival curves were depicted as cumulative incidence curves. For cause‐specific outcomes, consistency analyses were performed by Fine–Gray sub‐distributional hazards models treating death as a competing event. 15
Variables included in the multivariable models with missing entries were handled by multiple imputation (n = 10 imputed dataset) with variables labelled with ‘a’ in Table 1 included in the model. 16
All analyses were performed using R version 4.3.1 (2023‐06‐16 ucrt). The level of significance was set to 5%, two‐sided.
The R code for all data handling and statistical analyses are found at https://github.com/KIHeartFailure/swedehf‐previouscancer.
Results
Prevalence and types of previous cancer
Of 53 314 patients with chronic HF (47% HFrEF, 25% HFmrEF, 28% HFpEF), with median age 77 years (IQR 69–84) and 18 901 (35%) females, 9066 (17%) had previous cancer (16% with HFrEF, 18% with HFmrEF, and 18% with HFpEF) (Table 1 ). The median time between a previous diagnosis of cancer and the index date was 9.6 (IQR 5.2–16.2) years. The latest cancer diagnosis was recorded 2–3 years prior to the index date in 13% of patients with previous cancer, 4–5 years in 14%, 6–10 years in 27%, 11–15 years in 18%, and >15 years in 27%. Cancer sites were prostate (26%), breast (15%), colon (11%), blood (11%) urinary tract (10%), gynaecological organs (6%), skin (melanoma, 6%), endocrine organs (4%), other gastrointestinal (2%), lung (2%) and others (6%).
Patient characteristics according to a previous diagnosis of cancer
Heart failure patients with previous cancer were more likely female, older, encountered in an inpatient setting at the index HF visit, and referred to follow‐up in primary care than those without any cancer. They more likely had children and were less likely in the lowest income tertile. Interestingly, they less likely had a history of smoking, diabetes mellitus, or prior coronary revascularization, but more likely had hypertension, atrial fibrillation (AF), anaemia, valvular heart disease (VHD), and chronic obstructive pulmonary disease (COPD). They had higher New York Heart Association (NYHA) functional class, N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP) levels, and heart rate, but lower body mass index and estimated glomerular filtration rate (eGFR). Patients with previous cancer were less likely treated with HF medications (renin–angiotensin system inhibitors/ARNi, beta‐blockers, MRA), were less frequently on target doses of these drugs, were less likely treated with cardiac resynchronization therapy (CRT)/implantable cardioverter‐defibrillator (ICD), platelet inhibitors, and statins, but were more likely on oral anticoagulants, diuretics, digoxin, and nitrates (Table 1 ). These differences were largely consistent across the EF spectrum (online supplementary Tables S4–S6 ).
With increasing time from the latest cancer diagnosis, patients were more likely older, female, with HFpEF, impaired renal function, and referred for follow‐up in primary care, but less likely smokers, with a prior coronary revascularization, and anaemia (online supplementary Table S7 ). These results were overall consistent across EF strata (online supplementary Tables S8–S10 ).
Patient characteristics according to the site of the last recorded malignancy are presented in online supplementary Tables S11–S14 .
Independent associations between patient characteristics and previous cancer
Patient characteristics independently associated with previous cancer were, in descending order of magnitude, older age and a later period of registration in SwedeHF, higher income/education, anaemia, having children, COPD, AF, and lower eGFR, whereas use of oral anticoagulant, male sex, CRT/ICD, living alone, treatment with platelet inhibitors, higher body mass index, use of MRA/statins, and smoking were less likely associated with previous cancer (Figure 1 ).
Figure 1.
Patient characteristics independently associated with previous cancer. Odds ratios with 95% confidence intervals (CI) in multivariable logistic regression model (adjustment for variables labelled with ‘a’ in Table 1 ). ACEi, angiotensin‐converting enzyme inhibitor; ARB, angiotensin receptor blocker; ARNi, angiotensin receptor–neprilysin inhibitor; BMI, body mass index; COPD, chronic obstructive pulmonary disease; CRT, cardiac resynchronization therapy; eGFR, estimated glomerular filtration rate; HFmrEF, heart failure with mildly reduced ejection fraction; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction; ICD, implantable cardioverter‐defibrillator; MAP, mean arterial pressure; MRA, mineralocorticoid receptor antagonist; NT‐proBNP, N‐terminal pro‐B‐type natriuretic peptide; NYHA, New York Heart Association. eGFR was calculated by using the Chronic Kidney Disease Epidemiology Collaboration formula. Anaemia was defined as haemoglobin <120 g/L in females and <130 g/L in males.
These associations were mostly consistent across the EF spectrum with some exceptions. In patients with HFrEF, COPD was not associated with previous cancer (online supplementary Figure S2 ). In patients with HFmrEF, previous cancer was associated with VHD, but not with AF, lower eGFR, or COPD, nor was it negatively associated with MRA or CRT/ICD (online supplementary Figure S3 ). In HFpEF, previous cancer was not independently associated with AF, having children or education level, and the only factors associated with lower likelihood of previous cancer were living alone and male sex (online supplementary Figure S4 ).
In patients with HFrEF, previous cancer was independently associated with lower odds of being treated with MRA, triple therapy, and CRT/ICD. There was no independent association between time from latest malignancy diagnosis and likelihood of receiving HF treatments (Figure 2 ).
Figure 2.
Independent associations between previous cancer and heart failure with reduced ejection fraction therapy. Odds ratios with 95% confidence intervals (CI) in multivariable logistic regression model (adjustment for variables labelled with ‘a’ in Table 1 ). ACEi, angiotensin‐converting enzyme inhibitor; ARB, angiotensin receptor blocker; ARNi, angiotensin receptor–neprilysin inhibitor; CRT, cardiac resynchronization therapy; ICD, implantable cardioverter‐defibrillator; MRA, mineralocorticoid receptor antagonist. Triple therapy was defined as a combination of beta‐blocker, MRA, and ACEi, ARB or ARNi.
Clinical outcomes
Over a median follow‐up of 2.4 (0.0–5.0) years, 5360 (59%) patients with HF and previous cancer died versus 21 907 (49%) patients with HF without previous cancer, with event rates of 24 (95% CI 23–25) and 18 (95% CI 18–19) deaths per 100 patient‐years, respectively.
As shown in Figure 3 , cancer mortality was more likely in the former group (16% vs. 5.6%).
Figure 3.
Causes of death in patients with or without previous cancer. The columns display the absolute numbers and the percentages of patients without (light blue) or with (red) a history of cancer who died from the diseases listed below the graph. AF, atrial fibrillation; CV, cardiovascular; COVID‐19, coronavirus disease 2019; DM, diabetes mellitus; HF, heart failure; IHD, ischaemic heart disease; HF, heart failure; MI, myocardial infarction; VHD, valvular heart disease. ***p < 0.001; **p < 0.01.
In the overall cohort, previous cancer was associated with higher crude risk of all the investigated outcomes. After adjustments, previous cancer was independently associated with 14% higher risk of all‐cause death, 38% higher risk of non‐CV death, 11% higher risk of first all‐cause hospitalization, and a 7% higher risk of total HF hospitalizations, but there was no association with CV death or first HF hospitalization (Figure 4 ).
Figure 4.
Associations of previous cancer with clinical outcomes. Cumulative incidence curves of primary and secondary outcomes. Results are displayed as crude and adjusted hazard ratios (HR) or crude and adjusted risk ratio (RR). The variables considered for adjustment are labelled with ‘a’ in Table 1 . CI, confidence interval; CV, cardiovascular; CVD, cardiovascular death; HF, heart failure; HFH, heart failure hospitalization.
The independent associations between previous cancer and higher risk of all‐cause death, non‐CV death, and first all‐cause hospitalization were confirmed across the EF phenotypes (online supplementary Figures S5–S7 ). Previous cancer was also associated with a higher risk of first HF hospitalization and CV death in patients with HFmrEF, but not with HFrEF or HFpEF, and with higher rates of total HF hospitalizations in patients with HFmrEF and HFpEF, but not HFrEF.
Results for the cause‐specific outcomes were consistent in the Fine–Gray analyses (for competing risk), except that previous cancer was not independently associated with higher risk of CV death in HFmrEF, but it was in HFrEF (online supplementary Tables S15–S18 ).
The shorter the time since the last malignancy diagnosis was, the higher cancer mortality was: cancer accounted for 24.5%, 19.1%, 17.6%, 14.5%, and 10.0% of all deaths in patients with a diagnosis 2–3 years, 4–5 years, 6–10 years, 11–15 years, and >15 years before the index date, respectively (online supplementary Table S19 ). This trend persisted when patients with HFrEF, HFmrEF, and HFpEF were considered separately (online supplementary Tables S20–S22 ).
When time from cancer diagnosis was modelled as a continuous variable using restricted cubic splines, increasing time was associated with progressively lower risk of non‐CV death, while the risk of CV death increased (Figure 5 ). When time from cancer diagnosis was categorized as 2–3 years (reference group), 4–5 years, 6–10 years, 11–15 years, and ≥15 years before the index date, longer time since the last diagnosis of malignancy was associated with lower crude and adjusted risk of non‐CV death in the overall HF cohort and in the HFmrEF and HFpEF groups. The crude but not adjusted risk of CV death increased with time since cancer diagnosis (online supplementary Figure S8 ).
Figure 5.
Spline curves depicting the risk of cardiovascular (CV) and non CV death according to the time of previous cancer. Results are displayed as crude and adjusted hazard ratio (HR) with 95% confidence intervals (CI). HFmrEF, heart failure with mildly reduced ejection fraction; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction.
Discussion
With increasing HF prevalence and improved oncological therapies and, thereby, cancer survivorship, the population of patients with HF and previous cancer is growing. In this study, we characterized this specific patient group out of a cohort of >50 000 subjects with HF across the EF spectrum. Our main findings were that: (i) one in five patients with HF had a history of cancer >2 years, and generally much longer than 2 years, before the index date in SwedeHF; (ii) as compared with patients without previous cancer, those with previous cancer were older and more likely female, symptomatic from HF, and with comorbidities, such as AF, anaemia, COPD, and chronic kidney disease; (iii) guideline‐recommended therapies for HFrEF were less likely used in patients with HF and previous cancer; and (iv) previous cancer was mainly associated with higher risk of non‐CV death and hospitalizations, in the range of 10–60% higher risk in adjusted and unadjusted analyses (Graphical Abstract).
Prevalence and phenotype of patients with heart failure and previous cancer
The relationship between HF and cancer has mostly been analysed from the perspective of HF being associated with or even leading to incident cancer. Several studies have shown a higher risk of cancer in HF patients. 17 This association may be stronger with age 18 and in patients with HFrEF due to ischaemic aetiology, 19 and has been confirmed after excluding potential surveillance bias linked with a more recent HF diagnosis. 20
We instead focused on patients with HF and a history of malignancy. The prevalence of previous cancer in SwedeHF was similar to that reported in other real‐world HF cohorts. In over 150 000 patients with newly diagnosed HF from Danish nationwide administrative databases, 21 previous cancer was found in 17% of cases and was also linked to an increased risk of developing new cancers. In another large analysis of a national ambulatory cohort of US veterans with HF, 22 19% had a history of cancer, which was associated with a higher burden of comorbidities and mainly represented by two distinct malignancies, that is breast and prostate.
Also consistent with previous studies, 22 we found a slightly lower prevalence of prior cancer in HFrEF as compared with HFmrEF and HFpEF, which might be explained by heightened cancer risk with the well‐known heavier comorbidity burden with increasing EF. 23
By contrast, the prevalence of previous cancer was higher in our and other real‐world analyses than in HF RCTs. Prior cancer was recorded for 4.3% of patients with HFrEF in the pooled PARADIGM‐HF and ATMOSPHERE cohorts, and in 8.5% of patients with EF ≥45% in the combined PARAGON‐HF and CHARM‐Preserved cohorts. 24 This discrepancy may be explained by the younger age and less comorbid profile of participants in RCTs. 8
Patients with HF and previous cancer in our study were characterized by older age, female sex, and distinctive comorbidities, that is chronic kidney disease, AF, COPD, and anaemia.
The association of female sex with previous cancer might be due to greater survival from cancer in women than in men, because more aggressive malignancies, especially lung cancer, prevail in the latter. Sex differences have been conflictingly described in studies about incident cancer in HF. 24 , 25 , 26 It is possible that sex‐specific factors modulate the link between HF and cancer, for example sexual hormones and CTR‐CVT of sex‐specific cancer treatments.
Comorbidities such as chronic kidney disease and COPD share risk factors with both cancer and HF, and may also favour the development of either condition via common pathophysiological pathways, such as low‐grade systemic inflammation. 27 , 28 , 29 Experimental investigations also support the hypothesis that HF itself promotes tumour progression by stimulating cancer cell proliferation. 30 Additionally, neurohormonal activation, which is central to HFrEF and HFmrEF pathophysiology and might play a role in a subgroup of patients with HFpEF, has repeatedly been shown to facilitate cancer growth and spreading. 31 , 32
From the clinical standpoint, previous cancer identified a subset of HF patients, older and with comorbidities, who might benefit from closer surveillance and specialized medical care during follow‐up. However, subjects with previous cancer in SwedeHF were more likely to be referred to primary care, suggesting that HF management might be de‐prioritized in these patients in favour, for example, of a specialized follow‐up for cancer or other comorbidities.
We also found an association between higher income/education and previous cancer, which may be explained by easier access to the healthcare system for wealthier people, leading to higher chances of being diagnosed with and/or surviving from cancer. Conversely, obesity was inversely associated with previous cancer, possibly because malignancy induces some degree of weight loss that persists over time.
Smoking was also less common in patients with than without previous cancer, but this might be due to the fact that this habit was quitted upon cancer diagnosis. Indeed, COPD, which is strongly related to smoking, was more frequent in subjects with than without previous cancer.
In our HFrEF population, previous cancer was independently associated with a lower likelihood of receiving MRA, guideline‐directed triple medical therapy, and CRT or ICD, despite higher NYHA class and NT‐proBNP concentrations, which, instead, should prompt more aggressive HF treatment. Given the significant proportion of patients with a history of malignancy lasting more than 6 years, it can be hypothesized that the reduced use of therapy for HF was related to clinical inertia or bias rather than to poor tolerability or other factors limiting treatment implementation. The low referral to specialized follow‐up may also have contributed to the insufficient prescription of guideline‐directed medical therapy, since specialty care referral was associated with greater HF therapy use and improved outcomes in patients with HF. 33 , 34
Prognostic impact of previous cancer
In SwedeHF, previous cancer was independently associated with higher risk of all‐cause death and first all‐cause hospitalization, primarily due to non‐CV events, both in the overall population and across the EF strata.
We observed a three‐fold higher risk of cancer mortality in patients with previous cancer, which implies that the 2‐year cut‐off we adopted might not ensure that cancer was over. Accordingly, as many as one in four patients with a diagnosis of malignancy 2–3 years before the index date in SwedeHF died from cancer. As time from diagnosis extends, the chances of a still ongoing cancer might decrease. Nonetheless, survivors may face recurrent or new, subsequent malignancies, which can be the result of genetic predisposition, acquired risk factors, and systemic toxicity of chemotherapy and radiotherapy. 34 , 35 Our analysis also highlights this scenario. The excess risk of cancer death decreased with longer time from the last diagnosis but never disappeared: the proportion of deaths from cancer in HF patients diagnosed with a malignancy >15 years prior to the index date was still the double of the one in HF patients without any history of malignancy.
Advanced age and comorbidities are other factors potentially contributing to the worse outcomes of these patients. Long‐term consequences of cancer treatment other than CTR‐CVT may also play a role. Cancer therapies may cause senescence of multiple tissues, immune dysregulation and chronic inflammation, which in turn lead to multi‐organ, clinically relevant disorders, such as sarcopenia, neuropathy, and bone marrow failure. 36 , 37 These adverse effects may ultimately determine an increased risk of non‐CV hospitalization and death. Hence, previous cancer affects the prognosis of HF patients even several years later.
Considerations on the design of heart failure randomized controlled trials
Patients with HF and previous cancer are under‐represented in HF RCTs, 38 which usually exclude individuals with conditions carrying a significant competing risk of non‐CV death, as this reduces the relative incidence of CV outcomes and potential treatment effect. Life expectancy below a certain duration, as judged by the investigators, is the criterion used to determine whether a concomitant disease prevents from participating in a RCT. 39 By following this approach, a history of cancer over the last years preceding enrolment is often considered a reason for exclusion from HF RCTs. 38 In some HF RCTs, cancer is instead clearly mentioned as an exclusion criterion when it has occurred within a specific time. The most conservative RCT design leaves out any patient with cancer within 5 years, 39 while the most permissive reduces the interval to 2 years before enrolment. 13 , 14 This is the time cut‐off we considered for the present analysis, which therefore addresses the RCT setting involving most patients with prior malignancy.
Our results indicate that previous cancer, as defined by a diagnosis >2 years before, dictates prognosis because of non‐CV events and justify the adoption of more stringent cut‐offs. The inclusion in HF RCTs of patients with cancer within the previous 2–5 years might still be motivated whether cancer can be considered as cured. However, comprehensive oncological expertise, potentially beyond the scope of cardiologists and HF specialists, might be necessary to conclude that cancer has been completely eradicated in these patients.
Limitations
First, we cannot exclude unknown and unmeasured confounders. Second, patients enrolled in SwedeHF are younger, have fewer comorbidities, are better treated, and have better outcomes than patients with HF not enrolled in the registry, thereby limiting generalizability to the broader HF population. Third, despite the linkage of SwedeHF and the National Cancer Register, providing comprehensive data on cancer diagnoses, we did not have access to cancer staging and treatments that might mediate the association between former malignancies and adverse outcomes. Finally, we could not investigate the effects of HF pharmacotherapy on second cancer incidence and cancer mortality. This topic represents an emerging area of research, since preclinical studies suggest that HF drugs may favourably modulate tumour biology. 40
Conclusions
In a large nationwide HF population, approximately 20% of patients with HF had previous cancer. Irrespective of EF, they were characterized by older age, female sex, and heavier comorbidity burden, representing a frailer subset of patients with HF and poorer long‐term prognosis, mainly driven by non‐CV events. Previous cancer was also associated with less use of MRA, triple pharmacotherapy, and cardiac device in HFrEF patients, despite the higher burden of symptoms.
Our data highlight the need of better access to and optimization of HF care in HF patients with previous cancer, and to carefully balance the benefits in terms of generalizability deriving from their inclusion in RCTs with the potential harm deriving from competing events.
Supporting information
Appendix S1. Supporting Information.
Acknowledgements
We thank all staff members at all care units in Sweden for their contribution to the SwedeHF.
Funding
This study received support through grants from the Swedish Heart and Lung Foundation (project number 20220680) to dr. Savarese.
Conflict of interest: P.A. received speaker and/or advisor fees from Boehringer Ingelheim, Daiichi‐Sankyo, Janssen, MSD, and Gossamer Bio; the Department of Internal Medicine made an agreement with Bayer for scientific consultancy carried out by P.A. D.S. reports personal fees from Novartis, Merck, AstraZeneca, Janssen, Dompe', Bruno Farmaceutici, Gossamer Bio and Novo Nordisk, outside the submitted work. N.V.S. reports a grant from the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund (New National Excellence Program, EKÖP‐2024‐182). C.L. received speaker fees from AstraZeneca, Pfizer, Novo Nordisk, Boehringer Ingelheim and Pharmacosmos. J.B. served as consultant for Abbott, Adaptyx, American Regent, Amgen, AskBio, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol Myers Squibb, Cardiac Dimension, Cardior, CSL Vifor, CVRx, Cytokinetics, Daxor, Diastol, Edwards, Element Sciences, Faraday, Idorsia, Impulse Dynamics, Imbria, Innolife, Intellia, Inventiva, Levator, Lexicon, Eli Lilly, Mankind, Medtronic, Merck, New Amsterdam, Novartis, NovoNordisk, Pfizer, Pharmacosmos, Pharmain, Prolaio, Pulnovo, Regeneron, Renibus, Reprieve, Roche, Rycarma, Saillent, Salamandra, Salubris, SC Pharma, SQ Innovation, Secretome, Sequanna, Transmural, TekkunLev, Tenex, Tricog, Ultromic, Vera, Zoll. S.D.A. reports grants and personal fees from Vifor and Abbott Laboratories, and personal fees for consultancies, trial committee work and/or lectures from Actimed, Alleviant, AstraZeneca, Bayer, Berlin Heals, Boehringer Ingelheim, Brahms, Cardiac Dimensions, Cardior, Cordio, CVRx, Cytokinetics, Edwards, Impulse Dynamics, Lilly, Mankind Pharma, Medtronic, Novo Nordisk, Occlutech, Pfizer, Regeneron, Relaxera, Repairon, Scirent, Sensible Medical, Vectorious, Vivus, and V‐Wave; named co‐inventor of two patent applications regarding MR‐proANP (DE 102007010834 & DE 102007022367), but he does not benefit personally from the related issued patents. N.G. reports personal fees from Novartis, Bayer, AstraZeneca, Lilly, Boehringer, and Vifor, outside the submitted work. G.F. reports lecture fees and/or advisory and/or trial committee membership by Bayer, Boehringer Ingelheim, Servier, Novartis, Impulse Dynamics, Vifor, Medtronic, Cardior, Novo Nordisk, and research grants from the European Union. L.H.L. declares to author's institution unrelated to the present manuscript: grants and/or consulting from AstraZeneca, Novartis, Boehringer Ingelheim, Bayer, Pharmacosmos, Biopeutics, Owkin, Impulse Dynamics, Novo Nordisk. G.S. reports grants and personal fees from CSL Vifor, Boehringer Ingelheim, AstraZeneca, Servier, Novartis, Cytokinetics, Pharmacosmos, Medtronic, Bayer, and personal fees from Roche, Abbott, Edwards Lifesciences, TEVA, Menarini, INTAS, GETZ, and grants from Boston Scientific, Merck, all outside the submitted work. All other authors have nothing to disclose.
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Associated Data
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Supplementary Materials
Appendix S1. Supporting Information.