Clinical and Biomarker Profiles and Prognosis of Elderly Patients With Coronavirus Disease 2019 (COVID-19) With Cardiovascular Diseases and/or Risk Factors

Shingo Matsumoto; Shunsuke Kuroda; Takahide Sano; Takeshi Kitai; Taishi Yonetsu; Shun Kohsaka; Sho Torii; Takuya Kishi; Issei Komuro; Ken-ichi Hirata; Koichi Node; Yuya Matsue

doi:10.1253/circj.CJ-21-0160

Abstract

Background: This study investigated the effects of age on the outcomes of coronavirus disease 2019 (COVID-19) and on cardiac biomarker profiles, especially in patients with cardiovascular diseases and/or risk factors (CVDRF).

Methods and Results: A nationwide multicenter retrospective study included 1,518 patients with COVID-19. Of these patients, 693 with underlying CVDRF were analyzed; patients were divided into age groups (<55, 55–64, 65–79, and ≥80 years) and in-hospital mortality and age-specific clinical and cardiac biomarker profiles on admission evaluated. Overall, the mean age of patients was 68 years, 449 (64.8%) were male, and 693 (45.7%) had underlying CVDRF. Elderly (≥80 years) patients had a significantly higher risk of in-hospital mortality regardless of concomitant CVDRF than younger patients (P<0.001). Typical characteristics related to COVID-19, including symptoms and abnormal findings on baseline chest X-ray and computed tomography scans, were significantly less prevalent in the elderly group than in the younger groups. However, a significantly (P<0.001) higher proportion of elderly patients were positive for cardiac troponin (cTn), and B-type natriuretic peptide (BNP) and N-terminal pro BNP (NT-proBNP) levels on admission were significantly higher among elderly than younger patients (P<0.001 and P=0.001, respectively).

Conclusions: Elderly patients with COVID-19 had a higher risk of mortality during the hospital course, regardless of their history of CVDRF, were more likely to be cTn positive, and had significantly higher BNP/NT-proBNP levels than younger patients.

As of October 31, 2020, the World Health Organization reported that the case fatality ratio of coronavirus disease 2019 (COVID-19) was approximately 2.6% in the overall infected population,¹ but COVID-19 highlighted the particular vulnerabilities of the aging population.²^–⁴ In addition, elderly patients are generally characterized by a higher incidence of cardiovascular risk factors, such as hypertension, diabetes, dyslipidemia, and a history of cardiovascular diseases,⁵^,⁶ which are some of the comorbidities of COVID-19 that lead to worse outcomes.²^,⁴^,⁷ However, controversy remains regarding the associations between age, cardiovascular comorbidities, and outcomes in patients with COVID-19.⁸^–¹⁰ Furthermore, there have been few reports regarding the prognosis of COVID-19 infection from East Asian countries other than China, where the contribution of cardiovascular diseases and risk factors (CVDRF) is known to differ significantly from those in Western countries.¹¹ Thus, a better understanding of the clinical characteristics and prognosis of the older population in non-Western countries is of glowing importance for the appropriate management of COVID-19. In addition, further research focusing on vulnerable individuals, such as patients with underlying CVDRF, is warranted.

Based on increasing evidence linking COVID-19 with the incidence of de novo cardiovascular issues, cardiac biomarkers are receiving increased attention. Although it has been recognized that cardiac biomarkers, especially cardiac troponin (cTn) and B-type natriuretic peptide (BNP) or N-terminal pro BNP (NT-proBNP), are associated with disease severity and prognosis in patients with COVID-19,¹²^–¹⁴ age-related differences in the profiles of these biomarkers (cardiac and other inflammatory) obtained at the time of admission have not been clearly described.

Thus, the aim of the present study was to clarify the effects of age on outcomes during hospitalization for COVID-19 infection, as well as on cardiac biomarker profiles at the time of admission in hospitalized patients with COVID-19 and CVDRF.

Methods

Ethics Statements

This investigation conforms with the principles outlined in the Declaration of Helsinki. The study protocol, including the use of an opt-out consent method, was approved by the Ethics Committee of Toho University Omori Medical Center (No. M20253) and the local ethics committees of all participating institutions. Furthermore, this clinical study was registered with the University Hospital Medical Information Network (UMIN) Clinical Trial Registry (ID: UMIN000040598) before the first patient was enrolled, in accordance with the International Committee of Medical Journal Editors.

Study Design

The Clinical Outcomes of COVID-19 Infection in Hospitalized Patients with Cardiovascular Diseases and/or Risk Factors (CLAVIS-COVID) was a Japanese nationwide multicenter retrospective study endorsed by the Japanese Circulation Society; the study was designed to evaluate the clinical features and outcomes of hospitalized patients with COVID-19 during the first Japanese wave of the pandemic, namely from January 1, 2020 to May 31, 2020.¹⁵

Even though there are numerous hospitals in Japan, this study focused on including major acute care hospitals that accommodated patients with COVID-19 during the period, resulting in the enrollment of approximately 9.0% (1,518/16,851) of all Japanese confirmed cases of COVID-19 detected by a polymerase chain reaction (PCR) test as of May 31, 2020.¹⁶ In Japan, examinations and treatments for COVID-19 are performed by a single payer system funded by national insurance; thus, physicians have broad discretion to perform examinations (e.g., biomarkers and imaging tests) and treatments, including invasive ventilation and extracorporeal membrane oxygenation. The COVID-19 infection was diagnosed on the basis of a positive PCR test result of nasal or pharyngeal swab specimens in all patients. All 1,518 patients admitted to the participating hospitals and enrolled in this study had been discharged by November 8, 2020, the day of the deadline for data transfer.

Patient Population, Definitions, and Data Collection

In the present study, the main population consisted of patients with pre-existing CVDRF. CVDRF were defined as hypertension, diabetes, and dyslipidemia. The definition of pre-existing cardiovascular disease was a history and/or manifestations on admission of heart failure, coronary artery disease, myocardial infarction, peripheral artery disease, valvular heart disease, cardiac arrhythmia, pericarditis, myocarditis, congenital heart disease, pulmonary hypertension, deep vein thrombosis, pulmonary embolism, aortic dissection, aortic aneurysm, cerebral infarction/transient ischemic attack, the use of cardiac devices (e.g., a pacemaker, implantable cardioverter defibrillation, cardiac resynchronization therapy, and left ventricular assist device), heart transplantation, and cardiac arrest. Detailed definitions of each comorbidity are provided in Supplementary Table 1.

Among patients with COVID-19, those without CVDRF served as the control group. For the control group, only basic data (e.g., age, sex, dates of hospitalization and discharge, and in-hospital outcomes) were collected (Figure 1). The case report form used in the present study was based on the form proposed by the International Severe Acute Respiratory and Emerging Infection Consortium.¹⁷ Clinical data, including symptoms, demographics, medical history, home medications, baseline comorbidities, physical findings, laboratory test results, radiography and chest computed tomography (CT) findings, electrocardiography and cardiac echocardiography results, treatment information, and outcomes, were obtained from electronic medical records using data collection forms. All laboratory and imaging data collected were obtained at the time of admission. The date of COVID-19 onset was defined as the day of first symptoms or, if patients had no symptoms on admission, the day of the first positive PCR test result for SARS-CoV-2. In the analysis of cTn, a positive result was defined as elevation above the 99th percentile of the upper reference limit value in each assay. Patients who were <20 years of age on admission were excluded from the study. The primary endpoint was in-hospital death.

Figure 1.

Study flow chart. CLAVIS-COVID, Clinical Outcomes of COVID-19 Infection in Hospitalized Patients with Cardiovascular Diseases and/or Risk Factors; CVD, cardiovascular diseases; CVDRF, CVD and/or risk factors.

Hospitalization and Discharge Decisions for Patients With COVID-19

During the period of patient enrollment in this study, the Japanese government mandated the hospitalization of all patients diagnosed as having COVID-19 infection based on the results of a PCR test regardless of disease severity.¹⁶ The hospitals participating in the present study fundamentally followed this government recommendation. In addition, the discharge of all patients was determined by attending physicians based on Japanese government guidelines for the management of COVID-19, which recommend that patients should not be discharged until: (1) systemic conditions and respiratory symptoms had improved and were stable; (2) the patient’s body temperature was consistently <37.5℃ for at least 24 h; and (3) a negative PCR test result was obtained twice, at least 12 h apart.¹⁶

Statistical Analysis

Patients with CVDRF were divided into 4 categories based on their age (<55, 55–64, 65–79, and ≥80 years), and baseline characteristics were evaluated in each group. Categorical variables are reported as the presenting frequency (%), whereas continuous variables are presented as the mean±SD or as the median with interquartile range (IQR). To evaluate the significance of differences between groups, Fisher’s exact test was used in the case of categorical variables, and Student’s t-test or the Mann-Whitney test was used in the case of continuous variables. Comparisons of continuous variables between 2 groups were made using t-tests and between more than 2 groups were made using 1-way analysis of variance and the Kruskal-Wallis test. In analyses of laboratory and imaging findings, trend tests were performed using the Cochran-Armitage trend test and Mantel-Haenszel Chi-squared test. Logistic regression analysis was used to estimate age-specific differences in in-hospital outcomes. In addition, logistic regression analysis was used to analyze the effect of a positive cTn result and increases in BNP and NT-proBNP levels at the time of admission on in-hospital mortality. If necessary, variables were transformed for further analyses. All analyses were performed using Stata version 15 (StataCorp, College Station, TX, USA).

Results

Patient Characteristics

In the present study, 1,518 hospitalized patients with COVID-19 were analyzed. Of these patients, 693 (45.7%) had a history of CVDRF and 825 (54.3%; control group) had no underlying CVDRF (Figure 1). There were significant differences in the age and sex ratio between these 2 groups. The mean age and the prevalence of male patients were significantly higher in the CVDRF than control group (age: 68.3±14.9 vs. 48.4±17.3 years [P<0.001]; male sex: 64.8% vs. 52.4% [P<0.001]). The distribution of patients in different age groups for the overall study population and in the control group separately is summarized in Supplementary Table 2.

Among the 693 patients with CVDRF, the proportion of Japanese patients was 96.1%. Table 1 presents the baseline characteristics of patients with CVDRF stratified by age. Among the 4 groups, patients aged ≥80 years were more likely to be women, and they more frequently had a history of hypertension and cardiovascular diseases, such as heart failure, coronary artery disease, myocardial infarction, cardiac arrhythmia, and cerebral infarction or transient ischemic attack, than the other 3 groups. Chronic lung disease, chronic kidney disease, and cancer were more frequently observed in those aged ≥80 years. The use of a loop diuretic was significantly higher in the group aged ≥80 years than in the other groups, although the use angiotensin-converting enzyme (ACE) inhibitors, angiotensin II receptor blockers, and β-blockers did not differ significantly among the 4 groups.

Table 1. Characteristics at the Time of Admission of Patients With COVID-19 and Cardiovascular Diseases and/or Risk Factors (n=693)

Variable	No. patients with data	Overall (n=693)	Age group (years)				P value
Variable	No. patients with data	Overall (n=693)	<55 (n=133)	55–64 (n=142)	65–79 (n=246)	≥80 (n=172)	P value
Age	693	68.3±14.9	46.7±6.9	59.3±2.8	72.1±4.0	87.0±4.8	<0.001
Male sex	693	449 (64.8)	98 (73.7)	105 (73.9)	159 (64.6)	87 (50.6)	<0.001
Japanese patient	693	666 (96.1)	123 (92.5)	136 (95.8)	237 (96.3)	170 (98.8)	0.04
Height (cm)	588	163.0±10.6	169.1±8.6	167.2±8.5	162.0±9.5	155.0±10.3	0.07
BMI (kg/m²)	580	24.3±5.1	27.3±5.6	25.4±5.0	23.3±4.2	22.0±4.1	<0.001
Smoking at the time of admission	655	270 (41.2)	58 (44.3)	65 (47.8)	107 (45.9)	40 (25.8)	<0.001
Comorbidities
Hypertension	693	513 (74.0)	85 (63.9)	100 (70.4)	182 (74.0)	146 (84.9)	<0.001
Diabetes	693	266 (38.4)	50 (37.6)	61 (43.0)	109 (44.3)	46 (26.7)	0.002
Dyslipidemia	693	269 (38.8)	55 (41.4)	77 (54.2)	85 (34.6)	52 (30.2)	<0.001
Heart failure	693	60 (8.7)	1 (0.8)	6 (4.2)	18 (7.3)	35 (20.4)	<0.001
Coronary artery disease	693	70 (10.1)	6 (4.5)	10 (7.0)	26 (10.6)	28 (16.3)	0.004
Myocardial infarction	693	30 (4.3)	1 (0.8)	2 (1.4)	15 (6.1)	12 (7.0)	0.005
Cardiac arrhythmia	693	70 (10.1)	3 (2.3)	8 (5.6)	31 (12.6)	28 (16.3)	<0.001
CI/TIA	693	52 (7.5)	3 (2.3)	1 (0.7)	24 (9.8)	24 (14.0)	<0.001
Obesity	693	47 (6.8)	22 (16.5)	14 (9.9)	7 (2.9)	4 (2.3)	<0.001
Chronic lung diseases	693	35 (5.1)	0	3 (2.1)	17 (6.9)	15 (8.7)	<0.001
CKD	693	48 (6.9)	3 (2.3)	4 (2.8)	23 (9.4)	18 (10.5)	0.002
Cancer	693	67 (9.7)	2 (1.5)	10 (7.0)	27 (11.0)	28 (16.3)	<0.001
Baseline medication
ACEI	693	35 (5.1)	3 (2.3)	7 (4.9)	15 (6.1)	10 (5.8)	0.39
ARB	693	232 (33.5)	35 (26.3)	58 (40.9)	77 (31.3)	62 (36.1)	0.06
β-blocker	693	111 (16.0)	12 (9.0)	23 (16.2)	46 (18.7)	30 (17.4)	0.08
Loop diuretic	693	56 (8.1)	2 (1.5)	6 (4.2)	17 (6.9)	31 (18.0)	<0.001
Symptoms
Cough	693	333 (48.1)	80 (60.2)	69 (48.6)	118 (48.0)	66 (38.4)	0.002
Sore throat	693	76 (11.0)	21 (15.8)	21 (14.8)	22 (8.9)	12 (7.0)	0.03
Fatigue	693	228 (32.9)	45 (33.8)	47 (33.1)	84 (34.2)	52 (30.2)	0.86
Dyspnea	693	226 (32.6)	44 (33.1)	50 (35.2)	80 (32.5)	52 (30.2)	0.83
Anosmia	693	49 (7.1)	17 (12.8)	13 (9.2)	17 (6.9)	2 (1.2)	<0.001
Headache	693	53 (7.7)	15 (11.3)	19 (13.4)	15 (6.1)	4 (2.3)	<0.001
Arthritic pain	693	30 (4.3)	12 (9.0)	7 (4.9)	7 (2.9)	4 (2.3)	0.03
Chest pain	693	11 (1.6)	4 (3.0)	3 (2.1)	3 (1.2)	1 (0.6)	0.36
No symptoms	693	47 (6.8)	4 (3.0)	6 (4.2)	16 (6.5)	21 (12.2)	0.009
Physical findings
Maximum body temperature (℃)	644	38.0±0.9	38.1±0.9	38.2±0.8	37.9±0.9	37.9±0.9	0.009
Heart rate (beats/min)	687	86±18	91±17	87±17	85±17	84±19	0.009
SBP (mmHg)	688	133±22	132±21	130±19	134±23	135±22	0.10
Respiratory rate (/min)	540	21±6	20±6	21±6	21±6	21±6	0.39
SpO₂	689	95.0±4.7	95.7±4.1	95.4±3.4	94.8±4.5	94.2±6.1	<0.001

Values are expressed as n (%) or the mean±SD. ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; BMI, body mass index; CI, cerebral infarction; CKD, chronic kidney disease; COVID-19, coronavirus disease 2019; SBP, systolic blood pressure; SpO₂, peripheral oxygen saturation; TIA, transient ischemic attack.

Symptoms and Physical Findings

The most common symptoms confirmed at the time of admission were cough, which was observed in 48.1% of patients. There was a lower proportion of patients with cough, sore throat, anosmia, headache, and arthritic pain among those aged ≥80 years than among the younger age groups; in addition, asymptomatic patients were more prevalent in the older (≥80 years) than younger age groups (Table 1). In contrast, the prevalence of fatigue and dyspnea, which were relatively common symptoms, was not significantly different among the 4 groups, although the prevalence of both was still numerically lower among patients aged ≥80 years compared with the younger groups. Systolic blood pressure and respiratory rate on admission did not differ among the 4 groups, whereas maximum body temperature recorded after symptom onset, heart rate, and peripheral oxygen saturation at the time of admission were significantly lower in older (≥80 years) than younger patients.

Laboratory Findings

Baseline laboratory test results are summarized in Table 2. Although white blood cell levels did not show significant differences according to age, the percentage of lymphocytes and neutrophils decreased with increasing age. Moreover, there was a significant trend between older age and a lower ferritin level. In contrast, both D-dimer and Krebs von den Lungen-6 (KL-6) levels increased significantly with age. In addition, there was a significant increase in the cardiac biomarkers BNP and NT-proBNP with age. Based on current definitions,¹⁸ high-sensitivity troponin assays were used in 129 (92.1%) patients, and the proportion of those with a positive cTn result increased with age. In sensitivity analysis in which this association was examined only in patients without a history of coronary artery disease and myocardial infarction (n=123), the significant relationship between age and positive cTn was retained (P value for trend<0.001 for both). Similarly, significant associations between older age and higher BNP (P<0.001, P value for trend<0.001) and NT-proBNP (P=0.01, P value for trend=0.002) levels were obtained, even in those without a history of heart failure. Baseline serum C-reactive protein (CRP) and lactate dehydrogenase (LDH) levels differed significantly among age groups, but did not consistently increase with age.

Table 2. Examination Findings at the Time of Admission in Patients With COVID-19 and Cardiovascular Diseases and/or Risk Factors (n=693)

Variable	No. patients with data	Overall (n=693)	Age group (years)				P value	P value for trend
Variable	No. patients with data	Overall (n=693)	<55 (n=133)	55–64 (n=142)	65–79 (n=246)	≥80 (n=172)	P value	P value for trend
Laboratory data
WBC count (/μL)	678	5,700 [4,370–7,600]	5,500 [4,260–7,000]	5,550 [4,450–6,795]	6,050 [4,400–7,910]	5,790 [4,300–7,900]	0.34	0.17
Lymphocyte count (%)	646	16.1 [10.5–24.2]	20.5 [14.0–30.0]	15.9 [10.1–24.0]	15.0 [10.1–21.0]	16.3 [9.6–24.5]	<0.001	<0.001
Neutrophil count (%)	602	75.0 [64.6–82.7]	67.6 [59.0–77.9]	76.5 [65.5–83.7]	78.0 [67.0–84.0]	74.2 [63.9–82.9]	<0.001	0.002
Hemoglobin (g/dL)	679	13.4 [11.7–14.7]	14.5 [13.6–15.5]	14.1 [12.8–15.3]	13.0 [11.4–14.5]	12.1 [10.6–13.6]	<0.001	<0.001
eGFR (mL/min/1.73 m²)	679	86.5 [65.1–106.4]	99.3 [82.1–116.8]	87.4 [69.6–106.6]	85.1 [62.0–103.1]	77.2 [52.4–96.7]	<0.001	<0.001
Serum biomarker
CRP (mg/L)	667	5.8 [1.8–11.6]	3.8 [0.8–9.0]	6.3 [2.1–12.0]	7.4 [2.8–13.0]	4.9 [1.4–11.1]	0.001	0.04
LDH (IU/L)	617	290 [225–411]	264 [216–363]	324 [238–454]	306 [235–437]	267 [211–356]	<0.001	0.15
Ferritin (ng/mL)	317	526 [225–1,021]	481 [225–1,134]	782 [374–1,513]	612 [290–1,148]	253 [149–568]	<0.001	0.002
KL-6 (U/mL)	289	300 [209–458]	277 [200–348]	264 [190–365]	319 [230–473]	388 [240–595]	0.001	<0.001
D-dimer (ng/mL)	461	1.5 [0.8–3.0]	0.8 [0.5–1.5]	1.2 [0.7–2.3]	1.6 [0.9–2.6]	2.2 [1.2–5.2]	<0.001	<0.001
Cardiac biomarker
Positive troponin	140	72 (51.4)	8 (30.8)	10 (30.3)	28 (58.3)	26 (78.8)	<0.001	<0.001
BNP (pg/mL)	217	31.3 [11.4–134.0]	8.0 [5.8–17.2]	17.1 [10.4–74.2]	50.3 [19.2–162.6]	116.7 [48.1–363.0]	<0.001	<0.001
NT-proBNP (pg/mL)	95	476 [131–2,385]	109 [37–464]	249 [104–1,073]	333 [144–1,170]	1,601 [694–3,982]	0.001	<0.001
Chest X-ray
GGO/consolidation/ alveolar opacity	630	461 (73.2)	75 (65.2)	109 (82.6)	172 (76.1)	105 (66.9)	0.003	0.73
Congestion	630	53 (8.4)	3 (2.6)	10 (7.6)	18 (8.0)	22 (14.0)	0.03	0.001
Pleural effusion	630	45 (7.1)	0	6 (4.6)	19 (8.4)	20 (12.7)	<0.001	<0.001
Chest CT
GGO/consolidation	543	492 (90.6)	85 (86.7)	111 (93.9)	186 (93.9)	110 (83.3)	0.001	0.29
GGO	543	392 (72.2)	64 (65.3)	88 (76.5)	158 (79.8)	82 (62.1)	0.001	0.66
Consolidation	543	241 (44.4)	46 (46.9)	54 (47.0)	83 (41.9)	58 (43.9)	0.79	0.48
Pleural effusion	543	76 (14.0)	4 (4.1)	9 (7.8)	28 (14.1)	35 (26.5)	<0.001	<0.001

Values are presented as n (%) or as the median [interquartile range]. BNP, B-type natriuretic peptide; COVID-19, coronavirus disease 2019; CRP, C-reactive protein; CT, computed tomography; eGFR, estimated glomerular filtration rate; GGO, ground-glass opacity; KL-6, Krebs von den Lungen-6; LDH, lactate dehydrogenase; NT-proBNP, N-terminal pro BNP; WBC, white blood cell.

Imaging Findings

Among 693 patients with CVDRF, 630 (90.9%) and 543 (78.4%) patients underwent chest radiography and CT imaging on admission, respectively (Table 2). Overall, the proportion of ground-glass opacity (GGO)/consolidation/alveolar opacity on a chest X-ray was 73.2%, whereas that of GGO/consolidation on chest CT was 90.6%. Although there was a significant group difference in the presence of GGO/consolidation/alveolar opacity on chest X-ray, no statistically significant trend between the presence of these findings and age was observed. In contrast, the presence of congestion and pleural effusion on the baseline X-ray was significantly more prevalent in patients with older age. Similarly, on chest CT imaging, the percentage of those with GGO/consolidation at baseline showed a significant group difference without trends in relation to age. However, pleural effusion was observed significantly more in older than younger patients.

In-Hospital Outcomes

In-hospital mortality is summarized in Figure 2. There were no missing data on prognosis during the index hospitalization. Across the whole population (i.e., 1,518 patients with and without CVDRF), there were 140 deaths and the overall in-hospital mortality was 9.2%.

Figure 2.

In-hospital mortality in patients with COVID-19 overall, as well as in those with and without (control) cardiovascular diseases and/or risk factors (CVDRF) separately, stratified according to age.

In-hospital mortality was significantly higher for those with than without CVDRF (15.6% vs. 3.9%; P<0.001). Elderly patients were at a significantly higher risk of mortality during hospitalization regardless of their history of CVDRF (Figure 2; all P value for trend were <0.001, and the P value for the interaction between age and CVDRF on in-hospital mortality was 0.20). This trend was similar in further analysis focusing on only Japanese patients with COVID-19 and CVDRF (Supplementary Figure).

In further analysis, positive cTn and an increase in BNP on admission were significantly associated with higher in-hospital mortality, with odds ratios of 3.59 (95% confidence interval [CI] 1.41–9.12; P=0.007) and 1.01 (95% CI 1.00–1.01; P=0.004), respectively. There was a tendency for increases in baseline NT-proBNP levels being associated with higher in-hospital mortality, but the association was not significant (OR 1.00; 95% CI 0.99–1.00; P=0.07; Supplementary Table 3).

Discussion

The present study demonstrated several specific features of elderly patients with COVID-19. Older age was strongly associated with poorer in-hospital prognosis regardless of a patient’s history of CVDRF. Nevertheless, compared with younger patients, elderly patients with COVID-19 were more likely to be asymptomatic and showed relatively less severe non-cardiac biomarker profiles considering their high mortality rate. In contrast, with respect to cardiac biomarkers, cTn and BNP (or NT-proBNP) levels at the time of admission showed a strong positive association with age in patients with COVID-19 and CVDRF. In particular, approximately 80.0% of patients aged ≥80 years were positive for cTn at the time of admission, which is more than twice as high as the proportion of cTn-positive patients aged <55 years. These age-specific features can potentially help improve future risk stratification and strategies for patients with COVID-19.

Increased age is reportedly associated with a higher fatality rate after COVID-19 infection,²^–⁴ whereas there is limited information as to whether older age is significantly related to higher mortality in patients with CVDRF, which are representative comorbidities associated with age.⁹^,¹⁰ In the present study, we found that patients aged ≥80 years had excess in-hospital mortality, which was approximately 30% of patients regardless of CVDRF. Furthermore, there is limited information regarding associations between age and cardiovascular issues in East Asian cohorts with COVID-19 other than cohorts from China, where the contribution of CVDRF is known to differ significantly from that in Western countries.¹¹^,¹⁹

The profiles of prognostic biomarkers of COVID-19 suggested notable features of elderly patients with COVID-19 and CVDRF. In previous studies, it has been consistently shown that several biomarkers associated with inflammation, such as serum CRP, LDH, and ferritin levels, are increased and potentially associated with poor clinical outcomes.²⁰^–²² Thus, the elderly group, which is associated with particularly unfavorable outcomes after infection, was expected to show higher baseline levels of these biomarkers; however, our cohort did not show a consistent increase in these biomarkers in parallel with age. In addition, despite the strong association between age and mortality, older patients were less likely than younger patients to have typical clinical symptoms associated with COVID-19 infection, such as cough, sore throat, anosmia, and headache. Furthermore, based on baseline X-ray and CT imaging, typical pneumonia findings were less frequent and pleural effusion and congestion were more frequent in the elderly than younger age groups, despite older age being associated with associated with more hypoxia and worse outcomes. These unexpected features of the older population with COVID-19 at the time of admission (i.e., the paradoxically lower incidence of clinical symptoms and lower inflammation biomarker levels despite a more hypoxic state and high in-hospital mortality) should be considered when this high-risk subgroup of patients with COVID-19 is evaluated and treated.

It should be noted that the present study showed a strong association between cardiac biomarkers and older age. Furthermore, there were significant associations or trends for higher cardiac biomarker levels to be associated with higher morality, similar to the findings of previous studies.¹²^–¹⁴ COVID-19 interacts with and affects the cardiovascular system via the ACE2 receptor and gains entry into host cells, causing myocardial injury and cardiac dysfunction.²³ Several studies have suggested that patients with COVID-19 who develop myocardial injury during hospitalization, as detected by positive serum cTn, are more likely to have poor clinical outcomes,¹²^–¹⁴ particularly those with underlying cardiovascular diseases.¹⁴^,²⁴ In the present study, patients with positive cTn at admission were not rare and accounted for 51.4% of all patients, and the proportion of patients with positive cTn was markedly higher in the elderly than younger population. The same association was also observed for BNP and NT-proBNP levels measured at admission. It is important to be aware that an elevated BNP/NT-proBNP level does not directly mean the patient has heart failure. However, together with a higher prevalence of pleural effusion and congestion seen on chest X-ray and/or CT scans in the elderly groups, one possible hypothesis is that in addition to pre-existing heart failure, de novo heart failure could also play a crucial role in terms of prognosis, particularly in older patients. Indeed, one study showed that the development of heart failure after admission, which mostly occurred in those without a history of heart failure, was associated with higher in-hospital mortality.²⁵ Nevertheless, this hypothesis should be carefully examined in future studies not only because the present study had a limited number of patients with available data on cardiac biomarkers, but also because BNP/NT-proBNP levels can increase as a result of a wide range of cardiac conditions, including exposure to a cytokine storm, which has been associated with severe COVID-19.

Study Limitations

Several limitations of this study should be acknowledged. This study was a retrospective study and there were considerable missing values of baseline serum biomarkers, especially cTn and BNP/NT-proBNP. These missing data may have led to the different results regarding cardiac biomarkers in univariate and multivariate analysis models contrary to consistent results reported in previous studies.¹²^–¹⁴ Although the present study demonstrated specific characteristics of elderly patients with COVID-19 and CVDRF, it is unclear whether these results can be extrapolated to patients with COVID-19 in other countries. Indeed, our cohort demonstrated a lower in-hospital mortality for hospitalized patients with COVID-19 than reported in other countries, which is approximately 18–28%.⁴^,¹²^,²⁶^–²⁸ Although there may be many reasons for the relatively favorable results for hospitalized patients, the lower prevalence of comorbidities in our data (Supplementary Table 4), such as hypertension (30–64% in other countries⁴^,¹²^,²⁷^,²⁸), diabetes (19–34% in other countries⁴^,¹²^,²⁶^–²⁸), cardiovascular diseases (33% in the UK²⁶), and heart failure (6.9–10% in the US⁴^,¹²), may have contributed to the differences. In addition, the Japanese government mandated the hospitalization of all patients with COVID-19, regardless of disease severity, during patient enrollment, and this may have consequently been associated with the different prognosis in Japan.²⁹

Conclusions

Compared with younger patients, elderly (≥80 years) patients with COVID-19 had a higher risk of mortality during the hospital course regardless of their history of CVDRF. Moreover, elderly patients were more likely to have higher cTn and BNP/NT-proBNP levels, as well as congestion and pleural effusion on chest imaging tests, than younger patients, although they were more likely to be asymptomatic and show less severe abnormalities in inflammatory biomarkers at the time of admission. These findings may contribute not only to the better management of elderly patients with (and possibly with) COVID-19, but also to a deeper understanding of the effects of the disease on the cardiovascular system.

Acknowledgments

The authors acknowledge all the investigators who participated in CLAVIS-COVID (Supplementary Appendix) and the Japanese Circulation Society.

Sources of Funding

This study received financial supports from the Japanese Circulation Society.

Disclosures

T.Y. belongs to endowed departments of Abbott Vascular Japan, Boston Scientific Japan, Japan Lifeline, WIN International, and Takeyama KK. S. Kohsaka has received unrestricted research grants from the Department of Cardiology, Keio University School of Medicine provided by Daiichi Sankyo Co., Ltd. and Bristol-Meyers Squibb, as well as lecture fees from AstraZeneca and Bristol-Meyers Squibb. I.K. has received unrestricted research grants from Daiichi Sankyo Co., Ltd., Sumitomo Dainippon Pharma Co., Ltd., Takeda Pharmaceutical Co., Ltd., Mitsubishi Tanabe Pharma Corporation, Teijin Pharma Ltd., Idorsia Pharmaceuticals Ltd., Otsuka Pharmaceutical Co., Ltd., Bayer Yakuhin Ltd., Ono Pharmaceutical Co., Ltd., and Toa Eiyo Ltd., as well as lecture fees from AstraZeneca, Daiichi Sankyo Co., Ltd., Takeda Pharmaceutical Co., Ltd., Bayer Yakuhin Ltd., Pfizer Japan Inc., and Ono Pharmaceutical Co., Ltd. Y.M. is affiliated with a department endowed by Philips Respironics, ResMed, Teijin Home Healthcare, and Fukuda Denshi, and has received honoraria from Otsuka Pharmaceutical Co., Ltd. and Novartis Japan, consultant fees from Otsuka Pharmaceutical Co., Ltd., and joint research funds from Otsuka Pharmaceutical Co., Ltd. and Pfizer Inc.

I.K., K.H., and K.N. are members of Circulation Journal’s Editorial Team.

IRB Information

This investigation conforms with the principles outlined in the Declaration of Helsinki. The study protocol, including the use of an opt-out consent method, was approved by the Ethics Committee of Toho University Omori Medical Center (No. M20253) and the local ethics committees of all participating institutions. This clinical study was registered with the University Hospital Medical Information (UMIN) Network Clinical Trial Registry (ID: UMIN000040598) before the first patient was enrolled, in accordance with the International Committee of Medical Journal Editors.

Data Availability

Due to the nature of this research, the study participants did not agree for their data to be shared publicly or upon request. Hence, the data are not available.

Supplementary Files

Please find supplementary file(s);

http://dx.doi.org/10.1253/circj.CJ-21-0160

References

1. World Health Organization (WHO). WHO coronavirus disease (COVID-19) dashboard. https://covid19.who.int/ (accessed December 1, 2020).
2. Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: Summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention. JAMA 2020; 323: 1239–1242.
3. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 2020; 323: 1061–1069.
4. Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, et al. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area. JAMA 2020; 323: 2052–2059.
5. Jousilahti P, Vartiainen E, Tuomilehto J, Puska P. Sex, age, cardiovascular risk factors, and coronary heart disease: A prospective follow-up study of 14 786 middle-aged men and women in Finland. Circulation 1999; 99: 1165–1172.
6. Kovacic JC, Moreno P, Hachinski V, Nabel EG, Fuster V. Cellular senescence, vascular disease, and aging: Part 1 of a 2-part review. Circulation 2011; 123: 1650–1660.
7. Mehra MR, Desai SS, Kuy S, Henry TD, Patel AN. Cardiovascular disease, drug therapy, and mortality in COVID-19. N Engl J Med 2020; 382: e102.
8. Phelps M, Christensen DM, Gerds T, Fosbol E, Torp-Pedersen C, Schou M, et al. Cardiovascular comorbidities as predictors for severe COVID-19 infection or death. Eur Heart J Qual Care Clin Outcomes 2021; 7: 172–180.
9. Stokes EK, Zambrano LD, Anderson KN, Marder EP, Raz KM, El Burai Felix S, et al. Coronavirus disease 2019 case surveillance: United States, January 22–May 30, 2020. MMWR Morb Mortal Wkly Rep 2020; 69: 759–765.
10. Tisminetzky M, Delude C, Hebert T, Carr C, Goldberg RJ, Gurwitz JH. Age, multiple chronic conditions, and COVID-19: A literature review. J Gerontol A Biol Sci Med Sci, doi:10.1093/gerona/glaa320.
11. Ohira T, Iso H. Cardiovascular disease epidemiology in Asia: An overview. Circ J 2013; 77: 1646–1652.
12. Lala A, Johnson KW, Januzzi JL, Russak AJ, Paranjpe I, Richter F, et al. Prevalence and impact of myocardial injury in patients hospitalized with COVID-19 infection. J Am Coll Cardiol 2020; 76: 533–546.
13. Lombardi CM, Carubelli V, Iorio A, Inciardi RM, Bellasi A, Canale C, et al. Association of troponin levels with mortality in Italian patients hospitalized with coronavirus disease 2019: Results of a multicenter study. JAMA Cardiol 2020; 5: 1274–1280.
14. Sandoval Y, Januzzi JL Jr, Jaffe AS. Cardiac troponin for assessment of myocardial injury in COVID-19: JACC review topic of the week. J Am Coll Cardiol 2020; 76: 1244–1258.
15. Japanese Circulation Society COVID-19 Countermeasures Special Mission Team. The clinical outcomes of COVID-19 infection in hospitalized patients with cardiovascular diseases and/or risk factors (CLAVIS-COVID) [in Japanese]. https://www.j-circ.or.jp/covid-19/ (accessed December 25, 2020).
16. Ministry of Health, Labour and Welfare. Clinical management of patients with COVID-19 [in Japanese]. https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000121431_00111.html (accessed June 1, 2020).
17. International Severe Acute Respiratory and Emerging Infection Consortium. Clinical data collection: The COVID-19 case report forms (CRFs). https://isaric.org/research/covid-19-clinical-research-resources/covid-19-crf/ (accessed December 1, 2020).
18. Ibanez B, James S, Agewall S, Antunes MJ, Bucciarelli-Ducci C, Bueno H, et al. 2017 ESC guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation. Rev Esp Cardiol (Engl Ed) 2017; 70: 1082.
19. Ueshima H, Sekikawa A, Miura K, Turin TC, Takashima N, Kita Y, et al. Cardiovascular disease and risk factors in Asia: A selected review. Circulation 2008; 118: 2702–2709.
20. Wang G, Wu C, Zhang Q, Wu F, Yu B, Lv J, et al. C-reactive protein level may predict the risk of COVID-19 aggravation. Open Forum Infect Dis 2020; 7: ofaa153.
21. Zeng Z, Yu H, Chen H, Qi W, Chen L, Chen G, et al. Longitudinal changes of inflammatory parameters and their correlation with disease severity and outcomes in patients with COVID-19 from Wuhan, China. Crit Care 2020; 24: 525.
22. Kappert K, Jahic A, Tauber R. Assessment of serum ferritin as a biomarker in COVID-19: Bystander or participant? Insights by comparison with other infectious and non-infectious diseases. Biomarkers 2020; 25: 616–625.
23. Clerkin KJ, Fried JA, Raikhelkar J, Sayer G, Griffin JM, Masoumi A, et al. COVID-19 and cardiovascular disease. Circulation 2020; 141: 1648–1655.
24. Giannitsis E, Katus HA. Cardiac troponin level elevations not related to acute coronary syndromes. Nat Rev Cardiol 2013; 10: 623–634.
25. Rey JR, Caro-Codon J, Rosillo SO, Iniesta AM, Castrejon-Castrejon S, Marco-Clement I, et al. Heart failure in COVID-19 patients: Prevalence, incidence and prognostic implications. Eur J Heart Fail 2020; 22: 2205–2215.
26. Docherty AB, Harrison EM, Green CA, Hardwick HE, Pius R, Norman L, et al. Features of 20 133 UK patients in hospital with COVID-19 using the ISARIC WHO Clinical Characterisation Protocol: Prospective observational cohort study. BMJ 2020; 369: m1985.
27. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet 2020; 395: 1054–1062.
28. Inciardi RM, Adamo M, Lupi L, Cani DS, Di Pasquale M, Tomasoni D, et al. Characteristics and outcomes of patients hospitalized for COVID-19 and cardiac disease in northern Italy. Eur Heart J 2020; 41: 1821–1829.
29. Matsunaga N, Hayakawa K, Terada M, Ohtsu H, Asai Y, Tsuzuki S, et al. Clinical epidemiology of hospitalized patients with COVID-19 in Japan: Report of the COVID-19 REGISTRY JAPAN. Clin Infect Dis, doi:10.1093/cid/ciaa1470.

Corresponding author

Register with J-STAGE for free!