Journal Logo

Clinical Methods and Pathophisiology

Clinical impact of blood pressure variability in patients with COVID-19 and hypertension

Nam, Jong-Hoa; Park, Jong Ila; Kim, Byung-Juna; Kim, Hun-Taea; Lee, Jung-Heea; Lee, Chan-Heea; Son, Jang-Wona; Kim, Unga; Park, Jong-Seona; Shin, Dong-Gua; Hong, Kyung Soob; Jang, Jong Geolb; Ahn, June Hongb; Jin, Hyun Jungb; Choi, Eun Youngb; Shin, Kyeong-Cheolb; Chung, Jin Hongb; Lee, Kwan Hob; Hur, Jianc; Hong, Young-Hoonc; Lee, Choong-Kic

Author Information
doi: 10.1097/MBP.0000000000000544

Abstract

Introduction

In December 2019, pneumonia caused by an unknown pathogen broke out in Wuhan City, the capital of Hubei Province, China. In January 2020, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified as the pathogen underlying the disease [1]. As of 15 November 2020, the number of confirmed cases of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 has surpassed 53.7 million cases globally, resulting in 1.3 million deaths [2].

The clinical and epidemiological features of COVID-19 have been repeatedly reported, and one of the most common comorbidities among COVID-19 patients is hypertension [3–5]. Some studies have shown that hypertension is a risk factor for worse outcomes in patients with COVID-19 [6,7]. In other studies, hypertension was no longer an independent risk factor for outcomes of COVID-19 patients after multivariate analysis, despite being identified as a risk factor by univariate analysis [3,8]. Taken together, the prevalence of hypertension seems to be high among patients with COVID-19, but the available evidence so far is not solid enough to support the conclusion that hypertension is a real independent risk factor for clinical outcome in COVID-19 patients.

It has recently been suggested that the occurrence of cardiovascular complications may be related not only to the severity of blood pressure (BP) values but also to the degree of BP fluctuation. In fact, blood pressure variability (BPV) has been identified in various studies as a predictor of cardiovascular complications and organ damage marker in the general population as well as among hypertensive patients [9,10]. However, the risk associated with BPV in COVID-19 patients with hypertension has been less investigated.

In this study, we aimed to clarify the impact of BPV on the outcomes of COVID-19 patients with hypertension.

Methods

Study design and participants

This single-center, retrospective, observational study was performed at Yeungnam University Medical Center in Daegu, Republic of Korea. We analyzed adults (≥18 years old) with COVID-19 who were diagnosed according to the interim guidance of the WHO [11], and who were hospitalized in our hospital from 14 February 2020, to 08 April 2020. The electronic medical records of the patients were reviewed by two physicians (JH Nam and JI Park). Patient data during hospitalization, including demographics, comorbidities, laboratory findings, treatments and outcomes were collected and analyzed. The identification of patients with hypertension was based on a clearly documented medical history of hypertension with antihypertensive drugs or a systolic BP ≥ 140 mmHg or a diastolic BP ≥ 90 mmHg as the criteria [12]. This study was approved by the Institutional Review Board (IRB) of Yeungnam University Medical Center (YUMC 2020-04-080) and conformed to ethical guidelines of the 1975 Declaration of Helsinki. The IRB waived the need for informed consent from patients owing to the retrospective nature of the study and the absence of patients’ identification in the data presented.

Definitions

Acute respiratory distress syndrome was defined according to the Berlin Definition [13]. Sepsis and septic shock were defined according to the WHO interim guideline [11]. The acute cardiac injury was considered to occur when the level of creatine kinase-MB (CK-MB) was above the 99th percentile of the upper reference limit or when the level of N-terminal pro-B type natriuretic peptide (NT-proBNP) was ≥300 pg/mL [14,15]. Acute renal injury was defined as an increase in the serum creatinine level of >0.3 mg/dL within 48 h or 1.5 times the baseline level within 7 days and decreased urine output of <0.6 mL/kg/h for 6 h [16].

Blood pressure monitoring and assessment of blood pressure variability

Serial BP recordings during hospitalization were obtained from the electronic medical records. Noninvasive BP recordings were obtained twice a day (8 a.m. and 8 p.m.) using an automated oscillometric device (UA-767JP, A&D Company, Kitamoto-shi, Saitama, Japan). In the ICU, BP was recorded every hour with invasive BP monitoring via peripheral arterial lines. The invasive arterial catheter used was a BD Angiocath Plus 22G (Becton Dickinson Medical, Singapore) in the right or left radial artery. Invasive arterial BP was recorded with the invasive pressure device (TruWave, Edwards Lifesciences Corp., Irvine, California, USA) connected to the monitor (Bedside Monitor BSM-3763, NIHON KOHDEN, Tokyo, Japan). Invasive BP measurements at 8 a.m. and 8 p.m. were selected, which is the same timing as that of the noninvasive BP measurement.

BP profiles were described using parameters for mean arterial pressure (MAP): mean (MAPmean), SD (MAPSD) and coefficient of variation (equal to (SD × 100)/mean, MAPCV) values for MAP were measured and calculated for each individual. In our study, MAPCV was considered the parameter of BPV. BPV was classified as high BPV when the values of MAPCV were above the median and low BPV when the values were below the median.

Statistical analysis

Categorical variables are shown as frequencies or percentages, and continuous variables as mean ± SD or median. Categorical variables were compared using the χ2 test, Fisher’s exact test or linear by linear association. Continuous variables were compared using Student’s t test or Kruskal–Wallis test. The Pearson correlation was used to evaluate the relationship of MAPCV with age and levels of C-reactive protein (CRP), CK-MB, NT-proBNP and creatinine. Survival was estimated using the Kaplan–Meier method. Cox proportional hazard regression analysis was applied to determine the potential risk factors associated with in-hospital mortality. Variables that were considered clinically relevant or that showed a univariate relationship with in-hospital mortality (P < 0.05) were included in the multivariate regression analysis, and the results are reported as hazard ratios (HR) and 95% confidence interval (CI). All statistical analyses were performed using IBM SPSS version 20.0 (IBM Co., Armonk, New York, USA). A two-sided P value <0.05 was considered statistically significant.

Results

Clinical characteristics and outcomes of COVID-19 patients with or without hypertension

A flowchart of the data screening procedure is shown in Fig. 1. A total of 136 patients were enrolled for final analysis. Of the 136 patients, 51 (37.5%) had a medical history of hypertension. The clinical characteristics and outcomes of the COVID-19 patients with and without hypertension are reported in Table 1. When compared with patients without hypertension, patients with hypertension were older (70 ± 12 vs. 53 ± 17 years, P < 0.001), more likely to have a prior history of diabetes mellitus (17 [33.3%] vs. 9 [10.6%], P = 0.001) or chronic kidney disease (CKD; 5 [9.8%] vs. 0, P = 0.007). The CRP level was significantly higher in hypertensive patients than in nonhypertensive patients (9.429 ± 9.170 vs. 5.270 ± 8.205 mg/dL, P = 0.009). Patients with hypertension exhibited higher levels of NT-proBNP (2149.9 ± 7252.5 vs. 189.9 ± 466.3 pg/mL, P = 0.077) and creatinine (1.83 ± 4.05 vs. 0.80 ± 0.24, P = 0.076) than those without hypertension. Compared with the patients without hypertension, those with hypertension had higher MAPmean (95 ± 8 vs. 88 ± 13 mmHg, P = 0.001), MAPSD (11 ± 4 vs. 8 ± 3 mmHg, P < 0.001), and MAPCV (11 ± 5 vs. 9 ± 3, P = 0.002) during hospitalization.

Table 1 - Demographics, laboratory findings, blood pressure profiles, treatments and outcomes of COVID-19 patients with and without hypertension
All (n = 136) Hypertension (−) (n = 85) Hypertension (+) (n = 51) P value
Demographics
 Age, year 60 ± 17 53 ± 17 70 ± 12 <0.001
  <70 95 (69.9%) 72 (84.7%) 23 (45.1%) <0.001
  70–79 26 (19.1%) 9 (10.6%) 17 (33.3%)
  ≥80 15 (11.0%) 4 (4.6%) 11 (21.6%)
 Sex, men 64 (47.1%) 34 (40.0%) 30 (58.8%) 0.033
BP on admission
 SBP, mmHg 129 ± 20 126 ± 18 134 ± 21 0.027
 DBP, mmHg 80 ± 14 81 ± 11 79 ± 18 0.453
 MAP, mmHg 96 ± 14 96 ± 13 97 ± 16 0.632
Comorbidities
 Diabetes mellitus 26 (19.1%) 9 (10.6%) 17 (33.3%) 0.001
 CVA 5 (3.7%) 1 (1.2%) 4 (7.8%) 0.066
 CKD 5 (3.7%) 0 5 (9.8%) 0.007
 IHD 11 (8.1%) 5 (5.9%) 6 (11.8%) 0.33
 Heart failure 10 (7.4%) 6 (7.1%) 4 (7.8%) 1
Antihypertensive drugs on admission
 ACEis/ARBs 33 (64.7%) 33 (64.7%)
 Beta blockers 12 (23.5%) 12 (23.5%)
 CCBs 23 (45.1%) 23 (45.1%)
 Diuretics 11 (21.6%) 11 (21.6%)
 Any of drugs above 46 (90.2%) 46 (90.2%)
Symptoms on admission
 Fever 39 (28.7%) 28 (32.9%) 11 (21.6%) 0.763
 Cough 37 (27.2%) 28 (32.9%) 9 (17.6%)
 Dyspnea 29 (21.3%) 12 (14.1%) 17 (33.3%)
 Myalgia 18 (13.2%) 9 (10.6%) 9 (17.6%)
 Diarrhea 2 (1.5%) 0 2 (3.9%)
 Others 11 (8.1%) 8 (9.4%) 3 (5.9%)
Duration from symptom onset to admission, day 7.9 ± 7.3 8.4 ± 7.8 7.3 ± 6.5 0.436
Laboratory findings
 WBC, per μL 6.79 ± 3.45 6.33 ± 2.87 7.55 ± 4.17 0.045
 Hemoglobin, g/dL 12.8 ± 1.6 13.1 ± 1.4 12.4 ± 1.8 0.01
 Platelets, per μL 237 ± 103 244 ± 112 225 ± 86 0.312
 CRP, mg/dL 6.817 ± 8.777 5.270 ± 8.205 9.429 ± 9.170 0.009
 Procalcitonin, ng/dL 0.282 ± 1.118 0.298 ± 1.374 0.253 ± 0.374 0.829
 Creatinine, mg/dL 1.19 ± 2.52 0.80 ± 0.24 1.83 ± 4.05 0.076
 CK-MB, ng/mL 4.6 ± 6.6 3.8 ± 4.4 5.2 ± 7.8 0.411
 NT-proBNP, pg/mL 943.8 ± 4582.8 189.9 ± 466.3 2149.9 ± 7252.5 0.077
 Acute renal injury 31 (22.8%) 12 (14.1%) 19 (37.3%) 0.002
 Acute cardiac injury 32 (23.5%) 9 (11.8%) 23 (46.9%) <0.001
BP profiles during hospitalization
 MAPmean, mmHg 91 ± 12 87.9 ± 13.1 95.1 ± 8.1 0.001
 MAPSD, mmHg 8.9 ± 3.6 8.0 ± 3.1 10.6 ± 3.9 <0.001
 MAPCV 9.8 ± 4.0 8.9 ± 3.2 11.4 ± 4.8 0.002
Treatments
 Antibiotics 135 (99.3%) 85 (100%) 50 (98.0%) 0.375
 Lopinavir/Ritonavir 126 (92.6%) 79 (92.9%) 47 (92.2%) 1
 Hydroxychloroquine 125 (91.9%) 76 (89.4%) 49 (96.1%) 0.209
 Glucocorticoid 42 (30.9%) 21 (24.7%) 21 (41.2%) 0.044
 Intravenous immunoglobulin 3 (2.2%) 1 (1.2%) 2 (3.9%) 0.556
 Mechanical ventilation 15 (11.0%) 7 (8.3%) 8 (15.7%) 0.188
 Vasopressor use 17 (12.5%) 7 (8.2%) 10 (19.6%) 0.052
 RRT 4 (2.9%) 2 (2.4%) 2 (3.9%) 0.631
 ECMO 7 (5.1%) 4 (4.7%) 3 (5.9%) 1
Outcomes
 Sepsis 38 (27.9%) 17 (20.0%) 21 (41.2%) 0.008
 ARDS 30 (22.1%) 13 (15.3%) 17 (33.3%) 0.014w
 Shock 22 (16.2%) 7 (8.2%) 15 (29.4%) 0.001
 ICU admission 18 (13.2%) 8 (9.4%) 10 (19.6%) 0.089
 In-hospital mortality 15 (11.0%) 5 (5.9%) 10 (19.6%) 0.013
Values are presented as number (%) or mean ± SD.
ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; ARDS, acute respiratory distress syndrome; BP, blood pressure; BPV, blood pressure variability; CCB, calcium channel blocker; CKD, chronic kidney disease; CK-MB, creatinine kinase-MB; CRP, C-reactive protein; CV, coefficient of variation; CVA, cerebrovascular accident; DBP, diastolic blood pressure; ECMO, extracorporeal membrane oxygenation; IHD, ischemic heart disease; MAP, mean arterial pressure; NT-proBNP, N-terminal pro-B type natriuretic peptide; RRT, renal replacement therapy; SBP, systolic blood pressure; WBC, white blood cell.

Fig. 1
Fig. 1:
Study flow chart of patients included in the analysis. a,bThe term ‘low’ refers to values below the median and the term ‘high’ to values above the median. BPV, blood pressure variability.

Hypertensive patients developed more frequent complications, including sepsis (21 [41.2%] vs. 17 [20.0%], P = 0.008), ARDS (17 [33.3%] vs. 13 [15.3%], P = 0.014), and shock (15 [29.4%] vs. 7 [8.2%], P = 0.001) compared with nonhypertensive patients. Among the 136 COVID-19 patients included in the analysis, 15 (11.0%) died during hospitalization. More hypertensive patients died compared to nonhypertensive patients (10 [19.6%] vs. 5 [5.9%], P = 0.013).

Clinical characteristics and outcomes of COVID-19 patients grouped according to hypertension and blood pressure variability

The clinical characteristics and outcomes of the COVID-19 patients subgrouped according to the presence of hypertension and high BPV are reported in Table 2. Age, diabetes mellitus, CKD, levels of CRP, CK-MB, NT-proBNP, creatinine and in-hospital mortality increased from patients with low BPV to nonhypertensive patients with high BPV and hypertensive patients with high BPV (P < 0.001 for age, P < 0.001 for diabetes mellitus, P < 0.003 for CKD, P < 0.001 for CRP, P = 0.006 for CK-MB, P < 0.001 for NT-proBNP, P < 0.001 for creatinine and P < 0.001 for in-hospital mortality). Antihypertensive treatments on admission were generally comparable between hypertensive patients with and without high BPV (32 [88.9%] vs. 14 [93.3%], P = 1).

Table 2 - Demographics, laboratory findings, treatments, and outcomes of COVID-19 patients grouped according to hypertension and BPV
Hypertension (-),
Lowa BPV (n = 53)
Hypertension (+),
Lowa BPV (n = 15)
Hypertension (-),
Highb BPV (n = 32)
Hypertension (+),
Highb BPV (n = 36)
P vlaue
Demographics
 Age, year 48 ± 17 61 ± 10 62 ± 13 73 ± 11 <0.001
  <70 50 (94.3%) 12 (80.0%) 22 (68.8%) 11 (30.6%) <0.001
  70–79 1 (1.9%) 2 (13.3%) 8 (25.0%) 15 (41.7%)
  ≥80 2 (3.8%) 1 (6.7%) 2 (6.2%) 10 (27.8%)
 Sex, men 15 (28.3%) 8 (53.3%) 19 (59.4%) 22 (61.1%) 0.001
BP on admission
 SBP, mmHg 124 ± 18 140 ± 17 130 ± 18 131 ± 22 0.014
 DBP, mmHg 80 ± 11 80 ± 24 81 ± 13 78 ± 15 0.55
 MAP, mmHg 95 ± 12 100 ± 15 97 ± 14 96 ± 16 0.473
Comorbidities
 Diabetes mellitus 3 (5.7%) 4 (26.7%) 6 (18.8%) 13 (36.1%) 0.001
 CVA 0 2 (13.3%) 1 (3.1%) 2 (5.6%) 0.253
 CKD 0 0 0 5 (13.9%) 0.003
 IHD 0 0 5 (15.6%) 6 (16.7%) 0.001
 Heart failure 1 (1.9%) 0 5 (15.6%) 4 (11.1%) 0.03
Antihypertensive drugs on admission
 ACEis/ARBs 9 (60.0%) 24 (66.7%) 0.65
 Beta blockers 2 (13.3%) 10 (27.8%) 0.47
 CCBs 7 (46.7%) 16 (44.4%) 0.884
 Diuretics 2 (13.3%) 9 (25.0%) 0.472
 Any of drugs above 14 (93.3%) 32 (88.9%) 1
Symptoms on admission
 Fever 15 (28.3%) 6 (40.0%) 13 (40.6%) 5 (13.9%) 0.76
 Cough 22 (41.5%) 4 (26.7%) 6 (18.8%) 5 (13.9%)
 Dyspnea 7 (13.2%) 2 (13.3%) 5 (15.6%) 15 (41.7%)
 Myalgia 4 (7.5%) 2 (13.3%) 5 (15.6%) 7 (19.4%)
 Diarrhea 0 1 (6.7%) 0 1 (2.8%)
 Others 5 (9.4%) 0 3 (9.4%) 3 (8.3%)
Duration from symptom onset to admission, day 10.4 ± 9.0 9.2 ± 4.6 5.3 ± 4.0 6.3 ± 7.1 0.009
Laboratory Findings
 WBC, per μL 5649 ± 1824 5996 ± 2310 7467 ± 3812 8202 ± 4603 0.005
 Hemoglobin, g/dL 13.1 ± 1.3 13.5 ± 1.2 13.2 ± 1.6 11.9 ± 1.8 0.001
 Platelets, per μL 249 ± 98 242 ± 86 235 ± 133 218 ± 86 0.568
 CRP, mg/dL 2.354 ± 5.013 2.332 ± 3.947 10.227 ± 10.101 12.654 ± 9.076 <0.001
 Procalcitonin, ng/dL 0.039 ± 0.043 0.055 ± 0.043 0.717 ± 2.177 0.340 ± 0.420 <0.001
 Creatinine, mg/dL 0.73 ± 0.17 0.81 ± 0.24 0.92 ± 0.30 2.26 ± 4.77 <0.001
 CK-MB, ng/mL 1.0 ± 0.2 1.5 ± 1.2 5.1 ± 4.8 5.9 ± 8.3 0.006
 NT-proBNP, pg/mL 92.0 ± 125.7 159.3 ± 194.3 353.2 ± 722.9 2958.6 ± 8503.3 <0.001
 Acute renal injury 3 (5.7%) 1 (6.7%) 9 (28.1%) 18 (50.0%) <0.001
 Acute cardiac injury 1 (2.2%) 2 (14.3%) 8 (25.8%) 21 (60.0%) <0.001
Invasive BP monitoring 0 1 (6.7%) 7 (21.9%) 8 (22.2%) <0.001
Treatments
 Antibiotics 53 (100%) 15 (100%) 32 (100%) 35 (97.2%) 0.191
 Lopinavir/Ritonavir 49 (92.5%) 15 (100%) 30 (93.8%) 32 (88.9%) 0.553
 Hydroxychloroquine 45 (84.9%) 14 (93.3%) 31 (96.9%) 35 (97.2%) 0.021
 Glucocorticoid 6 (11.3%) 2 (13.3%) 15 (46.9%) 19 (52.8%) <0.001
 Intravenous immunoglobulin 0 0 1 (3.1%) 2 (5.6%) 0.07
 Mechanical ventilation 0 0 7 (22.6%) 8 (22.2%) <0.001
 Vasopressor use 0 0 7 (21.9%) 10 (27.8%) <0.001
 RRT 0 0 2 (6.2%) 2 (5.6%) 0.067
 ECMO 0 0 4 (12.5%) 3 (8.3%) 0.022
Outcomes
 Sepsis 3 (5.7%) 1 (6.7%) 14 (43.8%) 20 (55.6%) <0.001
 ARDS 1 (1.9%) 0 12 (37.5%) 17 (47.2%) <0.001
 Shock 0 0 7 (21.9%) 15 (41.7%) <0.001
 ICU admission 0 1 (6.7%) 8 (25.0%) 9 (25.0%) <0.001
 In-hospital mortality 0 0 5 (15.6%) 10 (27.8%) <0.001
Values are presented as number (%) or mean ± SD.
aThe term ‘low’ refers to values below the median.
bThe term ‘high’ to values above the median.
ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; ARDS, acute respiratory distress syndrome; BP, blood pressure; BPV, blood pressure variability; CCB, calcium channel blocker; CKD, chronic kidney disease; CK-MB, creatinine kinase-MB; CRP, C-reactive protein; CV, coefficient of variation; CVA, cerebrovascular accident; DBP, diastolic blood pressure; ECMO, extracorporeal membrane oxygenation; IHD, ischemic heart disease; MAP, mean arterial pressure; NT-proBNP, N-terminal pro-B type natriuretic peptide; RRT, renal replacement therapy; SBP, systolic blood pressure; WBC, white blood cell.

Associations between blood pressure variability and age, marker of inflammation, markers of acute cardiac injury, acute renal injury

The associations of MAPCV with age and levels of CRP, CK-MB, NT-proBNP and creatinine were analyzed in all patients (Fig. 2 and Supplementary Fig. 1, Supplemental digital content, https://links.lww.com/BPMJ/A137). Age and CRP levels were positively correlated with MAPCV (r = 0.402 and P < 0.001 for age, r = 0.519 and P < 0.001 for CRP, respectively; Fig. 2a,b). MAPCV was correlated with the levels of CK-MB, NT-proBNP and creatinine (r = 0.319 and P = 0.012 for CK-MB, r = 0.285 and P = 0.002 for NT-proBNP, r = 0.500 and P < 0.001 for creatinine, respectively; Supplementary Fig. 1a,b,c, Supplemental digital content, https://links.lww.com/BPMJ/A137).

Fig. 2
Fig. 2:
Scatterplots depicting the relationship between (a) age and MAPCV, (b) CRP and MAPCV. CRP, C-reactive protein; CV, coefficient of variation; MAP, mean arterial pressure.

Prognosis of COVID-19 patients grouped according to hypertension and blood pressure variability

The Kaplan–Meier curves showed that patients with hypertension were more likely to die than patients without hypertension, and patients with high BPV were more likely to die than those with low BPV (P = 0.02 and P < 0.001, respectively; Fig. 3a,b). The in-hospital mortality rates showed a gradual increase: patients with low BPV showed the lowest rates, followed by nonhypertensive patients with high BPV and hypertensive patients with high BPV, who had the highest rates (P < 0.001; Fig. 3c).

Fig. 3
Fig. 3:
Kaplan–Meier survival curves for mortality during hospitalization. (a) Patients with or without hypertension, (b) patients with high BPV or low BPV, (c) nonhypertensive patients with low BPV, hypertensive patients with low BPV, nonhypertensive patients with high BPV or hypertensive patients with high BPV. a,bThe term ‘low’ refers to values below the median and the term ‘high’ to values above the median. BPV, blood pressure variability; CV, coefficient of variation; MAP, mean arterial pressure.

To further explore the potential risk factors for in-hospital mortality, we performed a Cox proportional hazard regression analysis (Table 3). After adjusting for confounding factors, advanced age (≥80 years, HR 10.4, 95% CI 2.264–47.772, P = 0.003) and higher MAPCV (HR 1.617, 95% CI 1.281–2.040, P < 0.001) were significantly associated with in-hospital mortality.

Table 3 - Cox regression analysis on the potential risk factors associated with mortality in patients with COVID-19
Variable Univariate analysis Multivariate analysis
HR 95% CI P value HR 95% CI P value
Age, year            
 <70 Reference     Reference    
 70–79 4.392 (1.166–16.539) 0.029 5.563 (0.921–33.595) 0.061
 ≥80 10.95 (3.045–39.381) <0.001 10.4 (2.264–47.772) 0.003
Sex, men 1.492 (0.528–4.219) 0.451      
Diabetes mellitus 2.964 (1.054–8.334) 0.039      
Hypertension 3.317 (1.130–9.741) 0.029      
CVA 6.012 (1.660–21.778) 0.006      
CKD 7.088 (1.997–25.158) 0.002      
CRP, mg/dL 1.099 (1.049–1.152) <0.001      
Acute cardiac injury 5.55 (1.872–16.454) 0.002      
Acute renal injury 10.516 (3.346–33.045) <0.001      
MAPCV 1.425 (1.276–1.592) <0.001 1.617 (1.281–2.040) <0.001
Vasopressor use 7.876 (2.837–21.864) <0.001      
Body temperature ≥38°C 2.6 (0.925–7.309) 0.07      
Heart rate ≥100 bpm 3.444 (0.773–15.336) 0.105      
CI, confidence Interval; CKD, chronic kidney disease; CRP, C-reactive protein; CV, coefficient of variation; CVA, cerebrovascular accident; HR, hazard ratio; MAP, mean arterial pressure.

Discussion

The major findings of this study are that high BP fluctuation (i.e. BPV) was significantly associated with in-hospital mortality. Moreover, this high BPV had a proportional relationship with advanced age, high levels of inflammatory markers such as CRP, and worse clinical outcomes, including cardiac and renal injury.

Several reports have demonstrated that the prevalence of hypertension is high among patients with COVID-19 [3–5]. For example, Zhou et al. reported that comorbidities were present in nearly half of COVID-19 patients, with hypertension being the most common comorbidity (58 of 191 (30.4%); 56 years of median age) [3]. However, a Chinese hypertension survey showed that 44.6% of the population aged 55–64 years had hypertension [17]. Our study showed that 51 of 136 (37.5%, 60 years of mean age) COVID-19 patients had a medical history of hypertension. According to a report from the Korea Centers for Disease Control and Prevention, the prevalence in the Korean general population aged 60–69 years was 46.0% [18]. The actual prevalence of hypertension in COVID-19 patients may not be higher, considering the prevalence of hypertension among the same age group in the general population.

Patients with COVID-19 and hypertension have been reported to have an increased risk of adverse outcomes. Zhou et al. [3] found that hypertension had an HR of 3.05 for in-hospital mortality in 191 COVID-19 patients. However, hypertension was not included as a potential risk factor in the multivariate analysis. In another study by Simonnet et al. [8], hypertension was not found to be an independent risk factor for outcomes of COVID-19 patients after multivariate analysis, despite being identified as a risk factor by univariate analysis. The Centers for Disease Control and Prevention also informed that adults with any age with hypertension might be at an increased risk for severe illness from COVID-19 [19]. Our study revealed that COVID-19 patients with hypertension tended to show higher mortality than those without hypertension. However, multivariate Cox regression analysis showed that after adjusting for confounders, including age and other comorbidities, hypertension did not have a significant correlation with in-hospital mortality. This result may be because older individuals often have multiple comorbidities, such as hypertension, diabetes mellitus or CKD, and are therefore vulnerable to infection. At this point, it is unclear whether hypertension or a high mean BP value only are indeed independent risk factors for developing a severe disease in patients with COVID-19.

It is known that not only mean BP values but also BP fluctuations (i.e. BPV) may be related to cardiovascular events [9.10]. There are several factors that can affect and increase BPV [9]. One of these factors is advanced age. It is suggested that advanced age is associated with progressive stiffening of major arteries, and reduced arterial compliance increases both BPV and the risk of cardiovascular events [20,21]. In our study, BPV significantly increased with aging. Systemic inflammation is also a plausible factor that leads to high BPV [22]. Systemic inflammation following an infection, especially sepsis, increases inflammatory mediators that elicit diffuse vasodilation or transient suppression of myocardial function, and these changes can contribute to BP fluctuation. Impairment of myocardial function in severe COVID-19 patients has also been reported [23]. Altered vasomotor tone or impairment of myocardial function caused by systemic inflammation may explain our finding that inflammatory markers were positively correlated with BPV in COVID-19 patients.

BPV is associated with target organ damage and rate of cardiovascular events in both the general population and in patients with hypertension, independent of mean BP [9,10,24]. The pathophysiological mechanisms of BPV and cardiac injury caused by COVID-19 are not well defined. An increase in the markers of cardiac injury may be due to an increased oxygen demand by the myocardium or an inflammatory process caused by an exaggerated cytokine response by type 1 and 2 helper T cells, which could cause a reduction in coronary blood flow, decrease in oxygen supply, instability of the atherosclerotic plaque and microthrombogenesis [25]. It could be hypothesized that episodes of myocardial ischemia in COVID-19 patients could provoke sympathetic, angiotensin or other reactive responses that mediate systemic vasoconstriction and affect BP, thus more closely linking cardiovascular events and BPV [21]. Our analysis was also consistent with this hypothesis by showing significantly high BPV in patients with cardiac injury compared with those without. The clinical significance of BPV for acute renal injury may be related to hemodynamic alterations caused by systemic inflammation. As described above, viral and bacterial infections are known to cause excessive release of inflammatory cytokines that lead to microvascular dysfunction, increased vascular permeability and tissue damage, along with hypoperfusion, which affects kidney microcirculation [26]. This pathophysiologic mechanism may result in BP fluctuation.

Our study has several limitations. First, the sample size of this study was relatively small and the subgroups were not evenly distributed. Also, the number of invasive or noninvasive BP monitoring was not uniform among the subgroups and this could be a source of bias in our study. A larger cohort study is needed to support our conclusion. Second, serial BP measurements during hospitalization were obtained only twice a day (at 8 a.m. and 8 p.m.) owing to the limitations imposed in the isolation ward and the urgency of containing the COVID-19 epidemic. Increasing the time interval between BP measurements will result in less BP measurements, which can consequently increase BPV. Third, we could perform an echocardiographic exam in only 8 (5.9%) patients during hospitalization due to the complex vendor and transducer sterilization procedures and the difficulty acquiring echocardiographic images while wearing level-D personal protective equipment. Therefore, it is difficult to report the echocardiographic findings of our COVID-19 patients. Fourth, the decision to perform laboratory tests was left to the discretion of each physician in clinical practice; thus, some data, such as troponin I, as a marker of acute cardiac injury, were not adequately acquired. Fifth, although it is suggested that increased sympathetic discharge may exert detrimental effects on COVID-19 patients [27], we could not properly evaluate the relationship between autonomic dysfunction and BPV. However, we performed a Cox regression analysis by including the heart rate over 100 beats per minute, vasopressor use or body temperature over 38°C as confounding factors to adjust for the effects of autonomic dysfunction on BP. Sixth, we could not clarify whether the influence of ACEi/ARBs on COVID-19 is harmful, as only 33 COVID-19 patients with hypertension taking ACEi/ARBs were enrolled. However, in our subanalysis, the rate of in-hospital mortality was not different between hypertensive patients taking ACEi/ARBs and those not taking ACEi/ARBs [7 of 33 (21.2%) vs. 3 of 18 (16.7%), P = 1]. Seventh, as this study was retrospective, even though the association between BPV and outcome was significant and persisted after multivariate adjustments, our findings cannot establish a causal link between BPV and outcome.

In conclusion, COVID-19 patients with hypertension and high BP fluctuation had worse clinical outcomes than those without hypertension. Advanced age and severe systemic inflammation may be a potential mechanism for BPV. Further studies are needed to identify a causal link between BPV and clinical outcome will be needed.

Acknowledgements

This work was supported by a grant from the Chunma Medical Research Foundation of Yeungnam University, Republic of Korea, 2020. The authors would like to thank all the patients who were enrolled and the staff who helped to care for COVID-19 patients at Yeungnam University Medical Center in Daegu, Republic of Korea.

Conflicts of interest

There are no conflicts of interest.

References

1. Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, et al.; China Medical Treatment Expert Group for Covid-19. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 2020; 382:1708–1720.
2. World Health Organization. Coronavirus disease 2019 (COVID-19): situation reports. Updated November 15, 2020. https://www.who.int/publications/m/item/weekly-epidemiological-update---17-november-2020. Accessed November 20, 2020.
3. 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.
4. Grasselli G, Zangrillo A, Zanella A, Antonelli M, Cabrini L, Castelli A, et al.; COVID-19 Lombardy ICU Network. Baseline Characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the lombardy region, Italy. JAMA 2020; 323:1574–1581.
5. Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, et al.; the Northwell COVID-19 Research Consortium. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City Area. JAMA 2020; 323:2052–2059.
6. Shi Y, Yu X, Zhao H, Wang H, Zhao R, Sheng J. Host susceptibility to severe COVID-19 and establishment of a host risk score: findings of 487 cases outside Wuhan. Crit Care 2020; 24:108.
7. Li X, Xu S, Yu M, Wang K, Tao Y, Zhou Y, et al. Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan. J Allergy Clin Immunol 2020; 146:110–118.
8. Simonnet A, Chetboun M, Poissy J, Raverdy V, Noulette J, Duhamel A, et al.; LICORN and the Lille COVID-19 and Obesity study group. High prevalence of obesity in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) requiring invasive mechanical ventilation. Obesity (Silver Spring) 2020; 28:1195–1199.
9. Parati G, Ochoa JE, Lombardi C, Bilo G. Blood pressure variability: assessment, predictive value, and potential as a therapeutic target. Curr Hypertens Rep 2015; 17:537.
10. Mancia G, Bombelli M, Facchetti R, Madotto F, Corrao G, Trevano FQ, et al. Long-term prognostic value of blood pressure variability in the general population: results of the Pressioni Arteriose Monitorate e Loro Associazioni Study. Hypertension 2007; 49:1265–1270.
11. World Health Organization. Clinical management of severe acute respiratory infection when novel coronavirus (nCoV) infection is suspected. Published March 13, 2020. https://www.who.int/publications-detail/clinical-management-of-severe-acute-respiratoryinfection-when-novel-coronavirus-(ncov)-infection-is-suspected. Accessed January 28, 2020.
12. Williams B, Mancia G, Spiering W, Agabiti Rosei E, Azizi M, Burnier M, et al.; ESC Scientific Document Group. 2018 ESC/ESH Guidelines for the management of arterial hypertension. Eur Heart J 2018; 39:3021–3104.
13. Ranieri VM, Rubenfeld GD, Thompson BT, Ferguson ND, Caldwell E, Fan E, et al.; ARDS Definition Task Force. Acute respiratory distress syndrome: the Berlin definition. JAMA 2012; 307:2526–2533.
14. Thygesen K, Alpert JS, Jaffe AS, Simoons ML, Chaitman BR, White HD, et al.; Joint ESC/ACCF/AHA/WHF Task Force for Universal Definition of Myocardial Infarction; Authors/Task Force Members Chairpersons; Biomarker Subcommittee; ECG Subcommittee; Imaging Subcommittee; Classification Subcommittee; Intervention Subcommittee; Trials & Registries Subcommittee; Trials & Registries Subcommittee; Trials & Registries Subcommittee; Trials & Registries Subcommittee; ESC Committee for Practice Guidelines (CPG); Document Reviewers. Third universal definition of myocardial infarction. J Am Coll Cardiol 2012; 60:1581–1598.
15. Ponikowski P, Voors AA, Anker SD, Bueno H, Cleland JGF, Coats AJS, et al.; ESC Scientific Document Group. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: the task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC)Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur Heart J 2016; 37:2129–2200.
16. Khwaja A. KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract 2012; 120:c179–c184.
17. Wang Z, Chen Z, Zhang L, Wang X, Hao G, Zhang Z, et al.; China Hypertension Survey Investigators. Status of hypertension in China: results from the China hypertension survey, 2012-2015. Circulation 2018; 137:2344–2356.
18. Korea Centers for Disease Control and Prevention. Korea Health Statistics 2018. Korea National Health and Nutrition Examination Survey (KNHANES VII -3): Ministry of Health and Welfare. Updated January 09, 2020. https://knhanes.cdc.go.kr/knhanes/sub04/sub04_03.do?classType=7. Accessed November 15, 2020.
19. Centers for Disease Control and Prevention. People with Certain Medical Conditions. Updated November 2, 2020. https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-with-medical-conditions.html. Accessed November 15, 2020.
20. Izzo JJ. Arterial stiffness and the systolic hypertension syndrome. Curr Opin Cardiol 2004; 19:341–352.
21. Weber MA. Blood pressure variability and cardiovascular prognosis: implications for clinical practice. Eur Heart J 2017; 38:2823–2826.
22. Yu CM, Wong RS, Wu EB, Kong SL, Wong J, Yip GW, et al. Cardiovascular complications of severe acute respiratory syndrome. Postgrad Med J 2006; 82:140–144.
23. Kim M, Nam JH, Son JW, Kim SO, Son NH, Ahn CM, et al. Cardiac manifestations of coronavirus disease 2019 (COVID-19): a Multicenter Cohort Study. J Korean Med Sci 2020; 35:e366.
24. Parati G, Pomidossi G, Albini F, Malaspina D, Mancia G. Relationship of 24-hour blood pressure mean and variability to severity of target-organ damage in hypertension. J Hypertens 1987; 5:93–98.
25. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020; 395:497–506.
26. Hassanein M, Radhakrishnan Y, Sedor J, Vachharajani T, Vachharajani VT, Augustine J, et al. COVID-19 and the kidney. Cleve Clin J Med 2020; 87:619–631.
27. Porzionato A, Emmi A, Barbon S, Boscolo-Berto R, Stecco C, Stocco E, et al. Sympathetic activation: a potential link between comorbidities and COVID-19. FEBS J 2020; 287:3681–3688.
Keywords:

blood pressure variability; coronavirus disease 2019; coronavirus; SARS-CoV-2; hypertension

Supplemental Digital Content

Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.