Abstract
Aims
Mean arterial pressure (MAP) is widely used for evaluating organ perfusion, but its impact on clinical outcomes in patients with heart failure (HF) remains poorly understood. The aim of this study is to investigate the relationship between MAP and all‐cause mortality and readmission in patients with HF.
Methods and results
We retrospectively analysed data from PhysioNet, involving 2005 patients with HF admitted to Zigong Fourth People's Hospital between 2016 and 2019. The primary outcomes were composite outcomes of all‐cause mortality and readmission at 3 and 6 months. The secondary outcomes were readmission at 3 and 6 months. Multivariate‐adjusted Cox regression models, restricted cubic spline curves (RCS), and propensity score matching (PSM) were used to explore the relationship between MAP and clinical outcomes. Among 2005 patients with HF [≥70 years, 1460 (72.8%); male, 843 (42.0%)], the incidence of primary outcome at 3 months was 33.4% (223/668), 24.4% (163/668), and 22.7% (152/669), and at 6 months, it was 47.5% (317/668), 38.5% (257/668), and 38.0% (254/669) across MAP tertiles [from Tertile 1 (T1) to Tertile 3 (T3)], respectively. The RCS showed an ‘L‐shaped’ relationship between MAP and primary or secondary endpoints. Multivariate‐adjusted Cox models showed that a higher MAP was significantly associated with a lower risk of composite endpoints at 3 months [adjusted hazard ratio (aHR) 0.75, 95% confidence interval (CI) 0.61–0.92, P = 0.006, Tertile 2 (T2); aHR 0.69, 95% CI 0.56–0.86, P = 0.001, T3] and 6 months (aHR 0.79, 95% CI 0.67–0.93, P = 0.005, T2; aHR 0.77, 95% CI 0.64–0.91, P = 0.003, T3) compared with T1. After 1:1 PSM, the effect of maintaining a relatively higher MAP was slightly attenuated. Threshold analyses indicated that per 10 mmHg increase in MAP, there was a 21% and 14% decrease in composite endpoints at 3 and 6 months, respectively (aHR 0.79, 95% CI 0.69–0.91, P = 0.001), and 6 months (aHR 0.86, 95% CI 0.77–0.97, P = 0.013) in patients with MAP ≤ 93 mmHg. The associations were consistent in readmission (secondary outcomes), various subgroups, and sensitivity analysis.
Conclusions
A higher MAP was associated with a lower risk of a composite of all‐cause mortality and readmission. Maintaining a relatively higher MAP could potentially improve the clinical prognosis for patients with HF.
Keywords: Mean arterial pressure, Heart failure, Mortality, Readmission
Introduction
Heart failure (HF) is a complex clinical syndrome primarily caused by ventricular insufficiency, resulting in reduced cardiac output. HF is a leading cause of hospitalization and mortality, particularly in the elderly population. 1 Despite advances in the management and treatment of HF, the incidence of HF continues to rise, and the burden of hospitalization and mortality remains a significant concern. 2 Hospitalization and readmission account for a substantial proportion of HF costs. 3 Therefore, early identification of patients with HF at risk of poor outcomes is critical for optimizing treatment and improving symptoms and survival.
Mean arterial pressure (MAP) is a critical indicator for assessing organ perfusion. MAP reflects the balance between the systolic blood pressure (SBP) and the diastolic blood pressure (DBP) and plays a crucial role in maintaining adequate oxygen and nutrient supply to vital organs. 4 Previous studies have demonstrated that high MAP levels were associated with target organ damage, cardiovascular diseases, and cerebrovascular diseases, whereas low MAP levels might be detrimental in unstable haemodynamics. 5 , 6 , 7 , 8 However, most of these studies were conducted among critically ill patients, or those with shock, sepsis, or a medical history of cerebrovascular disease. 9 , 10 , 11 , 12 , 13 , 14 There was limited information on the association between MAP and clinical outcomes among patients with HF. 15 , 16 Therefore, we aimed to explore the relationship between MAP and all‐cause mortality and readmission among patients with HF in this study.
Methods
Study design, population, and data source
The study population was derived from a single‐centre retrospective database with open access, accessed through the PhysioNet platform (https://doi.org/10.13026/5m60‐vs44). 17 A detailed description of the study design and database information has been previously reported. 18 Briefly, the database consisted of 2008 adult patients with HF, defined according to the criteria of the European Society of Cardiology, 19 who were admitted to Zigong Fourth People's Hospital in Sichuan, China, between December 2016 and June 2019. The study was approved by the ethics committee of Zigong Fourth People's Hospital (Approval Number 2020‐010). Informed consent was waived due to the retrospective nature of the study. Only the first admission for a patient was included in the cohort if they were subsequently readmitted. Mandatory follow‐up visits were conducted at 3 and 6 months to collect data on subsequent hospital admissions and mortality. In cases where patients were unable to reach the clinical centre, the follow‐up visit was replaced by a telephone call. The study was conducted in accordance with the Declaration of Helsinki, and the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) statement was adhered to in the reporting of this study. 20
Mean arterial pressure measurements
Blood pressure (BP) measurements were taken in a seated position by trained clinicians following a resting period of 5 min using a mercury manometer. MAP was calculated using the following formula: DBP + 1/3 * (SBP − DBP).
Primary and secondary outcomes
This study focused on two primary endpoints and two secondary endpoints. The primary endpoints were a composite of all‐cause mortality and readmission at both the 3 and 6 month follow‐up timepoints. The secondary endpoints were readmission at both the 3 and 6 month follow‐up timepoints.
Covariates and other definitions
The dataset utilized in this study encompassed a comprehensive range of patient‐related information, such as demographic data, baseline clinical characteristics [including vital signs, echocardiography results, type, and New York Heart Association (NYHA) class of HF], comorbidities, laboratory tests, drug prescriptions, and clinical outcomes, as previously described in the database protocol. 18 Chronic kidney disease (CKD) was defined as an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD‐EPI) equation. 21 The body mass index (BMI) of patients was calculated by dividing weight in kilograms by height in metres squared.
Statistical analyses
To assess normal distribution of continuous variables, Shapiro–Wilk's normality test was used. Normally distributed continuous variables were presented as mean ± standard deviation (SD) and compared using one‐way ANOVA. Non‐normally distributed continuous variables were presented as median (interquartile range) and compared using Kruskal–Wallis's test. Categorical data were presented as number (percentage) and compared using Pearson's χ2 test, Fisher's exact test when one expected value was <5, and rank‐sum test for bivariate ordered categorical variables. The study utilized the Kaplan–Meier method to estimate the cumulative incidence of outcomes, with comparisons made using log‐rank test. The association between MAP and outcomes was analysed through Cox proportional hazards models. The Cox models included the crude model, Model 1, which was adjusted for age, sex, type of HF, NYHA class, myocardial infarction, history of HF, and brain natriuretic peptide (BNP), and Model 2, which was further adjusted for BMI, admission way, Charlson Comorbidity Index (CCI) score, diabetes, chronic obstructive pulmonary disease, malignant tumour, cerebrovascular disease, type II respiratory failure, acute kidney injury (AKI), diuretics, angiotensin‐converting enzyme inhibitor/angiotensin receptor blocker (ACEI/ARB), calcium channel blocker (CCB), beta‐blockers, anticoagulants, antiplatelets, lipid‐lowering agents, inotropes, vasodilators, eGFR, high‐sensitivity troponin T, haemoglobin, and albumin. The proportional hazards assumption was tested through plotting Schoenfeld's residuals against time, followed by visual inspection for uniformity. The variance inflation factor value for all predictors in our models was <5, indicating the absence of significant multicollinearity. Additionally, a 1:1 propensity score matching (PSM) method 22 was utilized to construct a balanced baseline dataset, enhancing the comparability between groups. A logistic regression model was built, using the covariates in Model 2, to estimate the propensity score.
Restricted cubic spline curves with four knots were used to explore the potentially non‐linear relationship between MAP and outcomes with adjustments for covariates in Model 2. Threshold analysis was conducted in the association between MAP and outcomes using a smoothing function with a two‐piecewise Cox regression model.
Subgroup analyses were performed on patients stratified by age (<70 vs. ≥70 years), sex, type of HF (unilateral vs. bilateral), NYHA class (II vs. ≥III), history of HF (yes vs. no), diabetes (yes vs. no), CKD (yes vs. no), CCI score (≤2 vs. 3–6), BNP [≤768 (median) vs. >768 pg/mL], BMI (<24 vs. ≥24 kg/m2), and concomitantly administered drugs (ACEI/ARB, CCB, beta‐blockers, and vasodilators, yes vs. no). An interaction term was added to the Cox proportional hazards regression model, adjusting for covariates listed in Model 2, to test for possible effect modification by the grouping factor. Furthermore, the modification effect of left ventricular ejection fraction (LVEF) on MAP and primary outcomes was explored by restricting the analysis to patients with available LVEF data.
Handling of missing data
Considering the varying degrees of missing variables, for variables with missing rates ≤ 15%, we performed an imputation process using a random forest model, which was a widely accepted method. 23 Variables with missing rates > 15% were not included in the model, because it might introduce data bias. The imputed dataset was then used for our main analysis.
Sensitivity analyses
To further validate the robustness of the study findings, several sensitivity analyses were conducted. First, we analysed the dataset prior to imputation to examine the impact of missing data on the results. Second, additional confounding factors such as dementia, peripheral vascular disease (PVD), liver disease, white blood cell count, blood lipids, and fasting blood glucose were further adjusted for in the Cox models based on Model 2. Third, a competing risk model was employed to validate the secondary outcomes. All analyses were performed using R, Version 4.1.2 (http://www.R‐project.org/), with two‐tailed P values < 0.05 considered statistically significant. Data were analysed from January 2022 to September 2022.
Results
Baseline characteristics of study population
In the study cohort, out of 2008 patients, 3 were excluded due to missing values for MAP. As a result, 2005 patients who were admitted with HF were finally included in the main analysis (Figure 1 ).
Figure 1.
Flow chart of patient selection. BNP, brain natriuretic peptide; eGFR, estimated glomerular filtration rate; hs‐TnT, high‐sensitivity troponin T; MAP, mean arterial pressure.
The distribution of missing variables was presented in Supporting Information, Table S1 . The clinical characteristics of patients were stratified by tertiles of MAP and were summarized in Table 1 . The mean (SD) MAP of patients in the three tertiles were 78 ± 6 mmHg [Tertile 1 (T1)], 94 ± 4 mmHg [Tertile 2 (T2)], and 113 ± 10 mmHg [Tertile 3 (T3)]. Of the 2005 patients, the majority were older than 70 years [1460 (72.8%)], were female [1162 (58.0%)], and had chronic HF (93.2%), bilateral HF (73.8%), and NYHA class ≥ III (82.4%). All participants received active treatment of HF, with the top three drugs being diuretics (98%), vasodilators (83.5%), and inotropes (51.4%). Patients with lower MAP were more likely to have a higher NYHA class and a higher BNP.
Table 1.
Baseline characteristics of patients with heart failure stratified by tertiles of mean arterial pressure
| Characteristic | Overall | Tertiles of MAP, mmHg | |||
|---|---|---|---|---|---|
| T1 (≤87) | T2 (>87 to 100) | T3 (>100) | P value | ||
| n | 2005 | 668 | 668 | 669 | |
| MAP, mmHg | 95 ± 16 | 78 ± 6 | 94 ± 4 | 113 ± 10 | <0.001 |
| Age, years, n (%) | <0.001 | ||||
| <70 | 545 (27.2) | 218 (32.6) | 180 (26.9) | 147 (22.0) | |
| ≥70 | 1460 (72.8) | 450 (67.4) | 488 (73.1) | 522 (78.0) | |
| Male (%) | 843 (42.0) | 295 (44.2) | 271 (40.6) | 277 (41.4) | 0.379 |
| BMI, kg/m2 | 20.8 [18.5, 23.4] | 20.1 [18.2, 22.4] | 20.7 [18.4, 23.4] | 21.5 [19.0, 24.5] | <0.001 |
| Admission way, n (%) | 0.005 | ||||
| Emergency | 953 (47.5) | 304 (45.5) | 297 (44.5) | 352 (52.6) | |
| Non‐emergency | 1052 (52.5) | 364 (54.5) | 371 (55.5) | 317 (47.4) | |
| Type of heart failure, n (%) | 0.061 | ||||
| Bilateral | 1479 (73.8) | 512 (76.6) | 474 (71.0) | 493 (73.7) | |
| Unilateral | 526 (26.2) | 156 (23.4) | 194 (29.0) | 176 (26.3) | |
| NYHA class, n (%) | 0.030 | ||||
| II | 353 (17.6) | 108 (16.2) | 106 (15.9) | 139 (20.8) | |
| ≥III | 1652 (82.4) | 560 (83.8) | 562 (84.1) | 530 (79.2) | |
| CCI score | 2.00 [1.00, 2.00] | 2.00 [1.00, 2.00] | 2.00 [1.00, 2.00] | 2.00 [1.00, 2.00] | 0.838 |
| Comorbidity, n (%) | |||||
| Myocardial infarction | 142 (7.1) | 50 (7.5) | 52 (7.8) | 40 (6.0) | 0.386 |
| History of heart failure | 1869 (93.2) | 613 (91.8) | 634 (94.9) | 622 (93.0) | 0.070 |
| Cerebrovascular disease | 150 (7.5) | 52 (7.8) | 49 (7.3) | 49 (7.3) | 0.936 |
| COPD | 233 (11.6) | 86 (12.9) | 79 (11.8) | 68 (10.2) | 0.297 |
| Type II respiratory failure | 114 (5.7) | 46 (6.9) | 32 (4.8) | 36 (5.4) | 0.233 |
| Diabetes | 465 (23.2) | 146 (21.9) | 156 (23.4) | 163 (24.4) | 0.550 |
| CKD | 888 (44.3) | 336 (50.3) | 270 (40.4) | 282 (42.2) | 0.001 |
| AKI | 7 (0.3) | 2 (0.3) | 3 (0.4) | 2 (0.3) | 0.866 |
| Dementia | 115 (5.7) | 39 (5.8) | 32 (4.8) | 44 (6.6) | 0.369 |
| PVD | 101 (5.0) | 21 (3.1) | 34 (5.1) | 46 (6.9) | 0.008 |
| Liver disease | 83 (4.1) | 35 (5.2) | 31 (4.6) | 17 (2.5) | 0.034 |
| Malignant tumour | 40 (2.0) | 10 (1.5) | 11 (1.6) | 19 (2.8) | 0.157 |
| Treatment, n (%) | |||||
| Diuretics | 1965 (98.0) | 662 (99.1) | 650 (97.3) | 653 (97.6) | 0.042 |
| ACEI/ARB | 768 (38.3) | 246 (36.8) | 249 (37.3) | 273 (40.8) | 0.261 |
| CCB | 464 (23.1) | 110 (16.5) | 145 (21.7) | 209 (31.2) | <0.001 |
| Beta‐blockers | 762 (38.0) | 228 (34.1) | 266 (39.8) | 268 (40.1) | 0.041 |
| Anticoagulants | 479 (23.9) | 162 (24.3) | 172 (25.7) | 145 (21.7) | 0.210 |
| Antiplatelets | 1193 (59.5) | 356 (53.3) | 382 (57.2) | 455 (68.0) | <0.001 |
| Lipid‐lowering agents | 820 (40.9) | 228 (34.1) | 270 (40.4) | 322 (48.1) | <0.001 |
| Inotropes | 1031 (51.4) | 392 (58.7) | 344 (51.5) | 295 (44.1) | <0.001 |
| Vasodilators | 1674 (83.5) | 565 (84.6) | 558 (83.5) | 551 (82.4) | 0.550 |
| BNP a , pg/mL | 768.1 [310.6, 1729.8] | 941.2 [387.4, 1958.4] | 717.4 [260.0, 1692.8] | 725.9 [308.3, 1535.8] | <0.001 |
| hs‐TnT a , pg/mL | 0.06 [0.02, 0.13] | 0.07 [0.03, 0.13] | 0.05 [0.02, 0.12] | 0.05 [0.02, 0.13] | 0.004 |
| eGFR a , mL/min/1.73 m2 | 66.0 [42.4, 89.5] | 59.9 [37.4, 80.9] | 69.8 [46.6, 93.3] | 67.6 [44.8, 90.3] | <0.001 |
| Haemoglobin a , g/L | 115.1 ± 24.4 | 114.2 ± 26.9 | 115.4 ± 23.3 | 115.7 ± 22.6 | 0.502 |
| Albumin a , g/L | 36.5 ± 4.9 | 36.0 ± 5.1 | 36.7 ± 4.9 | 36.9 ± 4.6 | 0.003 |
| Triglyceride a , mmol/L | 1.00 [0.73, 1.27] | 0.97 [0.72, 1.25] | 1.01 [0.75, 1.29] | 1.02 [0.74, 1.29] | 0.169 |
| Total cholesterol a , mmol/L | 3.66 [3.03, 4.23] | 3.50 [2.84, 4.00] | 3.63 [3.03, 4.22] | 3.80 [3.30, 4.49] | <0.001 |
| LDL‐C a , mmol/L | 1.80 [1.38, 2.22] | 1.73 [1.24, 2.11] | 1.78 [1.38, 2.23] | 1.87 [1.45, 2.34] | <0.001 |
| HDL‐C a , mmol/L | 1.09 [0.88, 1.28] | 1.03 [0.80, 1.20] | 1.09 [0.87, 1.26] | 1.14 [0.98, 1.36] | <0.001 |
| WBC, *109/L | 6.52 [5.10, 8.61] | 6.59 [5.21, 8.91] | 6.40 [4.98, 8.41] | 6.60 [5.17, 8.55] | 0.100 |
| hs‐CRP a , mg/L | 9.40 [3.98, 29.83] | 11.45 [4.27, 46.05] | 9.40 [4.30, 29.35] | 7.30 [3.20, 19.30] | <0.001 |
| FBG a , mmol/L | 7.81 (3.96) | 7.84 (4.24) | 7.98 (4.30) | 7.63 (3.32) | 0.503 |
ACEI/ARB, angiotensin‐converting enzyme inhibitor/angiotensin receptor blocker; AKI, acute kidney injury; BMI, body mass index; BNP, brain natriuretic peptide; CCB, calcium channel blocker; CCI, Charlson Comorbidity Index; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; eGFR, estimated glomerular filtration rate; FBG, fasting blood glucose; HDL‐C, high‐density lipoprotein cholesterol; hs‐CRP, high‐sensitive C‐reactive protein; hs‐TnT, high‐sensitivity troponin T; LDL‐C, low‐density lipoprotein cholesterol; MAP, mean arterial pressure; NYHA, New York Heart Association; PVD, peripheral vascular disease; T1, Tertile 1; T2, Tertile 2; T3, Tertile 3; WBC, white blood cell count.
As the missing rates of BNP, hs‐TnT, eGFR, haemoglobin, albumin, triglyceride, total cholesterol, LDL‐C, HDL‐C, and WBC were ≤15%, we performed an imputation process using random forest to fill in these missing values. And hs‐CRP and FBG were not included in random forest model, because the missing rates were >15%.
The baseline characteristics of patients stratified by composite outcomes were presented in Supporting Information, Table S2 . Patients who occurred outcomes were older and had a lower MAP, a higher NYHA class, and a higher proportion of comorbidities than patients without outcomes.
Association between mean arterial pressure levels and outcomes
During the study period, 538 (26.8%) and 828 (41.3%) participants occurred composite outcomes at 3 and 6 months, respectively. Kaplan–Meier curves revealed that the highest cumulative incidence rate of composite endpoints (Supporting Information, Figure S1A,B ) and readmission (Supporting Information, Figure S1C,D ) was observed in T1 compared with T2 and T3. The spline analysis showed an ‘L‐shaped’ relationship between MAP and primary or secondary endpoints in this population (Figure 2 A–D ).
Figure 2.
Relationship of mean arterial pressure (MAP) with risk of composite outcomes of all‐cause mortality and readmission based on restricted cubic spline curves. (A) Six month composite outcomes of all‐cause mortality and readmission, (B) 3 month composite outcomes of all‐cause mortality and readmission, (C) 6 month readmission, and (D) 3 month readmission. Adjusted for age, sex, type of heart failure, New York Heart Association class, myocardial infarction, history of heart failure, brain natriuretic peptide, body mass index, admission way, Charlson Comorbidity Index score, chronic obstructive pulmonary disease, diabetes, acute kidney injury, type II respiratory failure, cerebrovascular disease, malignant tumour, diuretics, angiotensin‐converting enzyme inhibitor/angiotensin receptor blocker, calcium channel blocker, beta‐blockers, anticoagulants, antiplatelets, lipid‐lowering agents, inotropes, vasodilators, high‐sensitivity troponin T, estimated glomerular filtration rate, haemoglobin, and albumin.
Cox models indicated that higher levels of MAP were significantly associated with a lower risk of composite endpoints [3 months: adjusted hazard ratio (aHR) 0.75, 95% confidence interval (CI) 0.61–0.92, P = 0.006, T2; aHR 0.69, 95% CI 0.56–0.86, P = 0.001, T3; 6 months: aHR 0.79, 95% CI 0.67–0.93, P = 0.005, T2; aHR 0.77, 95% CI 0.64–0.91, P = 0.003, T3] and readmission (3 months: aHR 0.71, 95% CI 0.57–0.88, P = 0.002, T2; aHR 0.69, 95% CI 0.55–0.86, P = 0.001, T3; 6 months: aHR 0.76, 95% CI 0.64–0.90, P = 0.002, T2; aHR 0.75, 95% CI 0.63–0.90, P = 0.002, T3) (Table 2 ). Further exploratory analysis combining T2 and T3 revealed a significantly lower risk of composite endpoints (3 months: aHR 0.72, 95% CI 0.60–0.86, P < 0.001, T2–T3; 6 months: aHR 0.78, 95% CI 0.67–0.90, P = 0.001, T2–T3) compared with T1 (Table 2 ). The results for secondary outcomes were consistent with those of the primary endpoints (Table 2 ).
Table 2.
The association of mean arterial pressure with 3 and 6 month primary and secondary outcomes for patients with heart failure
| MAP, mmHg | Total N | No. of events (%) | Crude model | Model 1 | Model 2 | |||
|---|---|---|---|---|---|---|---|---|
| HR (95% CI) | P value | HR (95% CI) | P value | HR (95% CI) | P value | |||
| Primary outcomes | ||||||||
| 3 month composite outcomes of all‐cause mortality and readmission | ||||||||
| Tertiles | ||||||||
| T1 (≤87) | 668 | 223 (33.4) | Ref | Ref | Ref | |||
| T2 (>87 to 100) | 668 | 163 (24.4) | 0.69 (0.56–0.84) | <0.001 | 0.71 (0.58–0.88) | 0.001 | 0.75 (0.61–0.92) | 0.006 |
| T3 (>100) | 669 | 152 (22.7) | 0.64 (0.52–0.78) | <0.001 | 0.66 (0.54–0.81) | <0.001 | 0.69 (0.56–0.86) | 0.001 |
| P for trend | <0.001 | <0.001 | <0.001 | |||||
| Categories | ||||||||
| T1 (≤87) | 668 | 223 (33.4) | Ref | Ref | Ref | |||
| T2–T3 (>87) | 1337 | 315 (23.6) | 0.66 (0.56–0.79) | <0.001 | 0.69 (0.58–0.82) | <0.001 | 0.72 (0.60–0.86) | <0.001 |
| 6 month composite outcomes of all‐cause mortality and readmission | ||||||||
| Tertiles | ||||||||
| T1 (≤87) | 668 | 317 (47.5) | Ref | Ref | Ref | |||
| T2 (>87 to 100) | 668 | 257 (38.5) | 0.74 (0.63–0.87) | <0.001 | 0.76 (0.64–0.90) | 0.001 | 0.79 (0.67–0.93) | 0.005 |
| T3 (>100) | 669 | 254 (38.0) | 0.72 (0.61–0.85) | <0.001 | 0.74 (0.63–0.87) | <0.001 | 0.77 (0.64–0.91) | 0.003 |
| P for trend | <0.001 | <0.001 | 0.003 | |||||
| Categories | ||||||||
| T1 (≤87) | 668 | 317 (47.5) | Ref | Ref | Ref | |||
| T2–T3 (>87) | 1337 | 511 (38.2) | 0.73 (0.63–0.84) | <0.001 | 0.75 (0.65–0.86) | <0.001 | 0.78 (0.67–0.90) | 0.001 |
| Secondary outcomes | ||||||||
| 3 month readmission | ||||||||
| Tertiles | ||||||||
| T1 (≤87) | 668 | 206 (30.8) | Ref | Ref | Ref | |||
| T2 (>87 to 100) | 668 | 148 (22.2) | 0.68 (0.55–0.84) | <0.001 | 0.70 (0.56–0.86) | 0.001 | 0.71 (0.57–0.88) | 0.002 |
| T3 (>100) | 669 | 143 (21.4) | 0.66 (0.53–0.81) | <0.001 | 0.67 (0.54–0.84) | <0.001 | 0.69 (0.55–0.86) | 0.001 |
| P for trend | <0.001 | <0.001 | 0.001 | |||||
| Categories | ||||||||
| T1 (≤87) | 668 | 206 (30.8) | Ref | Ref | Ref | |||
| T2–T3 (>87) | 1337 | 291 (21.8) | 0.67 (0.56–0.80) | <0.001 | 0.69 (0.57–0.82) | <0.001 | 0.70 (0.58–0.84) | <0.001 |
| 6 month readmission | ||||||||
| Tertiles | ||||||||
| T1 (≤87) | 668 | 295 (44.2) | Ref | Ref | Ref | |||
| T2 (>87 to 100) | 668 | 238 (35.6) | 0.74 (0.62–0.88) | 0.001 | 0.75 (0.63–0.89) | 0.001 | 0.76 (0.64–0.90) | 0.002 |
| T3 (>100) | 669 | 239 (35.7) | 0.74 (0.62–0.87) | <0.001 | 0.75 (0.63–0.89) | 0.001 | 0.75 (0.63–0.90) | 0.002 |
| P for trend | <0.001 | 0.002 | 0.003 | |||||
| Categories | ||||||||
| T1 (≤87) | 668 | 295 (44.2) | Ref | Ref | Ref | |||
| T2–T3 (>87) | 1337 | 477 (35.7) | 0.74 (0.64–0.85) | <0.001 | 0.75 (0.65–0.87) | <0.001 | 0.76 (0.65–0.88) | <0.001 |
CI, confidence interval; HR, hazard ratio; MAP, mean arterial pressure; Ref, reference; T1, Tertile 1; T2, Tertile 2; T3, Tertile 3.
Model 1: adjusted for age, type of heart failure, New York Heart Association class, myocardial infarction, history of heart failure, and brain natriuretic peptide. Model 2 (full model): Model 1 + further adjusted for body mass index, admission way, Charlson Comorbidity Index score, chronic obstructive pulmonary disease, diabetes, acute kidney injury, type II respiratory failure, cerebrovascular disease, malignant tumour, diuretics, angiotensin‐converting enzyme inhibitor/angiotensin receptor blocker, calcium channel blocker, beta‐blockers, anticoagulants, antiplatelets, lipid‐lowering agents, inotropes, vasodilators, high‐sensitivity troponin T, estimated glomerular filtration rate, haemoglobin, and albumin.
Additionally, a 1:1 PSM analysis was conducted to balance baseline characteristics between the T1 and T2–T3 groups (Supporting Information, Table S3 ), resulting in 641 pairs of patients. The Cox model showed that a higher MAP was significantly associated with a lower risk of composite endpoints (3 months: aHR 0.74, 95% CI 0.60–0.92, P = 0.005, T2–T3; 6 months: aHR 0.84, 95% CI 0.71–0.99, P = 0.037, T2–T3) and readmission (3 months: aHR 0.72, 95% CI 0.58–0.90, P = 0.003, T2–T3; 6 months: aHR 0.82, 95% CI 0.69–0.98, P = 0.025, T2–T3) (Table 3 ).
Table 3.
The association of mean arterial pressure with 3 and 6 month primary and secondary outcomes for patients with heart failure after 1:1 propensity score matching
| MAP, mmHg | Total N | No. of events (%) | Crude model | Model 1 | Model 2 | |||
|---|---|---|---|---|---|---|---|---|
| HR (95% CI) | P value | HR (95% CI) | P value | HR (95% CI) | P value | |||
| Primary outcomes | ||||||||
| 3 month composite outcomes of all‐cause mortality and readmission | ||||||||
| Categories | ||||||||
| T1 (≤87) | 641 | 213 (33.2) | Ref | Ref | Ref | |||
| T2–T3 (>87) | 641 | 162 (25.3) | 0.73 (0.59–0.90) | 0.003 | 0.74 (0.60–0.91) | 0.004 | 0.74 (0.60–0.92) | 0.005 |
| 6 month composite outcomes of all‐cause mortality and readmission | ||||||||
| Categories | ||||||||
| T1 (≤87) | 641 | 305 (47.6) | Ref | Ref | Ref | |||
| T2–T3 (>87) | 641 | 270 (42.1) | 0.83 (0.70–0.97) | 0.022 | 0.83 (0.71–0.98) | 0.030 | 0.84 (0.71–0.99) | 0.037 |
| Secondary outcomes | ||||||||
| 3 month readmission | ||||||||
| Categories | ||||||||
| T1 (≤87) | 641 | 197 (30.7) | Ref | Ref | Ref | |||
| T2–T3 (>87) | 641 | 147 (22.9) | 0.71 (0.58–0.88) | 0.002 | 0.73 (0.59–0.90) | 0.004 | 0.72 (0.58–0.90) | 0.003 |
| 6 month readmission | ||||||||
| Categories | ||||||||
| T1 (≤87) | 641 | 284 (44.3) | Ref | Ref | Ref | |||
| T2–T3 (>87) | 641 | 248 (38.7) | 0.82 (0.69–0.97) | 0.019 | 0.83 (0.70–0.98) | 0.028 | 0.82 (0.69–0.98) | 0.025 |
CI, confidence interval; HR, hazard ratio; MAP, mean arterial pressure; Ref, reference; T1, Tertile 1; T2, Tertile 2; T3, Tertile 3.
Model 1: adjusted for sex, age, type of heart failure, New York Heart Association class, myocardial infarction, history of heart failure, and brain natriuretic peptide. Model 2 (full model): Model 1 + further adjusted for body mass index, admission way, Charlson Comorbidity Index score, chronic obstructive pulmonary disease, diabetes, acute kidney injury, type II respiratory failure, cerebrovascular disease, malignant tumour, diuretics, angiotensin‐converting enzyme inhibitor/angiotensin receptor blocker, calcium channel blocker, beta‐blockers, anticoagulants, antiplatelets, lipid‐lowering agents, inotropes, vasodilators, high‐sensitivity troponin T, estimated glomerular filtration rate, haemoglobin, and albumin.
Threshold analysis consistently revealed that, for every 10 mmHg increase in MAP, there was a 21% and 14% decrease in composite endpoints at 3 and 6 months, respectively (aHR 0.79, 95% CI 0.69–0.91, P = 0.001; aHR 0.86, 95% CI 0.77–0.97, P = 0.013) (Table 4 ) in participants with MAP ≤ 93 mmHg. However, similar trends were not observed in participants with MAP > 93 mmHg. The above results were consistent with 3 and 6 month readmission (Table 4 ).
Table 4.
Threshold analyses of mean arterial pressure with 3 and 6 month primary outcomes for patients with heart failure using two‐piecewise regression models
| MAP, per 10 mmHg increase | Total N | No. of events (%) | Crude model | Model 1 | Model 2 | |||
|---|---|---|---|---|---|---|---|---|
| HR (95% CI) | P value | HR (95% CI) | P value | HR (95% CI) | P value | |||
| 3 month composite outcomes of all‐cause mortality and readmission | ||||||||
| ≤93 | 1008 | 308 (30.6) | 0.77 (0.68–0.88) | <0.001 | 0.79 (0.69–0.90) | <0.001 | 0.79 (0.69–0.91) | 0.001 |
| >93 | 997 | 230 (23.1) | 0.97 (0.87–1.10) | 0.665 | 0.99 (0.87–1.11) | 0.810 | 0.98 (0.86–1.11) | 0.749 |
| 6 month composite outcomes of all‐cause mortality and readmission | ||||||||
| ≤93 | 1008 | 454 (45.0) | 0.84 (0.75–0.94) | 0.002 | 0.86 (0.76–0.96) | 0.007 | 0.86 (0.77–0.97) | 0.013 |
| >93 | 997 | 374 (37.5) | 1.01 (0.92–1.10) | 0.882 | 1.02 (0.93–1.12) | 0.704 | 1.01 (0.91–1.11) | 0.908 |
| 3 month readmission | ||||||||
| ≤93 | 1008 | 286 (28.4) | 0.80 (0.70–0.92) | 0.002 | 0.82 (0.71–0.94) | 0.005 | 0.80 (0.69–0.93) | 0.005 |
| >93 | 997 | 211 (21.2) | 0.99 (0.88–1.12) | 0.923 | 1.01 (0.89–1.14) | 0.903 | 1.01 (0.89–1.16) | 0.831 |
| 6 month readmission | ||||||||
| ≤93 | 1008 | 424 (42.1) | 0.88 (0.78–0.99) | 0.027 | 0.89 (0.79–1.00) | 0.053 | 0.88 (0.77–0.99) | 0.034 |
| >93 | 997 | 348 (34.9) | 1.02 (0.93–1.12) | 0.616 | 1.04 (0.95–1.14) | 0.424 | 1.03 (0.94–1.14) | 0.500 |
CI, confidence interval; HR, hazard ratio; MAP, mean arterial pressure.
Model 1: adjusted for age, sex, type of heart failure, New York Heart Association class, myocardial infarction, history of heart failure, and brain natriuretic peptide. Model 2 (full model): Model 1 + further adjusted for body mass index, admission way, Charlson Comorbidity Index score, chronic obstructive pulmonary disease, diabetes, acute kidney injury, type II respiratory failure, cerebrovascular disease, malignant tumour, diuretics, angiotensin‐converting enzyme inhibitor/angiotensin receptor blocker, calcium channel blocker, beta‐blockers, anticoagulants, antiplatelets, lipid‐lowering agents, inotropes, vasodilators, high‐sensitivity troponin T, estimated glomerular filtration rate, haemoglobin, and albumin.
Subgroup analyses
Subgroup analyses revealed a consistent association between MAP and the risk of composite endpoints at 3 and 6 months (Figures 3 and 4 and Supporting Information, Figures S2 and S3 ). None of the analysed variables, including age, sex, type of HF, NYHA class, history of HF, diabetes, CKD, CCI score, BNP, CCB, beta‐blockers, or vasodilators use, significantly modified the relationship between MAP and composite endpoints at 3 and 6 months (all P for interaction > 0.05). However, we observed a stronger association between MAP and composite endpoint at 6 months in patients treated with ACEI/ARB (users: aHR 0.58, 95% CI 0.46–0.73 vs. non‐users: aHR 0.91, 95% CI 0.76–1.10, P for interaction = 0.002) and those with lower BMI (<24 kg/m2: aHR 0.72, 95% CI 0.61–0.84 vs. ≥24 kg/m2: aHR 1.08, 95% CI 0.75–1.56, P for interaction = 0.040) (Figure 4 ).
Figure 3.
The association between mean arterial pressure and the risk of composite outcomes of 3 month all‐cause mortality and readmission in various subgroups. ACEI/ARB, angiotensin‐converting enzyme inhibitor/angiotensin receptor blocker; BMI, body mass index; BNP, brain natriuretic peptide; CCB, calcium channel blocker; CCI, Charlson Comorbidity Index; CI, confidence interval; CKD, chronic kidney disease; HR, hazard ratio; NYHA, New York Heart Association; T1, Tertile 1; T2, Tertile 2; T3, Tertile 3.
Figure 4.
The association between mean arterial pressure and the risk of composite outcomes of 6 month all‐cause mortality and readmission in various subgroups. ACEI/ARB, angiotensin‐converting enzyme inhibitor/angiotensin receptor blocker; BMI, body mass index; BNP, brain natriuretic peptide; CCB, calcium channel blocker; CCI, Charlson Comorbidity Index; CI, confidence interval; CKD, chronic kidney disease; HR, hazard ratio; NYHA, New York Heart Association; T1, Tertile 1; T2, Tertile 2; T3, Tertile 3.
Furthermore, we conducted an additional analysis that included only patients with available LVEF data. Our findings suggested that LVEF (<50% vs. ≥50%) did not modify the association between MAP and composite endpoints at 3 and 6 months (all P for interaction > 0.05) (Supporting Information, Table S4 ).
Sensitivity analyses
The robustness of the association between MAP and outcomes was verified through various methods. After excluding patients with missing variables and using the non‐imputed dataset, the results remained consistent (Supporting Information, Table S5 ). Moreover, further adjusting for potential confounders including dementia, PVD, liver disease, white blood cell count, blood lipids, and fasting blood glucose did not substantially alter the association between MAP and outcomes (Supporting Information, Tables S6 – S8 ). Competing risk models were also used for secondary outcomes, and similar results were obtained (Supporting Information, Table S9 ).
Discussion
This retrospective study, conducted at a single centre and involving 2005 patients with HF, found that 26.8% and 41.3% of patients occurred a composite of all‐cause mortality and readmission within 3 and 6 months of hospital admission, respectively. The study showed that a higher MAP was significantly associated with a lower risk of a composite of all‐cause mortality and readmission. This association was consistent across various subgroups and sensitivity analyses. The results of this study suggested that MAP might be a prognostic indicator at admission to help stratify patient prognosis.
MAP is a crucial parameter that affected cardiac function and central artery wall properties in individuals throughout one cardiac cycle. When MAP falls below a certain threshold, organ blood flow rapidly decreases. For patients with septic shock, both the Surviving Sepsis Campaign guidelines 24 and the Task Force of the European Society of Intensive Care Medicine 25 recommended an initial MAP target of 65 mmHg, followed by individual titration of vasopressor agents. Although it was generally accepted that MAP should be maintained above 65 mmHg, the need for higher MAP targets in other patients, particularly those with HF, remained unclear. 26
Our study has shown an L‐shaped relationship between MAP and a composite of mortality and readmission, or readmission only. Furthermore, the threshold analysis supported a linear association with composite endpoints over the range of MAP ≤ 93 mmHg. These findings were consistent with previous studies. 15 , 16 Daniele Torres et al. recruited 136 patients with HF and found that patients with MAP ≥ 93.3 mmHg had a higher probability of survival free from all‐cause mortality than those with MAP < 93.3 mmHg. 15 However, this study had the limitation of a small sample size. In patients with HF with preserved ejection fraction (HFpEF), Fang‐Fei Wei et al. also found that lower MAP was associated with increased risk of the primary composite endpoint (a composite of all‐cause mortality, non‐fatal myocardial infarction, non‐fatal stroke, or hospitalization for HF). 16 However, this study only focused on HFpEF patients and was unable to assess the predictive value of MAP in patients with HF with reduced ejection fraction (HFrEF). Our research filled these gaps and provided clinical guidance for cardiologists.
Multiple studies 9 , 13 , 14 , 27 , 28 have yielded consistent results with our findings that lower MAP was associated with worse clinical outcomes. One large‐scale retrospective study involving over 5000 patients demonstrated that a prolonged duration of MAP below 65 mmHg was associated with an increased mortality. 9 Another retrospective study indicated that MAP < 80 mmHg could exacerbate myocardial injury and AKI. 13 In patients with severe head injury, lower MAP was independently and significantly associated with mortality, 14 consistent with a post hoc analysis of shocked patients (≥70 vs. <70 mmHg) in the DOREMI (DObutamine compaREd to MIlrinone) trial. 28 However, a study showed that targeting a MAP of 80–85 mmHg in patients with septic shock, compared with 65–70 mmHg, did not result in significant differences in 28 and 90 days of mortality. 29 Furthermore, two meta‐analyses including randomized controlled trials failed to demonstrate any benefits of a high MAP goal in terms of survival. 30 , 31 These findings suggested that a specific MAP goal or a one‐size‐fits‐all approach may not be clinically beneficial for all population. Thus, it was urgent to set patient‐specific optimal MAP goals. And for patients with HF, we recommended that they maintain a higher MAP for a better clinical prognosis based on our study results.
In subgroup analysis, we found that most variables did not significantly modify the association between MAP and the composite endpoints at 3 and 6 months. This suggested that the results of this study were generally applicable to most patients with HF. However, the association was stronger in patients treated with ACEIs/ARBs or lower BMI. ACEIs and ARBs were commonly used as first‐line therapy for patients with HF, and they have been shown to improve ventricular remodelling, reduce cardiac burden, and lower BP. Therefore, a higher MAP might have a stronger protective effect in patients receiving ACEIs or ARBs. Furthermore, epidemiological data unequivocally supported that higher BMI was one of the major risk factors for hypertension. 32 It was estimated that at least 75% of the incidence of hypertension was directly related to obesity. 33 Thus, the association between MAP and endpoints might be greatly attenuated in this particular population. However, in patients with a BMI < 24 kg/m2, our results were consistent with the main findings. Additionally, we found that the prognostic role of MAP was not related to traditional prognostic indicators such as BNP or LVEF (whether it is HFrEF or HFpEF). This further underscored the value of MAP in predicting clinical outcomes in patients with HF.
Some potential mechanisms supported our findings that maintaining a higher MAP was associated with a better prognosis. 34 , 35 , 36 , 37 , 38 , 39 , 40 Patients with HF typically have a decrease in cardiac output, which causes an increase in peripheral vascular resistance and peripheral vascular stiffness due to overactivation of neurohumoral regulatory mechanisms in the long run. 34 , 35 , 36 This pathophysiology was more pronounced in patients with low MAP. Overactive neurohumoral mechanisms have been shown to indicate poor prognosis in patients with HF. 37 Meanwhile, low MAP typically indicated insufficient blood supply to peripheral tissues and organs (particularly vital organs including the heart and brain), 38 and a prolonged state of ischaemia and hypoxia exacerbated organ failure and increased the risk of cardiovascular death and all‐cause mortality. 39 , 40
The present study had a major strength in utilizing a comprehensive database that contained patient‐level data, enabling detailed analyses of adjusting for potential confounding factors, as well as multiple sensitivity analyses. However, there were several limitations in this study that needed to be considered. First, because this was a retrospective investigation conducted at a single centre, no causal inferences could be drawn. The study population was limited to Southwest China, and therefore, further research was necessary to validate these findings in diverse populations. Second, the follow‐up period was relatively short, and the long‐term prognostic effects of MAP were unknown. Third, although certain covariates were adjusted in the regression model, there might still be unknown or unmeasured confounders. Fourth, BP data in this database only recorded the first measurement at admission, which might introduce bias in MAP variability. Therefore, the conclusions should be interpreted with greater caution. Moreover, due to the limited number of deaths, we were unable to investigate the association between MAP and mortality in detail [41 (2.8%) at 3 months and 56 (2.8%) at 6 months]. Finally, the database did not capture the reasons for death and readmission during follow‐up, precluding stratification of the analysis by cause of death and readmission. Therefore, larger clinical studies were needed to confirm these findings.
Conclusions
In conclusion, we found that a higher MAP was associated with a lower risk of a composite of all‐cause mortality and readmission in patients with HF. Our findings suggest that MAP could be a useful indicator for prognostic stratification at admission. Maintaining a relatively higher MAP could potentially improve the clinical prognosis for patients with HF.
Conflict of interest
None declared.
Funding
This work was supported by the Outstanding Youths Development Scheme of Nanfang Hospital, Southern Medical University (2020J005 to Dr S. Nie) and the Basic and Applied Basic Research Foundation of Guangdong Province (SL2022A04J02062 to Dr Y. Li).
Supporting information
Figure S1. Cumulative incidence of primary and secondary outcomes. *Composite outcomes of all‐cause mortality and readmission stratified by tertiles of MAP (A), Composite outcomes of all‐cause mortality and readmission stratified by categories of MAP (B), Readmission stratified by tertiles of MAP (C) and Readmission stratified by categories of MAP (D).
Figure S2. The association between MAP and the risk of 3‐month composite outcomes stratified by cutoff value of MAP.
Figure S3. The association between MAP and the risk of 6‐month composite outcomes stratified by cutoff value of MAP.
Table S1. Distribution of missing variables.
Table S2. Baseline characteristics of patients with heart failure stratified by outcomes.
Table S3. Baseline characteristics of patients with heart failure stratified by MAP tertiles after 1:1 propensity score matching.
Table S4. The association of MAP with 3‐ and 6‐month composite outcomes for patients with heart failure stratified by LVEF.
Table S5. The association of MAP with 3‐ and 6‐month primary and secondary outcomes for patients with heart failure using non‐imputed data.
Table S6. The association of MAP with 3‐ and 6‐month primary and secondary outcomes for patients with heart failure after adjusting for dementia, PVD and liver disease.
Table S7. The association of MAP with 3‐ and 6‐month primary and secondary outcomes for patients with heart failure after adjusting for white blood cell count and blood lipids.
Table S8. The association of MAP with 3‐ and 6‐month primary and secondary outcomes for patients with heart failure after adjusting for white blood cell count, blood lipids and fasting blood glucose.
Table S9. The association of MAP with 3‐ and 6‐month readmission for patients with heart failure using competitive risk model.
Acknowledgements
This analysis uses data from Zigong Fourth People's Hospital, which was interrogated in the PhysioNet. The authors thank the research team and the field team for collecting and providing the data. The authors also thank all patients and staff involved in this research.
Gao, Q. , Lin, Y. , Xu, R. , Zhang, Y. , Luo, F. , Chen, R. , Li, P. , Nie, S. , Li, Y. , and Su, L. (2023) Association between mean arterial pressure and clinical outcomes among patients with heart failure. ESC Heart Failure, 10: 2362–2374. 10.1002/ehf2.14401.
Qi Gao and Yuxin Lin contributed equally to the study.
Contributor Information
Sheng Nie, Email: niesheng0202@126.com.
Yanqin Li, Email: liyanqin819@163.com.
Licong Su, Email: slc666@smu.edu.cn.
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Associated Data
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Supplementary Materials
Figure S1. Cumulative incidence of primary and secondary outcomes. *Composite outcomes of all‐cause mortality and readmission stratified by tertiles of MAP (A), Composite outcomes of all‐cause mortality and readmission stratified by categories of MAP (B), Readmission stratified by tertiles of MAP (C) and Readmission stratified by categories of MAP (D).
Figure S2. The association between MAP and the risk of 3‐month composite outcomes stratified by cutoff value of MAP.
Figure S3. The association between MAP and the risk of 6‐month composite outcomes stratified by cutoff value of MAP.
Table S1. Distribution of missing variables.
Table S2. Baseline characteristics of patients with heart failure stratified by outcomes.
Table S3. Baseline characteristics of patients with heart failure stratified by MAP tertiles after 1:1 propensity score matching.
Table S4. The association of MAP with 3‐ and 6‐month composite outcomes for patients with heart failure stratified by LVEF.
Table S5. The association of MAP with 3‐ and 6‐month primary and secondary outcomes for patients with heart failure using non‐imputed data.
Table S6. The association of MAP with 3‐ and 6‐month primary and secondary outcomes for patients with heart failure after adjusting for dementia, PVD and liver disease.
Table S7. The association of MAP with 3‐ and 6‐month primary and secondary outcomes for patients with heart failure after adjusting for white blood cell count and blood lipids.
Table S8. The association of MAP with 3‐ and 6‐month primary and secondary outcomes for patients with heart failure after adjusting for white blood cell count, blood lipids and fasting blood glucose.
Table S9. The association of MAP with 3‐ and 6‐month readmission for patients with heart failure using competitive risk model.