Decreased pulmonary arterial proportional pulse pressure is associated with increased mortality in group 1 pulmonary hypertension

Abstract

Background

This study evaluated the utility of a novel index, pulmonary arterial (PA) proportional pulse pressure (PAPP; range 0–1, defined as [PA systolic pressure – PA diastolic pressure] / PA systolic pressure), in predicting mortality in patients with World Health Organization group 1 pulmonary hypertension (PH).

Hypothesis

Low PAPP is associated with increased 5‐year mortality independent of a validated contemporary risk‐prediction equation (Pulmonary Hypertension Connection [PHC] equation).

Methods

In a group of 262 patients in the National Institutes of Health Primary Pulmonary Hypertension (NIH‐PPH) Registry, PAPP and the PHC risk equation were used to predict mortality during 5 years of follow‐up using Cox proportional hazards models. Kaplan–Meier survival curves were used to compare mortality among PAPP quartiles, and significance was tested using the log‐rank test.

Results

Patients in the lowest quartile (PAPP ≤0.47) had a significantly higher 5‐year mortality than did patients in higher quartiles (log‐rank P = 0.016). In a Cox model adjusted for the PHC equation, PAPP remained significantly associated with 5‐year mortality (hazard ratio: 0.74 per 0.10 increase in PAPP, 95% confidence interval: 0.61‐0.90). The χ² statistic for the single PAPP covariate in this model was 8.8 (P = 0.003), which compared favorably with the χ² statistic of 15.2 (P < 0.0001) for the multivariable PHC equation.

Conclusions

PAPP, an index of ventricular‐arterial coupling, is independently associated with survival in World Health Organization group 1 PH. The use of this easily measurable index for guiding risk stratification needs further investigation.

1. INTRODUCTION

Pulmonary arterial hypertension (PAH) is a progressive disease that leads to right ventricular (RV) failure and death. There are limited clinical metrics available to guide risk stratification for PAH patients. PAH is defined hemodynamically as a mean pulmonary artery pressure (mPAP) ≥25 mm Hg at rest, typically measured using a pulmonary artery catheter,1, 2 accompanied by a pulmonary artery wedge pressure (PAWP) ≤15 mm Hg and a pulmonary vascular resistance (PVR) ≥3 Wood units (WU) in the absence of other causes of precapillary pulmonary hypertension (PH).3 Outcomes in PAH are inextricably linked to RV function.4, 5 With progression of PH, RV function correspondingly declines, leading to death. Ventricular‐arterial (VA) coupling describes the interaction between the ventricle and arterial conduits, and in the case of PH, the efficiency of RV contractility in overcoming afterload in the pulmonary artery. Pulmonary arterial proportional pulse pressure (PAPP) is a surrogate marker of the RV‐arterial interaction. We have recently shown that a low PAPP is independently associated with a poor prognosis in patients with advanced heart failure.6 PAPP is an integrated hemodynamic index of VA coupling that mirrors the RV adaptive response to increased afterload. The application of PAPP to patients with PH has, to our knowledge, never been evaluated. We therefore used data from the National Institutes of Health Primary Pulmonary Hypertension (NIH‐PPH) Registry to test the hypothesis that PAPP is independently associated with survival in PAH. The Pulmonary Hypertension Connection (PHC) risk equation is a contemporary risk model incorporating mPAP, mean right atrial pressure (mRAP), and cardiac index, and it has been shown to accurately predict survival in patients with idiopathic, heritable, and anorexigen‐associated PAH in large cohort studies.7

2. METHODS

2.1. PPH Registry

This study is a retrospective analysis of de‐identified public release data from the NIH‐PPH Registry from the National Heart, Lung, and Blood Institute. The NIH‐PPH Registry was one of the first registries established to study the natural history of PAH. It included patients with idiopathic, familial, and anorexigen‐associated PAH, who, by current nomenclature, fall under World Health Organization (WHO) group 1 PH. The methodology, enrollment, and 5‐year outcome determinants associated with this registry have been published.5 Briefly, the study included PAH patients; all but 2 patients had a PVR >3 WU, which is required to diagnose PAH per contemporary European Society of Cardiology PH guidelines.3 Patients were enrolled from 32 centers across the United States.

2.2. Study cohort and follow‐up

We included all patients with complete variables for the calculation of PAPP and the PHC equation (N = 262). The results were analyzed based on a 5‐year follow‐up time.

2.3. Statistical analysis

The statistical analysis was performed using SAS version 9.4 (SAS Institute, Inc., Cary, NC). Demographic variables were characterized as continuous (age, body mass index) or categorical (sex, race). The hemodynamic continuous variables included PAPP, mPAP, PAWP, PA systolic pressure (PASP), PA diastolic pressure (PADP), mRAP, transpulmonary gradient, PVR, PA pulse pressure, PA capacitance, RV stroke work index, and cardiac index. Continuous variables were characterized using the median and interquartile range, or mean and standard deviation, whereas categorical variables were categorized based on the frequency in each group. Differences in continuous variables between groups were assessed using the Kruskal‐Wallis test or, for normally distributed variables, analysis of variance. Differences in categorical variables between groups were assessed using a χ² test or Fisher exact test. In all cases, a P value of 0.05 was considered significant.

Survival analysis using Kaplan–Meier curves was used to assess survival among the PAPP quartiles. To assess how PAPP can identify patients with the worst prognosis, survival in patients in the lowest quartile of PAPP was also compared with survival in the upper 3 quartiles of PAPP. The significance of the differences between groups was determined using the log‐rank statistic.

Bivariable Cox proportional hazards regression was used to evaluate the association between PAPP and survival, and the proportional hazards assumption was confirmed. The hazard ratio (HR) and 95% confidence intervals (CI) for death were determined. PAPP was evaluated both as a continuous variable (per 0.1 increase) and dichotomized based on the lowest quartile vs the remaining quartiles. For reference, Cox regression was also used to determine the corresponding HR for death and 95% CI with the PHC risk score, based on weighting of mean PAP, RAP, and cardiac index. The associated χ² statistic was used to evaluate the strength of these associations relative to each other.

A multivariable Cox proportional hazards regression model was then constructed based on covariates with significant association with the outcome of death on bivariable regression. In particular, we evaluated the multivariable model with PAPP (as a continuous variable) and the PHC risk score. An alternative multivariable model with PAPP and the individual components of the PHC risk score was also considered. Again, the χ² statistic was used to evaluate the strength of association of each covariate in the adjusted models.

3. RESULTS

3.1. Baseline characteristics

The mean (± SD) age of patients was 37.5 ± 14.9 years; 62% were female, and 28% had race reported as other than white. The distributions of baseline demographic and hemodynamic characteristics by PAPP quartiles are shown in Tables 1 and 2, respectively. Patients in the higher PAPP quartiles were older, but age was not a significant predictor of survival in a bivariable Cox regression model for death (P = 0.85). There were no significant differences in sex or race among PAPP quartiles. Table 2 demonstrates that the following hemodynamic variables were significantly different among the PAPP quartiles: PADP, RAP, PA pulse pressure, PVR, and cardiac index. There was also a clinically meaningful trend in increasing concordant with the PAPP quartiles and RV stroke work index.

Table 1.

Demographics of the population cohort by quartiles of PAPP

Variables	PAPP Q1, <=0.47, n = 63	PAPP Q2, 0.48–0.53, n = 68	PAPP Q3, 0.54–0.59, n = 64	PAPP Q4, >0.59, n = 67	P Value
Age, y	31 (21–39)	36 (28–42)	40 (32–53)	43 (33–54)	<0.0001
Female sex	38 (60)	39 (57)	41 (64)	45 (67)	0.67
Race					0.49
White	50 (79)	48 (71)	41 (64)	49 (73)
African American	8 (13)	7 (10)	13 (20)	8 (12)
Other	5 (8)	13 (19)	10 (26)	10 (15)
BMI, kg/m2	22 (19–25)	24 (21–27)	22 (19–27)	23 (21–26)	0.037

Abbreviations: AA, African American; BMI, body mass index; IQR, interquartile range; PAPP, pulmonary artery proportional pulse pressure; Q, quartile.

Data are presented as n (%) or median (IQR).

Table 2.

Hemodynamic measurements and calculation by quartiles of PAPP

Variables	PAPP Q1, ≤0.47	PAPP Q2, 0.48–0.53	PAPP Q3, 0.54–0.59	PAPP Q4, >0.59	P Value
Directly measured hemodynamics
Mean PAP, mm Hg	62 (47–75)	56 (49–68)	56 (47–63)	54 (44–61)	0.06
PAWP, mm Hg	9 (6–12)	8 (6–10)	8 (6–11)	7 (5–10)	0.15
PASP, mm Hg	84 (65–102)	82 (72–100)	87 (75–98)	90 (75–106)	0.59
PADP, mm Hg	48 (39–66)	41 (35–50)	38 (33–44)	30 (26–40)	<0.0001
Mean RAP, mm Hg	10 (6–16)	9 (5–13)	8 (6–11)	6 (3–10)	0.018
Calculated hemodynamics
TPG, mm Hg	51 (38–64)	47 (39–61)	47 (40–55)	45 (36–54)	0.13
PVR, WU	16 (9–22)	14 (9–20)	13 (9–18)	11 (7–15)	0.03
PA pulse pressure, mm Hg	34 (26–46)	44 (37–51)	50 (43–55)	57 (48–70)	<0.0001
PA capacitance, mL/mm Hg	0.0008 (0.0005–0.0015)	0.0009 (0.0007–0.0013)	0.0009 (0.0007–0.0013)	0.0009 (0.0006–0.0015)	0.99
RVSWI, g/m2	14 (9–21)	15 (11–23)	18 (13–23)	19 (11–26)	0.095
CI, L/min/m2	1.9 (1–3)	2.1 (1–3)	2.2 (2–3)	2.3 (2–3)	0.018

Abbreviations: CI, cardiac index; IQR, interquartile range; PA, pulmonary artery; PADP, pulmonary artery diastolic pressure; PAP, pulmonary artery pressure; PAPP, pulmonary artery proportional pulse pressure; PASP, pulmonary artery systolic pressure; PCWP, pulmonary capillary wedge pressure; PVR, pulmonary vascular resistance; Q, quartile; RAP, right atrial pressure; RVSWI, right ventricular stroke work index; TPG, transpulmonary gradient; WU, Wood units.

Data are presented as median (IQR).

3.2. Survival analysis based on PAPP

With respect to the hypothesis that patients with the lowest PAPP would have greatest 5‐year mortality, the Kaplan–Meier curves in the Figure 1 show that 5‐year survival in the lowest quartile of PAPP (Q1) was indeed lower than in the other quartiles combined (Q2–Q4; P = 0.016).

Figure 1.

Survival curves comparing low PAPP with the remaining cohort. Survival is lower in patients with low PAPP (Q1) versus those in the rest of the PAPP quartiles combined (Q2–Q4). Abbreviations: PAPP, pulmonary arterial proportional pulse pressure

3.3. Bivariable Cox proportional hazards regression

The results of bivariable Cox proportional hazards regression for the outcome of death are shown in Table 3. As a continuous variable, PAPP had an HR of 0.71 per 0.1 increase (95% CI: 0.60‐0.85, P = 0.0001) for the outcome of death, indicating that increased PAPP was associated with a lower risk of death in group 1 PH. With PAPP as a categorical variable based on the lowest quartile vs the remaining patients, we found that patients in the lowest PAPP quartile (PAPP ≤0.47) had an HR of 1.58 for death (95% CI: 1.09‐2.31, P = 0.017); however, the χ² statistic was greater for PAPP as a continuous parameter, indicating a stronger association with survival with PAPP categorized in this way. For reference, the PHC equation had a χ² statistic of 18.0, which was only marginally greater than the χ² statistic for PAPP as a continuous parameter (14.4), indicating that the strength of the continuous form of PAPP was only slightly less than the complex PHC risk score. In the multivariable models for the outcome of death, the Wald χ² statistic increased from 18.0 to 28.0 (an increase of 56%) when PAPP was added to the model with the PHC score alone. Constituent PHC equation variables and PAPP are compared in Table 3. However, in a multivariable Cox model adjusted for PAPP, only RAP had a significant association with survival, whereas the mean PAP and cardiac index did not. In this multivariable Cox model for death with PAPP and RAP, PAPP had a HR of 0.75 (95% CI: 0.63‐0.90) per 0.1‐unit increase (χ² = 9.5, P = 0.002), and RAP had a HR of 1.06 (95% CI: 1.03‐1.09) per mm Hg increase (χ2 = 16.8, P < 0.0001).

Table 3.

Bivariable Cox regression HRs for the outcome of death

Covariate	HR	95% CI	χ2	P Value
PAPP (Q1 vs Q2–Q4)	1.58	1.09‐2.31	5.7	0.016
PAPP (per 0.1 increase)	0.71	0.60‐0.85	14.4	0.0001
PHC (per 0.01 increase)	0.96	0.93‐0.98	18.0	<0.0001
RAP (per 1–mm Hg increase)	1.07	1.04‐1.09	22.1	<0.0001
Mean PAP (per 1–mm Hg increase)	1.02	1.01‐1.03	15.5	<0.0001
Cardiac index (per L/min/m2)	0.68	0.53‐0.87	9.7	0.002

Abbreviations: CI, confidence interval; HR, hazard ratio; PAPP, pulmonary artery proportional pulse pressure; PHC, Pulmonary Hypertension Connection risk equation; PAP, pulmonary artery pressure; RAP, right atrial pressure.

3.4. Multivariable Cox proportional hazards regression

Multivariable Cox proportional hazards regression analysis (Table 4) shows that PAPP as a continuous measure was also independently associated with prognosis after adjustment for the PHC risk score. The χ² statistic for PAPP in this model was 8.8 (P = 0.003), which was less than the χ² statistic of 15.2 (P < 0.0001) for the more complex PHC equation, but was of similar magnitude.

Table 4.

Multivariable Cox regression HRs for the outcome of death

Covariate	HR	95% CI	χ2	P Value
PAPP (per 0.1 increase)	0.74	0.61‐0.90	8.8	0.003
PHC (per 0.01 increase)	0.96	0.93‐0.98	15.2	<0.0001

Abbreviations: CI, confidence interval; HR, hazard ratio; PAPP, pulmonary artery proportional pulse pressure; PHC, Pulmonary Hypertension Connection risk equation.

3.5. Sensitivity analysis

Of the 262 patients, only 2 patients had a PVR <3 WU (2.13 and 2.43 WU, respectively), whereas the rest had a PVR >3 WU (median, 12.84 WU [interquartile range, 8.86–18.54 WU]). A sensitivity analysis for the main findings in the Cox regression models excluding these 2 patients showed no significant change.

4. DISCUSSION

The present study investigated the prognostic implication of PAPP in PAH. We found that lower PAPP is strongly associated with 5‐year mortality in WHO group 1 PH, independent of the PHC risk equation. Patients in the lowest quartile of PAPP, Q1 (PAPP ≤0.47), had a significantly higher probability of mortality compared with patients in the higher quartiles of PAPP combined (Q2–Q4). Survival analysis demonstrated a significant difference in probability of mortality between the lowest quartile of PAPP (Q1) and the higher PAPP quartiles group (Q2–Q4). We further noted that patients in the lowest PAPP quartile showed significantly unfavorable hemodynamic variables (mRAP, mean PAP, PADP, and PVR) compared with the higher PAPP quartiles.

PAH is a relentless disease that invariably leads to RV failure. The status of RV function is the primary determinant of overall prognosis.4, 8, 9, 10, 11 Pathophysiologically, PAH is characterized by pulmonary vascular remodeling with increasing PVR that imposes an increased resistive and pulsatile load on the RV. With disease progression, this increased load impedes RV contractility, leading to RV failure and death.4, 8, 12 Under optimal conditions, the fluidic nexus between the RV and PA is facilitated by the RV‐PA coupling. In essence, the RV augments contractility to offset the pulmonary arterial load while the PA dilates to accommodate the stroke volume. With a sustained pressure load, the RV fails to generate sufficient contractility proportional to the increased arterial load, thereby impairing optimal VA coupling.1, 8, 12

Physiologically, PAPP is PA pulse pressure normalized to PASP. PA pulse pressure is an indirect measure of the combined effects of RV contractility and pulmonary vascular distensibility (pulmonary arterial capacitance).1, 13, 14 Taken in isolation, PA pulse pressure and PASP in prior studies have not been shown to have a linear correlation with mortality.15 PVR and pulmonary arterial compliance are inversely related and are both key markers of PAH disease progression. Decreases in pulmonary artery distensibility (compliance) are an early marker of increased PVR.12, 16 Worsening PAH leads to increased pulmonary vascular‐bed stiffness that hemodynamically manifests as increased PVR. In this analysis, we found these relationships (with other indices of RV failure) to be congruent to the polarity of PAPP and PAH severity. For instance, patients in the lowest PAPP quartile had significantly worse indices of RV function (elevated mRAP, higher PVR values, and lower cardiac index) compared with the rest of the PAPP quartiles, signaling advanced disease status in the respective strata. Elevated RAP has been shown to impact survival outcomes.5, 7, 12, 17, 18, 19 As expected, patients in the lowest quartile of PAPP demonstrated elevated mRAP compared with the rest of the PAPP quartiles. Cardiac index values were lower among patients in the lowest quartile of PAPP. Both PASP and PADP were notably higher in the lowest quartile of PAPP vs higher PAPP quartiles. In summary, a lower PAPP (<=0.47) was associated with markers of severe disease status and was independently associated with increased 5‐year mortality in NIH‐PPH registry patients.

Our study extends the application of PAPP in guiding prognosis to WHO group 1 PH. PAPP was originally shown to inform prognosis in patients with advanced heart failure, where a lower PAPP (<0.5) was associated with increased adverse events such as death, heart transplant, and left ventricular assist devices during the 6‐month follow‐up of the study.6 The finding that PAPP adds significant predictive value to the PHC risk equation in PAH with an improvement in the model Wald χ² statistic from 18.0 to 28.0 (an increase of 56%) warrants further investigation into the use of PAPP in a contemporary cohort of PAH patients. PAPP is a readily measurable index that may prove useful in risk stratification of PAH patients. In summary, future studies should evaluate how PAPP varies with therapeutic interventions and whether this novel index can be used as a target to guide therapy.

4.1. Study limitations

Our study was a retrospective analysis of NIH‐PPH registry, and patient characteristics have changed remarkably in the current era. Newer therapies have resulted in an older patient population with multiple medical comorbidities. The changing patient demographics in terms of age, sex, and comorbid medical conditions may limit the external validity of this study,4 as the NIH‐PPH registry had a relatively younger, predominantly female population, compared with more contemporary PAH registries.1, 20 Furthermore, survival in the current era of pulmonary vasodilator therapies has modestly improved, by about 10%, compared with patients in the era of our study.4, 17, 20, 21 Finally, we had no echocardiographic images for correlation with the hemodynamic data. However, these limitations are offset by the strengths of the study, which include a robust focus on objective hemodynamic measurements, use of the contemporary PHC equation, and the multicenter enrollment in the registry.

5. CONCLUSION

PAPP is independently associated with survival in WHO group 1 PH and adds significant predictive power for all‐cause mortality when used in combination with the PHC equation. These findings support further investigation into the use of this index in guiding risk stratification in WHO group 1 PH.

Conflicts of interest

The authors declare no potential conflicts of interest.

Supporting information

Figure S1. Survival Curves Comparing Low PAPP with the Remaining Cohort. As shown in the figure, survival is lower in patients with low PAPP (Q1) than rest of PAPP quartiles combined (Q2‐4).

Mwansa H, Bilchick K.C, Parker A.M, Harding W, Ruth B, Kennedy J.L.W, Mysore M, Kwon Y, Mihalek A, and Mazimba S. Decreased pulmonary arterial proportional pulse pressure is associated with increased mortality in group 1 pulmonary hypertension. Clin Cardiol. 2017;40:988–992. 10.1002/clc.22752