ABSTRACT
Identifying noninvasive measures to assess intravascular volume status and risk stratify patients with pulmonary hypertension (PH) and chronic kidney disease (CKD) is needed. We assessed the predictive value of estimated plasma volume status (ePVS) using the Strauss‐derived Duarte formula in PH–CKD patients. This single‐center retrospective cohort analysis included patients with PH and CKD Stage 3b (CKD3b), Stage 4 (CKD4), or Stage 5 (CKD5) who underwent right heart catheterization from 2018 to 2023. Patients were categorized into low ePVS (< 6.2) and high ePVS (≥ 6.2) using Youden's J statistics. We used the Cox‐proportional hazards model, adjusting for age, sex, and body mass index, to investigate the association between high ePVS and major adverse cardiovascular events (MACE) and all‐cause mortality within 1 year after ePVS measurement date. Of 305 patients with PH–CKD, 30% (n = 91) had low ePVS, and 70% (n = 215) had high ePVS. Compared to the low ePVS group, patients with high ePVS had higher left ventricular ejection fraction, right atrial pressure, pulmonary artery wedge pressure, and cardiac index, lower pulmonary vascular resistance, worse kidney function, and more chronic anemia. Among patients with precapillary or Cpc‐PH, high ePVS was associated with a greater incidence of 1‐year all‐cause mortality (adjusted HR = 2.11, 95% CI 1.06–4.22 p = 0.034). Among PH–CKD patients, high ePVS was associated with hyperdynamic circulation, worse kidney function, and anemia. High ePVS was associated with greater 1‐year all‐cause mortality among patients with a precapillary PH component.
Keywords: all‐cause mortality, chronic kidney disease, estimated plasma volume status, pulmonary hypertension, risk stratification
1. Introduction
Pulmonary hypertension (PH) is a prevalent complication in chronic kidney disease (CKD) associated with increased risk of cardiovascular outcomes and mortality [1, 2]. Patients with CKD are at risk of developing intravascular congestion due to compromised kidney function, which can lead to right ventricle (RV) failure and acute decompensation.
The identification of accurate, noninvasive, and cost‐effective measures to assess intravascular volume status in PH–CKD is of utmost importance. Of note, congestion is the leading cause of hospital readmission and mortality in decompensated heart failure (HF) [3]. Therefore, identifying congestion may be helpful for risk stratification and management of PH–CKD patients.
Estimated plasma volume status (ePVS) is a surrogate measure of intravascular congestion with prognostic value in HF [4, 5]. Furthermore, ePVS was validated and predicted short‐term mortality in precapillary PH patients [6]. In this study, we aim to assess the predictive value of ePVS in PH–CKD patients.
2. Methods and Statistical Analysis
This was a single‐center retrospective cohort analysis of patients with PH and CKD Stage 3b (CKD3b), CKD Stage 4 (CKD4), or CKD Stage 5 (CKD5) who underwent right heart catheterization (RHC) from 2018 to 2023. Of 649 patients with CKD3b–5 who underwent RHC, we excluded 344 subjects, including patients without PH (n = 112), patients with arteriovenous shunt or fistula placement or were receiving dialysis (n = 200), patients receiving erythropoietin stimulating agents (n = 31), and one patient without available hemoglobin and hematocrit (H&H) values within 30 days before or after RHC. We used the closest H&H values to RHC date, of which most were obtained within 24 h (n = 262), and almost all were collected within 1 week (n = 299). H&H values were used to calculate ePVS using the Strauss‐derived Duarte formula; ePVS = 100 × (1 − hematocrit)/hemoglobin [7].
PH was defined as a mean pulmonary arterial pressure (mPAP) of > 20 mmHg [8]. Precapillary PH was defined as a pulmonary vascular resistance (PVR) of > 2 Wood units (WU), and pulmonary arterial wedge pressure (PAWP) of ≤ 15 mmHg [8]. Combined pre‐ and postcapillary PH (Cpc‐PH) was defined as PVR > 2 WU and PAWP > 15 mmHg, whereas isolated postcapillary PH (Ipc‐PH) was defined as PVR ≤ 2 WU and PAWP > 15 mmHg [8]. Those with elevated mPAP but with normal PVR and PAWP were unclassifiable [8]. We used the CKD‐EPI creatinine formula for eGFR calculations [9]. CKD3b was defined as a baseline estimated glomerular filtration rate (eGFR) between 44 and 30 mL/min/1.73 m2, CKD4 with an eGFR between 15 and 29 mL/min/1.73 m2, and CKD5 with an eGFR of < 15 mL/min/1.73 m2 [10].
We gathered patient demographic, clinical, and outcome data through retrospective chart review from our institutional electronic medical records system. The diagnoses of comorbid conditions were determined based on available medical records using ICD‐10 coding or physician documentation. Transthoracic echocardiography (TTE) interpretations, including assessments for RV strain and dilation, were performed by board‐certified cardiologists as part of routine clinical care and in accordance with established guidelines. Evaluation of RV systolic function varied among echocardiographers, typically involving qualitative visual RV assessment. RV dilation was defined as an RV‐to‐LV ratio ≥ 1:1, inferior vena cava (IVC) dilation as an IVC diameter > 2.1 cm, and a noncollapsible IVC as < 50% inspiratory collapse [11]. On RHC, estimated cardiac output (CO) and cardiac index (CI) were calculated using the indirect Fick principle [12].
The outcomes of interest were major adverse cardiovascular events (MACE) and all‐cause mortality within 1 year after ePVS measurement date. MACE included cardiovascular death, nonfatal HF exacerbation, acute coronary syndrome, and cerebrovascular accident [13]. Thomas Jefferson University's Institutional Review Board approved our project (iRISID‐2023‐2277).
We stratified the data into low ePVS (< 6.2) and high ePVS (≥ 6.2) groups. The optimal ePVS cutpoint was established using Youden's J statistics. The sensitivity and specificity at this cutpoint to detect outcomes at 1 year (including MACE and all‐cause mortality) were 68% and 47%, respectively. We present continuous and categorical data using descriptive statistics. Bivariate analyses were conducted using χ 2 tests or Fisher's exact tests for categorical variables and Wilcoxon rank‐sum tests for continuous variables. We used Kaplan–Meier curves with log‐rank tests to compare MACE and all‐cause mortality between ePVS groups (low ePVS vs. high ePVS) among patients with Ipc‐PH and patients with precapillary and Cpc‐PH. We used the Cox‐proportional hazards model, adjusting for demographic variables (age, sex, and body mass index), to investigate the association between high ePVS and all‐cause mortality. A sensitivity analysis was done by stratifying the ePVS data into tertiles. A p < 0.05 was considered statistically significant. The statistical analyses used Stata (StataCorp. 2023. Stata Statistical Software: Release 18. College Station, TX: StataCorp LLC).
3. Results
3.1. Baseline Characteristics
Our analyses consisted of 305 patients with PH and CKD3b, CKD4, or CKD5. The median age was 67 years (IQR 60–75), 51% (n = 156) were females, and 70% (n = 213) were African American. In total, 21% (n = 63) had precapillary PH, 32% (n = 98) had Cpc‐PH, 37% (n = 114) had Ipc‐PH, and 10% (n = 30) were unclassifiable. The median mPAP was 34 mmHg (IQR 28–41), and the median PVR was 2.2 WU (IQR 1.3–3.4). The median creatinine and eGFR were 2.8 mg/dL and 20 mL/min/1.73 m2, respectively. The most common comorbidities were hypertension (93%, n = 283), hyperlipidemia (79%, n = 240), chronic anemia (61%, n = 185), and diabetes mellitus (60%, n = 185).
3.2. Comparison of ePVS Groups
In comparison, 30% (n = 90) of patients had low ePVS, whereas 70% (n = 215) had high ePVS. The median ePVS was 5.3 (IQR 4.6–5.8) and 8.4 (IQR 7.2–9.6) amongst groups, respectively. Precapillary PH was more common among patients with low ePVS (33% vs. 15%, p < 0.0001), whereas isolated postcapillary PH was more prevalent in patients with high ePVS (45% vs. 20%, p < 0.0001).
The high ePVS group was female predominant (56% vs. 40%, p = 0.012), had higher left ventricular ejection fraction (LVEF; 55% vs. 35%, p = 0.002), higher right atrial pressure (RAP; 13 vs. 10, p = 0.025), higher PAWP (20 vs. 18 mmHg, p = 0.007), lower PVR (2.9 vs. 1.9 WU, p < 0.0001), higher CO (6.6 vs. 5.3 L/min, p < 0.0001), higher CI (3.4 vs. 2.6 L/min/m2, p < 0.0001), and worse kidney function (median eGFR 19 vs. 22 mL/min/1.73 m2, p = 0.009). The prevalence of chronic anemia (69% vs. 40%, p < 0.0001) was greater in the high ePVS group. Additional information in Table 1.
Table 1.
Data stratified by estimated plasma volume status (ePVS) using cutoff 6.2.
| Variables | Low ePVS < 6.2 (n = 90) | High ePVS ≥ 6.2 (n = 215) | Total (n = 305) | p |
|---|---|---|---|---|
| Age in years | 67 (61–73) | 68 (59–76) | 67 (60–75) | 0.644 |
| Female sex—n (%) | 36 (40) | 120 (56) | 156 (51) | 0.012 |
| Race—n (%) | 0.006 | |||
| African American | 73 (81) | 140 (65) | 213 (70) | |
| Non‐African American | 17 (19) | 75 (35) | 92 (30) | |
| Median body mass index in kg/m2 (IQR) | 30 (26–38) | 30 (26–37) | 30 (26–37) | 0.921 |
| Laboratory analysis—Median (IQR) | ||||
| Hemoglobin concentration in g/dL | 11.8 (11.2–12.9) | 8.6 (7.8–9.7) | 9.5 (8–10.9) | < 0.0001 |
| Hematocrit in % | 37.4 (35.6–39.7) | 27.8 (24.8–30.5) | 30.1 (25.8–34.9) | < 0.0001 |
| Estimated plasma volume status (ePVS) | 5.3 (4.6–5.8) | 8.4 (7.2–9.6) | 7.4 (6–9.1) | < 0.0001 |
| RHC data—median (IQR) | ||||
| Precapillary PH—n (%) | 30 (33) | 33 (15) | 63 (21) | < 0.0001 |
| Combined pre‐ and postcapillary PH—n (%) | 32 (35) | 66 (31) | 98 (32) | 0.407 |
| Isolated postcapillary PH—n (%) | 18 (20) | 96 (45) | 114 (37) | < 0.0001 |
| Unclassifiable PH—n (%) | 10 (11) | 20 (9) | 30 (10) | 0.629 |
| RAP in mmHg | 10 (7–16) | 13 (8–18) | 12 (8–17) | 0.025 |
| mPAP in mmHg | 35 (26–42) | 34 (28–40) | 34 (28–41) | 0.976 |
| PAWP in mmHg | 18 (12–24) | 20 (16–26) | 20 (14–25) | 0.007 |
| Cardiac output in L/min | 5.3 (3.6–6.7) | 6.6 (5–9.1) | 6.1 (4.7–8.3) | < 0.0001 |
| Cardiac index in L/min/m2 | 2.6 (2–3.3) | 3.4 (2.5–4.5) | 3.1 (2.4–4.1) | < 0.0001 |
| PVR in Wood units | 2.9 (1.9–4.4) | 1.9 (1.1–3) | 2.2 (1.3–3.4) | < 0.0001 |
| RAP/PAWP | 0.62 (0.40–0.82) | 0.60 (0.44–0.81) | 0.60 (0.44–0.81) | 0.909 |
| Transthoracic echocardiographic parameters—n (%) | ||||
| Estimated LVEF in %—median (IQR) (n = 303) | 35 (20–55) | 55 (30–60) | 50 (30–50) | 0.0002 |
| Estimated PASP in mmHg—median (IQR) (n = 225) | 45 (40–60) | 45 (40–57) | 45 (40–60) | 0.923 |
| Estimated TRV in m/s—median (IQR) (n = 250) | 2.7 (2.3–3.3) | 2.8 (2.4–3.2) | 2.8 (2.4–3.2) | 0.648 |
| Diastolic dysfunction (n = 252) | 45 (60) | 104 (59) | 149 (59) | 0.854 |
| Right ventricular strain | 38 (42) | 73 (34) | 111 (36) | 0.171 |
| Right ventricular dilation | 32 (36) | 82 (38) | 114 (37) | 0.671 |
| Right atrial dilation | 37 (41) | 89 (41) | 126 (41) | 0.963 |
| Dilated IVC | 30 (33) | 91 (42) | 121 (40) | 0.143 |
| Noncollapsible IVC | 34 (38) | 94 (44) | 128 (42) | 0.337 |
| Kidney disease features and comorbidities—n (%) | ||||
| CKD3b | 12 (13) | 13 (6) | 25 (8) | 0.034 |
| CKD4 | 50 (55) | 138 (64) | 188 (62) | 0.157 |
| CKD5 | 28 (31) | 64 (30) | 92 (30) | 0.816 |
| Median eGFR in mL/min/1.73 m2 (IQR) | 22 (16–27) | 19 (14–25) | 20 (15–26) | 0.009 |
| Median creatinine in mg/dL (IQR) | 2.6 (2.1–3.3) | 2.9 (2.4–3.8) | 2.8 (2.3–3.7) | 0.012 |
| Hypertension | 86 (96) | 197 (92) | 283 (93) | 0.227 |
| Diabetes mellitus | 49 (54) | 136 (63) | 185 (61) | 0.151 |
| Hyperlipidemia | 74 (82) | 166 (77) | 240 (79) | 0.330 |
| Chronic lung disease | 32 (36) | 60 (28) | 92 (30) | 0.184 |
| Hypoventilation syndromes | 20 (22) | 38 (18) | 58 (19) | 0.356 |
| History of venous thromboembolism | 13 (14) | 27 (13) | 40 (13) | 0.656 |
| Chronic anemia | 36 (40) | 149 (69) | 185 (61) | < 0.0001 |
Abbreviations: CKD, chronic kidney disease; IVC, inferior vena cava; LVEF, left ventricular systolic function; mPAP, median pulmonary artery systolic pressure; PASP, pulmonary artery systolic pressure; PAWP, pulmonary artery wedge pressure; PH, pulmonary hypertension; PVR, pulmonary vascular resistance; RAP, right atrial pressure; TRV, tricuspid regurgitation velocity.
3.3. Clinical Outcomes at 1‐Year Follow‐up
Among patients with a precapillary PH component (precapillary PH and Cpc‐PH), patients with high ePVS had higher rates of all‐cause mortality (hazard ratio [HR] = 2.05, 95% confidence interval [95% CI] 1.04–4.05, p = 0.038). The median time from RHC to death was 15 days (IQR 5–64). The incidence of MACE was not different between groups (HR = 1.15, 95% CI 0.68–1.97; p = 0.601) (Table 2). Kaplan–Meier curves and log‐rank p values are shown in Figure 1A,B. On multivariable Cox analysis, after adjusting for demographic variables, high ePVS was independently associated with all‐cause mortality (HR = 2.11, 95% CI 1.06–4.22, p = 0.034). Meanwhile, among patients with Ipc‐PH, the incidence of MACE and all‐cause mortality was not different between ePVS groups (Figure 2A,B).
Table 2.
Clinical outcomes stratified by estimated plasma volume status (ePVS) according to hemodynamic groups.
| Variables | Low ePVS < 6.2 | High ePVS ≥ 6.2 | Unadjusted HR (95% CI) | p |
|---|---|---|---|---|
|
Precapillary PH + Cpc‐PH n = 161 |
n = 62 | n = 99 | ||
| MACE composite | 21 (34) | 39 (39) | 1.15 (0.68–1.97) | 0.601 |
| HF hospitalizations | 18 (29) | 30 (30) | — | — |
| Cardiovascular death | 1 (2) | 7 (7) | — | — |
| Acute coronary syndrome | 2 (3) | 2 (2) | — | — |
| Cerebrovascular accident | None | 2 (2) | — | — |
| All‐cause mortality | 11 (18) | 34 (34) | 2.05 (1.04–4.05)a | 0.038 |
|
Isolated postcapillary PH n = 114 |
n = 18 | n = 96 | n = 114 | |
| MACE composite | 1 (6) | 24 (25) | 5.13 (0.69–37.93) | 0.109 |
| HF hospitalizations | None | 20 (21) | — | — |
| Cardiovascular death | None | 5 (5) | — | — |
| Acute coronary syndrome | 1 (6) | 3 (3) | — | — |
| Cerebrovascular accident | None | 2 (2) | — | — |
| All‐cause mortality | 8 (44) | 30 (31) | 0.57 (0.26–1.25) | 0.160 |
Abbreviations: HF, heart failure; MACE, major cardiovascular events.
Adjusted HR = 2.11, 95% CI 1.06–4.22; p = 0.034. Adjusted for age, sex, and BMI.
Figure 1.
Kaplan–Meier curves of MACE and all‐cause mortality at 1‐year follow‐up among precapillary and Cpc‐PH patients. (A) MACE outcomes among precapillary and Cpc‐PH patients. Log rank p value 0.601. (B) All‐cause mortality among precapillary and Cpc‐PH patients. Log rank p value 0.034.
Figure 2.
Kaplan–Meier curves of MACE and all‐cause mortality at 1‐year follow‐up among postcapillary PH patients. (A) MACE outcomes among postcapillary PH patients. Log rank p value 0.074. (B) All‐cause mortality among postcapillary PH patients. Log rank p value 0.153.
3.4. Sensitivity Analysis
By stratifying ePVS data into tertiles, the parameters of intravascular congestion (higher RAP and PAWP) and hyperdynamic circulation (higher CO and CI, and lower PVR) were directly proportional to ePVS increase, whereas kidney function (lower eGFR and higher serum creatinine) was inversely proportional (Table 3).
Table 3.
Sensitivity analysis with estimated plasma volume status (ePVS) data stratified by tertiles.
| Variables—median (IQR) | ePVS 1st tertile | ePVS 2nd tertile | ePVS 3rd tertile | p |
|---|---|---|---|---|
| ePVS | 5.4 (4.8–6.0) | 7.4 (6.9–7.9) | 9.7 (9.2–10.3) | — |
| RAP in mmHg | 10 (7–16) | 13 (8–18) | 14 (10–18) | 0.031 |
| mPAP in mmHg | 35 (26–42) | 34 (29–40) | 33 (27–40) | 0.872 |
| PAWP in mmHg | 18 (12–24) | 20 (14–25) | 20 (17–26) | 0.046 |
| Cardiac output in L/min | 5.3 (3.9–6.8) | 6 (4.7–7.5) | 7.7 (5.8–10.8) | 0.0001 |
| Cardiac index in L/min/m2 | 2.6 (2–3.4) | 3.1 (2.3–3.6) | 4.1 (3–5.1) | 0.0001 |
| PVR in Wood units | 2.7 (1.9–4.3) | 2.2 (1.4–3.4) | 1.5 (0.9–2.5) | 0.0001 |
| Baseline eGFR in mL/min/1.73 m2 | 23 (16–27) | 19 (15–26) | 17 (14–23) | 0.0003 |
| Baseline serum creatinine in mg/dL | 2.5 (2–3.2) | 2.9 (2.2–3.7) | 3.1 (2.5–3.9) | 0.0003 |
Abbreviations: eGFR, estimated glomerular filtration rate‐; ePVS, estimated plasma volume status; mPAP, median pulmonary artery systolic pressure; PAWP, pulmonary artery wedge pressure; PVR, pulmonary vascular resistance; RAP, right atrial pressure.
4. Discussion
This study presents a retrospective cohort analysis of patients with PH and CKD3b–CKD5 who underwent hemodynamic evaluation and were stratified according to ePVS (low and high ePVS groups). High ePVS was associated with hyperdynamic circulation (LVEF, CO, CI, and PVR), as well as worse kidney function, and chronic anemia. Notably, high ePVS was only weakly associated with intravascular congestion (RAP, PAWP) and had no association with right ventricular dysfunction, which has been previously seen in patients with precapillary PH and high ePVS [6]. Additionally, among patients with a precapillary PH component, the estimated all‐cause mortality was two‐fold greater in the high ePVS group. These findings suggest that ePVS may be informative in PH–CKD patients and have prognostic value in those with a precapillary PH component.
For patients with PH–CKD, high ePVS is a more significant determinant of hyperdynamic circulation rather than intravascular congestion. In our study, comorbidities such as anemia and renal dysfunction both represent plausible moderators for the association between high ePVS and high output circulation. The mechanisms leading to increased ePVS in these patients are likely secondary to decreased red blood cell count (anemia) or increased plasma volume (kidney dysfunction). In such instances, hyperdynamic circulation may develop as a result of decreased cardiac preload (anemia) or decreased systemic vascular resistance due to more advanced kidney disease. A similar study found that high ePVS was associated with hyperdynamic circulation in patients with cirrhosis, in which renal dysfunction was one of several moderators [14].
Our findings highlight unique differences in the relationship between ePVS and PH–CKD compared to HF. Previously, ePVS was a provisional indicator of intravascular congestion and predicted adverse cardiovascular outcomes in HF with reduced and preserved ejection fraction [4, 15]. It is presumed that impaired systolic dysfunction contributed to intravascular congestion in these patients. However, subsequent analyses have demonstrated that hematocrit‐based equations are erroneous compared to quantitative measurements of plasma volume [16]. This is substantiated by the lack of correlation between ePVS and clinical edema [6], as well as indices of intravascular congestion using both TTE [17], and RHC [18].
Although high ePVS may have limited correlation with intravascular congestion, it stratifies mortality risk in PH–CKD patients. The association between high ePVS and all‐cause mortality in CKD patients with a precapillary PH component has been previously described in precapillary PH and HF [4, 6, 19, 20]. Nevertheless, the high short‐term mortality observed in our analysis was not attributed to MACE. It is conceivable that MACE were not significant between ePVS groups due to the heterogeneity of our patient population, in which RHC was performed for a variety of indications. Also, as we gathered clinical outcome data from a comprehensive retrospective chart review, there were many deaths that remained undifferentiated, and it is possible that some patients with MACE were admitted to outside institutions and were not included in this study.
Our single‐center retrospective study has other limitations. First, there is no formal reference range for ePVS as studies have used numerous statistical methods to determine low versus high ePVS without formal standardization [6], which greatly limits its generalizability. We used one‐time snapshots of renal function (serum creatinine) and invasive hemodynamic assessment (via RHC) to phenotype PH–CKD, which is prone to misclassification and insufficient for the diagnosis of CKD. Our findings may not be generalizable to all PH–CKD patients given our strict inclusion criteria and high representation of African American patients.
In summary, among patients with PH–CKD, high ePVS was associated with hyperdynamic circulation, worse kidney function, and more severe anemia. Furthermore, CKD patients with a precapillary PH component and high ePVS had greater 1‐year all‐cause mortality. Future studies are needed to further understand the underlying mechanisms associated with ePVS and adverse clinical outcomes among PH–CKD patients.
Author Contributions
Conceptualization: Andrew Geller, Jose Manuel Martinez Manzano. Data curation: Andrew Geller, Jose Manuel Martinez Manzano, Esteban Kosak Lopez, Phuuwadith Wattanachayakul, John Malin, Raul Leguizamon, Tara A. John, Rasha Khan, Ian McLaren, Alexander Prendergast, Simone A. Jarrett. Formal analysis: Andrew Geller, Jose Manuel Martinez Manzano, Kevin Bryan Lo, Christian Witzke. Investigation: Andrew Geller, Jose Manuel Martinez Manzano, Esteban Kosak Lopez, Phuuwadith Wattanachayakul, John Malin, Raul Leguizamon, Tara A. John, Rasha Khan, Ian McLaren, Alexander Prendergast, Simone A. Jarrett. Methodology: Andrew Geller, Jose Manuel Martinez Manzano, Esteban Kosak Lopez, Phuuwadith Wattanachayakul, John Malin, Raul Leguizamon, Tara A. John, Rasha Khan, Ian McLaren, Alexander Prendergast, Simone A. Jarrett. Supervision/guarantor: Kevin Bryan Lo, Christian Witzke. Writing – original draft: Andrew Geller, Jose Manuel Martinez Manzano. Writing – reviewing and editing: Andrew Geller, Jose Manuel Martinez Manzano, Kevin Bryan Lo, Christian Witzke.
Disclosure
The authors have nothing to report.
Ethics Statement
Thomas Jefferson University's Institutional Review Board approved our project (iRISID‐2023‐2277). The collection and evaluation of all patient health information was performed in a manner compliant with the Health Insurance Portability and Accountability Act (HIPAA).
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgments
The authors have nothing to report.
Andrew Geller and Jose Manuel Martinez Manzano contributed equally to this study.
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