Literature DB >> 34737226

COMPERA 2.0: a refined four-stratum risk assessment model for pulmonary arterial hypertension.

Marius M Hoeper1,2, Christine Pausch3, Karen M Olsson4,2, Doerte Huscher5, David Pittrow3,6, Ekkehard Grünig7, Gerd Staehler8, Carmine Dario Vizza9, Henning Gall2,10, Oliver Distler11, Christian Opitz12, J Simon R Gibbs13, Marion Delcroix14, H Ardeschir Ghofrani2,10,15, Da-Hee Park4, Ralf Ewert16, Harald Kaemmerer17, Hans-Joachim Kabitz18, Dirk Skowasch19, Juergen Behr20,21, Katrin Milger21, Michael Halank22, Heinrike Wilkens23, Hans-Jürgen Seyfarth24, Matthias Held25, Daniel Dumitrescu26, Iraklis Tsangaris27, Anton Vonk-Noordegraaf28, Silvia Ulrich29, Hans Klose30, Martin Claussen31, Tobias J Lange32, Stephan Rosenkranz33.   

Abstract

BACKGROUND: Risk stratification plays an essential role in the management of patients with pulmonary arterial hypertension (PAH). The current European guidelines propose a three-stratum model to categorise risk as low, intermediate or high, based on the expected 1-year mortality. However, with this model, most patients are categorised as intermediate risk. We investigated a modified approach based on four risk categories, with intermediate risk subdivided into intermediate-low and intermediate-high risk.
METHODS: We analysed data from the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA), a European pulmonary hypertension registry, and calculated risk at diagnosis and first follow-up based on World Health Organization functional class, 6-min walk distance (6MWD) and serum levels of brain natriuretic peptide (BNP) or N-terminal pro-BNP (NT-proBNP), using refined cut-off values. Survival was assessed using Kaplan-Meier analyses, log-rank testing and Cox proportional hazards models.
RESULTS: Data from 1655 patients with PAH were analysed. Using the three-stratum model, most patients were classified as intermediate risk (76.0% at baseline and 63.9% at first follow-up). The refined four-stratum risk model yielded a more nuanced separation and predicted long-term survival, especially at follow-up assessment. Changes in risk from baseline to follow-up were observed in 31.1% of the patients with the three-stratum model and in 49.2% with the four-stratum model. These changes, including those between the intermediate-low and intermediate-high strata, were associated with changes in long-term mortality risk.
CONCLUSIONS: Modified risk stratification using a four-stratum model based on refined cut-off levels for functional class, 6MWD and BNP/NT-proBNP was more sensitive to prognostically relevant changes in risk than the original three-stratum model.
Copyright ©The authors 2022.

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Year:  2022        PMID: 34737226      PMCID: PMC9260123          DOI: 10.1183/13993003.02311-2021

Source DB:  PubMed          Journal:  Eur Respir J        ISSN: 0903-1936            Impact factor:   33.795


Introduction

Risk stratification has become an integral part of the management of patients with pulmonary arterial hypertension (PAH). The 2015 joint pulmonary hypertension (PH) guidelines of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS) proposed a multidimensional risk stratification model based on 14 variables derived from nine assessments [1, 2]. Based on this model, risk is divided into three strata as low, intermediate or high with estimated 1-year mortality rates <5%, 5–10% and >10%, respectively. Achieving and maintaining a low risk profile is recommended as treatment goal in patients with PAH [1-3]. Since publication of these guidelines, several registry-based studies showed that simplified versions of the ESC/ERS tool provided reliable prognostication. In particular, a combination of functional class, 6-min walk distance (6MWD) and brain natriuretic peptide (BNP) or N-terminal pro-BNP (NT-proBNP) was found to have strong prognostic value, both at the time of diagnosis and even more so at follow-up, i.e. after initiation of targeted therapies [4-6]. These variables were also the most reliable predictive parameters in the Lite-2 version of the Registry to Evaluate Early and Long-term PAH Disease Management (REVEAL) risk calculator, a risk stratification tool developed in the United States [7-9]. Several modalities have been developed to calculate individual risk. French investigators used a panel of noninvasive (functional class, 6MWD, BNP/NT-proBNP) and invasive (right atrial pressure, cardiac index) variables and summed up the number of variables meeting low risk criteria. They found that combined assessment of functional class, 6MWD and BNP/NT-proBNP had the highest prognostic value [4]. This strategy was confirmed by the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) investigators [10]. In both series, patients who met low risk criteria for all three variables (functional class I or II, 6MWD >440 m, BNP <50 ng·L−1 or NT-proBNP <300 ng·L−1) while on therapy had 5-year survival rates >90%. However, these criteria were met only by 9–19% of the patients [4, 10]. The Swedish Pulmonary Arterial Hypertension Registry (SPAHR) group and the COMPERA group used an alternative approach: based on the cut-off levels proposed in the ESC/ERS guidelines, each variable was graded from 1 to 3, where 1 defined low risk, 2 intermediate risk and 3 high risk. The mean value was calculated by dividing the sum of all grades by the number of variables [5, 6]. As the French group, the SPAHR and COMPERA groups included haemodynamics (right atrial pressure, cardiac index, mixed-venous oxygen saturation) in addition to World Health Organization functional class (WHO FC), 6MWD and BNP/NT-proBNP. In line with the French observations, the COMPERA investigators found that the noninvasive variables had a higher predictive value than the haemodynamic variables and showed that risk assessment based on the noninvasive variables alone provided good discrimination demonstrating significant survival differences between the risk groups [6]. Based on these studies, functional class, 6MWD and BNP/NT-proBNP have been established as key elements of current risk assessment tools in PAH. However, it was noted in the SPAHR and COMPERA analyses that most patients did not meet the low risk criteria while receiving PAH treatment. In fact, the majority of patients met intermediate risk criteria (∼70% at baseline and ∼60% at follow-up) [5, 6]. In these patients, a more granular risk prediction is required, in particular for far-reaching therapeutic decisions including the need for parenteral prostanoid therapy and evaluation for lung transplantation. Several investigators have shown that the use of additional variables derived from echocardiography, right heart catheterisation or blood gas analysis improved risk prediction [11-14]. As an alternative model, the SPAHR group recently proposed a modification of their original approach defining a calculated score of 1.5–1.99 as intermediate-low risk and a score of 2.0–2.4 as intermediate-high risk [15]. This approach showed promising results with further discrimination within the intermediate risk group, albeit based on a relatively small sample size. We hypothesised that a subdivision into four risk strata (low, intermediate-low, intermediate-high, and high) based on more granularity within the cut-off levels of 6MWD, WHO FC and BNP/NT-proBNP might improve risk stratification. Here, we used the COMPERA database to investigate a refined risk stratification model (COMPERA 2.0) using modified cut-off levels, some of which have been proposed recently by the REVEAL group [7].

Methods

Database

Details of COMPERA (www.COMPERA.org; registered at clinicaltrials.gov with identifier NCT01347216) have been reported in previous communications [16, 17]. In summary, COMPERA is an ongoing PH registry launched in 2007 that prospectively collects baseline, follow-up and outcome data of patients who receive targeted therapies for any form of PH. Patients are enrolled within 6 months after PH diagnosis to ensure inclusion of newly diagnosed patients only. PH centres from several European countries participate (Austria, Belgium, Germany, Greece, Hungary, Italy, Latvia, Lithuania, the Netherlands, Slovakia, Switzerland, United Kingdom), with ∼80% of the enrolled patients coming from Germany. COMPERA has been approved by the ethics committees of all participating centres, and all patients provided written informed consent prior to inclusion.

Patients

For the present analysis, patients were selected from the COMPERA database by the following criteria. 1) Treatment-naïve patients aged ≥18 years newly diagnosed with any form of PAH between 1 January 2009 and 31 December 2020; 2) at least one follow-up available; 3) baseline haemodynamics showing mean pulmonary arterial pressure ≥25 mmHg, pulmonary arterial wedge pressure ≤15 mmHg and pulmonary vascular resistance >3 Wood units (240 dyn·s·cm−5); and 4) all three variables of interest (WHO FC, 6MWD, BNP or NT-proBNP) available at baseline. Patients with other forms of PH were excluded from this analysis as were patients with Eisenmenger syndrome and patients with confirmed or suspected pulmonary veno-occlusive disease or pulmonary capillary haemangiomatosis.

Refined risk stratification

The cut-off levels for WHO FC, 6MWD and NT-proBNP for the COMPERA 2.0 risk stratification model were modified from the ESC/ERS guidelines and from our previous analysis [2, 6] as follows. The refined cut-off values for 6MWD and BNP were adopted from REVEAL [8, 9]. As no NT-proBNP cut-off value to distinguish between intermediate-low risk and intermediate-high risk was available from REVEAL Lite 2, we determined the optimal cut-off from the present database by selecting the value with the highest predictive value, i.e. the lowest p-value of the log-rank test, for mortality in the group of patients with NT-proBNP levels between 300 ng·L−1 and 1100 ng·L−1 at baseline, using 50-ng·L−1 intervals. For functional class, we considered distinguishing between WHO FC I and II. However, as very few patients in the present dataset were classified as WHO FC I (n=7 at baseline), and as WHO FC II has repeatedly been shown to be associated with good long-term survival, we continued grouping WHO FC I and II as a single (low-risk) group. Based on the criteria shown in table 1, each variable was graded from 1 to 4, and the mean was calculated by dividing the sum of all grades by the number of variables and rounding to the next integer. For the three-stratum model, we defined a score of 1 as low risk, 2 as intermediate risk and 3 as high risk. For the four-stratum model, we used the following definitions: 1=low risk, 2=intermediate-low risk, 3=intermediate-high risk and 4=high risk. Risk stratification was performed at baseline, i.e. before initiation of PAH medications, and at first follow-up between 3 and 12 months after treatment initiation.
TABLE 1

Criteria for refined risk stratification in the three-stratum model and the four-stratum model based on World Health Organization functional class (WHO FC), 6-min walk distance (6MWD) and brain natriuretic peptide (BNP)/N-terminal pro-BNP (NT-proBNP)

Points assigned
1 2 3 4
Three-stratum model
 WHO FCI or IIIIIIV
 6MWD, m>440 440–165<165
 BNP,# ng·L−1<5050–800>800
 NT-proBNP,# ng·L−1<300300–1100>1100
Four-stratum model
 WHO FCI or IIIIIIV
 6MWD, m>440 440–320 319–165<165
 BNP,# ng·L−1<5050–199200–800>800
 NT-proBNP,# ng·L−1<300300–649650–1100>1100

#: the cut-off values for 6MWD and BNP were obtained from Registry to Evaluate Early and Long-term PAH Disease Management (REVEAL) Lite 2 [9], while the cut-off values for NT-proBNP were derived from receiver operating curve analysis of all patients from the present analysis with baseline NT-proBNP values between 300 and 1100 ng·L−1. When both BNP and NT-proBNP were available, NT-proBNP was used.

Criteria for refined risk stratification in the three-stratum model and the four-stratum model based on World Health Organization functional class (WHO FC), 6-min walk distance (6MWD) and brain natriuretic peptide (BNP)/N-terminal pro-BNP (NT-proBNP) #: the cut-off values for 6MWD and BNP were obtained from Registry to Evaluate Early and Long-term PAH Disease Management (REVEAL) Lite 2 [9], while the cut-off values for NT-proBNP were derived from receiver operating curve analysis of all patients from the present analysis with baseline NT-proBNP values between 300 and 1100 ng·L−1. When both BNP and NT-proBNP were available, NT-proBNP was used.

Statistical analyses

This was a post hoc analysis of prospectively collected data. Continuous data are presented as mean±sd or as median (interquartile range (IQR)), and categorical data as number and percentage. The dataset as of 30 June 2021, was analysed. Vital status was ascertained by on-site visits or phone calls to the patients or their caregivers. Patients who underwent lung transplantation and patients who were lost to follow-up were censored at the date of the last contact. No imputations were made for missing data. Survival was evaluated using Kaplan–Meier analysis and log-rank test. Survival analyses were done for the entire group and for subgroups of patients with idiopathic, heritable, drug-associated and connective tissue disease associated (CTD)-PAH according to risk at baseline and first follow-up (with survival time starting at first follow-up for the latter analysis). The effects of changes in risk from baseline to first follow-up on consecutive survival were evaluated using the Cox regression model. The associated hazard ratios (HR) and 95% confidence intervals were calculated. All statistical analyses were performed using R version 4.0.0.

Results

Baseline characteristics and survival of the entire cohort

Out of 10 825 patients enrolled into COMPERA, 9710 were excluded for the reasons shown in figure 1, including 136 patients who fulfilled all inclusion criteria, but who had no follow-up information or were lost to follow-up without any information. A total of 1655 patients were finally included in this analysis. The characteristics of these patients at baseline are shown in table 2.
FIGURE 1

Strengthening the Reporting of Observational Studies in Epidemiology diagram showing patient eligibility for analysis. #: more than one reason for exclusion could apply. COMPERA: Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension; PAH: pulmonary arterial hypertension; WHO FC: World Health Organization functional class; 6MWD: 6-min walk distance; BNP: brain natriuretic peptide; NT-proBNP: N-terminal pro-BNP; mPAP: mean pulmonary arterial pressure; PVR: pulmonary vascular resistance; PAWP: pulmonary arterial wedge pressure.

TABLE 2

Baseline characteristics

Low risk Intermediate-low risk Intermediate-high risk High risk All Available data
Patients 92 (5.6)401 (24.2)910 (55.0)252 (15.2)1655 (100)
Age, years 49.9±17.061.5±15.167.5±14.771.4±12.865.7±15.51655 (100.0)
Female 58 (63.0)259 (64.6)573 (63.0)174 (69.0)1064 (64.3)1655 (100.0)
BMI, kg·m−2 26.7±5.228.7±6.328.0±5.928.6±6.428.2±6.01604 (96.9)
Diagnosis 1655 (100.0)
 Idiopathic, drug-associated or  hereditary PAH59 (64.1)282 (70.3)659 (72.4)182 (72.2)1182 (71.4)
 CTD-PAH18 (19.6)68 (17.0)184 (20.2)60 (23.8)330 (19.9)
 CHD-PAH5 (5.4)21 (5.2)20 (2.2)0 (0.0)46 (2.8)
 HIV-PAH3 (3.3)4 (1.0)6 (0.7)1 (0.4)14 (0.8)
 PoPH7 (7.6)26 (6.5)41 (4.5)9 (3.6)83 (5.0)
WHO FC 1655 (100.0)
 I2 (2.2)5 (1.2)0 (0.0)0 (0.0)7 (0.4)
 II90 (97.8)111 (27.7)36 (4.0)0 (0.0)237 (14.3)
 III0 (0.0)281 (70.1)813 (89.3)115 (45.6)1209 (73.1)
 IV0 (0.0)4 (1.0)61 (6.7)137 (54.4)202 (12.2)
6MWD, m 488.7±84.5380.7±92.2279.0±92.8132.7±63.9293.0±125.81655 (100.0)
NT-proBNP, ng·L−1 130 (68–252)398 (194–642)1930 (1022–3468)4192 (2255–7122)1499 (512–3330)1389 (83.9)
BNP, ng·L−1 99 (58–131)82 (34–147)280 (134–543)543 (373–1035)214 (94–432)271 (16.4)
RAP, mmHg 6.2±3.66.8±4.38.3±4.710.3±5.28.2±4.81538 (92.9)
mPAP, mmHg 42.8±13.541.2±13.043.5±11.946.1±10.843.3±12.21655 (100.0)
PAWP, mmHg 8.4±3.29.1±3.49.6±3.39.5±3.49.4±3.31655 (100.0)
Cardiac index, L·min−1·m−2 2.8±0.82.4±0.82.1±0.71.9±0.72.2±0.71566 (94.6)
PVR, Wood units 7.7±4.47.6±4.29.6±4.711.7±5.79.3±4.91655 (100.0)
S vO 2 , % 70.0±6.467.3±6.162.0±7.759.2±7.963.2±8.01450 (87.6)
DLCO, % pred 60.7±18.158.5±21.850.3±20.942.4±20.551.5±21.61168 (70.6)
Comorbidities 0.9±0.91.5±1.11.8±1.32.0±1.31.7±1.21300 (78.5)
 Arterial hypertension31 (43.7)201 (59.3)514 (64.2)143 (70.1)889 (62.9)1414 (85.4)
 Coronary heart disease7 (10.4)66 (19.9)209 (27.0)61 (29.5)343 (24.9)1379 (83.3)
 Diabetes mellitus6 (8.7)77 (23.1)215 (27.2)70 (33.5)368 (26.2)1402 (84.7)
 BMI ≥30 kg·m−219 (21.8)150 (38.2)294 (33.0)90 (37.0)553 (34.2)1615 (97.6)

Data are presented as shown as n (%) of the respective population, mean±sd or median (interquartile range). BMI: body mass index; PAH: pulmonary arterial hypertension; CTD: connective tissue disease; CHD: congenital heart disease; PoPH: portopulmonary hypertension; WHO FC: World Health Organization functional class; 6MWD: 6-min walk distance; BNP: brain natriuretic peptide; NT-proBNP: N-terminal pro-BNP; RAP: right atrial pressure; mPAP: mean pulmonary arterial pressure; PAWP: pulmonary arterial wedge pressure; PVR: pulmonary vascular resistance; SvO: mixed-venous oxygen saturation; DLCO: diffusing capacity of the lung for carbon monoxide.

Strengthening the Reporting of Observational Studies in Epidemiology diagram showing patient eligibility for analysis. #: more than one reason for exclusion could apply. COMPERA: Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension; PAH: pulmonary arterial hypertension; WHO FC: World Health Organization functional class; 6MWD: 6-min walk distance; BNP: brain natriuretic peptide; NT-proBNP: N-terminal pro-BNP; mPAP: mean pulmonary arterial pressure; PVR: pulmonary vascular resistance; PAWP: pulmonary arterial wedge pressure. Baseline characteristics Data are presented as shown as n (%) of the respective population, mean±sd or median (interquartile range). BMI: body mass index; PAH: pulmonary arterial hypertension; CTD: connective tissue disease; CHD: congenital heart disease; PoPH: portopulmonary hypertension; WHO FC: World Health Organization functional class; 6MWD: 6-min walk distance; BNP: brain natriuretic peptide; NT-proBNP: N-terminal pro-BNP; RAP: right atrial pressure; mPAP: mean pulmonary arterial pressure; PAWP: pulmonary arterial wedge pressure; PVR: pulmonary vascular resistance; SvO: mixed-venous oxygen saturation; DLCO: diffusing capacity of the lung for carbon monoxide. The median (IQR) observation time was 2.6 (1.2–4.9) years. During follow-up, 640 (38.7%) patients died, 21 (1.3%) underwent lung transplantation and 90 (5.4%) were lost to follow-up. For the entire cohort, the Kaplan–Meier estimated survival rates 1, 3 and 5 years after diagnosis were 91.1%, 70.7% and 55.2%, respectively.

Determination of NT-proBNP cut-off level to distinguish between intermediate-low and intermediate-high risk

To determine the NT-proBNP cut-off level for the discrimination between intermediate-low and intermediate-high risk, we calculated the log-rank test for patients whose baseline NT-proBNP levels were between 300 ng·L−1 and 1100 ng·L−1 (n=374), using 50-ng·L−1 steps to split these patients into two groups. As shown in supplementary figure S1, the lowest p-value (p=0.091) was found for an NT-proBNP value of 650 ng·L−1. As this number was also close to this group's median, we used it as cut-off to distinguish between the two intermediate risk groups in all further analyses.

Risk at baseline and survival

At baseline, using the three-stratum model, 142 (8.6%) patients were classified as low risk, 1257 (76.0%) patients as intermediate risk and 256 (15.5%) patients as high risk. With the four-stratum model, 92 (5.6%) patients were classified as low risk, 401 (24.2%) patients as intermediate-low risk, 910 (55.0%) patients as intermediate-high risk and 252 (15.2%) as high risk (table 2). The Kaplan–Meier estimated survival rates 1, 3 and 5 years after diagnosis for the low risk group were 100%, 89.0% and 82.9%, respectively; for the intermediate-low risk group, 97.9%, 85.6% and 78.6%, respectively; for the intermediate-high risk group, 90.9%, 62.2% and 50.3%, respectively; and for the high risk group, 78.1%, 46.5% and 28.2%, respectively (p<0.001 for between-group comparisons; figure 2a). The survival estimates of the idiopathic, heritable, drug-associated and CTD-PAH subgroups in the four-risk-strata model were consistent with the overall group, as shown in supplementary figures S2 and S3.
FIGURE 2

Kaplan–Meier survival curves based on the four risk strata obtained a) at baseline, b) at first follow-up.

Kaplan–Meier survival curves based on the four risk strata obtained a) at baseline, b) at first follow-up.

Risk at follow-up and survival

Information on risk variables at first follow-up after treatment initiation (median 4.1 months) was available for 1414 patients (supplementary table S1). At that time, 64.1% of the patients were receiving monotherapy, 33.2% oral combination therapy and 1.3% combination therapy including intravenous or subcutaneous prostacyclin analogues (supplementary table S2). At first follow-up, with the three-stratum model, 282 (19.9%) patients were classified as low risk, 903 (63.9%) patients as intermediate risk and 229 (16.2%) as high risk. With the four-stratum model, 240 (17%) patients were classified as low risk, 395 patients (27.9%) as intermediate-low risk, 534 (37.8%) as intermediate-high risk and 245 (17.3%) as high risk. The Kaplan–Meier estimated survival rates 1, 3 and 5 years after diagnosis for the low risk at first follow-up group were 98.5%, 91.2% and 82.8%, respectively; for the intermediate-low risk group, 97.2%, 81.8% and 66.8%, respectively; for the intermediate-high risk group, 91.3%, 63.0% and 46.5%, respectively; and for the high risk group, 78.0%, 48.0% and 33.3%, respectively (p<0.001 for between-group comparisons; figure 2b).

Changes in risk from baseline to first follow-up and survival

Overall, risk improved from baseline to first follow-up (figure 3). When the three-stratum approach was applied to the current dataset, 440 (31.1%) patients changed their risk category (figure 3a and supplementary table S3). Changes in risk from baseline to follow-up were associated with changes in long-term mortality risk, as shown in figure 4a and b.
FIGURE 3

Change in risk from baseline to first follow-up. Risk at baseline and at first follow-up and changes in risk are shown for a) the three-stratum model and b) the four-stratum model.

FIGURE 4

Mortality risk of patients who changed their risk category from baseline to follow-up with the three-stratum model. Mortality risk of patients who changed from baseline to follow-up with the three-stratum model a) from intermediate risk to other risk categories and b) from high risk to intermediate risk. Data for patients coming from low risk at baseline and those from patients coming from high risk and improving to low risk are not shown due to small numbers. All comparisons were made against patients who remained in their original risk category. Analyses were done with Cox proportional hazard models and depicted as hazard ratio (HR) and 95% confidence intervals.

Change in risk from baseline to first follow-up. Risk at baseline and at first follow-up and changes in risk are shown for a) the three-stratum model and b) the four-stratum model. Mortality risk of patients who changed their risk category from baseline to follow-up with the three-stratum model. Mortality risk of patients who changed from baseline to follow-up with the three-stratum model a) from intermediate risk to other risk categories and b) from high risk to intermediate risk. Data for patients coming from low risk at baseline and those from patients coming from high risk and improving to low risk are not shown due to small numbers. All comparisons were made against patients who remained in their original risk category. Analyses were done with Cox proportional hazard models and depicted as hazard ratio (HR) and 95% confidence intervals. Using the refined four-stratum approach, 695 (49.2%%) patients changed their risk category from baseline to first follow-up including 263 (18.6%) patients who changed between the intermediate-low and intermediate-high strata (figure 3b and supplementary table S4). Changes in risk observed with the four-stratum model including those between intermediate-low and intermediate-high risk were associated with changes in long-term mortality risk (figure 5a–d). In patients who started at intermediate-low risk at baseline, the likelihood of death increased by 60.3% in those who deteriorated to intermediate-high risk at follow-up (n=65), compared to patients who remained at intermediate-low risk (HR 1.603, 95% CI 0.921–2.792); if these patients improved to low risk (n=102), the likelihood of death decreased by 35.5% (HR 0.645, 95% CI 0.343–1.214; figure 5a).
FIGURE 5

Mortality risk of patients who changed their risk category from baseline to follow-up with the four-stratum model. Survival of patients who changed from baseline to follow-up with the four-stratum model a) from intermediate-low risk to other risk categories, b) from intermediate-high risk to other risk categories, c) from high risk to other risk categories and d) from intermediate-high or high risk combined to intermediate-low or low risk. Data for patients coming from low risk at baseline and those from patients coming from high risk and improving to low risk are not shown due to small numbers. All comparisons were made against patients who remained in their original risk category. Analyses were done with Cox proportional hazard models and depicted as hazard ratio (HR) and 95% confidence intervals.

Mortality risk of patients who changed their risk category from baseline to follow-up with the four-stratum model. Survival of patients who changed from baseline to follow-up with the four-stratum model a) from intermediate-low risk to other risk categories, b) from intermediate-high risk to other risk categories, c) from high risk to other risk categories and d) from intermediate-high or high risk combined to intermediate-low or low risk. Data for patients coming from low risk at baseline and those from patients coming from high risk and improving to low risk are not shown due to small numbers. All comparisons were made against patients who remained in their original risk category. Analyses were done with Cox proportional hazard models and depicted as hazard ratio (HR) and 95% confidence intervals. In patients coming from intermediate-high risk at baseline, the likelihood of death decreased by 20.1% in those who improved to intermediate-low risk at follow-up (n=198) compared to patients who remained at intermediate-high risk (HR 0.799, 95% CI 0.611–1.046; figure 5b). If these patients deteriorated to high risk at follow-up (n=139), they had a 49.2% increased likelihood of death compared to patients who remained at intermediate-high risk (HR 1.492, 95% CI 1.122–1.983; figure 5b). Conversely, in patients coming from high risk at baseline, only a slightly decreased likelihood of death was seen when improving to intermediate-high risk at follow-up (n=80) compared to patients who remained at high risk (HR 0.895, 95% CI 0.608–1.317; figure 5c). Of note, of 995 patients who were classified as high or intermediate-high risk at baseline, only 75 (7.5%) improved to low risk at follow-up, but 216 (21.7%) improved to intermediate-low risk (supplementary table S4). In this group of patients, reaching an intermediate-low risk profile at follow up was associated with a 41.3% reduction in the likelihood of death compared to patients who did not improve their risk category (HR 0.587, 95% CI 0.459–0.749; figure 5d).

Discussion

In the present study, we evaluated a modified risk assessment strategy termed COMPERA 2.0 using four instead of three risk strata, and refined cut-off levels for 6MWD and BNP/NT-proBNP. The main findings were 1) very few patients (5.6%) were at low risk at the time of diagnosis; 2) with the three-stratum model, most patients presented with an intermediate risk profile at the time of diagnosis and at follow-up, and this group was further divided into intermediate-low and intermediate-high risk groups with the four-stratum model; 3) the long-term survival of patients presenting with low or intermediate-low risk at the time of diagnosis was almost identical; 4) at follow-up, all four risk strata were of reasonable size (17–38% of the patients), showing significant differences in long-term survival; 5) with the four-stratum model, changes in risk from baseline to first follow-up were documented in 49.2% of the patients, compared to 31.1% with the three-stratum model; 6) changes in risk from baseline to first follow-up observed with the four-stratum model, including changes between intermediate-low and intermediate-high risk, were associated with changes in long-term mortality risk; and 7) patients classified as high or intermediate-high risk at baseline had a very low likelihood of reaching a low risk profile, but a higher likelihood of reaching an intermediate-low risk profile, which was associated with a decreased mortality risk over time. It was not surprising to find a very low number of patients presenting with a low risk profile at the time of diagnosis. This is in line with previous data from SPAHR and COMPERA [5, 6] as well as findings from the French registry [4]. At baseline, the four-stratum model did not show a survival difference in patients classified as low or intermediate-low risk. However, risk assessment at the time of diagnosis is particularly important for identifying high-risk patients, for whom initial combination therapy including intravenous or subcutaneous prostacyclin analogues is recommended, whereas for all other patients, initial oral combination therapy is currently the preferred treatment [2, 3]. Hence the absence of a survival difference between patients presenting with low or intermediate-low risk at baseline is not considered a shortcoming of the proposed model. At the same time, an intermediate-high risk status at baseline may prompt physicians to initiate a more aggressive therapeutic approach, especially when keeping in mind a recent publication from France on the effects of initial treatment strategies on long-term survival [18]. Compared to risk assessment at the time of diagnosis, risk stratification after treatment initiation provides more reliable prognostic information as it incorporates the individual response to therapy [19]. It has already been shown by several groups that changes in risk translate into changes in long-term survival [4, 6, 9]. However, a substantial limitation of the three-stratum model is that most patients present at intermediate risk at baseline and during follow-up while the number of patients who change their risk category is relatively low. Applying the original three-stratum model to the present series, 76.0% of the patients were at intermediate risk at baseline and 63.9% at follow-up, and only 31.1% changed their risk category between baseline and first follow-up. Thus, the three-stratum model may not be sufficiently sensitive to prognostically relevant changes. With the refined four-stratum model, changes from baseline to first follow-up were observed in 49.2% of the patients. Changes between the intermediate-low and intermediate-high strata occurred in 18.6% of the patients, and these changes had an impact on consecutive survival. Hence, there was more between-group penetrability with the four-stratum model, which may be of relevance not only in clinical settings but also when risk stratification tools are considered as end-points in clinical trials, where it will have a substantial impact on sample size calculations if changes can be expected to occur in ~30% or in almost 50% of the participants. According to current guidelines, achieving and maintaining a low risk profile is a major treatment objective in PAH [1-3], but it has been shown that this goal is not reached in most patients [4, 6, 20]. In the present series, only 7.5% of the patients who were classified as high or intermediate-high risk at baseline reached the low risk category at follow-up, while 21.7% reached the intermediate-low risk category, which was associated with a decreased mortality risk, albeit less so than with reaching the low risk category. Thus, while improving from high or intermediate-high to intermediate low risk can be considered a partial treatment success, our data confirm that a low risk profile is an essential treatment goal in patients with PAH. At the same time, an intermediate-high risk category at follow-up was associated with a high mortality risk and should trigger treatment escalation whenever reasonably possible. Hence, the distinction between intermediate-low and intermediate-high risk can support clinical decision-making. In the present analysis, we used several cut-off values that have originally been proposed by the REVEAL group [7-9], and one may ask why not use REVEAL Lite 2 instead of COMPERA 2.0? REVEAL Lite 2 is a well validated and established tool, but we believe that our model has potential advantages. Firstly, REVEAL Lite 2 has no NT-proBNP cut-off to distinguish between intermediate-low and intermediate-high risk, which is a major drawback, as NT-proBNP is used more frequently than BNP as cardiac biomarker. Secondly, REVEAL Lite 2 includes heart rate and systolic blood pressure, i.e. two highly variable parameters, which were not obtained in a standardised manner in the original REVEAL registry [8]. The prognostic value of these parameters awaits independent confirmation, especially when added to functional class, 6MWD and BNP/NT-proBNP. Thirdly, REVEAL Lite 2 incorporates renal dysfunction, defined as estimated glomerular filtration rate <60 mL·min−1·1.73 m−2 or renal insufficiency deemed present by the investigator. While there is little doubt that kidney function is prognostically important in patients with PAH [21], we believe that this variable needs to be better defined and validated before being included in risk stratification models. The COMPERA 2.0 model is based only on parameters that have been thoroughly validated, and future studies are needed to determine whether the use of additional parameters increases the predictive value of this tool. The limitations of the present study are those inherent to registry analyses, including lack of standardised visit schedules and missing values. The number of patients lost to follow-up was small, but not negligible. Although the sample size was relatively large, the numbers became small for the subgroup analyses. This was particularly relevant for the number of patients who changed their risk category between baseline and follow-up, which became relatively small in some subsets, so that statistical significance could not be claimed for all possible changes in risk and their impact on consecutive survival. Furthermore, the NT-proBNP cut-off value was derived and validated in the same cohort, so that independent confirmation is necessary. In addition, we did not attempt to further calibrate and weigh the variables in our model. As a general limitation, all available simplified models provide a basic risk assessment, while individual risk is determined by numerous other factors including age [22, 23], sex [24], type of PAH [25], symptoms, signs, disease trajectories [26] and comorbidities [16]. In addition, risk stratification can be modified by variables derived from cardiac imaging [27-30], cardiopulmonary exercise testing [31] and right heart catheterisation [13, 32]. Hence, while combining functional class, 6MWD and NT-proBNP has proven useful for a primary risk assessment, all available information should be considered for individual treatment decisions. In summary, our data show that a four-stratum risk model based on refined cut-off levels for WHO FC, 6MWD and BNP/NT-proBNP was more sensitive than the three-stratum model to prognostically relevant changes in risk. Thus, it is possible that the four-stratum model may be more useful both in clinical practice and as a research tool in clinical trials. If these findings can be confirmed by other groups, the four-stratum model may replace the current three-stratum model as risk stratification tool in PAH. Please note: supplementary material is not edited by the Editorial Office, and is uploaded as it has been supplied by the author. Supplementary material ERJ-02311-2021.Supplement This one-page PDF can be shared freely online. Shareable PDF ERJ-02311-2021.Shareable
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1.  Mortality in pulmonary arterial hypertension: prediction by the 2015 European pulmonary hypertension guidelines risk stratification model.

Authors:  Marius M Hoeper; Tilmann Kramer; Zixuan Pan; Christina A Eichstaedt; Jens Spiesshoefer; Nicola Benjamin; Karen M Olsson; Katrin Meyer; Carmine Dario Vizza; Anton Vonk-Noordegraaf; Oliver Distler; Christian Opitz; J Simon R Gibbs; Marion Delcroix; H Ardeschir Ghofrani; Doerte Huscher; David Pittrow; Stephan Rosenkranz; Ekkehard Grünig
Journal:  Eur Respir J       Date:  2017-08-03       Impact factor: 16.671

2.  Risk assessment, prognosis and guideline implementation in pulmonary arterial hypertension.

Authors:  Athénaïs Boucly; Jason Weatherald; Laurent Savale; Xavier Jaïs; Vincent Cottin; Grégoire Prevot; François Picard; Pascal de Groote; Mitja Jevnikar; Emmanuel Bergot; Ari Chaouat; Céline Chabanne; Arnaud Bourdin; Florence Parent; David Montani; Gérald Simonneau; Marc Humbert; Olivier Sitbon
Journal:  Eur Respir J       Date:  2017-08-03       Impact factor: 16.671

3.  Impact of age and comorbidity on risk stratification in idiopathic pulmonary arterial hypertension.

Authors:  Clara Hjalmarsson; Göran Rådegran; David Kylhammar; Bengt Rundqvist; Jonas Multing; Magnus D Nisell; Barbro Kjellström
Journal:  Eur Respir J       Date:  2018-05-03       Impact factor: 16.671

4.  The added value of cardiopulmonary exercise testing in the follow-up of pulmonary arterial hypertension.

Authors:  Roberto Badagliacca; Silvia Papa; Roberto Poscia; Gabriele Valli; Beatrice Pezzuto; Giovanna Manzi; Roberto Torre; Daniele Gianfrilli; Susanna Sciomer; Paolo Palange; Robert Naeije; Francesco Fedele; Carmine Dario Vizza
Journal:  J Heart Lung Transplant       Date:  2018-12-06       Impact factor: 10.247

5.  Prognostic Value of Follow-Up Hemodynamic Variables After Initial Management in Pulmonary Arterial Hypertension.

Authors:  Jason Weatherald; Athénaïs Boucly; Denis Chemla; Laurent Savale; Mingkai Peng; Mitja Jevnikar; Xavier Jaïs; Yu Taniguchi; Caroline O'Connell; Florence Parent; Caroline Sattler; Philippe Hervé; Gérald Simonneau; David Montani; Marc Humbert; Yochai Adir; Olivier Sitbon
Journal:  Circulation       Date:  2017-10-25       Impact factor: 29.690

6.  A comprehensive risk stratification at early follow-up determines prognosis in pulmonary arterial hypertension.

Authors:  David Kylhammar; Barbro Kjellström; Clara Hjalmarsson; Kjell Jansson; Magnus Nisell; Stefan Söderberg; Gerhard Wikström; Göran Rådegran
Journal:  Eur Heart J       Date:  2018-12-14       Impact factor: 29.983

7.  Systemic Consequences of Pulmonary Hypertension and Right-Sided Heart Failure.

Authors:  Stephan Rosenkranz; Luke S Howard; Mardi Gomberg-Maitland; Marius M Hoeper
Journal:  Circulation       Date:  2020-02-24       Impact factor: 29.690

8.  Sex and haemodynamics in pulmonary arterial hypertension.

Authors:  Corey E Ventetuolo; Amy Praestgaard; Harold I Palevsky; James R Klinger; Scott D Halpern; Steven M Kawut
Journal:  Eur Respir J       Date:  2013-08-15       Impact factor: 16.671

9.  Magnetic Resonance Imaging in the Prognostic Evaluation of Patients with Pulmonary Arterial Hypertension.

Authors:  Andrew J Swift; Dave Capener; Chris Johns; Neil Hamilton; Alex Rothman; Charlie Elliot; Robin Condliffe; Athanasios Charalampopoulos; Smitha Rajaram; Allan Lawrie; Michael J Campbell; Jim M Wild; David G Kiely
Journal:  Am J Respir Crit Care Med       Date:  2017-07-15       Impact factor: 21.405

10.  Predicting mortality during long-term follow-up in pulmonary arterial hypertension.

Authors:  David Kylhammar; Clara Hjalmarsson; Roger Hesselstrand; Kjell Jansson; Mohammad Kavianipour; Barbro Kjellström; Magnus Nisell; Stefan Söderberg; Göran Rådegran
Journal:  ERJ Open Res       Date:  2021-05-31
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  8 in total

Review 1.  Chronic Thromboembolic Pulmonary Hypertension.

Authors:  Krittika Teerapuncharoen; Remzi Bag
Journal:  Lung       Date:  2022-05-29       Impact factor: 2.584

2.  Cross-cultural validation of the Chinese version of the EmPHasis-10 questionnaire in connective tissue disease patients with pulmonary arterial hypertension and its relationship with risk stratification.

Authors:  Yue Shi; Xingbei Dong; Xiaoyun Hu; Li Weng; Yongtai Liu; Jinzhi Lai; Zhuang Tian; Jiuliang Zhao; Mengtao Li; Jinmin Peng; Qian Wang; Xiaofeng Zeng
Journal:  BMC Pulm Med       Date:  2022-07-05       Impact factor: 3.320

3.  Carbon Monoxide Diffusion Capacity as a Severity Marker in Pulmonary Hypertension.

Authors:  Eleni Diamanti; Vasiliki Karava; Patrick Yerly; John David Aubert
Journal:  J Clin Med       Date:  2021-12-27       Impact factor: 4.241

4.  Impact of Pulmonary Arterial Hypertension on Employment, Work Productivity, and Quality of Life - Results of a Cross-Sectional Multi-Center Study.

Authors:  Jan Fuge; Da-Hee Park; Thomas von Lengerke; Manuel J Richter; Henning Gall; Hossein A Ghofrani; Jan C Kamp; Marius M Hoeper; Karen M Olsson
Journal:  Front Psychiatry       Date:  2022-02-08       Impact factor: 4.157

5.  Risk assessment in systemic lupus erythematosus-associated pulmonary arterial hypertension: CSTAR-PAH cohort study.

Authors:  Qian Wang; Junyan Qian; Mengtao Li; Xiao Zhang; Wei Wei; Xiaoxia Zuo; Ping Zhu; Shuang Ye; Wei Zhang; Yi Zheng; Wufang Qi; Yang Li; Zhuoli Zhang; Feng Ding; Jieruo Gu; Yi Liu; Can Huang; Jiuliang Zhao; Yongtai Liu; Zhuang Tian; Yanhong Wang; Miaojia Zhang; Xiaofeng Zeng
Journal:  Ther Adv Chronic Dis       Date:  2022-07-21       Impact factor: 4.970

6.  Pulmonary Vascular Research Institute GoDeep: A meta-registry merging deep phenotyping datafrom international PH reference centers.

Authors:  Raphael W Majeed; Martin R Wilkins; Luke Howard; Paul M Hassoun; Anastasia Anthi; Hector R Cajigas; John Cannon; Stephen Y Chan; Victoria Damonte; Jean Elwing; Kai Förster; Robert Frantz; Stefano Ghio; Imad Al Ghouleh; Anne Hilgendorff; Arun Jose; Ernesto Juaneda; David G Kiely; Allan Lawrie; Stylianos E Orfanos; Antonella Pepe; Joanna Pepke-Zaba; Yuriy Sirenko; Andrew J Swett; Olena Torbas; Roham T Zamanian; Kurt Marquardt; Achim Michel-Backofen; Tobiah Antoine; Jochen Wilhelm; Stephanie Barwick; Phillipp Krieb; Meike Fuenderich; Patrick Fischer; Henning Gall; Hossein-Ardeschir Ghofrani; Friedrich Grimminger; Khodr Tello; Manuel J Richter; Werner Seeger
Journal:  Pulm Circ       Date:  2022-07-01       Impact factor: 2.886

7.  Risk Stratification of Patients with Pulmonary Arterial Hypertension: The Role of Echocardiography.

Authors:  Valentina Mercurio; Hussein J Hassan; Mario Naranjo; Alessandra Cuomo; Jeremy A Mazurek; Paul R Forfia; Aparna Balasubramanian; Catherine E Simpson; Rachel L Damico; Todd M Kolb; Stephen C Mathai; Steven Hsu; Monica Mukherjee; Paul M Hassoun
Journal:  J Clin Med       Date:  2022-07-12       Impact factor: 4.964

8.  External validation of a refined four-stratum risk assessment score from the French pulmonary hypertension registry.

Authors:  Athénaïs Boucly; Jason Weatherald; Laurent Savale; Pascal de Groote; Vincent Cottin; Grégoire Prévot; Ari Chaouat; François Picard; Delphine Horeau-Langlard; Arnaud Bourdin; Etienne-Marie Jutant; Antoine Beurnier; Mitja Jevnikar; Xavier Jaïs; Gérald Simonneau; David Montani; Olivier Sitbon; Marc Humbert
Journal:  Eur Respir J       Date:  2022-06-30       Impact factor: 33.795

  8 in total

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