Systemic BP and Heart Rate as Prognostic Indicators in Pulmonary Arterial Hypertension

CHEST Original Research PULMONARY VASCULAR DISEASE Systemic BP and Heart Rate as Prognostic Indicators in Pulmonary Arterial Hypertension Malcolm M. Bersohn, MD, PhD ; Michelle P. Turner, MS ; Glenna L. Traiger, RN, MSN ; Adaani E. Frost, MD, FCCP ; and Shelley Shapiro, MD, PhD Background: Heart rate (HR) and systolic BP (SBP) are significant multivariate predictors of survival in patients with pulmonary arterial hypertension (PAH) as part of a 19-element formula. To what extent HR and BP alone predict survival and future hospitalization in patients with PAH is unknown. Methods: We analyzed data from the Registry to Evaluate Early and Long-term PAH Disease Management (REVEAL Registry), a prospective, observational study of patients with PAH. Patients were analyzed by quintile (Q) according to values of HR, SBP, and SBP/HR. Kaplan-Meier curves were calculated by Q for survival and freedom from hospitalization. Results: For patients in the worst Q, 1-year survival after enrollment was 85% 2% for SBP, 86% 2% for HR, and 84% 2% for SBP/HR vs 91% 1% for the middle three Qs ( P,.001). Hospitalization occurred more frequently than mortality but with a similar pattern among Qs. One-year survival after first follow-up of patients in the worst Q for change ( ) in SBP since enrollment was 85% 2% ( P 5.004), 86% 2% for HR ( P 5.12), and 84% 2% for SBP/HR ( P 5.024) vs the middle three Qs ( SBP: 91% 1%; HR: 90% 1%; SBP/HR: 90% 1%). Conclusions: Changes in vital signs from enrollment to first follow-up were less predictive of mortality than the values of vital-sign parameters at either enrollment or first follow-up. HR, SBP, and SBP/HR at enrollment identified high-risk groups with survival differences of 5% to 7% and freedom from hospitalization differences of 9% to 11% vs lower-risk groups. SBP/HR defines the highest-risk group, including most of the high-risk patients defined by HR and SBP separately. Trial registry: ClinicalTrials.gov; No.: NCT00370214; URL: www.clinicaltrials.gov CHEST 2013; 144(3):959 965 Abbreviations: 6MWD 5 6-min walk distance; bpm 5 beats per minute; HR 5 heart rate; mpap 5 mean pulmonary artery pressure; NYHA 5 New York Heart Association; PAH 5 pulmonary arterial hypertension; PCWP 5 pulmonary capillary wedge pressure; PH 5 pulmonary hypertension; Q 5 quintile; REVEAL Registry 5 Registry to Evaluate Early and Long-term PAH Disease Management; SBP 5 systolic BP; WHO 5 World Health Organization For patients with chronic congestive heart failure or acute coronary syndrome, many studies have assessed the prediction of survival and hospitalization. 1-5 Left ventricular ejection fraction is generally the best single predictor of survival, but other parameters are also useful, including heart rate (HR), BP, and serum parameters, such as sodium or brain natriuretic peptide levels. 1-6 For patients with pulmonary arterial hypertension (PAH), predictors of survival include New York Heart Association (NYHA) functional class, 6-min walk distance (6MWD), and right atrial pressure measured at cardiac catheterization. 7-9 HR and systolic BP (SBP) have been found to be significant multivariate predictors of survival in patients with PAH, as part of a 19-element formula. 10 However, to what extent HR and BP alone predict survival in patients with PAH, or how well they would predict future hospitalization, is unknown. We hypothesized that a high resting HR or a low resting SBP are significant independent predictors of a worse prognosis in patients with PAH, including the time to first hospitalization and decreased survival during 1 year of follow-up. We also hypothesized that serial changes in HR and SBP (ie, increasing resting HR or a decreasing resting SBP) between enrollment and the first vital-sign follow-up visit are significant journal.publications.chestnet.org CHEST / 144 / 3 / SEPTEMBER 2013 959

prognostic indicators. To investigate these hypoth eses, we analyzed data from the Registry to Evaluate Early and Long-term PAH Disease Management (REVEAL Registry), a large (54-center), prospective, US-based observational study designed to provide current information about the demographics, course, and management of 3,523 patients with World Health Organization (WHO) group 1 pulmonary hypertension (PH). 11,12 had at least one vital sign recorded (SBP and/or HR) at the first follow-up visit occurring within 1 year of enrollment and if they had at least 1 year of follow-up thereafter. The results for the middle three Qs (Q2-Q4) were similar and were combined in the final analyses. Log-rank analysis with a Bonferroni correction was used to assess differences (Bonferroni adjusted a 5 0.017). Hazard ratios were computed from univariate Cox regression models comparing patients in the worst Q for the parameter to all others. Study Population Materials and Methods The patient population of the REVEAL Registry has been described previously. 11,12 Patients were enrolled consecutively from March 2006 to December 2009 and followed through discontinuation or the end of study, which occurred in December 2012. Briefly, patients included in this analysis were either newly or previously diagnosed with WHO group 1 PH, were aged 19 years at enrollment, and had a mean pulmonary artery pressure (mpap). 25 mm Hg at rest or. 30 mm Hg with exercise, pulmonary capillary wedge pressure (PCWP) or left ventricular end-diastolic pressure 18 mm Hg at rest, and a pulmonary vascular resistance 240 dynes/s/cm5. Study Design Patients were analyzed by quintile (Q) according to the values of resting HR, resting SBP, and the ratio of resting SBP/HR at the enrollment visit and at the first vital-signs follow-up visit. In addition, patients were analyzed for serial changes in resting HR, resting SBP, and ratio of resting SBP/HR between enrollment and the first vitalsigns follow-up visit. For SBP, Q1 was defined as 102 mm Hg; Q2-Q4:. 102 to 130 mm Hg; and Q5:. 130 mm Hg. For HR, Q1 was defined as 70 beats per minute (bpm); Q2-Q4:. 70 to 95 bpm; and Q5:. 95 bpm. For SBP/HR ratio, Q1 was defined as 1.2 mm Hg/bpm; Q2-Q4:. 1.2 to 1.7 mm Hg/bpm; and Q5:. 1.7 mm Hg/bpm. Statistical Analyses For all data analyses, the data lock was January 14, 2010. Kaplan-Meier curves were calculated by Q for survival and for freedom from hospitalization for 1 year from the time of enrollment and for 1 year from the time of the first follow-up visit. For all first follow-up visit data analyses, patients were included if they Manuscript received October 18, 2012; revision accepted March 18, 2013. Affiliations: From the VA Greater Los Angeles Healthcare System and David Geffen School of Medicine (Drs Bersohn and Shapiro) at the University of California, Los Angeles, Los Angeles, CA; ICON Late Phase & Outcomes Research (Ms Turner), San Francisco, CA; University of California, Los Angeles (Ms Traiger), Los Angeles, CA; and Baylor College of Medicine (Dr Frost), Houston, TX. Funding /Support: Funding and support for the REVEAL Registry were provided by Cotherix Inc, and its affiliate Actelion Pharmaceuticals US Inc. Correspondence to: Malcolm M. Bersohn, MD, PhD, VA Greater Los Angeles Healthcare System, Cardiology (111E), 11301 Wilshire Blvd, Los Angeles, CA 90073; e-mail: mbersohn@ucla.edu 2013 American College of Chest Physicians. Reproduction of this article is prohibited without written permission from the American College of Chest Physicians. See online for more details. DOI: 10.1378/chest.12-2572 Results Patient Population and PAH Risk Factors at Enrollment A total of 2,830 patients were included in the analysis cohort ( Fig 1 ), and the maximal number with available data were used for each vital-sign data category even if other data were missing. The patient cohort at enrollment had a mean ( SD) age of 53 15 years, was predominantly female (79%), and was diagnosed as NYHA functional class I (7%), II (37%), III (50%), or IV (6%). The majority of patients (n 5 2,433; 86.0%) were on PAH medications at enrollment. Systemic hypertension was diagnosed in 34%, of whom 7% were on b -blockers. Another 6% were on b-blockers without a diagnosis of systemic hypertension. Table 1 shows the mpap and the enrollment 6MWD according to Qs of vital-signs data. There does not appear to be any systematic variation of these previously identified predictors of survival across Qs of vital-signs data. Survival Following Enrollment The Kaplan-Meier survival curves following enrollment are shown in Figure 2. Patients in the lowest Q of SBP at enrollment had significantly lower survival at 12 months (85% 2%) than patients in the middle three Qs (91% 1%) or the highest Q (91% 1%) ( P,.001 for both) ( Fig 2A ). Patients in the highest Q of HR had significantly lower survival at 12 months (86% 2%) than patients in the lowest Q (92% 1%) or the middle three Qs (91% 1%) ( P,.001 for both) ( Fig 2B ). Patients in the lowest Q of SBP/HR ratio had significantly lower survival at 12 months (84% 2%) compared with patients in the middle three Qs (91% 1%) or the highest Q (92% 1%) ( P,.001 for both) ( Fig 2C ). In addition, survival for the worst Q for SBP/HR ratio was lower than survival for the worst Q of either SBP or HR. Freedom From Hospitalization Following Enrollment Results for freedom from hospitalization are shown in Figure 3. As expected, hospitalization was a more frequent occurrence than mortality, with 72% 1% 960 Original Research

Figure 1. Strengthening the Reporting of Observational Studies in Epidemiology diagram. HR 5 heart rate; SBP 5 systolic BP. freedom from hospitalization for patients in the middle three Qs when grouped according to SBP, HR, and SBP/HR ratio. The pattern of results for hospitalization is similar to that seen for survival at 12 months, with 9% to 11% absolute differences between the highest-risk Q and the middle three Qs after 1 year of follow-up for SBP, HR, and SBP/HR ratio. Survival After First Follow-up and the Change Between Enrollment and First Follow-up The median time from enrollment to the first follow-up vital-signs evaluation within 1 year of enrollment was 3 months. Based on vital signs at the first follow-up, compared with the middle three Qs for each vital-signs parameter, the survival in patients Table 1 Traditional PAH Risk Factors at Enrollment According to Quintiles of Vital-Sign Parameters Quintile SBP, mm Hg HR, bpm SBP/HR, mm Hg/bpm Quintile limits Q1 102 70 1.2 Q2-Q4. 102 to 130. 70 to 95. 1.2 to 1.7 Q5. 130. 95. 1.7 Patients, No. 2,698 2,671 2,662 6MWD, mean SD, m Q1 353 126 370 122 349 129 Q2-Q4 375 125 364 125 372 125 Q5 346 122 360 129 358 121 mpap, mean SD, mm Hg Q1 51 15 47 15 52 15 Q2-Q4 49 14 50 14 50 14 Q5 48 14 51 14 46 14 6MWD 5 6-min walk distance; bpm 5 beats per minute; HR 5 heart rate; mpap 5 mean pulmonary artery pressure; PAH 5 pulmonary arterial hypertension; Q 5 quintile; SBP 5 systolic BP. journal.publications.chestnet.org CHEST / 144 / 3 / SEPTEMBER 2013 961

Figure 2. A-C, One-y survival estimates at time from enrollment by (A) SBP, (B) HR, and (C) SBP/HR ratio. bpm 5 beats per min ute; Q 5 quintile. See Figure 1 legend for expansion of other abbre viations. over the subsequent year in the worst Q for SBP was 83% 2% (P,.001), in the worst Q for HR was 84% 2% (P,.003), and in the worst Q for SBP/HR was 80% 2% ( P,.001) ( Table 2 ). The pattern of results at the first follow-up was similar to that following enrollment. One-year survival analyzed by the change in SBP/HR ratio from enrollment to the first follow-up is shown in Figure 4A. Patients in the worst Q of change in SBP/HR ratio had a lower survival at 12 months (84% 2%) than those in the middle three Qs (90% 1%) or the best Q (91% 1%). The hazard ratio for the worst Q was 1.3 ( P 5.011). Notably, the change in SBP/HR ratio ( Fig 4A ) was not as strong a predictor as the SBP/HR ratio at the first follow-up. Figure 3. A-C, One-y freedom from hospitalization at time from enrollment by (A) SBP, (B) HR, and (C) SBP/HR ratio. See Figure 1 and 2 legends for expansion of other abbreviations. Individual curves are not shown for the change in SBP or HR, but point estimates for survival 1 year after the first follow-up visit are shown in Table 2, with the overall pattern of results similar to those for enrollment data. The change in HR and BP were less predictive than the absolute values of HR and BP at the first follow-up. Freedom From Hospitalization After First Follow-up and the Change Between Enrollment and First Follow-up The SBP and SBP/HR at the first follow-up visit were predictors of significantly worse 1-year freedom from hospitalization for the worst Qs (62% 2% 962 Original Research

Table 2 Point Estimates of Survival and Freedom From Hospitalization 1 Y After the First Vital-Signs Follow-up by Quintiles of and Changes in Vital Signs Quintile SBP, mm Hg HR, bpm SBP/HR, mm Hg/bpm SBP, mm Hg HR, bpm SBP/HR, mm Hg/bpm Quintile limits Q1 102 70 1.2 12 10 0.2 Q2-Q4. 102 to 130. 70 to 95. 1.2 to 1.7. 12 to 11. 10 to 10. 0.2 to 0.2 Q5. 130. 95. 1.7. 11. 10. 0.2 Patients, No. 2,521 2,497 2,478 2,415 2,367 2,345 Survival estimate, mean SE, % Q1 83 2b 92 1 80 2b 85 2a 90 1 84 2 Q2-Q4 91 1 90 1 91 1 91 1 90 1 90 1 Q5 91 1 84 2 a 93 1 88 2 86 2 91 1 Hazard ratio 1.9 1.5 2.1 1.3 1.2 1.3 P value c,.001,.001,.001.006.091.011 Freedom from hospitalization, mean SE, % Q1 62 2 b 74 2 60 2b 66 2 70 2 70 2 Q2-Q4 73 1 70 1 72 1 72 1 71 1 69 1 Q5 73 2 66 2 75 2 71 2 69 2 74 2 Hazard ratio 1.4 1.3 1.6 1.1 1.1 1.1 P valuec,.001,.001,.001.059.35.11 5 change between enrollment value and follow-up value. See Table 1 legend for expansion of other abbreviations. a P,.01 vs Q2-Q4 from log-rank test. b P,.001 vs Q2-Q4 from log-rank test. c P value from Cox proportional hazards model. and 60% 2%, respectively) vs the middle three Qs (73% 1% and 72% 1%, respectively) (both P,.001) ( Table 2 ). The changes in HR, BP, and SBP/HR ratio between enrollment and first follow-up visits (shown as point estimates in Table 2 and graphically for SBP/HR in Fig 4B ) were not significant predictors of hospitalization during the year after the first follow-up visit. These results contrast with the improvement in hospitalization predicted by the best Q of SBP, SBP/HR ratio, or HR at the first follow-up ( Table 2 ). High-Risk Qs for Vital Signs To investigate the overlap among patients defined by the different vital-sign parameters, we constructed a Venn diagram of the worst Qs for survival according to SBP, HR, and SBP/HR ratio at enrollment ( Fig 5 ). There was only a modest overlap of patients in the lowest SBP and HR Qs, and the SBP/HR ratio identified only a few patients not already identified by their low Q for SBP or HR. However, almost all of the highest-risk patients are identified by the low SBP/HR Q. The point estimate for 1-year survival was 89% 2% for the 297 patients in the low SBP Q but not also in the low SBP/HR Q, and was 90% 2% for the 201 patients in the high HR Q but not also in the low SBP/HR Q. These survival estimates were not significantly different from the 91% 1% survival for the patients in the middle three Qs for each parameter. Discussion The severity of disease in individual patients with PAH is usually assessed with respect to present symptoms (eg, NYHA functional class) and present physical findings, such as jugular venous pressure and edema. 8,9 It is also important to assess the severity of prognosis in individual patients because a worse prognosis might influence the choice of diagnostic and therapeutic approaches. The care of patients with PAH could be improved if the analysis of basic vitalsigns data provided significant prognostic indicators for morbidity, cost of hospitalization, and survival. To our knowledge, the use of the SBP/HR ratio is a new idea that may improve discrimination between highand low-risk patient groups better than either HR or SBP alone. Comparison of the predictive value of changes in vital signs over time vs the vital signs measured at enrollment or the first follow-up visit revealed that current vital signs are much more important than the changes. Thus, where you are, whether at the start of the trial or at the first follow-up visit, is more important than how you got there. Our cohort included patients diagnosed as having WHO group 1 PH with a PCWP 18 mm Hg, which is higher than the traditional cutoff PCWP of 15 mm Hg and does not completely exclude PAH due to left-sided heart failure. A sensitivity analysis was performed excluding those patients with a PCWP. 15 mm Hg, thus limiting analysis to those journal.publications.chestnet.org CHEST / 144 / 3 / SEPTEMBER 2013 963

Figure 4. A, One-y survival estimates at time from first follow-up by change from enrollment in SBP/HR ratio. B, One-y freedom from hospitalization at time from first follow-up by change from enrollment in SBP/HR ratio. 5 change. See Figure 1 and 2 legends for expansion of other abbreviations. patients with traditional hemodynamic criteria of PCWP 15 mm Hg. This analysis did not have any effect on the results, implying that patients with PAH and mildly elevated PCWP were not different from other patients with PAH regarding the hypotheses tested. This has also been true for other REVEAL Registry analyses. 13,14 Figure 5. Venn diagram of worst quintile for SBP, HR, and SBP/HR ratio from enrollment data. See Figure 1 and 2 legends for expansion of abbreviations. Survival of patients with left-sided heart failure can also be predicted by resting HR and by SBP. 1,3,5 However, the predictive value of the ratio of SBP/HR in patients with left-sided heart failure has not been tested in the same way as we have done for patients with PAH in this study. A more sophisticated multivariate model for predicting survival in patients with PAH has been developed using the same patient cohort. 10 The model includes subgroups of patients diagnosed with WHO group 1 PH and uses a total of 19 historical and laboratory data items, including data from cardiac catheterization. Resting HR and SBP are significant predictors in this more complex model, which provides a more precise estimate of survival in an individual patient with PAH than HR and SBP alone. In the multivariate model, neither systemic hypertension nor b blocker use were significant predictors, even though they might have effects on HR and SBP. The SBP/HR ratio identified the 20% of patients from the overall population of patients with PAH who were at significantly higher risk than others for death or hospitalization over the next year, and had about the same mortality risk as the high-risk Q in the model by Benza et al. 10 This provides an opportunity for rapid assessment in the office using two readily available variables and one calculation. The SBP/HR ratio represents a potential way for tracking patients over time, in addition to 6MWD, and future studies could consider using it to alter therapy. However, without further study, it is unknown from these registry data whether a change in therapy in response to identifying a high-risk patient identified by a low SBP/HR ratio could mitigate poor prognosis. In summary, in a large cohort of adult patients with PAH, HR. 95 bpm, SBP 102 mm Hg, and a ratio of SBP/HR 1.2 mm Hg/bpm at enrollment identified high-risk groups of patients with 5% to 7% worse survival and 9% to 11% worse freedom from hospitalization vs lower-risk groups of patients. The hazard ratios over 1 year were 1.5 to 2.1 for mortality and 1.3 to 1.6 for hospitalization. Our data also provide added justification for the REVEAL Registry risk calculator 15 as an informative measure that can be used at any given point in time and without the need for a change score. Current values of vital signs were more useful in predicting mortality and hospitalization than changes in vital signs. The predictive value of vital signs at the first follow-up visit was similar to the predictive value of enrollment vital signs. Thus, the presence of lower SBP and/or higher HR at any time predicts a worse clinical outcome. Following the SBP/HR ratio alone, a value 1.2 identified 20% of patients with PH with a very high morbidity and mortality over the ensuing year. Whether that poor prognosis could be modified by changing therapy or follow-up cannot be determined from this registry study. 964 Original Research

Acknowledgments Author contributions: Dr Bersohn had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Dr Bersohn: contributed to the study design; collection, analysis, and approval of the final manuscript; and served as principal author. Ms Turner: contributed to the study design; collection, analysis, Ms Traiger: contributed to the study design; collection, analysis, Dr Frost: contributed to the study design; collection, analysis, and interpretation of data; drafting and critical review of the manuscript; Dr Shapiro: contributed to the study design; collection, analysis, Financial/nonfinancial disclosures : The authors have reported to CHEST the following conflicts of interest: Dr Bersohn has received grant/research support from Actelion Pharmaceuticals US Inc, Bayer, Biotronik SE & CoKG, Medtronic Inc, and St. Jude Medical Inc. Ms Turner is employed by ICON Late Phase & Outcomes Research, a company that receives research support from Actelion Pharmaceuticals US Inc on behalf of Cotherix Inc and other pharmaceutical companies. Ms Traiger serves as a consultant and is on the speakers bureau for Actelion Pharmaceuticals US Inc, United Therapeutics Corp, and Gilead Sciences Inc. Dr Frost serves as a consultant for Actelion Pharmaceuticals US Inc, and Gilead Sciences Inc; has received honoraria from Actelion Pharmaceuticals US Inc, Gilead Sciences Inc, and Pfizer Inc; has provided expert testimony on diet-pill litigation; has received (through Baylor University) funds for institutional review board-approved research from Gilead Sciences Inc, Actelion Pharmaceuticals US Inc, United Therapeutics Corp, Eli Lilly and Company, Pfizer Inc, Novartis Pharmaceuticals Corp., and Bayer Pharmaceuticals Corp.; and has received honoraria for her service on the REVEAL Registry Steering Committee, which is supported by Actelion Pharmaceuticals US Inc on behalf of Cotherix Inc. Dr Shapiro has received research grants from Actelion Pharmaceuticals US Inc, Pfizer Inc, Gilead Sciences Inc, United Therapeutics Corp, Medtronic Inc, GeNO LLC, and Bayer Pharmaceuticals, Inc; has served as a consultant for Gilead Sciences Inc and United Therapeutics Corp; and has served as a speaker for Actelion Pharmaceuticals US Inc, United Therapeutics Corp, and Gilead Sciences Inc. Role of sponsors : The sponsors had no role in the design of the study, the collection and analysis of the data, or the preparation of the manuscript. Other contributions: Medical writing support was provided by Latoya M. Mitchell, PhD, and Kathryn Leonard, of inscience Communications, Springer Healthcare. SAS programming support was provided by Ginny Lai of ICON Late Phase & Outcomes Research (ICON plc) and funding was provided by Actelion Pharmaceuticals US, Inc. We thank the principal investigators and their study coordinators for their participation in the REVEAL Registry (available in e-appendix 1). Additional information: The e-appendix can be found in the Supplemental Materials area of the online article. References 1. Levy WC, Mozaffarian D, Linker DT, et al. The Seattle Heart Failure Model: prediction of survival in heart failure. Circulation. 2006 ;113(11):1424-1433. 2. Komajda M, Carson PE, Hetzel S, et al. Factors associated with outcome in heart failure with preserved ejection fraction: findings from the Irbesartan in Heart Failure with Preserved Ejection Fraction Study (I-PRESERVE). Circ Heart Fail. 2011 ; 4 (1 ):27-35. 3. Pocock SJ, Wang D, Pfeffer MA, et al. Predictors of mortality and morbidity in patients with chronic heart failure. Eur Heart J. 2006 ;27 (1 ):65-75. 4. Wedel H, McMurray JJ, Lindberg M, et al ; CORONA Study Group. 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