Improving the Detection of Pulmonary Hypertension in Systemic Sclerosis Using Pulmonary Function Tests

Transcription

1 ARTHRITIS & RHEUMATISM Vol. 63, No. 11, November 2011, pp DOI /art , American College of Rheumatology Improving the Detection of Pulmonary Hypertension in Systemic Sclerosis Using Pulmonary Function Tests Benjamin E. Schreiber, 1 Christopher J. Valerio, 1 Gregory J. Keir, 2 Clive Handler, 1 Athol U. Wells, 2 Christopher P. Denton, 3 and John G. Coghlan 1 Objective. To construct a readily applicable formula for selecting patients with systemic sclerosis (SSc) for right-sided heart catheterization (RHC) based on the results of their pulmonary function tests (PFTs). Methods. The diagnostic value of PFT variables was quantified in 386 patients with SSc against data obtained from RHC. Results. We derived the following formula using data from 257 patients: predicted mpap 136 SpO DLCO % predicted, where mpap is the mean pulmonary artery pressure, SpO 2 is the oxygen saturation as measured by pulse oximetry, and DLCO is the diffusing capacity for carbon monoxide. We validated the formula in the remaining 129 SSc patients. The area under the curve was 0.75 (95% confidence interval [95% CI] 0.67, 0.84). Using a predicted threshold of 25 mm Hg, the sensitivity was 90.1% (95% CI 82, 96) and the specificity was 29.2% (95% CI 17, 44). When used as a Supported by the Royal Free Hampstead NHS Trust. 1 Benjamin E. Schreiber, MA, MBBS, MRCP, Christopher J. Valerio, MBChB, MRCP, Clive Handler, MBBS, BSc, MD, MRCP, John G. Coghlan, MD, MRCPI, FRCP: Royal Free Hampstead NHS Trust, London, UK; 2 Gregory J. Keir, MBBS, FRACP, Athol U. Wells, MBChB, FRACP, MD: Royal Brompton Hospital NHS Trust, London, UK; 3 Christopher P. Denton, PhD, FRCP: Royal Free and University College Medical School, London, UK. Dr. Schreiber has received speaking fees from Actelion (less than $10,000) and educational/travel grants from Actelion and Glaxo- SmithKline (less than $10,000 each). Drs. Valerio and Handler have received educational/travel grants from Actelion and Pfizer (less than $10,000 each). Dr. Denton has received consulting fees, speaking fees, and/or honoraria from Actelion, Pfizer, GlaxoSmithKline, and Sanofi- Aventis (less than $10,000 each) and research grants from Pfizer and Actelion. Dr. Coghlan has received consulting fees and speaking fees from GlaxoSmithKline, Bayer, Pfizer, and Eli Lilly (less than $10,000 each) and from Actelion (more than $10,000); he has received staff support and research grants from Actelion, Pfizer, and GlaxoSmithKline. Address correspondence to Benjamin E. Schreiber, MA, MBBS, MRCP, Rheumatology Department, Royal Free Hampstead NHS Trust, Pond Street, London NW3 2QG, UK. benjamin. schreiber@nhs.net. Submitted for publication January 5, 2011; accepted in revised form June 30, screening procedure in a typical scleroderma patient population, it is projected that those with an mpap below 25 mm Hg are unlikely to have pulmonary hypertension (PH; prevalence 4.4%), those with a predicted mpap of mm Hg are at average risk of having PH (prevalence of 11.3%), and those with a formulapredicted mpap above 35 mm Hg are likely to have PH (prevalence of 62.9%), thus justifying RHC. In patients with equivocal findings on echocardiography, a high formula-predicted mpap is strongly associated with the presence of PH. Conclusion. We derived and validated an easily applied formula for determining pulmonary function in patients with SSc that identifies subgroups with a low, average, or high prevalence of PH. It provides information that is complementary to echocardiography and that should improve the selection of patients for RHC. Pulmonary hypertension (PH) occurs in up to 12% of patients with systemic sclerosis (SSc) (1). Early identification and treatment of PH is important and may improve survival (2). Although right-sided heart catheterization (RHC) remains the gold standard test for diagnosing PH, it is invasive and is not ideal as a screening test. Pulmonary function tests (PFTs) are recommended as part of annual screening tests in patients with SSc. However, it is not clear which patients require RHC, based on the findings of the PFTs. In this study, we explored the utility of pulmonary function testing and assessment of oxygen saturation in detecting PH in a large cohort of patients with SSc who had undergone RHC. We sought to develop a formula based on oxygen saturation and PFT results that would help to select patients for whom RHC is required. In order to derive and validate the formula, we split our sample into separate derivation and validation cohorts. We compared our formula to other pulmonary function test based formulae. We then assessed the diagnostic 3531

2 3532 SCHREIBER ET AL use of our formula in patients undergoing contemporary echocardiography to examine whether PFTs provide information that is complementary to echocardiography and whether it may help in the decision-making in patients with equivocal echocardiographic findings. Finally, we tested the formula in a cohort of patients with other connective tissue diseases (CTDs) associated with PH. The information was derived from a large real-life clinical cohort, and the results should be readily applicable to scleroderma cohorts elsewhere. PATIENTS AND METHODS Patients. This study used the large cohort of SSc patients who underwent a diagnostic RHC study in our center. Of the 838 patients with SSc, 50 had additional features of overlap CTDs (systemic lupus erythematosus [SLE] in 23 patients, myositis in 15 patients, rheumatoid arthritis [RA] in 9 patients, Sjögren s syndrome in 7 patients, vasculitis in 1 patient, and antiphospholipid syndrome [APS] in 1 patient). Patients with SSc overlap syndromes were included. SSc was classified as limited cutaneous (lcssc) or diffuse cutaneous (dcssc) disease according to the criteria described by LeRoy et al (3). Of the 838 patients, 386 had undergone complete pulmonary function testing (forced vital capacity [FVC] % predicted, diffusing capacity for carbon monoxide [DLCO] % predicted, and carbon monoxide transfer coefficient [DLCO/ VA], or KCO % predicted) within 6 months of the RHC and oxygen saturation as measured by pulse oximetry (SpO 2 )atthe time of RHC and were included in the study. Patients with incomplete PFT data were excluded. There were no other exclusion criteria. Patients in the study cohort (n 386) were a little younger and more of them were male as compared with the excluded patients (n 452), but the mean pulmonary artery pressure (mpap) and mean oxygen saturation values were similar. The excluded patients did not differ significantly from the study cohort in terms of the RHC data or oxygen saturation. Investigations were performed as part of routine clinical care. Catheterization was performed in scleroderma patients in whom PH was clinically suspected. In general, RHC was considered in any patient with a maximum tricuspid regurgitation jet velocity (TRV max ) 3.2 meters/second, in any patient with a TRV max of meters/second if there was any clinical suspicion of PH, and in any patient with a TRV max of 2.8 meters/second if there was a strong clinical suspicion of PH or unexplained progression of exertional dyspnea. Patients were divided randomly in a 2:1 ratio into derivation and validation cohorts by generating a random number between 0 and 1 for each patient and allocating those with a number that was under two-thirds to the derivation cohort and the others to the validation cohort. We then applied the formula to a separate population of patients with CTDs other than SSc who had been referred to the Royal Free Pulmonary Hypertension Service (London, UK) and had undergone their first RHC between September 1996 and May There were 155 patients with a variety of CTDs in our database (59 SLE, 22 RA, 42 undifferentiated CTD [UCTD], 9 mixed CTD [MCTD], 12 dermatomyositis [DM], 15 polymyositis [PM], 12 Sjögren s syndrome, 2 antineutrophil cytoplasmic antibody associated vasculitis, 3 APS, and 1 Henoch-Schonlein purpura [HSP]). PFT data obtained within 6 months of RHC were available in 52 of these patients (17 SLE, 7 RA, 15 UCTD, 3 MCTD, 4 DM, 3 PM, 2 Sjögren s syndrome, and 1 HSP), and these patients were included in the validation analysis. Clinical data. Oxygen saturation measurements were taken at the time of the RHC. Measurements were taken with the patient breathing room air, and assessments were made at the finger if there was an excellent signal on pulse oximetry or by femoral arterial puncture and blood gas analysis. In patients taking supplementary oxygen, the oxygen was stopped in order to establish a baseline oxygen saturation level. One patient could not be taken off supplementary oxygen and was excluded from the analysis. Data on smoking status, weight, and height were collected at the time of PFT. PFTs were performed at the Royal Free Hospital or the Royal Brompton Hospital. We defined PH as a resting mpap of 25 mm Hg (4). Echocardiography was performed in the referring hospital or at the Royal Free Hospital. Tricuspid jet maximum velocity was assessed by Doppler echocardiography. Statistical analysis. Analyses were performed using Stata software version 10.0 for Windows (StataCorp). Data were expressed as the mean SD or as medians with ranges, depending on distribution. Group comparisons were made using Student s t-test or chi-square test as appropriate. P values less than 0.05 were considered significant. Univariable linear regression was performed to identify variables of interest for the multivariable linear regression analysis. Statistically significant variables (P 0.05) were included in the model. Using multivariable linear regression in the derivation cohort, we explored the relationship between the SpO 2, PFTs, clinical subtype, autoimmune serologic features, and the mpap on RHC and derived a formula for the predicted mpap. We used split-sample derivation and validation. We performed subgroup analysis by clinical subtypes (lcssc or dcssc), serologic subtype (anticentromere antibody [ACA] or anti topoisomerase I [Scl-70] antibody), smoking status, and lung function (5). We compared the performance of our formula to that of other existing formulae as well as echocardiography. Finally, we tested the formula in a population of patients with CTDs other than SSc who had undergone RHC at the same institution over the same timeframe. RESULTS Study population. Within the study sample of 386 patients, 243 patients (63.0%) had PH. The mean SD number of days between PFTs and RHC was Comparing patients with PH (mean mpap 37.7 mm Hg; n 243) to those without PH (mean mpap 19.2 mm Hg; n 143), we found that their age and sex were similar. Oxygen saturation was lower in patients

3 DERIVATION AND VALIDATION OF A FORMULA TO DETECT PH IN SSc 3533 with PH (mean 94.3%) than in those without PH (mean 96.3%; P 0.005). The DLCO % predicted was also lower in those with PH (mean 38.6%) than in those without (mean 50.0%; P 0.005). The proportion of patients with PH did not vary by clinical subtype of scleroderma (61.5% of those with dcssc versus 63.4% of those with lcssc; P 0.75). More patients with PH were ACA positive than those without (38.3% versus 28.2%), although the difference did not reach statistical significance (P 0.057). Patients with PH were less likely to carry the anti Scl-70 antibody (16.2%) than were those without PH (28.5%; P 0.007). Pulmonary hypertension was classified as respiratory disease associated (group 3 in the Dana Point classification of PH [6]) in 113 of the 243 patients (FVC predicted 80%), left-sided heart disease associated (defined by a pulmonary capillary wedge pressure [PCWP] 15 mm Hg at the time of RHC) in 33 patients (group 2), and pulmonary arterial hypertension in 114 patients (group 1). The derivation (n 257) and validation (n 129) cohorts were well matched with regard to age, sex, prevalence and severity of PH, clinical subtype, PFT data, autoimmune serologic features, and smoking status. Identifying variables of interest. Univariable linear regression was performed to identify variables of interest for multivariable linear regression. We found that the DLCO % predicted and the SpO 2 explained 13 14% of the variability in the mpap (R % and R %, respectively; P for each), the KCO % predicted provided information similar to that of the DLCO % predicted, and other variables were less informative (Table 1). We therefore proceeded to multivariable linear regression using DLCO % predicted and SpO 2. Derivation of a formula based on the DLCO % predicted and the SpO 2. Both the DLCO % predicted and the SpO 2 were significantly associated with the mpap (P ). Regression analysis using DLCO % predicted and SpO 2 yielded an R 2 value of 23.1%. This gave the following formula: Predicted mpap SpO DLCO % predicted which could be simplified as follows: Predicted mpap 136 SpO DLCO % predicted. No collinearity was found between the SpO 2 and the DLCO % predicted. We tested whether the time interval Table 1. Identification of variables of interest by univariable regression against mpap values in 386 patients with systemic sclerosis* Variable R 2,% P Sex Age DLCO SpO FVC KCO Anti Scl-70 antibody Anticentromere antibody Smoking status Weight Body mass index * Smoking status categories were scored as follows: 0 never, 0.5 previous, and 1 current smoker. mpap mean pulmonary artery pressure; DLCO diffusing capacity for carbon monoxide; SpO 2 oxygen saturation as measured by pulse oximetry; FVC forced vital capacity; KCO carbon monoxide transfer coefficient. between RHC and PFT influenced the results by analyzing separately patients in the derivation cohort whose PFTs were done within 60 days of their RHC (n 154) and those whose PFTs were done more than 60 days before or after their RHC (n 103). The area under the curve (AUC) was 0.75 in both cases (P 0.97), suggesting that the relationship between PFTs and the presence of PH was similar in the two groups. Influence of low FVC on the multivariable model. The FVC % predicted was not associated with pulmonary artery pressures in multivariable linear regression analysis (P 0.37). However, on separate analyses in patients with low or preserved FVC, a clear difference in the strength of association was seen between those with an FVC 60% predicted (R %; n 54) and those with an FVC 60% predicted (R %; n 203). Autoantibody status. Compared to the simple model described above (DLCO % predicted and SpO 2 ), addition of the ACA status to the model (1 if positive, 0 if negative) was associated with the mpap (P 0.006) and improved the model fit (R 2 increased from 23.1% to 26.4%). Anti Scl-70 antibody was more tightly associated (R 2 increased from 23.1% to 27.1%, P ), and when both anti Scl-70 and ACA were added to the model, anti Scl-70 remained strongly significant (P ), but ACA became nonsignificant (P 0.43). Testing the formula in the validation cohort. Applying the simplified formula to the validation cohort (n 129) gave an AUC of 0.75 (95% CI 0.67, 0.84), and an R 2 value of 22.3%. A scatterplot showing the correlation between the formula-predicted mpap and the

4 3534 SCHREIBER ET AL Figure 1. Scatterplot showing the correlation between the mean pulmonary artery pressure (mpap) as predicted by the formula and the mpap as measured during right-sided heart catheterization (RHC) in the validation cohort of patients with systemic sclerosis. actual mpap measured at the time of RHC is shown in Figure 1. Using a predicted threshold of 25 mm Hg for diagnosing PH, the sensitivity was 90.1% (95% CI 82, 96), the specificity was 29.2% (95% CI 17, 44), the positive likelihood ratio was 1.3 (95% CI 1.05, 1.55), and the negative likelihood ratio was 0.3 (95% CI 0.2, 0.7). A Bland-Altman plot of the data showed a mean difference between the predicted and measured mpap of 1.1 (95% CI 20.4, 22.6). As can be seen in Figure 2, there was better agreement for lower mpap values and a trend toward underestimation for very high mpap values. Using a predicted threshold of 30 mm Hg for PH diagnosis, the sensitivity was 59.3% (95% CI 47.8, 70.1) and the specificity was 70.8% (95% CI 55.9, 83.0). Using a predicted threshold of 35 mm Hg, the sensitivity fell to 25.9% (95% CI 16.8, 36.9), but the specificity rose to 97.9% (95% CI 88.9, 99.9). We can therefore estimate the positive predictive value of the formula in a typical scleroderma cohort with a prevalence of 12% (1). Patients with a formulapredicted mpap 25 mm Hg have a low prevalence of PH (4.4% [95% CI 2.1, 9.3]). Those with an intermediate formula-predicted mpap of mm Hg have an average risk of PH (11.3% [95% CI 9.0, 14.1]). Those with a formula-predicted mpap 35 mm Hg have a high prevalence of PH (62.9% [95% CI 19.1, 92.4]). Thus, a formula-predicted mpap below 25 mm Hg indicates a group with a low prevalence of PH, a formula-predicted mpap of mm Hg indicates a group with average prevalence, and a formula-predicted mpap above 35 mm Hg indicates a group with a high prevalence. Subgroup comparisons. We explored the relationship between the predicted and measured mpap values in subgroups within our combined study cohort. We examined the effect of disease subtype, autoantibody status, restrictive lung disease, increased PCWP, functional class, and smoking status on the relationship between predicted and measured mpap (Table 2). It is notable that the formula performed well in patients with ACAs, with an AUC of 0.87 (95% CI 0.80, 0.94). This may be because patients with ACAs have less restrictive lung disease than other patients (mean FVC % predicted 100.2% in patients with ACAs versus 77.8% in patients without ACAs; P ). Hypoxia in patients with ACAs is therefore likely to directly reflect pulmonary vasculopathy. The prevalence of PH was not significantly higher in patients with ACAs than in those without (68.1% versus 60.7%; P 0.17). Of the total 386 patients in our study, 34 had an increased PCWP ( 15 mm Hg). The AUC in these patients (0.60 [95% CI 0.37, 0.82]) was not different from that in patients with a normal PCWP (0.77 [95% CI 0.72, 0.82]; P 0.15). The sensitivity and specificity of the formula for predicting PH in the validation cohort were not significantly affected by the removal of the patients with a high PCWP (sensitivity 93.0% and specificity 37.1% without these patients compared to sensitivity 91.4% and specificity 37.9% overall). In 243 SSc patients, PH was identified at the time of their RHC (mpap 25 mm Hg). We analyzed the performance of our formula in identifying pulmonary Figure 2. Bland-Altman plot showing the level of agreement between the formula-predicted mean pulmonary artery pressure (mpap) and the measured mpap in the validation cohort of patients with systemic sclerosis.

5 DERIVATION AND VALIDATION OF A FORMULA TO DETECT PH IN SSc 3535 Table 2. Performance of our formula for predicting pulmonary artery pressures based on oxygen saturation and DLCO in various subgroups of patients with SSc* No. of patients AUC (95% CI) Sensitivity, % (95% CI) Specificity, % (95% CI) R 2,% Patient group All SSc patients (0.70, 0.80) 90.9 (86.6, 94.2) 35.0 (27.2, 43.4) 22.8 lcssc patients (0.70, 0.81) 91.1 (86.0, 94.8) 34.6 (25.6, 44.6) 26.6 dcssc patients (0.64, 0.85) 89.3 (78.1, 96.0) 37.1 (21.5, 55.1) 10.0 Serologic subtype ACA (0.80, 0.94) 89.9 (81.0, 95.5) 62.2 (44.8, 77.5) 41.8 Scl-70 antibody (0.64, 0.87) 96.9 (83.8, 99.9) 13.5 (4.54, 28.8) 23.0 FVC 80% (0.61, 0.77) 95.6 (90.0, 98.5) 13.0 (5.4, 24.9) % (0.73, 0.85) 86.9 (79.9, 92.2) 48.3 (37.6, 59.2) 29.6 Increased PCWP (0.37, 0.82) 76.9 (56.4, 91.0) 50.0 (15.7, 84.3) 17.8 Smoking status Past and present smokers (0.63, 0.82) 94.3 (87.2, 98.1) 34.8 (21.4, 50.2) 10.1 Nonsmokers (0.68, 0.82) 89.9 (81.7, 95.3) 35.9 (26.1, 46.5) 19.2 Functional class Class I II (0.53, 0.71) 78.9 (62.7, 90.4) 44.8 (31.7, 58.5) 5.1 Class III IV (0.52, 0.65) 92.7 (88, 95.9) 24.5 (13.3, 38.9) 17.4 *DLCO diffusing capacity for carbon monoxide; SSc systemic sclerosis; AUC area under the curve; 95% CI 95% confidence interval; lcssc limited cutaneous SSc; dcssc diffuse cutaneous SSc; ACA anticentromere antibody; FVC forced vital capacity; PCWP pulmonary capillary wedge pressure. arterial hypertension (defined as an mpap 25 mm Hg, a PCWP 15 mm Hg, and an FVC 80% predicted to exclude major interstitial lung disease). Of the total group of 386 study patients, 115 met this definition. In these patients, correlation between the predicted mpap and the actual PAP yielded an R 2 value of 0.45% compared to 0.35% in the 243 patients with PH. To clarify the effect of interstitial lung disease on the formula, we analyzed separately the performance of the formula in a subgroup of patients in the validation cohort with no significant restrictive lung disease (FVC 80% predicted) and no interstitial lung disease on computed tomography of the chest. The overall performance of the formula was not significantly improved in this group without significant lung disease (AUC 0.79 [95% CI 0.69, 0.88]) as compared to the rest of the validation cohort (AUC 0.74 [95% CI 0.67, 0.82]; P 0.49 for the comparison). The incidence of PH was not different in these two groups (66.0% in patients without lung disease versus 61.2% in those with lung disease; P 0.45). Comparison to echocardiography. Data from echocardiography performed within 3 months of the RHC were available in 96 patients (63 in the derivation cohort and 33 in the validation cohort). In these patients, the AUC for the echo-derived tricuspid valve gradient (AUC 0.73 [95% CI 0.63, 0.83]) was similar to that for the new formula in the same patients (AUC 0.75 [95% CI 0.64, 0.86]; P 0.78). There was similarly no difference between the AUCs for echocardiography and the formula in the 63 patients in the derivation cohort alone (P 0.93). Table 3. Performance of our formula in patients with equivocal echocardiographic findings and comparison to echocardiographic results* TRV max 2.8 meters/second meters/second Formula mpap threshold, mm Hg No. of patients Sensitivity, % (95% CI) 93.8 (69.8, 99.8) 92 (74, 99) Specificity, % (95% CI) 31.3 (11, 58.7) 52.6 (28.9, 75.6) Positive LR (95% CI) 1.36 (0.95, 1.94) 1.94 (1.19, 3.16) Negative LR (95% CI) 0.2 (0.03, 1.5) 0.15 (0.04, 0.61) * TRV max maximum tricuspid regurgitation jet velocity; mpap mean pulmonary artery pressure; 95% CI 95% confidence interval; LR likelihood ratio.

6 3536 SCHREIBER ET AL The recent European guidelines for the diagnosis and treatment of PH suggest that a TRV max of meters/second is equivocal for the presence of PH (4). In our cohort, a TRV max 3.4 meters/second was strongly predictive of PH, with a sensitivity of 31.7% (95% CI 20.3, 45), a specificity of 97.2% (95% CI 85.9, 99.9), a positive likelihood ratio of 11.4 (95% CI 1.6, 81.6), and a negative likelihood ratio of 0.70 (95% CI 0.6, 0.84). The high specificity of a TRV max 3.4 meters/second means that the PFT formula is not required in those patients. Among patients with equivocal echocardiographic findings (TRV max 3.4 meters/second), a formula-predicted mpap of 25 mm Hg was associated with an odds ratio of 9.5 (95% CI 2.5, 36.7, P 0.001; n 76) for the presence of PH (Table 3). In our PH-enriched population of 96 patients with echocardiographic data who underwent RHC, 60 had PH. Of these, only 19 had a TRV max 3.4 meters/ second. An additional 38 patients had a formulapredicted mpap 25 mm Hg. Thus, only 4 of the 60 patients had a TRV max 3.4 meters/second and a formula-predicted mpap 25 mm Hg. In contrast, in the 36 patients who did not have PH, only 1 had a TRV max 3.4 meters/second. A further 16 patients had a formula-predicted mpap 25 mm Hg. In this cohort, selecting patients with a TRV max 3.4 meters/second or a formula-predicted mpap 25 mm Hg had a sensitivity of 95% (95% CI 86.1, 99), a specificity of 41.7% (95% CI 25.5, 59.2), a positive likelihood ratio of 1.63 (95% CI 1.23, 2.16), and a negative likelihood ratio of 0.12 (95% CI 0.04, 0.40) for the presence of PH. If used in a screening context to interpret annual echocardiographic and PFT results in a scleroderma patient population with a prevalence of PH of 12%, a TRV max threshold 3.4 meters/second or a formulapredicted mpap threshold 25 mm Hg had a positive predictive value of 18.2% (95% CI 14.3, 22.7) and a negative predictive value of 98.4% (95% CI 95, 99.5) for the presence of PH. Choosing the higher threshold of 3.4 meters/second for the TRV max or 35 mm Hg for the formula-predicted mpap was associated with a much higher positive predictive value of 41.5% (95% CI 18.8, 68.5) and negative predictive value of 92.2% (95% CI 90.3, 93.8) Validation of the PFT formula in nonscleroderma CTDs. We tested the formula in 52 patients with non-ssc CTDs who underwent their first RHC at the same time in our institution. Application of the formula in this population gave an AUC of 0.64 (95% CI 0.45, 0.84). Using a threshold of 25 mm Hg, the sensitivity was 92.9%, the specificity was 18.2%, the positive likelihood ratio was 1.1, and the negative likelihood ratio was 0.4. The high sensitivity is encouraging, but the positive predictive value will depend on the prevalence of PH in the population under consideration. Performance of other proposed formulae. In our entire study population (n 386), we compared the performance of our formula and recommended thresholds with that of other methods. Our formula, with an AUC of 0.75 (95% CI 0.70, 0.80) performed significantly better than the FVC/DLCO, with an AUC of 0.66 (P 0.001) (7). It also performed better than the formula derived and validated in a cohort of patients with idiopathic pulmonary fibrosis (AUC 0.71 [95% CI 0.66, 0.76]; P 0.08) (8). Finally, our formula performed better than the DLCO/alveolar volume (VA) (AUC 0.70 [95% CI 0.65, 0.76]; P 0.08) (9). DISCUSSION In our large cohort of patients with SSc, we derived and validated a formula for improving the detection of pulmonary hypertension using pulse oximetry and the diffusing capacity for carbon monoxide, as follows: predicted mpap 136 SpO DLCO % predicted. A simple nomogram may be used to quickly establish whether a patient is in the low-risk (predicted mpap 25 mm Hg), average risk (predicted mpap mm Hg), or high-risk (predicted mpap 35 mm Hg) category (Figure 3). Echocardiography is widely used as the primary noninvasive test of pulmonary artery pressures. Analysis of echocardiographic data in our patients showed that Figure 3. Nomogram showing the risk of pulmonary hypertension in patients with systemic sclerosis according to a formula-predicted mean pulmonary artery pressure (mpap) of 25 mm Hg (low risk), mm Hg (average risk), or 35 mm Hg (high risk), based on the diffusing capacity for carbon monoxide (DLCO) and oxygen saturation.

7 DERIVATION AND VALIDATION OF A FORMULA TO DETECT PH IN SSc 3537 the two tests provide complementary information and that information derived from PFTs is as predictive as information derived from echocardiography. Our analysis combining echocardiography with pulmonary function testing shows a high prevalence of PH in patients with a high result for either analysis and suggests that patients with a TRV max 3.4 meters/second or a formula-predicted mpap 35 mm Hg should be considered for RHC. Those with low-risk findings on echocardiography and lung function testing (TRV max 3.4 meters/second and a formula-predicted mpap 25 mm Hg) can be reassured, and a longer followup period may be considered. There remains a group of patients with intermediate results on echocardiography and on pulmonary function testing who remain at average risk of PH. Our analysis was geared particularly toward establishing the diagnostic value of PFTs in detecting PH in patients with SSc. In this real-life cohort, different causes of PH were diagnosed, including PH associated with respiratory disease (group 3) and with cardiac disease (group 2), as well as pulmonary arterial hypertension (group 1). The diagnosis of PH is important in all patients. In patients with interstitial lung disease, those with PH have a worse prognosis than those without, and diagnosis aids in counseling and planning (10). Identifying PH due to left-sided heart disease is important because the condition is common, the prognosis is poor, and treatment should be targeted to the left-sided heart disease (4,11). Thus, detection of PH is important in all patients who have SSc. The formula explains only 23% of the variability in mpap, which is reflected in the wide confidence intervals for agreement between the predicted and measured mpap. These confidence intervals are very similar to those for echocardiography performed at the time of RHC (12). However, the receiver operating characteristics show that the lung function formula performs well at predicting the presence or absence of PH. We have shown that in patients with ACAs, the formula performs particularly well. The difference in antibody subgroups may be partly explained by a higher prevalence of interstitial lung disease in patients with anti Scl-70 antibodies. However, correcting for the FVC does not improve the model. The differences may be due to lung changes that are not reflected in the FVC, or the FVC may be reduced in patients with pulmonary arterial hypertension who are breathless. Our analysis showed that a disproportionate reduction in oxygen saturation for a given DLCO % predicted is seen in patients with PH. This mirrors the findings reported by Zissman et al (8) in patients with idiopathic interstitial lung disease. It seems that oxygen saturation and DLCO are influenced by somewhat different physiologic components, each of which affects pulmonary artery pressures. The DLCO correlates with the extent of fibrosis on high-resolution computed tomography in SSc (13). Hypoxemia may, in addition, relate to severe vascular intimal remodeling and obliteration causing ventilation/perfusion mismatch and to shunting through a patent foramen ovale. Pulmonary vasculopathy may be associated with impaired hypoxia-induced vasoconstriction, which profoundly amplifies ventilation/ perfusion mismatch, causing disproportionate hypoxia (14). Our results can be compared with those of other reported studies. A high ratio of FVC to DLCO % predicted was associated with pulmonary arterial hypertension in 49 patients with SSc (7), and a low DLCO/VA was associated with the subsequent development of PH in 110 SSc patients in France (9). In patients with idiopathic pulmonary fibrosis, a formula based on PFTs and oxygen saturation was shown to help in the detection of PH (8). One recent study found no significant correlation between PFTs and pulmonary hemodynamics on RHC, although oxygen saturation was not included in the analysis (15). Echocardiography is a recommended tool for the noninvasive diagnosis of PH (4,16). However, echocardiography cannot assess lung disease and is not always technically possible. Even when performed within hours of the RHC, there is only moderate agreement between echocardiography and the RHC mpap values, and false-negative results are common (7,12). Patients with scleroderma require regular pulmonary function testing including assessment of the DLCO to screen for interstitial lung disease (17 19). Using our formula, these tests can be used to detect patients who are more likely to have PH, thus improving the clinical utility and costeffectiveness of PFTs. Strengths of our study include the large patient cohort, inclusion of clinical diagnoses, serologic assessments, and oxygen saturation assessments, as well as the completeness of the RHC data. In addition, using splitsample validation, we were able to derive and validate our formula in separate but comparable patient groups. We also tested the formula in a different patient population with CTDs other than scleroderma. Our study had limitations. It was a retrospective study, with the associated limitations of missing data,

8 3538 SCHREIBER ET AL especially echocardiograms and PFTs, and of lack of rigorous selection of patients for RHC. We did not have echocardiographic data for most patients, and there may have been selection bias in the patients who had a second echocardiography performed at our hospital. So, the comparison to echocardiographic findings was limited and should be repeated in a prospective cohort. The prevalence of PH in the cohort was 63%, which indicates that the current selection of patients for RHC is fairly effective in our institution. Use of our formula would allow for further refinement. Patients without PFT data were excluded from the study. However, this is unlikely to have affected the results because the patients who were excluded had a similar incidence and severity of PH as those who were included. We did not include high-resolution computed tomography data, and we used the FVC % predicted to distinguish patients with pulmonary arterial hypertension from those with pulmonary hypertension due to lung disease. We performed a subgroup analysis for patients with preserved FVC and for patients with a normal PCWP. It is not known whether subtle interstitial lung disease in patients with PFT findings within the normal range may be sufficient to cause PH. Further work should explore combining PFT data with other noninvasive information, such as symptoms, 6-minute walking distance, and brain natriuretic peptide levels. We plan further prospective validation of this formula in patients recruited into the Detection of Pulmonary Artery Hypertension in Systemic Sclerosis (DETECT) study, a prospective observational cohort study to evaluate screening tests in scleroderma patients. Other important initiatives, including the Pulmonary Hypertension Registry of Scleroderma (PHAROS) and the ItinerAIR Scleroderma Study Group, are furthering our understanding of the clinical utility of noninvasive testing for earlier detection of PH. In conclusion, we have shown that PFTs and pulse oximetry are useful in screening for PH, providing information that is complementary to that obtained by echocardiography. Patients at high risk based on the findings of PFTs should be considered for RHC even if the findings of echocardiography are reassuring. Patients at low risk based on the findings of echocardiography and the findings of PFTs are very unlikely to have PH. Use of this simple formula may assist in the interpretation of results of PFTs performed as part of the routine screening protocol in patients with SSc as well as in better selection of patients for RHC. Addendum. Since the time this article was accepted for publication, another group of investigators published a formula based on the findings of pulmonary function tests, the Cochin Risk Prediction Score (RPS) (20). Application of the Cochin RPS to the data derived from our study cohort yielded an AUC of 0.73 (95% CI 0.68, 0.78) for detecting prevalent pulmonary hypertension. AUTHOR CONTRIBUTIONS All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Dr. Schreiber 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. Study conception and design. Schreiber, Keir, Handler, Wells, Denton, Coghlan. Acquisition of data. Schreiber, Valerio, Keir, Handler, Coghlan. Analysis and interpretation of data. Schreiber, Keir, Handler, Wells, Denton, Coghlan. REFERENCES 1. 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