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      string(196) "NCT00706082 - A Two-stage Prospective Observational Cohort Study in Scleroderma Patients to Evaluate Screening Tests and the Incidence of Pulmonary Arterial Hypertension and Pulmonary Hypertension"
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  ["project_title"]=>
  string(115) "Reassessment of the 2022 ESC/ERS echocardiographic algorithm to detect pulmonary hypertension in systemic sclerosis"
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  string(622) "Pulmonary hypertension (PH) is a serious condition that can be life-threatening if not detected early. Recent changes in how PH is diagnosed raise concerns about whether current screening methods remain reliable, particularly for patients with systemic sclerosis (SSc), who are at high risk. Using data from the DETECT study, this project evaluates the accuracy of the 2022 ESC/ERS screening algorithm and tries to identify more adequate cut-offs. By comparing it to other strategies, we aim to improve PAH detection, ensuring better care for SSc patients and others at risk, ultimately enhancing public health strategies."
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    ["first_name"]=>
    string(10) "Sébastien"
    ["last_name"]=>
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    ["degree"]=>
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    ["primary_affiliation"]=>
    string(20) "Université de Lille"
    ["email"]=>
    string(30) "sebastien.sanges@univ-lille.fr"
    ["state_or_province"]=>
    string(15) "Hauts-de-France"
    ["country"]=>
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  string(1639) "Background: Pulmonary hypertension (PH) is a severe complication of systemic sclerosis (SSc), requiring early detection through systematic screening. Recent changes in PH diagnosis criteria, lowering the mean pulmonary arterial pressure (mPAP) threshold to 20 mmHg, require reassessment of existing screening strategies. The ESC/ERS 2022 echocardiographic algorithm has been validated with the new definition in high-risk patients but not for systematic screening in asymptomatic SSc patients.

Objective: To evaluate the diagnostic accuracy of the ESC/ERS 2022 echocardiographic algorithm for PH screening in SSc patients using the updated hemodynamic definition.

Study Design: Retrospective analysis of SSc patients from the DETECT study, assessing echocardiographic signs of PH against hemodynamic confirmation via right heart catheterization (RHC).

Participants: All patients from the original DETECT trial (SSc patients that underwent systematic RHC)

Primary/Secondary Outcome Measures:

Primary: Diagnostic accuracy of echocardiographic classification of PH risk by the ESC/ERS 2022 algorithm to predict PH defined by RHC in SSc patients 

Secondary: Diagnostic accuracy of individual echocardiographic parameters to predict PH, as well as various PH subgroups, in SSc patients. Diagnostic accuracy of detection of PH. Creation of a new algorithm inspired by both ESC/ERS and DETECT to predict PH

Statistical Analysis: Computation of diagnostic accuracy metrics (area under receiver operator Characteristic curve (AUC), sensitivity, specificity, positive and negative predictive values)."
  ["project_brief_bg"]=>
  string(2855) "Pulmonary hypertension (PH) is a frequent and severe complication of systemic sclerosis (SSc) (1). Prognosis is improved by early diagnosis, which is facilitated by a systematic annual screening program (2). The optimal screening modality for PH in SSc is still debated, with various strategies developed over the years (3). Recently, the hemodynamic definition of PH has been modified, with a lowering of the mean pulmonary arterial pressure (mPAP) threshold at 20 mmHg, and the introduction of a pulmonary vascular resistance (PVR) criterion for pre-capillary PH (4); requiring a reassessment of previously established screening algorithms.

The DETECT algorithm has been validated as a 2-step strategy to screen for PH in a population of SSc patients that underwent systematic right-heart catheterization (RHC) (5). While originally designed with the previous definition of PH, its performance has recently been re-evaluated on the same cohort using the updated hemodynamic criteria (6). In this setting, the DETECT algorithm was associated with an adequate sensitivity (88%), but with unsatisfying rates of false positives (53%) and false negatives (12%) (6). Moreover, the DETECT algorithm has been validated to detect only patients with group 1 PH (i.e. pulmonary arterial hypertension, PAH), but recent data have suggested that other PH subsets may benefit from dedicated treatment strategies. As such, screening and detecting other PH subgroups may be relevant, but the performance of the DETECT algorithm in this setting is unknown.

The European Society of Cardiology (ESC)/European Respiratory Society (ERS) 2022 guidelines also recommend estimating the echocardiographic probability of PH using a dedicated algorithm, relying on direct (tricuspid regurgitation velocity) and indirect (e.g. right atrial surface, right ventricle dimension and function) signs of elevated pulmonary pressures (4). This algorithm was recently reassessed using the new hemodynamic criteria in a cohort of PH patients referred for RHC, and retained its diagnostic accuracy despite the change in mPAP threshold (7). However, as this evaluation was performed on a population with high pre-test probability (patients addressed to the cath lab), it is unclear whether the same holds true in the case of systematic screening in asymptomatic SSc patients.

The objective of this work is to reassess the performance of the ESC/ERS 2022 echocardiographic algorithm in a cohort of SSc patients that underwent systematic RHC, in order to determine its diagnostic accuracy in this setting. As secondary objectives, we will also assess the performance of the DETECT algorithm for the prediction of other PH subgroups, and try to elaborate an improved screening strategy, inspired by both the ESC/ERS and DETECT algorithms, whilst overcoming their limitations."
  ["project_specific_aims"]=>
  string(816) "The objectives of this project are:

1/ to assess the performance of the current ESC/ERS echocardiographic algorithm in discriminating patients with and without PH, using the updated hemodynamic definition (mPAP value less than 20 mmHg), in a cohort of SSc patients with systematic hemodynamic evaluation

2/ to evaluate the diagnostic accuracy of direct and indirect echocardiographic signs of PH in predicting an mPAP value less than 20 mmHg in this cohort, and identify new optimized cut-offs if necessary

3/ to assess the performance of the DETECT algorithm in classifying patients with various PH subgroups.

4/ to build a new algorithm inspired by both the current ESC/ERS and DETECT algorithms to discriminating patients with and without precapillary PH and help stratify RHC referral"
  ["project_study_design"]=>
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    ["label"]=>
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  ["project_purposes"]=>
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      ["label"]=>
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  ["project_research_methods"]=>
  string(143) "The patient sample for this study will include all the patients from the original DETECT trial. There is no exclusion criterion for this study."
  ["project_main_outcome_measure"]=>
  string(1605) "Outcome measure for objective 1:  Diagnostic accuracy (sensitivity, specificity, positive and negative predictive values) of the classification of the echocardiographic probability of PH by the ESC/ERS 2022 algorithm to predict PH as determined by RHC as gold standard



Outcome measures for objective 2: Diagnostic accuracy (sensitivity, specificity, AUC, positive negative predictive values) of echocardiographic signs of PH (tricuspid regurgitant jet velocity; tricuspid annular plane systolic excursion (TAPSE); right atrium (RA) area; right ventricle (RV) area; pulmonary regurgitant velocity; RV diameter; left ventricle (LV) end-diastolic dimension; LV end-systolic dimension; inferior vena cava; interventricular septum) to predict PH status defined by RHC 



Outcome measures for objective 3: Diagnostic accuracy (sensitivity, specificity, AUC, positive and negative predictive values) of the DETECT algorithm to predict PH (as determined by RHC as gold standard) and independently other sub-groups of PH (global precapillary, isolated precapillary and group 1/2/3) against the rest of the population



Outcome measures for objective 4: Diagnostic accuracy (sensitivity, specificity, positive and negative predictive values and AUC) of the new algorithm that predict precapillary PH. The new algorithm will be defined using all informative variables among the 112 variables of the dataset. We will implement multiple machine learning approach (logistic regression, decision tree, bagging) and define the most performing one using the maximization of the AUC"
  ["project_main_predictor_indep"]=>
  string(1805) "Main predictor for objective 1: 

Classification of the echocardiographic probability of PH by the ESC/ERS 2022 algorithm (3-level categorical variable: low vs. intermediate vs. high risk) and PH status as determined RHC (mPAP value>20 mmg) as gold standard



Main predictors for objective 2: 

Direct and indirect signs of PH (tricuspid regurgitant jet velocity; tricuspid annular plane systolic excursion (TAPSE); right atrium (RA) area; right ventricle (RV) area; pulmonary regurgitant velocity; RV diameter; left ventricle (LV) end-diastolic dimension; LV end-systolic dimension; inferior vena cava; interventricular septum) as measured by echocardiography according to standardized procedures (all quantitative variables) and PH status as determined RHC as gold standard.



Main predictors for objective 3:

PH status as determined RHC (mPAP value>20 mmHg) as gold standard, global precapillary PH (mPAP value>20 mmHg and pulmonary vascular resistance (PVR) >2 WU), isolated precapillary PH (mPAP value>20 mmHg and PVR>2 WU and pulmonary capillary wedge pressure (PCWP)≤15 mmHg), group 1/2/3 PH; FVC/DLCO % predicted; telangiectasias; serum ACA; serum NTproBNP; serum urate; right axis deviation; right atrium (RA) area, tricuspid regurgitant (TR) velocity.  



Main predictors for objective 4:

Direct and indirect signs of PH (tricuspid regurgitant jet velocity; tricuspid annular plane systolic excursion (TAPSE); tricuspid annulus S (Swave), right atrium (RA) area; pulmonary regurgitant velocity; RV diameter; LV diameter; inferior vena cava as measured by echocardiography according to standardized procedures, demographic, clinical, serum laboratory, and ECG variables, and precapillary PH as determined RHC as gold standard."
  ["project_other_variables_interest"]=>
  string(165) "In the context of this study, all 112 variables collected during the DETECT trial will be needed to describe the patient sample and/or adjust the performed analyses."
  ["project_stat_analysis_plan"]=>
  string(5041) "Categorical variables will be described using frequencies and percentages and quantitative variables will be summarized using the mean and standard deviation or median and interquartile range (IQR) in case of non-normal distribution. The normality of distributions will be assessed through graphical representations and the Shapiro-Wilk test. For each variable, the number of missing data will be reported.

To answer to primary objective, a contingency table between classification of ESC/ERS 2022 algorithm and PH status determined by RHC will be established. From this table, the diagnostic accuracy (sensitivity, specificity, negative and positive predictive values) of classification of ESC/ERS 2022 algorithm to predict PH as determined by RHC will be determined with theirs 95% confidence intervals (CIs). Primary analysis will be done by considering high vs. low and intermediate risk pooled together and secondary analysis will be done by considering high and intermediate pooled together vs. low risk. 

To answer to primary objective, the diagnostic performance of each individual echocardiographic parameters included in the ESC/ERS algorithm to predict PH will be determined. The diagnostic accuracy of the pre-specified threshold used in the ESC/ERS algorithm will be calculated with theirs 95%CIs. We will also determine the AUC of each parameters to predicted PH, and determined the optimal cut-off value by maximizing the Youden index; diagnostic accuracy associated with optimal cut-off values will be reported. 

To answer to objective 3, the diagnostic performance of the DETECT algorithm to predict different PH outcomes independently (PH, global precapillary PH, isolated precapillary PH, group 1/2/3 PH) against the rest of the population will be determined with their 95% confidence intervals (CIs).

To answer to objective 4, we will select variables in order to reduce the number of covariates likely to predict precapillary PH outcomes. This strategy will be implemented considering the significance of the covariates in univariate logistic regression models, their clinical utility, availability and colinearity, as well as descriptive statistics. We will then proceed to predictions using a two-step modelling process. The first step will focus solely on echocardiographic variables in order to achieve high specificity. The second step of the modelling incorporates all clinical, demographic, ECG and serum laboratory variables into a single model to achieve high sensibility. The second step will only use data from patients for whom we were unable to establish a diagnosis of precapillary PH at the end of the first step. Diagnostic performance characteristics will be reported for each step of the modelling process.

The modelling-process will compare the performance of three types of statistical model. 

1/ A logistic regression model will be used in both step 1 and 2 to estimate the probability of precapillary PH. The estimated probability will be compared to a specific threshold with the aim of maximizing specificity in the first step and sensitivity in the second step. Thresholds will be defined with the aim to obtain 97% of specificity in step 1 and 97% of sensitivity in step 2. A multiple imputation method using 20 imputed bases will be employed to avoid bias and improve the robustness of the results.

2/ A decision tree model will also be trained on the entire dataset. To ensure optimal performance of this model, hyperparameter tuning will be carried out using 10-fold cross-validation (CV). This process aims to determine the best set of model parameters that will maximize the AUC. We will also add a control parameter for the minimum number of observations in the terminal nodes of the tree in order to avoid overfitting. The PH probability will be estimated from the model and compared with thresholds specific to step 1 and 2, with the aim of maximizing specificity and sensitivity respectively.

3/ An ensemble machine learning method (bagged decision tree) train on the entire dataset will also be used, as inspired by Jing Zhang, et al. (8). Compared with the decision tree algorithm, this model overcomes the heterogeneity of the input data by using a bootstrapping approach. For this model, we will set the number of decision trees to 1,000 and use the same CV approach as that used in the decision tree model. The PH probability will be estimated from the model and compared with thresholds specific to step 1 and 2, with the aim of maximizing specificity and sensitivity respectively; the diagnostic sensitivity and specificity associated with the optimal cut-off values will be reported. 

The ROC curves of multivariable logistic regression, decision tree and bagged decision tree will be displayed on the same graph for visual comparison at each step and for the global process. Additionally, calibration curves could also be plotted on the same graph. 

All analyses will be done using complete available cases"
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  ["project_timeline"]=>
  string(187) "Anticipated start date: October 1st, 2025

Analysis completion date: M6

Manuscript drafted and first submission: M12

Report of results to the YODA project: M12

"
  ["project_dissemination_plan"]=>
  string(413) "The results generated in this project will be presented in national and international conferences of rheumatology (EULAR and ACR), cardiology (ESC and AHA) and pulmonology (ERS and ATS) societies.



They will also be submitted for publication in high-impact, peer-reviewed journals in the same fields (Annals of the Rheumatic Diseases, Arthritis and Rheumatology, European Respiratory Journal, Chest)"
  ["project_bibliography"]=>
  string(2095) "

Lefèvre G, Dauchet L, Hachulla E, Montani D, Sobanski V, Lambert M, et al. Survival and Prognostic Factors in Systemic Sclerosis-Associated Pulmonary Hypertension: A Systematic Review and Meta-Analysis: Survival and Prognosis in SSc-Associated Pulmonary Hypertension. Arthritis Rheum. 2013 Sep;65(9):2412–23.
Hachulla E, Gressin V, Guillevin L, Carpentier P, Diot E, Sibilia J, et al. Early detection of pulmonary arterial hypertension in systemic sclerosis: A French nationwide prospective multicenter study. Arthritis Rheum. 2005 Dec 1;52(12):3792–800.
Hao Y, Thakkar V, Stevens W, Morrisroe K, Prior D, Rabusa C, et al. A comparison of the predictive accuracy of three screening models for pulmonary arterial hypertension in systemic sclerosis. Arthritis Res Ther. 2015 Jan 18;17(1):7.
Humbert M, Kovacs G, Hoeper MM, Badagliacca R, Berger RMF, Brida M, et al. 2022 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension. Eur Heart J. 2022 Oct 11;43(38):3618–731.
Coghlan JG, Denton CP, Grünig E, Bonderman D, Distler O, Khanna D, et al. Evidence-based detection of pulmonary arterial hypertension in systemic sclerosis: the DETECT study. Ann Rheum Dis. 2014 Jul;73(7):1340–9.
Distler O, Bonderman D, Coghlan JG, Denton CP, Grünig E, Khanna D, et al. Performance of DETECT Pulmonary Arterial Hypertension Algorithm According to the Hemodynamic Definition of Pulmonary Arterial Hypertension in the 2022 European Society of Cardiology and the European Respiratory Society Guidelines. Arthritis Rheumatol Hoboken NJ. 2024 May;76(5):777–82.
D’Alto M, Di Maio M, Romeo E, Argiento P, Blasi E, Di Vilio A, et al. Echocardiographic probability of pulmonary hypertension: a validation study. Eur Respir J. 2022 Aug;60(2):2102548.
Jing Zhang, Wuyu Xiong, Jiajuan Yang, Ye Sang, et al. Enhanced machine learning models for predicting one-year mortality in individuals suffering from type A aortic dissection, The Journal of Thoracic and Cardiovascular Surgery 2025;169(4):1191-200.

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    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_highlight_button"]=>
  string(0) ""
  ["request_data_partner"]=>
  string(15) "johnson-johnson"
  ["request_overridden_res"]=>
  string(1) "3"
}
data partner
array(1) {
  [0]=>
  string(15) "johnson-johnson"
}


pi country
array(0) {
}


pi affil
array(0) {
}


products
array(1) {
  [0]=>
  string(3) "n-a"
}


num of trials
array(1) {
  [0]=>
  string(1) "1"
}


res
array(1) {
  [0]=>
  string(1) "3"
}