ReviewDiagnostics (Basel, Switzerland)2021
Screening for Pulmonary Hypertension in Systemic Sclerosis-A Primer for Cardio-Rheumatology Clinics.
Review in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- Predictive modeling of cardiovascular risk in systemic sclerosis: a single-center retrospective study integrating clinical and imaging data.Clinical rheumatology · 2026Article
- Emphysema elevates the DETECT scores: impact on pulmonary hypertension screening and diagnosis in systemic sclerosis.Rheumatology (Oxford, England) · 2025Article
- Leveraging Artificial Intelligence for the Diagnosis of Systemic Sclerosis Associated Pulmonary Arterial Hypertension: Opportunities, Challenges, and Future Perspectives.Advances in respiratory medicine · 2025Review
- Insights into Pulmonary Arterial Hypertension in Connective Tissue Diseases.Journal of clinical medicine · 2025Review
- A Practical Multidisciplinary Approach to Identifying Interstitial Lung Disease in Systemic Autoimmune Rheumatic Diseases: A Clinician's Narrative Review.Diagnostics (Basel, Switzerland) · 2024Review
- Epidemiology and Treatment of Systemic Sclerosis in Korea.Journal of rheumatic diseases · 2022Review
- Sclerodermic Cardiomyopathy-A State-of-the-Art Review.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Systemic sclerosis (SSc) is a rare disease, with unfavorable clinical course and prognosis, characterized by progressive multisystemic involvement. SSc associated pulmonary hypertension (SSc-PAH) and interstitial lung disease (ILD) are the most important factors for morbi-mortality in these patients, being responsible for more than 60% of total deaths. Though pulmonary arterial hypertension (PAH) is the dominant subtype seen in SSc, PH secondary to ILD, left-heart pathology, and pulmonary veno-occlusive disease (PVOD) are also possible occurrences. Initial evaluation of a SSc case is complex and should be performed with a multidisciplinary approach. Early detection of SSc-PAH is imperative, given the fact that new and effective medications are available and early treatment was shown to improve outcomes. Therefore, screening algorithms must be used adequately and in a cost-effective manner. Sensitivity and negative predictive value (NPV) are the most important performance measures in a screening test. Several algorithms were developed in the last decade (e.g., DETECT and ASIG) and demonstrated higher efficiency when compared to older algorithms. The present manuscript details the risk factors for SSc-PAH and includes a critical description of current detection algorithms, as a primer for clinicians working in the field of cardio-rheumatology.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.