Evidence map›Paper›PMID 40191459›Full record

ArticleCurrent treatment options in rheumatology2025

Personalized Medicine for Systemic Sclerosis-Associated Interstitial Lung Disease.

Angela Ma, Sydney B Montesi

Abstract read
In one paragraph

Article in Current treatment options in rheumatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Angela MaDivision of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114.
Sydney B MontesiDivision of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114.

Funding

Advanced Imaging for Pulmonary FibrosisR01HL171240 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Sydney Montesi · 2024 to 2026
$1.8M
Vascular Leak and Outcomes in IPFK23HL150331 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI MONTESI, SYDNEY · 2020 to 2024
$887k
NHLBI NIH HHS K23 HL150331NHLBI NIH HHS R01 HL171240
6 · The paper itself

Abstract

Purpose of the review: Systemic sclerosis (SSc) is a rare immune-mediated connective tissue disease with high morbidity and mortality. Interstitial lung disease (ILD) is now the leading cause of death for patients with SSc. While several therapeutic agents have been approved for SSc-ILD, opportunities remain for a personalized medicine approach to improve patient outcomes. The purpose of this narrative review is to summarize the current state of personalized medicine for SSc-ILD and future directions to facilitate earlier diagnosis, disease stratification, prognostication, and determination of treatment response. We also review opportunities for personalized medicine approaches within clinical trial design for SSc-ILD. Recent findings: The management of SSc-ILD remains challenging due to its variable clinical course and current deficits in predicting which individuals will develop progressive pulmonary fibrosis. There have additionally been many challenges in clinical trial design due to limitations in enrichment strategies. Emerging data suggest that serum, radiologic, and other novel biomarkers could be utilized to assess disease activity and treatment response on an individual level. Summary: Personalized medicine is emerging as a way to address unmet challenges in SSc-ILD and has applicability for identifying stratifying, prognostic, and therapeutic markers for routine clinical care and clinical trial design.

Indexed as

biomarkersinterstitial lung diseasepersonalized medicineprecision medicinesclerodermasystemic sclerosis

Identifiers

PMID40191459
PMCPMC11967446

What OpenQuestion holds

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Registered trials

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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.