Systemic Sclerosis Patient Biomarkers for Lung Phenotype Differentiation

Huichen Luo's dataset contains clinical and laboratory data for 314 consecutive systemic sclerosis inpatients from 2018 to 2023. The data includes inflammatory, immune, and nutritional markers used to differentiate between lung phenotypes like interstitial lung disease and pulmonary arterial hypertension. The study performed group comparisons, multinomial logistic regression, and k-means clustering on the biomarker profiles.

Use Cases

• Train classification models to predict lung phenotypes based on inflammatory markers like ESR and hs-CRP.
• Perform unsupervised clustering to identify patient subgroups driven by inflammation, mucosal immunity, and metabolic dysregulation.
• Build multivariable regression models to assess associations between biomarkers and disease outcomes.
• Analyze the co-occurrence of autoantibodies like anti-Scl-70 with specific lung involvement patterns.

Strengths

• Includes data from 314 patients, a substantial sample size for a clinical study.
• Contains a wide range of derived biomarkers, including NLR, PLR, SII, and SIRI.
• Analysis includes internal validation and clustering stability metrics, such as an Adjusted Rand Index of 1.00.
• Covers four distinct lung phenotype groups for comparative analysis.

Limitations

• Row count for the underlying data table is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• One phenotype group (ILD-/PAH+) had a small sample size of 12 patients and was excluded from some analyses.

Provenance

Source

Huichen Luo via figshare.

Collection Method

Retrospective cross-sectional study of consecutive inpatients.

Time Range

2018–2023

Freshness

Last updated 2026-04-14 04:19:43; freshness should be verified.