COVID-19 ARDS Risk Prediction Model with Plasma Hyaluronic Acid and Clinical Scores

A retrospective single-center cohort study of 502 adult COVID-19 patients admitted between September 2022 and February 2023. The dataset, authored by Genhua Mu, supports a nomogram for predicting ARDS progression and invasive ventilation need using plasma hyaluronic acid levels and clinical severity scores.

Use Cases

• Predicting ARDS diagnosis in COVID-19 patients based on plasma hyaluronic acid levels and clinical scores.
• Stratifying severe ARDS risk using biomarker levels combined with APACHE II and SOFA scores.
• Assessing endotracheal intubation risk based on multivariable logistic regression models.
• Building and validating clinical nomograms for early patient risk stratification.

Strengths

• Includes data from 502 adult COVID-19 patients, with 141 in the ARDS group.
• Reports strong model performance metrics, including an AUC of 0.904 for ARDS diagnosis and 0.953 for severe ARDS.
• Integrates multiple clinical variables such as age, lymphocyte count, CRP, CK, APACHE II, and SOFA scores.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data is from a single-center retrospective study, which may limit generalizability.

Provenance

Source

figshare

Collection Method

Retrospective single-center cohort study.

Time Range

September 2022 to February 2023

Freshness

Last updated 2026-04-16 05:25:20; freshness should be verified.

Geography

null