Inflammatory Indices and Left Atrial Thrombus in 623 Atrial Fibrillation Patients

A cross-sectional study of 623 patients with non-valvular atrial fibrillation, including 59 with left atrial thrombus. The dataset evaluates eight complete blood count-derived inflammatory indices for their association with thrombus risk. It was authored by You Zhou and published in 2026.

Use Cases

• Assess the predictive value of neutrophil-to-lymphocyte ratio (NLR) and systemic immune-inflammation index (SII) for left atrial thrombus risk using logistic regression.
• Compare the area under the curve (AUC) performance of neutrophil-to-mean platelet volume ratio (NMR), systemic inflammation response index (SIRI), and pan-immune-inflammation value (PIV) via ROC analysis.
• Investigate why monocyte-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and white blood cell-to-mean platelet volume ratio showed no significant association with thrombus presence.
• Build a multivariable risk model incorporating the five significant inflammatory indices (NLR, NMR, SII, SIRI, PIV) identified in the study.

Strengths

• Includes data from 623 patient records with a clearly defined outcome group of 59 left atrial thrombus cases.
• Evaluates eight distinct inflammatory indices derived from routine complete blood count data.
• Provides multivariable logistic regression and ROC curve analysis results with AUC values ranging from 0.582 to 0.601 for significant indices.

Limitations

• The dataset is small at 39.0 KB, likely containing summary results rather than the raw patient-level data.
• Cross-sectional design limits causal inference regarding inflammatory indices and thrombus formation.
• Moderate predictive accuracy of identified indices, with AUCs below 0.65, indicating limited discriminative power.

Provenance

Source

figshare, authored by You Zhou.

Collection Method

Cross-sectional study of patients with non-valvular atrial fibrillation.

Time Range

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Freshness

Last updated in March 2026.

Geography

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