Composite Inflammatory Indices for Prostate Cancer Risk and Mortality Prediction

Hengxin Chen analyzed data from the National Health and Nutrition Examination Survey (NHANES) 1999–2018, including 14,562 participants. The study evaluates associations between 14 composite inflammatory indices and prostate cancer risk and all-cause mortality. External validation was performed using a Chinese hospital-based cohort.

Use Cases

• Evaluating the predictive value of the Systemic Immune-Inflammation Index (SII) for prostate cancer risk based on NHANES data.
• Assessing the association between the Platelet-to-Lymphocyte Ratio (PLR) and all-cause mortality in prostate cancer patients.
• Training machine learning models for prognostic prediction using inflammation-related hematological features mentioned in the study.
• Validating the prognostic relevance of the Neutrophil-to-Lymphocyte Ratio (NLR) across different populations.

Strengths

• Dataset is based on a large, nationally representative sample of 14,562 participants from NHANES.
• Analysis includes 14 distinct composite inflammatory indices, such as SII, PLR, NLR, LMR, PNI, HALP, MHR, NPR, SIRI, AISI, CAR, CLR, and IBI.
• Findings were externally validated using a separate Chinese hospital-based cohort.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small at 10.8 KB, indicating limited scope, likely containing summary or analysis results rather than raw patient-level data.

Provenance

Source

National Health and Nutrition Examination Survey (NHANES) and a Chinese hospital-based cohort.

Collection Method

Analysis of NHANES data using multivariable logistic regression, Cox proportional hazards models, generalized estimating equations, generalized additive models, restricted cubic splines, Kaplan-Meier survival analysis, and machine learning models.

Time Range

NHANES data covers 1999–2018.

Freshness

Last updated 2026-05-11 04:23:44; freshness should be verified.

Geography

Primary data from the United States (NHANES), with external validation from China.