Table 4_Comparative evaluation of composite inflammatory indices for predicting prostate c

Data from the National Health and Nutrition Examination Survey (NHANES) 1999–2018, including 14,562 participants, was analyzed to evaluate associations between 14 composite inflammatory indices and prostate cancer risk and mortality. The study, authored by Hengxin Chen and published on figshare, also includes external validation using a Chinese hospital-based cohort and employs machine learning models for prognostic prediction.

Use Cases

• Evaluating the association between systemic immune-inflammation index (SII) and prostate cancer risk.
• Assessing the prognostic value of neutrophil-to-lymphocyte ratio (NLR) for all-cause mortality in prostate cancer patients.
• Validating the predictive performance of platelet-to-lymphocyte ratio (PLR) using external cohort data.
• Exploring non-linear relationships between inflammatory indices and disease incidence using restricted cubic spline analysis.
• Training machine learning models for prognosis based on inflammation-related hematological features.

Strengths

• Analyzes data from a large, nationally representative survey (NHANES) with 14,562 participants.
• Evaluates 14 distinct composite inflammatory indices, including SII, PLR, NLR, LMR, PNI, HALP, MHR, NPR, SIRI, AISI, CAR, CLR, and IBI.
• Includes external validation using a separate Chinese hospital-based cohort.
• Employs multiple statistical methods, including multivariable regression, Cox models, and machine learning.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the primary dataset is unknown, which may limit suitability assessment.
• Data may reflect geographic and temporal bias inherent to the NHANES and single external cohort sources.

Provenance

Source

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

Collection Method

Analysis of survey 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

1999–2018

Freshness

Last updated 2026-05-11 04:23:39; freshness should be verified.

Geography

United States (NHANES) and China (external validation cohort).