Development of a clinical nomogram for predicting sarcopenia in patients with chronic obstructive pulmonary disease based on NHANES data

TL;DR

This study creates a predictive model to identify sarcopenia in COPD patients using data from NHANES, aiming to enable early intervention.

Contribution

A novel clinical nomogram is developed for early sarcopenia prediction in COPD patients using NHANES data.

Findings

The nomogram includes four predictors: gender, height, BMI, and WWI.

The model showed strong predictive performance with AUCs of 0.94 (training) and 0.91 (validation).

The model's clinical applicability was thoroughly validated.

Abstract

The prevalence of sarcopenia in COPD patients is high, and the mutual influence between COPD and sarcopenia creates a vicious cycle. The goal of this research is to create a nomogram model that can forecast when sarcopenia will strike people with COPD.

2011 to 2018 data were retrieved from four NHANES database cycles. The 7:3 proportion was applied to split the dataset randomly to separate validation and training datasets. Multivariate logistical regression and LASSO regression were applied to design nomogram design and to select predictors. In addition, multicollinearity existence among final predictor variables remaining in model were tested, among other variables. Calibration curve, decision curve analysis (DCA), and area under receiver operating characteristic curve (AUC) were applied in testing performance in prediction model.

The nomogram was constructed based on four predictive factors: gender, height, BMI, and WWI. The AUC for the training set was 0.94 (95% CI 0.91–0.97), and the AUC for the validation set was 0.91 (95% CI 0.83–0.98), indicating excellent predictive performance. Furthermore, the clinical applicability of the model has been thoroughly validated.

We established a nomogram model to provide an easy and convenient way for early screening of sarcopenia in COPD patients, and to allow for effective guidance to perform early intervention and manage patient prognosis in an optimal way.

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