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

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.

Authors

Institutions

Publication Details

Journal
Frontiers in Medicine
Published
2025-07-30
DOI
https://doi.org/10.3389/fmed.2025.1612403
Primary Topic
Nutrition and Health in Aging
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Wenqin Tan, Xiaofang Li, Yang Luo, Shiping Liu et al.
1 links
Frontiers in Medicine
Nutrition and Health in Aging
article

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

Wenqin Tan, Xiaofang Li, Yang Luo, Shiping Liu, Zhao Jin, Xingfu Fan
article en

Abstract

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.

Frontiers in MedicineVol. 12
North Sichuan Medical University (CN), Affiliated Hospital of North Sichuan Medical College (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 76%
Nutrition and Health in Aging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.