Evidence map›Paper›PMID 38751887›Full record

ArticleFrontiers in neurology2024

A nomogram for predicting cerebral white matter lesions in elderly men.

Yi-Feng Pei, Xian-Dong Li, Quan-Ying Liu, Chu-Wei Zhang, Yi-Han Wang, Ming-Rui Chen, Hui-Sheng Chen

Abstract read
In one paragraph

Article in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yi-Feng PeiDepartment of Neurology, General Hospital of Northern Theater Command, Shenyang, China.
Xian-Dong LiDepartment of Neurology, General Hospital of Northern Theater Command, Shenyang, China.
Quan-Ying LiuDepartment of Neurology, General Hospital of Northern Theater Command, Shenyang, China.
Chu-Wei ZhangDepartment of Neurology, General Hospital of Northern Theater Command, Shenyang, China.
Yi-Han WangDepartment of Neurology, General Hospital of Northern Theater Command, Shenyang, China.
Ming-Rui ChenDepartment of Neurology, General Hospital of Northern Theater Command, Shenyang, China.
Hui-Sheng ChenDepartment of Neurology, General Hospital of Northern Theater Command, Shenyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to develop a nomogram tool to predict cerebral white matter lesions (WMLs) in elderly men. Methods: Based on a retrospective cohort from January 2017 to December 2019, a multivariate logistic analysis was performed to construct a nomogram for predicting WMLs. The nomogram was further validated using a follow-up cohort between January 2020 and December 2022. The calibration curve, receiver operating characteristics (ROC) curves, and the decision curves analysis (DCA) were used to evaluate discrimination and calibration of this nomogram. Result: A total of 436 male patients were enrolled in this study, and all 436 patients were used as the training cohort and 163 follow-up patients as the validation cohort. A multivariate logistic analysis showed that age, cystatin C, uric acid, total cholesterol, platelet, and the use of antiplatelet drugs were independently associated with WMLs. Based on these variables, a nomogram was developed. The nomogram displayed excellent predictive power with the area under the ROC curve of 0.951 [95% confidence interval (CI), 0.929-0.972] in the training cohort and 0.915 (95% CI, 0.864-0.966) in the validation cohort. The calibration of the nomogram was also good, as indicated by the Hosmer-Lemeshow test with Conclusion: We have developed and validated a novel nomogram tool for identifying elderly men at high risk of WMLs, which exhibits excellent predictive power, discrimination, and calibration.

Indexed as

cerebral white matter lesionselderlymalenomogramprediction model

Identifiers

PMID38751887
PMCPMC11094237

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.