Evidence map›Paper›PMID 40514591›Full record

ArticleNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2025

Development and multi-center validation of a high-performance predictive model for early detection of cognitive impairment in older adults: data-based on communities in Northern China.

Ming-Lin Li, Fei Zhang, Le-Tian Huang, Jia-He Wang

Abstract readMulticenter StudyValidation Study
PubMed Publisher
In one paragraph

Article in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2025. 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

4 authors.

Ming-Lin LiDepartment of Family Medicine, The Sixth Affiliated Hospital, School of Medicine, South China University of Technology, Foshan, Guangdong Province, China.ORCID http://orcid.org/0000-0002-7658-2639
Fei ZhangDepartment of Family Medicine, Shengjing Hospital of China Medical University, 39 Huaxiang Street, Tiexi District, Shenyang City, Liaoning Province, China.
Le-Tian HuangDepartment of Oncology, Shengjing Hospital of China Medical University, 39 Huaxiang Street, Tiexi District, Shenyang City, Liaoning Province, China. letian91k@163.com.
Jia-He WangDepartment of Family Medicine, Shengjing Hospital of China Medical University, 39 Huaxiang Street, Tiexi District, Shenyang City, Liaoning Province, China. wangjh1@sj-hospital.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe prevalence of cognitive impairment (CI) is progressively rising. Insufficient awareness, complex diagnostic procedures, and the absence of effective treatment methods contribute to the challenge. Therefore, our objective is to develop and validate a CI screening model specifically for the elderly population in China.

methodsThe multi-center cross-sectional study included 18 communities in four provinces, and data collection was performed on basic parameters, medical history, physical measurements, cognitive assessments, and blood sample tests. The least absolute shrinkage and selection operator (Lasso) and multiple-factor logistic regression analysis were used to select variables with best predictive performance to build the model that minimizes the mean squared error and build a nomogram of the CI screening model. The Receiver Operating Characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) are utilized to assess the discriminative ability and clinical utility of the model. Both internal and external datasets are utilized for model validation.

resultsThe development set of the model included a total of 1,479 participants, among whom 278 were identified as having cognitive impairment (CI), with a prevalence of 18.8%. Eight predictive factors for CI were selected as the final model, including age, falls times in 1-year, educational level, place of residence, hypertension, stroke, protein intake, and subcutaneous fat loss. The area under the curve (AUC) of the model was 0.873, indicating good discriminative ability. The calibration curve demonstrated good consistency between predicted and observed results. DCA and CIC analysis showed that the model had favorable clinical utility. The AUC, calibration curve, DCA, and CIC of both internal and external validation sets showed consistent results with the development set, indicating good consistency.

conclusionsWe developed an accurate and valid nomogram of CI screening with all the required factors easily available and four of them modifiable.

Indexed as

Cognitive DysfunctionAgedAged, 80 and overChinaCross-Sectional StudiesEarly DiagnosisFemaleHumansMaleMiddle AgedNomogramsROC CurveCognitive impairmentDynamic nomogramMulticenter studyNorthern ChinaOlder adultsPrediction model

Identifiers

PMID40514591

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.