Evidence map›Paper›PMID 37072704›Full record

ArticleBMC psychiatry2023

Construction and validation of cognitive frailty risk prediction model for elderly patients with multimorbidity in Chinese community based on non-traditional factors.

Shuzhi Peng, Juan Zhou, Shuzhen Xiong, Xingyue Liu, Mengyun Pei, Ying Wang, Xiaodong Wang, Peng Zhang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 3 pooled it
4.3field-weighted citation impact, top 5% of its field
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

9 citing papers in PubMed, 3 syntheses or guidelines pooled it, 24 citations in OpenAlex.

  1. Pooled it
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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

8 authors at 4 institutions in 1 country.

Shuzhi PengGraduate School, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Juan ZhouNursing Department, Funing People's Hospital, Jiangsu, China.
Shuzhen XiongICU, Funing People's Hospital, Jiangsu, China.
Xingyue LiuGraduate School, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Mengyun PeiGraduate School, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Ying WangGraduate School, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Xiaodong WangDepartment of Nephrology, Shuguang Hospital Affiliated, Shanghai University of Traditional Chinese Medicine, Shanghai, China. wxdong1223@shutcm.edu.cn.
Peng ZhangSchool of Management, Hainan Medical University, Haikou, China. zhangp@sumhs.edu.cn.
Shanghai University of Medicine and Health Sciences · CNHainan Medical University · CNShanghai University of Traditional Chinese Medicine · CNShuguang Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesEarly identification of risk factors and timely intervention can reduce the occurrence of cognitive frailty in elderly patients with multimorbidity and improve their quality of life. To explore the risk factors, a risk prediction model is established to provide a reference for early screening and intervention of cognitive frailty in elderly patients with multimorbidity.

methodsNine communities were selected based on multi-stage stratified random sampling from May-June 2022. A self-designed questionnaire and three cognitive frailty rating tools [Frailty Phenotype (FP), Montreal Cognitive Assessment (MoCA), and Clinical Qualitative Rating (CDR)] were used to collect data for elderly patients with multimorbidity in the community. The nomogram prediction model for the risk of cognitive frailty was established using Stata15.0.

resultsA total of 1200 questionnaires were distributed in this survey, and 1182 valid questionnaires were collected, 26 non-traditional risk factors were included. According to the characteristics of community health services and patient access and the logistic regression results, 9 non-traditional risk factors were screened out. Among them, age OR = 4.499 (95%CI:3.26-6.208), marital status OR = 3.709 (95%CI:2.748-5.005), living alone OR = 4.008 (95%CI:2.873-5.005), and sleep quality OR = 3.71(95%CI:2.730-5.042). The AUC values for the modeling and validation sets in the model were 0. 9908 and 0.9897. Hosmer and Lemeshow test values for the modeling set were χ2 = 3.857, p = 0.870 and for the validation set were χ2 = 2.875, p = 0.942.

conclusionThe prediction model could help the community health service personnel and elderly patients with multimorbidity and their families in making early judgments and interventions on the risk of cognitive frailty.

Indexed as

FrailtyAgedCognitionEast Asian PeopleFrail ElderlyGeriatric AssessmentHumansMultimorbidityQuality of LifeCognitive frailtyMultimorbidityNon-traditional factorsPrediction model

Identifiers

PMID37072704
PMCPMC10114438
OpenAlexW4366264552

What OpenQuestion holds

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LicenceCC BY
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 OpenQuestion graph.