Evidence map›Paper›PMID 38648746›Full record

ArticleCerebrovascular diseases extra2024

A Nomogram to Predict the Risk of Acute Ischemic Stroke in Patients with Maintenance Hemodialysis: A Retrospective Cohort Study.

Jingyi Tong, Tingting Ji, Nan Liu, Yibin Chen, Zongjun Li, Xuejuan Lin, Yi Xing, Qifu Li

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

Article in Cerebrovascular diseases extra, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 28% 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

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 2 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 1 institution in 1 country.

Jingyi TongDepartment of Neurology, The First Affiliated Hospital of Hainan Medical University, Haikou, China.
Tingting JiDepartment of Neurology, The First Affiliated Hospital of Hainan Medical University, Haikou, China.
Nan LiuDepartment of Nephrology, The First Affiliated Hospital of Hainan Medical University, Haikou, China.
Yibin ChenDepartment of Neurology, The First Affiliated Hospital of Hainan Medical University, Haikou, China.
Zongjun LiDepartment of Neurology, The First Affiliated Hospital of Hainan Medical University, Haikou, China.
Xuejuan LinDepartment of Neurology, The First Affiliated Hospital of Hainan Medical University, Haikou, China.
Yi XingDepartment of Neurology, The First Affiliated Hospital of Hainan Medical University, Haikou, China.
Qifu LiDepartment of Neurology, The First Affiliated Hospital of Hainan Medical University, Haikou, China.
Hainan Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAcute ischemic stroke (AIS) stands as a leading cause of death and disability globally. This study aimed to investigate the risk factors and relevance linked with AIS in patients undergoing maintenance hemodialysis (MHD) and to create and validate nomogram models.

methodsWe examined the medical records of 314 patients with stage 5 chronic kidney disease (CKD 5) undergoing MHD, who sought neurology outpatient department consultation for suspected AIS symptoms between January 2018 and December 2023. These 314 patients were randomly divided into the training cohort (n = 222) and validation cohort (n = 92). The least absolute shrinkage selection operator (LASSO) regression model was employed for optimal feature selection in the AIS risk model. Subsequently, multivariable logistic regression analysis was used to construct a predictive model incorporating the features selected through LASSO. This predictive model's performance was assessed using the C-index and the area under the receiver operating characteristic curve (AUC). Additionally, calibration and clinical utility were evaluated through calibration plots and decision curve analysis (DCA). The model's internal validation was conducted using the validation cohort.

resultsPredictors integrated into the prediction nomogram encompassed cardiovascular disease (CVD) (odds ratio [OR] 7.95, 95% confidence interval [CI] 2.400-29.979), smoking (OR 5.7, 95% CI: 1.661-21.955), dialysis time (OR: 5.91, 95% CI: 5.866-29.979), low-density lipoprotein (OR: 2.99, 95% CI: 0.751-13.007), and fibrin degradation products (OR: 5.47, 95% CI: 1.563-23.162). The model exhibited robust discrimination, with a C-index of 0.877 and 0.915 in the internal training and validation cohorts, respectively. The AUC for the training set was 0.857, and a similar AUC of 0.905 was achieved in the validation cohort. DCA demonstrated a positive net benefit within a threshold risk range of 2-96%.

conclusionThe proposed nomogram effectively identifies MHD patients at high risk of AIS at an early stage. This model holds the potential to aid clinicians in making preventive recommendations.

Indexed as

Ischemic StrokeNomogramsPredictive Value of TestsRenal DialysisAgedDecision Support TechniquesFemaleHumansMaleMiddle AgedPrognosisRenal Insufficiency, ChronicReproducibility of ResultsRetrospective StudiesRisk AssessmentRisk FactorsAcute ischemic strokeMaintenance hemodialysisNomogramPredict reviewRetrospective cohort study

Identifiers

PMID38648746
PMCPMC11250579
OpenAlexW4395013132

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.