Evidence map›Paper›PMID 35571506›Full record

ArticleJournal of inflammation research2022

Risk Factors and Predictive Models for Intravenous Immunoglobulin Resistance in Children with Recurrent Kawasaki Disease.

Xi Chen, Lu Gao, Zhen Zhen, Ying Wang, Jia Na, Wen Yu, Xinyuan Chu, Yue Yuan, Suyun Qian

Open access · goldAbstract read
In one paragraph

Article in Journal of inflammation research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
2.2field-weighted citation impact, top 13% 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 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

9 authors at 2 institutions in 1 country.

Xi ChenDepartment of Cardiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, People's Republic of China.
Lu GaoDepartment of Cardiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, People's Republic of China.
Zhen ZhenDepartment of Cardiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, People's Republic of China.
Ying WangDepartment of Cardiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, People's Republic of China.
Jia NaDepartment of Cardiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, People's Republic of China.
Wen YuDepartment of Cardiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, People's Republic of China.
Xinyuan ChuDepartment of Cardiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, People's Republic of China.
Yue YuanDepartment of Cardiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, People's Republic of China.
Suyun QianPediatric Intensive Care Unit, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, People's Republic of China.
Capital Medical University · CNBeijing Children’s Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To explore the risk factors and develop predictive models for intravenous immunoglobulin (IVIG) resistance in children with recurrent Kawasaki disease (KD). Patients and Methods: Patients with recurrent KD were retrospectively reviewed. Clinical and laboratory data at recurrence were collected and compared between patients with and without IVIG resistance. The patients were randomly divided into training and validation cohorts for model development and validation. All variables were subjected to standard Lasso and its variant group Lasso analyses, respectively, to construct predictive models. Model performance was evaluated by receiver operating characteristics (ROC) curves, calibration curves, and Hosmer-Lemeshow tests. Results: A total of 90 children with recurrent KD were included. A total of 16 cases were IVIG resistant. The patients with IVIG resistance had higher age and IVIG resistance probability at the first episode, increased CRP levels, neutrophil count, neutrophil percentage, direct bilirubin level, prothrombin time, and international normalized ratio, and decreased lymphocyte count, lymphocyte percentage, and serum sodium levels. Five variables including age and IVIG resistance at the first episode, lymphocytes count, serum sodium levels, and CRP levels were finally selected by standard Lasso (lLasso model) and four variables including age and IVIG resistance at the first episode, neutrophil percentage, and CRP levels were selected by group Lasso (gLasso). ROC curves suggested lLasso and gLasso models had similar excellent discrimination in both the training cohort (0.895 vs 0.906) and the validation cohort (0.855 vs 0.909). Hosmer-Lemeshow tests suggested the two models exerted a good calibration. Two nomograms were also constructed to facilitate the potential application of the two models. Conclusion: Age and IVIG resistance at the first episode and some laboratory variables may be risk factors for IVIG resistance in recurrent KD. Two predictive models for IVIG resistance with excellent performance were established in recurrent KD. External validation should be performed before clinical use.

Indexed as

intravenous immunoglobulinpredictive modelrecurrent Kawasaki diseaseresistancerisk factor

Identifiers

PMID35571506
PMCPMC9091694
OpenAlexW4229054721

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.