Evidence map›Paper›PMID 40594895›Full record

ArticleScientific reports2025

Development and validation of a risk prediction model for unplanned 7-day readmission to PICU.

Min Ding, Chunfeng Yang, Yumei Li

Abstract readValidation Study
In one paragraph

Article in Scientific reports, 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

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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

3 authors.

Min DingDepartment of Pediatric Intensive Care Unit, Children's Medical Center, The First Hospital of Jilin University, 1 Xinmin Street, Changchun, 130012, China.
Chunfeng YangDepartment of Pediatric Intensive Care Unit, Children's Medical Center, The First Hospital of Jilin University, 1 Xinmin Street, Changchun, 130012, China.
Yumei LiDepartment of Pediatric Intensive Care Unit, Children's Medical Center, The First Hospital of Jilin University, 1 Xinmin Street, Changchun, 130012, China. ym_li@jlu.edu.cn.ORCID http://orcid.org/0000-0002-7705-4950

Funding

Doctor of excellence program (DEP), the First Hospital of Jilin University JDYY-DEP-2023021
6 · The paper itself

Abstract

We studied risk factors and predicted the probability of a child being readmitted to the pediatric intensive care unit (PICU) within 7 days of being discharged home. From November 2011 and September 2022, a retrospective case-control study was conducted to develop a risk prediction model in the PICU. The case group included children aged 1 month to 18 years discharged home who required unplanned 7-day readmission to the PICU. Non-readmitted children were chosen as controls. Characteristics were collected on the first admission and divided into a developing set and a validation set. In the developing set, a nomogram was established to predict the risk of readmission, and its performance was assessed by receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Internal validation was eventually performed on the model. 5266 children were involved in the study, with 173 eligible children in the case group and 184 in the control group. The model included five risk characteristics: complex chronic conditions, higher Pediatric Logistic Organ Dysfunction 2 scores on admission and discharge, sedation, and the Functional Status Scale score. In the two datasets, the area under the curves was 0.851 and 0.811, respectively. The calibration curve and DCA both performed well. And the model showed great reproducibility. The model demonstrated good capability for assessing the risk of 7-day readmission to the PICU, which could support early detection and intervention.

Indexed as

Intensive Care Units, PediatricPatient ReadmissionAdolescentCase-Control StudiesChildChild, PreschoolFemaleHumansInfantMaleNomogramsPatient DischargeReproducibility of ResultsRetrospective StudiesRisk AssessmentRisk FactorsPediatric intensive care unitPediatricsPredictive modelPrognosisReadmission

Identifiers

PMID40594895
PMCPMC12216315

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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.