Evidence map›Paper›PMID 36367725›Full record

ArticleJAMA network open2022

Development and Validation of an Integrated Suite of Prediction Models for All-Cause 30-Day Readmissions of Children and Adolescents Aged 0 to 18 Years.

Denise M Goodman, Mia T Casale, Karen Rychlik, Michael S Carroll, Katherine A Auger, Tracie L Smith, Jenifer Cartland, Matthew M Davis

Abstract read
In one paragraph

Article in JAMA network open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Predicting Psychiatric Readmission of Children and Adolescents Using Machine Learning.Journal of child and adolescent psychopharmacology · 2026
    Article
  2. Article
  3. Observational
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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.

Denise M GoodmanDivision of Critical Care Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois.
Mia T CasaleData Analytics and Reporting, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois.
Karen RychlikDepartment of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
Michael S CarrollDepartment of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
Katherine A AugerDivision of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
Tracie L SmithData Analytics and Reporting, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois.
Jenifer CartlandDepartment of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
Matthew M DavisDepartment of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Readmission is often considered a hospital quality measure, yet no validated risk prediction models exist for children. Objective: To develop and validate a tool identifying patients before hospital discharge who are at risk for subsequent readmission, applicable to all ages. Design, Setting, and Participants: This population-based prognostic analysis used electronic health record-derived data from a freestanding children's hospital from January 1, 2016, to December 31, 2019. All-cause 30-day readmission was modeled using 3 years of discharge data. Data were analyzed from June 1 to November 30, 2021. Main Outcomes and Measures: Three models were derived as a complementary suite to include (1) children 6 months or older with 1 or more prior hospitalizations within the last 6 months (recent admission model [RAM]), (2) children 6 months or older with no prior hospitalizations in the last 6 months (new admission model [NAM]), and (3) children younger than 6 months (young infant model [YIM]). Generalized mixed linear models were used for all analyses. Models were validated using an additional year of discharges. Results: The derivation set contained 29 988 patients with 48 019 hospitalizations; 50.1% of these admissions were for children younger than 5 years and 54.7% were boys. In the derivation set, 4878 of 13 490 admissions (36.2%) in the RAM cohort, 2044 of 27 531 (7.4%) in the NAM cohort, and 855 of 6998 (12.2%) in the YIM cohort were followed within 30 days by a readmission. In the RAM cohort, prior utilization, current or prior procedures indicative of severity of illness (transfusion, ventilation, or central venous catheter), commercial insurance, and prolonged length of stay (LOS) were associated with readmission. In the NAM cohort, procedures, prolonged LOS, and emergency department visit in the past 6 months were associated with readmission. In the YIM cohort, LOS, prior visits, and critical procedures were associated with readmission. The area under the receiver operating characteristics curve was 83.1 (95% CI, 82.4-83.8) for the RAM cohort, 76.1 (95% CI, 75.0-77.2) for the NAM cohort, and 80.3 (95% CI, 78.8-81.9) for the YIM cohort. Conclusions and Relevance: In this prognostic study, the suite of 3 prediction models had acceptable to excellent discrimination for children. These models may allow future improvements in tailored discharge preparedness to prevent high-risk readmissions.

Indexed as

Patient DischargePatient ReadmissionAdolescentChildFemaleHospitalizationHumansInfantLength of StayMaleRetrospective Studies

Identifiers

PMID36367725
PMCPMC9652755

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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.