Evidence map›Paper›PMID 33231538›Full record

ArticleJournal of hospital medicine2020

Performance of Pediatric Readmission Measures.

Katherine A Auger, Michael C Ponti-Zins, Angela M Statile, Kris Wesselkamper, Beth Haberman, Samuel P Hanke

Abstract read
In one paragraph

Article in Journal of hospital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Katherine A AugerDivision of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
Michael C Ponti-ZinsCenter for Patient Family Experience, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
Angela M StatileDivision of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
Kris WesselkamperUniversity of Cincinnati School of Medicine, Cincinnati, Ohio.
Beth HabermanUniversity of Cincinnati School of Medicine, Cincinnati, Ohio.
Samuel P HankeJames M. Anderson Center for Healthcare Improvement, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.

Funding

LCenter for Clinical and Translational Science and TrainingUL1TR001425 · NCATS · UNIVERSITY OF CINCINNATI · PI HEUBI, JAMES E., KISSELA, BRETT M · 2015 to 2024
$37.5M
Predicting Pediatric Readmission: Development of a Validated Risk Prediction ToolK08HS024735 · AHRQ · CINCINNATI CHILDRENS HOSP MED CTR · PI AUGER, KATHERINE ANN SCOTT · 2017 to 2021
$742k
AHRQ HHS K08 HS024735NCATS NIH HHS UL1 TR001425
6 · The paper itself

Abstract

backgroundReadmission rates are frequently used as a hospital quality metric; yet multiple measures exist to evaluate pediatric readmission rates. We sought to assess how four different measures of pediatric readmission compare with assessment of both preventable and unplanned readmission.

methodsClinicians on hospital medicine, cardiology, neonatology, and neurology teams reviewed medical records for 30-day readmissions using an abstraction tool with high interrater reliability for preventability assessment. Readmissions between July 2014 and June 2016 were classified separately as preventable or not preventable and planned or unplanned. We compared the classifications to four existing readmission metrics: all-cause readmission, unplanned readmission/time flag classification, the pediatric all-condition readmission, and potentially preventable readmission. We calculated sensitivity and specificity for all readmission metrics.

resultsAmong 30-day readmissions considered, 1,643 were eligible for medical record review; 1,125 reviews were completed by the clinical teams (68%). On medical record review, the majority of readmissions were determined not preventable (85%). Only 15% were classified as unplanned and preventable. None of the four readmission measures had appropriate sensitivity or specificity for identifying preventable readmission. The unplanned readmission/time flag classification had the highest sensitivity (95%) and specificity (90%) in identifying unplanned readmissions.

conclusionNone of the existing pediatric readmission measures can reliably determine preventability. The unplanned readmission/time flag measure performed best in identifying unplanned readmissions.

Indexed as

Medical RecordsPatient ReadmissionChildHumansReproducibility of ResultsRetrospective Studies

Identifiers

PMID33231538
PMCPMC8034671

What OpenQuestion holds

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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.