Evidence map›Paper›PMID 31386613›Full record

ArticleJournal of hospital medicine2019

Discharge Medical Complexity, Change in Medical Complexity and Pediatric 30-day Readmission.

Katherine A Auger, Samir S Shah, Bin Huang, Patrick W Brady, Steven H Weinberg, Elyse Reamer, Kevin S Tanager, Katelin Zahn, Matthew M Davis

Abstract read
In one paragraph

Article in Journal of hospital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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.

Katherine A AugerDivision of Hospital Medicine, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
Samir S ShahDivision of Hospital Medicine, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
Bin HuangUniversity of Cincinnati College of Medicine, Cincinnati, Ohio.
Patrick W BradyDivision of Hospital Medicine, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
Steven H WeinbergDepartment of Pediatrics, University of North Carolina School of Medicine, Chapel Hill, North Carolina.
Elyse ReamerDivision of Epidemiology, The Ohio State University College of Public Health, Columbus, Ohio.
Kevin S TanagerDepartment of Pathology, University of Chicago Medicine, Chicago, Illinois.
Katelin ZahnDepartment of Obstetrics and Gynecology, University of North Carolina School of Medicine, Chapel Hill, North Carolina.
Matthew M DavisDivision of Academic General Pediatrics and Mary Ann & J. Milburn Smith Child Health Research, Outreach, and Advocacy Center, Stanley Manne Children's Research Institute, Ann and Robert H. Lurie Children's Hospital, Chicago, Illinois.

Funding

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 HS024735
6 · The paper itself

Abstract

backgroundWhile medical complexity is associated with pediatric readmission risk, less is known about how increases in medical complexity during hospitalization affect readmission risk.

methodsWe conducted a five-year retrospective, case-control study of pediatric hospitalizations at a tertiary care children's hospital. Cases with a 30-day unplanned readmission were matched to controls based on admission seasonality and distance from the hospital. Complexity variables included the number of medications prescribed at discharge, medical technology, and the need for home healthcare services. Change in medical complexity variables included new complex chronic conditions and new medical technology. We estimated odds of 30-day unplanned readmission using adjusted conditional logistic regression.

resultsOf 41,422 eligible index hospitalizations, we included 595 case and 595 control hospitalizations. Complexity: Polypharmacy after discharge was common. In adjusted analyses, being discharged with ≥2 medications was associated with higher odds of readmission compared with being discharged without medication; children with ≥5 discharge medications had a greater than four-fold higher odds of readmission. Children assisted by technology had higher odds of readmission compared with children without technology assistance. Change in complexity: New diagnosis of a complex chronic condition (Adjusted Odds Ratio (AOR) = 1.75; 1.11-2.75) and new technology (AOR = 1.84; 1.09-3.10) were associated with higher risk of readmission when adjusting for patient characteristics. However, these associations were not statistically significant when adjusting for length of stay.

conclusionPolypharmacy and use of technology at discharge pose a substantial readmission risk for children. However, added technology and new complex chronic conditions do not increase risk when accounting for length of stay.

Indexed as

ComorbidityHospitals, PediatricPolypharmacyAdolescentChildChild, PreschoolChronic DiseaseFemaleHospitalizationHumansInfantInfant, NewbornLength of StayLongitudinal StudiesMalePatient Readmission

Identifiers

PMID31386613
PMCPMC6686738

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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.