Evidence map›Paper›PMID 31752782›Full record

ArticleBMC pediatrics2019

Assessing the risk of early unplanned rehospitalisation in preterm babies: EPIPAGE 2 study.

Robert Anthony Reed, Andrei Scott Morgan, Jennifer Zeitlin, Pierre-Henri Jarreau, Héloïse Torchin, Véronique Pierrat, Pierre-Yves Ancel, Babak Khoshnood

Abstract read
In one paragraph

Article in BMC pediatrics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. 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

8 authors.

Robert Anthony ReedUniversité de Paris, Epidemiology and Statistics Research Center/CRESS, INSERM, INRA, F-75004, Paris, France.
Andrei Scott MorganUniversité de Paris, Epidemiology and Statistics Research Center/CRESS, INSERM, INRA, F-75004, Paris, France. andrei.morgan@inserm.fr.ORCID 0000-0003-4143-1977
Jennifer ZeitlinUniversité de Paris, Epidemiology and Statistics Research Center/CRESS, INSERM, INRA, F-75004, Paris, France.
Pierre-Henri JarreauUniversité de Paris, Epidemiology and Statistics Research Center/CRESS, INSERM, INRA, F-75004, Paris, France.
Héloïse TorchinUniversité de Paris, Epidemiology and Statistics Research Center/CRESS, INSERM, INRA, F-75004, Paris, France.
Véronique PierratUniversité de Paris, Epidemiology and Statistics Research Center/CRESS, INSERM, INRA, F-75004, Paris, France.
Pierre-Yves AncelUniversité de Paris, Epidemiology and Statistics Research Center/CRESS, INSERM, INRA, F-75004, Paris, France.
Babak KhoshnoodUniversité de Paris, Epidemiology and Statistics Research Center/CRESS, INSERM, INRA, F-75004, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGaining a better understanding of the probability, timing and prediction of rehospitalisation amongst preterm babies could help improve outcomes. There is limited research addressing these topics amongst extremely and very preterm babies. In this context, unplanned rehospitalisations constitute an important, potentially modifiable adverse event. We aimed to establish the probability, time-distribution and predictability of unplanned rehospitalisation within 30 days of discharge in a population of French preterm babies.

methodsThis study used data from EPIPAGE 2, a population-based prospective study of French preterm babies. Only those babies discharged home alive and whose parents responded to the one-year survey were eligible for inclusion in our study. For Kaplan-Meier analysis, the outcome was unplanned rehospitalisation censored at 30 days. For predictive modelling, the outcome was binary, recording unplanned rehospitalisation within 30 days of discharge. Predictors included routine clinical variables selected based on expert opinion.

resultsOf 3841 eligible babies, 350 (9.1, 95% CI 8.2-10.1) experienced an unplanned rehospitalisation within 30 days. The probability of rehospitalisation progressed at a consistent rate over the 30 days. There were significant differences in rehospitalisation probability by gestational age. The cross-validated performance of a ten predictor model demonstrated low discrimination and calibration. The area under the receiver operating characteristic curve was 0.62 (95% CI 0.59-0.65).

conclusionsUnplanned rehospitalisation within 30 days of discharge was infrequent and the probability of rehospitalisation progressed at a consistent rate. Lower gestational age increased the probability of rehospitalisation. Predictive models comprised of clinically important variables had limited predictive ability.

Indexed as

FemaleGestational AgeHumansInfant, NewbornInfant, Premature, DiseasesMalePatient ReadmissionProspective StudiesRisk AssessmentTime FactorsCohort studyDischargeEpidemiologyNeonatologyNewbornPredictionPrematurityRehospitalisationSurvival analysis

Identifiers

PMID31752782
PMCPMC6870221

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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.