Evidence map›Paper›PMID 27354072›Full record

SynthesisBMJ open2016

Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review.

Huaqiong Zhou, Phillip R Della, Pamela Roberts, Louise Goh, Satvinder S Dhaliwal

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in BMJ open, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 139 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
139citing papers in PubMed, 9 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

139 citing papers in PubMed, 9 syntheses or guidelines pooled it.

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79 more citing papers are in PubMed but not listed here.

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

5 authors.

Huaqiong ZhouClinical Nurse, General Surgical Ward, Princess Margaret Hospital for Children, Perth, Western Australia, Australia School of Nursing, Midwifery and Paramedicine, Curtin University, Perth, Western Australia, Australia.
Phillip R DellaSchool of Nursing, Midwifery and Paramedicine, Curtin University, Perth, Western Australia, Australia.
Pamela RobertsSchool of Nursing, Midwifery and Paramedicine, Curtin University, Perth, Western Australia, Australia.
Louise GohSchool of Nursing, Midwifery and Paramedicine, Curtin University, Perth, Western Australia, Australia.
Satvinder S DhaliwalSchool of Nursing, Midwifery and Paramedicine, Curtin University, Perth, Western Australia, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo update previous systematic review of predictive models for 28-day or 30-day unplanned hospital readmissions.

designSystematic review. SETTING/DATA SOURCE: CINAHL, Embase, MEDLINE from 2011 to 2015.

participantsAll studies of 28-day and 30-day readmission predictive model. OUTCOME MEASURES: Characteristics of the included studies, performance of the identified predictive models and key predictive variables included in the models.

resultsOf 7310 records, a total of 60 studies with 73 unique predictive models met the inclusion criteria. The utilisation outcome of the models included all-cause readmissions, cardiovascular disease including pneumonia, medical conditions, surgical conditions and mental health condition-related readmissions. Overall, a wide-range C-statistic was reported in 56/60 studies (0.21-0.88). 11 of 13 predictive models for medical condition-related readmissions were found to have consistent moderate discrimination ability (C-statistic ≥0.7). Only two models were designed for the potentially preventable/avoidable readmissions and had C-statistic >0.8. The variables 'comorbidities', 'length of stay' and 'previous admissions' were frequently cited across 73 models. The variables 'laboratory tests' and 'medication' had more weight in the models for cardiovascular disease and medical condition-related readmissions.

conclusionsThe predictive models which focused on general medical condition-related unplanned hospital readmissions reported moderate discriminative ability. Two models for potentially preventable/avoidable readmissions showed high discriminative ability. This updated systematic review, however, found inconsistent performance across the included unique 73 risk predictive models. It is critical to define clearly the utilisation outcomes and the type of accessible data source before the selection of the predictive model. Rigorous validation of the predictive models with moderate-to-high discriminative ability is essential, especially for the two models for the potentially preventable/avoidable readmissions. Given the limited available evidence, the development of a predictive model specifically for paediatric 28-day all-cause, unplanned hospital readmissions is a high priority.

Indexed as

Models, StatisticalComorbidityHumansLength of StayPatient ReadmissionPrognosisRisk AssessmentTime FactorsEPIDEMIOLOGYPUBLIC HEALTH

Identifiers

PMID27354072
PMCPMC4932323

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.