Universität zu Köln

Niehaus, Ines Marina ORCID: 0000-0002-4214-6898, Kansy, Nina, Stock, Stephanie, Doetsch, Joerg and Mueller, Dirk (2022). Applicability of predictive models for 30-day unplanned hospital readmission risk in paediatrics: a systematic review. BMJ Open, 12 (3). LONDON: BMJ PUBLISHING GROUP. ISSN 2044-6055

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Abstract

Objectives To summarise multivariable predictive models for 30-day unplanned hospital readmissions (UHRs) in paediatrics, describe their performance and completeness in reporting, and determine their potential for application in practice. Design Systematic review. Data source CINAHL, Embase and PubMed up to 7 October 2021. Eligibility criteria English or German language studies aiming to develop or validate a multivariable predictive model for 30-day paediatric UHRs related to all-cause, surgical conditions or general medical conditions were included. Data extraction and synthesis Study characteristics, risk factors significant for predicting readmissions and information about performance measures (eg, c-statistic) were extracted. Reporting quality was addressed by the 'Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis' (TRIPOD) adherence form. The study quality was assessed by applying six domains of potential biases. Due to expected heterogeneity among the studies, the data were qualitatively synthesised. Results Based on 28 studies, 37 predictive models were identified, which could potentially be used for determining individual 30-day UHR risk in paediatrics. The number of study participants ranged from 190 children to 1.4 million encounters. The two most common significant risk factors were comorbidity and (postoperative) length of stay. 23 models showed a c-statistic above 0.7 and are primarily applicable at discharge. The median TRIPOD adherence of the models was 59% (P-25-P-75, 55%-69%), ranging from a minimum of 33% to a maximum of 81%. Overall, the quality of many studies was moderate to low in all six domains. Conclusion Predictive models may be useful in identifying paediatric patients at increased risk of readmission. To support the application of predictive models, more attention should be placed on completeness in reporting, particularly for those items that may be relevant for implementation in practice.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Niehaus, Ines Marina UNSPECIFIED orcid.org/0000-0002-4214-6898 UNSPECIFIED Kansy, Nina UNSPECIFIED UNSPECIFIED UNSPECIFIED Stock, Stephanie UNSPECIFIED UNSPECIFIED UNSPECIFIED Doetsch, Joerg UNSPECIFIED UNSPECIFIED UNSPECIFIED Mueller, Dirk UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-662091
DOI:	10.1136/bmjopen-2021-055956
Journal or Publication Title:	BMJ Open
Volume:	12
Number:	3
Date:	2022
Publisher:	BMJ PUBLISHING GROUP
Place of Publication:	LONDON
ISSN:	2044-6055
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language QUALITY; SURGERY; CHILDREN; OUTCOMES; FUSION; RATES; CARE; ASSOCIATION; PROGNOSIS Multiple languages Medicine, General & Internal Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/66209