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Applicability of predictive models for 30-day unplanned hospital readmission risk in paediatrics: a systematic review
Ines Marina Niehaus1, Nina Kansy2, Stephanie Stock3
1Department of Business Administration and Health Care Management, University of Cologne, Cologne, Germany niehaus@wiso.uni-koeln.de.
Insights
Multivariable predictive models can identify children at high risk for 30-day unplanned hospital readmissions. Improving reporting completeness is crucial for practical application of these pediatric readmission risk models.
Area of Science:
- Pediatric healthcare research
- Clinical prediction modeling
- Health services research
Background:
- Unplanned hospital readmissions in children pose a significant burden on healthcare systems.
- Predictive models are being developed to identify pediatric patients at high risk for readmission.
- Assessing the performance and reporting quality of these models is essential for their clinical utility.
Purpose of the Study:
- To systematically review multivariable predictive models for 30-day unplanned hospital readmissions (UHRs) in pediatrics.
- To describe the performance and reporting completeness of these models.
- To determine their potential for practical application in clinical settings.
Main Methods:
- A systematic review of studies published in CINAHL, Embase, and PubMed up to October 2021.
- Inclusion of English or German studies developing or validating pediatric UHR predictive models.
- Extraction of study characteristics, significant risk factors, performance measures (e.g., c-statistic), and assessment of reporting quality using TRIPOD criteria.
Main Results:
- 37 predictive models were identified across 28 studies, with participant numbers ranging from 190 to 1.4 million.
- Comorbidity and length of stay were the most common significant risk factors.
- 23 models had a c-statistic > 0.7, indicating good predictive performance, primarily for use at discharge. Median TRIPOD adherence was 59%, with moderate to low study quality noted.
Conclusions:
- Multivariable predictive models show promise for identifying pediatric patients at elevated risk of readmission.
- Enhancing the completeness of reporting, especially for implementation-relevant items, is necessary to support the practical application of these models.
- Further research should focus on improving model transparency and addressing study quality to facilitate clinical integration.
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% (P25-P75, 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.
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