Multivariable predictive models for 30-day paediatric unplanned hospital readmissions showed a c-statistic >0.7 in 23 of 37 identified models, despite moderate to low overall study quality.
Systematic Review
What is the performance and reporting quality of multivariable predictive models for 30-day unplanned hospital readmissions in paediatrics?
While several predictive models for 30-day pediatric readmissions show acceptable discrimination (c-statistic >0.7), their clinical application is limited by incomplete reporting and moderate-to-low methodological quality.
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.
Niehaus et al. (Tue,) conducted a systematic review in 30-day unplanned hospital readmissions (UHRs) in paediatrics. Multivariable predictive models was evaluated on 30-day unplanned hospital readmissions (UHRs). Multivariable predictive models for 30-day paediatric unplanned hospital readmissions showed a c-statistic >0.7 in 23 of 37 identified models, despite moderate to low overall study quality.