Applicability of predictive models for 30-day unplanned hospital readmission risk in paediatrics: a systematic review

Abstract

ObjectivesTo 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.DesignSystematic review.Data sourceCINAHL, Embase and PubMed up to 7 October 2021.Eligibility criteriaEnglish 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 synthesisStudy 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.ResultsBased 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.ConclusionPredictive 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.

Data transforming: A concept analysis

“…Areas that may benefit include information technology infrastructure and operational, organizational, managerial, and strategic areas, such as marketing, business understanding, and reputation 46 . For example, the transformed data could be used for disaster response and preparedness, 47 and for the early identification of patients with increased risk 48 . Both bring value to the healthcare field by saving costs and improving the well‐being of the patients.…”

“…46 For example, the transformed data could be used for disaster response and preparedness, 47 and for the early identification of patients with increased risk. 48 Both bring value to the healthcare field by saving costs and improving the well-being of the patients.…”

Hospital readmission following acute illness among children 2–23 months old in sub-Saharan Africa and South Asia: a secondary analysis of CHAIN cohort

“… 8 , 18 In such settings, 30-day reported readmission frequencies ranged widely from 3.4% to 29% across different disease conditions, with as many as a third of readmissions being deemed potentially preventable with well-planned inpatient-to-outpatient care transition and post-discharge support. 8 , 19 , 20 …”