Risk factors associated with 30‐day all‐cause unplanned hospital readmissions at a tertiary children's hospital in Western Australia

Zhou, Huaqiong; Della, Phillip; Porter, Paul; Roberts, Pamela A.

doi:10.1111/jpc.14492

Cited by 9 publications

(15 citation statements)

References 24 publications

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“…Social history and English language proficiency are routinely assessed at the time of admission, and this study highlights the need for early commencement of discharge planning for these patients. 19 Patients identified as having significant social history at the time of admission require a designated hospital-based social worker to assess and provide social needs for the family/caregiver. The social worker should also collaborate with other healthcare providers to implement a discharge planning process that ensures continuity of care at home, post-discharge.…”

Section: Discussionmentioning

confidence: 99%

“…[16][17][18] However, the number of paediatric studies remains limited, and have so far only analysed administrative data. [11][12][13][14] In a recently published study, 19 we developed a logistic regression model based on 16 administratively collected variables as electronic medical records were not available. The model was found with moderate discriminative ability for 30-day all-cause readmission at a tertiary paediatric hospital in Western Australia (WA) (C-statistic ¼ 0.645).…”

Section: Introductionmentioning

confidence: 99%

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Using machine learning to predict paediatric 30-day unplanned hospital readmissions: a case-control retrospective analysis of medical records, including written discharge documentation

et al. 2021

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Objectives. To assess whether adding clinical information and written discharge documentation variables improves prediction of paediatric 30-day same-hospital unplanned readmission compared with predictions based on administrative information alone.Methods. A retrospective matched case-control study audited the medical records of patients discharged from a tertiary paediatric hospital in Western Australia (WA) between January 2010 and December 2014. A random selection of 470 patients with unplanned readmissions (out of 3330) were matched to 470 patients without readmissions based on age, sex, and principal diagnosis at the index admission. Prediction utility of three groups of variables (administrative, administrative and clinical, and administrative, clinical and written discharge documentation) were assessed using standard logistic regression and machine learning.Results. Inclusion of written discharge documentation variables significantly improved prediction of readmission compared with models that used only administrative and/or clinical variables in standard logistic regression analysis (x 2 17 ¼ 29.4, P ¼ 0.03). Highest prediction accuracy was obtained using a gradient boosted tree model (C-statistic ¼ 0.654), followed closely by random forest and elastic net modelling approaches. Variables highlighted as important for prediction included patients' social history (legal custody or patient was under the care of the Department for Child Protection), languages spoken other than English, completeness of nursing admission and discharge planning documentation, and timing of issuing discharge summary.Conclusions. The variables of significant social history, low English language proficiency, incomplete discharge documentation, and delay in issuing the discharge summary add value to prediction models.What is known about the topic? Despite written discharge documentation playing a critical role in the continuity of care for paediatric patients, limited research has examined its association with, and ability to predict, unplanned hospital readmissions. Machine learning approaches have been applied to various health conditions and demonstrated improved predictive accuracy. However, few published studies have used machine learning to predict paediatric readmissions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using machine learning to predict paediatric 30-day unplanned hospital readmissions: a case-control retrospective analysis of medical records, including written discharge documentation

et al. 2021

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“…Four of 28 studies additionally included census data in the analysis. 61 65 66 68 The period of data collection ranged from 1 year 31 53 54 60 63 68 to 17 years 69 70 . The majority of studies included patients up to an age of <18 or ≤18 years.…”

Section: Resultsmentioning

confidence: 99%

“…ORs for comorbidity ranged from 1.01 72 to 10.08 58 across predictive models. A length of stay of ≥15 days (OR=2.39) 61 and a postoperative length of stay of >4 days (hazard ratio=3.12) 56 were considered to be a major risk factor. For illness severity, ‘intensive care unit stay’ (OR=3.302) 67 and for principal procedures ‘isolated primary anterior spinal fusion’ (OR=7.65) 54 were one of the most pronounced risk factors, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Clinicians and decision makers should use predictive models with good discriminative ability (ie, c-statistic above 0.7) and sufficient data availability. Especially predictive models that are based on census data 61 65 66 68 or manual data entry (eg, written discharge documentation 22 ) may be more difficult to implement than models relying on centralised databases 31 53 54 56–60 69–78 . The TRIPOD score at the predictive model level (see table 4 ) can be used as a first indicator if the predictive model can be assessed and implemented with the given information.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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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.

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Prevalence and risk factors associated within 48-hour unplanned paediatric intensive care unit readmissions: An integrative review

Barnwell,

Zhou,

Erickson

2024

Australian Critical Care

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Risk factors associated with 30‐day all‐cause unplanned hospital readmissions at a tertiary children's hospital in Western Australia

Cited by 9 publications

References 24 publications

Using machine learning to predict paediatric 30-day unplanned hospital readmissions: a case-control retrospective analysis of medical records, including written discharge documentation

Using machine learning to predict paediatric 30-day unplanned hospital readmissions: a case-control retrospective analysis of medical records, including written discharge documentation

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

Prevalence and risk factors associated within 48-hour unplanned paediatric intensive care unit readmissions: An integrative review

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