Identifying Children at Readmission Risk: At-Admission versus Traditional At-Discharge Readmission Prediction Model

Association of the Child Opportunity Index and Inpatient Illness Severity in the United States, 2018-2019

OBJECTIVE

Children residing in impoverished neighborhoods have reduced access to health care resources. Our objective was to identify potential associations between Child Opportunity Index (COI), a composite score of neighborhood characteristics, and inpatient severity of illness and clinical trajectory among United States (US) children.

METHODS

This retrospective cohort study assessed data using the Pediatric Health Information System Registry from 2018 to 2019. Primary exposure variable was COI level (range: very low [CO1 1], low [COI 2], moderate [COI 3], high [COI 4], and very high [COI 5]). Markers of inpatient clinical severity included index mortality, Pediatric Intensive Care Unit (PICU) admission, invasive mechanical ventilation (IMV), and hospital length of stay (LOS). Subgroup analysis of COI and clinical outcome variation by United States Census Geographic Regions was conducted. Adjusted regression analysis was utilized to understand associations between COI and inpatient clinical severity outcomes.

RESULTS

Of the 132,130 encounters, 44% resided in very low or low COI neighborhoods. In adjusted models, very low COI was associated with increased mortality (aOR: 1.35, 95% CI: 1.05-1.74, P = .018), PICU admission (aOR: 1.06, 95% CI: 1.02-1.11, P = 0.014), IMV (aOR: 1.12, 95% CI: 1.04-1.21, P = .002), and higher hospital LOS (P = .045). Regional variation by COI depicted the East North Central region having the highest rate of mortality (20.5%), P < .001, and PICU admissions (23%), P = .014.

CONCLUSIONS

Our multicenter, retrospective study highlights the interaction between neighborhood-level deprivation and worsened health disparities, indicating a need for prospective study.

Machine learning for hospital readmission prediction in pediatric population

BACKGROUND AND OBJECTIVE

Pediatric readmissions are a burden on patients, families, and the healthcare system. In order to identify patients at higher readmission risk, more accurate techniques, as machine learning (ML), could be a good strategy to expand the knowledge in this area. The aim of this study was to develop predictive models capable of identifying children and adolescents at high risk of potentially avoidable 30-day readmission using ML.

METHODS

Retrospective cohort study was carried out with 9,080 patients under 18 years old admitted to a tertiary university hospital. Demographic, clinical, and biochemical data were collected from electronic databases. We randomly divided the dataset into training (75 %) and testing (25 %), applied downsampling, repeated cross-validation with five folds and ten repetitions, and the hyperparameter was optimized of each technique using a grid search via racing with ANOVA models. We applied six ML classification algorithms to build the predictive models, including classification and regression tree (CART), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), decision tree and logistic regression (LR). The area under the receiver operating curve (AUC), sensitivity, specificity, Youden's J-index and accuracy were used to evaluate the performance of each model.

RESULTS

The avoidable 30-day hospital readmissions rate was 9.5 %. Some algorithms presented similar AUC, both in the dataset training and in the dataset testing, such as XGBoost, RF, GBM and CART. Considering the Youden's J-index, the algorithm that presented the best index was XGBoost with bagging imputation, with AUC of 0.814 (J-index of 0.484). Cancer diagnosis, age, red blood cells, leukocytes, red cell distribution width and sodium levels, elective admission, and multimorbidity were the most important characteristics to classify between readmission and non-readmission groups.

CONCLUSION

Machine learning approaches, especially XGBoost, can predict potentially avoidable 30-day pediatric hospital readmission into tertiary assistance. If implemented in the computer hospital system, our model can help in the early and more accurate identification of patients at readmission risk, targeting health strategic interventions.

Identifying the Prevalence and Causes of 30-Day Hospital Readmission in Children: A Case Study from a Tertiary Pediatric Hospital

Introduction

The objectives of this study were to determine the prevalence of unplanned readmissions in the pediatric population within 30 days of discharge, identify the possible reasons behind them, and develop a predictive model for unplanned admissions.

Methods

A retrospective chart review study of 25,211 patients was conducted to identify the prevalence of readmissions occurring within 30 days of discharge from the King Abdullah Specialized Children's Hospital (KASCH) in Riyadh, Saudi Arabia, between Jan 1, 2019, and Dec 31, 2021. The data were collected using the BestCare electronic health records system and analyzed using Jamovi statistical software version 1.6.

Results

Among the 25,211 patients admitted to the hospital during the study period, the prevalence of unplanned readmission within 30 days was 1291 (5.12%). Of the 1291 patients, 1.91% had subsequent unplanned readmissions. In 57.8% of the cases, the cause of the first unplanned readmission was related to the cause of the first admission, and in 90.64% of the cases, the cause of the subsequent unplanned readmission was related to the cause of the first unplanned readmission. The most common reason for the first unplanned readmission was postoperative complications (18.75%), whereas pneumonia (10.81%) was the most common reason for subsequent unplanned readmissions. Most patients with subsequent unplanned readmissions were also found to have either isolated central nervous system pathology or chronic complex medical conditions.

Conclusion

Internationally, the rate of unplanned readmissions in pediatric patients has been estimated to be 6.5% within 30 days, which is comparable to the results of our study (5.12%). Most of the causes of first and subsequent unplanned readmission were found to be related to primary admission. The diagnosis/causes of readmission vary depending on the patient's age. A predictive model for pediatric readmission should be established so that preventive measures can be implemented.

Factors associated with hospital admission and 30-day readmission for children less than 18 years of age in 2018 in France: a one-year nationwide observational study

BACKGROUND

Nationwide data for children for short-stay hospitalisation (SSH) and associated factors are scarce. This retrospective study of children in France < 18 years of age followed after their birth or birthday in 2018 focused on at least one annual SSH, stay < 1 night or ≥ 1 night, or 30-day readmission ≥ 1 night.

METHODS

Children were selected from the national health data system (SNDS), which includes data on long-term chronic disease (LTD) status with full reimbursement and complementary universal coverage based on low household income (CMUC). Uni and multivariate quasi-Poisson regression were applied for each outcome.

RESULTS

Among 13.211 million children (94.4% population, 51.2% boys), CMUC was identified for 17.5% and at least one LTD for 4% (0-<1 year: 1.5%; 14-<18 year: 5.2%). The most frequent LTDs were pervasive developmental diseases (0.53%), asthma (0.24%), epilepsy (0.17%), and type 1 diabetes (0.15%). At least one SSH was found for 8.8%: SSH < 1 night (4.9%), SSH ≥ 1 night (4.5%), readmission (0.4%). Children with at least one SSH were younger (median 6 vs. 9 years) and more often had CMUC (21%), a LTD (12%), an emergency department (ED) visit (56%), or various primary healthcare visits than all children. Those with a SSH ≥1 night vs. < 1 night were older (median: 9 vs. 4 years). They had the same frequency of LTD (13.4%) but more often an ED visit (78% vs. 42%). Children with readmissions were younger (median 3 years). They had the highest levels of CMUC (29.3%), LTD (34%), EDs in their municipality (35% vs. 29% for the whole population) and ED visits (87%). In adjusted analysis, each outcome was significantly less frequent among girls than boys and more frequent for children with CMUC. LTDs with the largest association with SSH < 1 night were cystic fibrosis, sickle cell diseases (SCD), diabetes type 1, those with SSH ≥1 night type 1 diabetes epilepsy and SCD, and those for readmissions lymphoid leukaemia, malignant neoplasm of the brain, and SCD. Among all SSH admissions of children < 10 years, 25.8% were potentially preventable.

CONCLUSION

Higher SSH and readmission rates were found for children with certain LTD living in low-income households, suggesting the need or increase of specific policy actions and research.

Factors Associated with Hospital Readmission One Year Post-Discharge in Infants with Gastroschisis

INTRODUCTION

Gastroschisis is the most common congenital abdominal wall defect with a rising prevalence. Infants with gastroschisis are at risk for multiple complications, leading to a potential increased risk for hospital readmission after discharge. We aimed to find the frequency and factors associated with an increased risk of readmission.

METHODS

A retrospective analysis of infants born with gastroschisis between 2013 and 2019 who received initial surgical intervention and follow-up care in the Children's Wisconsin health system was performed. The primary outcome was the frequency of hospital readmission within 1 year of discharge. We also compared maternal and infant clinical and demographic variables between those readmitted for reasons related to gastroschisis, and those readmitted for other reasons or not readmitted.

RESULTS

Forty of 90 (44%) infants born with gastroschisis were readmitted within 1-year of the initial discharge date, with 33 (37%) of the 90 infants being readmitted due to reasons directly related to gastroschisis. The presence of a feeding tube (p < 0.0001), a central line at discharge (p = 0.007), complex gastroschisis (p = 0.045), conjugated hyperbilirubinemia (p = 0.035), and the number of operations during the initial hospitalization (p = 0.044) were associated with readmission. Maternal race/ethnicity was the only maternal variable associated with readmission, with Black race being less likely to be readmitted (p = 0.003). Those who were readmitted were also more likely to be seen in outpatient clinics and utilize emergency healthcare resources. There was no statistically significant difference in readmission based on socioeconomic factors (all p > 0.084).

CONCLUSION

Infants with gastroschisis have a high hospital readmission rate, which is associated with a variety of risk factors including complex gastroschisis, multiple operations, and the presence of a feeding tube or central line at discharge. Improved awareness of these risk factors may help stratify patients in need of increased parental counseling and additional follow-up.