BACKGROUND In this interventional study, we addressed the selection and application of clinical interventions on pediatric patients identified as at risk by a predictive model for readmissions. METHODS A predictive model for readmissions was implemented, and a team of providers expanded corresponding clinical interventions for at-risk patients at a freestanding children’s hospital. Interventions encompassed social determinants of health, outpatient care, medication reconciliation, inpatient and discharge planning, and postdischarge calls and/or follow-up. Statistical process control charts were used to compare readmission rates for the 3-year period preceding adoption of the model and clinical interventions with those for the 2-year period after adoption of the model and clinical interventions. Potential financial savings were estimated by using national estimates of the cost of pediatric inpatient readmissions. RESULTS The 30-day all-cause readmission rates during the periods before and after predictive modeling (and corresponding 95% confidence intervals [CI]) were 12.5% (95% CI: 12.2%–12.8%) and 11.1% (95% CI: 10.8%–11.5%), respectively. More modest but similar improvements were observed for 7-day readmissions. Statistical process control charts indicated nonrandom reductions in readmissions after predictive model adoption. The national estimate of the cost of pediatric readmissions indicates an associated health care savings due to reduced 30-day readmission during the 2-year predictive modeling period at $2 673 264 (95% CI: $2 612 431–$2 735 364). CONCLUSIONS A combination of predictive modeling and targeted clinical interventions to improve the management of pediatric patients at high risk for readmission was successful in reducing the rate of readmission and reducing overall health care costs. The continued prioritization of patients with potentially modifiable outcomes is key to improving patient outcomes.
OBJECTIVES: The rate of pediatric 7-day unplanned readmissions is often seen as a measure of quality of care, with high rates indicative of the need for improvement of quality of care. In this study, we used machine learning on electronic health records to study predictors of pediatric 7-day readmissions. We ranked predictors by clinical significance, as determined by the magnitude of the least absolute shrinkage and selection operator regression coefficients. METHODS: Data consisting of 50 241 inpatient and observation encounters at a single tertiary pediatric hospital were retrieved; 50% of these patients’ data were used for building a least absolute shrinkage and selection operator regression model, whereas the other half of the data were used for evaluating model performance. The categories of variables included were demographics, social determinants of health, severity of illness and acuity, resource use, diagnoses, medications, psychosocial factors, and other variables such as primary care no show. RESULTS: Previous hospitalizations and readmissions, medications, multiple comorbidities, longer current and previous lengths of stay, certain diagnoses, and previous emergency department use were the most significant predictors modifying a patient’s risk of 7-day pediatric readmission. The model achieved an area under the curve of 0.778 (95% confidence interval 0.763–0.793). CONCLUSIONS: Predictors such as medications, previous and current health care resource use, history of readmissions, severity of illness and acuity, and certain psychosocial factors modified the risk of unplanned 7-day readmissions. These predictors are mostly unmodifiable, indicating that intervention plans on high-risk patients may be developed through discussions with patients and parents to identify underlying modifiable causal factors of readmissions.
OBJECTIVES: To develop a model to assist clinicians in reducing 30-day unplanned pediatric readmissions and to enhance understanding of risk factors leading to such readmissions. METHODS: Data consisting of 38 143 inpatient clinical encounters at a tertiary pediatric hospital were retrieved, and 50% were used for training on a multivariate logistic regression model. The pediatric Rothman Index (pRI) was 1 of the novel candidate predictors considered. Multivariate model selection was conducted by minimization of Akaike Information Criteria. The area under the receiver operator characteristic curve (AUC) and values for sensitivity, specificity, positive predictive value, relative risk, and accuracy were computed on the remaining 50% of the data. RESULTS: The multivariate logistic regression model of readmission consists of 7 disease diagnosis groups, 4 measures of hospital resource use, 3 measures of disease severity and/or medical complexities, and 2 variables derived from the pRI. Four of the predictors are novel, including history of previous 30-day readmissions within last 6 months (P < .001), planned admissions (P < .001), the discharge pRI score (P < .001), and indicator of whether the maximum pRI occurred during the last 24 hours of hospitalization (P = .005). An AUC of 0.79 (0.77–0.80) was obtained on the independent test data set. CONCLUSIONS: Our model provides significant performance improvements in the prediction of unplanned 30-day pediatric readmissions with AUC higher than the LACE readmission model and other general unplanned 30-day pediatric readmission models. The model is expected to provide an opportunity to capture 39% of readmissions (at a selected operating point) and may therefore assist clinicians in reducing avoidable readmissions.
BACKGROUND: Malnutrition is known to be associated with higher morbidity and a risk factor of readmissions in the adult population. In this study, we explore the effect of malnutrition in pediatrics because it may differ from the adult population. METHODS: Data for all inpatient encounters at a tertiary children’s hospital within a 2-year period corresponding to 19 702 visits were obtained. The data included demographics, socioeconomic status, registered dietitian diagnosis of malnutrition, and variables of the LACE readmission model. We excluded all neonates and patients older than 21 years. A multivariable logistic model was obtained by implementing best subset regression on these variables, controlling for demographics and socioeconomic status, and considering all possible 2-way statistical interactions between malnutrition and the variables for demographics and socioeconomic status. RESULTS: We discovered a statistical interaction effect between a patient’s age and malnutrition status (P value = .002) with respect to odds of unplanned 7-day readmission. It is indicated in this interaction term that patients who were malnourished had higher odds of readmission than patients who were not malnourished. Furthermore, younger patients who were malnourished were at increased odds of readmission than their older peers, whereas among patients who were not malnourished, younger patients were at reduced odds of readmission. CONCLUSIONS: The statistical interaction effect revealed that a patient’s risk of readmission is jointly modified by the patient’s age and malnutrition status. This finding advances our understanding of the complex picture of the simultaneous risk factor of unplanned 7-day readmissions in pediatrics.