Characteristics and predictors of 7- and 30-day hospital readmissions to pediatric neurology

Abstract

ObjectiveHospital readmission is an important quality improvement measure that has not been well-studied in pediatric neurology. We examined predictors of 7-day and 30-day readmissions for pediatric patients hospitalized with a neurologic diagnosis.MethodsThis was a retrospective study of hospital readmission rates in pediatric neurology patients admitted to a tertiary children's hospital from January 2017 to December 2017. Inclusion criteria were age ≤18 years and a primary neurologic diagnosis on admission, with an unplanned readmission within 7 or 30 days. Demographic and clinical data were collected, including age, sex, income, insurance type, discharge occurring on a weekend, admission to the pediatric intensive care unit (PICU), use of multiple antiepileptic drugs (AEDs), and involvement of multiple subspecialties.ResultsThere were 923 neurology admissions, and 64 readmissions within 30 days. Total unplanned readmission rate was 6.9%, with 56% (36/64) readmitted within 30 days, 44% (28/64) readmitted within 7 days, and 11% (7/64) admitted multiple times within 30 days. The most common readmission diagnosis was seizure (62%), followed by other neurologic diagnosis (21%), headache (8%), encephalitis/meningitis (7%), stroke (1%), and ataxia (1%). Readmission was significantly associated with multiple AED, PICU admission, seizure with major complication or comorbidity, and presence of a major complication or comorbidity irrespective of diagnosis (p < 0.05).ConclusionsThis study identifies factors associated with higher rates of readmission for pediatric neurology patients. Patients with epilepsy and chronic neurologic conditions should be targeted for future discharge-related interventions to reduce hospital readmission and ensure safe transitions from the inpatient to the outpatient setting.

Predictors of pediatric readmissions among patients with neurological conditions

“…The number of previous unplanned readmissions as well as emergency room/department visits within the prior 6 months are associated with increased risk of readmission, whereas the risk of readmission is reduced if the index visit is a planned encounter. These results are consistent with prior studies mostly in the general pediatric and adult populations [3,10,16].…”

“…Previous studies have identified risk factors such as higher severity of illness, public health insurance, multiple antiepileptic drugs, intensive care admission, and seizures with major complication/comorbidity [3,8]. These findings are generally consistent with the results of this study.…”

“…Several proxies for severity of illness and comorbidity were all associated with increased risk of readmission. While one of the studies found that multiple antiepileptic drugs are associated with increased risk of readmission [3], our findings indicated that the higher the overall number of medications administered the higher the risk of readmission.…”

“…Improvements in the quality of care of these patients is paramount to ensuring higher quality of life and reduced morbidity and mortality. Patients with neurological conditions largely contribute to this category as they often require complex medical care, making unplanned readmissions undesirable [2][3][4]. There is a dearth of research in risk factors and predictive models of unplanned readmission among these patients, so further investigation is a key step to improving quality of care and reducing corresponding readmission rates [5].…”

“…Previous studies on unplanned readmission among patients with neurological conditions have indicated multiple anti-epilepsy drugs, pediatric intensive care unit admission, seizure with major complication or comorbidity, and presence of a major complication or comorbidity irrespective of diagnosis as factors associated with higher risk of unplanned readmission [3,8]. In this study, we aimed to identify novel factors that may predict a higher risk of unplanned readmission in pediatric neurology and provide a prediction model that may be useful to clinicians in their efforts to reduce readmission rates [9,10].…”