Skip to main content
Top
Published in: BMC Neurology 1/2021

Open Access 01-12-2021 | Research article

Predictors of pediatric readmissions among patients with neurological conditions

Authors: Ryan O’Connell, William Feaster, Vera Wang, Sharief Taraman, Louis Ehwerhemuepha

Published in: BMC Neurology | Issue 1/2021

Login to get access

Abstract

Background

Unplanned readmission is one of many measures of the quality of care of pediatric patients with neurological conditions. In this multicenter study, we searched for novel risk factors of readmission of patients with neurological conditions.

Methods

We retrieved hospitalization data of patients less than 18 years with one or more neurological conditions. This resulted in a total of 105,834 encounters from 18 hospitals. We included data on patient demographics, prior healthcare resource utilization, neurological conditions, number of other conditions/diagnoses, number of medications, and number of surgical procedures performed. We developed a random intercept logistic regression model using stepwise minimization of Akaike Information Criteria for variable selection.

Results

The most important neurological conditions associated with unplanned pediatric readmissions include hydrocephalus, inflammatory diseases of the central nervous system, sleep disorders, disease of myoneural junction and muscle, other central nervous system disorder, other spinal cord conditions (such as vascular myelopathies, and cord compression), and nerve, nerve root and plexus disorders. Current and prior healthcare resource utilization variables, number of medications, other diagnoses, and certain inpatient surgical procedures were associated with changes in odds of readmission. The area under the receiver operator characteristic curve (AUROC) on the independent test set is 0.733 (0.722, 0.743).

Conclusions

Pediatric patients with certain neurological conditions are more likely to be readmitted than others. However, current and prior healthcare resource utilization remain some of the strongest indicators of readmission within this population as in the general pediatric population.
Appendix
Available only for authorised users
Literature
1.
go back to reference Cohen E, Berry JG, Camacho X, Anderson G, Wodchis W, Guttmann A. Patterns and costs of health care use of children with medical complexity. Pediatrics. 2012;130(6):e1463–70.CrossRef Cohen E, Berry JG, Camacho X, Anderson G, Wodchis W, Guttmann A. Patterns and costs of health care use of children with medical complexity. Pediatrics. 2012;130(6):e1463–70.CrossRef
2.
go back to reference Leary JC, Price LL, Scott CER, Kent D, Wong JB, Freund KM. Developing prediction models for 30-day unplanned readmission among children with medical complexity. Hosp Pediatr. 2019;9(3):201–8.CrossRef Leary JC, Price LL, Scott CER, Kent D, Wong JB, Freund KM. Developing prediction models for 30-day unplanned readmission among children with medical complexity. Hosp Pediatr. 2019;9(3):201–8.CrossRef
4.
go back to reference Berry JG, Toomey SL, Zaslavsky AM, et al. Pediatric readmission prevalence and variability across hospitals. JAMA. 2013;309(4):372–80.CrossRef Berry JG, Toomey SL, Zaslavsky AM, et al. Pediatric readmission prevalence and variability across hospitals. JAMA. 2013;309(4):372–80.CrossRef
5.
go back to reference Simon TD, Berry J, Feudtner C, et al. Children with complex chronic conditions in inpatient hospital settings in the United States. Pediatrics. 2010;126(4):647–55.CrossRef Simon TD, Berry J, Feudtner C, et al. Children with complex chronic conditions in inpatient hospital settings in the United States. Pediatrics. 2010;126(4):647–55.CrossRef
9.
go back to reference Ehwerhemuepha L, Pugh K, Grant A, et al. A statistical-learning model for unplanned 7-day readmission in pediatrics. Hosp Pediatr. 2020;10(1):43–51.CrossRef Ehwerhemuepha L, Pugh K, Grant A, et al. A statistical-learning model for unplanned 7-day readmission in pediatrics. Hosp Pediatr. 2020;10(1):43–51.CrossRef
10.
go back to reference Ehwerhemuepha L, Finn S, Rothman MJ, Rakovski C, Feaster W. A novel model for enhanced prediction and understanding of unplanned 30-day pediatric readmission. Hosp Pediatr. 2018;8(9):578–87.CrossRef Ehwerhemuepha L, Finn S, Rothman MJ, Rakovski C, Feaster W. A novel model for enhanced prediction and understanding of unplanned 30-day pediatric readmission. Hosp Pediatr. 2018;8(9):578–87.CrossRef
11.
go back to reference Heinze G, Schemper M. A solution to the problem of separation in logistic regression. Stat Med. 2002;21(16):2409–19.CrossRef Heinze G, Schemper M. A solution to the problem of separation in logistic regression. Stat Med. 2002;21(16):2409–19.CrossRef
12.
go back to reference O’brien RM. A caution regarding rules of thumb for variance inflation factors. Qual Quant. 2007;41(5):673–90.CrossRef O’brien RM. A caution regarding rules of thumb for variance inflation factors. Qual Quant. 2007;41(5):673–90.CrossRef
13.
go back to reference Bozdogan H. Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions. Psychometrika. 1987;52(3):345-70. Bozdogan H. Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions. Psychometrika. 1987;52(3):345-70.
14.
go back to reference Ehwerhemuepha L, Gasperino G, Bischoff N, Taraman S, Chang A, Feaster W. HealtheDataLab - a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions. BMC Med Inform Decis Mak. 2020;20(1):1–12. https://doi.org/10.1186/s12911-020-01153-7.CrossRef Ehwerhemuepha L, Gasperino G, Bischoff N, Taraman S, Chang A, Feaster W. HealtheDataLab - a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions. BMC Med Inform Decis Mak. 2020;20(1):1–12. https://​doi.​org/​10.​1186/​s12911-020-01153-7.CrossRef
16.
go back to reference Kansagara D, Englander H, Salanitro A, et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688–98.CrossRef Kansagara D, Englander H, Salanitro A, et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688–98.CrossRef
Metadata
Title
Predictors of pediatric readmissions among patients with neurological conditions
Authors
Ryan O’Connell
William Feaster
Vera Wang
Sharief Taraman
Louis Ehwerhemuepha
Publication date
01-12-2021
Publisher
BioMed Central
Published in
BMC Neurology / Issue 1/2021
Electronic ISSN: 1471-2377
DOI
https://doi.org/10.1186/s12883-020-02028-0

Other articles of this Issue 1/2021

BMC Neurology 1/2021 Go to the issue