Literature DB >> 33402138

Predictors of pediatric readmissions among patients with neurological conditions.

Ryan O'Connell1,2,3, William Feaster1,2, Vera Wang1,4, Sharief Taraman1,2,5, Louis Ehwerhemuepha6,7,8.   

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.

Entities:  

Keywords:  Indicators; Neurology; Pediatric; Predictors; Readmission

Mesh:

Year:  2021        PMID: 33402138      PMCID: PMC7784269          DOI: 10.1186/s12883-020-02028-0

Source DB:  PubMed          Journal:  BMC Neurol        ISSN: 1471-2377            Impact factor:   2.474


  13 in total

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2.  Hospital readmissions and the Affordable Care Act: paying for coordinated quality care.

Authors:  Robert P Kocher; Eli Y Adashi
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3.  National characteristics and predictors of neurologic 30-day readmissions.

Authors:  Elan L Guterman; Vanja C Douglas; Maulik P Shah; Tennille Parsons; Julio Barba; S Andrew Josephson
Journal:  Neurology       Date:  2016-01-20       Impact factor: 9.910

Review 4.  Risk prediction models for hospital readmission: a systematic review.

Authors:  Devan Kansagara; Honora Englander; Amanda Salanitro; David Kagen; Cecelia Theobald; Michele Freeman; Sunil Kripalani
Journal:  JAMA       Date:  2011-10-19       Impact factor: 56.272

5.  A requirement to reduce readmissions: take care of the patient, not just the disease.

Authors:  Mark V Williams
Journal:  JAMA       Date:  2013-01-23       Impact factor: 56.272

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

Authors:  Annie Hong; Yash Shah; Kanwaljit Singh; Shefali Karkare; Sanjeev Kothare
Journal:  Neurology       Date:  2019-03-20       Impact factor: 9.910

7.  A Statistical-Learning Model for Unplanned 7-Day Readmission in Pediatrics.

Authors:  Louis Ehwerhemuepha; Karen Pugh; Alex Grant; Sharief Taraman; Anthony Chang; Cyril Rakovski; William Feaster
Journal:  Hosp Pediatr       Date:  2019-12-06

8.  Pediatric readmission prevalence and variability across hospitals.

Authors:  Jay G Berry; Sara L Toomey; Alan M Zaslavsky; Ashish K Jha; Mari M Nakamura; David J Klein; Jeremy Y Feng; Shanna Shulman; Vincent W Chiang; Vincent K Chiang; William Kaplan; Matt Hall; Mark A Schuster
Journal:  JAMA       Date:  2013-01-23       Impact factor: 56.272

9.  Patterns and costs of health care use of children with medical complexity.

Authors:  Eyal Cohen; Jay G Berry; Ximena Camacho; Geoff Anderson; Walter Wodchis; Astrid Guttmann
Journal:  Pediatrics       Date:  2012-11-26       Impact factor: 7.124

10.  HealtheDataLab - a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions.

Authors:  Louis Ehwerhemuepha; Gary Gasperino; Nathaniel Bischoff; Sharief Taraman; Anthony Chang; William Feaster
Journal:  BMC Med Inform Decis Mak       Date:  2020-06-19       Impact factor: 2.796

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

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