Developing Clinical Prediction Models for 30-day Readmission in the General and Medically Complex Pediatric Populations
readmissions negatively impact patient quality of life and incur substantial healthcare
costs. To target resources to prevent readmission, this study sought to develop clinical
prediction models for 30-day readmission in the general pediatric population and for
children with medical complexity (CMC). Sociodemographic and clinical characteristics
were extracted from electronic ... read morehealth records for pediatric patients aged 6 months to 18
years admitted at an urban academic medical center between October 1, 2010 and July 31,
2016. Factors associated with unplanned 30-day readmission on univariate screen were
candidates for the multivariable logistic regression models. Using backward selection,
we derived a model predicting readmission utilizing characteristics obtainable at
admission ("model at admission"). A second model was derived including variables
available by hospital discharge ("model at discharge"). Model performance was assessed
using c-statistic and calibration curves, and bootstrap resampling was performed for
internal validation. CMC-specific models were developed and evaluated by repeating these
procedures in the subgroup of medically complex children. Of the 7,068 general pediatric
index admissions during the study, 313 (4.4%) had an unplanned readmission within 30
days. The model at admission included the following variables: non-English language,
prior admissions, prior emergency department (ED) visits, number of home medications,
medical complexity, technology assistance, and medical versus surgical admission
(c-statistic 0.68). The model at discharge included all these variables plus length of
stay, weekday discharge, and discharge disposition (c-statistic 0.69). For the CMC
subgroup, of 2,296 index admissions, 188 (8.2%) had readmissions. The CMC model at
admission included prior admissions, prior ED visits, number of complex chronic
conditions and medical versus surgical admission (c-statistic 0.65). When including
variables available at discharge, the model also included length of stay, weekday
discharge, and discharge disposition (c-statistic 0.67). Patients in the highest risk
quartiles had 3.6 to 4.5 times higher readmission rates compared with patients in the
lowest risk quartiles for all models. Bootstrap samples had similar c-statistics, and
slopes did not suggest substantial overfitting in any model. In conclusion, easily
obtainable clinical characteristics are useful in identifying children at particularly
high risk for readmission. These high risk children may be an appropriate target for
interventions to prevent readmissions. Future proposals will involve external validation
of the models and will explore whether the models can be used to target resources aimed
at decreasing readmissions.
Thesis (M.S.)--Tufts University, 2018.
Submitted to the Dept. of Clinical & Translational Science.
Advisor: Karen Freund.
Committee: John Wong, David Kent, and Lori Lyn Price.
Keyword: Medicine.read less