Developing Prediction Models for 30-Day Unplanned Readmission Among Children With Medical Complexity

Abstract

A B S T R A C TOBJECTIVES: To target interventions to prevent readmission, we sought to develop clinical prediction models for 30-day readmission among children with complex chronic conditions (CCCs). METHODS:After extracting sociodemographic and clinical characteristics from electronic health records for children with CCCs admitted to an academic medical center, we constructed a multivariable logistic regression model to predict readmission from characteristics obtainable at admission and then a second model adding hospitalization and discharge variables to the first model. We assessed model performance using c-statistic and calibration curves and internal validation using bootstrapping. We then created readmission risk scoring systems from final model b-coefficients. RESULTS:Of the 2296 index admissions involving children with CCCs, 188 (8.2%) had unplanned 30-day readmissions. The model with admission characteristics included previous admissions, previous emergency department visits, number of CCC categories, and medical versus surgical admission (c-statistic 0.65). The model with hospitalization and discharge factors added discharge disposition, length of stay, and weekday discharge to the admission variables (c-statistic 0.67). Bootstrap samples had similar c-statistics, and slopes did not suggest significant overfitting for either model. Readmission risk was 3.6% to 4.9% in the lowest risk quartile versus 15.9% to 17.6% in the highest risk quartile (or 3.6-4.5 times higher) for both models. CONCLUSIONS:Clinical variables related to the degree of medical complexity and illness severity can stratify children with CCCs into groups with clinically meaningful differences in the risk of readmission. Future research will explore whether these models can be used to target interventions and resources aimed at decreasing readmissions.

Predictors of pediatric readmissions among patients with neurological conditions

“…Sharon Disney Lund Medical Intelligence and Innovation Institute (MI3) at CHOC, Orange, California, USA. 2 Children's Hospital of Orange County (CHOC), Orange, California, USA. 3 Department of Emergency Medicine, University of California -Irvine, Irvine, California, USA.…”

“…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].…”

Parent Perspectives During Hospital Readmissions for Children With Medical Complexity: A Qualitative Study

“…Also, by allowing for the inclusion of children with any type of complex chronic condition, different themes may have arisen than would have occurred within specific CMC subgroups (eg, technology-dependent children). We were unable to reach many families of patients with short readmissions; although the characteristics of our enrolled participants more closely reflect the institution's broader population of CMC compared with those not enrolled, 39 it is possible that families not enrolled may have provided different perspectives. We were only able to interview 1 family with a child who was originally discharged to a subacute facility, and it is reasonable to believe that families working with other facilities may experience additional challenges.…”

Development and Validation of a Web-Based Pediatric Readmission Risk Assessment Tool

“…Moreover, these scoring algorithms often either exclude children altogether 12 or otherwise exclude important pediatric subgroups, such as neonates. 14 Furthermore, PAs may rely on data elements that are not known until after the patient is discharged. 15,16 Also of concern is that some PAs use measures that are derived for adult populations and are inappropriate for pediatrics, 17 such as the Charlson Comorbidity Index score.…”

“…18,19 Time horizons for risk prediction also remain limited, with published pediatric PAs to date having focused solely on 30-day readmission risk. 14,20 Yet, URs that happen sooner may be of even more concern to young patients, their families, and the pediatric health care stakeholders who care for them. Evidence suggests that URs that occur soon after discharge are likely the readmissions that are more preventable 21,22 and may represent patients who, if identified before discharge, may be easier to intervene on in a hospital setting.…”