Development and Validation of an Integrated Suite of Prediction Models for All-Cause 30-Day Readmissions of Children and Adolescents Aged 0 to 18 Years

Abstract

ImportanceReadmission is often considered a hospital quality measure, yet no validated risk prediction models exist for children.ObjectiveTo develop and validate a tool identifying patients before hospital discharge who are at risk for subsequent readmission, applicable to all ages.Design, Setting, and ParticipantsThis population-based prognostic analysis used electronic health record–derived data from a freestanding children’s hospital from January 1, 2016, to December 31, 2019. All-cause 30-day readmission was modeled using 3 years of discharge data. Data were analyzed from June 1 to November 30, 2021.Main Outcomes and MeasuresThree models were derived as a complementary suite to include (1) children 6 months or older with 1 or more prior hospitalizations within the last 6 months (recent admission model [RAM]), (2) children 6 months or older with no prior hospitalizations in the last 6 months (new admission model [NAM]), and (3) children younger than 6 months (young infant model [YIM]). Generalized mixed linear models were used for all analyses. Models were validated using an additional year of discharges.ResultsThe derivation set contained 29 988 patients with 48 019 hospitalizations; 50.1% of these admissions were for children younger than 5 years and 54.7% were boys. In the derivation set, 4878 of 13 490 admissions (36.2%) in the RAM cohort, 2044 of 27 531 (7.4%) in the NAM cohort, and 855 of 6998 (12.2%) in the YIM cohort were followed within 30 days by a readmission. In the RAM cohort, prior utilization, current or prior procedures indicative of severity of illness (transfusion, ventilation, or central venous catheter), commercial insurance, and prolonged length of stay (LOS) were associated with readmission. In the NAM cohort, procedures, prolonged LOS, and emergency department visit in the past 6 months were associated with readmission. In the YIM cohort, LOS, prior visits, and critical procedures were associated with readmission. The area under the receiver operating characteristics curve was 83.1 (95% CI, 82.4-83.8) for the RAM cohort, 76.1 (95% CI, 75.0-77.2) for the NAM cohort, and 80.3 (95% CI, 78.8-81.9) for the YIM cohort.Conclusions and RelevanceIn this prognostic study, the suite of 3 prediction models had acceptable to excellent discrimination for children. These models may allow future improvements in tailored discharge preparedness to prevent high-risk readmissions.

Predictive Value of LACE Scores for Pediatric Readmissions

“… 31 However, other components of LACE score, namely, length of stay and emergency department visits, have been independently associated with pediatric readmissions. 32 The question is whether the LACE score or elements of it can be integrated into other algorithms that were derived and validated in the pediatric population, such as the pediatric complex chronic conditions classification system and the pediatric medical complexity algorithm, which stratify patients based on medical complexity rather than comorbidities. 33,34 …”

Predictive value of clinician impression for readmission and postdischarge mortality among neonates and young children in Dar es Salaam, Tanzania and Monrovia, Liberia

“…[2][3][4][5] Childhood mortality rates in the period immediately after hospitalisation for an illness (ie, the postdischarge period) approximate and may even outpace rates of mortality during hospitalisation. 5 6 Although clinical prediction rules for all-cause hospital readmissions among children in settings like the USA have been developed that include variables such as prior healthcare utilisation and markers of illness severity, 7 to our knowledge, there are currently no clinical prediction rules for hospital readmissions among children in sub-Saharan Africa, where readmissions are common. Differences in healthcare access and disease prevalence in the USA and sub-Saharan Africa necessitate the creation of clinical prediction rules catered to settings in sub-Saharan Africa.…”