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Development and Validation of a Web-Based Pediatric Readmission Risk Assessment Tool.
Hosp Pediatr. 2020 03; 10(3):246-256.HP

Abstract

OBJECTIVES

Accurately predicting and reducing risk of unplanned readmissions (URs) in pediatric care remains difficult. We sought to develop a set of accurate algorithms to predict URs within 3, 7, and 30 days of discharge from inpatient admission that can be used before the patient is discharged from a current hospital stay.

METHODS

We used the Children's Hospital Association Pediatric Health Information System to identify a large retrospective cohort of 1 111 323 children with 1 321 376 admissions admitted to inpatient care at least once between January 1, 2016, and December 31, 2017. We used gradient boosting trees (XGBoost) to accommodate complex interactions between these predictors.

RESULTS

In the full cohort, 1.6% of patients had at least 1 UR in 3 days, 2.4% had at least 1 UR in 7 days, and 4.4% had at least 1 UR within 30 days. Prediction model discrimination was strongest for URs within 30 days (area under the curve [AUC] = 0.811; 95% confidence interval [CI]: 0.808-0.814) and was nearly identical for UR risk prediction within 3 days (AUC = 0.771; 95% CI: 0.765-0.777) and 7 days (AUC = 0.778; 95% CI: 0.773-0.782), respectively. Using these prediction models, we developed a publicly available pediatric readmission risk scores prediction tool that can be used before or during discharge planning.

CONCLUSIONS

Risk of pediatric UR can be predicted with information known before the patient's discharge and that is easily extracted in many electronic medical record systems. This information can be used to predict risk of readmission to support hospital-discharge-planning resources.

Authors+Show Affiliations

Nicklaus Children's Research Institute, thomas.taylor@nicklaushealth.org. Nicklaus Children's Health System, Miami, Florida; and. Research Facilitation Laboratory, Northrop Grumman, Monterey, California.Nicklaus Children's Health System, Miami, Florida; and.Nicklaus Children's Research Institute. Nicklaus Children's Health System, Miami, Florida; and.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

32075853

Citation

Taylor, Thom, et al. "Development and Validation of a Web-Based Pediatric Readmission Risk Assessment Tool." Hospital Pediatrics, vol. 10, no. 3, 2020, pp. 246-256.
Taylor T, Altares Sarik D, Salyakina D. Development and Validation of a Web-Based Pediatric Readmission Risk Assessment Tool. Hosp Pediatr. 2020;10(3):246-256.
Taylor, T., Altares Sarik, D., & Salyakina, D. (2020). Development and Validation of a Web-Based Pediatric Readmission Risk Assessment Tool. Hospital Pediatrics, 10(3), 246-256. https://doi.org/10.1542/hpeds.2019-0241
Taylor T, Altares Sarik D, Salyakina D. Development and Validation of a Web-Based Pediatric Readmission Risk Assessment Tool. Hosp Pediatr. 2020;10(3):246-256. PubMed PMID: 32075853.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Development and Validation of a Web-Based Pediatric Readmission Risk Assessment Tool. AU - Taylor,Thom, AU - Altares Sarik,Danielle, AU - Salyakina,Daria, PY - 2020/2/23/pubmed PY - 2021/6/29/medline PY - 2020/2/21/entrez SP - 246 EP - 256 JF - Hospital pediatrics JO - Hosp Pediatr VL - 10 IS - 3 N2 - OBJECTIVES: Accurately predicting and reducing risk of unplanned readmissions (URs) in pediatric care remains difficult. We sought to develop a set of accurate algorithms to predict URs within 3, 7, and 30 days of discharge from inpatient admission that can be used before the patient is discharged from a current hospital stay. METHODS: We used the Children's Hospital Association Pediatric Health Information System to identify a large retrospective cohort of 1 111 323 children with 1 321 376 admissions admitted to inpatient care at least once between January 1, 2016, and December 31, 2017. We used gradient boosting trees (XGBoost) to accommodate complex interactions between these predictors. RESULTS: In the full cohort, 1.6% of patients had at least 1 UR in 3 days, 2.4% had at least 1 UR in 7 days, and 4.4% had at least 1 UR within 30 days. Prediction model discrimination was strongest for URs within 30 days (area under the curve [AUC] = 0.811; 95% confidence interval [CI]: 0.808-0.814) and was nearly identical for UR risk prediction within 3 days (AUC = 0.771; 95% CI: 0.765-0.777) and 7 days (AUC = 0.778; 95% CI: 0.773-0.782), respectively. Using these prediction models, we developed a publicly available pediatric readmission risk scores prediction tool that can be used before or during discharge planning. CONCLUSIONS: Risk of pediatric UR can be predicted with information known before the patient's discharge and that is easily extracted in many electronic medical record systems. This information can be used to predict risk of readmission to support hospital-discharge-planning resources. SN - 2154-1671 UR - https://www.unboundmedicine.com/medline/citation/32075853/Development_and_Validation_of_a_Web_Based_Pediatric_Readmission_Risk_Assessment_Tool_ L2 - https://hosppeds.aappublications.org/lookup/pmidlookup?view=long&pmid=32075853 DB - PRIME DP - Unbound Medicine ER -