Prolonged hospital length of stay after pediatric heart transplantation: A machine learning and logistic regression predictive model from the Pediatric Heart Transplant Society
Section snippets
Materials and methods
PHTS maintains a multicenter, prospective, event‐driven database for pediatric patients (<18 years) listed for HT. Collection of date of discharge began in 2005, therefore data query spanned January 2005-December 2018, and included patients from multiple institutions (Table S1). Institutional Review Board approval was obtained at each institution. Information was collected on demographics and other patient data at listing, patient and procedural data at HT, and event data surrounding listing,
Results
A total of 4827 patients received a HT during the study period. Of these, 413 were excluded as described in Figure 2 leaving 4414 patients for analysis.
Discussion
Our study is the first to provide insight into LOS trends after pediatric HT. The use of LOS as a quality metric is clearly important as it encompasses various domains of quality.1,24,25 Furthermore, an improved understanding of factors affecting LOS can allow transplant centers to estimate their expected LOS, identify patients at risk for PLOS and counsel families accurately. Due to a paucity of LOS data in pediatric HT, heterogeneity of diagnoses and unique characteristics of pediatric
Limitations
Limitations of our study include use of 30 days as the cutoff for PLOS. This was chosen after a careful analysis of the LOS distribution of the overall cohort. Knowing these limitations, we chose this cutoff as it also aligns well with other clinically important parameters including 30-day survival and readmission data. This study is additionally subject to limitations inherent to analyses of registry data, as they are bound by preexisting variables. However, PHTS provides a comprehensive,
Conclusion
In summary, we present novel findings of LOS distribution and define PLOS after pediatric HT. This study is the first to inform about factors influencing LOS and develop a prediction model for PLOS. Furthermore, this provides a quality metric for individual programs to utilize and further study trends in their own practice. This tool will allow better risk stratification and counseling about expected LOS. Future studies are needed to assess the accuracy of this prediction model and guide
Author contributions
All authors contributed to study design and data analysis. DG drafted the manuscript and all authors assisted with writing and critical revision of the manuscript.
Foundation
This study was supported by the Pediatric Heart Transplant Society.
Disclosure statement
None
Acknowledgments
The authors thank the other investigators, staff, and statisticians at the University of Alabama at Birmingham, Alabama, and the Pediatric Heart Transplant Society
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