Abstract
Backgrounds
New methods such as machine learning could provide accurate predictions with little
statistical assumptions. We seek to develop prediction model of pediatric surgical
complications based on pediatric National Surgical Quality Improvement Program(NSQIP).
Methods
All 2012–2018 pediatric-NSQIP procedures were reviewed. Primary outcome was defined
as 30-day post-operative morbidity/mortality. Morbidity was further classified as
any, major and minor. Models were developed using 2012–2017 data. 2018 data was used
as independent performance evaluation.
Results
431,148 patients were included in the 2012–2017 training and 108,604 were included
in the 2018 testing set. Our prediction models had high performance in mortality prediction
at 0.94 AUC in testing set. Our models outperformed ACS-NSQIP Calculator in all categories
for morbidity (0.90 AUC for major, 0.86 AUC for any, 0.69 AUC in minor complications).
Conclusions
We developed a high-performing pediatric surgical risk prediction model. This powerful
tool could potentially be used to improve the surgical care quality.
Keywords
Abbreviations:
ACS-NSQIP (American College of Surgeons- National Surgical Quality Improvement Program), CPT (Current Procedural Terminology), ICD-CM (International Classification of Disease diagnosis), CCS (Clinical Classification Software), HCUP (Healthcare Cost and Utilization Project)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: March 13, 2023
Accepted:
March 10,
2023
Received in revised form:
February 9,
2023
Received:
August 23,
2022
Publication stage
In Press Journal Pre-ProofIdentification
Copyright
© 2023 Elsevier Inc. All rights reserved.
