Prediction of Systemic-to-Pulmonary Artery shunt surgery outcomes using administrative data

Sara Moein, Hao Yan, Sanmay Das, Matthew Hall, Pirooz A. Eghtesady

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Systemic-to-Pulmonary Artery (SPA) shunt surgery, one of the most common cardiac surgical procedures in the newborn period, provides a means to palliate children with limited pulmonary blood flow, such as in Tetralogy of Fallot. Despite the simplicity of the procedure, it is associated with significant morbidity (such as need for extracorporeal membrane oxygenation (ECMO), and long post-operative length of stay (PLOS) in the hospital following surgery) and mortality. These outcomes are known to be impacted by a number of complex factors (including patient specific and procedure specific factors, perioperative related factors, etc.), whose relative importance in clinical decision making remains the domain of clinical judgment. The increasing availability of multi-modal data on patient care and outcomes opens up the opportunity to assess clinical practices from a more data-driven perspective. In this paper, we report results from a study of 1036 patients (from 44 children's hospitals across the US) during 2009-2014 that applies a machine learning approach to predicting post-operative outcomes for patients in the Pediatric Health Information System (PHIS) database. We demonstrate that it is feasible to achieve significant prediction benefits using a standard machine learning approach (random forests) on a carefully constructed dataset, showing the value of applying machine learning even with noisy administrative databases. The methods we describe can be used to identify potential important variables that lead to good clinical judgment as defined by desirable clinical outcomes.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Editorslng. Matthieu Schapranow, Jiayu Zhou, Xiaohua Tony Hu, Bin Ma, Sanguthevar Rajasekaran, Satoru Miyano, Illhoi Yoo, Brian Pierce, Amarda Shehu, Vijay K. Gombar, Brian Chen, Vinay Pai, Jun Huan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages737-741
Number of pages5
ISBN (Electronic)9781467367981
DOIs
StatePublished - Dec 16 2015
EventIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 - Washington, United States
Duration: Nov 9 2015Nov 12 2015

Publication series

NameProceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

Conference

ConferenceIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
CountryUnited States
CityWashington
Period11/9/1511/12/15

Keywords

  • Feature Importance
  • Prediction
  • Random Forest (RF)
  • Systemic-to-Pulmonary Artery (SPA) Shunt Surgery

Fingerprint Dive into the research topics of 'Prediction of Systemic-to-Pulmonary Artery shunt surgery outcomes using administrative data'. Together they form a unique fingerprint.

Cite this