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Resuscitation. 2014 Aug;85(8):1065-71. doi: 10.1016/j.resuscitation.2014.04.009. Epub 2014 May 9.

Developing and evaluating a machine learning based algorithm to predict the need of pediatric intensive care unit transfer for newly hospitalized children.

Author information

1
Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
2
Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; James M. Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
3
Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
4
Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; James M. Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. Electronic address: imre.solti@cchmc.org.

Abstract

BACKGROUND:

Early warning scores (EWS) are designed to identify early clinical deterioration by combining physiologic and/or laboratory measures to generate a quantified score. Current EWS leverage only a small fraction of Electronic Health Record (EHR) content. The planned widespread implementation of EHRs brings the promise of abundant data resources for prediction purposes. The three specific aims of our research are: (1) to develop an EHR-based automated algorithm to predict the need for Pediatric Intensive Care Unit (PICU) transfer in the first 24h of admission; (2) to evaluate the performance of the new algorithm on a held-out test data set; and (3) to compare the effectiveness of the new algorithm's with those of two published Pediatric Early Warning Scores (PEWS).

METHODS:

The cases were comprised of 526 encounters with 24-h Pediatric Intensive Care Unit (PICU) transfer. In addition to the cases, we randomly selected 6772 control encounters from 62516 inpatient admissions that were never transferred to the PICU. We used 29 variables in a logistic regression and compared our algorithm against two published PEWS on a held-out test data set.

RESULTS:

The logistic regression algorithm achieved 0.849 (95% CI 0.753-0.945) sensitivity, 0.859 (95% CI 0.850-0.868) specificity and 0.912 (95% CI 0.905-0.919) area under the curve (AUC) in the test set. Our algorithm's AUC was significantly higher, by 11.8 and 22.6% in the test set, than two published PEWS.

CONCLUSION:

The novel algorithm achieved higher sensitivity, specificity, and AUC than the two PEWS reported in the literature.

KEYWORDS:

Clinical care; Clinical status deterioration; EHR; Machine learning; PEWS; PICU

[Indexed for MEDLINE]
Free PMC Article

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