Monitoring Approaches for a Pediatric Chronic Kidney Disease Machine Learning Model

Keith E. Morse; Conner Brown; Scott Fleming; Irene Todd; Austin Powell; Alton Russell; David Scheinker; Scott M. Sutherland; Jonathan Lu; Brendan Watkins; Nigam H. Shah; Natalie M. Pageler; Jonathan P. Palma

doi:10.1055/s-0042-1746168

Applied Clinical Informatics, Table of Contents

Appl Clin Inform 2022; 13(02): 431-438
DOI: 10.1055/s-0042-1746168

Research Article

Monitoring Approaches for a Pediatric Chronic Kidney Disease Machine Learning Model

Authors

Keith E. Morse

¹Division of Pediatric Hospital Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States
Conner Brown

²Information Services Department, Lucile Packard Children's Hospital, Stanford, Palo Alto, California, United States
Scott Fleming

³Department of Biomedical Data Science, Stanford University, Palo Alto, California, United States
Irene Todd

²Information Services Department, Lucile Packard Children's Hospital, Stanford, Palo Alto, California, United States
Austin Powell

²Information Services Department, Lucile Packard Children's Hospital, Stanford, Palo Alto, California, United States
Alton Russell

⁴Harvard Medical School, Boston, Massachusetts, United States
David Scheinker

²Information Services Department, Lucile Packard Children's Hospital, Stanford, Palo Alto, California, United States
Scott M. Sutherland

⁵Division of Nephrology, Department of Pediatrics, Stanford University, Stanford, California, United States
Jonathan Lu

³Department of Biomedical Data Science, Stanford University, Palo Alto, California, United States
Brendan Watkins

²Information Services Department, Lucile Packard Children's Hospital, Stanford, Palo Alto, California, United States
Nigam H. Shah

³Department of Biomedical Data Science, Stanford University, Palo Alto, California, United States
Natalie M. Pageler

⁶Division of Pediatric Critical Care Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States

⁷Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States
Jonathan P. Palma

⁸Division of Neonatology, Department of Pediatrics, Orlando Health, Orlando, Florida, United States

Abstract

Objective The purpose of this study is to evaluate the ability of three metrics to monitor for a reduction in performance of a chronic kidney disease (CKD) model deployed at a pediatric hospital.

Methods The CKD risk model estimates a patient's risk of developing CKD 3 to 12 months following an inpatient admission. The model was developed on a retrospective dataset of 4,879 admissions from 2014 to 2018, then run silently on 1,270 admissions from April to October, 2019. Three metrics were used to monitor its performance during the silent phase: (1) standardized mean differences (SMDs); (2) performance of a “membership model”; and (3) response distribution analysis. Observed patient outcomes for the 1,270 admissions were used to calculate prospective model performance and the ability of the three metrics to detect performance changes.

Results The deployed model had an area under the receiver-operator curve (AUROC) of 0.63 in the prospective evaluation, which was a significant decrease from an AUROC of 0.76 on retrospective data (p = 0.033). Among the three metrics, SMDs were significantly different for 66/75 (88%) of the model's input variables (p <0.05) between retrospective and deployment data. The membership model was able to discriminate between the two settings (AUROC = 0.71, p <0.0001) and the response distributions were significantly different (p <0.0001) for the two settings.

Conclusion This study suggests that the three metrics examined could provide early indication of performance deterioration in deployed models' performance.

Keywords

learning algorithm - electronic health record - monitoring - machine learning - safety

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References

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