Prediction of Acute Kidney Injury With a Machine Learning Algorithm Using Electronic Health Record Data
Abstract
Abstract
Background:
Objective:
Design:
Setting:
Patients:
Measurements:
Methods:
Results:
Limitations:
Conclusions:
Abrégé
Contexte:
Objectif de l’étude:
Type d’étude:
Cadre de l’étude:
Participants:
Mesures:
Méthodologie:
Résultats:
Limites de l’étude:
Conclusion:
What was known before
What this adds
Introduction
Materials and Methods
Data Sources
Statistical Analysis
Data Processing
Imputation and Feature Creation
Gold Standard
Machine Learning and Experimental Methods
Results
Participants
Characteristic | BIDMC (%) | Stanford (%) |
---|---|---|
Gender | ||
Female | 43.66 | 51.19 |
Male | 56.44 | 48.81 |
Age (years) | ||
18-29 | 4.51 | 15.23 |
30-39 | 5.26 | 11.22 |
40-49 | 10.64 | 11.22 |
50-59 | 17.50 | 13.20 |
60-69 | 20.98 | 12.69 |
70+ | 40.91 | 14.07 |
Severe AKI based on NHS England algorithma | ||
Yes | 2.7% | 0.5% |
No | 97.3% | 99.5% |
In-hospital death | ||
Yes | 9.2% | 2.78% |
No | 90.8% | 97.22% |
Main Results
Prediction time | Onset | 12 hours | 24 hours | 48 hours | 72 hours | |||||
---|---|---|---|---|---|---|---|---|---|---|
Predictor | MLA | SOFA | MLA | SOFA | MLA | SOFA | MLA | SOFA | MLA | SOFA |
AUROC (95% CI) | 0.841 (0.837-0.844) | 0.762 | 0.749 (0.744-0.755) | 0.734 | 0.758 (0.754-0.762) | 0.716 | 0.707 (0.701-0.713) | 0.675 | 0.674 (0.669-0.679) | 0.653 |
Sensitivity | 0.81 | 0.55 | 0.77 | 0.54 | 0.83 | 0.78 | 0.83 | 0.84 | 0.82 | 0.82 |
Specificity | 0.75 | 0.79 | 0.62 | 0.78 | 0.56 | 0.57 | 0.48 | 0.41 | 0.45 | 0.39 |
Accuracy | 0.81 | 0.57 | 0.76 | 0.55 | 0.82 | 0.76 | 0.82 | 0.81 | 0.80 | 0.79 |
DOR | 13.1 | 4.8 | 5.5 | 4.2 | 6.2 | 4.7 | 4.5 | 3.6 | 3.7 | 3.0 |
LR+ | 3.3 | 2.7 | 2.0 | 2.5 | 1.9 | 1.8 | 1.6 | 1.4 | 1.5 | 1.3 |
LR− | 0.25 | 0.56 | 0.37 | 0.59 | 0.30 | 0.39 | 0.35 | 0.39 | 0.40 | 0.46 |
Prediction time | Onset | 12 hours | 24 hours | 48 hours | 72 hours | |||||
---|---|---|---|---|---|---|---|---|---|---|
Predictor | MLA | SOFA | MLA | SOFA | MLA | SOFA | MLA | SOFA | MLA | SOFA |
AUROC (95% CI) | 0.872 (0.867-0.878) | 0.815 | 0.800 (0.792-0.809) | 0.781 | 0.795 (0.785-0.804) | 0.764 | 0.761 (0.753-0.768) | 0.732 | 0.728 (0.719-0.737) | 0.720 |
Sensitivity | 0.77 | 0.73 | 0.75 | 0.73 | 0.79 | 0.55 | 0.85 | 0.53 | 0.78 | 0.51 |
Specificity | 0.82 | 0.78 | 0.73 | 0.74 | 0.64 | 0.83 | 0.51 | 0.79 | 0.53 | 0.81 |
Accuracy | 0.78 | 0.73 | 0.75 | 0.73 | 0.79 | 0.56 | 0.84 | 0.54 | 0.79 | 0.53 |
DOR | 15.5 | 9.7 | 8.0 | 7.3 | 6.9 | 5.9 | 5.8 | 4.3 | 4.4 | 4.3 |
LR+ | 4.3 | 3.4 | 2.7 | 2.7 | 2.2 | 3.2 | 1.7 | 2.6 | 1.7 | 2.7 |
LR− | 0.28 | 0.35 | 0.34 | 0.37 | 0.32 | 0.55 | 0.30 | 0.60 | 0.38 | 0.61 |
Trained on BIDMC | Trained on Stanford | |||||
---|---|---|---|---|---|---|
Onset | 12 hours | 24 hours | Onset | 12 hours | 24 hours | |
AUROC (95% CI) | 0.924 (0. 872-0.975) | 0.914 (0.814-0.999) | 0.882 (0.669-0.999) | 0.844 (0.716-0.972) | 0.826 (0.716-0.935) | 0.760 (0.591, 0.929) |
Sensitivity | 0.987 | 0.999 | 0.900 | 0.981 | 0.971 | 0.933 |
Specificity | 0.912 | 0.907 | 0.879 | 0.715 | 0.719 | 0.602 |
Discussion
Limitations
Conclusion
Acknowledgments
Ethics Approval and Consent to Participate
Consent for Publication
Declaration of Conflicting Interests
Funding
Data availability statement
References
Supplementary Material
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This article was published in Canadian Journal of Kidney Health and Disease.
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