Akhil Vaid, Lili Chan, Kumardeep Chaudhary, Suraj K. Jaladanki, Ishan Paranjpe, Adam J. Russak, Arash Kia, Prem Timsina, Matthew A. Levin, John Cijiang He, Erwin Böttinger, Alexander W. Charney, Zahi A. Fayad, Steven G. Coca, Benjamin S. Glicksberg, Girish N. Nadkarni
- Background and objectives
AKI treated with dialysis initiation is a common complication of coronavirus disease 2019 (COVID-19) among hospitalized patients. However, dialysis supplies and personnel are often limited.
Design, setting, participants, & measurements
Using data from adult patients hospitalized with COVID-19 from five hospitals from theMount Sinai Health System who were admitted between March 10 and December 26, 2020, we developed and validated several models (logistic regression, Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and eXtreme GradientBoosting [XGBoost; with and without imputation]) for predicting treatment with dialysis or death at various time horizons (1, 3, 5, and 7 days) after hospital admission. Patients admitted to theMount Sinai Hospital were used for internal validation, whereas the other hospitals formed part of the external validation cohort. Features included demographics, comorbidities, and laboratory and vital signs within 12 hours of hospital admission.
Results
ABackground and objectives
AKI treated with dialysis initiation is a common complication of coronavirus disease 2019 (COVID-19) among hospitalized patients. However, dialysis supplies and personnel are often limited.
Design, setting, participants, & measurements
Using data from adult patients hospitalized with COVID-19 from five hospitals from theMount Sinai Health System who were admitted between March 10 and December 26, 2020, we developed and validated several models (logistic regression, Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and eXtreme GradientBoosting [XGBoost; with and without imputation]) for predicting treatment with dialysis or death at various time horizons (1, 3, 5, and 7 days) after hospital admission. Patients admitted to theMount Sinai Hospital were used for internal validation, whereas the other hospitals formed part of the external validation cohort. Features included demographics, comorbidities, and laboratory and vital signs within 12 hours of hospital admission.
Results
A total of 6093 patients (2442 in training and 3651 in external validation) were included in the final cohort. Of the different modeling approaches used, XGBoost without imputation had the highest area under the receiver operating characteristic (AUROC) curve on internal validation (range of 0.93-0.98) and area under the precisionrecall curve (AUPRC; range of 0.78-0.82) for all time points. XGBoost without imputation also had the highest test parameters on external validation (AUROC range of 0.85-0.87, and AUPRC range of 0.27-0.54) across all time windows. XGBoost without imputation outperformed all models with higher precision and recall (mean difference in AUROC of 0.04; mean difference in AUPRC of 0.15). Features of creatinine, BUN, and red cell distribution width were major drivers of the model's prediction.
Conclusions
An XGBoost model without imputation for prediction of a composite outcome of either death or dialysis in patients positive for COVID-19 had the best performance, as compared with standard and other machine learning models.…
MetadatenAuthor details: | Akhil VaidORCiD, Lili ChanORCiD, Kumardeep ChaudharyORCiD, Suraj K. JaladankiORCiD, Ishan ParanjpeORCiD, Adam J. RussakORCiD, Arash KiaORCiD, Prem TimsinaORCiD, Matthew A. LevinORCiD, John Cijiang He, Erwin BöttingerORCiDGND, Alexander W. CharneyORCiD, Zahi A. FayadORCiD, Steven G. CocaORCiD, Benjamin S. GlicksbergORCiD, Girish N. NadkarniORCiD |
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DOI: | https://doi.org/10.2215/CJN.17311120 |
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ISSN: | 1555-9041 |
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ISSN: | 1555-905X |
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Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/34031183 |
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Title of parent work (English): | Clinical journal of the American Society of Nephrology : CJASN |
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Publisher: | American Society of Nephrology |
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Place of publishing: | Washington |
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Publication type: | Article |
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Language: | English |
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Date of first publication: | 2021/07/01 |
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Publication year: | 2021 |
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Creating corporation: | MSCIC |
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Release date: | 2023/10/12 |
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Tag: | AKI; COVID-19; dialysis; machine learning; prediction |
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Volume: | 16 |
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Issue: | 8 |
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Number of pages: | 11 |
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First page: | 1158 |
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Last Page: | 1168 |
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Funding institution: | NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [K23DK107908, R56DK126930]; National Institute of Diabetes and Digestive and Kidney Diseases career development grant [K23DK124645] |
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Organizational units: | An-Institute / Hasso-Plattner-Institut für Digital Engineering gGmbH |
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DDC classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
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Peer review: | Referiert |
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Publishing method: | Open Access / Bronze Open-Access |
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