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. 2021 Aug;16(8):1158-1168.
doi: 10.2215/CJN.17311120. Epub 2021 May 24.

Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19

Akhil Vaid  1   2 , Lili Chan  3   4 , Kumardeep Chaudhary  1   3 , Suraj K Jaladanki  1 , Ishan Paranjpe  1 , Adam Russak  1 , Arash Kia  1   5 , Prem Timsina  1   5 , Matthew A Levin  1   6   7 , John Cijiang He  4 , Erwin P Böttinger  2   8 , Alexander W Charney  1   7   9   10 , Zahi A Fayad  1   11 , Steven G Coca  4 , Benjamin S Glicksberg  1   2   7 , Girish N Nadkarni  1   2   3   4 ; MSCIC
Collaborators, Affiliations

Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19

Akhil Vaid et al. Clin J Am Soc Nephrol. 2021 Aug.

Abstract

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 the Mount 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 the Mount 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 precision-recall 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.

Podcast: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_07_09_CJN17311120.mp3.

Keywords: AKI; COVID-19; dialysis; machine learning; prediction.

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Figures

None
Graphical abstract
Figure 1.
Figure 1.
Overall schema of study. (A) Mount Sinai Hospital (MSH) was used for training and internal validation, whereas the other hospitals were used for external validation. (B) Study flow diagram. (C) Model predictions were generated for hospital day 1, 3, 5, and 7. COVID-19, coronavirus disease 2019; MSB, Mount Sinai Brooklyn; MSM, Mount Sinai Morningside; MSQ, Mount Sinai Queens; MSW, Mount Sinai West.
Figure 2.
Figure 2.
XGBoost without imputation had high area under the receiver operating characteristic (AUROC) curves. AUROC curves for the eXtreme Gradient Boosting (XGBoost) model without imputation at hospital day 1, 3, 5, and 7. Orange line represents results in the training set, blue line represents external validation, and the dotted red line is chance. OH, other hospitals.
Figure 3.
Figure 3.
XGBoost without imputation had high area under the precision-recall curves (AUPRC). AUPRC for the XGBoost model without imputation at hospital day 1, 3, 5, and 7. Orange line represents results in the training set, blue line represents external validation, and the dotted red line is chance. OH, other hospitals.
Figure 4.
Figure 4.
Top features contributing to model prediction were similar across time frames. SHapley Additive exPlanations (SHAP) summary plots showing features driving model prediction toward death or dialysis at 1, 3, 5, and 7 days postadmission. Each dot represents a single patient. In each of the four plots, features are listed on the left in decreasing order of importance. Higher values of a feature are shown in red, and lower values are shown in blue. Positive SHAP values (plotted to the right of the zero on the x axis) push the model toward a prediction of death or dialysis, whereas negative SHAP values push the model away from a prediction of death or dialysis. For example, creatinine levels are the most important feature driving prediction. Because creatinine levels are in red on the right, higher values of creatinine drive model prediction toward the outcome. Similarly, higher oxygen saturation is plotted in red on the left and is associated with the model’s prediction being driven away from death or dialysis. AST, aspartate aminotransferase; RDW, red cell distribution width.
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