TY  - JOUR
A1  - Vaid, Akhil
A1  - Chan, Lili
A1  - Chaudhary, Kumardeep
A1  - Jaladanki, Suraj K.
A1  - Paranjpe, Ishan
A1  - Russak, Adam J.
A1  - Kia, Arash
A1  - Timsina, Prem
A1  - Levin, Matthew A.
A1  - He, John Cijiang
A1  - Böttinger, Erwin
A1  - Charney, Alexander W.
A1  - Fayad, Zahi A.
A1  - Coca, Steven G.
A1  - Glicksberg, Benjamin S.
A1  - Nadkarni, Girish N.
T1  - Predictive approaches for acute dialysis requirement and death in COVID-19
JF  - Clinical journal of the American Society of Nephrology : CJASN
N2  - 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
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.
KW  - COVID-19
KW  - dialysis
KW  - machine learning
KW  - prediction
KW  - AKI
Y1  - 2021
U6  - https://doi.org/10.2215/CJN.17311120
SN  - 1555-9041
SN  - 1555-905X
VL  - 16
IS  - 8
SP  - 1158
EP  - 1168
PB  - American Society of Nephrology
CY  - Washington
ER  -