TY - JOUR
T1 - Using dipstick urinalysis to predict development of acute kidney injury in patients with COVID-19
AU - McAdams, Meredith C.
AU - Li, Michael
AU - Xu, Pin
AU - Gregg, Lucile Parker
AU - Patel, Jiten
AU - Willett, Duwayne L.
AU - Velasco, Ferdinand
AU - Lehmann, Christoph U.
AU - Hedayati, S. Susan
N1 - Funding Information:
M. McAdams is supported by training grant 5T32DK007257-38 from the National Institutes of Diabetes and Digestive and Kidney Diseases (NIDDK).
Funding Information:
The University of Texas Southwestern COVID-19 Registry Collaborative database was used to derive the data. We acknowledge the following people for their efforts in pulling data for this research: from the University of Texas Southwestern Medical Informatics, Mereeja Varghese, Clarie Wang, and Ki Lai; from the Texas Health Resources Medical Informatics, Chaitanya Katterapalli, Sohal Sukhraj, and Andrew Masica, M.D.; and from Parkland Health and Hospital System Medical Informatics, Christopher Clark and Brett Moran, M.D.
Funding Information:
S. S. Hedayati is supported by the Yin Quan-Yuen Distinguished Professorship in Nephrology at the
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - Background: Acute kidney injury (AKI) is a common complication in patients hospitalized with COVID-19 and may require renal replacement therapy (RRT). Dipstick urinalysis is frequently obtained, but data regarding the prognostic value of hematuria and proteinuria for kidney outcomes is scarce. Methods: Patients with positive severe acute respiratory syndrome-coronavirus 2 (SARS-CoV2) PCR, who had a urinalysis obtained on admission to one of 20 hospitals, were included. Nested models with degree of hematuria and proteinuria were used to predict AKI and RRT during admission. Presence of Chronic Kidney Disease (CKD) and baseline serum creatinine were added to test improvement in model fit. Results: Of 5,980 individuals, 829 (13.9%) developed an AKI during admission, and 149 (18.0%) of those with AKI received RRT. Proteinuria and hematuria degrees significantly increased with AKI severity (P < 0.001 for both). Any degree of proteinuria and hematuria was associated with an increased risk of AKI and RRT. In predictive models for AKI, presence of CKD improved the area under the curve (AUC) (95% confidence interval) to 0.73 (0.71, 0.75), P < 0.001, and adding baseline creatinine improved the AUC to 0.85 (0.83, 0.86), P < 0.001, when compared to the base model AUC using only proteinuria and hematuria, AUC = 0.64 (0.62, 0.67). In RRT models, CKD status improved the AUC to 0.78 (0.75, 0.82), P < 0.001, and baseline creatinine improved the AUC to 0.84 (0.80, 0.88), P < 0.001, compared to the base model, AUC = 0.72 (0.68, 0.76). There was no significant improvement in model discrimination when both CKD and baseline serum creatinine were included. Conclusions: Proteinuria and hematuria values on dipstick urinalysis can be utilized to predict AKI and RRT in hospitalized patients with COVID-19. We derived formulas using these two readily available values to help prognosticate kidney outcomes in these patients. Furthermore, the incorporation of CKD or baseline creatinine increases the accuracy of these formulas.
AB - Background: Acute kidney injury (AKI) is a common complication in patients hospitalized with COVID-19 and may require renal replacement therapy (RRT). Dipstick urinalysis is frequently obtained, but data regarding the prognostic value of hematuria and proteinuria for kidney outcomes is scarce. Methods: Patients with positive severe acute respiratory syndrome-coronavirus 2 (SARS-CoV2) PCR, who had a urinalysis obtained on admission to one of 20 hospitals, were included. Nested models with degree of hematuria and proteinuria were used to predict AKI and RRT during admission. Presence of Chronic Kidney Disease (CKD) and baseline serum creatinine were added to test improvement in model fit. Results: Of 5,980 individuals, 829 (13.9%) developed an AKI during admission, and 149 (18.0%) of those with AKI received RRT. Proteinuria and hematuria degrees significantly increased with AKI severity (P < 0.001 for both). Any degree of proteinuria and hematuria was associated with an increased risk of AKI and RRT. In predictive models for AKI, presence of CKD improved the area under the curve (AUC) (95% confidence interval) to 0.73 (0.71, 0.75), P < 0.001, and adding baseline creatinine improved the AUC to 0.85 (0.83, 0.86), P < 0.001, when compared to the base model AUC using only proteinuria and hematuria, AUC = 0.64 (0.62, 0.67). In RRT models, CKD status improved the AUC to 0.78 (0.75, 0.82), P < 0.001, and baseline creatinine improved the AUC to 0.84 (0.80, 0.88), P < 0.001, compared to the base model, AUC = 0.72 (0.68, 0.76). There was no significant improvement in model discrimination when both CKD and baseline serum creatinine were included. Conclusions: Proteinuria and hematuria values on dipstick urinalysis can be utilized to predict AKI and RRT in hospitalized patients with COVID-19. We derived formulas using these two readily available values to help prognosticate kidney outcomes in these patients. Furthermore, the incorporation of CKD or baseline creatinine increases the accuracy of these formulas.
KW - AKI
KW - COVID-19
KW - Hematuria
KW - Predictive model
KW - Proteinuria
KW - Urinalysis
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U2 - 10.1186/s12882-022-02677-y
DO - 10.1186/s12882-022-02677-y
M3 - Article
C2 - 35105331
AN - SCOPUS:85124061913
VL - 23
JO - BMC Nephrology
JF - BMC Nephrology
SN - 1471-2369
IS - 1
M1 - 50
ER -