1

Association of AKI-D with Urinary Findings and Baseline eGFR in

Hospitalized COVID-19 Patients

Dipal M. Patel1, Manali Phadke2, Feng Dai2, Michael Simonov3, Neera K. Dahl1, and Ravi

Kodali1

1Department of Internal Medicine, Section of Nephrology, Yale School of Medicine; 2Yale

Center for Analytical Sciences, Yale School of Public Health; 3Clinical and Translational

Research Accelerator, Yale New Haven Health System, New Haven, CT

Corresponding author:

Dr. Dipal Patel

330 Cedar Street

BB114

New Haven, CT 06510

dipal.patel@yale.edu

(908) 578-7944

Kidney360 Publish Ahead of Print, published on May 20, 2021 as doi:10.34067/KID.0001612021

Copyright 2021 by American Society of Nephrology.

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Key Points:

• We evaluated risk factors for acute kidney injury requiring dialysis (AKI-D) in a cohort

of 3186 patients hospitalized with COVID-19.

• Latino patients, men, and those with lower eGFR or obesity experienced more AKI-D.

Patients with AKI-D had increased odds of mortality.

• After adjustment for covariates including baseline kidney function, proteinuria and

hematuria were associated with increased odds of AKI-D.

Abstract:

Background: Acute kidney injury (AKI) is common in patients hospitalized with coronavirus

disease 2019 (COVID-19). Risk factors for AKI requiring dialysis (AKI-D) are not fully

understood. We aimed to identify risk factors associated with AKI-D and AKI not requiring

dialysis (AKI-ND).

Methods: We reviewed electronic health records of 3186 patients aged > 18 years old

hospitalized with COVID-19 across six hospitals. Patient characteristics, urinalysis findings, and

inflammatory markers were analyzed for association with in-hospital AKI status (AKI-D, AKI-

ND, or no AKI), and we subsequently evaluated mortality.

Results: After adjustment for multiple covariates, higher baseline eGFR was associated with 30%

lower odds of AKI-D and 11% lower odds of AKI-ND (OR 0.70, 95% CI 0.64-0.77 for AKI-D;

OR 0.89, 95% CI 0.85-0.92 for AKI-ND). Patients with obesity and Latino patients had

increased odds of AKI-D, whereas those with congestive heart failure or diabetes with

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complications had increased odds of AKI-ND. Females had lower odds of in-hospital AKI (OR

0.28, 95% CI 0.17-0.46 for AKI-D; OR 0.83, 95% CI 0.70-0.99 for AKI-ND). After adjustment

for covariates and baseline eGFR, 1-4+ protein on initial urinalysis was associated with a 9-fold

increase in odds of AKI-D (OR 9.00, 95% CI 2.16-37.38) and > 2-fold higher odds of AKI-ND

(OR 2.28, 95% CI 1.66-3.13). 1-3+ blood and trace glucose on initial urinalysis were also

associated with increased odds of both AKI-D and AKI-ND. AKI-D and AKI-ND were

associated with in-hospital death (OR 2.64, 95% CI 1.13-6.17 for AKI-D; OR 2.44, 95% CI

1.77-3.35 for AKI-ND).

Conclusions: Active urine sediments, even after adjustment for baseline kidney function, and

reduced baseline eGFR are significantly associated with increased odds of AKI-D and AKI-ND.

In-hospital AKI was associated with in-hospital death. These findings may help prognosticate

patients hospitalized with COVID-19.

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Introduction:

Acute kidney injury (AKI) is commonly seen in COVID-19 and is associated with poor

outcomes. Populations at risk for AKI in the setting of COVID-19 include those with chronic

kidney disease, diabetes, high body mass index, cardiovascular disease, and hypertension1. In

one meta-analysis, AKI incidence ranged from 4.5%-69.2% in patients hospitalized with

COVID-19, with average incidences of 28.6% in patients hospitalized in the United States (U.S.)

and Europe and 5.5% in patients hospitalized in China2. Pooled incidence of kidney replacement

therapy was 7.7% in patients hospitalized in the USA and Europe. AKI in COVID-19 has

consistently been associated with mortality3-9, with a risk ratio of 4.6 on meta-analysis2.

Additional data on risk factors for development of AKI in the setting of COVID-19, and risk

factors associated with progression to AKI requiring dialysis (AKI-D), may allow for better

triage and prognostication of patients hospitalized with COVID-19. The patient population at

risk for AKI-D is particularly important to identify, as mortality rates are greater than 60% for

patients hospitalized with COVID-19 in intensive care requiring renal replacement therapy10.

In this retrospective cohort analysis, we describe risk factors for the development of AKI-

D in 3186 patients hospitalized with COVID-19 across 6 medical centers in the Northeast U.S.

Our objectives were to identify patient characteristics, urinalysis findings, and inflammatory

markers associated with risk of developing AKI-D and AKI not requiring dialysis (AKI-ND).

We also evaluated the association of these clinical factors with in-hospital death. Based on our

clinical experiences, we hypothesized that kidney tubular damage (resulting in proteinuria and

hematuria) in addition to systemic inflammation (resulting in elevated inflammatory markers)

would be associated with increased risk of AKI-D and mortality.

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Methods:

Study Population:

The study was conducted across the Yale New Haven Health system, which includes five

major hospitals (Yale New Haven Hospital, New Haven, CT; Bridgeport Hospital, Bridgeport,

CT; Greenwich Hospital, Greenwich, CT; Lawrence + Memorial Hospital, New London, CT;

Westerly Hospital, Westerly, RI) and serves a large and diverse population of patients in the

Northeast U.S. De-identified patient data were obtained from electronic health records (EHRs)

from patients hospitalized in this network between March 1st and September 17th, 2020. The

study was approved by the Yale Institutional Review Board (2000028702).

Patients were included if aged 18 years or older and hospitalized within the Yale New

Haven Health System. All patients had a positive SARS-CoV-2 PCR test within 14 days prior to

hospitalization or during the hospitalization, or were flagged as SARS-CoV-2-positive in EHRs.

The database included only inpatient encounters in which the patient’s first positive COVID-19

test was performed. ‘Only ED Visits’ were excluded, as patients needed to be hospitalized, either

under observation status or under inpatient status. Patients were excluded if they did not have

inpatient creatinine (Cr) measurements or if they had preexisting end stage kidney disease

(ESKD) as per International Classification of Diseases Tenth Revision (ICD-10) coding.

Data Collection:

We collected information on demographics, comorbidities, procedures, medications,

laboratory data, urinalysis findings, and hospitalization metrics. All data were extracted from

Clarity, the relational database used for data extraction and analysis for the Epic electronic health

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record. Preexisting conditions were identified using ICD-10 codes as per Elixhauser ICD-based

coding11.

The primary definition for AKI was a 50% rise in serum Cr over the lowest Cr in the

preceding 7 days, or a 0.3 mg/dL increase over the lowest value in the preceding 48 hours,

corresponding to Kidney Disease: Improving Global Outcomes (KDIGO) stage 1 AKI or higher.

Our AKI definitions did not utilize urine output given a high degree of missingness in this

variable. Dialysis was measured by utilization of dialysis-specific medications during the

hospitalization; this definition was validated by manual inspection of a random sampling of

patient medical records and was seen to have greater than 95% sensitivity and specificity.

Historical baseline Cr and eGFR values were defined as a median of all Cr or eGFR

values from 7-365 days preceding hospitalization. For patients with no baseline information

available, baseline GFR was assumed to be 75 mL/min/1.73m2 and baseline Cr was derived

using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation12.

For urinalysis (UA) data, urine protein and urine glucose were condensed into the

following categories: 1-4+, trace, and absent. Urine blood was condensed into 1-3+, trace, and

absent. Values for urine renal tubular epithelial cells (RTEs) were combined into present or

absent.

Definitions of Outcomes:

The primary outcome was in-hospital AKI status [no AKI, AKI not requiring dialysis

(AKI-ND), or AKI requiring dialysis (AKI-D)]. The AKI-D group was defined as those who had

AKI and continuous renal replacement therapy (CRRT) or hemodialysis while hospitalized. All

others who had AKI but were not AKI-D formed the AKI-ND group. Those who did not meet

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criteria for AKI formed the no AKI group. The secondary outcome was in-hospital death,

defined as patients who were discharged from their hospitalization as deceased.

Statistical Analyses:

Continuous variables are presented by AKI status as mean ± standard deviation if

normally distributed or median (interquartile range: IQR) if not normally distributed. Differences

among groups were compared using analysis of variance (ANOVA) or Kruskal-Wallis tests.

Categorical variables were presented as n (%) and compared using Chi-square or Fisher’s exact

tests.

Multinomial logistic regression analysis was used to test the association of patient

characteristics, urinary findings, and inflammatory markers with the primary outcome (AKI-D,

AKI-ND, or no AKI). The first model, “model 1”, considered the following independent

variables of demographics and comorbidities: sex, age, race, Latino, obesity, hypertension

(HTN) without complication, HTN with complication, diabetes without complication, diabetes

with complication, malignancy, congestive heart failure (CHF), and historical baseline eGFR.

Subsequently, individual urinary findings and inflammatory markers were added to model 1 to

test their respective association with the primary outcome. Interleukin values (IL-2R, IL-6, and

IL-10) were log transformed for analysis.

Multivariable logistic regression was used to assess the association of all independent

variables in model 1 plus AKI status during hospitalization with in-hospital death (“model 2”).

Last, another multivariable regression model was fit by adding maximum serum Cr, first UA

protein, first UA glucose, first UA blood, maximum fibrinogen, maximum ferritin, maximum D-

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dimer, and maximum lactate in the independent variable list (“model 3”). Logistic regression

models are outlined in Supplemental Table 1 for reference.

To avoid multicollinearity in the regression analysis, if there were strong and significant

correlations among different continuous factors (r > 0.75), only the factor with the least

missingness was entered in the regression models. Potential important covariates that had a large

amount of missing data, such as interleukins and a few other immune parameters, were also not

included in multivariable models as indicated.

All analysis was done using SAS Version 9.4 (SAS Institute Inc, Cary, North Carolina).

No corrections for multiple testing were included. A two-sided p-value of less than 0.05 was

considered statistically significant. The odds ratio (OR) and 95% confidence interval (CI) of each

independent variable in the models were reported as a measure of effect size. A 95% CI not

containing the null value of 1 indicates statistical significance of the OR estimate. For AKI-D

and AKI-ND that were not directly compared due to the choice of reference level (i.e., no AKI)

in the logistic regression models, an indirect comparison was used by comparing the 95% CI

range for the OR estimate of AKI-D vs. no AKI to that of AKI-ND vs. no AKI. If CIs were non-

overlapping, that would indicate the existence of a statistically significant difference between

AKI-D vs. AKI-ND.

A priori sample size calculation and power analysis for this retrospective study were not

performed, because we planned to include all patients aged > 18 years who were hospitalized

with COVID-19 between 03/01/2020 and 09/17/2020 in our health care system.

Results:

Patient characteristics:

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Of 3426 patients meeting inclusion criteria, 65 were excluded for lack of data on kidney

function and 175 were excluded for preexisting ESKD, leaving a cohort of 3186 patients

included in analysis (Figure 1). 6 patients included in analysis had kidney transplants. 889

patients (27%) developed AKI and 95 patients (3% of all patients and 11% of AKI patients)

developed AKI-D. 45 patients required CRRT (1% of all patients, 5% of AKI patients, and 47%

of AKI-D patients).

Patient characteristics are presented in Table 1. 48% of patients were White, 25% were

Black/African American, and 27% were Latino. Males represented 76% of the AKI-D

population. Body mass index (BMI) values were higher in patients with AKI-D. Historical

baseline eGFR values were lower and obesity was more common in the AKI-D population

compared to AKI-ND patients or those without AKI, whereas prevalence of CHF, diabetes,

HTN, and malignancy were similar in AKI-D and AKI-ND groups. The median serum Cr on

admission was 1.5 (IQR 1.0-2.7) for AKI-D patients, compared to 1.2 (IQR 0.8-1.8) for AKI-ND

patients and 0.9 (0.7-1.2) for patients without AKI.

By univariate analysis, patients with higher BMI, obesity, CHF, diabetes, HTN, or higher

serum Cr on admission had greater odds of developing AKI-D compared to patients with no

AKI, while odds were reduced for those with higher baseline eGFR and for females

(Supplemental Table 2). In multinomial regression analysis (model 1), odds of AKI-D were

found to be significantly elevated in Latino patients and those with obesity (Latino: OR 2.54,

95% CI 1.28-5.05; obesity: OR 2.28, 95% CI 1.42-3.67). Females and those with higher baseline

eGFR had lower odds of AKI (females: OR 0.28, 95% CI 0.17-0.46 for AKI-D and OR 0.83,

95% CI 0.70-0.99 for AKI-ND; baseline eGFR: OR 0.70, 95% CI 0.64-0.77 for AKI-D and OR

0.89, 95% CI 0.85-0.92 for AKI-ND) (Figure 2, Supplemental Table 2). Odds of AKI-ND were

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higher for patients who were older (OR 1.01, 95% CI 1.00-1.02), Black/African American (OR

1.29, 95% CI 1.03-1.60), and for those with CHF (OR 1.32, 95% CI 1.03-1.70) or diabetes with

complication (OR 1.41, 95% CI 1.09-1.83).

Urinalysis findings:

We evaluated findings obtained from the first urinalysis (UA) of each patient’s

hospitalization (Table 2). Renal tubular epithelial cells (RTEs) were present in 88.6% of first

UAs, and 86.2% of all patients had some degree of proteinuria. While there was no significant

difference in the presence of RTEs between patient groups, patients with AKI-D more commonly

had 1-4+ protein, 1-3+ blood, and trace glucose on first UA compared to patients with AKI-ND

or those without AKI. 3-4+ protein was seen in 9.4% of all patients (6.5% of patients with no

AKI, 13.9% of patients with AKI-ND, and 21.7% of patients with AKI-D).

Maximum urine albumin/Cr ratios (UACR) and urine protein/Cr ratios (UPCR) were

higher in AKI-D patients compared to other groups, respectively averaging 236.0 mg/g (IQR

75.6-629.7) and 1.3 g/g (IQR 0.6-3.6 g/g) for AKI-D patients. UPCR values were > 3.5 g/g,

indicating nephrotic-range proteinuria, in 7.8% of patients with measured UPCR values (5.1% of

patients with no AKI, 9.0% of patients with AKI-ND, and 25.5% of patients with AKI-D).

After adjusting for demographics, comorbidities, and baseline eGFR (model 1), we found

increased odds of AKI-D in those presenting with 1-4+ protein, trace glucose, or 1-3+ blood on

first UA (1-4+ protein: OR 9.00, 95% CI 2.16-37.38; trace glucose: OR 3.96, 95% CI 1.64-9.56;

1-3+ blood: OR 2.96, 95% CI 1.78-4.93) (Figure 3, Supplemental Table 3). Odds of AKI-ND

were higher in patients presenting with 1-4+ protein (OR 2.28, 95% CI 1.66-3.13), trace glucose

(OR 2.23, 95% CI 1.34-3.73), 1-4+ glucose (OR 1.65, 95% CI 1.23-2.22), or 1-3+ blood (OR

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1.52, 95% CI 1.23-1.87) on initial UA. Higher maximum UACR and UPCR were also associated

with increased odds of both AKI-D and AKI-ND.

Inflammatory markers:

Maximum serum values of inflammatory markers (IL-2R, IL-6, IL-10, CRP, ESR,

lactate, ferritin, fibrinogen, D-dimer, and LDH) are shown in Supplemental Figure 1.

Inflammatory markers were generally higher in AKI-D patients compared to AKI-ND patients

and those without AKI. Both univariate and multivariable analysis showed that elevated IL-2R,

IL-6, IL-10, fibrinogen, ferritin, D-dimer, lactate, CRP, and LDH were associated with increased

odds for both AKI-ND and AKI-D (Table 3, Supplemental Table 4).

Hospitalization characteristics:

Characteristics of hospitalizations are shown in Supplemental Table 5. Patients in the

AKI-D group had an average maximum serum Cr of 6.3 (IQR 4.7-7.9) compared to 1.7 (IQR

1.2-2.6) for AKI-ND patients and 1.0 (IQR 0.8-1.2) for patients without AKI. Hospital length of

stay was longer for patients with AKI-D and AKI-ND, with median lengths of stay of 26 days

(IQR 14-37) for AKI-D patients, 15 days (IQR 9-24) for AKI-ND patients, and 6 days (IQR 3-

11) for patients without AKI. 73 of the 95 patients with AKI-D (76.8%) required intensive care

unit (ICU) care, with a median ICU stay of 13 days (IQR 7-24).

Patients with AKI-ND and AKI-D demonstrated more acidosis, hypercarbia, and

hypoxemia (Supplemental Table 5). Select medications administered during hospitalizations are

also shown. Patients with AKI-D more commonly received vasopressors, steroids, and

specialized COVID-19 therapies.

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Mortality:

Data on in-hospital death was captured for 1814 patients (91 with AKI-D, 633 with AKI-

ND, and 1090 with no AKI). 15.6% of this patient subset died while hospitalized: 52 of the 91

AKI-D patients (57%), 234 of the 633 AKI-ND patients (37%), and 212 of the 1090 patients

without AKI (19%).

Our multivariable logistic regression model (model 2) showed that AKI-D was associated

with > 18-fold higher odds of in-hospital death (OR 18.24, 95% CI 11.06-30.08) while AKI-ND

was associated with > 3-fold greater odds of in-hospital death (OR 3.23, 95% CI 2.57-4.07)

compared to patients without AKI (Supplemental Table 6). Patients of older age also had higher

odds of in-hospital death (OR 1.07, 95% CI 1.06-1.08), while females and those with higher

baseline eGFR had reduced odds of in-hospital death (females: OR 0.74, 95% CI 0.59-0.93;

baseline eGFR: OR 0.95, 95% CI 0.90-0.99).

In a separate logistic model after adjustment for the same list of independent variables in

model 2, the presence of 1-4+ protein or 1-3+ blood on initial urinalysis, in addition to elevated

maximum serum Cr during hospitalization and maximum IL-10, ferritin, D-dimer, or lactate

values, were associated with higher odds of in-hospital death (1-4+ UA protein: OR 1.92, 95%

CI 1.23-2.99; 1-3+ UA blood: OR 1.52, 95% CI 1.17-1.98; maximum serum Cr: OR 1.32, 95%

CI 1.20-1.45; maximum IL-10: OR 1.49, 95% CI 1.23-1.80; maximum ferritin: OR 1.02 (95% CI

1.01-1.02); maximum D-dimer: OR 1.06 (95% CI 1.04-1.07); maximum lactate: OR 1.36, 95%

CI 1.28-1.45) (Table 4).

Finally, in our most non-parsimonious regression model (model 3), increased odds of in-

hospital death were seen in patients with AKI-D (OR 2.64, 95% CI 1.13-6.17), AKI-ND (OR

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2.44, 95% CI 1.77-3.35), CHF (OR 1.60, 95% CI 1.08-2.38), older age (OR 1.08, 95% CI 1.06-

1.10), higher maximum serum Cr (OR 1.14, 95% CI 1.02-1.28), or elevated inflammatory

markers (maximum ferritin: OR 1.01, 95% CI 1.00-1.01; maximum D-dimer: OR 1.04, 95% CI

1.03-1.05; maximum lactate: OR 1.23, 95% CI 1.15-1.32) (Figure 4, Supplemental Table 7).

Females and Black/African American patients were at lower odds of in-hospital death (females:

OR 0.73, 95% CI 0.53-1.00; Black/African American: OR 0.59, 95% CI 0.40-0.87). Historical

baseline eGFR, after adjustments made in this model, was not associated with odds of in-hospital

mortality.

Discussion:

In this study, we investigated patient characteristics and clinical factors associated with

AKI-ND, AKI-D, and in-hospital death in patients hospitalized with COVID-19. Our study

reveals several key findings which may be applicable to similarly diverse patient populations.

We found that patients with higher baseline eGFR had a significant reduction in odds of

AKI-D and AKI-ND. This finding supports an association between CKD and in-hospital AKI in

the setting of COVID-191. Reduction in odds of in-hospital AKI was also found for female

patients.

Proteinuria and hematuria have been reported to be highly prevalent in patients with

COVID-19. In one cohort of patients with COVID-19, 64% presented with hematuria and 75%

with proteinuria on urine dipstick5. Notably, the incidence of proteinuria and hematuria may

actually exceed that of AKI, raising the possibility that urinary abnormalities could serve as an

earlier or more sensitive marker of disease16. Others have demonstrated an association of

proteinuria and hematuria with mortality in the setting of COVID-194, 17-25. However, to our

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knowledge, there has been only one other publication evaluating the association of proteinuria

with the need for dialysis in 84 patients with formal urine protein/creatinine ratios26. We

demonstrate an association of proteinuria and hematuria on initial urinalysis with higher odds for

AKI-D and AKI-ND, with 9-fold higher odds of AKI-D in patients with 1-4+ protein on initial

urinalysis. Importantly, this association stands despite adjustment for historical baseline eGFR.

Trace urine glucose, which may represent tubular dysfunction, was also associated with

increased odds of AKI-D. Urinalysis screening is likely attainable for most patients hospitalized

with COVID-19 and should be utilized for prognostication, even when unable to measure formal

urine protein/creatinine ratios in resource-limited settings.

While mechanistic evaluation with kidney biopsies was not reported in our study, we feel

that our data implicate a tubulointerstitial pattern of damage caused by the SARS-CoV2 virus,

resulting in non-albumin proteinuria, trace glucosuria, and hematuria20, 27, 28. 25.5% of AKI-D

patients had nephrotic-range proteinuria in our study, supporting what others have shown to be a

range of kidney injury pathologies in COVID-19 including both tubular injury and glomerular

processes29-38.

The SARS-CoV2 virus is proposed to trigger a systemic inflammatory response1, and we

therefore evaluated the association of inflammatory markers with outcomes in patients

hospitalized with COVID-19. Some studies have reported elevated IL-6 levels as being

associated with AKI38 and death39, 40, though others have reported that the association is not

significant after adjustment for covariates41 and on meta-analysis42. In our study, elevated

inflammatory marker values were associated with AKI-D and AKI-ND. Elevation of

inflammatory markers is associated with poor outcomes, but measurement of these factors may

15

not be as available in resource-limited settings, and furthermore may not be a risk factor

immediately apparent at the time of hospitalization.

AKI-D and AKI-ND were associated with higher odds of in-hospital death, similar to

data from additional patient cohorts3-9. Our data supports evidence that males and the elderly are

at increased risk of death in the setting of COVID-1943-46. In our patient cohort, Latinos had

higher odds of AKI-D but not in-hospital death, whereas Black/African American patients had

lower odds of in-hospital death. Black patients have been shown to be at higher risk of mortality

in the setting of COVID-19 infection even after adjustment for covariates43, 46, though others

have also shown decreased mortality44, 45. Further data is needed to understand variations in

patient populations, including socioeconomic factors, that may explain these disparate findings.

Strengths and limitations:

A major strength of our study was the inclusion of data from a large hospital network,

offering information on a diverse patient population. Our study is one of a few that examine

associations of urinary findings and inflammatory markers with outcomes of AKI-D and

mortality. We separated associations of trace versus significant proteinuria, hematuria, or

glucosuria with studied outcomes, and provided data on albuminuria and total proteinuria for

patient groups.

There were several limitations to our study. Pre-hospitalization baseline proteinuria was

not captured, though baseline kidney function was included in analysis. Given lack of data on

urine output for all patients, we did not categorize patients per Kidney Disease: Improving

Global Outcomes (KDIGO) stages of AKI, and instead categorized patients as AKI with or

without need for dialysis. Notably, patients who may have had indications for dialysis, but did

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not start dialysis due to goals of care or other issues such as resource allocation, were not

included in the AKI-D group. We did not track progression of AKI or post-hospitalization

outcomes of residual kidney disease and dialysis dependency, though this information has been

published elsewhere47. Outcomes for the small number of kidney transplant patients were not

separately analyzed. We did not specifically evaluate risk factors associated with need for

CRRT. We did not account for the influence of dialysis and/or medications in changes to

inflammatory marker values and did not evaluate the association of administered medications

with outcomes. In-hospital death was evaluated without time to event analysis.

Conclusions:

In a diverse population of 3186 patients hospitalized with COVID-19, 27% developed

AKI, of which 11% required initiation of dialysis. Patient with 1-4+ proteinuria on initial

urinalysis had the highest odds for development of AKI-D, and patients with AKI-D had the

highest odds for in-hospital death. The association of inflammatory markers with outcomes was

moderate. We advocate for standardized assessment of urinalysis in addition to traditional risk

factors including pre-existing kidney disease as tools to prognosticate patients admitted with

COVID-19.

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Disclosures:

N. Dahl reports the following: Consultancy Agreements: Otsuka Pharmaceuticals; Research

Funding: I am a PI for clinical trials sponsored by Kadmon, Allena, Regulus, Sanofi, and Reata;

Honoraria: National Kidney Foundation, Otsuka Pharmaceutical; Scientific Advisor or

Membership: PKD Foundation, Natera; Other Interests/Relationships: Medical Advisory Board,

NKF NE Chapter, Medical Advisory Board, ESRD Network, Region 1. All remaining authors

have nothing to disclose.

Funding:

The authors thank the Yale Department of Medicine COVID Explorer data repository made

possible by funding from the Department of Medicine, the George M. O'Brien Kidney Center at

Yale (P30DK079310), and resources from the Clinical and Translational Research Accelerator.

This publication was made possible by CTSA Grant Number UL1 TR001863 or KL2 TR001862

or TL1 TR001864 (as appropriate) from the National Center for Advancing Translational

Science (NCATS), a component of the National Institutes of Health (NIH).

Acknowledgements:

This article’s contents are solely the responsibility of the authors and do not necessarily represent

the official view of NIH.

We also thank Elyssa Noce, APRN and Mary Zorzanello, APRN for input during design of this

study, and Dr. Dennis Moledina for input regarding data analysis.

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Author Contributions:

D Patel: Conceptualization; Formal analysis; Investigation; Writing - original draft; Writing -

review and editing

M Phadke: Data curation; Formal analysis; Methodology

F Dai: Data curation; Formal analysis; Methodology

M Simonov: Data curation; Methodology; Software

N Dahl: Funding acquisition; Writing - review and editing

R Kodali: Supervision; Writing - review and editing

Supplemental Materials: Table of Contents

Supplemental Table 1. Description of logistic regression models.

Supplemental Table 2. Association of patient characteristics with AKI status.

Supplemental Table 3. Association of urinalysis findings with AKI status.

Supplemental Figure 1. Maximum inflammatory marker values.

Supplemental Table 4. Association of inflammatory markers with AKI status.

Supplemental Table 5. Hospitalization characteristics.

Supplemental Table 6: Association of patient characteristics with in-hospital death.

Supplemental Table 7. Association of patient characteristics, urinary findings, and inflammatory

markers with in-hospital death.

19

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23

Table 1: Patient characteristics.

In-hospital AKI status All (n=3186) No AKI

(n=2297)

AKI-ND

(n=794) AKI-D (n=95) p-value

Age 63 ± 19 61 ± 20 69 ± 16 63 ± 14 < 0.001

Female sex 1578 (50%) 1166 (51%) 389 (49%) 23 (24%) < 0.001

Race

0.016 Black/African American 798 (25%) 551 (24%) 218 (28%) 29 (31%)

White/Caucasian 1537 (48%) 1097 (48%) 397 (50%) 43 (45%)

Latino 845 (27%) 636 (28%) 179 (23%) 30 (32%) 0.010

BMIa 28.4 (24.3-33.8) 28.3 (24.4-33.4) 28.1 (23.8-34.5) 31.8 (27.1-39.1) < 0.001

Comorbidities

Obesity 1019 (32%) 686 (30%) 286 (36%) 47 (50%) < 0.001

CHF 669 (21%) 386 (17%) 258 (33%) 25 (26%) < 0.001

Diabetes, complicated 885 (28%) 522 (23%) 321 (40%) 42 (44%) < 0.001

Diabetes, uncomplicated 1141 (36%) 714 (31%) 382 (48%) 45 (47%) < 0.001

HTN, complicated 793 (25%) 452 (20%) 306 (39%) 35 (37%) < 0.001

HTN, uncomplicated 2013 (63%) 1339 (58%) 607 (77%) 67 (71%) < 0.001

Malignancy 364 (11%) 226 (10%) 124 (16%) 14 (15%) < 0.001

Baseline Cr (mg/dL) 1.0 (0.8-1.1) 1.0 (0.8-1.1) 1.0 (0.8-1.2) 1.1 (1.0-1.3) < 0.001

Baseline eGFR

(mL/min/1.73m2) 76.7 (51.5-98.3) 82.1 (59.8-101.9) 60.6 (39.6-86.6) 53.6 (29.8-85.1) <.0001

Serum Cr on admission (mg/dL) 1.0 (0.8-1.3) 0.9 (0.7-1.2) 1.2 (0.8-1.8) 1.5 (1.0-2.7) < 0.001

Continuous variables are presented as mean ± S.D. or median (IQR), and categorical values are presented as n (%). aBMI values were

available for 3068 patients: 2192 without AKI, 782 with AKI-ND, and 94 with AKI-D. HTN: hypertension; eGFR: estimated

glomerular filtration rate.

24

Table 2: Urinalysis findings.

In-hospital AKI status All No AKI AKI-ND AKI-D p-value

First UA RTEs (n=807)

0.09 Absent 92 (11%) 38 (9%) 49 (15%) 5 (7%)

Present 715 (89%) 374 (91%) 277 (85%) 64 (93%)

First UA Protein (n=2192)

< 0.001 Absent 302 (14%) 0242 (17%) 0058 (8%) 0002 (2%)

Trace 420 (19%) 0318 (23%) 0097 (14%) 0005 (5%)

1-4+ 1470 (67%) 0847 (60%) 0538 (78%) 0085 (92%)

First UA Glucose (n=2192)

< 0.001 Absent 1820 (83%) 1216 (86%) 0536 (77%) 0068 (74%)

Trace 75 (03%) 0033 (2%) 0034 (5%) 0008 (9%)

1-4+ 297 (14%) 0158 (11%) 0123 (18%) 0016 (17%)

First UA Blood (n=2192)

< 0.001 Absent 1099 (50%) 0779 (55%) 0295 (43%) 0025 (27%)

Trace 275 (13%) 0171 (12%) 0096 (14%) 0008 (9%)

1-3+ 818 (37%) 0457 (33%) 0302 (44%) 0059 (64%)

Max urine ACR (mg/g) 78.9 (32.3-324.0)

(n=436)

65.4 (26.7-176.9)

(n=279)

145.5 (41.8-548.2)

(n=124)

236.0 (75.6-629.7)

(n=33) < 0.001

Max urine PCR (g/g) 0.4 (0.2-1.1)

(n=722)

0.3 (0.2-0.7)

(n=449)

0.6 (0.3-1.3)

(n=222)

1.3 (0.6-3.6)

(n=51) < 0.001

Findings from first urinalyses and maximum proteinuria and albuminuria from hospitalization are shown. P-values assess differences

among groups. RTE: renal tubular epithelial cell; ACR: albumin/creatinine ratio; PCR: protein/creatinine ratio.

25

Table 3. Association of inflammatory markers with AKI status.

AKI-ND vs No AKI

OR (95% CI) p-value

AKI-D vs No AKI

OR (95% CI) p-value

Max IL-6 (n=1277) 1.36 (1.26-1.47) < 0.001 1.62 (1.37-1.91) < 0.001

Max IL-2R (n=1295) 1.39 (1.21-1.60) < 0.001 2.63 (1.89-3.65) < 0.001

Max IL-10 (n=1282) 1.47 (1.27-1.71) < 0.001 2.33 (1.75-3.11) < 0.001

Max fibrinogen (n=2890) 1.22 (1.16-1.30) < 0.001 1.46 (1.26-1.71) < 0.001

Max ferritin (n=2946) 1.02 (1.02-1.03) < 0.001 1.03 (1.02-1.03) < 0.001

Max D-dimer (n=3010) 1.06 (1.05-1.07) < 0.001 1.14 (1.12 -1.16) < 0.001

Max lactate (n=2226) 1.27 (1.20-1.34) < 0.001 1.46 (1.36-1.57) < 0.001

Max CRP (n=1426) 1.08 (1.06-1.09) < 0.001 1.17 (1.13-1.22) < 0.001

Max LDH (n=1456) 1.46 (1.37-1.56) < 0.001 1.51 (1.41-1.62) < 0.001

A separate multinomial logistic regression analysis was performed to test the association of each inflammatory marker with AKI

status, adjusting for patient characteristics listed in model 1 (Supplemental Table 1). Log-transformed values of IL-6, IL-2R, and IL-

10 were analyzed. For fibrinogen, ferritin, and LDH, a unit change of 100 was assumed in calculating OR.

26

Table 4. Association of urinalysis findings, maximum serum Cr, and inflammatory markers with in-hospital death.

OR (95% CI) for

in-hospital death p-value

First UA Protein (v. absent)

Trace 0.96 (0.56-1.64) 0.88

1-4+ 1.92 (1.23-2.99) 0.004

First UA Glucose (v. absent)

Trace 0.67 (0.33-1.36) 0.26

1-4+ 1.45 (0.99-2.14) 0.06

First UA Blood (v. absent)

Trace 1.14 (0.78-1.66) 0.51

1-3+ 1.52 (1.17-1.98) 0.002

Max serum Cr 1.32 (1.20-1.45) < 0.001

Max IL-6 1.10 (0.99-1.22) 0.06

Max IL-2R 0.92 (0.78-1.10) 0.36

Max IL-10 1.49 (1.23-1.80) < 0.001

Max fibrinogen 1.02 (0.95-1.10) 0.60

Max ferritin 1.02 (1.01-1.02) < 0.001

Max D-dimer 1.06 (1.04-1.07) < 0.001

Max lactate 1.36 (1.28-1.45) < 0.001

A separate multivariable logistic regression analysis was performed to test the association of each listed factor with in-hospital death,

after adjusting for the covariates listed in model 2 (Supplemental Table 1). Fibrinogen and ferritin were analyzed using a unit change

of 100. Log-transformed values of IL-6, IL-2R, and IL-10 were analyzed.

27

Figure legends:

Figure 1: Study flow diagram. A total of 3186 patients were studied. 794 developed acute

kidney injury not requiring dialysis (AKI-ND) and 95 developing AKI requiring dialysis (AKI-

D). ESKD: end-stage kidney disease.

Figure 2. Association of patient characteristics with AKI status. Odds ratios for the

development of AKI-ND or AKI-D on multinomial analysis (model 1), using patients with no

AKI as reference, are presented. Asterisks mark statistical significance (p-values are provided in

Supplemental Table 2).

Figure 3. Association of urinalysis findings with AKI status. Odds ratios for the development

of AKI-ND or AKI-D on multinomial analysis, using patients with no AKI as reference, are

presented. Separate multinomial regression models were run for each factor, with each including

patient characteristics listed in model 1. Asterisks mark statistical significance (p-values are

provided in Supplemental Table 3).

Figure 4. Association of patient characteristics, urinalysis findings, and inflammatory

markers with in-hospital death. Odds ratios for in-hospital death after adjustment for patient

demographics and comorbidities, AKI status, urinary findings, and inflammatory markers (model

3) are shown. Asterisks mark statistical significance (p-values are provided in Supplemental

Table 7).

Patients aged > 18yo hospitalized with

COVID-19 between

03/01/2020 and 09/17/2020 (n=3426)

Patients excluded: Missing data on kidney function (n=65)

Pre-existing ESKD (n=175)

Patients studied (n=3186)

No AKI (n=2297)

AKI-ND (n=794)

AKI-D (n=95)

Figure 1

In-hospital AKI status

Figure 1

Figure 2

OR (95% CI) for in-hospital AKI status

0 2 4 6

AKI-D vs. No AKI

AKI-ND vs. No AKI Age

Female sex

Black/African American (ref: White/Caucasian)

Latino

Baseline eGFR

Obesity

CHF

Diabetes, uncomplicated

Diabetes, complicated

HTN, complicated

Malignancy

HTN, uncomplicated

* *

* *

*

*

*

*

*

* *

Figure 2

OR (95% CI) for in-hospital AKI status

Figure 3

*

*

*

* *

* *

0 5 10 15 20 25 30 35 40

AKI-D vs. No AKI

AKI-ND vs. No AKI

Trace protein

1-4+ protein

Trace glucose

1-4+ glucose

Trace blood

1-3+ blood

RTEs

Maximum urine albumin/Cr

Maximum urine protein/Cr

* *

* *

*

Figure 3

Figure 4

OR (95% CI) for in-hospital death

* *

*

*

* *

*

*

* *

0 2 4 6 8

UA trace glucose

UA 1-4+ glucose

Age

Female sex

Black/African American (Ref: White)

Latino

Baseline eGFR

UA trace protein

Obesity CHF

Diabetes, uncomplicated Diabetes, complicated

HTN, uncomplicated

HTN, complicated

Malignancy

AKI-ND

AKI-D

UA 1-4+ protein

UA trace blood UA 1-3+ blood

Maximum serum Cr Maximum fibrinogen

Maximum ferritin Maximum D-dimer

Maximum lactate *

Figure 4