Use of an extended KDIGO definition to diagnose acute kidney injury in patients with COVID-19: A multinational study using the ISARIC-WHO clinical characterisation protocol.

BACKGROUND: Acute kidney injury (AKI) is one of the most common and significant problems in patients with Coronavirus Disease 2019 (COVID-19). However, little is known about the incidence and impact of AKI occurring in the community or early in the hospital admission. The traditional Kidney Disease Improving Global Outcomes (KDIGO) definition can fail to identify patients for whom hospitalisation coincides with recovery of AKI as manifested by a decrease in serum creatinine (sCr). We hypothesised that an extended KDIGO (eKDIGO) definition, adapted from the International Society of Nephrology (ISN) 0by25 studies, would identify more cases of AKI in patients with COVID-19 and that these may correspond to community-acquired AKI (CA-AKI) with similarly poor outcomes as previously reported in this population. METHODS AND
FINDINGS: All individuals recruited using the International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC)-World Health Organization (WHO) Clinical Characterisation Protocol (CCP) and admitted to 1,609 hospitals in 54 countries with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection from February 15, 2020 to February 1, 2021 were included in the study. Data were collected and analysed for the duration of a patient's admission. Incidence, staging, and timing of AKI were evaluated using a traditional and eKDIGO definition, which incorporated a commensurate decrease in sCr. Patients within eKDIGO diagnosed with AKI by a decrease in sCr were labelled as deKDIGO. Clinical characteristics and outcomes-intensive care unit (ICU) admission, invasive mechanical ventilation, and in-hospital death-were compared for all 3 groups of patients. The relationship between eKDIGO AKI and in-hospital death was assessed using survival curves and logistic regression, adjusting for disease severity and AKI susceptibility. A total of 75,670 patients were included in the final analysis cohort. Median length of admission was 12 days (interquartile range [IQR] 7, 20). There were twice as many patients with AKI identified by eKDIGO than KDIGO (31.7% versus 16.8%). Those in the eKDIGO group had a greater proportion of stage 1 AKI (58% versus 36% in KDIGO patients). Peak AKI occurred early in the admission more frequently among eKDIGO than KDIGO patients. Compared to those without AKI, patients in the eKDIGO group had worse renal function on admission, more in-hospital complications, higher rates of ICU admission (54% versus 23%) invasive ventilation (45% versus 15%), and increased mortality (38% versus 19%). Patients in the eKDIGO group had a higher risk of in-hospital death than those without AKI (adjusted odds ratio: 1.78, 95% confidence interval: 1.71 to 1.80, p-value < 0.001). Mortality and rate of ICU admission were lower among deKDIGO than KDIGO patients (25% versus 50% death and 35% versus 70% ICU admission) but significantly higher when compared to patients with no AKI (25% versus 19% death and 35% versus 23% ICU admission) (all p-values <5 × 10-5). Limitations include ad hoc sCr sampling, exclusion of patients with less than two sCr measurements, and limited availability of sCr measurements prior to initiation of acute dialysis.
CONCLUSIONS: An extended KDIGO definition of AKI resulted in a significantly higher detection rate in this population. These additional cases of AKI occurred early in the hospital admission and were associated with worse outcomes compared to patients without AKI.

Introduction

Acute kidney injury (AKI) has been identified as one of most common and significant problems in hospitalised patients with Coronavirus Disease 2019 (COVID-19) [1-3]. Observational studies have consistently shown that patients who develop AKI are more likely to be admitted to an intensive care unit (ICU), require invasive mechanical ventilation, have longer lengths of stay (LOS) and increased mortality [1,2,4]. Autopsy studies point to several potential pathophysiological pathways for AKI including acute tubular injury from hemodynamic shifts, local inflammatory and microvascular thrombotic changes from immune dysregulation as well as direct viral invasion through the angiotensin converting enzyme 2 (ACE2) receptor [5].

Until now, most studies looking at AKI in COVID-19 have used the traditional Kidney Disease Improving Global Outcomes (KDIGO) definition, which relies on the rise in serum creatinine (sCr), either by 26.5 μmol/l in 48 hours or by 50% from baseline over a 7-day period [6]. While this definition is likely to adequately capture AKI that develops during a hospital stay, it may fail to identify cases that have developed in the community and are potentially recovering by the time a patient presents to the hospital, thereby underestimating the true incidence of AKI. To address this potential limitation of the KDIGO definition, the International Society of Nephrology (ISN) 0by25 studies added a commensurate fall in sCr to their definition of AKI [7,8]. Using this modified criteria in the feasibility study, it was found that approximately 40% of the community-acquired AKI (CA-AKI) could be identified by a fall in the level of sCr early in the admission, making it a more comprehensive and inclusive definition [8].

The integration of this additional criterion to identify kidney injury has also been highlighted as one of the research priorities in the recent KDIGO report on controversies in AKI [9]. While other papers have indicated the need to revise various aspects of the KDIGO criteria, aside from the 0by25 studies, a decrease in sCr as a marker of AKI has only been explored in infants and neonates, prompting a need for further research in this area [10,11].

Given the global impact of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection across all income and resource settings, combined with the potentially significant burden of AKI occurring in the community, we hypothesised that an extended KDIGO definition, adapted from the ISN’s 0by25 studies, would identify more cases of AKI in patients with COVID-19. We also hypothesised that the additional cases identified using this extended criterion may correspond to CA-AKI and be associated with similarly poor outcomes as those shown in previous studies of AKI in COVID-19 [1,2,4].

Methods

Study design

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC)–World Health Organization (WHO) Clinical Characterisation Protocol (CCP) for Severe Emerging Infections provided a framework for prospective, observational data collection on hospitalised patients affected by pathogens of public health interest. The protocol, case report forms (CRFs), and study information are available online (https://isaric.org/research/covid-19-clinical-research-resources), of which only the core CRF was used in this study [12]. These CRFs were developed to standardise clinical data collection on patients admitted with suspected or confirmed COVID-19 and have been widely used since the start of the pandemic [13,14]. Collection of sCr measurements across all sites was not time standardised, and the frequency of collection was left to the discretion of each site.

This observational study required no change to clinical management and encouraged patient enrollment in other research projects. Protocol and consent forms are available at https://isaric.net/ccp. While written consent was obtained in most cases, for some sites, the local regulators and ethics committees approved oral consent, or waiver of consent, in the context of the pandemic.

The ISARIC–WHO CCP was approved by WHO Ethics Review Committee (RPC571 and RPC572, April 25, 2013). Ethical approval was obtained for each participating country and site according to local requirements (S1 Statement). A prospective analysis plan was used to guide the design of this study [15]. Only the first of the three aims proposed was addressed in this study, and the addition of an extended AKI definition was adapted from the 0by25 studies in order to better capture AKI occurring in the community or early in the hospital admission [7].

Study population

Inclusion and exclusion criteria

All individuals in the ISARIC–WHO CCP database with clinically diagnosed or laboratory-confirmed SARS-CoV-2 infection admitted to hospital from February 15, 2020 to February 1, 2021 (criteria for clinical diagnosis in S1 Table) were included in this analysis. Patients younger than 18 years of age and those on maintenance renal replacement therapy (RRT; dialysis or transplantation) were excluded. Patients with fewer than two sCr measurements during the admission and those with incomplete or unreliable laboratory data were also excluded (Fig 1).

Fig 1

Flowchart of the study.

AKI definition and diagnosis

AKI was identified biochemically using sCr and incidence rates calculated accordingly. Patients’ sCr levels throughout the admission were used to classify them as (i) not having AKI; (ii) having AKI according to the traditional KDIGO definition; or (iii) AKI according to the extended KDIGO (eKDIGO) criteria. For the purpose of this analysis, those patients in the eKDIGO group with AKI diagnosed by a fall in sCr were labelled as deKDIGO (Fig 2). The KDIGO definition of AKI requires a patient to have an increase in sCr by 26.5 μmol/l within 48 hours or an increase to more than 1.5 times the baseline sCr within 7 days [6]. The eKDIGO definition of AKI was adapted from the ISN’s 0by25 studies and included a fall in sCr by 26.5 μmol/l within 48 hours or a fall to more than 1.5 times the baseline sCr within 7 days [8]. AKI was then graded according to the corresponding staging criteria for each definition (Table 1). A moving window of 48 hours and 7 days was applied during the entire length of a patient’s admission to find the first instance of AKI as well as the highest stage reached. In the case of AKI diagnosis using an increment in sCr, the minimum sCr within that window was deemed as the baseline, while for a diagnosis using the decrement, the maximum sCr within that window became the baseline. Information on the timing of acute RRT was not always available so it was not possible to determine whether patients receiving RRT fell into the KDIGO or deKDIGO portions of the eKDIGO group. Given the low likelihood that a patient with a falling creatinine would be given acute RRT, all RRT patients were categorised as being stage 3 AKI within the KDIGO group. Urine volume criteria was not used for either definition as it was not routinely collected in the CRF.

Fig 2

Visual representation of the relationships between sCr trajectories within the AKI groups (KDIGO, eKDIGO, and deKDIGO).

AKI, acute kidney injury; eKDIGO, extended KDIGO; KDIGO, Kidney Disease Improving Global Outcomes; sCr, serum creatinine.

Table 1

AKI definitions.

	KDIGO	eKDIGO
Diagnosis	Increase in sCr by ≥26.5 μmol/l within 48 hours or increase in sCr to ≥1.5 times baseline, which is known or presumed to have occurred within the prior 7 days	Increase in sCr by ≥26.5 μmol/l or decrease in sCr by ≥26.5 μmol/l within 48 hours or increase in sCr to ≥1.5 times baseline or a decrease in sCr to ≥1.5 times baseline, which is known or presumed to have occurred within the prior 7 days*
Staging*
Stage 1	sCr increase to 1.5 to 1.9 times baseline or increase in sCr by ≥26.5 μmol/l	sCr increase to 1.5 to 1.9 times baseline or an increase in sCr by ≥26.5 μmol/l or sCr decrease to 1.5 to 1.9 times baseline or a decrease by ≥26.5 μmol/l
Stage 2	sCr increase to 2.0 to 2.9 times baseline	sCr increase to 2.0 to 2.9 times baseline or sCr decrease 2.0 to 2.9 times baseline
Stage 3	sCr increase to 3.0 times baseline or sCr increase by ≥353.6 μmol/l or initiation of RRT	sCr increase to 3.0 times baseline or sCr increase by ≥353.6 μmol/l or initiation of RRT or sCr decrease to 3.0 times baseline or sCr decrease to ≥353.6 μmol/l

*deKDIGO refers to the subgroup of patients diagnosed with AKI by eKDIGO ONLY by the decrease in sCr (see Fig 2).

AKI, acute kidney injury; eKDIGO, extended KDIGO; RRT, renal replacement therapy; sCr, serum creatinine.

Data collection and time to peak AKI

This study analysed data up until February 15 on patients for whom data collection commenced on or before February 1, 2021. Data were collected and analysed for the duration of a patient’s admission. A 14-day rule was applied to focus analysis on individuals who were more likely to have a recorded outcome. By excluding patients enrolled during the last 14 days, we aimed to reduce the number of incomplete data records, thus improving the generalisability of the results and the accuracy of the outcomes.

For both groups (KDIGO and eKDIGO), time to peak AKI from hospital admission and the respective counts for each day were compared by visual inspection of histograms using the first day that a peak stage was reached. From the prespecified data collected in the CRF, information was obtained on demographics and country income level divided according to the World Bank classification (https://data.worldbank.org/country) into high income (HIC), upper middle income (UMIC), and low and low middle-income countries (LLMIC) merged into a single category. Information was obtained on patients’ comorbidities and preadmission medications as well as signs and symptoms, observations, and laboratory results on admission. Information collected during the admission included acute treatments, complications, and outcomes. Outcomes included an admission to the ICU, use of invasive mechanical ventilation, and either discharge, transfer to another hospital, in-hospital death, or remaining in hospital. Definitions of all collected variables are provided in S2 Table. A comparison of these variables was performed among patients with eKDIGO AKI and no AKI; deKDIGO and KDIGO AKI; deKDIGO AKI and no AKI.

Patients were classed as lost to follow up if either (a) they were transferred to another facility or (b) they had an unknown outcome and the last date upon which any data were recorded for them was 45 days or before the date of data extraction. Patients with unknown outcome where the last recorded data were less than 45 days old are categorised as receiving ongoing care. Data on readmissions could not be obtained for patients in many countries.

Statistical analysis

For continuous variables, characteristics were reported as medians and interquartile ranges (IQRs). For categorical variables, counts and percentages were reported. All statistical tests were carried out as pairwise independent samples comparisons. Due to the number of statistical tests conducted, a conservative Bonferroni adjusted significance level of αb 5 × 10−5 was used to limit the study wide probability of a type I error [16]. For continuous variables, the Mann–Whitney U test was used. For categorical variables, Pearson chi-squared test was performed. Missing data were reported as a percentage of the relevant cohort for each variable in Tables 2–4, and further information on its distribution is presented in S3 Table.

Table 2

Characteristics of patients with no AKI and AKI diagnosed by eKDIGO definition.

		eKDIGO AKI	No AKI	Missingness (%)	p-Value
Total count
		23,892	51,772
Demographics
	Age, year, median (IQR)	68 (57.5, 78.5)	67 (53.5, 80.5)	0	p < 5 × 10−5
	Female (%)	8,375 (35)	22,031 (43)	0	p < 5 × 10−5
Country income level, n (%)
	HIC	20,513 (86)	45,686 (88)	0	p < 5 × 10−5
	UMIC	1,129 (5)	4,176 (8)	0	p < 5 × 10−5
	LLMIC	2,161 (9)	1,876 (4)	0	p < 5 × 10−5
AKI grades and RRT, n (%)
	AKI stage 1	13,746 (58)	-	0
	AKI stage 2	3,682 (15)	-	0
	AKI stage 3*	6,464 (27)	-	0
	RRT	4,252 (19)	-	9
Comorbidities ** , n (%)
	CKD	4,059 (18)	5,433 (11)	7	p < 5 × 10−5
	Chronic cardiac disease	6,225 (27)	12,772 (25)	2
	Chronic pulmonary disease	2,975 (13)	6,853 (14)	7
	Hypertension	8,447 (50)	16,429 (43)	28	p < 5 × 10−5
	Dementia	1,835 (9)	4,315 (9)	12
	Type 2 diabetes	7,996 (36)	14,051 (29)	6	p < 5 × 10−5
	Liver disease	842 (4)	1,653 (3)	4
	Malnutrition	501 (2)	963 (2)	12
	Obesity	3,792 (19)	6,404 (15)	18	p < 5 × 10−5
Medications on admission, n (%)
	NSAIDs	1,382 (9)	2,610 (8)	35
	ACEis	2,795 (17)	5,177 (15)	33	p < 5 × 10−5
	ARBs	1,942 (12)	3,101 (9)	33	p < 5 × 10−5
Signs and symptoms on admission, n (%)
	Altered consciousness/confusion	4,295 (23)	8,267 (20)	19	p < 5 × 10−5
	Diarrhea	3,740 (20)	7,983 (19)	19
	Fever	13,333 (64)	30,320 (66)	12
	Vomiting/nausea	3,387 (18)	8,099 (19)	19
	Muscle aches/joint pain	3,643 (21)	8,853 (23)	25	p < 5 × 10−5
	Headache	1,779 (10)	5,367 (14)	26	p < 5 × 10−5
	Sore throat	1,564 (9)	3,579 (9)	27
	Cough	12,961 (63)	29,549 (65)	12
	Shortness of breath	14,824 (72)	30,334 (66)	12	p < 5 × 10−5
	Runny nose	634 (4)	1,547 (4)	28
Observations on admission, median (IQR)
	Temperature, °C	37.2 (36.2, 38.2)	37.3 (36.8, 37.8)	3
	Systolic BP, mm Hg	127 (110.5, 143.5)	130 (115.0, 145.0)	6	p < 5 × 10−5
	Diastolic BP, mm Hg	71 (61.5, 80.5)	75 (66.0, 84.0)	7	p < 5 × 10−5
	Heart rate, BPM	93 (79.5, 106.5)	90 (77.5, 102.5)	7	p < 5 × 10−5
	Respiratory rate, per minute	23 (18.5, 27.5)	21 (17.0, 25.0)	13	p < 5 × 10−5
	Oxygen saturation, %	94 (90.5, 97.5)	95 (92.5, 97.5)	7	p < 5 × 10−5
Laboratory results on admission, median (IQR)
	WBC (× 109/L)	8.2 (5.2, 11.2)	7 (4.5, 9.5)	15	p < 5 × 10−5
	BUN (mmol/L)	10.9 (4.9, 16.9)	6.4 (3.9, 8.9)	22	p < 5 × 10−5
	Potassium (mmol/L)	4.2 (3.8, 4.6)	4.1 (3.7, 4.4)	15	p < 5 × 10−5
	CRP (mg/L)	95.3 (19.8, 170.8)	69 (12.5, 125.5)	22	p < 5 × 10−5
	sCr (umol/l)	110 (67.5, 152.5)	80 (63.0, 97.0)	11	p < 5 × 10−5
	eGFR (ml/min/1.73m2)	54.4 (29.4, 79.4)	80.3 (61.8, 98.8)	12	p < 5 × 10−5
Admission treatment, n (%)
	Antiviral and COVID-19 targeting agents	5,349 (26)	9,145 (21)	16	p < 5 × 10−5
	Antibiotic agents	20,905 (93)	40,430 (86)	8	p < 5 × 10−5
	Antifungal agents	2,459 (11)	2,564 (6)	11	p < 5 × 10−5
	Corticosteroids	8,553 (38)	11,905 (25)	9	p < 5 × 10−5
Complications ** , n (%)
	Bacterial pneumonia	3,827 (19)	5,761 (13)	16	p < 5 × 10−5
	Cardiac arrest	1,482 (7)	988 (2)	10	p < 5 × 10−5
	Coagulation disorder	1,414 (7)	1,321 (3)	15	p < 5 × 10−5
	Rhabdomyolysis	292 (1)	177 (0.4)	15	p < 5 × 10−5
Outcomes, n (%)
	ICU admission	12,579 (54)	11,652 (23)	2	p < 5 × 10−5
	Invasive mechanical ventilation	10,264 (45)	7,294 (15)	7	p < 5 × 10−5
	LOS (median, IQR)	13 (5.5, 20.5)	11 (5.0, 17.0)	4	p < 5 × 10−5
	Still in hospital	1,342 (6)	1,871 (4)	2	p < 5 × 10−5
	Transferred	2,052 (9)	3,457 (7)	2	p < 5 × 10−5
	Discharged	10,942 (47)	35,744 (70)	2	p < 5 × 10−5
	Death	8,890 (38)	9,794 (19)	2	p < 5 × 10−5

*Stage 3 includes patients requiring RRT.

**Definitions of comorbidities, complications, and outcomes from the CRFs are presented in S2 Table.

ACEi, angiotensin converting enzyme inhibitor; AKI, acute kidney injury; ARB, angiotensin II receptor blocker; BP, blood pressure; BPM, beats per minute; BUN, blood urea nitrogen; CKD, chronic kidney disease; COVID-19, Coronavirus Disease 2019; CRF, case report form; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate (estimated using the CKD-EPI equation); eKDIGO, extended KDIGO; HIC, high income; ICU, intensive care unit; IQR, interquartile range; LLMIC, low and low middle-income countries; LOS, length of stay; NSAID, nonsteroidal anti-inflammatory drug; RRT, renal replacement therapy; sCr, serum creatinine; UMIC, upper middle income; WBC, white blood cell.

Table 4

Characteristics of patients with AKI diagnosed using eKDIGO only by the decrease in sCr (deKDIGO) and no AKI.

		deKDIGO	No AKI	Missingness (%)	p-Value
Total count
		11,188	51,772
Demographics
	Age, year, median (IQR)	70 (58.5, 81.5)	67 (53.5, 80.5)	0	p < 5 × 10−5
	Female (%)	4,259 (38)	22,031 (43)	0	p < 5 × 10−5
Country income level, n (%)
	HIC	10,229 (92)	45,686 (88)	0	p < 5 × 10−5
	UMIC	333 (3)	4,176 (8)	0	p < 5 × 10−5
	LLMIC	600 (5)	1,876 (4)	0	p < 5 × 10−5
AKI Grades and RRT, n (%)
	AKI stage 1	9,169 (82)	0 (0.0)	0
	AKI stage 2	1,520 (14)	0 (0.0)	0
	AKI stage 3*	499 (4)	0 (0.0)	0
	RRT	-	-	9
Comorbidities * , n (%)
	CKD	1,797 (17)	5,433 (11)	7	p < 5 × 10−5
	Chronic cardiac disease	2,989 (27)	12,772 (25)	2
	Chronic pulmonary disease	1,599 (15)	6,853 (14)	7
	Hypertension	3,902 (48)	16,429 (43)	28	p < 5 × 10−5
	Dementia	1,316 (13)	4,315 (9)	12	p < 5 × 10−5
	Type 2 diabetes	3,443 (33)	14,051 (29)	6	p < 5 × 10−5
	Liver disease	380 (4)	1,653 (3)	4
	Malnutrition	284 (3)	963 (2)	12	p < 5 × 10−5
	Obesity	1,504 (16)	6,404 (15)	18
Medications on admission, n (%)
	NSAIDs	627 (8)	2,610 (8)	35
	ACEis	1,407 (18)	5,177 (15)	33	p < 5 × 10−5
	ARBs	926 (12)	3,101 (9)	33	p < 5 × 10−5
Signs and symptoms on admission, n (%)
	Altered consciousness/confusion	2,510 (28)	8,267 (20)	19	p < 5 × 10−5
	Diarrhea	1,885 (21)	7,983 (19)	19	p < 5 × 10−5
	Fever	6,126 (64)	30,320 (66)	12
	Vomiting/nausea	1,758 (20)	8,099 (19)	19
	Muscle aches/joint pain	1,617 (20)	8,853 (23)	25	p < 5 × 10−5
	Headache	743 (9)	5,367 (14)	26	p < 5 × 10−5
	Sore throat	628 (8)	3,579 (9)	27
	Cough	5,979 (63)	29,549 (65)	12
	Runny nose	230 (3)	1,547 (4)	28	p < 5 × 10−5
Observations on admission, median (IQR)
	Temperature, °C	37.3 (36.3, 38.3)	37.3 (36.8, 37.8)	3
	Systolic BP, mm Hg	123 (107.0, 139.0)	130 (115.0, 145.0)	6	p < 5 × 10−5
	Diastolic BP, mm Hg	70 (60.5, 79.5)	75 (66.0, 84.0)	7	p < 5 × 10−5
	Heart rate, BPM	92 (78.5, 105.5)	90 (77.5, 102.5)	7	p < 5 × 10−5
	Respiratory rate, per min	22 (17.5, 26.5)	21 (17.0, 25.0)	13	p < 5 × 10−5
	Oxygen saturation, %	95 (92.0, 98.0)	95 (92.5, 97.5)	7	p < 5 × 10−5
Laboratory results on admission, median (IQR)
	WBC (× 109/L)	8.1 (5.1, 11.1)	7 (4.5, 9.5)	15	p < 5 × 10−5
	BUN (mmol/L)	11.6 (5.6, 17.6)	6.4 (3.9, 8.9)	22	p < 5 × 10−5
	Potassium (mmol/L)	4.1 (3.7, 4.5)	4.1 (3.7, 4.4)	15	p < 5 × 10−5
	CRP (mg/L)	89 (20.0, 158.0)	69 (12.5, 125.5)	22	p < 5 × 10−5
	sCr (umol/L)	119 (80.0, 158.0)	80 (63.0, 97.0)	11	p < 5 × 10−5
	eGFR, (ml/min/1.73m2)	48.6 (28.1, 69.1)	80.3 (61.8, 98.8)	12	p < 5 × 10−5
Admission treatment, n (%)
	Antiviral and COVID-19 targeting agents	2,057 (22)	9,145 (21)	16
	Antibiotic agents	9,718 (91)	40,430 (86)	8	p < 5 × 10−5
	Antifungal agents	698 (7)	2,564 (6)	11	p < 5 × 10−5
	Corticosteroids	3,248 (31)	11,905 (25)	9	p < 5 × 10−5
Complications**, n (%)
	Bacterial pneumonia	1,461 (15)	5,761 (13)	16	p < 5 × 10−5
	Cardiac arrest	280 (3)	988 (2)	10
	Coagulation disorder	402 (4)	1,321 (3)	15	p < 5 × 10−5
	Rhabdomyolysis	70 (0.7)	177 (0.4)	15
Outcomes, n (%)
	ICU admission	3,836 (35)	11,652 (23)	2	p < 5 × 10−5
	Invasive mechanical ventilation	2,596 (24)	7,294 (15)	7	p < 5 × 10−5
	LOS (median, IQR)	12 (5.5, 18.5)	11 (5.0, 17.0)	4	p < 5 × 10−5
	Still in hospital	540 (5)	1,871 (4)	2	p < 5 × 10−5
	Transferred	924 (8)	3,457 (7)	2	p < 5 × 10−5
	Discharged	6,761 (62)	35,744 (70)	2	p < 5 × 10−5
	Death	2,692 (25)	9,794 (19)	2	p < 5 × 10−5

*Does not include RRT.

**Definitions of comorbidities, complications, and outcomes from the CRFs are presented in S2 Table.

ACEi, angiotensin converting enzyme inhibitor; AKI, acute kidney injury; ARB, angiotensin II receptor blocker; BP, blood pressure; BPM, beats per minute; BUN, blood urea nitrogen; CKD, chronic kidney disease; COVID-19, Coronavirus Disease 2019; CRF, case report form; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate (estimated using the CKD-EPI equation); HIC, high income; ICU, intensive care unit; IQR, interquartile range; LLMIC, low and low middle-income countries; LOS, length of stay; NSAID, nonsteroidal anti-inflammatory drug; RRT, renal replacement therapy; sCr, serum creatinine; UMIC, upper middle income; WBC, white blood cell.

A logistic regression was fitted to assess the association between eKDIGO AKI with in-hospital mortality. A t test with a significance threshold of 0.001 was used to assess the significance of predictors. Multiple Imputation by Chained Equations (MICE) imputation was used to address variable missingness and a sensitivity analysis showing the results without imputation can be found in S4 Table. Adjustments were made for factors indicating disease severity such as admission to ICU; need for mechanical ventilation; corticosteroid and antifungal treatment; and complicating factors such as bacterial pneumonia, cardiac arrest, coagulation disorders, and rhabdomyolysis. Adjustment was also made for factors known to increase the susceptibility to AKI such as age, sex, diabetes, chronic cardiac disease, chronic pulmonary disease, chronic kidney disease (CKD), hypertension, obesity, and use of renin–angiotensin system (RAS) blockers before admission [6].

The relationship between eKDIGO AKI and in-hospital death and discharge was described with a survival curve approximated using the Aalen–Johansen estimator, a multistate version of the Kaplan–Meier estimator [17]. The follow-up period began on the day of hospital admission and ended on the day of either discharge or death or 28 days postadmission if no event had occurred. Discharge from hospital was considered an absorbing state (once discharged there was no readmission or death).

All statistical analyses were performed using the R statistical programming language, version 4.0.2 [18,19]. This study is reported as per the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guideline (S5 Table).

Results

From February 15, 2020 to February 1, 2021, data were collected for 418,111 individuals admitted to hospital with clinically suspected or laboratory confirmed SARS-COV-2 infection from 1,609 sites and 54 countries. Of these, 75,670 were used as the analysis cohort (Fig 1). The median length of admission was 12 days (IQR 7, 20 days). Missing data were less than 10% for most variables—although averaging 20% for symptoms on admission—and distributed evenly between groups for those with higher missingness levels (S3 Table).

Incidence, staging, and timing of peak AKI

With the KDIGO definition 12,704 (16.8%) patients were identified as having AKI during their admission. Using the extended KDIGO definition, a total of 23,892 (31.6%) patients were diagnosed with AKI. A breakdown of the top 10 contributing countries for patients in each group can be found in S1 Fig. The peak stages of AKI with KDIGO and eKDIGO, respectively, were the following: stage 1: 36% and 58%; stage 2: 17% and 15%; and stage 3: 47% and 27%, with a total of 4,252 patients (overall 5.6% of all patients) requiring acute dialysis (Fig 3). Peak sCr occurred more frequently on days 3 and 6 from admission and diminished significantly after day 10 using a KDIGO definition. With the extended definition, an additional 4,019 patients had AKI on day 3 (70% of all AKI diagnosed on that day) and 1,808 on day 6 (64% of all AKI on that day) (Fig 4).

Fig 3

Staging of AKI using KDIGO and eKDIGO definitions.

AKI, acute kidney injury; eKDIGO, extended KDIGO; KDIGO, Kidney Disease Improving Global Outcomes; RRT, renal replacement therapy.

Fig 4

Day of peak AKI using KDIGO and eKDIGO definitions.

AKI, acute kidney injury; eKDIGO, extended KDIGO; KDIGO, Kidney Disease Improving Global Outcomes.

Demographic and clinical characteristics

Baseline characteristics at hospital admission, acute interventions, complications, and outcomes for patients with AKI diagnosed by eKDIGO versus no AKI, KDIGO versus deKDIGO, and deKDIGO versus no AKI are provided in Tables 2–4, respectively. A majority of patients from all groups were from high-income countries with the highest proportion from LLMICs in the KDIGO group (12% versus 5% in eKDIGO and 9% deKDIGO). Significantly more stage 1 AKI could be seen in the deKDIGO than in KDIGO patients (82% versus 36%), while more severe forms of AKI (stage 3) were dominant in the KDIGO group even after excluding RRT patients (13% versus 4%).

CKD, hypertension, type 2 diabetes, and obesity were significantly more common in patients who developed AKI. The use of ACE inhibitors (ACEis) and angiotensin receptor blockers (ARBs) medications were more common in the KDIGO group as compared to both the deKDIGO group and the no AKI group. Similarly, administration of antifungal agents and corticosteroids was more common among KDIGO-diagnosed than deKDIGO AKI patients and patients without AKI. Signs, symptoms, and observations at admission were very similar in all groups. At presentation, patients with eKDIGO AKI had a higher blood urea nitrogen (median 10.9 versus 6.4 mmol/l), C-reactive protein (median 95.3 versus 69 mg/L) and sCr (110 umol/l versus 80 umol/l), and lower estimated glomerular filtration rate (eGFR, estimated with CKD-EPI equation) (54 ml/min/1.73m2 versus 80 ml/min/1.73m2) compared to those without AKI. Renal function on admission was worse in the deKDIGO group than in KDIGO patients (eGFR 48 ml/min/1.73m2 versus 62 ml/min/1.73m2). In general, patients with AKI were more likely to have complications during their hospital stay.

Clinical outcomes

Patients who developed AKI using eKDIGO were more likely to be admitted to the ICU (54%), require invasive mechanical ventilation (45%), and die during their admission (38%) compared to patients without AKI. After adjusting for disease severity, this group of patients had a higher risk of in-hospital death (adjusted odds ratio: 1.77, 95% confidence interval: 1.7–1.85, p-value < 0.001) (Table 5), which is further illustrated in the survival curves shown in Fig 5. Patients in the deKDIGO group appeared to have better outcomes and less mortality than those diagnosed by KDIGO criteria, but still had significantly worse outcomes and mortality than patients with no AKI (Tables 3 and 4).

Table 5

Logistic regression fitted to assess the association between eKDIGO AKI with in-hospital mortality.

		95% confidence interval
Variable	Odds ratio	Lower	Upper	p-Value
(Intercept)	0.051	0.048	0.054	<0.001
AKI eKDIGO	1.776	1.705	1.85	<0.001
Female	0.78	0.749	0.812	<0.001
Age 18 to 65 (ref)	1.0	-	-	-
Age 65 to 85	3.969	3.778	4.17	<0.001
Age 85+	7.773	7.285	8.294	<0.001
CKD	1.317	1.248	1.39	<0.001
Chronic cardiac disease	1.331	1.271	1.393	<0.001
Chronic pulmonary disease	1.514	1.436	1.595	<0.001
Hypertension	0.955	0.896	1.019	0.153
Obesity	0.904	0.841	0.972	0.008
Type 2 diabetes	1.153	1.096	1.214	<0.001
Preadmission ACEis and ARBs	0.867	0.812	0.926	<0.001
Treatment with corticosteroids	1.14	1.091	1.192	<0.001
Treatment with antifungal agents	1.218	1.138	1.305	<0.001
ICU admission	1.585	1.482	1.695	<0.001
Mechanical ventilation	2.188	2.037	2.349	<0.001
Cardiac arrest	19.261	16.821	22.055	<0.001
Bacterial pneumonia	1.222	1.16	1.287	<0.001
Coagulation disorder	1.372	1.253	1.501	<0.001
Rhabdomyolysis	1.17	0.947	1.444	0.145

MICE imputation used for variable missingness.

ACEi, angiotensin converting enzyme inhibitor; AKI, acute kidney injury; ARB, angiotensin II receptor blocker; CKD, chronic kidney disease; eKDIGO, extended KDIGO; ICU, intensive care unit; MICE, Multiple Imputation by Chained Equations.

Fig 5

Aalen–Johansen survival plot.

Outcomes among patients with AKI diagnosed using eKDIGO criteria and no AKI. Confidence bars are used to illustrate a 95% confidence interval. AKI, acute kidney injury; eKDIGO, extended KDIGO.

Table 3

Characteristics of patients with AKI diagnosed using KDIGO definition versus patients diagnosed with AKI by eKDIGO only by the decrease in sCr (deKDIGO).

		deKDIGO	KDIGO	Missingness (%)	p-Value
Total count
		11,188	12,704
Demographics
	Age, year, median (IQR)	70 (58.5, 81.5)	66 (56.5, 75.5)	0	p < 5 × 10−5
	Female (%)	4,259 (38)	4,116 (33)	0	p < 5 × 10−5
Country income level, n (%)
	HIC	10,229 (92)	10,284 (81)	0	p < 5 × 10−5
	UMIC	333 (3)	796 (6)	0	p < 5 × 10−5
	LLMIC	600 (5)	1,561 (12)	0	p < 5 × 10−5
AKI grades and RRT, n (%)
	AKI stage 1	9,169 (82)	4,577 (36)	0	p < 5 × 10−5
	AKI stage 2	1,520 (14)	2,162 (17)	0	p < 5 × 10−5
	AKI stage 3 (no RRT)	499 (4)	1,713 (13)	0	p < 5 × 10−5
	AKI stage 3 (with RRT)	-	5,965 (47)	0
	RRT	-	4,252 (36)	9
Comorbidities * , n (%)
	CKD	1,797 (17)	2,262 (19)	7
	Chronic cardiac disease	2,989 (27)	3,236 (26)	2
	Chronic pulmonary disease	1,599 (15)	1,376 (12)	7	p < 5 × 10−5
	Hypertension	3,902 (48)	4,545 (51)	28
	Dementia	1,316 (13)	519 (5)	12	p < 5 × 10−5
	Type 2 diabetes	3,443 (33)	4,553 (39)	6	p < 5 × 10−5
	Liver disease	380 (4)	462 (4)	4
	Malnutrition	284 (3)	217 (2)	12	p < 5 × 10−5
	Obesity	1,504 (16)	2,288 (22)	18	p < 5 × 10−5
Medications on admission, n (%)
	NSAIDs	627 (8)	755 (9)	35
	ACEis	1,407 (18)	1,388 (17)	33
	ARBs	926 (12)	1,016 (12)	33
Signs and symptoms on admission, n (%)
	Altered consciousness/confusion	2,510 (28)	1,785 (18)	19	p < 5 × 10−5
	Diarrhea	1,885 (21)	1,855 (18)	19	p < 5 × 10−5
	Fever	6,126 (64)	7,207 (65)	12
	Vomiting/nausea	1,758 (20)	1,629 (16)	19	p < 5 × 10−5
	Muscle aches/joint pain	1,617 (20)	2,026 (22)	25
	Headache	743 (9)	1,036 (11)	26
	Sore throat	628 (8)	936 (10)	27	p < 5 × 10−5
	Cough	5,979 (63)	6,982 (64)	12
	Shortness of breath	6,680 (70)	8,144 (73)	12	p < 5 × 10−5
	Runny nose	230 (3)	404 (4)	28	p < 5 × 10−5
Observations on admission, median (IQR)
	Temperature, °C	37.3 (36.3, 38.3)	37.2 (36.2, 38.2)	3
	Systolic BP, mm Hg	123 (107.0, 139.0)	130 (113.5, 146.5)	6	p < 5 × 10−5
	Diastolic BP, mm Hg	70 (60.5, 79.5)	72 (62.0, 82.0)	7	p < 5 × 10−5
	Heart rate, BPM	92 (78.5, 105.5)	93 (79.5, 106.5)	7
	Respiratory rate, per min	22 (17.5, 26.5)	24 (19.5, 28.5)	13	p < 5 × 10−5
	Oxygen saturation, %	95 (92.0, 98.0)	94 (91.0, 97.0)	7	p < 5 × 10−5
Laboratory results on admission, median (IQR)
	WBC (× 109/L)	8.1 (5.1, 11.1)	8.3 (5.3, 11.3)	15
	BUN (mmol/L)	11.6 (5.6, 17.6)	10.1 (4.1, 16.1)	22	p < 5 × 10−5
	Potassium (mmol/L)	4.1 (3.7, 4.5)	4.2 (3.8, 4.6)	15
	CRP (mg/L)	89 (20.0, 158.0)	102.7 (21.2, 184.2)	22	p < 5 × 10−5
	sCr, (umol/l)	119 (80.0, 158.0)	101 (56.5, 145.5)	11	p < 5 × 10−5
	eGFR, ml/min/1.73m2	48.6 (28.1, 69.1)	62.1 (34.6, 89.6)	12	p < 5 × 10−5
Admission treatment, n (%)
	Antiviral and COVID-19 targeting agents	2,057 (22)	3,292 (31)	16	p < 5 × 10−5
	Antibiotic agents	9,718 (91)	11,187 (94)	8	p < 5 × 10−5
	Antifungal agents	698 (7)	1,761 (16)	11	p < 5 × 10−5
	Corticosteroids	3,248 (31)	5,305 (45)	9	p < 5 × 10−5
Complications * , n (%)
	Bacterial pneumonia	1,461 (15)	2,366 (22)	16	p < 5 × 10−5
	Cardiac arrest	280 (3)	1,202 (10)	10	p < 5 × 10−5
	Coagulation disorder	402 (4)	1,012 (9)	15	p < 5 × 10−5
	Rhabdomyolysis	70 (0.7)	222 (2)	15	p < 5 × 10−5
Outcomes, n (%)
	ICU admission	3,836 (35)	8,743 (70)	2	p < 5 × 10−5
	Invasive mechanical ventilation	2,596 (24)	7,668 (63)	7	p < 5 × 10−5
	LOS (median, IQR)	12 (5.5, 18.5)	15 (6.0, 24.0)	4	p < 5 × 10−5
	Still in hospital	540 (5)	802 (7)	2	p < 5 × 10−5
	Transferred	924 (8)	1,128 (9)	2
	Discharged	6,761 (62)	4,181 (34)	2	p < 5 × 10−5
	Death	2,692 (25)	6,198 (50)	2	p < 5 × 10−5

*Definitions of comorbidities, complications, and outcomes from the CRFs are presented in S2 Table.

ACEi, angiotensin converting enzyme inhibitor; AKI, acute kidney injury; ARB, angiotensin II receptor blocker; BP, blood pressure; BPM, beats per minute; BUN, blood urea nitrogen; CKD, chronic kidney disease; COVID-19, Coronavirus Disease 2019; CRF, case report form; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate (estimated using the CKD-EPI equation); eKDIGO, extended KDIGO; HIC, high income; ICU, intensive care unit; IQR, interquartile range; KDIGO, Kidney Disease Improving Global Outcomes; LLMIC, low and low middle-income countries; LOS, length of stay; NSAID, nonsteroidal anti-inflammatory drug; RRT, renal replacement therapy; sCr, serum creatinine; UMIC, upper middle income; WBC, white blood cell.

Discussion

In the largest, multinational cohort of hospitalised patients with COVID-19, it was found that an extended KDIGO criteria for the diagnosis of AKI, which includes a fall in sCr during admission, identified almost twice as many cases of AKI than the traditional KDIGO definition. The majority of these additional cases were stage 1 AKI, occurring early in the admission, supporting the hypothesis that they may represent recovering CA-AKI. This group had comparatively worse outcomes than patients without AKI, making their identification and exploration in future studies enormously important.

The estimated incidence of KDIGO AKI, 16.8%, is consistent with that reported in the first systematic review of AKI in COVID-19 patients [20], while the incidence of eKDIGO AKI fits those studies from the larger New York City cohorts, which had similar rates of ICU admission [2,21]. The mortality rate of 50% among KDIGO-diagnosed AKI patients falls within the range (34% to 50%) reported in previous studies using the same AKI definition [1,2,21-23]. While the inability to exclude readmitted patients may have introduced a degree of survival bias, the fact that readmission rates of less than 3% are seen in other large studies suggests that the effect of this bias is likely to be relatively small [2,21,23].

In line with what is known regarding AKI susceptibility and sequelae, patients identified in the present study as having AKI—by either definition—were more likely to have CKD, hypertension, and type 2 diabetes mellitus, be on an ACEi or ARB, and generally have more medical complications during their admission than patients who did not develop AKI.

The admission eGFR, sCr, and blood urea nitrogen levels of the eKDIGO AKI population, and specifically those in the deKDIGO group, demonstrated significant impairment early in the admission. This is suggestive of CA-AKI, which would otherwise have gone unrecognised. While these patients had comparatively milder AKI and disease severity than patients in the KDIGO group, they nonetheless incurred significantly more morbidity and mortality than patients without AKI, even after adjusting for confounding factors. With regard to the increased prevalence of stage 1 AKI using the eKDIGO definition, there is growing evidence that even mild episodes of AKI may contribute to the development of CKD [24-26]. This raises the important question of whether this new group of COVID-19 AKI patients would benefit from early management strategies to improve long-term outcomes. Such measures are typically simple—management of fluid balance and removal of nephrotoxic medication for example—and readily implementable, even in resource-poor environments. A follow-up study, similar to the 0by25 feasibility study [8], may be warranted to explore such questions.

The earlier timing of peak AKI in the hospital stay and large proportion of stage 1 cases in the eKDIGO group suggests several possible etiologies. It may point to a prerenal pattern of injury occurring in the setting of dehydration from gastrointestinal fluid losses, fever and anorexia—a finding supported by the identification of acute tubular injury in autopsy studies of patients with COVID-19 [27]. However, it is also possible that a proportion of these additional, milder, cases of AKI, captured by down trending sCr, are a consequence of early rehydration of patients with either previously normal kidney function or CKD. While the reduced admission eGFR of this group (median 54 ml/min/1.73 m2) makes the former less likely, preadmission sCr measurements would be required to reliably identify the latter. It is reassuring that the proportion of reported CKD in the KDIGO and eKDIGO groups is very similar (19% and 18%, respectively).

It is interesting to consider to what extent the large number of additional cases captured by the extended KDIGO definition are a COVID-specific consequence. While meta-analysis suggests that global estimates of AKI incidence in adult hospitalised patients range between 3% and 18% [28], there are no current estimates of global AKI incidence according to the eKDIGO definition. Moving forward, evaluation of the eKDIGO definition for the diagnosis of AKI in various hospital and community settings will be needed to shed light on whether our findings are particular to a COVID-affected population. In this context, it should be noted that approximately 20% of the analysis cohort had a diagnosis of COVID-19 made on clinical grounds, most likely due to testing shortages and high resource demands during surge phases of the pandemic. While this may have resulted in the potential inclusion of patients with other respiratory illnesses, given that other common respiratory illnesses were notably less prevalent during the pandemic [29,30], it is plausible that a significant proportion of these clinically diagnosed patients did in fact have COVID-19.

This study has some key limitations. The exclusion of patients without 2 sCr measurements may have introduced a degree of selection bias. This could be responsible for the absence of expected geographical differences found between the eKDIGO and no AKI groups and may also have resulted in an underestimation of AKI cases by both definitions [7].

The lack of a time-standardised collection of sCr across all sites also represents a limitation of the study. Patients having more frequent sCr collections may represent a population with more severe illness in whom AKI would be more readily detected, therefore affecting the overall AKI incidence rates and potentially generating a negative survival bias. Nevertheless, it is reassuring that the number of AKI cases are a small proportion of the total sCr collected on any given day (<18%) (S2 Fig), suggesting that the bias introduced by ad hoc sampling was low.

The lack of standardisation in sCr collection may have also affected the reporting of time to peak AKI, the magnitude of peak AKI reached in each individual patient and, in those experiencing both a rise and fall in sCr during their admission, whether AKI was captured during the former phase (KDIGO) or the latter (eKDIGO).

With regard to the distinction between community and hospital acquired AKI, often, a 48-hour threshold is used to identify CA-AKI [31]. Such a definition would preclude many patients in this study who were identified as having AKI on day 3 of admission. It is worth noting that these patients would be identified as CA-AKI (or transient hospital–associated AKI) by the definition proposed by Warnock and colleagues, which integrates sCr trajectories and does not adhere to the somewhat arbitrary 48-hour cutoff [32]. Whether or not the additional cases of AKI captured by eKDIGO are truly reflective of CA-AKI will ultimately require studies that assess this population in a variety of community settings.

To our knowledge, this is the first study to systematically examine an extended KDIGO definition for the identification of AKI against the traditional KDIGO criteria in hospitalised COVID-19 patients. Our population is, as far as we know, the largest and only multinational cohort of patients with COVID-19 from all income country levels. The use of an extended KDIGO definition to diagnose AKI in this population resulted in a significantly higher incidence rate compared to traditional KDIGO criteria. These additional cases of AKI appear to be occurring in the community or early in the hospital admission and are associated with significantly worse outcomes, highlighting the importance of examining their role and long-term impact in future studies.

Study ethics approval.

(DOCX)

Click here for additional data file.

Definitions used for clinical COVID-19.

COVID-19, Coronavirus Disease 2019.

(DOCX)

Click here for additional data file.

Definition of comorbidities, complications, and outcomes from the ISARIC CRFs.

ISARIC, International Severe Acute Respiratory and Emerging Infection Consortium; CRF, case report form.

(DOCX)

Click here for additional data file.

Distribution of missingness information between eKDIGO and No AKI patients.

AKI, acute kidney injury; eKDIGO, extended KDIGO.

(DOCX)

Click here for additional data file.

Logistic regression fitted to assess the association between eKDIGO AKI with in-hospital mortality without MICE imputation for variable missingness.

AKI, acute kidney injury; eKDIGO, extended KDIGO; MICE, Multiple Imputation by Chained Equations.

(DOCX)

Click here for additional data file.

STROBE checklist.

STROBE, STrengthening the Reporting of OBservational studies in Epidemiology.

(DOCX)

Click here for additional data file.

Breakdown of top contributing countries for patients diagnosed with AKI by KDIGO definition (A) and from deKDIGO group (B).

AKI, acute kidney injury; KDIGO, Kidney Disease Improving Global Outcomes.

(DOCX)

Click here for additional data file.

Number of AKI cases by AKI definition (A = KDIGO and B = eKDIGO) as a proportion of total number of sCrs collected each day.

(DOCX)

Click here for additional data file.

The ISARIC Clinical Characterisation Group.

ISARIC, International Severe Acute Respiratory and Emerging Infection Consortium.

(DOCX)

Click here for additional data file.

26 Nov 2021

Dear Dr Wainstein,

Thank you for submitting your manuscript entitled "Acute Kidney Injury in Patients with COVID-19 using an Extended KDIGO Definition" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff and I am writing to let you know that we would like to send your submission out for external peer review.

However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

Please re-submit your manuscript within two working days, i.e. by Nov 30 2021 11:59PM.

Login to Editorial Manager here: https://www.editorialmanager.com/pmedicine

Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed all checks it will be sent out for review.

Feel free to email us at plosmedicine@plos.org if you have any queries relating to your submission.

Kind regards,

Callam Davidson

Associate Editor

PLOS Medicine

24 Dec 2021

Dear Dr. Wainstein,

Thank you very much for submitting your manuscript "Acute Kidney Injury in Patients with COVID-19 using an Extended KDIGO Definition" (PMEDICINE-D-21-04861R1) for consideration at PLOS Medicine.

Your paper was evaluated by a associate editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

We hope to receive your revised manuscript by Jan 25 2022 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

Please use the following link to submit the revised manuscript:

https://www.editorialmanager.com/pmedicine/

Your article can be found in the "Submissions Needing Revision" folder.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Callam Davidson,

Associate Editor

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

Please revise your title according to PLOS Medicine's style. Your title must be nondeclarative and not a question. It should begin with main concept if possible. "Effect of" should be used only if causality can be inferred, i.e., for an RCT. Please place the study design (e.g. a "a cohort study") in the subtitle (ie, after a colon).

Please structure your abstract using the PLOS Medicine headings (Background, Methods and Findings, Conclusions).

Please combine the abstract Methods and Results sections into one section, “Methods and findings”.

Abstract Background: The final sentence should clearly state the study question.

Abstract Methods and Findings:

* Please include the study design, years during which the study took place, length of follow up, and main outcome measures.

* Please quantify the main results (with 95% CIs and p values).

* Please include the important dependent variables that are adjusted for in the analyses.

* In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology.

At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

Please review the submission guidelines (https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-financial-disclosure-statement) and confirm that your financial disclosure only contains the relevant information.

Please review the submission guidelines (https://journals.plos.org/plosmedicine/s/competing-interests) and confirm that your competing interests statement only contains the relevant information.

Relevant to the above two comments, it may be worth reviewing previous published articles in PLOS Medicine for an idea of how to present these statements (https://journals.plos.org/plosmedicine/)

The Data Availability Statement (DAS) requires revision. For the small subset of sites that have not agreed to pooled data sharing, the point of contact for inquiries cannot be a study author.

To facilitate future reviews, please include continuous line numbering throughout your manuscript.

Citations should be in square brackets, and preceding punctuation.

Please define the length of follow up (eg, in mean, SD, and range).

When a p value is given, please specify the statistical test used to determine it.

Please define the abbreviations in Figure 1.

Please provide the unadjusted comparisons as well as the adjusted comparisons in Table 3.

Please specify the variables controlled for in Table 3.

Please provide the name(s) of the institutional review board(s) that provided ethical approval (this can be provided in the supplementary materials if required).

Please specify whether informed consent was written or oral.

Please ensure that the study is reported according to the STROBE guideline, and include the completed STROBE checklist as Supporting Information.

Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist)."

The STROBE guideline can be found here: http://www.equator-network.org/reporting-guidelines/strobe/

When completing the checklist, please use section and paragraph numbers, rather than page numbers.

Did your study have a prospective analysis plan? Please state this (either way) early in the Methods section.

a) If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript.

b) If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place.

c) In either case, changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale.

Throughout, please ensure Supporting Information files are named and cited per our submission guidelines (https://journals.plos.org/plosmedicine/s/supporting-information).

In the first sentence of the concluding paragraph in your discussion, please temper claims of primacy by stating, "to our knowledge" or something similar.

Please remove the disclosures and author contributions sections from the end of the main text as this information should be captured in your responses to the submission forms.

References should only use et al after listing the first six authors, and should include dates accessed for internet sources. Please see our website for other reference guidelines https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references

Comments from the academic editor:

The manuscript addresses a topic of potential interest, which is very timely.

The Authors sough to assess the incidence and impact of acute kidney injury (AKI) occurring in patients with COVID-19 in the community or early after hospital admission using the large ISARIC database and an extended KDIGO definition of AKI. Although the study is very interesting, there are several shortcomings that in the present manuscript preclude sound conclusions, and that should be addressed. To mention some of them:

i) need to provide in the main manuscript the comparison of the outcomes between that part of eKDIGO criteria which otherwise would be missed by standard KDIGO criteria;

ii) need to provide a table showing differences between AKI diagnosed by deKDIGO and KDIGO criteria, and deKDIGO and no AKI;

iii) unclear whether there were any readmission, and how the authors treated readmission in the analysis;

iv) need to explain why in the present manuscript mortality may be lower than that reported in a recent UK study, since the first phase of COVID-19 pandemic was associated with high incidence of AKI and high mortality, especially in Europe;

v) need to explain in more depth the reasoning behind a decrease in sCr indicating AKI;

vi) need to clarify the criteria contributing to the number of sCr taken for each individual, and discuss the possible bias of this criteria;

vii) concern about the fact that the number of sCr measurements/tests taken would appear to affect the probability that a patient would meet the eKDIGO criteria;

viii) need to clarify and present the typical sCr measurement/test schedules that would also be relevant to the presentation for peak AKI stage;

ix) need to add for adjustment the age as possible confounder (Table 3);

x) need to stratify the analysis by age for validation of the findings;

xi) need to clarify how the confounders (Table 3) were chosen;

xii) provide a brief sensitivity analysis for the period before vaccination become available;

xiii) need to stratify analyses by country development;

xiv) discuss as a study limitation the fact that the involvement of patient without laboratory-confirmed SARS-CoV-2 infection, but with a clinical diagnosis of COVID-19, which relied on symptoms that could be ascribed to other infectious diseases, may have resulted in the inclusion of participants who were hospitalized for non-COVID-19-related conditions;

xv) need to add in the logistic Generalized Additive Model, several covariates which may have affected the risk of in-hospital mortality, such as ethnicity and co-morbidities (e.g. chronic kidney disease);

xvi) concern about the conclusions regarding the generalizability of the study findings, since only about 5.3% of the overall patient population was from low- and low/middle-income countries;

xvii) need to provide the proportion of patients who achieved recovery of kidney function by the time of hospital discharge among surviving patients who developed AKI, according to the traditional and the extended KDIGO definitions.

Comments from the reviewers:

Reviewer #1: This is a very interesting study using eKDIGO criteria for diagnosing AKI

The clinical features and outcomes of patients diagnosed by falling creatinine in subsequent 7 days is important. Comparison of the outcomes between that part of eKDIGO criteria which otherwise would be missed by standard KDIGO criteria is quite important and that should be in the main manuscript.

The main point of this study is the use of eKDIGO criteria and the added value of deKDIGO criteria for AKI. Hence, I would recommend table showing differences between AKI diagnosed by deKDIGO and KDIGO criteria and deKDIGO and no AKI in main manuscript.

Were there any readmissions? We know AKI patients have high rate of readmissions. If so, how did authors treat readmission in the analysis? There is risk of survival bias and how did authors take care of this. Interestingly, the mortality reported by authors in much lower than a UK study and I suspect that this is because of inclusion of readmissions (https://doi.org/10.1371/journal.pmed.1003406).

Can the authors explain why mortality may be lower as the first phase of COVID pandemic was associated with high incidence of AKI and high mortality, especially in UK and Europe.

I should confess that I am not well versed with GAM and not sure why authors did not perform logistic regression to adjust for the predictors. However, collinearity can still exist in GAM and wonder how did authors check this. Especially, multi-collinearity will exist between mechanical ventilation, cardiac arrest and ICU admission.

Going forward, it will be important to develop artificial intelligence to electronically detect AKI using the extended criteria in electronic patient record

Reviewer #2: "Acute Kidney Injury in Patients with COVID-19 using an Extended KDIGO Definition" promotes the use of an extended Kidney Disease Improving Global Outcomes (KDIGO) definition, to diagnose acute kidney injury (AKI). The current KDIGO definition depends on whether there is an increase in serum creatinine (sCr) beyond certain thresholds, over 48 hours or within 7 days (Table 1). eKDIGO further includes a decrease in sCr (i.e. deKDIGO) by the same amounts over the same timeframes, as additional criteria. Statistical analysis by pairwise independent samples comparisons adjusting for a number of common confounders suggested that the additionally-identified deKDIGO group would also expect worse outcomes compared to the control group without AKI (sTable5), although not to the extent of the original KDIGO group (sTable4). It is therefore suggested that eKDIGO (which diagnoses the additional deKDIGO group) has the potential to identify more patients with increased mortality (from AKI), that had not been previously diagnosed by the standard KDIGO definition.

This improved determination of risks appears potentially of great value in addressing the ongoing coronavirus pandemic. However, a number of issues might be considered, most importantly those relating to adjustment for confounders:

1. Given the focus on AKI in the study, it might be considered to further clarify the clinical validity of the proposed additions (i.e. decrease in sCr over time) to the KDIGO criteria. While some prior work ([6],[7]) was cited, it might be considered to explain the reasoning behind a decrease in sCr indicating AKI in slightly more depth in the paper, perhaps with empirical metrics/prior research on (expected) sCr variance in health individuals.

2. It is stated that individuals with <2 sCr measurements/tests were excluded, which accounts for n=293,619 of the original n=418,111 individual included in the study. Given that about 70% of the participants has <2 sCr, it might be clarified as to the criteria contributing to the number of sCr taken for each individual. Possible bias of this criteria to the study population might also be discussed further.

3. Moreover, the number of sCr measurements/tests taken would also appear to affect the probability that a patient would meet the (e)KDIGO criteria. For example, if a patient has his sCr increase by 2 times over baseline on the third day after admission, but have sCr decrease to just 1.2 times over baseline by the sixth day, then he would be considered to have AKI had he been tested for sCr around the third day (and possibly other days), but not to have AKI had he only been tested for sCr on the sixth day. sCr testing procedure and descriptive statistics relating to the number of sCr tests (i.e. sampling rate), might thus be discussed/presented.

4. The above clarifications on typical sCr measurement/test schedules would also seem relevant to the presentation of peak AKI stage (Figure 3), since it seems that a patient only has the potential to record a peak AKI on a day when his sCr was taken. Also, it might be clarified as to which day is reflected in Figure 3, if a patient maintaines his peak AKI stage over multiple days (e.g. the first day that the stage is reached)

5. From the confounders used for adjustment (Table 3), age appears a glaring omission, given how significantly it is known to affect mortality rates in particular (to the extent that it might be fair to say that the results may not be meaningful without it). Is there any reason why it was not considered as a confounder, especially as age is included as part of the demographics (Table 2)? Additionally, stratified analysis by age would seem especially relevant for validation of the findings.

6. Related to the above, it might be clarified as to how the confounders (Table 3) were chosen, given the number of available and potentially relevant patient features available from the demographics table (Table 2) alone, that were not included as confounders (e.g. obesity, diabetes, etc.)

7. Since vaccination status would also appear a particularly relevant confounder (for 2021 at least), a brief sensitivity analysis for the period before vaccination became available (if individual vaccination status is unavailable) might be appropriate.

8. Brief stratified analyses by country development (i.e. HIC/UMIC/LLMIC) might be considered.

9. It is stated that missing values were observed for some variables (as described in sTables 3 to 5). The treatment of missing values in the analysis (e.g. through imputation) might be further clarified.

10. There appears potential to further analyze outcomes according to a flexible (e)KDIGO definition, i.e. describe the change in outcomes as the threshold changes from 0.3ml/dl/48 hours, or >=1.5 times/7 days. This might be considered, perhaps in future work.

Reviewer #3: In the present manuscript, the authors evaluated the incidence and impact of AKI occurring in COVID-19 patients in the community or early after hospitalization by means of an extended KDIGO definition of AKI which captures a fall in serum creatinine upon admission. They found that among 75670 COVID-19 patients, the incidence of AKI was about two-fold higher when using the extended KDIGO criteria compared to the traditional KDIGO definition. Most of the additional cases identified were stage I AKI. Patients with AKI defined according to extended KDIGO criteria had worse kidney function at admission, more in-hospital complications and increased mortality risk compared to those without AKI.

The following drawbacks are for the Author's consideration:

1. The present study included patients from the ISARIC database with clinically diagnosed or laboratory-confirmed SARS-CoV-2 infection admitted to hospital from February 15th 2020, to February 1st 2021. Actually, the involvement of patients without laboratory-confirmed SARS-CoV-2 infection, but with a clinical diagnosis of COVID-19, which relied on symptoms (e.g., fever, cough, dyspnoea) that could be ascribed to other infectious diseases, may have resulted in the inclusion of participants who were hospitalized for non-COVID-19-related conditions. This issue should be acknowledged as a study limitation. Moreover, Table 2 lists signs and symptoms of patients at hospital admission, but key symptoms that were used to define clinically diagnosed COVID-19 (Table S1), such as cough, dyspnoea, changes in sense of smell and taste, were not provided. These data should be reported in Table 2.

2. According to the information reported in the Statistical analysis section, comparisons between patients with AKI defined according to the extended KDIGO criteria and those without AKI were performed using a conservative Bonferroni adjusted significance level of 5 x 10-5 (Page 8, lines 19-21). However, in Table 2, which provided characteristics of patients with or without AKI defined according to the extended KDIGO criteria, P values lower than 0.001 were outlined. It should be clarified for which of the comparisons provided in Table 2 P value was lower than 5 x 10-5.

3. The results showed that patients who developed AKI according to the extended KDIGO definition were more likely to require invasive mechanical ventilation and ICU admission compared to those without AKI. In this regard, a recent study found that among patients hospitalized with COVID-19 who required mechanical ventilation and had AKI, 74.5% developed AKI after initiation of mechanical ventilation (Am J Kidney Dis 2021; 77:204-215). Thus, information regarding the temporal relationship of AKI development with initiation of mechanical ventilation and ICU admission should be provided.

4. A logistic Generalized Additive Model was fitted to assess the association between AKI development diagnosed using extended KDIGO criteria with in-hospital mortality, adjusting for gender, requiring ICU admission and/or invasive mechanical ventilation, complications and treatment with corticosteroids or antifungal agents. However, several covariates which may have affected the risk of in-hospital mortality were not included in the model, such as ethnicity, with Black race having been reported to strengthen the association between AKI and the risk of death among patients hospitalized with COVID-19 (Clin J Am Soc Nephrol 2020; 16:14-25), and co-morbidities (e.g., chronic kidney disease). Moreover, the relationship between AKI diagnosed by extended KDIGO criteria and in-hospital mortality should also be described using a Kaplan-Meier survival curve.

5. In the Discussion it was argued that the considerable proportion of the study population from low- and middle-income countries afforded great generalizability of the study findings (Page 16, lines 16-18). Nevertheless, only about 5.3% of the overall patient population was from low- and low middle-income countries (i.e., 4037 out of 75670). Moreover, although 54 countries were involved in the present study, based on data in Figure S1 it can be inferred that more than half of the countries contributed with less than 35 patients with AKI defined according to serum creatinine criteria. Thus, the conclusions regarding the generalizability of the study findings should be tempered.

6. It would be valuable to assess the temporal variation of AKI rate, defined according to the traditional and the extended KDIGO definitions, during the observation period, in light of previous studies which suggested a fall in AKI incidence among COVID-19 patients following the first pandemic wave (Nephrol Dial Transplant 2021; doi: 10.1093/ndt/gfab303; Clin J Am Soc Nephrol 2020; 16:14-25; Kidney Int Rep 2021; 6:916-927).

7. The proportion of patients who achieved recovery of kidney function by the time of hospital discharge among surviving patients who had developed AKI, defined according to the traditional and the extended KDIGO definitions, should be provided.

Minor points:

- According to the flowchart of the study (Figure 1) individuals with laboratory confirmed SARS-CoV-2 infection admitted to hospital from February 15th 2020 to February 1st 2021 were included in the present study. It should be specified that also patients with clinically diagnosed COVID-19 were included.

- Several inconsistencies throughout the manuscript should be corrected. In particular: i) sixty countries were involved in the study based on the information in the Abstract, but they were 54 according to data in the Results section and in Figure 1; ii) 12740 patients were identified as having AKI according to the traditional KDIGO definition based on the information in the Results section, but they were 12704 according to data in Figure 2 and in Table S4; iii) 23982 patients were identified as having AKI according to the extended KDIGO definition based on the information in the Results section, but they were 23892 according to data in Figure 2 and in Table 2; iv) patients with AKI who required acute dialysis were 4262 according to the information in the Results section, whilst they were 4252 based on data in Table 2; v) the estimated incidence of AKI according to the traditional KDIGO definition was 18% according to the information in the Discussion (Page 14, line 10), but it was 16.8% throughout the manuscript.

- Regarding laboratory parameters, it is unclear whether urea levels were reported as either blood urea concentrations or blood urea nitrogen, and if median values were expressed as mg/dL (as indicated in Table 2) or as mmol/L (as indicated in the main text). As for kidney function, the equation used to estimate glomerular filtration rate (eGFR) should be specified, and it should be clarified whether the reported eGFR values were indexed for body surface area (as reported in the core paper) or not indexed for body surface area (as reported in Table 2).

- The last sentence of the paragraph entitled "Demographic and clinical characteristics" is incomplete.

- Figure S1 was never cited throughout the manuscript; this should be done.

Any attachments provided with reviews can be seen via the following link:

[LINK]

22 Feb 2022

Submitted filename: Reply letter_PLOS Med_Revisions_FINAL.docx

Click here for additional data file.

10 Mar 2022

Dear Dr. Wainstein,

Thank you very much for re-submitting your manuscript "Use of an extended KDIGO definition to diagnose acute kidney injury in patients with COVID-19: A prospective, multinational study of the ISARIC cohort." (PMEDICINE-D-21-04861R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by three reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We hope to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Mar 17 2022 11:59PM.

Sincerely,

Callam Davidson,

Associate Editor

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

In response to your authorship requests:

1. Any authors above 30 will be included as part of a collaborator group. Please see here for an example - https://pubmed.ncbi.nlm.nih.gov/34890407/. I would suggest that a similar approach would be appropriate here, but please let me know if you have further questions or comments.

2. As you have listed less than 30 authors as part of your preferred upper authorship group, this will not be a problem using the approach outlined above (these authors can be listed first, and any above 30 will be included in the collaborator group). I would suggest using the Acknowledgements section of the manuscript for any further specific acknowledgements.

For stylistic reasons, please remove the word ‘prospective’ from the title.

Please include the median length of admission (IQR) in the abstract.

Please ensure the main outcome measures are clearly described in the abstract.

Thank you for including a clear and interesting Author Summary. Can I please request the following:

• Please quantify the key findings such as percentages of patients with AKI identified by eKDIGO and KDIGO and the adjusted odds ratio for risk of in-hospital death.

• Please update the language in the final bullet to refer to associations.

The abbreviations ISN, CI, and RRT appear only once in the abstract, please write these out in full for clarity.

Line 26: Please define ICU on first use in the abstract.

Lines 38-40: You use the term significantly twice here. I believe the first use refers to clinical significance while the second refers to statistical – please clarify to avoid confusion.

Throughout: Please do not report P<5x10^-5, report instead as <0.001. This applies to tables/figures as well as the main text (Table 5 uses the correct format).

Line 90: Please define ISN on first use.

Please ensure Table S5 is labelled as the STROBE (Strengthening the reporting of observational studies in epidemiology) checklist.

Please confirm the y-axis for the S1 Figure is correct as the intervals are unusual.

S1 Figure Panel B: The term ‘delta eKDIGO’ appears nowhere else in the manuscript, please confirm this is correct.

Table 3 is missing a definition for the ** flag in the footnote.

Line 374: Please update to ‘19% and 18%, respectively’.

The axis labels on S2 Figure are too small to read, please enlarge these.

References: Journal name abbreviations should be those found in the National Center for Biotechnology Information (NCBI) databases.

Comments from Reviewers:

Reviewer #1: The authors have responded to all the queries. Though lack of readmission remains a concern, the authors have stated that in limitation

Reviewer #2: We thank the authors for addressing our previous comments, particularly with respect to age as a confounder/target of stratified analysis. While the responses on sCr frequency (e.g. Point 26) are reassuring, it might be considered to perform brief sensitivity analyses on the subset of patients with relatively high sCr frequency, to further confirm the findings.

Any attachments provided with reviews can be seen via the following link:

[LINK]

24 Mar 2022

Dear Dr Wainstein,

On behalf of my colleagues and the Academic Editor, Dr Giuseppe Remuzzi, I am pleased to inform you that we have agreed to publish your manuscript "Use of an extended KDIGO definition to diagnose acute kidney injury in patients with COVID-19: A multinational study using the ISARIC-WHO Clinical Characterisation Protocol" (PMEDICINE-D-21-04861R3) in PLOS Medicine.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

In the meantime, please log into Editorial Manager at http://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.

PRESS

We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate with medicinepress@plos.org. If you have not yet opted out of the early version process, we ask that you notify us immediately of any press plans so that we may do so on your behalf.

We also ask that you take this opportunity to read our Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visit http://www.plos.org/about/media-inquiries/embargo-policy/.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper.

Sincerely,

Callam Davidson

Associate Editor

PLOS Medicine