Identification and outcomes of acute kidney disease in patients presenting in Bolivia, Brazil, South Africa, and Nepal

Rhys D R Evans; Sanjib K Sharma; Rolando Claure-Del Granado; Brett Cullis; Emmanuel A Burdmann; FOS Franca; Junio Aguiar; Martyn Fredlund; Kelly Hendricks; Maria F Iturricha-Caceres; Mamit Rai; Bhupendra Shah; Shyam Kafle; David C Harris; Mike V Rocco

doi:10.1371/journal.pmed.1004495

. 2024 Nov 14;21(11):e1004495. doi: 10.1371/journal.pmed.1004495

Identification and outcomes of acute kidney disease in patients presenting in Bolivia, Brazil, South Africa, and Nepal

Rhys D R Evans ^1,^*, Sanjib K Sharma ², Rolando Claure-Del Granado ^3,⁴, Brett Cullis ⁵, Emmanuel A Burdmann ⁶, FOS Franca ⁶, Junio Aguiar ⁷, Martyn Fredlund ⁸, Kelly Hendricks ⁹, Maria F Iturricha-Caceres ¹⁰, Mamit Rai ², Bhupendra Shah ², Shyam Kafle ², David C Harris ¹¹, Mike V Rocco ¹²

Editor: Maarten W Taal¹³

¹Centre for Kidney and Bladder Health, University College London, London, United Kingdom

²B.P. Koirala Institute of Health Sciences, Dharan, Nepal

³IIBISMED, Universidad Mayor de San Simon, School of Medicine, Cochabamba, Bolivia

⁴Division of Nephrology, Hospital Obrero No 2 –CNS, Cochabamba, Bolivia

⁵University of Cape Town, Cape Town, South Africa

⁶LIM 12, Division of Nephrology, and Department of Infectious and Parasitic Diseases, University of Sao Paulo, Medical School, Sao Paulo, Brazil

⁷University of Para, Santarem, Brazil

⁸North Bristol NHS Trust, Bristol, United Kingdom

⁹ISN Programs, Denver, Colorado, United States of America

¹⁰Facultad de Medicina, Universidad Privada del Valle, Tiquipaya, Bolivia

¹¹Westmead Institute for Medical Research, University of Sydney, Sydney, Australia

¹²Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America

¹³Royal Derby Hospital, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND

RE has received honoraria from Therakos. EB has received honoraria from Baxter and AstraZeneca. Other authors have no conflicts to declare.

^✉

* E-mail: Rhys.evans5@nhs.net

Roles

Rhys D R Evans: Conceptualization, Data curation, Formal analysis, Project administration, Supervision, Writing – original draft, Writing – review & editing

Sanjib K Sharma: Investigation, Supervision, Writing – review & editing

Rolando Claure-Del Granado: Conceptualization, Investigation, Project administration, Supervision, Writing – review & editing

Brett Cullis: Conceptualization, Investigation, Project administration, Supervision, Writing – review & editing

Emmanuel A Burdmann: Conceptualization, Investigation, Project administration, Supervision, Writing – review & editing

FOS Franca: Investigation, Project administration, Writing – review & editing

Junio Aguiar: Investigation, Project administration, Writing – review & editing

Martyn Fredlund: Data curation, Investigation, Project administration, Supervision, Writing – review & editing

Kelly Hendricks: Funding acquisition, Project administration, Writing – review & editing

Maria F Iturricha-Caceres: Investigation, Project administration, Writing – review & editing

Mamit Rai: Investigation, Project administration, Writing – review & editing

Bhupendra Shah: Investigation, Project administration, Writing – review & editing

Shyam Kafle: Investigation, Project administration, Writing – review & editing

David C Harris: Conceptualization, Supervision, Validation, Visualization, Writing – review & editing

Mike V Rocco: Conceptualization, Data curation, Funding acquisition, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing

Maarten W Taal: Academic Editor

PMCID: PMC11611263 PMID: 39541400

Abstract

Background

The International Society of Nephrology proposes an acute kidney disease (AKD) management strategy that includes a risk score to aid AKD identification in low- and low-middle-income countries (LLMICs). We investigated the performance of the risk score and determined kidney and patient outcomes from AKD at multiple LLMIC sites.

Methods and findings

Adult patients presenting to healthcare facilities in Bolivia, Brazil, South Africa, and Nepal were screened using a symptom-based risk score and clinical judgment. Those at AKD risk underwent serum creatinine testing, predominantly with a point-of-care (POC) device. Clinical data were collected prospectively between September 2018 and November 2020. We analyzed risk score performance and determined AKD outcomes at discharge and over follow-up of 90 days. A total of 4,311 patients were at increased risk of AKD, and 2,922 (67.8%) had AKD confirmed. AKD prevalence was 80.2% in patients enrolled based on the risk score and 32.5% when enrolled on clinical judgment alone (p < 0.0001). The area under the receiver operating characteristic curve was 0.73 for the risk score to detect AKD. Death during admission occurred in 84 (2.9%) patients with AKD and 3 (0.2%) patients without kidney disease (p < 0.0001). Death after discharge occurred in 206 (9.7%) AKD patients, and 1865 AKD patients underwent reassessment of kidney function after discharge; 902 (48.4%) patients had persistent kidney disease including 740 (39.7%) patients reclassified with de novo or previously undiagnosed chronic kidney disease (CKD). The study was pragmatically designed to assess outcomes as part of routine healthcare, and there was heterogeneity in clinical practice and outcomes between sites, in addition to selection bias during cohort identification.

Conclusions

The use of a risk score can aid AKD identification in LLMICs. High rates of persistent kidney disease and mortality after discharge highlight the importance of AKD follow-up in low-resource settings.

Rhys Evans and team evaluate the performance of a risk score and determine kidney and patient outcomes of acute kidney disease in Bolivia, Brazil, Nepal, and South Africa.

Author summary

Why was this study done?

Acute kidney disease (AKD) is common in low-resource settings and leads to preventable deaths.
Management strategies that may improve outcomes for patients with AKD are untested when used within routine clinical care in low- and low-middle-income countries (LLMICs).
Outcomes after AKD episode in LLMICs are unknown.

What did the researchers do and find?

The researchers tested the performance of a scoring system to screen patients for risk of AKD in Bolivia, Brazil, Nepal, and South Africa and determined kidney and patient outcomes in the 3 months after AKD episode.
The risk score was effective in screening patients for AKD; 48.2% of patients with AKD had persistent kidney disease after 3 months.

What do these findings mean?

Use of a risk score facilitates AKD identification and can be incorporated within routine clinical care in LLMICs.
Frameworks need to be developed that allow patient follow-up after AKD episode as opportunities for chronic kidney disease (CKD) diagnosis are currently being missed.
There were differences in the clinical approach taken at different sites and whether the management strategy improves patient outcomes in the longer term is unknown.

Introduction

Kidney disease disproportionately affects disadvantaged populations in low- and low-middle-income countries (LLMICs) with poor access to care [1,2]. Greater than 850 million people are affected by kidney disease worldwide, with the majority of patients (e.g., 59% to 64% of chronic kidney disease [CKD]) concentrated in LLMICs [3–5]. Acute kidney disease (AKD) is common in these settings and is particularly important as it frequently affects young patients but often goes unrecognized or untreated leading to high mortality from the acute episode and may not recover leading to the development of CKD [6,7]. Undiagnosed and untreated CKD may progress to end-stage kidney disease (ESKD), which has devastating impacts for individuals and health systems in LLMICs; 90% of disadvantaged populations have no access to kidney replacement therapy (KRT) and when KRT can be provided it comes at significant economic cost. Moreover, there is an 80-fold difference in the number of nephrologists between low- and high-income countries, highlighting major deficiencies in LLMIC ability to provide good quality kidney care [2,5]. To address global inequities in kidney care, the International Society of Nephrology (ISN) launched the 0by25 initiative in 2013 with the ultimate aim of eliminating preventable deaths from acute kidney injury (AKI), present in a subset of patients with AKD, with a particular emphasis on people living in LLMICs [8,9]. An ambitious target was set to try and achieve this aim by 2025.

A key challenge in the management of AKD in LLMICs is the ability to identify patients at AKD risk and the capability to confirm AKD diagnosis with prompt serum creatinine (SCr) testing. This challenge is a consequence, at least in part, of a deficiency in nephrology education and training of healthcare workers [10–12] alongside a lack of consistent access to reliable laboratory measurement of SCr [13,14]. In response to these concerns, and within the 0by25 framework, the ISN developed a protocol for AKD management in LLMICs. This included the development of a symptom-based risk score to screen for patients at increased AKD risk and the use of devices to measure SCr at the point-of-care (POC) [15]. These efforts were underpinned with AKD education programs delivered to healthcare workers providing care to patients presenting with acute illness at risk of AKD. The feasibility of this approach to early identification and management of AKD was tested in a pilot study that included 2,101 patients presenting at increased AKD risk to low-resourced regions in Malawi, Nepal, and Bolivia [15]. The protocol was shown to be feasible and effective, with AKD confirmed in 1,199 (57%) patients. Difficulties, however, were faced in following patients after healthcare facility discharge, with 36% of patients lost to follow-up within 1 month. Patient follow-up in LLMICs presents a specific challenge due to a lack of established electronic health records to track outpatient results alongside a frequent inability to measure serum creatinine outside of the hospital setting. This lack of healthcare infrastructure, alongside a deficiency in trained nephrology staff, low health literacy, and resource restrictions impacting patient ability to travel for healthcare visits, means opportunities for the early detection of CKD and its subsequent management are being missed.

Having proven the feasibility of this AKD management strategy, the ISN subsequently established the Kidney Care Network (KCN) [16]. The aim of this service improvement project is to implement the 0by25 AKD management approach into routine clinical care in low-resource settings. The project was undertaken in 4 LLMICs over a 2-year period and utilized an updated AKD risk score, as outlined in the section below. The effectiveness of this approach in identifying AKD and the outcomes in AKD after management with this protocol as part of routine clinical care is unknown.

The main objectives of this study were to investigate the feasibility and performance of an updated symptom-based risk score to screen for AKD in LLMICs when applied as part of routine clinical care and to establish both patient and kidney outcomes from AKD in the short and medium term. We also aimed to provide further epidemiological data on AKD presenting in LLMICs including common causes and their management, in addition to the clinical variables associated with the development of AKD and its outcomes.

Methods

Ethics statement

Ethics approval was granted locally at each of the 4 study sites by the following ethics boards: the ethical committee of the Escola de Enfermagem da USP (University of Sao Paulo Nursing School), Brazil, approval 31670214; The Comité Regional de Enseñanza e Investigación, Hospital Obrero No 2—Caja Nacional de Salud, Cochabamba, Bolivia; the Nepal Health Research Council, Kathmandu, approval 205/2016; and the UKZN biomedical research ethics committee, South Africa, approval BE257/19. Consent was written in Brazil and Nepal and verbal in Bolivia. The requirement for consent was waived by the ethics board in South Africa as the project was categorized as a service improvement initiative.

Study design, setting, and participants

The study was undertaken as part of the KCN project in low-resourced regions of Brazil, Bolivia, South Africa, and Nepal. Patients were recruited from a variety of healthcare facility types including healthcare centers (HCCs), district hospitals, and tertiary hospitals (Table A in S1 Text). A protocol to identify and manage AKD was instituted. In short, an education and training program were delivered to healthcare workers working at each of the study sites on the management of AKD. The training was site specific and included face-face workshops delivered over multiple days. These were run by local nephrologists and attended by multidisciplinary healthcare professionals (clinicians, nurses, physician assistants) providing clinical care at the sites of project implementation. A symptom-based risk score coupled with the provision of devices to measure SCr at the POC were used to facilitate AKD identification. The risk score was developed using data from the cohort of patients presenting in the previous 0by25 Pilot Feasibility Study [15]. A logistic regression analysis was undertaken to determine the clinical variables associated with AKD in this study (variable included in this analysis are outlined in Table B in S1 Text); points within the scoring system are attributed to symptoms associated with AKD (Table 1). The area under the receiver operating characteristic curve was 0.824 for the risk score to detect AKD with an optimal cut-off score of 10 points (sensitivity 92.9% and specificity 58.9% at this cut-off) based on data from the pilot study [15]. As such, a score of 10 points or more was considered to represent increased risk of AKD, and these patients underwent SCr testing. Patients with a risk score of <10 points at presentation could also be considered at risk of AKD and undergo SCr testing according to the judgment of the clinical team.

Table 1. AKD risk score components.

A total score of ≥10 points represents increased risk of AKD.

Factor	Points
Vomiting	4
Low oral intake	2
Weakness	2
Oliguria reported by patient	8
Hypotension	8
Appetite loss	8
Swelling	5

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Variable description

Vomiting–presence of dehydration associated with vomiting as determined by clinical team.

Low oral intake–presence of dehydration associated with low oral intake as determined by clinical team.

Weakness–reported by patient.

Oliguria–reported by patient.

Hypotension–blood pressure <90/60 mmHg or relative hypotension as determined by clinical team.

Loss of Appetite–acute or chronic symptom reported by patient.

Swelling–presence of non-traumatic swelling on limbs, face, or entire body.

Adult (≥18 years) patients presenting to study sites between September 2018 and November 2020 were eligible to be screened for risk of AKD using the approach described. Those at increased kidney disease risk who underwent SCr testing were included. Patients on dialysis or with a kidney transplant, and those with missing data for presenting SCr or age category, were excluded. We report an observational cohort of patients managed with this approach. The size of the cohort reported represents a convenience sample of all patients managed within the prespecified timeframe of the project; a sample size calculation was not performed. The management of patients with AKD was left to the discretion of the treating clinician, who also determined contributors to the development of AKD and the most likely primary cause. Patient and kidney outcomes were recorded at the end of the healthcare facility admission and at 90 days thereafter. Of note, in the manuscript we refer to healthcare “admission” which includes both patients who attended a healthcare facility, underwent SCr testing (+- the relevant management) and were discharged on the same day, in addition to those patients that were admitted for an inpatient stay.

Variables and data sources/measurement

Clinical data were recorded prospectively at 3 time points: enrollment, at the end of the healthcare facility admission, and at post-discharge follow-up of up to 90 days. Devices to measure SCr at the POC were provided to each site (StatSensor Xpress CREA, Nova Biomedical, Waltham, Massachusetts, United States of America) [17,18]. SCr was either measured by the POC device or by an automated analyzer in a local laboratory. Clinical data were recorded on electronic devices using REDCap software (https://www.project-redcap.org) and exported as Microsoft Excel files for subsequent analysis.

Definitions

Glomerular filtration rate was estimated (eGFR) using the CKD-EPI equation without race adjustment [19,20]. Kidney disease was defined according to KDIGO SCr functional criteria (https://kdigo.org) and classified as either AKD or CKD (Table 2). In accordance with the latest KDIGO consensus statement, AKD was defined by “abnormalities of kidney function and/or structure with a duration of <3 months”; it was separated into AKD with and without AKI [21]. AKI was diagnosed and staged according to KDIGO criteria [22]. The latest SCr documented prior to healthcare facility admission and the lowest SCr during healthcare facility admission were used to determine the baseline SCr; an imputed baseline SCr based on an assumed eGFR was not used [23]. Urine output measurement and urinalysis data were not captured. Patients were categorized into those with and without kidney disease, and the nature of kidney disease was determined: AKD with AKI; AKD without AKI; or CKD. Kidney outcomes are defined in Table 2.

Table 2. Definitions of kidney disease and kidney recovery.

KIDNEY DISEASE
Kidney disease	Definition	Comments
*AKD with AKI*	Increase in SCr by 50% within 7 days OR Increase in SCr by 0.3 mg/dl within 2 days from baseline	Stage 1: SCr increase by 1.5–1.9 times baseline; Stage 2: SCr increase by 2.0–2.9 times baseline; Stage 3: SCr increase by ≥3 times baseline or increase in SCr to ≥4 mg/dl or initiated on KRT Urine output criteria not used as data not captured
*AKD without AKI*	eGFR <60 ml/min/1.73 m² OR Decrease in eGFR by ≥35% OR Increase in SCr by 50% occurring over ≤3 months (but not within 7 days)	GFR estimated by CKD-EPI equation (2021) Structural criteria (urinalysis) not used as data not captured
*CKD*	eGFR <60 ml/min/1.73 m² for >3 months
*NKD*	Not fulfilling criteria for AKD or CKD
*Baseline SCr*	Latest creatinine documented prior to healthcare facility admission OR Lowest creatinine during healthcare facility admission	Lowest value of 2 criteria used Imputed baseline creatinine based on an assumed eGFR not used
KIDNEY RECOVERY
*Complete recovery*	Has follow up creatinine and last recorded creatinine has returned to within 0.1 mg/dl of baseline value AND Last recorded eGFR is ≥60 ml/min/1.73 m²	No ongoing kidney disease
*Partial recovery / persistent kidney disease*	Has follow up creatinine and last recorded creatinine is less than highest creatinine but remains >0.1 mg/dl above baseline OR Creatinine has recovered to baseline but last recorded eGFR is <60 ml/min/1.73 m²
*No recovery / persistent kidney disease*	Has follow up creatinine and last recorded creatinine is highest creatinine during admission/follow-up period OR Remains dependent on KRT	Includes subset of patients on dialysis
*Unknown*	Creatinine not repeated during admission/follow-up period

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AKD, acute kidney disease; AKI, acute kidney injury; CKD, chronic kidney disease; NKD, no kidney disease; SCr, serum creatinine; eGFR, estimated glomerular filtration rate; KRT, kidney replacement therapy.

Outcome measures

Primary outcome measures included the prevalence of AKD in the enrolled cohort and the performance of the risk score to detect it. In addition, patient mortality and kidney outcome at end of healthcare facility admission and at 90-day follow-up were determined. Secondary outcomes included the causes of AKD and the treatments used in its management. We also compared clinical variables (demographics [age and sex], healthcare facility type where patient enrolled, and AKD risk score at patient presentation) and outcomes (patient mortality and kidney outcomes as described in Table 2) between those with AKD and no kidney disease (NKD), and investigated variables associated with AKD development and mortality.

Statistical methods

Data are presented as number and percentages for categorical variables and mean and standard deviation (SD) or median and interquartile range (IQR) for numerical variables depending on data distribution. Categorical variables were compared using the Fisher’s exact or Chi-squared test. Numerical variables were compared between 2 groups using the Mann–Whitney or an unpaired t test. Variables are compared across greater than 2 groups with a one-way analysis of variance. Multivariable logistic regression analysis was undertaken to determine factors associated with the development of AKD and mortality. Age, sex, country of enrolment, and risk score at presentation were included in the model for AKD development; the same variables in addition to the presence of AKD were included in the model for mortality. Odds ratios (OR) and 95% confidence intervals (CIs) determined for each variable. Variables were selected as these data were prespecified as required in all participants at study enrollment and due to differences in these variables in patients with AKD and in patient survival in univariable analyses. The performance of the risk score was assessed using the area under the receiver operating characteristics curve and with a sensitivity and specificity analysis. The optimal score was determined by Youden’s index. Youden’s index is defined by sensitivity + specificity– 1; it may be used to determine the cut-off representing the maximum potential effectiveness of the risk score. Analysis was performed using Graphpad Prism version 9 (www.graphpad.com). A p-value of ≤0.05 was considered statistically significant. A formal prospective analysis plan was not used; analyses were determined after data collection. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 STROBE Checklist).

Results

Participants

A total of 4,394 patients were screened for risk of AKD and 4,311 of these were deemed to be at increased AKD risk and enrolled (Fig 1), and 2,289 (53.1%) patients were female and median age was 57 (IQR 42–70) years. A total of 3,190 (74.0%) patients had an AKD risk score of ≥10 points, whereas 1,121 (26.0%) patients were deemed to be at risk of AKD by clinical judgment despite a risk score <10 points. Enrollment based on clinical judgment of AKD risk occurred predominantly at the South Africa site (Table C in S1 Text). Median risk score in all patients was 14 (IQR 8–19), ranging from 2 (IQR 0–8) in South Africa to 18 (IQR 14–22) in Nepal. The frequency of the presence of each component of the AKD risk score is outlined in Table D in S1 Text; reduced appetite and weakness were the most common symptoms. Data on the type of facility where patients presented were available in 4,293 patients; 1,356 (31.6%) patients presented to an HCC, 676 (15.7%) to a district hospital, and 2,259 (52.6%) to a tertiary hospital.

Measurement of kidney function and prevalence of AKD

Creatinine was measured by POC device in 3,145 (73.0%) patients. Median enrollment creatinine and eGFR were 1.4 (IQR 1.0–1.9) mg/dl and 52 (IQR 34–78) ml/min/1.73m², respectively (Fig A in S1 Text). Enrollment eGFR was <60 ml/min/1.73 m² in 2,597 (60.2%) patients. A historical creatinine measured prior to enrollment was available in 1,239 (28.7%) patients.

Kidney disease was present in 2,959 (68.6%) patients, which included 2,922 (67.8%) patients with AKD and 37 (0.9%) patients with CKD (Table 3 and Fig 1), and 2,288 (53.1%) patients had AKD without AKI and 634 (14.7%) patients had AKD with AKI. Of the 634 patients with AKI, stage 1 was present in 391 (61.7%) patients, stage 2 in 140 (22.1%) patients, and stage 3 in 103 (16.3%) patients. Patients with AKD were older than patients with NKD, a higher proportion were male, and they were more commonly enrolled from a tertiary hospital than an HCC (Table 4). In the multivariable analysis age (OR 1.04, 95% CI [1.03, 1.04]), risk score (OR 1.03, 95% CI [1.01, 1.04]), and presentation in Nepal (OR for presentation in Bolivia 0.38, 95% CI [0.31, 0.47], OR for presentation in Brazil 0.34, 95% CI [0.24, 0.49], OR for presentation in South Africa 0.09, 95% CI [0.07, 0.11]), were associated with the presence of AKD (Table E in S1 Text).

Table 3. Measurement of kidney function and kidney disease classification at each study site.

	Bolivia	Brazil	Nepal	South Africa	All patients
Measurement of kidney function
Number with data	951	197	1,952	1,211	4,311
Enrollment SCr (mg/dL) (median; IQR)	1.4 (1.0–1.9)	1.2 (0.9–1.8)	1.7 (1.4–2.3)	1.0 (0.8–1.2)	1.4 (1.0–1.9)
Enrollment eGFR (ml/min/1.73 m²) (median; IQR)	51 (33–75)	60 (36–85)	41 (28–56)	75 (57–93)	52 (34–78)
Enrollment eGFR <60 ml/min/1.73 m² (n; %)	612 (64.4)	97 (49.2)	1,550 (79.4)	338 (27.9)	2,597 (60.2)
SCr measured by POC device (n; %)	431 (45.3)	194 (98.5)	1,309 (67.2)	1,211 (100)	3,145 (73.0)
SCr documented prior to enrollment (n; %)	325 (34.2)	64 (32.5)	609 (31.2)	241 (19.9)	1,239 (28.7)
Kidney disease classification
Number with data	951	197	1,952	1,211	4,311
AKD with AKI (n; %)	116 (12.2)	14 (7.1)	404 (20.7)	100 (8.3)	634 (14.7)
AKI Stage 1 (n; % of AKI)	60 (51.7)	7 (50.0)	235 (58.2)	89 (89.0)	391 (61.7)
AKI Stage 2 (n; % of AKI)	39 (33.6)	1 (7.1)	93 (23.0)	7 (7.0)	140 (22.1)
AKI Stage 3 (n; % of AKI)	17 (14.7)	6 (42.9)	76 (18.8)	4 (4.0)	103 (16.3)
AKD without AKI (n; %)	568 (59.7)	123 (62.4)	1,295 (66.3)	302 (24.9)	2,288 (53.1)
CKD (n; %)	28 (2.9)	4 (2.0)	5 (0.3)	0 (0.0)	37 (0.9)
NKD (n; %)	239 (25.1)	56 (28.4)	248 (12.7)	809 (66.8)	1,352 (31.4)

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AKD, acute kidney disease; AKI, acute kidney injury; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; IQR, interquartile range; NKD, no kidney disease; POC, point of care; SCr, serum creatinine.

Table 4. Clinical variables and patient outcomes in patients with AKD and NKD.

	AKD	NKD	p-Value
Demographic
Sex, Female (n; %)	1,476 (50.5)	792 (58.6)	<0.0001
Age (years, median; IQR)	61 (46–72)	59 (43–70)	0.0005
Healthcare facility type where patient enrolled
HCC	458 (15.8)	897 (66.8)	<0.0001
District hospital	520 (17.9)	147 (11.0)
Tertiary hospital	1,928 (66.3)	297 (22.1)
Other/missing	1 (0.0)	1 (0.1)
Risk score
Total points (median; IQR)	15 (12–20)	8 (0–15)	<0.0001
Healthcare facility outcome
Number with data	2891	1339
Died (n; %)	84 (2.9)	3 (0.2)	<0.0001
Outcome at 90-day follow up
Number with data	2,119	422
Died (after discharge)	206 (9.3)	31 (7.3)	0.14

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AKD, acute kidney disease; HCC, healthcare centre; IQR, interquartile range; NKD, no kidney disease.

Performance of the risk score to detect AKD

AKD was present in 2,557 (80.2%) of the 3,190 patients enrolled based on the risk score and 365 (32.5%) of the 1,121 patients enrolled according to clinical judgment (p < 0.0001). The median risk scores in patients with AKD and NKD were 15 (IQR 12–20) and 8 (IQR 0–15), respectively (p < 0.0001; Table 4 and Fig 2). In this cohort, the area under the receiver operating characteristic curve was 0.73 for the risk score to detect AKD with an optimal cut-off score of 11 points (p < 0.0001; sensitivity 61.6% and specificity 77.2% at this cut-off; Fig 2).

Fig 2 — The score attributes points to clinical features associated with AKD, with a higher total score representing increased AKD risk. (A) Risk score (individual values with median and IQR plotted) in patients with AKD and NKD. (B) Receiver operating characteristic curve for the risk score to detect AKD. AKD, acute kidney disease; IQR, interquartile range; NKD, no kidney disease.

Causes and management of AKD

Contributors to the development of AKD, the primary causes of AKD, and strategies used in their management are outlined in Table 5. The main contributors to the development of AKD were infection, hypotension/shock, and dehydration, present in 1,280 (44.0%), 561 (19.3%), and 557 (19.1%) cases of AKD, respectively. In those with infection, the commonest types of infection were “other bacterial” infections (n = 805; 63.1%) and gastroenteritis (n = 365; 28.6%); the main sites of infection were the urinary (n = 523; 41.9%) and gastrointestinal (n = 401; 32.1%) tract (Table F in S1 Text). Infection was also the most common primary cause of AKD, responsible for 1,127 (40.6%) cases. The most common treatments were fluid resuscitation (intravenous in 2,013 [69.2%] patients and oral in 389 [13.4%] patients) and antibiotics (1,973 [67.8%] patients). KRT was indicated in 32 (1.1%) patients and provided in 26 (81.3%) patients. Hemodialysis was the KRT modality used in all cases.

Table 5. Aetiologies and management of AKD.

	Bolivia	Brazil	Nepal	South Africa	All patients
Contributors to the development of AKD*
Number with data	681	132	1,696	402	2,911
Dehydration (n; %)	419 (61.5)	19 (14.4)	86 (5.1)	33 (8.2)	557 (19.1)
Hypotension/shock (n; %)	94 (13.8)	5 (3.8)	444 (26.2)	18 (4.5)	561 (19.3)
Trauma (n; %)	7 (1.0)	0 (0.0)	23 (1.4)	5 (1.2)	35 (1.2)
Surgery (n; %)	12 (1.8)	0 (0.0)	0 (0.0)	3 (0.7)	15 (0.5)
Infection (n; %)	420 (61.7)	64 (48.5)	757 (44.6)	39 (9.7)	1,280 (44.0)
HIV (n; %)	17 (2.5)	0 (0.0)	4 (0.2)	23 (5.7)	44 (1.5)
Urinary obstruction (n; %)	77 (11.3)	9 (6.8)	44 (2.6)	9 (2.2)	139 (4.8)
Pregnancy related (n; %)	13 (1.9)	0 (0.0)	7 (0.4)	3 (0.7)	23 (0.8)
Allergic reaction (n; %)	2 (0.3)	0 (0.0)	3 (0.2)	1 (0.2)	6 (0.2)
Herbal medications (n; %)	63 (9.3)	0 (0.0)	1 (0.1)	2 (0.5)	66 (2.3)
Induced by other medications (n; %)	139 (20.4)	0 (0.0)	20 (1.2)	33 (8.2)	192 (6.6)
Poisoning (n; %)	13 (1.9)	1 (0.1)	14 (0.8)	3 (0.7)	31 (1.1)
Animal/insect bite (n; %)	10 (1.5)	18 (13.6)	0 (0.0)	1 (0.2)	29 (1.0)
Cardiorenal (n; %)	27 (4.0)	17 (12.9)	178 (10.5)	1 (0.2)	223 (7.7)
Hepatorenal (n; %)	11 (1.6)	1 (0.8)	199 (11.7)	1 (0.2	212 (7.3)
Other (n; %)	139 (20.4)	49 (37.1)	690 (40.7)	171 (42.5)	1,049 (36.0)
Primary cause of AKD*
Number with data	665	132	1,695	284	2,776
Dehydration (n; %)	138 (20.8)	13 (9.8)	35 (2.1)	24 (8.5)	210 (7.6)
Hypotension/shock (n; %)	34 (5.1)	5 (3.8)	180 (10.6)	11 (3.9)	230 (8.3)
Trauma (n; %)	3 (0.5)	0 (0.0)	22 (1.3)	4 (1.4)	29 (1.0)
Surgery (n; %)	5 (0.8)	0 (0.0)	2 (0.1)	2 (0.7)	9 (0.3)
Infection (n; %)	301 (45.3)	46 (34.8)	753 (44.4)	27 (9.5)	1,127 (40.6)
HIV (n; %)	6 (0.9)	0 (0.0)	2 (0.1)	14 (4.9)	22 (0.8)
Urinary obstruction (n; %)	47 (7.1)	7 (5.3)	44 (2.6)	3 (1.1)	101 (3.6)
Pregnancy related (n; %)	7 (1.1)	0 (0.0)	5 (0.3)	1 (0.4)	13 (0.5)
Allergic reaction (n; %)	2 (0.3)	0 (0.0)	3 (0.2)	0 (0.0)	5 (0.2)
Induced by other medications (n; %)	43 (6.5)	0 (0.0)	16 (0.9)	26 (9.2)	85 (3.1)
Poisoning (n; %)	10 (1.5)	1 (0.8)	14 (0.8)	2 (0.7)	27 (1.0)
Animal/insect bite (n; %)	8 (1.2)	17 (12.9)	0 (0.0)	1 (0.4)	26 (0.9)
Cardiorenal (n; %)	9 (1.4)	17 (12.9)	165 (9.7)	1 (0.4)	192 (6.9)
Hepatorenal (n; %)	5 (0.8)	1 (0.8)	176 (10.4)	0 (0.0)	182 (6.6)
Other (n; %)	47 (7.1)	25 (18.9)	278 (16.4)	168 (59.2)	518 (18.7)
Management of AKD
Number with data	681	132	1,696	402	2,911
Oral fluid (n; %)	321 (47.1)	11 (8.3)	31 (1.8)	26 (6.5)	389 (13.4)
IV fluid (n; %)	488 (71.7)	36 (27.3)	1,459 (86.0)	30 (7.5)	2,013 (69.2)
Blood products (n; %)	31 (4.6)	1 (0.7)	18 (1.1)	4 (1.0)	54 (1.9)
Antibiotics (n; %)	406 (59.6)	61 (46.2)	1,466 (86.4)	40 (10.0)	1,973 (67.8)
HIV therapy (n; %)	16 (2.3)	0 (0.0)	3 (0.2)	18 (4.5)	37 (1.3)
Anti-venom therapy (n; %)	9 (1.3)	10 (7.6)	1 (0.1)	1 (0.2)	21 (0.7)
Relief of urinary tract obstruction (n; %)	58 (8.5)	8 (6.1)	15 (0.9)	7 (1.7)	88 (3.0)
Antihypertensives (n; %)	48 (7.0)	16 (12.1)	32 (1.9)	11 (2.7)	107 (3.7)
Vasopressors (n; %)	22 (3.2)	1 (0.8)	12 (0.7)	1 (0.2)	36 (1.2)
Diuretics (n; %)	67 (9.8)	23 (17.4)	182 (10.7)	9 (2.2)	281 (9.7)

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*Contributors and causes of AKD were determined by the clinical judgment of the treating clinician.

AKD, acute kidney disease; HIV, human immunodeficiency virus; IV, intravenous.

Kidney and patient outcomes at end of healthcare facility admission

A total of 4,266 patients, including 2,891 patients with AKD, had a healthcare facility patient outcome recorded, and 4,172 (97.8%) patients were discharged alive, and 94 (2.2%) patients died. Death during healthcare facility admission was more common in patients with AKD (n = 84; 2.9%) than in patients with NKD (n = 3; 0.2%) (p < 0.0001; Table 4). In multivariable analysis, age (OR 1.03, 95% CI [1.01, 1.04]), risk score at presentation (OR 1.14, 95% CI [1.10, 1.17]), presentation in a country other than Nepal (OR for presentation in Bolivia 29,83, 95% CI [13.95, 77.47], OR for presentation in Brazil 10.30, 95% CI [2.90, 35.01], OR for presentation in South Africa 14.62, 95% CI [4.44, 49.41]), and the presence of AKD (OR 2.48, 95% CI [1.27, 5.33]) were associated with death during admission (Table G in S1 Text).

Kidney outcomes in patients with AKD are outlined in Table 6; outcomes in the subset of patients with AKD with AKI are outlined in Table H in S1 Text. Kidney function was not repeated prior to discharge in 1,346 (46.1%) AKD patients and as such kidney status was unknown; 1,492 (51.1%) patients with AKD had kidney status reassessed prior to discharge and 579 (38.8%) of these patients were discharged with known persistent kidney disease (Fig 1).

Table 6. Patient and kidney outcomes at healthcare facility discharge and at 90-day follow-up in patients with AKD.

	Bolivia	Brazil	Nepal	South Africa	Total
At healthcare facility discharge
Number with data	684	137	1,699	402	2,922
Unknown (no creatinine after enrolment) (n; %)	91 (13.3)	23 (16.8)	944 (55.6)	288 (71.6)	1,346 (46.1)
Partial recovery (n; %)	242 (35.4)	21 (15.3)	192 (11.3)	14 (3.5)	469 (16.1)
Complete recovery (n; %)	244 (35.7)	67 (48.9)	517 (30.4)	85 (21.1)	913 (31.2)
No kidney recovery (n; %)	39 (5.7)	22 (16.1)	40 (2.4)	9 (2.2)	110 (3.8)
Died (n; %)	68 (9.9)	4 (2.9)	6 (0.4)	6 (1.5)	84 (2.9)
At 90-day follow-up
Number with follow-up	490	89	1,418	122	2,119
Death after discharge (n; %)	1 (0.2)	8 (9.0)	197 (13.9)	0 (0.0)	206 (9.7)
Death during admission or post discharge follow-up (n; %)	69 (14.1)	12 (13.5)	203 (14.3)	6 (4.9)	290 (13.7)
Number with creatinine at follow-up (n; %)	487	63	1,219	96	1,865
Partial recovery (n; %)	154 (31.6)	29 (46.0)	561 (46.0)	34 (35.4)	778 (41.7)
Complete recovery (n; %)	309 (63.4)	20 (31.7)	584 (47.9)	50 (52.1)	963 (51.6)
No kidney recovery (n; %)	24 (4.9)	14 (22.2)	74 (6.1)	12 (12.5)	124 (6.6)

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Kidney and patient outcomes at 90-day follow-up

A total of 2,564 patients, including 2,119 patients with AKD, had follow-up after healthcare facility discharge; this occurred at a median of 91 (90 to 92) days after study enrollment. Death after discharge occurred in 237 (9.2%) patients. There was no difference in mortality after discharge between patients with AKD (n = 206; 9.7%) and NKD (n = 31; 7.3%) (p = 0.14). Death at any time up to 90 days after enrollment occurred in 290 (13.7%) patients with AKD (Table 4). In multivariable analysis, age (OR 1.03, 95% CI [1.02, 1.03]) and total risk score (OR 1.07, 95% CI [1.05, 1.09]) were associated with death at any stage during follow-up; there was a negative association of death at any stage with presentation in South Africa (OR 0.12, 95% CI [0.05, 0.26]) and Bolivia (OR 0.68, 95% CI [0.50, 0.89]) (Table G in S1 Text).

A total of 1,865 (66.4% of those discharged) patients with AKD had reassessment of kidney function at 90-day follow-up (Fig 1). Kidney outcomes in these patients are outlined in Table 6, and 902 (48.4%) patients had persistent kidney disease, including 12 patients who remained on dialysis (representing 0.6% of AKD patients followed to this time point). In 740 (39.7%) patients, eGFR was persistently <60 ml/min/1.73 m² over at least 90 days representing de novo or previously undiagnosed CKD.

Discussion

In this study, we investigated the effectiveness of an AKD management strategy to identify patients with AKD in LLMICs, and we determined patient and kidney outcomes from AKD when this management strategy was implemented as part of routine clinical care. We did this at multiple sites in 4 countries across 3 continents in patients presenting to a variety of healthcare facility types. We included a large number of patients (n = 2,922) with AKD. We demonstrated that the use of a symptom-based risk score, underpinned by an AKD education program, improved detection of AKD compared to clinical judgment alone. We provide data to support previous findings of more AKD in patients presenting to higher-level healthcare facilities from causes that were treatable by relatively simple means [15,24]. We demonstrated that with early identification and treatment, the requirements for KRT and in-hospital mortality were low. We highlighted that a large proportion of patients were discharged from the healthcare facility with either unknown kidney status or known persistent kidney disease. Furthermore, our unique data demonstrate that 1 in 10 patients with AKD die in the 90 days following discharge and that around one half of AKD patients will have persistent kidney disease at 3 months, many of whom are reclassified with de novo or previously undiagnosed CKD.

Large numbers of acutely unwell patients present to healthcare facilities in LLMICs each day, many of whom may be at risk of AKD. It is impractical, both logistically and financially, to undertake SCr testing in all patients, and as such efforts to risk stratify patients must be made. As part of the 0by25 initiative a symptom-based risk score was developed for this purpose and its use was previously shown to be feasible in a pilot study [15]. The risk score was subsequently updated for use in the current study: the number of variables within the score was reduced from 10 to 7 to simplify its use; moreover, its performance was improved using clinical variables associated with the development of AKD from real-world data in patients presenting to LLMICs. To the best of our knowledge, this study is the first to test the performance of the updated risk score; this was done with the score employed during routine clinical care and formed one of our main study objectives. We have demonstrated its use to be feasible and effective, with an area under the ROC curve of 0.73 for the score to detect AKD. We demonstrated a higher prevalence of AKD in patients enrolled based on the risk score compared to clinical judgment alone (80% versus 33%) and this may explain the reduction in AKD prevalence at the South Africa site where clinical judgment was predominantly used to determine AKD risk. Our data demonstrate a cut-off score of 11 (as opposed to 10) is optimal for AKD identification. As would be expected, greater specificity was demonstrated at this higher cut-off, which may be important when rationalizing the limited resources available for SCr measurement in LLMICs. The risk score was not only predictive of the presence of AKD, but we also found it to predict patient survival, both in the short and medium term, adding weight to its utility in stratifying overall patient risk at the time of presentation.

Through this study, we also provide important further data on the epidemiology of AKD in LLMICs. The cohort in this study was older than adults included in the pilot feasibility project and comparable to some higher income AKI cohorts [25,26]. The cohort was preselected for those at risk of AKD, and the prevalence of AKD in this study (67.8%) was similar to that in the feasibility project (66%). A comparison of the key findings in this study alongside the other main studies from the 0by25 initiative is outlined in Table I in S1 Text. As with previous studies, AKD was most common in patients presenting to higher levels of healthcare facility, while the predominant causes were related to infection and hypovolemia and as such treatable by relatively simple interventions. The requirement for KRT was low in this study, indicated in only 1.1% cases of AKD, and when AKI was confirmed, it was most commonly mild, more reflective of higher-income settings. This may result from the early identification of kidney disease facilitating timely interventions targeting treatable causes as described.

The other key objective of this study was to determine both the short- and medium-term outcomes from AKD. Importantly, we assessed these outcomes in the setting of AKD being managed as part of routine clinical care. Patient outcome at discharge was recorded in most patients (98.9%) with AKD. While healthcare facility mortality was low, it was higher in patients with AKD than NKD supporting the known impact of AKD on patient outcomes [27]. A significant proportion of patients left the healthcare facility without reassessment of kidney function. This may reflect deficiencies in the management strategy or a natural high turnover of patients at study sites; data on facility length of stay were not recorded. While most patients who had kidney function reassessed had some improvement in kidney function, 38.8% left the healthcare facility with persistent kidney disease providing evidence to support the need to monitor AKD patients after discharge.

A unique aspect of this study is the follow-up data after healthcare facility discharge, and 2,119 cases of AKD had a patient outcome recorded at 90-day follow-up and 1,865 patients had kidney function reassessed at this time point. This reflects 75.5% and 66.4% of patients discharged post AKD, respectively and represents one of the largest cohorts of AKD patients followed-up in LLMICs to date. Mortality post discharge in this study was 9.7%, this being similar to the post discharge mortality (10.3%) at the same time point in the feasibility study. Notable is the higher post discharge mortality compared to inpatient mortality. The reasons for this are unclear (cause of death was not recorded) but warrants further study. Moreover, this consistent finding across more than 1 study adds further support for the need to follow AKD patients closely after the initial episode. Post discharge mortality was not different in patients with AKD compared to NKD, albeit only a small proportion of patients with NKD was followed up. The overall mortality in AKD patients in this study of 13.7% is, however, lower than other previous studies from low-resourced parts of the world [8,28,29].

Further evidence for the need to monitor patients with AKD after discharge comes from the kidney outcomes determined at 3 months. Kidney disease was persistent in around one half of AKD patients and in 39.7% a new diagnosis of CKD was made. Given the lack of historical creatinine measurements we are unable to say whether these patients had de novo or previously undiagnosed CKD, but this finding highlights the close interconnection between AKD and CKD syndromes [30,31]. In this project, we used the more recent KDIGO concept of AKD which includes any disorder of kidney function present for less than 3 months, in contrast to the previous Acute Disease Quality Initiative (ADQI) concept of AKD being kidney disease that persists from days 7 to 90 after an episode of AKI [32]. The latter concept has been used in other studies previously [33–36]. Both concepts highlight the important continuum between acute and chronic kidney diseases, and reveal knowledge gaps in mechanisms that may improve outcomes post AKD episode [37]. Our data lend weight to the potential value of “post-AKD” clinics, which would provide a unique opportunity to test interventions that may enhance recovery from AKD and that would facilitate the early diagnosis of CKD. Early identification of CKD is of particular importance in low-resourced regions to allow interventions to be instituted to prevent progression to ESKD and the need for costly chronic KRT. The 0by25 initiative advocates following a “5R approach” to the management of AKD with the last of these phases representing rehabilitation [8]. This phase of the 5R approach is often neglected but the follow-up data in this project reinforce its importance as part of an overall strategy to tackle kidney disease in its entirety in disadvantaged populations worldwide.

We designed this pragmatic observational study to assess AKD identification and outcomes as part of routine healthcare. This was undertaken in a variety of healthcare facilities managed within different health systems across 3 continents. This led to marked heterogeneity in clinical practice and outcomes between sites (e.g., significantly less AKD in South Africa; lower inpatient mortality in Nepal) and results should be interpreted with this in mind. Further, selection bias likely occurred during cohort identification, given the high proportion (98%) of patients deemed to be at AKD risk, and there may have also been selection bias in those patients with AKD that were followed up. The performance of the risk score reported is based on this preselected cohort, and hence this may not be representative of its performance in a broader population. We deliberately collected only a minimum data set, recording data that would be documented as part of standard clinical practice, and we do not have data on healthcare facility length of stay. As such, we are also missing a comprehensive assessment of serial SCr measurements and we did not record data on urine output and urinalysis, which may have led to an underestimation of AKD prevalence. Moreover, as in previous studies in LLMICs, most patients did not have a recorded baseline creatinine, which may have led to the misclassification of kidney status in some. We did not use an imputed baseline creatinine based on an assumed “normal” eGFR as we felt it inappropriate to make such an assumption in a population at high risk of kidney disease. The high number of patients reclassified with CKD during follow-up supported this assumption. Furthermore, there is a lack of data to inform what a “normal” eGFR is and whether this is the same across the diverse populations studied. We used the CKD-EPI equation to estimate GFR but are aware that based on recent findings this may have underestimated true prevalence of kidney disease, specifically in African patients [38]. We did not measure kidney function in those that were not deemed to be at risk for AKD, which would have allowed a more comprehensive analysis of the risk score. We undertook follow-up at 90 days to facilitate the determination of CKD prevalence, but follow-up beyond this time point was not undertaken. Whilst follow-up occurred in a large proportion of patients, it was not undertaken in all and there were natural variations in clinical approaches and degree of follow-up between the study sites. Similarly, variation in clinical expertise and resources available at different sites may have impacted outcomes, and we were not able to control for these issues. The study was not designed to detect a statistically significant change in clinical outcomes for patients albeit we frame outcomes in this cohort with those in previous similar studies in the sections above. We included patients from a range of healthcare facilities across 3 continents and as such our findings are generalizable to many low-resourced healthcare systems but not necessarily all.

In conclusion, we have demonstrated the use of a symptom-based risk score is feasible and effective in helping identify patients at risk of AKD during routine clinical care in LLMICs. We have provided unique outcome data in many patients at multiple LLMIC sites. We have demonstrated a high mortality rate (9.7%) in patients in the 3-month period after AKD admission and the persistence of kidney disease in around one half of patients. Our findings support the ongoing development of AKD management strategies for use in LLMICs, which should include resource for close patient follow-up after the presenting episode.

Supporting information

S1 STROBE Checklist. STROBE Statement—checklist of items that should be included in reports of observational studies.

(DOCX)

pmed.1004495.s001.docx^{(35.5KB, docx)}

S1 Text

Table A. Study sites. Table B. Clinical variables included in the logistic regression analysis used to create the risk score. Table C. Enrollment and demographic data at each study site. Table D. Presence of individual components of the AKD risk score in all patients and at each site. Table E. Multivariable analysis of factors associated with the development of AKD. Table F. Type of infection and main site of infection in patients with AKD in whom infection was a contributor to AKD development. Table G. Multivariable analysis of factors associated with mortality. Table H. Patient and kidney outcomes at healthcare facility discharge and at 90-day follow-up in patients with AKD with AKI. Table I. Prevalence, causes of kidney disease, and clinical outcomes in this and other studies within the 0by25 initiative. Fig A. Histograms of enrollment creatinine and eGFR in the entire cohort.

(DOCX)

pmed.1004495.s002.docx^{(76.8KB, docx)}

Acknowledgments

We thank the healthcare workers who provided clinical care during this study and, above all, the patients for their participation. We acknowledge the work of the ISN in obtaining the funding for the study and for their administrative support throughout. We thank Nova Biomedical (https://www.novabiomedical.com) for their in-kind support.

Abbreviations

ADQI: Acute Disease Quality Initiative
AKD: acute kidney disease
AKI: acute kidney injury
CI: confidence interval
CKD: chronic kidney disease
eGFR: estimated glomerular filtration rate
ESKD: end-stage kidney disease
HCC: healthcare center
IQR: interquartile range
ISN: International Society of Nephrology
KCN: Kidney Care Network
KRT: kidney replacement therapy
LLMIC: low- and low-middle-income country
NKD: no kidney disease
OR: odds ratio
POC: point-of-care
SD: standard deviation

Data Availability

Original data is owned by the International Society of Nephrology and will be made freely available upon reasonable request (research@theisn.org).

Funding Statement

The study was funded by a grant from the Stavros Niarchos Foundation (https://www.snf.org), which was provided directly to the International Society of Nephrology (through its executive director in 2017, Luca Segantini). Funds for the project were subsequently managed by KH on behalf of the ISN. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Med. doi: 10.1371/journal.pmed.1004495.r001

Decision Letter 0

Alexandra Tosun

4 Jul 2024

Dear Dr Evans,

Thank you for submitting your manuscript entitled "Identification and outcomes of acute kidney disease in patients presenting in low-resource settings" 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 Jul 08 2024.

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 me at atosun@plos.org or us at plosmedicine@plos.org if you have any queries relating to your submission.

Kind regards,

Alexandra Tosun, PhD

Associate Editor

PLOS Medicine

PLoS Med. doi: 10.1371/journal.pmed.1004495.r002

Decision Letter 1

Alexandra Tosun

7 Aug 2024

Dear Dr Evans,

Many thanks for submitting your manuscript "Identification and outcomes of acute kidney disease in patients presenting in low-resource settings" (PMEDICINE-D-24-02124R1) to PLOS Medicine. The paper has been reviewed by subject experts and a statistician; their comments are included below and can also be accessed here: [LINK]

As you will see, the reviews were positive, but the reviewers, especially the statistical reviewer, pointed out a general lack of detail in the manuscript. After discussing the paper with the editorial team and an academic editor with relevant expertise, I'm pleased to invite you to revise the paper in response to the reviewers' comments. We plan to send the revised paper to some or all of the original reviewers, and we cannot provide any guarantees at this stage regarding publication.

When you upload your revision, please include a point-by-point response that addresses all of the reviewer and editorial points, indicating the changes made in the manuscript and either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please also be sure to check the general editorial comments at the end of this letter and include these in your point-by-point response. When you resubmit your paper, please include a clean version of the paper as the main article file and a version with changes tracked as a marked-up manuscript. It may also be helpful to check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper.

We ask that you submit your revision by Aug 28 2024. However, if this deadline is not feasible, please contact me by email, and we can discuss a suitable alternative.

Don't hesitate to contact me directly with any questions (atosun@plos.org).

Best regards,

Alexandra

Alexandra Tosun, PhD

Associate Editor

PLOS Medicine

atosun@plos.org

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Comments from the academic editor:

1. Interpretation of the data is limited by a problem of selection bias at every stage of the study. 98% of those screened were deemed to be at high risk for AKD, implying a high degree of selection. Follow-up after discharge occurred in only 59% and repeat creatinine testing occurred in 64% of those with AKD. In addition it is clear that there was a big difference in practice in the centre in South Africa versus the other centres. The impact of this problem of selection bias should be discussed in more detail in the limitations section.

2. The paper would benefit from more discussion of the concept of AKD and why it is particularly relevant in low resource settings.

3. The study is reported to have recruited persons “presenting” to healthcare centres but it is implied that all were admitted. The authors should clarify this and perhaps specify that all participants were admitted.

4. The ROC curve in this case was based only on data from a highly selected group of patients considered to be at high risk. Performance in this population would not necessarily be representative of performance in a broader population.

5. More detail should be presented on the multivariable analyses.

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Comments from the reviewers:

Reviewer #1: The study evaluates the performance of a risk score for Acute Kidney Disease (AKD) identification and outcomes in low- and low-middle-income countries (LLMICs). Conducted across healthcare facilities in Bolivia, Brazil, South Africa, and Nepal from September 2018 to November 2020, adult patients were screened using a symptom-based risk score and clinical judgment. Serum creatinine testing confirmed AKD in 2922 out of 4311 at-risk patients (67.8%). The risk score's area under the curve was 0.73, indicating moderate accuracy. AKD prevalence was higher when using the risk score compared to clinical judgment alone (80.2% vs. 32.5%). Mortality during admission was 2.9% for AKD patients, and 9.7% after discharge, with 48.4% of AKD patients showing persistent kidney disease at follow-up. The study underscores the risk score's utility in AKD identification and the need for diligent follow-up in LLMICs.

Although the study was carefully designed and a lot of data were collected over the study period, I found the methods section, particularly the statistical methods section, to be lacking. Below are my specific comments.

1. Please add line numbers for easier reference.

2. Introduction: The introduction mentions that kidney disease disproportionately affects disadvantaged populations in LLMICs but lacks specific statistics or data to quantify this burden. The authors could include concrete data or statistics on the prevalence and impact of kidney disease in LLMICs to strengthen the argument and provide a clearer picture of the problem's scale.

3. Introduction: The pilot study results are mentioned, but the challenges faced, such as the high rate of loss to follow-up, are not discussed in depth. Consider providing a more detailed analysis of the pilot study's challenges, including reasons for the high loss to follow-up rate and potential strategies to mitigate this issue in future implementations.

4. Introduction: The introduction briefly mentions the establishment of the KCN but lacks details on its implementation and scope. It could be helpful to include more information about the KCN's implementation, such as the specific interventions, the roles of participating healthcare facilities, and how the updated AKD risk score is integrated into routine clinical care.

5. Method, Study Design, Setting, and Participants: It would be beneficial to provide a detailed description of the regression used to predict the risk score. Specifically, what type of regression was used and outline the variables included in the regression models.

6. Outcome Measures: It would be helpful to categorize the outcomes into primary and secondary outcomes. Additionally, specify the clinical variables and outcomes compared between the two groups. A detailed description of these variables and outcomes would enhance clarity.

7. Statistical Methods: Provide more details about the exact multivariable analysis performed to determine the factors associated with AKD and mortality. Clarify if logistic regression was used and explain why certain variables (e.g., age, sex, country of enrollment) were included in the models. Describe the selection process for these variables. Additionally, explain Youden's index in the main text for better understanding. Overall, the statistical methods section needs more comprehensive information.

Reviewer #2: In this study, the authors attempted to validate the performance of the AKD risk stratification tool. According to reference 10, the initial design and rationale of this AKD tool are based on the pre-hospital kidney disease classification (NKD, AKD, CKD). This AKD concept might slightly differ from the AKD concept used in large cohort studies or from the concept according to the ADQI consensus (non-recovery status after AKI). However, considering prior exposure to a community-acquired AKI episode, this concept might still be applicable.

Comment 1: Should Table 2 be cited as Table 1 (with the current Table 1 changing to Table 2) according to the main text in the manuscript?

Comment 2: According to reference 18, the authors decided to use baseline creatinine based on "The latest SCr documented prior to healthcare facility admission and the lowest SCr during healthcare facility admission were used to determine the baseline SCr; an imputed baseline SCr based on an assumed eGFR was not used." Did the authors consider using the lowest creatinine level from -7 to -365 days before this index admission as baseline creatinine, as well?

Comment 3: I understand the authors followed the KDIGO AKD concept. However, I suggest that the authors also discuss the slightly different concept of AKD from ADQI. As mentioned above, the score used to identify patients at risk for AKD might account for an under-diagnosed community-acquired AKI episode prior. The authors should also cite the ADQI reference in this article.

ref 1: Acute kidney disease and renal recovery: consensus report of the Acute Disease Quality Initiative (ADQI) 16 Workgroup. Nat Rev Nephrol. 2017 Apr;13(4):241-257. doi: 10.1038/nrneph.2017.2

Comment 4: In the discussion section, I suggest the authors also discuss the post-AKD follow-up and the differences in patient outcomes in this study compared to other prior studies. Additionally, please cite some recently published articles provided below.

ref 1: Unveiling the enigma of acute kidney disease: predicting prognosis, exploring interventions, and embracing a multidisciplinary approach. Kidney Res Clin Pract. 2024 Jul;43(4):406-416. doi: 10.23876/j.krcp.23.289

ref 2: Incidence and Transition of Acute Kidney Injury, Acute Kidney Disease to Chronic Kidney Disease after Acute Type A Aortic Dissection Surgery. J Clin Med. 2021 Oct 18;10(20):4769.

ref 3: Comprehensive versus standard care in post-severe acute kidney injury survivors, a randomized controlled trial. Crit Care. 2021 Aug 31;25(1):322. doi: 10.1186/s13054-021-03747-7

ref 4: Acute Kidney Disease After Acute Decompensated Heart Failure. Kidney Int Rep. 2022 Jan 3;7(3):526-536. doi: 10.1016/j.ekir.2021.12.033

ref 4: Quality of Care for Acute Kidney Disease: Current Knowledge Gaps and Future Directions. Kidney Int Rep. 2020 Aug 6;5(10):1634-1642. doi: 10.1016/j.ekir.2020.07.031

Comment 5: For the causes of AKD listed in Table 5, could the authors provide supplemental table or appendix information regarding how each etiology for AKD, such as infection, cardiorenal, and hepatorenal, was defined?

Comment 6: Could the authors also provide the long-term kidney outcomes (such as the need for maintenance dialysis) or MAKE-90 after identifying AKD patients in this cohort? (since the authors mentioned "We undertook follow-up at 90 days")

Comment 7: The 90-day mortality after AKD is reported as "13.7%." This seems a little high. Do the authors have further discussion or explanation, with more detailed information? (since the in-hospital rate only 2.9% ?)

Comment 8: According to reference 10, the AKI episode after admission might be considered a recurrent AKI episode or AKD/AKI progression following a previously unidentified community AKI episode (as shown in reference 10, Fig 1: the AKI develops and is identified after AKD). Did the authors use the same concept in this article (AKD first, then AKI)? If so, please clarify this more clearly in the introduction and methods section since, as mentioned above, this differs from the ADQI AKD concept.

Reviewer #3: This is a very interesting and important work performed by Evans et al. The researchers included adult patients who were at increased risk of AKD and who had a creatinine measurement performed.

Few queries

1) Could you please clarify why the multivariable analysis includes the presence of AKD in the model if the study aims to compare clinical variables and outcomes between those with AKD and those without kidney disease (NKD)?

2) If the inclusion criteria was availability of creatinine measurement, I am unsure why 28.7% of patients had creatinine prior to enrolment - "Those at increased kidney disease risk who underwent SCr testing were included. Patients on dialysis or with a kidney transplant, and those with missing data for presenting SCr or age category, were excluded."

3) Can Table 3 show number of patients please.

4) I am unclear with regards to serum creatinine measurement. Did the author measure serum creatinine by POC at enrolment for patients who did not have pre-enrolment creatinine? Or, some patients had creatinine measure twice - pre-enrolment and at the time of enrolment?

5) Authors need to provide a brief on Education and training program delivered to healthcare workers.

6) The multivariable analysis has been included as supplementary table instead of being in the main document.

Additional minor comment:

1) There are too many tables

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

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PLoS Med. 2024 Nov 14;21(11):e1004495. doi: 10.1371/journal.pmed.1004495.r003

Author response to Decision Letter 1

24 Aug 2024

Attachment

Submitted filename: Reply to reviewers.docx

pmed.1004495.s003.docx^{(32.8KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1004495.r004

Decision Letter 2

Alexandra Tosun

2 Oct 2024

Dear Dr. Evans,

Thank you very much for re-submitting your manuscript "Identification and outcomes of acute kidney disease in patients presenting in low-resource settings" (PMEDICINE-D-24-02124R2) for review by PLOS Medicine.

Thank you for your detailed response to the reviewers' comments. I have discussed the paper with my colleagues and the academic editor, and it has also been seen again by two of the original reviewers. The changes made to the paper were mostly satisfactory to the reviewers. As such, we intend to accept the paper for publication, pending your attention to the reviewers' and editors' comments below in a further revision. When submitting your revised paper, please once again include a detailed point-by-point response to the editorial comments.

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]

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.

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.

We ask that you submit your revision within 1 week (Oct 09 2024). However, if this deadline is not feasible, please contact me by email, and we can discuss a suitable alternative.

Please do not hesitate to contact me directly with any questions (atosun@plos.org). If you reply directly to this message, please be sure to 'Reply All' so your message comes directly to my inbox.

We look forward to receiving the revised manuscript.

Sincerely,

Alexandra Tosun, PhD

Associate Editor

PLOS Medicine

plosmedicine.org

***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.***

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ACADEMIC EDITOR COMMENTS

The authors have responded comprehensively and adequately to the reviewers' comments. The study still has multiple limitations but importantly these are now more completely acknowledged and discussed. There are a few typo's to correct and I picked up two minor language issues as follows:

Line76-77: “…high mortality from the acute AKD episode…” suggest rephrase to “…high mortality from the acute episode…” or “…high mortality from the AKD episode…”

Line 93-94: “This challenge is a consequence, at least in part, due to a deficiency in nephrology education…” suggest rephrase to: “This challenge is a consequence, at least in part, of a deficiency in nephrology education…”

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Requests from Editors:

Please note that we always require a point-by-point response to not only reviewer comments, but all editorial comments, including general editorial requests. Please be sure to provide such a document.

FINANCIAL DISCLOSURE

The funding statement should include: specific grant numbers, initials of authors who received each award, URLs to sponsors’ websites. Also, please state whether any sponsors or funders (other than the named authors) played any role in study design, data collection and analysis, the decision to publish, or preparation of the manuscript. If they had no role in the research, include this sentence: “The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”

DATA AVAILABILITY

The Data Availability Statement (DAS) requires revision. For each data source used in your study:

CODE AVAILABILITY

We expect all researchers with submissions to PLOS in which author-generated code underpins the findings in the manuscript to make all author-generated code available without restrictions upon publication of the work. In cases where code is central to the manuscript, we may require the code to be made available as a condition of publication. Authors are responsible for ensuring that the code is reusable and well documented. Please make any custom code available, either as part of your data deposition or as a supplementary file. Please add a sentence to your data availability statement regarding any code used in the study, e.g. "The code used in the analysis is available from Github [URL] and archived in Zenodo [DOI link]" Please review our guidelines at https://journals.plos.org/plosmedicine/s/materials-software-and-code-sharing and ensure that your code is shared in a way that follows best practice and facilitates reproducibility and reuse. Because Github depositions can be readily changed or deleted, we encourage you to make a permanent DOI'd copy (e.g. in Zenodo) and provide the URL.

TITLE

We suggest changing the title to “Identification and outcomes of acute kidney disease in patients presenting in Bolivia, Brazil, South Africa and Nepal”.

ABSTRACT

1) Abstract: Please structure your abstract using the PLOS Medicine headings (Background, Methods and Findings, Conclusions). Please combine the Methods and Findings sections into one section.

2) l. 36: Please define ‘AKD’ at first use.

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

AUTHOR SUMMARY

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. Ideally each sub-heading should contain 2-3 single sentence, concise bullet points containing the most salient points from your study. In the final bullet point of 'What Do These Findings Mean?', please include the main limitations of the study in non-technical language. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary.

INTRODUCTION

1) Please cite the reference numbers in square brackets. Citations should precede punctuation. Please revise throughout the entire manuscript.

2) Please remove any subheadings from the Introduction section.

METHODS AND RESULTS

1) Please state in the Methods section whether the study had a prospective protocol or analysis plan. 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 document(s) 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. 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. 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.

2) Please ensure that the study is reported according to the STROBE guideline (please use RECORD if you feel it is more appropriate), 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)."

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

3) l.247: We suggest introducing the abbreviation ‘CI’ for ‘confidence intervals’ here.

4) l.265, please state the statistical meaning of the numbers in parentheses, e.g. "57 (interquartile range (IQR): 42-70) years". Please revise throughout.

5) l.290ff: Throughout, we suggest reporting statistical information as follows to improve clarity for the reader "22% (95% CI [13%,28%]; p</=)". When reporting 95% CIs please separate upper and lower bounds with commas instead of hyphens as the latter can be confused with reporting of negative values. For example: “(OR 1.04, 95% CI [1.03-1.04])”

6) Table 1: Please spell out ‘AKD’ in the title or define ‘AKD’ below the table.

7) Table 3: Please define ‘POC’ and ‘IQR’ below the table.

8) Table 4: Please spell out ‘AKD’ and ‘NKD’ in the title or define both below the table. Please add a unit for age (years). Please define ‘HCC’, ‘IQR’. Please change the first demographic to “Sex, Female (n; %)”.

9) Table 5: Please spell out ‘AKD’ in the title or define ‘AKD’ below the table. Please define ‘HIV’ and ‘IV’. Please provide a definition of the numerical values (e.g. n (%)).

10) Table 6: Please spell out ‘AKD’ in the title or define ‘AKD’ below the table. Please provide a definition of the numerical values (e.g. n (%)).

11) Table 7: Please define ‘AKD’, ‘AKI’, ‘N/A’, ‘CKD’, ‘LLMIC’ and ‘HIC’.

12) Please provide titles and legends for all figures (including those in Supporting Information files).

13) Figure 1: Please define ‘AKD’, ‘KRT’, ‘NKD’, ‘CKD’.

14) Figure 2: Please define ‘AKD’ and ‘NKD’. Please indicate in the figure caption the meaning of the bars and whiskers as well as the lines and dots. Please add a brief explanation on the risk score.

DISCUSSION

1) Please remove any subheadings from the Discussion section.

2) l.396: Please temper claims of primacy of results by stating, "to our knowledge" or something similar.

3) Please note that we prefer not to introduce new tables/figures in the Discussion section. We suggest moving Table 7 to the Supplementary Material.

REFERENCES

1) PLOS uses the numbered citation (citation-sequence) method and first six authors, et al.

2) Please ensure that journal name abbreviations match those found in the National Center for Biotechnology Information (NCBI) databases (http://www.ncbi.nlm.nih.gov/nlmcatalog/journals), and are appropriately formatted and capitalised.

3) Where website addresses are cited, please include the complete URL and specify the date of access (e.g. [accessed: 12/06/2024]).

4) Please also see https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references for further details on reference formatting.

SUPPLEMENTARY MATERIAL

1) Please note that supplementary material will be posted as supplied by the authors. Therefore, please amend it according to the relevant comments outlined here and in the previous decision letter.

2) In the published article, supporting information files are accessed only through a hyperlink attached to the captions. For this reason, you must list captions at the end of your manuscript file. You may include a caption within the supporting information file itself, as long as that caption is also provided in the manuscript file. Do not submit a separate caption file.

When SI files are contained with a single file:

Please label the file as ‘S1 Supporting Information’.

Please apply alphabetical labelling to each table and figure contained within the S1 file. For example, ‘Fig A’ to ‘Fig Z’ and ‘Table A’ to ‘Table Z’.

Plain text does not need to be labelled and can just be given a title as necessary. For example, ‘Statistical Analysis Plan’.

Please cite tables/figures as ‘Fig A in S1 Supporting Information’ and/or ‘Table A in S1 Supporting Information’, for example.

Please cite plain text as, ‘Statistical Analysis Plan in S1 Supporting Information’, for example.

When SI files are uploaded as separate files:

Please label tables as ‘S1 Table’ (so on) and figures as ‘S1 Fig’ (and so on).

Any additional documents (protocols/analysis plans etc.) can be labelled as ‘S1 Protocol’, for example. Please cite items as exactly as labelled.

SOCIAL MEDIA

To help us extend the reach of your research, please provide any X (formerly known as Twitter) handle(s) that would be appropriate to tag, including your own, your co-authors’, your institution, funder, or lab. Please enter in the submission form any handles you wish to be included when we post about this paper.

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Comments from Reviewers:

Reviewer #1: I would like to thank the authors for their thoughtful revisions in response to my and the other reviewers' comments. After reviewing the authors' responses and the revised manuscript, I am satisfied that they have adequately addressed the concerns raised. I am pleased to recommend this paper for acceptance and publication.

Reviewer #2: The authors have addressed my previous question regarding the different AKD definitions compared to the ADQI consensus and the recent KDIGO criteria. Additionally, detailed information about MAKE-90 and the etiologies of AKD/AKI has been provided in the revised manuscript.

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

[LINK]

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General Editorial Requests

1) 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.

2) 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.

3) 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.

PLoS Med. 2024 Nov 14;21(11):e1004495. doi: 10.1371/journal.pmed.1004495.r005

Author response to Decision Letter 2

7 Oct 2024

Attachment

Submitted filename: Reply (second) to reviewers.docx

pmed.1004495.s004.docx^{(27.4KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1004495.r006

Decision Letter 3

Alexandra Tosun

14 Oct 2024

Dear Dr. Evans,

Thank you very much for re-submitting your manuscript "Identification and outcomes of acute kidney disease in patients presenting in Bolivia, Brazil, South Africa and Nepal" (PMEDICINE-D-24-02124R3) to PLOS Medicine.

There are a few minor editorial issues that need to be addressed before we can accept the manuscript for publication; these are outlined at the end of this email. Please revise the paper accordingly, and submit the final revision within 1 week.

A reminder that 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 directly at hvanepps@plos.org.

We look forward to receiving the revised manuscript by Oct 21st.

Sincerely,

Heather Van Epps, PhD

Executive Editor, PLOS Medicine

[on behalf of]

Alexandra Tosun, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

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Requests from Editors:

1. Abstract, ll 55-58: Please clarify the denominators used to calculate the percentages for persistent kidney disease (48.4%) and reclassification (39.7%). Should the denominator be the 2119 patients with AKD who had follow up (per line 368 in the Results)? If so, the percentage with persistent kidney disease would be 42.6% (902 of 2119), and the percentage who were reclassified would be 34.9% (740 of 2119). Please check and clarify if needed, and please accept my apologies if I have missed something.

2. Please format your Author summary using bullet points, rather than a continuous narrative. Each sub-heading should contain 2-3 single sentence, concise bullet points containing the most salient points from your study. In the final bullet point of ‘What Do These Findings Mean?’, please include the main limitations of the study in non-technical language. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary.

3. Results, ll 294-297; Please report the denominator used to calculate these percentages and explain why this is different from the total number (n=4311). Is this due to missing data for type of center visited?

4. Financial disclosure statement: please include grant numbers and the initials of the author to whom the grants were awarded.

5. STROBE checklist – please remove page numbers (leaving only line numbers) as these will not correspond to the final published document.

PLoS Med. 2024 Nov 14;21(11):e1004495. doi: 10.1371/journal.pmed.1004495.r007

Author response to Decision Letter 3

21 Oct 2024

Attachment

Submitted filename: Reply (third) to reviewers.docx

pmed.1004495.s005.docx^{(15.3KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1004495.r008

Decision Letter 4

Alexandra Tosun

22 Oct 2024

Dear Dr Evans,

On behalf of my colleagues and the Academic Editor, Maarten W. Taal, I am pleased to inform you that we have agreed to publish your manuscript "Identification and outcomes of acute kidney disease in patients presenting in Bolivia, Brazil, South Africa and Nepal" (PMEDICINE-D-24-02124R4) in PLOS Medicine.

I appreciate your thorough responses to the reviewers' and editors' comments throughout the editorial process. We look forward to publishing your manuscript, and editorially there are only a few remaining minor stylistic/presentation points that should be addressed prior to publication. We will carefully check whether the changes have been made. If you have any questions or concerns regarding these final requests, please feel free to contact me at atosun@plos.org.

Please see below the minor points that we request you respond to:

1) Author Summary: Please introduce abbreviations the first time they are used (AKD, LLMICs) or spell them out if they are used only once, such as CKD.

2) STROBE checklist: We apologize for the miscommunication regarding page and line numbers. We ask that you replace the line numbers with paragraph numbers per section (e.g. "Methods, paragraph 1").

3) Citations: Please note and revise that multiple citations should be combined into a single pair of parentheses. For example: “Kidney disease disproportionately affects disadvantaged populations in low- and low-middle income countries (LLMICs) with poor access to care [1,2]”. Please note the lack of spacing between citations.

4) References: Where website addresses are cited, please use the word “accessed” when specifying the date of access (e.g. [accessed: 12/06/2024]).

5) In reference [15], please change ‘PLOS Med’ to ‘PLoS Med’.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email (including the editorial points above). 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/.

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

Sincerely,

Alexandra Tosun, PhD

Associate Editor

PLOS Medicine

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 STROBE Checklist. STROBE Statement—checklist of items that should be included in reports of observational studies.

(DOCX)

pmed.1004495.s001.docx^{(35.5KB, docx)}

S1 Text

(DOCX)

pmed.1004495.s002.docx^{(76.8KB, docx)}

Attachment

Submitted filename: Reply to reviewers.docx

pmed.1004495.s003.docx^{(32.8KB, docx)}

Attachment

Submitted filename: Reply (second) to reviewers.docx

pmed.1004495.s004.docx^{(27.4KB, docx)}

Attachment

Submitted filename: Reply (third) to reviewers.docx

pmed.1004495.s005.docx^{(15.3KB, docx)}

Data Availability Statement

Original data is owned by the International Society of Nephrology and will be made freely available upon reasonable request (research@theisn.org).

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PERMALINK

Identification and outcomes of acute kidney disease in patients presenting in Bolivia, Brazil, South Africa, and Nepal

Rhys D R Evans

Sanjib K Sharma

Rolando Claure-Del Granado

Brett Cullis

Emmanuel A Burdmann

FOS Franca

Junio Aguiar

Martyn Fredlund

Kelly Hendricks

Maria F Iturricha-Caceres

Mamit Rai

Bhupendra Shah

Shyam Kafle

David C Harris

Mike V Rocco

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Methods

Ethics statement

Study design, setting, and participants

Table 1. AKD risk score components.

Variables and data sources/measurement

Definitions

Table 2. Definitions of kidney disease and kidney recovery.

Outcome measures

Statistical methods

Results

Participants

Fig 1. Cohort description.

Measurement of kidney function and prevalence of AKD

Table 3. Measurement of kidney function and kidney disease classification at each study site.

Table 4. Clinical variables and patient outcomes in patients with AKD and NKD.

Performance of the risk score to detect AKD

Fig 2. Performance of the risk score to detect AKD.

Causes and management of AKD

Table 5. Aetiologies and management of AKD.

Kidney and patient outcomes at end of healthcare facility admission

Table 6. Patient and kidney outcomes at healthcare facility discharge and at 90-day follow-up in patients with AKD.

Kidney and patient outcomes at 90-day follow-up

Discussion

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Alexandra Tosun

Roles

Decision Letter 1

Alexandra Tosun

Roles

Author response to Decision Letter 1

Decision Letter 2

Alexandra Tosun

Roles

Author response to Decision Letter 2

Decision Letter 3

Alexandra Tosun

Roles

Author response to Decision Letter 3

Decision Letter 4

Alexandra Tosun

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES