Burden and Excess Risk of Adverse Outcomes in Patients With Type 1 Diabetes Using KDIGO Classification: A National Cohort Study

Chronic kidney disease (CKD) associated with diabetes is a leading cause of kidney failure (KF) worldwide and is linked to increased risks of cardiovascular (CV) disease (CVD), dialysis dependence, and mortality (1–4). Consequently, there is a strong socioeconomic and health imperative to improve outcomes of CKD associated with diabetes.

CKD associated with diabetes is characterized by persistent albuminuria and/or low glomerular filtration rate (GFR) in the absence of other apparent alternative causes (1,2) and is traditionally seen as a gradual, linear progression from normo- to micro- to macroalbuminuria culminating in GFR loss (2,5–7). However, this traditional view is rapidly changing. Emerging evidence indicates that a notable subset of patients with type 1 or 2 diabetes may follow a normoalbuminuric pathway to KF, even with renoprotective treatments (8–10). Thus, while albuminuria is an important factor, it is not always a reliable sole predictor of CKD progression and complications, questioning its universal applicability as a prognostic marker (7,9,11).

CKD develops in ∼30–40% of individuals with type 1 diabetes (12,13). However, there are significant gaps in our understanding of various phenotypes, outcomes, and risk factors for progression in this population (13). Studies in individuals with type 1 diabetes, in contrast to those with type 2 diabetes, have been restricted by small sample sizes, short observation periods, and a predominant focus on advanced CKD stages (estimated GFR [eGFR] of <60 mL/min/1.73 m²) (13).

Early identification of CKD through regular screening, monitoring, and improved risk prediction in type 1 diabetes is of paramount importance to delay CKD and reduce CVD, the primary cause of death (5,14). The Kidney Disease: Improving Global Outcomes (KDIGO) Clinical Practice Guideline for the Evaluation and Management of CKD, endorsed by the American Diabetes Association, recommends classifying patients by GFR and albuminuria for better prognostication (1,2). This stratification could significantly improve risk assessment and enable timely, targeted interventions but remains underused in type 1 diabetes studies (15). Data quantifying the increased risk of disease progression in these patients across various levels of kidney function, especially in early KDIGO category with nearly normal eGFR (60–89 mL/min/1.73 m²) and no albuminuria, which is conventionally perceived as low risk for adverse outcomes, are limited.

This observational study used the Swedish National Diabetes Register (NDR) to assess whether analyzing KDIGO categories independently can provide new insights and improve risk prediction for kidney and other adverse outcomes in type 1 diabetes. We examined the following among individuals with type 1 diabetes: 1) the prevalence of KDIGO categories at baseline, 2) the incidence of adverse kidney and CV outcomes, including mortality, in each category, and 3) the association of baseline categories with excess risk of major outcomes.

Data were extracted and retrospectively analyzed in individuals with type 1 diabetes residing in Sweden and registered in the NDR. The study was approved by the Regional Ethical Review Board, Gothenburg, Sweden.

Several national health registries were used through patients’ unique personal identity numbers for extracting their baseline data and outcomes (details are described in the Supplementary Methods).

Our study enrolled 40,199 adult patients diagnosed with type 1 diabetes at least 1 year prior to the study start. The study's baseline was the time of entry into the national registry. The study covered the period from 1 January 2007 to 31 December 2018, with longitudinal data collection from NDR and other national registries until 30 September 2019.

Epidemiological definition of type 1 diabetes was used (i.e., age of onset <30 years and exclusive insulin treatment). This diagnosis of type 1 diabetes has been validated as accurate in 97% of cases, which was reported in a previous study from this register (16). Individuals with a preexisting diagnosis of KF (KDIGO category G5), at or before the baseline, or malignancies within 5 years of the baseline were excluded. KF was defined as receiving kidney replacement therapy (dialysis or kidney/combined kidney-pancreas transplantation) or having an eGFR <15 mL/min/1.73 m², without acute kidney injury (AKI).

NDR encourages annual eGFR data submissions from collaborators, including urine albumin-to-creatinine ratio (UACR) measurements. The eGFR was calculated using the CKD-EPI equation (17) at baseline and during follow-up, with at least one additional measurement ≥3 months apart required for inclusion. Participants were categorized at baseline into KDIGO G1–G4 based eGFR and A1–A3 based albuminuria, with G1 further divided into G1a (eGFR ≥105 mL/min/1.73 m²) and G1b (eGFR ≥90–104 mL/min/1.73 m²) (18). The KDIGO classification is detailed in the Supplementary Methods and Supplementary Table 1. Statistical analyses were performed using this stratification. To ensure accurate assessment of chronicity, CKD at baseline was defined as at least two eGFR values <60 mL/min/1.73 m² ≥3 months apart and/or UACR >30 mg/g (i.e., encompassing all KDIGO categories except G1A1 and G2A1 [non-CKD]) (18,19). Thus, non-CKD categories represented eGFR ≥60 mL/min/1.73 m² and normoalbuminuria.

Participant’s demographic, baseline, and clinical data were obtained, with inputs up to 1 year before study inclusion. Comorbidities were collected over 10 years preceding the baseline. The baseline variables are presented in Table 1.

Kidney outcomes of interest included AKI, a 40% decline in eGFR from baseline, KF (as defined above), renal death, and a composite outcome termed major adverse kidney events (MAKE). The outcome MAKE comprised any of the following renal events: AKI, a 40% decline in eGFR from baseline value, or renal death. The CV outcomes were coronary heart disease (CHD, incorporating acute myocardial infarction and unstable angina), stroke, CV death, the composite outcome “four-point major adverse CV events (MACE)” that included either of the above CV outcomes and heart failure. The last outcome examined was all-cause mortality. Renal death was defined as any death occurring within 4 weeks after MAKE, while CV death was defined as death due to CHD or stroke. All diagnoses and outcomes, except for 40% eGFR decline, were extracted from other national registries using the ICD-10 codes, which are specified in Supplementary Table 2 for each outcome.

The statistical analyses were performed using R 4.2.0 software. Descriptive statistics, including means, SDs, medians, interquartile range, and frequency distributions, were computed. The cumulative incidence of outcomes was assessed using Kaplan-Meier survival curves, and incidence rate was calculated per 1,000 person-years (py). Cox proportional hazards regression models were applied to analyze the association between the baseline KDIGO category and the outcomes of 40% eGFR decline, KF, MAKE, MACE, and mortality. Hazard ratios (HRs) and 95% CIs, both crude and adjusted, were determined, and proportionality testing was conducted.

Adjusted HRs (aHRs) were obtained by accounting for covariates at the baseline, including age, debut age of diabetes, sex, smoking, CVD, heart failure, duration of diabetes, systolic blood pressure, HbA_1c, LDL cholesterol, triglycerides, use of ACE inhibitor (ACEi) and/or angiotensin receptor blocker (ARB), and lipid-lowering agents. The follow-up period ended at the earliest manifestation of KF, death, or the study’s conclusion. For composite outcomes, follow-up ended at the occurrence of a first event of any of the outcomes. Missing data were generally <10%, except for albuminuria, which was unavailable in 13.8% (n = 5,384). In adjusted regression, missing data were omitted. The number of eGFR measurements by KDIGO category during follow-up is displayed in Table 1.

The regression analysis employed three distinct models, each with a different reference category. First, the combined G1A1 + G2A1 (eGFR ≥60 mL/min/1.73 m² and normoalbuminuria), representing non-CKD and traditionally viewed as low-risk by KDIGO, served as the reference to investigate relative risk in other categories. Next, G1A1 (eGFR ≥90 mL/min/1.73 m² and normoalbuminuria) was used as the reference to specifically evaluate the early G2A1 category (eGFR 60–89 mL/min/1.73 m² and normoalbuminuria) for increased risk. Finally, G1bA1 (eGFR 90–104 mL/min/1.73 m² and normoalbuminuria) reference category provided the basis to assess whether individuals with the highest eGFR (≥105 mL/min/1.73 m²) in the cohort were at an increased risk. This report focuses solely on the HR for the baselines KDIGO categories. A comprehensive analysis of other risk factors, including sex-differentiated assessments, is beyond the scope of this report and will be addressed in a separate manuscript.

The data sets created and analyzed in this study can be accessed by contacting the corresponding author upon a reasonable request.

Of 40,199 patients with type 1 diabetes in NDR, 171 were excluded due to missing eGFR values at baseline and 917 due to past cancer diagnosis. Among the remaining 39,111 patients, 44 (0.1%) were already at KF stage G5, which was an outcome of interest, and were subsequently removed from further analysis (Supplementary Fig. 1). The final study cohort comprised 39,067 individuals with a mean age 35.4 ± 15.3 years and 45% women. Baseline characteristics of the entire study cohort stratified into eGFR categories are presented in Table 1, and in different categories of age, diabetes duration, systolic blood pressure, diastolic blood pressure, and HbA_1c in Supplementary Table 3.

The mean follow-up time was 9.1 ± 3.6 years, with ∼350,000 py of observation. The median number of eGFR measurements after baseline was 9.0 (interquartile range 5.0, 13.0), and mean diabetes duration at time of inclusion was 20.8 ± 15.3 years. Debut age of diabetes was similar in all categories, with a mean of 14.9 ± 7.6 years. Mean HbA_1c was 64.9 ± 16.2 mmol/mol, with no significant differences between the categories.

At baseline, 5.1% had CHD, 1.5% had a history of stroke, 1.5% had heart failure, 22.1% received lipid-lowering treatment, and 24.2% received ACEi and/or ARB. No individual with the diagnostic code of isolated pancreas transplantation was identified in the cohort.

Table 2 displays the prevalence of KDIGO GFR categories for the cohort at baseline: 58.5% were classified as G1a and 20.9% G1b, 15.5% as G2, 4.3% as G3 (2.7% G3a, 1.6% G3b), and 0.9% as G4. Most patients (71.9%) had normoalbuminuria, 9.5% had microalbuminuria, and 4.8% had macroalbuminuria. Albuminuria data was missing in the remaining 13.8%. According to the KDIGO classification, the risk for disease progression was low in 70.7%, moderate in 9.4%, high in 3.7%, and very high in 2.7% (excluding the patients with missing albuminuria data).

After excluding 5,374 patients with missing albuminuria data, the overall prevalence of CKD (UACR >30 mg/g and/or eGFR <60 mL/min/1.73 m²) was 18.5% (n = 6,278). At baseline, 31.5% (470 of 1,492) in G3 and 8.0% (25 of 314) in G4 were normoalbuminuric. Thus, the normoalbuminuric CKD phenotype was present in 1.5% of the whole cohort, in 8.1% in individuals with any CKD, and in 27.4% of those with GFR <60 mL/min/1.73 m².

The cumulative incidence and incidence rate/1,000 py for different outcomes over the entire observation period are described below (Table 3 and Supplementary Figs. 2–4).

Renal outcomes (Table 3 and Supplementary Fig. 2) include the following. A cumulative incidence of 2.1% was observed for AKI, 2.5% for 40% eGFR decline, 2.7% for KF, and 3.7% for MAKE in the entire cohort. The incidence rate/1,000 py of all renal outcomes increased progressively from G1a to G4 stages—AKI from 0.8 to 39.4, 40% eGFR decline 1.0 to 43.4, KF from 0.4 to 169.6, renal death from 0.1 to 45.4, and MAKE from 1.1 to 179.3, respectively.

CV outcomes (Table 3 and Supplementary Fig. 3) include the following. The cumulative incidence was 9.6% for CHD, 3.0% for stroke, 1.1% for CV mortality, 11.8% for MACE, and 4.8% for heart failure in the entire cohort. The incidence rate of all CV outcomes increased across the G1a to G4 categories from 2.6 to 119.9 for CHD, from 1.1 to 26.1 for stroke, from 0.2 to 18.3 for CV death, from 2.5 to 133.5 for MACE, and from 1.0 to 78.6 for heart failure.

All-cause mortality (Table 3 and Supplementary Fig. 4) includes the following. In the entire cohort, there were 3,027 deaths (7.7%). The incidence rate of death showed a progressive increase from 2.3 in G1a to 74.1 in G4.

The aHRs for the five major outcomes in the initial and repeat analyses are presented in Table 4. Supplementary Table 4 demonstrates the crude HR for these analyses for G1A1 + G2A1 and G1A reference categories.

For 40% eGFR decline from baseline, the risk gradually increased across G1 to G4, from 2.79 in G1A2 to 19.11 in G4A3, except in the G3bA1, where the aHR was not significant.

For KF, advancing GFR categories from G1 to G4 were associated with increasing risk, with the aHR increasing from 3.35 in G1A2 to 491.94 in G4A3.

For MAKE, the risk increased significantly across G1 to G4, with the aHR increasing from 2.71 in G1A2 to 168.10 in G4A3.

For MACE, the aHR demonstrated an incremental increase across G2 to G4, from 1.36 in G2A2 to 5.09 in G4A3.

For all-cause mortality, the aHR increased significantly across G1 to G4, from 1.32 in G1A2 to 9.34 in G4A3. In both G3 and G4, the risk of mortality was significantly higher, even in the normoalbuminuric category, and it increased progressively with worsening albuminuria.

Notably, the risk for all of the above outcomes was significantly higher even in the early G1 and G2 categories with albuminuria except for MACE, for which an increased risk was not observed in G1. Furthermore, an increased risk was found also in normoalbuminuric G3 and G4 categories, the exceptions being MACE and a 40% eGFR decline in G3b, where the risk was not significantly different compared with reference.

The repeat analysis revealed significant increases in aHRs for all five major outcomes even in G2A1 compared with G1A1, as below: 3.17 for 40% eGFR decline, 6.82 for KF, 3.93 for MAKE, 1.26 for MACE, and 1.35 for all-cause mortality. Substantial increases in the aHR for all outcomes across all other categories were observed compared with the initial analysis, as can be seen in Table 4.

Upon stratifying the G1 category into G1a and G1b, the individuals with albuminuria (G1aA2 and G1aA3), but not those with normoalbuminuria (G1aA1) demonstrated an increased risk for adverse outcomes relative to the G1bA1 reference category (Table 4).

In this large national cohort of adults with type 1 diabetes, CKD prevalence was 18.5% based on the KDIGO classification, with 27.4% of those with eGFR <60 mL/min/1.73 m² displaying a normoalbuminuric phenotype. Incidence of all adverse kidney and CV outcomes, including mortality, progressively increased with advancing GFR categories. Multivariable analysis showed a clear graded rise in risk for all investigated major outcomes with advancing KDIGO categories, starting as early as G1 categories with albuminuria. Individuals with eGFR ≥105 mL/min/1.73 m² and normoalbuminuria had no increased risk, while normoalbuminuric CKD (G3/G4) exhibited elevated risk compared with the non-CKD group. Interestingly, in subsequent analysis using G1A1 alone as the reference, a significantly higher risk for all major outcomes was observed even in G2A1 (representing 10% of the entire cohort)—a category traditionally considered as non-CKD and low risk.

This study uniquely applied the KDIGO classification to a large national cohort with type 1 diabetes to assess the incidence of CKD and its adverse outcomes, incorporating both kidney function and albuminuria (2,18). Few studies have applied the KDIGO categorization to such a large population with type 1 diabetes, and none, to our knowledge, have comprehensively evaluated the burden and excess risk of CV and renal complications, including 40% eGFR decline and the composite outcome MAKE. Our findings reveal a substantial burden of adverse outcomes already in early KDIGO categories, challenging the notion that early-stage CKD carries minimal risk. This underscores the need to reassess management strategies for early CKD, possibly by advocating for early specialist involvement and optimized resource allocation to address its substantial impact. Timely intervention and vigilant monitoring are crucial for preventing progression and improving long-term outcomes. Furthermore, the quantification of disease burden across different KDIGO categories in this study can guide design and sample size calculations for clinical intervention trials in type 1 diabetes.

The implications of normoalbuminuria in individuals with CKD in this population with type 1 diabetes remain inconclusive (7,9,11). In our cohort, the normoalbuminuric CKD was prevalent in 8.1% of those with CKD and in 27.4% of individuals with eGFR <60 mL/min/1.73 m², which was higher than the 15.5% found in the FinnDiane study (20) and similar to the 24% reported in the Diabetes Control and Complications Trial/ Epidemiology of Diabetes Interventions and Complications study (21). This phenotype emerged as an independent risk factor for all outcomes in our cohort, reinforcing that CKD progression and associated complications can occur in absence of albuminuria (8,9).

The incidence of all kidney outcomes progressively increased across declining GFR categories. Notably, rise in adverse kidney outcomes was evident as early as the G1b category.

AKI, characterized by a precipitous drop in kidney function within hours to days, is a serious complication occurring at a higher rate in type 1 diabetes than in those without diabetes and potentially resulting in irreversible kidney damage (22). Previous studies have established that AKI can accelerate CKD progression (23), likely due to multifactorial mechanisms (23,24). In our cohort, the cumulative incidence of AKI was 2.1%, rising exponentially from G1b onward, indicating that the risk of AKI increases with worsening kidney function. These findings suggest the need for heightened vigilance and preventive strategies to manage AKI effectively in people with type 1 diabetes, particularly in those with CKD.

For the first time in a large cohort with type 1 diabetes, this study evaluated both the 40% decline in eGFR and the composite kidney outcome MAKE. A 40% decline in eGFR is a well-accepted surrogate marker for KF, widely used in clinical trials and allowing consistency in evaluation of disease progression and comparison across cohorts (25). Our analysis of this measure aligns with existing literature reinforcing its role as a robust predictor of CKD progression.

A single measure of kidney function or adverse event may not fully capture the complexity of kidney disease, and obtaining hard end points in nephrology is challenging, often requiring lengthy follow-up periods. MAKE addresses this challenge by assessing multiple clinically relevant outcomes within a shorter time frame. In addition, it enables consistent and comparable investigation of interventions across various settings. Therefore, MAKE is increasingly used in kidney research, including trials assessing CKD progression and interventions (26).

The rising incidence and excess risk of both a 40% eGFR decline and MAKE found across advancing KDIGO categories in our study provides new insights into kidney disease burden and progression. Tracking and comparing the incidence of these key outcomes across different KDIGO categories not only deepens our understanding of the disease burden and risk associated with each stage of kidney disease but also facilitates the design and interpretation of clinical trials. This approach may support the development of more targeted and effective interventions in type 1 diabetes.

Although the incidence of KF in type 1 diabetes has reduced over time (27), the risk remains significantly higher than in the general population (28). Previous studies have shown that renal events, including KF and mortality, increase with a GFR decline <60 mL/min/1.73 m² and with a progression from micro- to macroalbuminuria, in both general and type 1 diabetes populations (2,29,30). However, the incidence of KF in these studies was not delineated category-wise. Our study addresses this knowledge gap by examining KF rates for each KDIGO category and reveals that the rise in renal events begins as early as G1b and increases substantially in more advanced stages.

From extensive body of research, it is evident that CVD stands as the primary driver of both morbidity and mortality in individuals with type 1 diabetes (31,32). In our study, a progressively increasing cumulative incidence of all CV outcomes and heart failure were observed across G1 up to G3a; however, this trend plateaued somewhat thereafter. The incidence rate increased dramatically across GFR and albuminuria categories, in line with findings from previous studies summarized in recent reviews and guidelines (18,33).

While mortality rates in individuals with type 1 diabetes have declined over past decades, their life expectancy remains lower compared with populations without diabetes (34,35). A prior observational study conducted within the NDR showed individuals with type 1 diabetes who had CKD, CVD, or heart failure, and particularly those with a combination of these conditions, had a clearly increased mortality risk (30,36). In our study, all-cause mortality also followed a pattern similar to the other outcomes, with increasing incidence rates from G1 to G4, highlighting that kidney function deterioration significantly contributes to elevated mortality in type 1 diabetes.

Our multivariable analysis revealed significantly elevated risks for all major outcomes across KDIGO categories, including early stages, compared with reference groups (G1A1 + G2A1 or G1A1 alone). Importantly, excess risk was found even in the earlier G1/G2 and in normoalbuminuric G3/G4 categories for most outcomes. The aHR increased markedly, by 14- to 16-fold, for KF and MAKE, whereas by 2- to 3-fold for 40% eGFR decline and MACE, progressing from G3aA1 to G4A3. The modest increase in the aHR for 40% eGFR decline or MACE with increasing albuminuria in G4 may be attributable to competing risk with other outcomes, such as KF or renal death, or a limited sample size in this advanced category. For all-cause mortality, the aHR consistently increased with worsening eGFR and albuminuria, regardless of each other, indicating elevated risk as kidney disease progresses, even in early stages.

Interestingly, significant increases in the aHRs were observed even in G2A1 for all outcomes when only G1A1 was used as the reference. Additionally, in the G1a category with eGFR ≥105 mL/min/1.73 m², elevated risk was found only in the albuminuria categories, suggesting that elevated GFR, potentially indicative of glomerular hyperfiltration, does not alone accelerate renal damage in the absence of albuminuria. In both early G1 or G2 categories, progression from micro- to macroalbuminuria increased the aHRs for all analyzed outcomes, regardless of ACEi/ARB use, reiterating the importance of albuminuria screening even in earlier categories (2). While the association between albuminuria and adverse kidney outcomes and mortality in patients with type 1 diabetes with CKD is well established (37), our results also inform about the excess risk of these complications in patients with preserved GFR and no albuminuria (G2A1), considered non-CKD and low risk. These findings highlight that careful monitoring and management of CKD in type 1 diabetes should not be limited to patients with overt albuminuria or advanced CKD stages.

Current guidelines from American Diabetes Association/European Association for the Study of Diabetes recommend screening for CKD in type 1 diabetes to guide therapy only if diabetes duration is ≥5 years, by testing for UACR and creatinine at least annually and twice a year in patients with albuminuria ≥300 mg/g creatinine (A3) and/or an eGFR 30–60 mL/min/1.73 m² (G3) (38), which does not cover a substantial portion of population at risk (which corresponds to 18.5% of our cohort in G1A2, G2A1 and G2A2 categories who were at an increased risk of adverse outcomes). KDIGO supports this approach in general as it recommends multidisciplinary management (2). In a large German database analysis, adherence to annual screening for CKD by at least one eGFR and one UACR determination was found in less than half of patients with type 1 diabetes (39). While underuse even of the current guidelines exists, our data advocate for revisiting these guidelines, to promote early and frequent monitoring of both eGFR and albuminuria even in early GFR stages in type 1 diabetes. This approach could enhance early detection, improve risk stratification, and optimize intervention strategies to prevent disease progression and reduce mortality.

The strengths of our study are inclusion of a large cohort covering nearly all type 1 diabetes patients in Sweden with their detailed clinical attributes, past and present diagnoses as well as medications, allowing for robust multivariable analysis. However, we must acknowledge several limitations that are mostly inherent to the retrospective design, use of registry data received from multiple clinics with a potential for residual confounding from uncaptured factors, and reliance on ICD codes for definition of outcomes, without access to primary care data, which may have led to an underestimation of certain events. Furthermore, albuminuria status was recorded only once at baseline, and the data were missing for 13.8% of the cohort. Additionally, albuminuria was categorized into normo-, micro-, and macroalbuminuria, and the precise numerical values of the UACRs were not available. Lastly, our findings are applicable to a Swedish, predominantly Caucasian population, and may not be generalizable to other ethnic or geographic groups.

In conclusion, to the best of our knowledge, this is the first study to extensively analyze the burden and risk of adverse outcomes, including mortality, across different KDIGO categories in a large population with type 1 diabetes. The significant excess risk identified in individuals in the early KDIGO category, even those with preserved GFR and no albuminuria, calls for early and regular screening with UACR and eGFR together with optimized comprehensive management strategies. This approach could mitigate disease progression and markedly improve outcomes.

Acknowledgments. The authors express their gratitude to the late Associate Professor Ann-Marie Svensson for her tremendous dedication to the NDR and to the statistician Caddie Zhou, NDR, Centre of Registries in Region Western Sweden, Gothenburg, Sweden, for her assistance in data extraction.

Funding. The study received partial funding from the John and Brit Wennerström’s Research Foundation.

The funders had no direct participation in shaping the scientific content of this research.

Duality of Interest. B.E. receives support from Novo Nordisk, Eli Lilly, Sanofi, Bayer, AstraZeneca, Amgen, Boehringer Ingelheim, and Merck Sharp & Dohme, unrelated to the work presented herein. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. K.M., B.E., H.K.C., and S.B.-A. reviewed the manuscript. K.M. and S.B.-A. wrote the first draft of the manuscript. K.M. and S.B.-A. edited the manuscript. B.E. and H.K.C. provided the data from the NDR register. All authors approved the final version of the manuscript. K.M. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Preliminary results of this study were shared as an oral presentation at the 61st European Renal Association (ERA) Congress, Stockholm, Sweden, and virtual, 23–26 May 2024.

Handling Editors. The journal editors responsible for overseeing the review of the manuscript were Elizabeth Selvin and Csaba P. Kovesdy.