A key challenge in the design and conduct of randomized controlled trials (RCTs) of CKD is that kidney failure typically develops over a long period of time, thus studies seeking to detect effects on this outcome require substantial follow-up time. As a result, there has been substantial effort from investigators, regulatory authorities, and sponsors toward identifying robust alternative endpoints for kidney failure, particularly for the early stages of CKD and for early-phase trials.12345–6
A 2018 scientific workshop convened by the National Kidney Foundation, United States Food and Drug Administration, and European Medicines Agency evaluated the evidence for rate of change in GFR (i.e., GFR slope) as an alternative endpoint for kidney disease progression in RCTs.7 On the basis of two separate meta-analyses of observational cohorts and RCTs, the workshop concluded that treatment effects on GFR slope accurately predicted treatment effects on clinical outcomes, indicating GFR slope may be a viable alternative endpoint for kidney disease progression. On the basis of these data, some ongoing studies are using GFR slope as an endpoint.8910111213–14 Interventions that affect CKD progression often produce early, short-term effects on GFR (referred to hereon in as acute effects) that differ from their long-term treatment effects (referred to hereon in as chronic slope), as, for example, are seen with agents that block the renin-angiotensin system (RAS) and sodium-glucose cotransporter 2 (SGLT2) inhibitors.151617–18 The presence of acute effects may complicate the design and interpretation of RCTs in which GFR slope is the primary outcome. For example, negative acute effects may increase risk of falsely concluding no benefit, whereas positive acute effects may increase the risk of falsely concluding treatment benefit.
Although such acute effects are common, there is little understanding of them. We sought to describe the nature and magnitude of acute treatment effects on GFR in RCTs in which kidney disease progression was assessed, and evaluate the consistency of these effects across key study level characteristics, including intervention type, GFR, and albuminuria.
Methods
Datasets
As part of our previous work, we developed a pooled database of RCTs by performing a systematic literature search to identify relevant trials and obtaining individual participant data for these studies.1,192021222324252627282930313233343536373839404142434445464748495051525354555657585960–61 A complete list of search terms used is provided in Supplemental Table 1 and the study inclusion criteria are listed in Supplemental Table 2. Risks of bias for each study were assessed using the risk-of-bias tool of the Cochrane collaboration62 (Supplemental Figure 1). As we have done previously, we included a separate randomized treatment comparison for each independent treatment versus control comparison for trials that evaluated more than one intervention, but unlike in our prior work, we did not pool small trials that had <100 participants if the disease and intervention were the same.1,34,4243–44,48,49,515253–54,6364–65 For this analysis, we excluded four RCTs that did not have at least one follow-up study visit at or before 6 months postrandomization.636465–66 The study was approved by Tufts Medical Center Institutional Review Board.
GFR
GFR was estimated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2009 creatinine equation.67 Creatinine was standardized to isotope dilution mass spectroscopy traceable reference methods using direct comparison, or was reduced by 5%, as has previously been described.68
Estimation of Acute Effects
Our primary definition of the acute treatment effect was the mean difference in the change in GFR from baseline to 3 months between the treatment and control arms. The primary analysis used analysis of covariance to estimate the acute effect while adjusting for baseline GFR as a covariate. This analysis was restricted to studies with baseline GFR and at least one planned measurement of GFR between month 3 and month 6. For patients who had measurements at 4, 5, or 6 months, the acute effect was computed at that time. As we have previously performed, we categorized the observed mean acute effect (ml/min per 1.73 m2) as very large negative (≤−2.5), moderate-to-large negative (>−2.5 to <−1.25), small negative (−1.25 to <0), small positive (>0 to <1.25), moderate-to-large positive (1.25 to <2.5), and very large positive (≥2.5).69 In a sensitivity analysis, we fitted a linear mixed effects model to GFR between months 3 and 24 of follow-up, with covariate adjustment for the baseline GFR level. This model includes fixed effects for treatment assignment, baseline GFR and the interactions of these factors with time, and random slopes and intercepts for characterizing variation in GFR trajectories across patients. For studies that had follow-up longer than 24 months, the follow-up time was truncated at 24 months to ensure the long-term trajectory did not overly influence the estimation of the acute effect. This sensitivity analysis was able to formally evaluate mean GFR change to 3 months, even if the initial GFR follow-up assessment occurred later than month 3, on the basis of the assumption of linear mean decline between months 3 and 24. With the exception of the analyses that related the size of the acute effects to baseline GFR and urinary albumin-creatinine ratio (UACR) levels (see below), acute effects were estimated on the basis of change in GFR on the linear (raw) scale and expressed in units of ml/min per 1.73 m2.
Meta-analyses
We performed separate random effects meta-analyses to model the distribution of “true” treatment effects on the acute GFR change to 3 months across all studies, and then separately for subgroups of studies on the basis of intervention type. Our random effects models assumed the acute effects were normally distributed across studies. We used these models to obtain the mean acute effect across the studies included in each analysis, with a 95% confidence interval. In addition, to assess heterogeneity of acute effects between studies, we computed 95% coverage intervals from the mean and the between-study SD of the acute effects from the random-effects meta-analysis. The coverage interval provides lower and upper limits that included 95% of the acute effects across the studies, under the assumption that the acute effects are normally distributed.
We performed separate univariable metaregression analyses to explore the effect of mean baseline GFR and median baseline UACR on the magnitude of the acute effects across studies. For these analyses, we analyzed the longitudinal GFR measurements for estimation of the acute effect on both on the linear scale and natural log scale. When the longitudinal GFR measurements were expressed on the linear scale, the acute effect is expressed in absolute units of ml/min per 1.73 m2. When the longitudinal GFR measurements were expressed on the natural log scale, the acute effect is expressed as a relative effect and expressed as a ratio of geometric mean GFR levels between the treatment and control groups. Median baseline UACR was log transformed in these analyses irrespective of whether the longitudinal GFR measurements were expressed on the raw scale or natural log scale. Baseline GFR was expressed on the linear scale when the longitudinal GFR measurements were expressed on the linear scale and on the natural log scale when the longitudinal GFR measurements were log transformed. In addition, to consider whether standard of care at the time of the study affected the magnitude of the acute effects, we also performed separate univariable metaregression analyses by year of publication.
We also performed multivariable metaregression to further assess the effect of baseline mean GFR and median UACR on acute effects after adjusting for intervention type, and diabetes status. In additional sensitivity analyses, we analyzed acute effects by quartiles of baseline GFR within individual studies to compare associations observed at the study and individual participant levels.
Analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC) and R 3.16.1 (R Project for Statistical Computing, www.r-project.org).
Results
We included 53 randomized studies enrolling 56,413 participants that had at least one visit by 6 months after randomization. Aggregate characteristics of included studies stratified by intervention are summarized in Table 1, Supplemental Table 3 (stratified by disease), and Supplemental Table 4. The median baseline UACR was higher in trials with lower mean baseline GFR, except for immunosuppression studies (Supplemental Figure 2).
Table 1. -
Clinical characteristics of the population overall and stratified by intervention
|
N studies |
N participants |
Age |
Female |
Black |
Diabetes |
GFR |
ACR |
| Overall |
53 |
56413 |
61.5 (11.2) |
22514 (37.4) |
4601 (7.6) |
45342 (75.3) |
61.8 (26.3) |
59 (13, 539) |
| Intervention |
| RASB versus control |
19 |
25157 |
61.7 (11.1) |
9964 (37.9) |
1720 (6.5) |
22650 (86.1) |
64.0 (23.8) |
83 (14, 691) |
| RASB versus CCB |
4 |
1884 |
57.6 (9.1) |
832 (36.3) |
862 (37.6) |
1520 (66.3) |
52.1 (20.5) |
836 (105, 1983) |
| BP
a
|
5 |
2438 |
51.0 (12.8) |
1157 (40.4) |
1235 (43.1) |
435 (15.2) |
55.9 (26.3) |
68 (24, 393) |
| Diet
b
|
2 |
731 |
51.8 (12.4) |
332 (39.6) |
66 (7.9) |
43 (5.1) |
34.5 (13.5) |
192 (42, 904) |
| IS
a
|
15 |
1039 |
41.7 (12.9) |
407 (33.0) |
19 (1.5) |
4 (0.3) |
72.1 (29.2) |
1557 (898, 2814) |
| Other |
8 |
25164 |
64.0 (9.1) |
9822 (36.8) |
699 (2.6) |
20690 (77.4) |
61.4 (28.3) |
30 (9, 186) |
Values for age and GFR are presented as mean (SD) and for ACR as median (25th, 75th percentile). Values for other characteristics are presented as number (%).The N participants presented here are for the primary analysis (analysis of covariance). RASB, renin angiotensin system blockers.
aBP, low versus usual BP control.
bDiet, low versus high protein diet.
cIS, various immunosuppression treatment versus control studies.
Acute Effects Overall and by Intervention
Across all studies, the mean acute effect (difference in GFR between randomized groups) was −0.21 ml/min per 1.73 m2 over the first 3 months (95% confidence interval, –0.63 to 0.22). There was substantial heterogeneity across studies; the 95% coverage interval for the acute effect across studies ranged from –2.50 to +2.08 ml/min per 1.73 m2 (Figure 1, Supplemental Table 5, Supplemental Figure 3, and Supplemental Figure 4). Results were similar in sensitivity analyses using the simple linear mixed models (Supplemental Table 5 and Supplemental Figure 5). For specific intervention types, there is evidence that RAS blockade versus calcium channel blockers (CCB), RAS blockade versus control, SGLT2 inhibitors versus placebo, and intensive BP lowering led to negative average acute effects, and that immunosuppressive agents led to a positive average acute effect (Figure 1, Supplemental Table 5, and Supplemental Figure 4), although in most cases statistical significance was not reached. The substantial variation in acute effects across different trials persisted within the individual treatment comparison classes (Supplemental Table 5 and Supplemental Figure 4), and by year of publication (Supplemental Figure 6). Heterogeneity was greatest among immunosuppression (95% coverage interval across studies, −2.34–6.29 ml/min per 1.73 m2) and renin-angiotensin receptor blockers versus CCB trials (95% coverage interval across studies, −4.28–1.07 ml/min per 1.73 m2).
;)
Figure 1.: Distribution and estimated mean acute effect on GFR by intervention. Coverage interval refers to the interval under which 95% of the studies fall. 95% CI, 95% confidence interval; RASB, renin-angiotensin receptor blockers.
Supplemental Table 6 shows the categories of large, moderate, or small observed mean acute effects overall and by intervention. Overall, moderate to large (>−2.5 to ≤−1.25 ml/min per 1.73 m2) or very large (≤−2.5 ml/min per 1.73 m2) negative acute effects were observed in 13 trials, moderate to large (≥1.25 to <2.5 ml/min per 1.73 m2) or very large (≥2.5 ml/min per 1.73 m2) positive acute effects were observed in 13 trials, and small positive or negative acute effects were observed in the remaining studies. Reflecting a combination of true variation in acute effects and random sampling error, which predominates in smaller trials, in these descriptive analyses negative acute effects were observed in four out of four studies comparing RAS blockade with CCBs, in 13 out of 23 studies comparing RAS blockade to control, in one trial comparing SGLT2 inhibitors to control, and four out of five trials of immunosuppressive agents. Positive acute effects were observed in 11 of the 15 trials of immunosuppressive agents (Supplemental Table 6).
Acute Effects by Baseline GFR
Larger negative acute effects (expressed in units of ml/min per 1.73 m2) tended to be observed in studies with higher levels of mean baseline GFR (P=0.02; Figure 2). The association between the acute effect and baseline GFR remained mostly consistent after adjustment for intervention and diabetic status at the study level (Supplemental Table 7). For trials of RAS blockade versus control, the mean acute effect (SE) varied from 0.24 (0.36) when the mean baseline GFR was 20 ml/min per 1.73 m2 compared with −1.57 (0.42), when mean baseline GFR was 80 ml/min per 1.73 m2 (Figure 3). For trials comparing low versus usual BP control the mean acute effect varied from 0.24 (0.43) when mean baseline GFR was 20 ml/min per 1.73 m2 compared with −2.25 (0.55) when mean baseline GFR was 80 ml/min per 1.73 m2 (Figure 3). There was no clear association between the acute effect and mean baseline GFR for RAS blockade versus CCB or for immunosuppression trials (Figure 3), although the wide confidence bands indicate low statistical power for these analyses. The association between the acute effect and baseline GFR did not substantively change after removing trials of immunosuppressive agents (data not shown). The effect persisted when GFR was log transformed (Supplemental Figure 7 and Supplemental Figure 8). For RAS blockade versus control, there was a similar relationship between the acute effect and baseline GFR when participants were categorized by GFR quartiles within individual studies (Supplemental Figure 9).
Figure 2.: Meta regression of acute treatment effect on GFR by baseline GFR (top panel) and UACR (bottom panel). β refers to the coefficient for the slope through the meta-regression of the association of acute effects by mean baseline level of GFR in each study and is expressed per 10 ml/min per 1.73 m2 higher levels of GFR. RASB, renin-angiotensin receptor blockers; CCB, calcium channel blockers; SGLT2, sodium-glucose cotransporter-2; UACR, urine albumin: creatinine ratio.
Figure 3.: Meta regression plot of variation in acute effect on GFR according to baseline GFR by intervention. β refers to the coefficient for the slope through the meta-regression of the association of acute effects by mean baseline level of GFR in each study and is expressed per 10 ml/min per 1.73 m2 higher levels of GFR. RASB, renin-angiotensin receptor blockers; CCB, calcium channel blockers.
Acute Effects by Baseline UACR
Acute effects were also somewhat more negative for studies with lower baseline UACR (Figure 2), although there was an attenuation of the effect after adjustment for baseline GFR (Supplemental Table 7). When stratified by intervention type, the association between the acute effect and baseline UACR was only observed in studies evaluating low versus usual BP targets, with no association observed between baseline UACR and the magnitude of acute effects for other interventions (Figure 4). Results were similar in sensitivity analyses when the acute effect was estimated on the basis of log transformed GFR (Supplemental Figure 7 and Supplemental Figure 8).
Figure 4.: Meta regression plot of variation in acute effect on GFR according to UACR by intervention. β refers to the coefficient for the slope through the meta-regression of the association of acute effects by mean baseline level of GFR in each study and is expressed per 10 ml/min per 1.73 m2 higher levels of GFR. RASB, renin-angiotensin receptor blockers; CCB, calcium channel blockers; UACR, urine albumin: creatinine ratio.
Discussion
We provide a comprehensive assessment of the magnitude and consistency of acute treatment effects on GFR, across a range of interventions and disease states in 53 RCTs evaluating treatments for CKD progression. Negative acute effects were observed in the majority of RAS blockade, BP lowering, and SGLT2 inhibitor trials, whereas positive acute effects were observed in immunosuppression trials. However, even within interventions there was substantial variability in the observed acute effects. These findings have important implications for the design of clinical trials assessing kidney disease progression, and highlight the importance of understanding the nature and magnitude of acute effects for specific interventions in early-phase trials to inform the design and analysis of longer-term studies, specifically, the decision to use a slope-based outcome or a clinical endpoint.
Negative acute effects are known to be common in interventions for CKD progression. For example, RAS blockade, SGLT2 inhibitors, and BP lowering all lead to hemodynamic changes in GFR, which are thought to differ from the long-term protective effect on the kidney. These hemodynamic effects influence the function of individual nephrons, not the number of nephrons, and are reversible on treatment discontinuation. Negative acute effects may also be a result of changes in non-GFR determinants of creatinine, as in, for example, decreased creatinine secretion by the tubules. Regardless of the mechanism, negative acute effects can increase the risk of false negative conclusions about the treatment benefit. Negative acute effects may also reduce the utility of slope-based analyses or time-to-event analyses, with endpoints defined by 30% or 40% GFR declines by eliminating power advantages of total GFR slope, or time to lesser GFR decline compared with the clinical endpoint.6970–71 In a post-hoc analysis of the CANVAS Program assessing the effect of the SGLT2 inhibitor canagliflozin on different GFR decline thresholds (i.e., 50%, 40%, and 30% declines in GFR), the power advantage of using lesser declines in GFR was only observed after excluding the negative acute hemodynamic effect of canagliflozin.72
One potential solution to overcome negative acute effects and utilize slope as an endpoint is to use chronic (rather than total) GFR slope, which computes the change in GFR slope after the acute phase. However, this approach may introduce bias from attenuation of the acute effect over time or early discontinuation of the study treatment.69,73,74 Another potential strategy is to assess GFR slope from baseline to off-treatment measures, when it is anticipated that acute and reversible hemodynamic effects will no longer be present. Ongoing trials of sparsentan and atrasentan in people with FSGS and IgA nephropathy plan to account for anticipated negative acute effects by employing both approaches: excluding the acute effect from baseline to week 6 and by assessing slope from baseline to 4 weeks off treatment.9,10 A third approach would be to leverage an active run-in period to assign different baseline GFRs to the active and control arms. In the Efficacy and Safety of Selonsertib in Participants with Moderate to Advanced DKD (MOSAIC, NCT04026165) trial, testing the effects of selonsertib (which causes a negative acute effect by inhibiting creatinine secretion) in people with diabetic kidney disease, the GFR at the beginning of the run-in period is taken as the baseline measure for placebo-treated participants, whereas the GFR at randomization is used as the baseline measure in the active treatment arm.14 Finally, artificial censoring of GFR values after treatment discontinuation while using weighting or imputation to reduce the risk of selection bias may limit bias due to reversal of acute effects after treatment discontinuation.69 As we have previously shown, if negative acute effects are relatively small (e.g., <−1.5 ml/min 1.73 m2), adequate statistical power can be achieved to detect treatment effects using total slope if there is sufficient follow-up relative to the mean rate of progression in the study population.69
We observed positive acute effects primarily in studies of immunosuppressive agents. The cause is not well understood, but it may be related to the early anti-inflammatory action of immunosuppressive agents in glomerular diseases, or the positive hemodynamic effect of steroids.75 If an intervention has a positive acute effect but leads to harm on the longer-term chronic slope, then positive acute effects could lead to false conclusions about treatment benefits if assessment of the treatment effect is performed on the total GFR slope. Bardoxolone produces an early increase in GFR, which has been hypothesized to be due to an increase in mesangial surface area in addition to its longer-term effect on reducing inflammation.76 Trials evaluating bardoxolone have computed change in GFR using off-treatment GFR values to help determine whether the positive acute effect is a false conclusion or a lasting effect of the drug on the kidney. This underscores the importance of understanding the mechanisms for earlier versus longer-term changes and designing trials accordingly.
The use of GFR slope to assess kidney disease progression may be particularly relevant to trials conducted in the early stages of CKD. The association between larger negative acute effects and higher baseline mean GFR remained significant after adjustment for intervention and diabetes status. Thus, studies seeking to use GFR slope should carefully consider the mechanism of action of an intervention, study design and population characteristics (for example, the proportions of participants recruited with different levels of GFR), to ensure efficiency, and protect against the risk of a falsely positive or negative conclusion. Additionally, trials using GFR slope as an outcome must also ensure sufficient information on safety, given the timeframes over which individuals with CKD are treated.
We observed a high degree of heterogeneity in the magnitude of acute effects, even within interventions where the nature and magnitude of the acute effect is anticipated due to well-understood mechanisms of action. The reason for this observation is unclear. Possible variation in study quality may be one explanation of the heterogeneity; indeed, there was greater heterogeneity among immunosuppressive studies, which in general were smaller, and of lower quality. Because the acute effect was defined as the difference in change in GFR between treatment and control arms, differences in the care in the control arm (i.e., placebo versus active treatment) across different trials may have also contributed to the observed heterogeneity, although accounting for year of publication did not affect the results. Future work including newer studies with more consistent standard medical care in the control arm may help to evaluate this hypothesis. In addition, as we previously demonstrated, combined assessment of changes in GFR and albuminuria may predict treatment effects and outcomes better than either alone.7 Further work will assess the utility of combined assessments of treatment effects on GFR slope and change in albuminuria that could overcome the challenges from acute effects.
This study has a number of strengths, including a systematic literature search, inclusion of a large number of studies with a diverse range of interventions, analyses of individual participant data, and the use of multiple methods to estimate acute GFR effects. However, a number of limitations should be considered when interpreting our findings. First, we estimated the acute effect at 3 months, but not all studies had measurements at this time point. For some studies, the acute effect might have occurred over a different time period and might not be linear. For example, the negative acute effect with SGLT2 inhibition calculated at 3 months is less than previously reported at 1 month, possibly due to an attenuation of the acute effect over time.77,78 The timing of an acute effect is important to appreciate due to its implications for study power; as the timing of the acute effect increases, power to detect treatment effects on total slope decreases. Second, acute effects were calculated on the basis of estimated GFR rather than measured GFR; although potentially important at an individual level, this is less likely to affect our conclusions at a population level. Third, although these analyses examined the effect of intervention type, GFR and UACR individually on the observed acute effects, effects might be multifactorial and variation in acute effects could be due to other factors not captured. We also had relatively few studies for some interventions, including trials of SGLT2 inhibitors, and had no studies with pediatric participants. Finally, despite the large number of studies and participants overall, there were an insufficient number of trials to evaluate the magnitude of acute effects for individual classes of immunosuppressive agents with different mechanisms of action and to assess more granular subgroups, which could have explained the causes of the observed heterogeneity within these groups.
In summary, the magnitude and consistency of acute effects is variable across different interventions and may be larger at higher baseline GFR. Future work will involve understanding the timing of the acute effect and the associations of the acute effects with subsequent outcomes and longer-term treatments effects. Understanding the magnitude, timing and nature of the acute effect for a specific intervention and population can help inform the optimal design of randomized trials in CKD.
Disclosures
B.L. Neuen reports having consultancy agreements with and receiving personal fees for advisory boards, scientific presentations, steering committee roles, and travel support from AstraZeneca, Bayer, and Janssen, all paid to institution. C. Wanner reports receiving personal fees from Boehringer-Ingelheim during the conduct of the study, and reports receiving personal fees from Astellas, AstraZeneca, Bayer, Chiesi, Eli-Lilly, Fresenius Medical Care, Merck Sharp & Dohme Corp., Sanofi-Genzyme, and Shire-Takeda outside the submitted work; reports having consultancy agreements with Akebia, Bayer, Boehringer-Ingelheim, Gilead, GlaxoSmothKline, Merck Sharp & Dohme Corp., Sanofi-Genzyme, Triceda, and Vifor; reports receiving research funding from Idorsia and Sanofi-Genzyme, grant to institution; and reports having other interests/relationships with the European Renal Association–European Dialysis and Transplant Association. D. Xie reports having other interests/relationships as an International Society of Nephrology member. E.F. Vonesh reports serving as a paid biostatistics consultant for the National Kidney Foundation for the express purpose of developing statistical models for use in the estimation and comparison of GFR slopes as a surrogate endpoint in CKD RCTs; reports serving as a biostatistics consultant to Prometic and Tricida, Inc., in which some of the work entailed consulting on the design and analysis of clinical trials in patients with CKD; and reports having consultancy agreements with Fresenius-Kabi, and Sarepta Therapeutics, Inc.; and reports other interests/relationships with the National Kidney Foundation. E. Imai reports receiving honoraria fees from AstraZeneca, Bayer, Boehringer-Ingelheim, Daiichi Sankyo, and Eli-Lilly; and reports receiving research funding from AstraZeneca, Boehringer Ingelheim, and Tanabe Mitsubishi. F. Fervenza reports having consultancies with Alexion, Alnylam, BioCryst, GlaxoSmithKline Morphosys, Novartis, Otsuka, and Takeda; reports receiving research funding from Chemocentryx, Genentech, Janssen Pharmaceutical, Questcor/Mallinckrodt, and Retrophin; reports receiving honoraria from UpToDate; reports being a scientific advisor or member of Journal of the American Society of Nephrology, Kidney International, Nephrology, Nephrology Dialysis and Transplantation, and Up-To-Date. F. Locatelli reports receiving personal fees from Amgen, Astellas, Bayer, Baxter, Norgine, Otzuka, Roche, and Vifor Fresenius Pharma outside of the submitted work; reports having consultancy agreements with Amgen and Baxter; reports being a scientific advisor or member of Accelsior, Amgen, Astellas, AstraZeneca, GlaxoSmithKline, Norgine, Otsuca Roche, Travere, and Vifor Pharma; reports speakers bureau from Amgen, Astellas, and Roche; and reports having other interests/relationships with European Renal Association, Italian Society of Nephrology, Dialysis Outcomes and Practice Patterns Study, and Kidney Disease: Improving Global Outcomes. F.P. Schena reports receiving a grant from the University of Bari. H.J L. Heerspink reports having consultancy agreements with AbbVie, AstraZeneca, Bayer, Boehringer-Ingelheim, CSL Pharma, Chinook, Dimerix Fresenius, Gilead, Janssen, Merck, Mundi Pharma, Mitsubishi Tanabe, NovoNordisk, and Travere Pharmaceuticals; reports receiving research funding from AbbVie, AstraZeneca, Boehringer Ingelheim, and Janssen (grant funding directed to employer); and reports speakers bureau from AstraZeneca. J.B. Lewis reports being a principal investigator in the study that contributed data to this analysis; reports current consulting fees from BIOVIE and CSL Behring; and reports being a scientific advisor or member of treasurer and board member of the Collaborative Study Group for a not-for-profit academic research organization. J. Floege reports receiving consultancy honoraria and/or speaker fees from Alnylam, Amgen, Astellas, AstraZeneca, Bayer, Calliditas, Fresenius, Omeros, Novo Nordisk, Travere, Visterra, and Vifor; and reports being a scientific advisor or member of Calliditas, Omeros, and Travere. J. Wetzels reports receiving grants from Amgen, Pfizer, and Sanofi; reports receiving other from Achillion, Shire, and Vifor Fresenius; reports having consultancy agreements with Novartis and Travere; reports receiving research funding from Alexion and Chemocentryx; reports receiving honoraria from Travere and UptoDate; reports being a scientific advisor or member of Kidney International; reports speakers bureau from Novartis; and reports having other interests/relationships with an Advisor Patient organization in the Netherlands. L.A. Inker reports receiving funding to institute for research and contracts with the National Institutes of Health, National Kidney Foundation, Reata Pharmaceuticals, Retrophin, Omeros, and Travere; reports having consulting agreements with Diamtrix, Omeros Corp., and Tricida Inc.; reports being a scientific advisor or member of Alport's Foundation, Diametrix, and Goldfinch; reports having other interests/relationships as a member of the American Society of Nephrology, National Kidney Disease Education Program, and National Kidney Foundation. M. Goicoechea reports receiving consultancy honorary and/or speaker fees from Alexion, Amgen, Astellas, AstraZeneca, Genzyme, Menarini, Novo-Nordisk, and Vifor. M. Praga reports receiving consultancy honoraria and/or speaker fees from Alexion, AstraZeneca, Novartis, Silence, and Travere; and reports receiving research funding from Amgen, Astellas, and Novartis. M. Woodward reports receiving personal fees from Amgen, Freeline, and Kyowa Kirin. P.K.T. Li reports receiving speaker honorarium (personal fees) from AstraZeneca, Baxter, and FibroGen; reports having consultancy agreements with (2019) Fibrogen, Co-Chair of advisory board Treatment of Anemia in Peritoneal, Dialysis Patients What is the Asia Pacific Experience; reports being a scientific advisor or member of Kidney International, Peritoneal Dialysis International, Co-Chair for Fibrogen Advisory Board Meeting on Anemia in Peritoneal Dialysis Patients (2019); and reports having other interests/relationships as Immediate Past President of Asian Pacific Society of Nephrology, President of International Association of Chinese Nephrologists, and President of Hong Kong College of Physicians. R.D. Perrone reports receiving personal fees from KluwersWolter, Otsuka, Palladiobio, Sanofi-Genzyme, and UpToDate; reports receiving research funding from Kadmon, Otsuka, Palladiobio, Reata, and Sanofi-Genzyme outside of the submitted work; reports having consultancy agreements with Caraway, Navitor, Otsuka, Palladiobio, Reata, and Sanofi-Genzyme; reports being a scientific advisor or member of Otsuka, PalladioBio, Sanofi-Genzyme, and Up-to-Date; and reports having other interests/relationships with the Polycystic Kidney Disease Foundation and UpToDate. T. Greene reports receiving grants from the National Kidney Foundation during the conduct of the study; reports receiving personal fees from DURECT Corporation, Janssen Pharmaceuticals, and Pfizer Inc. outside the submitted work; reports having consultancy agreements with AstraZeneca, Invokana, Janssen Pharmaceuticals, Pfizer Inc., and Novartis; and reports receiving research funding from AstraZeneca, Boehringer-Ingelheim, CSL, and Vertex. T.M. Chan reports receiving grants from Astellas, Baxter, and Kyowa Kirin; and reports having consultancy agreements with GSK, Novartis, and Visterra. W. Herrington reports being supported by a Medical Research Council Kidney Research UK Professor David Kerr Clinician Scientist Award, and reports receiving institutional grants from Boehringer-Ingelheim, British Heart Foundation, Eli-Lilly, and Health Data Research (UK); reports adhering to a department policy not to accept honoraria or other payments from the pharmaceutical industry, expect for the reimbursement of costs to participate in scientific meetings; and reports being a scientific advisor or member of Clinical Kidney Journal subject editor and the Nephrology Dialysis and Transplantation. All remaining authors have nothing to disclose.
Funding
The study was funded by the National Kidney Foundation.
Acknowledgments
T. Greene, H. Heerspink, L. Inker, and E. Vonesh conceived of the study and design; T.M. Chan, F. C. Fervenza, J. Floege, M. Goicoechea, W. Herrington, E. Imai, T. Jafar, J. Lewis, P. Li, F. Locatelli, B. Maes, R. Perrone, M. Praga, A. Perna, F. Schena, C. Wanner, J. Wetzels, M. Woodward, and D. Xie and their collaborators acquired the data; J. Chaudhari, T. Greene, S. Miao, H. Tighiouart, and E. Vonesh analyzed the data; all authors took part in the interpretation of the data; T. Greene, L. Inker, and B. Neuen drafted the manuscript; all authors provided critical revisions of the manuscript for important intellectual content; all collaborators who shared data were given the opportunity to comment on the manuscript; T. Greene, H. Heerspink, and L. Inker obtained funding for CKD-EPI, and the individual cohort support is listed in Supplemental Appendix 1. We thank all investigators, study teams, and patients for participating in the studies included in the meta-analysis. Study acronyms are listed in Supplemental Appendix 2 along with other abbreviations. A variety of sources have supported the RCTs included in the CKD-EPI Clinical Trials. These fundingsources include government agencies, such as the National Institutes of Health and medical research councils, and foundations and industry sponsors listed in Supplemental Appendix 1.
Supplemental Material
This article contains the following supplemental material online at http://jasn.asnjournals.org/lookup/suppl/doi:10.1681/ASN.2021070948/-/DCSupplemental.
Supplemental Appendix 1. Abbreviations, units, and terms.
Supplemental Appendix 2. Study funding sources.
Supplemental Table 1. Search terms.
Supplemental Table 2. Study inclusion criteria.
Supplemental Table 3. Clinical characteristics of the population overall and stratified by disease.
Supplemental Table 4. Patient characteristics by study.
Supplemental Table 5. Estimated acute effects using different methods to compute the acute effect on GFR, overall, and by intervention and disease.
Supplemental Table 6. Magnitude of acute effects on GFR, by intervention.
Supplemental Table 7. Multivariable metaregression of acute effects on GFR, both for GFR and ACR.
Supplemental Figure 1. Evaluation of bias in studies included in meta-analysis.
Supplemental Figure 2. Mean baseline GFR and median baseline UACR across studies.
Supplemental Figure 3. Distribution and estimated mean acute effect on GFR by disease.
Supplemental Figure 4. Forest plot of acute effect on GFR by intervention, all studies, ANCOVA method.
Supplemental Figure 5. Forest plot of acute effects on GFR by intervention, all studies, sensitivity analysis using the linear mixed model.
Supplemental Figure 6. Variation in acute effect on GFR by year of study publication.
Supplemental Figure 7. Meta regression plot of variation in acute effect by (A) baseline natural log-transformed eGFR and (B) natural log acute effect by UACR.
Supplemental Figure 8. Meta regression plot of variation in acute effect by intervention by (A) baseline natural log-transformed eGFR and (B) natural log acute effect by UACR.
Supplemental Figure 9. Variation in acute effect on GFR by within-study GFR quartiles, by intervention .
Supplemental References.
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