
==== Front
Contemp Clin Trials Commun
Contemp Clin Trials Commun
Contemporary Clinical Trials Communications
2451-8654
Elsevier

S2451-8654(24)00094-2
10.1016/j.conctc.2024.101347
101347
Article
Assessing core outcome set uptake in randomized controlled trials for chronic kidney disease: Cross-sectional analysis
Hagood Alex alexhagoodresearch@gmail.com
a⁎
Corwin Logan Patrick logan.corwin@okstate.edu
a
Modi Jay S. jay.modi@okstate.edu
a
Jones Garrett A. garrett.jones11@okstate.edu
a
Fitzgerald Kyle J. kyle.fitzgerald@okstate.edu
a
Magana Kimberly J. kimberly.magana@okstate.edu
a
Ward Shaelyn A. shaelyn.ward@okstate.edu
a
Magee Trevor R. trevor.magee@okstate.edu
a
Hughes Griffin K. griffinhughesresearch@gmail.com
a
Ford Alicia Ito alicia.ford@okstate.edu
ab
Vassar Matt matt.vassar@okstate.edu
ab
a Office of Medical Student Research, Oklahoma State University Center for Health Sciences, Tulsa, OK, USA
b Department of Psychiatry and Behavioral Sciences, Oklahoma State University Center for Health Sciences, Tulsa, OK, USA
⁎ Corresponding author. Oklahoma State University Center for Health Sciences, 1111 W 17th St., Tulsa, OK, 74107, USA. alexhagoodresearch@gmail.com
15 8 2024
10 2024
15 8 2024
41 1013471 1 2024
27 6 2024
11 8 2024
© 2024 The Authors. Published by Elsevier Inc.
2024

https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Main problem

Chronic kidney disease (CKD) is a progressive condition that affects millions of people worldwide. A standardized core outcome set (COS) was developed for CKD by the International Consortium for Health Outcomes and Measurements in 2019. This study aims to evaluate the frequency of measurement for these outcomes before and after the publication of the COS.

Methods

A literature search was done to gather the phase III/IV clinical trials evaluating chronic kidney disease through ClinicalTrials.gov. Data extraction of included studies was completed in a masked, duplicate fashion. The included studies were evaluated for characteristics such as survival, burden of disease, patient-reported health-related quality of life, and treatment modality-specific outcomes.

Results

Our results showed that the majority of all COS domains were inadequately measured in CKD clinical trials before and after publication of the COS. Despite the increase in COS measurements following publication, the average percent of COS outcomes measured was less than 40 % per year even after four years.

Conclusion

There is a notable deficiency in the complete measurement of COS among all domains both before and after COS publication. We suggest efforts be made to improve the adoption of consistent outcome measures that would benefit the growing population of patients affected by CKD.

Keywords

Core Out Come Set
Uptake
RCT
==== Body
pmc1 Introduction

Chronic kidney disease (CKD) is a progressive condition seen in more than 800 million people worldwide, and is projected to become the fifth most common chronic disease by 2040 [1,2]. Patients with CKD are subject to aggressive monitoring, involving routine visits with specialists and intrusive treatments such as dialysis and possible kidney transplant in later stages [3,4]. Constant monitoring and treatment can have significant effects on quality of life and economic standing of patients with CKD. For example, Kefale et al. found declining physical functioning, vitality, and social functioning in all stages of CKD [5]. Furthermore, in 2018, the global estimated cost of CKD was 79 billion dollars and is expected to increase to 95 billion dollars by the end of 2023 [6]. Societal costs continue to rise due to CKD patients commonly having a wide array of complications, such as cardiovascular events, hyperlipidemia, anemia, and others [7].Given the increasing prevalence, financial burden, and complications associated with CKD, consistent measurement of outcomes within evidence-based trials is essential to guide clinical decision-making and improve patient care [8].

Randomized controlled trials (RCTs) are a means of studying the efficacy and safety of new treatments and are among the highest forms of evidence to which physicians look when making clinical decisions [9]. However, when comparing results across different treatment studies, researchers have found that heterogeneity in the outcomes measured can make it difficult or impossible to draw clinically meaningful comparisons [10,11]. Such a lack of standardization of outcomes hinders the comparability and generalizability of RCT findings, thus diminishing the usefulness of the research [12].Several measures have been taken to mitigate inconsistent outcome measuring, one of which is the development of core outcome sets (COS).

A COS is a standardized, minimum set of outcomes that all trials in a specific field should measure [12]. Standardization of outcome measurements help facilitate meta-analyses, minimize risk of reporting bias, and allows for the collection of results that are valuable to the treatment and care of patients [13]. In 2019, the International Consortium for Health Outcomes and Measurements (ICHOM) developed a standardized COS for use in CKD clinical trials through consultation of patients, physicians, and researchers [14]. The use of the ICHOM COS enables trial endpoints to be assessed, monitored, and compared consistently over time, thus improving the quality of CKD care internationally [14]. However, there has yet to be a study analyzing adherence to the uptake of COS in CKD by clinical trialists. Therefore, the aim of this study is to assess the completeness with which clinical trials for CKD measure the outcomes detailed in the COS.

2 Methods

2.1 Reproducibility and study Design

Before conducting the study, we performed a pilot test of the search strategies, inclusion criteria, and data extraction materials. To ensure comprehensive reporting of our data, we adhered to the PRISMA 2020 checklist. We followed a comparable approach to Kirkham et al., as they conducted a previous study looking at the core outcome set uptake in rheumatoid arthritis [15]. Our methodology was strictly followed and made publicly available on Open Science Framework (OSF) prior to the start of the study to ensure replicability [16]. Our study was submitted to the Institutional Review Board for proper determination status, and was determined to not involve human participants.

2.2 Search string

The Core Outcome Measures in Effectiveness Trials (COMET) Initiative is an organization that houses a database which was created to enhance the reproducibility of clinical trials by compiling COSs [17]. Using the COMET initiative database, the COS published by Verberne et al. in March of 2019 was identified and selected for uptake analysis [14].The search was conducted on June 26, 2023. Following Kirkham et al.’s methodology, only phase III/IV clinical trials were included [15]. To identify phase III/IV RCTs associated with CKD, the ClinicalTrials.gov database, an electronic clinical trial registry, was used. ClinicalTrials.gov automatically searches for synonyms of a certain condition or disease. We have included these synonyms in Supplemental File 1. When using the ClinicalTrials.gov database, the following filters were applied: "conditions: chronic kidney disease", "study type: interventional studies", "phase: 3 and 4″, “date: January 03, 2014 to 06/26/2023,” and no restrictions regarding recruitment status were applied. We obtained data five years prior to the publication of our COS to allow for an adequate baseline of outcome measures.

2.3 Training

Prior to screening and extraction, all investigators received thorough training on the purposes and methodology of COS uptake. Training on COS consisted of COMET Initiative tutorial video presentations [18], a subsequent review of the COMET Initiative handbook [19] and comprehensive group discussion.

2.4 Screening/eligibility criteria

The inclusion criteria for this study included the following characteristics: subjects are adult patients with CKD and/or complications of CKD, study was registered five years prior to publication of the COS to June 26, 2023, and assessed the effectiveness or efficacy of interventions. Trials that do not meet these criteria, such as those not exclusively focused on CKD or complications of CKD, non-randomized trials, those focused on diagnostic test accuracy, those focused on drug pharmacokinetics or pharmacodynamics, those measuring side effects due to medications, and single-group assignment trials, were excluded from the study. The RCTs identified from the comprehensive search were compiled into a Google Sheet. In the initial screening, two authors (AH, LC) independently evaluated the clinical trial registry search results to assess for inclusion within the study. All screening was performed in a masked and duplicate manner. Upon completion of the clinical trial registry screening, both authors convened to reconcile decisions regarding study inclusion/exclusion. If a consensus regarding the inclusion/exclusion of a study could not be reached, a third party (JM) was consulted.

2.5 Data extraction

For each eligible trial, general study characteristics and COS data was extracted in a masked, duplicate fashion by two investigators (AH, LC), using a pilot-tested Google Form. The following general characteristics were recorded for each RCT: year of trial start date, if before/after publication of COS, National Clinical Trial number, trial continent affiliation(s), phase of trial, recruitment status, funding type, enrollment number, trial duration, and type of intervention. Specific outcomes related to survival, disease burden, quality of life, and treatment modality–as defined by the authors of the COS [14]–were also extracted; the full list can be found in Table 1. Additionally, if available, the timing and method for each collected outcome measure were recorded in the Google Form. In the event trialists use an established screening instrument (i.e. Short Form Survey-36, RAND-36, etc.), authors consulted the referenced scale to determine if the appropriate core outcomes were included in the screening instrument. For standardization, data was independently extracted from the first five trials in the sample by two separate investigators and any inconsistencies were resolved through discussion. A third investigator (JM) was available if necessary. Investigators then completed the rest of the data extraction within the sample.Table 1 Trial characteristics.

Table 1Characteristic	N = 198	
Year, n (%)	
 2015	29 (14.7)	
 2016	29 (14.7)	
 2018	25 (12.6)	
 2017	22 (11.1)	
 2022	22 (11.1)	
 2019	20 (10.1)	
 2014	14 (7.1)	
 2021	14 (7.1)	
 2020	13 (6.6)	
 2023	10 (5.1)	
Phase, n (%)	
 3	132 (66.7)	
 4	66 (33.3)	
Continent, n (%)	
 Asia	61 (30.8)	
 North America	51 (25.8)	
 Multiple	31 (15.7)	
 Europe	29 (14.6)	
 Not Listed	11 (5.6)	
 Africa	9 (4.5)	
 South America	5 (2.5)	
 Australia	1 (0.5)	
Recruitment Status, n (%)	
 Completed	97 (49.0)	
 Recruiting	36 (18.2)	
 Unknown	26 (13.1)	
 Active, but Not Recruiting	14 (7.1)	
 Not Yet Recruiting	8 (4.0)	
 Terminated	7 (3.5)	
 Enrolling by Invitation	5 (2.5)	
 Withdrawn	5 (2.5)	
Funding Type, n (%)	
 Industry	90 (45.5)	
 University	34 (17.2)	
 Multiple With Industry	25 (12.6)	
 Hospital	20 (10.1)	
 Multiple Without Industry	19 (9.6)	
 Private	6 (3.0)	
 Government	4 (2.0)	
Enrollment Number, Median (IQR)	198 (68–391)	
 Unknown	2	
Trial Duration in Months, Median (IQR)	28 (18–49)	
Type of Intervention, n (%)	
 Multiple	82 (41.4)	
 Drugs Related to CKD Complications	49 (24.7)	
 Conservative Care	31 (15.7)	
 Drugs with HD	11 (5.6)	
 Drugs with KT	10 (5.1)	
 Behavioral Health	6 (3.0)	
 Drugs with PD	5 (2.5)	
 Other	3 (1.5)	
 Hemodialysis	1 (0.5)	

2.6 Data analysis

For our primary analysis, we evaluated the uptake of the COS using an interrupted time series analysis. Regardless of trial status, the authors evaluated the percentage of registered trials that intended to measure the COS. The trial characteristics in our sample were evaluated using descriptive statistics. To assess overall uptake, each trial was first evaluated for percentage of COS adherence, which is the number of COS outcomes measured relative to the total possible number of outcomes; we describe these as “COS-defined outcomes”. From those percentages of adherence, we derived a mean percentage completion score by month for all trials published in that particular month. This model uses the slopes of the pre and post publication trials to determine if the difference between the two are significant. We ran a second analysis using one-way ANOVAs, which evaluated the effect that ‘Continent’, ‘Funding Type’, and ‘Recruitment Status’ had on the variation of “COS-defined outcomes”. Additionally, a correlation analysis was conducted to assess the relationship between ‘Enrollment Number’ and ‘Trial Duration (Months)’ and the percent completion of outcomes measured. Analyses were completed using Stata/BE 17.0 (StataCorp, LLC, College Station, TX), R (version 4.2.1) and RStudio. All original data, final reconciled data, and statistical analysis approaches have been uploaded to OSF.

To systematically assess the overlap between outcomes reported in clinical trials and those specified in the COS, we evaluated trials following the release of the COS in March 2019 for outcomes of exact and partial matches. Exact matches were defined as exactly meeting the criteria as defined in the COS. A partial match was defined as outcomes that were conceptually similar to the COS outcomes but differed slightly. Additionally, we conducted an overlap analysis to evaluate the alignment between the timing recommended by the COS and those employed in clinical trials. These were defined as ‘matches timing’ and ‘does not match timing’. Furthermore, a variance analysis was done to investigate the relationship between types of interventions and uptake of COS. Interventions included drugs, multiple modality, conservative care, behavioral health, and hemodialysis. Additionally, an analysis was done to compare the uptake of COS and the income status of the country of trial completion. Income status for each trial was categorized as ‘High-Income’, ‘High-Middle-Income’, ‘Low-Middle-Income’, or ‘Low-Income’ based on the World Bank Income Classification list [20].

3 Results

3.1 Trial inclusions and exclusions

Our initial search string from ClinicalTrials.gov returned 4507 trials for potential inclusion. Trials were first excluded for being the wrong trial phase or for being outside of the search dates, which left 444 studies. Further studies were then excluded for having the wrong topic, disease, population, focus or being non-randomized. The final inclusion number for our study was 198 trials. All reasons for exclusion can be seen in Fig. 1.Fig. 1 Flow diagram of study selection.

Fig. 1

3.2 Trial characteristics

The top three interventions used among the included clinical trials were multiple modality (41.4 %, 82/198), drugs related to CKD complications (24.7 %, 49/198), and conservative care at (15.7 %, 31/198). ‘Multiple’ modality encompassed the use of more than one intervention in a clinical trial and ‘conservative care’ was defined as the treatment of CKD without dialysis. The least-reported singular intervention was hemodialysis at 0.5 % (1/198). Additional characteristics that were assessed for included trials can be seen in Table 1.

3.3 Analysis of COS uptake

In March 2014, five years prior to the COS publication, 16.53 % of trialists measured outcomes from the COS. From March 2014 to March 2019, there was a non-significant increase of approximately 0.01 % (p = 0.96, 95 % CI = [−0.22, 0.23]) in “COS-defined outcome” measurements per month. Subsequently, there was a slight non-significant, 0.59 % (p = 0.06, 95 % CI = [−0.32, 1.21]) decrease in the monthly trend of completion outcome measures relative to the pre-COS trend. After the publication of the CKD COS, there was a statistically significant overall increase of 0.59 % (p = 0.04, 95 % CI = [0.02, 1.17]) in “COS-defined outcome” measurements. Fig. 2 illustrates these findings, with a representation of a one-year grace period to allow for the uptake of the COS by CKD clinical trialists. Despite the increase in COS measurements following publication, the average percent of COS outcomes measured was less than 40 % per year even after four years (Fig. 3). Additionally, ‘survival’, ‘burden of disease’, and ‘patient reported outcomes for health related quality of life (HRQoL)’ were assessed against all trials while treatment specific outcomes were analyzed against the specific modality. The core outcome domains that were evaluated for every trial were shown to be under measured as seen in Table 2. The patient reported HRQoL domain included the subgroups of ‘pain’, ‘fatigue’, ‘physical function’, ‘depression’, ‘daily activity’, and ‘overall HRQoL’. The most frequently measured HRQoL outcome was ‘physical function’ at 19.7 % (39/198), while ‘fatigue’ had the lowest incidence of measurement at 14.6 % (29/198). The treatment-specific COS domain continued the trend of an overall lack of outcome measuring with ‘bacteremia’ being measured by only 4.7 % (5/106) of trials. However, the ‘kidney allograft function’ for the transplant treatment modality demonstrated the highest incidence of measurement out of the entire COS at 78.6 % (11/14).Fig. 2 Interrupted time series analysis demonstrating percent of the COS measured before and after COS publication.

Fig. 2

Fig. 3 Mean percentage of outcomes measured by year.

Fig. 3

Table 2 Frequency of measurements of core outcomes in chronic kidney disease RCTs.

Table 2Domain	Outcome Set Item	N = 198	
Survival	Survival, n (%)		
No	152 (76.8)	
Yes	46 (23.2)	
Burden of Disease	Hospitalization, n (%)		
No	159 (80.3)	
Yes	39 (19.7)	
CV Events, n (%)		
No	166 (83.8)	
Yes	32 (16.2)	
Patient-Reported Outcomes for HRQoL	HRQoL, n (%)		
No	161 (81.3)	
Yes	37 (18.7)	
Pain, n (%)		
No	165 (83.3)	
Yes	33 (16.7)	
Fatigue, n (%)		
No	169 (85.4)	
Yes	29 (14.6)	
Physical Function, n (%)		
No	159 (80.3)	
Yes	39 (19.7)	
Depression, n (%)		
No	166 (83.8)	
Yes	32 (16.2)	
Daily Activity, n (%)		
No	163 (82.3)	
Yes	35 (17.7)	
Treatment Modality–Specific Outcomes	Kidney Function, n (%)		
No	57 (59.4)	
Yes	39 (40.6)	
Not Applicable	102	
Albuminuria, n (%)		
No	82 (78.1)	
Yes	23 (21.9)	
Not Applicable	93	
Bacteremia, n (%)		
No	101 (95.3)	
Yes	5 (4.7)	
Not Applicable	92	
Vascular Access Survival, n (%)		
No	81 (91.0)	
Yes	8 (9.0)r	
Not Applicable	109	
PD Modality Survival, n (%)		
No	26 (92.9)	
Yes	2 (7.1)	
Not Applicable	170	
Peritonitis, n (%)		
No	25 (89.3)	
Yes	3 (10.7)	
Not Applicable	170	
Kidney Allograft Function, n (%)		
No	3 (21.4)	
Yes	11 (78.6)	
Not Applicable	184	
Kidney Allograft Survival, n (%)		
No	8 (57.1)	
Yes	6 (42.9)	
Not Applicable	184	
Acute Rejection, n (%)		
No	8 (57.1)	
Yes	6 (42.9)	
Not Applicable	184	
Malignancies, n (%)		
No	13 (92.9)	
Yes	1 (7.1)	
Not Applicable	184	

3.4 Relationship between trial characteristics and outcome measurements

A one-way ANOVA indicated a statistically significant association between where the trial was conducted and the COS outcome measurements. In the analysis of variance, ‘Continent’ explained approximately 9 % (F = 2.67, p = 0.01, η2 = 0.09) of the month-to-month variation in “COS-defined outcomes'' measurements. ‘Funding Type’ (F = 1.55, p = 0.16, η2 = 0.05) was statistically non-significant. Additionally, we conducted a Pearson correlation analysis between ‘Enrollment Number’ (r = 0.29, t = 4.25, p=<0.001) and ‘Trail Duration (Months)’ (r = 0.23, t = 3.30, p = 0.001) against the COS outcome measurements, finding a statistically significant, positive correlation (Table 3).Table 3 ANOVA and Pearson Result: Association between trial characteristics and COS outcomes.

Table 3Characteristic	N = 198a	F-statisticb	p-valueb	(ηb)b	
Continent		2.67	0.01	0.09	
 Africa	11.11 (18.06)				
 Asia	14.86 (21.62)				
 Australia	58.82 (NA)				
 Europe	23.47 (29.61)				
 Multiple	30.39 (24.92)				
 North America	12.43 (18.96)				
 Not Listed	19.56 (22.73)				
 South America	17.79 (30.98)				
Funding Type		1.55	0.16	0.05	
 Government	6.12 (7.52)				
 Hospital	13.80 (23.08)				
 Industry	18.78 (24.05)				
 Multiple With Industry	20.32 (22.47)				
 Multiple Without Industry	30.95 (30.71)				
 Private	10.72 (13.57)				
 University	13.95 (20.43)				
Recruitment Status		0.16	>0.99	5.71x10−3	
 Active, but Not Recruiting	18.93 (14.75)				
 Completed	18.13 (24.98)				
 Enrolling by Invitation	25.80 (21.33)				
 Not Listed	18.11 (25.68)				
 Not Yet Recruiting	23.86 (33.29)				
 Recruiting	16.87 (21.42)				
 Terminated	16.88 (26.47)				
 Withdrawn	17.12 (15.92)				
Income Status of Countries		3.43	0.01	0.07	
 High	16.12 (23.25)				
 Lower Middle	8.24 (15.20)				
 Mixed	31.49 (25.53)				
 Not Listed	18.79 (23.81)				
 Upper Middle	17.16 (22.95)				
Type of Intervention		1.25	0.27	0.05	
 Behavioral Health	39.39 (30.24)				
 Conservative Care	20.55 (23.16)				
 Drugs Related to CKD Complications	21.89 (28.44)				
 Drugs with HD	15.03 (21.55)				
 Drugs with KT	19.84 (12.77)				
 Drugs with PD	23.33 (34.05)				
 Hemodialysis	0.00 (NA)				
 Multiple	14.10 (20.66)				
 Other	14.39 (12.92)				
Characteristic	t-statisticc	p-valuec	(r)c	
Trial Duration (Months)	3.30	0.001	0.23	
Enrollment Number	4.25	<0.001	0.29	
a Mean (SD).

b One-way ANOVA, η2.

c Pearson Correlation Coefficient.

3.5 Variance analysis of income status and interventions

A variance analysis was also performed on ‘Income Status of Countries’ and ‘Type of Interventions’. The analysis revealed that the ‘Income status of Countries’ had a statistically significant impact of 7 % (F = 3.43, p = 0.01, η2 = 0.07) on the month-to-month variance in the COS measurements. Trials conducted in countries with ‘Mixed’ income statuses had the highest mean COS outcomes (M = 31.40, SD = 25.53) while those in ‘Lower Middle’ income countries had the lowest (M = 8.24, SD = 15.20). Interestingly, ‘Upper Middle’ (M = 17.16, SD = 22.95) income status had higher mean COS measurements than ‘High’ (M = 16.12, SD = 23.25) income status. In contrast, the ‘Type of Interventions’ variable explained 5 % (F = 1.25, p = 0.27, η2 = 0.05) of the overall variance in COS measurements. The intervention, ‘Behavioral Health’ had the highest mean COS outcomes (M = 39.39, SD = 30.24). In contrast, the intervention ‘Hemodialysis’ (M = 0.00, SD=N/A), which was represented by only one trial in our sample, had the lowest mean COS outcome (M = 0.00, SD not available).

3.6 Exact vs partial matches

The evaluation between exact and partial matches of COS outcomes revealed significant discrepancies. For outcomes involving ‘Survival’, ‘Hospitalization’, and ‘Cardiovascular Events’, no trials achieved an exact match with the COS. Specifically, ‘Survival’ outcomes had 26.3 % of trials showing a partial match and 73.7 % not measuring the outcome at all.

The outcomes with the highest rate of exact matches included ‘Kidney Allograft Survival’, ‘Kidney Function’, and ‘Albuminuria’ with 66.7 % (4/6), 53.1 % (17/32), and 36.1 % (13/36) respectively. ‘Bacteremia’, ‘Acute Rejection’, and ‘Malignancies’ all had a not measured rate of 100 %. HRQoL and pain outcomes had exact matches of 2.6 %, with partial matches of 13.2 % and 11.8 %, respectively. Fatigue outcomes had a slightly higher exact match rate at 3.9 % (3/76), with 5.3 % (4/76) being partial matches and 90.8 % (69/76) not being measured. Peritoneal dialysis patients showed a 15.4 % (2/13) exact match rate for both ‘PD Modality Survival’ and ‘Peritonitis’ outcomes. Kidney transplant patients had varying results with ‘Kidney Allograft Function’ having exact match rates of 66.7 % (4/6), ‘Kidney Allograft Survival’ of 33.3 % (2/6) (Table 4).Table 4 Exact and partial matches of COS outcomes.

Table 4Outcome	Exact Match	Partial Match	Did not measure	
Survival	0 % (0/76)	26.3 % (20/76)	73.7 % (56/76)	
Hospitalization	0 % (0/13)	17.1 % (13/76)	82.9 % (63/76)	
CV Events	0 % (0/76)	15.8 % (12/76)	84.2 % (64/76)	
HRQoL	2.6 % (2/76)	13.2 % (10/76)	84.2 % (64/76)	
Quality of Pain	2.6 % (2/76)	11.8 % (9/76)	85.5 % (65/76)	
Fatigue	3.9 % (3/76)	5.3 % (4/76)	90.8 % (69/76)	
Physical Function	3.9 %(3/76)	10.5 % (8/76)	85.6 % (65/76)	
Depression	2.6 % (2/76)	10.5 % (8/76)	86.8 % (66/76)	
Daily Activity	3.9 % (3/76)	10.5 % (8/76)	85.6 % (65/76)	
Pre-ESKD and Conservative care patients only:
Kidney Function (eGFR and/or SCr)	53.1 % (17/32)	3.1 % (1/32)	43.8 % (14/32)	
Pre-ESKD, KT, and Conservative care patients only:
Albuminuria	36.1 % (13/36)	0 % (0/36)	63.9 % (23/36)	
HD, PD, and KT patients only:
Bacteremia (positive blood culture with clinical signs)	0 % (0/45)	0 % (0/45)	100 % (45/45)	
HD patients only: vascular access survival	5.3 % (2/38)	0 % (0/38)	94.7 % (36/38)	
PD patients only:
PD modality survival	15.4 % (2/13)	0 % (0/13)	84.6 % (11/13)	
PD patients only: peritonitis	15.4 % (2/13)	0 % (0/13)	84.6 % (11/13)	
KT patients only:
Kidney allograft function (eGFR and/or SCr)	66.7 % (4/6)	0 % (0/6)	33.3 % (2/6)	
KT patients only:
Kidney allograft survival	33.3 % (2/6)	0 % (0/6)	66.7 % (4/6)	
KT patients only: acute rejection	0 % (0/6)	0 % (0/6)	100 % (6/6)	
KT patients only: malignancies	0 % (0/6)	0 % (0/6)	100 % (6/6)	

3.7 Timing of COS measurements

The comparison of timing between trial outcomes and the standard COS outcome set revealed that, on average, 14.71 % of trials did not align with the COS timing, while only 4.63 % of trials did align with the timing. Of the outcomes that matched timing, ‘Kidney Allograft Survival’ had the highest match at 16.6 % (1/6) followed by ‘Survival’ at 14.5 % (11/76). Timing was not measured for the outcomes ‘Bacteremia’, ‘Acute Rejection’, and ‘Malignancies’ in any of the trials. Timing was unmeasured in 80.65 % of the outcomes assessed (Table 5).Table 5 Comparison of timing between trial outcomes and standard COS outcomes.

Table 5Outcome	Matches Timing	Does Not Match Timing	Did not measure	
Survival	14.5 % (11/76)	11.8 % (9/76)	73.7 % (56/76)	
Hospitalization	1.3 % (1/76)	15.8 % (12/76)	82.9 % (63/76)	
CV Events	2.6 % (2/76)	11.8 % (9/76)	85.6 % (65/76)	
HRQoL	3.9 % (3/76)	11.8 % (9/76)	84.2 % (64/76)	
Quality of Pain	3.9 % (3/76)	10.5 % (8/76)	85.6 % (65/76)	
Fatigue	5.3 % (4/76)	3.9 % (3/76)	90.8 % (69/76)	
Physical Function	3.9 % (3/76)	10.5 % (8/76)	85.5 % (65/76)	
Depression	3.9 % (3/76)	9.2 % (7/76)	86.8 % (66/76)	
Daily Activity	3.9 % (3/76)	10.5 % (8/76)	85.5 % (65/76)	
Pre-ESKD and Conservative care patients only:
Kidney Function (eGFR and/or SCr)	9.3 % (3/32)	46.9 % (15/32)	43.8 % (14/32)	
Pre-ESKD, KT, and Conservative care patients only:
Albuminuria	11.1 % (4/36)	25.0 % (9/36)	63.9 % (23/36)	
HD, PD, and KT patients only:
Bacteremia (positive blood culture with clinical signs)	0.0 % (0/45)	0 % (0/45)	100.0 % (45/45)	
HD patients only: vascular access survival	0.0 % (0/38)	5.3 % (2/38)	94.7 % (36/38)	
PD patients only:
PD modality survival	0.0 % (0/13)	15.4 % (2/13)	84.6 % (11/13)	
PD patients only: peritonitis	7.7 % (1/13)	7.7 % (1/13)	84.6 % (11/13)	
KT patients only:
Kidney allograft function (eGFR and/or SCr)	0.0 % (0/6)	66.7 % (4/6)	33.3 % (2/6)	
KT patients only:
Kidney allograft survival	16.6 % (1/6)	16.6 % (1/6)	66.7 % (4/6)	
KT patients only: acute rejection	0.0 % (0/6)	0.0 % (0/6)	100 % (6/6)	
KT patients only: malignancies	0.0 % (0/6)	0.0 % (0/6)	100 % (6/6)	
Average	4.63 %	14.71 %	80.65 %	

4 Discussion

Our study found that there was a statistically significant increase in COS measurement within CKD clinical trials after the publication of the ICHOM COS in March of 2019. However, despite the statistical improvement, the average percent of COS outcomes measured was less than 40 % per year, even after four years. Additionally, we found that there was a significant discrepancy in the accuracy of clinical trial measurement in regards to an exact match vs. partial match of the COS guidelines. The majority of trials completed after the ICHOM COS publication did not measure any of the non-treatment specific recommendations. These findings demonstrate inadequate measuring of the ICHOM COS and emphasize a need to improve compliance with the standardized measurements. Hughes et al. suggests that the lack of patients and other key stakeholders in the development process, along with insufficient use of validated measures and poor awareness of the COS, are potential barriers to COS adoption [21]. Because the specific outcomes of the ICHOM COS were developed by relevant stakeholders, improvement efforts should focus on promoting familiarity with the COS among CKD researchers and clinicians and training on use of the recommended measurement tools [[10], [11]].

The implementation of a standardized COS for patients affected by CKD is essential, as this population is at risk for negative HRQoL when compared to the general population. Our findings highlight inadequate measurement of the HRQoL within clinical trials of kidney disease and associated treatment modalities. The ICHOM COS recommends the assessment tools Short Form Survey-36, RAND-36, and a combination of PROMIS and PROMIS-36 for the evaluation of patient-reported HRQoL. Within our sample, approximately one-fourth of the studies used the Kidney Disease Quality of Life (KDQOL) questionnaire–which is not recommended for use by the COS–as their method to assess quality of life. Of the studies that used KDQoL, half of the trials were registered after the COS publication. In 2020, a study found that evaluating HRQoL is a valuable method to predict mortality and morbidity in patients with CKD [22]. Additionally, the ICHOM states that the measurement of HRQoL outcomes rank highest in importance among patients [14]. Considering its significance to both survival and patient satisfaction, future measurements of HRQoL should be optimized.

Additionally, within the ICHOM COS the measurements of survival, burden of disease, and patient reported HRQoL are listed as Tier 1 essential outcomes measurements for all clinical trials. Our findings highlight the lack of measurement and completeness pertaining to these outcomes within clinical trials completed after the publication of the ICHOM COS. The majority of clinical trials did not assess these outcomes and of the trials that did, less than 5 % were exact matches to the COS guidelines. This lack of reporting and accuracy may lead to a lack of generalizability of results and hinder the comparison of clinical trial findings to formulate clinical practice guidelines that are valuable to the stakeholders of CKD.

Another critical outcome measurement category within the ICHOM COS are the outcomes for specific treatment modalities, including kidney transplantation, hemodialysis, and peritoneal dialysis. Over 2 million people worldwide are currently treated with dialysis and the increasing prevalence of CKD correlates to an increase in kidney transplant patients [23,24] Patients within each treatment modality have unique adverse events compared to pre-CKD and ‘conservative care’ patients [25,26,27,28] The outcomes for specific treatments demonstrated a consistent lack of measurement. For example, in hemodialysis patients, the incidence of ‘bacteremia’ was measured only 4.7 % of the time. Such lack of outcome data for specific treatment modalities impedes the comparability between different clinical trial outcomes and weakens evidence synthesis for clinician use [29].

4.1 Strength and Limitations

Our study had several notable strengths. First, the extraction of data was performed in a masked and duplicate fashion to avoid bias and enhance the validity of our findings. Second, to promote transparency and reproducibility, we uploaded our data to OSF. Finally, we retrieved our studies from ClinicalTrials.gov which is a comprehensive registry that offers a vast range of clinical trials, resulting in a robust collection of studies for our sample. To minimize the potential for error, our data extraction was performed in masked, duplicate fashion and had a third author to resolve disputes. However, our sample may not represent all clinical trials on CKD as we extracted only from ClinicalTrials.gov and included clinical trials from five years before the publication of our COS.

5 Translational statement

Our study emphasizes the global effect chronic kidney disease (CKD) has on patients and the impact randomized clinical trials (RCT) have on physicians making clinical decisions. Inconsistent measurements of RCT outcomes makes it difficult to draw clinically meaningful comparisons of data and diminishes research quality. Measuring a minimum standardized outcome set for CKD RCTs enables trial endpoints to be compared, thus improving the quality of CKD care internationally.

6 Conclusion

Our study highlights the importance of implementing the ICHOM COS in CKD clinical trials. We found that there is a notable deficiency in the complete COS measurement before and after the COS publication date among all domains. Despite the increase in COS measurements following publication, the average percent of COS outcomes measured was less than 40 % per year even after four years. We suggest efforts be made to improve the adoption of consistent outcome measures that would benefit the growing population of patients affected by CKD.

Funding

This study was not funded.

CRediT authorship contribution statement

Alex Hagood: Writing – review & editing, Writing – original draft, Methodology, Investigation, Conceptualization. Logan Patrick Corwin: Writing – review & editing, Writing – original draft, Data curation, Conceptualization. Jay S. Modi: Writing – review & editing, Visualization, Methodology, Investigation. Garrett A. Jones: Writing – review & editing, Project administration, Methodology, Conceptualization. Kyle J. Fitzgerald: Validation, Supervision, Software, Methodology. Kimberly J. Magana: Writing – review & editing, Supervision, Methodology. Shaelyn A. Ward: Writing – review & editing, Visualization, Validation, Supervision, Methodology. Trevor R. Magee: Writing – review & editing, Validation, Supervision, Software, Project administration, Methodology, Investigation, Data curation. Griffin K. Hughes: Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology. Alicia Ito Ford: Visualization, Validation, Supervision, Software, Resources, Project administration. Matt Vassar: Writing – review & editing, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.

Declaration of competing interest

MV reports receipt of funding from the 10.13039/100000026 National Institute on Drug Abuse , the 10.13039/100000027 National Institute on Alcohol Abuse and Alcoholism , the U.S. Office of Research Integrity, Oklahoma Center for Advancement of Science and Technology, and internal grants from 10.13039/100019803 Oklahoma State University Center for Health Sciences — all outside of the present work. AF reports receipt of funding from the Center for Integrative Research on Childhood Adversity, the Oklahoma Shared Clinical and Translational Resources, and internal grants from 10.13039/100007069 Oklahoma State University and 10.13039/100019803 Oklahoma State University Center for Health Sciences — all outside of the present work. All other authors have nothing to report.

Appendix A Supplementary data

The following are the Supplementary data to this article.Multimedia component 1

Multimedia component 1

Multimedia component 2

Multimedia component 2

Data availability

All data has been uploaded to Open Science Framework

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.conctc.2024.101347.
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