Available via license: CC BY-NC-ND 4.0
Content may be subject to copyright.
A CKD Clinical Decision Support System: A Cluster
Randomized Clinical Trial in Primary Care Clinics
JoAnn Sperl-Hillen, A. Lauren Crain, James B. Wetmore, Lilian N. Chumba, and
Patrick J. O’Connor
Rationale & Objective: The study aimed to
develop, implement, and evaluate a clinical deci-
sion support (CDS) system for chronic kidney
disease (CKD) in a primary care setting, with the
goal of improving CKD care in adults.
Study Design: This was a cluster randomized trial.
Setting & Participants: A total of 32 Midwestern
primary care clinics were randomly assigned to
either receive usual care or CKD-CDS intervention.
Between April 2019 and March 2020, we enrolled
6,420 patients aged 18-75 years with laboratory-
defined glomerular filtration rate categories of
CKD Stage G3 and G4, and 1 or more of 6 CKD
care gaps: absence of a CKD diagnosis,
suboptimal blood pressure or glycated hemoglobin
levels, indication for angiotensin-converting enzyme
inhibitor or angiotensin receptor blocker but not
prescribed, a nonsteroidal anti-inflammatory agent
on the active medication list, or indication for a
nephrology referral.
Intervention: The CKD-CDS provided
personalized suggestions for CKD care
improvement opportunities directed to both
patients and clinicians at primary care encounters.
Outcomes: Weassessedtheproportionofpa-
tients meeting each of 6 CKD-CDS quality
metrics representing care gap resolution after
18 months.
Results: The adjusted proportions of patients
meeting quality metrics in CKD-CDS versus usual
care were as follows: CKD diagnosis
documented (26.6%vs 21.8%; risk ratio [RR],
1.17; 95%CI, 0.91-1.51); angiotensin-converting
enzyme inhibitor or angiotensin receptor blocker
prescribed (15.9%vs 16.1%; RR, 0.95; 95%CI,
0.76-1.18); blood pressure control (20.4%vs
20.2%; RR, 0.98; 95%CI, 0.84-1.15); glycated
hemoglobin level control (21.4%vs 22.1%;RR,
1.00; 95%CI, 0.80-1.24); nonsteroidal anti-
inflammatory agent not on the active medication
list (51.5%vs 50.4%; RR, 1.03; 95%CI, 0.90-
1.17); and referral or visit to a nephrologist
(38.7%vs 36.1%; RR, 1.02; 95%CI, 0.79-1.32).
Limitations: We encountered an overall reduction
in expected primary care encounters and obstacles
to point-of-care CKD-CDS utilization because of
the coronavirus disease 2019 pandemic.
Conclusions: The CKD-CDS intervention did not
lead to a significant improvement in CKD quality
metrics. The challenges to CDS use during the
coronavirus disease 2019 pandemic likely
influenced these results.
Funding: National Institute of Diabetes and
Digestive and Kidney Diseases (R18DK118463).
Trial Registration: clinicaltrials.gov Identifier:
NCT03890588.
Primary care clinicians play a crucial role in caring for
adults with chronic kidney disease (CKD). Effective care
by primary care clinicians is essential to mitigate the world-
wide burden of CKD.
1,2
Substantial evidence exists that CKD
progression can often be delayed or prevented by optimal
control of blood pressure (BP) and glycated hemoglobin
(A1C),
3
use of medications like angiotensin-converting
enzyme inhibitors (ACEis) or angiotensin receptor blockers
(ARBs),
3
and avoiding nonsteroidal anti-inflammatory drugs
(NSAIDs). Furthermore, referrals to nephrologists are rec-
ommended for individuals with advanced CKD.
2-4
However, many patients with CKD in glomerular filtra-
tion rate (GFR) categories G3 or G4 (GFR of 15-59 mL/
min/1.73 m
2
) do not receive the recommended guideline-
based care.
3-9
Preliminary data for this study showed that
nearly two-thirds of these patients had BP equal to or
exceeding 130/80 mm Hg, half of those with diabetes had
an A1C of 7% or higher, and only 1 in 4 had undergone
albuminuria testing within the past year. For those with
hypertension or albuminuria (without hyperkalemia), only
about half were prescribed an ACEi or ARB. Moreover, less
than half of patients with CKD stages G3bA2, G3bA3, or G4
had seen a nephrologist within the last 2 years.
The deficiencies in CKD care are more prevalent when
patients with laboratory-confirmed CKD and/or their cli-
nicians are unaware of the CKD diagnosis.
10
Data from a
2018 Veterans Affairs CKD national surveillance study
showed that more than half of patients with CKD based on
GFR values had no diagnostic codes for CKD.
11
National
health and nutrition examination survey data indicated no
significant improvement in patients with CKD awareness
between 1999 and 2016.
12,13
Primary care clinicians frequently cite various barriers
to delivering optimal CKD care, including incomplete
knowledge of CKD guidelines, challenges in translating
guidelines into practice, competing clinical demands,
difficulties in care coordination, and a low prioritization of
CKD care quality perhaps because of the lack of CKD-
specific quality measures.
14-16
In this context, there is
reason to believe that deploying well-designed clinical
Complete author and article
information provided before
the references.
Correspondence to J. Sperl-
Hillen (ann.m.harste@
healthpartners.com)
Kidney Med. 6(3):100777.
Published online December
12, 2023.
doi: 10.1016/
j.xkme.2023.100777
©
2023 The Authors.
Published by Elsevier Inc.
on behalf of the National
Kidney Foundation, Inc. This
is an open access article
under the CC BY-NC-ND
license (http://
creativecommons.org/
licenses/by-nc-nd/4.0/).
Kidney Med Vol 6 | Iss 3 | March 2024 | 100777 1
Original Research
decision support (CDS) during primary care encounters for
patients having laboratory evidence of CKD could enhance
recognition and management.
16
In previous work we developed, implemented, and
evaluated primary care CDS systems that were aimed at
improving uncontrolled cardiometabolic conditions, such
as diabetes and hypertension. The results of clinic ran-
domized trials for these earlier CDS systems demonstrated
significant improvement in glycemic and BP control,
17
reduced 10-year cardiovascular risk,
18,19
improved BP
recognition in adolescents,
20
and cost effectiveness.
21,22
Clinician surveys during these trials demonstrate high
levels of primary care clinician satisfaction with the CDS
system.
18
Given the positive outcomes of CDS for other
chronic diseases, there was great potential for the CKD-
CDS to improve CKD care management in the same pri-
mary care setting using a similar implementation strategy.
This trial faced unforeseen challenges because of dis-
ruptions caused by the coronavirus disease 2019 (COVID-
19) pandemic, such as a sharp decrease in office-based
clinical encounters, clinic closures, and the sudden wide-
spread use of virtual encounters. These challenges and
necessary study adaptations have been previously docu-
mented.
23
Recognizing this limitation, we report the re-
sults of this NIH-funded project to develop, implement,
and rigorously evaluate a CKD-specific primary care CDS
intervention in a cluster randomized trial. The goal was to
improve CKD detection and care using a CDS system that
minimized disruption to clinic workflows.
17-19
METHODS
Hypothesis, Study Design, and Study Site
This cluster randomized trial aimed to assess whether
implementing the CKD-CDS intervention in primary care
would improve CKD recognition by primary care clinicians
and improve essential aspects of evidence-based CKD care.
The study took place in 32 primary care clinics within a
47-clinic multispecialty care system across Minnesota and
Wisconsin. Clinics were selected based on specific criteria:
they needed a sufficient number of adults with CKD GFR
categories G3 and G4 (GFR of 15-59 mL/min/1.73 m
2
),
use EpiCare electronic health record (EHR) software,
already use the CDS system for cardiovascular disease and
diabetes, and be within a 30-mile radius of 1 of the 2
nephrologist subspecialty care groups associated with the
care system, which also documented their encounters
within the same EHR. Referrals to nephrology outside of
these 2 nephrologist specialty groups were rare in these
clinics.
The study employed a clinic cluster randomized trial
design, with 32 eligible clinics randomized into either
CKD-CDS intervention or the usual care group (Fig 1). The
CKD-CDS intervention occurred during primary care en-
counters of eligible patients in intervention clinics over 12
months, with outcome assessment conducted 18 months
after their index visit. With almost no staff crossover be-
tween clinics, the risk of clinician contamination in the
usual care group was minimal with the clinic randomized
design.
Randomization Procedure
Eligible clinics were randomized in a 1:1 ratio to either
receive the CKD-CDS intervention or usual care using
covariate constrained restricted randomization.
24
This
computerized process balanced clinic characteristics that
could affect the intervention or its implementation. Five
clinic covariates were balanced across treatment groups,
including care delivery system affiliation; participation
in a concurrent clinical trial on CDS to improve medi-
cation adherence, the number of patients with CKD in
the year preceding randomization, the proportion of
patients with Medicaid health insurance coverage; and
scores on the care system’s quality measure for hyper-
tension. The study team was blinded during the
randomization process, but after randomization, the
study team could not be blinded to allocation because of
staff training and implementation requirements at the
intervention clinics.
Study Participants
Patients aged 18-75 years with GFR laboratory evidence of
CKD stage G3 (GFR of 30-59 mL/min/1.73 m
2
on the
most recent GFR in the last 5 years and confirmed with
GFR 15-59 mL/min/1.73 m
2
on the next most recent
GFR) or CKD stage G4 (GFR 15-29 mL/min/1.73 m
2
on
the most recent GFR result in the last 5 years) were
identified at clinic encounters. The index visit was defined
as the first visit at a randomized clinic after the CDS
intervention go-live date when a patient who met these
GFR criteria also had evidence of 1 of the 6 CKD care gaps
defined in Table 1. Patients with evidence of kidney failure
PLAIN-LANGUAGE SUMMARY
This study aimed to improve the management of
chronic kidney disease (CKD) through a clinical deci-
sion support (CDS) system. It involved 32 primary care
clinics and 6,420 patients with CKD who had 1 or more
of 6 CKD care improvement opportunities. The CDS
provided personalized suggestions to both patients and
clinicians about CKD care opportunities during primary
care visits. After 18 months, the study found no sig-
nificant differences between patients in clinics with
CKD-CDS compared with usual care in diagnosing CKD,
prescribing recommended medications, controlling
blood pressure or glycated hemoglobin, nonsteroidal
anti-inflammatory agent usage, or nephrology referrals.
The coronavirus disease 2019 pandemic may have
influenced results by introducing unforeseen imple-
mentation challenges, reduced visits, and less than ex-
pected CDS exposure.
Sperl-Hillen et al
2Kidney Med Vol 6 | Iss 3 | March 2024 | 100777
(kidney failure diagnosis, receiving dialysis, after kidney
transplant, or GFR of <15 mL/min/1.73 m
2
), recent
pregnancy, active cancer, or use of hospice or palliative
care did not qualify for an index visit at that encounter.
Study patients were accrued between April 2019 and
March 2020, with an 18-month observation period
beginning on their index date. Patients were assigned to
the study arm of the clinic where their index visit
occurred.
Protection of Human Participants
Study procedures were reviewed in advance, approved,
and monitored by the HealthPartners institutional review
board (17-353). The institutional review board granted a
waiver of written informed consent for primary care cli-
nicians and patients because CDS was limited to evidence-
based recommendations included in national guidelines.
Intervention
The CKD-CDS intervention comprised 3 main features:
exchange and evaluation of EHR data at every primary care
encounter to identify CKD study–eligible patients with care
gaps and generate evidence-based personalized care
suggestions; provision of these CDS-generated care sug-
gestions on printed interfaces to both primary care clini-
cians and patients at clinical encounters; and display of the
CDS-generated suggestions within the EHR with facilita-
tion of clinician actions through quick orders for care
suggestions involving test ordering (eg, creatinine/GFR
and albumin-to-creatinine ratio [ACR]), prescribing
medications (eg, ACEi or ARB), or referrals to nephrology.
A description of the development process, technology,
and security process for the CKD-CDS has been previously
documented.
23
The patient interface listed kidney health as
a priority to encourage discussion about CKD with their
primary care clinician (see example in Fig 2). The primary
care clinician interface and EHR display listed the patient’s
individualized care gaps and offered treatment suggestions
using algorithms that incorporated laboratory data, medi-
cations, comorbid conditions, allergies, and other treat-
ment considerations (see example in Fig 3). The CKD-CDS
was incorporated into a larger CDS system already in place
within the care system that included CDS for diabetes and
cardiovascular risk factors such as hypertension, lipids,
smoking, and obesity. An important aspect of the stan-
dardized CDS workflow in all clinics was reliance on
Primary Care clinics assessed for eligibility (n =47)
Eligible clinics (n =32)
Paents idenfied
with CKDs tage G3
and G4 at index
(n = 85 05)
Clinics excluded (n = 15):
Too fe w CKD pa en ts (n = 13 ),
Outside the metro/ poor acce ss to nephrology (n =2)
Paents idenfied
with CKD stage G3
and G4 at index
(n = 90 09)
Post-index visits per paent
total: median =2, P
10
=0, P
90
=6
CDS eligible: median =1, P
10
=0, P
90
=5
CDS printed: median 0, P
10
=0, P
90
=3
Total number analyzed (16 clinics, 2988 paents):
Analyzed per care gap (n, percent of total):
CKD diagnosis (n =1568, 52%),
ACEI/ARB indicate d(n =95 2, 32%),
BP > 130/80 (n= 1633, 55%),
A1C >7% (n= 659, 22%),
NSAID use (n= 520, 17%),
Nephrology referral indicate d (n = 238, 8%)
Total number analyzed (16 clinics, 3432 paents):
Ana ly zed per ca re g ap (n, pe rce nt of to ta l):
CKD diagnosis (n =1783, 52%),
ACEI/ARB indicated (n = 1192, 35%),
BP > 130/80 (n =1917, 56%)
A1C >7% (n= 719, 21%)
NSAID use (n= 583, 17%),
Nephrology referral indicated (n =263, 8%)
Enrollment
Randomizaon
Follow Up
Analysis
CKD-CDS intervenon (n = 16 clinics)
Usual Care (n =16 clinics)
Paent s excluded
(n= 5517):
Age < 20 or >75 (n=
3211), nursing
home/hospice/kidne y
failure (n= 343), no
CK D c ar e ga p (n = 19 63)
Total Paents Accrued (n =2988)
clinic median =134, clinic range = 58-818
Total Paents Accrued (n =3432)
cl in ic m ed ia n = 1 88 , cl in ic r an ge = 8 1-4 32
Paent s excluded
(n= 5517):
Age < 20 or >75 (n=
3171), nursing
home/hospice/kidne y
failure (n= 360), no
CK D ca re gap (n = 20 46)
Po st -ind ex vis it s p er pa en t
total: median =2, P
10
=0, P
90
=6
CDS eligible: median =1, P
10
=0, P
90
=5
CDS printed: median 0, P
10
=0, P
90
=0
Accrual
Figure 1. CONSORT flow diagram.
Kidney Med Vol 6 | Iss 3 | March 2024 | 100777 3
Sperl-Hillen et al
rooming staff (the staff who typically prepare a patient for
a primary care visit and obtain vital signs) to open and
print the patient and clinician interfaces at the beginning of
the encounter using a best practice alert programmed to
appear on the EHR screen for targeted patients within
seconds of a BP entry. The rooming staff could open and
print the patient and clinician interface with only 1 click
on a URL link embedded in the best practice alert. The
rooming staff handed the patient version to the patients to
review while they were waiting to be seen.
Data Collection
The CKD-CDS system collected data from the EHR during
all visits of study-eligible patients across all randomized
clinics. Data elements included demographics, vitals,
medications, comorbid conditions, and laboratory data
from 2 years preceding each visit. Data elements for
calculating study outcomes were collected from EHR
production tables and CDS web service analytic tables over
the 18 months following the index visit. Missing labora-
tory values, vital signs, or medications were interpreted as
care processes or tests not performed or medication not
prescribed rather than missing values.
Statistical Analysis
Each of the 6 quality metric outcomes representing care
gap resolution was analyzed separately using data from
patients with each care gap at their index visit. Intent-to-
treat analyses modeled the binary outcomes for all pa-
tients, regardless of the number of postindex visits or the
clinic location in the subsequent 18 months. A generalized
linear mixed model (GzLMM) with a binomial distribu-
tion, log link function, and random clinic intercept were
used to account for the intraclass correlation (ICC) among
patients within clinics. The primary predictor was a fixed
treatment group indicator with covariates for balancing
variables. RRs and 2-sided 95% confidence intervals (CIs)
are presented to characterize treatment effects. Unadjusted
linear mixed models were used to compare patient char-
acteristics at index by treatment group.
Secondary analyses were conducted for each CKD
quality metric using data from patients who accrued into
the study early, up to September 13, 2019. These patients
had at least 6 months of follow-up before the COVID-
19–related clinic disruptions that began on March 13,
2020. These models followed the same specifications as
the primary analysis but with a smaller sample.
Additional secondary analyses were performed using
data from patients with any of the CKD-specific care gaps
at index. These models predicted binary outcomes that
indicated fewer or no remaining CKD care gaps at 18
months after index, with a fixed predictor for the number
of CKD care gaps at index. The models were otherwise
specified similarly to the primary analysis.
A priori power analyses estimated the minimum
detectable difference for each study outcome based on
Table 1. The Six CKD Quality Metric Outcomes—Definitions and Eligibility Requirements
CKD Quality Metric
Definition of Care Gap Identified at
Index that was Required for Each
Outcome Denominator
Quality Metric Definition—Evaluated
Through 18 Mo After Index
CKD recognized A diagnosis of CKD is not identified on
the problem list or at more than one
encounter diagnoses in the previous 2 y
(ICD10 N18.3 or higher or N19)
CKD diagnosis code assigned at an
outpatient encounter, or the entry of CKD
diagnosis on the problem list, from index
through 18 mo after index
ACEi or ARB
prescribed
No ACEi or ARB on the active
medication list when indicated for either
diagnosed hypertension or urine ACR ≥
30 mg/g, and GFR ≥30 mL/min/1.73 m2,
and no hyperkalemia in the last year
Prescription for an ACEi or ARB
medication in the 18 mo after index
Blood pressure at goal BPs at the current and most recent visit ≥
130/80 mm Hg
Mean of the last 2 systolic BPs <130
mm Hg and mean of the last 2 diastolic
BP’s<80 mm Hg in the 18 mo after
index using outpatient office BP
measurements
A1C at goal Diagnosed diabetes and most recent
A1C in the last 12 mo ≥7. 0 %
Last A1C value in the 18 mo after
index <7. 0 %
NSAID not identified
on
the active medication
list
≥1 NSAID medications (other than
aspirin) on the active medication list
No NSAID medications (other than
aspirin) on the active medication list at
last visit in 18 mo after index
Nephrology referral or
visit completed
No nephrology visit in the last 12 mo for
patients with G4 CKD (GFR 15-29 mL/
min/1.73 m2), or G3bA2 CKD (GFR 30-
44 mL/min/1.73 m2with ACR ≥30 mg/g),
or A3 (ACR ≥300 mg/g)
Referral or consult order to nephrology or
a nephrology visit identified in the EHR in
the 18 mo after index
Abbreviations: ACEi, angiotensin-converting enzyme inhibitor; ACR, albumin-to-creatinine ratio; ARB, angiotensin receptor blocker; BP, blood pressure; CKD, chronic
kidney disease; EHR, electronic health record; G FR, glomerular filtration rate; NSAID, nonsteroidal anti-inflammatory drugs.
4Kidney Med Vol 6 | Iss 3 | March 2024 | 100777
Sperl-Hillen et al
sample size, event rates, and ICC estimated from pilot data.
The study was powered (80%, 2-sided α=0.05) to detect
clinically meaningful between-group differences of at least
10% (BP control, ACEi or ARB use, and glucose control) or
20% (CKD recognition and nephrology referral).
RESULTS
Characteristics of Study-Eligible Patients
Table 2 shows the characteristics of the study sample
consisting of 6,420 patients with laboratory-defined CKD
who had an index visit with at least one identified care gap.
At the index visit, the mean age was 66.1 years, 56.4%
were female, and 84.1% were White. Among them, 72.4%
had CKD stage G3a, 22.2% had CKD stage G3b, and 5.5%
had CKD stage G4. Patients in CKD-CDS clinics had slightly
higher diastolic BP (DBP) at index (CKD-CDS mean BP =
77.4 mm Hg, usual care mean BP =76.4 mm Hg), and,
among those with a hypertension diagnosis, 60.6% in
CKD-CDS versus 57.1% in usual care were prescribed an
ACEi or ARB. More patients with CKD-CDS had an ACR test
value at index (CKD-CDS 47.9% and usual care 44.3%). No
other statistically significant treatment group differences
were observed in patient characteristics or in the propor-
tion of patients eligible for each care gap analysis. Table 3
displays the number and percent of eligible study patients
with each of the 6 care gaps at index. The number eligible
for each care gap analysis ranged from 501 (nephrology
referral) to 3,551 (BP control).
Main Outcomes
Table 4 presents the main outcomes of the trial. The
greatest improvement in quality metric was observed for
CKD recognition, in which 26.6% of CKD-CDS and 21.8%
of usual care patients had a CKD diagnosis documented in
the EHR within 18 months after index (risk ratio [RR],
1.17; 95% CI, 0.91-1.51), although this was not statisti-
cally significant (P=0.21). None of the CKD quality
Figure 2. Patient version of clinical decision support-chronic kidney disease interface.
Kidney Med Vol 6 | Iss 3 | March 2024 | 100777 5
Sperl-Hillen et al
metric improvements were clinically or statistically sig-
nificant. In absolute terms, patients in the CKD-CDS group,
relative to usual care, had higher percentages of NSAIDs
not identified on the active medication list (51.5% vs
50.4%) and nephrology referral or visits (38.7% vs
36.1%), but lower percentages of orders for ACEi or ARBs
(15.9% vs 16.1%) and lower percentage with BP less than
130/80 mm Hg.
Postindex Patient Visit Patterns
Fig 4 illustrates the pattern of intervention-eligible visits.
Of note, the care system began restricting in-person pri-
mary care visits in March 2020, resulting in a sharp
decrease in the number of CKD-CDS eligible postindex
visits. Postindex visits reached their nadir in April 2020
and largely returned to prepandemic levels as of June
2020. The print rates of the CDS interfaces for eligible
patient encounters were about 67% in CKD-CDS clinics in
the months before March 2020. The CKD-CDS interface
and print capabilities were disabled in March 2020 because
of major clinic disruptions and restored in August 2020
with added functionality to view interfaces in video visits.
The print rates improved but did not return to prepandemic
levels after the intervention was restored. Fig 4 also dem-
onstrates some crossover contamination of usual care pa-
tients who were seen in CKD-CDS clinics for postindex visits
(2.6%). After August 2020, the CKD-CDS was programmed
not to display for usual care patients at CKD-CDS clinics and
that risk of contamination was essentially eliminated.
Secondary Analysis
Table 5 summarizes the results of secondary analyses,
estimating CKD-CDS effectiveness among the 3,833 pa-
tients with at least 6 months of follow-up before the
COVID-19 pandemic clinic disruptions. Adjusted RRs for
CKD diagnosis (RR, 1.16; 95% CI, 0.90-1.50), ACEi or
ARB orders (RR, 0.93; 95% CI, 0.71-1.22), and NSAID not
on the active medication list (RR, 1.05; 95% CI, 0.88-
1.24) were similar to those of the primary analysis.
Adjusted RRs for BP <130/80 mm Hg (RR =1.06),
Figure 3. Clinician version of clinical decision support-chronic kidney disease interface.
6Kidney Med Vol 6 | Iss 3 | March 2024 | 100777
Sperl-Hillen et al
A1C <7% (RR =1.11), and nephrology referral (RR =
1.19) among early enrollees were more favorable than
those observed in the whole sample, but for all outcomes,
confidence limits spanned unity. We did not make direct
comparisons between early and late enrollees.
Among the 4,079 patients who had at least 1 CKD-
specific care gap at index, a higher proportion of those
in the CKD-CDS had fewer remaining care gaps at 18
months after index (RR, 1.24; 95% CI, 0.92-1.68) or no
(RR, 1.21; 95% CI, 0.87-1.68) (Table 6). However, the
estimated CKD-CDS effectiveness for the composite care
gap outcome, and the individual care gaps, did not reach
statistical significance even when limited to the smaller
sample of patients with at least 6 months of follow-up
Table 2. Index Visit Characteristics of Patients with CKD and at Least 1 Care Gap by Treatment Group
Usual Care CKD-CDS All P
Study eligible N 3,432 2,988 6,420
Male n (%) 1,445 (42.1) 1,353 (45.3) 2,798 (43.6) 0.06
Female n (%) 1,987 (57.9) 1,635 (54.7) 3,622 (56.4)
Age (y) Mean ±SD 66.1 ±7.9 66.2 ±7.6 66.1 ±7.8 0.69
Native American, Alaskan n (%) 16 (0.5) 12 (0.4) 28 (0.4) 0.71
Asian n (%) 142 (4.1) 119 (4.0) 261 (4.1) 0.63
Black, African American n (%) 260 (7.6) 278 (9.3) 538 (8.4) 0.28
Hawaiian, Pacific Islander n (%) 7 (0.2) 3 (0.1) 10 (0.2) 0.29
White n (%) 2,903 (84.6) 2,496 (83.5) 5,399 (84.1) 0.26
Other, Unknown, Multiple n (%) 104 (3.0) 80 (2.7) 184 (2.9) 0.93
Hispanic, Latino n (%) 77 (2.2) 48 (1.6) 125 (1.9) 0.34
not Hispanic, Latino n (%) 3,355 (97.8) 2,940 (98.4) 6,295 (98.1)
GFR category for CKD
G3a, GFR 45-59 n (%) 2,472 (72.0) 2,173 (72.7) 4,645 (72.4) 0.74
G3b, GFR 30-44 n (%) 764 (22.3) 659 (22.1) 1,423 (22.2) 0.99
G4, GFR 15-29 n (%) 196 (5.7) 156 (5.2) 352 (5.5) 0.55
Diabetes
Diabetes diagnosed n (%) 1,276 (37.2) 1,139 (38.1) 2,415 (37.6) 0.48
A1C documented n (%) 1,273 (99.8) 1,131 (99.3) 2,404 (99.5) 0.13
A1C M (SD) 7.6 (3.8) 7.5 (1.5) 7.5 (3.0) 0.33
Median 7.2 7.2 7.2
A1C ≥8%n(%) 369 (29.0) 313 (27.7) 682 (28.4) 0.60
A1C <8%n(%) 904 (71.0) 818 (72.3) 1,722 (71.6)
Hypertension
Hypertension diagnosed n (%) 2,928 (85.3) 2,512 (84.1) 5,440 (84.7) 0.72
Index SBP M (SD) 134.2 (17.3) 134.4 (17.4) 134.0 (17.4) 0.27
Index DBP M (SD) 76.4 (11.9) 77.4 (11.9) 76.9 (11.9) 0.02
BP greater or equal to
140/90 mm Hg
n(
%) 904 (30.9) 795 (31.6) 1,699 (31.2) 0.09
ACR category
Urine ACR documented n (%) 1,519 (44.3) 1,430 (47.9) 2,949 (45.9) 0.03
A2 or A3, (urine ACR ≥30) n (%) 698 (20.3) 680 (22.8) 1,378 (21.5) 0.19
A1, urine ACR <30 n (%) 821 (23.9) 750 (25.1) 1,571 (24.5)
Medications identified on the active
medication list at index visit
Antihypertensive n (%) 2,932 (85.4) 2,542 (85.1) 5,474 (85.3) 0.91
Lipid n (%) 2,262 (65.9) 2,008 (67.2) 4,270 (66.5) 0.36
Aspirin n (%) 1,996 (58.2) 1,771 (59.3) 3,767 (58.7) 0.80
Glucose, oral n (%) 866 (25.2) 794 (26.6) 1,660 (25.9) 0.58
Glucose, insulin n (%) 511 (14.9) 462 (15.5) 973 (15.2) 0.86
NSAID n (%) 636 (18.5) 565 (18.9) 1,201 (18.7) 0.80
ACEi or ARB n (%) 1,720 (50.1) 1,575 (52.7) 3,295 (51.3) 0.08
ACEi or ARB, if diagnosed
hypertension
n(
%) 1,672 (57.1) 1,523 (60.6) 3,195 (58.7) 0.05
ACEi or ARB, if proteinuria n (%) 465 (66.6) 483 (71.0) 948 (68.8) 0.18
Abbreviations: A1C, glycated hemoglobin; ACEi, angiotensin-converting enzyme inhibitor; ACR, albumin-to-creatinine ratio; ARB, angiotensin receptor blocker; BP,
blood pressure; CDS, clinical decision support; CKD, chronic kidney disease; DBP, diastolic blood pressure; GFR, glomerular filtration rate; NSAID, nonsteroidal anti-
inflammatory drug, SBP, systolic blood pressure.
Kidney Med Vol 6 | Iss 3 | March 2024 | 100777 7
Sperl-Hillen et al
before the COVID-19 pandemic clinic disruptions (fewer
gaps RR, 1.27; 95% CI, 0.94-1.70; no gaps RR, 1.25; 95%
CI, 0.89-1.75).
DISCUSSION
The substantial gaps in care management for patients with
CKD have been well established. Our study sought to
address this issue by integrating CKD decision support into
a pre-existing CDS system primarily aimed at managing
cardiovascular risk factors and diabetes. The CDS system
previously demonstrated significant improvements in pa-
tient outcomes in NIH-funded randomized trials,
providing a solid basis for our hypothesis that CKD-specific
CDS could similarly improve CKD care outcomes within
the same clinical setting. The results of the study yield
several key insights.
First, the CKD-CDS intervention did not lead to signif-
icant improvements in the quality metrics associated with
CKD care, encompassing CKD recognition, BP and A1C
control, ACEi or ARB use, NSAID use, or nephrology re-
ferrals. Unfortunately, other studies of CKD-CDS alone
have also failed to significantly improve the care of patients
with kidney disease.
25-29
For example, a clustered ran-
domized trial of 30 clinical practices using EHRs showed
that CKD-CDS plus practice facilitation intervention
significantly improved the primary outcome of annualized
GFR decline compared with the control group that
received CKD-CDS alone.
27
The study was limited by an
imbalance of higher dropout of control practices. For
another example, an electronic decision support system for
CKD and hypertension in a primary care environment with
and without pharmacist counseling may have increased
provider awareness of CKD but did not improve the pri-
mary outcome of the BP control.
29
Another intervention
by Samal et al
28
that aimed to increase nephrology referrals
paradoxically decreased nephrology referral rates. These
studies, along with ours, highlighted important challenges
to evaluating CDS interventions in real-world settings
including low CDS use rates with noninterruptive point-
of-care CDS, clinician contamination with patient
randomized trials involving primary care clinician-based
interventions, small sample sizes, and the potential need
to include both patients and clinicians in the CDS process.
Our study intended to overcome the limitations of previ-
ous studies by achieving high CDS use rates, employing
clinic randomization to avoid contamination, a large
sample size, and including both patient and clinician-
directed CDS. However, our intervention did not bring
about significant changes in CKD care even though we had
seen positive results with similar interventions for dia-
betes, hypertension, and cardiovascular risk. The findings
suggest that the mere deployment of CKD-CDS many not
suffice to drive substantial changes in CKD care. Perhaps
there are unique challenges to CKD-CDS that should not be
underestimated. It is important that future studies incor-
porate evaluations of patient perceptions, values, self-
determination (the right for patients to refuse treatment
options), and cultural and ethnic preferences for how
health information is divulged.
COVID-19 introduced unforeseen challenges, such as
reduced in-person visits and decreased intervention
Table 3. Care Gaps Identified at Index Visit
Usual Care
(n =3,432)
CKD-CDS
(2,988)
All
(6,420) P
CKD not recognized n(
%) 1,783 (52.0) 1,568 (52.5) 3,351 (52.2) 0.69
ACEi or ARB use indicated n(
%) 1,192 (34.7) 952 (31.7) 2,144 (33.4) 0.10
BP over goal n(
%) 1,917 (55.9) 1,633 (54.7) 3,550 (55.3) 0.88
A1C over goal n(
%) 719 (20.9) 659 (22.1) 1,378 (21.5) 0.48
NSAID on the active medication list n(
%) 583 (17.0) 520 (17.4) 1,103 (17.2) 0.87
Nephrology referral indicated n(
%) 263 (7.7) 238 (8.0) 501 (7.8) 0.95
Abbreviations: A1C, glycated hemoglobin; ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; BP, blood pressure; CDS, clinical
decision support; CKD, chronic kidney disease; NSAID, nonsteroidal anti-inflammatory drug.
Table 4. Main Results: Number of Eligible Patients Meeting the CKD Quality Metric at 18 Months Postindex Visit by Treatment
Group
Usual care CKD-CDS RRa95%CI P
CKD diagnosis 389/1,783 (21.8%) 417/1,568 (26.6%) 1.17 (0.91-1.51) 0.21
ACEi/ARB order 192/1,192 (16.1%) 151/952 (15.9%) 0.95 (0.76-1.18) 0.61
BP <130/80 mm Hg 388/1,917 (20.2%) 334/1,633 (20.4%) 0.98 (0.84-1.15) 0.84
A1C<7%159/719 (22.1%) 141/659 (21.4%) 1.00 (0.80-1.24) 0.99
NSAID not on the active medication list 294/583 (50.4%) 268/520 (51.5%) 1.03 (0.90-1.17) 0.67
Nephrology referral or visit 95/263 (36.1%) 92/238 (38.7%) 1.02 (0.79-1.32) 0.86
Abbreviations: A1C, glycated hemoglobin; ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; BP, blood pressure; CDS, clinical
decision support; CKD, chronic kidney disease; NSAID, nonsteroidal anti-inflammatory drug.
a
Risk ratios (RRs) comparing the proportions in CKD-CDS relative to usual care are adjusted for clinic level balancing covariates.
8Kidney Med Vol 6 | Iss 3 | March 2024 | 100777
Sperl-Hillen et al
exposure. A secondary analysis conducted on a subgroup
of patients with longer prepandemic follow-up produced
more favorable point estimates for several outcomes but
still did not reach statistical significance. Even after the
pandemic subsided, the CDS workflows did not fully
recover during the study, affecting the print rates of CKD-
CDS materials. Print rates of CDS materials in the care
system have only recently returned to prepandemic levels
(70%-75% of targeted encounters), and the slow recovery
may have been because of a combination of residual
problems the care system experienced such as staff burnout
and staffing shortages, patient access challenges and
increased complexity of visits because of deferred care, and
difficulty keeping up with training new staff on CDS
workflow. In addition, although the intervention was
adapted for primary care clinician viewing at telehealth
encounters, the patient-direct aspects of the CDS designed
to promote patient engagement were effectively abolished
by inability to screen share or print materials. Future
research should explore better ways to deploy CDS during
telehealth visits, which have become a permanent part of
health care delivery.
These study findings should also be considered in the
light of the care system demographics (84% White) and
potential cointerventions related to the health care system’s
focus on improving quality measures for hypertension,
diabetes, and heart disease. Given that CDS was already in
place for these conditions in usual care, it made it more
difficult to isolate the potential effects of the CKD-specific
intervention. The study also raised a dilemma posed by a
CDS system that used a generalized patient-centered
approach covering multiple chronic conditions. For pa-
tients with multiple care improvement opportunities, the
order in which conditions and treatment suggestions are
listed on interfaces could imply a higher priority for those
listed first, or greater attention may be given to what is on
the top of the list. In some cases, lower prioritization of
CKD relative to other conditions like very poorly
controlled diabetes or hypertension could decrease the
likelihood of action for some CKD care opportunities.
Further research on how the prioritization of clinical
content on CDS materials influences outcomes is of
interest.
In addition to the CKD care opportunities discussed
within the scope of this study, clinical evidence now
strongly supports the adoption of sodium-glucose
cotransporter 2 (SGLT2) inhibitor use for patients with
CKD to reduce the burden of kidney-related complica-
tions.
30-32
The guideline recommendations for SGLT2 in-
hibitors emerged after the CKD-CDS for this study was
deployed. However, a treatment suggestion to consider
initiating an SGLT2 inhibitor when indicated was inte-
grated into the CKD-CDS later in the observation period.
Future evaluation to assess the impact of this added treat-
ment recommendation is warranted because SGLT2 in-
hibitors could further enhance CKD care and patient
outcomes.
In summary, we conducted an ambitious randomized
trialofCDSforpatientswithCKDstagesG3andG4
designed to improve CKD recognition and care in a
primary care setting. The CKD-CDS intervention did not
significantly improve any of the 6 CKD care quality
metrics. The COVID-19 challenges underscore the
importance of considering external factors when evalu-
ating the impact of interventions in real-world settings.
Despite difficulty drawing definitive conclusions from
these results, the study highlights the high frequency of
CKDcaregapsandtheimportanceofimprovingCKD
care management. Future research should explore
alternative interventions that integrate CKD-CDS with
0
50
100
150
200
250
300
350
400
450
500
0
10
20
30
40
50
60
70
80
Oct 19 Jan 20 Apr
20
Jul 20 Oct 20
UC, % printed CKD-CDS, % printed
UC, n visits CKD-CDS, n visits
Figure 4. Intervention-eligible after index visits. Number of visits
and percentage printed by treatment group.
Table 5. Main Results Limited to the Subset of Patients Who Had at Least 6 months of Follow-Up Before the CKD-CDS was
Suspended Because of the Coronavirus Disease Pandemic
Usual care CKD-CDS R Ra95%CI P
CKD diagnosis 237/960 (24.7%) 251/864 (29.0%) 1.16 (0.90-1.50) 0.25
ACEi/ARB order 130/762 (17.1%) 101/608 (16.6%) 0.93 (0.71-1.22) 0.59
BP <130/80 mm Hg 243/1,158 (21.0%) 216/970 (22.3%) 1.06 (0.87-1.28) 0.57
A1C<7%109/500 (21.8%) 102/423 (24.1%) 1.11 (0.86-1.44) 0.42
NSAID not on active medication list 166/342 (48.5%) 155/305 (50.8%) 1.05 (0.88-1.24) 0.58
Nephrology referral 65/185 (35.1%) 62/149 (41.6%) 1.19 (0.87-1.63) 0.25
Abbreviations: A1C, glycated hemoglobin; ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; BP, blood pressure; CDS, clinical
decision support; CKD, chronic kidney disease; NSAID, nonsteroidal anti-inflammatory drug.
a
Risk ratios (RRs) comparing the proportions in CKD-CDS relative to usual care are adjusted for clinic level balancing covariates.
Kidney Med Vol 6 | Iss 3 | March 2024 | 100777 9
Sperl-Hillen et al
other strategies to address the complex challenges of
CKD care.
ARTICLE INFORMATION
Authors’Full Names and Academic Degrees: JoAnn Sperl-Hillen,
MD, A. Lauren Crain, PhD, James B. Wetmore, MD, MS, Lilian N.
Chumba, MBChB, MScGH, Patrick J. O’Connor MD, MA, MPH
Authors’Affiliations: HealthPartners Institute, Minneapolis,
Minnesota (JS-H, ALC, LNC, PJOC); Center for Chronic Care
Innovation, HealthPartners Institute, Minneapolis, Minnesota (JS-H,
LNC, PJOC); Division of Nephrology, Hennepin Healthcare;
Chronic Disease Research Group, Hennepin Healthcare Research
Institute, Minneapolis, MN (JBW).
Address for Correspondence: JoAnn Sperl-Hillen, MD,
HealthPartners Institute, 8170 33rd Avenue South, Bloomington,
MN 55425. Email: ann.m.harste@healthpartners.com
Authors’Contributions: Research idea and study design: JS-H,
PJOC, and ALC; data analysis/interpretation: JS-H, ALC, JBW,
LNC, and PJOC; statistical analysis: ALC; supervision or
mentorship: JSH and PJOC. Each author contributed important
intellectual content during manuscript drafting or revision and
accepts accountability for the overall work by ensuring that
questions pertaining to the accuracy or integrity of any portion of
the work are appropriately investigated and resolved.
Support: This work was supported by the National Institute of
Diabetes and Digestive and Kidney Diseases (R18DK118463).
Financial Disclosure: The authors declare that they have no
relevant financial interests.
Peer Review: Received June 15, 2023. Evaluated by 2 external peer
reviewers, with direct editorial input by the Statistical Editor, an
Associate Editor, and the Editor-in-Chief. Accepted in revised form
October 15, 2023.
REFERENCES
1. Chen TK, Knicely DH, Grams ME. Chronic kidney disease
diagnosis and management: a review. JAMA. 2019;322(13):
1294-1304. doi:10.1001/jama.2019.14745
2. Kinchen KS, Sadler J, Fink N, et al. The timing of specialist
evaluation in chronic kidney disease and mortality. Ann Intern
Med. 2002;137(6):479-486. doi:10.7326/0003-4819-137-6-
200209170-00007
3. Levin A, Stevens PE, Bilous RW, et al. Kidney disease:
improving global outcomes (KDIGO) CKD work group. KDIGO
2012 clinical practice guideline for the evaluation and man-
agement of chronic kidney disease. Kidney Int Suppl.
2013;3(1):1-150.
4. Vassalotti JA, Centor R, Turner BJ, et al. Practical approach to
detection and management of chronic kidney disease for the
primary care clinician. Am J Med. 2016;129(2):153-162.e7.
doi:10.1016/j.amjmed.2015.08.025
5. Kidney Disease: Improving Global Outcomes (KDIGO) Dia-
betes Work Group. KDIGO 2020 Clinical Practice Guideline
for Diabetes Management in Chronic Kidney Disease. Kidney
Int. 2020;98(4S):S1-S115. doi:10.1016/j.kint.2020.06.019
6. National Kidney Foundation. KDOQI clinical practice guideline
for diabetes and CKD: 2012 update. Am J Kidney Dis.
2012;60(5):850-886. doi:10.1053/j.ajkd.2012.07.005
7. de Boer IH, Caramori ML, Chan JCN, et al. Executive summary
of the 2020 KDIGO Diabetes Management in CKD Guideline:
evidence-based advances in monitoring and treatment. Kidney
Int. 2020;98(4):839-848. doi:10.1016/j.kint.2020.06.024
8. Manns L, Scott-Douglas N, Tonelli M, et al. A population-based
analysis of quality indicators in CKD. Clin J Am Soc Nephrol.
2017;12(5):727-733. doi:10.2215/CJN.08720816
9. Tummalapalli SL, Powe NR, Keyhani S. Trends in quality of care
for patients with CKD in the United States. Clin J Am Soc
Nephrol. 2019;14(8):1142-1150. doi:10.2215/CJN.
00060119
10. Whaley-Connell A, Shlipak MG, Inker LA, et al. Awareness of
kidney disease and relationship to end-stage renal disease and
mortality. Am J Med. 2012;125(7):661-669. doi:10.1016/j.
amjmed.2011.11.026
11. Saran R, Pearson A, Tilea A, et al. Burden and cost of caring for
US veterans with CKD: initial findings from the VA renal infor-
mation system (VA-REINS). Am J Kidney Dis. 2021;77(3):397-
405. doi:10.1053/j.ajkd.2020.07.013
12. Centers for Disease Control and Prevention. National Diabetes
Statistics Report website. Accessed December 29, 2023.
https://www.cdc.gov/diabetes/data/statistics-report/index.html
13. Chu CD, McCulloch CE, Banerjee T, et al. CKD awareness
among US adults by future risk of kidney failure. Am J Kidney
Dis. 2020;76(2):174-183. doi:10.1053/j.ajkd.2020.01.007
14. Sperati CJ, Soman S, Agrawal V, et al. Primary care physicians’
perceptions of barriers and facilitators to management of
chronic kidney disease: a mixed methods study. PLOS ONE.
2019;14(8):e0221325. doi:10.1371/journal.pone.0221325
15. Ramakrishnan C, Tan NC, Yoon S, et al. Healthcare pro-
fessionals’perspectives on facilitators of and barriers to CKD
management in primary care: a qualitative study in Singapore
clinics. BMC Health Serv Res. 2022;22(1):560. doi:10.1186/
s12913-022-07949-9
16. Abdel-Kader K, Greer RC, Boulware LE, Unruh ML. Primary
care physicians’familiarity, beliefs, and perceived barriers to
practice guidelines in non-diabetic CKD: a survey study. BMC
Nephrol. 2014;15:64. doi:10.1186/1471-2369-15-64
Table 6. Composite Measures of Fewer or no Remaining CKD-specific Care Gaps at 18 Months Among Patients With at Least One
Gap at Index, Overall and in the Subset of People who had at Least 6 months of Follow-up Before the CKD-CDS was Suspended
Because of the Coronavirus Disease 2019 Pandemic
Usual Care CKD-CDS RR 95%CI P
Overall, n 2,140 1,939
Fewer gaps 704 (32.9%) 725 (37.4%) 1.24 (0.92-1.68) 0.15
No gaps 500 (23.4%) 535 (27.6%) 1.21 (0.87-1.68) 0.25
6-mo follow-up, n 1,207 1,095
Fewer gaps 421 (34.9%) 431 (39.4%) 1.27 (0.94-1.70) 0.11
No gaps 311 (25.8%) 331 (30.2%) 1.25 (0.89-1.75) 0.19
Note: Risk ratios (RRs) comparing proportions in CKD-C DS relative to usual care are adjusted for clinic balancing covariates and number of CKD-specific care gaps
at index.
Abbreviations: CDS, clinical decision support; CKD, chronic kidney disease.
10 Kidney Med Vol 6 | Iss 3 | March 2024 | 100777
Sperl-Hillen et al
17. O’Connor PJ, Sperl-Hillen JM, Rush WA, et al. Impact of
electronic health record clinical decision support on diabetes
care: a randomized trial. Ann Fam Med. 2011;9(1):12-21. doi:
10.1370/afm.1196
18. Sperl-Hillen JM, Crain AL, Margolis KL, et al. Clinical decision
support directed to primary care patients and providers re-
duces cardiovascular risk: a randomized trial. JAmMed
Inform Assoc. 2018;25(9):1137-1146. doi:10.1093/jamia/
ocy085
19. Rossom RC, Crain AL, O’Connor PJ, et al. Effect of clinical
decision support on cardiovascular risk among adults with bi-
polar disorder, schizoaffective disorder, or schizophrenia: a
cluster randomized clinical trial. JAMA Netw Open. 2022;5(3):
e220202. doi:10.1001/jamanetworkopen.2022.0202
20. Kharbanda EO, Asche SE, Sinaiko AR, et al. Clinical decision
support for recognition and management of hypertension: a
randomized trial. Pediatrics. 2018;141(2). doi:10.1542/peds.
2017-2954
21. Gilmer TP, O’Connor PJ, Sperl-Hillen JM, et al. Cost-effective-
ness of an electronic medical record based clinical decision
support system. Health Serv Res. 2012;47(6):2137-2158. doi:
10.1111/j.1475-6773.2012.01427.x
22. Sperl-Hillen JM, Anderson JP, Margolis KL, et al. Bolstering the
business case for adoption of shared decision-making systems
in primary care: randomized controlled trial. JMIR Form Res.
2022;6(10):e32666. doi:10.2196/32666
23. Sperl-Hillen JM, Crain AL, Chumba L, et al. Pragmatic clinic
randomized trial to improve chronic kidney disease care: design
and adaptation due to COVID disruptions. Contemp Clin Trials.
2021;109:106501. doi:10.1016/j.cct.2021.106501
24. Moulton LH. Covariate-based constrained randomization of
group-randomized trials. Clin Trials. 2004;1(3):297-305. doi:
10.1191/1740774504cn024oa
25. Galbraith L, Jacobs C, Hemmelgarn BR, Donald M, Manns BJ,
Jun M. Chronic disease management interventions for people
with chronic kidney disease in primary care: a systematic review
and meta-analysis. Nephrol Dial Transplant. 2018;33(1):112-
121. doi:10.1093/ndt/gfw359
26. Abdel-Kader K, Fischer GS, Li J, Moore CG, Hess R,
Unruh ML. Automated clinical reminders for primary care pro-
viders in the care of CKD: a small cluster-randomized
controlled trial. Am J Kidney Dis. 2011;58(6):894-902. doi:
10.1053/j.ajkd.2011.08.028
27. Carroll JK, Pulver G, Dickinson LM, et al. Effect of 2 clinical
decision support strategies on chronic kidney disease outcomes
in primary care: a cluster randomized trial. JAMA Netw Open.
2018;1(6):e183377. doi:10.1001/jamanetworkopen.2018.3377
28. Samal L, D’Amore JD, Gannon MP, et al. Impact of kidney
failure risk prediction clinical decision support on monitoring
and referral in primary care management of CKD: a randomized
pragmatic clinical trial. Kidney Med. 2022;4(7):100493. doi:
10.1016/j.xkme.2022.100493
29. Peralta CA, Livaudais-Toman J, Stebbins M, et al. Electronic
decision support for management of CKD in primary care: a
pragmatic randomized trial. Am J Kidney Dis. 2020;76(5):636-
644. doi:10.1053/j.ajkd.2020.05.013
30. Perkovic V, Jardine MJ, Neal B, et al. Canagliflozin and renal
outcomes in Type 2 diabetes and nephropathy. N Engl J Med.
2019;380(24):2295-2306. doi:10.1056/NEJMoa1811744
31. Heerspink HJL, Stef
ansson BV, Correa-Rotter R, et al. Dapa-
gliflozin in patients with chronic kidney disease. N Engl J Med.
2020;383(15):1436-1446. doi:10.1056/NEJMoa2024816
32. Neuen BL, Young T, Heerspink HJL, et al. SGLT2 inhibitors for the
prevention of kidney failure in patients with type 2 diabetes: a
systematicreview and meta-analysis. Lancet DiabetesEndocrinol.
2019;7(11):845-854. doi:10.1016/S2213-8587(19)30256-6
Kidney Med Vol 6 | Iss 3 | March 2024 | 100777 11
Sperl-Hillen et al
