Kidney Coordinated Health Management Partnership
Phase: NA
Conditions: Chronic Kidney Diseases
Sponsor: University of Pittsburgh

 
1 
 
Electronic Health Record based Population health 
management to optimize care in CKD: Kidney 
Coordinated HeAlth Management Partnership 
(Kidney CHAMP) trial 
 
Trial registration: ClinicalTrials.gov   NCT03832595 
 
 
Funding Sponsor: 
1R01DK116957 (PI, Abdel-Kader & Jhamb) 
National Institutes of Diabetes and Digestive 
and Kidney Diseases 
National Institutes of Health 
2 Democracy Plaza 
6707 Democracy Boulevard 
Bethesda, MD  20892-5458 
 
Protocol version 1.0 
Jan 1, 2019 
 
 
 

 
2 
 
 
I. 
Study Summary 
Synopsis 
Title 
Kidney Coordinated HeAlth Management Partnership (Kidney CHAMP) 
trial 
Short Title 
K CHAMP Trial 
Study 
Description 
This is a randomized clinical trial to test the effectiveness of a 
multifaceted EHR-based PHM intervention to improve evidence-based 
CKD care in high-risk patients 
Objectives 
Primary Objective 
To perform a 42-month pragmatic, cluster RCT comparing the effect of 
an EHR-based PHM intervention versus usual care on key processes 
of care in 1,650 high-risk CKD patients. 
Aim 1a: To examine the effect of the intervention on systolic blood 
pressure (SBP) in HTN patients. 
Aim 1b: To examine the effect of the intervention on RAASi use in 
albuminuric patients. 
Aim 1c: To examine the effect of the intervention on exposures to 
potentially unsafe medications. 
 
Secondary Objective 
To test the clinical effectiveness of a multifaceted PHM intervention in 
reducing kidney disease progression in 1,700 high-risk CKD patients 
enrolled in a 42-month pragmatic, cluster RCT 
Primary 
Outcome 
≥40% decline in estimated glomerular filtration rate (eGFR) or end stage 
kidney disease 
Secondary 
Outcomes 
• blood pressure control 
• renin-angiotensin aldosterone system inhibitors use 
• exposure to potentially unsafe medications  
Study 
Population 
1,650 high-risk CKD patients not presently seeing a nephrologist  
Phase or Trial 
Type 
Effectiveness  
Description of 
Sites/Facilities 
Enrolling 
Participants 
100 University of Pittsburgh Medical Center (UPMC)-affiliated PCP 
practices located across southwest Pennsylvania University of 
Pittsburgh; University of Pennsylvania 
 
 
Description of 
Study 
Intervention 
Intervention bundle includes nephrology electronic consults, pharmacist-
led medication reviews and nurse-led CKD education  
Usual Care: per PCP as routine 

 
3 
 
Study Duration 
42 months 
Participant 
Duration 
24 months 
 
 

 
 
 
4. GOAL 
Lower risk for
ESRD 
 
II. 
Introduction  
 
CKD is associated with an unacceptably high human and financial cost. Over 12 million US 
adults have CKD stage 3-5.6 As the population ages and diabetes (DM), HTN, and obesity 
rates increase, the prevalence of CKD will grow.6 Kidney disease is the 9th leading cause of 
death48 and attributable Medicare expenditures are $80 billion.8,19,49 Over 1/3rd of this is 
spent on ESRD patients, who represent < 5% of patients with CKD.19 
The overwhelming burden of CKD care falls to PCPs. PCPs deliver most care to patients with 
non-dialysis dependent CKD due to its growing prevalence and the relative dearth of 
nephrologists.10-12,50-52 However, PCPs report that limited CKD knowledge, time constraints, 
complex case-mix, and inadequate system-based resources contribute to gaps in CKD 
care.12,14,15,17,38,51,53 These gaps include poor patient education,16 inadequate diagnostic 
evaluation,12,27,38,54 suboptimal treatment of HTN and use of RAASi in albuminuric 
patients,19,23,55-57 inappropriate medications or dosages,21,58-61 and late referrals of high-risk 
patients.13,19,20  These shortcomings inevitably lead to increased CKD progression, 
hospitalizations, and mortality.20,25,26,62-64 
Novel system-based interventions are needed. The above observations underscore an 
urgent need for system-based interventions to improve CKD care and outcomes. We 
recently conducted a national survey and found the overwhelming majority of PCPs endorse 
systematic interventions to improve CKD care.14 One potentially high-impact, low-cost 
intervention that has improved outcomes in other chronic diseases is EHR- based PHM.65,66 
CKD is an ideal setting to evaluate the impact of EHR-based PHM due to the: 1) high 
prevalence of disease, 2) ability to detect high-risk disease with widely used biomarkers (i.e., 
creatinine/eGFR, change in eGFR, urine albuminuria), 3) baseline gaps in care that provide 
opportunities for improvement, and 4) patient benefit and health system savings conferred by 
avoiding or delaying catastrophic outcomes (e.g., ESRD).19,31,67 National primary care 
organizations have called for the use of EHR-based PHM in primary care46 and a recent 
NIDDK conference advocated for urgent research examining the effectiveness of CKD 
PHM.68 
A conceptual model. A conceptual model of care69,70 provides an approach to examine care 
deficiencies.71 Disease management consists of 7 simplified steps 
(Figure 1). At each step, the 
potential for lapses in care exists. 
Leveraging IT tools to risk stratify 
patients, deliver decision support, 
and to provide electronic guidance 
with specific recommendations 
overcomes barriers at nearly each 
step by providing timely cognitive 
support to aid risk assessment, 
diagnostic evaluation, and treatment 
selection while lowering the burden 
on PCPs.72,73 However, studies are 
needed to test the feasibility and 
effectiveness of these strategies in 
improving CKD outcomes. 
 
5. INTENTION
Suppress proteinuria 
with RAASi, control BP, 
avoid nephrotoxins 
6. ACTION
SPECIFICATION 
Enter Rx orders, review
med list, educate
patient 
7. EXECUTE 
Lisinopril ordered,
NSAIDs stopped, and 
patient educated 
1. PERCEIVE 
Patient with 
decreased eGFR 
& proteinuria 
2. INTERPRET 
Patient has 
progressive CKD 
3. EVALUATE
CKD from DM with 
high-risk for ESRD 
Fig 1. A Conceptual Model for CKD Management 

 
 
 
Improving alignment between patient risk and treatment intensity. PCPs struggle to recognize 
high-risk patients early in their course.13,14,19 In the US, 2/3rds of incident dialysis patients have 
less than 1 year of nephrology care before initiating dialysis, leading to greater morbidity and 
mortality.13,19,20 However, fewer than 1/3rd of non-dialysis dependent CKD patients with an 
eGFR< 60 are at high risk for poor outcomes.74 Given the scarcity of nephrologists, a vital need 
exists for tools to effectively risk stratify the CKD population and improve the efficiency of 
resource allocation.1,76 This study identifies high-risk patients earlier in their disease course, 
when outcome trajectories can be improved. 
Actionable strategies to improve CKD care and outcomes in high-risk disease. Key 
evidence-based process of care targets in CKD are improved: 1) HTN control,19,44,55,56 2) 
RAASi use in proteinuric CKD,23,57 3) avoidance of inappropriate medications or 
dosages,21,58-61 and 4) timely nephrology referrals in high-risk CKD.13,19,20 These strategies 
have been demonstrated to slow CKD progression, prevent ESRD, decrease 
hospitalizations, and improve patient safety.13,19-21,23,55-61 Several approaches have been 
shown to enhance adoption of these critical processes of care: a) electronically delivered 
expert guidance by nephrologists,13,77 which slowed CKD progression, b) pharmacist led 
medication reconciliation,43,78-80 which decreased medication related problems, and c) patient 
education,56,57 which increased patient engagement and self- management.81,82 However, 
large pragmatic studies validating the effectiveness of these interventions are lacking. 
Summary and Implications: Combining complementary interventions in a highly pragmatic, 
cluster RCT of EHR-based PHM for high-risk CKD patients could establish a novel, 
exportable strategy to improve patient care, safety, and outcomes (Figure 2). Our 
multifaceted intervention will improve CKD risk stratification, resource allocation, adoption of 
evidence-based interventions, and patient medication safety. The intervention may thereby 
improve CKD outcomes and transform approaches to CKD care. Further, the study will 
deliver templates, algorithms, and code to enable dissemination to other settings.  
 
 
 

 
 
 
 
. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 2. PHM to improve CKD care 

 
 
 
 
 
III. 
Study Objectives 
 
The overarching aim of the Kidney Coordinated HeAlth Management Partnership 
(Kidney CHAMP) trial is to test the effectiveness of a multifaceted EHR-based PHM 
intervention to improve evidence-based CKD care in high-risk patients in a highly 
pragmatic, cluster randomized trial (CRT). By combining timely nephrology guidance, 
pharmacist-led medication management services, and CKD patient education, our 
intervention will improve CKD risk stratification, resource allocation, adoption of 
evidence-based interventions, and medication safety and efficacy, while minimizing the 
PCP and patient burden 
 
Aim 1: To perform a 42-month pragmatic, cluster RCT comparing the effect of an EHR-
based PHM intervention versus usual care on key processes of care in 1,700 high-risk 
CKD patients. 
Aim 1a: To examine the effect of the intervention on systolic blood pressure (SBP) in 
HTN patients. 
Aim 1b: To examine the effect of the intervention on RAASi use in albuminuric patients. 
Aim 1c: To examine the effect of the intervention on exposures to potentially unsafe 
medications. 
 
Aim 2: To test the clinical effectiveness of a multifaceted PHM intervention in reducing 
kidney disease progression in 1,700 high-risk CKD patients enrolled in a 42-month 
pragmatic, cluster RCT.   
 
 
 

 
 
 
IV. Study Design  
 
Study design. A 42-month cluster RCT with randomization occurring at the practice level (to 
minimize contamination) and stratified by number of high-risk CKD patients in the practice. All 
90 practices will be randomized. Patient enrollment will continue for 18 months, with a 
minimum of 24 months of follow-up (Fig 3). 
 
 
 
 
 
 

 
 
 
 
Population and Setting. The study leverages a PCP network that includes 90 practices; 330 
PCPs; and over 480,000 patients to conduct a cluster RCT testing the effectiveness of an 
EHR-based CKD PHM intervention. In this pragmatic study, practices will be randomized to 
the intervention. Secondarily, their high- risk CKD patients who are not seeing a nephrologist 
will be enrolled. 
Eligibility. Inclusion criteria are (Figure 3): a) age > 18 and <85, b) most recent eGFR < 60 
ml/min/yr,c) UPMC health plan insurance, d) established care with UPMC PCP, e) high-risk CKD 
(see Table 1). Exclusion criteria are: a) history of kidney transplant, b) receiving maintenance 
dialysis, c) recent (within 12 months) outpatient nephrology visit, d) baseline eGFR < 15ml/min, 
or e) expected survival < 6 months/hospice.We will use the validated 4- variable KFRE1,35,83 to 
estimate 5-year ESRD risk. We will supplement these criteria by including other patients who 
are high risk for poor outcomes (Table 1).37,74  
 
 
 
 
 
 
 
 
PCP Recruitment and Engagement.  
Before implementation at each site, we will provide a remote/on-site presentation during a 
scheduled practice meeting. Communication with lead physicians will continue during the 
study at least biannually by teleconference or on-site lunches to discuss feedback and 
concerns and document comments on a standardized feedback form. Study/Site PIs will also 
meet with practice physicians as needed to address concerns. We will anonymously survey 
intervention arm PCPs every 6 months to assess their experience with the intervention 
bundle. This will include: ease of use, questions communicated by patients, effects on patient 
medication use and adherence, utility of the recommendations, and temporal burden of the 
intervention. Minor adjustments to PHM intervention workflow will be made as needed to 
ensure continued workflow optimization. 
 
Randomization. The unit of randomization will be the practice (Fig. 3), stratified by estimated 
number of eligible CKD patients within the practice (small [<15 patients], medium/large [>15 
patients]). Randomization will use a computer generated random number sequence with 
random block sizes of 4 and 6. 
CKD Registry and PHM Dashboard. Our CKD registry includes all outpatients with a recent 
eGFR <  60 ml/min followed by a UPMC PCP. The CKD registry identifies patients with CKD, 
phenotypically characterizes them, and stores information on labs, medications of interest, 
upcoming PCP appointments, and information about CKD outreach (including dates of 
electronic outreach, medication reviews, patient education, and pending labs/studies). The 
registry is updated automatically with activity in the patient chart. 
Dashboards are medical informatics data representations that can be employed when decisions 
need to be made about a population of patients. These tools display groups of patients based 
on clinical characteristics and allow providers to stratify, filter, and sort by relevant variables. 
Thus, subgroups of patients can be rapidly identified and targeted for more intensive therapy. 
Dashboards can improve evidence-based care; however, they have been slow to enter clinical 
eGFR 15-29ml/min OR 
5 year risk of ESRD > 4% determined using the validated 4-variable 
kidney failure risk equation35,83 (urine dipstick substituted for ACR when 
necessary) OR 
Rapid decline in eGFR operationalized as annualized eGFR decline 
>=5ml/min/yr74* 
*determined from 2 eGFR values at least 12 months apart 
Table 1: High risk CKD  

 
 
 
practice because of the sophisticated underlying data requirements.84,93 
Our CKD PHM dashboard (Figure 4) includes population-based reports built off of the registry 
(e.g., high-risk patients with PCP appointment in October), as well as graphical and tabular 
displays of key metrics (e.g.,identified subpopulation with albuminuria who are not on RAASi). 
Population reports accessible through the dashboard will allow the study coordinator to sort 
patients based on phenotypic data and upcoming PCP appointments, and include filters such 
as CKD stage, RAASi use, and PCP group. Longitudinal tracking and notation functions 
interface with the dashboard and allow documentation of study inclusion, and dates of 
scheduled/completed aspects of the intervention. Dashboard reports will allow the coordinator 
to rapidly flag patients for urgent concerns, send routine reminders to PCPs to implement 
recommended interventions, ensure follow-up on pending components of the intervention, 
and actively monitor follow-up status. Dashboard reports will be actionable and will allow the 
manager to view more detailed information in a viewing pane without leaving the report. The 
reports allow the coordinator to jump directly into activities to communicate with the patient, 
the patient’s PCP, and the respective nephrologist or pharmacist. 
 
 
 
Patient screening and enrollment. Each month, the coordinator will use the CKD PHM tool to 
review high-risk patients with an upcoming (within 1 month) appointment with their PCP to 
determine eligibility. Possible decisions will include actively enroll, permanently exclude, or not 
presently eligible but may rescreen if deemed high-risk at next PCP appointment. Once 
delineated, the patient’s status is noted in the EHR with a modifiable study flag not visible to 
providers, but that is continually tracked, updated, and ascertained through the PHM 
dashboard. 
For intervention patients, the coordinator will send a scripted EHR message with associated 
decision support to the PCP about 1-3 weeks prior to their scheduled PCP appointment. 
When the PCP accepts the decision support recommendations for an electronic nephrology 
consultation, a medication reconciliation and safety review, and standardized patient 
Figure 4. PHM

 
 
 
education, the orders will be placed . Patients enrolled to practices randomized to usual care 
will receive care as they currently do. 
A study coordinator is needed to screen patients for eligibility to ensure identical enrollment 
criteria are used in the intervention and usual care arms. Because coordinators will review 
recent patient documentation for potential enrollees in the intervention arm, they will note if a 
patient has a terminal condition, etc. However, this information may only be recorded in the 
text portion of documents. Hence, we will use a standardized form to screen patients in both 
arms to ensure comparable enrollment. To minimize potential bias from the nurse 
coordinator’s non-blinded status, a local PI blinded to PCP group assignment will review 
randomly selected screened patients on a weekly basis to ensure accurate implementation of 
the eligibility criteria. Potential discrepancies will be resolved by consensus and further 
training as necessary.  
Intervention. Approximately 1-3 weeks prior to an appointment, PCPs of enrolled patients will 
receive an EHR message informing them of the patient’s high-risk CKD status. Linked with 
the notification is a decision support alert (Figure 5) that asks the provider to a) order a CKD 
care bundle that includes an electronic nephrology consultation, pharmacist led medication 
reconciliation and safety review, and a CKD education session, or b) order a traditional 
nephrology consult, or c) provide a reason why neither choice is warranted. Notification 
messages and PCPs’ responses will be documented in the EHR.  
 
If the care bundle is ordered, a nephrologist will review the chart and provide specific prior to 
the appointment. In addition, a telephonic appointment with a pharmacist will be made and 
s/he will contact the patient and review their medication list and provide safety 
recommendations to the PCP through the EHR. 
 
During the patient visit, a real time decision support alert will remind the PCP to review the 
nephrology e- consult and pharmacist recommendations, to inform the patient about their 
CKD status, and to refer the patient for complimentary CKD education. Following the visit, 
the patient will have their CKD education sessions scheduled. Subsequent nephrology 
electronic follow-ups will be scheduled according to the patient’s clinical needs (generally 
every 3-6 months) and coordinated through the PHM dashboard. The intervention has been 
refined to facilitate care enhancements, preserve workflow, and minimize PCP and patient 
burden. 
Figure 5. EHR message linked to decision support alert 

 
 
 
We have also taken steps to enhance PCP acceptance of the intervention: a) high-level support 
from UPMC Health Plan, PCP network (Community Medicine Incorporated, including 90 
practices and 330 PCPs), and the Nephrology Division; b) meeting with lead physicians and 
refining the intervention to harmonize with workflow; c) documenting PCP responses in the 
EHR; d) ongoing communication with PCP groups and leadership. 
Electronic nephrology consult. Orders for e-consults will be received in an electronic basket 
monitored by the study coordinator. The consults will be routed to 1 of 12 board certified 
nephrology clinicians, who will undergo training to standardize consult focus and 
communication. The notes will adopt a “Situation, Background, Assessment, 
Recommendation”94-97 template to ensure clear, concise communication. The note will be 
completed at least several days prior to the patient’s appointment and routed to the PCP’s 
message basket and documented in the chart to allow time for review and clarification. The 
initial consult will focus primarily on HTN control , proteinuria assessment and suppression, 
RAASi use, and medication safety. The note will include a bolded list of recommendations 
with orders placed in a pended status (i.e., entered, but awaiting acceptance from the PCP). 
To enhance communication and care coordination, the e- consultant will ensure CKD is 
added to the problem list.98 In addition, the problem list will convey that the patient is 
followed by the e-consult team, reachable via listed message baskets and phone numbers. 
The nephrologist will include an order for remote electronic follow-up, which will be captured 
in the PHM dashboard. Electronic messages will be sent to the nephrologist to perform the 
follow-up approximately 3 weeks before the recommended interval. When necessary, 
traditional office evaluations will be suggested. 
Medication reconciliation and safety review. Prior to their upcoming PCP appointment, 
patients will be contacted to schedule a remote medication review with a study pharmacist 
(PharmD) who has expertise in medication therapy management.43 Appointment availability 
will include evenings to maximize patient convenience. Patients will be asked to have their 
medications available for the review. Prior to and during the review, the PharmD will gather 
clinical data from the EHR’s active and recently discontinued medications. S/he will also 
note the patient’s most recent medication dispensing record for each medication (usually 
documented in the EHR with electronic scripts). The study pharmacist will assess the 
patient’s self- reported medication regimen, administration routine, adherence, and will 
reconcile this with the medication information contained in the EHR. S/he will assess over 
the counter (OTC) medications and herbal products, will deliver guidance on OTC 
medications to avoid, and will screen for possible adverse effects of all medications. 
Thomas D. Nolin, Associate Professor, PharmD, PhD (Co-I), will guide the medication 
review, perform intermittent audits to ensure intervention fidelity, and direct additional staff 
training if necessary.91 
The pharmacist will document their findings in the EHR using a standard medication 
reconciliation and review template. The note will be sent to the PCP’s EHR inbox. Specific, 
concise recommendations and reasoning will be listed at the top of the note. Thereafter, 
pharmacist medication reviews will be scheduled quarterly. 
Standardized patient education. Once an order for CKD education is placed, the research 
team will schedule individual or group education session. Caregivers will be encouraged to 
attend. Study nurse educators will deliver the CKD education. New nurse educators will 
undergo an intensive 3- to 6-month training period under the guidance of the PIs and 
existing UPMC CKD nurse educators and dietitians. Simulated and authentic patient 
education sessions will be observed to judge readiness. 
Print and video based education material from the National Kidney Disease Education 
Program (NKDEP) and the National Kidney Foundation that reviews the role of the kidneys, 
CKD risk factors, dietary guidelines, pharmacotherapy, medication adherence and safety, 
frequently asked questions, and dialysis modalities will be used. The nurse will document 

 
 
 
the session in the EHR using a brief templated education note (that is captured by the PHM 
dashboard), and route it to the PCP and nephrologist. After the initial sessions, annual 
refresher sessions will be scheduled. 
Multi-disciplinary Case Discussions 
Prior to providing recommendations to PCPs, every patient’s management will be discussed in 
case conference calls (2-4 times per week) attended by APPs, nephrologists, and pharmacists, 
to arrive at consensual individualized recommendations for patients.  
Intervention Fidelity 
All nephrologists, nurse educators, and PharmDs delivering a component of the intervention 
will undergo standardized training until they achieve consistent and acceptable performance. 
This will include review of concise educational materials, observation and apprenticeship 
with existing providers at the local site, the use of checklists operationalizing key aspects of 
each intervention, the use of SBAR (situation, background, assessment, recommendation) 
EHR templates,94-97 and direct observation during role played and actual interventions. After 
initial implementation, the study/site PIs will randomly audit 5-10% of e- consults every 3 
months. Dr. Nolin will randomly audit 5-10% of pharmacy communications every 3 months. 
Providers will receive targeted feedback based on findings. Providers will also continue to 
use checklists to document completion of key aspects of the intervention as well as 
deviations throughout the study. Refreshers will occur every 6-12 months and remediation 
will occur as dictated by observed performance (i.e., <80% fidelity with items on checklist). 
 
 

 
 
 
V. 
Data collection and Outcomes 
 Routinely collected EHR and administrative data will be abstracted for outcomes assessment 
as shown in Table 2. PCP practice level data will be obtained from public records.  
Table 2: Key variables and covariates for usual care and intervention patients 
 
 
Primary Outcome 
A >40% decline in eGFR or ESRD.92 eGFR decline will be adjudicated based on the 
baseline creatinine and eGFR determined from the CKD-EPI equation and measured 
routinely in clinical practice.99 ESRD will be defined as an eGFR < 10ml/min to account for 
patients with markedly reduced baseline eGFR values (i.e., 16-20ml/min). 
The 40% decline surrogate outcome may increase power and precision by capturing 
additional events while maintaining a similar risk of type I error92 compared to the standard 
doubling of serum creatinine outcome. To limit surveillance bias, we will use 6-month 
ascertainment windows and average all values within each window. Our data indicate 75% 
of high-risk patients have an outpatient eGFR value every 6-months. Additional analyses 
will compare changes in eGFR slope over time (using splines to account for non-linearity.  
 
Secondary outcomes - Process of care outcomes  
1. HTN control. Outpatient, sitting BP values measured during each outpatient encounter and 
recorded in the EHR. BP will be treated as a continuous variable. To minimize 
Socio- 
demographics 
• Age, gender, race, ethnicity, marital status, insurance, and zip code 
for linkage with neighborhood median household income.39 
• Baseline values defined on the date of baseline visit 
EHR 
Comorbid 
conditions 
• DM, HTN, hyperlipidemia, CAD, cerebrovascular disease, peripheral 
vascular disease, CHF, arrhythmia, gout, chronic lung disease, 
chronic liver disease, mood disorder, and malignancy. 
• Baseline values defined on the date of baseline visit and using a 24 month 
“look back” period. 
Phenotypes using 
administrative & clinical 
codes, meds, & lab 
values validated in local 
EHR.12,38,39 
Blood 
Pressure 
• Baseline BP - mean outpatient BP from the date of the baseline 
study visit with the PCP until 180 days prior to the baseline visit. 
• Follow-up BP – all outpatient BPs after patient enrollment 
Office visit vital signs 
recorded in the EHR 
Medication 
use 
• RAASi, NSAID and other medications deemed a potential safety 
concern (e.g., allopurinol, gemfibrozil, glyburide, metformin, etc.) 
• Medication related problems and drug record discrepancies 
EHR medication list; 
medication review 
(intervention patients) 
Laboratory 
values 
• Common laboratory tests (e.g., K+, cholesterol, etc.) 
• Baseline values - determined using the most recent value from 
baseline visit up to 365 days prior to the visit. 
EHR (restricted to 
outpatient labs) 
Urine 
albuminuria 
• Quantitative urine albuminuria - most recent ACR from the baseline 
visit up to 365 days prior to the visit. 
• Urine dipstick albuminuria - median of outpatient values available from 
the date of baseline visit up to 365 days prior to the visit.100 
Outpatient lab values 
from EHR 
Variables 
Variable descriptions 
Data source details 
Renal function 
and rate of 
change in renal 
function 
• Baseline serum creatinine – most recent creatinine from date of 
study enrollment visit with PCP up to 365 days prior to the visit. 
• Baseline eGFR – Calculated using CKD-EPI.99 
• Baseline rates of change in eGFR - determined from baseline eGFR and 
prior eGFRs between 365 to 730 days before the baseline value. 
EHR (restricted to 
outpatient labs) 

 
 
 
ascertainment bias, we will use 6-month ascertainment windows to determine an average 
BP for each patient for each 6-mo period. Patients lacking an outpatient value will have 
their last value carried forward  
2. Use of RAASi. Will be determined by active use of an ACEi or ARB based on the EHR 
medication list at each outpatient encounter. Analyses will compare cumulative person-time 
exposure during the study. 
3. Medication safety. We will examine the rates of use of several high-risk 
medications21,43,54,61,80,101 that can be associated with adverse outcomes in progressive 
CKD. Medication exposure will be determined by presence of the specified medication 
on the patient's EHR medication list at each outpatient encounter. Analyses will compare 
cumulative person-time exposure during the study. 
a. Use of NSAIDs: use examined for all study patients 
b. Use of glyburide: use examined for all diabetic study patients 
c. Use of metformin: use examined for diabetic study patients with eGFR<30 
d. Use of gemfibrozil: use examined for all study patients with eGFR<30. 
 
 
Exploratory outcomes. Mortality, hyperkalemia, and health utilization (i.e., costs) including 
hospitalizations, emergency department visits, and outpatient encounters will be ascertained 
by a combination of EHR and administrative data from the UPMC health plan. The 
accessibility of administrative data that captures events outside the health system and 
supplements the EHR is a unique and complementary resource that will be leveraged in 
future study analyses.  
 
Adverse events 
While it is unlikely that systematically providing evidence-based recommendations to PCPs of 
high-risk CKD patients by trained nephrologists and pharmacists will worsen overall clinical 
outcomes, we will monitor safety signals through the EHR, including hyperkalemia, ER visits, 
hospitalizations and mortality.  
 
 
 

 
 
 
VI. 
Regulatory and Oversight Considerations 
This study meets the criteria for human subject’s research. 
 
We will conduct a cluster RCT of PCP practices, implementing a multifaceted PHM 
intervention for patients with high-risk CKD. The intervention targets improvements in the 
delivery of evidence-based care and outcomes. Patients with high-risk CKD will be identified 
through demographics and laboratory tests that are collected for clinical purposes. The PHM 
dashboard, which includes estimates from validated risk prediction models and eGFR 
trajectories, will be used to identify and track enrolled patients. Shortly before their regularly 
scheduled visit with an enrolled patient, PCPs randomized to the intervention arm will receive 
a decision support message and subsequent reminders notifying the provider of the patient’s 
high-risk CKD status and recommending the following bundle: 1) an order for electronic 
nephrology guidance or formal nephrology office consultation if preferred; 2) medication 
therapy management by a PharmD, specifically including a medication reconciliation and 
safety review, and 3) CKD patient education. These interventions are all consistent with 
standard of care practices for patients with high-risk CKD and the PCP will always be 
permitted to accept or refuse these suggestions according to their clinical judgement. If the 
provider accepts these suggestions, the individual intervention components will be ordered, 
scheduled, coordinated, and tracked. If the provider refuses any of the components, they will 
be able to justify why their reasons for refusal. Refusal responses will be randomly audited to 
ensure accuracy. 
 
a. Recruitment and Informed Consent: 
We will include all primary care providers at UPMC with an active primary care continuity 
clinic. We will identify these providers through provider rosters given to us by the respective 
departments and practices. We will maintain contact with each practice through biannual 
sessions held during their regular practice meetings or lunch meetings held at their offices. 
We will remind providers of the aims of the study and the intervention. We will also seek 
regular feedback from providers to identify unforeseen issues that were not encountered 
during the pilot study and will work collaboratively with them to identify suitable solutions. We 
will survey providers on their experience with the intervention using both multiple choice and 
open-ended questions. We will also collect feedback from them using standardized, 
templated forms during planned meetings. The study's PIs and nurses will meet with the 
practices as described above. 
 
Per the University of Pittsburgh IRB and UPMC Quality Improvement committee guidance, 
the study will not require consent from PCP or patient to enroll them in the study. Both PCP 
and patients will be given information about the study and an opportunity to opt-out. We will 
continue to meet with PCPs to understand barriers that may arise and to develop solutions 
that ensure PCP burden is minimized and workflow is preserved. 
 
Notably, all practicing PCPs who partake in this study are licensed providers. They vary in 
age from approximately 30 years to >60 years. All PCPs from UPMC primary care practices 
are included in this research; no one is excluded. The PCPs care for approximately 480,000 
patients annually, including ~10,000 patients with high-risk CKD. Individual patients seen will 
vary greatly in age and health status as expected in any large group PCP practice. 
 
The intervention will only target patients who have high-risk CKD using validated risk 
prediction models. Low- risk patients will be excluded because outcomes occur at a lower 
rate in this setting. Including these patients would necessitate a larger, longer, and costlier 
study to ensure adequate power to detect small but meaningful differences. Alternatively, 

 
 
 
including these patients using the current study size and follow-up would result in an 
underpowered trial with significant potential for a type II error. Patients > 85 years of age will 
be excluded as best practices for CKD treatment are less clear in the very aged. Patients < 
18 years will also be excluded (see inclusion of children below). Patients with a history of 
renal transplant, end-stage renal disease, or already under the care of a nephrologist will be 
excluded because these patients are generally receiving specialized care in addition to their 
PCP’s care. In addition, patients with very limited prognoses (e.g., metastatic cancer, COPD 
on continuous oxygen, stage IV heart failure) will be excluded during the screening process 
due to the difficulty of substantially altering their course with CKD treatment enhancements. 
 
Patients that are pregnant or prisoners will be excluded from the study if they are seen by a 
participating PCP during the study enrollment period. Evidence-based CKD care differs 
during pregnancy (e.g., contraindication  of RAASi, different BP goals) and pregnancy and 
peripartum course are known to affect eGFR and albuminuria thereby uniquely affecting 
potential outcomes. In addition, because 2 components of our intervention bundle require 
scheduling a group CKD education session and a telephonic medication reconciliation and 
safety review with a PharmD, enrolling incarcerated patients would be impracticable. 
 
a. Potential Risk: 
PCPs will be at minimal risk with regards to their reputation, finances, legal liability, or position 
in their department or practice. Their exposure is limited to intermittent outreach from the 
investigators and EHR communications/reminders regarding their enrolled patients. These 
messages will recommend evidence-based care (e.g., implementation of RAASi, avoidance of 
NSAIDs, checking a urinary albumin to creatinine ratio) for a high-risk CKD population. The 
provider can then choose to enact or ignore the suggestions, and document reasons for 
refusal. The messages and communication have been designed to activate in a manner that 
harmonizes with existing PCP workflow. Processing these messages should require far less 
than 1 minute per patient, exposing the PCPs to a minimal risk of temporal inconvenience. 
Assuming a uniform distribution of high-risk CKD patients throughout the practices, the 
average PCP will have ~5 patients included in the study. 
However, because patient distribution is not uniform, we estimate a single PCP may have up 
to ~15 patients in the study. This will still pose a minimal temporal burden over the course of 
the trial. 
 
All data analysis on provider performance will be reported in the aggregate; hence, 
performance of an individual PCP will not be identifiable. In the unlikely event of a breach of 
confidentiality, the physicians will be exposed to minimal risks to their reputation, job 
security/finances, or legal liability as physician data will be strictly de-identified with a 
password protected “key” stored separately in a password protected file on a secure 
university server. Even if the key was stolen (which we deem to be very unlikely to happen), 
the data regarding physicians would be of limited implication as there are no formal metrics 
regarding CKD care and we will not gather any high-risk provider information (e.g., dob, 
SSN, etc). Alternative treatments at this time are to continue usual care (e.g., continuing 
medical education activities to PCPs) which has proven ineffective in optimizing PCP 
performance including treatment of CKD. 
 
This study will also subject the enrolled patients of participating providers to minimal risks. At 
baseline, enrolled patients are at high-risk for poor outcomes including catastrophic 
outcomes such as ESRD. The predictive models used to make these estimates have been 
externally validated in multiple populations. When the PCP accepts the recommended 
intervention bundle the following events will occur. First, electronic nephrology guidance will 

 
 
 
be provided, which will give the PCP recommendations on how to improve CKD related care 
prior to their visit with the patient. This intervention is strictly provider facing (i.e., the patient 
is not contacted by the nephrologist). The PHM dashboard will be used to track whether 
recommendations are implemented and to send reminder messages to PCPs unless reasons 
for non-implementation are provided. Second, the patient  will have a telephonic appointment 
made with a PharmD to review their medications. If an emergent  medication hazard is 
discovered, the PharmD will ask the patient to hold the offending medication(s) and will 
immediately contact PIs, and the patient’s PCP. These emergent events are likely to be quite 
infrequent. Otherwise, following completion of the medication reconciliation and safety review, 
the pharmacist’s findings will be reported to the PCP so that they may be reviewed with the 
patient and appropriate changes made (in accordance with the PCP’s clinical judgement). 
Third, the patient will be scheduled for a CKD nurse education session following their 
appointment with their PCP (thereby allowing the PCP to share the CKD diagnosis with the 
patient). The nurse education session will provide information on the role of the kidneys, 
kidney function assessment, strategies to protect kidney function, medication safety, and 
general information about ESRD treatment options. Given patients’ high-risk CKD status, 
CKD education isconsidered standard of care and patients may opt out of the education 
session if they find it distressing. 
 
Some of these interventions may pose a small psychological risk to the patient. Patients who 
were previously unaware, may become aware of their CKD status. However, the patient will 
have discovered the presence of a serious illness with potentially severe complications and 
(most importantly) available treatments. Further  studies to delineate the exact etiology of the 
CKD and subsequent treatments can be initiated. The intervention may assist with proper 
medication dosing, avoidance of nephrotoxic medications, and avoidance of potentially risky 
procedures (imaging with intravenous contrast or gadolinium containing compounds). The 
intervention may also help delay the need for dialysis and ensure patients are prepared for 
dialysis if it becomes necessary. In addition, PCPs can choose to defer discussions and 
treatment if they feel a patient is unlikely to have high- risk CKD or benefit from any of the 
aspects of the intervention. The presently available alternative approach is to continue 
current practice with suboptimal PCP treatment of CKD. 
 
While it is unlikely that systematically providing evidence-based CKD recommendations to 
PCPs of high risk patients by board certified nephrologists and pharmacists will worsen 
overall clinical outcomes, we will monitor several safety signals. We will gather these safety 
data annually through the EHR, minimizing additional patient or study cost burden. Potential 
adverse events that will be monitored include: 
1) Rates of hyperkalemia (K>5.5, K>6) 
2) Rates of emergency department visits and hospitalizations 
3) Rates of death. 
 
Tests and treatments implemented by PCPs based on nephrologist’s or pharmacist’s 
recommendations are likely to be relatively inexpensive and non-invasive and should expose 
the patient to minimal financial or bodily risk. The board certified PCP is the final arbiter of 
medical decisions regarding their patients. 
 
No patient will be excluded based on gender, race, or ethnicity. However, we are restricting the 
intervention to UPMC health plan patients (including UPMC Medicare Advantage) for several 
reasons. First, this allows us to inform PCPs and patients that we have partnered with the 
patient’s insurer in an effort to optimize care, thereby mitigating potential financial concerns. 
Second, the health plan’s support will allow us to supplement EHR data with administrative 

 
 
 
data to adjudicate outcomes while minimizing misclassification. Our findings will inform future 
efforts to extend the intervention while preserving fiscal sustainability. 
 
b. Protections against Risk: 
Data will be stored in secured databases (e.g., MS access, REDCap) on secure university 
servers accessible only through password protected computers in locked rooms. All working 
datasets will be de-identified limited datasets (i.e., dates of tests, labs will remain). Identifiers 
will be stored in a separate, password protected file. Access to the PHM dashboard similarly 
requires 1) a valid password to access the university computer, 2) a valid password to 
access the EHR, and 3) clearance/privileges to access the PHM dashboard. 
 
The risk of breach of confidentiality is low. Further, all information associated with provider 
performance or patient information will be de-identified (using limited data sets with dates). 
Hence, if there is a breach in confidentiality it is unlikely to expose the providers or patients to 
any significant harm or discomfort. Given the lack of published literature on the usefulness or 
futility of PHM in CKD, breach of confidentiality regarding provider participation or 
randomization assignment is also very unlikely to result in any foreseeable harm or 
discomfort. Patient information will be de-identified and high-risk variables (e.g., DOB, etc) 
removed from all working datasets to minimize the risk of breach of confidentiality. The 
minimum data necessary for the study will be accessed. In addition, all researchers involved 
are clinically competent and certified in HIPAA compliance. All stored electronic data will be 
kept on University secure computers and secure servers in locked departmental offices. The 
corresponding electronic databases are password protected. All paper records associated 
with the study will be stored in a locked file cabinet in a locked room. These measures are 
likely to be effective. 
 
To minimize the risks of the PHM intervention (e.g., possible psychological distress), we are 
limiting our intervention to patients with high-risk CKD based on clinically validated risk 
prediction models. This model will limit the targeted population thereby restricting those 
subjected to the above-mentioned risks.  Additionally, all communications are targeted to 
licensed providers who will exercise their clinical judgment and can ignore recommendations 
they feel would subject the patient to an undue burden. In this manner, the intervention is 
non-invasive and nonbinding (i.e., it is always left to the provider’s discretion whether to 
follow suggestions). Together, these are likely to successfully limit unnecessary 
interventions and the placement of undue psychological or financial risks on patients. 
 
Data Safety and Monitoring Plan. 
A data and safety monitoring plan (DSMP) will be implemented by Drs. Jhamb and Abdel-
Kader, and members of the research team, to ensure that there are no changes in the 
risk/benefit ratio during the course of the study and that confidentiality of research data is 
maintained. Investigators and study personnel will meet monthly to discuss the study (e.g. 
study goals and modifications of those goals; subject recruitment and retention; progress in 
data coding and analysis; documentation, identification of adverse events or research subject 
complaints; violations of confidentiality) and address any issues or concerns at that time. 
Minutes will be kept for these meetings and will be maintained in the study regulatory binder. 
The status of recruitment and data collection will be discussed and addressed among the 
attendees with confirmation that proper protocol has been followed. Technical problems if any 
will be discussed and plans developed to address them. Any instances of serious adverse 
events will be reported immediately to the University of Pittsburgh IRB using standard forms 
and/or procedures that have been established by the IRB. 

 
 
 
 
The yearly IRB renewal for this study will include a summary report of the DSMP findings 
from the prior year.  
 
Inclusion of Women and Minorities: 
Given the study design of randomizing practices, the subject selection criteria are all PCP 
practices previously specified.  Secondarily, the patients with high-risk CKD who are seen by 
a participating PCP will be included. We are unable to control the gender, race, or ethnicity of 
the PCPs or of their patients.  However, we will include all PCPs regardless of gender, race, 
or ethnicity. We will also include all of their eligible patients between the ages of 18 and 85 
who have high-risk CKD. No patient will be excluded based on their gender or race or 
ethnicity.  Indeed, because patients with high-risk CKD are often of minority race or ethnicity, 
we expect higher proportions of these groups than the general population. However, there will 
not be any proposed outreach program for recruiting members of a specific gender or 
racial/ethnic group as subjects. We do not suspect that one gender or racial group will be 
excluded or underrepresented given the baseline patient demographics of the PCP practices 
(55% women, 15% African-Americans) and data from the USRDS annual report revealing that 
nearly 30% of incident dialysis patients are African-American and that women have a greater 
prevalence of CKD stages 3-5. While we acknowledge that the local Hispanic/Latino-American 
population is relatively small compared to the national average, we will attempt to include 
every patient deemed to have high-risk CKD seen by a participating PCP. The racial and 
ethnic diversity of patients included in the study will be entirely based on the diversity of the 
local PCP practices and is outside our control. We will not exclude any patient based on their 
gender, race, or ethnicity. 
 
 
Inclusion of Children 
Participants in this study are enrolled at two levels.  First, we are directly enrolling PCPs. 
None of the PCPs are children and hence no children will be recruited at this stage.  
However, patients with high-risk CKD are secondarily included in the study when they are 
seen by a participating PCP. The participating PCPs generally see patients >18 years old 
(some family practice physicians see both children and adults). However, we will only be 
targeting patients > 18 years. Hence, children will not be included. This is justified for several 
reasons: 
1) the prevalence of CKD in this age range is low and there will be few patients who meet 
these criteria, 2) CKD in the adult population has different etiologies, natural history, and 
treatments, 3) well validated risk prediction models to determine high-risk status are not 
available to our knowledge. All of these reasons make including children impracticable in 
this study. 

 
 
 
 
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Statistical Analysis Plan 
Original Statistical Analysis Plan (from grant application) 
 
Clinical outcome (primary study outcome): A ≥40% decline in eGFR or ESRD.(1) eGFR 
decline will be adjudicated based on the baseline creatinine and eGFR determined from the CKD-EPI 
equation and measured routinely in clinical practice.(2) To limit ascertainment bias, we will also use a 
once yearly decision support alert to remind study PCPs in both arms to order a BMP on study patients, 
if results are not available in the last 6 months. ESRD will be defined as an eGFR < 10ml/min to account 
for patients with markedly reduced baseline eGFR values (i.e., 16-20ml/min). To limit surveillance bias, 
in addition to the above alert, we will use 6-month ascertainment windows and average all values within 
each window. Additional analyses will compare changes in eGFR slope over time (using splines to 
account for non-linearity). Researchers involved in outcome assessment will be strictly blinded. 
 
Process of care outcomes (secondary outcomes): 1) HTN control. Outpatient, sitting BP values 
measured during each outpatient encounter and recorded in the EHR. BP will be treated as a continuous 
variable. To minimize ascertainment bias, we will use 6-month ascertainment windows to determine an 
average BP for each patient for each 6-mo period. Patients lacking an outpatient value will have their 
last value carried forward. 2) Use of RAASi. Will be determined by active use of an ACEi or ARB based on 
the EHR medication list at each outpatient encounter. Analyses will compare cumulative person-time 
exposure during the study. 3) Medication safety. We will examine the rates of use of several high-risk 
medications that can be associated with adverse outcomes in progressive CKD. Medication exposure will 
be determined by presence of the specified medication on the patient's EHR medication list at each 
outpatient encounter. Analyses will compare cumulative person-time exposure during the study. a) Use 
of NSAIDs: use examined for all study patients, b) Use of glyburide: use examined for all diabetic study 
patients, c) Use of metformin: use examined for diabetic study patients with eGFR<30, d) Use of 
gemfibrozil: use examined for all study patients with eGFR<30. 
 
Sample Size: We base our sample size justifications on computational techniques that match our study 
design and proposed analytical approach within the constraints of published methodologies (PASS 13 
Power Analysis and Sample Size Software (2014). NCSS, LLC. Kaysville, Utah, USA). We selected our 
sample size to attain adequate power to assess differences in the primary outcome using the design 
effect method for time-to-event approach.(3) We assumed two-sided tests at α = 0.05, an Intra-Class 
Correlation (ICC) of 0.01 (as recommended for health services research when no preliminary data on ICC 
are available)(4), a cluster size of 19 patients per practice, and an 18-month enrollment period with an 
additional follow-up of 24 months after the accrual period. For an individually randomized trial, a total 
sample size of 1,102 provides at least 80% power to detect a hazard ratio of 0.64 (or a 0.05 difference in 
event proportions) assuming the control event proportion at 24 months is 0.15, based on our 
preliminary data. Accounting for the clustered design and 20% attrition (e.g., patients leaving the health 
system), the required sample size is 1,653.  
For the secondary outcome (BP in patients with HTN and total person-time of medication exposures), 
this sample size achieves at least 80% power to detect a small effect size, standardized mean difference 
of at least 0.2. In the 96% of the cohort estimated to have a diagnosis of HTN, this is equivalent to 
detecting a mean SBP difference of 3mmHg (based on preliminary data with SBP standard deviation [SD] 
= 15mmHg), a mean RAASi use difference of ~78 person-years (modeled SD = 390 person-years, based 
on simulation), a mean medication use exposure difference of 72 person-years (modeled SD= 360 
person-years based on simulation). 
 
Preliminary Analyses: Preliminary analyses will focus on data checks for completeness and accuracy and 
address any issues with data quality. Descriptive summaries will be examined overall and by 
intervention group and time point. We will compare distributions of baseline characteristics for 
practices and patients between randomized groups to assess the effectiveness of randomization. All 

 
 
 
primary analyses for intervention group comparisons will use an intention-to-treat approach and results 
will be reported using the CONSORT extension to cluster RCTs.(5) We will adjust for statistical or clinical 
differences in secondary analyses. 
 
General Approach: We will use linear mixed models (LMM) or generalized LMM (GLMM) to account for 
clustering. These models will include random practice intercepts to account for correlation of 
observations from patients within the same practice. For analysis involving repeated measurements, 
random patient intercepts nested within practice effects will also be included. Unadjusted models will 
test a fixed intervention effect; adjusted models may include stratification variables used in 
randomization, patient and practice characteristics exhibiting imbalance between intervention groups, 
and variables associated with missingness. 
 
Primary clinical outcome (Aim 2: eGFR decline ≥40% or ESRD): Our primary analysis will use discrete-
time survival methods to examine the occurrence of the composite endpoint at 6 month intervals from 
baseline. At each of these discrete time points, the average of all eGFR measurements within a +/- 3 
month window will be used to determine event occurrence. This accounts for random eGFR fluctuations 
and will minimize the impact of potential ascertainment bias related to more frequent eGFR 
measurements in the intervention group. We will use a GLMM for binary outcomes with complementary 
log-log link and piecewise-constant hazards. This model will include random practice intercepts to 
account for practice-level clustering. This is analogous to a Cox model with frailty for continuous-time 
survival data (i.e., a random effects survival analysis model). As a secondary analysis, we will treat eGFR 
as a continuous variable and compare the rate of decline over time between the intervention and 
control group using GLMM with an identity link under the normal family. This will utilize all repeated 
outpatient eGFRs from each patient. Random patient intercepts nested within random practice 
intercepts will be included to account for within patient and within practice correlations. The unadjusted 
model will include fixed effects for intervention, time, and treatment by time interaction. We will test 
for significance of the treatment by time interaction to test intervention effects. As sensitivity analyses, 
we will a) fit smoothing-spline mixed-effects models since eGFR trajectories may be nonlinear, and b) 
require 2 consecutive eGFR values below the 40% decline/ESRD threshold.  
 
Secondary process of care outcomes (Aim 1): To assess the intervention effect on HTN control, we will 
compare mean SBP between intervention and control at each 6-month time point using LMM with 
random practice intercepts. At each of these time points, the average of all BP measurements within a 
+/- 3 month window will be used to account for random fluctuations. As an alternative approach, we will 
analyze BP as a binary outcome defined by achieving BP goal of <140/90 via GLMM with logit link and 
binomial family. In examining medications (e.g., RAASi), we will calculate the total number of medication 
days for each patient. The average medication duration will be compared between intervention and 
control using LMM with random practice effect.  
 
Exploratory analyses: Although this study is not powered to conduct subgroup analyses, we will perform 
analyses of the secondary outcomes stratified by DM status, HTN with baseline BP (>140/90), and RAASi 
use to explore whether heterogeneous intervention effects exist among these subgroups. 
Missing Data: The extent of missing data will be described. We will investigate the randomness of 
missing data using available information on patient and provider characteristics to identify possible 
covert missing data mechanisms. The analytical models used can handle data that are missing at 
random, but other strategies to handle missing data (multiple imputation, selection models, pattern-
mixture models) will also be implemented. In addition, adjusted LMM or GLMM will be used to account 
for variables associated with missingness. 
 
Final Statistical Analysis Plan  
Clinical outcome (primary study outcome): A ≥40% decline in eGFR or ESRD.(1)  eGFR decline will be 
adjudicated based on the baseline creatinine and eGFR determined from the CKD-EPI creatinine (2021) 
equation.(6). To limit ascertainment bias, we will also use a once yearly decision support alert to remind 

 
 
 
study PCPs in both arms to order a BMP on study patients, if results are not available in the last 6 
months. ESRD will be defined as an eGFR < 10ml/min to account for patients with markedly reduced 
baseline eGFR values (i.e., 16-20ml/min). To limit surveillance bias, in addition to the above alert, we will 
use 6-month ascertainment windows and average all values within each window. (Table 1). Researchers 
involved in outcome assessment will be strictly blinded. 
 
Process of care outcomes (secondary outcomes): 1) HTN control. Outpatient, sitting BP values 
measured during each outpatient encounter and recorded in the EHR. BP will be treated as a continuous 
variable. To minimize ascertainment bias, we will use 6-month ascertainment windows to determine an 
average BP for each patient for each 6-mo period. 2) Use of RAASi. Will be determined by active use of 
an ACEi or ARB based on the EHR medication list at each outpatient encounter. Analyses will compare 
cumulative person-time exposure during the study. 3) Medication safety. We will examine the rates of 
use of several high-risk medications that can be associated with adverse outcomes in progressive CKD. 
Medication exposure will be determined by presence of the specified medication on the patient's EHR 
medication list at each outpatient encounter. Analyses will compare cumulative person-time exposure 
during the study. a) Use of NSAIDs: use examined for all study patients, b) Use of glyburide: use 
examined for all diabetic study patients, c) Use of metformin: use examined for diabetic study patients 
with eGFR<30, d) Use of gemfibrozil: use examined for all study patients with eGFR<30. 
 
Sample Size: We base our sample size justifications on computational techniques that match our study 
design and proposed analytical approach within the constraints of published methodologies (PASS 13 
Power Analysis and Sample Size Software (2014). NCSS, LLC. Kaysville, Utah, USA). We selected our 
sample size to attain adequate power to assess differences in the primary outcome using the design 
effect method for time-to-event approach.(3) We assumed two-sided tests at α = 0.05, an Intra-Class 
Correlation (ICC) of 0.01 (as recommended for health services research when no preliminary data on ICC 
are available)(4), a cluster size of 19 patients per practice, and an 18-month enrollment period with an 
additional follow-up of 24 months after the accrual period. For an individually randomized trial, a total 
sample size of 1,102 provides at least 80% power to detect a hazard ratio of 0.64 (or a 0.05 difference in 
event proportions) assuming the control event proportion at 24 months is 0.15, based on our 
preliminary data. Accounting for the clustered design and 20% attrition (e.g., patients leaving the health 
system), the required sample size is 1,653.  
For the secondary outcome (BP in patients with HTN and total person-time of medication exposures), 
this sample size achieves at least 80% power to detect a small effect size, standardized mean difference 
of at least 0.2. In the 96% of the cohort estimated to have a diagnosis of HTN, this is equivalent to 
detecting a mean SBP difference of 3mmHg (based on preliminary data with SBP standard deviation [SD] 
= 15mmHg), a mean RAASi use difference of ~78 person-years (modeled SD = 390 person-years, based 
on simulation), a mean medication use exposure difference of 72 person-years (modeled SD= 360 
person-years based on simulation). 
 
Preliminary Analyses: Preliminary analyses will focus on data checks for completeness and accuracy and 
address any issues with data quality. Descriptive summaries will be examined overall and by 
intervention group and time point. We will compare distributions of baseline characteristics for 
practices and patients between randomized groups to assess the effectiveness of randomization. All 
primary analyses for intervention group comparisons will use an intention-to-treat approach and results 
will be reported using the CONSORT extension to cluster RCTs.(5) We will adjust for statistical or clinical 
differences in secondary analyses. 
 
General Approach: We will use linear mixed models (LMM) or generalized LMM (GLMM) to account for 
clustering. These models will include random practice intercepts to account for correlation of 
observations from patients within the same practice. For analysis involving repeated measurements, 
random patient intercepts nested within practice effects will also be included. Unadjusted models will 
test a fixed intervention effect; adjusted models will include stratification variable used in randomization 
(practice size) and patient characteristics (age, sex, race, baseline levels). All statistical analyses will be 
performed in R using glmmTMB and lme4 packages to fit discrete-time survival models, GLMM, and 

 
 
 
LMM and use the marginaleffects package to estimate adjusted average responses and contrasts. 
 
Primary clinical outcome (Aim 2: eGFR decline ≥40% or ESRD): Our primary analysis will use discrete-
time survival methods to examine the occurrence of the composite endpoint at 6-month intervals from 
baseline. To determine progression at each of these discrete time points, the average of all eGFR 
measurements within a +/- 3-month window will be used in order to account for random eGFR 
fluctuations and potential ascertainment bias. We will employ generalized GLMM with complementary 
log-log link with random practice intercepts to account for practice-level clustering. This is analogous to 
a Cox model with frailty for continuous-time survival data (i.e., a random effects survival analysis 
model). The unadjusted model will include fixed effects for intervention, and time. The functional form 
of time (categorical or continuous with restricted cubic splines) will be selected based on the Akaike 
Information Criteria (AIC). Adjusted models will include pre-specified patient (age, sex, race, baseline 
eGFR) and practice variables (practice size). MMWD, being moved to hospice care, or mortality will be 
treated as competing events. Those that did not reach any endpoint will be censored at the end of study 
(July 31, 2022). In secondary analysis, we will use eGFR as a continuous variable and include random 
patient intercepts nested within random practice intercepts. The unadjusted model included fixed 
effects for intervention, time, and treatment by time interaction. We will compare the rate of eGFR 
decline over time between groups. As sensitivity analyses, we evaluated a) smoothing-spline mixed-
effects models since eGFR trajectories may be nonlinear, and b) requiring 2 consecutive eGFR values 
below the 40% decline/ESKD threshold.  
 
Secondary process of care outcomes (Aim 1): To assess the intervention effect on hypertension control, 
we will compare the mean outpatient systolic blood pressure (SBP) between intervention and control at 
each 6-month time point using LMM with fixed effects for treatment, time, and treatment by time 
interaction. At each time point, the average of all BP measurements within a +/- 3-month window will 
be used to account for random fluctuations. As an alternative approach, we will analyze BP as a binary 
outcome defined by achieving BP goal of <140/90 mm Hg or <130/80 mm Hg via GLMM with logit link 
and binomial family. If linearity of time is reasonable, we will compare the slopes between intervention 
and control. These models will include random patient intercepts nested within random practice 
intercepts and will be adjusted for pre-specified covariates (age, sex, race, and practice size). In 
examining medication use, we will compare the average medication exposure days between the arms 
using LMM with random practice effect. For each medication class, the number of exposure days will be 
determined by counting the number of days from medication order start date to either the 
discontinuation date, survival endpoint date (progression, ESKD, death/hospice, MMWD) or study end 
date, whichever comes first. (refer to Table 1 for details). To account for potential overdispersion, we 
will also fit count models including Poisson, generalized Poisson, and negative binomial mixed models 
and select the final model based on minimum Akaike Information Criteria (AIC) or Bayesian Information 
Criteria (BIC). If count models provide better fit, we will report the rate of exposure days per year for 
each medication. We will adjust for baseline medication exposure days in addition to pre-specified 
covariates with the log of the number follow-up days as offset. In post-hoc analyses, we will evaluate the 
effect of intervention on albuminuria minimization among patients with at least one urine-albumin-to-
creatinine ratio (UACR) measure during the study period. Similar to the approach we used for examining 
changes in eGFR, we will fit a mixed effects model utilizing all available UACR for each patient adjusting 
for age, sex, race, baseline eGFR and practice size. We will also adjust for diabetes since we found that it 
is associated with having at least one UACR measure during the study period. Changes in UACR from 
baseline to 18 months (approximately the median follow-up time) were calculated by group, and 
between group differences were compared using contrasts. 
 
Exploratory analyses: Although this study is not powered to conduct subgroup analyses, we will perform 
analyses stratified by age, sex, CKD stage, DM status, HTN with baseline BP (>140/90 mmHg or >130/80 
mmHg), and ACEi/ARB in albuminuric patients, or SGLT-2i use to explore whether heterogeneous 
intervention effects exist among these subgroups. Subgroup by race will also be examined. 
 
Missing Data: The extent of missing data will be described. We will investigate the randomness of 

 
 
 
missing data using available information on patient and provider characteristics to identify possible 
covert missing data mechanisms. The analytical models used can handle data that are missing at 
random, but other strategies to handle missing data (multiple imputation, selection models, pattern-
mixture models) will also be implemented. In addition, adjusted LMM or GLMM will be used to account 
for variables associated with missingness. 
 
Table 1. Determination of outcomes and covariates based from electronic health record (EHR) 
 
Determination from EHR 
General principles 
Baseline time point (T0) 
• Office or telemedicine encounters with the PCP after 
the date the patient was determined to be eligible 
will be extracted. The first PCP encounter within 1 
year of the date of eligibility determination will be 
used as the baseline time point (T0). 
Outpatient records 
• Outpatient data will be extracted from the EHR. 
Laboratory results or vital records obtained within a 
span of 2 consecutive days will be excluded, as these 
are more likely to stem from inpatient encounters.  
Primary outcome 
Composite of ≥40% decline in 
eGFR from baseline or ESKD 
• The CKD-EPI creatinine (2021) equation will be used 
to calculate eGFR. (6) 
• The baseline eGFR will be calculated by averaging the 
two most recent serum creatinine values, which 
should be at least 90 days apart, within a 3-year look-
back period from the first PCP visit (T0). Improbable 
creatinine values (>10) will be excluded. If a second 
creatinine measurement beyond 90 days is not 
available, we will progressively reduce the time 
requirement to at least 60 days and then at least 30 
days. In cases where a second creatinine value cannot 
be found, the single closest creatinine value to T0 will 
be utilized. 
• eGFR at follow-up will be determined by averaging all 
eGFR measurements within +/- 3 months from the 
landmark time points (6, 12, 18, and 24 months). 
• Progression or ESKD flags will be generated at each 
follow-up time point. Progression will be identified if 
the averaged eGFR at the respective time point is ≥
40% of the baseline eGFR. ESKD will be recognized if 
the averaged eGFR at the corresponding time point is 
<10 ml/min, or if ICD-10 or CPT codes indicating ESKD 
or kidney transplant are detected within the time 
window of that specific time point. 

 
 
 
• At each index time point, endpoints for MMWD and 
hospice/death will be determined based on ICD-10 
codes or the date of death within the specified time 
window.  
• Individuals who do not reach any endpoint will be 
considered censored at the conclusion of the study 
(July 31, 2022). Patients with no EHR records at the 
follow-up point will be presumed not to have reached 
any endpoint and will also be censored. 
Secondary outcomes 
eGFR (continuous) 
• Each follow-up eGFR, calculated using the CKDI-EPI 
(2021), will be used without averaging. Elapsed time 
from T0 will be determined based on the date the 
creatinine was drawn.  
Composite of confirmed 
progression or ESKD 
• For the purpose of sensitivity analyses, we will 
consider progression confirmed if the subsequent 
eGFR measurement, taken at least a month after the 
initial progression, continues to show a decline of 
≥40% from the baseline value. The composite 
outcome is determined using a similar approach to 
the primary outcome. 
Blood pressure (continuous) 
• Baseline BP will be determined by taking the most 
recent systolic (SBP) and diastolic BP (DBP) prior to 
T0. 
• BP at follow-up will be determined by averaging all BP 
measurements within +/- 3 months of the landmark 
time points (6, 12, 18, and 24 months). 
• Implausible BP values (systolic BP<70 or >250; 
diastolic BP<10 or >180) will be excluded. 
Hypertension control 
• Hypertension control will be defined as SBP<140 and 
DBP<90. Alternative definition with SBP<130 and 
DBP<80 will also be used to reflect more recent 
guidelines. 
• Continuous BP measurements described above will be 
used to determine hypertension control at baseline 
and at each of the landmark time points (6, 12, 18, 
and 24 months). 
Medication use and exposure 
days 
• Medication list will be extracted from EHR medication 
orders that were flagged as sent. Subcutaneous and 
oral medications were excluded. 
• Medications will be classified into ACEi/ARB, NSAID, 
Glyburide, Metformin, and Gemfibrozil. Use of insulin, 

 
 
 
SGLT2, GLP1, and moderate to high intensity statin 
will also be determined. 
• For each medication class, the count of exposure days 
will be determined by calculating the duration from 
the medication order start date up to the earlier of 
the discontinuation date, survival endpoint date 
(progression, ESKD, death/hospice, MMWD), or the 
study end date. In the case of chronic medications, if 
the discontinuation date is unknown, an expiration 
date of one year from the start date will be assumed. 
For NSAIDs, exposure days will be computed based on 
medication refills, as these are not consistently 
prescribed for extended periods. In situations 
involving multiple medication episodes, the 
cumulative exposure days across all episodes will be 
utilized. 
• At baseline, a medication will be considered active if 
the start date was within 1 year prior to T0 and has 
not been discontinued by T0. 
Other covariates 
Urine albuminuria 
• The most recent urine albumin and urine creatinine 
within 2-years prior to T0 will be used to calculate 
urine albumin-to-creatinine ratio (UACR). If this is 
missing, it will be estimated from protein 
quantification using conversion formulas for urine 
protein-creatinine ratio or urine dipstick protein.(7)  
Kidney failure risk equation 
(KFRE) 
• At baseline, 5-year risk and 2-year risk of ESKD will be 
calculated from baseline eGFR and UACR using a 
validated 4-variable KFRE.(8)  
• A 5 year KFRE ≥ 4% will be considered high-risk CKD. 
Laboratory values 
• K+, hemoglobin, albumin, hemoglobin A1c 
• Baseline laboratory values will be determined by 
averaging the two most recent lab measurements 
within 1 year prior to T0. 
• Implausible lab values were excluded 
(hemoglobin≥27; hemoglobin A1c>20) or censored 
(albumin<1 censored at 1). 
Comorbid conditions 
• Diabetes (type 1 and type 2), hypertension, 
hyperlipidemia, coronary artery disease, 
cerebrovascular disease, peripheral vascular disease, 
congestive heart failure, arrhythmia, gout, chronic 
lung disease, chronic liver disease, mood disorder, 
malignancy, Charlson comorbidity score. 

 
 
 
• Comorbid conditions will be identified based on ICD-9 
or ICD10 records, requiring at least 1 incidence in the 
problem list or 2 incidences from the diagnosis list.  
• Baseline values will be defined on the date of first PCP 
visit (T0). 
Sodiodemographics 
• age, sex, race, ethnicity, marital status, median 
income at zip code of residence, area deprivation 
index, Rural-Urban category (RUCA score), BMI  
• Baseline values will be defined on the date of first PCP 
visit (T0). 
• For BMI, the most recent height and weight 
measurements prior to T0 will be used in the 
calculation.  
Care utilization 
• The number of visits to the PCP, cardiologist, ER, and 
hospitalizations at baseline will be determined using a 
1 year look-back period from T0.  
 
 
Summary of Major Revisions to the SAP 
 
The major changes in the SAP and their underlying reasoning are itemized below. These determinations 
were made prior to the initial disclosure of the study outcomes to the research team in March 2023. 
• 
Establishment of Baseline Date: The baseline date was set as the first primary care provider 
(PCP) visit within one year of the eligibility determination date. This choice was made to ensure 
a common starting point (time 0) for both study arms, which is crucial for conducting time-to-
event analyses. 
• 
eGFR Calculation: To align with current clinical practice, the CKD-EPI 2021 equation was adopted 
for eGFR calculations. 
• 
Competing Events: Events such as Medication Management Without Dialysis (MMWD), 
transition to hospice care, or mortality were considered as competing events as these endpoints 
may informatively censor the primary outcome (progression or ESKD). An exploration of the 
intervention's impact on mortality and the composite of progression, ESKD, or mortality was 
planned. 
• 
Adjusted Models: Efficiency enhancement was pursued through the inclusion of pre-specified 
covariates in adjusted models: age, sex, race, and baseline eGFR. These factors were anticipated 
to be linked with the primary outcome. 
• 
Subgroup Analyses: Subgroups were formed based on age, sex, and CKD stage. The aim was to 
scrutinize whether treatment effects differed across diverse patient demographic and clinical 
characteristics. 
• 
SAP Layout and Detail Enhancement: The SAP's layout was restructured, and more 
comprehensive details about variable extraction from EHR were added. 
 
Further alterations were enacted following the unveiling of the initial study results to the research team 
in March 2023. 
 

 
 
 
• 
Hypertension Control Definition: To remain consistent with contemporary guidelines, a blood 
pressure (BP) goal of <130/80 mm Hg (SBP/DBP) was integrated as an alternate hypertension 
control endpoint. This definition was also added to subgroup analyses related to the primary 
outcome. 
• 
Medication Exposure Days Calculation: To limit missing data in medication exposure days 
calculation, chronic medications were assumed to be active for one year from the medication 
start date when discontinuation date is unknown. For NSAIDs, medication refills were used as 
these are not consistently prescribed for extended periods. 
• 
Model Considerations for Count Data: Considering the skewed nature of the data, models for 
count data were explored for the analysis of medication exposure days. The final model was 
determined based on AIC and BIC criteria. 
• 
Intervention effect on UACR: Following the suggestion of a reviewer, we examined the effect of 
the intervention on albuminuria minimization by analyzing the between group changes in UACR. 
• 
Race subgroup: To address a reviewer’s concern about representativeness of the population 
with respect to race, we performed subgroup analysis by race. 
 
 
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