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American Journal of Kidney Diseases

Electronic Decision Support for Management of CKD in Primary Care: A Pragmatic Randomized Trial

Open AccessPublished:July 30, 2020DOI:https://doi.org/10.1053/j.ajkd.2020.05.013

      Rationale & Objective

      Most adults with chronic kidney disease (CKD) in the United States are cared for by primary care providers (PCPs). We evaluated the feasibility and preliminary effectiveness of an electronic clinical decision support system (eCDSS) within the electronic health record with or without pharmacist follow-up to improve the management of CKD in primary care.

      Study Design

      Pragmatic cluster-randomized trial.

      Setting & Participants

      524 adults with confirmed creatinine-based estimated glomerular filtration rates of 30 to 59 mL/min/1.73 m2 cared for by 80 PCPs at the University of California San Francisco. Electronic health record data were used for patient identification, intervention deployment, and outcomes ascertainment.

      Interventions

      Each PCP’s eligible patients were randomly assigned as a group into 1 of 3 treatment arms: (1) usual care; (2) eCDSS: testing of creatinine, cystatin C, and urinary albumin-creatinine ratio with individually tailored guidance for PCPs on blood pressure, potassium, and proteinuria management, cardiovascular risk reduction, and patient education; or (3) eCDSS plus pharmacist counseling (eCDSS-PLUS).

      Outcomes

      The primary clinical outcome was change in blood pressure over 12 months. Secondary outcomes were PCP awareness of CKD and use of angiotensin-converting enzyme inhibitor/angiotensin receptor blocker and statin therapy.

      Results

      All 80 eligible PCPs participated. Mean patient age was 70 years, 47% were nonwhite, and mean estimated glomerular filtration rate was 56 ± 0.6 mL/min/1.73 m2. Among patients receiving eCDSS with or without pharmacist counseling (n = 336), 178 (53%) completed laboratory measurements and 138 (41%) had laboratory measurements followed by a PCP visit with eCDSS deployment. eCDSS was opened by the PCP for 102 (74%) patients, with at least 1 suggested order signed for 83 of these 102 (81%). Changes in systolic blood pressure were −2.1 ± 1.5 mm Hg with usual care, −2.8 ± 1.8 mm Hg with eCDSS, and −1.1 ± 1.1 with eCDSS-PLUS (P = 0.7). PCP awareness of CKD was 16% with usual care, 26% with eCDSS, and 32% for eCDSS-PLUS (P = 0.09). In as-treated analyses, PCP awareness of CKD was significantly greater with eCDSS and eCDSS-PLUS (73% and 69%) versus usual care (47%; P = 0.002).

      Limitations

      Recruitment of smaller than intended sample size and limited uptake of the testing component of the intervention.

      Conclusions

      Although we were unable to demonstrate the effectiveness of eCDSS to lower blood pressure and uptake of the eCDSS was limited by low testing rates, eCDSS use was high when laboratory measurements were available and was associated with higher PCP awareness of CKD.

      Funding

      Grants from government (National Institutes of Health) and not-for-profit (American Heart Association) entities.

      Trial Registration

      Registered at ClinicalTrials.gov with study number NCT02925962.

      Index Words

      Most adults with chronic kidney disease (CKD) in the United States are cared for by primary care providers (PCPs). We conducted a clinical trial to evaluate the feasibility and effectiveness of an electronic clinical decision support system within the electronic health record designed to help primary care physicians improve CKD care. We studied 524 adults with CKD, cared for by 80 PCPs in San Francisco. Although this study had limited power and did not show significant differences in blood pressure, electronic clinical decision support increased primary care physicians’ awareness of CKD.
      Editorial, p. 613
      The enormous burden that is due to chronic kidney disease (CKD) and kidney failure in the United States is in the national spotlight after signature of the “Advancing American Kidney Health” executive order. Part of this initiative aims to improve the identification and management of people with earlier stages of kidney disease. Because most adults with CKD in the United States receive medical care from primary care providers (PCPs), improved CKD management in primary care is imperative.
      Many persons with earlier stages of CKD can be safely managed in primary care without the need for nephrology comanagement,
      • Samal L.
      • Wright A.
      • Waikar S.S.
      • Linder J.A.
      Nephrology co-management versus primary care solo management for early chronic kidney disease: a retrospective cross-sectional analysis.
      ,
      • Ricardo A.C.
      • Roy J.A.
      • Tao K.
      • et al.
      Influence of nephrologist care on management and outcomes in adults with chronic kidney disease.
      but most adults with CKD are undiagnosed, improperly risk stratified, and undertreated.
      • Tuot D.S.
      • Plantinga L.C.
      • Hsu C.Y.
      • Powe N.R.
      Is awareness of chronic kidney disease associated with evidence-based guideline-concordant outcomes?.
      • Plantinga L.C.
      • Tuot D.S.
      • Powe N.R.
      Awareness of chronic kidney disease among patients and providers.
      • Tuot D.S.
      • Plantinga L.C.
      • Hsu C.Y.
      • et al.
      Chronic kidney disease awareness among individuals with clinical markers of kidney dysfunction.
      • Allen A.S.
      • Forman J.P.
      • Orav E.J.
      • Bates D.W.
      • Denker B.M.
      • Sequist T.D.
      Primary care management of chronic kidney disease.
      These gaps persist despite international guidelines that recommend risk stratification with both a measure of filtration (estimated glomerular filtration rate [eGFR]) and one of damage (urinary albumin-creatinine ratio [UACR]), followed by evidence-based treatments that can reduce complications.
      Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group
      KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease.
      Barriers that hinder effective CKD care in primary care settings include lack of awareness and understanding of guidelines for risk stratification and management of CKD, confusion regarding appropriate referral criteria and timing, and lack of confidence in managing CKD.
      • Tahir M.A.
      • Dmitrieva O.
      • de Lusignan S.
      • et al.
      Confidence and quality in managing CKD compared with other cardiovascular diseases and diabetes mellitus: a linked study of questionnaire and routine primary care data.
      Additionally, PCPs have limited time to manage complex visit agendas.
      • Tai-Seale M.
      • McGuire T.G.
      • Zhang W.
      Time allocation in primary care office visits.
      ,
      • Abbo E.D.
      • Zhang Q.
      • Zelder M.
      • Huang E.S.
      The increasing number of clinical items addressed during the time of adult primary care visits.
      The advent of electronic health records (EHRs) has propelled an interest in using electronic decision supports to improve care.
      However, whether EHR-embedded automated decision support can improve outcomes for those with CKD managed in primary care is not well understood. Prior interventions have been hindered by alert fatigue, lack of individualization of care recommendations that are actionable by the PCP, limited focus on PCP education, the need for additional clinical personnel, and limited pretrial design phases to allow close integration into the primary care clinical workflow.
      • Navaneethan S.D.
      • Jolly S.E.
      • Schold J.D.
      • et al.
      Pragmatic randomized, controlled trial of patient navigators and enhanced personal health records in CKD.
      • Major R.W.
      • Brown C.
      • Shepherd D.
      • et al.
      The Primary-Secondary Care Partnership to Improve Outcomes in Chronic Kidney Disease (PSP-CKD) Study: a cluster randomized trial in primary care.
      • Tuot D.S.
      • McCulloch C.E.
      • Velasquez A.
      • et al.
      Impact of a primary care CKD registry in a US public safety-net health care delivery system: a pragmatic randomized trial.
      Moreover, the paucity of experience in conducting pragmatic randomized trials in the field of kidney care adds to our inability to understand the impact of electronic decision support tools on CKD care in the real world.
      • de Boer I.H.
      • Kovesdy C.P.
      • Navaneethan S.D.
      • et al.
      Pragmatic clinical trials in CKD: opportunities and challenges.
      Therefore, we designed an electronic automated CKD decision support tool (electronic clinical decision support system [eCDSS]) embedded into the EHR to provide guidance on risk stratification and individualized CKD management optimization in primary care. We then conducted a 3-arm pragmatic randomized trial to evaluate the feasibility of implementation, usability, and preliminary effectiveness of the eCDSS to improve CKD management.

      Methods

      Design

      We previously published the rationale, design, pretrial pilot activities, and preliminary implementation metrics for this study.
      • Khoong E.C.
      • Karliner L.
      • Lo L.
      • et al.
      A pragmatic cluster randomized trial of an electronic clinical decision support system to improve chronic kidney disease management in primary care: design, rationale, and implementation experience.
      In brief, we used the EHR to identify participants, deliver the eCDSS, and ascertain study outcomes. The 3 arms were: (1) eCDSS; (2) eCDSS-PLUS, which added a pharmacist telephone call to reinforce CKD-related education after a PCP visit in which the eCDSS was used; and (3) usual care.
      Study intervention dates were October 4, 2017, to October 4, 2018, with an additional 9 months of follow-up for a total 21-month duration. This trial was registered on https://ClinicalTrials.gov (NCT02925962), and the University of California San Francisco Human Research Protection Program approved the protocol.

      Eligibility and Consent

      All providers practicing in the general internal medicine practice with a primary care panel were eligible. Providers received an e-mail explaining the study and had 2 weeks to opt out.
      We identified eligible patients through the EHR who were aged 18 to 80 years; preferred language of English, Spanish, or Chinese (Cantonese or Mandarin); had at least 2 outpatient eGFRs of 30 to 59 mL/min/1.73 m2 using the CKD Epidemiology Collaboration (CKD-EPI) creatinine equation at least 90 days apart; and had a primary care visit with their assigned PCP in the prior 18 months. We excluded persons with ongoing nephrology follow-up; additional exclusions are noted in Item S1. For patients, we mailed letters to eligible persons randomly assigned to the intervention arms with a subsequent 2-week opt-out period.

      Randomization and Blinding

      We block-randomized at the PCP level based on panel size (Item S2). The study statistician was blinded to the identification of the PCPs and to allocation of patients to each arm during the study period.

      Interventions

      Electronic Clinical Decision Support System

      The first step of the intervention consisted of obtaining appropriate laboratory testing for risk stratification. Study staff ordered triple-marker testing (serum creatinine, serum cystatin C, and UACR) for all participants randomly assigned to either intervention arm to be done the next time they visited the laboratory for usual clinical care. We programmed the eCDSS to deploy at a subsequent visit with their assigned PCP only if all 3 tests had results available. Details have been previously published.
      • Khoong E.C.
      • Karliner L.
      • Lo L.
      • et al.
      A pragmatic cluster randomized trial of an electronic clinical decision support system to improve chronic kidney disease management in primary care: design, rationale, and implementation experience.
      In brief, the eCDSS was designed to follow PCP workflow during a patient encounter and was built into the current EHR (EpicCare; Epic Systems). The eCDSS appeared as an alert at the time the encounter was opened. The eCDSS first calculated eGFRcr and eGFRcys using the CKD-EPI creatinine equation and the CKD-EPI cystatin C equation, respectively. eCDSS then stratified participants into low-risk unconfirmed CKD (eGFRcr < 60 but eGFRcys > 60 mL/min/1.73 m2, with UACR < 30 mg/g) versus higher risk confirmed CKD (all others with eGFRcr and eGFRcys < 60 mL/min/1.73 m2 or with eGFRcr or eGFRcys > 60 mL/min/1.73 m2 with UACR > 30 mg/g); the designation of low risk is consistent with guidelines.
      Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group
      KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease.
      ,
      • Peralta C.A.
      • Shlipak M.G.
      • Judd S.
      • et al.
      Detection of chronic kidney disease with creatinine, cystatin C, and urine albumin-to-creatinine ratio and association with progression to end-stage renal disease and mortality.
      If the patient was categorized as low risk, the alert notified the PCP along with recommendation for re-testing in 6 months. For patients for whom CKD was confirmed, the alert allowed navigation to a SmartSet, which contained tailored recommendations individualized to each patient and with prepopulated orders.
      The eCDSS SmartSet delivered individualized guideline-concordant recommendations to the PCP: statin use for those with CKD who were older than 50 years,
      Kidney Disease: Improving Global Outcomes (KDIGO) Lipid Work Group
      KDIGO Clinical Practice gGuideline for Lipid Management in Chronic Kidney Disease.
      dietary and diuretic recommendations for those with mild hyperkalemia (potassium level of 5.2-5.5 mg/dL), initiation or uptitration of angiotensin-converting enzyme (ACE)-inhibitor/angiotensin receptor blocker (ARB) therapy, and nephrology referral for the highest risk participants.
      Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group
      KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease.
      Highest risk was defined as any of the following: eGFRcys < 30 mL/min/1.73 m2, potassium level > 5.5 mEq/L, UACR > 300 μg/mg, systolic blood pressure (BP) > 150 mm Hg on therapy with 3 or more agents including a diuretic; and >3% probability of 5-year progression to kidney failure based on the Kidney Failure Risk Equation.
      Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group
      KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease.
      ,
      • Tangri N.
      • Stevens L.A.
      • Griffith J.
      • et al.
      A predictive model for progression of chronic kidney disease to kidney failure.
      The eCDSS also included education materials (based on National Kidney Disease Education Program materials and translated into Spanish and Chinese) regarding CKD general information, avoidance of nonsteroidal anti-inflammatory drugs, and dietary recommendations to be printed in the patient’s after-visit summary. If the eCDSS was ignored, we allowed redeployment at up to 2 subsequent PCP visits during the study period.

      eCDSS-PLUS

      In the second intervention arm, a pharmacist scheduled a follow-up visit by telephone within 2 weeks of the PCP visit when eCDSS deployed. The call was scripted to reinforce medication changes ordered at the PCP visit, CKD-related teaching, and a comprehensive medication review. Information on the telephone encounter was documented in the EHR and sent to the PCP.
      A study nephrologist (L.L.) reviewed weekly laboratory results to identify: eGFRcr decline > 30% from baseline, UACR ≥ 1,000 μg/mg, adherence to nephrology referrals, and any discordance >30% between eGFRcr and eGFRcys to ensure appropriate follow-up.

      Data Collection

      We used the EHR and Epic systems data warehouse to identify participants and ascertain participant characteristics and study outcomes (Item S3). Details on variable definitions have been previously published.
      • Khoong E.C.
      • Karliner L.
      • Lo L.
      • et al.
      A pragmatic cluster randomized trial of an electronic clinical decision support system to improve chronic kidney disease management in primary care: design, rationale, and implementation experience.
      In addition to EHR-derived outcomes, we surveyed participating PCPs who were randomly assigned to intervention to ascertain their perception of study burden using the question: “What level of burden did the eCDSS place on your practice?” Response options were high, medium, low, or none.

      Outcomes

      The primary clinical outcomes were changes from baseline in systolic and diastolic BP. Because this study was initiated before the most recent American Heart Association/American College of Cardiology guidelines, we defined adequate control as BP < 140/90 mm Hg as a secondary clinical outcome. We assessed BP at the end of the intervention period (12 months) and then 9 months after study completion using BP measures only from encounters at the general internal medicine practice.
      The process outcome of primary interest was PCP awareness of the participant’s CKD defined as inclusion of CKD-related International Classification of Diseases, Tenth Revision codes on the problem list or visit diagnosis. We measured CKD awareness overall at study end and also newly recorded among patients without a CKD diagnosis at baseline. Additional secondary outcomes included prespecified clinical process outcomes: use of an ACE inhibitor/ARB and use of a statin (for persons aged >50 years), defined as having an active prescription for an ACE inhibitor/ARB or statin, respectively. We estimated overall use at study end, and new use, defined as having a new prescription among those who were not using these agents, at baseline. We also estimated total and new use of a diuretic. Finally, we report on implementation metrics based on the RE-AIM framework (reach, effectiveness, adoption, implementation, and maintenance) for pragmatic interventions.

      Analyses

      We compared baseline demographics, clinical characteristics, and study outcomes by study arm. For bivariate-unadjusted comparisons across study groups, we specified PCPs as the cluster-level variable (Item S4). In adjusted analyses, we compared outcomes across study arms using multilevel mixed-effects models accounting for clustering within PCPs and specifying robust standard errors. Primary analyses followed intention-to-treat principles.
      We also performed prespecified “as-treated” analyses restricted to participants who completed testing. To understand sources of potential bias, we compared characteristics by study arm (intervention vs usual care) including only those who received the intervention. Because we found some differences in age, sex, and diabetes status, we adjusted for these to understand the association of the intervention with outcomes in as-treated analyses. Finally, we estimated use of ACE inhibitors/ARBs in those with albuminuria among patients tested within each intervention arm.
      As previously reported,
      • Khoong E.C.
      • Karliner L.
      • Lo L.
      • et al.
      A pragmatic cluster randomized trial of an electronic clinical decision support system to improve chronic kidney disease management in primary care: design, rationale, and implementation experience.
      we powered this study for the clinical outcome of BP change. We anticipated that if we recruited 1,400 participants, we would have 80% power to detect a difference of 1.27 mm Hg in mean BP between arms (Item S5 has original sample-size calculations). All analyses were performed using Stata, version 14.2 (StataCorp LLC).

      Results

      Setting and Participants

      All 80 eligible PCPs (49 attendings, 28 residents, and 3 nurse practitioners) agreed to participate (100%). We excluded providers with no eligible patients.
      We identified 995 patients who met initial criteria for participation. Among these, 326 were excluded based on protocol, with an additional 87 patients excluded by PCPs. Details on reasons for exclusions have been previously published and are documented in the footnote of Figure 1.
      • Khoong E.C.
      • Karliner L.
      • Lo L.
      • et al.
      A pragmatic cluster randomized trial of an electronic clinical decision support system to improve chronic kidney disease management in primary care: design, rationale, and implementation experience.
      Only 55 (9%) intervention patients opted out or withdrew after receiving study information by mail. The total final sample was 524 patient participants randomly assigned across the 3 arms.
      Figure thumbnail gr1
      Figure 1Study flow. Among 995 initially eligible persons, 326 were excluded due to kidney failure, 53; 2+ nephrology visits, 136; excluded language, 57; kidney transplant, 42; and dementia, 38. Physicians directed additional exclusions: 87 (18 death). An additional 55 opted out or withdrew, and an additional 3 were ultimately found to be ineligible.
      • Khoong E.C.
      • Karliner L.
      • Lo L.
      • et al.
      A pragmatic cluster randomized trial of an electronic clinical decision support system to improve chronic kidney disease management in primary care: design, rationale, and implementation experience.
      Abbreviations: eCDSS, electronic clinical decision support system; eCDSS-PLUS, eCDSS plus pharmacist counseling.
      Patient participant characteristics were well balanced at baseline, as shown in Table 1. Characteristics of those who opted out versus those who participated were similar, as previously reported.
      • Khoong E.C.
      • Karliner L.
      • Lo L.
      • et al.
      A pragmatic cluster randomized trial of an electronic clinical decision support system to improve chronic kidney disease management in primary care: design, rationale, and implementation experience.
      Overall, patient participants were older adults who had relatively early CKD and had high rates of CKD guideline-concordant care with the exception of baseline albuminuria testing. Baseline CKD awareness by PCP was limited, with only 47% of patients having a CKD diagnosis on the problem list or at a prior visit (Table 1).
      Table 1Baseline Patient Participant Characteristics by Study Arm
      Usual CareeCDSSeCDSS-PLUSTotalP
      P values account for clustering of patients within physicians.
      No. of patients188165171524
      No. of PCPs27252880
      Age, y71.1 ± 8.470.2 ± 8.669.4 ± 9.670.3 ± 8.90.4
      Male sex113 (60.1%)90 (54.5%)85 (49.7%)288 (55.0%)0.4
      Race/ethnicity0.2
       White99 (52.7%)98 (59.8%)80 (46.8%)277 (53.0%)
       Black/African American17 (9.0%)21 (12.8%)31 (18.1%)69 (13.2%)
       Asian53 (28.2%)29 (17.7%)35 (20.5%)117 (22.4%)
       Hispanic15 (8.0%)9 (5.5%)14 (8.2%)38 (7.3%)
       Other4 (2.1%)7 (4.3%)11 (6.4%)22 (4.2%)
      Preferred language0.4
       English159 (84.6%)153 (92.7%)157 (91.8%)469 (89.5%)
       Chinese22 (11.7%)9 (5.5%)8 (4.7%)39 (7.4%)
       Spanish7 (3.7%)3 (1.8%)6 (3.5%)16 (3.1%)
      Comorbid conditions
       Cerebrovascular disease17 (9.0%)9 (5.5%)10 (5.8%)36 (6.9%)0.3
       Congestive heart failure17 (9.0%)14 (8.5%)9 (5.3%)40 (7.6%)0.4
       Coronary artery disease39 (20.7%)29 (17.6%)27 (15.8%)95 (18.1%)0.5
       Diabetes mellitus75 (39.9%)55 (33.3%)69 (40.4%)199 (38.0%)0.3
       Hyperlipidemia115 (61.2%)97 (58.8%)90 (52.6%)302 (57.6%)0.4
       Hypertension137 (72.9%)122 (73.9%)118 (69.0%)377 (72.0%)0.6
      Medication use
       ACEi/ARB115 (61.2%)102 (61.8%)102 (59.7%)319 (60.9%)0.9
       Diuretic73 (38.8%)66 (40.0%)60 (35.1%)199 (38.0%)0.5
       Statin136 (72.3%)107 (64.9%)110 (64.3%)353 (67.4%)0.3
      CKD-related variables
       Had albuminuria test90 (47.9%)59 (35.8%)71 (41.5%)220 (42.0%)0.3
       CKD diagnosis98 (52.1%)78 (47.3%)71 (41.5%)247 (47.1%)0.3
       Systolic BP, mm Hg130 [118-142]126 [116-137]131 [118-143]128 [117-140]0.2
       Diastolic BP, mm Hg68 [63-75]68 [62-76]68 [63-73]68 [63-75]0.8
       BP controlled (<140/90 mm Hg)130 (69.2%)128 (77.6%)115 (67.3%)373 (71.2%)0.09
       eGFRcr, mL/min/1.73 m256 ± 12.255 ± 11.558 ± 11.456 ± 11.80.1
      Note: N = 524. Values for continuous variables given as mean ± standard deviation or median [interquartile range]; for categorical variables, as count (percentage).
      Abbreviations: ACEi/ARB, angiotensin-converting enzyme inhibitor; angiotensin receptor blocker; BP, blood pressure; CKD, chronic kidney disease; eCDSS, electronic clinical decision support system; eCDSS-PLUS, electronic clinical decision support system with pharmacist counseling; eGFRcr, estimated glomerular filtration rate using creatinine level; PCP, primary care provider.
      a P values account for clustering of patients within physicians.

      Adoption and Implementation

      At study end, 178 intervention patient participants completed a triple marker screen. Among these, 138 (78%) had a subsequent visit with their PCP during the intervention period. The eCDSS was highly used by the PCPs. Among the 138 encounters with an eligible PCP visit, the eCDSS was opened by the PCP for 102 (74%) participants, and during these 102 encounters, orders were signed or patient education was given from the SmartSet for 83 participants (81%; Fig 1). Among those with orders signed, 67 had confirmed high-risk CKD and 16 had low-risk unconfirmed CKD (Item S6 details orders signed).
      The eCDSS identified 33 (10% of intervention) patients who met criteria for nephrology referral.
      During the entire study period, a total of 425 (81%) patients had a PCP visit (69% usual care, 89% eCDSS, and 87% eCDSS-PLUS). Of 524 patients, 22 (4%) changed PCPs from one in an intervention group to one in the usual-care group before they had an eligible intervention visit.

      CKD Risk Stratification and Utility of the Triple Marker

      Among the 178 patients who completed the triple marker screening, we found that 40 (22%) had CKD that was not confirmed by either eGFRcys < 60 mL/min/1.73 m2 or UACR ≥ 30 mg/g (considered low-risk CKD). Of the remaining 138 patients who had CKD confirmed, 69 (50%) were confirmed by eGFRcys < 60 mL/min/1.73 m2 only (UACR < 30 mg/g). The remaining 69 (50%) patients had all 3 tests consistent with CKD (eGFRcr < 60 mL/min/1.73 m2, eGFRcys < 60 mL/min/1.73 m2, and UACR ≥ 30 mg/g), the highest-risk category.

      Intention-to-Treat Analyses

      We found no significant differences in BP change or BP control between arms (Table 2). When analyzing only those with uncontrolled BP at baseline, there were no differences in BP control achieved (12%, 14%, and 12% for usual care, eCDSS, and eCDSS-PLUS, respectively).
      Table 2eCDSS and Outcomes: Intention-to-Treat Analyses at 12 Months
      Usual Care (188 pts, 27 PCPs)eCDSS (165 pts, 25 PCPs)eCDSS-PLUS (171 pts, 28 PCPs)Total (524 pts, 80 PCPs)P
      P values account for clustering of patients within physicians.
      Clinical Primary and Secondary Outcomes
      Change in systolic BP, mm Hg
      Only of 480 patients with valid BP measure during study period (number missing BP: 20 usual care, 11 eCDSS, and 13 eCDSS-PLUS).
      −2.1 ± 18.2−2.8 ± 20.9−1.1 ± 20.2−2.0 ± 19.70.7
      Change in diastolic BP, mm Hg
      Only of 480 patients with valid BP measure during study period (number missing BP: 20 usual care, 11 eCDSS, and 13 eCDSS-PLUS).
      −0.2 ± 10.40.1 ± 12.0−0.4 ± 10.8−0.2 ± 11.00.9
      Controlled BP
      Only of 480 patients with valid BP measure during study period (number missing BP: 20 usual care, 11 eCDSS, and 13 eCDSS-PLUS).
      (<140/90 mm Hg)
      109 (65%)114 (74%)100 (63%)323 (67%)0.1
      CKD Awareness: Process Outcome
      PCP awareness of CKD diagnosis at study end
      Inclusion on problem list or visit diagnosis.
      88 (47%)86 (52%)86 (50.3%)260 (50%)0.7
      PCP new awareness
      New diagnosis from study baseline.
      14 (16%)23 (26%)32 (32%)69 (25%)0.09
      Secondary Clinical Process Outcomes
      ACEi/ARB use
      ACEi/ARB, statin therapy, and diuretic medication “use” include only patients who are still usng the medication at the end of the study period.
      95 (51%)86 (52%)75 (44%)256 (49%)0.3
      ACEi/ARB initiation
      Inclusion on problem list or visit diagnosis.
      5 (7%)6 (9%)3 (4%)14 (7%)0.5
      Statin therapy use
      ACEi/ARB, statin therapy, and diuretic medication “use” include only patients who are still usng the medication at the end of the study period.
      112 (61%)79 (49%)94 (58%)285 (56%)0.03
      Statin therapy initiation
      New diagnosis from study baseline.
      ,
      ACEi/ARB, statin therapy, and diuretic medication “use” include only patients who are still usng the medication at the end of the study period.
      3 (6%)3 (5%)4 (7%)10 (6%)0.9
      Diuretic use at end of study
      ACEi/ARB, statin therapy, and diuretic medication “use” include only patients who are still usng the medication at the end of the study period.
      47 (25%)35 (21%)32 (19%)114 (22%)0.3
      Diuretic initiation
      New use.
      5 (4%)3 (3%)1 (1%)9 (3%)0.4
      Note: N = 524. Values for continuous variables give as mean ± standard deviation; for categorical variables, as count (percentage).
      Abbreviations: ACEi/ARB, angiotensin-converting enzyme inhibitor; angiotensin receptor blocker; BP, blood pressure; CKD, chronic kidney disease; eCDSS, electronic clinical decision support system; eCDSS-PLUS, electronic clinical decision support system with pharmacist counseling; PCP, primary care provider; pt, patient.
      a P values account for clustering of patients within physicians.
      b Only of 480 patients with valid BP measure during study period (number missing BP: 20 usual care, 11 eCDSS, and 13 eCDSS-PLUS).
      c Inclusion on problem list or visit diagnosis.
      d New diagnosis from study baseline.
      e ACEi/ARB, statin therapy, and diuretic medication “use” include only patients who are still usng the medication at the end of the study period.
      f New use.
      The overall proportion of patients with a CKD diagnosis documented at study end was somewhat higher among the intervention groups compared with usual care, whereas the proportion of patients with new documentation of a CKD diagnosis was almost double than in the intervention arms compared with usual care after 12 months, although these differences did not reach statistical significance (Table 2). Use of ACE inhibitors/ARBs and statins remained high during the study period. There were no differences in total use or new use of ACE inhibitors/ARBs by study arm. Although statin use remained higher in usual care as seen at baseline, there were no differences in new use of statins by study arm (Table 2). Results were not materially different with an additional 9 months of follow-up after study completion (Table S1). In exploratory analyses, we found that patients in the intervention arms had lower rates of nephrology consult (9%) compared with usual care (14%), although this was not statistically significant (P = 0.2).

      As-Treated Analyses

      We first compared baseline characteristics of participants who completed testing by study arm and with usual care. Overall, characteristics remained balanced, except for higher rates of diabetes in the intervention arms compared with usual care (Table S2). Among the intervention arms, compared with patients who did not have the eCDSS deploy during a visit, those who completed triple marker tests and had an eligible PCP visit (ie, received the intervention) were more likely to be nonwhite, report a non–English-speaking preference, more likely to have heart disease or diabetes, and were more likely to be on ACE-inhibitor/ARB and statin treatment at baseline (Table S3).
      PCP total and new awareness were significantly higher among intervention arms versus usual care (Table 3). Compared with usual care, the odds of PCP CKD awareness was higher in the eCDSS (odds ratio [OR], 3.18; 95% confidence interval [CI], 1.29-7.82) and eCDSS PLUS (OR, 2.49; 95% CI, 1.21-5.10) groups after adjustment. Similarly, the adjusted odds of PCP “new” CKD awareness was higher in the eCDSS (OR, 10.3; 95% CI, 1.48-71.37) and eCDSS-PLUS (OR, 8.34; 95% CI, 1.90-36.62) groups (Table S4). There were no significant differences in BP change or BP control. In extended follow-up analyses, we found higher use of ACE inhibitors/ARBs in the intervention arms (Table S5).
      Table 3Outcome Measures by Study Arm at 12 Months: As-Treated Analyses
      Usual Care (188 pts, 27 PCPs)eCDSS (63 pts, 17 PCPs)eCDSS-PLUS (75 pts, 21 PCPs)Total (326 pts, 65 PCPs)P
      P values account for clustering of patients within physicians.
      Clinical Primary and Secondary Outcome
      Change in systolic BP,
      Only of 306 patients with valid BP measure during study period (number missing BP: 20 usual care, 0 eCDSS, and 0 eCDSS-PLUS).
      mm Hg
      −2.1 ± 18.2−3.9 ± 20.7−0.9 ± 18.1−2.2 ± 18.70.6
      Change in diastolic BP,
      Only of 306 patients with valid BP measure during study period (number missing BP: 20 usual care, 0 eCDSS, and 0 eCDSS-PLUS).
      mm Hg
      −0.2 ± 10.4−1.3 ± 12.00.5 ± 9.0−0.3 ± 10.40.7
      Controlled BP
      Only of 306 patients with valid BP measure during study period (number missing BP: 20 usual care, 0 eCDSS, and 0 eCDSS-PLUS).
      (<140/90 mm Hg)
      109 (65%)43 (68%)50 (67%)202 (66%)0.9
      CKD Awareness: Process Outcome
      PCP awareness of CKD at study end
      Inclusion on problem list or visit diagnosis.
      88 (47%)46 (73%)52 (69%)186 (57%)0.002
      PCP new awareness
      New diagnosis from study baseline.
      14 (16%)18 (55%)22 (52%)54 (33%)<0.001
      Secondary Clinical Process Outcomes
      ACEi/ARB use
      ACEi/ARB, statin therapy, and diuretic medication “use” includes only patients who are still using the medication by the end of the study period.
      95 (51%)35 (56%)40 (53%)170 (52%)0.7
      ACEi/ARB initiation
      ACEi/ARB, statin therapy, and diuretic medication “use” includes only patients who are still using the medication by the end of the study period.
      ,
      New use.
      5 (7%)5 (24%)1 (5%)11 (10%)0.04
      Statin therapy use
      ACEi/ARB, statin therapy, and diuretic medication “use” includes only patients who are still using the medication by the end of the study period.
      112 (61%)36 (59%)44 (61%)192 (61%)0.9
      Statin therapy initiation
      ACEi/ARB, statin therapy, and diuretic medication “use” includes only patients who are still using the medication by the end of the study period.
      ,
      New use.
      3 (2%)2 (3%)2 (3%)7 (2%)0.7
      Diuretic use
      ACEi/ARB, statin therapy, and diuretic medication “use” includes only patients who are still using the medication by the end of the study period.
      47 (25%)16 (25%)16 (21%)79 (24%)0.8
      Diuretic initiation
      New use.
      5 (4%)2 (7%)0 (0%)7 (4%)0.5
      Note: N = 326. Values for continuous variables give as mean ± standard deviation; for categorical variables, as count (percentage).
      Abbreviations: ACEi/ARB, angiotensin-converting enzyme inhibitor; angiotensin receptor blocker; BP, blood pressure; CKD, chronic kidney disease; eCDSS, electronic clinical decision support system; eCDSS-PLUS, electronic clinical decision support system with pharmacist counseling; PCP, primary care provider; pt, patient.
      a P values account for clustering of patients within physicians.
      b Only of 306 patients with valid BP measure during study period (number missing BP: 20 usual care, 0 eCDSS, and 0 eCDSS-PLUS).
      c Inclusion on problem list or visit diagnosis.
      d New diagnosis from study baseline.
      e ACEi/ARB, statin therapy, and diuretic medication “use” includes only patients who are still using the medication by the end of the study period.
      f New use.
      Among 138 persons in the intervention arms who were tested and had eCDSS deploy, 53 (38%) had UACR ≥ 30 mg/g. Among these 53 participants, use of ACE inhibitors/ARBs was 64% and 61% in the eCDSS and eCDSS-PLUS arms, respectively (P = 0.9).

      Burden

      A total of 35 (66%) providers randomly assigned to an intervention arm responded to the physician survey and 27 of 35 (77%) recalled seeing the eCDSS. Among those, 20 (74%) reported low or no burden from the eCDSS on their practice.

      Discussion

      In this study, we efficiently used the EHR to identify patient participants, deploy the intervention, and ascertain outcomes. We also demonstrated that when deployed during an eligible encounter, >70% of the PCPs engaged with the electronic clinical decision support tool. Moreover, use of the eCDSS improved documented recognition of CKD. However, due to the limited uptake of the patient laboratory testing part of the intervention, we were unable to determine whether the eCDSS can improve CKD-related management and clinical outcomes in primary care.
      This study is important given recent payment reforms announced by the US Department of Health and Human Services to incentivize earlier detection, risk stratification, and evidence-based management of CKD. To do so, we must begin by improving our ability to identify individuals with CKD at earlier stages, distinguish those at highest risk for complications, and empower PCPs to manage patients with CKD who may not require nephrology comanagement. With the use of an automated tool integrated into the EHR and using a triple-marker approach to testing and risk stratification, we found that one-fifth of patients with the previous 2 eGFRcr values < 60 mL/min/1.73 m2 had CKD that was not confirmed by eGFRcys or albuminuria. These individuals have lower risk for complications, and international guidelines consider them as not having CKD.
      Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group
      KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease.
      Our tool, which recommended guideline-concordant care interventions and identified patients in need of nephrology referral, was opened at 74% of the eligible encounters. This very high rate suggests that when designed with physician input to follow workflow and incorporate individualized action items for each patient, electronic decision tools can be highly used. Although we were unable to conclusively determine whether the use of the automated tool improved clinical outcomes, a prespecified as-treated analysis showed that the eCDSS increased CKD documentation by PCPs. Given prior reports on the low levels of awareness of CKD by PCPs,
      • Plantinga L.C.
      • Tuot D.S.
      • Powe N.R.
      Awareness of chronic kidney disease among patients and providers.
      we believe that our tool could lead to improved outcomes by raising clinician awareness of the diagnosis.
      For clinical decision support to be useful, it must be integrated into the EHR workflow, which is challenging due to the need to program complex clinical information in the background, use current evidence, and provide clinically meaningful recommendations.
      • Porat T.
      • Delaney B.
      • Kostopoulou O.
      The impact of a diagnostic decision support system on the consultation: perceptions of GPs and patients.
      ,
      • Collins I.M.
      • Breathnach O.
      • Felle P.
      Electronic clinical decision support systems attitudes and barriers to use in the oncology setting.
      Prior CKD studies using enhanced EHRs or registries had been limited in their ability to integrate into PCP workflows and provide individualized recommendations
      • Navaneethan S.D.
      • Jolly S.E.
      • Schold J.D.
      • et al.
      Pragmatic randomized, controlled trial of patient navigators and enhanced personal health records in CKD.
      ,
      • Tuot D.S.
      • McCulloch C.E.
      • Velasquez A.
      • et al.
      Impact of a primary care CKD registry in a US public safety-net health care delivery system: a pragmatic randomized trial.
      and required additional clinical staff on site, which can be costly.
      • Major R.W.
      • Brown C.
      • Shepherd D.
      • et al.
      The Primary-Secondary Care Partnership to Improve Outcomes in Chronic Kidney Disease (PSP-CKD) Study: a cluster randomized trial in primary care.
      This study extends these prior studies to inform the field on how to design and conduct pragmatic real-world interventions leveraging the EHR. With this study design, costs were low and our success in using the EHR for patient identification, deployment of the intervention, and ascertainment of outcomes constitutes an important example for future interventions. The low opt-out rate by physicians and patients demonstrated willingness to participate in research and allowed us to include a diverse cohort of participants with early stages of CKD, including non-English speakers.
      Despite these successes, we also had some difficulties in the implementation of the protocol. These challenges provide important insights that can inform future research and clinical implementation. Our sample size was smaller than planned due both to fewer patients meeting inclusion criteria and to exclusion criteria applying to more patients than we had expected, thus limiting our power to detect differences between arms.
      Due to the limited intervention time frame of this study, one of the investigators ordered all the triple marker tests with the expectation that intervention group patients would get the tests done the next time they visited the laboratory for clinical care. However, only 41% of participants randomly assigned to an intervention arm obtained the required testing and had a subsequent follow-up appointment in which the eCDSS launched. In a larger trial, incorporating triple-marker test ordering into the PCP-facing intervention may improve uptake. Patient reminders about laboratory tests would also likely increase intervention uptake, but may reduce pragmatism.
      We also found that pharmacists were frequently unable to reach patients for follow up. Future studies could consider other forms of communication such as texting or patient-facing tools instead of telephone calls to provide guidance. Use of BP measures from the EHR may be limited by missing data and lack of standardization, although the medical assistants in the studied general medicine practice undergo certification of appropriate technique. Although deployment in a single academic practice may also limit generalizability, the patients included are representative of the San Francisco area. An additional consideration for pragmatic trial implementation relates to consent procedures. The requirement to send a letter and then wait 2 weeks for patients to have the opportunity to opt out of a clinician-facing intervention limited our ability to test patients as they became eligible for the study. In the future, a rolling enrollment procedure could increase intervention uptake, but this would require modified consent procedures or waived consent.
      In summary, although we are unable to determine whether a CKD eCDSS can improve clinical outcomes for patients with CKD in primary care, this study represents a foundation for pragmatic trials in nephrology that use the EHR to embed kidney-related interventions in primary care. The findings that 1 in 5 patients with CKD by creatinine level had low-risk CKD by other markers, coupled with the high engagement with the tool and increased documented recognition of confirmed CKD diagnoses, argue for a larger study of primary-care embedded electronic decision support.

      Article Information

      Authors’ Full Names and Acadmic Degrees

      Carmen A. Peralta, MD, MAS, Jennifer Livaudais-Toman, PhD, Marilyn Stebbins, PharmD, Lowell Lo, MD, Andrew Robinson, BS, Sarita Pathak, MPH, Rebecca Scherzer, PhD, and Leah S. Karliner, MD, MAS.

      Authors’ Contributions

      Research idea and study design: CAP, LSK; data analysis and interpretation: CAP, LSK; clinical oversight, protocol design: LL; data analysis: JL-T; conducted study and/or recruited and interacted with participants: MS, AR, SP, RS. Each author contributed important intellectual content during manuscript drafting or revision and agrees to be personally accountable for the individual’s own contributions and to ensure that questions pertaining to the accuracy or integrity of any portion of the work, even one in which the author was not directly involved, are appropriately investigated and resolved, including with documentation in the literature if appropriate.

      Support

      This was work was supported by the National Institute of Diabetes and Digestive and Kidney Diseases , grant number R18DK110959 (PI: Peralta, Karliner). Dr Peralta was also supported by an American Heart Association Established Investigator Award (PI: Peralta; 17IEA33410161 ). Dr Karliner received additional support for this work from the National Institute on Aging , grant number P30AG015272 (PI: Karliner). The funders had no role in study design; data collection, analysis, or reporting; or the decision to submit for publication.

      Financial Disclosure

      Dr Peralta is the Chief Medical Officer of Cricket Health. The remaining authors declare that they have no relevant financial interests.

      Data Sharing

      Per the National Institutes of Health’s policy, no identifiable data are available to the public. However, the study protocol and statistical analysis plan is available at ClinicalTrials.gov.

      Peer Review

      Received December 2, 2019. Evaluated by 3 external peer reviewers, with direct editorial input from a Statistics/Methods Editor, an Associate Editor, and the Editor-in-Chief. Accepted in revised form May 3, 2020.

      Supplementary Material

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      Linked Article

      • CKD Management in Primary Care: Supporting Systems Change
        American Journal of Kidney DiseasesVol. 76Issue 5
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          Persistent gaps exist in care for patients with chronic kidney disease (CKD), such as inadequate blood pressure (BP) control and low use of angiotensin-converting enzyme (ACE) inhibitors or angiotensin receptor blockers (ARBs) and statins.1 There are multiple barriers to optimal CKD care, including: (1) low general public awareness about CKD, its risk factors, and its consequences; (2) lack of understanding about individual CKD risk; (3) inadequate urinary albumin-creatinine ratio (UACR) testing to diagnose early CKD or appropriately risk-stratify patients; (4) lack of patient or provider awareness of CKD; and (5) lack of provider- or patient-targeted evidence-based interventions to reduce CKD progression or cardiovascular disease (CVD; Fig 1).  
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