Dataset Information

A CKD Clinical Decision Support System: A Cluster Randomized Clinical Trial in Primary Care Clinics.

ABSTRACT:

Rationale & objective

The study aimed to develop, implement, and evaluate a clinical decision support (CDS) system for chronic kidney disease (CKD) in a primary care setting, with the goal of improving CKD care in adults.

Study design

This was a cluster randomized trial.

Setting & participants

A total of 32 Midwestern primary care clinics were randomly assigned to either receive usual care or CKD-CDS intervention. Between April 2019 and March 2020, we enrolled 6,420 patients aged 18-75 years with laboratory-defined glomerular filtration rate categories of CKD Stage G3 and G4, and 1 or more of 6 CKD care gaps: absence of a CKD diagnosis, suboptimal blood pressure or glycated hemoglobin levels, indication for angiotensin-converting enzyme inhibitor or angiotensin receptor blocker but not prescribed, a nonsteroidal anti-inflammatory agent on the active medication list, or indication for a nephrology referral.

Intervention

The CKD-CDS provided personalized suggestions for CKD care improvement opportunities directed to both patients and clinicians at primary care encounters.

Outcomes

We assessed the proportion of patients meeting each of 6 CKD-CDS quality metrics representing care gap resolution after 18 months.

Results

The adjusted proportions of patients meeting quality metrics in CKD-CDS versus usual care were as follows: CKD diagnosis documented (26.6% vs 21.8%; risk ratio [RR], 1.17; 95% CI, 0.91-1.51); angiotensin-converting enzyme inhibitor or angiotensin receptor blocker prescribed (15.9% vs 16.1%; RR, 0.95; 95% CI, 0.76-1.18); blood pressure control (20.4% vs 20.2%; RR, 0.98; 95% CI, 0.84-1.15); glycated hemoglobin level control (21.4% vs 22.1%; RR, 1.00; 95% CI, 0.80-1.24); nonsteroidal anti-inflammatory agent not on the active medication list (51.5% vs 50.4%; RR, 1.03; 95% CI, 0.90-1.17); and referral or visit to a nephrologist (38.7% vs 36.1%; RR, 1.02; 95% CI, 0.79-1.32).

Limitations

We encountered an overall reduction in expected primary care encounters and obstacles to point-of-care CKD-CDS utilization because of the coronavirus disease 2019 pandemic.

Conclusions

The CKD-CDS intervention did not lead to a significant improvement in CKD quality metrics. The challenges to CDS use during the coronavirus disease 2019 pandemic likely influenced these results.

Funding

National Institute of Diabetes and Digestive and Kidney Diseases (R18DK118463).

Trial registration

clinicaltrials.gov Identifier: NCT03890588.

SUBMITTER: Sperl-Hillen J

PROVIDER: S-EPMC10906435 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Publications

A CKD Clinical Decision Support System: A Cluster Randomized Clinical Trial in Primary Care Clinics.

Sperl-Hillen JoAnn J Crain A Lauren AL Wetmore James B JB Chumba Lilian N LN O'Connor Patrick J PJ

Kidney medicine 20231212 3

<h4>Rationale & objective</h4>The study aimed to develop, implement, and evaluate a clinical decision support (CDS) system for chronic kidney disease (CKD) in a primary care setting, with the goal of improving CKD care in adults.<h4>Study design</h4>This was a cluster randomized trial.<h4>Setting & participants</h4>A total of 32 Midwestern primary care clinics were randomly assigned to either receive usual care or CKD-CDS intervention. Between April 2019 and March 2020, we enrolled 6,420 patient ...[more]

PMID: 38435072

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Project description:BackgroundThe EBMeDS system is the computerized clinical decision support (CCDS) system of EBPNet, a national computerized point-of-care information service in Belgium. There is no clear evidence of more complex CCDS systems to manage chronic diseases in primary care practices (PCPs). The objective of this study was to assess the effectiveness of EBMeDS use in improving diabetes care.MethodsA cluster-randomized trial with before-and-after measurements was performed in Belgian PCPs over 1 year, from May 2017 to May 2018. We randomly assigned 51 practices to either the intervention group (IG), to receive the EBMeDS system, or to the control group (CG), to receive usual care. Primary and secondary outcomes were the 1-year pre- to post-implementation change in HbA1c, LDL cholesterol, and systolic and diastolic blood pressure. Composite patient and process scores were calculated. A process evaluation was added to the analysis. Results were analyzed at 6 and 12 months. Linear mixed models and logistic regression models based on generalized estimating equations were used where appropriate.ResultsOf the 51 PCPs that were enrolled and randomly assigned (26 PCPs in the CG and 25 in the IG), 29 practices (3815 patients) were analyzed in the study: 2464 patients in the CG and 1351 patients in the IG. No change differences existed between groups in primary or secondary outcomes. Change difference between CG and IG after 1-year follow-up was - 0.09 (95% CI - 0.18; 0.01, p-value = 0.06) for HbA1c; 1.76 (95% CI - 0.46; 3.98, p-value = 0.12) for LDL cholesterol; and 0.13 (95% CI - 0.91; 1.16, p-value = 0.81) and 0.12 (95% CI - 1.25;1.49, p-value = 0.86) for systolic and diastolic blood pressure respectively. The odds ratio of the IG versus the CG for the probability of no worsening and improvement was 1.09 (95% CI 0.73; 1.63, p-value = 0.67) for the process composite score and 0.74 (95% CI 0.49; 1.12, p-value = 0.16) for the composite patient score. All but one physician was satisfied with the EBMeDS system.ConclusionsThe CCDS system EBMeDS did not improve diabetes care in Belgian primary care. The lack of improvement was mainly caused by imperfections in the organizational context of Belgian primary care for chronic disease management and shortcomings in the system requirements for the correct use of the EBMeDS system (e.g., complete structured records). These shortcomings probably caused low-use rates of the system.Trial registrationClinicalTrials.gov, NCT01830569, Registered 12 April 2013.

Project description:ImportanceUniversal early screening for autism spectrum disorder (ASD) is recommended but not routinely performed.ObjectiveTo determine whether computer-automated screening and clinical decision support can improve ASD screening rates in pediatric primary care practices.Design, setting, and participantsThis cluster randomized clinical trial, conducted between November 16, 2010, and November 21, 2012, compared ASD screening rates among a random sample of 274 children aged 18 to 24 months in urban pediatric clinics of an inner-city county hospital system with or without an ASD screening module built into an existing decision support software system. Statistical analyses were conducted from February 6, 2017, to June 1, 2018.InterventionsFour clinics were matched in pairs based on patient volume and race/ethnicity, then randomized within pairs. Decision support with the Child Health Improvement Through Computer Automation system (CHICA) was integrated with workflow and with the electronic health record in intervention clinics.Main outcomes and measuresThe main outcome was screening rates among children aged 18 to 24 months. Because the intervention was discontinued among children aged 18 months at the request of the participating clinics, only results for those aged 24 months were collected and analyzed. Rates of positive screening results, clinicians' response rates to screening results in the computer system, and new cases of ASD identified were also measured. Main results were controlled for race/ethnicity and intracluster correlation.ResultsTwo clinics were randomized to receive the intervention, and 2 served as controls. Records from 274 children (101 girls, 162 boys, and 11 missing information on sex; age range, 23-30 months) were reviewed (138 in the intervention clinics and 136 in the control clinics). Of 263 children, 242 (92.0%) were enrolled in Medicaid, 138 (52.5%) were African American, and 96 (36.5%) were Hispanic. Screening rates in the intervention clinics increased from 0% (95% CI, 0%-5.5%) at baseline to 68.4% (13 of 19) (95% CI, 43.4%-87.4%) in 6 months and to 100% (18 of 18) (95% CI, 81.5%-100%) in 24 months. Control clinics had no significant increase in screening rates (baseline, 7 of 64 children [10.9%]; 6-24 months after the intervention, 11 of 72 children [15.3%]; P = .46). Screening results were positive for 265 of 980 children (27.0%) screened by CHICA during the study period. Among the 265 patients with positive screening results, physicians indicated any response in CHICA in 151 (57.0%). Two children in the intervention group received a new diagnosis of ASD within the time frame of the study.Conclusions and relevanceThe findings suggest that computer automation, when integrated with clinical workflow and the electronic health record, increases screening of children for ASD, but follow-up by physicians is still flawed. Automation of the subsequent workup is still needed.Trial registrationClinicalTrials.gov identifier: NCT01612897.

Project description:Rationale & objectiveMost 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 designPragmatic cluster-randomized trial.Setting & participants524 adults with confirmed creatinine-based estimated glomerular filtration rates of 30 to 59mL/min/1.73m2 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.InterventionsEach 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).OutcomesThe 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.ResultsAll 80 eligible PCPs participated. Mean patient age was 70 years, 47% were nonwhite, and mean estimated glomerular filtration rate was 56±0.6mL/min/1.73m2. 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±1.5mm Hg with usual care, -2.8±1.8mm 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).LimitationsRecruitment of smaller than intended sample size and limited uptake of the testing component of the intervention.ConclusionsAlthough 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.FundingGrants from government (National Institutes of Health) and not-for-profit (American Heart Association) entities.Trial registrationRegistered at ClinicalTrials.gov with study number NCT02925962.

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Project description:Rationale & objectiveTo design and implement clinical decision support incorporating a validated risk prediction estimate of kidney failure in primary care clinics and to evaluate the impact on stage-appropriate monitoring and referral.Study designBlock-randomized, pragmatic clinical trial.Setting & participantsTen primary care clinics in the greater Boston area. Patients with stage 3-5 chronic kidney disease (CKD) were included. Patients were randomized within each primary care physician panel through a block randomization approach. The trial occurred between December 4, 2015, and December 3, 2016.InterventionPoint-of-care noninterruptive clinical decision support that delivered the 5-year kidney failure risk equation as well as recommendations for stage-appropriate monitoring and referral to nephrology.OutcomesThe primary outcome was as follows: Urine and serum laboratory monitoring test findings measured at one timepoint 6 months after the initial primary care visit and analyzed only in patients who had not undergone the recommended monitoring test in the preceding 12 months. The secondary outcome was nephrology referral in patients with a calculated kidney failure risk equation value of >10% measured at one timepoint 6 months after the initial primary care visit.ResultsThe clinical decision support application requested and processed 569,533 Continuity of Care Documents during the study period. Of these, 41,842 (7.3%) documents led to a diagnosis of stage 3, 4, or 5 CKD by the clinical decision support application. A total of 5,590 patients with stage 3, 4, or 5 CKD were randomized and included in the study. The link to the clinical decision support application was clicked 122 times by 57 primary care physicians. There was no association between the clinical decision support intervention and the primary outcome. There was a small but statistically significant difference in nephrology referral, with a higher rate of referral in the control arm.LimitationsContamination within provider and clinic may have attenuated the impact of the intervention and may have biased the result toward null.ConclusionsThe noninterruptive design of the clinical decision support was selected to prevent cognitive overload; however, the design led to a very low rate of use and ultimately did not improve stage-appropriate monitoring.FundingResearch reported in this publication was supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under award K23DK097187.Trial registrationClinicalTrials.gov Identifier: NCT02990897.

Project description:BackgroundThe diagnosis of chronic kidney disease (CKD) is based on laboratory results easily extracted from electronic health records; therefore, CKD identification and management is an ideal area for targeted electronic decision support efforts. Early CKD management frequently occurs in primary care settings where primary care providers (PCPs) may not implement all the best practices to prevent CKD-related complications. Few previous studies have employed randomized trials to assess a CKD electronic clinical decision support system (eCDSS) that provided recommendations to PCPs tailored to each patient based on laboratory results.ObjectiveThe aim of this study was to report the trial design and implementation experience of a CKD eCDSS in primary care.MethodsThis was a 3-arm pragmatic cluster-randomized trial at an academic general internal medicine practice. Eligible patients had 2 previous estimated-glomerular-filtration-rates by serum creatinine (eGFRCr) <60 mL/min/1.73m2 at least 90 days apart. Randomization occurred at the PCP level. For patients of PCPs in either of the 2 intervention arms, the research team ordered triple-marker testing (serum creatinine, serum cystatin-c, and urine albumin-creatinine-ratio) at the beginning of the study period, to be completed when acquiring labs for regular clinical care. The eCDSS launched for PCPs and patients in the intervention arms during a regular PCP visit subsequent to completing the triple-marker testing. The eCDSS delivered individualized guidance on cardiovascular risk-reduction, potassium and proteinuria management, and patient education. Patients in the eCDSS+ arm also received a pharmacist phone call to reinforce CKD-related education. The primary clinical outcome is blood pressure change from baseline at 6 months after the end of the trial, and the main secondary outcome is provider awareness of CKD diagnosis. We also collected process, patient-centered, and implementation outcomes.ResultsA multidisciplinary team (primary care internist, nephrologists, pharmacist, and informaticist) designed the eCDSS to integrate into the current clinical workflow. All 81 PCPs contacted agreed to participate and were randomized. Of 995 patients initially eligible by eGFRCr, 413 were excluded per protocol and 58 opted out or withdrew, resulting in 524 patient participants (188 usual care; 165 eCDSS; and 171 eCDSS+). During the 12-month intervention period, 53.0% (178/336) of intervention patient participants completed triple-marker labs. Among these, 138/178 (77.5%) had a PCP appointment after the triple-marker labs resulted; the eCDSS was opened for 73.9% (102/138), with orders or education signed for 81.4% (83/102).ConclusionsSuccessful integration of an eCDSS into primary care workflows and high eCDSS utilization rates at eligible visits suggest this tailored electronic approach is feasible and has the potential to improve guideline-concordant CKD care.Trial registrationClinicalTrials.gov NCT02925962; https://clinicaltrials.gov/ct2/show/NCT02925962 (Archived by WebCite at http://www.webcitation.org/78qpx1mjR).International registered report identifier (irrid)DERR1-10.2196/14022.

Project description:ImportanceBest practices for improving access to assessment of hereditary cancer risk in primary care are lacking.ObjectiveTo compare 2 population-based engagement strategies for identifying primary care patients with a family or personal history of cancer and offering eligible individuals genetic testing for cancer susceptibility.Design, setting, and participantsThe EDGE (Early Detection of Genetic Risk) clinical trial cluster-randomized 12 clinics from 2 health care systems in Montana, Wyoming, and Washington state to 1 of 2 engagement approaches for assessment of hereditary cancer risk in primary care. The study population included 95 623 English-speaking patients at least 25 years old with a primary care visit during the recruitment window between April 1, 2021, and March 31, 2022.InterventionThe intervention comprised 2 risk assessment engagement approaches: (1) point of care (POC), conducted by staff immediately preceding clinical appointments, and (2) direct patient engagement (DPE), where letter and email outreach facilitated at-home completion. Patients who completed risk assessment and met prespecified criteria were offered genetic testing via a home-delivered saliva testing kit at no cost.Main outcomes and measuresPrimary outcomes were the proportion of patients with a visit who (1) completed the risk assessment and (2) completed genetic testing. Logistic regression models were used to compare the POC and DPE approaches, allowing for overdispersion and including clinic as a design factor. An intention-to-treat analysis was used to evaluate primary outcomes.ResultsOver a 12-month window, 95 623 patients had a primary care visit across the 12 clinics. Those who completed the risk assessment (n = 13 705) were predominately female (64.7%) and aged between 65 and 84 years (39.6%). The POC approach resulted in a higher proportion of patients completing risk assessment than the DPE approach (19.1% vs 8.7%; adjusted odds ratio [AOR], 2.68; 95% CI, 1.72-4.17; P < .001) but a similar proportion completing testing (1.5% vs 1.6%; AOR, 0.96; 95% CI, 0.64-1.46; P = .86). Among those eligible for testing, POC test completion was approximately half of that for the DPE approach (24.7% vs 44.7%; AOR, 0.49; 95% CI, 0.37-0.64; P < .001). The proportion of tested patients identified with an actionable pathogenic variant was significantly lower for the POC approach than the DPE approach (3.8% vs 6.6%; AOR, 0.61; 95% CI, 0.44-0.85; P = .003).Conclusions and relevanceIn this cluster randomized clinical trial of risk assessment delivery, POC engagement resulted in a higher rate of assessment of hereditary cancer risk than the DPE approach but a similar rate of genetic testing completion. Using a combination of engagement strategies may be the optimal approach for greater reach and impact.Trial registrationClinicalTrials.gov Identifier: NCT04746794.

Dataset Information

A CKD Clinical Decision Support System: A Cluster Randomized Clinical Trial in Primary Care Clinics.

Rationale & objective

Study design

Setting & participants

Intervention

Outcomes

Results

Limitations

Conclusions

Funding

Trial registration

Publications

A CKD Clinical Decision Support System: A Cluster Randomized Clinical Trial in Primary Care Clinics.

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