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Aims Chronic kidney disease (CKD) increases risk of cardiovascular disease (CVD). Less is known about how CVD associates with future risk of kidney failure with replacement therapy (KFRT). Methods and results The study included 25 903 761 individuals from the CKD Prognosis Consortium with known baseline estimated glomerular filtration rate (eGFR) and evaluated the impact of prevalent and incident coronary heart disease (CHD), stroke, heart failure (HF), and atrial fibrillation (AF) events as time-varying exposures on KFRT outcomes. Mean age was 53 (standard deviation 17) years and mean eGFR was 89 mL/min/1.73 m2, 15% had diabetes and 8.4% had urinary albumin-to-creatinine ratio (ACR) available (median 13 mg/g); 9.5% had prevalent CHD, 3.2% prior stroke, 3.3% HF, and 4.4% prior AF. During follow-up, there were 269 142 CHD, 311 021 stroke, 712 556 HF, and 605 596 AF incident events and 101 044 (0.4%) patients experienced KFRT. Both prevalent and incident CVD were associated with subsequent KFRT with adjusted hazard ratios (HRs) of 3.1 [95% confidence interval (CI): 2.9–3.3], 2.0 (1.9–2.1), 4.5 (4.2–4.9), 2.8 (2.7–3.1) after incident CHD, stroke, HF and AF, respectively. HRs were highest in first 3 months post-CVD incidence declining to baseline after 3 years. Incident HF hospitalizations showed the strongest association with KFRT [HR 46 (95% CI: 43–50) within 3 months] after adjustment for other CVD subtype incidence. Conclusion Incident CVD events strongly and independently associate with future KFRT risk, most notably after HF, then CHD, stroke, and AF. Optimal strategies for addressing the dramatic risk of KFRT following CVD events are needed.

SIGNIFICANCE STATEMENT: The kidney failure risk equation (KFRE) uses age, sex, GFR, and urine albumin-to-creatinine ratio (ACR) to predict 2- and 5-year risk of kidney failure in populations with eGFR

OBJECTIVE To predict adverse kidney outcomes for use in optimizing medical management and clinical trial design. RESEARCH DESIGN AND METHODS In this meta-analysis of individual participant data, 43 cohorts (N = 1,621,817) from research studies, electronic medical records, and clinical trials with global representation were separated into development and validation cohorts. Models were developed and validated within strata of diabetes mellitus (presence or absence) and estimated glomerular filtration rate (eGFR; ≥60 or RESULTS There were 17,399 and 24,591 events in development and validation cohorts, respectively. Models predicting ≥40% eGFR decline or kidney failure incorporated age, sex, eGFR, albuminuria, systolic blood pressure, antihypertensive medication use, history of heart failure, coronary heart disease, atrial fibrillation, smoking status, and BMI, and, in those with diabetes, hemoglobin A1c, insulin use, and oral diabetes medication use. The median C-statistic was 0.774 (interquartile range [IQR] = 0.753, 0.782) in the diabetes and higher-eGFR validation cohorts; 0.769 (IQR = 0.758, 0.808) in the diabetes and lower-eGFR validation cohorts; 0.740 (IQR = 0.717, 0.763) in the no diabetes and higher-eGFR validation cohorts; and 0.750 (IQR = 0.731, 0.785) in the no diabetes and lower-eGFR validation cohorts. Incorporating the previous 2-year eGFR slope minimally improved model performance, and then only in the higher-eGFR cohorts. CONCLUSIONS Novel prediction equations for a decline of ≥40% in eGFR can be applied successfully for use in the general population in persons with and without diabetes with higher or lower eGFR.

Aims The 2021 European Society of Cardiology (ESC) guideline on cardiovascular disease (CVD) prevention categorizes moderate and severe chronic kidney disease (CKD) as high and very-high CVD risk status regardless of other factors like age and does not include estimated glomerular filtration rate (eGFR) and albuminuria in its algorithms, systemic coronary risk estimation 2 (SCORE2) and systemic coronary risk estimation 2 in older persons (SCORE2-OP), to predict CVD risk. We developed and validated an ‘Add-on’ to incorporate CKD measures into these algorithms, using a validated approach. Methods In 3,054 840 participants from 34 datasets, we developed three Add-ons [eGFR only, eGFR + urinary albumin-to-creatinine ratio (ACR) (the primary Add-on), and eGFR + dipstick proteinuria] for SCORE2 and SCORE2-OP. We validated C-statistics and net reclassification improvement (NRI), accounting for competing risk of non-CVD death, in 5,997 719 participants from 34 different datasets. Results In the target population of SCORE2 and SCORE2-OP without diabetes, the CKD Add-on (eGFR only) and CKD Add-on (eGFR + ACR) improved C-statistic by 0.006 (95%CI 0.004–0.008) and 0.016 (0.010–0.023), respectively, for SCORE2 and 0.012 (0.009–0.015) and 0.024 (0.014–0.035), respectively, for SCORE2-OP. Similar results were seen when we included individuals with diabetes and tested the CKD Add-on (eGFR + dipstick). In 57 485 European participants with CKD, SCORE2 or SCORE2-OP with a CKD Add-on showed a significant NRI [e.g. 0.100 (0.062–0.138) for SCORE2] compared to the qualitative approach in the ESC guideline. Conclusion Our Add-ons with CKD measures improved CVD risk prediction beyond SCORE2 and SCORE2-OP. This approach will help clinicians and patients with CKD refine risk prediction and further personalize preventive therapies for CVD.

BACKGROUND: The association between cost-sharing and receipt of medication for opioid use disorder (MOUD) is unknown. METHODS: We constructed a cohort of 10,513 commercially insured individuals with a new diagnosis of opioid use disorder (OUD) and information on insurance cost-sharing in a large national de-identified claims database. We examined four cost-sharing measures: 1) pharmacy deductible; 2) medical service deductible; 3) pharmacy medication co-pay; and 4) medical office co-pay. We measured MOUD (naltrexone, buprenorphine, or methadone) initiation (within 14 days of diagnosis), engagement (second receipt within 34 days of first), and 6-month retention (continuous receipt without 14-day gap). We used multivariable logistic regression to assess the association between cost-sharing and MOUD initiation, engagement, and retention. We calculated total out-of-pocket costs in the 30 days following MOUD initiation for each type of MOUD. RESULTS: Of 10,513 individuals with incident OUD, 1,202 (11%) initiated MOUD, 742 (7%) engaged, and 253 (2%) were retained in MOUD at six months. A high ($1,000+) medical deductible was associated with a lower odds of initiation compared to no deductible (odds ratio: 0.85, 95% CI: 0.74-0.98). We found no significant associations between other cost-sharing measures for initiation, engagement, or retention. Median initial 30-day out-of-pocket costs ranged from $100 for methadone to $710 for extended-release naltrexone. CONCLUSION: Among insurance plan cost-sharing measures, only medical services deductible showed an association with decreased MOUD initiation. Policy and benefit design should consider ways to reduce cost barriers to initiation and retention in MOUD.

Background Recombinant zoster vaccine (RZV) was approved by the Food and Drug Administration for prevention of herpes zoster (HZ) in adults aged ≥50 years in 2017 and recommended for this population by the Advisory Committee on Immunization Practices (ACIP) in 2018. Since the ACIP recommendation, vaccination rates have been increasing but are suboptimal for adults aged 50-59 years compared to adults aged ≥50 years overall. The objective of this study was to model the change in outcomes associated with improved vaccination rates in this population. Methods A multicohort Markov model was used to compare a scenario using the real-world vaccination rate for adults in the US aged 50-59 years in 2020 to a scenario assuming a higher vaccination rate based on that of adults aged 60-64 years in 2020. Outcomes, based on a lifetime horizon, included HZ cases and complications averted, quality-adjusted life-years (QALY), and costs. Model inputs, including age-specific HZ epidemiology, RZV efficacy, real-world RZV series completion, utilities, and costs (direct and indirect), were based on published literature and US sources. The size of the population aged 50-59 years was based on 2020 US census estimates. Schematic overview of Markov model structure HZ: herpes zoster; PHN: postherpetic neuralgia Results For the 43 million adults aged 50-59 years, increasing the percentage who received at least one dose of RZV from the current estimate of 7.3% in 2020 to 14.6%, assuming 80% series completion, could avoid an additional 504,468 HZ cases. An estimated additional 42,077 post-herpetic neuralgia cases and an additional 56,247 other complication cases of HZ could be avoided. Based on the estimated cases and complications avoided, an additional 5,230 discounted QALYs would be gained. Direct costs could be reduced by an additional $638 million (M) and indirect costs by $493M. With incremental vaccination costs of approximately $1,032M, from a societal perspective, cost savings of $99M would be realized. Conclusion Increasing RZV vaccination among adults aged 50-59 years could reduce the burden associated with HZ cases, complications, and result in fewer QALY losses. From a societal perspective, cost savings could be achieved in this population in addition to improved clinical outcomes. These findings demonstrate the potential value of increasing RZV vaccination for adults in the US aged 50-59 years. Disclosures David Singer, PharmD, MS, The GSK group of companies: Employed|The GSK group of companies: Stocks/Bonds Ahmed Salem, MSc, The GSK group of companies: Employed Nikita Stempniewicz, MSc, The GSK group of companies: Employed Siyu Ma, PhD, MBBS, The GSK group of companies: Grant/Research Support Sara Poston, PharmD, The GSK group of companies: Employed|The GSK group of companies: Stocks/Bonds Desmond Curran, PhD, The GSK group of companies: Employed|The GSK group of companies: Stocks/Bonds.

Albuminuria is an under-recognized component of chronic kidney disease definition, staging, and prognosis. Guidelines, particularly for hypertension, conflict on recommendations for urine albumin-to-creatinine ratio (ACR) measurement. Separately among 1 344 594 adults with diabetes and 2 334 461 nondiabetic adults with hypertension from the chronic kidney disease Prognosis Consortium, we assessed ACR testing, estimated the prevalence and incidence of ACR ≥30 mg/g and developed risk models for ACR ≥30 mg/g. The ACR screening rate (cohort range) was 35.1% (12.3%–74.5%) in diabetes and 4.1% (1.3%–20.7%) in hypertension. Screening was largely unrelated to the predicted risk of prevalent albuminuria. The median prevalence of ACR ≥30 mg/g across cohorts was 32.1% in diabetes and 21.8% in hypertension. Higher systolic blood pressure was associated with a higher prevalence of albuminuria (odds ratio [95% CI] per 20 mm Hg in diabetes, 1.50 [1.42–1.60]; in hypertension, 1.36 [1.28–1.45]). The ratio of undetected (due to lack of screening) to detected ACR ≥30 mg/g was estimated at 1.8 in diabetes and 19.5 in hypertension. Among those with ACR

To estimate the risk of a patient with osteoarthritis (OA) developing chronic opioid use (COU) within 1 year of a new opioid prescription by using electronic health record (EHR) data and predictive models.We used EHR data from 13 health care organizations to identify patients with OA with an opioid prescription between March 1, 2017 and February 28, 2019 and no record of opioid use in the prior 6 months. We evaluated 4 machine learning models to estimate patients' risk of COU (≥3 prescriptions ≥84 days, maximum gap ≤60 days). We also estimated the transportability of models to organizations outside the training set.The cohort consisted of 33,894 patients with OA, of whom 2,925 (8.6%) developed COU within 1 year. All models demonstrated good discrimination, with the best-performing model (random forest) achieving an area under the receiver operating characteristic curve (AUC) of 0.728 (95% CI 0.711-0.745), but the simplest regression model performed nearly as well (AUC 0.717 [95% CI 0.699-0.734]). Predicted risk deciles spanned a range of 2% risk for the 10th percentile to 18% risk for the 90th percentile for well-calibrated models. Models showed highly variable discrimination across organizations (AUC 0.571-0.842).We found that EHR-based predictive models could estimate the risk of future COU among patients with OA to help inform care decisions. Black-box methods did not have significant advantages over more interpretable models. Care should be taken when extending all models into organizations not included in model training because of a high variability in performance across held-out organizations.

Objective: To predict adverse kidney outcomes for use in optimizing medical management and clinical trial design. Research Design and Methods: In this individual participant data meta-analysis, 43 cohorts (N=1,621,817) from research studies, electronic medical records, and clinical trials with global representation were separated into development and validation cohorts. Models were developed and validated within strata of diabetes mellitus (presence or absence) and eGFR (≥60 or Results: There were 17,399 and 24,591 events in development and validation cohorts, respectively. Models predicting ≥40% eGFR decline or kidney failure incorporated age, sex, eGFR, albuminuria, systolic blood pressure, anti-hypertensive medication use, history of heart failure, coronary heart disease, atrial fibrillation, smoking status, and body-mass index (and hemoglobin A1c, insulin use, and oral diabetes medication use in those with diabetes). The median C-statistic was 0.774 (interquartile range [IQR]: 0.753, 0.782) in the diabetes/higher eGFR validation cohorts, 0.769 (IQR: 0.758, 0.808) in the diabetes/lower eGFR validation cohorts, 0.740 (interquartile range [IQR]: 0.717, 0.763) in the no diabetes/higher eGFR validation cohorts, and 0.750 (IQR: 0.731, 0.785) in the no diabetes/lower eGFR validation cohorts. Incorporating previous 2-year eGFR slope minimally improved model performance, and only in the higher eGFR cohorts. Conclusions: Novel prediction equations for an eGFR decline of ≥40% eGFR can be applied successfully for use in the general population in persons with and without diabetes with higher or lower eGFR.

Anemia is common in chronic kidney disease (CKD); although anemia is associated with adverse outcomes, the available treatments are not ideal. We characterized the burden, risk factors for, and risks associated with anemia by estimated glomerular filtration rate (eGFR) and hemoglobin level.Cross-sectional and prospective cohort study.Outpatient data from 5,004,957 individuals across 57 health care centers in the United States from 2016 to 2019, extracted from the Optum Labs Data Warehouse.Severity of anemia, presence of low iron test results, eGFR.Incident kidney failure with replacement therapy, cardiovascular disease, coronary heart disease, stroke, heart failure, death.The prevalences of anemia, low iron test results, vitamin BThe mean age was 54 years, and 42% were male. Lower eGFR was very strongly associated with increased prevalence of anemia, even after adjustment. Although iron studies were checked infrequently in patients with anemia, low iron test results were highly prevalent in those tested: 60.4% and 81.3% of men and women, respectively. ESA use was uncommon, with a prevalence of use of4%. Lower hemoglobin was independently associated with increased risk of incident kidney failure with replacement therapy, cardiovascular disease, coronary heart disease, stroke, heart failure, and death.Reliance on ICD codes for medical diagnoses, death information obtained from claims data, observational study.Severe anemia was common and strongly associated with lower eGFR and multiple adverse outcomes. Low-iron test results were highly prevalent in those tested despite iron studies being checked infrequently. ESA use in nondialysis CKD patients was uncommon.

Objective: Clinical guidelines for people with diabetes recommend chronic kidney disease (CKD) testing at least annually using estimated glomerular filtration rate (eGFR) and urinary albumin-creatinine ratio (uACR). We aimed to understand CKD testing among people with type 2 diabetes in the United States. Research Design and Methods: Electronic health record data were analyzed from 513,165 adults with type 2 diabetes, receiving primary care from 24 health care organizations and 1,164 clinical practice sites. We assessed the percentage of patients with both ≥1 eGFR and ≥1 uACR, and each test individually, in the 1, 2, and 3 years ending September 2019, by health care organization and clinical practice site. Elevated albuminuria was defined by uACR ≥30 mg/g. Results: The 1-year median testing rate across organizations was 51.6% for both uACR and eGFR, 89.5% for eGFR, and 52.9% for uACR. uACR testing varied (10th–90th percentile) from 44.7% to 63.3% across organizations and from 13.3% to 75.4% across sites. Over 3 years, the median testing rate for uACR across organizations was 73.7%. Overall, the prevalence of detected elevated albuminuria was 15%. The average prevalence of detected elevated albuminuria increased linearly with uACR testing rates at sites, with estimated prevalence of 6%, 15%, and 30%, at uACR testing rates of 20%, 50%, and 100%. Conclusions: While eGFR testing rates are uniformly high among people with type 2 diabetes, testing rates for uACR are suboptimal and highly variable across and within the organizations examined. Guideline-recommended uACR testing should increase detection of CKD.

INTRODUCTION: Medications for opioid use disorder (MOUD) are highly effective, but barriers along the cascade of care for opioid use disorder (OUD) from diagnosis to treatment limit their reach. For individuals desiring MOUD, the final step in the cascade is filling a written prescription, and fill rates have not been described. METHODS: We used data from a large de-identified database linking individuals’ electronic medical records (EMR) and administrative claims data and employed a previously developed algorithm to identify individuals with a new diagnosis of OUD. We included individuals with a prescription for buprenorphine or naltrexone recorded in the EMR. The outcome was a prescription fill within 30 days as reported in claims data. We compared demographic and clinical characteristics between those who did and did not fill the prescription and used a Kaplan-Meier curve to assess whether fill rates differed based on patient copay. RESULTS: We identified 264 individuals with a new diagnosis of OUD who had a prescription written for buprenorphine or oral naltrexone. Of these, 70% (184) filled the prescription within 30 days, and more than half (57%) filled the prescription on the day it was written. Individuals with prescription copay at or below the mean had a 75% fill rate at 30 days compared with 63% for those with copay above the mean (p

IMPORTANCE Early identification of individuals at elevated risk of developing chronic kidney disease (CKD) could improve clinical care through enhanced surveillance and better management of underlying health conditions.OBJECTIVE To develop assessment tools to identify individuals at increased risk of CKD, defined by reduced estimated glomerular filtration rate (eGFR).DESIGN, SETTING, AND PARTICIPANTS Individual-level data analysis of 34 multinational cohorts from the CKD Prognosis Consortium including 5 222 711 individuals from 28 countries. Data were collected from April 1970 through January 2017. A 2-stage analysis was performed, with each study first analyzed individually and summarized overall using a weighted average. Because clinical variables were often differentially available by diabetes status, models were developed separately for participants with diabetes and without diabetes. Discrimination and calibration were also tested in 9 external cohorts (n = 2 253 540).EXPOSURES Demographic and clinical factors.MAIN OUTCOMES AND MEASURES Incident eGFR of less than 60 mL/min/1.73 m(2).RESULTS Among 4 441 084 participants without diabetes (mean age, 54 years, 38% women), 660 856 incident cases (14.9%) of reduced eGFR occurred during a mean follow-up of 4.2 years. Of 781 627 participants with diabetes (mean age, 62 years, 13% women), 313 646 incident cases (40%) occurred during a mean follow-up of 3.9 years. Equations for the 5-year risk of reduced eGFR included age, sex, race/ethnicity, eGFR, history of cardiovascular disease, ever smoker, hypertension, body mass index, and albuminuria concentration. For participants with diabetes, the models also included diabetes medications, hemoglobin A(1c), and the interaction between the 2. The risk equations had a median C statistic for the 5-year predicted probability of 0.845 (interquartile range [IQR], 0.789-0.890) in the cohorts without diabetes and 0.801 (IQR, 0.750-0.819) in the cohorts with diabetes. Calibration analysis showed that 9 of 13 study populations (69%) had a slope of observed to predicted risk between 0.80 and 1.25. Discrimination was similar in 18 study populations in 9 external validation cohorts; calibration showed that 16 of 18 (89%) had a slope of observed to predicted risk between 0.80 and 1.25.CONCLUSIONS AND RELEVANCE Equations for predicting risk of incident chronic kidney disease developed from more than 5 million individuals from 34 multinational cohorts demonstrated high discrimination and variable calibration in diverse populations. Further study is needed to determine whether use of these equations to identify individuals at risk of developing chronic kidney disease will improve clinical care and patient outcomes.

OBJECTIVE Clinical guidelines for people with diabetes recommend chronic kidney disease (CKD) testing at least annually using estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (uACR). We aimed to understand CKD testing among people with type 2 diabetes in the U.S. RESEARCH DESIGN AND METHODS Electronic health record data were analyzed from 513,165 adults with type 2 diabetes receiving primary care from 24 health care organizations and 1,164 clinical practice sites. We assessed the percentage of patients with both one or more eGFRs and one or more uACRs and each test individually in the 1, 2, and 3 years ending September 2019 by health care organization and clinical practice site. Elevated albuminuria was defined as uACR ≥30 mg/g. RESULTS The 1-year median testing rate across organizations was 51.6% for both uACR and eGFR, 89.5% for eGFR, and 52.9% for uACR. uACR testing varied (10th–90th percentile) from 44.7 to 63.3% across organizations and from 13.3 to 75.4% across sites. Over 3 years, the median testing rate for uACR across organizations was 73.7%. Overall, the prevalence of detected elevated albuminuria was 15%. The average prevalence of detected elevated albuminuria increased linearly with uACR testing rates at sites, with estimated prevalence of 6%, 15%, and 30% at uACR testing rates of 20%, 50%, and 100%, respectively. CONCLUSIONS While eGFR testing rates are uniformly high among people with type 2 diabetes, testing rates for uACR are suboptimal and highly variable across and within the organizations examined. Guideline-recommended uACR testing should increase detection of CKD.

BACKGROUND: The four-variable kidney failure risk equation (KFRE) is a well-validated tool for patients with GFR

BACKGROUND: Although measuring albuminuria is the preferred method for defining and staging chronic kidney disease (CKD), total urine protein or dipstick protein is often measured instead.OBJECTIVE: To develop equations for converting urine protein-creatinine ratio (PCR) and dipstick protein to urine albumin-creatinine ratio (ACR) and to test their diagnostic accuracy in CKD screening and staging.DESIGN: Individual participant-based meta-analysis.SETTING: 12 research and 21 clinical cohorts.PARTICIPANTS: 919 383 adults with same-day measures of ACR and PCR or dipstick protein.MEASUREMENTS: Equations to convert urine PCR and dipstick protein to ACR were developed and tested for purposes of CKD screening (ACR ≥30 mg/g) and staging (stage A2: ACR of 30 to 299 mg/g; stage A3: ACR ≥300 mg/g).RESULTS: Median ACR was 14 mg/g (25th to 75th percentile of cohorts, 5 to 25 mg/g). The association between PCR and ACR was inconsistent for PCR values less than 50 mg/g. For higher PCR values, the PCR conversion equations demonstrated moderate sensitivity (91%, 75%, and 87%) and specificity (87%, 89%, and 98%) for screening (ACR >30 mg/g) and classification into stages A2 and A3, respectively. Urine dipstick categories of trace or greater, trace to +, and ++ for screening for ACR values greater than 30 mg/g and classification into stages A2 and A3, respectively, had moderate sensitivity (62%, 36%, and 78%) and high specificity (88%, 88%, and 98%). For individual risk prediction, the estimated 2-year 4-variable kidney failure risk equation using predicted ACR from PCR had discrimination similar to that of using observed ACR.LIMITATION: Diverse methods of ACR and PCR quantification were used; measurements were not always performed in the same urine sample.CONCLUSION: Urine ACR is the preferred measure of albuminuria; however, if ACR is not available, predicted ACR from PCR or urine dipstick protein may help in CKD screening, staging, and prognosis.PRIMARY FUNDING SOURCE: National Institute of Diabetes and Digestive and Kidney Diseases and National Kidney Foundation.

Background Granular clinical and laboratory data available in electronic health record (EHR) databases provide researchers the opportunity to conduct investigations that would not be possible in insurance claims databases; however, for pharmacoepidemiology studies, accurate classification of medication exposure is critical. Objective The aim of this study was to evaluate the validity of classifying medication exposure using EHR prescribing (EHR-Rx) data. Methods We conducted a retrospective cohort study among patients with linked claims and EHR data in OptumLabs™ Data Warehouse. The agreement between EHR-Rx data and pharmacy claims (PC-Rx) data (for 40 medications) was determined using the positive predictive value (PPV) and medication possession ratio (MPR)—calculated in 1- and 12-month medication exposure periods (MEPs). Secondary analyses were restricted to incident vs prevalent EHR-Rxs, age ≥65 vs

Non-adherence to medications is a major challenge in diabetes care. The objective of this brief report is to compare adherence rates for 6 major classes of diabetes medications: metformin, sulfonylurea, thiazolidinedione, basal insulin, DPP-4 inhibitors, and GLP-1 receptor agonists. We used a data source that linked electronic prescriptions with insurance claims to assess whether new electronic prescriptions for diabetes medications were followed by dispensing claims consistent with that prescription. After one year of follow-up, the daily medication possession probability (MPP) - a measure of overall adherence - at one year for sulfonylurea was 0.49 and for metformin was 0.46. Thiazolidinediones and basal insulin had a similar final daily MPP at 0.36 and 0.39, respectively, which was significantly lower than that for sulfonylurea or metformin (P < .05). GLP-1 receptor agonists and DPP-4 inhibitors were also comparable to one another at a final daily MPP of .30 and .21, respectively (P < .05 compared to any of the aforementioned drug classes). In summary, the rates at which diabetes drugs are prescribed, and the rates at which patients actually take them, differ substantially. Physicians should be aware of potentially significant challenges concerning adherence to newer agents.

Objective: To measure time from positive diabetes screening test to appropriate follow-up action, i.e., diagnosis, pharmacologic treatment, or negative confirmatory test, using a large, geographically diverse clinical dataset. Study Design: Retrospective descriptive analysis in a clinical database with over 25 million patient records. Population Studied: From 2013-2017, 61,162 patients aged 18-75 from 23 health care organizations (HCOs) with ≥ 1 outpatient visit in last 12 months of data, a laboratory finding of HbA1c ≥ 6.5 or fasting plasma glucose (FPG) ≥ 126 in the last 12-24 months, and no prior diabetes diagnosis or prescriptions (except metformin). Principal Findings: Positive diabetes tests were indicated by HbA1c (28%), FPG (61%), or both (11%). A total of 5.7% of patients had an FPG ≥ 200, and 6.9% had an HbA1c ≥ 8.0. Sixty-five percent (range 55-77% across 23 HCOs) had appropriate action in 3 months following a positive screening: 35% had a diagnosis or prescription for an antidiabetes medication; 30% had neither diagnosis nor prescription but had a subsequent negative diabetes test result. The remaining 35% had no diagnosis nor prescription, and either re-tested positive for diabetes or had no follow-up. By 12 months, 83% of all patients received appropriate follow-up. Among patients with HbA1c ≥ 8.0, 92% and 96% had appropriate follow-up within 3 and 12 months, respectively. Most initial prescriptions were for metformin (84%) with variation in use of 2nd or 3rd line diabetes medications: sulfonylurea, 1-12%; DPP-4, 0-10%; SGLT2, 0-10%; GLP-1, 0-3%; and insulin, 1-12%. Conclusions: Within a year of a positive diabetes screen, most patients received appropriate action. Those with higher lab values were more likely to receive appropriate action sooner. Among those with prescriptions, most were for metformin. The proportion of patients with appropriate action, time to action, and type of action, e.g., use of 2nd and 3rd line agents, all varied considerably across HCOs. Disclosure N. Stempniewicz: None. E.L. Ciemins: None. C.R. Rattelman: None. J.K. Cuddeback: None. Funding Novo Nordisk

Item does not contain fulltext BACKGROUND: Change in albuminuria as a surrogate endpoint for progression of chronic kidney disease is strongly supported by biological plausibility, but empirical evidence to support its validity in epidemiological studies is lacking. We aimed to assess the consistency of the association between change in albuminuria and risk of end-stage kidney disease in a large individual participant-level meta-analysis of observational studies. METHODS: In this meta-analysis, we collected individual-level data from eligible cohorts in the Chronic Kidney Disease Prognosis Consortium (CKD-PC) with data on serum creatinine and change in albuminuria and more than 50 events on outcomes of interest. Cohort data were eligible if participants were aged 18 years or older, they had a repeated measure of albuminuria during an elapsed period of 8 months to 4 years, subsequent end-stage kidney disease or mortality follow-up data, and the cohort was active during this consortium phase. We extracted participant-level data and quantified percentage change in albuminuria, measured as change in urine albumin-to-creatinine ratio (ACR) or urine protein-to-creatinine ratio (PCR), during baseline periods of 1, 2, and 3 years. Our primary outcome of interest was development of end-stage kidney disease after a baseline period of 2 years. We defined an end-stage kidney disease event as initiation of kidney replacement therapy. We quantified associations of percentage change in albuminuria with subsequent end-stage kidney disease using Cox regression in each cohort, followed by random-effects meta-analysis. We further adjusted for regression dilution to account for imprecision in the estimation of albuminuria at the participant level. We did multiple subgroup analyses, and also repeated our analyses using participant-level data from 14 clinical trials, including nine clinical trials not in CKD-PC. FINDINGS: Between July, 2015, and June, 2018, we transferred and analysed data from 28 cohorts in the CKD-PC, which included 693 816 individuals (557 583 [80%] with diabetes). Data for 675 904 individuals and 7461 end-stage kidney disease events were available for our primary outcome analysis. Change in ACR was consistently associated with subsequent risk of end-stage kidney disease. The adjusted hazard ratio (HR) for end-stage kidney disease after a 30% decrease in ACR during a baseline period of 2 years was 0.83 (95% CI 0.74-0.94), decreasing to 0.78 (0.66-0.92) after further adjustment for regression dilution. Adjusted HRs were fairly consistent across cohorts and subgroups (ie, estimated glomerular filtration rate, diabetes, and sex), but the association was somewhat stronger among participants with higher baseline ACR than among those with lower baseline ACR (pinteraction

Objective: To characterize diabetes screening patterns among eligible patients per ADA Standards of Medical Care in Diabetes using a large, geographically diverse clinical dataset. Study Design: Retrospective descriptive analysis conducted in a clinical database containing over 25 million patient records. Population Studied: A total of 5.1 million patients aged 18-75 in 23 health care organizations (HCOs) with at least one outpatient visit in the study period (7/2016-6/2017), no prior diagnosis of diabetes mellitus, and no prior diabetes medication prescriptions (except metformin). Principal Findings: Among 5.1 million patients, 74% were eligible for screening and 55% were screened according to guidelines. Adequate screening ranged from 47%-76% across 23 HCOs. Adults 65 and older were most likely to be adequately screened (61%; P < .01). Least likely to be screened were patients with low-income insurance types (P < .01); non-white race (P < .01) or lower education by zip code (P < .01). Of 1.7 million patients screened, 2.8% and 36.5% had evidence of diabetes and prediabetes, respectively. Diabetes/prediabetes was identified in 30% of patients aged 35-44 and 48% of patients 65 and older. Patients with lower education were less likely to be adequately screened, but yielded greater rates of diabetes/prediabetes, 47% to 39% from least to most educated (P < .01). More African Americans (46%) screened positive for diabetes/prediabetes compared to other race/ethnicities (42%) (P < .01). Conclusions: Screening practices vary greatly, mostly within and across HCOs. Patients least likely to be adequately screened were often those with highest yields of diabetes/prediabetes. The identification of inadequately screened, low-income, lower education, or race/ethnic minority populations may facilitate targeted efforts to decrease disparities in screening practices. Identification of organizations with the highest net yield may also provide insight into strategies for efficient screening practices. Disclosure N. Stempniewicz: None. J.K. Cuddeback: Research Support; Self; Novo Nordisk Inc.. Other Relationship; Self; Johnson & Johnson Diabetes Institute, LLC., Merck & Co., Inc., Sanofi US, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company. C.R. Rattelman: None. E.L. Ciemins: None.

Background and objective Controlling blood pressure (BP) for patients with stage 2 hypertension remains challenging. This research aimed to: (i) identify predictors of failure to achieve BP control, (ii) determine the association of adding one additional antihypertensive class with achieving BP control, and (iii) describe the prescribed antihypertensive regimens. Methods Electronic medical record data from 25 multi-specialty medical groups in the USA were used. The study cohort included patients with stage 2 hypertension in 2012. BP control rates were determined at 6months from the date of the stage 2 BP. Using multivariable logistic regression and validation by Monte Carlo simulation, we determined independent baseline predictors of not achieving BP control (

Non-adherence to medications is a major challenge in diabetes care. The objective of this brief report is to compare adherence rates for 6 major classes of diabetes medications: metformin, sulfonylurea, thiazolidinedione, basal insulin, DPP-4 inhibitors, and GLP-1 receptor agonists. We used a data source that linked electronic prescriptions with insurance claims to assess whether new electronic prescriptions for diabetes medications were followed by dispensing claims consistent with that prescription. After one year of follow-up, the daily medication possession probability (MPP) - a measure of overall adherence - at one year for sulfonylurea was 0.49 and for metformin was 0.46. Thiazolidinediones and basal insulin had a similar final daily MPP at 0.36 and 0.39, respectively, which was significantly lower than that for sulfonylurea or metformin (P.05). GLP-1 receptor agonists and DPP-4 inhibitors were also comparable to one another at a final daily MPP of .30 and .21, respectively (P.05 compared to any of the aforementioned drug classes). In summary, the rates at which diabetes drugs are prescribed, and the rates at which patients actually take them, differ substantially. Physicians should be aware of potentially significant challenges concerning adherence to newer agents.

Controlling blood pressure (BP) for patients with stage 2 hypertension remains challenging. This research aimed to: (i) identify predictors of failure to achieve BP control, (ii) determine the association of adding one additional antihypertensive class with achieving BP control, and (iii) describe the prescribed antihypertensive regimens.Electronic medical record data from 25 multi-specialty medical groups in the USA were used. The study cohort included patients with stage 2 hypertension in 2012. BP control rates were determined at 6 months from the date of the stage 2 BP. Using multivariable logistic regression and validation by Monte Carlo simulation, we determined independent baseline predictors of not achieving BP control (140/90).Included were 107 903 patients. Baseline predictors of failure to achieve BP control included the following: a prior stage 2 BP, systolic BP ≥ 165, Black race, male sex, income ≤ $35 000, body mass index ≥ 30, age ≥ 65 years, and no office visits. Increasing from single-class to dual-class antihypertensive therapy was associated with a 42% increased odds of achieving BP control (odds ratio 1.42; 95% CI 1.22, 1.64); however, this effect was attenuated as the number of baseline antihypertensive classes increased. The 10 most frequently prescribed regimens accounted for only 40% of all antihypertensive regimens.Among patients with stage 2 hypertension, a prior stage 2 BP, a systolic BP ≥ 165, and fewer office visits were strong predictors of failure to achieve BP control. Increasing to dual-class antihypertensive therapy was significantly associated with achieving BP control. There is broad heterogeneity in the antihypertensive regimens prescribed.