Characteristics of patients referred for treatment of end-stage renal disease in a defined population.

Hiatt, Robert A.; Friedman, G D

doi:10.2105/ajph.72.8.829

Cited by 39 publications

(19 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Second, the KP membership from which the study population was drawn makes up approximately 30% of the Northern California population and is sociodemographically representative of the general population, giving this study public health relevance and generalizability. [20][21][22]61 Third, we were able to control for a wide variety of potential confounding variables, including diabetes self-care behaviors. 62 Fourth, the finding that the inverse relationship between alcohol and A1C persisted during the period 1-2 years after baseline suggests that the relationship is robust.…”

Section: Discussionmentioning

confidence: 99%

The Relationship Between Alcohol Consumption and Glycemic Control Among Patients with Diabetes: The Kaiser Permanente Northern California Diabetes Registry

et al. 2008

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

The Relationship Between Alcohol Consumption and Glycemic Control Among Patients with Diabetes: The Kaiser Permanente Northern California Diabetes Registry

et al. 2008

View full text Add to dashboard Cite

“…The source population included adults (age Ն18 yr) living in or near the San Francisco Bay Area who were receiving medical care within KPNC, a population generally representative of the local and statewide insured adult population. (13,18) Between October 28, 2001 and December 31, 2003, 3,179 subjects from the source population living in or near the San Francisco Bay Area were recruited into five separate groups: 1) subjects with clinically significant CAD at an age Ͻ45 yr for males and Ͻ55 yr for females (n ϭ 500), 2) subjects with incident stable angina at an older age (n ϭ 468), 3) subjects with incident acute myocardial infarction (AMI) at an older age (n ϭ 924), 4) young subjects with no history of CAD (n ϭ 264), and 5) subjects aged 60 to 72 at the time of their study clinic visit with no history of CAD, ischemic stroke (CVA), or peripheral arterial disease (PAD) (n ϭ 1,023). Extensive details of the eligibility criteria, sampling strategy and the recruitment statistics for all cohorts are available elsewhere (http://med.stanford.edu/advance/) (11,15,29).…”

Section: Methodsmentioning

confidence: 99%

Circulating chemokines accurately identify individuals with clinically significant atherosclerotic heart disease

Ardigò

Assimes

Fortmann

et al. 2007

Physiological Genomics

View full text Add to dashboard Cite

Serum inflammatory markers correlate with outcome and response to therapy in subjects with cardiovascular disease. However, current individual markers lack specificity for the diagnosis of coronary artery disease (CAD). We hypothesize that a multimarker proteomic approach measuring serum levels of vascular derived inflammatory biomarkers could reveal a “signature of disease” that can serve as a highly accurate method to assess for the presence of coronary atherosclerosis. We simultaneously measured serum levels of seven chemokines [CXCL10 (IP-10), CCL11 (eotaxin), CCL3 (MIP1α), CCL2 (MCP1), CCL8 (MCP2), CCL7 (MCP3), and CCL13 (MCP4)] in 48 subjects with clinically significant CAD (“cases”) and 44 controls from the ADVANCE Study. We applied three classification algorithms to identify the combination of variables that would best predict case-control status and assessed the diagnostic performance of these models with receiver operating characteristic (ROC) curves. The serum levels of six chemokines were significantly higher in cases compared with controls ( P < 0.05). All three classification algorithms entered three chemokines in their final model, and only logistic regression selected clinical variables. Logistic regression produced the highest ROC of the three algorithms (AUC = 0.95; SE = 0.03), which was markedly better than the AUC for the logistic regression model of traditional risk factors of CAD without (AUC = 0.67; SE = 0.06) or with CRP (AUC = 0.68; SE = 0.06). A combination of serum levels of multiple chemokines identifies subjects with clinically significant atherosclerotic heart disease with a very high degree of accuracy. These results need to be replicated in larger cross-sectional studies and their prognostic value explored.

show abstract

“…A strength of the study is that all subjects received care at a single health plan (KPNC), a nonprofit, fully-integrated healthcare delivery system that provides comprehensive medical services to over 3.2 million members. While members resemble the general population except for the extreme tails of the income distribution, [25][26][27] there are ways that they differ from the general population. KPNC typically dispenses 3 months' supply of medication.…”

Section: Research and Applicationsmentioning

confidence: 99%

An algorithm to identify medication nonpersistence using electronic pharmacy databases

Parker

Moffet

Adams

et al. 2015

Journal of the American Medical Informatics Association

View full text Add to dashboard Cite

Objective Identifying patients who are medication nonpersistent (fail to refill in a timely manner) is important for healthcare operations and research. However, consistent methods to detect nonpersistence using electronic pharmacy records are presently lacking. We developed and validated a nonpersistence algorithm for chronically used medications. Materials and Methods Refill patterns of adult diabetes patients (n ¼ 14,349) prescribed cardiometabolic therapies were studied. We evaluated various grace periods (30-300 days) to identify medication nonpersistence, which is defined as a gap between refills that exceeds a threshold equal to the last days' supply dispensed plus a grace period plus days of stockpiled medication. Since data on medication stockpiles are typically unavailable for ongoing users, we compared nonpersistence to rates calculated using algorithms that ignored stockpiles. Results When using grace periods equal to or greater than the number of days' supply dispensed (i.e., at least 100 days), this novel algorithm for medication nonpersistence gave consistent results whether or not it accounted for days of stockpiled medication. The agreement (Kappa coefficients) between nonpersistence rates using algorithms with versus without stockpiling improved with longer grace periods and ranged from 0.63 (for 30 days) to 0.98 (for a 300-day grace period). Conclusions Our method has utility for health care operations and research in prevalent (ongoing) and new user cohorts. The algorithm detects a subset of patients with inadequate medication-taking behavior not identified as primary nonadherent or secondary nonadherent. Healthcare systems can most comprehensively identify patients with short-or long-term medication underutilization by identifying primary nonadherence, secondary nonadherence, and nonpersistence.

show abstract

Characteristics of patients referred for treatment of end-stage renal disease in a defined population.

Cited by 39 publications

References 5 publications

The Relationship Between Alcohol Consumption and Glycemic Control Among Patients with Diabetes: The Kaiser Permanente Northern California Diabetes Registry

The Relationship Between Alcohol Consumption and Glycemic Control Among Patients with Diabetes: The Kaiser Permanente Northern California Diabetes Registry

Circulating chemokines accurately identify individuals with clinically significant atherosclerotic heart disease

An algorithm to identify medication nonpersistence using electronic pharmacy databases

Contact Info

Product

Resources

About