Impact of remote management of diabetes via computer: the 360 study--a proof-of-concept randomized trial.

BACKGROUND: Previous studies have provided limited guidance regarding the clinical efficacy and cost-effectiveness of interventions using "telemedicine" models in the management of diabetes mellitus. We conducted a study to determine if routine clinical assessments of diabetes patients could be effectively conducted via computer and telephone interaction with patients and still provide clinical results similar to traditional office care. SUBJECTS AND METHODS: We enrolled 100 subjects with diabetes in this 12-month, randomized, controlled, non-inferiority study. Subjects were randomized (1:1 ratio) to a control group (CG) or study group (SG). Baseline characteristics were similar. CG subjects participated in quarterly office visits; SG subjects participated in two office visits (months 6 and 12) and two telemedicine interactions (months 3 and 9). Changes in clinical measurements (hemoglobin A1c [HbA1c], blood pressure, lipids, body mass index [BMI], and body weight) and clinician time requirements were assessed. RESULTS: Seventy subjects completed the study (CG, n=37; SG, n=33). No significant between-group differences in HbA1c, blood pressure, lipids, or BMI were seen at 12 months. SG subjects showed significantly greater reductions in mean (SD) body weight compared with CG subjects: -5.2 (1.6) pounds versus -0.7 (1.5) pounds, respectively (P=0.04). Clinician time requirements for SG subjects were reduced by >40%. CONCLUSIONS: Our study demonstrated that use of a telemedicine-based treatment protocol in diabetes patients is feasible and efficient and yields similar clinical outcomes compared with traditional, clinic-based protocols. Telemedicine applications of computer software can potentially expand access to care for patients and may reduce costs for patients, providers, and payers.

Changes in A1C levels are significantly associated with changes in levels of the cardiovascular risk biomarker hs-CRP: results from the SteP study.

OBJECTIVE: The effect of therapeutic strategies on cardiovascular (CV) disease can be evaluated by monitoring changes in CV risk biomarkers. This study investigated the effect of a structured self-monitoring of blood glucose (SMBG) protocol and the resulting improvements in glycemic control on changes in high-sensitivity C-reactive protein (hs-CRP) in insulin-naïve patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: The Structured Testing Program (STeP) study was a prospective, cluster-randomized, multicenter trial in which 483 poorly controlled, insulin-naïve patients with type 2 diabetes were randomized to active control (ACG) or structured testing (STG) that included quarterly structured SMBG. Changes in A1C, hs-CRP, and glycemic variability (STG subjects only) were measured at baseline and quarterly. RESULTS: Reductions in geometric mean hs-CRP values were significantly greater in the STG group at months 3 (P = 0.005), 6 (P = 0.0003), and 12 (P = 0.04) than in the ACG group. STG patients at high CV risk (>3 mg/L) showed significantly greater reductions in hs-CRP levels than ACG patients at high CV risk: -3.64 mg/dL (95% CI -4.21 to -3.06) versus -2.18 mg/dL (-2.93 to -1.43), respectively (P = 0.002). There was a strong correlation between reductions in hs-CRP and A1C in both groups: standardized coefficient (ß) was 0.25 for the entire cohort (P < 0.0001), 0.31 for STG (P < 0.0001), and 0.16 for ACG (P = 0.02). CONCLUSIONS: Reductions in hs-CRP level are associated with reductions in A1C but not reductions in lipids or glycemic variability. Comprehensive structured SMBG-based interventions that lower A1C may translate into improvements in CV risk, as evidenced by levels of the biomarker hs-CRP.

Use of an automated decision support tool optimizes clinicians' ability to interpret and appropriately respond to structured self-monitoring of blood glucose data.

OBJECTIVE: We evaluated the impact of an automated decision support tool (DST) on clinicians' ability to identify glycemic abnormalities in structured self-monitoring of blood glucose (SMBG) data and then make appropriate therapeutic changes based on the glycemic patterns observed. RESEARCH DESIGN AND METHODS: In this prospective, randomized, controlled, multicenter study, 288 clinicians (39.6% family practice physicians, 37.9% general internal medicine physicians, and 22.6% nurse practitioners) were randomized to structured SMBG alone (STG; n = 72); structured SMBG with DST (DST; n = 72); structured SMBG with an educational DVD (DVD; n = 72); and structured SMBG with DST and the educational DVD (DST+DVD; n = 72). Clinicians analyzed 30 patient cases (type 2 diabetes), identified the primary abnormality, and selected the most appropriate therapy. RESULTS: A total of 222 clinicians completed all 30 patient cases with no major protocol deviations. Significantly more DST, DVD, and DST+DVD clinicians correctly identified the glycemic abnormality and selected the most appropriate therapeutic option compared with STG clinicians: 49, 51, and 55%, respectively, vs. 33% (all P < 0.0001) with no significant differences among DST, DVD, and DST+DVD clinicians. CONCLUSIONS: Use of structured SMBG, combined with the DST, the educational DVD, or both, enhances clinicians' ability to correctly identify significant glycemic patterns and make appropriate therapeutic decisions to address those patterns. Structured testing interventions using either the educational DVD or the DST are equally effective in improving data interpretation and utilization. The DST provides a viable alternative when comprehensive education is not feasible, and it may be integrated into medical practices with minimal training.

The impact of structured blood glucose testing on attitudes toward self-management among poorly controlled, insulin-naïve patients with type 2 diabetes.

INTRODUCTION: Patients with T2DM often view self-monitoring of blood glucose (SMBG) as burdensome and pointless, which may affect their broader attitudes toward diabetes management. We examined how a structured SMBG protocol influenced diabetes self-efficacy and autonomous motivation over time, and linked these to changes in glycemic control. MATERIALS AND METHODS: The Structured Testing Program (STeP) is a 12-month, two-arm, cluster-randomized trial that assessed the efficacy of structured SMBG in 483 insulin-naïve T2DM patients. Measures included: Confidence in Diabetes Self-Care for Type 2 patients (CIDS-T2), Diabetes-related Autonomous Motivation (DRAM), self-management behaviors and HbA1c. RESULTS: Intent-to-treat (ITT) and per-protocol (PP) analyses showed significant increases in CIDS-T2 scores over time (main effects p<0.0001). PP analysis showed significant between-group differences (p<0.05), with adherent STG patients displaying greater CIDS-T2 improvement than ACG patients. PP analyses showed main and between-group effects in DRAM with similar findings. Changes in CIDS-T2 were related to HbA1c changes over time; no self-management variable mediated this relationship. CIDS-T2 and HbA1c displayed a significant time-concordant relationship. DISCUSSION: Structured SMBG leads to significant increases in self-confidence and autonomous motivation associated with diabetes self-management. Changes in self-confidence are linked to changes in glycemic control and share a time-concordant relationship.

A structured self-monitoring of blood glucose approach in type 2 diabetes encourages more frequent, intensive, and effective physician interventions: results from the STeP study.

BACKGROUND: We evaluated how a structured patient/physician self-monitoring of blood glucose (SMBG) intervention influenced the timing, frequency, and effectiveness of primary care physicians' treatment changes with type 2 diabetes mellitus (T2DM) patients over 12 months. METHODS: The Structured Testing Program (STeP) study was a cluster-randomized, multicenter trial with 483 poorly controlled, insulin-naive T2DM subjects. Primary care practices were randomized to the Active Control Group (ACG) or the Structured Testing Group (STG), the latter of which included quarterly review of structured SMBG results. STG patients used a paper tool that graphs seven-point glucose profiles over 3 consecutive days; physicians received a treatment algorithm based on SMBG patterns. Impact of structured SMBG on physician treatment modification recommendations (TMRs) and glycemic outcomes was examined. RESULTS: More STG than ACG patients received a TMR at each study visit (P < 0.0001). Of patients who received at least one TMR, STG patients demonstrated a greater reduction in glycated hemoglobin A1c (HbA1c) than ACG patients (-1.2% vs. -0.8%, P < 0.03). Patients with a baseline HbA1c ≥8.5% who received a TMR at the Month 1 visit experienced greater reductions in HbA1c (P = 0.002) than patients without an initial TMR. More STG than ACG patients were started on incretins (P < 0.01) and on thiazolidinediones (P = 0.004). The number of visits with a TMR was unrelated to HbA1c change over time. CONCLUSIONS: Patient-provided SMBG data contribute to glycemic improvement when blood glucose patterns are easy to detect, and well-trained physicians take timely action. Collaborative use of structured SMBG data leads to earlier, more frequent, and more effective TMRs for poorly controlled, non-insulin-treated T2DM subjects.

Structured self-monitoring of blood glucose significantly reduces A1C levels in poorly controlled, noninsulin-treated type 2 diabetes: results from the Structured Testing Program study.

OBJECTIVE: To assess the effectiveness of structured blood glucose testing in poorly controlled, noninsulin-treated type 2 diabetes. RESEARCH DESIGN AND METHODS: This 12-month, prospective, cluster-randomized, multicenter study recruited 483 poorly controlled (A1C ≥ 7.5%), insulin-naïve type 2 diabetic subjects from 34 primary care practices in the U.S. Practices were randomized to an active control group (ACG) with enhanced usual care or a structured testing group (STG) with enhanced usual care and at least quarterly use of structured self-monitoring of blood glucose (SMBG). STG patients and physicians were trained to use a paper tool to collect/interpret 7-point glucose profiles over 3 consecutive days. The primary end point was A1C level measured at 12 months. RESULTS: The 12-month intent-to-treat analysis (ACG, n = 227; STG, n = 256) showed significantly greater reductions in mean (SE) A1C in the STG compared with the ACG: -1.2% (0.09) vs. -0.9% (0.10); Δ = -0.3%; P = 0.04. Per protocol analysis (ACG, n = 161; STG, n = 130) showed even greater mean (SE) A1C reductions in the STG compared with the ACG: -1.3% (0.11) vs. -0.8% (0.11); Δ = -0.5%; P < 0.003. Significantly more STG patients received a treatment change recommendation at the month 1 visit compared with ACG patients, regardless of the patient's initial baseline A1C level: 179 (75.5%) vs. 61 (28.0%); <0.0001. Both STG and ACG patients displayed significant (P < 0.0001) improvements in general well-being (GWB). CONCLUSIONS: Appropriate use of structured SMBG significantly improves glycemic control and facilitates more timely/aggressive treatment changes in noninsulin-treated type 2 diabetes without decreasing GWB.

The value of episodic, intensive blood glucose monitoring in non-insulin treated persons with Type 2 Diabetes: design of the Structured Testing Program (STeP) study, a cluster-randomised, clinical trial [NCT00674986].

BACKGROUND: The value and utility of self-monitoring of blood glucose (SMBG) in non-insulin treated T2DM has yet to be clearly determined. Findings from studies in this population have been inconsistent, due mainly to design differences and limitations, including the prescribed frequency and timing of SMBG, role of the patient and physician in responding to SMBG results, inclusion criteria that may contribute to untoward floor effects, subject compliance, and cross-arm contamination. We have designed an SMBG intervention study that attempts to address these issues. METHODS/DESIGN: The Structured Testing Program (STeP) study is a 12-month, cluster-randomised, multi-centre clinical trial to evaluate whether poorly controlled (HbA1c >or= 7.5%), non-insulin treated T2DM patients will benefit from a comprehensive, integrated physician/patient intervention using structured SMBG in US primary care practices. Thirty-four practices will be recruited and randomly assigned to an active control group (ACG) that receives enhanced usual care or to an enhanced usual care group plus structured SMBG (STG). A total of 504 patients will be enrolled; eligible patients at each site will be randomly selected using a defined protocol. Anticipated attrition of 20% will yield a sample size of at least 204 per arm, which will provide a 90% power to detect a difference of at least 0.5% in change from baseline in HbA1c values, assuming a common standard deviation of 1.5%. Differences in timing and degree of treatment intensification, cost effectiveness, and changes in patient self-management behaviours, mood, and quality of life (QOL) over time will also be assessed. Analysis of change in HbA1c and other dependent variables over time will be performed using both intent-to-treat and per protocol analyses. Trial results will be available in 2010. DISCUSSION: The intervention and trial design builds upon previous research by emphasizing appropriate and collaborative use of SMBG by both patients and physicians. Utilization of per protocol and intent-to-treat analyses facilitates a comprehensive assessment of the intervention. Use of practice site cluster-randomisation reduces the potential for intervention contamination, and inclusion criteria (HbA1c >or= 7.5%) reduces the possibility of floor effects. Inclusion of multiple dependent variables allows us to assess the broader impact of the intervention, including changes in patient and physician attitudes and behaviours. TRIAL REGISTRATION: Current Controlled Trials NCT00674986.

Primary care physicians identify and act upon glycemic abnormalities found in structured, episodic blood glucose monitoring data from non-insulin-treated type 2 diabetes.

BACKGROUND: The purpose of this study was to determine if primary care physicians could utilize data collection tools to accurately identify glycemic abnormalities in structured, episodic self-monitoring of blood glucose (SMBG) data from patients with non-insulin-treated type 2 diabetes and whether use of these SMBG data would influence their therapeutic decisions. METHODS: Twenty-three case studies demonstrating several glycemic states (normoglycemia, elevated fasting glucose, elevated postprandial glucose, all elevated glucose, and hypoglycemia) were presented to 61 primary care physicians who evaluated the cases based upon A1C data, alone and then in combination with SMBG data. SMBG data were presented in five formats. Participants were to identify the specific glucose pattern, determine the necessity for therapy change, and select specific therapeutic changes. Participant assessments were compared with assessments made by a panel of diabetes care specialists. RESULTS: Most (78%) participants identified the same primary blood glucose feature identified by the diabetes specialists; 93.8% agreed with the diabetes care specialists regarding the need for therapy modification. The majority (77%) of participants changed the way they would manage the case after evaluating case studies with SMBG data made available to them. Eighty-six percent of participants considered the SMBG data to be of equal value or more valuable than an A1C test result; 71% of participants strongly agreed that they are now more likely to recommend structured, episodic SMBG to their non-insulin-treated type 2 diabetes mellitus patients. CONCLUSIONS: Primary care physicians can correctly identify glycemic abnormalities in SMBG data obtained through structured, episodic SMBG. Additional studies are needed to determine the clinical impact of similar testing regimens in primary care practice settings.

Genes that contribute to cancer fusion genes are large and evolutionarily conserved.

Numerous cancer fusion genes have been identified and studied, and in some cases, therapy or diagnostic techniques have been designed that are specific to the fusion protein encoded by the fusion gene. There has been little progress, however, in understanding the general features of cancer fusion genes in a way that could provide the foundation for an algorithm for predicting the occurrence of a fusion gene once the chromosomal translocation points have been identified by karyotype analyses. In this study, we used publicly available data sets to characterize 59 cancer fusion genes. The results indicate that all but 17% of the genes involved in fusion events are either relatively large, compared to neighboring genes, or are highly conserved in evolution. These results support a basis for designing algorithms that could have a high degree of predictive value in identifying fusion genes once conventional microscopic analyses have identified the chromosomal breakpoints.