Comparative Effectiveness of Metformin Dosage Uptitration Versus Adding Another Antihyperglycemic Medication on Glycemic Control in Type 2 Diabetes Patients Failing Initial Metformin Monotherapy: A Retrospective Cohort Study.

Metformin is recommended as first-line treatment for type 2 diabetes (T2D). A disadvantage of metformin is the possibility of gastrointestinal adverse effects in some patients. Many T2D patients are not able to achieve/maintain glycemic control from initial metformin treatment and receive treatment intensification by means of metformin dosage uptitration or addition of a T2D drug. This retrospective study evaluated the comparative effectiveness of these 2 treatment intensification strategies. The study cohort included T2D patients at a US integrated health care system who: were initiated on metformin monotherapy (MM) during January 2009 - September 2013; had an uncontrolled HbA1c (≥7%) after at least 90 days of MM; and received metformin dosage uptitration or an additional T2D medication within 6 months of the uncontrolled HbA1c reading. Statistical techniques included Kaplan-Meier curves and Cox proportional hazards regression. The study cohort included 1167 patients, 52.4% male and 65.1% white, with a mean age of 55.3 (±11.9) years. Of these, 49.1% received metformin dosage uptitration and 50.9% received an additional T2D medication. Metformin dosage uptitration was as effective as adding another T2D medication with the probability of not achieving glycemic control (P = 0.599) and rate of glycemic control (adjusted hazard ratio = 1.28, 95% confidence interval = 0.98-1.68) within 6 months of intensification not significantly different between the 2 groups. Metformin dosage uptitration could be a preferable initial intensification strategy in patients failing initial MM unless there is a concern for gastrointestinal adverse effects, in which case adding a T2D medication might be preferable.

Whole Genome Sequencing detects Inter-Facility Transmission of Carbapenem-resistant Klebsiella pneumoniae.

OBJECTIVES: To identify transmission patterns of Carbapenem-resistant Klebsiella pneumoniae infection during an outbreak at a large, tertiary care hospital and to detect whether the outbreak organisms spread to other facilities in the integrated healthcare network. METHODS: We analyzed 71 K. pneumoniae whole genome sequences collected from clinical specimens before, during and after the outbreak and reviewed corresponding patient medical records. Sequence and patient data were used to model probable transmissions and assess factors associated with the outbreak. RESULTS: We identified close genetic relationships among carbapenem-resistant K. pneumoniae isolates sampled during the study period. Transmission tree analysis combined with patient records uncovered extended periods of silent colonization in many study patients and transmission routes that were likely the result of asymptomatic patients transitioning between facilities. CONCLUSIONS: Detecting how and where Carbapenem-resistant K. pneumoniae infections spread is challenging in an environment of rising prevalence, asymptomatic carriage and mobility of patients. Whole genome sequencing improved the precision of investigating inter-facility transmissions. Our results emphasize that containment of Carbapenem-resistant K. pneumoniae infections requires coordinated efforts between healthcare networks and settings of care that acknowledge and mitigate transmission risk conferred by undetected carriage and by patient transfers between facilities.

Impact of Timely Treatment Intensification on Glycemic Goal Achievement in Patients With Type 2 Diabetes Failing Metformin Monotherapy.

Purpose The purpose of the study was to examine the association between timely treatment intensification (TTI) and glycemic goal achievement in patients with type 2 diabetes (T2D) failing metformin monotherapy (MM). Methods This study was set at a large integrated health care system in the United States. The study cohort included T2D patients aged 18 to 85 years who were on MM between January 2009 and September 2013 and had an uncontrolled glycated hemoglobin (A1C) reading (≥8%) after at least 3 months of MM (corresponding date was index date). Secondary analyses were performed using A1C <7% as T2D control. TTI was defined as receipt of an add-on therapy within 180 days after the index date. Impact of TTI on glycemic goal achievement was determined using multivariate Cox proportional hazards regression. Patients were censored at their last A1C reading or health care visit during 2 years after the index date. Results The study cohort consisted of 996 patients, ~58% male and ~59% Caucasian, with a mean age of ~54 (±12) years. TTI was observed in 50.2% of the patients. The rate of glycemic goal achievement was higher in patients with TTI compared with patients without TTI (hazards ratio = 1.632, 95% confidence interval = 1.328-2.006). The results for the secondary analyses were largely consistent with the primary findings. Conclusions TTI positively affected glycemic goal achievement among T2D patients failing MM and could be a useful strategy to increase the currently low proportion of patients with their T2D controlled in the United States.

Patient and Provider Factors Affecting Clinical Inertia in Patients With Type 2 Diabetes on Metformin Monotherapy.

PURPOSE: Our aim was to determine the extent of clinical inertia and the associated patient and provider factors in patients with type 2 diabetes on metformin monotherapy (MM) at a large integrated health care system in the United States. METHODS: The study cohort included patients with type 2 diabetes aged 18 to 85 years, on MM between January 2009 and September 2013, who experienced MM failure (had an uncontrolled glycosylated hemoglobin [HbA1c] reading (≥8.0% [64 mmol/mol]) after at least 90 days of MM). Clinical inertia was defined as absence of treatment intensification with an add-on therapy within 180 days after the MM failure (index date). The impact of patient and provider factors on clinical inertia was determined using generalized estimating equations. FINDINGS: The study cohort consisted of 996 patients; 58% were men and 59% were white, with a mean age of 53 (11.8) years. Of these, 49.8% experienced clinical inertia. Lower HbA1c at index date, absence of liver diseases, absence of renal diseases, and greater provider age were associated with clinical inertia. The clinical inertia rate in a secondary analysis considering HbA1c <7.0% (53 mmol/mol) as glycemic control was 67.9%. Greater patient age, lower HbA1c at index date, greater provider age, and being a primary care physician were associated with clinical inertia. IMPLICATIONS: Considerable clinical inertia rates were observed in our real-world patient population, suggesting the need of interventions to reduce clinical inertia in clinical practice. Information about patient and provider factors affecting clinical inertia provided by this study could help healthcare policymakers plan and implement such interventions.

Traditional Risk Indices as Predictors of Future Utilization and Charges in the Context of Population Health for an Uninsured Cohort.

INTRODUCTION: The uninsured population presents unique challenges to the application of an integrated approach to population health. Our objective is to compare and test population risk indices for identifying a cohort of uninsured patients at high-risk for avoidable healthcare utilization and costs. METHODS: Patients who had a least one visit at a safety-net clinic, had a primary address in Mecklenburg County, were aged 18-74, and had the most recent healthcare visit coded as 'uninsured' were identified in the baseline period. The five risk indices used were: the HHS Hierarchical Conditions Category (HCC), the Charlson Comorbidity Index (CCI), Total Cost Index, Total Inpatient Visits Index, and Total Emergency Department Visits Index. First, agreement across the five indices was analyzed. Then, the accuracy of the five risk indices was tested in predicting future utilization and costs for the subsequent 12-month follow-up period. RESULTS: Kappa statistics and percent overlap values showed below average to poor agreement between indices when comparing scorers.The strongest predictors of being in the 90th percentile of total cost during the 12 months follow-up period were the Total Cost Index at baseline (C statistic=0.75) and the HCC (C-statistic=0.73). The CCI and Total Inpatient Visit Index's demonstrated the lowest accuracy for predicting an unnecessary ED visit (C-statistic=0.51, for both). DISCUSSION/CONCLUSION: Prior cost and ED utilization were key in predicting their corresponding 12-month metrics. In contrast, the Total Inpatient Visit Index had the worst predictive performance for future hospitalization rates. Some indices were similarly predictive as compared to insured cohorts but others showed contrasting results.