Sequence symmetry analysis graphic adjustment for prescribing trends.

BACKGROUND: Sequence symmetry analysis (SSA) is a signal detection method that can be used to assist with adverse drug event detection. It provides both a risk estimate and a data visualisation. Published methods provide results in the form of an adjusted sequence ratio, which adjusts for underlying market trends of medicine use, however no method for adjusting the visualisation is available. We aimed to develop and evaluate another method of adjustment for prescribing trends and apply it to the SSA visualisation. METHODS: The SSA method relies on incident prescriptions for pairs of medicines of interest. Smoothing curves were fitted to the frequency distributions of incident medicine use. When divided and normalised, these curves yielded a set of proportions related to differences in prescribing trends over follow-up. These were then used to adjust the unit counts for incident prescriptions in the SAA visualisation and to calculate the sequence ratio. Curve fitting was also used to identify the proportional relationship between sequences over time for SSA and is presented as a supplementary visualisation to the SSA. We compared the sensitivity and specificity of our method with that from the SSA method of Tsiropolous et al. RESULTS: Curve-fit adjusted SSA visualisations yielded adjusted sequence ratios very close to those of Tsiropolous, with a p-value of 0.999 for the two sample Kolmogorov-Smirnov test. Results for sensitivity and specificity derived from adjusted sequence ratios were also practically the same. The curve-fit method graphically indicates the proportionality of the sequence and provides a useful supplement of net differences between the two sides of the SSA visualisation. Additionally, we found that incident prescriptions for patients common to both distributions are best precluded from adjustment calculations, leaving only incident prescriptions for patients unique to one or other distribution. This improved the accuracy of SSA in some atypical instances with negligible affect on accuracy elsewhere. CONCLUSIONS: Our curve-fit method is equivalent to current methods in the literature for adjusting prescribing trends in SAA, with the advantage of providing adjustment incorporated in the SAA visualisation.

Analysis of Luminex-based Algorithms to Define Unacceptable HLA Antibodies in CDC-crossmatch Negative Kidney Transplant Recipients.

BACKGROUND: HLA-specific antibodies detected by solid phase assays are increasingly used to define unacceptable HLA antigen mismatches (UAM) before renal transplantation. The accuracy of this approach is unclear. METHODS: Day of transplant sera from 211 complement-dependent cytotoxicity crossmatch-negative patients were retrospectively analyzed for donor-specific anti-HLA antibodies (DSA) using Luminex technology. HLA were defined as UAM if DSA had mean fluorescence intensity above (I) 3000 (patients retransplanted and those with DSA against HLA class I and II) or 5000 (all other patients), (II) 5000 for HLA-A, -B, and -DR and 10 000 for HLA DQ or (III) 10 000 (all HLA). We then studied the accuracy of these algorithms to identify patients with antibody-mediated rejection (AMR) and graft loss. UAM were also determined in 256 transplant candidates and vPRA levels calculated. RESULTS: At transplantation, 67 of 211 patients had DSA. Of these, 31 (algorithm I), 24 (II) and 17 (III) had UAM. Nine (I and II) and 8 (III) of 11 early AMR episodes and 7 (I), 6 (II) and 5 (III) of 9 graft losses occurred in UAM-positive patients during 4.9 years of follow-up. Algorithms I and II identified patients with persistently lower glomerular filtration rate even in the absence of overt AMR. Of the waiting list patients, 22-33% had UAM with median virtual panel reactive antibody of 69.2% to 79.1%. CONCLUSIONS: Algorithms I and II had comparable efficacy but were superior to Algorithm III in identifying at-risk patients at an acceptable false-positive rate. However, Luminex-defined UAM significantly restrict the donor pool of affected patients, which might prolong waiting time.

Comorbid diabetes and COPD: impact of corticosteroid use on diabetes complications.

OBJECTIVE: To identify if there is a dose-dependent risk of diabetes complications in patients treated with corticosteroids who have both diabetes and chronic obstructive pulmonary disorder (COPD). RESEARCH DESIGN AND METHODS: A retrospective study of administrative claims data from the Australian Government Department of Veterans' Affairs, from 1 July 2001 to 30 June 2008, of diabetes patients newly initiated on metformin or sulfonylurea. COPD was identified by dispensings of tiotropium or ipratropium in the 6 months preceding study entry. Total corticosteroid use (inhaled and systemic) in the 12 months after study entry was determined. The outcome was time to hospitalization for a diabetes-related complication. Competing risks and Cox proportional hazard regression analyses were conducted with adjustment for a number of covariates. RESULTS: A total of 18,226 subjects with diabetes were identified, of which 5.9% had COPD. Of those with COPD, 67.2% were dispensed corticosteroids in the 12 months from study entry. Stratification by dose of corticosteroids demonstrated a 94% increased likelihood of hospitalization for a diabetes complication for those who received a total defined daily dose (DDD) of corticosteroids≥0.83/day (subhazard ratio 1.94 [95% CI 1.14-3.28], P=0.014), by comparison with those who did not receive a corticosteroid. Lower doses of corticosteroid (<0.83 DDD/day) were not associated with an increased risk of diabetes-related hospitalization. CONCLUSIONS: In patients with diabetes and COPD, an increased risk of diabetes-related hospitalizations was only evident with use of high doses of corticosteroids. This highlights the need for constant revision of corticosteroid dose in those with diabetes and COPD, to ensure that the minimally effective dose is used, together with review of appropriate response to therapy.

Does antidepressant medication use affect persistence with diabetes medicines?

PURPOSE: This study aimed to examine the effect of antidepressant use on persistence with newly initiated oral antidiabetic medicines in older people. METHODS: A retrospective study of administrative claims data from the Australian Government Department of Veterans' Affairs, from 1 July 2000 to 30 June 2008 of new users of oral antidiabetic medicines (metformin or sulfonylurea). Antidepressant medicine use was determined in the 6 months preceding the index date of the first dispensing of an oral antidiabetic medicine. The outcome was time to discontinuation of diabetes therapy in those with antidepressant use compared with those without. Competing risks regression analyses were conducted with adjustment for covariates. RESULTS: A total of 29,710 new users of metformin or sulfonylurea were identified, with 7171 (24.2%) dispensed an antidepressant. Median duration of oral antidiabetic medicines was 1.81 years (95% CI 1.72­1.94) for those who received an antidepressant at the time of diabetes medicine initiation, by comparison to 3.23 years (95% CI 3.10­3.40) for those who did not receive an antidepressant. Competing risk analyses showed a 42% increased likelihood of discontinuation of diabetes medications in persons who received an antidepressant (subdistribution hazard ratio 1.42, 95% CI 1.37­1.47, p < 0.001). CONCLUSIONS: The results of this large population-based study demonstrate that depression may be contributing to non-compliance with medicines for diabetes and highlight the need to provide additional services to support appropriate medicine use in those initiating diabetes medicines with co-morbid depression.

Major bleeding risk associated with warfarin and co-medications in the elderly population.

PURPOSE: Warfarin management in the elderly population is complex as medicines prescribed for concomitant diseases may further increase the risk of major bleeding associated with warfarin use. We aimed to quantify the excess risk of bleeding-related hospitalisation when warfarin was co-dispensed with potentially interacting medicines. METHODS: A retrospective cohort study was undertaken over a 4-year period from July 2002 to June 2006 to examine bleeding risk associated with medications co-administered in patients taking warfarin using an administrative claims database from the Australian Department of Veterans' Affairs. All veterans aged 65 years and over who were new users of warfarin were followed until death or study end. Risk of bleeding was assessed using a Poisson GEE model adjusting for age, gender, socioeconomic status, co-morbidity index, previous bleeding related hospitalisations and indicators of health service use. RESULTS: Overall, 17661 veterans who used warfarin at any time during the study period were included. The overall incidence rate of bleeding-related hospitalisations was 4.1 (95% CI 3.7-4.6) per 100 person-years in veterans who were not receiving potentially interacting medicines. Bleeding-related hospitalisation rates were significantly increased when warfarin was co-prescribed with low-dose aspirin (Adjusted rate ratio (AdjRR) 1.44, 95% CI 1.00-2.07), clopidogrel (AdjRR 2.23, 95% CI 1.48­3.36), clopidogrel with aspirin (AdjRR 3.44, 95% CI 1.28-9.23), amiodarone (AdjRR 3.33, 95% CI 1.38­8.00) and antibiotics (AdjRR 2.34, 95% CI 1.55-3.54). CONCLUSIONS: Models assessing bleeding risk with warfarin should take account of the range of potentially harmful medicine combinations used in elderly people with comorbid conditions.

Influence of comorbidities on therapeutic progression of diabetes treatment in Australian veterans: a cohort study.

BACKGROUND: This study assessed whether the number of comorbid conditions unrelated to diabetes was associated with a delay in therapeutic progression of diabetes treatment in Australian veterans. METHODOLOGY/PRINCIPAL FINDINGS: A retrospective cohort study was undertaken using data from the Australian Department of Veterans' Affairs (DVA) claims database between July 2000 and June 2008. The study included new users of metformin or sulfonylurea medicines. The outcome was the time to addition or switch to another antidiabetic treatment. The total number of comorbid conditions unrelated to diabetes was identified using the pharmaceutical-based comorbidity index, Rx-Risk-V. Competing risk regression analyses were conducted, with adjustments for a number of covariates that included age, gender, residential status, use of endocrinology service, number of hospitalisation episodes and adherence to diabetes medicines. Overall, 20,134 veterans were included in the study. At one year, 23.5% of patients with diabetes had a second medicine added or had switched to another medicine, with 41.4% progressing by 4 years. The number of unrelated comorbidities was significantly associated with the time to addition of an antidiabetic medicine or switch to insulin (subhazard ratio [SHR] 0.87 [95% CI 0.84-0.91], P<0.001). Depression, cancer, chronic obstructive pulmonary disease, dementia, and Parkinson's disease were individually associated with a decreased likelihood of therapeutic progression. Age, residential status, number of hospitalisations and adherence to anti-diabetic medicines delayed therapeutic progression. CONCLUSIONS/SIGNIFICANCE: Increasing numbers of unrelated conditions decreased the likelihood of therapeutic progression in veterans with diabetes. These results have implications for the development of quality measures, clinical guidelines and the construction of models of care for management of diabetes in elderly people with comorbidities.

Assessing overall duration of cardiovascular medicines in veterans with established cardiovascular disease.

BACKGROUND: This study aimed to determine persistence, adherence, and time without therapy with cardiovascular medicines over all episodes of use among veterans following hospitalization for ischemic heart disease. METHODS: Retrospective cohort study using Department of Veterans' Affairs database including 9635 veterans with a hospitalization for acute myocardial infarction, angina, or ischemic heart disease, and who had been dispensed cardiovascular medicines in the 3 months posthospitalization. The main outcome measures were duration of first treatment episode, duration of overall treatment episode, and adherence with recommended therapies: angiotensin-converting enzyme (ACE) inhibitors, angiotensin II receptor blockers (ARBs), lipid-lowering therapy, calcium channel blockers (CCBs), beta-blockers, and antiplatelet therapy. RESULTS: The median duration of overall treatment was 6.2 years [95% confidence interval (CI): 6.0-6.4] for lipid-lowering therapy, 5.4 years (95% CI: 5.1-5.5) for ACE inhibitors/ARBs, 5.0 years (95% CI: 4.8-5.1) for antiplatelets, 3.4 years (95% CI: 3.3-3.6) for beta-blockers, and 2.8 years (95% CI: 2.6-3.0) for CCBs. Adherence was 72% for CCBs, 75% for ACE inhibitors/ARBs, 84% for lipid-lowering therapy, and 84% for antiplatelets other than aspirin. The median time without therapy was 4.5 months or less for ACE inhibitors/ARBs, antiplatelets, and lipid-lowering therapy. CONCLUSION: Problems with medication adherence can relate to either persistence or compliance during treatment. This novel method provides a way to determine which of these factors is most problematic when considering chronic therapies. We found that Australian veterans with established cardiovascular disease are persistent with their cardiovascular therapy, with only small gaps in therapy.