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Background Trends and clinical factors associated with prescribing choices for oral P2Y12 inhibitors (P2Y12-I) remain unknown for patients on chronic dialysis, i.e., with end-stage renal disease (ESRD). Methods From 2011–2014 U.S. Renal Data System registry, we identified 36,542 ESRD patients who received new prescriptions for P2Y12-I (median age 64.0 years and 54% males). Of the cohort, 93% were receiving hemodialysis and 7% on peritoneal dialysis. We analyzed trends and investigated clinical factors associated with specific P2Y12-I prescribed. Results Clopidogrel was prescribed for 95%, prasugrel for 3%, and ticagrelor for 2%. Clopidogrel was favored for those ≥75 years (18% of cohort). Compared to Caucasians, African Americans (36% of cohort) and Hispanics (19% of cohort) were less likely to receive prasugrel and ticagrelor (P Conclusion Between 2011 and 2014, clopidogrel remained the most common P2Y12-I whereas ticagrelor and prasugrel remained underutilized in ESRD patients. Prescribing practices for these drugs were based upon clinically approved indication for their use in the general population as well as perceived complexity of an ESRD patient including demographics, dialysis-related factors and comorbidities. Comparative effectiveness studies involving ESRD patients are needed to prove that ticagrelor and prasugrel are just as safe and effective as clopidogrel before clinicians can make informed decisions for choice of P2Y12-I in this patient population.

NCCN guidelines recommend genetic testing for all triple-negative breast cancer (TNBC) patients aged ≤60 years. However, due to the lack of prospective information in unselected patients, these guidelines are not uniformly adopted by clinicians and insurance carriers. The aim of this study was to determine the prevalence of BRCA mutations and evaluate the utility of NCCN guidelines in unselected TNBC population. Stage I–IV TNBC patients were enrolled on a prospective registry at academic and community practices. All patients underwent BRCA1/2 testing. Significant family history (SFH) was defined >1 relative with breast cancer at age ≤50 or ≥1 relative with ovarian cancer. Mutation prevalence in the entire cohort and subgroups was calculated. 207 TNBC patients were enrolled between 2011 and 2013. Racial/ethnic distribution: Caucasian (80 %), African–American (14 %), Ashkenazi (1 %). Deleterious BRCA1/2 mutations were identified in 15.4 % (32/207) of patients (BRCA1:11.1 %, BRCA2:4.3 %). SFH reported by 36 % of patients. Mutation prevalence in patients with and without SFH was 31.6 and 6.1 %, respectively. When assessed by age at TNBC diagnosis, the mutation prevalences were 27.6 % (≤50 years), 11.4 % (51–60 years), and 4.9 % (≥61 years). Using SFH or age ≤50 as criteria, 25 and 34 % of mutations, respectively, were missed. Mutation prevalence in patients meeting NCCN guidelines was 18.3 % (32/175) and 0 % (0/32) in patients who did not meet guidelines (p = .0059). In this unselected academic and community population with negligible Ashkenazi representation, we observed an overall BRCA mutation prevalence rate of 15.4 %. BRCA testing based on NCCN guidelines identified all carriers supporting its routine application in clinical practice for TNBC.

Background Little is known about the effect of multiple, or subsequent, ischemic strokes in patients receiving hemodialysis. Methods We undertook a retrospective cohort study of incident hemodialysis patients with Medicare coverage who had experienced a first ischemic stroke. Factors associated with either a subsequent ischemic stroke or death following a first new stroke were modeled. A multistate model with Cox proportional hazards was used to predict transition probabilities from first ischemic stroke to either subsequent stroke or to death, and the demographic and clinical factors associated with the respective transition probabilities were determined. Effect of a subsequent ischemic stroke on survival was quantified. Results Overall, 12,054 individuals (mean age 69.7 years, 41.3 % male, 53.0 % Caucasian and 34.0 % African-American) experienced a first new ischemic stroke. Female sex was associated with an increased risk of having a subsequent ischemic stroke (adjusted hazard ratio 1.37, 95 % confidence intervals 1.20 – 1.56, P 15 %, in absolute terms, from 0.254 to 0.096, with substantial drops observed at subsequent time points such that the probability of survival was more than halved. Conclusions Likelihood of subsequent ischemic stroke and of survival in hemodialysis patients appears to vary by sex and race: females are more likely than males to experience a subsequent ischemic stroke, and Caucasians are more likely than African-Americans to die after a first new ischemic stroke. The risk of a transitioning to a subsequent stroke (after having had a first) increases until about 1 year, then decreases. Subsequent strokes are associated with decreased probability of survival, an effect which increases as time since first stroke elapses. This information may be of assistance to clinicians when counseling hemodialysis patients about the implications of recurrent ischemic stroke.

Background Within-class comparative effectiveness studies of β-blockers have not been performed in the chronic dialysis setting. With widespread cardiac disease in these patients and potential mechanistic differences within the class, we examined whether mortality and morbidity outcomes varied between cardio-selective and non-selective β-blockers. Methods Retrospective observational study of within class β-blocker exposure among a national cohort of new chronic dialysis patients (N = 52,922) with hypertension and dual eligibility (Medicare-Medicaid). New β-blocker users were classified according to their exclusive use of one of the subclasses. Outcomes were all-cause mortality (ACM) and cardiovascular morbidity and mortality (CVMM). The associations of cardio-selective and non-selective agents on outcomes were adjusted for baseline characteristics using Cox proportional hazards. Results There were 4938 new β-blocker users included in the ACM model and 4537 in the CVMM model: 77 % on cardio-selective β-blockers. Exposure to cardio-selective and non-selective agents during the follow-up period was comparable, as measured by proportion of days covered (0.56 vs. 0.53 in the ACM model; 0.56 vs 0.54 in the CVMM model). Use of cardio-selective β-blockers was associated with lower risk for mortality (AHR = 0.84; 99 % CI = 0.72–0.97, p = 0.0026) and lower risk for CVMM events (AHR = 0.86; 99 % CI = 0.75–0.99, p = 0.0042). Conclusion Among new β-blockers users on chronic dialysis, cardio-selective agents were associated with a statistically significant 16 % reduction in mortality and 14 % in cardiovascular morbidity and mortality relative to non-selective β-blocker users. A randomized clinical trial would be appropriate to more definitively answer whether cardio-selective β-blockers are superior to non-selective β-blockers in the setting of chronic dialysis.

Despite uncertainty about their effectiveness in chronic dialysis patients, statin use has increased in recent years. Little is known about the demographic, clinical, and geographic factors associated with statin exposure in end-stage renal disease (ESRD) patients.To analyze the demographic, clinical, and geographic factors associated with use of statins among chronic dialysis patients.Cross-sectional analysis.Prevalent dialysis patients across the U.S.55,573 chronic dialysis patients who were dually eligible for Medicaid and Medicare services during the last four months of 2005.Using Medicaid prescription drug claims and United States Renal Data System core data, we examined demographics, comorbid conditions, and state of residence using hierarchical logistic regression models to determine their associations with statin use.Prescription for a statin.Factors associated with a prescription for a statin.Statin exposure was significantly associated with older age, female sex, Caucasian (versus African-American) race, body mass index, use of self-care dialysis, diabetes, and comorbidity burden. Moreover, there was substantial state-by-state variation in statin use, with a greater than 2.3-fold difference in adjusted odds ratios between the highest- and lowest-prescribing states.Among publicly insured chronic dialysis patients, there were marked differences between states in the use of HMG-CoA reductase inhibitors above and beyond patient characteristics. This suggests substantial clinical uncertainty about the utility of these medications. Understanding how such regional variations impact patient care in this high-risk population is an important focus for future work.

We examined the relationship between the proportion of time spent in the mammography exam room relative to the total visit time and the rate of repeat visits across mammography facilities. We hypothesized that the means for these proportions would be greater among facilities with high repeat rates compared to those with low repeat rates. The beta distribution was used to model the proportions. However, not all of the observations were observed as some were (right- or interval-) censored due to the inability to observe complete information (e.g., time of arrival at the facility) to calculate the proportions for some observations. Using a maximum likelihood approach, we estimated the mean proportion of time spent during appointments on the actual exams. To analyze these data we utilized survival analysis methods, which typically are applied to data on time scales, to analyze these data restricted to the range (0, 1). We found that facilities with high repeat mammography rates on average had a greater proportion of their visits spent receiving the exams than the low repeat rate facilities (p = 0.0003). Women from low repeat rate facilities spent on average 45 % of their appointment time engaging in the exam; thus, these women averaged more time waiting for the exam to begin than receiving the exam. Conversely, those from high repeat rate facilities averaged more of their appointment time engaging in the mammographic exams. These findings were consistent with our hypothesis that women would have greater satisfaction with their mammography appointments—and thus be more likely to return for routine screening—in facilities where their appointments were more efficient.

The inpatient principal diagnosis in Medicare claims identified 96% of hip fractures in hospitalized nursing home residents with high rates of confirmation by other claims files.Hip fracture is typically identified in Medicare claims by examining only the principal diagnosis in the inpatient file, but this simple approach might be inadequate for nursing home residents. Our objective was to examine the impact of varied operational definitions for identifying hip fracture hospitalizations in administrative claims data.We conducted a retrospective examination of Medicare inpatient and outpatient claims data for dually Medicaid- and Medicare-eligible nursing home residents in 1999 in California, Florida, Missouri, New Jersey, and Pennsylvania (n = 197,514). We determined the number of hip fractures identified in inpatient (Medicare A) diagnoses codes using differing definitions that varied according to whether or not hip fracture was required to be the principal diagnosis and whether or not confirmatory imaging and procedure codes were required to be found in other (Medicare B) claims files.Hip fractures were found in any inpatient diagnosis position in 4,680 subjects, with 4,479 of these found in the principal diagnosis position. With either approach to diagnosis position, confirmatory imaging and procedure codes were identified for 95% of persons hospitalized with hip fracture.The principal diagnosis alone will identify 96% of hip fracture diagnoses in hospitalized nursing home residents. Such diagnoses are confirmed at very high rates by other sources of claims data. Researchers may be confident using a simple approach to identifying hip fracture hospitalizations in this population, using inpatient claims alone and interrogating only the principal diagnosis position.

Background In the literature, different shapes of associations have been found between body mass index (BMI) and mortality and some of the findings were opposite to each other. The association of BMI and mortality in a single cohort has been found to be dynamic that can lead to different findings under different settings. The identified dynamic features were consistent with the heterogeneity in the literature. It is meaningful to find out whether such dynamic associations exist in other populations. Methods Data of six different cohorts were used for analysis and comparison. The proportional hazards assumptions for BMI in Cox models were tested to identify dynamic associations in each cohort. Time-dependent covariates Cox model was used to model the association of BMI and mortality risk as functions of follow-up time. The Cox model was applied to the pooled data with survival times censored at 5 to 40 years to show the potential impact of the dynamic association on traditional Meta-analysis. Results and discussion Dynamic associations were identified in six models (4 for men and 2 for women), four of which showed the same changing pattern: the elevated mortality risk for low BMI decreased while that for high BMI increased with follow-up time. When the Cox model was applied to the pooled data excluding the largest and also the shortest cohort, low BMI was but high BMI was not associated with high mortality for men with censoring at 5 years but the association for low BMI became weaker and that for high BMI became much stronger when censoring time was at 40 years. The dynamic association indicated that shorter studies tend to obtain inverse associations between BMI and mortality while longer studies tend to obtain J-shaped associations. Conclusions Different or even opposite results about body weight and mortality in the literature may be in part due to the underlying dynamic association of BMI and mortality. The dynamic features need to be taken into consideration in future studies.

Background Baseline and trend data for oral and pharyngeal cancer incidence is limited. A new algorithm was derived using the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked database to create an algorithm to identify incident cases of oral and pharyngeal cancer using Medicare claims. Methods Using a split-sample approach, Medicare claims’ procedure and diagnosis codes were used to generate a new algorithm to identify oral and pharyngeal cancer cases and validate its operating characteristics. Results The algorithm had high sensitivity (95%) and specificity (97%), which varied little by age group, sex, and race and ethnicity. Conclusion Examples of the utility of this algorithm and its operating characteristics include using it to derive baseline and trend estimates of oral and pharyngeal cancer incidence. Such measures could be used to provide incidence estimates where they are lacking or to serve as comparator estimates for tumor registries.

Background Many nursing and health related research studies have continuous outcome measures that are inherently non-normal in distribution. The Box-Cox transformation provides a powerful tool for developing a parsimonious model for data representation and interpretation when the distribution of the dependent variable, or outcome measure, of interest deviates from the normal distribution. The objectives of this study was to contrast the effect of obtaining the Box-Cox power transformation parameter and subsequent analysis of variance with or without a priori knowledge of predictor variables under the classic linear or linear mixed model settings. Methods Simulation data from a 3 × 4 factorial treatments design, along with the Patient Falls and Patient Injury Falls from the National Database of Nursing Quality Indicators (NDNQI®) for the 3rd quarter of 2007 from a convenience sample of over one thousand US hospitals were analyzed. The effect of the nonlinear monotonic transformation was contrasted in two ways: a) estimating the transformation parameter along with factors with potential structural effects, and b) estimating the transformation parameter first and then conducting analysis of variance for the structural effect. Results Linear model ANOVA with Monte Carlo simulation and mixed models with correlated error terms with NDNQI examples showed no substantial differences on statistical tests for structural effects if the factors with structural effects were omitted during the estimation of the transformation parameter. Conclusions The Box-Cox power transformation can still be an effective tool for validating statistical inferences with large observational, cross-sectional, and hierarchical or repeated measure studies under the linear or the mixed model settings without prior knowledge of all the factors with potential structural effects.

Background The National Database for Nursing Quality Indicators® (NDNQI®) was established in 1998 to assist hospitals in monitoring indicators of nursing quality (eg, falls and pressure ulcers). Hospitals participating in NDNQI transmit data from nursing units to an NDNQI data repository. Data are summarized and published in reports that allow participating facilities to compare the results for their units with those from other units across the nation. A disadvantage of this reporting scheme is that the sampling variability is not explicit. For example, suppose a small nursing unit that has 2 out of 10 (rate of 20%) patients with pressure ulcers. Should the nursing unit immediately undertake a quality improvement plan because of the rate difference from the national average (7%)? Methods In this paper, we propose approximating 95% credible intervals (CrIs) for unit-level data using statistical models that account for the variability in unit rates for report cards. Results Bayesian CrIs communicate the level of uncertainty of estimates more clearly to decision makers than other significance tests. Conclusion A benefit of this approach is that nursing units would be better able to distinguish problematic or beneficial trends from fluctuations likely due to chance.