Evaluating the stability of opioid efficacy over 12 months in patients with chronic noncancer pain who initially demonstrate benefit from extended release oxycodone or hydrocodone: harmonization of Food and Drug Administration patient-level drug safety study data.

ABSTRACT: Opioids relieve acute pain, but there is little evidence to support the stability of the benefit over long-term treatment of chronic noncancer pain. Previous systematic reviews consider only group level published data which did not provide adequate detail. Our goal was to use patient-level data to explore the stability of pain, opioid dose, and either physical function or pain interference in patients treated for 12 months with abuse deterrent formulations of oxycodone and hydrocodone. All available studies in the Food and Drug Administration Document Archiving, Reporting, and Regulatory Tracking System were included. Patient-level demographics, baseline data, exposure, and outcomes were harmonized. Individual patient slopes were calculated from a linear model of pain, physical function, and pain interference to determine response over time. Opioid dose was summarized by change between baseline and the final month of observation. Patients with stable or less pain, stable or lower opioid dose, and stable or better physical function (where available) met our prespecified criteria for maintaining long-term benefit from chronic opioids. Of the complete data set of 3192 patients, 1422 (44.5%) maintained their pain level and opioid dose. In a secondary analysis of 985 patients with a measured physical function, 338 (34.3%) maintained their physical function in addition to pain and opioid dose. Of 2040 patients with pain interference measured, 788 (38.6%) met criteria in addition. In a carefully controlled environment, about one-third of patients successfully titrated on opioids to treat chronic noncancer pain demonstrated continued benefit for up to 12 months.

Testosterone Therapy in Relation to Prostate Cancer in a U.S. Commercial Insurance Claims Database.

BACKGROUND: We conducted a study to assess whether testosterone therapy (TT) alters prostate cancer risk using a large U.S. commercial insurance research database. METHODS: From the HealthCore Integrated Research Database (HIRD), we selected men ages 30 years or greater who were new users of TT during 2007 to 2015. We selected two comparison groups: (i) unexposed (matched 10:1) and (ii) new users of phosphodiesterase type 5 inhibitor (PDE5i). Incident prostate cancer was defined as diagnosis of prostate cancer within 4 weeks following prostate biopsy. Propensity scores and inverse probability of treatment weights were used in Poisson regression models to estimate adjusted incidence rates, incidence rate ratios (IRR), and 95% confidence intervals (CI). Subgroup analyses included stratification by prostate cancer screening, hypogonadism, and follow-up time. RESULTS: The adjusted prostate cancer IRR was 0.77 (95% CI, 0.68-0.86) when comparing TT with the unexposed group and 0.85 (95% CI, 0.79-0.91) in comparison with the PDE5i group. Inverse associations between TT and prostate cancer were observed in a majority of subgroup analyses, although in both comparisons estimates generally attenuated with increasing time following initial exposure. Among TT users, duration of exposure was not associated with prostate cancer. CONCLUSIONS: Men who received TT did not have a higher rate of prostate cancer compared with the unexposed or PDE5i comparison groups. The inverse association between TT and prostate cancer could be the result of residual confounding, contraindication bias, or undefined biological effect. IMPACT: This study suggests that limited TT exposure does not increase risk of prostate cancer in the short term.

Health Plan Enrollment and Disenrollment of Individuals With and Without Established Chronic Disease in a U.S. Commercially Insured and Medicare Advantage Population.

BACKGROUND: Chronic disease is associated with increased health care resource utilization and costs. Effective development and implementation of health care management and clinical intervention programs require an understanding of health plan member enrollment and disenrollment behavior. OBJECTIVE: To examine the health plan enrollment and disenrollment behavior of commercially insured and Medicare Advantage members with established chronic disease compared with matched members without the disease of interest, using data from a large national health insurer in the United States. METHODS: This retrospective matched cohort study used administrative claims data from the HealthCore Integrated Research Database from January 1, 2006, to November 30, 2015, to identify adults with chronic disease (type 2 diabetes mellitus [T2DM], cardiovascular disease [CVD], chronic obstructive pulmonary disease [COPD], rheumatoid arthritis [RA], and breast cancer [BC]). Members with no established chronic disease (controls) were directly matched to members with established chronic disease (cases) on demographic characteristics. The earliest date on which members met the criteria for a given disease was defined as the index date. Controls had the same index date as the matched cases. All members had ≥ 12 months of continuous health plan enrollment before the index date. Outcomes included health plan member disenrollment and enrollment duration. Incidence rates per 1,000 member-years for member disenrollment were evaluated along with incidence rate ratios (relative risk) using a Poisson model. Time to disenrollment was analyzed by Cox proportional hazard models and Kaplan-Meier survival curves. Sensitivity analyses were conducted where death was included as a disenrollment event. RESULTS: 70,907 health plan members with BC (99.7% female, mean age 60.5 years); 28,883 members with COPD (52.3% female, mean age 66.7); 835,358 members with CVD (50.5% female, mean age 62.7 years); 210,936 members with T2DM (45.2% female, mean age 53.6 years); and 31,954 members with RA (72.0% female, mean age 55.5 years) were matched to controls and met the study criteria. The incidence rates of health plan disenrollment ranged from 155 to 192 members per 1,000 members per year. Compared with controls, members with chronic disease were 30%-40% less likely to disenroll from a health plan (P < 0.001 for all comparisons). Among those who disenrolled, enrollment duration ranged from 2.3 to 2.7 years among cases and 1.5 to 1.8 years among matched controls (P ≤ 0.001 for all comparisons). CONCLUSIONS: This real-world study demonstrated that members with chronic disease had a significantly lower rate of disenrollment and a longer duration of enrollment compared with matched controls and were continuously enrolled for almost a year longer than members without a diagnosed chronic disease. Understanding health plan enrollment and disenrollment behavior may provide a valuable context for determining the time frame for the effect of health care programs and initiatives. DISCLOSURES: Funding for this study was provided by HealthCore, a wholly owned subsidiary of Anthem. Chung, Deshpande, Zolotarjova, Quimbo, and Willey are employees of HealthCore. Kern and Cochetti are former employees of HealthCore. Quimbo, Cochetti, and Willey are shareholders of Anthem. HealthCore receives funding from multiple pharmaceutical companies to perform various research studies outside of the submitted work. The preliminary results of this study were presented at AMCP Nexus 2015; March 26-29, 2015; Orlando, FL, and the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) 2017 Conference; May 20-24, 2017; Boston, MA.

Predictive model algorithms identifying early and advanced stage ER+/HER2- breast cancer in claims data.

PURPOSE: Claims databases offer large populations for research, but lack clinical details. We aimed to develop predictive models to identify estrogen receptor positive (ER+) and human epidermal growth factor negative (HER2-) early breast cancer (ESBC) and advanced stage breast cancer (ASBC) in a claims database. METHODS: Female breast cancer cases in Anthem's Cancer Care Quality Program served as the gold standard validation sample. Predictive models were developed from clinical knowledge and empirically from claims data using logistic and lasso regression. Model performance was assessed by classification rates and c-statistics. Models were applied to the HealthCore Integrated Research Database (claims) to identify cohorts of women with ER+/HER2- ESBC and ASBC. RESULTS: The validation sample included 3184 women with ER+/HER2- ESBC and 1436 with ER+/HER2- ASBC. Predictive models for ER+/HER2- ESBC and ASBC included 25 and 20 factors, respectively. Models had robust discrimination in identifying cases (c-stat = 0.92 for ESBC and 0.95 for ASBC). Compared with a traditional a priori algorithm developed with clinical insight alone, the ER+/HER2- ASBC-predictive model had better positive predictive value (PPV) (0.91, 95% CI, 0.90-0.93, vs 0.69, 95% CI, 0.66-0.73) and sensitivity (0.54 vs 0.35). Models were applied to the claims database to identify cohorts of 33 001 and 3198 women with ER+/HER2- ESBC and ASBC. CONCLUSION: We conducted a validation study and developed predictive models to identify in a claims database cohorts of women with ER+/HER2- ESBC and ASBC. The models identified large cohorts in the claims data that can be used to characterize indications in the evaluation of targeted therapies.

Pretransplant physical activity predicts all-cause mortality in kidney transplant recipients.

BACKGROUND: Low physical activity (PA) has been associated with higher rates of cardiovascular disease (CVD) and mortality in the general population. Despite the benefits of kidney transplantation, kidney transplant recipients (KTRs) remain at elevated risk for CVD and mortality compared to individuals without kidney disease. METHODS: A prospective cohort of 507 adult KTRs from three academic centers completed the Physical Activity Scale for the Elderly (PASE) at transplantation. PASE scores were divided into tertiles. RESULTS: PA was lower with older age, history of CVD, smoking, and diabetes. During the median 8-year follow-up period, 128 individuals died, among whom 101 had a functioning allograft. In multivariable Cox regression for all-cause mortality, greater PA was strongly associated with better survival (HR: 0.52 for most active vs. inactive tertiles, 95% CI: 0.31-0.87, p = 0.01). Secondary analyses, in which (1) death with a functioning graft was the primary outcome, and (2) PASE scores were converted to the metabolic equivalent of task, revealed similar results. We did not find an association between change of PA after transplantation and mortality. CONCLUSIONS: PA at the time of kidney transplantation is a strong predictor of all-cause mortality and death with graft function. Evaluation of PA level among kidney transplant candidates may be a useful method to risk-stratify patients for survival after kidney transplantation. Kidney transplant candidates and recipients should also be encouraged to be physically active.

Association of metabolic syndrome with development of new-onset diabetes after transplantation.

BACKGROUND: New-onset diabetes after transplantation (NODAT) is a major posttransplant complication associated with lower allograft and recipient survival. Our objective was to determine whether metabolic syndrome pretransplant is independently associated with NODAT development. METHODS: We recruited 640 consecutive incident nondiabetic renal transplant recipients from three academic centers between 1999 and 2004. NODAT was defined as the use of hypoglycemic medication, a random plasma glucose level more than 200 mg/dL, or two fasting glucose levels more than or equal to 126 mg/dL beyond 30 days posttransplant. RESULTS: Metabolic syndrome was common pretransplant (57.2%). NODAT developed in 31.4% of recipients 1 year posttransplant. Participants with metabolic syndrome were more likely to develop NODAT compared with recipients without metabolic syndrome (34.4% vs. 27.4%, P=0.057). Recipients with increasing number of positive metabolic syndrome components were more likely to develop NODAT (metabolic syndrome score prevalence at 1 year: 0 components-0.0%, 1-24.2%, 2-29.3%, 3-31.0%, 4-34.8%, and 5-73.7%, P=0.001). After adjustment for demographics, age by decade (hazard ratio [HR] 1.34 [1.20-1.50], P<0.0001), African American race (HR 1.35 [1.01-1.82], P=0.043), cumulative prednisone dosage (HR 1.18 [1.07-1.30], P=0.001), and metabolic syndrome (HR 1.34 [1.00-1.79], P=0.047) were independent predictors of development of NODAT at 1 year posttransplant. In a multivariable analysis incorporating the individual metabolic syndrome components themselves as covariates, the only pretransplant metabolic syndrome component to remain an independent predictor of NODAT was low high-density lipoprotein (hazard ratio [HR] 1.37 [1.01-1.85], P=0.042). CONCLUSIONS: Metabolic syndrome is an independent predictor for NODAT and is a possible target for intervention to prevent NODAT. Future studies to evaluate whether modification of metabolic syndrome factors pretransplant reduces NODAT development are needed.