Longitudinal Patterns in Testosterone Prescribing After US FDA Safety Communication in 2014.

BACKGROUND: The objective of this study was to describe changes in testosterone prescribing following a 2014 US Food and Drug Administration (FDA) safety communication and how changes varied by physician characteristics. METHODS: Data were extracted from a 20% random sample of Medicare fee-for-service administrative claims data from 2011 through 2019. The sample included 1,544,604 unique male beneficiaries who received evaluation and management (E&M) services from 58,819 unique physicians that prescribed testosterone between 2011 and 2013. Patients were categorized based on presence of coronary artery disease (CAD) and non-age-related hypogonadism. Physician characteristics were identified in the OneKey database and included specialty and affiliations with teaching hospitals, for-profit hospitals, hospitals in integrated delivery networks, and hospitals in the top decile of case mix index. Linear segmented models described how testosterone prescriptions changed following a 2014 FDA safety communication and how changes were associated with physician and organizational characteristics. RESULTS: Among 65,089,560 physician-patient-quarter-year observations, mean (standard deviation) age ranged from 72.16 (5.84) years for observations without CAD or non-age-related hypogonadism to 75.73 (6.92) years with CAD and without non-age-related hypogonadism. Following the safety communication, immediate changes in off-label testosterone prescription levels fell by 0.22 percentage points (pp) (95% confidence interval [CI] -0.33 to -0.11) for patients with CAD and by -0.16 pp (95% CI -0.19 to -0.16) for patients without CAD. A similar change was noticed in on-label prescribing levels. Off-label testosterone prescription quarterly trend, however, increased for patients with CAD and without CAD; on-label testosterone prescription trends declined for both groups. Declines in off-label prescribing were larger when treated by primary care physicians vs. non-primary care physicians, and physicians affiliated with teaching compared to nonteaching hospitals. Physician and organizational characteristics were not associated with changes in on-label prescribing. CONCLUSION: On-label and off-label testosterone therapy declined following the FDA safety communication. Certain physician characteristics were associated with changes in off-label, but not on-label, prescribing.

Time to publication of cost-effectiveness analyses for medical devices.

OBJECTIVES: Academic researchers and physicians have called for greater use of cost-effectiveness analyses in informing treatment and reimbursement decisions. This study examines the availability of cost-effectiveness analyses for medical devices, in terms of both the number of studies and when studies are published. STUDY DESIGN: Analysis of the number of years between FDA approval/clearance and publication for cost-effectiveness analyses of medical devices in the United States published between 2002 and 2020 (n = 86). METHODS: Cost-effectiveness analyses of medical devices were identified using the Tufts University Cost-Effectiveness Analysis Registry. Studies in which the model and manufacturer of the medical device used in the intervention were identifiable were linked to FDA databases. Years between FDA approval/clearance and publication of cost-effectiveness analyses were calculated. RESULTS: A total of 218 cost-effectiveness analyses of medical devices in the United States published between 2002 and 2020 were identified. Of these studies, 86 (39.4%) were linked to FDA databases. Studies examining devices approved via premarket approval were published a mean of 6.0 years after the device received FDA approval (median, 4 years), whereas studies examining devices that were cleared via the 510(k) process were published a mean of 6.5 years after the device received FDA clearance (median, 5 years). CONCLUSIONS: There are few studies describing the cost-effectiveness of medical devices. Most of these studies' findings are not published until several years after the studied devices received FDA approval/clearance, meaning that decision makers will likely not have evidence of cost-effectiveness when making initial decisions related to newly available medical devices.

Association Between Regulatory Submission Characteristics and Recalls of Medical Devices Receiving 510(k) Clearance.

Importance: Most regulated medical devices enter the US market via the 510(k) regulatory submission pathway, wherein manufacturers demonstrate that applicant devices are "substantially equivalent" to 1 or more "predicate" devices (legally marketed medical devices with similar intended use). Most recalled medical devices are 510(k) devices. Objective: To examine the association between characteristics of predicate medical devices and recall probability for 510(k) devices. Design, Setting, and Participants: In this exploratory cross-sectional analysis of medical devices cleared by the US Food and Drug Administration (FDA) between 2003 and 2018 via the 510(k) regulatory submission pathway, linear probability models were used to examine associations between a 510(k) device's recall status and characteristics of its predicate medical devices. Public documents for the 510(k) medical devices were collected using FDA databases. A text extraction algorithm was applied to identify predicate medical devices cited in 510(k) regulatory submissions. Algorithm-derived metadata were combined with 2003-2020 FDA recall data. Exposures: Citation of predicate medical devices with certain characteristics in 510(k) regulatory submissions, including the total number of predicate medical devices cited by the applicant device, the age of the predicate medical devices, the lack of similarity of the predicate medical devices to the applicant device, and the recall status of the predicate medical devices. Main Outcomes and Measures: Class I or class II recall of a 510(k) medical device between its FDA regulatory clearance date and December 31, 2020. Results: The sample included 35 176 medical devices, of which 4007 (11.4%) were recalled. The applicant devices cited a mean of 2.6 predicate medical devices, with mean ages of 3.6 years and 7.4 years for the newest and oldest, respectively, predicate medical devices. Of the applicant devices, 93.9% cited predicate medical devices with no ongoing recalls, 4.3% cited predicate medical devices with 1 ongoing class I or class II recall, 1.0% cited predicate medical devices with 2 ongoing recalls, and 0.8% cited predicate medical devices with 3 or more ongoing recalls. Applicant devices citing predicate medical devices with 3 or more ongoing recalls were significantly associated with a 9.31-percentage-point increase (95% CI, 2.84-15.77 percentage points) in recall probability compared with devices without ongoing recalls of predicate medical devices, or an 81.2% increase in recall probability relative to the mean recall probability. A 1-SD increase in the total number of predicate medical devices cited by the applicant device was significantly associated with a 1.25-percentage-point increase (95% CI, 0.62-1.87 percentage points) in recall probability, or an 11.0% increase in recall probability relative to the mean recall probability. A 1-SD increase in the newest age of a predicate medical device was significantly associated with a 0.78-percentage-point decrease (95% CI, 1.29-0.30 percentage points) in recall probability, or a 6.8% decrease in recall probability relative to the mean recall probability. Conclusions and Relevance: This exploratory cross-sectional study of 510(k) medical devices cleared by the FDA between 2003 and 2018 demonstrated significant associations between 510(k) submission characteristics and recalls of medical devices. Further research is needed to understand the implications of these associations.