; for the PCORnet Bariatric Study Collaborative IMPORTANCE Additional data comparing longer-term problems associated with various bariatric surgical procedures are needed for shared decision-making. OBJECTIVE To compare the risks of intervention, operation, endoscopy, hospitalization, and mortality up to 5 years after 2 bariatric surgical procedures. DESIGN, SETTING, AND PARTICIPANTS Adults who underwent Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy (SG) between January 1, 2005, and September 30, 2015, within the National Patient-Centered Clinical Research Network. Data from 33 560 adults at 10 centers within 4 clinical data research networks were included in this cohort study. Information was extracted from electronic health records using a common data model and linked to insurance claims and mortality indices. Analyses were conducted from January 2018 through October 2019. EXPOSURES Bariatric surgical procedures. MAIN OUTCOMES AND MEASURES The primary outcome was time until operation or intervention. Secondary outcomes included endoscopy, hospitalization, and mortality rates. RESULTS Of 33 560 adults, 18 056 (54%) underwent RYGB, and 15 504 (46%) underwent SG. The median (interquartile range) follow-up for operation or intervention was 3.4 (1.6-5.0) years for RYGB and 2.2 (0.9-3.6) years for SG. The overall mean (SD) patient age was 45.0 (11.5) years, and the overall mean (SD) patient body mass index was 49.1 (7.9). The cohort was composed predominantly of women (80%) and white individuals (66%), with 26% of Hispanic ethnicity. Operation or intervention was less likely for SG than for RYGB (hazard ratio, 0.72; 95% CI, 0.65-0.79; P < .001). The estimated, adjusted cumulative incidence rates of operation or intervention at 5 years were 8.94% (95% CI, 8.23%-9.65%) for SG and 12.27% (95% CI, 11.49%-13.05%) for RYGB. Hospitalization was less likely for SG than for RYGB (hazard ratio, 0.82; 95% CI, 0.78-0.87; P < .001), and the 5-year adjusted cumulative incidence rates were 32.79% (95% CI, 31.62%-33.94%) for SG and 38.33% (95% CI, 37.17%-39.46%) for RYGB. Endoscopy was less likely for SG than for RYGB (hazard ratio, 0.47; 95% CI, 0.43-0.52; P < .001), and the adjusted cumulative incidence rates at 5 years were 7.80% (95% CI, 7.15%-8.43%) for SG and 15.83% (95% CI, 14.94%-16.71%) for RYGB. There were no differences in all-cause mortality between SG and RYGB. CONCLUSIONS AND RELEVANCE Interventions, operations, and hospitalizations were relatively common after bariatric surgical procedures and were more often associated with RYGB than SG.

; for the PCORnet Bariatric Study Collaborative IMPORTANCE Bariatric surgery can lead to substantial improvements in type 2 diabetes (T2DM), but outcomes vary across procedures and populations. It is unclear which bariatric procedure has the most benefits for patients with T2DM. OBJECTIVE To evaluate associations of bariatric surgery with T2DM outcomes. DESIGN, SETTING, AND PARTICIPANTS This cohort study was conducted in 34 US health system sites in the National Patient-Centered Clinical Research Network Bariatric Study. Adult patients with T2DM who had bariatric surgery between January 1, 2005, and September 30, 2015, were included. Data analysis was conducted from April 2017 to August 2019. INTERVENTIONS Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy (SG). MAIN OUTCOME AND MEASURES Type 2 diabetes remission, T2DM relapse, percentage of total weight lost, and change in glycosylated hemoglobin (hemoglobin A 1c). RESULTS A total of 9710 patients were included (median [interquartile range] follow-up time, 2.7 [2.9] years; 7051 female patients [72.6%]; mean [SD] age, 49.8 [10.5] years; mean [SD] BMI, 49.0 [8.4]; 6040 white patients [72.2%]). Weight loss was significantly greater with RYGB than SG at 1 year (mean difference, 6.3 [95% CI, 5.8-6.7] percentage points) and 5 years (mean difference, 8.1 [95% CI, 6.6-9.6] percentage points). The T2DM remission rate was approximately 10% higher in patients who had RYGB (hazard ratio, 1.10 [95% CI, 1.04-1.16]) than those who had SG. Estimated adjusted cumulative T2DM remission rates for patients who had RYGB and SG were 59.2% (95% CI, 57.7%-60.7%) and 55.9% (95% CI, 53.9%-57.9%), respectively, at 1 year and 86.1% (95% CI, 84.7%-87.3%) and 83.5% (95% CI, 81.6%-85.1%) at 5 years postsurgery. Among 6141 patients who experienced T2DM remission, the subsequent T2DM relapse rate was lower for those who had RYGB than those who had SG (hazard ratio, 0.75 [95% CI, 0.67-0.84]). Estimated relapse rates for those who had RYGB and SG were 8.4% (95% CI, 7.4%-9.3%) and 11.0% (95% CI, 9.6%-12.4%) at 1 year and 33.1% (95% CI, 29.6%-36.5%) and 41.6% (95% CI, 36.8%-46.1%) at 5 years after surgery. At 5 years, compared with baseline, hemoglobin A 1c was reduced 0.45 (95% CI, 0.27-0.63) percentage points more for patients who had RYGB vs patients who had SG. CONCLUSIONS AND RELEVANCE In this large multicenter study, patients who had RYGB had greater weight loss, a slightly higher T2DM remission rate, less T2DM relapse, and better long-term glycemic control compared with those who had SG. These findings can help inform patient-centered surgical decision-making.

Following the release of the framework for the Real-World Evidence (RWE) Program, the US Food and Drug Administration (FDA) is actively evaluating and exploring ways to optimize the utility of real-world data (RWD) and RWE to support regulatory decision making. For rare conditions, conducting traditional randomized clinical trials may not always be feasible, and RWD and RWE have played and will continue to play an important role. We use three case examples-cerliponase alfa, asfotase alfa, and uridine triacetate-to illustrate how RWD from disease registries, medical records with chart review, and literature, respectively, have been used to generate RWE to support regulatory decisions for selected rare diseases. These

IMPORTANCEPrior observational studies have suggested that fluoroquinolone use may be associated with more than 2-fold increased risk of aortic aneurysm or aortic dissection (AA/AD). These studies, however, did not fully consider the role of coexisting infections and the risk of fluoroquinolones relative to other antibiotics. OBJECTIVE To estimate the risk of AA/AD associated with infections and to assess the comparative risk of AA/AD associated with fluoroquinolones vs other antibiotics with similar indication profiles among patients with the same types of infections. DESIGNS, SETTINGS, AND PARTICIPANTSThis nested case-control study identified 21 651 176 adult patients from a nationwide population-based health insurance claims database from January 1, 2009, to November 30, 2015. Each incident case of AA/AD was matched with 10 control individuals by age, sex, and follow-up duration in the database using risk-set sampling. Analysis of the data was conducted from April 2019 to March 2020.EXPOSURES Infections and antibiotic use within a 60-day risk window before the occurrence of AA/AD. MAIN OUTCOMES AND MEASURESConditional logistic regression was used to estimate the odds ratios (ORs) and 95% CIs comparing infections for which fluoroquinolones are commonly used with no infection within a 60-day risk window before outcome occurrence, adjusting for baseline confounders and concomitant antibiotic use. The adjusted ORs comparing fluoroquinolones with antibiotics with similar indication profiles within patients with indicated infections were also estimated. RESULTSA total of 28 948 cases and 289 480 matched controls were included (71.37% male; mean [SD] age, 67.41 [15.03] years). Among these, the adjusted OR of AA/AD for any indicated infections was 1.73 (95% CI, 1.66-1.81). Septicemia (OR, 3.16; 95% CI, 2.63-3.78) and intra-abdominal infection (OR, 2.99; 95% CI, 2.45-3.65) had the highest increased risk. Fluoroquinolones were not associated with an increased AA/AD risk when compared with combined amoxicillin-clavulanate or combined ampicillin-sulbactam (OR, 1.01; 95% CI, 0.82-1.24) or with extended-spectrum cephalosporins (OR, 0.88; 95% CI, 0.70-1.11) among patients with indicated infections. The null findings for fluoroquinolone use remained robust in different subgroup and sensitivity analyses.CONCLUSIONS AND RELEVANCE These results highlight the importance of accounting for coexisting infections while examining the safety of antibiotics using real-world data; the findings suggest that concerns about AA/AD risk should not deter fluoroquinolone use for patients with indicated infections.

Purpose of ReviewAdministrative claims databases, which collect reimbursement-related information generated from healthcare encounters, are increasingly used to evaluate medication safety in pregnancy. We reviewed the strengths and limitations of claims-only databases and how other data sources may be used to improve the accuracy and completeness of information critical for studying medication safety in pregnancy.Recent FindingsResearch on medication safety in pregnancy requires information on pregnancy episodes, mother-infant linkage, medication exposure, gestational age, maternal and birth outcomes, confounding factors, and (in some studies) long-term follow-up data. Claims data reliably identifies live births and possibly other pregnancies. It allows mother-infant linkage and has prospectively collected prescription medication information. Its diagnosis and procedure information allows estimation of gestational age. It captures maternal medical conditions but generally has incomplete data on reproductive and lifestyle factors. It has information on certain, typically short-term maternal and infant outcomes that may require chart review confirmation. Other data sources including electronic health records and birth registries can augment claims data or be analyzed alone. Interviews, surveys, or biological samples provide additional information. Nationwide and regional birth and pregnancy registries, such as those in several European and North American countries, generally contain more complete information essential for pregnancy research compared to claims-only databases.SummaryClaims data offers several advantages in medication safety in pregnancy research. Its limitations can be partially addressed by linking it with other data sources or supplementing with primary data collection. Rigorous assessment of data quality and completeness is recommended regardless of data sources.

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Background European studies reported an increased risk of non-melanoma skin cancer associated with hydrochlorothiazide (HCTZ)-containing products. We examined the risks of basal cell (BCC) and squamous cell carcinoma (SCC) associated with HCTZ compared to angiotensin-converting enzyme inhibitors (ACEIs) in a US population. Methods We conducted a retrospective cohort study in the US Food and Drug Administration’s Sentinel System. From the date of HCTZ or ACEI dispensing, patients were followed until a SCC or BCC diagnosis requiring excision or topical chemotherapy treatment on or within 30 days after the diagnosis date; or a censoring event. Using Cox proportional hazards regression models, we estimated the hazard ratios (HRs), overall and separately by age, sex, and race. We also examined site- and age-adjusted incidence rate ratios (IRRs) by cumulative HCTZ dose within the matched cohort. Results Among 5.2 million propensity-score matched HCTZ and ACEI users, the incidence rate (per 1,000 person-years) of BCC was 2.78 and 2.82 respectively, and 1.66 and 1.60 for SCC. Overall, there was no difference in risk between HCTZ and ACEIs for BCC (HR = 0.99, 95% CI = 0.97–1.00), but an increased risk for SCC (HR = 1.04, 95% CI = 1.02–1.06). HCTZ use was associated with higher risks of BCC (HR = 1.09, 95% CI = 1.07–1.11) and SCC (HR = 1.15, 95% CI = 1.12–1.17) among Caucasians. Cumulative HCTZ dose ≥50,000mg was associated with an increased risk of SCC in the overall population (IRR =1.19, 95% CI = 1.05–1.35) and among Caucasians (IRR = 1.27, 95% CI = 1.10–1.47). Conclusions Among Caucasians, we identified small increased risks of BCC and SCC with HCTZ compared to ACEI. Appropriate risk mitigation strategies should be taken while using HCTZ.

Purpose of review: Electronic health records (EHRs) contain valuable data for identifying health outcomes, but these data also present numerous challenges when creating computable phenotyping algorithms. Machine learning methods could help with some of these challenges. In this review, we discuss four common scenarios that researchers may find helpful for thinking critically about when and for what tasks machine learning may be used to identify health outcomes from EHR data. Recent findings: We first consider the conditions in which machine learning may be especially useful with respect to two dimensions of a health outcome: 1) the characteristics of its diagnostic criteria, and 2) the format in which its diagnostic data are usually stored within EHR systems. In the first dimension, we propose that for health outcomes with diagnostic criteria involving many clinical factors, vague definitions, or subjective interpretations, machine learning may be useful for modeling the complex diagnostic decision-making process from a vector of clinical inputs to identify individuals with the health outcome. In the second dimension, we propose that for health outcomes where diagnostic information is largely stored in unstructured formats such as free text or images, machine learning may be useful for extracting and structuring this information as part of a natural language processing system or an image recognition task. We then consider these two dimensions jointly to define four common scenarios of health outcomes. For each scenario, we discuss the potential uses for machine learning – first assuming accurate and complete EHR data and then relaxing these assumptions to accommodate the limitations of real-world EHR systems. We illustrate these four scenarios using concrete examples and describe how recent studies have used machine learning to identify these health outcomes from EHR data. Summary: Machine learning has great potential to improve the accuracy and efficiency of health outcome identification from EHR systems, especially under certain conditions. To promote the use of machine learning in EHR-based phenotyping tasks, future work should prioritize efforts to increase the transportability of machine learning algorithms for use in multi-site settings.