Citations

Abstract Privacy protection is a core principle of genomic but not proteomic research. We identified independent single nucleotide polymorphism (SNP) quantitative trait loci (pQTL) from COPDGene and Jackson Heart Study (JHS), calculated continuous protein level genotype probabilities, and then applied a naïve Bayesian approach to match proteomes to genomes for 2,812 independent subjects from COPDGene, JHS, SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) and Multi-Ethnic Study of Atherosclerosis (MESA). We were able to correctly match 90%-95% of proteomes to their correct genome and for 95%-99% we could match the proteome to the 1% most likely genome. The accuracy of matching in subjects with African ancestry was lower (~ 60%) unless training included diverse subjects. With larger profiling (SomaScan 5K) in the Atherosclerosis Risk Communities (ARIC) correct identification was > 99% even in mixed ancestry populations. When serial proteomes are available, the matching algorithm can be used to identify and correct mislabeled samples. This work also demonstrates the importance of including diverse populations in omics research and that large proteomic datasets (> 1,000 proteins) can be accurately linked to a specific genome through pQTL knowledge and should not be considered unidentifiable.

Background: Administrative data such as Medicare data are commonly used for health services research and comparative effectiveness studies. These databases are readily available and a good source of long-term data on healthcare utilization and health outcomes in real-world settings. Outcomes are captured using diagnostic codes, but the evidence on the validity of these codes is scarce in contemporary Medicare data. Methods: We linked the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study data using SSN, date of birth and sex to 2003-2009 Medicare claims. In addition to the events captured by REGARDS phone interviews, medical charts were pursued for Medicare-identified strokes not previously identified in REGRADS. Events were adjudicated by stroke specialists using the retrieved medical charts. Using all the adjudicated strokes as gold standard, we calculated the sensitivity, specificity, PPV and NPV of inpatient stroke algorithms with ICD-9-CM codes [430, 431, 433.x1, 434.x1, 436] in the primary discharge diagnosis. Results: We successfully linked 15,089 REGARDS participants with mean age of 69.3, 52% female and 37% Black. We adjudicated 457 strokes, of which 48 were identified by claims only and not by interviews. The tested algorithms had high specificity (99.8-100%), PPV (88.6-90.5%) and NPV (98.9-99.9%), but low sensitivity (58.6-67.4%) (Table). Conclusions: High specificity and PPV of our algorithms to identify strokes in contemporary Medicare populations support their use in etiological and comparative effectiveness studies. While our inpatient Medicare algorithms identified 12% extra cases of strokes, their usefulness for estimating stroke incidence or related healthcare utilizations is limited by their low sensitivity. Linking cohorts to Medicare data is feasible and should be considered to increase the completeness of follow-up. Further studies are needed to evaluate more sensitive Medicare algorithms.

BACKGROUND: Patients with inflammatory bowel disease (IBD), particularly ulcerative colitis (UC), have a higher risk of Clostridium difficile infection (CDI) than the general population (1). The incidence of CDI is increasing among patients with IBD, and certain UC treatments may be associated with greater risk (1). We report CDI events in the tofacitinib UC clinical program, and the incidence of CDI among patients receiving other treatments for UC. METHODS: For the tofacitinib UC program, rates of CDI (patients with an adverse event [AE] of CDI, Clostridium difficile colitis, or Clostridium test-positive, plus concomitant oral metronidazole or vancomycin, counted once per cohort) are reported for Induction Cohort (patients from an 8-week Phase [P]2 and two 8-week P3 induction studies; placebo or tofacitinib 10 mg twice daily [BID; NCT00787202; NCT01465763; NCT01458951]); Maintenance Cohort (patients from a 52-week maintenance study; placebo or tofacitinib 5 or 10 mg BID [NCT01458574]); and Overall Cohort (all tofacitinib-treated patients in P2/3 induction and maintenance studies and an ongoing, open-label, long-term extension study [as of December 2016; NCT01470612]). Positive Clostridium difficile test at screening was an exclusion criterion for program entry. Overall Cohort incidence rate (IR) was calculated as number of unique patients with events per 100 patient-years (PY). For contextualization, CDI rates among patients receiving other UC medications were obtained from a retrospective cohort study utilizing data from the Truven MarketScan ® Database, an administrative healthcare claims database in the US. Patients with moderately to severely active UC, with either an inpatient or outpatient diagnosis code of CDI, plus a prescription for oral therapy (including metronidazole, vancomycin, and fidaxomicin) within 14 days of CDI diagnosis (observation period: October 1, 2010–September 30, 2015), were included. Truven cohort IRs were calculated as number of patients with events per 100 PY. RESULTS: In the tofacitinib Induction Cohort, CDI occurred in 3 patients (placebo, 1/282 [0.4%]; tofacitinib 10 mg BID, 2/938 [0.2%]). In the Maintenance Cohort, CDI occurred in 3 patients (placebo, 3/198 [1.5%]; tofacitinib 5 or 10 mg BID, 0/394 [0.0%]). In the Overall Cohort of all tofacitinib-treated patients (1,664 PY of follow-up), CDI occurred in 8/1,157 patients (0.7%), with an IR (95% confidence interval [CI]) of 0.48 (0.21, 0.95). Two cases were reported as serious AEs. All were mild (n = 4) or moderate (n = 4) in severity; 6 continued with study treatment, one temporarily discontinued, one permanently discontinued. All CDI cases resolved with treatment. In the Truven Cohort, 160 CDI events occurred during 4,918 PY of follow-up, with an overall CDI IR (95% CI) of 3.25 (2.77, 3.80); of which 116 were CDI-associated hospitalizations (4,936 PY of follow-up). Among patients receiving azathioprine, 6-mercaptopurine, or tumor necrosis factor inhibitors, CDI IRs were 3.23 (2.18, 4.62), 2.31 (1.26, 3.87), and 3.58 (2.95, 4.29), respectively. CONCLUSION(S): In the tofacitinib UC program, CDI rates among patients receiving tofacitinib were similar to or lower than those observed with placebo. The IR among patients receiving tofacitinib was numerically lower than those observed for immunomodulators and tumor necrosis factor inhibitors in the Truven Cohort.

ObjectiveWe performed a systematic review and meta‐analysis to evaluate the comparative effects of tumor necrosis factor inhibitors (TNFi), non‐TNFi biologics, and conventional synthetic disease‐modifying antirheumatic drugs (csDMARDs) on cardiovascular risk in rheumatoid arthritis (RA).MethodsUsing a systematic search through May 8, 2018, we included 14 observational studies in adults with RA treated with TNFi, non‐TNFi biologics, tofacitinib, or csDMARDs, reporting the risk of major adverse cardiovascular events (MACE) or stroke. Only studies reporting active comparators were included. We performed random effects meta‐analysis and estimated odds ratios (ORs) and 95% confidence intervals (95% CIs).ResultsAs compared to TNFi, tocilizumab was associated with a decreased risk of MACE (OR 0.59 [95% CI 0.34–1.00]), whereas csDMARDs were associated with an increased risk of MACE (csDMARDs including methotrexate OR 1.45 [95% CI 1.09–1.93]; without methotrexate OR 2.57 [95% CI 1.32–5.00]), without heterogeneity (I2 = 0%); there was no difference in risk of MACE between abatacept and TNFi (OR 0.89 [95% CI 0.71–1.11]), or between tocilizumab and abatacept (OR 0.81 [0.57–1.16]). Based on 11 cohorts (n = 135,053 patients), as compared to TNFi, csDMARDs were associated with an increased risk of stroke (OR 1.17 [95% CI 1.01–1.36]); there was no difference in risk of stroke between different biologics (tocilizumab versus TNFi OR 0.98 [95% CI 0.59–1.61]; abatacept versus TNFi OR 1.08 [0.86–1.34]; tocilizumab versus abatacept OR 0.73 [95% CI 0.39–1.38]), without heterogeneity (I2 = 0%). No comparative studies on cardiovascular risk with tofacitinib were identified.ConclusionBased on meta‐analysis, as compared to TNFi, tocilizumab may be associated with a reduced risk of MACE, whereas csDMARDs may be associated with an increased risk of MACE and stroke.

ObjectiveThe Outcome Measures in Rheumatology (OMERACT) 3×3 method analyzes the occurrence of benefit and harm simultaneously at the individual patient level. We applied this method to 2 recent rheumatoid arthritis (RA) trial data sets.MethodsThe Treatment of Early Aggressive Rheumatoid Arthritis (TEAR) and the Rheumatoid Arthritis Comparison of Active Therapies (RACAT) randomized trial outcomes for safety were defined according to OMERACT as having no adverse events (AEs), non‐serious AEs, and serious AEs. Treatment efficacy was defined as good, moderate, or no response. A good treatment response without any AEs was labeled an unqualified success, and no treatment response but at least 1 AE was considered an unmitigated failure. The association between benefit and harm was assessed by chi‐square or exact tests, as appropriate.ResultsIn TEAR, 612 of 755 patients had response data at 48 weeks: 14% of patients experienced unqualified success and 9% had unmitigated failure, with no difference between the treatment arms. Treatment response and AE rates were not correlated. In RACAT, 309 of 353 patients had response data at 48 weeks: 6% of patients experienced unqualified success and 11% had unmitigated failure, with no differences between the treatment arms. Response and AE rates were negatively correlated. The frequency of AEs and serious AEs increased as response decreased (P = 0.008).ConclusionWe found some evidence that clinical response may be reduced by the co‐occurrence of AEs.

ObjectiveTo develop a new evidence‐based, pharmacologic treatment guideline for rheumatoid arthritis (RA).MethodsWe conducted systematic reviews to synthesize the evidence for the benefits and harms of various treatment options. We used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology to rate the quality of evidence. We employed a group consensus process to grade the strength of recommendations (either strong or conditional). A strong recommendation indicates that clinicians are certain that the benefits of an intervention far outweigh the harms (or vice versa). A conditional recommendation denotes uncertainty over the balance of benefits and harms and/or more significant variability in patient values and preferences.ResultsThe guideline covers the use of traditional disease‐modifying antirheumatic drugs (DMARDs), biologic agents, tofacitinib, and glucocorticoids in early (<6 months) and established (≥6 months) RA. In addition, it provides recommendations on using a treat‐to‐target approach, tapering and discontinuing medications, and the use of biologic agents and DMARDs in patients with hepatitis, congestive heart failure, malignancy, and serious infections. The guideline addresses the use of vaccines in patients starting/receiving DMARDs or biologic agents, screening for tuberculosis in patients starting/receiving biologic agents or tofacitinib, and laboratory monitoring for traditional DMARDs. The guideline includes 74 recommendations: 23% are strong and 77% are conditional.ConclusionThis RA guideline should serve as a tool for clinicians and patients (our two target audiences) for pharmacologic treatment decisions in commonly encountered clinical situations. These recommendations are not prescriptive, and the treatment decisions should be made by physicians and patients through a shared decision‐making process taking into account patients’ values, preferences, and comorbidities. These recommendations should not be used to limit or deny access to therapies.

Objective To develop a new evidence‐based, pharmacologic treatment guideline for rheumatoid arthritis (RA). Methods We conducted systematic reviews to synthesize the evidence for the benefits and harms of various treatment options. We used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology to rate the quality of evidence. We employed a group consensus process to grade the strength of recommendations (either strong or conditional). A strong recommendation indicates that clinicians are certain that the benefits of an intervention far outweigh the harms (or vice versa). A conditional recommendation denotes uncertainty over the balance of benefits and harms and/or more significant variability in patient values and preferences. Results The guideline covers the use of traditional disease‐modifying antirheumatic drugs (DMARDs), biologic agents, tofacitinib, and glucocorticoids in early (<6 months) and established (≥6 months) RA. In addition, it provides recommendations on using a treat‐to‐target approach, tapering and discontinuing medications, and the use of biologic agents and DMARDs in patients with hepatitis, congestive heart failure, malignancy, and serious infections. The guideline addresses the use of vaccines in patients starting/receiving DMARDs or biologic agents, screening for tuberculosis in patients starting/receiving biologic agents or tofacitinib, and laboratory monitoring for traditional DMARDs. The guideline includes 74 recommendations: 23% are strong and 77% are conditional. Conclusion This RA guideline should serve as a tool for clinicians and patients (our two target audiences) for pharmacologic treatment decisions in commonly encountered clinical situations. These recommendations are not prescriptive, and the treatment decisions should be made by physicians and patients through a shared decision‐making process taking into account patients’ values, preferences, and comorbidities. These recommendations should not be used to limit or deny access to therapies.

Abstract Objective To examine the association of serum inflammatory markers (erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP)) and serum lipid measures (low-density lipoprotein (LDL)- and high-density lipoprotein (HDL)-cholesterol) with risk of myocardial infarction (MI) and ischaemic stroke (IS) among rheumatoid arthritis (RA) patients. Methods We conducted a retrospective cohort study using 2005–2010 data from a US commercial health plan. Eligible patients had two or more physician diagnoses of RA during a baseline period of at least 180 days with continuous medical and pharmacy coverage. We computed age-adjusted incidence rates of MI and IS, and used spline regression to assess non-linear associations and Cox-regression to quantify the independent association between the laboratory values and the outcomes. Results We identified 44 418 eligible RA patients (mean age 49 years; 76% women). CRP&gt;10 mg/L compared with &lt;1 mg/L was associated with increased MI risk (HR 2.12; 95% CI 1.02 to 4.38). ESR&gt;42 mm/h compared with &lt;14 mm/h was associated with increased risk of MI (HR 2.53; 95% CI 1.48 to 4.31) and IS (HR 2.51; 95% CI 1.33 to 4.75) risk. HDL-cholesterol ≥60 mg/dL (1.6 mmol/L) compared with &lt;40 mg/dL (1.0 mmol/L) was associated with reduced MI risk (HR 0.37; 0.21 to 0.66). The association between LDL and MI was not linear; the lowest risk was observed among patients with LDL between 70 mg/L (1.8 mmol/L) and 100 mg/L (2.6 mmol/L). We did not observe a significant association between LDL and IS. Conclusions This study provides evidence supporting the hypothesis that RA-related systemic inflammation plays a role in determining cardiovascular risk and a complex relationship between LDL and cardiovascular risk.