Development and external validation of prediction models for adverse health outcomes in rheumatoid arthritis: A multinational real-world cohort analysis.

BACKGROUND: Identification of rheumatoid arthritis (RA) patients at high risk of adverse health outcomes remains a major challenge. We aimed to develop and validate prediction models for a variety of adverse health outcomes in RA patients initiating first-line methotrexate (MTX) monotherapy. METHODS: Data from 15 claims and electronic health record databases across 9 countries were used. Models were developed and internally validated on Optum® De-identified Clinformatics® Data Mart Database using L1-regularized logistic regression to estimate the risk of adverse health outcomes within 3 months (leukopenia, pancytopenia, infection), 2 years (myocardial infarction (MI) and stroke), and 5 years (cancers [colorectal, breast, uterine] after treatment initiation. Candidate predictors included demographic variables and past medical history. Models were externally validated on all other databases. Performance was assessed using the area under the receiver operator characteristic curve (AUC) and calibration plots. FINDINGS: Models were developed and internally validated on 21,547 RA patients and externally validated on 131,928 RA patients. Models for serious infection (AUC: internal 0.74, external ranging from 0.62 to 0.83), MI (AUC: internal 0.76, external ranging from 0.56 to 0.82), and stroke (AUC: internal 0.77, external ranging from 0.63 to 0.95), showed good discrimination and adequate calibration. Models for the other outcomes showed modest internal discrimination (AUC < 0.65) and were not externally validated. INTERPRETATION: We developed and validated prediction models for a variety of adverse health outcomes in RA patients initiating first-line MTX monotherapy. Final models for serious infection, MI, and stroke demonstrated good performance across multiple databases and can be studied for clinical use. FUNDING: This activity under the European Health Data & Evidence Network (EHDEN) has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806968. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA.

Real-world evidence in Alzheimer's disease: The ROADMAP Data Cube.

INTRODUCTION: The ROADMAP project aimed to provide an integrated overview of European real-world data on Alzheimer's disease (AD) across the disease spectrum. METHODS: Metadata were identified from data sources in catalogs of European AD projects. Priority outcomes for different stakeholders were identified through systematic literature review, patient and public consultations, and stakeholder surveys. RESULTS: Information about 66 data sources and 13 outcome domains were integrated into a Data Cube. Gap analysis identified cognitive ability, functional ability/independence, behavioral/neuropsychiatric symptoms, treatment, comorbidities, and mortality as the outcomes collected most. Data were most lacking in caregiver-related outcomes. In general, electronic health records covered a broader, less detailed data spectrum than research cohorts. DISCUSSION: This integrated real-world AD data overview provides an intuitive visual model that facilitates initial assessment and identification of gaps in relevant outcomes data to inform future prospective data collection and matching of data sources and outcomes against research protocols.

Challenges for Optimizing Real-World Evidence in Alzheimer's Disease: The ROADMAP Project.

ROADMAP is a public-private advisory partnership to evaluate the usability of multiple data sources, including real-world evidence, in the decision-making process for new treatments in Alzheimer's disease, and to advance key concepts in disease and pharmacoeconomic modeling. ROADMAP identified key disease and patient outcomes for stakeholders to make informed funding and treatment decisions, provided advice on data integration methods and standards, and developed conceptual cost-effectiveness and disease models designed in part to assess whether early treatment provides long-term benefit.

Uterine effects of estrogen plus progestin therapy and raloxifene: adjudicated results from the EURALOX study.

OBJECTIVE: To compare the incident rate of abnormal endometrial findings in postmenopausal women receiving treatment with either 60 mg of raloxifene or a continuous combined estrogen plus progestin therapy containing 2 mg of 17 beta-estradiol plus 1 mg of norethisterone acetate for a duration of up to 12 months. METHODS: One thousand eight asymptomatic postmenopausal women with osteoporosis or cardiovascular risk factors with an endometrial thickness of less than 5 mm at baseline participated in this prospective, randomized, double-blind trial that lasted 6 months; 347 of these women also participated in a 6-month extension. Women with repeated bleeding or an increase in endometrial thickness to above 5 mm were subjected to saline-infused sonohysterography or hysteroscopy with biopsy. Sonographic, histologic, and clinical findings were adjudicated by a panel of 4 experts blinded with respect to patients' treatments. All adjudicated patients were grouped into 15 diagnostic categories according to predefined criteria. RESULTS: Three hundred thirty-four women needed adjudication during the core phase, 73 (14.7%) of those taking raloxifene and 261 (50.9%) taking continuous combined estrogen plus progestin therapy (P <.001). Compared with raloxifene, women using continuous combined estrogen plus progestin therapy had significantly higher rates of benign endometrial proliferation (8.8 versus 1.2%, P <.001), endometrial polyps (4.3 versus 2.0%, P =.048), and cystic atrophy (5.5 versus 1.2%, P <.001). CONCLUSION: Women using continuous combined estrogen plus progestin therapy more often have benign endometrial pathology and, in our study, more often required the protocol-specific gynecological follow-up assessments for safety reasons, as compared with those using raloxifene. These findings are of clinical relevance when choosing the most appropriate therapy for postmenopausal health risks such as osteoporosis.