Standard-of-care treatment for haemophilia A or B is to maintain adequate coagulation factor levels through clotting factor administration. The current study aimed to evaluate annualised bleeding rates (ABR) and treatment adherence for haemophilia A or B patients receiving standard half-life (SHL) vs. extended half-life (EHL) factor replacement products. We analysed data from the Adelphi Disease-Specific Programmes, a health record–based survey of United States and European haematologists. Analysis included 651 males with moderate-to-severe haemophilia A or B (the United States, n = 132; Europe, n = 519). The haemophilia A analysis included 501 patients (SHL, n = 435; EHL, n = 66). In the combined United States/European population, mean (SD) ABR was 1.7 (1.69) for the SHL group and 1.8 (2.00) for the EHL group. A total of 72% of patients receiving SHL factor VIII and 75% of patients receiving EHL factor VIII in the combined population were fully adherent (no doses missed of the last 10 doses), as reported by physicians. The haemophilia B analysis included 150 patients (SHL, n = 114; EHL, n = 36). The mean (SD) ABR in the combined population was 2.1 (2.16) for patients receiving SHL factor IX (FIX) and 1.4 (1.48) for patients receiving EHL FIX. The percentage of fully adherent patients (physician-reported) was similar in both treatment groups (SHL FIX, 68%; EHL FIX, 73%). In this preliminary real-world survey in a relatively small sample of patients, measures of ABR and adherence between SHL and EHL products were evaluated. Additional real-world research on prescribing patterns, SHL vs. EHL effectiveness, and adherence is warranted.
To complement real-world evidence (RWE) guidelines, the 2019 Structured Preapproval and Postapproval Comparative study design framework to generate valid and transparent real-world Evidence (SPACE) framework elucidated a process for designing valid and transparent real-world studies. As an extension to SPACE, here, we provide a structured framework for conducting feasibility assessments-a step-by-step guide to identify decision grade, fit-for-purpose data, which complements the United States Food and Drug Administration (FDA)'s framework for a RWE program. The process was informed by our collective experience conducting systematic feasibility assessments of existing data sources for pharmacoepidemiology studies to support regulatory decisions. Used with the SPACE framework, the Structured Process to Identify Fit-For-Purpose Data (SPIFD) provides a systematic process for conducting feasibility assessments to determine if a data source is fit for decision making, helping ensure justification and transparency throughout study development, from articulation of a specific and meaningful research question to identification of fit-for-purpose data and study design. BACKGROUNDAccess to extensive and diverse real-world data (RWD) sources has grown exponentially over the past decade. [1][2][3] Receptivity to using RWD in real-world evidence (RWE) to complement clinical trial evidence has simultaneously increased, 4-7 leading to more frequent inclusion of RWD studies in regulatory and payer submission packages, 8,9 but with mixed success. Whereas particular therapeutic areas, such as oncology and rare diseases, have historically utilized RWE, advances are being made to understand the optimal settings for producing RWE fit for decision making by regulators, payers, and health technology assessment agencies. 10 Standardssuch as guidance documents, step-by-step processes, and templates, developed to guide researchers on the design and conduct of RWD studies-support validity and transparency, and ultimately bolster confidence in RWE. These good practices cover the continuum 11 from articulating a clear research question 12 to transparency in study conduct and reporting of results, [13][14][15][16] and include consideration of the hypothetical target trial, 12,17 identifying confounders by constructing causal diagrams, 12,18,19 identifying a fit-for-purpose design, 12,20 protocol development, [21][22][23][24][25][26][27] and visualizing the study design. 20 A Structured Preapproval and Postapproval Comparative study design framework to generate valid and transparent RWE (SPACE) framework elucidated a step-by-step process for designing valid and transparent real-world studies and provides templates to capture decision making and justification at each step. 12 The structured template for planning and reporting on the implementation of RWE studies (STaRT-RWE) picks up where SPACE leaves off, providing detailed templates to capture the final design and implementation details (e.g., specific algorithms for each study variable). Taken...
Generating evidence from real‐world data requires fit‐for‐purpose study design and data. In addition to validity, decision makers require transparency in the reasoning that underlies study design and data source decisions. The 2019 Structured Preapproval and Postapproval Comparative Study Design Framework to Generate Valid and Transparent Real‐World Evidence (SPACE) and the 2021 Structured Process to Identify Fit‐For‐Purpose Data (SPIFD)—intended to be used together—provide a step‐by‐step guide to identify decision grade, fit‐for‐purpose study design and data. In this update (referred to as “SPIFD2” to encompass both the design and data aspects) we provide an update to these frameworks that combines the templates into one, more explicitly calls for articulation of the hypothetical target trial and sources of bias that may arise in the real‐world emulation, and provides explicit references to the Structured Template and Reporting Tool for Real‐World Evidence (STaRT‐RWE) tables that we suggest using immediately after invoking the SPIFD2 framework. Following the steps recommended in the SPIFD2 process requires due diligence on the part of the researcher to ensure that every aspect of study design and data selection is rationalized and supported by evidence. The resulting stepwise documentation enables reproducibility and clear communication with decision makers, and it increases the likelihood that the evidence generated is valid, fit‐for‐purpose, and sufficient to support healthcare and regulatory decisions.
Background Invasive mucormycosis (IM) is a rare and often life-threatening fungal infection, for which clinical and epidemiological understanding is lacking. Electronic health record (EHR) data can be utilized to elucidate large populations of patients with IM to address this unmet need. This study aimed to descriptively assess data on patients with IM using the Optum® EHR dataset. Methods US patient data from the Optum® deidentified EHR dataset (2007–2019) were analyzed to identify patients with IM. Patients with hematologic malignancies (HM), at high risk of IM, were selected and sorted by IM diagnosis (ICD9 117.7; ICD10 B46). Demographics, comorbidities/other diagnoses, and treatments were analyzed in patients with IM. Results In total, 1133 patients with HM and IM were identified. Most were between 40 and 64 years of age, Caucasian, and from the Midwest. Essential primary hypertension (50.31%) was the most common comorbidity. Of the 1133 patients, only 33.72% were prescribed an antifungal treatment. The most common antifungal treatments were fluconazole (24.27%) and posaconazole (16.33%), which may have been prophylactic, and any AmB (15.62%). Conclusions A large population of patients with IM were identified, highlighting the potential of analyzing EHR data to investigate epidemiology, diagnosis, and the treatment of apparently rare diseases.
OBJECTIVES:To compare clinical outcomes of type 2 diabetic patients with and without TZDs therapy after patients receiving DES. METHODS: We conducted a retrospective cohort study using the National Health Insurance Database (NHIRD). The type 2 diabetes mellitus patients were included if they received first limuseluting stent or paclitaxel-eluting stent placement and were identified by presence of a hospital claim during the period from December 1, 2006, through December 31, 2007. Follow-up data were available through December 31, 2008. Patients were classified into two groups based on the antidiabetic agents they took from pharmacy records for use of TZD (rosiglitazone, pioglitazone) or non-TZD within 3 months after the index date of hospitalization. A total of 1,743 patients who received stents during the study period were identified as the study subjects. Our measure of effectiveness was the prevalence of death, myocardial infarction and repeat revascularization, defined as any PCI, whether or not the patient received a stent, or crossed over to CABG within one year after index hospitalization. RESULTS: There were 268 patients in TZD group, 1,475 patients in non-TZD group. Compared with non-TZD group, there were no significant difference in adjusted hazard ratio of death, myocardial infarction and repeat revascularization between limus-eluting stent group and paclitaxel-eluting stent group. In stratified analysis, patients who received limus-eluting stent with history of myocardial infarction and treated with TZDs were associated with a higher risk of myocardial infarction (HRϭ 5.292,. CONCLUSIONS: Our findings suggest that TZDs could not improve clinical outcomes in type 2 diabetes patients after drug-eluting stent implantation. TZDs may contribute to higher risk of myocardial infarction in patient with limus-eluting stent and history of myocardial infarction. For the pleiotropic effects of TZDs, balance between benefit and risk for cardiovascular events to different subgroups, may be different.Further studies are required to investigate this relationship.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.