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Objective: We introduce and review the concept of a study-a-thon as a catalyst for open science in medicine, utilizing harmonized real world, observation health data, tools, skills, and methods to conduct network studies, generating insights for those wishing to use study-a-thons for future research. Materials and Methods: A series of historical study-a-thons since 2017 to present were reviewed for thematic insights as to the opportunity to accelerate the research method to conduct studies across therapeutic areas. Review of publications and experience of the authors generated insights to illustrate the conduct of study-a-thons, key learning, and direction for those wishing to conduct future such study-a-thons. Results: A review of six study-a-thons have provided insights into their scientific impact, and 13 areas of insights for those wishing to conduct future study-a-thons. Defining aspects of the study-a-thon method for rapid, collaborative research through network studies reinforce the need to clear scientific rationale, tools, skills, and methods being collaboratively to conduct a focused study. Well-characterized preparatory, execution and postevent phases, coalescing skills, experience, data, clinical input (ensuring representative clinical context to the research query), and well-defined, logical steps in conducting research via the study-a-thon method are critical. Conclusions: A study-a-thon is a focused multiday research event generating reliable evidence on a specific medical topic across different countries and health systems. In a study-a-thon, a multidisciplinary team collaborate to create an accelerated contribution to scientific evidence and clinical practice. It critically accelerates the research process, without inhibiting the quality of the research output and evidence generation, through a reproducible process.
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BACKGROUND: Heart failure (HF) with reduced ejection fraction (HFrEF) has a worse prognosis than HF with preserved EF (HFpEF). The study aimed to evaluate whether different comorbidity profiles of HFrEF- and HFpEF-patients or HF-specific mechanisms contribute to a greater extent to this difference. METHODS: We linked data from two health insurances to data from a cardiology clinic hospital information system. Patients with a hospitalization with HF in 2005-2011, categorized as HFrEF (EF < 45%) or HFpEF (EF ≥ 45%), were propensity score (PS) matched to controls without HF on comorbidites and medication to assure similar comorbidity profiles of patients and their respective controls. The balance of the covariates in patients and controls was compared via the standardized difference (SDiff). Age-standardized 1-year mortality rates (MR) with 95% confidence intervals (CI) were calculated. RESULTS: 777 HFrEF-patients (1135 HFpEF-patients) were PS-matched to 3446 (4832) controls. Balance between patients and controls was largely achieved with a SDiff < 0.1 on most variables considered. The age-standardized 1-year MRs per 1000 persons in HFrEF-patients and controls were 267.8 (95% CI 175.9-359.8) and 86.1 (95% CI 70.0-102.3). MRs in HFpEF-patients and controls were 166.2 (95% CI 101.5-230.9) and 61.5 (95% CI 52.9-70.1). Thus, differences in MRs between patients and their controls were higher for HFrEF (181.7) than for HFpEF (104.7). CONCLUSIONS: Given the similar comorbidity profiles between HF-patients and controls, the higher difference in mortality rates between HFrEF-patients and controls points more to HF-specific mechanisms for these patients, whereas for HFpEF-patients a higher contribution of comorbidity is suggested by our results.
