Citations

Abstract Objectives Translational research in laboratory medicine and pathology is transforming health care based on recent quantum advances in omics technologies, computational science, and precision medicine. However, high-volume clinical settings where this research may be most effective often have significant constraints on faculty time that challenge productivity. The objective of this study was to assess our experience with a new resource designed to address this challenge. Methods In September 2016, we established the Research and Innovation Office (RIO) in the Department of Laboratory Medicine and Pathology, Mayo Clinic–Rochester. The goal was to provide infrastructure to unburden faculty from performing administrative, organizational, and operational tasks related to research. Over 30 months, we solicited faculty input into desired services, conducted surveys on utility and ease of use, and tracked metrics, including faculty participation, numbers of projects, and publications. Results Faculty members were highly satisfied with the range of services, ease of use, and service-oriented approach of RIO. Initial services included project organization, institutional process support (eg, IRB protocol management), biospecimen requests and handling, and data management. Based on early feedback, patient consenting, budget support, and manuscript assistance were added as additional services. Total RIO staff increased from 6 to 10 during in the first 30 months. Over this time, 97 of 160 faculty members (60%) utilized RIO services in over 1,000 discrete project requests. Annual departmental publications increased by 50% during this time. Conclusion Faculty time is a critical element for translational research productivity in laboratory medicine and pathology departments. Numerous tasks previously performed by faculty could be managed effectively by a team of research facilitators that unburdened constraints on research time and led to high levels of faculty engagement, satisfaction, and productivity. This model may promote advances in transformative laboratory medicine and pathology research in high-volume clinical settings.

Background The greatest potential to reduce the burden of stroke is by primary prevention of first-ever stroke, which constitutes three quarters of all stroke. In addition to population-wide prevention strategies (the ‘mass’ approach), the ‘high risk’ approach aims to identify individuals at risk of stroke and to modify their risk factors, and risk, accordingly. Current methods of assessing and modifying stroke risk are difficult to access and implement by the general population, amongst whom most future strokes will arise. To help reduce the burden of stroke on individuals and the population a new app, the Stroke Riskometer™, has been developed. We aim to explore the validity of the app for predicting the risk of stroke compared with current best methods. Methods 752 stroke outcomes from a sample of 9501 individuals across three countries (New Zealand, Russia and the Netherlands) were utilized to investigate the performance of a novel stroke risk prediction tool algorithm (Stroke Riskometer™) compared with two established stroke risk score prediction algorithms (Framingham Stroke Risk Score [FSRS] and QStroke). We calculated the receiver operating characteristics (ROC) curves and area under the ROC curve (AUROC) with 95% confidence intervals, Harrels C-statistic and D-statistics for measure of discrimination, R2 statistics to indicate level of variability accounted for by each prediction algorithm, the Hosmer-Lemeshow statistic for calibration, and the sensitivity and specificity of each algorithm. Results The Stroke Riskometer™ performed well against the FSRS five-year AUROC for both males (FSRS = 75·0% (95% CI 72·3%–77·6%), Stroke Riskometer™ = 74·0(95% CI 71·3%–76·7%) and females [FSRS = 70·3% (95% CI 67·9%–72·8%, Stroke Riskometer™ = 71·5% (95% CI 69·0%–73·9%)], and better than QStroke [males–59·7% (95% CI 57·3%–62·0%) and comparable to females = 71·1% (95% CI 69·0%–73·1%)]. Discriminative ability of all algorithms was low (C-statistic ranging from 0·51–0·56, D-statistic ranging from 0·01–0·12). Hosmer-Lemeshow illustrated that all of the predicted risk scores were not well calibrated with the observed event data ( P < 0·006). Conclusions The Stroke Riskometer™ is comparable in performance for stroke prediction with FSRS and QStroke. All three algorithms performed equally poorly in predicting stroke events. The Stroke Riskometer™ will be continually developed and validated to address the need to improve the current stroke risk scoring systems to more accurately predict stroke, particularly by identifying robust ethnic/race ethnicity group and country specific risk factors.