70 research outputs found

    TECLA—an innovative technical approach for prostate cancer registries

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    The article has been peer-reviewed, but does not include the publisher’s layout, page numbers and proof-corrections.Citation for the published paper: Christiansen, O., Bratt, O., Haug, E. S., Vaktskjold, A., Selnes, A. & Jordhøy, M. S. (2019). TECLA—an innovative technical approach for prostate cancer registries. Scandinavian Journal of Urology and Nephrology, 53(4), 229-234. DOI: http://dx.doi.org/10.1080/21681805.2019.1634148Objective: To present a code-driven, electronic database for patients TrEated with robotic-assisted radiCaL prostAtectomy (TECLA), developed at Innlandet Hospital (IH), Trust, Norway, for research, local quality control and to deliver data to the National Cancer Registry of Norway (CRN). Clinical data are directly extracted from the structured documentation in the electronic medical record (EMR). Materials and methods: The urological department at IH treats about 200 patients with robotic-assisted radical prostatectomy (RARP) annually. All consenting patients registered with the procedure code for RARP are included in TECLA. Clinical data are obtained automatically from the EMR, by structured forms. Patient-reported outcome and experience measures (PROMs and PREMs) are filled in by the patients on an iPad or a smartphone. Results: The basic construct of TECLA is presented. From August 2017 to June 2018, 200 men were treated with RARP, of which 182 (91%) provided consent for inclusion in the register. Of these, 97% completed the PROM survey before treatment and 91% at 3 months follow-up. PREMs were completed by 78%. All clinical variables for the hospital stay and for the 6-week follow-up were more than 95% complete. Conclusion: This entirely electronic surgical quality register is easy to use, both for patients and clinicians, and has a high capture rate. The data collection is linked to the clinicians’ workflow, without double data entry, so entering data does not add any extra work. The register design can be used by other hospitals for various surgical procedures.acceptedVersio

    Predictors of upgrading from low-grade cancer at prostatectomy in men with biparametric magnetic resonance imaging

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    Introduction: Prostate-specific antigen (PSA) density has previously been identified as a predictor of histological upgrading at radical prostatectomy, but how information from pre-treatment biparametric magnetic resonance imaging (bpMRI) contributes needs further clarification. The objective of this register-based study was to identify predictors of upgrading at prostatectomy in men with Grade group (GG) 1 and pre-treatment bpMRI. Material and methods: This single-center study included men with GG 1 cancer on prediagnostic biopsy, who underwent bpMRI and robotic-assisted radical prostatectomy (RARP) between March 2014 and September 2019. We estimated logistic regression models to explore predictors for upgrading. The explored potential predictors were age, PSA density, tumor stage and Prostate Imaging Reporting and Data System (PI-RADS) score (dichotomised 1-3 versus 4-5). Results: Upgrading was observed in 56% (73/130) of the men. PSA density was the only significant predictor for upgrading (unadjusted OR = 1.7, 95% CI 1.2; 2.4 adjusted OR = 1.7, 95% CI 1.2; 2.5). The probability of upgrading was lower for men with a PIRADS 1-3 than for PIRADS 4-5, but the difference was not statistically significant (adjusted OR 0.4, 95% CI 0.2; 1.1, p = 0.082). Among men with PI-RADS 1-3, the probability increased with increasing PSA density (p = 0.036). With PI-RADS 4-5 the probability of upgrading was high over the entire PSA density range. Conclusions: PSA density is a clinically important factor to predict upgrading from GG1 when bpMRI shows PI-RADS 1-3. In men with PI-RADS 4-5 on bpMRI, the probability of an undetected GG 2-5 cancer is high regardless of the PSA density.publishedVersio

    Quantitative Methods for Tracking Cognitive Change 3 Years After Coronary Artery Bypass Surgery

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    Background: The analysis and interpretation of change in cognitive function test scores after Coronary Artery Bypass Grafting (CABG). Longitudinal studies with multiple outcomes present considerable statistical challenges. Application of hierarchical linear statistical models can estimate the effects of a surgical intervention on the time course of multiple biomarkers. Methods: We use an analyze then summarize approach whereby we estimate the intervention effects separately for each cognitive test and then pool them, taking appropriate account of their statistical correlations. The model accounts for dropouts at follow-up, the chance of which may be related to past cognitive score, by implicitly imputing the missing data from individuals’ past scores and group patterns. We apply this approach to a study of the effects of CABG on the time course of cognitive function as measured by 16 separate neuropsychological test scores, clustered into 8 cognitive domains. The study includes measurements on 140 CABG patients and 92 nonsurgical controls at baseline, and 3, 12, and 36 months. Including a nonsurgical control group allows comparison of changes in cognition over time between the surgery group and patients with similar risk factors, controlling for potential effects of aging and vascular disease. Results: We find that CABG patients have very longitudinal changes from baseline in cognitive function similar to those observed for nonsurgical controls. Any small differences tend to favor greater improvement in CABG patients than in the nonsurgical controls. Conclusions: The methods used have application to a wide range of intervention studies in which multiple biomarkers are followed over time to quantify health effects. Software to implement the methods in commonly used statistical packages is available from the authors at http://www.biostat.jhsph.edu/research/software.shtml

    Impact of Differential Attrition on the Association of Education With Cognitive Change Over 20 Years of Follow-up: The ARIC Neurocognitive Study

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    Studies of long-term cognitive change should account for the potential effects of education on the outcome, since some studies have demonstrated an association of education with dementia risk. Evaluating cognitive change is more ideal than evaluating cognitive performance at a single time point, because it should be less susceptible to confounding. In this analysis of 14,020 persons from a US cohort study, the Atherosclerosis Risk in Communities (ARIC) Study, we measured change in performance on 3 cognitive tests over a 20-year period, from ages 48–67 years (1990–1992) through ages 70–89 years (2011–2013). Generalized estimating equations were used to evaluate the association between education and cognitive change in unweighted adjusted models, in models incorporating inverse probability of attrition weighting, and in models using cognitive scores imputed from the Telephone Interview for Cognitive Status for participants not examined in person. Education did not have a strong relationship with change in cognitive test performance, although the rate of decline was somewhat slower among persons with lower levels of education. Methods used to account for selective dropout only marginally changed these observed associations. Future studies of risk factors for cognitive impairment should focus on cognitive change, when possible, to allow for reduction of confounding by social or cultural factors
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