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dc.creatorRoganović, Maša
dc.creatorStanojković, Tatjana
dc.creatorNikitović, Marina
dc.creatorPetrović, Nina
dc.creatorĐurić, Ana
dc.creatorMatić, Ivana
dc.creatorJovanović, Marija
dc.creatorVučićević, Katarina
dc.date.accessioned2023-07-07T07:15:16Z
dc.date.available2023-07-07T07:15:16Z
dc.date.issued2023
dc.identifier.issn1871-6032
dc.identifier.urihttps://farfar.pharmacy.bg.ac.rs/handle/123456789/4899
dc.description.abstractIntroduction: The second most common form of cancer in the male population is prostate cancer. Therapeutic options include radical prostatectomy, different forms of radiotherapy, hormone treatment and chemotherapy. Radical prostatectomy and radiotherapy are the most frequently used strategies and enable a long survival for patients diagnosed on time. Because of the prostate anatomy, patients that are on radiotherapy experience a great range of side effects, and even after the therapy is finished, side effects such as urogenital and gastrointestinal toxicity can persist for years. Although survival is long regardless of the therapeutic option used, choosing the appropriate therapeutic options for the patients in terms of efficacy and safety is very important [1]. Objectives: We aimed in developing a model for repeated count data, i.e. fatigue, which is the most common adverse event that patients experience during radiotherapy. In addition, the objective of the study is to assess the effect of various covariates on the probability of the event happening using different modelling approaches [2]. Methods: Data collected from prostate cancer patients included: age, concentrations of glutamine and glutamate before radiotherapy and after 5, 15, 25 and 30 fractions of radiotherapy, as well as a month after the last fraction of radiotherapy (first follow – up visit), genetic testing results regarding variants in glutamine metabolic pathway, signs and symptoms of acute or chronic urogenital and gastrointestinal toxicity, fatigue, details of their treatment (e.g. radical prostatectomy, hormone therapy), their smoking and alcohol intake status, presence of hypertension or diabetes mellitus type II, and other laboratory findings of significance. Analysis was performed using nonlinear mixed effects modelling approach using NONMEM® software (version 7.4). We tested two approaches: modelling using the first-order estimation method and the Laplace method of estimation in order to create a Poisson model for count data. NONMEM outputs were handled in R software (graphical diagnostics). Model evaluation has been performed using numerical and visual approaches. Covariate model building was performed using a stepwise covariate procedure (SCM). Covariates that were tested are age, glutamine/glutamate concentrations (continuous, time-varying covariates). The influence of categorical covariates was also examined (smoking and alcohol intake, presence of aforementioned comorbidities). Results: In total, we analysed 143 data records from 28 male patients aged 53-82 years (mean±sd: 72.67±6.64), mainly older people (>65 years old) that were included in the analysis. The probability of fatigue occurrence was 78.3%, which was rather high but expected. The objective function value of the developed base model using the Laplace method of estimation was 546.346. The average number of fatigue events occurring in the period from the start of the radiotherapy until the first follow-up visit was estimated to be 2.48 with a 95% confidence interval of 1.655 - 3.305 and RSE of 17%. Interindividual variability in the number of fatigue events per patient was estimated at 48.3%, with a shrinkage of 11.1%. The inclusion of the covariates in the base model did not improve the model fit, so they were not kept in the model. Conclusion: Our results confirm that fatigue is one of the most common side effects of radiotherapy. Although our model did not show that examined covariates have an effect on the average number of fatigue events, further analysis will aim at testing different modelling approaches when it comes to modelling side effects of radiotherapy in order to minimize them in cancer patients. [1] Retrieved from https://www.nhs.uk/conditions/prostate-cancer/treatment/ . Last access: 19.3.2023. [2] Plan E.L. Modeling and simulation of count data. CPT Pharmacometrics Syst. Pharmacol. 2014: 3 (8): p. e129.sr
dc.language.isoensr
dc.rightsopenAccesssr
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourcePage. Abstracts of the Annual Meeting of the Population Approach Group in Europesr
dc.titleModelling fatigue events in prostate cancer patients on radiotherapysr
dc.typeconferenceObjectsr
dc.rights.licenseBYsr
dc.description.otherPAGE 31 (2023)sr
dc.description.otherPoster: Drug/Disease Modelling - Oncology
dc.identifier.fulltexthttp://farfar.pharmacy.bg.ac.rs/bitstream/id/13404/Modelling_fatigue_events_2023.pdf
dc.identifier.rcubhttps://hdl.handle.net/21.15107/rcub_farfar_4899
dc.type.versionpublishedVersionsr


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