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Baptista de Lemos Guerra de Oliveira, Raquel Maria
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- Cross-cultural adaptation and psychometric evaluation of the perceived ability to cope with trauma scale in portuguese patients with breast cancerPublication . Lemos, Raquel; Costa, Beatriz; Frasquilho, Diana; Almeida, Sílvia; Sousa, Berta; Maia, Albino J. Oliveira
- Criterion and construct validity of the Beck Depression Inventory (BDI-II) to measure depression in patients with cancer: The contribution of somatic itemsPublication . Almeida, Sílvia; Camacho, Marta; Barahona-Corrêa, J. Bernardo; Oliveira, José; Lemos, Raquel; Da Silva Rodrigues, Daniel; da Silva, Joaquim Alves; Baptista, Telmo Mourinho; Grácio, Jaime; Oliveira-Maia, Albino J.Background/Objective: Screening for depression in patients with cancer can be difficult due to overlap between symptoms of depression and cancer. We assessed validity of the Beck Depression Inventory (BDI-II) in this population. Method: Data was obtained in an outpatient neuropsychiatry unit treating patients with and without cancer. Psychometric properties of the BDI-II Portuguese version were assessed separately in 202 patients with cancer, and 376 outpatients with mental health complaints but without cancer. Results: Confirmatory factor analysis suggested a three-factor structure model (cognitive, affective and somatic) provided best fit to data in both samples. Criterion validity was good for detecting depression in oncological patients, with an area under the ROC curve (AUC) of 0.85 (95% confidence interval [CI], 0.760.91). A cut-off score of 14 had sensitivity of 87% and specificity of 73%. Excluding somatic items did not significantly change the ROC curve for BDI-II (difference AUCs = 0.002, p=0.9). A good criterion validity for BDI-II was also obtained in the non-oncological population (AUC = 0.87; 95% CI 0.810.91), with a cut-off of 18 (sensitivity=84%; specificity=73%). Conclusions: The BDI-II demonstrated good psychometric properties in patients with cancer, comparable to a population without cancer. Exclusion of somatic items did not affect screening accuracy
- Predicting effective adaptation to breast cancer to help women BOUNCE back: Protocol for a Multicenter Clinical Pilot StudyPublication . Pettini, Greta; Sanchini, Virginia; Pat-Horenczyk, Ruth; Sousa, Berta; Masiero, Marianna; Marzorati, Chiara; Galimberti, Viviana Enrica; Munzone, Elisabetta; Mattson, Johanna; Vehmanen, Leena; Utriainen, Meri; Roziner, Ilan; Lemos, Raquel; Frasquilho, Diana; Cardoso, Fatima; Oliveira-Maia, Albino J; Kolokotroni, Eleni; Stamatakos, Georgios; Leskelä, Riikka-Leena; Haavisto, Ira; Salonen, Juha; Richter, Robert; Karademas, Evangelos; Poikonen-Saksela, Paula; Mazzocco, KettiBackground: Despite the continued progress of medicine, dealing with breast cancer is becoming a major socioeconomic challenge, particularly due to its increasing incidence. The ability to better manage and adapt to the entire care process depends not only on the type of cancer but also on the patient’s sociodemographic and psychological characteristics as well as on the social environment in which a person lives and interacts. Therefore, it is important to understand which factors may contribute to successful adaptation to breast cancer. To our knowledge, no studies have been performed on the combination effect of multiple psychological, biological, and functional variables in predicting the patient’s ability to bounce back from a stressful life event, such as a breast cancer diagnosis. Here we describe the study protocol of a multicenter clinical study entitled “Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back” or, in short, BOUNCE. Objective: The aim of the study is to build a quantitative mathematical model of factors associated with the capacity for optimal adjustment to cancer and to study resilience through the cancer continuum in a population of patients with breast cancer. Methods: A total of 660 women with breast cancer will be recruited from five European cancer centers in Italy, Finland, Israel, and Portugal. Biomedical and psychosocial variables will be collected using the Noona Healthcare platform. Psychosocial, sociodemographic, lifestyle, and clinical variables will be measured every 3 months, starting from presurgery assessment (ie, baseline) to 18 months after surgery. Temporal data mining, time-series prediction, sequence classification methods, clustering time-series data, and temporal association rules will be used to develop the predictive model. Results: The recruitment process stared in January 2019 and ended in November 2021. Preliminary results have been published in a scientific journal and are available for consultation on the BOUNCE project website. Data analysis and dissemination of the study results will be performed in 2022. Conclusions: This study will develop a predictive model that is able to describe individual resilience and identify different resilience trajectories along the care process. The results will allow the implementation of tailored interventions according to patients’ needs, supported by eHealth technologies. Trial Registration: ClinicalTrials.gov NCT05095675; https://clinicaltrials.gov/ct2/show/NCT05095675 International Registered Report Identifier (IRRID): DERR1-10.2196/34564
- A prospective observational study for a Federated Artificial Intelligence solution for monitoring mental health status after cancer treatment (FAITH): study protocolPublication . Lemos, Raquel; Areias-Marques, Sofia; Ferreira, Pedro; O’Brien, Philip; Beltrán-Jaunsarás, María Eugenia; Ribeiro, Gabriela; Martín, Miguel; del Monte-Millán, María; López-Tarruella, Sara; Massarrah, Tatiana; Luís-Ferreira, Fernando; Frau, Giuseppe; Venios, Stefanos; McManus, Gary; Oliveira-Maia, Albino J.Background: Depression is a common condition among cancer patients, across several points in the disease trajec‑ tory. Although presenting higher prevalence rates than the general population, it is often not reported or remains unnoticed. Moreover, somatic symptoms of depression are common in the oncological context and should not be dismissed as a general symptom of cancer. It becomes even more challenging to track psychological distress in the period after the treatment, where connection with the healthcare system typically becomes sporadic. The main goal of the FAITH project is to remotely identify and predict depressive symptoms in cancer survivors, based on a federated machine learning (ML) approach, towards optimization of privacy. Methods: FAITH will remotely analyse depression markers, predicting their negative trends. These markers will be treated in distinct categories, namely nutrition, sleep, activity and voice, assessed in part through wearable technolo‑ gies. The study will include 300 patients who have had a previous diagnosis of breast or lung cancer and will be recruited 1 to 5 years after the end of primary cancer. The study will be organized as a 12-month longitudinal pro‑ spective observational cohort study, with monthly assessments to evaluate depression symptoms and quality of life among cancer survivors. The primary endpoint is the severity of depressive symptoms as measured by the Hamilton Depression Rating Scale (Ham-D) at months 3, 6, 9 and 12. Secondary outcomes include self-reported anxiety and depression symptoms (HADS scale), and perceived quality of life (EORTC questionnaires), at baseline and monthly. Based on the predictive models gathered during the study, FAITH will also aim at further developing a conceptual fed‑ erated learning framework, enabling to build machine learning models for the prediction and monitoring of depres‑ sion without direct access to user’s personal data. Discussion: Improvements in the objectivity of psychiatric assessment are necessary. Wearable technologies can provide potential indicators of depression and anxiety and be used for biofeedback. If the FAITH application is