Objective To assess the prevalence of burn-out syndrome in healthcare workers working on the front line (FL) in Spain during COVID-19. Design Cross-sectional, online survey-based study. Settings Sampling was performed between 21st April and 3rd May 2020. The survey collected demographic data and questions regarding participants’ working position since pandemic outbreak. Participants Spanish healthcare workers working on the FL or usual ward were eligible. A total of 674 healthcare professionals answered the survey. Main outcomes and measures Burn-out syndrome was assessed by the Maslach Burnout Inventory-Medical Personnel. Results Of the 643 eligible responding participants, 408 (63.5%) were physicians, 172 (26.8%) were nurses and 63 (9.8%) other technical occupations. 377 (58.6%) worked on the FL. Most participants were women (472 (73.4%)), aged 31–40 years (163 (25.3%)) and worked in tertiary hospitals (>600 beds) (260 (40.4%)). Prevalence of burn-out syndrome was 43.4% (95% CI 39.5% to 47.2%), higher in COVID-19 FL workers (49.6%, p<0.001) than in non- COVID-19 FL workers (34.6%, p<0.001). Women felt more burn-out (60.8%, p=0.016), were more afraid of self-infection (61.9%, p=0.021) and of their performance and quality of care provided to the patients (75.8%, p=0.015) than men. More burn-out were those between 20 and 30 years old (65.2%, p=0.026) and those with more than 15 years of experience (53.7%, p=0.035). Multivariable logistic regression analysis revealed that, working on COVID-19 FL (OR 1.93; 95% CI 1.37 to 2.71, p<0.001), being a woman (OR 1.56; 95% CI 1.06 to 2.29, p=0.022), being under 30 years old (OR 1.75; 95% CI 1.06 to 2.89, p=0.028) and being a physician (OR 1.64; 95% CI 1.11 to 2.41, p=0.011) were associated with high risk of burn-out syndrome. Conclusions This survey study of healthcare professionals reported high rates of burn-out syndrome. Interventions to promote mental well-being in healthcare workers exposed to COVID-19 need to be immediately implemented.

Advances in the knowledge of the biology of non-small cell lung cancer (NSCLC) have revealed molecular information used for systemic cancer therapy targeting metastatic disease, with an important impact on patients overall survival (OS) and quality of life. However, a biopsy of overt metastases is an invasive procedure limited to certain locations and not easily acceptable in the clinic. Moreover, a single biopsy cannot reflect the clonal heterogeneity of the tumor. The analysis of peripheral blood samples of cancer patients represents a new source of cancer-derived material, known as liquid biopsy, and its components can be obtained from almost all body fluids. These components have shown to reflect characteristics of the status of both the primary and metastatic diseases, helping the clinicians to move towards a personalized medicine. The present review focuses on the liquid biopsy components: circulating tumor cells (CTCS), circulating free DNA (cfDNA), exosomes and tumor-educated platelets (TEP); the isolation technologies used and their potential use for non-invasive screening, early diagnosis, prognosis, response to treatment and real time monitoring of the disease, in NSCLC patients.

Background: Artificial intelligence (AI) has contributed substantially in recent years to the resolution of different biomedical problems, including cancer. However, AI tools with significant and widespread impact in oncology remain scarce. The goal of this study is to present an AI-based solution tool for cancer patients data analysis that assists clinicians in identifying the clinical factors associated with poor prognosis, relapse and survival, and to develop a prognostic model that stratifies patients by risk. Materials and Methods: We used clinical data from 5275 patients diagnosed with non-small cell lung cancer, breast cancer, and non-Hodgkin lymphoma at Hospital Universitario Puerta de Hierro-Majadahonda. Accessible clinical parameters measured with a wearable device and quality of life questionnaires data were also collected. Results: Using an AI-tool, data from 5275 cancer patients were analyzed, integrating clinical data, questionnaires data, and data collected from wearable devices. Descriptive analyses were performed in order to explore the patients’ characteristics, survival probabilities were calculated, and a prognostic model identified low and high-risk profile patients. Conclusion: Overall, the reconstruction of the population’s risk profile for the cancer-specific predictive model was achieved and proved useful in clinical practice using artificial intelligence. It has potential application in clinical settings to improve risk stratification, early detection, and surveillance management of cancer patients.

PURPOSE: Stratifying patients with cancer according to risk of relapse can personalize their care. In this work, we provide an answer to the following research question: How to use machine learning to estimate probability of relapse in patients with early-stage non–small-cell lung cancer (NSCLC)? MATERIALS AND METHODS: For predicting relapse in 1,387 patients with early-stage (I-II) NSCLC from the Spanish Lung Cancer Group data (average age 65.7 years, female 24.8%, male 75.2%), we train tabular and graph machine learning models. We generate automatic explanations for the predictions of such models. For models trained on tabular data, we adopt SHapley Additive exPlanations local explanations to gauge how each patient feature contributes to the predicted outcome. We explain graph machine learning predictions with an example-based method that highlights influential past patients. RESULTS: Machine learning models trained on tabular data exhibit a 76% accuracy for the random forest model at predicting relapse evaluated with a 10-fold cross-validation (the model was trained 10 times with different independent sets of patients in test, train, and validation sets, and the reported metrics are averaged over these 10 test sets). Graph machine learning reaches 68% accuracy over a held-out test set of 200 patients, calibrated on a held-out set of 100 patients. CONCLUSION: Our results show that machine learning models trained on tabular and graph data can enable objective, personalized, and reproducible prediction of relapse and, therefore, disease outcome in patients with early-stage NSCLC. With further prospective and multisite validation, and additional radiological and molecular data, this prognostic model could potentially serve as a predictive decision support tool for deciding the use of adjuvant treatments in early-stage lung cancer.