The COVID-19 pandemic has caused a profound change in health organizations at both the primary and hospital care levels. This cross-sectional study aims to investigate the impact of the COVID-19 pandemic in the annual rate of new cancer diagnosis in two university-affiliated hospitals. This study includes all the patients with a pathological diagnosis of cancer attended in two hospitals in Málaga (Spain) during the first year of pandemic. This study population was compared with the patients diagnosed during the previous year 2019. To analyze whether the possible differences in the annual rate of diagnoses were due to the pandemic or to other causes, the patients diagnosed during 2018 and 2017 were also compared. There were 2340 new cancer diagnosis compared to 2825 patients in 2019 which represented a decrease of −17.2% (p = 0.0001). Differences in the number of cancer patients diagnosed between 2018 and 2019 (2840 new cases; 0.5% increase) or 2017 and 2019 (2909 new cases; 3% increase) were not statistically significant. The highest number of patients lost from diagnosis in 2020 was in breast cancer (−26.1%), colorectal neoplasms (−16.9%), and head and neck tumors (−19.8%). The study of incidence rates throughout the first year of the COVID-19 pandemic shows that the diagnosis of new cancer patients has been significantly impaired. Health systems must take the necessary measures to restore pre-pandemic diagnostic procedures and to recover lost patients who have not been diagnosed.
We have retrospectively analyzed a series of 155 sequential cases of T1N0M0 ductal carcinomas of which 51 tumors had a ductal carcinoma in situ (DCIS) component for correlation between the presence of DCIS and clinicopathological variables, recurrence and patient survival. No correlations between the presence of DCIS and age, menopausal status, size, estrogen or progesterone receptors were found. High-grade infiltrative tumors tended not to present a DCIS component (P = 0.08). Patients with tumors associated with DCIS form a subgroup with few recurrences (P = 0.003) and good survival (P = 0.008). When tumors were classified by size, an association between large tumors (>1.0 cm) and increased recurrence and shortened overall survival was found. The presence of DCIS in this subgroup significantly reduced the relative risk of death.
The vascular endothelial growth factor (VEGF) and receptor is a therapeutic target because of the importance of this pathway in carcinogenesis. This pathway regulates and promotes angiogenesis as well as increases endothelial cell proliferation, permeability, and cancer survival. Ramucirumab is a new fully human monoclonal antibody that targets the VEGF receptor-2, an important key receptor implicated in angiogenesis. Ramucirumab has been approved for the treatment of second-line advanced or metastatic non-small cell lung cancer (NSCLC) in combination with the chemotherapy agent docetaxel. This was based on the result of the randomized trial REVEL of 1,253 patients with metastatic NSCLC previously treated with a platinum-based combination therapy. The authors observed a significant improvement in overall survival (OS) with an acceptable toxicities profile. In this study, patients were randomized to receive ramucirumab plus docetaxel or placebo with docetaxel. The combination of docetaxel and ramucirumab showed an improved OS (hazard ratio [HR]: 0.86; 95% CI: 0.75, 0.98). Median OS was 10.5 months in the ramucirumab arm versus 9.1 months in the placebo arm. Regarding side effects, the toxicity described on the ramucirumab arm were principally diarrhea, fatigue, and neutropenia. The most common (5%) adverse reactions of grade 3 and 4 in the ramucirumab arm were fatigue, neutropenia, febrile neutropenia, leukopenia, and hypertension. Adding ramucirumab to docetaxel improves QoL of patients, and does not impair symptoms or functioning. There are currently several trials in progress evaluating the effects of ramucirumab in combination with other drugs in patients with advanced NSCLC.
2042 Background: Lung cancer patients commonly need unplanned visits to ED. Many of these visits could be potentially avoidable if it were possible to identify patients at risk when the previous scheduled visit takes place. At that moment, it would be possible to perform elective actions to manage patients at risk to consult the ED in the near future. Methods: Unplanned visits of patients in active cancer therapy (i.e. chemo or immunotherapy) are attended in our own ED facilities. Our Electronic Health Record (EHR) includes specific modules for first visit, scheduled visits and unplanned visits. Lung cancer patients with at least two visits were eligible. The event of interest was patient visit to ED within 21 or 28 days (d) from previous visit. Free text data collected in the three modules were obtained from EHR in order to generate a feature vector composed of the word frequencies for each visit. We evaluate five different machine learning algorithms to predict the event of interest. Area under the ROC curve (AUC), F1 (harmonic mean of precision and recall), True Positive Rate (TPR) and True Negative Rate (TNR) were assessed using 10-fold cross validation. Results: 2,682 lung cancer patients treated between March 2009 and October 2019 were included from which 819 patients were attended at ED. There were 2,237 first visits, 47,465 scheduled visits (per patient: range 1-174; median 12) and 2,125 unplanned visits (per patient: range 1-20; median 2). Mean age at diagnosis was 64 years. The majority of patients had late stage disease (34.24 % III, 51.56 % IV). The Adaptive Boosting Model yields the best results for both 21 d or 28 d prediction. Conclusions: Using unstructured data from real-world EHR enables the possibility to build an accurate predictive model of unplanned visit to an ED within the 21 or 28 following d after a scheduled visit. Such utility would be very useful in order to prevent ED visits related with cancer symptoms and to improve patients care. [Table: see text]
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