ABSTRACT
Background: Strategies for locally advanced rectal cancer LARC usually consisted of neoadjuvant concomitant chemoradiotherapy (CRT) followed by adjuvant chemotherapy, or short-course radiotherapy (SCRT). TNT is a novel approach for LARC, with several randomized clinical trials exploring its role and paving the way for implementation in clinical practice. Nevertheless, the COVID-19 pandemic represented a challenge for a timely diagnosis, implementation and follow-up of new treatment strategies in these pts. Methods: Records of all the pts diagnosed with LARC and stage IV rectal cancer evaluated in the Oncology department of Vall d’Hebron Hospital between Jan 1st, 2017 and Dec 31th 2021 were included. The period 2017-19 was considered pre-pandemic (PP) and 2020-2021 during-pandemic (DP). Patients with LARC receiving neoadjuvant and/or adjuvant treatment were analyzed, including those treated with SCRT, CRT, and TNT. Data regarding demographics, diagnosis and staging, preoperative treatment received, surgical outcomes, including treatment response, and pathological stage were collected. Results: 390 patients were included (31.28% female, 68.71% Male, median age 69). LARC pts characteristics included 123 (31.54%) either cT4 or cN2, 59 low rectal cancers, 4 with signet ring cells. Neoadjuvant treatment was done in 160 pts (CRT) and 59 pts (TNT). pCR was achieved in 20% and 22% for CRT, and TNT respectively (p0.84). 32 pts received only SCRT with 6.25% pCR. An increased ratio of stage IV pts compared to LARC was evident during the pandemic (stage IV 26.38% 2017-2019, 37.14% 2020-2021, p=0.044). The proportion of high risk LARC increased during pandemic (34.89% PP vs 39.04% DP, p=0.041). No difference was found in terms of pCR amongst the PP and DP patients (25.3% vs 27%, p=0.83) nor different strategies (TNT: 26.47% PP and 26.6% PD, p=0.98 and CRT 23.89% PP and 27.27 % PD, p=0.82). Conclusions: Efficacy of LARC neoadjuvant treatment measured by pCR was maintained in pts before and during COVID-19 pandemic despite an increasing proportion of new LARC high-risk pts. Evaluation of TNT impact in LARC outcomes was challenging because of pandemic confounding role. Real-world data in a post-pandemic setting is essential to evaluate outcome trends in LARC pts;an increase in high-risk LARC and metastatic pts should be expected. Legal entity responsible for the study: The authors. Funding: Has not received any funding. Disclosures: A. García Álvarez: Speaker Bureau / Expert testimony: ANGELINI PHARMA ESPAÑA;Travel / Accommodation / Expenses: Pfizer, Ipsen, Eisai Europe. All other authors have declared no conflicts of interest.
ABSTRACT
Background: Data about neuroendocrine neoplasms (NENs) patients with SARS-CoV-2 are scanty and specific indications for clinical practice are missing. Our aim is to create an international registry about NENs patients affected by SARS-CoV-2 positivity to give a worldwide representation of the situation. Methods: This is an observational retrospective/prospective multicenter survey based on a direct email invitation. All types of NEN are eligible except SCLC and MiNEN. Patients fulfilling the inclusion criteria from March 1st, 2020 up to the end pf pandemic will be recruited. Electronic CRFs are filled out using the REDCap database. Local Institutional Review Board approval is required before data entry. Results: Eighty-one centers, 24 in and 57 outside Italy, representing 39 countries and 5 continents, were contacted. Fourteen centers did not respond and 15 declined the invitation. Among the remaining 52, 6 centers are currently recruiting and 8 are active but not recruiting yet. As of the current abstract submission deadline, 67 patients have been included, mostly GEP-NET (75%), nonfunctioning (86%), metastatic (85%) and on active treatment [47% somatostatin analogs (SSAs), 10% everolimus, 12% tirosin-kinase inhibitors (TKIs), 10% peptide receptor radionuclide therapy (PRRT), 15% chemotherapy]. Median age at NEN diagnosis was 57 years whilst the median age at SARSCoV-2 diagnosis was 62 years. Almost all patients reported COVID-19-related symptoms, more commonly fever (67% of patients), cough (55%) and dyspnea (51%), in 28% of cases exertional and in 22% at rest. More than one third of patients (n, 24) had a pneumonia. The most common therapies for COVID-19 were antibiotics, steroids and hydroxychloroquine. Eleven patients (16%) needed oxygen therapy, three (4%) sub-intensive care and none intensive care. Fifty-six patients (84%) clinically recovered, six (9%) with sequelae, whereas five (7%) died. Among these latter 4 had a NET and 1 a NEC, and no patients have been receiving chemotherapy. With regard to the anti-tumor therapy no change occurred in 66% of patients, temporary interruption in 30%, and definitive discontinuation in 3%, 2% missing. Conclusions: Most of the NEN patients completely recovered from COVID-19, in many cases without any antitumor therapy adjustment. No clear correlations were observed with type, primary site and treatment of NEN. The recruitment is ongoing.
ABSTRACT
OBJECTIVES: Diagnostic work-up following any COVID-19 associated symptom will lead to extensive testing, potentially overwhelming laboratory capacity whilst primarily yielding negative results. We aimed to identify optimal symptom combinations to capture most cases using fewer tests with implications for COVID-19 vaccine developers across different resource settings and public health. METHODS: UK and US users of the COVID-19 Symptom Study app who reported new-onset symptoms and an RT-PCR test within seven days of symptom onset were included. Sensitivity, specificity, and number of RT-PCR tests needed to identify one case (test per case [TPC]) were calculated for different symptom combinations. A multi-objective evolutionary algorithm was applied to generate combinations with optimal trade-offs between sensitivity and specificity. FINDINGS: UK and US cohorts included 122,305 (1,202 positives) and 3,162 (79 positive) individuals. Within three days of symptom onset, the COVID-19 specific symptom combination (cough, dyspnoea, fever, anosmia/ageusia) identified 69% of cases requiring 47 TPC. The combination with highest sensitivity (fatigue, anosmia/ageusia, cough, diarrhoea, headache, sore throat) identified 96% cases requiring 96 TPC. INTERPRETATION: We confirmed the significance of COVID-19 specific symptoms for triggering RT-PCR and identified additional symptom combinations with optimal trade-offs between sensitivity and specificity that maximize case capture given different resource settings.
Subject(s)
COVID-19 , COVID-19 Vaccines , Fever , Humans , Prospective Studies , SARS-CoV-2ABSTRACT
OBJECTIVES: Diagnostic work-up following any COVID-19 associated symptom will lead to extensive testing, potentially overwhelming laboratory capacity whilst primarily yielding negative results. We aimed to identify optimal symptom combinations to capture most cases using fewer tests with implications for COVID-19 vaccine developers across different resource settings and public health. METHODS: UK and US users of the COVID-19 Symptom Study app who reported new-onset symptoms and an RT-PCR test within seven days of symptom onset were included. Sensitivity, specificity, and number of RT-PCR tests needed to identify one case (test per case [TPC]) were calculated for different symptom combinations. A multi-objective evolutionary algorithm was applied to generate combinations with optimal trade-offs between sensitivity and specificity. FINDINGS: UK and US cohorts included 122,305 (1,202 positives) and 3,162 (79 positive) individuals. Within three days of symptom onset, the COVID-19 specific symptom combination (cough, dyspnoea, fever, anosmia/ageusia) identified 69% of cases requiring 47 TPC. The combination with highest sensitivity (fatigue, anosmia/ageusia, cough, diarrhoea, headache, sore throat) identified 96% cases requiring 96 TPC. INTERPRETATION: We confirmed the significance of COVID-19 specific symptoms for triggering RT-PCR and identified additional symptom combinations with optimal trade-offs between sensitivity and specificity that maximize case capture given different resource settings.