Objectives. This study aimed to compare the characteristics of fully and partially vaccinated or unvaccinated coronavirus disease 2019 (COVID-19) patients who were hospitalised in a population of 220,000 habitants. Methods: Retrospective, observational, and population studies were conducted on patients who were hospitalised due to COVID-19 from March to October 2021. We assessed the impact of vaccination and other risk factors through Cox multivariate analysis. Results: A total of 500 patients were hospitalised, among whom 77 (15.4%) were fully vaccinated, 86 (17.2%) were partially vaccinated, and 337 (67.4%) were unvaccinated. Fully vaccinated (FV) patients were older and had a higher Charlson index than those of partially vaccinated and unvaccinated patients (NFV). Bilateral pneumonia was more frequent among NFV (259/376 (68.9%)) than among FV patients (32/75 (42.7%)). The former had more intensive care unit admissions (63/423) than the latter (4/77); OR: 2.80; CI (1.07–9.47). Increasing age HZ: 1.1 (1.06–1.14)) and haematological disease at admission HZ: 2.99 (1.26–7.11)) were independent risk factors for higher mortality during the first 30 days of hospitalisation. The probability of an earlier discharge in the subgroup of 440 patients who did not die during the first 30 days of hospitalisation was related to age (older to younger: HZ: 0.98 (0.97–0.99)) and vaccination status. Conclusions: Among the patients hospitalised because of COVID-19, complete vaccination was associated with less severe forms of COVID-19, with an earlier discharge date. Age and haematological disease were related to a higher mortality rate during the first 30 days of hospitalisation.

Small cell carcinoma (SCC) of the prostate is an uncommon condition; there are very few cases in which presenting symptoms are consistent with Cushing Syndrome (CS). We report a new case in which CS triggers the suspicion of an SCC of the prostate and a review of the published cases of SCC of the prostate presenting with CS. The origin of these neoplasms is still unclear. It may be suspected when laboratory features appear in patients diagnosed with prostatic adenocarcinoma which becomes resistant to specific therapy. SCC usually occurs after the 6th decade. Patients suffering SCC of the prostate presenting with CS usually present symptoms such as hypertension, hyperglycemia, alkalosis or hypokalemia; cushingoid phenotype is less frequent. Cortisol and ACTH levels are often high. Prostatic-specific antigen levels are usually normal. CT scan is the preferred imaging test to localize the lesion, but its performance may be improved by adding other tests, such as FDG-PET scan. All patients have metastatic disease at the time of diagnosis. Lymph nodes, liver and bone are the most frequent metastases sites. Surgery and Ketokonazole are the preferred treatments for CS. The prognosis is very poor: 2- and 5-year survival rates are 27.5 and 14.3%, respectively. Key messages When a patient presents with ectopic Cushing Syndrome but lungs are normal, an atypical localization should be suspected. We should suspect a prostatic origin if Cushing Syndrome is accompanied by obstructive inferior urinary tract symptoms or in the setting of a prostatic adenocarcinoma with rapid clinical and radiological progression with relatively low PSA levels. Although no imaging test is preferred to localize these tumors, FDG-PET-TC can be very useful. Hormone marker scintigraphy (e.g. somatostatin) could be used too. As Cushing Syndrome is a paraneoplastic phenomenon, treatment of the underlying disease may help control hypercortisolism manifestations. These tumors are usually metastatic by the time of diagnosis. They have very poor prognosis.

Introduction: Hospitalized patients with COVID-19 are at increased risk for venous thromboembolism (VTE), but also for bleeding. We previously derived a prognostic score including four variables (elevated D-dimer, elevated ferritin, critical illness, and therapeutic-dose anticoagulation) that identified those at increased risk for major bleeding. Methods: We aimed to validate the score in a subsequent cohort of hospitalized patients with COVID-19 receiving standard-, intermediate- or therapeutic doses of VTE prophylaxis. We evaluated its capacity to predict major bleeding, non-major bleeding, and bleeding-related death. Results: The cohort included 972 patients from 29 hospitals, of whom 280 (29%) received standard-; 412 (42%) intermediate-, 157 (16%) therapeutic doses of VTE prophylaxis and 123 (13%) other drugs. Median duration of prophylaxis was 14.7 ± 10.3 days. Major bleeding occurred in 65 patients (6.7%) and non-major bleeding in 67 (6.9%). Thirty patients with major bleeding (46%) died within the first 30 days after bleeding. The prognostic score identified 203 patients (21%) at very low risk, 285 (29%) at low risk, 263 (27%) intermediate-risk and 221 (23%) at high risk for bleeding. Major bleeding occurred in 1.0%, 2.1%, 8.7% and 15.4% of the patients, respectively. Non-major bleeding occurred in 0.5%, 3.5%, 9.5% and 14.2%, respectively. The c-statistics was: 0.74 (95% confidence intervals [CI]: 0.68–0.79) for major bleeding, 0.73 (95% CI: 0.67–0.78) for non-major bleeding and 0.82 (95% CI: 0.76–0.87) for bleeding-related death. Conclusions: In hospitalized patients with COVID-19, we validated that a prognostic score including 4 easily available items may identify those at increased risk for bleeding.

PURPOSE: Recent studies have suggested that machine learning (ML) could be used to predict venous thromboembolism (VTE) in cancer patients with high accuracy. METHODS: We aimed to evaluate the performance of ML in predicting VTE events in patients with cancer. PubMed, Web of Science, and EMBASE to identify studies were searched. RESULTS: Seven studies involving 12,249 patients with cancer were included. The combined results of the different ML models demonstrated good accuracy in the prediction of VTE. In the training set, the global pooled sensitivity was 0.87, the global pooled specificity was 0.87, and the AUC was 0.91, and in the test set 0.65, 0.84, and 0.80, respectively. CONCLUSION: The prediction ML models showed good performance to predict VTE. External validation to determine the result’s reproducibility is necessary.

(1) Background: COVID-19 has evolved during seven epidemic waves in Spain. Our objective was to describe changes in mortality and severity in our hospitalized patients. (2) Method: This study employed a descriptive, retrospective approach for COVID-19 patients admitted to the Hospital de Fuenlabrada (Madrid, Spain) until 31 December 2022. (3) Results: A total of 5510 admissions for COVID-19 were recorded. The first wave accounted for 1823 (33%) admissions and exhibited the highest proportion of severe patients: 65% with bilateral pneumonia and 83% with oxygen saturation under 94% during admission and elevated levels of CRP, IL-6, and D-dimer. In contrast, the seventh wave had the highest median age (79 years) and comorbidity (Charlson: 2.7), while only 3% of patients had bilateral pneumonia and 3% required intubation. The overall mortality rate was 10.3%. The first wave represented 39% of the total. The variables related to mortality were age (OR: 1.08, 1.07–1.09), cancer (OR: 1.99, 1.53–2.60), dementia (OR: 1.82, 1.20–2.75), the Charlson index (1.38, 1.31–1.47), the need for high-flow oxygen (OR: 6.10, 4.94–7.52), mechanical ventilation (OR: 11.554, 6.996–19.080), and CRP (OR: 1.04, 1.03–1.06). (4) Conclusions: The variables associated with mortality included age, comorbidity, respiratory failure, and inflammation. Differences in the baseline characteristics of admitted patients explained the differences in mortality in each wave. Differences observed between patients admitted in the latest wave and the earlier ones suggest that COVID-19 has evolved into a distinct disease, requiring a distinct approach.