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ilustraciones, gráficas, tablas En el presente trabajo se ajusta un modelo espacio-temporal para el número de muertes a causa de COVID-19 en Cundinamarca y Bogotá. En los municipios más alejados de Cundinamarca lo normal es que no se presentarán muertes en mucho tiempo, por esta razón puede ser considerado un problema con exceso de ceros de espacio-tiempo. Además, se realizó un análisis exploratorio de la base de datos el cual permitió detectar la presencia de relación temporal y espacial. Por lo tanto, en una primera parte se exponen y detallan las metodologías y conceptos que pueden ayudar a manejar el exceso de ceros y posteriormente, se hace énfasis en el modelo espacio-temporal con exceso de ceros. En la búsqueda de la literatura se encontró que una buena alternativa para ajustar un modelo de este estilo es hacerlo mediante un modelo jerárquico Bayesiano usando el método de la aproximación de Laplace integrada anidada (INLA). Se realizó un análisis descriptivo de la vacunación en Colombia dejando algunos detalles que permitieron complementar el análisis del ajuste de los modelos. Finalmente, se obtuvo que el modelo que mejor se ajustó a la luz de la media del error absoluto de predicción (MAPE), el criterio de información de la devianza (DIC) y del contexto del exceso de ceros fue el modelo Poisson Cero Inflado. Asi, se puede afirmar que las muertes a causa de COVID-19 en Cundinamarca y Bogotá es un fenómeno espacio-temporal con exceso de ceros. (Texto tomado de la fuente). In the present work, a spatio-temporal model is fitted for the number of deaths due to COVID-19 in Cundinamarca department and Bogota city. In the most remote municipalities of Cundinamarca, it is normal that there will be no deaths in a long time, for this reason it can be considered a problem with of space-time excess of zeros. In addition, an exploratory analysis of the database was carried out, which allowed detecting the presence of a temporal and spatial relationship. Therefore, in the first part, the methodologies and concepts that can help to manage the excess of zeros are presented and detailed, and subsequently, emphasis is placed on the spatio-temporal model with excess of zeros. In the literature, it was found that the best alternative to fit a model of this style is usign a Bayesian hierarchical model Integrated Nested Laplace Approximation (INLA) method. A descriptive analysis of vaccination in Colombia was carried out, leaving some details that allowed complementing the analysis of model fitting. Finally, it was obtained that the best fitting model in light of the mean absolute prediction error (MAPE), the deviancy information criterion (DIC) and the context of the excess of zeros was the Zero Inflated Poisson model. Therefore, it can be affirmed that deaths due to COVID-19 in Cundinamarca and Bogotá is a spatio-temporal phenomenon with an excess of zeros. Incluye anexos Maestría Magíster en Ciencias - Estadística

In a large observational study in patients with COVID-19 and cancer, survival was not significantly influenced by receipt of COVID-19 treatments, except hydroxychloroquine plus any other treatment, which was associated with reduced survival., Among 2,186 U.S. adults with invasive cancer and laboratory-confirmed SARS-CoV-2 infection, we examined the association of COVID-19 treatments with 30-day all-cause mortality and factors associated with treatment. Logistic regression with multiple adjustments (e.g., comorbidities, cancer status, baseline COVID-19 severity) was performed. Hydroxychloroquine with any other drug was associated with increased mortality versus treatment with any COVID-19 treatment other than hydroxychloroquine or untreated controls; this association was not present with hydroxychloroquine alone. Remdesivir had numerically reduced mortality versus untreated controls that did not reach statistical significance. Baseline COVID-19 severity was strongly associated with receipt of any treatment. Black patients were approximately half as likely to receive remdesivir as white patients. Although observational studies can be limited by potential unmeasured confounding, our findings add to the emerging understanding of patterns of care for patients with cancer and COVID-19 and support evaluation of emerging treatments through inclusive prospective controlled trials. Significance: Evaluating the potential role of COVID-19 treatments in patients with cancer in a large observational study, there was no statistically significant 30-day all-cause mortality benefit with hydroxychloroquine or high-dose corticosteroids alone or in combination; remdesivir showed potential benefit. Treatment receipt reflects clinical decision-making and suggests disparities in medication access. This article is highlighted in the In This Issue feature, p. 1426

Contains fulltext : 229846.pdf (Publisher’s version ) (Closed access) BACKGROUND: Early reports on patients with cancer and COVID-19 have suggested a high mortality rate compared with the general population. Patients with thoracic malignancies are thought to be particularly susceptible to COVID-19 given their older age, smoking habits, and pre-existing cardiopulmonary comorbidities, in addition to cancer treatments. We aimed to study the effect of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on patients with thoracic malignancies. METHODS: The Thoracic Cancers International COVID-19 Collaboration (TERAVOLT) registry is a multicentre observational study composed of a cross-sectional component and a longitudinal cohort component. Eligibility criteria were the presence of any thoracic cancer (non-small-cell lung cancer [NSCLC], small-cell lung cancer, mesothelioma, thymic epithelial tumours, and other pulmonary neuroendocrine neoplasms) and a COVID-19 diagnosis, either laboratory confirmed with RT-PCR, suspected with symptoms and contacts, or radiologically suspected cases with lung imaging features consistent with COVID-19 pneumonia and symptoms. Patients of any age, sex, histology, or stage were considered eligible, including those in active treatment and clinical follow-up. Clinical data were extracted from medical records of consecutive patients from Jan 1, 2020, and will be collected until the end of pandemic declared by WHO. Data on demographics, oncological history and comorbidities, COVID-19 diagnosis, and course of illness and clinical outcomes were collected. Associations between demographic or clinical characteristics and outcomes were measured with odds ratios (ORs) with 95% CIs using univariable and multivariable logistic regression, with sex, age, smoking status, hypertension, and chronic obstructive pulmonary disease included in multivariable analysis. This is a preliminary analysis of the first 200 patients. The registry continues to accept new sites and patient data. FINDINGS: Between March 26 and April 12, 2020, 200 patients with COVID-19 and thoracic cancers from eight countries were identified and included in the TERAVOLT registry; median age was 68.0 years (61.8-75.0) and the majority had an Eastern Cooperative Oncology Group performance status of 0-1 (142 [72%] of 196 patients), were current or former smokers (159 [81%] of 196), had non-small-cell lung cancer (151 [76%] of 200), and were on therapy at the time of COVID-19 diagnosis (147 [74%] of 199), with 112 (57%) of 197 on first-line treatment. 152 (76%) patients were hospitalised and 66 (33%) died. 13 (10%) of 134 patients who met criteria for ICU admission were admitted to ICU; the remaining 121 were hospitalised, but were not admitted to ICU. Univariable analyses revealed that being older than 65 years (OR 1.88, 95% 1.00-3.62), being a current or former smoker (4.24, 1.70-12.95), receiving treatment with chemotherapy alone (2.54, 1.09-6.11), and the presence of any comorbidities (2.65, 1.09-7.46) were associated with increased risk of death. However, in multivariable analysis, only smoking history (OR 3.18, 95% CI 1.11-9.06) was associated with increased risk of death. INTERPRETATION: With an ongoing global pandemic of COVID-19, our data suggest high mortality and low admission to intensive care in patients with thoracic cancer. Whether mortality could be reduced with treatment in intensive care remains to be determined. With improved cancer therapeutic options, access to intensive care should be discussed in a multidisciplinary setting based on cancer specific mortality and patients' preference. FUNDING: None.

Summary Background Data on patients with COVID-19 who have cancer are lacking. Here we characterise the outcomes of a cohort of patients with cancer and COVID-19 and identify potential prognostic factors for mortality and severe illness. Methods In this cohort study, we collected de-identified data on patients with active or previous malignancy, aged 18 years and older, with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection from the USA, Canada, and Spain from the COVID-19 and Cancer Consortium (CCC19) database for whom baseline data were added between March 17 and April 16, 2020. We collected data on baseline clinical conditions, medications, cancer diagnosis and treatment, and COVID-19 disease course. The primary endpoint was all-cause mortality within 30 days of diagnosis of COVID-19. We assessed the association between the outcome and potential prognostic variables using logistic regression analyses, partially adjusted for age, sex, smoking status, and obesity. This study is registered with ClinicalTrials.gov, NCT04354701, and is ongoing. Findings Of 1035 records entered into the CCC19 database during the study period, 928 patients met inclusion criteria for our analysis. Median age was 66 years (IQR 57–76), 279 (30%) were aged 75 years or older, and 468 (50%) patients were male. The most prevalent malignancies were breast (191 [21%]) and prostate (152 [16%]). 366 (39%) patients were on active anticancer treatment, and 396 (43%) had active (measurable) cancer. At analysis (May 7, 2020), 121 (13%) patients had died. In logistic regression analysis, independent factors associated with increased 30-day mortality, after partial adjustment, were: increased age (per 10 years; partially adjusted odds ratio 1·84, 95% CI 1·53–2·21), male sex (1·63, 1·07–2·48), smoking status (former smoker vs never smoked: 1·60, 1·03–2·47), number of comorbidities (two vs none: 4·50, 1·33–15·28), Eastern Cooperative Oncology Group performance status of 2 or higher (status of 2 vs 0 or 1: 3·89, 2·11–7·18), active cancer (progressing vs remission: 5·20, 2·77–9·77), and receipt of azithromycin plus hydroxychloroquine (vs treatment with neither: 2·93, 1·79–4·79; confounding by indication cannot be excluded). Compared with residence in the US-Northeast, residence in Canada (0·24, 0·07–0·84) or the US-Midwest (0·50, 0·28–0·90) were associated with decreased 30-day all-cause mortality. Race and ethnicity, obesity status, cancer type, type of anticancer therapy, and recent surgery were not associated with mortality. Interpretation Among patients with cancer and COVID-19, 30-day all-cause mortality was high and associated with general risk factors and risk factors unique to patients with cancer. Longer follow-up is needed to better understand the effect of COVID-19 on outcomes in patients with cancer, including the ability to continue specific cancer treatments. Funding American Cancer Society, National Institutes of Health, and Hope Foundation for Cancer Research.

We analyzed the spread of the COVID-19 epidemic in 6 metropolitan regions with similar demographic characteristics, daytime commuting population and business activities: the New York metropolitan area, the Île-de-France region, the Greater London county, Bruxelles-Capital, the Community of Madrid and the Lombardy region. The highest mortality rates 30-days after the onset of the epidemic were recorded in New York (81.2 x 100,000) and Madrid (77.1 x 100,000). Lombardy mortality rate is below average (41.4 per 100,000), and it is the only situation in which the capital of the region (Milan) has not been heavily impacted by the epidemic wave. Our study analyzed the role played by containment measures and the positive contribution offered by the hospital care system. (www.actabiomedica.it).

We analyzed the spread of the COVID-19 epidemic in 6 metropolitan regions with similar demographic characteristics, daytime commuting population and business activities: the New York metropolitan area, the Île-de-France region, the Greater London county, Bruxelles-Capital, the Community of Madrid and the Lombardy region. The highest mortality rates 30-days after the onset of the epidemic were recorded in New York (81.2 x 100,000) and Madrid (77.1 x 100,000). Lombardy mortality rate is below average (41.4 per 100,000), and it is the only situation in which the capital of the region (Milan) has not been heavily impacted by the epidemic wave. Our study analyzed the role played by containment measures and the positive contribution offered by the hospital care system. (www.actabiomedica.it).

ilustraciones, gráficas, tablas Introducción : en este trabajo se describe una cohorte de pacientes hospitalizados por covid-19 y se proponen modelos pronósticos que estiman la probabilidad de presentar enfermedad grave, trombosis y muerte a partir de variables de ingreso. Métodos : estudio retrospectivo analítico de la población de pacientes con diagnóstico confirmado de covid-19, atendidos hospitalariamente en un centro de alta complejidad, entre los meses de marzo de 2020 y abril de 2021. Se determinó la asociación entre variables de ingreso y la ocurrencia de muerte, curso clínico adverso y trombosis y se construyeron y validaron internamente modelos pronósticos para estos desenlaces utilizando métodos estadísticos tradicionales y de aprendizaje automático. Resultados: se incluyeron datos de 642 pacientes, de los cuales 328 (51 %) presentaron deterioro clínico y 180 (28 %) fallecieron durante la hospitalización. Se documentaron eventos trombóticos en 67 (10 %) y de sangrado en 19 pacientes (3 %). Se encontro asociación entre biomarcadores de inflamación, trombosis y disfunción orgánica y los desenlaces de interés. Los modelos de aprendizaje automático GBM (gradient boosting machine), presentaron el mayor rendimiento pronóstico para mortalidad: AUC-ROC de 80 % (IC 95 %: 76 %-84 %) y AUC-PR de 69 % (IC 95 %: 63 %-75 %), deterioro clínico: AUC-ROC de 79 % (IC 95 %: 75 %-84 %) y AUC-PR de 75 % (IC 95 %: 71 %-81 %), y trombosis: AUC-ROC de 69 % (IC 95 %: 60 %-78 %) y AUC-PR de 37 % (IC 95 %: 27 %-49 %). Las variables pronósticas utilizadas fueron: edad, SAFI, relación de neutrofilos sobre linfocitos, proteína C reactiva, dímero D, creatinina y deshidrogenasa láctica. Conclusión : se desarrollaron modelos con buen rendimiento pronóstico para muerte y enfermedad grave y con moderado rendimiento para trombosis en pacientes hospitalizados por covid19. El modelo con mejor desempeno para todos los desenlaces fue GBM. Para determinar su utilidad en la práctica clínica y de investigación, estos modelos deben ser evaluados en otras cohortes. Se construyó una aplicación para visualizar su funcionamiento y facilitar su validación: https://hga-p-w.shinyapps.io/covid_gbm_app/ (Texto tomado de la fuente). Introduction: We propose to characterize a cohort of patients hospitalized for covid-19 and to create models that estimate the probability of presenting clinical deterioration, thrombosis, and in-hospital mortality based on data obtainable at hospital admission. Methods: Retrospective analytical study of a population of patients with a confirmed diagnosis of covid-19 in a health center of high complexity, between March 2020 and April 2021. The association between entry variables and the outcomes of interest was determined, and prognostic models were developed and validated using traditional statistical methods and machine learning techniques. Results: Data from 642 patients were recorded. The outcome of clinical deterioration occurred in 328 patients (51 %) and death in 180 patients (28 %). Thrombotic events were documented in 67 patients (10 %) and bleeding in 19 patients (3 %). Correlations were found between biomarkers of inflammation, thrombosis, organ dysfunction, and the aforementioned outcomes of interest. GBM (gradient boosting machine), a machine learning technique, presented the best performance for mortality: AUC-ROC of 80 % (95 % CI: 76 % -84 %) and AUC-PR of 69 % (95 % CI: 63 % -75 %), and clinical deterioration: AUC-ROC of 79 % (95 % CI: 75 % -84 %) and AUC-PR of 75 % (95 % CI: 71 % -81 % ), and thrombosis: AUC-ROC of 69 % (95 % CI: 60 % -78 %) and AUC-PR of 37 % (95 % CI: 27 % -49 %). The prognostic variables used by the models were: age, SpO2/FiO2 ratio, neutrophil to lymphocyte ratio, C-reactive protein, D-dimer, creatinine, and lactate dehydrogenase. Conclusion: Models with good prognostic performance for death and severe disease and moderate performance for thrombosis were developed. GBM exhibited the best performance for all outcomes of interest. To determine the usefulness of these models in clinical and research practice, they should be further assessed in other cohorts. An app was built to visualize them and to facilitate their validation: https://hga-p-w.shinyapps.io/covid_gbm_app/ Incluye anexos Especialidades Médicas Especialista en Hematología

Objective To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. Design Population based cohort study. Setting and participants QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020. Main outcome measures The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period. Results 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R 2 ); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell’s C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19. Conclusion The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.

OBJECTIVE: To address the issue of limited national data on the prevalence and distribution of underlying conditions among COVID-19 deaths between sexes and across age groups.PATIENTS AND METHODS: All adult (≥18 years) deaths recorded in England and Wales (March 1, 2020, to May 12, 2020) were analyzed retrospectively. We compared the prevalence of underlying health conditions between COVID and non-COVID-related deaths during the COVID-19 pandemic and the age-standardized mortality rate (ASMR) of COVID-19 compared with other primary causes of death, stratified by sex and age group.RESULTS: Of 144,279 adult deaths recorded during the study period, 36,438 (25.3%) were confirmed COVID deaths. Women represented 43.2% (n=15,731) of COVID deaths compared with 51.9% (n=55,980) in non-COVID deaths. Overall, COVID deaths were younger than non-COVID deaths (82 vs 83 years). ASMR of COVID-19 was higher than all other common primary causes of death, across age groups and sexes, except for cancers in women between the ages of 30 and 79 years. A linear relationship was observed between ASMR and age among COVID-19 deaths, with persistently higher rates in men than women across all age groups. The most prevalent reported conditions were hypertension, dementia, chronic lung disease, and diabetes, and these were higher among COVID deaths. Pre-existing ischemic heart disease was similar in COVID (11.4%) and non-COVID (12%) deaths.CONCLUSION: In a nationwide analysis, COVID-19 infection was associated with higher age-standardized mortality than other primary causes of death, except cancer in women of select age groups. COVID-19 mortality was persistently higher in men and increased with advanced age.

The population-level case-fatality rate (CFR) associated with COVID-19 varies substantially, both across countries at any given time and within countries over time. We analyze the contribution of two key determinants of the variation in the observed CFR: the age-structure of diagnosed infection cases and age-specific case-fatality rates. We use data on diagnosed COVID-19 cases and death counts attributable to COVID-19 by age for China, Germany, Italy, South Korea, Spain, the United States, and New York City. We calculate the CFR for each population at the latest data point and also for Italy, Germany, Spain, and New York City over time. We use demographic decomposition to break the difference between CFRs into unique contributions arising from the age-structure of confirmed cases and the age-specific case-fatality. In late June 2020, CFRs varied from 2.2% in South Korea to 14.0% in Italy. The age-structure of detected cases often explains more than two-thirds of cross-country variation in the CFR. In Italy, the CFR increased from 4.2% to 14.0% between March 9 and June 30, 2020, and more than 90% of the change was due to increasing age-specific case-fatality rates. The importance of the age-structure of confirmed cases likely reflects several factors, including different testing regimes and differences in transmission trajectories; while increasing age-specific case-fatality rates in Italy could indicate other factors, such as the worsening health outcomes of those infected with COVID-19. Our findings lend support to recommendations for data to be disaggregated by age, and potentially other variables, to facilitate a better understanding of population-level differences in CFRs. They also show the need for well-designed seroprevalence studies to ascertain the extent to which differences in testing regimes drive differences in the age-structure of detected cases.

Background Concerns over the safety of non-steroidal anti-inflammatory drug (NSAID) use during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have been raised. We studied whether use of NSAIDs was associated with adverse outcomes and mortality during SARS-CoV-2 infection. Methods and findings We conducted a population-based cohort study using Danish administrative and health registries. We included individuals who tested positive for SARS-CoV-2 during the period 27 February 2020 to 29 April 2020. NSAID users (defined as individuals having filled a prescription for NSAIDs up to 30 days before the SARS-CoV-2 test) were matched to up to 4 non-users on calendar week of the test date and propensity scores based on age, sex, relevant comorbidities, and use of selected prescription drugs. The main outcome was 30-day mortality, and NSAID users were compared to non-users using risk ratios (RRs) and risk differences (RDs). Secondary outcomes included hospitalization, intensive care unit (ICU) admission, mechanical ventilation, and acute renal replacement therapy. A total of 9,236 SARS-CoV-2 PCR-positive individuals were eligible for inclusion. The median age in the study cohort was 50 years, and 58% were female. Of these, 248 (2.7%) had filled a prescription for NSAIDs, and 535 (5.8%) died within 30 days. In the matched analyses, treatment with NSAIDs was not associated with 30-day mortality (RR 1.02, 95% CI 0.57 to 1.82, p = 0.95; RD 0.1%, 95% CI −3.5% to 3.7%, p = 0.95), risk of hospitalization (RR 1.16, 95% CI 0.87 to 1.53, p = 0.31; RD 3.3%, 95% CI −3.4% to 10%, p = 0.33), ICU admission (RR 1.04, 95% CI 0.54 to 2.02, p = 0.90; RD 0.2%, 95% CI −3.0% to 3.4%, p = 0.90), mechanical ventilation (RR 1.14, 95% CI 0.56 to 2.30, p = 0.72; RD 0.5%, 95% CI −2.5% to 3.6%, p = 0.73), or renal replacement therapy (RR 0.86, 95% CI 0.24 to 3.09, p = 0.81; RD −0.2%, 95% CI −2.0% to 1.6%, p = 0.81). The main limitations of the study are possible exposure misclassification, as not all individuals who fill an NSAID prescription use the drug continuously, and possible residual confounding by indication, as NSAIDs may generally be prescribed to healthier individuals due to their side effects, but on the other hand may also be prescribed for early symptoms of severe COVID-19. Conclusions Use of NSAIDs was not associated with 30-day mortality, hospitalization, ICU admission, mechanical ventilation, or renal replacement therapy in Danish individuals who tested positive for SARS-CoV-2. Trial registration The European Union electronic Register of Post-Authorisation Studies EUPAS34734, In a nationwide cohort study, Lars C. Lund, Kasper B. Kristensen, and colleagues investigate the association of NSAID use and outcomes in COVID-19 patients., Author summary Why was this study done? During the early phases of the pandemic of coronavirus disease 2019 (COVID-19), concerns were raised that ibuprofen, a drug commonly used to treat weak pain and fevers, may lead to a more severe course of coronavirus disease. If this risk is verified, it would be important to reduce the use of ibuprofen and ibuprofen-like drugs, commonly referred to as non-steroidal anti-inflammatory drugs (NSAIDs), among patients at risk of COVID-19. What did the researchers do and find? We identified all Danish residents who tested positive for the infectious agent of COVID-19 and grouped them into users and non-users of NSAIDs. The risks of being hospitalized, admitted to the intensive care unit, or dying were compared between the 2 groups. Overall, risks for all studied outcomes were similar between users and non-users of ibuprofen and other NSAIDs. What do these findings mean? NSAIDs do not lead to more severe coronavirus disease according to this study.

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Objective To determine predictors of in-hospital mortality related to COVID-19 in older patients. Design Retrospective cohort study. Setting and Participants Patients aged 65 years and older hospitalized for a diagnosis of COVID-19. Methods Data from hospital admission was collected from the electronic medical records. Logistic regression and Cox proportional-hazard models were used to predict mortality, our primary outcome. Variables at hospital admission were categorized according to the following domains: demographics, clinical history, comorbidities, previous treatment, clinical status, vital signs, clinical scales and scores, routine laboratory analysis and imaging results. Results Of a total of 235 Caucasian patients, 43% were male, with a mean age of 86 ± 6.5 years. Seventy-six patients (32%) died. Non-survivors had a shorter number of days from initial symptoms to hospitalization (p=0.007) and the length of stay in acute wards than survivors (p, Brief Summary: This study showed that male sex, functional decline, an increased fraction of inspired oxygen and the presence of crackles were the best predictors of mortality in the oldest old with COVID-19 and differences in age were not a significant prognostic factor within this very old group.