Chronic Oral Anticoagulation Therapy and Prognosis of Patients Admitted to Hospital for COVID-19: Insights from the HOPE COVID-19 Registry.

Background: Most evidence regarding anticoagulation and COVID-19 refers to the hospitalization setting, but the role of oral anticoagulation (OAC) before hospital admission has not been well explored. We compared clinical outcomes and short-term prognosis between patients with and without prior OAC therapy who were hospitalized for COVID-19. Methods: Analysis of the whole cohort of the HOPE COVID-19 Registry which included patients discharged (deceased or alive) after hospital admission for COVID-19 in 9 countries. All-cause mortality was the primary endpoint. Study outcomes were compared after adjusting variables using propensity score matching (PSM) analyses. Results: 7698 patients were suitable for the present analysis (675 (8.8%) on OAC at admission: 427 (5.6%) on VKAs and 248 (3.2%) on DOACs). After PSM, 1276 patients were analyzed (638 with OAC; 638 without OAC), without significant differences regarding the risk of thromboembolic events (OR 1.11, 95% CI 0.59-2.08). The risk of clinically relevant bleeding (OR 3.04, 95% CI 1.92-4.83), as well as the risk of mortality (HR 1.22, 95% CI 1.01-1.47; log-rank p value = 0.041), was significantly increased in previous OAC users. Amongst patients on prior OAC only, there were no differences in the risk of clinically relevant bleeding, thromboembolic events, or mortality when comparing previous VKA or DOAC users, after PSM. Conclusion: Hospitalized COVID-19 patients on prior OAC therapy had a higher risk of mortality and worse clinical outcomes compared to patients without prior OAC therapy, even after adjusting for comorbidities using a PSM. There were no differences in clinical outcomes in patients previously taking VKAs or DOACs. This trial is registered with NCT04334291/EUPAS34399.

Non-invasive ventilation for SARS-CoV-2 acute respiratory failure: a subanalysis from the HOPE COVID-19 registry.

BACKGROUND: The COVID-19 pandemic has seriously challenged worldwide healthcare systems and limited intensive care facilities, leading to physicians considering the use of non-invasive ventilation (NIV) for managing SARS-CoV-2-related acute respiratory failure (ARF). METHODS: We conducted an interim analysis of the international, multicentre HOPE COVID-19 registry including patients admitted for a confirmed or highly suspected SARS-CoV-2 infection until 18 April 2020. Those treated with NIV were considered. The primary endpoint was a composite of death or need for intubation. The components of the composite endpoint were the secondary outcomes. Unadjusted and adjusted predictors of the primary endpoint within those initially treated with NIV were investigated. RESULTS: 1933 patients who were included in the registry during the study period had data on oxygen support type. Among them, 390 patients (20%) were treated with NIV. Compared with those receiving other non-invasive oxygen strategy, patients receiving NIV showed significantly worse clinical and laboratory signs of ARF at presentation. Of the 390 patients treated with NIV, 173 patients (44.4%) met the composite endpoint. In-hospital death was the main determinant (147, 37.7%), while 62 patients (15.9%) needed invasive ventilation. Those requiring invasive ventilation had the lowest survival rate (41.9%). After adjustment, age (adjusted OR (adj(OR)) for 5-year increase: 1.37, 95% CI 1.15 to 1.63, p<0.001), hypertension (adj(OR) 2.95, 95% CI 1.14 to 7.61, p=0.03), room air O2 saturation <92% at presentation (adj(OR) 3.05, 95% CI 1.28 to 7.28, p=0.01), lymphocytopenia (adj(OR) 3.55, 95% CI 1.16 to 10.85, p=0.03) and in-hospital use of antibiotic therapy (adj(OR) 4.91, 95% CI 1.69 to 14.26, p=0.003) were independently associated with the composite endpoint. CONCLUSION: NIV was used in a significant proportion of patients within our cohort, and more than half of these patients survived without the need for intubation. NIV may represent a viable strategy particularly in case of overcrowded and limited intensive care resources, but prompt identification of failure is mandatory to avoid harm. Further studies are required to better clarify our hypothesis. TRIAL REGISTRATION NUMBERS: NCT04334291/EUPAS34399.

Does there exist an obesity paradox in COVID-19? Insights of the international HOPE-COVID-19-registry.

BACKGROUND: Obesity has been described as a protective factor in cardiovascular and other diseases being expressed as 'obesity paradox'. However, the impact of obesity on clinical outcomes including mortality in COVID-19 has been poorly systematically investigated until now. We aimed to compare clinical outcomes among COVID-19 patients divided into three groups according to the body mass index (BMI). METHODS: We retrospectively collected data up to May 31st, 2020. 3635 patients were divided into three groups of BMI (<25 kg/m2; n = 1110, 25-30 kg/m2; n = 1464, and >30 kg/m2; n = 1061). Demographic, in-hospital complications, and predictors for mortality, respiratory insufficiency, and sepsis were analyzed. RESULTS: The rate of respiratory insufficiency was more recorded in BMI 25-30 kg/m2 as compared to BMI < 25 kg/m2 (22.8% vs. 41.8%; p < 0.001), and in BMI > 30 kg/m2 than BMI < 25 kg/m2, respectively (22.8% vs. 35.4%; p < 0.001). Sepsis was more observed in BMI 25-30 kg/m2 and BMI > 30 kg/m2 as compared to BMI < 25 kg/m2, respectively (25.1% vs. 42.5%; p = 0.02) and (25.1% vs. 32.5%; p = 0.006). The mortality rate was higher in BMI 25-30 kg/m2 and BMI > 30 kg/m2 as compared to BMI < 25 kg/m2, respectively (27.2% vs. 39.2%; p = 0.31) (27.2% vs. 33.5%; p = 0.004). In the Cox multivariate analysis for mortality, BMI < 25 kg/m2 and BMI > 30 kg/m2 did not impact the mortality rate (HR 1.15, 95% CI: 0.889-1.508; p = 0.27) (HR 1.15, 95% CI: 0.893-1.479; p = 0.27). In multivariate logistic regression analyses for respiratory insufficiency and sepsis, BMI < 25 kg/m2 is determined as an independent predictor for reduction of respiratory insufficiency (OR 0.73, 95% CI: 0.538-1.004; p = 0.05). CONCLUSIONS: HOPE COVID-19-Registry revealed no evidence of obesity paradox in patients with COVID-19. However, Obesity was associated with a higher rate of respiratory insufficiency and sepsis but was not determined as an independent predictor for a high mortality.

Clinical profile and predictors of in-hospital mortality among older patients hospitalised for COVID-19.

BACKGROUND: the coronavirus disease 2019 (COVID-19) is characterized by poor outcomes and mortality, particularly in older patients. METHODS: post hoc analysis of the international, multicentre, 'real-world' HOPE COVID-19 registry. All patients aged ≥65 years hospitalised for COVID-19 were selected. Epidemiological, clinical, analytical and outcome data were obtained. A comparative study between two age subgroups, 65-74 and ≥75 years, was performed. The primary endpoint was all cause in-hospital mortality. RESULTS: about, 1,520 patients aged ≥65 years (60.3% male, median age of 76 [IQR 71-83] years) were included. Comorbidities such as hypertension (69.2%), dyslipidaemia (48.6%), cardiovascular diseases (any chronic heart disease in 38.4% and cerebrovascular disease in 12.5%), and chronic lung disease (25.3%) were prevalent, and 49.6% were on ACEI/ARBs. Patients aged 75 years and older suffered more in-hospital complications (respiratory failure, heart failure, renal failure, sepsis) and a significantly higher mortality (18.4 vs. 48.2%, P < 0.001), but fewer admissions to intensive care units (11.2 vs. 4.8%). In the overall cohort, multivariable analysis demonstrated age ≥75 (OR 3.54), chronic kidney disease (OR 3.36), dementia (OR 8.06), peripheral oxygen saturation at admission <92% (OR 5.85), severe lymphopenia (<500/mm3) (OR 3.36) and qSOFA (Quick Sequential Organ Failure Assessment Score) >1 (OR 8.31) to be independent predictors of mortality. CONCLUSION: patients aged ≥65 years hospitalised for COVID-19 had high rates of in-hospital complications and mortality, especially among patients 75 years or older. Age ≥75 years, dementia, peripheral oxygen saturation <92%, severe lymphopenia and qSOFA scale >1 were independent predictors of mortality in this population.

COVID-19 anosmia and gustatory symptoms as a prognosis factor: a subanalysis of the HOPE COVID-19 (Health Outcome Predictive Evaluation for COVID-19) registry.

Olfactory and gustatory dysfunctions (OGD) are a frequent symptom of coronavirus disease 2019 (COVID-19). It has been proposed that the neuroinvasive potential of the novel SARS-CoV-2 could be due to olfactory bulb invasion, conversely studies suggest it could be a good prognostic factor. The aim of the current study was to investigate the prognosis value of OGD in COVID-19. These symptoms were recorded on admission from a cohort study of 5868 patients with confirmed or highly suspected COVID-19 infection included in the multicenter international HOPE Registry (NCT04334291). There was statistical relation in multivariate analysis for OGD in gender, more frequent in female 12.41% vs 8.67% in male, related to age, more frequent under 65 years, presence of hypertension, dyslipidemia, diabetes, smoke, renal insufficiency, lung, heart, cancer and neurological disease. We did not find statistical differences in pregnant (p = 0.505), patient suffering cognitive (p = 0.484), liver (p = 0.1) or immune disease (p = 0.32). There was inverse relation (protective) between OGD and prone positioning (0.005) and death (< 0.0001), but no with ICU (0.165) or mechanical ventilation (0.292). On univariable logistic regression, OGD was found to be inversely related to death in COVID-19 patients. The odds ratio was 0.26 (0.15-0.44) (p < 0.001) and Z was - 5.05. The presence of anosmia is fundamental in the diagnosis of SARS.CoV-2 infection, but also could be important in classifying patients and in therapeutic decisions. Even more knowing that it is an early symptom of the disease. Knowing that other situations as being Afro-American or Latino-American, hypertension, renal insufficiency, or increase of C-reactive protein (CRP) imply a worse prognosis we can make a clinical score to estimate the vital prognosis of the patient. The exact pathogenesis of SARS-CoV-2 that causes olfactory and gustative disorders remains unknown but seems related to the prognosis. This point is fundamental, insomuch as could be a plausible way to find a treatment.

Underlying heart diseases and acute COVID-19 outcomes.

BACKGROUND: The presence of any underlying heart condition could influence outcomes during the coronavirus disease 2019 (COVID-19). METHODS: The registry HOPE-COVID-19 (Health Outcome Predictive Evaluation for COVID-19, NCT04334291) is an international ambispective study, enrolling COVID-19 patients discharged from hospital, dead or alive. RESULTS: HOPE enrolled 2798 patients from 35 centers in 7 countries. Median age was 67 years (IQR: 53.0-78.0), and most were male (59.5%). A relevant heart disease was present in 682 (24%) cases. These were older, more frequently male, with higher overall burden of cardiovascular risk factors (hypertension, dyslipidemia, diabetes mellitus, smoking habit, obesity) and other comorbidities such renal failure, lung, cerebrovascular disease and oncologic antecedents (p < 0.01, for all). The heart cohort received more corticoids (28.9% vs. 20.4%, p < 0.001), antibiotics, but less hydroxychloroquine, antivirals or tocilizumab. Considering the epidemiologic profile, a previous heart condition was independently related with shortterm mortality in the Cox multivariate analysis (1.62; 95% CI 1.29-2.03; p < 0.001). Moreover, heart patients needed more respiratory, circulatory support, and presented more in-hospital events, such heart failure, renal failure, respiratory insufficiency, sepsis, systemic infammatory response syndrome and clinically relevant bleedings (all, p < 0.001), and mortality (39.7% vs. 15.5%; p < 0.001). CONCLUSIONS: An underlying heart disease is an adverse prognostic factor for patients suffering COVID-19. Its presence could be related with different clinical drug management and would benefit from maintaining treatment with angiotensin converting enzyme inhibitors or angiotensin receptor blockers during in-hospital stay.

Prognostic Impact of Hyponatremia and Hypernatremia in COVID-19 Pneumonia. A HOPE-COVID-19 (Health Outcome Predictive Evaluation for COVID-19) Registry Analysis.

Dysnatremia is associated with increased mortality in patients with community-acquired pneumonia. SARS-COV2 (Severe-acute-respiratory syndrome caused by Coronavirus-type 2) pneumonia can be fatal. The aim of this study was to ascertain whether admittance dysnatremia is associated with mortality, sepsis, or intensive therapy (IT) in patients hospitalized with SARS-COV2 pneumonia. This is a retrospective study of the HOPE-COVID-19 registry, with data collected from January 1th through April 31th, 2020. We selected all hospitalized adult patients with RT-PCR-confirmed SARS-COV2 pneumonia and a registered admission serum sodium level (SNa). Patients were classified as hyponatremic (SNa <135 mmol/L), eunatremic (SNa 135-145 mmol/L), or hypernatremic (SNa >145 mmol/L). Multivariable analyses were performed to elucidate independent relationships of admission hyponatremia and hypernatremia, with mortality, sepsis, or IT during hospitalization. Four thousand six hundred sixty-four patients were analyzed, median age 66 (52-77), 58% males. Death occurred in 988 (21.2%) patients, sepsis was diagnosed in 551 (12%) and IT in 838 (18.4%). Hyponatremia was present in 957/4,664 (20.5%) patients, and hypernatremia in 174/4,664 (3.7%). Both hyponatremia and hypernatremia were associated with mortality and sepsis. Only hyponatremia was associated with IT. In conclusion, hyponatremia and hypernatremia at admission are factors independently associated with mortality and sepsis in patients hospitalized with SARS-COV2 pneumonia. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT04334291, NCT04334291.

Prevalence and 30-Day Mortality in Hospitalized Patients With Covid-19 and Prior Lung Diseases.

INTRODUCTION: Patients with pre-existing respiratory diseases in the setting of COVID-19 may have a greater risk of severe complications and even death. METHODS: A retrospective, multicenter, cohort study with 5847 COVID-19 patients admitted to hospitals. Patients were separated in two groups, with/without previous lung disease. Evaluation of factors associated with survival and secondary composite end-point such as ICU admission and respiratory support, were explored. RESULTS: 1,271 patients (22%) had a previous lung disease, mostly COPD. All-cause mortality occurred in 376 patients with lung disease (29.5%) and in 819 patients without (17.9%) (p < 0.001). Kaplan-Meier curves showed that patients with lung diseases had a worse 30-day survival (HR = 1.78; 95%C.I. 1.58-2.01; p < 0.001) and COPD had almost 40% mortality. Multivariable Cox regression showed that prior lung disease remained a risk factor for mortality (HR, 1.21; 95%C.I. 1.02-1.44; p = 0.02). Variables independently associated with all-cause mortality risk in patients with lung diseases were oxygen saturation less than 92% on admission (HR, 4.35; 95% CI 3.08-6.15) and elevated D-dimer (HR, 1.84; 95% CI 1.27-2.67). Age younger than 60 years (HR 0.37; 95% CI 0.21-0.65) was associated with decreased risk of death. CONCLUSIONS: Previous lung disease is a risk factor for mortality in patients with COVID-19. Older age, male gender, home oxygen therapy, and respiratory failure on admission were associated with an increased mortality. Efforts must be done to identify respiratory patients to set measures to improve their clinical outcomes.

INTRODUCCIÓN: Los pacientes con enfermedades respiratorias preexistentes pueden tener en el contexto de la covid-19 un mayor riesgo de complicaciones graves e incluso de muerte. MÉTODOS: Estudio de cohortes multicéntrico y retrospectivo de 5.847 pacientes con covid-19 ingresados en hospitales. Los pacientes se separaron en 2 grupos, sin y con enfermedad pulmonar previa. Se evaluaron factores asociados con la supervivencia y criterios combinados de valoración secundarios, como el ingreso en la UCI y la necesidad de asistencia respiratoria. RESULTADOS: Mil doscientos setenta y un (1.271) pacientes (22%) tenían una enfermedad pulmonar previa, principalmente EPOC. La mortalidad por todas las causas ocurrió en 376 pacientes con enfermedad pulmonar (29,5%) y en 819 pacientes sin enfermedad pulmonar (17,9%; p < 0,001). Las curvas de Kaplan-Meier mostraron que los pacientes con enfermedades pulmonares tenían una peor supervivencia a los 30 días (HR: 1,78; IC del 95%: 1,58-2,01; p < 0,001) y la EPOC tenía una mortalidad de casi el 40%. La regresión de Cox multivariante mostró que la enfermedad pulmonar previa seguía siendo un factor de riesgo de mortalidad (HR: 1,21; IC del 95%: 1,02-1,44; p = 0,02). Las variables asociadas de forma independiente con el riesgo de muerte por todas las causas en pacientes con enfermedades pulmonares fueron la saturación de oxígeno inferior al 92% al ingreso (HR: 4,35; IC del 95%: 3,08-6,15) y el dímero D elevado (HR: 1,84; IC del 95%: 1,27-2,67). La edad menor de 60 años (HR: 0,37; IC del 95%: 0,21-0,65) se asoció con una disminución del riesgo de muerte. CONCLUSIONES: La enfermedad pulmonar previa es un factor de riesgo de muerte en pacientes con covid-19. La edad avanzada, el sexo masculino, la oxigenoterapia domiciliaria y la insuficiencia respiratoria al ingreso se asociaron con un aumento de la mortalidad. Se deben realizar esfuerzos para identificar a los pacientes respiratorios y establecer medidas para mejorar sus resultados clínicos.

Prognosis Prediction in Covid-19 Patients from Lab Tests and X-ray Data through Randomized Decision Trees (preprint)

AI and Machine Learning can offer powerful tools to help in the fight against Covid-19. In this paper we present a study and a concrete tool based on machine learning to predict the prognosis of hospitalised patients with Covid-19. In particular we address the task of predicting the risk of death of a patient at different times of the hospitalisation, on the base of some demographic information, chest X-ray scores and several laboratory findings. Our machine learning models use ensembles of decision trees trained and tested using data from more than 2000 patients. An experimental evaluation of the models shows good performance in solving the addressed task.