Clinical Characteristics and Outcomes of Covid-19 Patients With Underlying Dementia: Results From a Large Multicenter Propensity-matched Brazilian Cohort Study (preprint)

Background  Although dementia has emerged as an important risk factor for severe SARS-CoV-2 infection, results on COVID-19-related complications and mortality are not consistent. We examined the clinical presentations and outcomes of COVID-19 in a multicentre cohort of in-hospital patients, comparing those with and without dementia.  Methods  This retrospective observational study comprises COVID-19 laboratory-confirmed patients aged ≥60 years admitted to 38 hospitals from 19 cities in Brazil. Data were obtained from electronic hospital records. A propensity score analysis was used to match patients with and without dementia (up to 3:1) according to age, sex, comorbidities, year and hospital of admission. Our primary outcome was in-hospital mortality. We also assessed admission to the intensive care unit (ICU), invasive mechanical ventilation (IMV), kidney replacement therapy (KRT), sepsis, nosocomial infection, and thromboembolic events.  Results  Among 8,947 eligible patients, 405 (4.5%) had a diagnosis of dementia and were matched with 1,151 patients without dementia. Compared to a group of similar demographics and comorbidities, patients with dementia presented a lower duration of symptoms (5.0 vs. 7.0 days; p<0.001) and frequency of dyspnoea, cough, myalgia, headache, ageusia, and anosmia. Fever and delirium were more frequent in patients with dementia than the control group. Patients with dementia also received more palliative care than the control group. Dementia was associated with lower admission (32.7% vs. 47.1%, p<0.001) and length of stay (7 vs. 9 days, p<0.026) in the ICU, frequency of sepsis (17% vs. 24%, p=0.005), KRT (6.4% vs. 13%, p<0.001), and IVM (4.6% vs. 9.8%, p=0.002). We did not find differences in hospital mortality among those with and without dementia.  Conclusion  Clinical manifestations of COVID-19 differ in older patients with and without dementia in the hospital, with delirium being highly prevalent among those with dementia. Our findings indicate that dementia alone might not explain higher short-term mortality after severe COVID-19. Clinicians should include other risk factors such as acute morbidity severity and baseline frailty when evaluating the prognosis of COVID-19 in the hospital.

COVID-19 outcomes in people living with HIV: peering through the waves (preprint)

Background: HIV infection remains a public health concern, especially in low- and middle-income countries. Data regarding exposure of COVID-19 in HIV infected patients remains scarce. We evaluated clinical characteristics and outcomes of COVID-19 patients infected with HIV, and compared with a paired sample without HIV infection. Methods: This is a substudy of a large Brazilian cohort that comprised two periods (2020 and 2021). Data was obtained through the retrospective review of medical records to collect variables of interest and primary outcomes: intensive care admission, mechanical ventilation and death. COVID-19 patients infected with HIV were compared to COVID-19 patients without concomitant diagnosis of HIV infection using the Chi-Square Test and Fisher's exact test for categorical variables and the Wilcoxon test for numerical variables. Both groups were matched for age, sex, number of comorbidities and hospital of origin using the technique of propensity score matching (up to 4:1).Results: Throughout the study, 17,101 COVID-19 patients were hospitalized, 130 (0.76%) of these infected with HIV. The median age was 54 (IQR: 43.0;64.0) years in 2020 and 53 (IQR: 46.0;63.5) years in 2021, with predominance of females in both periods. People living with HIV (PLHIV) and their controls showed similar prevalence for the admission in the ICU and mechanical ventilation requirement in the two periods, with no significant differences. In 2020, in-hospital mortality was higher in the PLHIV compared to the controls (27.9% vs 17.7%; p=0.049), but there was no difference in mortality between groups in 2021 (25.0% vs 25.1%; p>0.999). Conclusion: Our results reiterate that PLHIV were at higher risk of COVID-19 mortality in the early stages of the pandemic, however, this finding did not sustain in 2021, indicating that measures such as large-scale immunization programs have successfully contributed to reducing the excess mortality seen in PLHIV.

Mechanical Ventilation and Death During Pregnancy Complicated by COVID-19: A Prognostic Analysis from the Brazilian COVID-19 Registry Score (preprint)

Background: Assessing predictors of critical outcomes in COVID-19 may advise timely treatments and better prepare facilities to overcome extra adversities during pregnancy. However, many clinical parameters of existent scores are deeply modified by physiologic adaptations. Our aim was to assess the feasibility of a prognosis score developed for general hospitalized adults with COVID-19 in Brazil to predict clinical adverse outcomes in pregnant women upon hospital admission. Methods This is a multicenter retrospective substudy of the Brazilian COVID-19 Registry, a multicenter cohort analysis in Brazilian hospitals, which provided an accurate score to predict in-hospital death. The present analysis assessed the performance of this model, ABC 2 -SPH, based on data of 3978 patients, to assess poor clinical outcomes in data from 85 pregnant women admitted due to COVID-19 from March 1, 2020, to May 5, 2021, in 19 Brazilian hospitals. The primary outcomes were death and the composite mechanical ventilation or death, and secondary were pregnancy outcomes and severe/critical Covid-19. The overall discrimination of the model was presented as the area under the receiver operating characteristic curve (AUROC). Results Thirty-one (36.5%) pregnant women had critical or severe COVID-19. Most of them had no previous comorbidities (64.7%). The median gestational age was 31.0 (26.0, 36.2) weeks; 38 (44.7%) women gave birth during hospitalization by Covid-19, most of them by C-section (76.3%). The need for mechanical ventilation or death occurred in 14 (17.3%) pregnant women. Severe and critical COVID-19 in pregnant women was associated with diabetes, inflammatory markers, and abnormal vital signals observed at admission. The model was not able to identify adverse clinical outcomes. The AUROC of predicting severe/critical Covid-19 illness was 0.595 (95% CI: 0.424-0.754); AUROC of the inpatient death discrimination was 0.683 (95% CI: 0.293-0.945), as the AUROC of mechanical ventilation or death discrimination was 0.591 (95% CI: 0.434-0.75). Conclusions The model ABC 2 -SPH developed in Brazilian general patients was not able to identify adverse clinical outcomes in pregnant women with COVID-19. We warn against the use of general inpatients COVID-19 prognosis in pregnant women. A more useful model for clinical prognosis is necessary concerning the specificities of pregnancy affected by COVID-19.

Systematic Review and Meta-analysis of Ivermectin for Treatment of COVID-19: Evidence Beyond the Hype (preprint)

Background: The role of ivermectin in the treatment of COVID-19 is still under debate, yet the drug has been widely used in some parts of the world, as shown by impressive market data and ivermectin-related adverse event reports. The available body of evidence may have changed over the last months, as studies have been retracted and "standards of care" (SOC) used in control groups have changed with rapidly evolving knowledge on COVID-19 rapidly. This review aims to summarize and critically appraise the evidence of randomized controlled trials (RCTs) of ivermectin, assessing clinical outcomes in COVID-19 patients. Methods: : RCTs evaluating the effects of ivermectin in adult patients with COVID-19 were searched through December 14, 2021, in four databases, L.OVE platform, clinical trial registries and pre-prints platforms. Primary endpoints included all-cause mortality and invasive ventilation requirement. Secondary endpoint was the occurrence of adverse events. Risk of bias was evaluated using the Cochrane Risk of Bias 2.0 tool. Random-effects were used to pool the risk ratios (RRs) of individual trials. The quality of evidence was evaluated using GRADE. Subgroup and sensitivity analysis were performed. The protocol was register in PROSPERO (CRD42021257471) Results: : Twenty-one RCTs fulfilled inclusion criteria (n=2592). Of those, 12 compared ivermectin with placebo and in seven ivermectin associated with SOC was compared to SOC. Most RCTs had some concerns or high risk of bias, mostly due to lack of concealment of the randomization sequence and allocation, lack of blinding and high number of missing cases. Ivermectin did not show an effect in reducing mortality (RR=0.76; 95%CI:0.44 to 1.32) or mechanical ventilation (RR=0.83; 95%CI:0.31 to 2.22) in COVID-19 patients. This effect was consistent when comparing ivermectin vs. placebo, and ivermectin associated with SOC vs. SOC, as well as in sensitivity analysis. Additionally, there was very low quality of evidence regarding adverse effects (RR=0.95; 95%CI: 0.86 to 1.04). Conclusions: : The evidence suggests that ivermectin does not reduce mortality risk and the risk of mechanical ventilation requirement. Although we did not observe an increase in the risk of adverse effects, the evidence is very uncertain regarding this endpoint. Funding: Research agencies FAPEMIG and CNPq.

Development and validation of the MMCD score to predict kidney replacement therapy in COVID-19 patients (preprint)

Background: Acute kidney injury (AKI) is frequently associated with COVID-19 and the need for kidney replacement therapy (KRT) is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting the need for KRT in hospitalized COVID-19 patients. Methods: This study is part of the multicentre cohort, the Brazilian COVID-19 Registry. A total of 5,212 adult COVID-19 patients were included between March/2020 and September/2020. We evaluated four categories of predictor variables: (1) demographic data; (2) comorbidities and conditions at admission; (3) laboratory exams within 24 h; and (4) the need for mechanical ventilation at any time during hospitalization. Variable selection was performed using generalized additive models (GAM) and least absolute shrinkage and selection operator (LASSO) regression was used for score derivation. The accuracy was assessed using the area under the receiver operating characteristic curve (AUCROC). Risk groups were proposed based on predicted probabilities: non-high (up to 14.9%), high (15.0 to 49.9%), and very high risk ([≥] 50.0%). Results: The median age of the model-derivation cohort was 59 (IQR 47-70) years, 54.5% were men, 34.3% required ICU admission, 20.9% evolved with AKI, 9.3% required KRT, and 15.1% died during hospitalization. The validation cohort had similar age, sex, ICU admission, AKI, required KRT distribution and in-hospital mortality. Thirty-two variables were tested and four important predictors of the need for KRT during hospitalization were identified using GAM: need for mechanical ventilation, male gender, higher creatinine at admission, and diabetes. The MMCD score had excellent discrimination in derivation (AUROC = 0.929; 95% CI 0.918-0.939) and validation (AUROC = 0.927; 95% CI 0.911-0.941) cohorts an good overall performance in both cohorts (Brier score: 0.057 and 0.056, respectively). The score is implemented in a freely available online risk calculator (https://www.mmcdscore.com/). Conclusion: The use of the MMCD score to predict the need for KRT may assist healthcare workers in identifying hospitalized COVID-19 patients who may require more intensive monitoring, and can be useful for resource allocation.

Effectiveness, Explainability and Reliability of Machine Meta-Learning Methods for Predicting Mortality in Patients with COVID-19: Results of the Brazilian COVID-19 Registry (preprint)

The majority prognostic scores proposed for early assessment of coronavirus disease 19 (COVID-19) patients are bounded by methodological flaws. Our group recently developed a new risk score - ABC 2 SPH - using traditional statistical methods (least absolute shrinkage and selection operator logistic regression - LASSO). In this article, we provide a thorough comparative study between modern machine learning (ML) methods and state-of-the-art statistical methods, represented by ABC 2 SPH, in the task of predicting in-hospital mortality in COVID-19 patients using data upon hospital admission. We overcome methodological and technological issues found in previous similar studies, while exploring a large sample (5,032 patients). Additionally, we take advantage of a large and diverse set of methods and investigate the effectiveness of applying meta-learning, more specifically Stacking, in order to combine the methods' strengths and overcome their limitations. In our experiments, our Stacking solutions improved over previous state-of-the-art by more than 26% in predicting death, achieving 87.1% of AUROC and MacroF1 of 73.9%. We also investigated issues related to the interpretability and reliability of the predictions produced by the most effective ML methods. Finally, we discuss the adequacy of AUROC as an evaluation metric for highly imbalanced and skewed datasets commonly found in health-related problems.

Frequency and burden of neurological manifestations in hospitalized patients with COVID-19: findings from a large Brazilian cohort (preprint)

Background: Scientific data regarding the prevalence of COVID-19 neurological manifestations and prognosis in Latin America countries is still lacking. Therefore, the study aims to understand neurological manifestations of SARS-CoV 2 infection in the Brazilian population and its association with patient outcomes, such as in-hospital mortality. Methods This study is part of the Brazilian COVID-19 Registry, a multicentric COVID-19 cohort, including data from 37 Brazilian hospitals. For the analysis, patients were grouped according to the presence of self-reported vs. clinically-diagnosed neurological manifestations and matched with patients without neurological manifestations by age, sex, number of comorbidities, hospital, and whether or not patients ha neurological underlying disease. Results From 7,232 hospitalized patients with COVID-19, 27.8% presented self-reported neurological manifestations, 9.9% were diagnosed with a clinically-defined neurological syndrome and 1.2% did not show any neurological symptoms. In patients with self-reported symptoms, the most common ones were headache (19.3%), ageusia (10.4%) and anosmia (7.4%). Meanwhile, in the group with clinically-defined neurological syndromes, acute encephalopathy was the most common diagnosis (10.5%), followed by coma (0.6%1) and seizures (0.4%). Men and younger patients were more likely to self-report neurological symptoms, while women and older patients were more likely to develop a neurological syndrome. Patients with clinically-defined neurological syndromes presented a higher prevalence of comorbidities, as well as lower oxygen saturation and blood pressure at hospital admission. In the paired analysis, it was observed that patients with clinically-defined neurological syndromes were more likely to require ICU admission (46.9 vs. 37.9%), mechanical ventilation (33.4 vs. 28.2%), to develop acute heart failure (5.1 vs. 3.0%, p=0.037) and to die (40.7 vs. 32.3%, p<0.001) when compared to controls. Conclusion Neurological manifestations are an important cause of morbidity in COVID-19 patients. More specifically, patients with clinically defined neurological syndromes presented a poorer prognosis for the disease when compared to matched controls.

Creation of a free, accessible event fighting fake news regarding Covid-19

IntroductionOur aim was to describe the First Brazilian Congress of Clinical Evidence on COVID-19, which took place in Brazil, where the pandemic has hugely impacted the population.MethodsThe organizing committee consisted of a multidisciplinary team of 23 volunteers. As official communication tools, WhatsApp, email, shared folders in Google Drive and online meetings were used. We hoped that the event would boost the fight against fake news and help raise awareness on COVID-19 prevention methods. A major concern of the organizing team was to spread factual knowledge about COVID-19. To this end we had simultaneous translation and interpretation in Brazilian Sign Language to ensure accessibility.ResultsIn total, 23,573 participants registered for the event, and over 30,000 views on the first day, with an overall reach of 85,000 views for the original audio version (up to September 5th, 2021). At the end of the event, a satisfaction survey with participants showed that expectations were exceeded for 97.5%, and 86.7% reported acquiring new knowledge about COVID-19.DiscussionThe conference was an important opportunity for teamwork and for providing reliable information to Brazil’s population. Technology allowed us to reach participants from all Brazilian states and even other countries. However, the majority of speakers were physicians and some healthcare areas were not represented. The team decided to host a Second Congress, including speakers from different specialties to account for knowledge gaps. With the increasing levels of contamination and deaths by COVID-19 in Brazil and the lack of effective public policies to combat the virus, the dissemination of good, reliable information concerning COVID-19 was urgent.ConclusionHosting a free, online, evidence-based event to disseminate knowledge on COVID-19 is feasible, and it is possible to engage a large number of participants.

Hypothyroidism does not lead to worse prognosis in COVID-19: findings from the Brazilian COVID-19 registry (preprint)

Background: It is not clear whether previous thyroid diseases influence the course and outcomes of COVID-19. The study aims to compare clinical characteristics and outcomes of COVID-19 patients with and without hypothyroidism. Methods: The study is a part of a multicentric cohort of patients with confirmed COVID-19 diagnosis, including data collected from 37 hospitals. Matching for age, sex, number of comorbidities and hospital was performed to select the patients without hypothyroidism for the paired analysis. Results: From 7,762 COVID-19 patients, 526 had previously diagnosed hypothyroidism (50%) and 526 were selected as matched controls. The median age was 70 (interquartile range 59.0-80.0) years-old and 68.3% were females. The prevalence of underlying comorbidities were similar between groups, except for coronary and chronic kidney diseases, that had a higher prevalence in the hypothyroidism group (9.7% vs. 5.7%, p=0.015 and 9.9% vs. 4.8%, p=0.001, respectively). At hospital presentation, patients with hypothyroidism had a lower frequency of respiratory rate > 24 breaths per minute (36.1% vs 42.0%; p=0.050) and need of mechanical ventilation (4.0% vs 7.4%; p=0.016). D-dimer levels were slightly lower in hypothyroid patients (2.3 times higher than the reference value vs 2.9 times higher; p=0.037). In-hospital management was similar between groups, but hospital length-of-stay (8 vs 9 days; p=0.029) and mechanical ventilation requirement (25.4% vs. 33.1%; p=0.006) were lower for patients with hypothyroidism. There was a trend of lower in-hospital mortality in patients with hypothyroidism (22.1% vs. 27.0%; p=0.062). Conclusion: In this large Brazilian COVID-19 Registry, patients with hypothyroidism had a lower requirement of mechanical ventilation, and showed a trend of lower in-hospital mortality. Therefore, hypothyroidism does not seem to be associated with a worse prognosis, and should not be considered among the comorbidities that indicate a risk factor for COVID-19 severity.

Effectiveness, Explainability and Reliability of Machine Meta-Learning Methods for Predicting Mortality in Patients with COVID-19: Results of the Brazilian COVID-19 Registry (preprint)

Objective: To provide a thorough comparative study among state ofthe art machine learning methods and statistical methods for determining in-hospital mortality in COVID 19 patients using data upon hospital admission; to study the reliability of the predictions of the most effective methods by correlating the probability of the outcome and the accuracy of the methods; to investigate how explainable are the predictions produced by the most effective methods. Materials and Methods: De-identified data were obtained from COVID 19 positive patients in 36 participating hospitals, from March 1 to September 30, 2020. Demographic, comorbidity, clinical presentation and laboratory data were used as training data to develop COVID 19 mortality prediction models. Multiple machine learning and traditional statistics models were trained on this prediction task using a folded cross validation procedure, from which we assessed performance and interpretability metrics. Results: The Stacking of machine learning models improved over the previous state of the art results by more than 26% in predicting the class of interest (death), achieving 87.1% of AUROC and macroF1 of 73.9%. We also show that some machine learning models can be very interpretable and reliable, yielding more accurate predictions while providing a good explanation for the why. Conclusion: The best results were obtained using the meta learning ensemble model Stacking. State of the art explainability techniques such as SHAP values can be used to draw useful insights into the patterns learned by machine-learning algorithms. Machine learning models can be more explainable than traditional statistics models while also yielding highly reliable predictions. Key words: COVID-19; prognosis; prediction model; machine learning

COVID-19 in Brazilian children and adolescents: findings from 21 hospitals (preprint)

IntroductionChildren and adolescents with Covid-19 have been shown lower mortality less intense symptoms when compared to adults, but studies in Brazil have been based on the compulsory notifying system only. ObjectiveTo analyse clinical, laboratory, radiological characteristics and outcomes of hospitalized patients under 20 years with Covid-19. MethodsCases series of hospitalized patients with confirmed Covid-19 under 20 years, obtained from a cohort study in 37 hospitals from five states of Brazil. ResultsFrom 36 patients, 20 (55.5%) were adolescentes, 20 (55.5%) were male, 18 (50.0%) had comorbidities, 2 were pregnant and in 7 (19.4%), initial symptoms occurred during hospitalization for other causes, of whom 3 were possibly infected in the hospital. Fever (61.1%), dyspnea (33.3%) and neurological symptoms (33.0%) were the most common complaints. C-reactive protein was higher than 50mg/L in 16.7% and D-dimer was above the reference limit in 22.2%. Chest X-rays were performed in 20 (55.5%) patients, 9 had abnormalities, and chest tomography in 5. Hospital length of stay ranged from 1-40 days (median 5 [interquartile range 3-10]), 16 (44.4%) needed intensive therapy, 6 (16.7%) required mechanical ventilation and one patient (2.8%) died. ConclusionIn case series patients under 20 years from hospitals from 5 states of Brazil, comorbidities were frequent, and most common symptoms were fever, dyspnea and neurological symptoms. Forty-four percent required intensive therapy, showing that the disease was not as mild as it was expected, and one patient died.

Chagas disease and SARS-CoV-2 coinfection does not lead to worse in-hospital outcomes (preprint)

Chagas disease (CD) continues to be a major public health burden in Latina America. Information on the interplay between COVID-19 and CD is lacking. Our aim was to assess clinical characteristics and in-hospital outcomes of patients with CD and COVID-19, and to compare it to non-CD patients. Consecutive patients with confirmed COVID-19 were included from March to September 2020. Genetic matching for sex, age, hypertension, diabetes mellitus and hospital was performed in a 4:1 ratio. Of the 7,018 patients who had confirmed COVID-19, 31 patients with CD and 124 matched controls were included (median age 72 (64.-80) years-old, 44.5% were male). At baseline, heart failure (25.8% vs. 9.7%) and atrial fibrillation (29.0% vs. 5.6%) were more frequent in CD patients than in the controls (p 

Extracorporeal membrane oxygenation in COVID-19 patients and in-hospital mortality: results from the Brazilian Registry using a propensity score matched analysis (preprint)

Around 5% of coronavirus disease 2019 (COVID-19) patients develop critical disease, with severe pneumonia and acute respiratory distress syndrome (ARDS). In these cases, extracorporeal membrane oxygenation (ECMO) may be considered when conventional therapy fails. This study aimed to assess the clinical characteristics and in-hospital outcomes of COVID-19 patients with ARDS refractory to standard lung-protective ventilation and pronation treated with ECMO support and to compare them to patients who did not receive ECMO. Patients were selected from the Brazilian COVID-19 Registry. At the moment of the analysis, 7,646 patients were introduced in the registry, eight of those received ECMO support (0.1%). The convenience sample of patients submitted to ECMO was compared to control patients selected by genetic matching for gender, age, comorbidities, pronation, ARDS and hospital, in a 5:1 ratio. From the 48 patients included in the study, eight received ECMO and 40 were matched controls. There were no significant differences in demographic, clinical and laboratory characteristics. Mortality was higher in the ECMO group (n = 7; 87.5%) when compared with controls (n = 17; 42.5%), (p=0.048). In conclusion, COVID 19 patients with ARDS refractory to conventional therapy who received ECMO support had worse outcomes to patients who did not receive ECMO. Our findings are not different from previous studies including a small number of patients, however there is a huge difference from Extracorporeal Life Support Organization results, which encourages us to keep looking for our best excellence.

Chagas disease and SARS-CoV-2 coinfection does not lead to worse in-hospital outcomes: results from the Brazilian COVID-19 Registry (preprint)

Objective: Chagas disease (CD) continues to be a major public health burden in Latina America, where co-infection with SARS-CoV-2 can occur. However, information on the interplay between COVID-19 and Chagas disease is lacking. Our aim was to assess clinical characteristics and in-hospital outcomes of patients with CD and COVID-19, and to compare it to non-CD patients. Methods: Patients with COVID-19 diagnosis were selected from the Brazilian COVID-19 Registry, a prospective multicenter cohort, from March to September, 2020. CD diagnosis was based on hospital record at the time of admission. Study data were collected by trained hospital staff using Research Electronic Data Capture (REDCap) tools. Genetic matching for sex, age, hypertension, DM and hospital was performed in a 4:1 ratio. Results: Of the 7,018 patients who had confirmed infection with SARS-CoV-2 in the registry, 31 patients with CD and 124 matched controls were included. Overall, the median age was 72 (64.-80) years-old and 44.5% were male. At baseline, heart failure (25.8% vs. 9.7%) and atrial fibrillation (29.0% vs. 5.6%) were more frequent in CD patients than in the controls (p<0.05 for both). C-reactive protein levels were lower in CD patients compared with the controls (55.5 [35.7, 85.0] vs. 94.3 [50.7, 167.5] mg/dL). Seventy-two (46.5%) patients required admission to the intensive care unit. In-hospital management, outcomes and complications were similar between the groups. Conclusions: In this large Brazilian COVID-19 Registry, CD patients had a higher prevalence of atrial fibrillation and chronic heart failure compared with non-CD controls, with no differences in-hospital outcomes. The lower C-reactive protein levels in CD patients require further investigation.

ABC2-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores (preprint)

Objective: To develop and validate a rapid scoring system at hospital admission for predicting in-hospital mortality in patients hospitalized with coronavirus disease 19 (COVID-19), and to compare this score with other existing ones. Design: Cohort study Setting: The Brazilian COVID-19 Registry has been conducted in 36 Brazilian hospitals in 17 cities. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients that were admitted between March-July, 2020. The model was then validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients. Participants: Consecutive symptomatic patients ([≥]18 years old) with laboratory confirmed COVID-19 admitted to participating hospitals. Patients who were transferred between hospitals and in whom admission data from the first hospital or the last hospital were not available were excluded, as well those who were admitted for other reasons and developed COVID-19 symptoms during their stay. Main outcome measures: In-hospital mortality Results: Median (25th-75th percentile) age of the model-derivation cohort was 60 (48-72) years, 53.8% were men, in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. From 20 potential predictors, seven significant variables were included in the in-hospital mortality risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829 to 0.859), which was confirmed in the Brazilian (0.859) and Spanish (0.899) validation cohorts. Our ABC2-SPH score showed good calibration in both Brazilian cohorts, but, in the Spanish cohort, mortality was somewhat underestimated in patients with very high (>25%) risk. The ABC2-SPH score is implemented in a freely available online risk calculator (https://abc2sph.com/). Conclusions: We designed and validated an easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation, for early stratification for in-hospital mortality risk of patients with COVID-19.

Coronavirus Disease (COVID-19) Pandemic: An Overview of Systematic Reviews (preprint)

Background: Navigating the rapidly growing body of scientific literature on the SARS-CoV-2 pandemic is challenging and ongoing critical appraisal of this output is essential. We aimed to collate and summarize all published systematic reviews on the coronavirus disease (COVID-19). Methods: Nine databases (Medline, EMBASE, Cochrane Library, CINAHL, Web of Sciences, PDQ-Evidence, WHO's Global Research, LILACS and Epistemonikos) were searched from December 1, 2019 to March 24, 2020. Systematic reviews analyzing primary studies of COVID-19 were included. Two authors independently undertook screening, selection, extraction (data on clinical symptoms, prevalence, pharmacological and non-pharmacological interventions, diagnostic test assessment, laboratory and radiological findings) and quality assessment (AMSTAR 2). Meta-analysis on prevalence of clinical outcomes was performed. Results: Eighteen systematic reviews were included; one was empty. Using AMSTAR 2, confidence in the results of 13 reviews was rated as "critically low", one as "low", one as "moderate" and two as "high". Symptoms of COVID-19 were (range values of point estimates): fever (82-95%), cough with or without sputum (58-72%), dyspnea (26-59%), myalgia or muscle fatigue (29-51%), sore throat (10-13%), headache (8-12%) and gastrointestinal complaints (5-9%). Severe symptoms were more common in men. Elevated C-reactive protein (associated with lymphocytopenia) and lactate dehydrogenase, and slightly elevated aspartate and alanine aminotransferase, were commonly described. Thrombocytopenia and elevated levels of procalcitonin and cardiac troponin I were associated with severe disease. Chest imaging described a frequent pattern of uni- or bilateral multilobar ground-glass opacity. Only one review investigated the impact of medication (chloroquine) but found no verifiable clinical data. All-cause mortality ranged from 0.3% to 14%. Conclusions: Confidence in the results of most reviews was "critically low". Future studies and systematic reviews should adhere to established methodologies. The majority of included systematic reviews were hampered by imprecise search strategy and no previous protocol submission. Protocol registration: This is an extension of a PROSPERO protocol (CRD42020170623); protocol available on Open Science Framework (https://osf.io/6xtyw).