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

Association between positive serology for COVID-19 and chagas cardiomyopathy progression: The SaMi-Trop project

BACKGROUND

Chagas Disease (CD) can cause Chagas cardiomyopathy. The new coronavirus disease (COVID-19) also affects the cardiovascular system and may worsen Chagas cardiomyopathy. However, the cardiac evolution of patients with CD infected by COVID-19 is not known. Thus, the objective of this study is to assess, within one year, whether there was cardiac progression after COVID-19 in CD.

METHODS

Longitudinal study with CD patients. The outcome was cardiac progression, defined as the appearance of new major changes in the current ECG compared to the previous ECG considered from the comparison of electrocardiograms (ECGs) performed with an interval of one year. Positive Anti-SARS-CoV2 Serology was the independent variable of interest. For each analysis, a final multiple model was constructed, adjusted for sociodemographic, clinical, and pandemic-related characteristics.

RESULTS

Of the 404 individuals included, 22.8 % had positive serology for COVID-19 and 10.9 % had cardiac progression. In the final model, positive serology for COVID-19 was the only factor associated with cardiac progression in the group as a whole (OR = 2.65; 95 % CI = 1.27-5.53) and for new-onset cardiomyopathy in the group with normal previous ECG (OR = 3.50; 95 % CI = 1.21-10.13).

CONCLUSION

Our study shows an association between COVID-19 and progression of Chagas cardiomyopathy, evaluated by repeated ECGs, suggesting that COVID-19 accelerated the natural history of CD.

AB2CO risk score for in-hospital mortality of COVID-19 patients admitted to intensive care units

PURPOSE

To develop a mortality risk score for COVID-19 patients admitted to intensive care units (ICU), and to compare it with other existing scores.

MATERIALS AND METHODS

This retrospective observational study included consecutive adult patients with laboratory-confirmed COVID-19 admitted to ICUs of 18 hospitals from nine Brazilian cities, from September 2021 to July 2022. Potential predictors were selected based on the literature review. Generalized Additive Models were used to examine outcomes and predictors. LASSO regression was used to derive the mortality score.

RESULTS

From 558 patients, median age was 69 years (IQR 58-78), 56.3 % were men, 19.7 % required mechanical ventilation (MV), and 44.8 % died. The final model comprised six variables: age, pO2/FiO2, respiratory function (respiratory rate or if in MV), chronic obstructive pulmonary disease, and obesity. The AB2CO had an AUROC of 0.781 (95 % CI 0.744 to 0.819), good overall performance (Brier score = 0.191) and an excellent calibration (slope = 1.063, intercept = 0.015, p-value = 0.834). The model was compared with other scores and displayed better discrimination ability than the majority of them.

CONCLUSIONS

The AB2CO score is a fast and easy tool to be used upon ICU admission.

COVID-19: The Development and Validation of a New Mortality Risk Score

Background: The coronavirus disease 2019 (COVID-19) pandemic has found the whole world unprepared for its correct management. Italy was the first European country to experience the spread of the SARS-CoV-2 virus at the end of February 2020. As a result of hospital overcrowding, the quality of care delivered was not always optimal. A substantial number of patients admitted to non-ICU units could have been treated at home. It would have been extremely useful to have a score that, based on personal and clinical characteristics and simple blood tests, could have predicted with sufficient reliability the probability that a patient had or did not have a disease that could have led to their death. This study aims to develop a scoring system to identify which patients with COVID-19 are at high mortality risk upon hospital admission, to expedite and enhance clinical decision making. Methods: A retrospective analysis was performed to develop a multivariable prognostic prediction model. Results: Derivation and external validation cohorts were obtained from two Italian University Hospital databases, including 388 (10.31% deceased) and 1357 (7.68% deceased) patients with confirmed COVID-19, respectively. A multivariable logistic model was used to select seven variables associated with in-hospital death (age, baseline oxygen saturation, hemoglobin value, white blood cell count, percentage of neutrophils, platelet count, and creatinine value). Calibration and discrimination were satisfactory with a cumulative AUC for prediction mortality of 0.924 (95% CI: 0.893-0.944) in derivation cohorts and 0.808 (95% CI: 0.886-0.828) in external validation cohorts. The risk score obtained was compared with the ISARIC 4C Mortality Score, and with all the other most important scores considered so far, to evaluate the risk of death of patients with COVID-19. It performed better than all the above scores to evaluate the predictability of dying. Its sensitivity, specificity, and AUC were higher than the other COVID-19 scoring systems when the latter were calculated for the 388 patients in our derivation cohort. Conclusions: In conclusion, the CZ-COVID-19 Score may help all physicians by identifying those COVID-19 patients who require more attention to provide better therapeutic regimens or, on the contrary, by identifying those patients for whom hospitalization is not necessary and who could therefore be sent home without overcrowding healthcare facilities. We developed and validated a new risk score based on seven variables for upon-hospital admission of COVID-19 patients. It is very simple to calculate and performs better than all the other similar scores to evaluate the predictability of dying.

Fully independent validation of eleven prognostic scores predicting progression to critically ill condition in hospitalized patients with COVID-19

INTRODUCTION

COVID-19 remains an important threat to global health and maintains the challenge of COVID-19 hospital care. To assist decision making regarding COVID-19 hospital care many instruments to predict COVID-19 progression to critical condition were developed and validated.

OBJECTIVE

To validate eleven COVID-19 progression prediction scores for critically ill hospitalized patients in a Brazilian population.

METHODOLOGY

Observational study with retrospective follow-up, including 301 adults confirmed for COVID-19 sequentially. Participants were admitted to non-critical units for treatment of the disease, between January and April 2021 and between September 2021 and February 2022. Eleven prognostic scores were applied using demographic, clinical, laboratory and imaging data collected in the first 48 of the hospital admission. The outcomes of greatest interest were as originally defined for each score. The analysis plan was to apply the instruments, estimate the outcome probability reproducing the original development/validation of each score, then to estimate performance measures (discrimination and calibration) and decision thresholds for risk classification.

RESULTS

The overall outcome prevalence was 41.8 % on 301 participants. There was a greater risk of the occurrence of the outcomes in older and male patients, and a linear trend with increasing comorbidities. Most of the patients studied were not immunized against COVID-19. Presence of concomitant bacterial infection and consolidation on imaging increased the risk of outcomes. College of London COVID-19 severity score and the 4C Mortality Score were the only with reasonable discrimination (ROC AUC 0.647 and 0.798 respectively) and calibration. The risk groups (low, intermediate and high) for 4C score were updated with the following thresholds: 0.239 and 0.318 (https://pedrobrasil.shinyapps.io/INDWELL/).

CONCLUSION

The 4C score showed the best discrimination and calibration performance among the tested instruments. We suggest different limits for risk groups. 4C score use could improve decision making and early therapeutic management at hospital care.

Assessment of the ABC2-SPH risk score to predict invasive mechanical ventilation in COVID-19 patients and comparison to other scores

Background

Predicting the need for invasive mechanical ventilation (IMV) is important for the allocation of human and technological resources, improvement of surveillance, and use of effective therapeutic measures. This study aimed (i) to assess whether the ABC2-SPH score is able to predict the receipt of IMV in COVID-19 patients; (ii) to compare its performance with other existing scores; (iii) to perform score recalibration, and to assess whether recalibration improved prediction.

Methods

Retrospective observational cohort, which included adult laboratory-confirmed COVID-19 patients admitted in 32 hospitals, from 14 Brazilian cities. This study was conducted in two stages: (i) for the assessment of the ABC2-SPH score and comparison with other available scores, patients hospitalized from July 31, 2020, to March 31, 2022, were included; (ii) for ABC2-SPH score recalibration and also comparison with other existing scores, patients admitted from January 1, 2021, to March 31, 2022, were enrolled. For both steps, the area under the receiving operator characteristic score (AUROC) was calculated for all scores, while a calibration plot was assessed only for the ABC2-SPH score. Comparisons between ABC2-SPH and the other scores followed the Delong Test recommendations. Logistic recalibration methods were used to improve results and adapt to the studied sample.

Results

Overall, 9,350 patients were included in the study, the median age was 58.5 (IQR 47.0-69.0) years old, and 45.4% were women. Of those, 33.5% were admitted to the ICU, 25.2% received IMV, and 17.8% died. The ABC2-SPH score showed a significantly greater discriminatory capacity, than the CURB-65, STSS, and SUM scores, with potentialized results when we consider only patients younger than 80 years old (AUROC 0.714 [95% CI 0.698-0.731]). Thus, after the ABC2-SPH score recalibration, we observed improvements in calibration (slope = 1.135, intercept = 0.242) and overall performance (Brier score = 0.127).

Conclusion

The ABC2-SPHr risk score demonstrated a good performance to predict the need for mechanical ventilation in COVID-19 hospitalized patients under 80 years of age.

Assessment of risk scores to predict mortality of COVID-19 patients admitted to the intensive care unit

Objectives

To assess the ABC2-SPH score in predicting COVID-19 in-hospital mortality, during intensive care unit (ICU) admission, and to compare its performance with other scores (SOFA, SAPS-3, NEWS2, 4C Mortality Score, SOARS, CURB-65, modified CHA2DS2-VASc, and a novel severity score).

Materials and methods

Consecutive patients (≥ 18 years) with laboratory-confirmed COVID-19 admitted to ICUs of 25 hospitals, located in 17 Brazilian cities, from October 2020 to March 2022, were included. Overall performance of the scores was evaluated using the Brier score. ABC2-SPH was used as the reference score, and comparisons between ABC2-SPH and the other scores were performed by using the Bonferroni method of correction. The primary outcome was in-hospital mortality.

Results

ABC2-SPH had an area under the curve of 0.716 (95% CI 0.693-0.738), significantly higher than CURB-65, SOFA, NEWS2, SOARS, and modified CHA2DS2-VASc scores. There was no statistically significant difference between ABC2-SPH and SAPS-3, 4C Mortality Score, and the novel severity score.

Conclusion

ABC2-SPH was superior to other risk scores, but it still did not demonstrate an excellent predictive ability for mortality in critically ill COVID-19 patients. Our results indicate the need to develop a new score, for this subset of patients.

Prognostic models in COVID-19 infection that predict severity: a systematic review

Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.

Potential and limitations of machine meta-learning (ensemble) methods for predicting COVID-19 mortality in a large inhospital Brazilian dataset

The majority of early prediction scores and methods to predict COVID-19 mortality are bound by methodological flaws and technological limitations (e.g., the use of a single prediction model). Our aim is to provide a thorough comparative study that tackles those methodological issues, considering multiple techniques to build mortality prediction models, including modern machine learning (neural) algorithms and traditional statistical techniques, as well as meta-learning (ensemble) approaches. This study used a dataset from a multicenter cohort of 10,897 adult Brazilian COVID-19 patients, admitted from March/2020 to November/2021, including patients [median age 60 (interquartile range 48-71), 46% women]. We also proposed new original population-based meta-features that have not been devised in the literature. Stacking has shown to achieve the best results reported in the literature for the death prediction task, improving over previous state-of-the-art by more than 46% in Recall for predicting death, with AUROC 0.826 and MacroF1 of 65.4%. The newly proposed meta-features were highly discriminative of death, but fell short in producing large improvements in final prediction performance, demonstrating that we are possibly on the limits of the prediction capabilities that can be achieved with the current set of ML techniques and (meta-)features. Finally, we investigated how the trained models perform on different hospitals, showing that there are indeed large differences in classifier performance between different hospitals, further making the case that errors are produced by factors that cannot be modeled with the current predictors.

Myocardial Injury and Prognosis in Hospitalized COVID-19 Patients in Brazil: Results From The Brazilian COVID-19 Registry

BACKGROUND

Cardiovascular complications of COVID-19 are important aspects of the disease's pathogenesis and prognosis. Evidence on the prognostic role of troponin and myocardial injury in Latin American hospitalized COVID-19 patients is still scarce.

OBJECTIVES

To evaluate myocardial injury as independent predictor of in-hospital mortality and invasive mechanical ventilation support in hospitalized patients, from the Brazilian COVID-19 Registry.

METHODS

This cohort study is a substudy of the Brazilian COVID-19 Registry, conducted in 31 Brazilian hospitals of 17 cities, March-September 2020. Primary outcomes included in-hospital mortality and invasive mechanical ventilation support. Models for the primary outcomes were estimated by Poisson regression with robust variance, with statistical significance of p<0.05.

RESULTS

Of 2,925 patients (median age of 60 years [48-71], 57.1% men), 27.3% presented myocardial injury. The proportion of patients with comorbidities was higher among patients with cardiac injury (median 2 [1-2] vs. 1 [0-2]). Patients with myocardial injury had higher median levels of brain natriuretic peptide, lactate dehydrogenase, creatine phosphokinase, N-terminal pro-brain natriuretic peptide, and C-reactive protein than patients without myocardial injury. As independent predictors, C-reactive protein and platelet counts were related to the risk of death, and neutrophils and platelet counts were related to the risk of invasive mechanical ventilation support. Patients with high troponin levels presented a higher risk of death (RR 2.03, 95% CI 1.60-2.58) and invasive mechanical ventilation support (RR 1.87, 95% CI 1.57-2.23), when compared to those with normal troponin levels.

CONCLUSION

Cardiac injury was an independent predictor of in-hospital mortality and the need for invasive mechanical ventilation support in hospitalized COVID-19 patients.

Clinical characteristics and outcomes of hospital-manifested COVID-19 among Brazilians

OBJECTIVES

To analyze the clinical characteristics and outcomes of admitted patients with the hospital- versus community-manifested COVID-19 and to evaluate the risk factors related to mortality in the first population.

METHODS

This retrospective cohort included consecutive adult patients with COVID-19, hospitalized between March and September 2020. The demographic data, clinical characteristics, and outcomes were extracted from medical records. Patients with hospital-manifested COVID-19 (study group) and those with community-manifested COVID-19 (control group) were matched by the propensity score model. Logistic regression models were used to verify the risk factors for mortality in the study group.

RESULTS

Among 7,710 hospitalized patients who had COVID-19, 7.2% developed symptoms while admitted for other reasons. Patients with hospital-manifested COVID-19 had a higher prevalence of cancer (19.2% vs 10.8%) and alcoholism (8.8% vs 2.8%) than patients with community-manifested COVID-19 and also had a higher rate of intensive care unit requirement (45.1% vs 35.2%), sepsis (23.8% vs 14.5%), and death (35.8% vs 22.5%) (P <0.05 for all). The factors independently associated with increased mortality in the study group were increasing age, male sex, number of comorbidities, and cancer.

CONCLUSION

Hospital-manifested COVID-19 was associated with increased mortality. Increasing age, male sex, number of comorbidities, and cancer were independent predictors of mortality among those with hospital-manifested COVID-19 disease.

Severity of Illness Scores and Biomarkers for Prognosis of Patients with Coronavirus Disease 2019

The spectrum of disease severity and the insidiousness of clinical presentation make it difficult to recognize patients with coronavirus disease 2019 (COVID-19) at higher risk of worse outcomes or death when they are seen in the early phases of the disease. There are now well-established risk factors for worse outcomes in patients with COVID-19. These should be factored in when assessing the prognosis of these patients. However, a more precise prognostic assessment in an individual patient may warrant the use of predictive tools. In this manuscript, we conduct a literature review on the severity of illness scores and biomarkers for the prognosis of patients with COVID-19. Several COVID-19-specific scores have been developed since the onset of the pandemic. Some of them are promising and can be integrated into the assessment of these patients. We also found that the well-known pneumonia severity index (PSI) and CURB-65 (confusion, uremia, respiratory rate, BP, age ≥ 65 years) are good predictors of mortality in hospitalized patients with COVID-19. While neither the PSI nor the CURB-65 should be used for the triage of outpatient versus inpatient treatment, they can be integrated by a clinician into the assessment of disease severity and can be used in epidemiological studies to determine the severity of illness in patient populations. Biomarkers also provide valuable prognostic information and, importantly, may depict the main physiological derangements in severe disease. We, however, do not advocate the isolated use of severity of illness scores or biomarkers for decision-making in an individual patient. Instead, we suggest the use of these tools on a case-by-case basis with the goal of enhancing clinician judgment.

Mechanical ventilation and death in pregnant patients admitted for COVID-19: a prognostic analysis from the Brazilian COVID-19 registry score

BACKGROUND

The assessment of clinical prognosis of pregnant COVID-19 patients at hospital presentation is challenging, due to physiological adaptations during pregnancy. Our aim was to assess the performance of the ABC₂-SPH score to predict in-hospital mortality and mechanical ventilation support in pregnant patients with COVID-19, to assess the frequency of adverse pregnancy outcomes, and characteristics of pregnant women who died.

METHODS

This multicenter cohort included consecutive pregnant patients with COVID-19 admitted to the participating hospitals, from April/2020 to March/2022. Primary outcomes were in-hospital mortality and the composite outcome of mechanical ventilation support and in-hospital mortality. Secondary endpoints were pregnancy outcomes. The overall discrimination of the model was presented as the area under the receiver operating characteristic curve (AUROC). Overall performance was assessed using the Brier score.

RESULTS

From 350 pregnant patients (median age 30 [interquartile range (25.2, 35.0)] years-old]), 11.1% had hypertensive disorders, 19.7% required mechanical ventilation support and 6.0% died. The AUROC for in-hospital mortality and for the composite outcome were 0.809 (95% IC: 0.641-0.944) and 0.704 (95% IC: 0.617-0.792), respectively, with good overall performance (Brier = 0.0384 and 0.1610, respectively). Calibration was good for the prediction of in-hospital mortality, but poor for the composite outcome. Women who died had a median age 4 years-old higher, higher frequency of hypertensive disorders (38.1% vs. 9.4%, p < 0.001) and obesity (28.6% vs. 10.6%, p = 0.025) than those who were discharged alive, and their newborns had lower birth weight (2000 vs. 2813, p = 0.001) and five-minute Apgar score (3.0 vs. 8.0, p < 0.001).

CONCLUSIONS

The ABC₂-SPH score had good overall performance for in-hospital mortality and the composite outcome mechanical ventilation and in-hospital mortality. Calibration was good for the prediction of in-hospital mortality, but it was poor for the composite outcome. Therefore, the score may be useful to predict in-hospital mortality in pregnant patients with COVID-19, in addition to clinical judgment. Newborns from women who died had lower birth weight and Apgar score than those who were discharged alive.

Frequency and burden of neurological manifestations upon hospital presentation in COVID-19 patients: Findings from a large Brazilian cohort

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 and outcomes in the Brazilian population.

METHODS

This study is part of the Brazilian COVID-19 Registry, a multicentric cohort, including data from 37 hospitals. For the present analysis, patients were grouped according to the presence of reported symptoms (i.e., headache; anosmia and ageusia; syncope and dizziness) vs. clinically-diagnosed neurological manifestations (clinically-defined neurological syndrome: neurological signs or diagnoses captured by clinical evaluation) and matched with patients without neurological manifestations by age, sex, number of comorbidities, hospital of admission, and whether or not patients had underlying neurological disease.

RESULTS

From 6,635 hospitalized patients with COVID-19, 30.8% presented reported neurological manifestations, 10.3% were diagnosed with a neurological syndrome and 60.1% did not show any neurological manifestations. In patients with reported symptoms, the most common ones were headache (20.7%), ageusia (11.1%) and anosmia (8.0%). In patients with neurological syndromes, acute encephalopathy was the most common diagnosis (9.7%). In the matched analysis, patients with neurological syndromes presented more cases of septic shock (17.0 vs. 13.0%, p = 0.045), intensive care unit admission (45.3 vs. 38.9%, p = 0.023), and mortality (38.7 vs. 32.6%, p = 0.026; and 39.2 vs. 30.3%, p < 0.001) when compared to controls.

CONCLUSION

COVID-19 in-hospital patients with clinically defined neurological syndromes presented a higher incidence of septic shock, ICU admission and death when compared to controls.

Hospital characteristics associated with COVID-19 mortality: data from the multicenter cohort Brazilian Registry

The COVID-19 pandemic caused unprecedented pressure over health care systems worldwide. Hospital-level data that may influence the prognosis in COVID-19 patients still needs to be better investigated. Therefore, this study analyzed regional socioeconomic, hospital, and intensive care units (ICU) characteristics associated with in-hospital mortality in COVID-19 patients admitted to Brazilian institutions. This multicenter retrospective cohort study is part of the Brazilian COVID-19 Registry. We enrolled patients ≥ 18 years old with laboratory-confirmed COVID-19 admitted to the participating hospitals from March to September 2020. Patients' data were obtained through hospital records. Hospitals' data were collected through forms filled in loco and through open national databases. Generalized linear mixed models with logit link function were used for pooling mortality and to assess the association between hospital characteristics and mortality estimates. We built two models, one tested general hospital characteristics while the other tested ICU characteristics. All analyses were adjusted for the proportion of high-risk patients at admission. Thirty-one hospitals were included. The mean number of beds was 320.4 ± 186.6. These hospitals had eligible 6556 COVID-19 admissions during the study period. Estimated in-hospital mortality ranged from 9.0 to 48.0%. The first model included all 31 hospitals and showed that a private source of funding (β = - 0.37; 95% CI - 0.71 to - 0.04; p = 0.029) and location in areas with a high gross domestic product (GDP) per capita (β = - 0.40; 95% CI - 0.72 to - 0.08; p = 0.014) were independently associated with a lower mortality. The second model included 23 hospitals and showed that hospitals with an ICU work shift composed of more than 50% of intensivists (β = - 0.59; 95% CI - 0.98 to - 0.20; p = 0.003) had lower mortality while hospitals with a higher proportion of less experienced medical professionals had higher mortality (β = 0.40; 95% CI 0.11-0.68; p = 0.006). The impact of those association increased according to the proportion of high-risk patients at admission. In-hospital mortality varied significantly among Brazilian hospitals. Private-funded hospitals and those located in municipalities with a high GDP had a lower mortality. When analyzing ICU-specific characteristics, hospitals with more experienced ICU teams had a reduced mortality.

Predictors of venous thromboembolism in COVID-19 patients: results of the COVID-19 Brazilian Registry

Previous studies that assessed risk factors for venous thromboembolism (VTE) in COVID-19 patients have shown inconsistent results. Our aim was to investigate VTE predictors by both logistic regression (LR) and machine learning (ML) approaches, due to their potential complementarity. This cohort study of a large Brazilian COVID-19 Registry included 4120 COVID-19 adult patients from 16 hospitals. Symptomatic VTE was confirmed by objective imaging. LR analysis, tree-based boosting, and bagging were used to investigate the association of variables upon hospital presentation with VTE. Among 4,120 patients (55.5% men, 39.3% critical patients), VTE was confirmed in 6.7%. In multivariate LR analysis, obesity (OR 1.50, 95% CI 1.11-2.02); being an ex-smoker (OR 1.44, 95% CI 1.03-2.01); surgery ≤ 90 days (OR 2.20, 95% CI 1.14-4.23); axillary temperature (OR 1.41, 95% CI 1.22-1.63); D-dimer ≥ 4 times above the upper limit of reference value (OR 2.16, 95% CI 1.26-3.67), lactate (OR 1.10, 95% CI 1.02-1.19), C-reactive protein levels (CRP, OR 1.09, 95% CI 1.01-1.18); and neutrophil count (OR 1.04, 95% CI 1.005-1.075) were independent predictors of VTE. Atrial fibrillation, peripheral oxygen saturation/inspired oxygen fraction (SF) ratio and prophylactic use of anticoagulants were protective. Temperature at admission, SF ratio, neutrophil count, D-dimer, CRP and lactate levels were also identified as predictors by ML methods. By using ML and LR analyses, we showed that D-dimer, axillary temperature, neutrophil count, CRP and lactate levels are risk factors for VTE in COVID-19 patients.

A Machine Learning Model for Predicting Hospitalization in Patients with Respiratory Symptoms during the COVID-19 Pandemic

A machine learning approach is a useful tool for risk-stratifying patients with respiratory symptoms during the COVID-19 pandemic, as it is still evolving. We aimed to verify the predictive capacity of a gradient boosting decision trees (XGboost) algorithm to select the most important predictors including clinical and demographic parameters in patients who sought medical support due to respiratory signs and symptoms (RAPID RISK COVID-19). A total of 7336 patients were enrolled in the study, including 6596 patients that did not require hospitalization and 740 that required hospitalization. We identified that patients with respiratory signs and symptoms, in particular, lower oxyhemoglobin saturation by pulse oximetry (SpO₂) and higher respiratory rate, fever, higher heart rate, and lower levels of blood pressure, associated with age, male sex, and the underlying conditions of diabetes mellitus and hypertension, required hospitalization more often. The predictive model yielded a ROC curve with an area under the curve (AUC) of 0.9181 (95% CI, 0.9001 to 0.9361). In conclusion, our model had a high discriminatory value which enabled the identification of a clinical and demographic profile predictive, preventive, and personalized of COVID-19 severity symptoms.

The role of a noninvasive index 'Spo2/ Fio2' in predicting mortality among patients with COVID-19 pneumonia

INTRODUCTION

Noninvasive risk assessment is crucial in patients with COVID-19 in emergency department. Since limited data is known about the role of noninvasive parameters, we aimed to evaluate the role of a noninvasive parameter 'SpO2/FiO2' in independently predicting 30-day mortality in patients with COVID-19 and its prognostic utility in combination with a noninvasive score 'CRB-65'.

METHODS

A retrospective study was performed in a tertiary training and research hospital, which included 272 patients with COVID-19 pneumonia diagnosed with polymerase chain reaction in emergency department. Data on characteristics, vital signs, and laboratory parameters were recorded from electronic medical records. The primary outcome of the study was 30-day mortality, and we assessed the discriminative ability of SpO2/FiO2 in predicting mortality in patients with COVID-19 pneumonia and its prognostic utility in combination with conventional pneumonia risk assessment scores.

RESULTS

Multivariate analysis revealed that only SpO2/FiO2 level was found to be an independent parameter associated with 30-day mortality (OR:0.98, 95% CI: 0.98-0.99, p = 0.003). PSI and CURB-65 were found to be better scores than CRB-65 in predicting 30-day mortality (AUC: 0.79 vs 0.72, p = 0.04; AUC: 0.76 vs 0.72, p = 0.01 respectively). Both SpO2/FiO2 combined with CRB-65 and SpO2/FiO2 combined with CURB-65 have good discriminative ability and seemed to be more favorable than PSI in predicting 30-days mortality (AUC: 0.83 vs 0.75; AUC: 0.84 vs 0.75), however no significant difference was found (p = 0.21 and p = 0.06, respectively).

CONCLUSION

SpO2/FiO2 is a promising index in predicting mortality. Addition of SpO2/FiO2 to CRB-65 improved the role of CRB-65 alone, however it performed similar to PSI. The combined noninvasive model of SpO2/FiO2 and CRB-65 may help physicians quickly stratify COVID-19 patients on admission, which is expected to be particularly important in hospitals still stressed by pandemic volumes.

Neurological manifestations by sex and age group in COVID-19 inhospital patients

Introduction

Neurological manifestations have been associated with a poorer prognosis in COVID-19. However, data regarding their incidence according to sex and age groups is still lacking.

Methods

This retrospective multicentric cohort collected data from 39 Brazilian hospitals from 17 cities, from adult COVID-19 admitted from March 2020 to January 2022. Neurological manifestations presented at hospital admission were assessed according to incidence by sex and age group.

Results

From 13,603 COVID-19 patients, median age was 60 years old and 53.0% were men. Women were more likely to present with headaches (22.4% vs. 17.7%, p < 0.001; OR 1.36, 95% confidence interval [CI] 1.22–1.52) than men and also presented a lower risk of having seizures (OR 0.43, 95% CI 0.20–0.94). Although delirium was more frequent in women (6.6% vs. 5.7%, p = 0.020), sex was not associated with delirium in the multivariable logistc regresssion analysis. Delirium, syncope and coma increased with age (1.5% [18–39 years] vs. 22.4% [80 years or over], p < 0.001, OR 1.07, 95% CI 1.06–1.07; 0.7% vs. 1.7%, p = 0.002, OR 1.01, 95% CI 1.00–1.02; 0.2% vs. 1.3% p < 0.001, OR 1.04, 95% CI 1.02–1.06), while, headache (26.5% vs. 7.1%, OR 0.98, 95% CI 0.98–0.99), anosmia (11.4% vs. 3.3%, OR 0.99, 95% CI] 0.98–0.99 and ageusia (13.1% vs. 3.5%, OR 0.99, CI 0.98–0.99) decreased (p < 0.001 for all).

Conclusion

Older COVID-19 patients were more likely to present delirium, syncope and coma, while the incidence of anosmia, ageusia and headaches decreased with age. Women were more likely to present headache, and less likely to present seizures.

•

Older COVID-19 patients were more likely to present delirium and coma.

•

Younger COVID-19 patients were more likely to report anosmia, ageusia and headache.

•

Women with COVID-19 are more likely to present headache.

Exposing and Overcoming Limitations of Clinical Laboratory Tests in COVID-19 by Adding Immunological Parameters; A Retrospective Cohort Analysis and Pilot Study

Background

Two years since the onset of the COVID-19 pandemic no predictive algorithm has been generally adopted for clinical management and in most algorithms the contribution of laboratory variables is limited.

Objectives

To measure the predictive performance of currently used clinical laboratory tests alone or combined with clinical variables and explore the predictive power of immunological tests adequate for clinical laboratories. Methods: Data from 2,600 COVID-19 patients of the first wave of the pandemic in the Barcelona area (exploratory cohort of 1,579, validation cohorts of 598 and 423 patients) including clinical parameters and laboratory tests were retrospectively collected. 28-day survival and maximal severity were the main outcomes considered in the multiparametric classical and machine learning statistical analysis. A pilot study was conducted in two subgroups (n=74 and n=41) measuring 17 cytokines and 27 lymphocyte phenotypes respectively.

Findings

1) Despite a strong association of clinical and laboratory variables with the outcomes in classical pairwise analysis, the contribution of laboratory tests to the combined prediction power was limited by redundancy. Laboratory variables reflected only two types of processes: inflammation and organ damage but none reflected the immune response, one major determinant of prognosis. 2) Eight of the thirty variables: age, comorbidity index, oxygen saturation to fraction of inspired oxygen ratio, neutrophil-lymphocyte ratio, C-reactive protein, aspartate aminotransferase/alanine aminotransferase ratio, fibrinogen, and glomerular filtration rate captured most of the combined statistical predictive power. 3) The interpretation of clinical and laboratory variables was moderately improved by grouping them in two categories i.e., inflammation related biomarkers and organ damage related biomarkers; Age and organ damage-related biomarker tests were the best predictors of survival, and inflammatory-related ones were the best predictors of severity. 4) The pilot study identified immunological tests (CXCL10, IL-6, IL-1RA and CCL2), that performed better than most currently used laboratory tests.

Conclusions

Laboratory tests for clinical management of COVID 19 patients are valuable but limited predictors due to redundancy; this limitation could be overcome by adding immunological tests with independent predictive power. Understanding the limitations of tests in use would improve their interpretation and simplify clinical management but a systematic search for better immunological biomarkers is urgent and feasible.

Demographics, clinical characteristics, and outcomes of 27,256 hospitalized COVID-19 patients in Kermanshah Province, Iran: a retrospective one-year cohort study

BACKGROUND

Since the first official report of SARS-CoV-2 infection in Iran on 19 February 2020, our country has been one of the worst affected countries by the COVID-19 epidemic in the Middle East. In addition to demographic and clinical characteristics, the number of hospitalized cases and deaths is an important factor for evidence-based decision-making and disease control and preparing the healthcare system to face the future challenges of COVID-19. Therefore, this cohort study was conducted to determine the demographics, clinical characteristics, and outcomes of hospitalized COVID-19 patients in Kermanshah Province, west of Iran.

METHODS

This multicenter retrospective cohort study included all suspected, probable, and confirmed cases of COVID-19 hospitalized in Kermanshah Province, Iran during the first year of the COVID-19 pandemic. Demographics, clinical characteristics, outcomes and other additional information of hospitalized patients were collected from the COVID-19 database of the Medical Care Monitoring Center (MCMC) of Kermanshah Province.

RESULTS

Kermanshah Province experienced three waves of COVID-19 infection considering the hospitalization and mortality rates between February 20, 2020 and February 19, 2021. A total of 27,256 patients were included in the study: 5203 (19.09%) subjects were suspected, 9136(33.52%) were probable, and 12,917 (47.39%) were confirmed COVID-19 cases. The mean age of the patients was 53.34 ± 22.74 years and 14,648 (53.74%) were male. The median length of hospital stay among COVID-19 survivors and non-survivors patients were 4 (interquartile range [IQR] 1-6) and 4 (IQR 1-8) days, respectively. Among patients with COVID-19, 2646 (9.71%) died during hospitalization. A multivariable logistic regression revealed that odds of death among patients ≥ 85 years was significantly greater than among patients < 15 years (adjusted odds ratio [aOR] 4.79, 95% confidence interval [CI] = 3.43-6.71, p≤ 0.001). Patients with one (aOR 1.38, 95% CI 1.21-1.59, p = 0.04), two (aOR 1.56, 95% CI 1.27-1.92, p = 0.001) or more (aOR 1.50, 95% CI 1.04-2.17, p = 0.03) comorbidities had higher odds of in-hospital death compared to those without comorbidities. The male sex (aOR 1.20, 95% CI 1.07- 1.35, p = 0.002), ICU admission (aOR 4.35, 95% CI 3.80-4.97, p < 0.001), intubation (aOR 11.09, 95% CI 9.58-12.84, p < 0.001), respiratory distress (aOR 1.40, 95% CI 1.22-1.61, p < 0.001), loss of consciousness (aOR 1.81, 95% CI 1.45-2.25, p < 0.001), anorexia (aOR 1.36, 95% CI 1.09-1.70, p = 0.006) and peripheral oxygen saturation (SpO2) < 93(aOR 2.72, 95% CI 2.34-3.16, p < 0.001) on admission were associated with increased risk of death in patients with SARS-CoV-2 infection. Having cough (aOR 0.82, 95% CI 0.72-0.93, p = 0.003) and headache (aOR 0.70, 95% CI 0.50-0.97, p = 0.03) decreased the odds of death.

CONCLUSION

The mortality rate of the patients admitted to the general wards and ICU can be a guide for allocating resources and making appropriate plans to provide better medical interventions during the COVID-19 pandemic. Several risk factors are associated with the in-hospital mortality of COVID-19, including advanced age, male sex, ICU admission, intubation, having comorbidity, SpO2 < 93, respiratory distress, loss of consciousness, headache, anorexia, and cough. These risk factors could help clinicians identify patients at high risk for death.

Usefulness of the C2HEST Score in Predicting the Clinical Outcomes of COVID-19 in Diabetic and Non-Diabetic Cohorts

BACKGROUND

Diabetes mellitus is among the most frequent comorbidities worsening COVID-19 outcome. Nevertheless, there are no data regarding the optimal risk stratification of patients with diabetes and COVID-19. Since individual C₂HEST components reflect the comorbidities, we assumed that the score could predict COVID-19 outcomes.

MATERIAL AND METHODS

A total of 2184 medical records of patients hospitalized for COVID-19 at the medical university center were analyzed, including 473 diabetic patients and 1666 patients without any glucose or metabolic abnormalities. The variables of patients' baseline characteristics were retrieved to calculate the C₂HEST score and subsequently the diabetic and non-diabetic subjects were assigned to the following categories: low-, medium- or high-risk. The measured outcomes included: in-hospital mortality; 3-month and 6-month all-cause mortality; non-fatal end of hospitalization (discharged home/sudden-deterioration/rehabilitation) and adverse in-hospital clinical events.

RESULTS

A total of 194 deaths (41%) were reported in the diabetic cohort, including 115 in-hospital deaths (24.3%). The 3-month and 6-month in-hospital mortality was highest in the high-risk C₂HEST stratum. The C₂HEST score revealed to be more sensitive in non-diabetic-group. The estimated six-month survival probability for high-risk subjects reached 0.4 in both cohorts whereas for the low-risk group, the six-month survival probability was 0.7 in the diabetic vs. 0.85 in the non-diabetic group-levels which were maintained during whole observation period. In both cohorts, receiver operating characteristics revealed that C₂HEST predicts the following: cardiogenic shock; acute heart failure; myocardial injury; and in-hospital acute kidney injury.

CONCLUSIONS

We demonstrated the usefulness and performance of the C₂HEST score in predicting the adverse COVID-19 outcomes in hospitalized diabetic subjects.

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

Background

It is not clear whether previous thyroid diseases influence the course and outcomes of COVID-19.

Methods

The study is a part of a multicentric cohort of patients with confirmed COVID-19 diagnosis from 37 hospitals. Matching for age, sex, number of comorbidities, and hospital was performed for the paired analysis.

Results

Of 7,762 patients with COVID-19, 526 had previously diagnosed hypothyroidism and 526 were matched controls. The median age was 70 years, and 68.3% were females. The prevalence of comorbidities was similar, except for coronary and chronic kidney diseases that were higher in the hypothyroidism group (p=0.015 and p=0.001). D-dimer levels were lower in patients with hypothyroid (p=0.037). In-hospital management was similar, but hospital length-of-stay (p=0.029) and mechanical ventilation requirement (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

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.

Extracorporeal membrane oxygenation outcomes in COVID-19 patients: Case series from the Brazilian COVID-19 Registry

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 describe the clinical characteristics and in-hospital outcomes of COVID-19 patients with ARDS refractory to lung-protective ventilation and prone positioning on ECMO support, as well as to review the available literature on ECMO use and COVID-19 patients' outcome. Patients from this case series were selected from the Brazilian COVID-19 Registry. From the 7646 patients included in the registry, only eight received ECMO support (0.1%), in four hospitals. The median age of the entire sample was 59 (interquartile range 54.2-64.4) years old and 87.5% were male. Hypertension (50.0%), diabetes mellitus (50.0%) and obesity (37.5%) were the most frequent comorbidities. The indications for ECMO were PaO₂ /FiO₂ ratio <80 mm Hg for more than 6 h or PaO₂ /FiO₂ ratio <60 mm Hg for more than 3 h. The mortality rate was 87.5%. In conclusion, in this case series of COVID-19 patients with ARDS refractory to conventional therapy who received ECMO support, a very high mortality was observed. 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 improvement.

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

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 7018 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 < 0.05). 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). In-hospital management, outcomes and complications were similar between the groups. 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.