Patients hospitalized with active tuberculosis and Covid-19 coinfection: A matched case-control from the Brazilian Covid-19 Registry

Although control of Covid-19 has improved, the virus continues to cause infections, such as tuberculosis, that is still endemic in many countries, representing a scenario of coinfection. To compare Covid-19 clinical manifestations and outcomes between patients with active tuberculosis infection and matched controls. This is a matched case-control study based on data from the Brazilian Covid-19 Registry, in hospitalized patients aged 18 or over with laboratory confirmed Covid-19 from March 1, 2020, to March 31, 2022. Cases were patients with tuberculosis and controls were Covid-19 patients without tuberculosis. From 13,636 Covid-19, 36 also had active tuberculosis (0.0026%). Pulmonary fibrosis (5.6% vs 0.0%), illicit drug abuse (30.6% vs 3.0%), alcoholism (33.3% vs 11.9%) and smoking (50.0% vs 9.7%) were more common among patients with tuberculosis. They also had a higher frequency of nausea and vomiting (25.0% vs 10.4%). There were no significant differences in in-hospital mortality, mechanical ventilation, need for dialysis and ICU stay. Patients with TB infection presented a higher frequency of pulmonary fibrosis, abuse of illicit drugs, alcoholism, current smoking, symptoms of nausea and vomiting. The outcomes were similar between them.

Neutrophil-Lymphocyte Ratio as a Predictor of the Risk of Death in Severe Cases of COVID-19

BACKGROUND

Identifying clinical characteristics and risk factors, comorbid conditions, and complications arising from SARS-CoV-2 infection is important to predict the progression to more severe forms of the disease among hospitalized individuals to enable timely intervention and to prevent fatal outcomes. The aim of the study is to assess the possible role of the neutrophil/lymphocyte ratio (NLR) as a biomarker of the risk of death in patients with comorbidities hospitalized with COVID-19 in a tertiary hospital in southern Brazil.

METHODS

This is a prospective cohort study on patients with SARS-CoV-2 infection admitted to a hospital in the metropolitan region of Porto Alegre from September 2020 to March 2022.

RESULTS

The sample consisted of 185 patients with associated comorbidities, namely, hypertension, diabetes mellitus, obesity, cardiovascular, pulmonary, and renal diseases, hospitalized with COVID-19. Of these, 78 died and 107 were discharged alive. The mean age was 66.5 years for the group that died and 60.1 years for the group discharged. Statistical analysis revealed that a difference greater than or equal to 1.55 in the NLR, from hospitalization to the 5th day, was associated with a relative risk of death greater than 2.

CONCLUSIONS

Measuring a simple inflammatory marker such as NLR may improve the risk stratification of comorbid patients with COVID-19 and can be considered a useful biomarker.

Socioeconomic and regional inequalities of pap smear coverage

Objectives To identify socioeconomic and regional inequalities of pap smear coverage in the state of Rio Grande do Sul. Methods An ecological study based on data of the 2011-2012 national health information system to estimate the annual coverage of pap smears for the overall female population of the state and for women without private health insurance. We estimated annual pap smear coverage according to the Municipal Social Vulnerability Index and health macro-regions and regions of the state. Results The percentage of women without private health insurance ranged from 38.1% to 94.2% in the health regions. Pap smear coverage was 17.3% for the overall female population and 23.8% for women without private health insurance. Pap smear coverage was higher in more socially vulnerable municipalities and regions with a higher percentage of women with private health insurance. Conclusions The prevalence of private health insurance should be considered in studies that address the coverage of the Brazilian Unified Health System (SUS).

Temporal validation of the MMCD score to predict kidney replacement therapy and in-hospital mortality in COVID-19 patients

BACKGROUND

Acute kidney injury has been described as a common complication in patients hospitalized with COVID-19, which may lead to the need for kidney replacement therapy (KRT) in its most severe forms. Our group developed and validated the MMCD score in Brazilian COVID-19 patients to predict KRT, which showed excellent performance using data from 2020. This study aimed to validate the MMCD score in a large cohort of patients hospitalized with COVID-19 in a different pandemic phase and assess its performance to predict in-hospital mortality.

METHODS

This study is part of the "Brazilian COVID-19 Registry", a retrospective observational cohort of consecutive patients hospitalized for laboratory-confirmed COVID-19 in 25 Brazilian hospitals between March 2021 and August 2022. The primary outcome was KRT during hospitalization and the secondary was in-hospital mortality. We also searched literature for other prediction models for KRT, to assess the results in our database. Performance was assessed using area under the receiving operator characteristic curve (AUROC) and the Brier score.

RESULTS

A total of 9422 patients were included, 53.8% were men, with a median age of 59 (IQR 48-70) years old. The incidence of KRT was 8.8% and in-hospital mortality was 18.1%. The MMCD score had excellent discrimination and overall performance to predict KRT (AUROC: 0.916 [95% CI 0.909-0.924]; Brier score = 0.057). Despite the excellent discrimination and overall performance (AUROC: 0.922 [95% CI 0.914-0.929]; Brier score = 0.100), the calibration was not satisfactory concerning in-hospital mortality. A random forest model was applied in the database, with inferior performance to predict KRT requirement (AUROC: 0.71 [95% CI 0.69-0.73]).

CONCLUSION

The MMCD score is not appropriate for in-hospital mortality but demonstrates an excellent predictive ability to predict KRT in COVID-19 patients. The instrument is low cost, objective, fast and accurate, and can contribute to supporting clinical decisions in the efficient allocation of assistance resources in patients with COVID-19.

Serum of COVID-19 patients changes neuroinflammation and mitochondrial homeostasis markers in hippocampus of aged rats

Patients affected by COVID-19 present mostly with respiratory symptoms but acute neurological symptoms are also commonly observed. Furthermore, a considerable number of individuals develop persistent and often remitting symptoms months after infection, characterizing the condition called long-COVID. Since the pathophysiology of acute and persistent neurological manifestations is not fully established, we evaluated the expression of different genes in hippocampal slices of aged rats exposed to the serum of a post-COVID (sPC) individual and to the serum of patients infected by SARS-CoV-2 [Zeta (sZeta) and Gamma (sGamma) variants]. The expression of proteins related to inflammatory process, redox homeostasis, mitochondrial quality control and glial reactivity was determined. Our data show that the exposure to sPC, sZeta and sGamma differentially altered the mRNA levels of most inflammatory proteins and reduced those of antioxidant response markers in rat hippocampus. Furthermore, a decrease in the expression of mitochondrial biogenesis genes was induced by all serum samples, whereas a reduction in mitochondrial dynamics was only caused by sPC. Regarding the glial reactivity, S100B expression was modified by sPC and sZeta. These findings demonstrate that changes in the inflammatory response and a reduction of mitochondrial biogenesis and dynamics may contribute to the neurological damage observed in COVID-19 patients.

Multicenter comparative analysis of local and aggregated data training strategies in COVID-19 outcome prediction with Machine learning

Machine learning (ML) is a promising tool in assisting clinical decision-making for improving diagnosis and prognosis, especially in developing regions. It is often used with large samples, aggregating data from different regions and hospitals. However, it is unclear how this affects predictions in local centers. This study aims to compare data aggregation strategies of several hospitals in Brazil with a local training strategy in each hospital to predict two COVID-19 outcomes: Intensive Care Unit admission (ICU) and mechanical ventilation use (MV). The study included 6,046 patients from 14 hospitals, with local sample sizes ranging from 47 to 1500 patients. Machine learning models were trained using extreme gradient boosting, lightGBM, and catboost for structured data. Seven data aggregation strategies based on hospital geographic regions were compared with local training, and the best strategy was determined by analyzing the area under the ROC curve (AUROC). SHAP (Shapley Additive exPlanations) values were used to assess the contribution of variables to predictions. Additionally, a metafeatures analysis examined how hospital characteristics influence the selection of the best strategy. The study found that the local training strategy was the most effective approach, in the case of ICU outcomes, for 11 of the 14 hospitals (79%), and, in the case of MV, for 10 hospitals (71%). Metafeatures analysis suggested that hospitals with smaller sample sizes generally performed better using an aggregated data strategy compared to local training. Our study brings to light an important concern about the impact of grouping data from different hospitals in predictive machine learning models. These findings contribute to the ongoing debate about the trade-off between increasing sample size and bringing together heterogeneous scenarios.

Comprehensive statistical analysis reveals significant benefits of COVID-19 vaccination in hospitalized patients: propensity score, covariate adjustment, and feature importance by permutation

BACKGROUND

COVID-19 vaccines effectively prevent infection and hospitalization. However, few population-based studies have compared the clinical characteristics and outcomes of patients hospitalized for COVID-19 using advanced statistical methods. Our objective is to address this evidence gap by comparing vaccinated and unvaccinated patients hospitalized for COVID-19.

METHODS

This retrospective cohort included adult COVID-19 patients admitted from March 2021 to August 2022 from 27 hospitals. Clinical characteristics, vaccination status, and outcomes were extracted from medical records. Vaccinated and unvaccinated patients were compared using propensity score analyses, calculated based on variables associated with vaccination status and/or outcomes, including waves. The vaccination effect was also assessed by covariate adjustment and feature importance by permutation.

RESULTS

From the 3,188 patients, 1,963 (61.6%) were unvaccinated and 1,225 (38.4%) were fully vaccinated. Among these, 558 vaccinated individuals were matched with 558 unvaccinated ones. Vaccinated patients had lower rates of mortality (19.4% vs. 33.3%), invasive mechanical ventilation (IMV-18.3% vs. 34.6%), noninvasive mechanical ventilation (NIMV-10.6% vs. 22.0%), intensive care unit admission (ICU-32.0% vs. 44.1%) vasoactive drug use (21.1% vs. 32.6%), dialysis (8.2% vs. 14.7%) hospital length of stay (7.0 vs. 9.0 days), and thromboembolic events (3.9% vs.7.7%), p < 0.05 for all. Risk-adjusted multivariate analysis demonstrated a significant inverse association between vaccination and in-hospital mortality (adjusted odds ratio [aOR] = 0.42, 95% confidence interval [CI]: 0.31-0.56; p < 0.001) as well as IMV (aOR = 0.40, 95% CI: 0.30-0.53; p < 0.001). These results were consistent in all analyses, including feature importance by permutation.

CONCLUSION

Vaccinated patients admitted to hospital with COVID-19 had significantly lower mortality and other severe outcomes than unvaccinated ones during the Delta and Omicron waves. These findings have important implications for public health strategies and support the critical importance of vaccination efforts, particularly in low-income countries, where vaccination coverage remains suboptimal.

Cohort study protocol of the Brazilian collaborative research network on COVID-19: strengthening WHO global data

INTRODUCTION

With the COVID-19 pandemic, hospitals in low-income countries were faced with a triple challenge. First, a large number of patients required hospitalisation because of the infection's more severe symptoms. Second, there was a lack of systematic and broad testing policies for early identification of cases. Third, there were weaknesses in the integration of information systems, which led to the need to search for available information from the hospital information systems. Accordingly, it is also important to state that relevant aspects of COVID-19's natural history had not yet been fully clarified. The aim of this research protocol is to present the strategies of a Brazilian network of hospitals to perform systematised data collection on COVID-19 through the WHO platform.

METHODS AND ANALYSIS

This is a multicentre project among Brazilian hospitals to provide data on COVID-19 through the WHO global platform, which integrates patient care information from different countries. From October 2020 to March 2021, a committee worked on defining a flowchart for this platform, specifying the variables of interest, data extraction standardisation and analysis.

ETHICS AND DISSEMINATION

This protocol was approved by the Research Ethics Committee (CEP) of the Research Coordinating Center of Brazil (CEP of the Hospital Nossa Senhora da Conceicao), on 29 January 2021, under approval No. 4.515.519 and by the National Research Ethics Commission (CONEP), on 5 February 2021, under approval No. 4.526.456. The project results will be explained in WHO reports and published in international peer-reviewed journals, and summaries will be provided to the funders of the study.

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.

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.

Real-Life Data on Hydroxychloroquine or Chloroquine with or Without Azithromycin in COVID-19 Patients: A Retrospective Analysis in Brazil

BACKGROUND

Despite no evidence showing benefits of hydroxychloroquine and chloroquine with or without azithromycin for COVID-19 treatment, these medications have been largely prescribed in Brazil.

OBJECTIVES

To assess outcomes, including in-hospital mortality, electrocardiographic abnormalities, hospital length-of-stay, admission to the intensive care unit, and need for dialysis and mechanical ventilation, in hospitalized COVID-19 patients who received chloroquine or hydroxychloroquine, and to compare outcomes between those patients and their matched controls.

METHODS

A retrospective multicenter cohort study that included consecutive laboratory-confirmed COVID-19 patients from 37 Brazilian hospitals from March to September 2020. Propensity score was used to select matching controls by age, sex, cardiovascular comorbidities, and in-hospital use of corticosteroid. A p-value <0.05 was considered statistically significant.

RESULTS

From 7,850 COVID-19 patients, 673 (8.6%) received hydroxychloroquine and 67 (0.9%) chloroquine. The median age in the study group was 60 years (46 - 71) and 59.1% were women. During hospitalization, 3.2% of patients presented side effects and 2.2% required therapy discontinuation. Electrocardiographic abnormalities were more prevalent in the chloroquine/hydroxychloroquine group (13.2% vs. 8.2%, p=0.01), and the long corrected QT interval was the main difference (3.6% vs. 0.4%, p<0.001). The median hospital length of stay was longer in the HCQ/CQ + AZT group than in controls (9.0 [5.0, 18.0] vs. 8.0 [4.0, 14.0] days). There was no statistical differences between groups in intensive care unit admission (35.1% vs. 32.0%; p=0.282), invasive mechanical ventilation support (27.0% vs. 22.3%; p=0.074) or mortality (18.9% vs. 18.0%; p=0.682).

CONCLUSION

COVID-19 patients treated with chloroquine or hydroxychloroquine had a longer hospital length of stay, when compared to matched controls. Intensive care unit admission, invasive mechanical ventilation, dialysis and in-hospital mortality were similar.

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.

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 protection: comparing histological effects of single-dose cardioplegic solutions-study protocol for a secondary analysis of the CARDIOPLEGIA trial

Background

Myocardial protection is crucial for successful cardiac surgery, as it prevents heart muscle damage that can occur during the procedure. Prolonged hypoxia without proper protection can lead to adenosine triphosphate consumption, microvilli loss, blister formation, and edema. Custodiol, del Nido, and modified del Nido are single-dose cardioplegic solutions with proven safety and significance in modern surgery. While each has been independently assessed for patient outcomes, limited research directly compares them. This study aims to compare their myocardial protection using histological analysis.

Methods

In a double-blind clinical trial, at least 90 patients will be randomly assigned to receive one of the three cardioplegic solutions. Myocardial biopsies will be collected before cardiopulmonary bypass and 15 minutes after reperfusion. The surgical, anesthetic and perfusion techniques will be the same for all patients, following the Institution's standard protocols.

Discussion

The ideal cardioplegic solution does not exist, and its selection remains challenging for surgeons. In modern surgical practice, understanding the behavior of these solutions and the ischemic tissue damage caused during induced cardiac arrest allows for safer surgical procedures. The results of this clinical trial can help in understanding the behavior of cardioplegic solutions and their tissue effects. Thus, by selecting the best cardioplegic solution, ischemic damage can be minimized, enhancing the effectiveness of this essential technique in cardiac procedures. The study may aid in implementing clinical protocols in several institutions, aiming to choose the solution with a superior myocardial protection profile, increasing safety, and reducing expenses.

Trial Registration

Brazilian Clinical Trials Registry (ReBEC, http://ensaiosclinicos.gov.br/): RBR-997tqhh. Registered: January 26th, 2022.

A New Natural Language Processing-Inspired Methodology (Detection, Initial Characterization, and Semantic Characterization) to Investigate Temporal Shifts (Drifts) in Health Care Data: Quantitative Study

BACKGROUND

Proper analysis and interpretation of health care data can significantly improve patient outcomes by enhancing services and revealing the impacts of new technologies and treatments. Understanding the substantial impact of temporal shifts in these data is crucial. For example, COVID-19 vaccination initially lowered the mean age of at-risk patients and later changed the characteristics of those who died. This highlights the importance of understanding these shifts for assessing factors that affect patient outcomes.

OBJECTIVE

This study aims to propose detection, initial characterization, and semantic characterization (DIS), a new methodology for analyzing changes in health outcomes and variables over time while discovering contextual changes for outcomes in large volumes of data.

METHODS

The DIS methodology involves 3 steps: detection, initial characterization, and semantic characterization. Detection uses metrics such as Jensen-Shannon divergence to identify significant data drifts. Initial characterization offers a global analysis of changes in data distribution and predictive feature significance over time. Semantic characterization uses natural language processing-inspired techniques to understand the local context of these changes, helping identify factors driving changes in patient outcomes. By integrating the outcomes from these 3 steps, our results can identify specific factors (eg, interventions and modifications in health care practices) that drive changes in patient outcomes. DIS was applied to the Brazilian COVID-19 Registry and the Medical Information Mart for Intensive Care, version IV (MIMIC-IV) data sets.

RESULTS

Our approach allowed us to (1) identify drifts effectively, especially using metrics such as the Jensen-Shannon divergence, and (2) uncover reasons for the decline in overall mortality in both the COVID-19 and MIMIC-IV data sets, as well as changes in the cooccurrence between different diseases and this particular outcome. Factors such as vaccination during the COVID-19 pandemic and reduced iatrogenic events and cancer-related deaths in MIMIC-IV were highlighted. The methodology also pinpointed shifts in patient demographics and disease patterns, providing insights into the evolving health care landscape during the study period.

CONCLUSIONS

We developed a novel methodology combining machine learning and natural language processing techniques to detect, characterize, and understand temporal shifts in health care data. This understanding can enhance predictive algorithms, improve patient outcomes, and optimize health care resource allocation, ultimately improving the effectiveness of machine learning predictive algorithms applied to health care data. Our methodology can be applied to a variety of scenarios beyond those discussed in this paper.

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

[This corrects the article DOI: 10.3389/fmed.2023.1130218.].

Clinical characteristics and outcomes of COVID-19 patients with preexisting dementia: a large multicenter propensity-matched Brazilian cohort study

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 1,556 patients included in the study, 405 (4.5%) had a diagnosis of dementia and 1,151 were matched controls. When compared to matched controls, patients with dementia had a lower frequency of dyspnoea, cough, myalgia, headache, ageusia, and anosmia; and higher frequency of fever and delirium. They also had a lower frequency of ICU admission (32.7% vs. 47.1%, p < 0.001) and shorter ICU length of stay (7 vs. 9 days, p < 0.026), and a lower 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). There were no differences in hospital mortality between groups.

CONCLUSION

Clinical manifestations of COVID-19 differ between older inpatients with and without dementia. We observed that dementia alone could not explain the higher short-term mortality following severe COVID-19. Therefore, clinicians should consider other risk factors such as acute morbidity severity and baseline frailty when evaluating the prognosis of older adults with dementia hospitalised with COVID-19.

Unraveling relevant cross-waves pattern drifts in patient-hospital risk factors among hospitalized COVID-19 patients using explainable machine learning methods

BACKGROUND

Several studies explored factors related to adverse clinical outcomes among COVID-19 patients but lacked analysis of the impact of the temporal data shifts on the strength of association between different predictors and adverse outcomes. This study aims to evaluate factors related to patients and hospitals in the prediction of in-hospital mortality, need for invasive mechanical ventilation (IMV), and intensive care unit (ICU) transfer throughout the pandemic waves.

METHODS

This multicenter retrospective cohort included COVID-19 patients from 39 hospitals, from March/2020 to August/2022. The pandemic was divided into waves: 10/03/2020-14/11/2020 (first), 15/11/2020-25/12/2021 (second), 26/12/2021-03/08/2022 (third). Patient-related factors included clinical, demographic, and laboratory data, while hospital-related factors covered funding sources, accreditation, academic status, and socioeconomic characteristics. Shapley additive explanation (SHAP) values derived from the predictions of a light gradient-boosting machine (LightGBM) model were used to assess potential risk factors for death, IMV and ICU.

RESULTS

Overall, 16,958 adult patients were included (median age 59 years, 54.7% men). LightGBM achieved competitive effectiveness metrics across all periods. Temporal drifts were observed due to a decrease in various metrics, such as the recall for the positive class [ICU: 0.4211 (wave 1) to 0.1951 (wave 3); IMV: 0.2089 (wave 1) to 0.0438 (wave 3); death: 0.2711 (wave 1) to 0.1175 (wave 3)]. Peripheral arterial oxygen saturation to the fraction of inspired oxygen ratio (SatO2/FiO2) at admission had great predictive capacity for all outcomes, with an optimal cut-off value for death prediction of 227.78. Lymphopenia had its association strength increased over time for all outcomes, optimal threshold for death prediction of 643 × 109/L. Thrombocytopenia was the most important feature in wave 2 (ICU); overall, values below 143,000 × 109/L were more related to death.

CONCLUSION

Data drifts were observed in all scenarios, affecting potential predictive capabilities of explainable machine learning methods. Upon admission, SatO2/FiO2 values, platelet and lymphocyte count were significant predictors of adverse outcomes in COVID-19 patients. Overall, inflammatory response markers were more important than clinical characteristics. Limitations included sample representativeness and confounding factors. Integrating the drift's knowledge into models to improve effectiveness is a challenge, requiring continuous updates and monitoring of performance in real-world applications.

CLINICAL TRIAL NUMBER

Not applicable.

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.

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.