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.

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.

Aims: This pilot study aimed to evaluate the usefulness of a sequential lung ultrasound score (LUS) in immunosuppressed patients with oncohematologic diseases and acute respiratory dysfunction hospitalized in an intensive care unit (ICU).Materials and methods: LUS was calculated at ICU admission, after 24 h, 48 h and at discharge. A score ranging from 0 to 26 was attributed according to the number of B lines, presence of lung consolidation and pleural effusion.Results: Twenty-six patients were included. The median age was 50 years [interquartile range (IQR) 21] and 14 (54%) were male. LUS on the day of ICU admission was significantly higher in non-survivors compared to survivors (13 [5] vs 9 [9], respectively; p=0.047). The median delta LUS (LUS_D2 – LUS_D1) did not show difference between survivors and non-survivors (2 [0-7.5] vs 1 [-1.5 – 5], p=0.33). Among patients initially submitted to noninvasive mechanical ventilation (NIMV), no difference in LUS at inclusion or after 24 h was found between those who succeeded or failed on this support.Conclusion: The use of LUS to quantify lung aeration loss in oncohematologic patients hospitalized in an ICU due to acute respiratory dysfunction might be a helpful tool to predict the severity of the illness.

Acute kidney injury (AKI) is a frequent complication during the postoperative period following liver transplantation. Occurrence of AKI in intensive care unit (ICU) patients is associated with increased mortality and higher costs. To evaluate occurrences of moderate or severe AKI among patients admitted to the ICU after liver transplantation and investigate characteristics associated with this complication. Single-center retrospective cohort study in a public hospital, Belo Horizonte, Brazil. Forty-nine patients admitted to the ICU between January 2015 and April 2017 were included. AKI was defined from a modified Kidney Disease Improving Global Outcomes (KDIGO) score (i.e. based exclusively on serum creatinine levels). Eighteen patients (36.7%) developed AKI KDIGO 2 or 3; mostly KDIGO 3 (16 out of the 18 patients). Lactate level within the first six hours after ICU admission (odds ratio, OR: 1.3; 95% confidence interval, CI: 1.021-1.717; P = 0.034) and blood transfusion requirement within the first week following transplantation (OR: 8.4; 95% CI: 1.687-41.824; P = 0.009) were independently associated with development of AKI. Patients with AKI KDIGO 2 or 3 underwent more renal replacement therapy (72.2% versus 3.2%; P < 0.01), had longer hospital stay (20 days versus 15 days; P = 0.001), higher in-hospital mortality (44.4% versus 6.5%; P < 0.01) and higher mortality rate after one year (44.4% versus 9.7%; P = 0.01). Need for blood transfusion during ICU stay and hyperlactatemia within the first six postoperative hours after liver transplantation are independently associated with moderate or severe AKI. Developing AKI is apparently associated with poor outcomes.

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.

Background: COVID-19 occurred in successive waves driven by different SARS-CoV-2 variants and shaped by vaccine availability and public health measures. This study analyzes differences in clinical characteristics and outcomes of hospitalized patients across three waves in Brazil. Methods: This retrospective cohort study included adult COVID-19 patients admitted to 41 hospitals across six Brazilian states from March 2020 to August 2022. Data on demographics, clinical characteristics, and outcomes were collected from medical records and compared across three pandemic waves. Categorical variables were analyzed using Chi-square or Fisher’s exact tests, with post hoc Z tests and Bonferroni correction. Continuous variables were analyzed using Analysis of Variance (ANOVA) with Tukey’s test or Kruskal-Wallis with Dunn’s test and Bonferroni correction. Results: Among 18,632 patients, 37% were hospitalized during the first wave, 55% in the second, and 8% in the third. The median age decreased during the second wave but increased in the third (60 vs 58 vs 66 years; P < 0.001). A higher proportion of patients with three or more comorbidities were admitted during the third wave (15.9% vs 11.9% vs 20.6%; P < 0.001). Anosmia, ageusia, and fever were less frequently reported in the third wave ( P < 0.001). Intensive care unit admissions (38.7% vs 37.1% vs 25.5%; P < 0.001) and in-hospital mortality (21.3% vs 23.7% vs 18.2%; P < 0.001) declined throughout the pandemic. Conclusion: Clinical manifestations and outcomes evolved across the pandemic waves. The third wave demonstrated fewer chemosensory symptoms, lower severity at admission, and reduced mortality, despite an older and more comorbid patient population. Graphical