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

Objective To evaluate clinical characteristics and outcomes of COVID-19 patients infected with HIV, and to compare with a paired sample without HIV infection. Methods This is a substudy of a Brazilian multicentric cohort that comprised two periods (2020 and 2021). Data was obtained through the retrospective review of medical records. Primary outcomes were admission to the intensive care unit, invasive mechanical ventilation, and death. Patients with HIV and controls were matched for age, sex, number of comorbidities, and hospital of origin using the technique of propensity score matching (up to 4:1). They were compared using the Chi-Square or Fisher's Exact tests for categorical variables and the Wilcoxon for numerical variables. Results Throughout the study, 17,101 COVID-19 patients were hospitalized, and 130 (0.76%) of those were infected with HIV. The median age was 54 (IQR: 43.0;64.0) years in 2020 and 53 (IQR: 46.0;63.5) years in 2021, with a predominance of females in both periods. People Living with HIV (PLHIV) and their controls showed similar prevalence for admission to the ICU and invasive mechanical ventilation requirement in the two periods, with no significant differences. In 2020, in-hospital mortality was higher in the PLHIV compared to the controls (27.9% vs. 17.7%;p = 0.049), but there was no difference in mortality between groups in 2021 (25.0% vs. 25.1%;p > 0.999). Conclusions Our results reiterate that PLHIV were at higher risk of COVID-19 mortality in the early stages of the pandemic, however, this finding did not sustain in 2021, when the mortality rate is similar to the control group.

COVID-19 outcomes in people living with HIV: Peering through the waves.

OBJECTIVE: To evaluate clinical characteristics and outcomes of COVID-19 patients infected with HIV, and to compare with a paired sample without HIV infection. METHODS: This is a substudy of a Brazilian multicentric cohort that comprised two periods (2020 and 2021). Data was obtained through the retrospective review of medical records. Primary outcomes were admission to the intensive care unit, invasive mechanical ventilation, and death. Patients with HIV and controls were matched for age, sex, number of comorbidities, and hospital of origin using the technique of propensity score matching (up to 4:1). They were compared using the Chi-Square or Fisher's Exact tests for categorical variables and the Wilcoxon for numerical variables. RESULTS: Throughout the study, 17,101 COVID-19 patients were hospitalized, and 130 (0.76%) of those were infected with HIV. The median age was 54 (IQR: 43.0;64.0) years in 2020 and 53 (IQR: 46.0;63.5) years in 2021, with a predominance of females in both periods. People Living with HIV (PLHIV) and their controls showed similar prevalence for admission to the ICU and invasive mechanical ventilation requirement in the two periods, with no significant differences. In 2020, in-hospital mortality was higher in the PLHIV compared to the controls (27.9% vs. 17.7%; p = 0.049), but there was no difference in mortality between groups in 2021 (25.0% vs. 25.1%; p > 0.999). CONCLUSIONS: Our results reiterate that PLHIV were at higher risk of COVID-19 mortality in the early stages of the pandemic, however, this finding did not sustain in 2021, when the mortality rate is similar to the control group.

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.

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.

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 (ß = - 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 (ß = - 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 (ß = - 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 (ß = 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.

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

BACKGROUND: Acute kidney injury (AKI) is frequently associated with COVID-19, and the need for kidney replacement therapy (KRT) is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting the need for KRT in hospitalised COVID-19 patients, and to assess the incidence of AKI and KRT requirement. METHODS: This study is part of a multicentre cohort, the Brazilian COVID-19 Registry. A total of 5212 adult COVID-19 patients were included between March/2020 and September/2020. Variable selection was performed using generalised additive models (GAM), and least absolute shrinkage and selection operator (LASSO) regression was used for score derivation. Accuracy was assessed using the area under the receiver operating characteristic curve (AUC-ROC). RESULTS: The median age of the model-derivation cohort was 59 (IQR 47-70) years, 54.5% were men, 34.3% required ICU admission, 20.9% evolved with AKI, 9.3% required KRT, and 15.1% died during hospitalisation. The temporal validation cohort had similar age, sex, ICU admission, AKI, required KRT distribution and in-hospital mortality. The geographic validation cohort had similar age and sex; however, this cohort had higher rates of ICU admission, AKI, need for KRT and in-hospital mortality. Four predictors of the need for KRT were identified using GAM: need for mechanical ventilation, male sex, higher creatinine at hospital presentation and diabetes. The MMCD score had excellent discrimination in derivation (AUROC 0.929, 95% CI 0.918-0.939) and validation (temporal AUROC 0.927, 95% CI 0.911-0.941; geographic AUROC 0.819, 95% CI 0.792-0.845) cohorts and good overall performance (Brier score: 0.057, 0.056 and 0.122, respectively). The score is implemented in a freely available online risk calculator ( https://www.mmcdscore.com/ ). CONCLUSIONS: The use of the MMCD score to predict the need for KRT may assist healthcare workers in identifying hospitalised COVID-19 patients who may require more intensive monitoring, and can be useful for resource allocation.