The Association Between Prepandemic ICU Performance and Mortality Variation in COVID-19: A Multicenter Cohort Study of 35,619 Critically Ill Patients

BACKGROUND

During the COVID-19 pandemic, ICUs remained under stress and observed elevated mortality rates and high variations of outcomes. A knowledge gap exists regarding whether an ICU performing best during nonpandemic times would still perform better when under high pressure compared with the least performing ICUs.

RESEARCH QUESTION

Does prepandemic ICU performance explain the risk-adjusted mortality variability for critically ill patients with COVID-19?

STUDY DESIGN AND METHODS

This study examined a cohort of adults with real-time polymerase chain reaction-confirmed COVID-19 admitted to 156 ICUs in 35 hospitals from February 16, 2020, through December 31, 2021, in Brazil. We evaluated crude and adjusted in-hospital mortality variability of patients with COVID-19 in the ICU during the pandemic. Association of baseline (prepandemic) ICU performance and in-hospital mortality was examined using a variable life-adjusted display (VLAD) during the pandemic and a multivariable mixed regression model adjusted by clinical characteristics, interaction of performance with the year of admission, and mechanical ventilation at admission.

RESULTS

Thirty-five thousand six hundred nineteen patients with confirmed COVID-19 were evaluated. The median age was 52 years, median Simplified Acute Physiology Score 3 was 42, and 18% underwent invasive mechanical ventilation. In-hospital mortality was 13% and 54% for those receiving invasive mechanical ventilation. Adjusted in-hospital mortality ranged from 3.6% to 63.2%. VLAD in the most efficient ICUs was higher than the overall median in 18% of weeks, whereas VLAD was 62% and 84% in the underachieving and least efficient groups, respectively. The least efficient baseline ICU performance group was associated independently with increased mortality (OR, 2.30; 95% CI, 1.45-3.62) after adjusting for patient characteristics, disease severity, and pandemic surge.

INTERPRETATION

ICUs caring for patients with COVID-19 presented substantial variation in risk-adjusted mortality. ICUs with better baseline (prepandemic) performance showed reduced mortality and less variability. Our findings suggest that achieving ICU efficiency by targeting improvement in organizational aspects of ICUs may impact outcomes, and therefore should be a part of the preparedness for future pandemics.

What we talk about when we talk about COVID-19 vaccination campaign impact: a narrative review

Background

The lack of precise definitions and terminological consensus about the impact studies of COVID-19 vaccination leads to confusing statements from the scientific community about what a vaccination impact study is.

Objective

The present work presents a narrative review, describing and discussing COVID-19 vaccination impact studies, mapping their relevant characteristics, such as study design, approaches and outcome variables, while analyzing their similarities, distinctions, and main insights.

Methods

The articles screening, regarding title, abstract, and full-text reading, included papers addressing perspectives about the impact of vaccines on population outcomes. The screening process included articles published before June 10, 2022, based on the initial papers' relevance to this study's research topics. The main inclusion criteria were data analyses and study designs based on statistical modelling or comparison of pre- and post-vaccination population.

Results

The review included 18 studies evaluating the vaccine impact in a total of 48 countries, including 32 high-income countries (United States, Israel, and 30 Western European countries) and 16 low- and middle-income countries (Brazil, Colombia, and 14 Eastern European countries). We summarize the main characteristics of the vaccination impact studies analyzed in this narrative review.

Conclusion

Although all studies claim to address the impact of a vaccination program, they differ significantly in their objectives since they adopt different definitions of impact, methodologies, and outcome variables. These and other differences are related to distinct data sources, designs, analysis methods, models, and approaches.

Effectiveness of a multicomponent intervention to face the COVID-19 pandemic in Rio de Janeiro's favelas: difference-in-differences analysis

INTRODUCTION

Few community-based interventions addressing the transmission control and clinical management of COVID-19 cases have been reported, especially in poor urban communities from low-income and middle-income countries. Here, we analyse the impact of a multicomponent intervention that combines community engagement, mobile surveillance, massive testing and telehealth on COVID-19 cases detection and mortality rates in a large vulnerable community (Complexo da Maré) in Rio de Janeiro, Brazil.

METHODS

We performed a difference-in-differences (DID) analysis to estimate the impact of the multicomponent intervention in Maré, before (March-August 2020) and after the intervention (September 2020 to April 2021), compared with equivalent local vulnerable communities. We applied a negative binomial regression model to estimate the intervention effect in weekly cases and mortality rates in Maré.

RESULTS

Before the intervention, Maré presented lower rates of reported COVID-19 cases compared with the control group (1373 vs 1579 cases/100 000 population), comparable mortality rates (309 vs 287 deaths/100 000 population) and higher case fatality rates (13.7% vs 12.2%). After the intervention, Maré displayed a 154% (95% CI 138.6% to 170.4%) relative increase in reported case rates. Relative changes in reported death rates were -60% (95% CI -69.0% to -47.9%) in Maré and -28% (95% CI -42.0% to -9.8%) in the control group. The case fatality rate was reduced by 77% (95% CI -93.1% to -21.1%) in Maré and 52% (95% CI -81.8% to -29.4%) in the control group. The DID showed a reduction of 46% (95% CI 17% to 65%) of weekly reported deaths and an increased 23% (95% CI 5% to 44%) of reported cases in Maré after intervention onset.

CONCLUSION

An integrated intervention combining communication, surveillance and telehealth, with a strong community engagement component, could reduce COVID-19 mortality and increase case detection in a large vulnerable community in Rio de Janeiro. These findings show that investment in community-based interventions may reduce mortality and improve pandemic control in poor communities from low-income and middle-income countries.

Antimicrobial consumption and drug utilization patterns among COVID-19 and non-COVID-19 patients

OBJECTIVES

To understand differences in antimicrobial use between COVID-19 and non-COVID-19 patients. To compare two metrics commonly used for antimicrobial use: Defined Daily Dose (DDD) and Days of Therapy (DOT). To analyse the order in which antimicrobials were prescribed to COVID-19 patients using process mining techniques.

METHODS

We analysed data regarding all ICU admissions from 1 January 2018 to 14 September 2020, in 17 Brazilian hospitals. Our main outcome was the antimicrobial use estimated by the DDD and DOT (Days of Therapy). We compared clinical characteristics and antimicrobial consumption between COVID-19 and non-COVID-19 patients. We used process mining to evaluate the order in which the antimicrobial schemes were prescribed to each COVID-19 patient.

RESULTS

We analysed 68 405 patients admitted before the pandemic, 12 319 non-COVID-19 patients and 3240 COVID-19 patients. Comparing those admitted during the pandemic, the COVID-19 patients required advanced respiratory support more often (42% versus 12%). They also had longer ICU length of stay (6 versus 3 days), higher ICU mortality (18% versus 5.4%) and greater use of antimicrobials (70% versus 39%). Most of the COVID-19 treatments started with penicillins with ß-lactamase inhibitors (30%), third-generation cephalosporins (22%), or macrolides in combination with penicillins (19%).

CONCLUSIONS

Antimicrobial prescription increased in Brazilian ICUs during the COVID-19 pandemic, especially during the first months of the epidemic. We identified greater use of broad-spectrum antimicrobials by COVID-19 patients. Overall, the DDD metric overestimated antimicrobial use compared with the DOT metric.

Maré cohort-profile: a prospective cohort study based in a socially vulnerable community during the COVID-19 pandemic in Rio de Janeiro, Brazil

Background: Socially vulnerable populations were vastly affected by the COVID-19 pandemic. The pandemic significantly impacted Brazil, pressuring its healthcare system for several months, with high mortality rates, even among the youngest population. Cohort studies combining disease surveillance are essential for understanding virus circulation in the community, surrogates of protection, vaccine effectiveness, and demand for health resources. Methods: Here, we present the protocol for a community-based prospective cohort study in the largest complex of favelas (slums) in Rio de Janeiro, Brazil (Complexo da Maré). The study participants are residents initially recruited during a massive vaccination campaign in the community. Five waves of data collection at approximately six-month intervals were planned. The first two waves have been completed at the time of writing this study protocol, and the third is underway. The protocol comprises interviews, blood sampling, and records linkage with secondary data to enrich the profiles of cohort participants and community information. We will describe COVID-19 seroprevalence, socio-demographic characteristics, and the burden of COVID-19, followed by estimating the association of socioeconomic factors and the burden of disease with seroprevalence. Discussion: The primary aims of the study are to assess COVID-19 clinical, epidemiological and genomic profiles and outcomes in residents from Maré, including vaccine effectiveness, surrogates of immune protection, virus transmission in households, and the overall burden of the pandemic.

Seroepidemiology of SARS-CoV-2 on a partially vaccinated island in Brazil: Determinants of infection and vaccine response

Background

A vaccination campaign targeted adults in response to the pandemic in the City of Rio de Janeiro.

Objective

We aimed to evaluate the seroprevalence of SARS-CoV-2 antibodies and identify factors associated with seropositivity on vaccinated and unvaccinated residents.

Methods

We performed a seroepidemiologic survey in all residents of Paquetá Island, a neighborhood of Rio de Janeiro city, during the COVID-19 vaccine roll-out. Serological tests were performed from June 16 to June 19, 2021, and adjusted seropositivity rates were estimated by age and epidemiological variables. Logistic regression models were used to estimate adjusted ORs for risk factors to SARS-CoV-2 seropositivity in non-vaccinated individuals, and potential determinants of the magnitude of antibody responses in the seropositive population.

Results

We included in the study 3,016 residents of Paquetá (83.5% of the island population). The crude seroprevalence of COVID-19 antibodies in our sample was 53.6% (95% CI = 51.0, 56.3). The risk factors for SARS-CoV-2 seropositivity in non-vaccinated individuals were history of confirmed previous COVID-19 infection (OR = 4.74; 95% CI = 3.3, 7.0), being a household contact of a case (OR = 1.93; 95% CI = 1.5, 2.6) and in-person learning (OR = 2.01; 95% CI = 1.4, 3.0). Potential determinants of the magnitude of antibody responses among the seropositive were hybrid immunity, the type of vaccine received, and time since the last vaccine dose. Being vaccinated with Pfizer or AstraZeneca (Beta = 2.2; 95% CI = 1.8, 2.6) determined higher antibody titers than those observed with CoronaVac (Beta = 1.2; 95% CI = 0.9, 1.5).

Conclusions

Our study highlights the impact of vaccination on COVID-19 collective immunity even in a highly affected population, showing the difference in antibody titers achieved with different vaccines and how they wane with time, reinforcing how these factors should be considered when estimating effectiveness of a vaccination program at any given time. We also found that hybrid immunity was superior to both infection-induced and vaccine-induced immunity alone, and online learning protected students from COVID-19 exposure.

Data-driven methodology to predict the ICU length of stay: A multicentre study of 99,492 admissions in 109 Brazilian units

PURPOSE

The length of stay (LoS) is one of the most used metrics for resource use in Intensive Care Units (ICU). We propose a structured data-driven methodology to predict the ICU length of stay and the risk of prolonged stay, and its application in a large multicenter Brazilian ICU database.

METHODS

Demographic data, comorbidities, complications, laboratory data, and primary and secondary diagnosis were prospectively collected and retrospectively analysed by a data-driven methodology, which includes eight different machine learning models and a stacking model. The study setting included 109 mixed-type ICUs from 38 Brazilian hospitals and the external validation was performed by 93 medical-surgical ICUs of 55 hospitals in Brazil.

RESULTS

A cohort of 99,492 adult ICU admissions were included from the 01^st of January to the 31^st of December 2019. The stacking model combining Random Forests and Linear Regression presented the best results to predict ICU length of stay (RMSE = 3.82; MAE = 2.52; R² = 0.36). The prediction model for the risk of long stay were accurate to early identify prolonged stay patients (Brier Score = 0.04, AUC = 0.87, PPV = 0.83, NPV = 0.95).

CONCLUSION

The data-driven methodology to predict ICU length of stay and the risk of long-stay proved accurate in a large multicentre cohort of general ICU patients. The proposed models are helpful to predict the individual length of stay and to early identify patients with high risk of prolonged stay.

Using data envelopment analysis to perform benchmarking in intensive care units

BACKGROUND

Studies using Data Envelopment Analysis to benchmark Intensive Care Units (ICUs) are scarce. Previous studies have focused on comparing efficiency using only performance metrics, without accounting for resources. Hence, we aimed to perform a benchmarking analysis of ICUs using data envelopment analysis.

METHODS

We performed a retrospective analysis on observational data of patients admitted to ICUs in Brazil (ORCHESTRA Study). The outputs in our data envelopment analysis model were the performance metrics: Standardized Mortality Ratio (SMR) and Standardized Resource Use (SRU); whereas the inputs consisted of three groups of variables that represented staffing patterns, structure, and strain, thus resulting in three models. We compared efficient and non-efficient units for each model. In addition, we compared our results to the efficiency matrix method and presented targets to each non-efficient unit.

RESULTS

We performed benchmarking in 93 ICUs and 129,680 patients. The median age was 64 years old, and mortality was 12%. Median SMR was 1.00 [interquartile range (IQR): 0.79-1.21] and SRU was 1.15 [IQR: 0.95-1.56]. Efficient units presented lower median physicians per bed ratio (1.44 [IQR: 1.18-1.88] vs. 1.7 [IQR: 1.36-2.00]) and nursing workload (168 hours [IQR: 168-291] vs 396 hours [IQR: 336-672]) but higher nurses per bed ratio (2.02 [1.16-2.48] vs. 1.71 [1.43-2.36]) compared to non-efficient units. Units from for-profit hospitals and specialized ICUs presented the best efficiency scores. Our results were mostly in line with the efficiency matrix method: the efficiency units in our models were mostly in the "most efficient" quadrant.

CONCLUSION

Data envelopment analysis provides managers the information needed to identify not only the outcomes to be achieved but what are the levels of resources needed to provide efficient care. Different perspectives can be achieved depending on the chosen variables. Its use jointly with the efficiency matrix can provide deeper understanding of ICU performance and efficiency.

Evolving changes in mortality of 13,301 critically ill adult patients with COVID-19 over 8 months

PURPOSE

Clinical characteristics and management of COVID-19 patients have evolved during the pandemic, potentially changing their outcomes. We analyzed the associations of changes in mortality rates with clinical profiles and respiratory support strategies in COVID-19 critically ill patients.

METHODS

A multicenter cohort of RT-PCR-confirmed COVID-19 patients admitted at 126 Brazilian intensive care units between February 27^th and October 28^th, 2020. Assessing temporal changes in deaths, we identified distinct time periods. We evaluated the association of characteristics and respiratory support strategies with 60-day in-hospital mortality using random-effects multivariable Cox regression with inverse probability weighting.

RESULTS

Among the 13,301 confirmed-COVID-19 patients, 60-day in-hospital mortality was 13%. Across four time periods identified, younger patients were progressively more common, non-invasive respiratory support was increasingly used, and the 60-day in-hospital mortality decreased in the last two periods. 4188 patients received advanced respiratory support (non-invasive or invasive), from which 42% underwent only invasive mechanical ventilation, 37% only non-invasive respiratory support and 21% failed non-invasive support and were intubated. After adjusting for organ dysfunction scores and premorbid conditions, we found that younger age, absence of frailty and the use of non-invasive respiratory support (NIRS) as first support strategy were independently associated with improved survival (hazard ratio for NIRS first [95% confidence interval], 0.59 [0.54-0.65], p < 0.001).

CONCLUSION

Age and mortality rates have declined over the first 8 months of the pandemic. The use of NIRS as the first respiratory support measure was associated with survival, but causal inference is limited by the observational nature of our data.

App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning

BACKGROUND

Tests are scarce resources, especially in low and middle-income countries, and the optimization of testing programs during a pandemic is critical for the effectiveness of the disease control. Hence, we aim to use the combination of symptoms to build a predictive model as a screening tool to identify people and areas with a higher risk of SARS-CoV-2 infection to be prioritized for testing.

MATERIALS AND METHODS

We performed a retrospective analysis of individuals registered in "Dados do Bem," a Brazilian app-based symptom tracker. We applied machine learning techniques and provided a SARS-CoV-2 infection risk map of Rio de Janeiro city.

RESULTS

From April 28 to July 16, 2020, 337,435 individuals registered their symptoms through the app. Of these, 49,721 participants were tested for SARS-CoV-2 infection, being 5,888 (11.8%) positive. Among self-reported symptoms, loss of smell (OR[95%CI]: 4.6 [4.4-4.9]), fever (2.6 [2.5-2.8]), and shortness of breath (2.1 [1.6-2.7]) were independently associated with SARS-CoV-2 infection. Our final model obtained a competitive performance, with only 7% of false-negative users predicted as negatives (NPV = 0.93). The model was incorporated by the "Dados do Bem" app aiming to prioritize users for testing. We developed an external validation in the city of Rio de Janeiro. We found that the proportion of positive results increased significantly from 14.9% (before using our model) to 18.1% (after the model).

CONCLUSIONS

Our results showed that the combination of symptoms might predict SARS-Cov-2 infection and, therefore, can be used as a tool by decision-makers to refine testing and disease control strategies.

Sociodemographic factors associated with COVID-19 in-hospital mortality in Brazil

OBJECTIVES

The coronavirus disease 2019 (COVID-19) pandemic has highlighted inequalities in access to healthcare systems, increasing racial disparities and worsening health outcomes in these populations. This study analysed the association between sociodemographic characteristics and COVID-19 in-hospital mortality in Brazil.

STUDY DESIGN

A retrospective analysis was conducted on quantitative reverse transcription polymerase chain reaction-confirmed hospitalised adult patients with COVID-19 with a defined outcome (i.e. hospital discharge or death) in Brazil. Data were retrieved from the national surveillance system database (SIVEP-Gripe) between February 16 and August 8, 2020.

METHODS

Clinical characteristics, sociodemographic variables, use of hospital resources and outcomes of hospitalised adult patients with COVID-19, stratified by self-reported race, were investigated. The primary outcome was in-hospital mortality. The association between self-reported race and in-hospital mortality, after adjusting for clinical characteristics and comorbidities, was evaluated using a logistic regression model.

RESULTS

During the study period, Brazil had 3,018,397 confirmed COVID-19 cases and 100,648 deaths. The study population included 228,196 COVID-19-positive adult in-hospital patients with a defined outcome; the median age was 61 years, 57% were men, 35% (79,914) self-reported as Black/Brown and 35.4% (80,853) self-reported as White. The total in-hospital mortality was 37% (85,171/228,196). Black/Brown patients showed higher in-hospital mortality than White patients (42% vs 37%, respectively), were admitted less frequently to the intensive care unit (ICU) (32% vs 36%, respectively) and used more invasive mechanical ventilation (21% vs 19%, respectively), especially outside the ICU (17% vs 11%, respectively). Black/Brown race was independently associated with high in-hospital mortality after adjusting for sex, age, level of education, region of residence and comorbidities (odds ratio = 1.15; 95% confidence interval = 1.09-1.22).

CONCLUSIONS

Among hospitalised Brazilian adults with COVID-19, Black/Brown patients showed higher in-hospital mortality, less frequently used hospital resources and had potentially more severe conditions than White patients. Racial disparities in health outcomes and access to health care highlight the need to actively implement strategies to reduce inequities caused by the wider health determinants, ultimately leading to a sustainable change in the health system.

Impact of urgent coronary artery bypass grafting on acute kidney injury : A matched cohort study

OBJECTIVES

There is limited knowledge regarding the specific interrelationships between urgent coronary artery bypass graft (U-CABG) surgery and postoperative acute kidney injury (AKI). We aimed to (1) analyze the impact of urgent CABG (U-CABG) on the incidence and severity of postoperative AKI, (2) estimate the influence of AKI after U‑CABG or elective CABG (E-CABG) on mortality and (3) identify risk factors for AKI depending on the urgency of operation.

RESULTS

U‑CABG patients showed a higher incidence of AKI (49.8% vs. E‑CABG: 39.7%; p = 0.026), especially for higher AKI stages 2 + 3. In-hospital mortality was higher in U‑CABG patients (12.6%) compared to E‑CABG patients (2.3%; p < 0.001). The impact of AKI on mortality did not differ, but showed a strong coherency between higher AKI stages (2 + 3) and mortality (stage 1: OR 2.409, 95% CI 1.017-5.706; p = 0.046 vs. stage 2 + 3: OR 5.577; 95% CI 2.033-15.3; p = 0.001). Univariate logistic regression analysis revealed that preoperative renal impairment, peripheral vascular disease and transfusion of more than two red blood cell concentrates were predictors for postoperative AKI in both groups.

CONCLUSIONS

U‑CABG is a risk factor for postoperative AKI and even "mild" AKI leads to a significantly higher mortality. Hence, the prevention of modifiable risk factors might reduce the incidence of postoperative AKI and thus improve outcome.

Characterisation of the first 250,000 hospital admissions for COVID-19 in Brazil: a retrospective analysis of nationwide data

Background

Most low-income and middle-income countries (LMICs) have little or no data integrated into a national surveillance system to identify characteristics or outcomes of COVID-19 hospital admissions and the impact of the COVID-19 pandemic on their national health systems. We aimed to analyse characteristics of patients admitted to hospital with COVID-19 in Brazil, and to examine the impact of COVID-19 on health-care resources and in-hospital mortality.

Methods

We did a retrospective analysis of all patients aged 20 years or older with quantitative RT-PCR (RT-qPCR)-confirmed COVID-19 who were admitted to hospital and registered in SIVEP-Gripe, a nationwide surveillance database in Brazil, between Feb 16 and Aug 15, 2020 (epidemiological weeks 8–33). We also examined the progression of the COVID-19 pandemic across three 4-week periods within this timeframe (epidemiological weeks 8–12, 19–22, and 27–30). The primary outcome was in-hospital mortality. We compared the regional burden of hospital admissions stratified by age, intensive care unit (ICU) admission, and respiratory support. We analysed data from the whole country and its five regions: North, Northeast, Central-West, Southeast, and South.

Findings

Between Feb 16 and Aug 15, 2020, 254 288 patients with RT-qPCR-confirmed COVID-19 were admitted to hospital and registered in SIVEP-Gripe. The mean age of patients was 60 (SD 17) years, 119 657 (47%) of 254 288 were aged younger than 60 years, 143 521 (56%) of 254 243 were male, and 14 979 (16%) of 90 829 had no comorbidities. Case numbers increased across the three 4-week periods studied: by epidemiological weeks 19–22, cases were concentrated in the North, Northeast, and Southeast; by weeks 27–30, cases had spread to the Central-West and South regions. 232 036 (91%) of 254 288 patients had a defined hospital outcome when the data were exported; in-hospital mortality was 38% (87 515 of 232 036 patients) overall, 59% (47 002 of 79 687) among patients admitted to the ICU, and 80% (36 046 of 45 205) among those who were mechanically ventilated. The overall burden of ICU admissions per ICU beds was more pronounced in the North, Southeast, and Northeast, than in the Central-West and South. In the Northeast, 1545 (16%) of 9960 patients received invasive mechanical ventilation outside the ICU compared with 431 (8%) of 5388 in the South. In-hospital mortality among patients younger than 60 years was 31% (4204 of 13 468) in the Northeast versus 15% (1694 of 11 196) in the South.

Interpretation

We observed a widespread distribution of COVID-19 across all regions in Brazil, resulting in a high overall disease burden. In-hospital mortality was high, even in patients younger than 60 years, and worsened by existing regional disparities within the health system. The COVID-19 pandemic highlights the need to improve access to high-quality care for critically ill patients admitted to hospital with COVID-19, particularly in LMICs.

Funding

National Council for Scientific and Technological Development (CNPq), Coordinating Agency for Advanced Training of Graduate Personnel (CAPES), Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro (FAPERJ), and Instituto de Salud Carlos III.

What factors predict length of stay in the intensive care unit? Systematic review and meta-analysis

PURPOSE

Studies have shown that a small percentage of ICU patients have prolonged length of stay (LoS) and account for a large proportion of resource use. Therefore, the identification of prolonged stay patients can improve unit efficiency. In this study, we performed a systematic review and meta-analysis to understand the risk factors of ICU LoS.

MATERIALS AND METHODS

We searched MEDLINE, Embase and Scopus databases from inception to November 2018. The searching process focused on papers presenting risk factors of ICU LoS. A meta-analysis was performed for studies reporting appropriate statistics.

RESULTS

From 6906 citations, 113 met the eligibility criteria and were reviewed. A meta-analysis was performed for six factors from 28 papers and concluded that patients with mechanical ventilation, hypomagnesemia, delirium, and malnutrition tend to have longer stay, and that age and gender were not significant factors.

CONCLUSIONS

This work suggested a list of risk factors that should be considered in prediction models for ICU LoS, as follows: severity scores, mechanical ventilation, hypomagnesemia, delirium, malnutrition, infection, trauma, red blood cells, and PaO2:FiO2. Our findings can be used by prediction models to improve their predictive capacity of prolonged stay patients, assisting in resource allocation, quality improvement actions, and benchmarking analysis.

Effect of seasonal and temperature variation on hospitalizations for stroke over a 10-year period in Brazil

BACKGROUND

Seasonal variation in stroke incidence remains controversial.

AIMS

We aimed to describe the pattern of seasonality in hospitalizations for stroke in Brazil.

METHODS

We evaluated age-adjusted hospitalization rates for stroke per month using data from the Brazilian Unified Healthcare System and median monthly temperature data obtained from the National Institute of Meteorology. To detect a seasonality pattern in time series, we used seasonal-trend decomposition using LOESS. We calculated a seasonal strength statistic and used Kruskal-Wallis test to evaluate the presence of seasonality in Brazil and its five regions. We also assessed the association of temperature and stroke hospitalization rates using Spearman's rho correlation.

RESULTS

We identified 1,422,496 stroke-related hospitalizations between 2009 and 2018. Mean age was 67 years, 51% were male and 77.5% of stroke diagnoses were not specified as ischemic or hemorrhagic. Median temperature was 23.8℃ (IQR 22.3-24.4). Age-adjusted hospitalizations demonstrated significant seasonal variation during all the years analyzed, with increased rates during the winter. When regional differences were analyzed, seasonal behavior was present in the south, southeast and northeast regions of the country. These were also the regions with lower median temperatures during the winter months and greater amplitude of average temperatures between warmer and colder months.

CONCLUSIONS

In this large national cohort of stroke patients in Brazil, we demonstrated the presence of seasonal variation in the age-adjusted hospitalization rate, with peak rates during the winter months. The regional gradient of incidence of stroke was directly associated with colder winters and greater amplitude of temperature.

Analysis of COVID-19 under-reporting in Brazil

Objective

To estimate the reporting rates of coronavirus disease 2019 (COVID-19) cases for Brazil as a whole and states.

Methods

We estimated the actual number of COVID-19 cases using the reported number of deaths in Brazil and each state, and the expected case-fatality ratio from the World Health Organization. Brazil’s expected case-fatality ratio was also adjusted by the population’s age pyramid. Therefore, the notification rate can be defined as the number of confirmed cases (notified by the Ministry of Health) divided by the number of expected cases (estimated from the number of deaths).

Results

The reporting rate for COVID-19 in Brazil was estimated at 9.2% (95%CI 8.8% - 9.5%), with all the states presenting rates below 30%. São Paulo and Rio de Janeiro, the most populated states in Brazil, showed small reporting rates (8.9% and 7.2%, respectively). The highest reporting rate occurred in Roraima (31.7%) and the lowest in Paraiba (3.4%).

Conclusion

The results indicated that the reporting of confirmed cases in Brazil is much lower as compared to other countries we analyzed. Therefore, decision-makers, including the government, fail to know the actual dimension of the pandemic, which may interfere with the determination of control measures.

Structure and process associated with the efficiency of intensive care units in low-resource settings: An analysis of the CHECKLIST-ICU trial database

PURPOSE

Characteristics of structure and process impact ICU performance and the outcomes of critically ill patients. We sought to identify organizational characteristics associated with efficient ICUs in low-resource settings.

MATERIALS AND METHODS

This is a secondary analysis of a multicenter cluster-randomized clinical trial in Brazil (CHECKLIST-ICU). Efficient units were defined by standardized mortality ratio (SMR) and standardized resource use (SRU) lower than the overall medians and non-efficient otherwise. We used a regularized logistic regression model to evaluate associations between organizational factors and efficiency.

RESULTS

From 118 ICUs (13,635 patients), 47 units were considered efficient and 71 non-efficient. Efficient units presented lower incidence rates (median[IQR]) of central line-associated bloodstream infections (4.95[0.00-22.0] vs 6.29[0.00-25.6], p = .04), utilization rates of mechanical ventilation (0.41[0.07-0.73] vs 0.58[0.19-0.82], p < .001), central venous catheter (0.67[0.15-0.98] vs 0.78[0.33-0.98], p = .04), and indwelling urinary catheter (0.62[0.22-0.95] vs 0.76[0.32-0.98], p < .01) than non-efficient units. The reported active surveillance of ventilator-associated pneumonia (OR = 1.72; 95%CI, 1.16-2.57) and utilization of central venous catheters (OR = 1.94; 95%CI, 1.32-2.94) were associated with efficient ICUs.

CONCLUSIONS

In low-resource settings, active surveillance of nosocomial infections and the utilization of invasive devices were associated with efficiency, supporting the management and evaluation of performance indicators as a starting point for improvement in ICU.

Progression of confirmed COVID-19 cases after the implementation of control measures

OBJECTIVE

To analyse the measures adopted by countries that have shown control over the transmission of coronavirus disease 2019 (COVID-19) and how each curve of accumulated cases behaved after the implementation of those measures.

METHODS

The methodology adopted for this study comprises three phases: systemizing control measures adopted by different countries, identifying structural breaks in the growth of the number of cases for those countries, and analyzing Brazilian data in particular.

RESULTS

We noted that China (excluding Hubei Province), Hubei Province, and South Korea have been effective in their deceleration of the growth rates of COVID-19 cases. The effectiveness of the measures taken by these countries could be seen after 1 to 2 weeks of their application. In Italy and Spain, control measures at the national level were taken at a late stage of the epidemic, which could have contributed to the high propagation of COVID-19. In Brazil, Rio de Janeiro and São Paulo adopted measures that could be effective in slowing the propagation of the virus. However, we only expect to see their effects on the growth of the curve in the coming days.

CONCLUSION

Our results may help decisionmakers in countries in relatively early stages of the epidemic, especially Brazil, understand the importance of control measures in decelerating the growth curve of confirmed cases.

Predicting Patient No-show Behavior: a Study in a Bariatric Clinic

PURPOSE

No-shows of patients to their scheduled appointments have a significant impact on healthcare systems, including lower clinical efficiency and higher costs. The purpose of this study was to investigate the factors associated with patient no-shows in a bariatric surgery clinic.

MATERIALS AND METHODS

We performed a retrospective study of 13,230 records for 2660 patients in a clinic located in Rio de Janeiro, Brazil, over a 17-month period (January 2015-May 2016). Logistic regression analyses were conducted to explore and model the influence of certain variables on no-show rates. This work also developed a predictive model stratified for each medical specialty.

RESULTS

The overall proportion of no-shows was 21.9%. According to multiple logistic regression, there is a significant association between the patient no-shows and eight variables examined. This association revealed a pattern in the increase of patient no-shows: appointment in the later hours of the day, appointments not in the summer months, post-surgery appointment, high lead time, higher no-show history, fewer numbers of previous appointments, home address 20 to 50 km away from the clinic, or scheduled for another specialty other than a bariatric surgeon. Age group, forms of payment, gender, and weekday were not significant predictors. Predictive models were developed with an accuracy of 71%.

CONCLUSION

Understanding the characteristics of patient no-shows allows making improvements in management practice, and the predictive models can be incorporated into the clinic dynamic scheduling system, allowing the use of a new appointment policy that takes into account each patient's no-show probability.

Public hospitalizations for stroke in Brazil from 2009 to 2016

Background

Stroke is the third major cause of death in the world and the second in Brazil. The purpose of this work was to assess the stroke-related hospitalization, in-hospital mortality, and case fatality rates under the Brazilian Unified Health System (SUS) from 2009 to 2016.

Methods

We evaluated the hospital admissions for stroke and their associated outcomes using data from the Hospital Information available at the Informatics Department of SUS. We selected hospitalization registries according to stroke diagnosis codes from the International Statistical Classification of Diseases and Related Health Problems (ICD-10). We identified the association of age and sex with patient death through multiple logistic regression and calculated the rates of hospitalization, mortality and case-fatality per 100,000 inhabitants using age-adjustment methodology.

Results

We analyzed 1,113,599 stroke hospitalizations. From 2009 to 2016, the number of admissions increased from 131,122 to 146,950 and the absolute number of in-hospital deaths increased from 28,731 to 31,937. Younger age and male sex were significantly associated with patient survival. Our results showed that the annual age-adjusted hospitalization and in-hospital mortality rates decreased by 11.8% and 12.6%, respectively, but the case fatality rate increased for patients older than 70 years.

Conclusions

Although the age-adjusted hospitalization and in-hospital mortality rates declined, the total number of hospitalization and deaths have increased. It is expected a continuous increase over the next years of stroke admissions with the rapid aging of the Brazilian population. Efforts should be renewed targeting risk factors, access to hospital and rehabilitation in particular for the elderly population.

Enzyme autoinduction by mitotane supported by population pharmacokinetic modelling in a large cohort of adrenocortical carcinoma patients

OBJECTIVE

Mitotane is used for the treatment of adrenocortical carcinoma. High oral daily doses of typically 1- 6 g are required to attain therapeutic concentrations. The drug has a narrow therapeutic index and patient management is difficult because of a high volume of distribution, very long elimination half-life, and drug interaction through induction of metabolizing enzymes. The present evaluation aimed at the development of a population pharmacokinetic model of mitotane to facilitate therapeutic drug monitoring.

METHODS

Appropriate dosing information, plasma concentrations (1137 data points) and covariates were available from therapeutic drug monitoring (TDM) of 76 adrenocortical carcinoma patients treated with mitotane. Using nonlinear mixed effects modeling, a simple structural model was first developed, with subsequent introduction of metabolic autoinduction. Covariate data were analyzed to improve overall model predictability. Simulations were performed to assess the attainment of therapeutic concentrations with clinical dosing schedules.

RESULTS

A one-compartment pharmacokinetic model with first order absorption was found suitable to describe the data, with an estimated central volume of distribution of 6086 L related to a high interindividual variability of 81.5%. Increase in clearance of mitotane during treatment could be modeled by a linear enzyme autoinduction process. Body mass index was found to have an influence upon disposition kinetics of mitotane. Model simulations favor a high dose regimen to rapidly attain therapeutic concentrations, with the first TDM suggested on day 16 of treatment to avoid systemic toxicity.

CONCLUSION

The proposed model describes mitotane pharmacokinetics and can be used to facilitate therapy by predicting plasma concentrations.

Optimizing Workover Rig Fleet Sizing and Scheduling Using Deterministic and Stochastic Programming Models

We present deterministic and stochastic programming models for the workover rig problem, one of the most challenging problems in the oil industry. In the deterministic approach, an integer linear programming model is used to determine the rig fleet size and schedule needed to service wells while maximizing oil production and minimizing rig usage cost. The stochastic approach is an extension of the deterministic method and relies on a two-stage stochastic programming model to define the optimal rig fleet size considering uncertainty in the intervention time. In this approach, different scenario-generation methods are compared. Several experiments were performed using instances based on real-world problems. The results suggest that the proposed methodology can be used to solve large instances and produces quality solutions in computationally reasonable times.

Are there effects of age, gender, height, and body fat on the functional muscle-bone unit in children and adults?

The aim was to describe the effect of age, gender, height, different stages of human life, and body fat on the functional muscle-bone unit. All these factors had a significant effect on the functional muscle-bone unit and should be addressed when assessing functional muscle-bone unit in children and adults.

INTRODUCTION

For the clinical evaluation of the functional muscle-bone unit, it was proposed to evaluate the adaptation of the bone to the acting forces. A frequently used parameter for this is the total body less head bone mineral content (TBLH-BMC) determined by dual-energy X-ray absorptiometry (DXA) in relation to the lean body mass (LBM by DXA). LBM correlates highly with muscle mass. Therefore, LBM is a surrogate parameter for the muscular forces acting in everyday life. The aim of the study was to describe the effect of age and gender on the TBLH-BMC for LBM and to evaluate the impact of other factors, such as height, different stages of human life, and of body fat.

METHODS

As part of the National Health and Nutrition Examination Survey (NHANES) study, between the years 1999-2006 whole-body DXA scans on randomly selected Americans from 8 years of age were carried out. From all eligible DXA scans (1999-2004), three major US ethnic groups were evaluated (non-Hispanic Whites, non-Hispanic Blacks, and Mexican Americans) for further statistical analysis.

RESULTS

For the statistical analysis, the DXA scans of 8190 non-Hispanic White children and adults (3903 female), of 4931 non-Hispanic Black children and adults (2250 female) and 5421 of Mexican-American children and adults (2424 female) were eligible. Age, gender, body height, and especially body fat had a significant effect on the functional muscle-bone unit.

CONCLUSIONS

When assessing TBLH-BMC for LBM in children and adults, the effects of age, gender, body fat, and body height should be addressed. These effects were analyzed for the first time in such a large cohort.

No-shows in appointment scheduling - a systematic literature review

No-show appointments significantly impact the functioning of healthcare institutions, and much research has been performed to uncover and analyze the factors that influence no-show behavior. In spite of the growing body of literature on this issue, no synthesis of the state-of-the-art is presently available and no systematic literature review (SLR) exists that encompasses all medical specialties. This paper provides a SLR of no-shows in appointment scheduling in which the characteristics of existing studies are analyzed, results regarding which factors have a higher impact on missed appointment rates are synthetized, and comparisons with previous findings are performed. A total of 727 articles and review papers were retrieved from the Scopus database (which includes MEDLINE), 105 of which were selected for identification and analysis. The results indicate that the average no-show rate is of the order of 23%, being highest in the African continent (43.0%) and lowest in Oceania (13.2%). Our analysis also identified patient characteristics that were more frequently associated with no-show behavior: adults of younger age; lower socioeconomic status; place of residence is distant from the clinic; no private insurance. Furthermore, the most commonly reported significant determinants of no-show were high lead time and prior no-show history.

The functional muscle-bone unit in children with cerebral palsy

Our results suggest that the prevalence of bone health deficits in children with CP was overestimated, when using only age- and height-adjusted bone mineral content (BMC) and areal bone mineral density (aBMD). When applying the functional muscle-bone unit diagnostic algorithm (FMBU-A), the prevalence of positive results decreased significantly. We recommend applying the FMBU-A when assessing bone health in children with CP.

INTRODUCTION

The prevalence of bone health deficits in children with cerebral palsy (CP) might be overestimated because age- and height-adjusted reference percentiles for bone mineral content (BMC) and areal bone mineral density (aBMD) assessed by dual-energy X-ray absorptiometry (DXA) do not consider reduced muscle activity. The aim of this study was to compare the prevalence of positive DXA-based indicators for bone health deficits in children with CP to the prevalence of positive findings after applying a functional muscle-bone unit diagnostic algorithm (FMBU-A) considering reduced muscle activity.

METHODS

The present study was a monocentric retrospective analysis of 297 whole body DXA scans of children with CP. The prevalence of positive results of age- and height-adjusted BMC and aBMD defined as BMC and aBMD below the P3 percentile and of the FMBU-A was calculated.

RESULTS

In children with CP, the prevalence of positive results of age-adjusted BMC were 33.3% and of aBMD 50.8%. Height-adjusted results for BMC and aBMD were positive in 16.8 and 36.0% of cases. The prevalence of positive results applying the FMBU-A regarding BMC and aBMD were significantly (p < 0.001) lower than using age- and height-adjusted BMC and aBMD (8.8 and 14.8%).

CONCLUSIONS

Our results suggest that the prevalence of bone health deficits in children with CP was overestimated, when using age- and height-adjusted BMC and aBMD. When applying the FMBU-A, the prevalence decreased significantly. We recommend applying the FMBU-A when assessing bone health in children with CP.

Optimization of biodiesel supply chains based on small farmers: a case study in Brazil

This article presents a methodology for conceiving and planning the development of an optimized supply chain of a biodiesel plant sourced from family farms and taking into consideration agricultural, logistic, industrial, and social aspects. This model was successfully applied to the production chain of biodiesel fuel from castor oil in the semi-arid region of Brazil. Results suggest important insights related to the optimal configuration of the crushing units, regarding its location, technology, and when it should be available, as well as the configuration of the production zones along the planning horizon considered. Moreover, a sensitivity analysis is performed in order to measure how possible variations in the considered conjecture can affect the robustness of the solutions.

Economic assessment of biodiesel production from waste frying oils

Waste frying oils (WFO) can be a good source for the production of biodiesel because this raw material is not part of the food chain, is low cost and can be used in a way that resolves environmental problems (i.e. WFO is no longer thrown into the sewage network). The goal of this article is to propose a method to evaluate the costs of biodiesel production from WFO to develop an economic assessment of this alternative. This method embraces a logistics perspective, as the cost of collection of oil from commercial producers and its delivery to biodiesel depots or plants can be relevant and is an issue that has been little explored in the academic literature. To determine the logistics cost, a mathematical programming model is proposed to solve the vehicle routing problem (VRP), which was applied in an important urban center in Brazil (Rio de Janeiro), a relevant and potential center for biodiesel production and consumption. Eighty-one biodiesel cost scenarios were compared with information on the commercialization of biodiesel in Brazil. The results obtained demonstrate the economic viability of biodiesel production from WFO in the urban center studied and the relevance of logistics in the total biodiesel production cost.

[Extracellular matrix -- from basic research to clinical significance. An overview with special consideration of matrix metalloproteinases]

Matrix metalloproteinases (MMPs) hydrolyze most components of the extracellular matrix (ECM). These proteinases play a central role in many biological processes such as normal tissue remodeling, embryogenesis, wound healing and angiogenesis. Currently about 26 MMP genes have been identified, and most are multidomain zinc endopeptidases. Knowledge of their tertiary structure is crucial for the understanding of the functional properties of MMPs. In healthy tissue a strict regulation of MMPs is critical in order to maintain proper ECM homeostasis. Among other levels of regulation, MMPs are precisely regulated by their main endogenous protein inhibitors (TIMPs). Disruption of this balance results in serious diseases such as fibrosis, arthritis, and tumour growth. Several studies have documented the importance of MMP-mediated ECM destruction for tumour initiation, growth, migration, angiogenesis, invasion and metastasis. Certain MMPs such as gelatinases (MMP-2, MMP-9) have special mechanisms to localize at leading edges in tumour tissue. MMPs can no longer be thought of solely as ECM destructionists, but as part of an elegant communication system through which epithelial and tumor cells interact with the stroma.