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.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Purpose
To assess whether intensive care unit (ICU) outcomes for patients not affected by coronavirus disease 2019 (COVID-19) worsened during the COVID-19 pandemic.
Methods
Retrospective cohort study ...including prospectively collected information of patients admitted to 165 ICUs in a hospital network in Brazil between 2011 and 2020. Association between admission in 2020 and worse hospital outcomes was performed using different techniques, including assessment of changes in illness severity of admitted patients, a variable life-adjusted display of mortality during 2020, a multivariate mixed regression model with admission year as both fixed effect and random slope adjusted for SAPS 3 score, an analysis of trends in performance using standardized mortality ratio (SMR) and standardized resource use (SRU), and perturbation analysis.
Results
A total of 644,644 admissions were considered. After excluding readmissions and patients with COVID-19, 514,219 patients were available for analysis. Non-COVID-19 patients admitted in 2020 had slightly lower age and SAPS 3 score but a higher mortality (6.4%) when compared with previous years (2019: 5.6%; 2018: 6.1%). Variable-adjusted life display (VLAD) in 2020 increased but started to decrease as the number of COVID-19 cases increased; this trend reversed as number of COVID cases reduced but recurred on the second wave. After logistic regression, being admitted in 2020 was associated with higher mortality when compared to previous years from 2016 and 2019. Individual ICUs standardized mortality ratio also increased during 2020 (higher SMR) while resource use remained constant, suggesting worsening performance. A perturbation analysis further confirmed changes in ICU outcomes for non-COVID-19 patients.
Conclusion
Hospital outcomes of non-COVID-19 critically ill patients worsened during the pandemic in 2020, possibly resulting in an increased number of deaths in critically ill non-COVID patients.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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.
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.
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.
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.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.
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.
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.
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.
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.
•The static offline operational Patient Admission Scheduling Problem is addressed.•An exact method is proposed to solve a mixed integer programming formulation.•The method is parameter-free and does ...not require pre-processing of penalties.•New best known solutions are generated for 9 out of 13 benchmark instances.•Proof of optimality of two best known solutions reported in the literature.
Among the many challenges involved in efficient healthcare resource planning, the Patient Admission Scheduling Problem is of particular significance, impacting organizational decisions at all planning levels. The problem of scheduling patient admissions involves assigning patients to beds over a given time horizon so as to maximize treatment efficiency, patient comfort and hospital utilization, while satisfying all necessary medical constraints and taking into consideration patient preferences as much as possible. A number of different variants of the Patient Admission Scheduling Problem exist at the strategic, tactical, and operational levels. In this paper, we consider a static offline operational level variant for which we propose a comprehensive mixed integer programming formulation and advance an exact solution method. We generate new best found solutions for 9 out of 13 benchmark instances from a publicly available repository. Additionally, we prove the optimality of two best known solutions reported in the literature.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Mathematical models, usually implemented in computer programs known as simulators, are widely used in all areas of science and technology to represent complex real-world phenomena. Simulators are ...often so complex that they take appreciable amounts of computer time or other resources to run. In this context, a methodology has been developed based on building a statistical representation of the simulator, known as an emulator. The principal approach to building emulators uses Gaussian processes. This work presents some diagnostics to validate and assess the adequacy of a Gaussian process emulator as surrogate for the simulator. These diagnostics are based on comparisons between simulator outputs and Gaussian process emulator outputs for some test data, known as validation data, defined by a sample of simulator runs not used to build the emulator. Our diagnostics take care to account for correlation between the validation data. To illustrate a validation procedure, we apply these diagnostics to two different data sets.
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BFBNIB, CEKLJ, GIS, IJS, INZLJ, KISLJ, NMLJ, NUK, PNG, UL, UM, UPUK, ZRSKP
Brazil detected community transmission of COVID-19 on March 13, 2020. In this study we identified which areas in the country were the most vulnerable for COVID-19, both in terms of the risk of ...arrival of cases, the risk of sustained transmission and their social vulnerability. Probabilistic models were used to calculate the probability of COVID-19 spread from São Paulo and Rio de Janeiro, the initial hotspots, using mobility data from the pre-epidemic period, while multivariate cluster analysis of socio-economic indices was done to identify areas with similar social vulnerability. The results consist of a series of maps of effective distance, outbreak probability, hospital capacity and social vulnerability. They show areas in the North and Northeast with high risk of COVID-19 outbreak that are also highly socially vulnerable. Later, these areas would be found the most severely affected. The maps produced were sent to health authorities to aid in their efforts to prioritize actions such as resource allocation to mitigate the effects of the pandemic. In the discussion, we address how predictions compared to the observed dynamics of the disease.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.
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.
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 (OR95%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).
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.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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