Challenges for the care delivery for critically ill COVID-19 patients in developing countries: the Brazilian perspective

Salluh, Jorge I.; Lisboa, Thiago; Bozza, Fernando A.

doi:10.1186/s13054-020-03278-7

Cited by 22 publications

(20 citation statements)

References 13 publications

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“…Nonsurvivor patients were older than survivors (p < 0.001). Median values of the APACHE II score and SOFA score were 17 (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26) and 5 (3)(4)(5)(6)(7)(8), respectively. APACHE and SOFA scores of the non-survivor patients were higher than the survivors (p < 0.001 for both).…”

Section: Resultsmentioning

confidence: 99%

“…In Turkey, these numbers are 348 and 301, respectively. During the COVID-19 pandemic, surge-capacity were enhanced in many countries [25][26][27]. In a study conducted in Australia, the number of intensive care beds in the rst wave of pandemic was increased by 191%, the number of ventilators by 120%, the number of senior doctors by 240% and the number of intensive care nurses by 249% [28].…”

Section: Discussionmentioning

confidence: 99%

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Clinical Outcomes and Independent Risk Factors for 90-day Mortality in Critically-ill Patients Infected with SARS CoV-2: A Multicenter Study in Turkish Intensive Care Units

Gündoğan

Akbudak

Hanci

et al. 2021

Preprint

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PurposeThere are limited data on long-term outcome of COVID-19 from different parts of the world. The aim of this study was to determine risk factors of 90-day mortality in critically-ill patients infected with SARS-CoV-2 in Turkish ICUs. MethodsPatients with COVID-19 from26 ICUs in Turkey were included in the study. Demographics, clinical characteristics, laboratory variables, treatment, and survival data were recorded. ResultsA total of 421 patients were included. The median age was 67 (IQR: 57-76) years, and 251 patients (59.6%) were men. 90-day mortality rate was 55.1%. Factors independently associated with 90-day mortality were invasive mechanical ventilation (HR 4.09 [95% CI: 2.20-7.63]), lactate level > 2 mmol/L (2.78 [1.93-4.01]), age ≥ 60 years (2.45 [1.48-4.06)]), cardiac arrhythmia during ICU stay (2.01 [1.27-3.20]), vasopressor treatment (1.94 [1.32-2.84]), positive fluid balance of ≥ 600 ml/per day (1.68 [1.21-2.34]), PaO2/FiO2 ratio of ≤ 150 mmHg (1.66 [1.18-2.32], and ECOG score ≥ 1 (1.42 [1.00-2.02]. ConclusionThis study has shown that long-term mortality was high in critically-ill COVID-19 patients in Turkish ICUs. Invasive mechanical ventilation, high lactate level, older age, presence of cardiac arrhythmia, need for vasopressor treatment, positive fluid balance, severe hypoxemia and not having fully-active performance were related with mortality.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Clinical Outcomes and Independent Risk Factors for 90-day Mortality in Critically-ill Patients Infected with SARS CoV-2: A Multicenter Study in Turkish Intensive Care Units

Gündoğan

Akbudak

Hanci

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In Turkey, these numbers are 348 and 301, respectively. During the COVID-19 pandemic, surge-capacity were enhanced in many countries 28-30] [24]. In a study conducted in Australia, the number of intensive care beds in the rst wave of pandemic was increased by 191%, the number of ventilators by 120%, the number of senior doctors by 240% and the number of intensive care nurses by 249% [29].…”

Section: Discussionmentioning

confidence: 99%

Clinical Outcomes and Independent Risk Factors for 90-day Mortality in Critically-ill Patients Infected With SARS CoV-2: a Multicenter Study in Turkish Intensive Care Units

Gündoğan

Akbudak

Hanci

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: There are limited data on long-term outcome and mortality predictors of COVID-19 from different parts of the world. The aim of this study was to determine risk factors of 90-day mortality in critically-ill patients infected with SARS-CoV-2 in Turkish ICUs. Methods: This multicenter, retrospective study was performed in 26 ICUs in Turkey. All patients with confirmed SARS-CoV2 infection, requiring more than 24 hours of ICU follow-up were included in the study. Demographics, clinical characteristics, laboratory variables, respiratory support, treatment types, and survival data were recorded. Results: A total of 421 patients were included in the study. The median age was 67 (IQR: 57-76) years, and 251 patients (59.6%) were men. 90-day mortality rate was 55.1%. Factors independently associated with 90-day mortality were receiving invasive mechanical ventilation (HR 4.09 [95% CI: 2.20-7.63]), admission lactate level > 2 mmol/L (2.78 [1.93-4.01]), age ≥ 60 years (2.45 [1.48-4.06)]), having cardiac arrhythmia during ICU stay (2.01 [1.27-3.20]), receiving vasopressor treatment (1.94 [1.32-2.84]), positive fluid balance of ≥ 600 ml/per day during ICU follow-up (1.68 [1.21-2.34]), admission PaO2/FiO2 ratio of ≤ 150 mmHg (1.66 [1.18-2.32], and baseline ECOG score ≥ 1 (1.42 [1.00-2.02]. Conclusion: This study has shown that long-term mortality was high in critically-ill COVID-19 patients in Turkish ICUs. Invasive mechanical ventilation, high lactate level, older age, presence of cardiac arrhythmia, need for vasopressor treatment, positive fluid balance, severe hypoxemia and not having fully-active performance were related with mortality.

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“…A neural network model for hospitalization prediction using the proposed method has been incorporated in the mobile app ContraCovid, which is available for public download 1 . The app is intended for the self-monitoring of patients suffering from COVID-19 and has used the proposed predictor as one of the metrics to recommend patients to seek treatment.…”

Section: System Availabilitymentioning

confidence: 99%

Machine Learning Prediction of Hospitalization due to COVID-19 based on Self-Reported Symptoms: A Study for Brazil*

Miranda¹,

Cardoso²,

Pahar³

et al. 2021

Preprint

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Predicting the need for hospitalization due to COVID-19 may help patients to seek timely treatment and assist health professionals to monitor cases and allocate resources. We investigate the use of machine learning algorithms to predict the risk of hospitalization due to COVID-19 using the patient's medical history and self-reported symptoms, regardless of the period in which they occurred. Three datasets containing information regarding 217,580 patients from three different states in Brazil have been used. Decision trees, neural networks, and support vector machines were evaluated, achieving accuracies between 79.1% to 84.7%. Our analysis shows that better performance is achieved in Brazilian states ranked more highly in terms of the official human development index (HDI), suggesting that health facilities with better infrastructure generate data that is less noisy. One of the models developed in this study has been incorporated into a mobile app that is available for public use.

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Challenges for the care delivery for critically ill COVID-19 patients in developing countries: the Brazilian perspective

Cited by 22 publications

References 13 publications

Clinical Outcomes and Independent Risk Factors for 90-day Mortality in Critically-ill Patients Infected with SARS CoV-2: A Multicenter Study in Turkish Intensive Care Units

Clinical Outcomes and Independent Risk Factors for 90-day Mortality in Critically-ill Patients Infected with SARS CoV-2: A Multicenter Study in Turkish Intensive Care Units

Clinical Outcomes and Independent Risk Factors for 90-day Mortality in Critically-ill Patients Infected With SARS CoV-2: a Multicenter Study in Turkish Intensive Care Units

Machine Learning Prediction of Hospitalization due to COVID-19 based on Self-Reported Symptoms: A Study for Brazil*

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