Intensive care for COVID-19 in low- and middle-income countries: research opportunities and challenges

Salluh, Jorge I.; Burghi, Gastón

doi:10.1007/s00134-020-06285-y

Cited by 30 publications

(32 citation statements)

References 7 publications

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“…In spite of these efforts, COVID-19 study involvement, particularly in low- and middle-income countries, remains poor. The COALIZÃO trials in Brazil [ 11 , 12 ] have circumvented some of these challenges using a pre-existing research network and pragmatic study design. However, there remains a significant discrepancy between trial participation in high-income and low-income countries.…”

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confidence: 99%

COVID-19 research in critical care: the good, the bad, and the ugly

2021

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confidence: 99%

COVID-19 research in critical care: the good, the bad, and the ugly

2021

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“… 8 As the pandemic progresses, ventilatory capacity will be completely overwhelmed in LMICs. 9 Even high‐income countries such as the US, have struggled with access to ventilators as the prevalence of SARS‐CoV‐2 reaches overwhelmingly high rates. 10 In fact, there is growing evidence to suggest that LMICs with a high SARS‐CoV‐2 prevalence have already been suffering such shortages, such as in India, 9 , 11 , 12 which has recorded over 10.5 million cases and 150, 000 deaths, as of January 2021.…”

Section: Global Challengesmentioning

confidence: 99%

“…9 Even high-income countries such as the US, have struggled with access to ventilators as the prevalence of SARS-CoV-2 reaches overwhelmingly high rates. 10 In fact, there is growing evidence to suggest that LMICs with a high SARS-CoV-2 prevalence have already been suffering such shortages, such as in India, 9,11,12 which has recorded over 10.5 million cases and 150, 000 deaths, as of January 2021. 13 Another vital factor to consider is the significant relative shortage of trained medical staff in LMICs.…”

Section: Global Challengesmentioning

confidence: 99%

A low‐cost field ventilator: An urgent global need

Nachiappan

Koo

Chockalingam

et al. 2021

Health Science Reports

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Modern ventilators are increasingly compact and able to deliver a wide range of ventilator modes and sophisticated monitoring capabilities. However, the global availability of ventilators is woefully short of demand. Data on intensive care units (ICUs), a proxy measure for hospital ventilator capacity in low and middle‐income countries (LMIC's), suggest that capacity is extremely limited where it exists at all. In LMIC's, the four most common indications for mechanical ventilation (MV) in ICUs are post‐surgical care, sepsis, trauma, and maternal peripartum or neonatal complications. A significant majority of these cases can be managed with intervention involving a short course of MV. Widespread and timely access to MV can thus effectively be used to help patients in these settings and improve outcomes. This paper implores this need and highlights the requirements for a low‐cost ventilator or a respiratory support device.

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“…The rapid propagation of the SARS-CoV-2 virus and the possible need for hospitalization by patients who develop COVID-19 have overloaded healthcare systems in several countries around the world. The shortage of hospital beds, especially those in intensive care units (ICU), has been one of the main challenges to fight this disease, affecting medical and governmental decision making [1], [2].…”

Section: Introductionmentioning

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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Intensive care for COVID-19 in low- and middle-income countries: research opportunities and challenges

Cited by 30 publications

References 7 publications

COVID-19 research in critical care: the good, the bad, and the ugly

COVID-19 research in critical care: the good, the bad, and the ugly

A low‐cost field ventilator: An urgent global need

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

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