scholarly journals Incidence and outcomes of acute respiratory distress syndrome in intensive care units of mainland China: a multicentre prospective longitudinal study

Critical Care ◽  
2020 ◽  
Vol 24 (1) ◽  
Author(s):  
Xu Huang ◽  
◽  
Ruoyang Zhang ◽  
Guohui Fan ◽  
Dawei Wu ◽  
...  
Keyword(s):  
Acute Respiratory Distress Syndrome ◽  
Intensive Care ◽  
Longitudinal Study ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Intensive Care Units ◽  
Distress Syndrome ◽  
Mainland China ◽  
Prospective Longitudinal Study ◽  
Prospective Longitudinal

scholarly journals Probabilistic analysis of COVID-19 patients’ individual length of stay in Swiss intensive care units

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0247265
Author(s):  
Alexander Henzi ◽  
Gian-Reto Kleger ◽  
Matthias P. Hilty ◽  
Pedro D. Wendel Garcia ◽  
Johanna F. Ziegel ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Acute Respiratory Distress Syndrome ◽  
Intensive Care ◽  
Length Of Stay ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Intensive Care Units ◽  
Distress Syndrome ◽  
Unit Length ◽  
Training Data

Rationale The COVID-19 pandemic induces considerable strain on intensive care unit resources. Objectives We aim to provide early predictions of individual patients’ intensive care unit length of stay, which might improve resource allocation and patient care during the on-going pandemic. Methods We developed a new semiparametric distributional index model depending on covariates which are available within 24h after intensive care unit admission. The model was trained on a large cohort of acute respiratory distress syndrome patients out of the Minimal Dataset of the Swiss Society of Intensive Care Medicine. Then, we predict individual length of stay of patients in the RISC-19-ICU registry. Measurements The RISC-19-ICU Investigators for Switzerland collected data of 557 critically ill patients with COVID-19. Main results The model gives probabilistically and marginally calibrated predictions which are more informative than the empirical length of stay distribution of the training data. However, marginal calibration was worse after approximately 20 days in the whole cohort and in different subgroups. Long staying COVID-19 patients have shorter length of stay than regular acute respiratory distress syndrome patients. We found differences in LoS with respect to age categories and gender but not in regions of Switzerland with different stress of intensive care unit resources. Conclusion A new probabilistic model permits calibrated and informative probabilistic prediction of LoS of individual patients with COVID-19. Long staying patients could be discovered early. The model may be the basis to simulate stochastic models for bed occupation in intensive care units under different casemix scenarios.

scholarly journals Probabilistic analysis of COVID-19 patients' individual length of stay in Swiss intensive care units

2020 ◽  
Author(s):  
Alexander Henzi ◽  
Gian-Reto Kleger ◽  
Matthias P. Hilty ◽  
Pedro D. Wendel Garcia ◽  
Johanna F. Ziegel
Keyword(s):  
Intensive Care Unit ◽  
Acute Respiratory Distress Syndrome ◽  
Intensive Care ◽  
Length Of Stay ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Intensive Care Units ◽  
Distress Syndrome ◽  
Unit Length ◽  
Training Data

Rationale: The COVID-19 pandemic induces considerable strain on intensive care unit resources. Objectives: We aim to provide early predictions of individual patients' intensive care unit length of stay, which might improve resource allocation and patient care during the on-going pandemic. Methods: We developed a new semiparametric distributional index model depending on covariates which are available within 24h after intensive care unit admission. The model was trained on a large cohort of acute respiratory distress syndrome patients out of the Minimal Dataset of the Swiss Society of Intensive Care Medicine. Then, we predict individual length of stay of patients in the RISC-19-ICU registry. Measurements: The RISC-19-ICU Investigators for Switzerland collected data of 557 critically ill patients with COVID-19. Main Results: The model gives probabilistically and marginally calibrated predictions which are more informative than the empirical length of stay distribution of the training data. However, marginal calibration was worse after approximately 20 days in the whole cohort and in different subgroups. Long staying COVID-19 patients have shorter length of stay than regular acute respiratory distress syndrome patients. We found differences in LoS with respect to age categories and gender but not in regions of Switzerland with different stress of intensive care unit resources. Conclusion: A new probabilistic model permits calibrated and informative probabilistic prediction of LoS of individual patients with COVID-19. Long staying patients could be discovered early. The model may be the basis to simulate stochastic models for bed occupation in intensive care units under different casemix scenarios.

scholarly journals Return to work and lost earnings after acute respiratory distress syndrome: a 5-year prospective, longitudinal study of long-term survivors

Thorax ◽  
2017 ◽  
Vol 73 (2) ◽  
pp. 125-133 ◽  
Author(s):  
Biren B Kamdar ◽  
Kristin A Sepulveda ◽  
Alexandra Chong ◽  
Robert K Lord ◽  
Victor D Dinglas ◽  
...  
Keyword(s):  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Return To Work ◽  
Respiratory Distress ◽  
Distress Syndrome ◽  
Prospective Longitudinal Study ◽  
Mechanical Ventilation Duration ◽  
Healthcare Coverage ◽  
Prospective Longitudinal

BackgroundDelayed return to work is common after acute respiratory distress syndrome (ARDS), but has undergone little detailed evaluation. We examined factors associated with the timing of return to work after ARDS, along with lost earnings and shifts in healthcare coverage.MethodsFive-year, multisite prospective, longitudinal cohort study of 138 2-year ARDS survivors hospitalised between 2004 and 2007. Employment and healthcare coverage were collected via structured interview. Predictors of time to return to work were evaluated using Fine and Grey regression analysis. Lost earnings were estimated using Bureau of Labor Statistics data.ResultsSixty-seven (49%) of the 138 2-year survivors were employed prior to ARDS. Among 64 5-year survivors, 20 (31%) never returned to work across 5-year follow-up. Predictors of delayed return to work (HR (95% CI)) included baseline Charlson Comorbidity Index (0.77 (0.59 to 0.99) per point; p=0.04), mechanical ventilation duration (0.67 (0.55 to 0.82) per day up to 5 days; p<0.001) and discharge to a healthcare facility (0.49 (0.26 to 0.93); p=0.03). Forty-nine of 64 (77%) 5-year survivors incurred lost earnings, with average (SD) losses ranging from US$38 354 (21,533) to US$43 510 (25,753) per person per year. Jobless, non-retired survivors experienced a 33% decrease in private health insurance and concomitant 37% rise in government-funded coverage.ConclusionsAcross 5-year follow-up, nearly one-third of previously employed ARDS survivors never returned to work. Delayed return to work was associated with patient-related and intensive care unit/hospital-related factors, substantial lost earnings and a marked rise in government-funded healthcare coverage. These important consequences emphasise the need to design and evaluate vocation-based interventions to assist ARDS survivors return to work.

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