scholarly journals Semi-supervised deep learning from time series clinical data for acute respiratory distress syndrome prediction: model development and validation study (Preprint)

10.2196/28028 ◽  
2021 ◽  
Author(s):  
Carson Lam ◽  
Chak Foon Tso ◽  
Abigail Green-Saxena ◽  
Emily Pellegrini ◽  
Zohora Iqbal ◽  
...  
Keyword(s):  
Time Series ◽  
Acute Respiratory Distress Syndrome ◽  
Deep Learning ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Prediction Model ◽  
Validation Study ◽  
Distress Syndrome ◽  
Model Development ◽  
Development And Validation

scholarly journals A Prediction Model of Extubation Failure Risk in Preterm Infants

2021 ◽  
Vol 9 ◽  
Author(s):  
Zimei Cheng ◽  
Ziwei Dong ◽  
Qian Zhao ◽  
Jingling Zhang ◽  
Su Han ◽  
...  
Keyword(s):  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Prediction Model ◽  
Preterm Infants ◽  
Gestational Age ◽  
Distress Syndrome ◽  
Extubation Failure ◽  
Concordance Index ◽  
Training Cohort

Objectives: This study aimed to identify variables and develop a prediction model that could estimate extubation failure (EF) in preterm infants.Study Design: We enrolled 128 neonates as a training cohort and 58 neonates as a validation cohort. They were born between 2015 and 2020, had a gestational age between 250/7 and 296/7 weeks, and had been treated with mechanical ventilation through endotracheal intubation (MVEI) because of acute respiratory distress syndrome. In the training cohort, we performed univariate logistic regression analysis along with stepwise discriminant analysis to identify EF predictors. A monogram based on five predictors was built. The concordance index and calibration plot were used to assess the efficiency of the nomogram in the training and validation cohorts.Results: The results of this study identified a 5-min Apgar score, early-onset sepsis, hemoglobin before extubation, pH before extubation, and caffeine administration as independent risk factors that could be combined for accurate prediction of EF. The EF nomogram was created using these five predictors. The area under the receiver operator characteristic curve was 0.824 (95% confidence interval 0.748–0.900). The concordance index in the training and validation cohorts was 0.824 and 0.797, respectively. The calibration plots showed high coherence between the predicted probability of EF and actual observation.Conclusions: This EF nomogram was a useful model for the precise prediction of EF risk in preterm infants who were between 250/7 and 296/7 weeks' gestational age and treated with MVEI because of acute respiratory distress syndrome.

scholarly journals External Validation of an Acute Respiratory Distress Syndrome Prediction Model Using Radiology Reports

2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Anoop Mayampurath ◽  
Matthew M. Churpek ◽  
Xin Su ◽  
Sameep Shah ◽  
Elizabeth Munroe ◽  
...  
Keyword(s):  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Prediction Model ◽  
Distress Syndrome ◽  
External Validation ◽  
Radiology Reports

scholarly journals Risk Factors Related to Acute Respiratory Distress Syndrome and Death in Patients with COVID-19: A Retrospective Cohort Study in Wuhan, China

2020 ◽  
Author(s):  
Jing Wang ◽  
Lu Wang ◽  
Meng Jin ◽  
Zequn Lu ◽  
Jun Xiao ◽  
...  
Keyword(s):  
Risk Factors ◽  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Prediction Model ◽  
Distress Syndrome ◽  
Immune Cell ◽  
Risk Model ◽  
Early Stage ◽  
Increased Risk

Abstract Background: The COVID-19 pandemic has been considered as the great threat to global public health. We aimed to clarify the risk factors associated with the development of acute respiratory distress syndrome (ARDS) and progression from ARDS to death and construct a risk prediction model.Methods: In this single-centered, retrospective, and observational study, 796 COVID-19 patients developed ARDS and 735 COVID-19 patients without ARDS were matched by propensity score at an approximate ratio of 1:1 based on age, sex and comorbidities. Demographic data, symptoms, radiological findings, laboratory examinations, and clinical outcomes were compared between with or without ARDS. Univariable and multivariable logistic regression models were applied to explore the risk factors for development of ARDS and progression from ARDS to death and establish a comprehensive risk model. Results: Higher SOFA, qSOFA, APACHE II and SIRS scores, elevated inflammatory cytokines, dysregulated multi-organ damage biomarkers, decreased immune cell subsets were associated with higher proportion of death (34.17% vs 1.22%; P<0.001) and increased risk odds of death (OR=57.216, 95%CI=28.373-115.378; P<0.001) in COVID-19 patients with ARDS. In addition to previous reported risk factors related to ARDS development and death, such as neutrophils, IL-6, D-Dimer, leukocytes and platelet, we identified elevated TNF-α (OR=1.146, 95%CI=1.100-1.194; P<0.001), CK-MB (OR=1.350, 95%CI=1.180-1.545; P<0.001), declined ALB (OR=0.834, 95%CI=0.799-0.872; P<0.001), CD8+ T cells (OR=0.983, 95%CI=0.976-0.990; P<0.001) and CD3-CD19+ B cells (OR=0.992, 95%CI=0.988-0.997; P=0.003) as novel risk factors. Most importantly, the predictive accuracy of the combined model integrating four score systems and these risk factors demonstrated highest among all models for the development of ARDS (AUC= 0.904) and the progression from ARDS to death (AUC= 0.959).Conclusion: COVID-19 patients with ARDS were more likely to develop into death. The potential risk factors and the comprehensive prediction model could be helpful to identify patients developed ARDS with poor prognosis at an early stage, which might help physicians to formulate a timely therapeutic strategy.

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