On the use of decision trees for ICU outcome prediction in sepsis patients treated with statins

Ribas, Vicent; López, J. Caballero; Ruiz‐Rodríguez, Juan Carlos; Ruiz‐Sanmartin, Adolf; Rello, Jordi; Vellido, Alfredo

doi:10.1109/cidm.2011.5949439

Cited by 8 publications

(4 citation statements)

References 25 publications

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“…The leading causes of mortality are cardiovascular collapses (Benjamin et al, 2017 ), multi-organ failures (Marshall et al, 1995 ), and sepsis (Kissoon et al, 2016 ). Studies have shown that prolonged hospital stays lead to increased life-threatening outcomes, which are associated with adverse events and other risks in the ICU environment (Celi et al, 2011 ; Hunziker et al, 2012 ), such as severe infection (Fagon et al, 1994 ; Ribas et al, 2011 ), cardiac arrest (Nemati et al, 2011 ), extended invasive ventilation (Fagon et al, 1993 ; de Rooij et al, 2006 ), mortality within 1 year (Mandelbaum et al, 2011 ; Celi et al, 2012 ), ICU/hospital readmissions (Fialho et al, 2012 ), acute kidney injury (Mandelbaum et al, 2011 ; Celi et al, 2012 ), and hypotension (Lee and Mark, 2010 ; Hug et al, 2011 ; Lee et al, 2012 ). Studies also report that these complications will also lead to a significant increase in the costs incurred (Wheeler et al, 2011 ; Kwon et al, 2012 ; Hutchinson et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Domain Adaptation Using Convolutional Autoencoder and Gradient Boosting for Adverse Events Prediction in the Intensive Care Unit

Zhu

Venugopalan

Zhang

et al. 2022

Front. Artif. Intell.

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More than 5 million patients have admitted annually to intensive care units (ICUs) in the United States. The leading causes of mortality are cardiovascular failures, multi-organ failures, and sepsis. Data-driven techniques have been used in the analysis of patient data to predict adverse events, such as ICU mortality and ICU readmission. These models often make use of temporal or static features from a single ICU database to make predictions on subsequent adverse events. To explore the potential of domain adaptation, we propose a method of data analysis using gradient boosting and convolutional autoencoder (CAE) to predict significant adverse events in the ICU, such as ICU mortality and ICU readmission. We demonstrate our results from a retrospective data analysis using patient records from a publicly available database called Multi-parameter Intelligent Monitoring in Intensive Care-II (MIMIC-II) and a local database from Children's Healthcare of Atlanta (CHOA). We demonstrate that after adopting novel data imputation on patient ICU data, gradient boosting is effective in both the mortality prediction task and the ICU readmission prediction task. In addition, we use gradient boosting to identify top-ranking temporal and non-temporal features in both prediction tasks. We discuss the relationship between these features and the specific prediction task. Lastly, we indicate that CAE might not be effective in feature extraction on one dataset, but domain adaptation with CAE feature extraction across two datasets shows promising results.

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Section: Introductionmentioning

confidence: 99%

Domain Adaptation Using Convolutional Autoencoder and Gradient Boosting for Adverse Events Prediction in the Intensive Care Unit

Zhu

Venugopalan

Zhang

et al. 2022

Front. Artif. Intell.

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“…We then examined the relationship between the DNA methylation profiles and the Sequential Organ Failure Assessment (SOFA) score, which is used in ICUs to calculate organ damage. The score ranges from 0 to 24, with values greater than 6 being associated with a significant increase in the risk of mortality [55]. Using Spearman's correlation coefficient to assess specific hypermethylated or hypomethylated CpGs with SOFA, we identified 1375 CpG sites whose methylation levels positively correlated with SOFA (increased methylation) (rho < 0.4 and p < 0.01) and 1497 CpG sites with an inverse correlation with SOFA (decreased methylation) (rho < − 0.4 and p < 0.01) (Fig.…”

Section: Monocytes From Severe Covid-19 Patients Display Increased Dn...mentioning

confidence: 99%

Epigenetic and transcriptomic reprogramming in monocytes of severe COVID-19 patients reflects alterations in myeloid differentiation and the influence of inflammatory cytokines

et al. 2022

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Background COVID-19 manifests with a wide spectrum of clinical phenotypes, ranging from asymptomatic and mild to severe and critical. Severe and critical COVID-19 patients are characterized by marked changes in the myeloid compartment, especially monocytes. However, little is known about the epigenetic alterations that occur in these cells during hyperinflammatory responses in severe COVID-19 patients. Methods In this study, we obtained the DNA methylome and transcriptome of peripheral blood monocytes from severe COVID-19 patients. DNA samples extracted from CD14 + CD15- monocytes of 48 severe COVID-19 patients and 11 healthy controls were hybridized on MethylationEPIC BeadChip arrays. In parallel, single-cell transcriptomics of 10 severe COVID-19 patients were generated. CellPhoneDB was used to infer changes in the crosstalk between monocytes and other immune cell types. Results We observed DNA methylation changes in CpG sites associated with interferon-related genes and genes associated with antigen presentation, concordant with gene expression changes. These changes significantly overlapped with those occurring in bacterial sepsis, although specific DNA methylation alterations in genes specific to viral infection were also identified. We also found these alterations to comprise some of the DNA methylation changes occurring during myeloid differentiation and under the influence of inflammatory cytokines. A progression of DNA methylation alterations in relation to the Sequential Organ Failure Assessment (SOFA) score was found to be related to interferon-related genes and T-helper 1 cell cytokine production. CellPhoneDB analysis of the single-cell transcriptomes of other immune cell types suggested the existence of altered crosstalk between monocytes and other cell types like NK cells and regulatory T cells. Conclusion Our findings show the occurrence of an epigenetic and transcriptional reprogramming of peripheral blood monocytes, which could be associated with the release of aberrant immature monocytes, increased systemic levels of pro-inflammatory cytokines, and changes in immune cell crosstalk in these patients.

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Section: Monocytes From Severe Covid-19 Patients Display Increased Dn...mentioning

confidence: 99%

Epigenetic and transcriptomic reprogramming in monocytes of severe COVID-19 patients reflects alterations in myeloid differentiation and the influence of inflammatory cytokines

Godoy-Tena

Barmada

Morante-Palacios

et al. 2022

Preprint

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COVID-19 manifests with a wide spectrum of clinical phenotypes, ranging from asymptomatic and mild to severe and critical. Severe and critical COVID-19 patients are characterized by marked changes in the myeloid compartment, especially monocytes. However, little is known about the epigenetic alterations that occur in these cells during hyperinflammatory responses in severe COVID-19 patients. In this study, we obtained the DNA methylome and transcriptome of peripheral blood monocytes from severe COVID-19 patients. DNA samples extracted from CD14+CD15- monocytes of 48 severe COVID-19 patients and 11 healthy controls were hybridized on MethylationEPIC BeadChip arrays. In parallel, single-cell transcriptomics of 10 severe COVID-19 patients were generated. CellPhoneDB was used to infer changes in the crosstalk between monocytes and other immune cell types. We observed DNA methylation changes in CpG sites associated with interferon-related genes and genes associated with antigen presentation, concordant with gene expression changes. These changes significantly overlapped with those occurring in bacterial sepsis, although specific DNA methylation alterations in genes specific to viral infection were also identified. We also found these alterations to comprise some of the DNA methylation changes occurring during myeloid differentiation and under the influence of inflammatory cytokines. A progression of DNA methylation alterations in relation to the Sequential Organ Failure Assessment (SOFA) score was found to be related to interferon-related genes and T-helper 1 cell cytokine production. CellPhoneDB analysis of the single-cell transcriptomes of other immune cell types suggested the existence of altered crosstalk between monocytes and other cell types like NK cells and regulatory T cells. Our findings show the occurrence of an epigenetic and transcriptional reprogramming of peripheral blood monocytes, which could be associated with the release of aberrant immature monocytes, increased systemic levels of pro-inflammatory cytokines, and changes in immune cell crosstalk in these patients.

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On the use of decision trees for ICU outcome prediction in sepsis patients treated with statins

Cited by 8 publications

References 25 publications

Domain Adaptation Using Convolutional Autoencoder and Gradient Boosting for Adverse Events Prediction in the Intensive Care Unit

Domain Adaptation Using Convolutional Autoencoder and Gradient Boosting for Adverse Events Prediction in the Intensive Care Unit

Epigenetic and transcriptomic reprogramming in monocytes of severe COVID-19 patients reflects alterations in myeloid differentiation and the influence of inflammatory cytokines

Epigenetic and transcriptomic reprogramming in monocytes of severe COVID-19 patients reflects alterations in myeloid differentiation and the influence of inflammatory cytokines

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