Introduction Materials Results Conclusions Acknowledgements Severe Sepsis Mortality Prediction with Relevance Vector Machines Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain August 30, 2011 Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
Sepsis is a transversal pathology and one of the main causes of death at the Intensive Care Unit (ICU). It has in fact become the tenth most common cause of death in western societies. Its mortality rates can reach up to 45.7\% for septic shock, its most acute manifestation. For these reasons, the prediction of the mortality caused by sepsis is an open and relevant medical research challenge. This problem requires prediction methods that are robust and accurate, but also readily interpretable. This is paramount if they are to be used in the demanding context of real-time decision making at the ICU. In this brief paper, such a method is presented. It is based on a variant of the well-known support vector machine (SVM) model and provides an automated ranking of relevance of the mortality predictors. The reported results show that it outperforms in terms of accuracy alternative techniques currently in use, while simultaneously assessing the relative impact of individual pathology indicators.
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Sepsis is a clinical syndrome defined by the presence of bothinfection and Systemic Inflammatory Response Syndrome(SIRS).
This can lead to severe sepsis (organ dysfunction) or to septicshock (severe sepsis with hypotension refractory to fluidadministration) and multiorgan failure.
In western countries, septic patients account for as much as25% of ICU bed utilization and the pathology occurs in 1% -2% of all hospitalizations.
The mortality rates of sepsis range from 12.8% for sepsis and20.7% for severe sepsis, and up to 45.7% for septic shock.
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines
Sepsis is a clinical syndrome defined by the presence of bothinfection and Systemic Inflammatory Response Syndrome(SIRS).
This can lead to severe sepsis (organ dysfunction) or to septicshock (severe sepsis with hypotension refractory to fluidadministration) and multiorgan failure.
In western countries, septic patients account for as much as25% of ICU bed utilization and the pathology occurs in 1% -2% of all hospitalizations.
The mortality rates of sepsis range from 12.8% for sepsis and20.7% for severe sepsis, and up to 45.7% for septic shock.
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines
Sepsis is a clinical syndrome defined by the presence of bothinfection and Systemic Inflammatory Response Syndrome(SIRS).
This can lead to severe sepsis (organ dysfunction) or to septicshock (severe sepsis with hypotension refractory to fluidadministration) and multiorgan failure.
In western countries, septic patients account for as much as25% of ICU bed utilization and the pathology occurs in 1% -2% of all hospitalizations.
The mortality rates of sepsis range from 12.8% for sepsis and20.7% for severe sepsis, and up to 45.7% for septic shock.
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines
Sepsis is a clinical syndrome defined by the presence of bothinfection and Systemic Inflammatory Response Syndrome(SIRS).
This can lead to severe sepsis (organ dysfunction) or to septicshock (severe sepsis with hypotension refractory to fluidadministration) and multiorgan failure.
In western countries, septic patients account for as much as25% of ICU bed utilization and the pathology occurs in 1% -2% of all hospitalizations.
The mortality rates of sepsis range from 12.8% for sepsis and20.7% for severe sepsis, and up to 45.7% for septic shock.
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines
The medical management of sepsis and the study of itsprognosis and outcome is a relevant medical researchchallenge.
Provided that such methods are to be used in a clinicalenvironment (ICU), it requires prediction methods that arerobust, accurate and readily interpretable.
Here we present a method based on Relevance VectorMachines (RVM) and compare it with other well establishedshrinkage methods.
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines
The medical management of sepsis and the study of itsprognosis and outcome is a relevant medical researchchallenge.
Provided that such methods are to be used in a clinicalenvironment (ICU), it requires prediction methods that arerobust, accurate and readily interpretable.
Here we present a method based on Relevance VectorMachines (RVM) and compare it with other well establishedshrinkage methods.
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines
The medical management of sepsis and the study of itsprognosis and outcome is a relevant medical researchchallenge.
Provided that such methods are to be used in a clinicalenvironment (ICU), it requires prediction methods that arerobust, accurate and readily interpretable.
Here we present a method based on Relevance VectorMachines (RVM) and compare it with other well establishedshrinkage methods.
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines
A prospective observational cohort study of adult patients withsevere sepsis was conducted at the Critical Care Departmentof the Vall d’ Hebron University Hospital (Barcelona, Spain).
Data from 354 patients with severe sepsis was collected atthis ICU between June, 2007 and December, 2010.
The mean age of the patients in the database was 57.08 (withstandard deviation ±16.65) years.
40% of patients were female.
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines
A prospective observational cohort study of adult patients withsevere sepsis was conducted at the Critical Care Departmentof the Vall d’ Hebron University Hospital (Barcelona, Spain).
Data from 354 patients with severe sepsis was collected atthis ICU between June, 2007 and December, 2010.
The mean age of the patients in the database was 57.08 (withstandard deviation ±16.65) years.
40% of patients were female.
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines
A prospective observational cohort study of adult patients withsevere sepsis was conducted at the Critical Care Departmentof the Vall d’ Hebron University Hospital (Barcelona, Spain).
Data from 354 patients with severe sepsis was collected atthis ICU between June, 2007 and December, 2010.
The mean age of the patients in the database was 57.08 (withstandard deviation ±16.65) years.
40% of patients were female.
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines
A prospective observational cohort study of adult patients withsevere sepsis was conducted at the Critical Care Departmentof the Vall d’ Hebron University Hospital (Barcelona, Spain).
Data from 354 patients with severe sepsis was collected atthis ICU between June, 2007 and December, 2010.
The mean age of the patients in the database was 57.08 (withstandard deviation ±16.65) years.
40% of patients were female.
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines
The coefficients corresponding to the rest of attributes wereset to values close to zero as part of the training process. Thisreduces the number of attributes (34 to just 4) and improvesits interpretability.
The negative weights (number of dysfunctioning organs,mechanical ventilation, APACHE II) are related to a highermortality risk.
The SSC bundles (resuscitation bundles) are associated to aprotective effect (i.e. antibiotics administration, performanceof haemocultures, administration of volume and vasoactivedrugs and so on).
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines
The coefficients corresponding to the rest of attributes wereset to values close to zero as part of the training process. Thisreduces the number of attributes (34 to just 4) and improvesits interpretability.
The negative weights (number of dysfunctioning organs,mechanical ventilation, APACHE II) are related to a highermortality risk.
The SSC bundles (resuscitation bundles) are associated to aprotective effect (i.e. antibiotics administration, performanceof haemocultures, administration of volume and vasoactivedrugs and so on).
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines
The coefficients corresponding to the rest of attributes wereset to values close to zero as part of the training process. Thisreduces the number of attributes (34 to just 4) and improvesits interpretability.
The negative weights (number of dysfunctioning organs,mechanical ventilation, APACHE II) are related to a highermortality risk.
The SSC bundles (resuscitation bundles) are associated to aprotective effect (i.e. antibiotics administration, performanceof haemocultures, administration of volume and vasoactivedrugs and so on).
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines
The three shrinkage methods evaluated in this section agreedin detecting as prognostic factors the Severity measured bythe APACHE II score and acidosis.
Organ dysfunction and mechanical ventilation or otherparameters related to it like PaO2/FiO2 also play a role in theprognosis of Sepsis.
The three shrinkage methods evaluated in this section agreedin detecting as prognostic factors the Severity measured bythe APACHE II score and acidosis.
Organ dysfunction and mechanical ventilation or otherparameters related to it like PaO2/FiO2 also play a role in theprognosis of Sepsis.
The three shrinkage methods evaluated in this section agreedin detecting as prognostic factors the Severity measured bythe APACHE II score and acidosis.
Organ dysfunction and mechanical ventilation or otherparameters related to it like PaO2/FiO2 also play a role in theprognosis of Sepsis.
The Risk-of-Death (ROD) formula based on the APACHE IIscore can be expressed as:
ln
(ROD
1 − ROD
)= −3.517 + 0.146 · A + ε
where A is the APACHE II score and ε is a correction factordepending on clinical traits at admission in the ICU.
The application of this ROD formula to the population understudy yields an error rate of 0.28 (higher than RVM), asensitivity of 0.55 (very low) and a specificity of 0.80. TheAUC was 0.70 (lower than RVM).
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines
The Risk-of-Death (ROD) formula based on the APACHE IIscore can be expressed as:
ln
(ROD
1 − ROD
)= −3.517 + 0.146 · A + ε
where A is the APACHE II score and ε is a correction factordepending on clinical traits at admission in the ICU.
The application of this ROD formula to the population understudy yields an error rate of 0.28 (higher than RVM), asensitivity of 0.55 (very low) and a specificity of 0.80. TheAUC was 0.70 (lower than RVM).
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines
In the assessment of ROD for critically ill patients, sensitivityis important due to the fact that more aggressive treatmentand therapeutic actions may result in better outcomes for highrisk patients.
We have put forward an RVM-based method for theprediction of ROD in septic patients, which has been shown toproduce accurate, specific results, while improving theinterpretability and actionability of the results.
This method has proven to be superior in terms of accuracy(error rate, specificity and AUC) than other well establishedshrinkage methods (Lasso and Ridge).
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines
In the assessment of ROD for critically ill patients, sensitivityis important due to the fact that more aggressive treatmentand therapeutic actions may result in better outcomes for highrisk patients.
We have put forward an RVM-based method for theprediction of ROD in septic patients, which has been shown toproduce accurate, specific results, while improving theinterpretability and actionability of the results.
This method has proven to be superior in terms of accuracy(error rate, specificity and AUC) than other well establishedshrinkage methods (Lasso and Ridge).
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines
In the assessment of ROD for critically ill patients, sensitivityis important due to the fact that more aggressive treatmentand therapeutic actions may result in better outcomes for highrisk patients.
We have put forward an RVM-based method for theprediction of ROD in septic patients, which has been shown toproduce accurate, specific results, while improving theinterpretability and actionability of the results.
This method has proven to be superior in terms of accuracy(error rate, specificity and AUC) than other well establishedshrinkage methods (Lasso and Ridge).
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines
From a medical viewpoint, this study shows that it is possibleto derive a reliable prognostic score from a parsimonious set ofphysiopathologic and therapeutic variables, which are availableat the onset of severe sepsis for medical experts at the ICU.
The method may be understood as a generalization of theROD formula which takes not only the contribution of theAPACHE II score into consideration, but also other importantlife-threatening clinical traits
The performance of the proposed method has been evaluatedin a single ICU and a limited population sample. Future workshould lead towards a multi-centric prospective study, in orderto validate its generalizability.
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines
From a medical viewpoint, this study shows that it is possibleto derive a reliable prognostic score from a parsimonious set ofphysiopathologic and therapeutic variables, which are availableat the onset of severe sepsis for medical experts at the ICU.
The method may be understood as a generalization of theROD formula which takes not only the contribution of theAPACHE II score into consideration, but also other importantlife-threatening clinical traits
The performance of the proposed method has been evaluatedin a single ICU and a limited population sample. Future workshould lead towards a multi-centric prospective study, in orderto validate its generalizability.
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines
From a medical viewpoint, this study shows that it is possibleto derive a reliable prognostic score from a parsimonious set ofphysiopathologic and therapeutic variables, which are availableat the onset of severe sepsis for medical experts at the ICU.
The method may be understood as a generalization of theROD formula which takes not only the contribution of theAPACHE II score into consideration, but also other importantlife-threatening clinical traits
The performance of the proposed method has been evaluatedin a single ICU and a limited population sample. Future workshould lead towards a multi-centric prospective study, in orderto validate its generalizability.
Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain
Severe Sepsis Mortality Prediction with Relevance Vector Machines