Rvm sepsis mortality_index_beamer

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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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Rvm sepsis mortality_index_beamer

  1. 1. 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
  2. 2. Introduction Materials Results Conclusions Acknowledgements Contents 1 Introduction 2 Materials 3 Results 4 Conclusions Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  3. 3. Introduction Materials Results Conclusions Acknowledgements Contents 1 Introduction 2 Materials 3 Results 4 Conclusions Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  4. 4. Introduction Materials Results Conclusions Acknowledgements Introduction Sepsis is a clinical syndrome defined by the presence of both infection and Systemic Inflammatory Response Syndrome (SIRS). Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  5. 5. Introduction Materials Results Conclusions Acknowledgements Introduction Sepsis is a clinical syndrome defined by the presence of both infection and Systemic Inflammatory Response Syndrome (SIRS). This can lead to severe sepsis (organ dysfunction) or to septic shock (severe sepsis with hypotension refractory to fluid administration) and multiorgan failure. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  6. 6. Introduction Materials Results Conclusions Acknowledgements Introduction Sepsis is a clinical syndrome defined by the presence of both infection and Systemic Inflammatory Response Syndrome (SIRS). This can lead to severe sepsis (organ dysfunction) or to septic shock (severe sepsis with hypotension refractory to fluid administration) and multiorgan failure. In western countries, septic patients account for as much as 25% of ICU bed utilization and the pathology occurs in 1% 2% of all hospitalizations. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  7. 7. Introduction Materials Results Conclusions Acknowledgements Introduction Sepsis is a clinical syndrome defined by the presence of both infection and Systemic Inflammatory Response Syndrome (SIRS). This can lead to severe sepsis (organ dysfunction) or to septic shock (severe sepsis with hypotension refractory to fluid administration) and multiorgan failure. In western countries, septic patients account for as much as 25% 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 and 20.7% 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
  8. 8. Introduction Materials Results Conclusions Acknowledgements The medical management of sepsis and the study of its prognosis and outcome is a relevant medical research challenge. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  9. 9. Introduction Materials Results Conclusions Acknowledgements The medical management of sepsis and the study of its prognosis and outcome is a relevant medical research challenge. Provided that such methods are to be used in a clinical environment (ICU), it requires prediction methods that are robust, accurate and readily interpretable. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  10. 10. Introduction Materials Results Conclusions Acknowledgements The medical management of sepsis and the study of its prognosis and outcome is a relevant medical research challenge. Provided that such methods are to be used in a clinical environment (ICU), it requires prediction methods that are robust, accurate and readily interpretable. Here we present a method based on Relevance Vector Machines (RVM) and compare it with other well established shrinkage methods. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  11. 11. Introduction Materials Results Conclusions Acknowledgements Contents 1 Introduction 2 Materials 3 Results 4 Conclusions Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  12. 12. Introduction Materials Results Conclusions Acknowledgements Materials A prospective observational cohort study of adult patients with severe sepsis was conducted at the Critical Care Department of the Vall d’ Hebron University Hospital (Barcelona, Spain). Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  13. 13. Introduction Materials Results Conclusions Acknowledgements Materials A prospective observational cohort study of adult patients with severe sepsis was conducted at the Critical Care Department of the Vall d’ Hebron University Hospital (Barcelona, Spain). Data from 354 patients with severe sepsis was collected at this ICU between June, 2007 and December, 2010. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  14. 14. Introduction Materials Results Conclusions Acknowledgements Materials A prospective observational cohort study of adult patients with severe sepsis was conducted at the Critical Care Department of the Vall d’ Hebron University Hospital (Barcelona, Spain). Data from 354 patients with severe sepsis was collected at this ICU between June, 2007 and December, 2010. The mean age of the patients in the database was 57.08 (with standard deviation ±16.65) years. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  15. 15. Introduction Materials Results Conclusions Acknowledgements Materials A prospective observational cohort study of adult patients with severe sepsis was conducted at the Critical Care Department of the Vall d’ Hebron University Hospital (Barcelona, Spain). Data from 354 patients with severe sepsis was collected at this ICU between June, 2007 and December, 2010. The mean age of the patients in the database was 57.08 (with standard 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
  16. 16. Introduction Materials Results Conclusions Acknowledgements The collected data show the worst values for all variables during the first 24 hours of evolution of Severe Sepsis. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  17. 17. Introduction Materials Results Conclusions Acknowledgements The collected data show the worst values for all variables during the first 24 hours of evolution of Severe Sepsis. Organ dysfunction was evaluated by means of the SOFA score system, which objectively measures organ dysfunction for 6 organs/systems. Cardiovascular (CV) Respiratory (RESP) Central Nerv. Sys. (CNS) Hepatic (HEPA) Renal (REN) Vicent J. Ribas 2.86 2.31 0.48 0.48 1.06 (1.62) (1.15) (1.00) (0.92) (1.20) Haematologic (HAEMATO) Global SOFA score Dysf. Organs (SOFA 1-2) Failure Organs (SOFA 3-4) Total Dysf. Organs 0.78 7.94 1.68 1.51 3.18 (1.14) (3.86) (1.09) (1.02) (1.32) SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  18. 18. Introduction Materials Results Conclusions Acknowledgements The collected data show the worst values for all variables during the first 24 hours of evolution of Severe Sepsis. Organ dysfunction was evaluated by means of the SOFA score system, which objectively measures organ dysfunction for 6 organs/systems. Cardiovascular (CV) Respiratory (RESP) Central Nerv. Sys. (CNS) Hepatic (HEPA) Renal (REN) 2.86 2.31 0.48 0.48 1.06 (1.62) (1.15) (1.00) (0.92) (1.20) Haematologic (HAEMATO) Global SOFA score Dysf. Organs (SOFA 1-2) Failure Organs (SOFA 3-4) Total Dysf. Organs 0.78 7.94 1.68 1.51 3.18 (1.14) (3.86) (1.09) (1.02) (1.32) Severity was evaluated by means of the APACHE II score, which was 23.03 ± 9.62 for the population under study. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  19. 19. Introduction Materials Results Conclusions Acknowledgements The collected data show the worst values for all variables during the first 24 hours of evolution of Severe Sepsis. Organ dysfunction was evaluated by means of the SOFA score system, which objectively measures organ dysfunction for 6 organs/systems. Cardiovascular (CV) Respiratory (RESP) Central Nerv. Sys. (CNS) Hepatic (HEPA) Renal (REN) 2.86 2.31 0.48 0.48 1.06 (1.62) (1.15) (1.00) (0.92) (1.20) Haematologic (HAEMATO) Global SOFA score Dysf. Organs (SOFA 1-2) Failure Organs (SOFA 3-4) Total Dysf. Organs 0.78 7.94 1.68 1.51 3.18 (1.14) (3.86) (1.09) (1.02) (1.32) Severity was evaluated by means of the APACHE II score, which was 23.03 ± 9.62 for the population under study. The mortality rate intra-ICU for our study population was 29.44%. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  20. 20. Introduction Materials Results Conclusions Acknowledgements List of attributes used in this study: Age Gender Sepsis Focus Germ Class Polimicrobial Infection Base Pathology Dysfunctioning Organs for SOFA 1-2 Haemocultures 6h Dysfunctioning Organs for SOFA 3-4 Volume 6h Total Number of Dysfunctioning Organs Haematocrit 6h Antibiotics 6h O2 Central Venous Saturation 6h Mechanical Ventilation Transfusions 6h Dobutamine 6h Respiratory SOFA Score Oxygenation Index PaO2 /FiO2 CNS SOFA score Vasoactive Drugs Surviving Sepsis Campaign Bundles 24h Hepatic SOFA Score Platelet Count Glycaemia 24h Renal SOFA Score APACHE II Score PPlateau Haematologic SOFA Score Surviving Sepsis Campaign Bundles 6h Worst Lactate Cardiovascular SOFA Score Total SOFA Score Vicent J. Ribas O2 Central Venous Saturation SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  21. 21. Introduction Materials Results Conclusions Acknowledgements Contents 1 Introduction 2 Materials 3 Results 4 Conclusions Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  22. 22. Introduction Materials Results Conclusions Acknowledgements Mortality Prediction with RVM The model performance was evaluated by means of 10-Fold Cross-Validation. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  23. 23. Introduction Materials Results Conclusions Acknowledgements Mortality Prediction with RVM The model performance was evaluated by means of 10-Fold Cross-Validation. The RVM yielded an accuracy of mortality prediction of 0.80; a prediction error of 0.24; a sensitivity of 0.66; and a specificity of 0.80. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  24. 24. Introduction Materials Results Conclusions Acknowledgements Mortality Prediction with RVM The model performance was evaluated by means of 10-Fold Cross-Validation. The RVM yielded an accuracy of mortality prediction of 0.80; a prediction error of 0.24; a sensitivity of 0.66; and a specificity of 0.80. RVM selected the following attributes (corresponding to weights): Number of dysfunctioning organs (w1 = −0.039) Mechanical Ventilation (w2 = −0.101) APACHE II (w3 = −0.337) Resuscitation Bundles (6h) (w4 = 0.037) Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  25. 25. Introduction Materials Results Conclusions Acknowledgements The coefficients corresponding to the rest of attributes were set to values close to zero as part of the training process. This reduces the number of attributes (34 to just 4) and improves its interpretability. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  26. 26. Introduction Materials Results Conclusions Acknowledgements The coefficients corresponding to the rest of attributes were set to values close to zero as part of the training process. This reduces the number of attributes (34 to just 4) and improves its interpretability. The negative weights (number of dysfunctioning organs, mechanical ventilation, APACHE II) are related to a higher mortality risk. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  27. 27. Introduction Materials Results Conclusions Acknowledgements The coefficients corresponding to the rest of attributes were set to values close to zero as part of the training process. This reduces the number of attributes (34 to just 4) and improves its interpretability. The negative weights (number of dysfunctioning organs, mechanical ventilation, APACHE II) are related to a higher mortality risk. The SSC bundles (resuscitation bundles) are associated to a protective effect (i.e. antibiotics administration, performance of haemocultures, administration of volume and vasoactive drugs and so on). Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  28. 28. Introduction Materials Results Conclusions Acknowledgements Comparison with Shrinkage Feature Selection Methods for Logistic Regression The predictive ability of the RVM has also been compared to that of other well established shrinkage methods for regression. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  29. 29. Introduction Materials Results Conclusions Acknowledgements Comparison with Shrinkage Feature Selection Methods for Logistic Regression The predictive ability of the RVM has also been compared to that of other well established shrinkage methods for regression. The selected attributes and coefficients for each method were: Lasso: Age (w1 = 0.007) Ridge Regression: Number of dysfunctioning organs for SOFA 3-4 (w1 = −0.021) APACHE II (w2 = −0.127) Worst Lactate (w3 = −0.126). Germ Class (w2 = 0.005) PaO2 /FiO2 (w3 = 0.001) APACHE II (w4 = −0.006) SvcO2 6h (w5 = −0.001) Haematocrit 6h (w6 = 0.009) Worst Lactate (w7 = −0.023) Logistic Regression with backward feature selection: Intercept (w1 = 4.16) Number of Dysfunctioning Organs (w1 = −0.57) APACHE II (w2 = −0.09) Worst Lactate (w3 = −0.30) SvcO2 (w8 = −0.006). Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  30. 30. Introduction Materials Results Conclusions Acknowledgements The three shrinkage methods evaluated in this section agreed in detecting as prognostic factors the Severity measured by the APACHE II score and acidosis. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  31. 31. Introduction Materials Results Conclusions Acknowledgements The three shrinkage methods evaluated in this section agreed in detecting as prognostic factors the Severity measured by the APACHE II score and acidosis. Organ dysfunction and mechanical ventilation or other parameters related to it like PaO2 /FiO2 also play a role in the prognosis of Sepsis. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  32. 32. Introduction Materials Results Conclusions Acknowledgements The three shrinkage methods evaluated in this section agreed in detecting as prognostic factors the Severity measured by the APACHE II score and acidosis. Organ dysfunction and mechanical ventilation or other parameters related to it like PaO2 /FiO2 also play a role in the prognosis of Sepsis. The accuracy of each method was the following: Method RVM Logistic Ridge Lasso Vicent J. Ribas AUC 0.80 0.77 0.69 0.70 Error Rate 0.24 0.27 0.28 0.32 Sens. 0.66 0.66 0.67 0.67 Spec. 0.80 0.76 0.73 0.68 SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  33. 33. Introduction Materials Results Conclusions Acknowledgements Comparison with the APACHE II Mortality Score The Risk-of-Death (ROD) formula based on the APACHE II score can be expressed as: ln ROD 1 − ROD = −3.517 + 0.146 · A + where A is the APACHE II score and is a correction factor depending on clinical traits at admission in the ICU. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  34. 34. Introduction Materials Results Conclusions Acknowledgements Comparison with the APACHE II Mortality Score The Risk-of-Death (ROD) formula based on the APACHE II score can be expressed as: ln ROD 1 − ROD = −3.517 + 0.146 · A + where A is the APACHE II score and is a correction factor depending on clinical traits at admission in the ICU. The application of this ROD formula to the population under study yields an error rate of 0.28 (higher than RVM), a sensitivity of 0.55 (very low) and a specificity of 0.80. The AUC 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
  35. 35. Introduction Materials Results Conclusions Acknowledgements Contents 1 Introduction 2 Materials 3 Results 4 Conclusions Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  36. 36. Introduction Materials Results Conclusions Acknowledgements Conclusions In the assessment of ROD for critically ill patients, sensitivity is important due to the fact that more aggressive treatment and therapeutic actions may result in better outcomes for high risk patients. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  37. 37. Introduction Materials Results Conclusions Acknowledgements Conclusions In the assessment of ROD for critically ill patients, sensitivity is important due to the fact that more aggressive treatment and therapeutic actions may result in better outcomes for high risk patients. We have put forward an RVM-based method for the prediction of ROD in septic patients, which has been shown to produce accurate, specific results, while improving the interpretability and actionability of the results. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  38. 38. Introduction Materials Results Conclusions Acknowledgements Conclusions In the assessment of ROD for critically ill patients, sensitivity is important due to the fact that more aggressive treatment and therapeutic actions may result in better outcomes for high risk patients. We have put forward an RVM-based method for the prediction of ROD in septic patients, which has been shown to produce accurate, specific results, while improving the interpretability and actionability of the results. This method has proven to be superior in terms of accuracy (error rate, specificity and AUC) than other well established shrinkage 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
  39. 39. Introduction Materials Results Conclusions Acknowledgements From a medical viewpoint, this study shows that it is possible to derive a reliable prognostic score from a parsimonious set of physiopathologic and therapeutic variables, which are available at the onset of severe sepsis for medical experts at the ICU. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  40. 40. Introduction Materials Results Conclusions Acknowledgements From a medical viewpoint, this study shows that it is possible to derive a reliable prognostic score from a parsimonious set of physiopathologic and therapeutic variables, which are available at the onset of severe sepsis for medical experts at the ICU. The method may be understood as a generalization of the ROD formula which takes not only the contribution of the APACHE II score into consideration, but also other important life-threatening clinical traits Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  41. 41. Introduction Materials Results Conclusions Acknowledgements From a medical viewpoint, this study shows that it is possible to derive a reliable prognostic score from a parsimonious set of physiopathologic and therapeutic variables, which are available at the onset of severe sepsis for medical experts at the ICU. The method may be understood as a generalization of the ROD formula which takes not only the contribution of the APACHE II score into consideration, but also other important life-threatening clinical traits The performance of the proposed method has been evaluated in a single ICU and a limited population sample. Future work should lead towards a multi-centric prospective study, in order to validate its generalizability. Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines
  42. 42. Introduction Materials Results Conclusions Acknowledgements THANK YOU Vicent J. Ribas SOCO Research Group Techical University of Catalonia (UPC) Barcelona, Spain Severe Sepsis Mortality Prediction with Relevance Vector Machines

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