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Group Capstone Project

This is the presentation for a group capstone project that I helped to create as a member of the team.

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Group Capstone Project

  1. 1. Joseph Guillén, Jiankun Liu, Margaret Furr, Tianyao Wang Client: Christopher Moore, MD Division of Infectious Diseases and International Health Funded by: Faculty Advisers: Northrop Grumman Corporation Laura Barnes and Abigail Flower
  2. 2. ! Background ! Goals ! Predictive Models ! Clinical Utility ! Future Research 2
  3. 3. !  A syndrome of organ dysfunction in the setting of infection !  Accepted clinical definitions are imprecise and miss 1 in 8 cases !  High lactate concentration reflects organ hypoperfusion and is a quantitative method of determining mortality risk in sepsis !  Our criteria for a severe sepsis event: •  High lactate (>4 mmol/L) •  Infection (blood culture acquisition) 3 Severe Sepsis in the United States •  2% of hospital patients •  10% of ICU patients •  750,000 cases per year •  Mortality Rate: 20-30% D Angus, 2013
  4. 4. ! Early intervention leads to better patient outcomes •  Antimicrobial therapy •  Fluid resuscitation 4 A Kumar, 2006
  5. 5. ! Strongly associated with improved outcomes ! Delayed clearance indicative of organ dysfunction or continual shock ! More aggressive resuscitation for patients with high risk of delayed clearance ! Definition of clearance: •  >10% reduction in lactate within 6 hours 5
  6. 6. ! Overcome limitations of existing Sepsis models by: •  Early Detection Models "  Build predictive models for the early detection of severe sepsis based on new definition utilizing clinical vitals + laboratory data •  Lactate Clearance Model "  For patients classified as having severe sepsis, build predictive models to identify patients who will be unable to have lactate cleared 6 Modeling Approaches •  Logistic Regression •  Support Vector Machines •  Logistic Model Trees •  Random Forest
  7. 7. ! ICUs •  Bedside physio-logic monitoring for real- time risk assessment 7 ! Medical (Floor) •  Less frequent monitoring •  Periodic vital check ! Consider models for patients across the care continuum (ICU vs. Non-ICU environments): •  Vital only •  Vital + Lab •  Lab only models
  8. 8. !  Source •  MIMIC-II Database: Beth Israel Deaconess Medical Center (Cambridge, MA) !  Patient Cohort !  Variables •  Vital Signs:Temperature, Heart Rate, Blood Pressure, Respiratory Rate •  Laboratory Values: "  Severe Sepsis Prediction: Anion Gap, Bicarbonate, Blood Urea Nitrogen, Calcium, Creatinine, Glucose, Hematocrit, Hemoglobin, Magnesium, Phosphate, Platelet Count,White Blood Cell Count "  Lactate Clearance: Base Excess, Oxygen Saturation, PCO2, pH Arterial, PO2, Potassium, Sodium, Protime, Partial Thromboplastin Time 8 24-hour 48-hour Clearance Control 2,925 2,644 658 Target 521 245 280 Total 3,446 2,889 938
  9. 9. !  Data capped at the 1st and 99th percentiles for each variable to control for extreme outliers !  Derived features •  minimum, maximum, median, standard deviation of all features !  Keep features recorded for at least 50% of patients !  Keep patients with at least 50% of features !  Imputation by k-nearest neighbors 9
  10. 10. ! Feature Selection •  Logistic Regression "  Forward step selection, vif, BIC backward reduction ! Evaluation •  10-fold cross-validation ! Metrics •  Sensitivity, specificity, PPV, NPV, AUC 10
  11. 11. 11 Variable Estimate Std. Error p-value x0 (Intercept) -5.357 0.918 5.45e-09 x1 Glucose - Median 0.006 0.001 3.94e-06 x2 CO2 - Median -0.059 0.016 1.46e-04 x3 Anion Gap - Minimum 0.154 0.024 1.61e-10 x4 Magnesium - Minimum -0.556 0.191 3.55e-03 x5 Hemoglobin - Minimum -0.548 0.064 < 2e-16 x6 White Blood Cell Count - Minimum 0.025 0.009 5.67e-03 x7 Creatinine - Maximum 0.194 0.055 3.70e-04 x8 Hematocrit - Maximum 0.239 0.023 < 2e-16 x9 Heart Rate - Minimum 0.039 0.004 < 2e-16 x10 Arterial BP - Minimum -0.037 0.005 3.14e-16 x11 Respiratory Rate - Minimum 0.075 0.013 2.34e-08
  12. 12. Predicted 1 0 Actual 1 323 198 0 191 2734 12
  13. 13. 13 Variable Mean Decrease in Gini Coefficient 1 Anion Gap - Maximum 68.5 2 Anion Gap – Median 44.0 3 Heart Rate – Minimum 42.7 4 White Blood Cell Count - Maximum 41.1 5 CO2 - Minimum 39.3 6 Heart Rate - Median 37.5 7 Arterial BP - Median 34.3
  14. 14. 14 Predicted 1 0 Actual 1 333 188 0 177 2748
  15. 15. 15
  16. 16. 16 Variable Estimate Std. Error p-value x0 Intercept -7.435 0.657 < 2e-16 x1 Anion Gap – Minimum 0.149 0.027 4.11e-08 x2 Platelet Count – Median -0.004 0.001 1.94e-06 x3 White Blood Cell Count - Minimum 0.038 0.013 2.37e-03 x4 Creatinine – Maximum 0.260 0.063 3.63e-05 x5 Heart Rate – Median 0.022 0.005 2.76e-06 x6 Respiratory Rate - Minimum 0.064 0.017 1.48e-04
  17. 17. Predicted 1 0 Actual 1 81 164 0 157 2487 17
  18. 18. 18 Variable Mean Decrease in Gini Coefficient 1 Platelet Count - Minimum 37.6 2 Heart Rate - Median 35.6 3 Blood Urea Nitrogen - Maximum 33.7 4 Platelet Count – Median 32.8 5 White Blood Cell Count - Minimum 32.4 6 Temperature - Maximum 32.0 7 White Blood Cell Count - Median 31.7
  19. 19. 19 Predicted 1 0 Actual 1 74 171 0 163 2481
  20. 20. 20
  21. 21. 21
  22. 22. !  Models can be applied in different clinical settings based on available data !  Understand the effect of clinical variables on risk scores for severe sepsis and lactate clearance !  Lead to earlier detection of and intervention for severe sepsis !  More aggressive resuscitation for patients with higher risk of delayed lactate clearance 22 Mortality Rate No severe sepsis Severe Sepsis 16.0% 44.1% Mortality Rate Clearance No Clearance 19.9% 42.5%
  23. 23. !  Analyze factors that may contribute to the predictive model !  Extend feature derivation and selection work !  Compare mortality between our working definition of severe sepsis and the traditional risk scores and SIRS criteria !  Investigate survival models (e.g. Cox) for severe sepsis !  Extend lactate clearance prediction to include mortality prediction !  Validate MIMIC-II data with UVA electronic health data 23
  24. 24. 24 References [1] D. C. Angus and T. van der Poll, "Severe sepsis and septic shock," New England Journal of Medicine,vol. 369, pp. 840-851, 2013. [2] A. Kumar, D. Roberts, K. E.Wood, B. Light, J. E. Parrillo, S. Sharma,et al., "Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock*," Critical care medicine,vol. 34, pp. 1589-1596, 2006.

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This is the presentation for a group capstone project that I helped to create as a member of the team.

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