group capstone project
TRANSCRIPT
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
! Background ! Goals ! Predictive Models ! Clinical Utility ! Future Research
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! 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)
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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
! Early intervention leads to better patient outcomes • Antimicrobial
therapy • Fluid resuscitation
4 A Kumar, 2006
! 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
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! 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
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Modeling Approaches
• Logistic Regression • Support Vector Machines • Logistic Model Trees • Random Forest
! ICUs • Bedside physio-logic
monitoring for real-time risk assessment
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! 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
! 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
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24-hour 48-hour Clearance
Control 2,925 2,644 658
Target 521 245 280
Total 3,446 2,889 938
! 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
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! Feature Selection • Logistic Regression " Forward step selection, vif, BIC backward reduction
! Evaluation • 10-fold cross-validation
! Metrics • Sensitivity, specificity, PPV, NPV, AUC
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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
Predicted
1 0
Actual 1 323 198
0 191 2734
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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
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Predicted
1 0
Actual 1 333 188
0 177 2748
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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
Predicted
1 0
Actual 1 81 164
0 157 2487
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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
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Predicted
1 0
Actual 1 74 171
0 163 2481
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! 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
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Mortality Rate
No severe sepsis Severe Sepsis
16.0% 44.1%
Mortality Rate
Clearance No Clearance
19.9% 42.5%
! 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
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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.