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SepRisk
- 1. SepRisk: Know your sepsis risk. Save your life. STEVE MAKKAR Consulting project for Patch’d Medical
- 2. Sepsis: exaggerated immune system reaction to infection 1 million affected annually 1/3 don’t survive! ! ! ! ! Annual healthcare costs of sepsis: $24 billion Sepsis and its impacts
- 3. Patchd, Inc. Developed the Patch’d Device Continuously measures and tracks vital signs to detect sepsis before it occurs Magnitude Time
- 4. Working with the data Data • 45K ICU patients • Sepsis status and time • Vital signs • Demographics • ICD9/10 codes • Medical history Pre-Processing • Vital sign feature engineering • Calculated Charlson Comorbidity Index • Removed outliers • Dimension reduction • Multiple imputation • Undersampling Feature Selection • Univariate Selection • Non-collinear features • Significant features • Interaction terms
- 5. Data exploration – Relationship between vital signs and sepsis • Both the LEVEL and VARIABILITY of vital signs were predictive of sepsis Blood Pressure Blood Pressure
- 7. Model evaluation • Baseline Vital Signs • Sex, Age, BMI Baseline Model 0.64 Concordanc e Time. Dep. AUC • Feature engineered vital signs • Medical conditions • Comorbidity • Imputation Cox model 0.75 8hr AUC: 0.84 12 hr AUC: 0.81 16 hr AUC: 0.80 5hr AUC: 0.88 • Undersampling • Hyperparameter tuning Gradient Boosted Cox Model 0.78 8hr AUC: 0.84 12 hr AUC: 0.81 16 hr AUC: 0.81 5hr AUC: 0.86 8hr AUC: 0.85 12 hr AUC: 0.84 16 hr AUC: 0.83 Best published model 4hr AUC: 0.87 Time. Dep. AUC
- 9. App Demo
Editor's Notes
- Sepsis occurs when our body has an exaggerated response to infection, thereby leading to severe organ damage and death 1 million people in the US are affected by sepsis every year and almost one-third don’t survive Sepsis accounts for 24 billion dollars in annual healthcare costs which is a 1.5 billion dollar increase since 2015; 3.5 billion dollars of that, is due to sepsis readmissions. Sepsis thus takes a massive toll on both the economy and human wellbeing
- Worn by patients at home Seeking a ML model they can integrate into their software – predicts both IF and WHEN sepsis will occur And identifies relative feature importance. At present – they have a black box algorithm that only predicts probability of sepsis
- Plenty missing data
- We can see there is a GREATER PROPORTION OF SEPTIC PATIENTS at higher quintiles of heart rate, and at lower quintiles of blood pressure Whereas the
- And event importance Which is directly in line with the client’s needs
- We have model that can predict sepsis risk with high accuracy, at multiple time points >> comparable to the best published model (using hospital based equipment) Patch’d believe this will be of tremendous value to their customers using their Patchd device at home By Pts and Caregivers knowing their sepsis risk relative to time – they can take prompt immediate action to prevent their condition escalating
- Worked in the areas of mental health, health policy, applied social research, and ageing Using data to heal, and make a difference in the world