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Medical University of South Carolina: Using Big Data and Predictive Analytics to Drive Clinical Quality Improvement Initiatives

Medical University of South Carolina: Using Big Data and Predictive Analytics to Drive Clinical Quality Improvement Initiatives

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Medical University of South Carolina: Using Big Data and Predictive Analytics to Drive Clinical Quality Improvement Initiatives

  1. 1. Using Big Data and Predictive Analytics to Drive Clinical Quality Improvement Initiatives: A Case of Kidney Transplantation. Titte R Srinivas, David J. Taber, Zemin Su, Jingwen Zhang, Girish Mour, David Northrup, Arun Tripathi, Brendan J Fowkes, Justin E. Marsden, William P. Moran, Patrick Mauldin Medical University of South Carolina, Charleston IBM Corporation, Armonk NY Session ID: 1854
  2. 2. “A good hockey player plays where the puck is. “…A GREAT hockey player plays where the puck is going to be.”
  3. 3. The challenge of quality…. n Safe n Patient centered n Timely n Effective n Efficient n Equitable
  4. 4. Joint Principles of the Patient-Centered Medical Home AAFP,ACP, AOA, AAP March, 2007 n Personal physician n Practice in teams n Whole person orientation n Care is coordinated and integrated n Quality and safety are hallmarks n Enhanced access
  5. 5. Background • Predictive models in kidney transplantation derived from national data (UNOS, SRTR) lack longitudinal patient level data, thereby limiting accuracy. • Adding patient level data capturing dynamic post-transplant clinical evolution to predictive models, may improve predictive accuracy for graft loss (GL) risk. • Complete capture of patient level clinical data in real time would require an approach that extracts, collates and curates both structured and unstructured data from electronic health records (EHR) • These large amounts of data are notable for volume, velocity, variety and, verified veracity; An operational definition of Big Data • As such analytic techniques would also need to handle such data
  6. 6. Predictive Diagnostics in Different Medical Contexts 1 - Specificity Sensitivity 0 0.5 1 0 0.5 1 C=0.5Stroke: c = 0.87 AMI: c = 0.85 CABG: c = 0.74 Transplant: c = 0.653 • Less acuity • Longer follow up window • Competing Risk • CURRENT MODELS DO NOT CAPTURE LONGITUDINAL CLINICAL EVOLUTION Why are Transplant Models Inferior? Courtesy: JD Schold (modified with permission)
  7. 7. Attributes of the Ideal Predictive Model for Graft Loss • Appropriate to Center’s Population and customizable • Ability to discriminate across levels of risk • Feasibility of build around clinically actionable variables • Uses data available within the EMR that are collected in the context of standard patient care • Biologically relevant to the extent of current understanding including social determinants and care processes • Ability to inform on individual patient trajectories and capture dynamic longitudinal clinical evolution in the temporal context of routine clinical care
  8. 8. Objectives • Articulate an approach to capture longitudinal post-transplant clinical evolution among kidney transplant recipients by capturing structured and unstructured elements from the EHR • Build predictive models for graft loss and mortality using patient level data • Compare model performance with those derived of national data • Deploy predictive models in a clinician facing interface through the electronic medical record to drive post transplant clinical care
  9. 9. Workflow of Data Extraction, Storage, Analysis and Deployment Watson Natural Language Processing Hadoop data storage Predictive analytic, Data Processing and Scoring
  10. 10. IBM SPSS Modeler & C&DS • Predictive modeling. • Data integration and processing • Creation of dynamic variables (e.g. eGFR trajectory) • Predictive model building. • Model deployment and scoring Automation. (C&DS: Collaboration & Deployment Services)
  11. 11. Watson Natural Language Processing (NLP) • Blood pressure • Heart rate • Temperature
  12. 12. eGFR: TRAJECTORY Similar approaches can be used to incorporate hemoglobin, blood pressure and heart rates into longitudinal models Time to first Max Standard Deviation Slope from last Max To day 90 (forced at Max)
  13. 13. Statistical Analysis § Risk models were developed for 1 & 3 year GL and 3 year Mortality) Each of these risk models incorporated variables as follows: § Model 1: OPTN/UNOS/SRTR variables § Model 2: UNOS + Tx Database Variables § Model 3: UNOS + Tx Database + EHR Comorbidities § Model 4: UNOS + Tx Database + NLP variables + Trajectory variables
  14. 14. Statistical Analysis • Backward selection in the Multivariable Firth logistic model using an exit p-value at the 20 percent level for variable selection • For verification of selection, o Step-wise AIC variable selection criteria o Lasso variable selection technique • Clinical adjudication if discrepancies of variables were revealed between methods • Methods to account for over-fitting were utilized
  15. 15. Rationale for Variable Inclusion Variable Source; Category KDRI UNOS; Kidney Quality Caregiver Status Transplant Database; Social Determinant Education Status Transplant Database; Social Determinant ICD-10 Comorbidities EHR; Comorbidities; Cardiometabolic risk Banff Lesion Scores EHR, NLP; Immunologic Risk CMV, BKV PCRs EHR: Immunologic Risk Cardiovascular events EHR; CV Risk; Access to care Blood Pressures, Blood sugars EHR, NLP; Biology, Cardiometabolic risk Hemoglobin and eGFR Slopes/trajectories EHR; Biology of Kidney Function Readmit Counts EHR; Processes of care, Access to care
  16. 16. Rationale for Variable Inclusion Variable Source; Category KDRI UNOS; Kidney Quality Caregiver Status Transplant Database; Social Determinant Education Status Transplant Database; Social Determinant ICD-10 Comorbidities EHR; Comorbidities; Cardiometabolic risk Banff Lesion Scores EHR, NLP; Immunologic Risk CMV, BKV PCRs EHR: Immunologic Risk Cardiovascular events EHR; Immunologic Risk Blood Pressures, Blood sugars EHR, NLP; Biology, Cardiometabolic risk Hemoglobin and eGFR Slopes/trajectories EHR; Biology of Kidney Function Readmit Counts EHR; Processes of care, Access to care
  17. 17. Rationale for Variable Inclusion Variable Source; Category KDRI UNOS; Kidney Quality Caregiver Status Transplant Database; Social Determinant Education Status Transplant Database; Social Determinant ICD-10 Comorbidities EHR; Comorbidities; Cardiometabolic risk Banff Lesion Scores EHR, NLP; Immunologic Risk CMV, BKV PCRs EHR: Immunologic Risk Cardiovascular events EHR; CV Risk, Access to care Blood Pressures, Blood sugars EHR, NLP; Biology, Cardiometabolic risk Hemoglobin and eGFR Slopes/trajectories EHR; Biology of Kidney Function Readmit Counts EHR; Processes of care, Access to care
  18. 18. Rationale for Variable Inclusion Variable Source; Category KDRI UNOS; Kidney Quality Caregiver Status Transplant Database; Social Determinant Education Status Transplant Database; Social Determinant ICD-10 Comorbidities EHR; Comorbidities; Cardiometabolic risk Banff Lesion Scores EHR, NLP; Immunologic Risk CMV, BKV PCRs EHR: Immunologic Risk Cardiovascular events EHR; CV Risk, Access to care Blood Pressures, Blood sugars EHR, NLP; Biology, Cardiometabolic risk Hemoglobin and eGFR Slopes/trajectories EHR; Biology of Kidney Function Readmit Counts EHR; Processes of care, Access to care
  19. 19. Rationale for Variable Inclusion Variable Source; Category KDRI UNOS; Kidney Quality Caregiver Status Transplant Database; Social Determinant Education Status Transplant Database; Social Determinant ICD-10 Comorbidities EHR; Comorbidities; Cardiometabolic risk Banff Lesion Scores EHR, NLP; Immunologic Risk CMV, BKV PCRs EHR: Immunologic Risk Cardiovascular events EHR; CV Risk, Access to care Blood Pressures, Blood sugars EHR, NLP; Biology, Cardiometabolic risk Hemoglobin and eGFR Slopes/trajectories EHR; Biology of Kidney Function Readmit Counts EHR; Processes of care, Access to care
  20. 20. Rationale for Variable Inclusion Variable Source; Category KDRI UNOS; Kidney Quality Caregiver Status Transplant Database; Social Determinant Education Status Transplant Database; Social Determinant ICD-10 Comorbidities EHR; Comorbidities; Cardiometabolic risk Banff Lesion Scores EHR, NLP; Immunologic Risk CMV, BKV PCRs EHR: Immunologic Risk Cardiovascular events EHR; CV Risk, Access to care Blood Pressures, Blood sugars EHR, NLP; Biology, Cardiometabolic risk Hemoglobin and eGFR Slopes/trajectories EHR; Biology of Kidney Function Readmit Counts EHR; Processes of care, Access to care
  21. 21. Rationale for Variable Inclusion Variable Source; Category KDRI UNOS; Kidney Quality Caregiver Status Transplant Database; Social Determinant Education Status Transplant Database; Social Determinant ICD-10 Comorbidities EHR; Comorbidities; Cardiometabolic risk Banff Lesion Scores EHR, NLP; Immunologic Risk CMV, BKV PCRs EHR: Immunologic Risk Cardiovascular events EHR; CV Risk, access to care Blood Pressures, Blood sugars EHR, NLP; Biology, Cardiometabolic risk Hemoglobin and eGFR Slopes/trajectories EHR; Biology of Kidney Function Readmit Counts EHR; Processes of care, Access to care
  22. 22. Rationale for Variable Inclusion Variable Source; Category KDRI UNOS; Kidney Quality Caregiver Status Transplant Database; Social Determinant Education Status Transplant Database; Social Determinant ICD-10 Comorbidities EHR; Comorbidities; Cardiometabolic risk Banff Lesion Scores EHR, NLP; Immunologic Risk CMV, BKV PCRs EHR: Immunologic Risk Cardiovascular events EHR; CV Risk, access to care Blood Pressures, Blood sugars EHR, NLP; Biology, Cardiometabolic risk Hemoglobin and eGFR Slopes/trajectories EHR; Biology of Kidney Function Readmit Counts EHR; Processes of care, Access to care
  23. 23. 3 Year Graft Loss Risk and Data Sources Odds Ratios; Logistic (Firth) Odds Ratio 95% Profile-Likelihood Confidence Limits p≤0.05 Model 1: UNOS KDRI 3.975 2.197 7.194 * Age at Transplant 0.987 0.970 1.004 Female 0.588 0.360 0.936 * Blood Type B 1.542 0.883 2.596 Model 2: UNOS + Transplant Database KDRI 4.160 2.290 7.566 * Age at Transplant 0.985 0.968 1.002 Female 0.624 0.381 0.997 * Primary Care Giver 0.469 0.295 0.756 *
  24. 24. 3 Year Graft Loss Risk and Data Sources Odds Ratios; Logistic (Firth) Odds Ratio 95% Profile-Likelihood Confidence Limits p≤0.05 Model 1: UNOS KDRI 3.975 2.197 7.194 * Age at Transplant 0.987 0.970 1.004 Female 0.588 0.360 0.936 * Blood Type B 1.542 0.883 2.596 Model 2: UNOS + Transplant Database KDRI 4.160 2.290 7.566 * Age at Transplant 0.985 0.968 1.002 Female 0.624 0.381 0.997 * Primary Care Giver 0.469 0.295 0.756 *
  25. 25. 3 Year Graft Loss Risk and Data Sources Model 3: UNOS + Transplant Database + Comorbidity OR 95% CI p≤0.05 KDRI 4.239 2.330 7.734 * Age at Transplant 0.985 0.968 1.002 Female 0.623 0.375 1.012 Primary Care Giver 0.492 0.307 0.797 * Cerebrovascular Disease 0.248 0.027 0.978 * Cardiac Arrhythmias 1.712 1.031 2.788 * Alcohol Abuse 2.446 0.793 6.463 Depression 1.829 0.928 3.419
  26. 26. 3 Year Graft Loss Risk and Data Sources Model 3: UNOS + Transplant Database + Comorbidity OR 95% CI p≤0.05 KDRI 4.239 2.330 7.734 * Age at Transplant 0.985 0.968 1.002 Female 0.623 0.375 1.012 Primary Care Giver 0.492 0.307 0.797 * Cerebrovascular Disease 0.248 0.027 0.978 * Cardiac Arrhythmias 1.712 1.031 2.788 * Alcohol Abuse 2.446 0.793 6.463 Depression 1.829 0.928 3.419
  27. 27. 3 Year Graft Loss Risk and Data Sources Model 4: UNOS + Tx Database + Comorbidity + Post-Transplant Trajectory OR 95% CI p≤0.05 KDRI_MED_Imputed 3.06 1.49 6.28 * AgeAtTransplant 0.98 0.96 1.00 * Female 0.57 0.32 0.99 * Primarycaregiver 0.40 0.23 0.69 * Cerebrovascular Disease 0.23 0.02 1.13 Obesity 0.66 0.38 1.11 Alcohol Abuse 3.22 0.89 9.78 Depression 1.91 0.86 3.98 Pulse Pressure_SDev_1yr 1.14 1.06 1.22 * AcuteMI_comp_1yr 11.14 2.15 54.30 * Cardiac_or_Vascular_Event_1yr 2.98 1.74 5.10 * HGBSlope_7dto1yr_Imputed 0.72 0.56 0.87 * Pulse_Mean_1yr 1.03 1.00 1.07 * MaxEGFR_1yr 0.99 0.98 1.00 * ED visit count_1yr 1.31 0.93 1.74 Acute_Banff_Score_Max_2_1yr 1.37 1.22 1.54 *
  28. 28. 3 Year Graft Loss Risk and Data Sources Model 4: UNOS + Tx Database + Comorbidity + Post-Transplant Trajectory OR 95% CI p≤0.05 KDRI_MED_Imputed 3.06 1.49 6.28 * AgeAtTransplant 0.98 0.96 1.00 * Female 0.57 0.32 0.99 * Primarycaregiver 0.40 0.23 0.69 * Cerebrovascular Disease 0.23 0.02 1.13 Obesity 0.66 0.38 1.11 Alcohol Abuse 3.22 0.89 9.78 Depression 1.91 0.86 3.98 Pulse Pressure_SDev_1yr 1.14 1.06 1.22 * AcuteMI_comp_1yr 11.14 2.15 54.30 * Cardiac_or_Vascular_Event_1yr 2.98 1.74 5.10 * HGBSlope_7dto1yr_Imputed 0.72 0.56 0.87 * Pulse_Mean_1yr 1.03 1.00 1.07 * MaxEGFR_1yr 0.99 0.98 1.00 * ED visit count_1yr 1.31 0.93 1.74 Acute_Banff_Score_Max_2_1yr 1.37 1.22 1.54 *
  29. 29. 3 Year Graft Loss Risk and Data Sources Model 4: UNOS + Tx Database + Comorbidity + Post-Transplant Trajectory OR 95% CI p≤0.05 KDRI_MED_Imputed 3.06 1.49 6.28 * AgeAtTransplant 0.98 0.96 1.00 * Female 0.57 0.32 0.99 * Primarycaregiver 0.40 0.23 0.69 * Cerebrovascular Disease 0.23 0.02 1.13 Obesity 0.66 0.38 1.11 Alcohol Abuse 3.22 0.89 9.78 Depression 1.91 0.86 3.98 Pulse Pressure_SDev_1yr 1.14 1.06 1.22 * AcuteMI_comp_1yr 11.14 2.15 54.30 * Cardiac_or_Vascular_Event_1yr 2.98 1.74 5.10 * HGBSlope_7dto1yr_Imputed 0.72 0.56 0.87 * Pulse_Mean_1yr 1.03 1.00 1.07 * MaxEGFR_1yr 0.99 0.98 1.00 * ED visit count_1yr 1.31 0.93 1.74 Acute_Banff_Score_Max_2_1yr 1.37 1.22 1.54 *
  30. 30. Effect of Layering Data Sources on Model Performance
  31. 31. Model 1: UNOS Model 2: UNOS + Transplant Database Model 3: UNOS + Transplant Database + Comorbidity Model 4: UNOS + Tx Database + Comorbidity + Post-Transplant Trajectory
  32. 32. Pairwise Model Comparison Model 1: UNOS only Model 2: UNOS + Transplant Database Model 3: UNOS + Transplant Databases + EHR Comorbidity Model 4: UNOS + Transplant database + NLP + EHR Comorbidity + EHR Post- Transplant Trajectory 1 Year Graft Loss 3 Year Graft Loss 3 Year Mortality Data Model A vs Data Model B P-value Data Model A vs Data Model B P-value Data Model A vs Data Model B P-value 2 (AUC=0.741) vs 1 (AUC=0.716) 0.416 2 (AUC=0.665) vs 1 (AUC=0.661) 0.838 2 (AUC=0.765) vs 1 (AUC=0.765) 1.000 3 (AUC=0.769) vs 1 (AUC=0.716) 0.170 3 (AUC=0.712) vs 1 (AUC=0.661) 0.021 3 (AUC=0.768) vs 1 (AUC=0.765) 0.476 4 (AUC=0.873) vs 1 (AUC=0.716) <.001 4 (AUC=0.846) vs 1 (AUC=0.661) <.001 4 (AUC=0.838) vs 1 (AUC=0.765) 0.004 3 (AUC=0.769) vs 2 (AUC=0.741) 0.119 3 (AUC=0.712) vs 2 (AUC=0.665) 0.006 3 (AUC=0.768) vs 2 (AUC=0.765) 0.476 4 (AUC=0.873) vs 2 (AUC=0.741) <.001 4 (AUC=0.846) vs 2 (AUC=0.665) <.001 4 (AUC=0.838) vs 2 (AUC=0.765) 0.004 4 (AUC=0.873) vs 3 (AUC=0.769) 0.001 4 (AUC=0.846) vs 3 (AUC=0.712) <.001 4 (AUC=0.838) vs 3 (AUC=0.768) 0.007
  33. 33. Pairwise Model Comparison Model 1: UNOS only Model 2: UNOS + Transplant Database Model 3: UNOS + Transplant Databases + EHR Comorbidity Model 4: UNOS + Transplant database + NLP + EHR Comorbidity + EHR Post- Transplant Trajectory 1 Year Graft Loss 3 Year Graft Loss 3 Year Mortality Data Model A vs Data Model B P-value Data Model A vs Data Model B P-value Data Model A vs Data Model B P-value 2 (AUC=0.741) vs 1 (AUC=0.716) 0.416 2 (AUC=0.665) vs 1 (AUC=0.661) 0.838 2 (AUC=0.765) vs 1 (AUC=0.765) 1.000 3 (AUC=0.769) vs 1 (AUC=0.716) 0.170 3 (AUC=0.712) vs 1 (AUC=0.661) 0.021 3 (AUC=0.768) vs 1 (AUC=0.765) 0.476 4 (AUC=0.873) vs 1 (AUC=0.716) <.001 4 (AUC=0.846) vs 1 (AUC=0.661) <.001 4 (AUC=0.838) vs 1 (AUC=0.765) 0.004 3 (AUC=0.769) vs 2 (AUC=0.741) 0.119 3 (AUC=0.712) vs 2 (AUC=0.665) 0.006 3 (AUC=0.768) vs 2 (AUC=0.765) 0.476 4 (AUC=0.873) vs 2 (AUC=0.741) <.001 4 (AUC=0.846) vs 2 (AUC=0.665) <.001 4 (AUC=0.838) vs 2 (AUC=0.765) 0.004 4 (AUC=0.873) vs 3 (AUC=0.769) 0.001 4 (AUC=0.846) vs 3 (AUC=0.712) <.001 4 (AUC=0.838) vs 3 (AUC=0.768) 0.007
  34. 34. Mutable factors associated with 1 & 3 year graft loss and mortality fall into 2 categories Transplant associated risk (historically managed by transplant nephrology) Non-Transplant associated risk (historically managed by primary care)
  35. 35. Model Deployment in The Clinic
  36. 36. 37
  37. 37. FROM….. TO…..
  38. 38. Limitations • This is a proof of concept which needs to be evaluated in the clinic • Single center study that needs to be replicated in other centers and across EMR and analytic platforms • Rules of engagement relevant to real time deployment in the clinical setting are not defined
  39. 39. Conclusion • A Big Data approach to curating diverse data sources adds significant accuracy to 3 year graft loss prediction models, and makes meaningful use of data in EHRs • Our approach captures clinical complexity and longitudinal dynamic evolution across several clinically mutable domains. • This solution is executable daily through EHR workflows and could help optimize outcomes and deliver value • This approach can be extended to most disease states using the care processes that characterize clinical transplantation
  40. 40. Team MUSC • Titte Srinivas, MD • David Taber, Pharm D • Patrick Mauldin, PhD • Jingwen Zhang, MS • Zemin Su, MS • Justin Marsden, MS • William Moran, MD, MS • David Northrup, MS • Mark Daniels, MS • Karthick Gourisankaran, MS • Leslie Lenert, MD, MS • Katie Reilly, BA • Martha Sylvia, RN,MSN IBM • Haroon Anwar, MS • Arun Tripathi, Ph.D. • Salvatore Galascio, MS
  41. 41. Questions ?
  42. 42. Thank You !
  43. 43. MUSC Kidney Transplant Modeling One year Graft Loss (GL) (Transplant dates between 1/2007 and 6/2015) • 1,176 patients analyzed • 45 had 1yr GL (3.8%) • Logistic Firth • 90 days exposure period • Backward Selection + Stepwise AIC • AUC = 0.873 (0.807 – 0.939) • Harrell’s Adjusted AUC = 0.829 • BCA (Bias Corrected and Accelerated) Bootstrap AUC = 0.764 – 0.913
  44. 44. MUSC Kidney Transplant Modeling Three year Graft Loss (GL) (Transplant dates between 1/2007 and 6/2015) • 891 patients analyzed • 89 had 3yr GL (10.0%) • Logistic Firth • 1 year exposure • Backward Selection + Stepwise AIC • AUC = 0.846 • Harrell’s Adjusted AUC = 0.811 • BCA (Bias Corrected and Accelerated) Bootstrap AUC = 0.791 – 0.869
  45. 45. MUSC Kidney Transplant Modeling Three year Mortality (ML) (Transplant dates between 1/2007 and 6/2015) • 880 patients analyzed • 76 had 3yr ML (8.9%) • Logistic Firth • 1 year exposure • Backward Selection + Stepwise AIC • AUC = 0.838 • Harrell’s Adjusted AUC = 0.800 • BCA (Bias Corrected and Accelerated) Bootstrap AUC = 0.766 – 0.868
  46. 46. MUSC Kidney Transplant Modeling (Transplant dates between 1/2007 and 6/2015) • Firth Logistic Regression • 90 days exposure period for 1 year survival models and 1 year exposure for 3 year models • Backward Selection + Stepwise AIC
  47. 47. Logistic (Firth) regression odds ratio and 95% confidence intervals (Model 4: UNOS + Velos + EHR Comorbidity + EHR Post-Transplant Trajectory) Odds Ratio (95% CI) 1 Year Graft Loss 3 Year Graft Loss 3 Year Mortality UNOS Age at Transplant 0.98 (0.96, 1.00)* 1.03 (1.00, 1.05)* Female 0.57 (0.32, 0.99)* African American 0.46 (0.26, 0.81)* KDRI 2.48 (0.95, 6.43) 3.06 (1.49, 6.28)* 1.81 (0.88, 3.69) Blood Type B 3.41 (1.55, 7.35)* Waitlisting Time (Years) 0.76 (0.56, 0.98)* First Week Dialysis 2.48 (1.20, 5.00)* Diabetes 1.67 (0.93, 3.02) Obesity 0.50 (0.23, 1.06) 0.66 (0.38, 1.11) 0.65 (0.36, 1.14) Private Insurance 0.46 (0.21, 0.91)* Previous Kidney Transplant 2.31 (0.99, 5.05) Graft Loss by 1 Year 2.77 (1.13, 6.54)* Transplant Database Finish High School 0.47 (0.23, 0.97)* Smoker 2.61 (0.87, 6.91) Primary Care Giver Identified at Transplant 0.40 (0.23, 0.69)* EHR*** Cerebrovascular Disease 0.07 (<0.01, 0.65)* 0.23 (0.02, 1.13) Cardiac Arrhythmias 2.16 (1.01, 4.52)* Alcohol Abuse 3.71 (0.87, 12.73) 3.22 (0.89, 9.78) Drug Abuse 3.55 (0.63, 15.50) Depression 1.91 (0.86, 3.98) 0.44 (0.14, 1.13) Transplant LOS (Days) 1.08 (1.01, 1.26)* Acute MI during Exposure** 11.14 (2.15, 54.30)* Cardiac or Vascular Event during Exposure 2.48 (1.06, 5.66)* 2.98 (1.74, 5.10)* 2.23 (1.21, 4.08)* BK>500 during Exposure 1.93 (0.90, 3.88) CMV>500 during Exposure 0.20 (0.02, 0.88)* Pulse Mean during Exposure 1.03 (1.00, 1.07)* 1.04 (1.00, 1.07)* SBP Mean during Exposure 0.97 (0.94, 1.00)* Pulse Pressure SDev during Exposure 1.14 (1.04, 1.25)* 1.14 (1.06, 1.22)* Glucose Mean during Exposure 0.99 (0.98, 1.00) HGB Slope perMonth - Day7 until End of Exposure 0.78 (0.58, 0.93)* 0.72 (0.56, 0.87)* Max eGFR during Exposure 0.97 (0.95, 0.99)* 0.99 (0.98, 1.00)* eGFR Slope from Max Value until End of Exposure 0.83 (0.74, 0.99)* 0.87 (0.73, 0.98)* Tacrolimus_SDev_during Exposure 1.18 (0.97, 1.42) Inpatient Readm Count during Exposure 1.42 (1.03, 1.93)* 1.16 (0.98, 1.37) Emergency Department Visit Count during Exposure 1.31 (0.93, 1.74) NLP Max Acute Banff Score during Exposure 1.37 (1.22, 1.54)*
  48. 48. Statistical Analysis • Assess Overfit: To assess and adjust for overfitting, results were validated using the Harrell Optimism Correction; The Harrell optimism corrected c statistic is an “honest” estimate of internal validity, penalizing for overfitting • Internal Validation o Bootstrapping with Bias Correction methodology were used for model internal validation and AUCs for the ROC curve were used to determine and compare model accuracy o Empirical cross check between actual observed events and model predicted risk scores was conducted by clinicians • Assess Methodology: To assess the “robustness” of the logistic methodology, models were also created using survival analysis (Cox Proportional Hazard)
  49. 49. Supplemental Material: Cox proportional Hazard Model Results Supplemental Table 1: One Year Graft Loss Green: represents variables in Logistic Model but not in Cox Model Yellow: represents variables in Cox Model but not in Logistic Model Cox Proportional Hazard Model (Concordance (C-index) = 0.873, se = 0.043) Effect Hazard Ratio LHR ULR p-value KDRI Blood Type B 3.080 1.496 6.343 0.002 Obesity 0.588 0.288 1.202 0.145 Waiting Time (Years) 0.776 0.608 0.990 0.042 Finish High School 0.589 0.297 1.166 0.129 Smoker 2.688 1.086 6.655 0.033 Cerebrovascular Disease Cardiac Arrhythmias 2.206 1.113 4.372 0.023 Alcohol Abuse 2.809 0.845 9.332 0.092 Drug Abuse Cardiac or Vascular Event_90days 2.539 1.206 5.345 0.014 CMV>500_90days 0.128 0.017 0.959 0.046 SBP Mean_90days 0.974 0.949 1.000 0.048 Pulse Pressure SDev_90days 1.134 1.043 1.234 0.003 Glucose Mean_90days 0.985 0.972 0.997 0.018 HGB Slope perMonth from Day7 until 90days 0.814 0.753 0.879 <.0001 eGFR Slope from Max Value until 90days 0.820 0.724 0.932 0.002 Max eGFR_90days 0.965 0.946 0.985 0.001 Inpatient Readm Count_90days 1.282 0.977 1.682 0.073 Female 0.486 0.233 1.013 0.054 Age At Transplant 1.027 0.998 1.056 0.068 Married 0.501 0.243 1.032 0.061 Depression 0.382 0.101 1.444 0.156 HGB Mean from Day7 until 90days 0.738 0.542 1.004 0.053 Max Acute Banff Score_90days 1.151 0.992 1.334 0.063 Tacrolimus Mean_90days 0.827 0.656 1.042 0.107
  50. 50. Supplemental Table 2: Three Year Graft Loss Cox Proportional Model (Concordance (C-index) = 0.843, se = 0.031) Effect Hazard Ratio LHR ULR p-value KDRI 2.739 1.480 5.069 0.001 Age at Transplant 0.983 0.964 1.002 0.081 Female 0.546 0.333 0.896 0.017 Obesity 0.646 0.401 1.039 0.071 Primary Care Giver 0.581 0.361 0.935 0.025 Cerebrovascular Disease 0.081 0.008 0.798 0.031 Alcohol Abuse Depression Acute MI_1yr 4.746 1.537 14.652 0.007 Cardiac or Vascular Event_1yr 2.411 1.461 3.979 0.001 Pulse Pressure SDev_1yr 1.147 1.078 1.221 <.0001 HGB Slope perMonth from Day7 until 1yr 0.817 0.758 0.881 <.0001 Pulse Mean_1yr 1.027 0.998 1.056 0.067 Max eGFR_1yr 0.985 0.975 0.995 0.004 Max Acute Banff Score_1yr 1.296 1.194 1.407 <.0001 ED Readm Count_1yr 1.260 0.944 1.682 0.117 Diabetes 0.647 0.375 1.116 0.118 Receive Disability 0.676 0.413 1.108 0.120 Smoker 1.733 0.837 3.592 0.139 Cardiac Arrhythmias 1.764 1.076 2.894 0.025 BK>500_1yr 0.522 0.252 1.078 0.079 CMV>500_1yr 0.648 0.357 1.177 0.154 eGFR Slope from Max Value until 1yr 0.862 0.762 0.972 0.017 Inpatient Readm Count_1yr 1.152 1.000 1.326 0.049 Tacrolimus Mean_1yr 0.865 0.725 1.033 0.109 Tacrolimus SDev_1yr 1.144 0.956 1.368 0.141 Green: represents variables in Logistic Model but not in Cox Model Yellow: represents variables in Cox Model but not in Logistic Model
  51. 51. Supplemental Table 3: Three Year Mortality Cox Proportional Model (Concordance (C-index) = 0.815, se = 0.033) Effect Hazard Ratio LHR ULR p-value KDRI Age at Transplant 1.029 1.007 1.050 0.008 African American 0.649 0.393 1.074 0.093 Private Insurance 0.491 0.249 0.965 0.039 Obesity First Week Dialysis Diabetes 1.567 0.930 2.638 0.091 Previous Kidney Transplant 2.481 1.244 4.949 0.010 Graft Loss_1yr 2.363 1.126 4.957 0.023 Depression 0.447 0.177 1.125 0.087 Transplant LOS (Days) 1.049 1.014 1.086 0.006 Cardiac or Vascular Event_1yr 1.990 1.164 3.401 0.012 BK>500_1yr Pulse Mean_1yr eGFR Slope from Max Value until 1yr 0.850 0.774 0.932 0.001 Inpatient Readm Count_1yr 1.113 0.968 1.280 0.133 Tacrolimus SDev_1yr 1.166 0.990 1.373 0.066 Distance to MUSC (Miles) 1.001 1.000 1.003 0.155 Receive Disability 0.687 0.408 1.157 0.158 Smoker 1.832 0.808 4.151 0.147 Peripheral Vascular Disorders 1.521 0.784 2.950 0.215 CMV>500_1yr 1.673 0.948 2.951 0.076 SBP Mean_1yr 0.980 0.962 0.999 0.040 HGB Mean from Day7 until 1yr 0.783 0.643 0.954 0.015 HGB Slope perMonth from Day7 until 1yr 0.796 0.565 1.123 0.194 Green: represents variables in Logistic Model but not in Cox Model Yellow: represents variables in Cox Model but not in Logistic Model
  52. 52. CMV Rejection BK DM Smoker MI Married Smoker College Grad Immunologic Cardiometabolic Psychosocial Date BK PCR 1/27/16 2.33E4 12/23/15 Neg 11/22/15 Neg Date Blood Sugar 1/27/16 185 12/23/15 109 11/22/15 93 Model Output Model Output

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