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Developing Computable Phenotype for Chronic Diseases using PEDSnet

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Panel presentation at AMIA 2016

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Developing Computable Phenotype for Chronic Diseases using PEDSnet

  1. 1. Designing Computable Phenotypes for Chronic Diseases using PEDSnet Ritu Khare, PhD The Children’s Hospital of Philadelphia November 16 2016
  2. 2. Background • Cohort identification • Certain conditions or features • Key in pediatric research • Traditional practice • Chart abstractions • Administrative data (billing code) • Computable Phenotype • EHR-based, pragmatic • Multiple clinical elements • e.g. eMERGE network True Cases Diagnoses Lab Interventions Encounters
  3. 3. Workflow for Computable Phenotype Development • Case • Non-case Definition • Medications • Diagnoses • Labs • Encounter Exposures • Combination • Configuration Algorithm Design • Internal • External Validation • Framework • Criteria Algorithm Selection • Type 2 diabetes • Crohn’s disease • Glomerular disease • Autism
  4. 4. Crohn’s Disease Computable Phenotype Exposures • Diagnosis (SNOMED-CT) • Methods = string match, concept relationships • 49 Crohn’s disease, 34 ulcerative colitis, 208 other GI concepts • Medications (RxNorm) • Method • Identify ingredient-level codes: balsalazide, mesalamine, sulfasalazine, ciprofloxacin, levofloxacin, … (Ritchie et al. 2010) • Determine granular codes: Clinical drug form, Clinical drug, Branded drug, Branded pack, … • 1086 RxNorm concepts for Crohn’s disease medications • Others • Encounter type • Clinic or provider specialty • Diagnoses type
  5. 5. Crohn’s Disease Computable Phenotype Algorithm design Version Exposures #Case Patients in PEDSnet Diagnosis* Meds Exclusion Diagnosis Case v1 >=1 Crohn’s disease Ulcerative colitis 10,669 Case v2 >=1 Crohn’s disease Crohn’s disease medications Ulcerative colitis 8,567 Case v3 >=3 Crohn’s disease Ulcerative colitis 7,570 Case v4 >=3 Crohn’s disease Crohn’s disease medications Ulcerative colitis 7,030 Noncase v1 >=1 Other GI disease Crohn’s disease, ulcerative colitis 1,169,861 Noncase v2 >= 1Other GI disease** Crohn’s disease, ulcerative colitis 290,081 *limited to office visits or problem lists **limited to specialty clinics
  6. 6. Crohn’s Disease Computable Phenotype Internal Validation: cross-site comparison Site Case v1 Case v2 Case v3 Case v4 Non- case v1 Non- case v2 s1 0.34% 0.21% 0.24% 0.19% 22.09% 8.28% s2 0.09% 0.06% 0.07% 0.06% 26.25% 4.93% s3 0.16% 0.12% 0.13% 0.11% 22.45% 4.73% s4 0.21% 0.17% 0.17% 0.15% 15.86% 4.60% s5 0.23% 0.16% 0.19% 0.16% 19.24% 7.13% s6 0.22% 0.16% 0.16% 0.13% 24.67% 0% s7 0.26% 0.19% 0.24% 0.18% 24.60% 6.55% s8 0.16% 0.12% 0.13% 0.11% 22.13% 7.86%
  7. 7. Crohn’s Disease Computable Phenotype Internal validation: Logistic Regression Cohort = Patients with GI disease (#1,060,127) Endoscopy GI Radiological exam #Crohn’s disease encounters (1,2,3,4,5+) 1.24 (1.21-1.27) 1.56 (1.54 – 1.58) Crohn’s disease medications (yes/no) 3.80 (3.65-3.97) 3.39 (3.27-3.51) ExposureVariables Outcomes (procedures) Odds ratio (95% confidence interval)
  8. 8. Crohn’s Disease Computable Phenotype Internal Validation: Longitudinal Reliability 60.00% 65.00% 70.00% 75.00% 80.00% 85.00% 90.00% 95.00% 100.00% Y1 -> Y2 % case patients with a follow- up visit 60.00% 65.00% 70.00% 75.00% 80.00% 85.00% 90.00% 95.00% 100.00% Y1 -> Y2 % case patients with a follow-up Crohn’s disease visit v1 v2 v3 v4
  9. 9. Crohn’s Disease Computable Phenotype External Validation: Gold Standard (CHOP) 1 Dx 3Dx + Meds Manually curated list Manually curated list Precision (PPV) = 0.66 Recall(sensitivity) = 0.99 Precision (PPV) = 0.81 Recall (sensitivity) = 0.98
  10. 10. Conclusions and Lessons Learned • Evaluation • patient-level chart reviews are expensive • several possibilities of internal validation • Data quality issues • sites with no GI providers or care sites! • sites with no patients on Crohn’s disease medications! • Algorithm Selection • known data quality issues • internal validation findings • sensitivity vs. specificity balance

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