Computable Phenotype to Identify ADPKD Patients, A Validation Study
Bibliographic record
Abstract
Background: Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited kidney disease. Patients with ADPKD are at high risk of end-stage kidney disease as the disease lacks a cure. It is important to facilitate the recruitment of ADPKD patients in clinical trials. Here, we sought to validate a computable phenotype based on the ICD-9/10 codes for ADPKD in the electronic medical record (EMR). Methods: We retrospectively identified 495 patients with ICD-9/10 for ADPKD or renal cysts and were following up at the University of Iowa Hospitals and Clinics (UIHC). We conducted a chart review to verify if they had ADPKD. We stratified patients into four groups: Group A, following in the nephrology clinic with ICD-9/10 codes of ADPKD; Group B, following in the nephrology clinic with ICD-9/10 codes for renal cysts; Group C, following in the non-nephrology clinic with ICD-9/10 codes for ADPKD; and Group D, following in the non-nephrology clinic with ICD-9/10 codes for renal cysts. Results: Of the 495 patients, 134 (27%) patients were evaluated in the nephrology clinic and 360 (73%) in the non-nephrology clinic. A total of 117 patients had ICD-9/10 codes for ADPKD, where 108 (92%) had confirmed ADPKD and 9 (8%) didn’t have ADPKD. A total of 377 had ICD-9/10 codes for renal cysts, where only 1 (0.3%) had ADPKD, while 376 (99.7%) didn’t have ADPKD. The overall sensitivity of the ICD-9/10 codes for ADPKD was 92.3% and the specificity was 99.7%. In the nephrology clinic, the sensitivity was 94.6% and the specificity was 100%. In the non-nephrology clinic, the sensitivity was 88.4% and the specificity was 99.7% (Figure.1).Figure 1:: Diagnostic Accuracy Test Results For ADPKDConclusions: Utilizing ICD-9/10 to identify patients with ADPKD is of excellent overall sensitivity, specificity, PPV, and NPP. The sensitivity tends to be higher in the nephrology clinic Vs non-nephrology clinic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".