Validity of the <i>International Classification of Diseases, Tenth Revision</i> code for acute kidney injury in elderly patients at presentation to the emergency department and at hospital admission
Bibliographic record
Abstract
OBJECTIVE: To evaluate the validity of the International Classification of Diseases, Tenth Revision (ICD-10) code N17x for acute kidney injury (AKI) in elderly patients in two settings: at presentation to the emergency department and at hospital admission. DESIGN: A population-based retrospective validation study. SETTING: Southwestern Ontario, Canada, from 2003 to 2010. PARTICIPANTS: Elderly patients with serum creatinine measurements at presentation to the emergency department (n=36 049) or hospital admission (n=38 566). The baseline serum creatinine measurement was a median of 102 and 39 days prior to presentation to the emergency department and hospital admission, respectively. MAIN OUTCOME MEASURES: Sensitivity, specificity and positive and negative predictive values of ICD-10 diagnostic coding algorithms for AKI using a reference standard based on changes in serum creatinine from the baseline value. Median changes in serum creatinine of patients who were code positive and code negative for AKI. RESULTS: The sensitivity of the best-performing coding algorithm for AKI (defined as a ≥2-fold increase in serum creatinine concentration) was 37.4% (95% CI 32.1% to 43.1%) at presentation to the emergency department and 61.6% (95% CI 57.5% to 65.5%) at hospital admission. The specificity was greater than 95% in both settings. In patients who were code positive for AKI, the median (IQR) increase in serum creatinine from the baseline was 133 (62 to 288) µmol/l at presentation to the emergency department and 98 (43 to 200) µmol/l at hospital admission. In those who were code negative, the increase in serum creatinine was 2 (-8 to 14) and 6 (-4 to 20) µmol/l, respectively. CONCLUSIONS: The presence or absence of ICD-10 code N17× differentiates two groups of patients with distinct changes in serum creatinine at the time of a hospital encounter. However, the code underestimates the true incidence of AKI due to a limited sensitivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".