Health Care Provider Awareness of AKI in Patients with Advanced CKD Undergoing Surgery: A Descriptive Study of Preoperative Notes
Bibliographic record
Abstract
Background: Postoperative acute kidney injury (AKI) is a frequent complication of surgery, with an incidence of 18-47%. The KDIGO guidelines offer a bundle of preventative strategies to mitigate AKI risk. Our study examined how often the risk of AKI and elements of the KDIGO bundle were mentioned by healthcare providers in their preoperative notes. Methods: We retrospectively identified patients ≥ 18 years followed in a tertiary care advanced chronic kidney disease (CKD) clinic who underwent elective surgery with a preoperative anesthesia assessment between January 1, 2017 and December 31, 2022. We extracted data on provider awareness of AKI, inclusion of KDIGO bundle elements in preoperative assessments, and qualitative comments on AKI risk assessment. We analyzed the quantitative data descriptively, stratifying our findings by preoperative healthcare provider awareness of AKI. We used a constant comparison technique and consensus to analyze qualitative comments. Results: Of 91 patients, the mean age was 76 (±13) years, 75% were male, and 96% had category 4 or 5 CKD. Postoperative AKI and dialysis frequencies were 13% and 1%, respectively. Few anesthesia providers (n=22/91, 24%) mentioned the risk of postoperative AKI. The most commonly documented KDIGO recommendations included holding ACEi/ARB medications (n=15/21, 80%), obtaining a preoperative creatinine measure (n=66/91, 73%), and considering hemodynamic monitoring (n=28/91, 31%). The least mentioned KDIGO recommendations included avoiding radiocontrast media (n=0/91, 0%), controlling hyperglycemia (n=2/91, 2%), and documenting the need for a postoperative creatinine (n=2/91, 2%). There was no difference in the inclusion of KDIGO bundle elements when stratifying preoperative awareness of AKI. In qualitative comments, providers who recognized postoperative AKI risk frequently mentioned the potential for dialysis (n=12/22, 55%), rarely utilized risk scores (n=1/22, 5%), and did not provide any actionable postoperative orders (n=0/22, 0%). Conclusion: Among patients with advanced CKD, only 24% of anesthesia providers mentioned the risk of AKI in their pre-operative assessments. Opportunities may exist to improve the perioperative care of patients with advanced CKD by increasing awareness of AKI risk and the KDIGO bundle elements.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".