Effect of fluid challenge on renal resistive index after major orthopaedic surgery: A prospective observational study using Doppler ultrasonography
Bibliographic record
Abstract
A postoperative renal resistive index (RRI) > 0.70 has the best threshold to early predict acute kidney injury (AKI). The response of RRI to a postoperative fluid challenge (FC) is unknown. The aim of our study was to assess the impact of a FC on RRI in suspected hypovolaemia patients after orthopaedic surgery. In this single-centre observational study, we prospectively screened 156 patients in the recovery room after having undergone a hip or knee replacement. Forty-six patients with a RRI > 0.70 and requiring FC were included. RRI and cardiac output (CO) were measured before and immediately after a fluid challenge with 500 mL of isotonic saline. A decrease in RRI > 5% was considered significant (renal responders). Overall, FC resulted in a consistent decrease in RRI (from 0.74 [0.72–0.79] to 0.70 [0.68–0.73], P < 0.01). Thirty-four patients (74%) showed a significant decrease in their RRI (from 0.74 [0.73–0.79] to 0.69 [0.67–0.72], P < 0.05, versus non-responders: from 0.73 [0.72–0.75] to 0.72 [0.71–0.79], P = NS). CO increased equally among renal responders and non-responders (P = 0.56). No correlation was found between changes in RRI and CO (r2 = 0.04; P = 0.064). AKI was more common in renal non-responders (7/12) than in responders (3/34, P = 0.001). After major orthopaedic surgery, a FC can decrease RRI in suspected hypovolaemia patients at risk of postoperative AKI, but the changes are not correlated to changes in CO. Decreases in RRI were associated with better renal outcome.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".