Acute Kidney Injury After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy in a Portuguese Population
Bibliographic record
Abstract
Background: Acute kidney injury (AKI) after cytoreductive surgery followed by the infusion of hyperthermic intraperitoneal chemotherapy (CRS/HIPEC) is associated with a higher rate of major complications, resulting in prolonged hospitalization and increased mortality. Our objective was to evaluate the incidence of AKI and further progression to chronic kidney disease (CKD) in patients submitted to this procedure and recognize the associated risk factors. Methods: This retrospective study collected demographic, tumor-related, intraoperative, and postoperative data from 182 patients who underwent CRS/HIPEC from January 2016 to December 2019. Renal impairment was defined according to Kidney Disease Improving Global Outcomes criteria for AKI. We conducted univariate and multiple logistic regression analyses to assess the association between variables of interest and AKI. Results: Twenty-three patients (12.6%) developed AKI. In the AKI group, the risk for developing CKD was six times higher (odds ratio (OR) 6.48, confidence interval (CI) 1.601 - 26.255). Multivariate regression identified higher risk of developing AKI in patients who underwent HIPEC with cisplatin (OR 12.21, CI 1.26 - 109.70, P = 0.025), in each additional day spent in the intensive care unit (ICU) (OR 2.42, CI 1.07 - 5.45, P = 0.033), and an association for each unit increase in estimated glomerular filtration rate (eGFR) before HIPEC (OR 0.96, CI 0.94 - 0.98, P = 0.037) and AKI development. Conclusion: Patients who are at higher risk of AKI after CRS/HIPEC include those who performed cisplatin HIPEC regimen, had poorer preoperative renal function and had longer ICU stays. Early institution of preventive measures and frequent monitoring should be considered to minimize AKI risk and its associated morbidity, such as CKD progression. World J Oncol. 2022;13(6):370-378 doi: https://doi.org/10.14740/wjon1540
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".