Prevalence and risk factors for perioperative complications of CKD patients undergoing elective hip surgery
Bibliographic record
Abstract
PURPOSE: Chronic kidney disease (CKD) is known to increase morbidity and mortality after orthopedic surgery. The purpose of this study is to investigate how CKD affects perioperative complications in hip surgery patients. MATERIAL AND METHODS: From 2013 to 2016, a total of 230 patients (30 patients with CKD and 200 without CKD) undergoing hip surgery were enrolled in this study. Preoperative, intraoperative, and postoperative data was collected and analyzed between CKD and non-CKD patients. Logistic regression was used to evaluate the independent risk factor for postoperative complications. RESULTS: There were significant differences in the number of people with hypertension (90.0% vs 27.3%, P < 0.001), diabetes (33.3% vs 8.7%, P = 0.01), coronary heart disease (20.0% vs 2.0%, P = 0.001), smoking habits (56.7% vs 22.7%, P = 0.016), anemia (90.0% vs 19.3%, P < 0.001), and low hemoglobin levels (94.1 ± 19.7 vs 121.3 ± 18.8, P < 0.001) between CKD and non-CKD patients before surgery. Receiving a blood transfusion was significantly more common in CKD patients (50% vs 28.5%, P = 0.018). Postoperatively, significant differences were detected in the average number of patients who transferred to the ICU (73.3% vs 19.3%, P < 0.001). Furthermore, differences were found in the quantity of hemoglobin (92.5 ± 16.8 vs 107.5 ± 18.3, P < 0.001) and albumin (32.4 ± 4.1 vs 34.9 ± 5.5, P = 0.02) measured between CKD and non-CKD patients. Logistic regression analysis indicated that diabetes, alcohol, and anemia were all independent risk factors for obtaining a blood transfusion, while age, CKD, and osteoporosis were all independent risk factors for ICU transfers. CONCLUSION: Compared with non-CKD patients, CKD patients were accompanied with more cardiac diseases preoperatively. In addition, CKD patients were more likely to receive a blood transfusion and transfer to the ICU after hip surgery. Preoperative anemia should be restored sufficiently to decrease the incidence of blood transfusions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".