Occurrence and predictive factors of acute renal injury following hip and knee arthroplasty
- PMID: 32201917
- DOI: 10.1007/s10157-020-01874-z
Occurrence and predictive factors of acute renal injury following hip and knee arthroplasty
Abstract
Background: To analyze the incidence of early acute kidney injury (AKI) and perioperative factors following hip and knee joint replacement.
Methods: A total of 6281 patients from the department of orthopedics from January 2016 to July 2018 were enrolled, and 1490 patients undergoing hip and knee arthroplasty met the inclusion criteria. The preoperative, intraoperative and postoperative parameters were recorded. The retrospective cohort study was carried out to analyze predictors for AKI and postoperative creatinine elevation following hip and knee joint replacement.
Results: Eighty patients (5.4%) met AKI criteria. Age, American Society of Anesthesiologists (ASA) physical status and preoperative diabetes were identified as independent predictors for postoperative AKI in patients undergoing hip and knee arthroplasty (p < 0.05). Age, male, preoperative diabetes, hypertension, and preoperative creatinine were identified as independent predictors for postoperative creatinine elevation (p < 0.05). Patients with AKI were more likely to enter the ICU than non-AKI patients (25% vs 5.6%, p < 0.05). Compared with non-AKI patients, the total hospital stay (16 [11-22] vs 13 [10-16] days) and postoperative hospital stay (11 [8-14] vs 8 [7-11] days) for AKI patients were significantly prolonged (p < 0.05).
Conclusion: The study shows age, male, preoperative diabetes, hypertension, and preoperative creatinine were independent predictors for postoperative creatinine elevation. In addition, age, ASA physical status and preoperative diabetes are independent predictors for postoperative AKI in patients undergoing hip and knee joint replacement. Postoperative AKI seems to increase ICU admission and significantly prolonged hospital stay.
Keywords: Acute kidney injury; Hip and knee arthroplasty; Predictive factors.
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