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10.5489/cuaj.4378

http://scihub22266oqcxt.onion/10.5489/cuaj.4378
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C5962945!5962945!29106360
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suck abstract from ncbi


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pmid29106360      Can+Urol+Assoc+J 2017 ; 11 (12): 388-93
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  • Complications in the 90-day postoperative period following kidney transplant and the relationship of the Charlson Comorbidity Index #MMPMID29106360
  • Levine MA; Schuler T; Gourishankar S
  • Can Urol Assoc J 2017[Dec]; 11 (12): 388-93 PMID29106360show ga
  • Introduction: Renal transplant experiences widespread success, but little is published regarding the postoperative complications. The Charlson Comorbidity Index (CCI) is a system of mortality risk assessment. Our purpose is to assess the 90-day postoperative complications after renal transplantation. The secondary objective is to clarify whether CCI predicts complications. We hypothesized increased CCI corresponds to worse complication on the Clavien scale. Methods: This is a retrospective analysis of renal recipients at our institution (2011?2013) who were ?18 years old and received complete follow up. CCI, age, gender, body mass index (BMI), and graft type were extracted from the electronic medical records. Complications were scored using the Clavien scale. Descriptive statistics and logistic regression were used to analyze 198 patients. Results: The mean age was 53 (standard deviation [SD] 14), mean BMI 27.4 (SD 14), median CCI 1. Grade 2 or higher (significant) complications occurred in 60% of patients and Grade 3b or higher (severe) in 15% of patients in the 90-day postoperative period. Sixty-eight different complications were identified, the most common being blood transfusion (19%). Logistic regression suggests a predictive value of CCI (odds ratio [OR] 1.70; 95% confidence interval [CI] 1.3?2.3) for severe complications, with diabetes mellitus and peripheral vascular disease conferring increased risk. Conclusions: Renal transplant carries significant risk. This data can be used to improve patient counselling on the likely postoperative course. Study limitations include the retrospective design, predisposing to potential bias in data capture.
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