医学
外科
肾移植
患者满意度
泌尿系统
麻醉
移植
内科学
作者
Ahmed Halawa,Stephen A. Rowe,F. W. Roberts,C.V. Senthil Nathan,Ahmed Hamody Hassan,Avneesh Kumar,Branislav Suvakov,Ben Edwards,Cavin Gray
出处
期刊:Experimental and Clinical Transplantation
[Baskent University Publishers]
日期:2018-04-01
卷期号:16 (2)
被引量:14
标识
DOI:10.6002/ect.2016.0304
摘要
Our aim was to apply the principles of enhanced recovery in renal transplant recipients and to assess the changes in the quality of patient care and patient satisfaction.Our study included 286 consecutive renal transplant patients. Of these, 135 patients went through the enhanced recovery program and 151 patients had traditional recovery. Patient education and discharge planning were commenced on admission. For enhanced recovery, prolonged preoperative fasting was avoided by carbohydrate loading. Goal-directed fluid management was aided by transesophageal Doppler to avoid central line insertion. Intrathecal diamorphine and ultrasonography-guided transversus abdominis plane blocks were used to achieve adequate analgesia. Patients started oral intake a few hours postoperatively. The urinary catheter was removed 2 to 4 days after transplant.The postoperative patient-controlled analgesia requirement for morphine was significantly reduced in the enhanced recovery versus traditional recovery group (median of 9.5 vs 47 mg; P < 0.001). The length of stay was significantly reduced for living-donor (median 5 vs 7 days; P < .001) and for deceased-donor transplant recipients (median 5 vs 8.5 days; P < 0.001) with enhanced recovery versus recipients who had traditional recovery. Implementing enhanced recovery saves £2160 per living-donor transplant and £3078 per deceased-donor transplant. In the enhanced recovery group, readmission within 10 days after transplant was 5%.Our service evaluation demonstrated that enhanced recovery benefits both types of renal transplant (living and deceased grafts) procedures, with excellent patient satisfaction and reduction of hospital length of stay.
科研通智能强力驱动
Strongly Powered by AbleSci AI