Received : 18 April 2026
Accepted : 23 September 2026
Published : 28 September 2026
DOI: 10.30726/ijmrss/v13.i3.2026.13372
Living-Donor Versus Deceased-Donor Renal Transplant Outcomes: A Multi-Centre Observational Study: Ten-Year Graft and Patient Survival, Early Complications, and Causes of Allograft Loss
Living-donor and deceased-donor renal transplantation provide qualitatively similar treatment for end-stage kidney disease but differ in donor characteristics, ischaemic exposure, and immunological matching. We conducted a multi-centre observational comparative study across three tertiary transplant centres, including 380 adult kidney transplant recipients (190 living-donor, 190 deceased-donor) transplanted between 2013 and 2018 with follow-up to 2023. Death-censored graft survival at 10 years was 81.1% in living-donor recipients versus 63.2% in deceased-donor recipients (log-rank p < 0.001). Patient survival at 10 years was 88.4% versus 79.5%. Delayed graft function occurred in 2.1% of living-donor recipients but in 22.4% of deceased-donor recipients with cold ischaemic time 12-24 hours, rising to 38.6% with cold ischaemic time exceeding 24 hours. Acute rejection in year one rose from 4.2% with short-CIT living-donor transplants to 18.4% with long-CIT deceased-donor transplants. The strongest independent predictor of 5-year graft loss was non-adherence to immunosuppression (adjusted HR 4.21), followed by acute rejection in year one and prolonged cold ischaemic time. The findings support continued strategic prioritisation of living-donor transplantation and active management of cold ischaemic time and post-transplant adherence in deceased-donor recipients.[/mysc_spoiler]
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[1] Agarwal, A., Kumar, D., & S, P. M. (2026). Optimizing clinical effectiveness of enhanced recovery after surgery (ERAS): Multidisciplinary pathways, patient-centered outcomes, and data-driven performance analytics. International Innovations & Scholarly Trends Journal, 2(2).
[2] Agarwal, P., Khatoon, N., & Kumar, A. (2026). Infection control and implant survival in orthopaedic surgery: Evidence-based strategies, antimicrobial innovation, and digital surveillance. International Journal of Scientific Research in Engineering and Management, 10(03).
[3] Ahluwalia, M. G. C. S., Gupta, S. K., & Chaudhary, A. (2026). Precision-oriented hemodynamic monitoring in critical care practice: Evidence-based strategies, clinical integration, and outcome optimization. International Journal of Scientific Research and Technology.
[4] Aumose, L., & Raj, M. A. (2026). Applications of intelligent motion control systems in promoting mental health and human-centered support for higher secondary students. In Intelligent motion control for human-centered systems (pp. 127–152). IGI Global.
[5] Bhatnagar, M., Kumar, N., & Shivam. (2026). Quality improvement frameworks in modern surgical practice: Evidence-based models, implementation science, and outcome-oriented performance evaluation. International Journal of Scientific Research and Engineering Development, 9(2).
[6] Bhatnagar, V., Tyagi, N., & John, L. (2026). Simulation-based competency development in anesthesia training: Educational theory, assessment validity, and clinical impact. International Journal of Versatile Research and Analysis, 4(2).
[7] Catherine, S., Gupta, N., Gopi, E., & Swadhi, R. (2025). Enhancing patient engagement and outcomes through digital transformation: Machine learning in medical marketing. In Impact of digital transformation on business growth and performance (pp. 285–312). Deepa, R., Swadhi, R., Udayavani, V., Lakshmi, R., & Rafiq, S. (2026). Motion-controlled wearables for physiological monitoring and predictive diagnostics. In R. Vettriselvan & N. Suresh (Eds.), Intelligent motion control for human-centered systems (pp. 1–28).
[8] Gautam, M., Samyal, M., & Chaudhary, S. (2026). Preoperative risk stratification and surgical outcome prediction: Integrating clinical scoring systems, data-driven models, and patient-centered optimization. International Innovations & Scholarly Trends Journal, 2(3).
[9] Jagar, K. D., Kumar, A., & Yadav, A. (2026). Multimodal analgesia in acute and chronic pain management: Mechanistic rationale, clinical applications, and outcome-focused strategies. Journal of Advanced Academic Research and Findings, 4(2).
[10] Jha, S. C., Kumar, P., & Neha. (2026). Artificial intelligence-assisted decision support in internal medicine: Enhancing clinical judgment, precision care, and health system performance. International Journal of Scientific Development and Research, 11(2).
[11] Kumar, A., Kumar, N., & Dhabhai, M. (2026). Optimizing outcomes through evidence-based protocols in postoperative intensive care: Clinical standards, implementation strategies, and quality improvement. International Journal of Scientific Research and Technology.
[12] Kumar, P., Gautam, S., & Maitiy, S. (2026). Diagnostic utility of biomarkers in early disease stratification: Clinical applications, predictive value, and emerging innovations. Journal of Emerging Technologies and Innovative Research, 13(2).
[13] Kumar, R., Sharma, K., & Gupta, S. K. (2026). Multimorbidity patterns and therapeutic complexity in adult medical practice: Implications for polypharmacy and patient-centred care. International Journal of Creative Research Thoughts, 14(2).
[14] Mishra, A., Choudhary, A., & Kumar, S. (2026). Infection prevention strategies in operative care: Evidence-based interventions, systems integration, and outcome-oriented practice. Asian Journal of Multidimensional Research.
[15] Selvi, K., Anbarasan, P., Madhumita, G., Janaki, L., & Devi, K. K. (2026). Governance, security, and ethical considerations in AI-driven motion control systems. In Methodologies and applications of intelligent motion control systems (pp. 217–242). IGI Global.
[16] Sharma, S., Sharma, K., & Tyagi, N. (2026). Geriatric psychiatry and cognitive decline: Clinical evaluation, neuropsychological determinants and evidence-based management. International Journal of Management Research and Social Science, 13(2).
[17] Singhal, A., Kumar, A., & Kataria, N. (2026). Advances in wound healing and tissue regeneration: Biomaterials, regenerative strategies, and translational challenges. International Innovations & Scholarly Trends Journal, 2(2).
[18] Swadhi, R., Gayathri, K., Suresh, N. V., Catherine, S., & Velmurugan, P. R. (2025). Leveraging machine learning for enhanced patient engagement and outcomes: Revolutionizing healthcare marketing. In Impact of digital transformation on business growth and performance (pp. 313–340). IGI Global.
[19] Vettriselvan, R., Ramya, R., Selvalakshmi, V., Jyothi, P., & Velmurugan, P. R. (2026). Empowering patients through knowledge: Educational strategies in rehabilitation. In Holistic approaches to health recovery (pp. 263–290). IGI Global.
[20] Vettriselvan, R., Velmurugan, P. R., Varshney, K. R., EP, J., & Deepika, R. (2025). Health impacts of smartphone and internet addictions across age groups: Physical and mental health across generations. In Impacts of digital technologies across generations (pp. 187–210). IGI Global.
[21] Vijayalakshmi, M., Subramani, A. K., Vettriselvan, R., Velmurugan, P. R., & Hasine, J. (2025). Strategic collaborations in medical innovation and AI-driven globalization: Advancing healthcare startups. In Navigating strategic partnerships for sustainable startup growth (pp. 85–110). IGI Global.
[22] Vinodh, N., Subramani, A. K., & Vettriselvan, R. (2026). Transforming the future of management and medical education: AI-driven innovations in curriculum design. In AI education strategies for future-proofing curriculum design (pp. 459–476). IGI Global.
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