Individual dynamic prediction and prognostic analysis for long-term allograft survival after kidney transplantation

Zhang QL, Rothenbacher D. Prevalence of chronic kidney disease in population-based studies: systematic review. BMC Public Health. 2008;8:117. https://doi.org/10.1186/1471-2458-8-117.

Article  PubMed  PubMed Central  Google Scholar 

Purnell TS, Auguste P, Crews DC, Lamprea-Montealegre J, Olufade T, Greer R, et al. Comparison of life participation activities among adults treated by hemodialysis, peritoneal dialysis, and kidney transplantation: a systematic review. Am J Kidney Dis. 2013;62(5):953–73. https://doi.org/10.1053/j.ajkd.2013.03.022.

Article  PubMed  Google Scholar 

Topuz K, Zengul FD, Dag A, Almehmi A, Yildirim MB. Predicting graft survival among kidney transplant recipients: a Bayesian decision support model. Decis Support Syst. 2018;106:97–109. https://doi.org/10.1016/j.dss.2017.12.004.

Article  Google Scholar 

Chesnaye NC, Tripepi G, Dekker FW, Zoccali C, Zwinderman AH, Jager KJ. An introduction to joint models—applications in nephrology. Clin Kidney J. 2020;13(2):143–9. https://doi.org/10.1093/ckj/sfaa024.

Article  PubMed  PubMed Central  Google Scholar 

Rizopoulos D, Ghosh P. A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-to-event. Stat Med. 2011;30(12):1366–80. https://doi.org/10.1002/sim.4205.

Article  PubMed  Google Scholar 

Lubetzky M, Tantisattamo E, Molnar MZ, Lentine KL, Basu A, Parsons RF, et al. The failing kidney allograft: a review and recommendations for the care and management of a complex group of patients. Am J Transplant. 2021;21(9):2937–49. https://doi.org/10.1111/ajt.16717.

Article  PubMed  Google Scholar 

Kaboré R, Haller MC, Harambat J, Heinze G, Leffondré K. Risk prediction models for graft failure in kidney transplantation: a systematic review. Nephrol Dial Transplant. 2017;32(suppl_2):ii68–76. https://doi.org/10.1093/ndt/gfw405.

Article  PubMed  Google Scholar 

Loupy A, Aubert O, Orandi BJ, Naesens M, Bouatou Y, Raynaud M, et al. Prediction system for risk of allograft loss in patients receiving kidney transplants: international derivation and validation study. BMJ. 2019;366:l4923. https://doi.org/10.1136/bmj.l4923.

Article  PubMed  PubMed Central  Google Scholar 

Udomkarnjananun S, Townamchai N, Kerr SJ, Tasanarong A, Noppakun K, Lumpaopong A, et al. The first Asian kidney transplantation prediction models for long-term patient and allograft survival. Transplantation. 2020;104(5):1048–57. https://doi.org/10.1097/TP.0000000000002918.

Article  PubMed  Google Scholar 

Senanayake S, Kularatna S, Healy H, Graves N, Baboolal K, Sypek MP, et al. Development and validation of a risk index to predict kidney graft survival: the kidney transplant risk index. BMC Med Res Methodol. 2021;21(1):127. https://doi.org/10.1186/s12874-021-01319-5.

Article  PubMed  PubMed Central  Google Scholar 

Miller G, Ankerst DP, Kattan MW, Hüser N, Vogelaar S, Tieken I, et al. Kidney Transplantation Outcome Predictions (KTOP): A Risk Prediction Tool for Kidney Transplants from Brain-dead Deceased Donors Based on a Large European Cohort. Eur Urol. 2022 (In Press). https://doi.org/10.1016/j.eururo.2021.12.008.

van Walraven C, Austin PC, Knoll G. Predicting potential survival benefit of renal transplantation in patients with chronic kidney disease. CMAJ. 2010;182(7):666–72. https://doi.org/10.1503/cmaj.091661.

Article  PubMed  PubMed Central  Google Scholar 

Hernández D, Rufino M, Bartolomei S, Lorenzo V, González-Rinne A, Torres A. A novel prognostic index for mortality in renal transplant recipients after hospitalization. Transplantation. 2005;79(3):337–43. https://doi.org/10.1097/01.tp.0000151003.30089.31.

Article  PubMed  Google Scholar 

Tiong HY, Goldfarb DA, Kattan MW, Alster JM, Thuita L, Yu C, et al. Nomograms for predicting graft function and survival in living donor kidney transplantation based on the UNOS registry. J Urol. 2009;181(3):1248–55. https://doi.org/10.1016/j.juro.2008.10.164.

Article  CAS  PubMed  Google Scholar 

Hernández D, Sánchez-Fructuoso A, González-Posada JM, Arias M, Campistol JM, Rufino M, et al. A novel risk score for mortality in renal transplant recipients beyond the first posttransplant year. Transplantation. 2009;88(6):803–9. https://doi.org/10.1097/TP.0b013e3181b4ac2f.

Article  PubMed  Google Scholar 

Dekker FW, Mutsert R, van Dijk PC, Zoccali C, Jager KJ. Survival analysis: time-dependent effects and time-varying risk factors. Kidney Int. 2008;74(8):994–7. https://doi.org/10.1038/ki.2008.328.

Article  PubMed  Google Scholar 

Yang Z, Wu H, Hou Y, Yuan H, Chen Z. Dynamic prediction and analysis based on restricted mean survival time in survival analysis with nonproportional hazards. Comput Methods Prog Biomed. 2021;207:106155. https://doi.org/10.1016/j.cmpb.2021.106155.

Article  Google Scholar 

Yang Z, Hou Y, Lyu J, Liu D, Chen Z. Dynamic prediction and prognostic analysis of patients with cervical cancer: a landmarking analysis approach. Ann Epidemiol. 2020;44:45–51. https://doi.org/10.1016/j.annepidem.2020.01.009.

Article  PubMed  Google Scholar 

Li L, Yang Z, Hou Y, Chen Z. Moving beyond the cox proportional hazards model in survival data analysis: a cervical cancer study. BMJ Open. 2020;10(7):e033965. https://doi.org/10.1136/bmjopen-2019-033965.

Article  PubMed  PubMed Central  Google Scholar 

Van Houwelingen HC. Dynamic prediction by landmarking in event history analysis. Scand J Stat. 2007;34(1):70–85. https://doi.org/10.1111/j.1467-9469.2006.00529.x.

Article  Google Scholar 

Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361–87. https://doi.org/10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4.

Graf E, Schmoor C, Sauerbrei W, Schumacher M. Assessment and comparison of prognostic classification schemes for survival data. Stat Med. 1999;18(17–18):2529–45. https://doi.org/10.1002/(sici)1097-0258(19990915/30)18:17/18<2529::aid-sim274>3.0.co;2-5.

Van Houwelingen HC, Putter H. Dynamic prediction in clinical survival analysis. Boca Raton: CRC Press; 2012.

Google Scholar 

Kosorok MR, Laber EB. Precision medicine. Annu Rev Stat Appl. 2019;6:263–86. https://doi.org/10.1146/annurev-statistics-030718-105251.

Article  PubMed  PubMed Central  Google Scholar 

Schumacher M, Hieke S, Ihorst G, Engelhardt M. Dynamic prediction: a challenge for biostatisticians, but greatly needed by patients, physicians and the public. Biom J. 2020;62(3):822–35. https://doi.org/10.1002/bimj.201800248.

Article  PubMed  Google Scholar 

Ferrer L, Putter H, Proust-Lima C. Individual dynamic predictions using landmarking and joint modelling: validation of estimators and robustness assessment. Stat Methods Med Res. 2019;28(12):3649–66. https://doi.org/10.1177/0962280218811837.

Article  PubMed  Google Scholar 

Liao L, Mark DB. Clinical prediction models: are we building better mouse traps? J Am Coll Cardiol. 2003;42(5):851–3. https://doi.org/10.1016/s0735-1097(03)00836-2.

Article  PubMed  Google Scholar 

Fournier MC, Foucher Y, Blanche P, Legendre C, Girerd S, Ladrière M, et al. Dynamic predictions of long-term kidney graft failure: an information tool promoting patient-centred care. Nephrol Dial Transplant. 2019;34(11):1961–9. https://doi.org/10.1093/ndt/gfz027.

Article  PubMed  Google Scholar 

Kaboré R, Ferrer L, Couchoud C, Hogan J, Cochat P, Dehoux L, et al. Dynamic prediction models for graft failure in paediatric kidney transplantation. Nephrol Dial Transplant. 2021;36(5):927–35. https://doi.org/10.1093/ndt/gfaa180.

Article  PubMed  Google Scholar 

Shah N, Al-Khoury S, Afzali B, Covic A, Roche A, Marsh J, et al. Posttransplantation anemia in adult renal allograft recipients: prevalence and predictors. Transplantation. 2006;81(8):1112–8. https://doi.org/10.1097/01.tp.0000205174.97275.b5.

Article  PubMed  Google Scholar 

Chhabra D, Grafals M, Skaro AI, Parker M, Gallon L. Impact of anemia after renal transplantation on patient and graft survival and on rate of acute rejection. Clin J Am Soc Nephrol. 2008;3(4):1168–74. https://doi.org/10.2215/CJN.04641007.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ahmad MS, Fatima R, Farooq H, Maham SN. Hemoglobin, ferritin levels and RBC indices among children entering school and study of their correlation with one another. J Pak Med Assoc. 2020;70(9):1582–6. https://doi.org/10.5455/JPMA.15046.

Article  PubMed  Google Scholar 

留言 (0)

沒有登入
gif