TY - JOUR
T1 - Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence
T2 - an observational, international, multicohort study
AU - Raynaud, Marc
AU - Aubert, Olivier
AU - Divard, Gillian
AU - Reese, Peter P.
AU - Kamar, Nassim
AU - Yoo, Daniel
AU - Chin, Chen Shan
AU - Bailly, Élodie
AU - Buchler, Matthias
AU - Ladrière, Marc
AU - Le Quintrec, Moglie
AU - Delahousse, Michel
AU - Juric, Ivana
AU - Basic-Jukic, Nikolina
AU - Crespo, Marta
AU - Silva, Helio Tedesco
AU - Linhares, Kamilla
AU - Ribeiro de Castro, Maria Cristina
AU - Soler Pujol, Gervasio
AU - Empana, Jean Philippe
AU - Ulloa, Camilo
AU - Akalin, Enver
AU - Böhmig, Georg
AU - Huang, Edmund
AU - Stegall, Mark D.
AU - Bentall, Andrew J.
AU - Montgomery, Robert A.
AU - Jordan, Stanley C.
AU - Oberbauer, Rainer
AU - Segev, Dorry L.
AU - Friedewald, John J.
AU - Jouven, Xavier
AU - Legendre, Christophe
AU - Lefaucheur, Carmen
AU - Loupy, Alexandre
N1 - Publisher Copyright:
© 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license
PY - 2021/12
Y1 - 2021/12
N2 - Background: Kidney allograft failure is a common cause of end-stage renal disease. We aimed to develop a dynamic artificial intelligence approach to enhance risk stratification for kidney transplant recipients by generating continuously refined predictions of survival using updates of clinical data. Methods: In this observational study, we used data from adult recipients of kidney transplants from 18 academic transplant centres in Europe, the USA, and South America, and a cohort of patients from six randomised controlled trials. The development cohort comprised patients from four centres in France, with all other patients included in external validation cohorts. To build deeply phenotyped cohorts of transplant recipients, the following data were collected in the development cohort: clinical, histological, immunological variables, and repeated measurements of estimated glomerular filtration rate (eGFR) and proteinuria (measured using the proteinuria to creatininuria ratio). To develop a dynamic prediction system based on these clinical assessments and repeated measurements, we used a Bayesian joint models—an artificial intelligence approach. The prediction performances of the model were assessed via discrimination, through calculation of the area under the receiver operator curve (AUC), and calibration. This study is registered with ClinicalTrials.gov, NCT04258891. Findings: 13 608 patients were included (3774 in the development cohort and 9834 in the external validation cohorts) and contributed 89 328 patient-years of data, and 416 510 eGFR and proteinuria measurements. Bayesian joint models showed that recipient immunological profile, allograft interstitial fibrosis and tubular atrophy, allograft inflammation, and repeated measurements of eGFR and proteinuria were independent risk factors for allograft survival. The final model showed accurate calibration and very high discrimination in the development cohort (overall dynamic AUC 0·857 [95% CI 0·847–0·866]) with a persistent improvement in AUCs for each new repeated measurement (from 0·780 [0·768–0·794] to 0·926 [0·917–0·932]; p<0·0001). The predictive performance was confirmed in the external validation cohorts from Europe (overall AUC 0·845 [0·837–0·854]), the USA (overall AUC 0·820 [0·808–0·831]), South America (overall AUC 0·868 [0·856–0·880]), and the cohort of patients from randomised controlled trials (overall AUC 0·857 [0·840–0·875]). Interpretation: Because of its dynamic design, this model can be continuously updated and holds value as a bedside tool that could refine the prognostic judgements of clinicians in everyday practice, hence enhancing precision medicine in the transplant setting. Funding: MSD Avenir, French National Institute for Health and Medical Research, and Bettencourt Schueller Foundation.
AB - Background: Kidney allograft failure is a common cause of end-stage renal disease. We aimed to develop a dynamic artificial intelligence approach to enhance risk stratification for kidney transplant recipients by generating continuously refined predictions of survival using updates of clinical data. Methods: In this observational study, we used data from adult recipients of kidney transplants from 18 academic transplant centres in Europe, the USA, and South America, and a cohort of patients from six randomised controlled trials. The development cohort comprised patients from four centres in France, with all other patients included in external validation cohorts. To build deeply phenotyped cohorts of transplant recipients, the following data were collected in the development cohort: clinical, histological, immunological variables, and repeated measurements of estimated glomerular filtration rate (eGFR) and proteinuria (measured using the proteinuria to creatininuria ratio). To develop a dynamic prediction system based on these clinical assessments and repeated measurements, we used a Bayesian joint models—an artificial intelligence approach. The prediction performances of the model were assessed via discrimination, through calculation of the area under the receiver operator curve (AUC), and calibration. This study is registered with ClinicalTrials.gov, NCT04258891. Findings: 13 608 patients were included (3774 in the development cohort and 9834 in the external validation cohorts) and contributed 89 328 patient-years of data, and 416 510 eGFR and proteinuria measurements. Bayesian joint models showed that recipient immunological profile, allograft interstitial fibrosis and tubular atrophy, allograft inflammation, and repeated measurements of eGFR and proteinuria were independent risk factors for allograft survival. The final model showed accurate calibration and very high discrimination in the development cohort (overall dynamic AUC 0·857 [95% CI 0·847–0·866]) with a persistent improvement in AUCs for each new repeated measurement (from 0·780 [0·768–0·794] to 0·926 [0·917–0·932]; p<0·0001). The predictive performance was confirmed in the external validation cohorts from Europe (overall AUC 0·845 [0·837–0·854]), the USA (overall AUC 0·820 [0·808–0·831]), South America (overall AUC 0·868 [0·856–0·880]), and the cohort of patients from randomised controlled trials (overall AUC 0·857 [0·840–0·875]). Interpretation: Because of its dynamic design, this model can be continuously updated and holds value as a bedside tool that could refine the prognostic judgements of clinicians in everyday practice, hence enhancing precision medicine in the transplant setting. Funding: MSD Avenir, French National Institute for Health and Medical Research, and Bettencourt Schueller Foundation.
UR - http://www.scopus.com/inward/record.url?scp=85119511452&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85119511452&partnerID=8YFLogxK
U2 - 10.1016/S2589-7500(21)00209-0
DO - 10.1016/S2589-7500(21)00209-0
M3 - Article
C2 - 34756569
AN - SCOPUS:85119511452
SN - 2589-7500
VL - 3
SP - e795-e805
JO - The Lancet Digital Health
JF - The Lancet Digital Health
IS - 12
ER -