Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multicohort study.

Autor:	Raynaud M; Paris Translational Research Centre for Organ Transplantation, INSERM, PARCC, Université de Paris, Paris, France., Aubert O; Paris Translational Research Centre for Organ Transplantation, INSERM, PARCC, Université de Paris, Paris, France; Kidney Transplant Department, Necker Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France., Divard G; Paris Translational Research Centre for Organ Transplantation, INSERM, PARCC, Université de Paris, Paris, France; Kidney Transplant Department, Necker Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France., Reese PP; Paris Translational Research Centre for Organ Transplantation, INSERM, PARCC, Université de Paris, Paris, France; Renal Electrolyte and Hypertension Division, University of Pennsylvania School of Medicine, Philadelphia, PA, USA., Kamar N; Université Paul Sabatier, INSERM, Department of Nephrology and Organ Transplantation, CHU Rangueil & Purpan, Toulouse, France., Yoo D; Paris Translational Research Centre for Organ Transplantation, INSERM, PARCC, Université de Paris, Paris, France., Chin CS; Deep Learning in Medicine and Genomics, DNAnexus, San Francisco, CA, USA., Bailly É; Nephrology and Immunology Department, Bretonneau Hospital, Tours, France., Buchler M; Nephrology and Immunology Department, Bretonneau Hospital, Tours, France., Ladrière M; Nephrology Dialysis Transplantation Department, University of Lorraine, Centre Hospitalier Universitaire Nancy, Nancy, France., Le Quintrec M; Department of Nephrology, Centre Hospitalier Universitaire Montpellier, Montpellier, France., Delahousse M; Department of Transplantation, Nephrology and Clinical Immunology, Foch Hospital, Suresnes, France., Juric I; Department of Nephrology, Arterial Hypertension, Dialysis and Transplantation, University Hospital Centre Zagreb, School of Medicine University of Zagreb, Zagreb, Croatia., Basic-Jukic N; Department of Nephrology, Arterial Hypertension, Dialysis and Transplantation, University Hospital Centre Zagreb, School of Medicine University of Zagreb, Zagreb, Croatia., Crespo M; Department of Nephrology, Hospital del Mar Barcelona, Spain., Silva HT Jr; Hospital do Rim, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil., Linhares K; Hospital do Rim, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil., Ribeiro de Castro MC; Renal Transplantation Service, Hospital das Clinicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil., Soler Pujol G; Unidad de Trasplante Renopancreas, Centro de Educacion Medica e Investigaciones Clinicas Buenos Aires, Buenos Aires, Argentina., Empana JP; Paris Translational Research Centre for Organ Transplantation, INSERM, PARCC, Université de Paris, Paris, France., Ulloa C; Kidney Transplantation Department, Clinica Alemana de Santiago, Santiago, Chile., Akalin E; Renal Division Montefiore Medical Centre, Kidney Transplantation Program, Albert Einstein College of Medicine, NY, USA., Böhmig G; Division of Nephrology and Dialysis, Department of Medicine III, General Hospital Vienna, Vienna, Austria., Huang E; Department of Medicine, Division of Nephrology, Comprehensive Transplant Centre, Cedars Sinai Medical Centre, Los Angeles, CA, USA., Stegall MD; William J von Liebig Centre for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, MN, USA., Bentall AJ; William J von Liebig Centre for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, MN, USA., Montgomery RA; New York University Langone Transplant Institute, New York, NY, USA., Jordan SC; Department of Medicine, Division of Nephrology, Comprehensive Transplant Centre, Cedars Sinai Medical Centre, Los Angeles, CA, USA., Oberbauer R; Nephrology, Medical University of Vienna, Vienna, Austria., Segev DL; Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Friedewald JJ; Kidney Transplantation Department, Northwestern University Feinberg School of Medicine, Chicago, IL, USA., Jouven X; Paris Translational Research Centre for Organ Transplantation, INSERM, PARCC, Université de Paris, Paris, France; Cardiology Department, European Georges Pompidou Hospital, Paris, France., Legendre C; Paris Translational Research Centre for Organ Transplantation, INSERM, PARCC, Université de Paris, Paris, France; Kidney Transplant Department, Necker Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France., Lefaucheur C; Paris Translational Research Centre for Organ Transplantation, INSERM, PARCC, Université de Paris, Paris, France; Kidney Transplant Department, Saint-Louis Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France., Loupy A; Paris Translational Research Centre for Organ Transplantation, INSERM, PARCC, Université de Paris, Paris, France; Kidney Transplant Department, Necker Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France. Electronic address: alexandre.loupy@inserm.fr.
Jazyk:	angličtina
Zdroj:	The Lancet. Digital health [Lancet Digit Health] 2021 Dec; Vol. 3 (12), pp. e795-e805. Date of Electronic Publication: 2021 Oct 28.
DOI:	10.1016/S2589-7500(21)00209-0
Abstrakt:	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. Competing Interests: Declaration of interests AL holds shares in Cibiltech, a company that develops software and IT solutions. C-SC is affiliated with Deep Learning in Medicine and Genomics, DNAnexus. All other authors declare no competing interests. (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)
Databáze:	MEDLINE
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