{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T15:47:44Z","timestamp":1776959264020,"version":"3.51.4"},"reference-count":38,"publisher":"American Medical Association (AMA)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n Importance<\/jats:title>\n Accurate prediction of patient mortality after kidney transplant is an unmet need.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To develop and validate an integrative prediction model that predicts short- and long-term patient mortality for kidney recipients.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n This international cohort study included patients who underwent transplants between 2004 and 2023 from 14 academic medical centers from Europe and the US. The derivation cohort consisted of 1566 consecutive adult kidney recipients in a deeply phenotyped cohort prospectively recruited in 1 French center between 2004 and 2014. External validation cohorts consisted of 10\u202f951 kidney recipients from 7 centers in France, 3 centers in Europe, and 3 centers in North America who underwent kidney transplants between 2006 and 2023. Data were analyzed from January 2023 to June 2025.<\/jats:p>\n <\/jats:sec>\n \n Main Outcome and Measures<\/jats:title>\n All-cause mortality was assessed, and 121 candidate prognostic factors, comprising demographics and clinical, biological, imaging, and immunological parameters, were collected.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Among 12\u202f517 kidney transplant recipients, including 1566 in the derivation cohort (mean [SD] age, 50.05 [14.31] years; 942 male [60.15%]) and 10\u202f951 in validation cohorts (mean [SD] age, 53.32 [13.97] years; 6766 male [61.78%]), 2486 patients (19.9%) died after a median (IQR) follow-up of 5.08 (2.97-7.00) years. Fourteen prognostic factors (including clinical, biological, and imaging risk factors) were independently associated with patient death (eg, patient age: hazard ratio per 1-year increase in age, 1.07 [95% CI, 1.06-1.08];\n P<\/jats:italic>\n \u2009&amp;lt;\u2009.001) and were combined into a risk prediction model (mBox). The model exhibited accurate calibration and discrimination in the derivation cohort, with C statistics of 0.82 (95% CI, 0.77-0.87) and 0.80 (95% CI, 0.78-0.82) at 1 and 10 years, respectively, after the transplant. Abbreviated models were developed and validated to ensure model generalizability. Performance of abbreviated models was confirmed in external validation cohorts from France (C statistic, 0.76 [95% CI, 0.73-0.78]), Europe (C statistic, 0.74 [95% CI, 0.72-0.76]), the US (C statistic, 0.74 [95% CI, 0.70-0.78]), the Greater Paris University Hospital database (C statistic, 0.79 [95% CI, 0.77-0.81]), and the University of California at San Francisco database (C statistic, 0.70 [95% CI, 0.65-0.74]). The model was also validated in a series of subpopulations (eg, women vs men: C statistic, 0.81 [95% CI, 0.77-0.84] vs 0.79 [95% CI, 0.77-0.82]) and clinical scenarios (eg, before COVID-19 era: C statistic, 0.79 [95% CI, 0.77-0.81]) with good and stable performance.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n In this study, an accurate prediction model for mortality among kidney recipients, computable at the time of transplant, was developed and externally validated. Results suggest that this model may help stratify patient risk of death, allowing for improved medical decisions.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2026.7452","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T15:30:21Z","timestamp":1776958221000},"page":"e267452","source":"Crossref","is-referenced-by-count":0,"title":["A Prediction Model for Risk of Death in Kidney Transplant Recipients"],"prefix":"10.1001","volume":"9","author":[{"given":"Charlotte","family":"Debiais-Deschamps","sequence":"first","affiliation":[{"name":"Paris Institute for Transplantation and Organ Regeneration, Institut National de la Sant\u00e9 et de la Recherche M\u00e9dicale U970, Universit\u00e9 Paris Cit\u00e9, Paris, France"},{"name":"Kidney Transplant Department, Necker Hospital, Assistance Publique-H\u00f4pitaux de Paris, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc","family":"Raynaud","sequence":"additional","affiliation":[{"name":"Paris Institute for Transplantation and Organ Regeneration, Institut National de la Sant\u00e9 et de la Recherche M\u00e9dicale U970, Universit\u00e9 Paris Cit\u00e9, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Agathe","family":"Truchot","sequence":"additional","affiliation":[{"name":"Paris Institute for Transplantation and Organ Regeneration, Institut National de la Sant\u00e9 et de la Recherche M\u00e9dicale U970, Universit\u00e9 Paris Cit\u00e9, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yannis","family":"Lombardi","sequence":"additional","affiliation":[{"name":"Paris Institute for Transplantation and Organ Regeneration, Institut National de la Sant\u00e9 et de la Recherche M\u00e9dicale U970, Universit\u00e9 Paris Cit\u00e9, Paris, France"},{"name":"Department of Nephrology and Renal Transplantation, H\u00f4pital Tenon, Assistance Publique-H\u00f4pitaux de Paris, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gillian","family":"Divard","sequence":"additional","affiliation":[{"name":"Paris Institute for Transplantation and Organ Regeneration, Institut National de la Sant\u00e9 et de la Recherche M\u00e9dicale U970, Universit\u00e9 Paris Cit\u00e9, Paris, France"},{"name":"Kidney Transplant Department, Saint-Louis Hospital, Assistance Publique-H\u00f4pitaux de Paris, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vivek A.","family":"Rudrapatna","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology and Hepatology, Department of Medicine, University of California, San Francisco"},{"name":"Bakar Computational Health Sciences Institute, University of California, San Francisco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soufian","family":"Meziyerh","sequence":"additional","affiliation":[{"name":"Leiden Transplant Center, Leiden University Medical Center, Leiden University, Leiden, the Netherlands"},{"name":"Division of Nephrology, Department of Internal Medicine, Leiden University Medical Center, Leiden University, Leiden, the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jesper","family":"Kers","sequence":"additional","affiliation":[{"name":"Leiden Transplant Center, Leiden University Medical Center, Leiden University, Leiden, the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danny","family":"van der Helm","sequence":"additional","affiliation":[{"name":"Leiden Transplant Center, Leiden University Medical Center, Leiden University, Leiden, the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aiko P. 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