{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T21:46:14Z","timestamp":1790718374300,"version":"4.1.0"},"reference-count":32,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2013,4,25]],"date-time":"2013-04-25T00:00:00Z","timestamp":1366848000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J American Geriatrics Society"],"published-print":{"date-parts":[[2013,5]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>\n                      To develop and validate a model to predict 1\u2010year risk of end\u2010stage renal disease (\n                      <jats:styled-content style=\"fixed-case\">ESRD<\/jats:styled-content>\n                      ) in elderly subjects with advanced chronic kidney disease (\n                      <jats:styled-content style=\"fixed-case\">CKD<\/jats:styled-content>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>Retrospective.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>\n                      <jats:styled-content style=\"fixed-case\">V<\/jats:styled-content>\n                      eterans\n                      <jats:styled-content style=\"fixed-case\">A<\/jats:styled-content>\n                      ffairs\n                      <jats:styled-content style=\"fixed-case\">M<\/jats:styled-content>\n                      edical\n                      <jats:styled-content style=\"fixed-case\">C<\/jats:styled-content>\n                      enter.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Participants<\/jats:title>\n                    <jats:p>\n                      Individuals aged 65 and older with\n                      <jats:styled-content style=\"fixed-case\">CKD<\/jats:styled-content>\n                      with an estimated glomerular filtration rate (e\n                      <jats:styled-content style=\"fixed-case\">GFR<\/jats:styled-content>\n                      ) less than 30\u00a0mL\/min per 1.73\u00a0m\n                      <jats:sup>2<\/jats:sup>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Measurements<\/jats:title>\n                    <jats:p>\n                      The outcome was\n                      <jats:styled-content style=\"fixed-case\">ESRD<\/jats:styled-content>\n                      within 1\u00a0year of the index e\n                      <jats:styled-content style=\"fixed-case\">GFR<\/jats:styled-content>\n                      . Cox regression was used to develop a predictive model (\n                      <jats:styled-content style=\"fixed-case\">V<\/jats:styled-content>\n                      eterans\n                      <jats:styled-content style=\"fixed-case\">A<\/jats:styled-content>\n                      ffairs (\n                      <jats:styled-content style=\"fixed-case\">VA<\/jats:styled-content>\n                      ) risk score) that was validated in a separate cohort.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Of the 1,866 participants in the developmental cohort, 77 developed\n                      <jats:styled-content style=\"fixed-case\">ESRD<\/jats:styled-content>\n                      . Risk factors for\n                      <jats:styled-content style=\"fixed-case\">ESRD<\/jats:styled-content>\n                      in the final model were age, congestive heart failure, systolic blood pressure, e\n                      <jats:styled-content style=\"fixed-case\">GFR<\/jats:styled-content>\n                      , potassium, and albumin. In the validation cohort, the\n                      <jats:styled-content style=\"fixed-case\">C<\/jats:styled-content>\n                      index for the\n                      <jats:styled-content style=\"fixed-case\">VA<\/jats:styled-content>\n                      risk score was 0.823. The risk for developing\n                      <jats:styled-content style=\"fixed-case\">ESRD<\/jats:styled-content>\n                      at 1\u00a0year from lowest to highest tertile was 0.08%, 2.7%, and 11.3% (\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0&lt;\u00a0.001). The\n                      <jats:styled-content style=\"fixed-case\">C<\/jats:styled-content>\n                      \u2010index for the recently published\n                      <jats:styled-content style=\"fixed-case\">T<\/jats:styled-content>\n                      angri model in the validation cohort was 0.780.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      A new model using commonly available clinical measures shows excellent ability to predict the onset of\n                      <jats:styled-content style=\"fixed-case\">ESRD<\/jats:styled-content>\n                      within the next year in elderly adults. The\n                      <jats:styled-content style=\"fixed-case\">T<\/jats:styled-content>\n                      angri model also had good predictive ability. Individuals and physicians can use these risk models to inform decisions regarding preparation for renal replacement therapy in individuals with advanced\n                      <jats:styled-content style=\"fixed-case\">CKD<\/jats:styled-content>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jgs.12223","type":"journal-article","created":{"date-parts":[[2013,4,25]],"date-time":"2013-04-25T14:26:46Z","timestamp":1366900006000},"page":"762-768","source":"Crossref","is-referenced-by-count":48,"title":["A Simple Tool to Predict End\u2010Stage Renal Disease within 1\u00a0Year in Elderly Adults with Advanced Chronic Kidney Disease"],"prefix":"10.1111","volume":"61","author":[{"given":"Paul E.","family":"Drawz","sequence":"first","affiliation":[{"name":"Division of Renal Diseases and Hypertension University of Minnesota Minneapolis Minnesota"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Puja","family":"Goswami","sequence":"additional","affiliation":[{"name":"Division of Nephrology University of Michigan Ann Arbor Michigan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reem","family":"Azem","sequence":"additional","affiliation":[{"name":"Case Western Reserve University Cleveland Ohio"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Denise C.","family":"Babineau","sequence":"additional","affiliation":[{"name":"Case Western Reserve University Cleveland Ohio"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahboob","family":"Rahman","sequence":"additional","affiliation":[{"name":"Case Western Reserve University Cleveland Ohio"},{"name":"Division of Nephrology and Hypertension Louis Stokes Cleveland Veterans Affairs Medical Center University Hospitals\u2014Case Medical Center Cleveland Ohio"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2013,4,25]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.298.17.2038"},{"key":"e_1_2_7_3_1","first-page":"1","article-title":"In the clinic. 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