{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T09:57:24Z","timestamp":1776938244096,"version":"3.51.4"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"7-8","license":[{"start":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T00:00:00Z","timestamp":1769299200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/deed.de"},{"start":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T00:00:00Z","timestamp":1769299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/deed.de"}],"funder":[{"DOI":"10.13039\/501100011852","name":"Paracelsus Medical University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100011852","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Wien Med Wochenschr"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The red cell distribution width (RDW) measures erythrocyte size variability and is linked to increased mortality in various diseases, including cardiovascular, kidney, and liver conditions. In critically ill patients, particularly those with sepsis, RDW is a\u00a0prognostic marker. While its role in cardiac surgery patients is well established, its value in surgical patients is less well explored. This study investigates whether a\u00a0single postoperative RDW measurement predicts short- and long-term mortality surgical intensive care unit (ICU) patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective cohort study analyzed data from 2312 surgery patients admitted to a\u00a0surgical ICU over 2\u00a0years. The RDW was measured within 2\u202fh of surgery and analyzed as a\u00a0continuous and binary variable (threshold\u00a015). Mortality at 30\u00a0days and 1\u00a0year was assessed using univariable and multivariable logistic regression and Cox regression models, adjusting for factors like the simplified acute physiology score\u00a03 (SAPS3), lactate levels, age, sex, and comorbidities. Interaction analyses evaluated the impact of cofactor on RDW\u2019s mortality prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Of 2312 patients, 1687 (73.0%) had RDW &lt;\u202f15 and 625 (27.0%) had RDW\u202f\u2265\u200915. The RDW\u202f\u2265\u200915\u00a0patients were older (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&lt;\u20090.001), had higher SAPS3 (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&lt;\u20090.001), and more comorbidities. Elevated RDW was independently associated with increased 30-day and 1\u2011year mortality. In univariable analysis, each unit increase in RDW was linked to higher 30-day mortality (HR 1.169, 95% CI 1.110\u20131.230;\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&lt;\u20090.001) and 1\u2011year mortality (HR 1.153, 95% CI 1.122\u20131.186;\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&lt;\u20090.001). Moreover, RDW\u202f\u2265\u200915 significantly increased the risk of 30-day (HR [Hazard Ratio] 3.247, 95% CI 2.352\u20134.482;\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&lt;\u20090.001) and 1\u2011year mortality (HR 3.278, 95% CI 2.654\u20134.048;\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&lt;\u20090.001). Interaction analyses showed that lactate levels and pre-existing lung diseases influenced RDW\u2019s mortality prediction. Including RDW in multivariable models improved predictive accuracy, as indicated by the Akaike information criterion.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Postoperative RDW is a\u00a0reliable and cost-effective marker for predicting short- and long-term mortality in surgical patients. An RDW threshold of 15\u00a0identifies high-risk patients who may benefit from targeted follow-up, thus potentially improving outcomes. In summary, RDW enhances postoperative risk stratification and management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s10354-026-01129-8","type":"journal-article","created":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T15:58:39Z","timestamp":1769356719000},"page":"206-215","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A\u00a0single postoperative red cell distribution width measurement predicts short- and long-term mortality in surgical patients","Eine einzelne postoperative Messung der Erythrozytenverteilungsbreite sagt kurz- und langfristige Mortalit\u00e4t bei chirurgischen Patient:innen voraus"],"prefix":"10.1007","volume":"176","author":[{"given":"Daniel","family":"Dankl","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernhard","family":"Wernly","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Niklas","family":"Rodemund","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"Rezar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Barbara","family":"Schreiber","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Kok\u00f6fer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,25]]},"reference":[{"key":"1129_CR1","doi-asserted-by":"publisher","DOI":"10.1038\/srep43420","volume":"7","author":"H Hou","year":"2017","unstructured":"Hou\u00a0H, Sun\u00a0T, Li\u00a0C, Li\u00a0Y, Guo\u00a0Z, Wang\u00a0W, Deng\u00a0M, Li\u00a0L, Tian\u00a0H. 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