{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T18:25:23Z","timestamp":1793125523583,"version":"4.5.0"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Urosepsis is a critical condition originating from urinary tract infections, characterized by rapid progression and high mortality. The Pan-immune-inflammation Value (PIV), a novel composite index reflecting integrated immune and inflammatory status, has shown prognostic value in various critically ill and septic populations. However, its independent prognostic value in urosepsis remains inadequately explored. This study therefore aims to systematically evaluate the ability of PIV to predict 28-day adverse outcomes in patients with urosepsis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>This study employed a retrospective dual-cohort design. The internal training cohort comprised adult ICU patients with urosepsis from the MIMIC-IV database, while the external validation cohort was derived from the electronic medical record system of Anshun Municipal People\u2019s Hospital. To investigate the association between the PIV and short-term adverse outcomes in urosepsis patients, we utilized a range of statistical methods, including multivariable Cox regression, restricted cubic spline (RCS) analysis, subgroup analysis, and Kaplan-Meier survival curves. To develop a parsimonious predictive model, the internal cohort was randomly split into training and testing sets at a 7:3 ratio. Within the training set, an ensemble machine learning strategy\u2014incorporating the Boruta algorithm, LASSO-Cox regression, random forest (RF), gradient boosting (GBDT), and support vector machine (SVM)\u2014was applied to identify key predictive variables from serological tests, comorbidities, demographic characteristics, and vital signs. Based on the selected features, prognostic models were constructed using multivariable Cox regression in the training, testing, and external validation sets, respectively. The discriminatory power of these models against traditional disease severity scores was assessed using receiver operating characteristic (ROC) curves, with the area under the curve (AUC) quantifying predictive performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Result<\/jats:title>\n                    <jats:p>\n                      A total of 1,686 patients with severe urosepsis were included in this study. In the fully adjusted model, both continuous PIV and PIV quartiles were independently associated with 28-day adverse outcomes. For 28-day ICU mortality, each unit increase in continuous PIV was associated with a 76.4% higher risk (HR 1.764, 95% CI 1.340\u20132.323,\n                      <jats:italic>p<\/jats:italic>\n                      &amp;lt; 0.001); compared with the lowest quartile (Q1), the highest quartile (Q4) showed a significantly increased risk of 84.3% (HR 1.843, 95% CI 1.186\u20132.846,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.007). For 28-day in-hospital mortality, the HR for continuous PIV was 1.639 (95% CI 1.214\u20132.214,\n                      <jats:italic>p<\/jats:italic>\n                      &amp;lt; 0.001), and the HR for Q4 was 1.789 (95% CI 1.110\u20132.885,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.039). In the external validation cohort, the predictive value of PIV for 28-day ICU mortality remained consistent and was even more pronounced (continuous PIV: HR 1.79, 95% CI 1.21\u20132.64,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.004). Restricted cubic spline analysis further confirmed a significant positive dose-response relationship between PIV and mortality (overall\n                      <jats:italic>p<\/jats:italic>\n                      &amp;lt; 0.001). The risk prediction model based on PIV demonstrated good discriminative ability, with areas under the receiver operating characteristic curve of 0.71, 0.73, and 0.76 in the training, internal testing, and external validation sets, respectively, all of which were higher than those of traditional severity scores (e.g., SOFA, APACHE II).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study confirms PIV as an independent predictor of 28-day mortality risk in patients with urosepsis across two cohorts. The prediction model incorporating PIV and four other clinical variables exhibited good discrimination and calibration, with prognostic performance superior to that of traditional disease severity scores. Future prospective, multicenter studies involving diverse geographic and ethnic populations are needed to further validate the generalizability of this model, thereby facilitating precise risk stratification for patients with urosepsis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1802352","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T05:39:38Z","timestamp":1783661978000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["The relationship between pan immune inflammatory index (PIV) and 28 day mortality in patients with severe urinary sepsis: a retrospective study of a multinational dual cohort study"],"prefix":"10.3389","volume":"13","author":[{"given":"Wenjun","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Urology, Anshun People\u2019s Hospital","place":["Anshun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunyuan","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Urology, Xingyi People\u2019s Hospital","place":["Xingyi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaobo","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Urology, The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine","place":["Guiyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guosheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Urology, Anshun People\u2019s Hospital","place":["Anshun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengjia","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Urology, Anshun People\u2019s Hospital","place":["Anshun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Urology, Anshun People\u2019s Hospital","place":["Anshun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaifa","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Urology, The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine","place":["Guiyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1001\/jama.2016.0287","article-title":"The third international consensus definitions for sepsis and septic shock 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