{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T08:24:11Z","timestamp":1786955051587,"version":"build-2736575974"},"reference-count":34,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"vor","delay-in-days":228,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82372206"],"award-info":[{"award-number":["82372206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017962","name":"Jiangsu Commission of Health","doi-asserted-by":"publisher","award":["H2023107"],"award-info":[{"award-number":["H2023107"]}],"id":[{"id":"10.13039\/100017962","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014358","name":"Second Affiliated Hospital of Soochow University","doi-asserted-by":"publisher","award":["XKTJ-XK202408-2"],"award-info":[{"award-number":["XKTJ-XK202408-2"]}],"id":[{"id":"10.13039\/501100014358","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Emergency Medicine International"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cardiac arrest (CA) causes systemic circulatory failure, leading to postcardiac arrest syndrome (PCAS) and high mortality. Lactic clearance rate (LCR) and albumin\u2010corrected anion gap (ACAG) reflect metabolic status but lack combined validation for predicting short\u2010term mortality in CA patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study explores the predictive value of LCR at different time points and ACAG for 7\u2010day mortality in post\u2010CA patients and constructs a predictive model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Using the MIMIC\u2010IV 3.0 database, we included eligible CA patients and calculated LCR at 6, 12, and 24\u2009h, along with initial ACAG. ROC curves and logistic regression were used to assess predictive value, while restricted cubic splines (RCS) explored mortality risk. The best predictors were selected for model construction, with multivariate logistic regression identifying independent risk factors. Internal and external validations were performed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 821 patients were included, with a 7\u2010day mortality rate of 34.47%. LCR was lower, and ACAG was higher in nonsurvivors (\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.05). The 24\u2010h LCR combined with ACAG had the highest predictive efficacy (area under the ROC curve [AUC]\u2009=\u20090.716). Independent risk factors included 24\u2010h LCR, ACAG, CK\u2013MB, shockable rhythm, and CA etiology. The final model achieved an AUC of 0.775 (internal) and 0.743 (external).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The 24\u2010h LCR combined with ACAG predicts short\u2010term mortality in post\u2010CA patients who survive the first 24\u2009h, supporting risk stratification after early postresuscitation stabilization.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/emmi\/3964486","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T07:29:38Z","timestamp":1786951778000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Construction of a Mortality Prediction Model for Cardiac Arrest Patients Using Lactate Clearance Rate and Albumin\u2010Corrected Anion Gap"],"prefix":"10.1155","volume":"2026","author":[{"given":"Yani","family":"Gao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4344-2411","authenticated-orcid":false,"given":"Sheng","family":"Bi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9021-0831","authenticated-orcid":false,"given":"Haoran","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojin","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0740-6694","authenticated-orcid":false,"given":"Chaogang","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9386-3869","authenticated-orcid":false,"given":"Xuming","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8179-0128","authenticated-orcid":false,"given":"Yan","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,17]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.1161\/cir.0000000000001123"},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.1161\/cir.0000000000001209"},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.1097\/ana.0000000000000921"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.1161\/cir.0000000000000697"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jchf.2023.01.013"},{"key":"e_1_2_12_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.redox.2024.103194"},{"key":"e_1_2_12_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.resuscitation.2024.110149"},{"key":"e_1_2_12_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.burns.2020.06.003"},{"key":"e_1_2_12_9_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40560-018-0302-z"},{"key":"e_1_2_12_10_2","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s00063-019-0538-y","article-title":"Metabolische Azidose: Diagnostik und Therapie","volume":"115","author":"Schricker S.","year":"2020","journal-title":"Medizinische Klinik-Intensivmedizin und Notfallmedizin"},{"key":"e_1_2_12_11_2","doi-asserted-by":"publisher","DOI":"10.1093\/ajhp\/zxab423"},{"key":"e_1_2_12_12_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2023.1256804"},{"key":"e_1_2_12_13_2","doi-asserted-by":"publisher","DOI":"10.5761\/atcs.oa.12.01942"},{"key":"e_1_2_12_14_2","doi-asserted-by":"publisher","DOI":"10.29271\/jcpsp.2025.03.267"},{"key":"e_1_2_12_15_2","volume-title":"Medical Statistics","author":"Sun Z. 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