{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T06:30:34Z","timestamp":1772692234047,"version":"3.50.1"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T00:00:00Z","timestamp":1734480000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T00:00:00Z","timestamp":1734480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Universit\u00e4tsklinikum Augsburg"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Blood transfusion (BT) is a critical aspect of medical care for surgical patients in the Intensive Care Unit (ICU). Timely and accurate identification of BT needs can enhance patient outcomes and healthcare resource management.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>This study aims to determine whether a machine learning (ML) model can be trained to predict the need for blood transfusion (BT) in patients on the ICU after a wide range of surgeries, utilizing only data from the ICU.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>This retrospective study analyzed data from 9,118 surgical ICU patients from the Amsterdam University Medical Centers database (UMCdb). The study included a primary analysis using data from 6\u00a0h before ICU admission up to 1, 2, 3, and 6\u00a0h after admission, and a secondary analysis using only the data from 6\u00a0h before ICU admission and only the data from the first hour after admission. The model integrated 32 relevant clinical variables and compared the performance of XGBoost and logistic regression (LR) algorithms.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>The model demonstrated an effective BT prediction, with XGBoost outperforming LR, particularly for a 12-hour prediction window. Notable differences in patient characteristics were observed among those who received BT and those who did not receive BT. The study establishes the feasibility of using ML for the prediction of BT in surgical ICU patients. It underlines the potential of ML models as decision support tools in healthcare, enabling early identification of BT needs.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s12911-024-02800-z","type":"journal-article","created":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T16:31:06Z","timestamp":1734539466000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Predicting blood transfusion demand in intensive care patients after surgery by comparative analysis of temporally extended data selection"],"prefix":"10.1186","volume":"24","author":[{"given":"Seyedmostafa","family":"Sheikhalishahi","sequence":"first","affiliation":[]},{"given":"Sebastian","family":"Goss","sequence":"additional","affiliation":[]},{"given":"Lea K.","family":"Seidlmayer","sequence":"additional","affiliation":[]},{"given":"Sarra","family":"Zaghdoudi","sequence":"additional","affiliation":[]},{"given":"Ludwig C.","family":"Hinske","sequence":"additional","affiliation":[]},{"given":"Mathias","family":"Kaspar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,18]]},"reference":[{"key":"2800_CR1","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1097\/SLA.0b013e31824a55b9","volume":"255","author":"W-C Wu","year":"2012","unstructured":"Wu W-C, Trivedi A, Friedmann PD, Henderson WG, Smith TS, Poses RM, et al. Association between Hospital Intraoperative Blood Transfusion Practices for Surgical Blood loss and Hospital Surgical Mortality Rates. Ann Surg. 2012;255:708\u201314.","journal-title":"Ann Surg"},{"key":"2800_CR2","doi-asserted-by":"crossref","first-page":"2316","DOI":"10.1016\/j.joms.2020.07.213","volume":"78","author":"ZQ Liu","year":"2020","unstructured":"Liu ZQ, Wu HX, Cheng S, Liu XQ, Wang CL, Cao MH. Unnecessary blood transfusion prolongs length of Hospital Stay of patients who Undergo Free Fibular Flap Reconstruction of Mandibulofacial defects: a Propensity score\u2013matched study. J Oral Maxillofac Surg. 2020;78:2316\u201327.","journal-title":"J Oral Maxillofac Surg"},{"key":"2800_CR3","doi-asserted-by":"crossref","first-page":"e899","DOI":"10.1016\/j.jormas.2022.03.005","volume":"123","author":"C Wang","year":"2022","unstructured":"Wang C, Han Z, Wang M, Hu C, Ji F, Cao M, et al. 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The steering group of AmsterdamUMCdb has granted permission for the use of its data by third parties for research purposes, as per the data use agreement. This research, conducted on anonymized data, is exempt from the requirement of ethical review\u00a0(see []).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"397"}}