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Early risk identification models that include high-dimensional nursing data may improve the delivery of preventive interventions. This study aims to develop and validate models predicting functional impairment at hospital discharge (Activity Measure for Post Acute Care [AMPAC] score &lt;18) using electronic health record (EHR) data from the first 48\u2009h of ICU admission. We identified 799 sepsis survivors hospitalized in the ICU (April 2016\u2013May 2020) from a Midwestern health system\u2019s data warehouse. We extracted demographics, illness severity, nursing assessments, and ICU interventions. Given the limited availability of real-world EHR data, we employed CTAB-GAN, a generative adversarial network, to synthesize training data, enabling more robust model development. After feature engineering, 53 of 99 features were selected. We trained an eXtreme Gradient Boosting (XGBoost) classification model and used SHapley Additive exPlanations (SHAP) analysis to identify key predictors. Model performance was evaluated using the area under the receiver operating characteristic curves (AUC). For the 24-h model, the most critical features were first documented AMPAC score, age, mobility level, Braden Scale score, and walking device, while the 48-h model added body mass index and sequential organ failure assessment (SOFA) score as key predictors. Leveraging these findings, lightweight models were constructed using only the most important (top 5\/10) predictors, which achieved results comparable to the full predictor model, with AUCs of 0.83 (24\u2009h) and 0.83 (48\u2009h), respectively. Our model, which includes patient characteristics and nurse assessments, can identify patients during early ICU admission who are at high risk for functional impairment at hospital discharge. Our streamlined modeling approach highlights the potential for integration into EHR systems, providing a practical and efficient tool for clinical decision support while maintaining predictive accuracy.<\/jats:p>","DOI":"10.1177\/10547738251342845","type":"journal-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T02:56:43Z","timestamp":1750820203000},"page":"332-339","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Post-ICU Functional Impairment During Early ICU Admission Using Real-world Electronic Health Record Data"],"prefix":"10.1177","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0815-9441","authenticated-orcid":false,"given":"Anna","family":"Krupp","sequence":"first","affiliation":[{"name":"College of Nursing, University of Iowa, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"You","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Nursing, University of Iowa, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"name":"Industrial and Systems Engineering, College of Engineering, University of Iowa, Iowa City, IA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicholas M.","family":"Mohr","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Carver College of Medicine, University of Iowa, Iowa City, IA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laura","family":"Frey-Law","sequence":"additional","affiliation":[{"name":"Department of Physical Therapy and Rehabilitation Science, Carver College of Medicine, University of Iowa, Iowa City, IA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Barbara","family":"Rakel","sequence":"additional","affiliation":[{"name":"College of Nursing, University of Iowa, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,6,4]]},"reference":[{"key":"e_1_3_4_2_1","doi-asserted-by":"crossref","unstructured":"Auriemma C. 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