{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T17:10:28Z","timestamp":1778605828245,"version":"3.51.4"},"reference-count":30,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objectives: Failure to rescue deteriorating ward patients before irreversible organ injury remains a leading cause of preventable in-hospital mortality, yet current rapid response team (RRT) research relies predominantly on cross-sectional comparisons at the moment of activation, overlooking the short-horizon physiological changes that precede it. Methods: This retrospective cohort study at a tertiary academic hospital in South Korea included 549 adults (191 ICU-transferred, 358 ward-remaining) with a first RRT activation between September 2023 and August 2025. Generalised estimating equations (GEE) with a time \u00d7 group interaction modelled differential changes in 12 laboratory variables and the DeepCARS AI-derived risk score between 24 h before activation (T\u221224 h) and the moment of activation (T0). At T\u221224 h, physiological profiles were largely similar between groups, indicating that conventional static assessment failed to identify patients destined for ICU transfer. Results: Over the ensuing 24 h, patients subsequently transferred to the ICU showed a steeper decline in SpO2\/FiO2 (S\/F) ratio (383.4 \u2192 167.1 vs. 369.1 \u2192 260.3; B = \u22120.547, p &lt; 0.001) and steeper increases in lactate (2.91 \u2192 4.02 vs. 2.05 \u2192 2.98 mmol\/L; B = 0.154, p = 0.045), creatinine (B = 0.076, p = 0.038), potassium (B = 0.019, p = 0.001), and DeepCARS score (B = 0.073, p = 0.028) compared with patients remaining on the ward. All five variables retained significance under Benjamini\u2013Hochberg false discovery rate correction (q &lt; 0.10). Seven inflammatory and haematological markers showed no differential change. Procalcitonin was excluded from the primary analysis because of very high missingness at the pre-activation time point (approximately 75%). Conclusions: These findings demonstrate that short-horizon deterioration in oxygenation, perfusion, and renal function\u2014rather than any single earlier measurement\u2014distinguishes patients requiring ICU transfer, supporting the development of change-based early warning criteria to enable earlier clinical escalation.<\/jats:p>","DOI":"10.3390\/jcm15103722","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:04:36Z","timestamp":1778601876000},"page":"3722","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Early Physiological Changes Before Rapid Response Team Activation Differentiate Patients Requiring ICU Transfer: A Retrospective Cohort Study"],"prefix":"10.3390","volume":"15","author":[{"given":"Bumin","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Nursing, Keimyung University Dongsan Hospital, Daegu 42601, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sumin","family":"Gwon","sequence":"additional","affiliation":[{"name":"Department of Nursing, Keimyung University Dongsan Hospital, Daegu 42601, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaeun","family":"Kim","sequence":"additional","affiliation":[{"name":"College of Nursing, Keimyung University, Daegu 42601, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.resuscitation.2016.02.005","article-title":"The value of vital sign trends for detecting clinical deterioration on the wards","volume":"102","author":"Churpek","year":"2016","journal-title":"Resuscitation"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1097\/01.CCM.0000254826.10520.87","article-title":"Can physiological variables and early warning scoring systems allow early recognition of the deteriorating surgical patient?","volume":"35","author":"Cuthbertson","year":"2007","journal-title":"Crit. Care Med."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"DeVita, M.A., Hillman, K., Bellomo, R., Odell, M., Jones, D.A., Winters, B.D., and Lighthall, G.K. (2017). Textbook of Rapid Response Systems: Concept and Implementation, Springer.","DOI":"10.1007\/978-3-319-39391-9"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lee, S.I., Koh, J.S., Kim, Y.J., Kang, D.H., and Lee, J.E. (2022). Characteristics and outcomes of patients screened by rapid response team who transferred to the intensive care unit. BMC Emerg. Med., 22.","DOI":"10.1186\/s12873-022-00575-y"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"25061","DOI":"10.1038\/s41598-024-75432-y","article-title":"Characteristics and outcomes of patients screened by the rapid response team and transferred to intensive care unit in South Korea","volume":"14","author":"Nam","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6450","DOI":"10.1111\/jocn.16678","article-title":"The illness severity of ward remaining patients reviewed by the medical emergency team: A retrospective cohort study","volume":"32","author":"Batterbury","year":"2023","journal-title":"J. Clin. Nurs."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1097\/CCM.0000000000000818","article-title":"Differences in vital signs between elderly and nonelderly patients prior to ward cardiac arrest","volume":"43","author":"Churpek","year":"2015","journal-title":"Crit. Care Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1093\/ajcp\/aqy163","article-title":"Time-series analysis of laboratory values in the context of long-term hospitalised patient mortality","volume":"151","author":"Boroujeni","year":"2019","journal-title":"Am. J. Clin. Pathol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.resuscitation.2021.04.013","article-title":"A multicentre validation study of the deep learning-based early warning score for predicting in-hospital cardiac arrest in patients admitted to general wards","volume":"163","author":"Lee","year":"2021","journal-title":"Resuscitation"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jang, H., Yoo, W., Hwang, S., and Lee, K. (2025). DeepCARS-identified high-risk patients: Clinical interventions and outcomes during the Korean healthcare crisis. Medicina, 61.","DOI":"10.3390\/medicina61111896"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"20076","DOI":"10.1038\/s41598-021-99581-6","article-title":"Understanding critically ill sepsis patients with normal serum lactate levels: Results from US and European ICU cohorts","volume":"11","author":"Sauer","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_12","first-page":"e14439","article-title":"The advancement in detecting sepsis and its outcome: Usefulness of procalcitonin in diagnosing sepsis and predicting fatal outcomes in patients admitted to intensive care unit","volume":"13","author":"Malik","year":"2021","journal-title":"Cureus"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1016\/S0140-6736(07)61602-X","article-title":"The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement","volume":"370","author":"Altman","year":"2007","journal-title":"Lancet"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1093\/biomet\/73.1.13","article-title":"Longitudinal data analysis using generalised linear models","volume":"73","author":"Liang","year":"1986","journal-title":"Biometrika"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"E70","DOI":"10.1503\/cjs.005722","article-title":"Predictors of clinical deterioration and intensive care unit admission in trauma patients: A retrospective cohort study","volume":"67","author":"Moon","year":"2024","journal-title":"Can. J. Surg."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1097\/MEJ.0000000000000375","article-title":"Patient physiological status during emergency care and rapid response team or cardiac arrest team activation during early hospital admission","volume":"24","author":"Considine","year":"2017","journal-title":"Eur. J. Emerg. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1186\/s13613-024-01377-0","article-title":"Organ crosstalk and dysfunction in sepsis","volume":"14","author":"Borges","year":"2024","journal-title":"Ann. Intensive Care"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2247","DOI":"10.1097\/CCM.0000000000001239","article-title":"Timing is everything: Do we need to redesign the afferent limb in rapid response?","volume":"43","author":"Danesh","year":"2015","journal-title":"Crit. Care Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"103041","DOI":"10.1016\/j.iccn.2021.103041","article-title":"Use of the pre-medical emergency team tier of rapid response systems: A scoping review","volume":"65","author":"Sprogis","year":"2021","journal-title":"Intensive Crit. Care Nurs."},{"key":"ref_20","first-page":"50","article-title":"Physiological antecedents and ward clinician responses before medical emergency team activation","volume":"19","author":"Sprogis","year":"2017","journal-title":"Crit. Care Resusc."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kwack, W.G., Lee, D.S., Min, H., Choi, Y.Y., Yun, M., Kim, Y., Lee, S.H., Song, I., Park, J.S., and Cho, Y.-J. (2018). Evaluation of the SpO2\/FiO2 ratio as a predictor of intensive care unit transfers in respiratory ward patients for whom the rapid response system has been activated. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0201632"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1164\/rccm.200912-1918OC","article-title":"Early lactate-guided therapy in intensive care unit patients: A multicenter, open-label, randomised controlled trial","volume":"182","author":"Jansen","year":"2010","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Gao, X.P., Zheng, C.F., Liao, M.Q., He, H., Liu, Y.-H., Jing, C.-X., Zeng, F.-F., and Chen, Q.-S. (2019). Admission serum sodium and potassium levels predict survival among critically ill patients with acute kidney injury: A cohort study. BMC Nephrol., 20.","DOI":"10.1186\/s12882-019-1505-9"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1016\/j.ekir.2021.02.038","article-title":"Hyperkalaemia and its association with mortality, cardiovascular events, hospitalisations, and ICU admissions in a population-based retrospective cohort","volume":"6","author":"Hougen","year":"2021","journal-title":"Kidney Int. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1373\/clinchem.2015.250993","article-title":"Biological variation of plasma and urinary markers of acute kidney injury in patients with chronic kidney disease","volume":"62","author":"Carter","year":"2016","journal-title":"Clin. Chem."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6539","DOI":"10.1038\/s41598-025-91194-7","article-title":"Hyperkalemia is associated with short- and mid-term mortalities in critically ill patients in the MIMIC IV database","volume":"15","author":"Xu","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"A245","DOI":"10.1016\/S0735-1097(18)30786-1","article-title":"Prognostic value of electrolyte imbalance at the time of admission in patients with acute myocardial infarction","volume":"71","author":"Takano","year":"2018","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e008678","DOI":"10.1161\/JAHA.118.008678","article-title":"An algorithm based on deep learning for predicting in-hospital cardiac arrest","volume":"7","author":"Kwon","year":"2018","journal-title":"J. Am. Heart Assoc."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Park, M.H., Kim, M., Lee, M.J., Kim, A.J., Cho, K.-J., Jang, J., Jung, J., Chang, M., Yoo, D., and Kim, J.S. (2026). Clinical effectiveness of an artificial intelligence-based prediction model for cardiac arrest in general ward-admitted patients: A non-randomised controlled trial. Diagnostics, 16.","DOI":"10.3390\/diagnostics16020335"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1097\/EJA.0000000000000798","article-title":"Improving detection of patient deterioration in the general hospital ward environment","volume":"35","author":"Vincent","year":"2018","journal-title":"Eur. J. Anaesthesiol."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/10\/3722\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:30:26Z","timestamp":1778603426000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/10\/3722"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,12]]},"references-count":30,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["jcm15103722"],"URL":"https:\/\/doi.org\/10.3390\/jcm15103722","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,12]]}}}