Federated learning for predicting critical intervention and poor clinical outcomes at emergency department triage stage

Background: Early detection and timely intervention of patients at risk during the triage stage can significantly improve patient outcomes. This study aimed to predict requirements for critical respiratory or cardiovascular intervention and poor clinical outcome using federated learning (FL). Methods: Patients of two tertiary hospitals who visited the emergency department (ED) were included. Local models for each hospital and FL models to predict high flow nasal cannula or endotracheal intubation (model 1), central venous catheter insertion or vasopressor administration (model 2), and admission to intensive care unit or cardiac arrest during ED stay (model 3) were developed and internally validated with data from 2017 to 2020. These models were then externally validated with data from 2021. Available information such as underlying disease, recent blood test results, age, sex, and initial vital signs at triage stage were used as input variables. Performances of models were evaluated using area under the receiver operating characteristic (AUROC) with 95% confidence interval. Results: A total of 262,283 and 180,261 ED visits from Samsung Medical Center (hospital A) and Korea University ANAM Hospital (hospital B) respectively, were included. AUROC values of three local and three FL models in both hospitals all exceeded 0.85 in internal validation. For hospital B, local models showed better performance than the FL model, including model 2 (0.942 (0.938–0.946) vs. 0.890 (0.884–0.896)) and model 3 (0.910 (0.905–0.914) vs. 0.886 (0.881–0.891)). AUROC values of local and FL models also exceeded 0.85 in external validation. The FL model showed comparable performance except model 3 of hospital B. Conclusions: Federated learning models demonstrated comparable performance to local models in predicting critical interventions and poor clinical outcomes at triage.

Critical intervention; Emergency department; Triage; Federated learning

[1] Gaieski DF, Agarwal AK, Mikkelsen ME, Drumheller B, Cham Sante S, Shofer FS, et al. The impact of ED crowding on early interventions and mortality in patients with severe sepsis. The American Journal of Emergency Medicine. 2017; 35: 953–960.

[2] Gruen RL, Jurkovich GJ, McIntyre LK, Foy HM, Maier RV. Patterns of errors contributing to trauma mortality: lessons learned from 2,594 deaths. Annals of Surgery. 2006; 244: 371–380.

[3] Hasegawa K, Sullivan AF, Tsugawa Y, Turner SJ, Massaro S, Clark S, et al. Comparison of US emergency department acute asthma care quality: 1997–2001 and 2011–2012. The Journal of Allergy and Clinical Immunology. 2015; 135: 73–80.

[4] Rathore SS, Curtis JP, Chen J, Wang Y, Nallamothu BK, Epstein AJ, et al. Association of door-to-balloon time and mortality in patients admitted to hospital with ST elevation myocardial infarction: national cohort study. The BMJ. 2009; 338: b1807.

[5] Goto T, Camargo CA III, Faridi MK, Freishtat RJ, Hasegawa K. Machine learning-based prediction of clinical outcomes for children during emergency department triage. JAMA Network Open. 2019; 2: e186937.

[6] Raita Y, Goto T, Faridi MK, Brown DFM, Camargo CA III, Hasegawa K. Emergency department triage prediction of clinical outcomes using machine learning models. Critical Care. 2019; 23: 64.

[7] Khera R, Haimovich J, Hurley NC, McNamara R, Spertus JA, Desai N, et al. Use of machine learning models to predict death after acute myocardial infarction. JAMA Cardiology. 2021; 6: 633–641.

[8] Sánchez-Salmerón R, Gómez-Urquiza JL, Albendín-García L, Correa-Rodríguez M, Martos-Cabrera MB, Velando-Soriano A, et al. Machine learning methods applied to triage in emergency services: a systematic review. International Emergency Nursing. 2022; 60: 101109.

[9] Levin S, Toerper M, Hamrock E, Hinson JS, Barnes S, Gardner H, et al. Machine-learning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index. Annals of Emergency Medicine. 2018; 71: 565–574.e2.

[10] Kamunga BLG, Bearnot CJ, Martin KD, Uwamahoro DL, Cattermole GN. Epidemiology and outcomes of critically ill patients in the emergency department of a tertiary teaching hospital in Rwanda. International Journal of Emergency Medicine. 2024; 17: 170.

[11] Mohr NM, Wessman BT, Bassin B, Elie-Turenne MC, Ellender T, Emlet LL, et al. Boarding of critically ill patients in the emergency department. Critical Care Medicine. 2020; 48: 1180–1187.

[12] Pang PS, Levy P, Shah SJ. Treatment of acute heart failure in the emergency department. Expert Review of Cardiovascular Therapy. 2013; 11: 1195–1209.

[13] Hwang U, Weber EJ, Richardson LD, Sweet V, Todd K, Abraham G, et al. A research agenda to assure equity during periods of emergency department crowding. Academic Emergency Medicine. 2011; 18: 1318–1323.

[14] Liu Y, Gao J, Liu J, Walline JH, Liu X, Zhang T, et al. Development and validation of a practical machine-learning triage algorithm for the detection of patients in need of critical care in the emergency department. Scientific Reports. 2021; 11: 24044.

[15] Chang H, Yu JY, Yoon S, Kim T, Cha WC. Machine learning-based suggestion for critical interventions in the management of potentially severe conditioned patients in emergency department triage. Scientific Reports. 2022; 12: 10537.

[16] Dou Q, So TY, Jiang M, Liu Q, Vardhanabhuti V, Kaissis G, et al. Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study. NPJ Digital Medicine. 2021; 4: 60.

[17] Sheller MJ, Edwards B, Reina GA, Martin J, Pati S, Kotrotsou A, et al. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Scientific Reports. 2020; 10: 12598.

[18] Alreshidi FS, Alsaffar M, Chengoden R, Alshammari NK. Fed-CL—an atrial fibrillation prediction system using ECG signals employing federated learning mechanism. Scientific Reports. 2024; 14: 21038.

[19] Zhou J, Wang X, Li Y, Yang Y, Shi J. Federated-learning-based prognosis assessment model for acute pulmonary thromboembolism. BMC Medical Informatics and Decision Making. 2024; 24: 141.

[20] Jordan S, Fontaine C, Hendricks-Sturrup R. Selecting privacy-enhancing technologies for managing health data use. Frontiers in Public Health. 2022; 10: 814163.

[21] Malin B, Goodman K; Section Editors for the IMIA Yearbook Special Section. Between access and privacy: challenges in sharing health data. Yearbook of Medical Informatics. 2018; 27: 55–59.

[22] Yazijy S, Schölly R, Kellmeyer P. Towards a toolbox for privacy-preserving computation on health data. Studies in Health Technology and Informatics. 2022; 290: 234–237.

[23] Zhang P, Kamel Boulos MN. Privacy-by-design environments for large-scale health research and federated learning from data. International Journal of Environmental Research and Public Health. 2022; 19: 11876.

[24] Rodríguez-Barroso N, Stipcich G, Jiménez-López D, Ruiz-Millán JA, Martínez-Cámara E, González-Seco G, et al. Federated learning and differential privacy: software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy. Information Fusion. 2020; 64: 270–292.

[25] Dugas AF, Kirsch TD, Toerper M, Korley F, Yenokyan G, France D, et al. An electronic emergency triage system to improve patient distribution by critical outcomes. The Journal of Emergency Medicine. 2016; 50: 910–918.

[26] Lambden S, Laterre PF, Levy MM, Francois B. The SOFA score-development, utility and challenges of accurate assessment in clinical trials. Critical care. 2019; 23: 374.

[27] McMahan B, Moore E, Ramage D, Hampson S, y Arcas BA. Communication-efficient learning of deep networks from decentralized data. Proceedings of Machine Learning Research. 2017; 54: 1273–1282.

[28] Van der werf AJ. Federated learning in the emergency department [Bachelor’s thesis]. Tilburg University. 2022.

[29] Sarma KV, Harmon S, Sanford T, Roth HR, Xu Z, Tetreault J, et al. Federated learning improves site performance in multicenter deep learning without data sharing. Journal of the American Medical Informatics Association. 2021; 28: 1259–1264.

[30] Cho KJ, Kim JS, Lee DH, Lee SM, Song MJ, Lim SY, et al. Prospective, multicenter validation of the deep learning-based cardiac arrest risk management system for predicting in-hospital cardiac arrest or unplanned intensive care unit transfer in patients admitted to general wards. Critical Care. 2023; 27: 346.

[31] Scheeren TWL, Bakker J, De Backer D, Annane D, Asfar P, Boerma EC, et al. Current use of vasopressors in septic shock. Annals of Intensive Care. 2019; 9: 20.

[32] Sanfilippo F, La Via L, Carpinteri G, Astuto M. Timing of intubation, beds in intensive care and inter-hospital transfer: rings of a complex chain during pandemic conditions. Critical Care. 2022; 26: 44.

[33] Gopinath B, Sachdeva S, Kumar A, Kumar G. Advancing emergency airway management by reducing intubation time at a high-volume academic emergency department. BMJ Open Quality. 2021; 10: e001448.

[34] Guerci P, Belveyre T, Mongardon N, Novy E. When to start vasopressin in septic shock: the strategy we propose. Critical Care. 2022; 26: 125.

[35] Evans HL, Zonies DH, Warner KJ, Bulger EM, Sharar SR, Maier RV, et al. Timing of intubation and ventilator-associated pneumonia following injury. Archives of Surgery. 2010; 145: 1041–1046.

[36] Oredsson S, Jonsson H, Rognes J, Lind L, Göransson KE, Ehrenberg A, et al. A systematic review of triage-related interventions to improve patient flow in emergency departments. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine. 2011; 19: 43.

[37] Fekonja Z, Kmetec S, Fekonja U, Mlinar Reljić N, Pajnkihar M, Strnad M. Factors contributing to patient safety during triage process in the emergency department: a systematic review. Journal of Clinical Nursing. 2023; 32: 5461–5477.

[38] Chiu YM, Vanasse A, Courteau J, Chouinard MC, Dubois MF, Dubuc N, et al. Persistent frequent emergency department users with chronic conditions: a population-based cohort study. PLOS ONE. 2020; 15: e0229022.

[39] Wang J, Liu Q, Liang H, Joshi G, Poor HV. A novel framework for the analysis and design of heterogeneous federated learning. IEEE Transactions on Signal Processing. 2021; 69: 5234–5249.