SEPRES: Intensive Care Unit Clinical Data Integration System to Predict Sepsis

Abstract

Background The lack of information interoperability between different devices and systems in the intensive care unit (ICU) hinders further utilization of data, especially for early warning of specific diseases in the ICU.

Objectives We aimed to establish a data integration system. Based on this system, the sepsis prediction module was added to compose the Sepsis PREdiction System (SEPRES), where real-time early warning of sepsis can be implemented at the bedside in the ICU.

Methods Data are collected from bedside devices through the integration hub and uploaded to the integration system through the local area network. The data integration system was designed to integrate vital signs data, laboratory data, ventilator data, demographic data, pharmacy data, nursing data, etc. from multiple medical devices and systems. It integrates, standardizes, and stores information, making the real-time inference of the early warning module possible. The built-in sepsis early warning module can detect the onset of sepsis within 5 hours preceding at most.

Results Our data integration system has already been deployed in Ruijin Hospital, confirming the feasibility of our system.

Conclusion We highlight that SEPRES has the potential to improve ICU management by helping medical practitioners identify at-sepsis-risk patients and prepare for timely diagnosis and intervention.

Keywords

sepsis - information system - intensive care units

• References

• 1 Singer M, Deutschman CS, Seymour CW. et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 2016; 315 (08) 801-810
• 2 Cecconi M, Evans L, Levy M, Rhodes A. Sepsis and septic shock. Lancet 2018; 392 (10141): 75-87
• 3 Angus DC, Linde-Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med 2001; 29 (07) 1303-1310
• 4 Marik PE, Farkas JD. The changing paradigm of sepsis: early diagnosis, early antibiotics, early pressors, and early adjuvant treatment. Crit Care Med 2018; 46 (10) 1690-1692
• 5 Filbin MR, Lynch J, Gillingham TD. et al. Presenting symptoms independently predict mortality in septic shock: Importance of a previously unmeasured confounder. Crit Care Med 2018; 46 (10) 1592-1599
• 6 Henry KE, Hager DN, Pronovost PJ, Saria S. A targeted real-time early warning score (TREWScore) for septic shock. Sci Transl Med 2015; 7 (299) 299ra122
• 7 Thiel SW, Rosini JM, Shannon W, Doherty JA, Micek ST, Kollef MH. Early prediction of septic shock in hospitalized patients. J Hosp Med 2010; 5 (01) 19-25
• 8 Bhattacharjee P, Edelson DP, Churpek MM. Identifying patients with sepsis on the hospital wards. Chest 2017; 151 (04) 898-907
• 9 Calvert JS, Price DA, Chettipally UK. et al. A computational approach to early sepsis detection. Comput Biol Med 2016; 74: 69-73
• 10 Kam HJ, Kim HY. Learning representations for the early detection of sepsis with deep neural networks. Comput Biol Med 2017; 89: 248-255
• 11 Lauritsen SM, Kalør ME, Kongsgaard EL. et al. Early detection of sepsis utilizing deep learning on electronic health record event sequences. Artif Intell Med 2020; 104: 101820
• 12 Futoma J, Hariharan S, Heller K. et al. An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection. PMLR; 2017. :243–254
• 13 Desautels T, Calvert J, Hoffman J. et al. Prediction of sepsis in the intensive care unit with minimal electronic health record data: a machine learning approach. JMIR Med Inform 2016; 4 (03) e28
• 14 Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, Buchman TG. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit Care Med 2018; 46 (04) 547-553
• 15 Moor M, Horn M, Rieck B, Roqueiro D, Borgwardt K. Early recognition of sepsis with gaussian process temporal convolutional networks and dynamic time warping. machine learning for healthcare conference. PMLR 2019; 106: 2-26
• 16 Barton C, Chettipally U, Zhou Y. et al. Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs. Comput Biol Med 2019; 109: 79-84
• 17 Aşuroğlu T, Oğul H. A deep learning approach for sepsis monitoring via severity score estimation. Comput Methods Programs Biomed 2021; 198: 105816
• 18 Rosnati M, Fortuin V. MGP-AttTCN: an interpretable machine learning model for the prediction of sepsis. PLoS One 2021; 16 (05) e0251248
• 19 Persson I, Östling A, Arlbrandt M, Söderberg J, Becedas D. A machine learning sepsis prediction algorithm for intended intensive care unit use (NAVOY sepsis): proof-of-concept study. JMIR Form Res 2021; 5 (09) e28000
• 20 Teng AK, Wilcox AB. A review of predictive analytics solutions for sepsis patients. Appl Clin Inform 2020; 11 (03) 387-398
• 21 Johnson AE, Pollard TJ, Shen L. et al. MIMIC-III, a freely accessible critical care database. Sci Data 2016; 3: 160035
• 22 Lloyd JK, Ahrens EA, Clark D, Dachenhaus T, Nuss KE. Automating a manual sepsis screening tool in a pediatric emergency department. Appl Clin Inform 2018; 9 (04) 803-808
• 23 Gibbs KD, Shi Y, Sanders N. et al. Evaluation of a sepsis alert in the pediatric acute care setting. Appl Clin Inform 2021; 12 (03) 469-478
• 24 Dewan M, Vidrine R, Zackoff M. et al. Design, implementation, and validation of a pediatric ICU sepsis prediction tool as clinical decision support. Appl Clin Inform 2020; 11 (02) 218-225
• 25 Smielewski P, Czosnyka M, Steiner L, Belestri M, Piechnik S, Pickard JD. ICM+: software for on-line analysis of bedside monitoring data after severe head trauma. Acta Neurochir Suppl (Wien) 2005; 95: 43-49
• 26 Meyer MA, Levine WC, Egan MT. et al. A computerized perioperative data integration and display system. Int J Comput Ass Rad 2007; 2: 191-202
• 27 Goldstein B, McNames J, McDonald BA. et al. Physiologic data acquisition system and database for the study of disease dynamics in the intensive care unit. Crit Care Med 2003; 31 (02) 433-441
• 28 Gjermundrod H, Papa M, Zeinalipour-Yazti D. et al. Intensive Care Window: A Multi-Modal Monitoring Tool for Intensive Care Research and Practice. Paper presented at: Twentieth IEEE International Symposium on Computer-based Medical Systems; June 20, 2007; Maribor, Slovenia
• 29 Sun Y, Guo F, Kaffashi F, Jacono FJ, DeGeorgia M, Loparo KA. INSMA: An integrated system for multimodal data acquisition and analysis in the intensive care unit. J Biomed Inform 2020; 106: 103434
• 30 Shimabukuro DW, Barton CW, Feldman MD, Mataraso SJ, Das R. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respir Res 2017; 4 (01) e000234
• 31 Burdick H, Pino E, Gabel-Comeau D. et al. Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals. BMJ Health Care Inform 2020; 27 (01) e100109
• 32 McCoy A, Das R. Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units. BMJ Open Qual 2017; 6 (02) e000158
• 33 Chen Q, Li R, Lin C. et al. Transferability and interpretability of the sepsis prediction models in the intensive care unit. medRxiv 2021;
• 34 Lundberg S, Lee SI. A Unified approach to interpreting model predictions. Paper presented at: Proceedings of the 31st international conference on neural information processing systems; 2017;4768–4777
• 35 Desai MD, Tootooni MS, Bobay KL. Can prehospital data improve early identification of sepsis in emergency department? An integrative review of machine learning approaches. Appl Clin Inform 2022; 13 (01) 189-202