Subscribe to RSS
DOI: 10.1055/s-0040-1710525
A Review of Predictive Analytics Solutions for Sepsis Patients
Funding This work was supported by the U.S. Department of Health and Human Services, National Library of Medicine Training Grant T15LM007442Publication History
02 October 2019
02 April 2020
Publication Date:
27 May 2020 (online)
![](https://www.thieme-connect.de/media/10.1055-s-00035026/202003/lookinside/thumbnails/10-1055-s-0040-1710525_190211rev-1.jpg)
Abstract
Background Early detection and efficient management of sepsis are important for improving health care quality, effectiveness, and costs. Due to its high cost and prevalence, sepsis is a major focus area across institutions and many studies have emerged over the past years with different models or novel machine learning techniques in early detection of sepsis or potential mortality associated with sepsis.
Objective To understand predictive analytics solutions for sepsis patients, either in early detection of onset or mortality.
Methods and Results We performed a systematized narrative review and identified common and unique characteristics between their approaches and results in studies that used predictive analytics solutions for sepsis patients. After reviewing 148 retrieved papers, a total of 31 qualifying papers were analyzed with variances in model, including linear regression (n = 2), logistic regression (n = 5), support vector machines (n = 4), and Markov models (n = 4), as well as population (range: 24–198,833) and feature size (range: 2–285). Many of the studies used local data sets of varying sizes and locations while others used the publicly available Medical Information Mart for Intensive Care data. Additionally, vital signs or laboratory test results were commonly used as features for training and testing purposes; however, a few used more unique features including gene expression data from blood plasma and unstructured text and data from clinician notes.
Conclusion Overall, we found variation in the domain of predictive analytics tools for septic patients, from feature and population size to choice of method or algorithm. There are still limitations in transferability and generalizability of the algorithms or methods used. However, it is evident that implementing predictive analytics tools are beneficial in the early detection of sepsis or death related to sepsis. Since most of these studies were retrospective, the translational value in the real-world setting in different wards should be further investigated.
Protection of Human and Animal Subjects
Human and/or animal subjects were not included in this project.
-
References
- 1 Data & Reports | Sepsis | CDC. Centers for Disease Control and Prevention. Available at: https://www.cdc.gov/sepsis/datareports/index.html . Accessed August 11, 2019
- 2 O'Brien J. . The Cost of Sepsis. Centers for Disease Control and Prevention. Published November 18, 2016 . Available at: https://blogs.cdc.gov/safehealthcare/the-cost-of-sepsis/ . Accessed August 11, 2019
- 3 Sepsis. Mayo Clinic. Published November 16, 2018 . Available at: https://www.mayoclinic.org/diseases-conditions/sepsis/symptoms-causes/syc-20351214 . Accessed August 11, 2019
- 4 Balk RA. Systemic inflammatory response syndrome (SIRS): where did it come from and is it still relevant today?. Virulence 2014; 5 (01) 20-26
- 5 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
- 6 Foster KR, Koprowski R, Skufca JD. Machine learning, medical diagnosis, and biomedical engineering research - commentary. Biomed Eng Online 2014; 13 (01) 94
- 7 Cheng T-H, Wei C-P, Tseng VS. Feature Selection for Medical Data Mining: Comparisons of Expert Judgment and Automatic Approaches. In: 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06). Salt Lake City, UT: IEEE; 2006: 165-170
- 8 Carrara M, Baselli G, Ferrario M. Mortality prediction in septic shock patients: towards new personalized models in critical care. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Milan: IEEE; 2015: 2792-2795
- 9 Danner OK, Hendren S, Santiago E, Nye B, Abraham P. Physiologically-based, predictive analytics using the heart-rate-to-systolic-ratio significantly improves the timeliness and accuracy of sepsis prediction compared to SIRS. Am J Surg 2017; 213 (04) 617-621
- 10 Capp R, Horton CL, Takhar SS. , et al. Predictors of patients who present to the emergency department with sepsis and progress to septic shock between 4 and 48 hours of emergency department arrival. Crit Care Med 2015; 43 (05) 983-988
- 11 Faisal M, Scally A, Richardson D. , et al. Development and external validation of an automated computer-aided risk score for predicting sepsis in emergency medical admissions using the patient's first electronically recorded vital signs and blood test results. Crit Care Med 2018; 46 (04) 612-618
- 12 Ho JC, Lee CH, Ghosh J. Imputation-enhanced prediction of septic shock in ICU patients categories and subject descriptors. HI-KDD 2012: ACM SIGKDD Workshop on Health Informatics; 2012
- 13 Langley RJ, Tsalik EL, van Velkinburgh JC. , et al. An integrated clinico-metabolomic model improves prediction of death in sepsis. Sci Transl Med 2013; 5 (195) 195ra95
- 14 Sutherland A, Thomas M, Brandon RA. , et al. Development and validation of a novel molecular biomarker diagnostic test for the early detection of sepsis. Crit Care 2011; 15 (03) R149
- 15 Gultepe E, Green JP, Nguyen H, Adams J, Albertson T, Tagkopoulos I. From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system. J Am Med Inform Assoc 2014; 21 (02) 315-325
- 16 Horng S, Sontag DA, Halpern Y, Jernite Y, Shapiro NI, Nathanson LA. Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLoS One 2017; 12 (04) e0174708
- 17 Thottakkara P, Ozrazgat-Baslanti T, Hupf BB. , et al. Application of machine learning techniques to high-dimensional clinical data to forecast postoperative complications. PLoS One 2016; 11 (05) e0155705
- 18 Vieira SM, Mendonça LF, Farinha GJ, Sousa JMC. Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients. Appl Soft Comput 2013; 13 (08) 3494-3504
- 19 Ghosh S, Li J, Cao L, Ramamohanarao K. Septic shock prediction for ICU patients via coupled HMM walking on sequential contrast patterns. J Biomed Inform 2017; 66: 19-31
- 20 Peelen L, de Keizer NF, Jonge Ed, Bosman RJ, Abu-Hanna A, Peek N. Using hierarchical dynamic Bayesian networks to investigate dynamics of organ failure in patients in the intensive care unit. J Biomed Inform 2010; 43 (02) 273-286
- 21 Stanculescu I, Williams CKI, Freer Y. A hierarchical switching linear dynamical system applied to the detection of sepsis in neonatal condition monitoring. UAI'14 Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence; 2014: 752-761
- 22 Stanculescu I, Williams CKI, Freer Y. Autoregressive hidden Markov models for the early detection of neonatal sepsis. IEEE J Biomed Health Inform 2014; 18 (05) 1560-1570
- 23 Gultepe E, Nguyen H, Albertson T, Tagkopoulos I. . A Bayesian network for early diagnosis of sepsis patients: a basis for a clinical decision support system. 2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2012; 2012: 1-5
- 24 Nachimuthu SK, Haug PJ. Early detection of sepsis in the emergency department using Dynamic Bayesian Networks. AMIA Annu Symp Proc 2012; 2012: 653-662
- 25 Calvert J, Desautels T, Chettipally U. , et al. High-performance detection and early prediction of septic shock for alcohol-use disorder patients. Ann Med Surg (Lond) 2016; 8: 50-55
- 26 Calvert JS, Price DA, Chettipally UK. , et al. A computational approach to early sepsis detection. Comput Biol Med 2016; 74: 69-73
- 27 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
- 28 Mao Q, Jay M, Hoffman JL. , et al. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open 2018; 8 (01) e017833
- 29 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
- 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 Lukaszewski RA, Yates AM, Jackson MC. , et al. Presymptomatic prediction of sepsis in intensive care unit patients. Clin Vaccine Immunol 2008; 15 (07) 1089-1094
- 32 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
- 33 Pereira RDMA, Fialho AS, Cismondi F. , et al. Predicting septic shock outcomes in a database with missing data using fuzzy modeling: influence of pre-processing techniques on real-world data-based classification. IEEE International Conference on Fuzzy Systems; 2011: 2507-2512
- 34 Johnson AE, Pollard TJ, Shen L. , et al. MIMIC-III, a freely accessible critical care database. Sci Data 2016; 3 (01) 160035
- 35 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
- 36 Ribas VJ, Lopez JC, Ruiz-Sanmartin A. , et al. Severe sepsis mortality prediction with relevance vector machines. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Boston, MA: IEEE; 2011: 100-103
- 37 Shashikumar SP, Stanley MD, Sadiq I. , et al. Early sepsis detection in critical care patients using multiscale blood pressure and heart rate dynamics. J Electrocardiol 2017; 50 (06) 739-743
- 38 Sawyer AM, Deal EN, Labelle AJ. , et al. Implementation of a real-time computerized sepsis alert in nonintensive care unit patients. Crit Care Med 2011; 39 (03) 469-473
- 39 Taylor RA, Pare JR, Venkatesh AK. , et al. Prediction of in-hospital mortality in emergency department patients with sepsis: a local big data-driven, machine learning approach. Acad Emerg Med 2016; 23 (03) 269-278
- 40 Clark M. Retrospective versus prospective cohort study designs for evaluating treatment of pressure ulcers: a comparison of 2 studies. J Wound Ostomy Continence Nurs 2008; 35 (04) 391-394 , quiz 395–396
- 41 Raith EP, Udy AA, Bailey M. , et al. Australian and New Zealand Intensive Care Society (ANZICS) Centre for Outcomes and Resource Evaluation (CORE). Prognostic accuracy of the SOFA score, SIRS criteria, and qSOFA score for in-hospital mortality among adults with suspected infection admitted to the intensive care unit. JAMA 2017; 317 (03) 290-300
- 42 Govindan S, Prescott HC, Chopra V, Iwashyna TJ. Sample size implications of mortality definitions in sepsis: a retrospective cohort study. Trials 2018; 19 (01) 198
- 43 UCSF Case Study: Dascena. Available at: https://www.dascena.com/ucsf . Accessed August 11, 2019
- 44 Nassir Ghaemi S, Shirzadi AA, Filkowski M. Publication bias and the pharmaceutical industry: the case of lamotrigine in bipolar disorder. Medscape J Med 2008; 10 (09) 211
- 45 Lever J, Krzywinski M, Altman N. Points of significance: model selection and overfitting. Nat Methods 2016; 13 (09) 703-704
- 46 Worster A, Fan J, Ismaila A. Understanding linear and logistic regression analyses. CJEM 2007; 9 (02) 111-113
- 47 Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 2018; 19 (06) 1236-1246
- 48 Wang H, Wu Z, Xing EP. Removing confounding factors associated weights in deep neural networks improves the prediction accuracy for healthcare applications. Pac Symp Biocomput 2019; 24: 54-65
- 49 Bishop CM, Bayesian PCA. In: Kearns MJ, Solla SA, Cohn DA. , eds. Advances in Neural Information Processing Systems 11. MIT Press; 1999: 382-388 . Available at: http://papers.nips.cc/paper/1549-bayesian-pca.pdf . Accessed April 17, 2020
- 50 Ravindra K, Rattan P, Mor S, Aggarwal AN. Generalized additive models: building evidence of air pollution, climate change and human health. Environ Int 2019; 132: 104987
- 51 Schinkel M, Paranjape K, Nannan Panday RS, Skyttberg N, Nanayakkara PWB. Clinical applications of artificial intelligence in sepsis: a narrative review. Comput Biol Med 2019; 115: 103488
- 52 Fleuren LM, Klausch TLT, Zwager CL. , et al. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Med 2020; 46 (03) 383-400
- 53 Peiffer-Smadja N, Rawson TM, Ahmad R. , et al. Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. Clin Microbiol Infect 2019; (September): S1198-743X(19)30494-X
- 54 Salvador-Oliván JA, Marco-Cuenca G, Arquero-Avilés R. Errors in search strategies used in systematic reviews and their effects on information retrieval. J Med Libr Assoc 2019; 107 (02) 210-221