Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach.
Sepsis is one of the leading causes of mortality in hospitalized patients. Despite this fact, a reliable means of predicting sepsis onset remains elusive. Early and accurate sepsis onset predictions could allow more aggressive and targeted therapy while maintaining antimicrobial stewardship. Existing detection methods suffer from low performance and often require time-consuming laboratory test results.



To study and validate a sepsis prediction method, InSight, for the new Sepsis-3 definitions in retrospective data, make predictions using a minimal set of variables from within the electronic health record data, compare the performance of this approach with existing scoring systems, and investigate the effects of data sparsity on InSight performance.



We apply InSight, a machine learning classification system that uses multivariable combinations of easily obtained patient data (vitals, peripheral capillary oxygen saturation, Glasgow Coma Score, and age), to predict sepsis using the retrospective Multiparameter Intelligent Monitoring in Intensive Care (MIMIC)-III dataset, restricted to intensive care unit (ICU) patients aged 15 years or more. Following the Sepsis-3 definitions of the sepsis syndrome, we compare the classification performance of InSight versus quick sequential organ failure assessment (qSOFA), modified early warning score (MEWS), systemic inflammatory response syndrome (SIRS), simplified acute physiology score (SAPS) II, and sequential organ failure assessment (SOFA) to determine whether or not patients will become septic at a fixed period of time before onset. We also test the robustness of the InSight system to random deletion of individual input observations.



In a test dataset with 11.3% sepsis prevalence, InSight produced superior classification performance compared with the alternative scores as measured by area under the receiver operating characteristic curves (AUROC) and area under precision-recall curves (APR). In detection of sepsis onset, InSight attains AUROC = 0.880 (SD 0.006) at onset time and APR = 0.595 (SD 0.016), both of which are superior to the performance attained by SIRS (AUROC: 0.609; APR: 0.160), qSOFA (AUROC: 0.772; APR: 0.277), and MEWS (AUROC: 0.803; APR: 0.327) computed concurrently, as well as SAPS II (AUROC: 0.700; APR: 0.225) and SOFA (AUROC: 0.725; APR: 0.284) computed at admission (P<.001 for all comparisons). Similar results are observed for 1-4 hours preceding sepsis onset. In experiments where approximately 60% of input data are deleted at random, InSight attains an AUROC of 0.781 (SD 0.013) and APR of 0.401 (SD 0.015) at sepsis onset time. Even with 60% of data missing, InSight remains superior to the corresponding SIRS scores (AUROC and APR, P<.001), qSOFA scores (P=.0095; P<.001) and superior to SOFA and SAPS II computed at admission (AUROC and APR, P<.001), where all of these comparison scores (except InSight) are computed without data deletion.



Despite using little more than vitals, InSight is an effective tool for predicting sepsis onset and performs well even with randomly missing data.
Dascena, Inc, Hayward, CA, United States.
Thomas
0000-0001-6853-981X
T
Desautels
Dascena, Inc, Hayward, CA, United States.
Jacob
0000-0001-9173-0946
J
Calvert
Dascena, Inc, Hayward, CA, United States.
Jana
0000-0002-7745-3900
J
Hoffman
Dascena, Inc, Hayward, CA, United States.
Melissa
0000-0002-8740-4803
M
Jay
Department of Clinical Informatics, Stanford University School of Medicine, Stanford, CA, United States|Department of Emergency Medicine, Kaiser Permanente Redwood City Medical Center, Redwood City, CA, United States
Yaniv
0000-0002-6855-710X
Y
Kerem
Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
Lisa
0000-0003-2729-315X
L
Shieh
Division of Critical Care Medicine, Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, United States
David
0000-0002-6742-9173
D
Shimabukuro
Department of Emergency Medicine, Kaiser Permanente South San Francisco Medical Center, South San Francisco, CA, United States|Department of Emergency Medicine, University of California San Francisco, San Francisco, CA, United States
Uli
0000-0001-8833-5478
U
Chettipally
Division of General Internal Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, United States
Mitchell D
0000-0003-2772-9742
MD
Feldman
Department of Emergency Medicine, University of California San Francisco, San Francisco, CA, United States
Chris
0000-0002-5842-2949
C
Barton
Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
David J
0000-0002-3555-6645
DJ
Wales
Dascena, Inc, Hayward, CA, United States.
Ritankar
0000-0002-1326-844X
R
Das
clinical decision support systems; electronic health records; machine learning; medical informatics; sepsis
Curr Opin Anaesthesiol. 2015 Dec;28(6):685-90
26539788
Crit Care Clin. 2000 Apr;16(2):179-92
10768078
Sci Transl Med. 2015 Aug 5;7(299):299ra122
26246167
Ann Med Surg (Lond). 2016 May 10;8:50-5
27489621
J Am Med Inform Assoc. 2017 Jan;24(1):24-29
27026611
IEEE J Biomed Health Inform. 2014 Sep;18(5):1560-70
25192568
Crit Care Med. 2009 May;37(5):1649-54
19325482
JAMA. 2016 Feb 23;315(8):762-74
26903335
Am J Respir Crit Care Med. 2016 Feb 1;193(3):259-72
26414292
Crit Care Med. 2001 Jul;29(7):1303-10
11445675
Sci Data. 2016 May 24;3:160035
27219127
J Trauma. 2005 Oct;59(4):821-8; discussion 828-9
16374268
Comput Biol Med. 2016 Jul 1;74:69-73
27208704
JAMA. 1993 Dec 22-29;270(24):2957-63
8254858
Intensive Care Med. 1996 Jul;22(7):707-10
8844239
AMIA Annu Symp Proc. 2012;2012:653-62
23304338
Emerg Med J. 2006 Nov;23(11):841-5
17057134
JAMA. 2016 Feb 23;315(8):801-10
26903338