Deconstructing Intensive Care Unit Triage
Identifying factors influencing admission decisions using
natural language processing and semantic technology
Kusum S. Mathews, MD,
Yue Liu,
2
MS;
Evan Patton,
2
MS;
Heng Ji,
2
PhD;
Deborah L. McGuinness,
2,3
PhD
Critical Care, & Sleep Medicine, Dept. of Medicine; Dept. of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY; 2Tetherless World Constellation, Dept. of Computer Science, Rensselaer Polytechnic Institute (RPI), Troy, NY, 3Dept. of Cognitive Science, RPI, Troy, NY
• To develop a semantically-enabled
natural learning processing (NLP) model
to capture subjective elements of
Intensive Care Unit (ICU) triage decisions
• To determine and characterize effect of
subjective assessments on odds of ICU
admission and in-patient mortality.
Figure 1: Semantic analysis & Acronym expansion
“61 y.o. M pt with hx of COPD, HTN, … etc.”
Online resource
Domain Ontology
Input
Distributional
Semantic
Computation
Text
Normalization
Natural
Language
Processing
BACKGROUND
• ICU admission decisions can hinge on rapid clinical
assessments, often involving factors that are not easily
captured within electronic health record (EHR) data.
• Ontology mapping has primarily focused on clinical
data and diagnoses, but the semantics of “clinical
gestalt” require more customized approaches.
• NLP and semantic technology can be used to analyze
various data formats and can be customized to
qualitatively and quantitatively study subjective
elements of the ICU triage process.
METHODS
• Setting: Academic tertiary care hospital, with a 14-bed
Medical ICU, operating at 91% average occupancy
• Study population: All adult patients referred for Medical
ICU admission over a 12-month period
• Data source: Prospectively collected Medical ICU physician
semi-structured consult documentation, augmented with
clinical data (patient characteristics/clinical course,
University HealthSystems Consortium (UHC) expected
mortality risk)
• Computer NLP model: Fact extraction training and
evaluation on 1160 patient consults, for pattern recognition
within free-text sections of consult logs. (Figures 1-3)
• Analysis: Chi-square testing and multivariate regression,
incorporating a priori selected clinical elements and NLP
themes to predict (1) odds of ICU “accept” decision, and (2)
in-hospital mortality or hospice discharge
• Domain-specific
resources combined
with a trained
model can expand
acronyms, interpret
jargon, and extract
semantic relations
between entities.
DISCUSSION
RESULTS
Table 1: Cohort characteristics
Accept (N=703)
Reject (N=457)
Age, years (mean ± SD)***
59.3 ± 16.6
64.0 ± 16.2
Male gender (%)*
361 (51.4)
209 (45.7)
UHC Expected Mortality (median, IQR)
0.54 (0.01-0.23)
0.45 (0.00-0.19)
Pre-consult LOS, days (median, IQR)
0.8 (0.3-4.9)
0.8 (0.3-6.4)
ED origin (%)**
226 (54.6)
188 (45.4)
Discharge disposition (%)
• In-hospital mortality
• Hospice
204 (29.0)
55 (7.8)
126 (27.6)
45 (9.8)
• Illness severity,
as captured by
UHC expected
mortality risk
index, was
similar for both
Accept and
Reject cases.
*p<0.05 **p<0.01 ***p<0.001.
Human correction
& validation
Entity
Recognition
Output
“61 year old Male patient
with history of chronic
obstructive pulmonary
disease, hypertension, … etc.”
Relation
Extraction
Automatic
Profiling
Figure 4: Word clouds for emergent descriptors, filtered against a
priori selected themes, presented by weight
REJECT
RESEARCH OBJECTIVES
NATURAL LANGUAGE MODEL DEVELOPMENT
ACCEPT
1Pulmonary,
1
MPH;
• Analysis of ICU consult documentation, written
in “real time,” provides insight into the
perceptions shaping triage decisions, including
disease severity, pre-existing care limitations,
and previous receipt of life-sustaining care.
• Subjective assessments of overall prognosis and
likelihood to benefit significantly predicted odds
of receiving an ICU “accept” decision.
• Although the emergent descriptors were similar
in both Accept and Reject cases, the prevalence
varied greatly with larger differences seen in
more subjective terms.
• Next steps include additional analysis on
emergent themes, incorporating background
knowledge to help produce more refined and
discriminating descriptors (e.g., contextualizing
poor prognosis with past clinical data).
Figure 2: Sample “Accept” case annotation†
84 year old woman with hypertension, hyperlipidemia, colon cancer
status post colectomy and with remote pulmonary metastasis
status/post resection. Good functional status until a recent
hospitalization for aspiration pneumonia. Was discharged to
rehabilitation center about a week ago then had witnessed emesis
with aspiration, and in septic shock and respiratory failure. Just
extubated yesterday.
• Significant differences were seen for the following terms:
cognitive limitations, ** family,*** limited goals of care,**
metastatic,*** poor prognosis,*** trach/PEG,* and transplant.**
Table 2: Logit models for ICU “accept” decision and in-hospital
mortality or hospice discharge
Figure 3: Sample “Reject” case annotation†
83 year old female with history of metastatic colon cancer, extensive
liver metastases, peritoneal carcinomatosis, hemorrhagic CVA,
advanced dementia, TPN-dependent, minimal functional status,
admitted for pneumonia and possible initiation of chemotherapy.
Treated with antibiotics, lasix, now with worsening hypoxic
respiratory failure. Patient was DNR/DNI, but reversed when
patient had decompensation. Now intubated, listed to step down.
†
Only partial clinical entities and associated relations displayed
for illustrative purposes.
Model: Triage‡
Model: Mortality‡
CONCLUSIONS
• NLP modeling and semantics can be
valuable tools in qualitative and
quantitative ICU triage decision analysis.
• Captured through NLP analysis,
prognosis and “likelihood to benefit”
determinations at triage significantly
influence admission decisions and are
predictive of mortality and hospice
utilization.
Predictors (n=892)
Odds ratio (CI)
P value Odds ratio (CI)
P value
UHC Expected Mortality
1.12 (1.04-1.21)
0.004
1.79 (1.61-1.98)
<0.001
Consult originating from ED
0.68 (0.49-0.95)
0.025
0.43 (0.29-0.64)
<0.001
Pre-existing care limitations
0.35 (0.16-0.76)
0.008
2.21 (0.98-5.02)
0.057
Poor prognosis per ICU team
0.22 (0.13-0.36)
<0.001
2.99 (1.68-5.33)
<0.001
ACKNOWLEDGMENTS & FUNDING
Known cancer diagnosis
0.64 (0.45-0.91)
0.012
2.35 (1.61-3.44)
<0.001
---
0.99 (0.69-1.42)
0.961
The study described was supported by Award Number 1K12HL109005-01 (Mathews) from the
National Heart, Lung, and Blood Institute (NHLBI) and by the Rensselaer Polytechnic Institute
(RPI) Tetherless World Constellation (Ji, Liu, McGuinness, Patton). The content is solely the
responsibility of the authors and does not necessarily represent the official views of the NHLBI,
the National Institutes of Health, or RPI.
ICU “Accept” Decision
‡
---
Model adjusted for Gender, Race, & Ethnicity; Day shift excluded (stepwise removal; p>0.10)