Intensive Care Med (2016) 42:361–369
DOI 10.1007/s00134-015-4203-5
Christin Edmark
Mark J. W. McPhail
Max Bell
Tony Whitehouse
Julia Wendon
Kenneth B. Christopher
ORIGINAL
LiFe: a liver injury score to predict outcome
in critically ill patients
Abstract Purpose: To develop a
liver function-related risk prediction
tool to identify acute-on-chronic liver
failure patients at greatest risk of inhospital mortality. Methods: The
LiFe (liver, injury, failure, evaluaC. Edmark and M. J. W. McPhail
contributed equally to this work.
tion) score, was constructed based on
the opinions of 157 intensivists within
Electronic supplementary material
the European Society for Intensive
The online version of this article
(doi:10.1007/s00134-015-4203-5) contains Care Medicine. Experts were sursupplementary material, which is available veyed and instructed to weigh the
to authorized users.
diagnostic importance of each feature
of a proposed prediction model. We
performed a retrospective cohort
study of 1916 patients with chronic
liver disease admitted to a medical or
C. Edmark
surgical ICU between 1997, and 2011
Department of Anaesthesiology, Critical
in three large hospitals in Boston,
Care and Surgical Services, Karolinska
University Hospital, Solna, Sweden
USA, and London, UK, with arterial
lactate, total bilirubin and INR drawn
M. J. W. McPhail J. Wendon
at ICU admission. The derivation
Liver Intensive Therapy Unit, Institute of
cohort consisted of ICU patients from
Liver Studies, Kings College Hospital,
Brigham and Women’s Hospital and
London, UK
Massachusetts General Hospital in
Boston (n = 945), and the validation
M. Bell
Department of Anesthesiology and
cohort comprised patients from Kings
Intensive Care, Karolinska Institutet, Solna, College Hospital, London, admitted
Sweden
to the Liver Intensive Therapy Unit
(n = 971). A clinical prediction
T. Whitehouse
model was derived and validated
Department of Anaesthesia and Critical
based on a logistic regression model
Care, University Hospital Birmingham,
describing the risk of in-hospital
Birmingham, UK
mortality as a function of the predicK. B. Christopher ())
tors (arterial lactate 0–1.9, C2.0–3.9,
The Nathan E. Hellman Memorial
C4.0–5.9, C6.0 mg/dL; total bilirubin
Laboratory, Renal Division, Brigham and
0–1.9,
C2.0–3.9, C4.0–5.9, C6.0 mg/
Women’s Hospital, 75 Francis Street, MRB
dL; INR 0–1.9, C2.0–3.9, C4.0–5.9,
418, Boston, MA 02115, USA
C6.0) at ICU admission. Performance
e-mail: [email protected]
Received: 22 July 2015
Accepted: 17 November 2015
Published online: 28 January 2016
Ó Springer-Verlag Berlin Heidelberg and
ESICM 2016
analysis of the LiFe score against
SOFA, CLIF-SOFA, APACHE II and
SAPS II was completed in the validation cohort of critically ill cirrhotic
patients. Results: The derivation
cohort (n = 941) was 53 % male
with a mean age of 65 years and an
in-hospital mortality rate of 30 %.
The validation cohort (n = 971) was
63 % male with mean age of 51 years
and an in-hospital mortality rate of
52 %. The C statistic for the prediction model was 0.74 (95 % CI
0.70–0.77) in the derivation cohort
and 0.77 (95 % CI 0.74–0.80) in the
validation cohort. In the validation
cohort, in-hospital mortality was
17 % in the low-risk group (0 risk
score points), 28 % in the intermediate-risk group (1–3 points), 47 % in
the high-risk group (4–8 points), and
77 % in the very high-risk group ([8
points). In the validation cohort, the C
statistics for SOFA, CLIF-SOFA,
APACHE II, and SAPS II were 0.80,
0.81, 0.77, and 0.78, respectively.
Further, a significant positive correlation exists between LiFe score and
acute-on-chronic liver failure grade,
(r = 0.478, P \ 0.001). Conclusions: Our LiFe score calculated
from arterial lactate, total bilirubin
and INR at ICU admission is a simple, quick and easily understandable
score that may increase clinical utility
for risk prediction in ICU patients
with acute-on-chronic liver failure.
The LiFe score can be used in place
362
of physiological based scores for
early risk prediction in patients with
chronic liver disease but is not
intended to replace CLIF-SOFA as a Keywords Acute liver failure Intensive care Mortality prediction
benchmark for prognostication.
Introduction
Acute-on-chronic liver failure (ACLF) is an acute
decompensation of liver function in patients with chronic
liver disease, either due to additional liver injury or due to
systemic factors leading to multi-organ dysfunction [1].
Acute hepatic dysfunction in the critically ill has substantial mortality [2–6]. Early hepatic dysfunction is
shown to occur in 11 % of critically ill patients and is
associated with in-hospital mortality [2]. Valid prediction
models that can discriminate patients by the risk of death
allow for the discovery of high risk subgroups which may
benefit most from intervention [7]. Several scoring systems are available for primary liver disease [8–10], for
cirrhotic patients admitted to an Intensive Care Unit
(ICU) [11–14] and for cirrhotic patients with acute
decompensation [15, 16], but no laboratory-based score
has been developed for ACLF in the critically ill.
To address this issue, we sought to assess the potential of
a laboratory-based acute liver failure score to serve as a
predictor of mortality in critically ill patients, termed the
Liver injury and Failure evaluation (LiFe) score. Through a
survey of ESICM members on acute liver failure in ICU
patients, we concluded three variables (INR, total bilirubin
and arterial lactate) were considered to be commonly
available markers of metabolism, excretion and synthesis
and of clinical importance. Our primary study objective was
to derive and validate a risk prediction score based on INR,
total bilirubin and arterial lactate and examine its discrimination for in-hospital mortality. We hypothesized that such
an acute liver failure risk prediction score would have robust
discrimination for in-hospital mortality in patients with
chronic liver disease following critical care admission. To
test this hypothesis, we performed a three-center observational cohort study of 1916 adults with chronic liver disease,
hospitalized for critical care from 1997 to 2011.
research committee within the ESICM. Details of the survey
design can be found in Supplemental Methods. Of the 157
intensivists who responded, 76 % were from Europe, 16 %
from Asia and 8 % from Africa, the Middle East, Australia
and the Americas. The majority of responders (72 %)
worked in closed intensive care units, with (80 %) of beds
having ventilator capacity. Thirty-six percent of responders
were from university hospitals with specialist Hepatobiliary
Services. Responders indicated that INR, total bilirubin and
arterial lactate which reflected the synthetic, excretory and
metabolic properties of liver function, respectively, and were
widely considered useful, were obtainable, and practical.
Further, the responders indicated that increases of INR, total
bilirubin and arterial lactate by 2-unit increments were
considered important for discriminating between those with
acute liver failure and those without and corresponded to
increasing severity of acute liver failure.
Source population and data sources
For the derivation cohort, we abstracted patient-level
demographic, administrative and laboratory data from two
academic medical centers in Boston, Massachusetts:
Brigham and Women’s Hospital (BWH), and Massachusetts General Hospital (MGH). Data on all patients
admitted to BWH or MGH between November 3, 1997, and
December 30, 2011, were obtained through the Research
Patient Data Registry (RPDR), a computerized registry
which serves as a central data warehouse for all inpatient
and outpatient records at BWH and MGH [7]. For the
external validation cohort, consecutive admissions to the
Liver Intensive Therapy Unit (LITU) at King’s College
Hospital from January 2000 to December 2010 had patientlevel demographic, administrative and laboratory data
prospectively captured by dedicated auditors [17].
Approval for the study was granted by the Partners Human
Research Committee (Institutional Review Board) and by
the South East London Research Ethics Committee.
Materials and methods
Survey
Study population
Using an internet-based survey tool, we conducted a
descriptive, structured, cross-sectional, self-administered
survey of intensivists within the European Society for
Intensive Care Medicine (ESICM) from July to October
2013. The survey questions chosen were based on test utility,
cut off levels and test availability and were reviewed and
approved by the Metabolism, Endocrinology and Nutrition
During the study period, there were 92,886 individual
inpatients at BWH or MGH in Boston, age C18 years,
who were admitted to a medical or surgical ICU [18], who
were assigned a Diagnostic Related Group classification
[19] and had a social security number. DRG classification
was used to exclude patients assigned the CPT code
99291 who received Emergency Room care but were not
363
Fig. 1 Study flow chart
admitted to BWH or MGH. We excluded: 85,838 patients
who did not have chronic liver disease [20], 6116 patients
who did not have arterial lactate, total bilirubin and INR
drawn at ICU admission, and 13 patients post-liver
transplant or transplanted during their hospital stay. Thus,
the derivation cohort was comprised of 945 patients (270
from BWH and 675 from MGH) (Fig. 1). During the
study period, there were 1032 individual inpatients at
Kings College Hospital, London, age C16 years, who
were admitted to the Liver Intensive Therapy Unit. We
excluded 61 Kings College Hospital patients who had
acute liver failure, hepatocellular carcinoma, chronic liver
disease not consistent with cirrhosis and malignancy and
patients post-liver transplant or transplanted during their
hospital stay. In the validation cohort, the presence of
cirrhosis was determined from clinical, biochemical,
radiological or histopathological results. Thus, the validation cohort was comprised of 971 patients with
cirrhosis from Kings College Hospital [17] (Fig. 1).
Covariates
Definition and determination of the following derivation
cohort covariates are outlined in Supplemental Methods:
ICU admission [18], race, medical versus surgical patient
admission ‘type’ [21], Deyo–Charlson index [22–24],
chronic liver disease [20], chronic kidney disease stage [25],
categories of acute organ failure [7, 26], sepsis [27], noncardiogenic acute respiratory failure [28, 29] and acute
kidney injury [30, 31]. For severity of illness risk adjustment,
we utilized the Acute Organ Failure score [7]. The Acute
Organ Failure score is an ICU risk-prediction score derived
and validated from demographics (age, race), patient
admission ‘type’ as well as ICD-9-CM code based comorbidity, sepsis and acute organ failure covariates which has
similar discrimination for 30-day mortality as APACHE II
[7]. Definition and determination of the following validation
cohort covariates are outlined in Supplemental Methods:
Acute Physiology and Chronic Health Evaluation (APACHE
II) [32], Simplified Acute Physiology Score (SAPS-II) [33],
Sequential organ failure assessment (SOFA) [34], chronic
liver failure–sequential organ failure score (CLIF-SOFA)
[15]. For the diagnositc criteria for acute-on-chronic liver
failure (ACLF) we utilized the CLIF Acute-on-Chronic
Liver Failure in Cirrhosis (CANONIC) study graded
approach [15]. The CANONIC study approach was utilized
to define the absence of ACLF: no organ failure; a single
‘‘non-kidney’’ organ failure with serum creatinine level
\1.5 mg/dL and no hepatic encephalopathy; or single
cerebral failure with serum creatinine level\1.5 mg/dL [15].
End points
The primary end point was all-cause in-hospital mortality
following ICU admission. Vital status in the derivation
cohort was obtained from hospital records and the Social
Security Administration Death Master File [35] which is
shown to be valid approach for in-hospital in the BWH/MGH
administrative database [18]. Vital status in the validation
cohort was determined by hospital records [17]. In-hospital
mortality data were available for 100 % of the cohort.
Power calculations and statistical analysis
Previously, acute hepatic dysfunction in the ICU was
shown to occur in 10 % of ICU patients and be associated
with a 14 % increase in hospital mortality [2]. From these
data, we assumed that in-hospital mortality would be
14 % higher among the current patient derivation cohort
with ACLF compared to those without. With an alpha
error level of 5 % and a power of 80 %, the sample size
required for our primary end point (in-hospital mortality)
was 705 patients without acute hepatic dysfunction and 71
patients with acute hepatic dysfunction.
The derivation cohort consisted of critically ill patients
with chronic liver disease treated at BWH and MGH
(n = 945) and the validation cohort comprised critically ill
364
cirrhotic patients treated at King’s College Hospital
(n = 971). Categorical variables were described by frequency distribution while continuous variables were
examined graphically and by summary statistics. The primary outcome was in-hospital all-cause mortality.
Univariate logistic regression was performed to determine
the unadjusted association between in-hospital mortality and
potential predictors. A clinical prediction model was created
based on a logistic regression model describing the risk of inhospital mortality in the derivation cohort as a function of
predictors (arterial lactate 0–1.9, C2.0–3.9, C4.0–5.9,
C6.0 mg/dL; total bilirubin 0–1.9, C2.0–3.9, C4.0–5.9,
C6.0 mg/dL; INR 0–1.9, C2.0–3.9, C4.0–5.9, C6.0) at ICU
admission. The model was transformed to a simplified
integer-based score, with a score for each predictor variable
assigned by dividing its b-coefficient by the smallest coefficient in the model, multiplying by a factor of 2 and rounding
up to the nearest integer. A risk score was then calculated for
each patient, and the population was divided into four categories: patients at low risk, patients at intermediate risk,
patients at high risk and patients at very high risk for death.
The discriminatory ability for in-hospital mortality was
quantified using the c-statistic. The DeLong method was
used for Area Under ROC (AUC) comparisons [36]. Calibration was assessed using the Hosmer–Lemeshow v2
goodness-of-fit test and the accompanying p value. We tested for effect modification by year of hospitalization by
adding an interaction term to the multivariate models. In all
analyses, p-values are two-tailed and values below 0.05 were
considered statistically significant. All analyses were performed using STATA 13.1MP statistical software
(StataCorp, College Station, TX, USA).
Results
Survival analysis and risk-scoring system
Table 1 shows demographic characteristics of the
derivation cohort. The majority of derivation cohort
patients were white (77 %), medical (71 %) and 59 %
were male. The mean age at hospital admission was
60 years. The in-hospital mortality rate was 32 %. Survival improved over the course of the study period with
in-hospital mortality rates prior to 2007 of 44 % and after
Table 1 Population characteristics of the development cohort and unadjusted association of potential prognostic determinants with inhospital mortality
Characteristic
Age in years (mean ± SD)
Male sex
Non-white race
Surgical patient type
Deyo–Charlson index)
0–1
2–3
4–6
C7
Chronic kidney disease stage
0–2
3
4
5
Trauma
Sepsis
Acute organ failure score (mean ± SD)
RIFLE class
No AKI
Risk
Injury
Failure
Noncardiogenic acute respiratory failure
Days from hospital to ICU admission median (IQR)
In-hospital mortality
Data presented as n (%) unless otherwise indicated
a
RIFLE present in 787 of the derivation cohort
Derivation cohort
Survivors
(n = 644)
Non-survivors
(n = 301)
All patients
(n = 945)
Unadjusted OR (95 % CI)
for in-hospital mortality
in derivation cohort
59.2 ± 14.6
374 (58)
136 (21)
184 (29)
60.7 ± 13.7
181 (60)
80 (27)
87 (29)
59.7 ± 14.3
555 (59)
216 (23)
271 (29)
1.01
1.09
1.35
1.02
(1.00–1.02)
(0.82–1.44)
(0.98–1.86)
(0.75–1.37)
49 (8)
159 (25)
271 (42)
165 (26)
12 (4)
60 (20)
150 (50)
79 (26)
61 (6)
219 (23)
421 (45)
244 (26)
1.00
1.54
2.26
1.96
(Referent)a
(0.77–3.10)
(1.17–4.38)
(0.98–3.88)
399 (64)
144 (23)
57 (9)
27 (4)
8 (1)
230 (36)
13.9 ± 4.7
153 (52)
81 (27)
40 (14)
21 (7)
12 (4)
181 (60)
17.0 ± 4.7
552 (60)
225 (24)
97 (11)
48 (5)
20 (2)
411 (43)
14.9 ± 4.9
1.00
1.47
1.83
2.03
3.30
2.72
1.15
(Referent)a
(1.05–2.04)
(1.17–2.86)
(1.11–3.70)
(1.33–8.16)
(2.05–3.60)
(1.12–1.19)
316 (61)
73 (14)
62 (12)
71 (14)
109 (17)
0 (0,1)
91 (34)
46 (17)
51 (19)
77 (29)
80 (27)
1 (0.5)
407 (52)
119 (15)
113 (14)
148 (19)
189 (20)
0 (0.2)
301 (31.9)
1.00
2.19
2.86
3.77
1.78
1.12
(Referent)a
(1.41–3.39)
(1.84–4.43)
(2.53–5.61)
(1.28–2.47)
(1.08–1.16)
365
Table 2 Risk of in-hospital death in the development and validation cohorts, according to risk category
Risk category
Low
Intermediate
High
Very high
Derivation cohort (n = 945)
Validation cohort (n = 971)
n (%)
In-hospital death % (95 % CI)
n (%)
In-hospital death % (95 % CI)
256
297
195
197
12.5
22.9
39.0
63.5
138
168
251
414
17.4
28.0
47.0
76.6
(27.1)
(31.4)
(20.6)
(20.9)
(9.0–17.2)
(18.5–28.0)
(32.4–46.0)
(56.5–69.9)
(14.2)
(17.3)
(25.9)
(42.6)
(11.9–24.7)
(21.7–35.3)
(40.9–53.2)
(72.2–80.4)
The risk category was calculated by adding the points for each of index was categorized in four groups: a low-risk group (0 points),
the following risk factors: arterial lactate 0–1.9, C2.0–3.9, an intermediate-risk group (1–3 points), a high-risk group (4–8
C4.0–5.9, C6.0 mg/dL; total bilirubin 0–1.9, C2.0–3.9, C4.0–5.9, points), and a very high-risk group ([8 points)
C6.0 mg/dL; INR 0–1.9, C2.0–3.9, C4.0–5.9, C6.0. The prognostic
of 23 %. The details of the validation cohort from Kings
College Hospital have been previously described [17].
The majority of the validation cohort patients were male
(63 %), the mean age at hospital admission was 51 years
and the in-hospital mortality rate was 52 %. The validation cohort had a mean APACHE II of 21.8, a mean
SOFA of 9.5, a mean SAPS II of 46.4, and a mean CLIFSOFA of 10.7. ACLF was absent (ACLF grade 0) in
18.9 % of validation cohort patients.
Table 1 shows that comorbidities and the development of
sepsis and acute organ failure were associated with the risk of
death in the derivation cohort. To calculate a risk score, we
utilized derivation cohort data and assigned levels of INR,
total bilirubin and arterial lactate a number of points proportional to its regression coefficient (Supplemental Table 1). A
score was calculated for each patient by adding the points
corresponding to the cut points of risk factors (arterial lactate
0–1.9, C2.0–3.9, C4.0–5.9, C6.0 mg/dL; total bilirubin
0–1.9, C2.0–3.9, C4.0–5.9, C6.0 mg/dL; INR 0–1.9,
C2.0–3.9, C4.0–5.9, C6.0). The patients were then divided
into 4 categories on the basis of score distribution, which
ranged from 0 to 20: a low-risk group (0 points), an intermediate-risk group (1–3 points), a high-risk group (4–8 points),
and a very high-risk group ([8 points) (Table 2; Fig. 2).
In the derivation and validation cohorts, the in-hospital
mortality rates for the low, intermediate, high and very
high risk categories show similar exposure–response
relationships (Table 2). The odds of in-hospital mortality
for validation patients with prognostic index of intermediate, high and very high risk categories was 1.85 (95 %
CI 1.06–3.21), 4.21 (95 % CI 2.54–6.98) and 15.52 (95 %
CI 9.46–25.48) relative to patients with low risk prognostic index. The AUC for the continuous risk score point
model was 0.74 in the derivation cohort and 0.77 in the
validation cohort (Fig. 3). For both the derivation and
validation cohorts, the Hosmer–Lemeshow v2 P values
indicated good model fit (Fig. 3). Thus, the risk score
point model showed good calibration and similar good
discrimination in the validation cohort.
Individually running the risk score point model in the
validation cohort with and without terms for year of hospitalization, the in-hospital mortality estimates in each case are
similar indicating that the risk score–mortality relationship is
not materially confounded by year (data not shown). There is
significant effect modification of the risk score–mortality
association on the basis of year of hospitalization (P interaction
\0.001). With regard to year, when the validation cohort is
separately analyzed before and after 2007, the directionality
and significance of the risk score–mortality association is
preserved.
We next assessed the performance of the risk score point
model compared to other scoring systems in the validation
cohort (Table 3). APACHE II score had similar accuracy as
the risk score (v2 0.03, P = 0.86) as did the SAPS II score
compared to the risk score (v2 0.32, P = 0.57). There was a
significant difference in discrimination between the risk
score and SOFA (v2 4.05, P = 0.044), as well as between
the risk score and CLIF-SOFA (v2 11.28, P \ 0.001).
ACLF was associated with increases in risk score. Using the
definitions from the CANONIC study for ACLF grade [15],
the mean (SD) risk score differed according to ACLF grade:
grade 0 = 3.7 (4.0), grade 1 = 6.7 (4.8), grade 2 = 7.9
(4.6), grade 3 = 10.9 (4.4). There was a statistically significant difference in risk score between ACLF grade as
determined by one-way ANOVA (F9961 = 105.7,
P \ 0.001). Finally, a Pearson’s product-moment correlation shows a significant positive correlation between risk
score and ACLF grade, r(969) = 0.49, P \ 0.001.
Discussion
The present study aimed to derive and validate a clinical
risk prediction score relevant to ACLF in the critically ill.
Our study has several novel findings. First, we show a riskprediction score created from commonly collected laboratory data has good calibration and discrimination for inhospital mortality in critically ill patients with chronic liver
disease. Further, in the validation cohort of patients with
cirrhosis, we show that our laboratory-based risk-prediction score has good discrimination for in-hospital mortality,
approaches the performance of physiological-based scoring systems and correlates with ACLF grade.
366
Fig. 2 Time-to-event curves
for mortality for the derivation
cohort. Unadjusted all-cause
mortality rates were calculated
with the use of the Kaplan–
Meier methods and compared
with the use of the log-rank test.
Categorization of risk groups is
per the primary analysis. The
global comparison log rank
p value is \0.001
Fig. 3 Performance of the LiFe score Area under the ROC Curve
(C Statistic) for continuous LiFe score point model predicting inhospital mortality of 0.738 (95 % CI 0.704–0.772) in the derivation
cohort (a) and 0.771 (95 % CI 0.742–0.800) in the validation cohort
(b). The black line indicates reference values. The unadjusted Odds
Ratio for in-hospital mortality (95 % CI) per one point increase of
LiFe score is 1.20 (1.16–1.24) in the derivation cohort and 1.25
(1.21–1.29) in the validation cohort. The LiFe score showed good
calibration in the derivation and validation cohorts (HL v2 4.87,
P = 0.56 and HL v2 4.67, P = 0.59, respectively)
While liver-related risk prediction scores have been
described before [8–16, 37, 38], this is the first study to
validate a laboratory-based score in the critically ill. In
cirrhotic patients, SOFA score is noted to have the best
predictive accuracy for survival in the ICU [11]. Chronic
liver failure-specific modification of the SOFA score
(CLIF-SOFA) may have higher accuracy in patients with
cirrhosis [39]. The CLIF-SOFA components include:
PaO2/FiO2 or SpO2/FiO2, INR, hypotension (mean aterial
pressure, vasopressor use), hepatic encephalopathy grade,
and serum creatinine [15], which may not be available in
observational datasets. While the SOFA-based scores
have better discriminiation of in-hospital mortality in our
validation cohort, our simple risk predition score may be
easier to utilize at the bedside and have higher utility in
observational datasets that lack physiologic parameters.
We do note that there are potential limitations to our
approach. Observational studies may be limited by confounding, bias and/or reverse causation. Importantly, we
cannot determine causality in our study. Our derivation and
validation cohorts are from large acute care hospitals that
may not be generalizable to all critically ill patients with
367
Table 3 Validation of the risk score point model (N = 971)
Outcome and statistics
Risk score
Discrimination for in-hospital mortality
C-Statistic
0.771
95 % confidence interval
0.74–0.80
SOFA
CLIF-SOFA
APACHE II
SAPS II
0.799
0.77–0.83
0.813
0.79–0.84
0.768
0.74-0.80
0.781
0.75–0.81
Risk score, SOFA, CLIF-SOFA, APACHE II and SAPS II were determined on day 1 of admission to Kings College Hospital Liver ICU
chronic liver disease [40]. Ascertainment bias may be present as a small fraction of patients with chronic liver disease
so the parent BWH/MGH ICU cohort was utilized to derive
the risk score. As arterial lactate is not routinely measured in
BWH/MGH ICU patients, those included in the derivation
cohort have a higher severity of illness and higher mortality
than the parent BWH/MGH ICU chronic liver disease
cohort. In the parent BWH/MGH ICU chronic liver disease
cohort, there is a substantial mortality difference between
patients with lactate measured on the ICU admission day and
those without, (in-hospital mortality 30.7 vs. 16.7 %,
respectively). Differences with regard to sepsis are present
between derivation cohort patients and the parent BWH/
MGH ICU chronic liver disease cohort (43.5 vs. 22.1 %).
Thus, our derivation cohort represents a sicker population of
patients with chronic liver disease.
INR, total bilirubin and arterial lactate may not be
reflective of liver issues alone. Importantly, marked abnormalities could exist in some or all of these markers and be
associated with mortality and yet not be the result of specific
liver injury or dysfunction but of illness severity. Lactate is
marker for tissue hypo-perfusion in both hepatic and nonhepatic endothelial beds and is further elevated with
decreased hepatic clearance. Bilirubin can rise in hematological conditions in the critically ill, and by not
differentiating between conjugated and unconjugated bilirubin, we may underestimate the role of hemolytic causes.
Coagulation disorders are similarly common in trauma,
hematological conditions, sepsis, and renal failure as well as
primary hepatic conditions. Overall, whether or not the cause
of critical illness in the cohorts under study is primarily acute
hepatic failure, the combination of these markers is predictive
for short-term mortality and correlates with ACLF grade.
The present study has several strengths. The risk score is
simple and easy to calculate from laboratory measures
(INR, total bilirubin and arterial lactate) that are commonly
available or obtainable in critically ill patients and
administrative datasets. The parent dataset where the
derivation cohort was obtained is well studied [7, 18]. The
use of CPT code 99291 to identify ICU patients and ICD-9CM code combinations to identify sepsis and chronic liver
disease are previously validated in the parent dataset under
study [18, 20, 27]. The risk score had good model performance for in-hospital mortality and was comparable to
other physiological scores in a validation cohort of critically ill patients with a high proportion of ACLF. Our
simple risk score has potential clinical utility in the triage
and management of critically ill patients with ACLF. In
addition, our risk score can be utilized for outcome studies
in observational datasets that include laboratory measures.
Conclusion
In aggregate, these data demonstrate that a risk score
based on INR, total bilirubin and arterial lactate drawn at
ICU admission is predictive for short-term mortality and
correlate with ACLF grade in critically ill patients. This
risk score can be quickly, easily and conveniently utilized
at the bedside for early risk prediction in patients with
chronic liver disease with performance that approaches
but does not match that of SOFA or CLIF-SOFA.
Acknowledgments This manuscript is dedicated to the memory of
our dear friend and colleague Nathan Edward Hellman, MD, PhD.
The authors thank Shawn Murphy and Henry Chueh and the
Partners Health Care Research Patient Data Registry group for
facilitating use of their database.
Compliance with ethical standards
Conflicts of interest
interest.
The authors disclose no potential conflicts of
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