The HALOS model
Page |1
The HALOS (Hospital Admission Length Of Stay) model: A
new tool for predicting hospital length of stay after liver
transplantation
Kartik R Krishnan, MD1, Renuka Bhattacharya, MD2, Arema Perreira, MD2, Robert L
Carithers, Jr., MD2, Jorge Reyes, MD3 and James D Perkins, MD3. 1Internal Medicine,
University of Washington, Seattle, WA, United States; 2Gastroenterology, University of
Washington, Seattle, WA, United States and 3Transplant Surgery, University of
Washington, Seattle, WA, United States.
ABSTRACT
Background: Liver transplant centers are under increasing pressure to understand and
optimize transplant-related costs. Hospital admission length of stay (LOS) is known to
be a significant driver and useful surrogate for cost. We sought to develop and evaluate
mathematical models predictive of LOS through retrospective analysis of the large,
multi-institutional United Network for Organ Sharing Standard Transplant Analysis
and Research files.
Methods: Of 45,256 adults (≥18 years) who underwent liver transplantation for any
indication between January 1, 2003 and November 30, 2010, 3,705 were excluded for
missing LOS data or death less than 19 days into the hospital stay, leaving 41,551
recipients in the study. For predictive model building internal split-group validation
was employed, with 75% and 25% of the study population randomly assigned to the
training and validation sets, respectively. Multiple linear regression was used to
generate four models predicting LOS, correlating to the stages of the transplantation
process: (1) using only recipient factors; (2) using a combination of recipient factors and
payment source; (3) using a combination of recipient factors, payment source, and
donor factors; and (4) using a combination of recipient factors, payment source, donor
factors, and one posttransplant factor from the transplant admission (acute cellular
rejection episode). Use of the models projected the validation patients as low (≤8 days),
moderate (>8 to 18 days), or high risk (>18 days) for prolonged LOS. Using receiver
operating characteristic curve analysis from logistic regression of the three groups, we
measured discrimination of our models by calculating the area under each curve
(AUC).
Results: LOS ranged from 1 to 528 days, with a mean of 17.38 days (SD of 22). Overall,
xx recipient factors, primary payment sources, x donor factors, and postoperative acute
cellular rejection during the transplant admission were identified as predictors of LOS.
The HALOS model
Page |2
For prediction of the high risk group, the AUC was 0.73, 0.73, 0.74, and 0.75 for models
(1) through (4), respectively. For prediction of the moderate risk group, the AUC was
0.55, 0.55, 0.56, and 0.57 for models (1) through (4), respectively. For prediction of the
low risk group, the AUC was 0.68, 0.68, 0.70, and 0.71 for models (1) through (4),
respectively.
Conclusion: This study is the first to identify mathematical models predictive of the
risk for prolonged LOS, with each predictor variably weighted based on the data. Most
importantly, the models accurately identify patients at high risk for prolonged LOS, and
can serve as valuable tools to transplant centers striving to understand transplantrelated costs.
INTRODUCTION
As their operating costs continue to rise, there is increasing pressure on liver
transplant (LT) centers to understand and optimize transplant-associated costs. Patients
who are deemed medically “high risk” are sometimes delayed for transplant listing by
transplant centers to obtain better insurance coverage, out of concern that their care will
be prohibitively expensive to the transplant center. Conversely, financial approval is
easier to obtain for those patients thought to be at low risk for an expensive, long
hospitalization after LT. Unfortunately, to date these costs have been largely
unpredictable at the time of listing, resulting in unnecessary delays in listing while
waiting for insurance coverage.
Transplant admission hospital length of stay (LOS) is a well-known driver and
useful surrogate of transplant-associated costs [1]. While the model for end-stage liver
disease (MELD) and donor risk index (DRI) have each been found to be predictors of
LOS [2-7], no model currently exists to more directly identify potential LT recipients at
highest risk for a prolonged LOS. To aid the difficult decisions involved in recipient
listing, donor selection, and postoperative management, there remains an important
need to understand the factors that affect costs following LT.
Our study uses retrospective analysis of a large, multi-institutional database to
construct mathematical models predictive of LOS. Factors examined in the database
include recipient factors, payment factors, donor factors, and one postoperative factor
(acute cellular rejection). Identification of recipient factors affecting LOS may improve
the pretransplant evaluation and listing process, while an understanding of donor
factors may assist surgeons in graft selection.
The HALOS model
Page |3
In our experience, postoperative events such as acute cellular rejection (ACR),
biliary stenosis, hepatic artery thrombosis and portal vein thrombosis lengthen hospital
LOS, as time is spent managing these complications. Of these events, data on ACR are
carefully recorded in the multi-institutional database, making this important
complication accessible for our retrospective analysis. ACR was evaluated for any
potential correlation with LOS, and to determine its influence on recipient and donor
selection factors.
PATIENTS AND METHODS
Protocol, Design, Data Sources, and Inclusion Criteria
The expedited review process of the institutional review board of the University
of Washington was used to approve this project. A retrospective cohort study was
conducted, including data from LT recipients during the MELD era obtained from the
United Network for Organ Sharing Standard Transplant Analysis and Research (UNOS
STAR) files. The UNOS STAR files include data submitted by the members of the Organ
Procurement and Transplantation Network (OPTN) on all waitlisted candidates,
transplant recipients, and donors [8]. The Health Resources and Services
Administration within the U.S. Department of Health and Human Services provides
oversight for the activities of the OPTN/UNOS contractor.
For the present study, inclusion was restricted to adult patients (≥ 18 years) who
underwent liver transplantation between January 1, 2003 and November 30, 2010 for
any indication. Patients with multiple organ transplants were included, while those
lacking length of stay data were excluded. Length of stay was defined within the UNOS
STAR files as the number of consecutive hospitalization days from 24 hours prior to
transplantation to the day of discharge.
Patients who died within 18 days of transplant during the initial posttransplant
hospital stay were excluded, and we refer to this group as the early death patients. This
was done to avoid skewing the data. Our concern was that patients dying
posttransplant without first enduring a long hospital stay would share more predictors
with those patients with prolonged LOS than with those surviving patients with short
or moderate LOS. Therefore, we feared that inclusion of these early death patients
would obscure the characteristics of the surviving patients who also did not have
prolonged hospital stays. LOS for the entire available population was divided into
quartiles, and then the cutoff number of 18 days was chosen because this was the LOS
The HALOS model
Page |4
of the 75th percentile of patients. This cutoff point was chosen to include in the study
those patients who died only after a prolonged (falling within the uppermost quartile)
hospital stay, while excluding those who died earlier.
Missing categorical data was recorded as “unknown”. For missing continuous
data, the mean of the existing data for that variable was substituted. No continuous
data variable had data missing for greater than 1% of patients.
This work was supported in part by Health Resources and Services
Administration contract 234-2005-370011C. The content is the responsibility of the
authors alone and does not necessarily reflect the views or policies of the Department of
Health and Human Services, nor does mention of trade names, commercial products, or
organizations imply endorsement by the U.S. Government.
Clinical Outcomes and Covariate Definitions
The primary outcome was LOS, a useful marker of cost. The secondary outcome
was recipient mortality. Recipient covariates included age, gender, body mass index
(BMI), self-identified ethnicity, liver disease diagnosis, blood type, medical condition at
the time of transplant (living at home, living in a non-intensive care unit (ICU) hospital
room, or living in the ICU), primary mode of payment, secondary mode of payment,
dialysis in the week before transplant, encephalopathy prior to transplant, ascites prior
to transplant, use of any type of life support, use of mechanical ventilation, diabetes
mellitus (as recorded in the UNOS STAR files), peripheral vascular disease, portal vein
thrombosis, history of previous abdominal surgery, transjugular intrahepatic
portosystemic shunt (TIPS) present at the time of transplant, any history of variceal
bleeding prior to transplant, serum albumin, laboratory MELD score, receiving
exception MELD points for hepatocellular carcinoma (HCC), history of previous
transplant, number of previous transplants, days on transplant list, receipt of multiple
transplanted organs, allograft type, and transplant procedure (whole or reduced/split).
The UNOS database defines life support as mechanical ventilation, intraaortic balloon
pump, or a mechanical heart device. Other definitions used by the UNOS database have
been described elsewhere [8]. Recipient bilirubin, INR, and serum creatinine were not
included in the statistical analysis, given their close correlation with the laboratory
MELD score.
Model-building was conducted in four broad stages, involving (1) recipient
factor analysis, (2) payment factor analysis, (3) donor factor analysis, and finally (4)
The HALOS model
Page |5
postoperative factor analysis. The staged approach was taken to simulate the
transplantation process through its different stages, including recipient listing (at which
time payment source is determined), donor selection, and postoperative management,
with the goal of maximizing the clinical utility of this study at each of these
transplantation process stages.
For the purposes of statistical analysis, liver disease diagnoses were grouped into
larger umbrella diagnoses. Specifically, the umbrella diagnosis liver cancer was used for
all cases with the recorded diagnoses of hepatocellular carcinoma (HCC); HCC and
cirrhosis;
fibrolamellar
carcinoma;
cholangiocarcinoma;
hepatoblastoma;
hemangioendothelioma, hemangiosarcoma, or angiosarcoma; and bile duct cancer. The
umbrella diagnosis of cholestatic disease includes recorded diagnoses of primary biliary
cirrhosis and all types of secondary biliary cirrhosis. The umbrella diagnosis of viral
cirrhosis was assigned to all cases with a hepatitis A, B, C, D, or non-A, non-B
diagnosis. The umbrella diagnosis of metabolic disease was assigned to all cases with a
recorded diagnosis of α-1 antitrypsin deficiency; Wilson’s disease or other copper
metabolism disorder; hemochromatosis or hemosiderosis; glycogen storage disease type
I or II; hyperlipidemia or homozygous hypercholesterolemia; tyrosinemia; primary
oxalosis/oxaluria or hyperoxaluria; maple syrup urine disease; or metabolic disease:
other. The umbrella diagnosis of benign tumor was assigned to all cases with a recorded
diagnosis of hepatic adenoma, polycystic liver disease, or benign tumor: other.
Also for the purposes of statistical analysis, American Indian, Pacific Islander,
and multiracial patients were combined into one group.
Donor covariates included age, gender, blood type, cause of death, ethnicity,
donor after cardiac death (DCD), extended donor criteria (renal transplant definition)
[9], weight, height, BMI, serum creatinine at death, aspartate aminotransferase, alanine
aminotransferase, total bilirubin, diabetes, chronic hypertension, hepatitis C virus
(HCV) infection, hepatitis B virus (HBV) infection, donor clinical infection, organ
sharing region, distance between donor and recipient, and cold ischemia time (CIT).
Donor and recipient ABO blood type match was also included in this analysis as a
covariate.
One postoperative variable, ACR during the transplant admission, was included
in separate analysis.
Statistical Analysis
The HALOS model
Page |6
We used descriptive statistics of mean ± SD for the continuous data and
percentages for categorical data. Linear regression with standard least squares was used
to perform univariable and multivariable analysis to determine factors associated with
increasing LOS. A P-value of 0.05 was used to determine statistical significance.
Survival curves were generated using Kaplan-Meier analysis with log-rank test for
significance. Analyses were performed using JMP version 9.0.2 (SAS Institute, Cary,
NC).
To build a predictive model, internal split-group validation was employed; 75%
and 25% of the study population were randomly assigned to the training and validation
sets, respectively. Multiple linear regression was used to generate four models
predicting LOS, correlating to the stages of the transplantation process: (1) a model
using only recipient factors; (2) one using a combination of recipient factors and
payment source; (3) one using a combination of recipient factors, payment source, and
donor factors; (4) and one using a combination of recipient factors, payment source,
donor factors, and one posttransplant factor from the transplant admission (acute
cellular rejection episode).
Model-building was approached in stages, as described above, to simulate the
transplantation process through its different stages (recipient listing, including
payment/financial analysis; donor selection; and postoperative management) and to
maximize the clinical utility of the study at each of these transplantation stages. For the
purposes of our analysis, living donor was included as a covariate in the recipient
factors analysis, rather than in the subsequent donor factors analysis. This approach
was taken because the presence of a potential living donor is information available
around the time of listing, and is therefore information present at the same time other
recipient factors become known to the transplant team. This approach is consistent with
our goal of maximizing the clinical utility of the study.
The four models were then used to predict which of three categories each
validation patient would fall into: low (≤8 days), moderate (>8 to 18 days), or high risk
(>18 days) for prolonged LOS. The parameters of these categories are based on the
quartiles of the entire study cohort’s LOS distribution, with an LOS of 8 days
representing the 25th percentile and an LOS of 18 days representing the 75th percentile.
Using receiver operating characteristic curve analysis from logistic regression of the
three groups, we measured discrimination of our models by calculating the area under
each curve (AUC).
The HALOS model
Page |7
For multivariable analysis, both backward and forward analyses were
performed, including variables with a p-value of less than 0.25 and setting a p-value of
0.1 for initial placement in the model. To be kept in the model, a significance of p < 0.05
was required.
Primary source of payment was analyzed separately, after the main univariable
and multivariable recipient factor analysis. This separate treatment of the payment
source variable was done with the goal of better assisting transplant centers in financial
analysis. Multivariable analysis was performed on the primary payment variables,
controlling for the significant recipient predictors identified earlier.
Donor predictors of LOS were likewise identified through univariable and
multivariable analysis while controlling for the recipient predictors and payment source
already identified. The postoperative variable of transplant admission ACR episode
was then analyzed through the same process, now controlling for the significant
recipient, payment, and donor predictors already identified.
Linear regression, a parametric test, was chosen despite the non-Gaussian
distribution of the LOS data. This approach is based on the central limit theorem, which
allows the application of linear regression to a non-Gaussian distribution of greater than
100 data points [10].
RESULTS
Recipient demographics
Between January 1, 2003 and November 30, 2010, a total of 45,256 adult liver
transplants were performed and recorded in the UNOS STAR files. Of these transplants,
3,705 cases were excluded due to either missing LOS data or recipient death less than 19
days after transplantation. The mean LOS was 17.38 days (SD of 22), with a minimum
LOS of 1 day for a surviving patient and a maximum of 528 days. The mean follow-up
period was 1,032 days (764).
Of the 41,551 patients included in our analysis, 67.2% were male (table 1). The
mean BMI was 28.0 (SD of 5.7) and the mean age was 52.9 years (10.2). A total of 72.6%
self-identified as white, 12.7% as Hispanic, and 9.3% as black. The primary liver disease
diagnosis was viral cirrhosis for 29.9% of recipients. A majority of recipients, 71.7%,
were living at home immediately prior to transplant, while 16.9% were confined to a
general hospital floor bed and 11.4% were ICU-bound. Most (61.1%) relied on private
The HALOS model
Page |8
insurance as a primary source of payment, while Medicare/Medicaid was the primary
source for 34.5% of recipients. A large majority of recipients, 65.9%, were reported as
exhibiting hepatic encephalopathy prior to transplant, while 77.4% were reported as
exhibiting ascites. There were 6,440 cases involving recipients with diabetes, 364
involving recipients with peripheral vascular disease, and 2,287 involving recipients
with portal vein thrombosis. A large minority of cases, 40.4%, involved recipients with a
history of prior abdominal surgery, with 2,926 cases involving a recipient with a history
of previous liver transplant. The mean serum albumin was 2.9 mg/dl (0.7) and mean
MELD score was 21.2, with a large SD (9.9).
Donor demographics
The mean donor age was 40.9 (17), while 60% of donors were male (table 2).
Stroke (40.5% of deceased donors) or head trauma (37.5% of deceased donors) were the
leading causes of death among deceased donors, while anoxia was the cause of death
for 15.5% of donors. A large majority of donors, 68.4%, were white, while 12.8% and
15.4% of donors were Hispanic and black, respectively. A total of 1,729 donors were
DCD donors, while 24.9% met extended donor criteria. The mean donor was
overweight (BMI 26.7 (5.8)) and had renal insufficiency at the time of death (mean
serum creatinine 1.5 (1.6)). A total of 7.1% and 30.9% of donors were known to have
diabetes and hypertension, respectively. A small portion of donors were HCV-positive
(2.8% with serum HCV-antibody) and HBV-positive (0.2% with serum HBV surface
antigen), while a significant portion of donors, 35.6%, had a clinically-significant
infection at the time of death.
The mean distance from the donor to recipient was 140 miles (247), and the mean
cold ischemia time was 7.2 hours (3.7).
Hospital course
A total of 6.3% of recipients experienced ACR during the transplant admission,
with 78.7% of these patients receiving anti-rejection treatment (table 3).
Recipient factors associated with hospital LOS
Univariable analysis
The HALOS model
Page |9
A large group of recipient factors emerged as statistically significant predictors of
LOS in univariable analysis of the training set, and was included in multivariable
analysis (table 4). This included the liver disease diagnoses of acute hepatic necrosis,
autoimmune disease, liver cancer, cholestatic disease, cryptogenic disease, alcoholrelated disease, metabolic disease, NASH, and other, with viral cirrhosis as the
reference variable. Hospital- and ICU-bound recipient status at transplant were
significant in univariable analysis, with home status as the reference variable. Dialysis
in the week prior to transplant emerged as a significant predictor in univariable
analysis, as did life support and ventilator status. The presence of any degree of
encephalopathy was a significant predictor in univariable analysis, with the absence of
encephalopathy as the reference variable. Other variables emerging from univariable
analysis included recipient diabetes, peripheral vascular disease, portal vein
thrombosis, previous abdominal surgery, TIPS presence, history of variceal bleeding,
serum albumin, MELD score, exception MELD points for HCC, previous liver
transplantation, time on the waiting list, simultaneous transplantation of multiple
organs, and living donor.
Simultaneous pancreas transplant and simultaneous intestine transplant were
variables that each emerged from univariable analysis. However, given that 91% of
subjects who underwent pancreas transplantation also underwent intestine
transplantation at the same time, the pancreas transplantation variable was dropped
after the univariable analysis. Similarly, 88% of patients who were requiring mechanical
ventilation at the time of transplant were also requiring life support (as defined above);
therefore, the ventilation variable was dropped after univariable analysis despite
achieving statistical significance in the univariable analysis.
Multivariable analysis
A large group of covariates emerged as predictors of LOS, with backward and
forward analyses each yielding the same set of predictors (table 4). The strongest such
predictors included simultaneous intestine transplant (estimate 34.58, 95% CI 31.29 to
37.87), simultaneous heart transplant (18.70, 13.10 to 24.37), two or more previous LT
(10.34, 7.60 to 13.10), life support at the time of transplant (9.64, 8.35 to 10.93), and ICU
status (7.25, 6.19 to 8.30). Each of these variables was associated with a longer LOS.
Other predictors of longer LOS included age in years (0.13, 0.11 to 0.15), hospitalbound status (4.49, 3.78 to 5.20), dialysis within the week prior to transplant (3.62, 2.72
to 4.52), encephalopathy (3.02, 2.18 to 3.87), moderate ascites (1.51, 0.94 to 2.08), diabetes
The HALOS model
P a g e | 10
(0.91, 0.37 to 1.46), portal vein thrombosis (2.33, 1.31 to 3.37), previous abdominal
surgery (0.96, 0.45 to 1.47), TIPS at transplant (1.18, 0.32 to 2.05), one previous LT (5.40,
4.39 to 6.42), and laboratory MELD score (0.14, 0.11 to 0.18).
Several variables were instead predictors of a shorter LOS, including male gender
(-1.42, -1.93 to -0.92), acute hepatic necrosis (-2.67, -3.80 to -1.54), exception MELD points
for HCC (-1.89, -2.56 to -1.21), and living donor (-2.50, -1.27 to -3.74).
One covariate, time on transplant list (0.0005, 0.00045 to 0.001) achieved statistical
significance, but had an estimate coefficient of minimal clinical significance.
Survival analysis
Kaplan-Meier survival with log-rank analysis was performed using the groups of
patients at low, moderate, and high risk for LOS. Those subjects at high risk for LOS
had the worst predicted survival (figure 1). Based on the slope of these curves, this
increase in graft failure and mortality risk is most pronounced within the first year after
transplant.
Payment factors associated with hospital LOS
Multivariable analysis
Each non-private source of primary payment predicted longer LOS, including
Medicare/Medicaid (1.24, 0.84 to 1.85), and Veterans Administration (VA) (3.48, 1.84 to
5.13). Medicare/Medicaid as secondary payment source was also a predictor of longer
LOS (1.46, 0.38 to 2.54) (table 4).
Donor factors associated with hospital LOS
Univariable analysis
Donor covariates that emerged from univariable analysis included male gender,
living donor, unknown extended criteria donor status, donor weight and height, AST,
unknown clinical infection status, and right lobe-only graft, each of which was
predictive of a shorter LOS (table 5). Hispanic donor race, non-heart beating donor,
regional or national organ sharing, donor height 20% greater than recipient height,
The HALOS model
P a g e | 11
transplanted left lobe only, ABO incompatible or compatible but not identical, and cold
ischemia time were all donor covariates emerging from univariable analysis as
predictors of longer LOS.
Multivariable analysis
Donor covariates that emerged as predictors of longer LOS included non-heart
beating donor (3.40, 2.23 to 4.60) and national organ sharing (1.39, 0.34 to 2.44) (table 5).
Additionally, CIT emerged as a predictor of longer LOS (0.36, 0.29 to 0.44), independent
of national organ sharing, and there was less than a 30% correlation between CIT and
sharing area. Donor serum creatinine in mg/dl (0.24, 0.09 to 0.40) and donor age (0.04,
0.02 to 0.05) were statistically significant predictors but had estimate coefficients of
minimal clinical significance. Left (6.04, 1.8 to 10.2) and right (2.56, 0.22 to 4.91)
transplanted lobes in split graft surgeries were predictive of longer LOS, as was donor
HCV-positive status (1.44, 0.03 to 2.85), all with wide confidence intervals.
Posttransplant events
Univariable and multivariable analysis
ACR episode during the transplant admission emerged as a predictor of longer
LOS on univariable analysis, whether treated or untreated (table 5). After controlling for
the identified recipient, payment, and donor predictors, any ACR emerged as an
independent predictor of longer LOS in multivariable analysis (8.20, 7.24 to 9.18).
Models Comparison
Comparing predicted and actual LOS of the validation set, Model 1 (recipient
factors only) yielded a correlation coefficient of 0.9182 and an R-square value of 0.1107
when predicted and actual LOS of the validation set were compared. Model 1 ROC
curves for the high, moderate, and low risk groups had AUC values of 0.73, 0.55, and
0.68 respectively. Models 2 through 4 were predictive of LOS to a similar degree, with
respective correlation coefficients of 0.9170, 0.9120, and 0.9077, and respective R-square
values of 0.113, 0.116, and 0.120. ROC curves constructed from Model 2 (recipient
factors and payment source) were no different than Model 1, with the same AUC values
for each respective risk group. Model 3 (recipient, payment, and donor factors) ROC
The HALOS model
P a g e | 12
curves had slightly better AUC values of 0.74, 0.56, and 0.70 for the high, moderate, and
low risk groups respectively. Additionally, Model 4 (recipient, payment, donor, and
postoperative factors) ROC curves slightly improved upon Model 3, with AUC values
of 0.75, 0.57, and 0.71 respectively.
DISCUSSION
We began this study mindful of the mounting pressure on liver transplant
centers to optimize costs. Through retrospective analysis of the large, multicenter
UNOS STAR files, we sought to create a mathematical model for estimating the risk of
prolonged hospitalization, a driver of higher costs to the transplant center. The current
study was conducted in stages focused on recipient, payment, donor, and
posttransplant factors, yielding models on which to base decisions at each stage of the
transplantation process.
The HALOS model can first add valuable information during recipient selection,
when in reality financial considerations loom large. Among the four models derived in
this study, the two models that are based only on information available during recipient
selection - Models 1 and 2 - were nearly as accurate as Models 3 and 4 in projecting
validation set patients into the three risk groups. Importantly, this means that Models 1
and 2 can be used by transplant centers at the recipient selection stage to predict
postoperative LOS, before donor or postoperative information is known.
Using the HALOS model calculator, patients can therefore be categorized as
being at low (≤8 days), moderate (>8 to ≤18 days), or high risk (>18 days) for prolonged
LOS, based on information available at any stage of the transplantation process. The
calculator is available for free use at http://cbatl.surgery.washington.edu/, to allow
providers to assess LOS risk for potential LT recipients.
This ability to identify patients at high risk of a prolonged hospitalization can
solve an important problem. Transplant centers are at increased financial risk since the
advent of the MELD era in 2003, during which organ allocation has appropriately
favored the sickest patients [7]. With their financial viability heavily dependent on
hospitalization profit margins [1], transplant centers have struggled to adequately curb
their financial risk by identifying the cases most likely to become costly outliers. The
expense of these cases is usually driven by LOS. Attempting to control their financial
exposure, transplant centers are forced to require “high risk” patients to first acquire the
broadest possible insurance coverage before proceeding to LT. The difficulty comes
The HALOS model
P a g e | 13
with identification of these high risk cases, an endeavor fraught with guesswork. The
unfortunate reality is that these insurance issues can cause a delay in transplantation,
and some patients cannot afford this delay.
Our hope is that the HALOS model, more precisely tailored toward identifying
such high risk cases prior to LT, can help. The HALOS model best achieves accuracy
when identifying patients at either high or low risk of prolonged LOS, rather than
moderate risk. Identification of patients at low risk may allow transplant centers to
allow these patients to proceed to LT, without delay, with lesser insurance coverage.
Transplant centers can then direct efforts to limit financial risk, a necessary endeavor,
specifically towards those patients identified as high risk by the HALOS model.
All four models are most accurate when used to identify those patients who are
at highest risk for prolonged LOS (AUC of 0.73, 0.73, 0.74, and 0.75 for Models 1
through 4, respectively). Each model was also accurate in identifying patients at low
risk of prolonged LOS (AUC of 0.68, 0.68, 0.70, and 0.71 for Models 1 through 4,
respectively). The models were not as accurate in identifying the moderate risk group.
The HALOS model can next be useful during donor selection. Recipient LOS
categorization may guide transplant surgeons in choosing donor organs. For example,
they may choose to decline organs from donors who confer high risk for LOS on behalf
of recipients already at high or moderate risk for prolonged LOS. Indeed, a recent study
by Salvalaggio et al. found higher DRI and MELD scores to have a synergistic impact on
transplant-related costs [11]. This means, though the survival benefit of high DRI
donors appears to be limited to recipients with high MELD scores [12, 13], that this
particular donor-recipient pairing leads to expensive transplantations.
Most recipient factors that emerged as predictors of LOS were unsurprising.
Several factors associated with an increased LOS are seen in clinically decompensated
patients, including ascites, encephalopathy, TIPS, higher laboratory MELD scores, life
support, and hospital- or ICU-bound status [14]. Others likely predict a more
complicated surgery, including previous abdominal surgeries or transplants and
simultaneous transplantation of other organs. Recipient diabetes predicted a longer
LOS, likely related to an increased risk for wound healing, infectious complications, and
cardiovascular events [15]. Increasing age, which also predicted a longer LOS, likely
The HALOS model
P a g e | 14
diminishes the overall ability to recover from the stresses associated with major surgery
[16].
Fewer donor factors emerged as predictors. Organ sharing on a national basis,
compared with local sharing, was predictive of a longer LOS. Increasing CIT,
representing a risk of increased physiological damage, was also associated with a longer
LOS [17]. Interestingly, though, national organ sharing did not strongly correlate with a
longer CIT. Donor livers that are shared on a national basis have usually already been
assessed and declined by several closer transplant teams. Therefore, organs that are
eventually shared on a national basis may be of inferior quality, potentially causing
problems with the transplantation (such as primary graft dysfunction) and producing a
longer LOS. The prolonged LOS with nationally shared organs should be carefully
considered in proposed national organ allocation policies. Similarly, DCD donors were
predictive of longer LOS, perhaps related to their association with increased graft
failure and biliary complications [18-22].
Split liver grafts, both right and left lobes, were associated with prolonged LOS.
Previous studies using (like the current study) pooled registry data [23, 24], in contrast
to studies limited to specialized centers [25-30], have shown poorer graft and patient
survival with split liver transplantation than whole liver transplantation. The reasons
behind the survival differences seen in pooled registry data (such as center inexperience
with split grafts, predisposing to ischemic or other complications) may also affect
LOS[31].
The one posttransplant factor evaluated, ACR, was also associated with an
increased LOS, adding 8 hospital days. This quantitative result may further crystallize
the debate about the use of immunosuppression induction agents. There exists
controversy among transplant surgeons about whether to utilize these agents in the
postoperative setting, revolving around their extremely high cost [32]. Surgeons
commonly elect to forgo immunosuppression induction and instead to simply treat
episodes of ACR that may result. With the knowledge that ACR lengthens LOS, the
more cost-effective strategy may be to prevent ACR with immunosuppression
induction, regardless of the initial expense. Even though other posttransplant factors
like biliary stenosis and hepatic artery thrombosis are not recorded in the UNOS STAR
files, each of these posttransplant factors seem to increase the LOS in our experience.
Importantly, our data indicate recipients with a high risk LOS categorization also
have a higher mortality risk than recipients falling into the other categories (figure 1),
The HALOS model
P a g e | 15
consistent with prior studies [33]. One potential criticism of this result is based on our
exclusion of patients dying within 18 days of transplant in the hospital. These patients
were excluded to avoid skewing the data as previously described. The question that
follows: does this exclusion artificially lower the mortality rates within the low or
moderate risk LOS categories? To answer this question, a post-hoc analysis was
performed. The donor, recipient, and posttransplant predictors identified on
multivariable analysis were found to be significantly more prevalent in the excluded
early death patients than among those included patients who likewise did not have a
prolonged LOS (data not shown). This finding supports our initial assumption that the
excluded early death patients were not representative (in terms of LOS risk factors) of
the surviving patients who also did not have a prolonged LOS. This did not hold true
for one recipient LOS predictor, male gender, which was not significantly different in
proportion between these two groups.
Criticism can also potentially be directed towards the use of LOS as a proxy for
costs, given one study that found the last days of hospitalization after trauma surgery to
be the less costly than the first days [34]. However, another study found an increased
risk of adverse events with each added hospital day [35]. Additionally, opportunity cost
may be an important consideration, in that a prolonged LOS limits a transplant center
from caring for other patients because of bed availability. This may lead to fewer
operations and procedures, events which produce needed revenue.
In conclusion, this study provides the first analysis of a large database
identifying predictors of hospital length of stay for potential liver transplant recipients.
The resultant HALOS mathematical model, with variable weighting of predictors, can
serve as an important tool for transplant centers striving to better understand and
optimize transplant-related costs.
The HALOS model
P a g e | 16
REFERENCES
1. Abecassis, M. M. (2006). Financial outcomes in transplantation--a provider’s perspective.
American journal of transplantation : official journal of the American Society of
Transplantation and the American Society of Transplant Surgeons, 6(6), 1257-63.
doi:10.1111/j.1600-6143.2006.01329.x
2. Abecassis, M. (2009). Making Dollars and Sense out of Liver. Transplantation,
1159-1161. doi:10.1002/lt.
3. Axelrod, D. a, Koffron, A. J., Baker, T., Al-Saden, P., Dixler, I., McNatt, G.,
Sumner, S., et al. (2005). The economic impact of MELD on liver transplant
centers. American journal of transplantation : official journal of the American Society of
Transplantation and the American Society of Transplant Surgeons, 5(9), 2297-301.
doi:10.1111/j.1600-6143.2005.01025.x
4. Feng, S., Goodrich, N. P., Bragg-Gresham, J. L., Dykstra, D. M., Punch, J. D.,
DebRoy, M. a, Greenstein, S. M., et al. (2006). Characteristics associated with liver
graft failure: the concept of a donor risk index. American journal of transplantation :
official journal of the American Society of Transplantation and the American Society of
Transplant Surgeons, 6(4), 783-90. doi:10.1111/j.1600-6143.2006.01242.x
5. Buchanan, P., Dzebisashvili, N., Lentine, K. L., Axelrod, D. A., Schnitzler, M. A.,
& Salvalaggio, P. R. (2009). Liver Transplantation Cost in the Model for EndStage Liver Disease Era : Looking Beyond the Transplant Admission. Liver, 12701277. doi:10.1002/lt.
6. Washburn, W. K., Pollock, B. H., Nichols, L., Speeg, K. V., & Halff, G. (2006).
Impact of recipient MELD score on resource utilization. American journal of
transplantation : official journal of the American Society of Transplantation and the
American Society of Transplant Surgeons, 6(10), 2449-54. doi:10.1111/j.16006143.2006.01490.x
The HALOS model
P a g e | 17
7. Axelrod, D. a, Schnitzler, M., Salvalaggio, P. R., Swindle, J., & Abecassis, M. M. (2007).
The economic impact of the utilization of liver allografts with high donor risk index.
American journal of transplantation : official journal of the American Society of
Transplantation and the American Society of Transplant Surgeons, 7(4), 990-7.
doi:10.1111/j.1600-6143.2006.01724.x
8. Data collection forms. Available at:
http://www.unos.org/data/about/collection.asp. Accessed July 2011.
9. Metzger, R. a, Delmonico, F. L., Feng, S., Port, F. K., Wynn, J. J., & Merion, R. M.
(2003). Expanded criteria donors for kidney transplantation. American Journal of
Transplantation, 3(s4), 114-125. doi:10.1034/j.1600-6143.3.s4.11.x
10. Katz, M. (2002). Study Design and Statistical Analysis (Third Edit., p. 81). New
York, NY: Cambridge University Press.
11. Salvalaggio, P. R., Dzebisashvili, N., Macleod, K. E., Lentine, K. L., Gheorghian,
A., Schnitzler, M. A., Hohmann, S., et al. (2011). The Interaction Among Donor
Characteristics , Severity of Liver Disease , and the Cost of Liver Transplantation.
Liver Transplantation, 233-242. doi:10.1002/lt.
12. de Vera, M. E., Lopez-Solis, R., Dvorchik, I., Campos, S., Morris, W., Demetris, a
J., Fontes, P., et al. (2009). Liver transplantation using donation after cardiac
death donors: long-term follow-up from a single center. American journal of
transplantation : official journal of the American Society of Transplantation and the
American Society of Transplant Surgeons, 9(4), 773-81. doi:10.1111/j.16006143.2009.02560.x
13. Schaubel, D. E., Sima, C. S., Goodrich, N. P., Feng, S., & Merion, R. M. (2008). The
survival benefit of deceased donor liver transplantation as a function of
candidate disease severity and donor quality. American journal of transplantation :
official journal of the American Society of Transplantation and the American Society of
Transplant Surgeons, 8(2), 419-25. doi:10.1111/j.1600-6143.2007.02086.x
14. Al Sibae, M. R., & Cappell, M. S. (2011). Accuracy of MELD scores in predicting
mortality in decompensated cirrhosis from variceal bleeding, hepatorenal syndrome,
alcoholic hepatitis, or acute liver failure as well as mortality after non-transplant surgery
or TIPS. Digestive diseases and sciences, 56(4), 977-87. doi:10.1007/s10620-010-1390-3
15. Moon, J., Barbeito, R., Faradji, R., Gaynor, J., & Tzakis, A. (2006). Negative impact
of new-onset diabetes mellitus on patient and graft survival after liver
transplantation: Long-term follow up. Transplantation, 82(12), 1625-8.
16. Watt, K. D. S., Pedersen, R. a, Kremers, W. K., Heimbach, J. K., & Charlton, M. R.
(2010). Evolution of causes and risk factors for mortality post-liver transplant:
results of the NIDDK long-term follow-up study. American journal of
transplantation : official journal of the American Society of Transplantation and the
American Society of Transplant Surgeons, 10(6), 1420-7. doi:10.1111/j.16006143.2010.03126.x
17. Colina, F. (1992). The role of histopathology in hepatic transplantation. Semin Diagn
Pathol, 9(3), 200-9.
The HALOS model
P a g e | 18
18. Jay, C., Lyuksemburg, V., Ladner, D., Wang, E., Caicedo, J., Holl, J., Abecassis,
M., et al. (2011). Ischemic cholangiopathy after controlled donation after cardiac
death liver transplantation: a meta-analysis. Annals of Surgery, 253(2), 259-64.
19. Chan, E. Y., Olson, L. C., Kisthard, J. A., Perkins, J. D., Bakthavatsalam, R., Halldorson,
J. B., Reyes, J. D., et al. (2008). Ischemic Cholangiopathy Following Liver Death
Donors. Liver, 604-610. doi:10.1002/lt.
20. Kaczmarek, B., Manas, M. D., Jaques, B. C., & Talbot, D. (2007). Ischemic
cholangiopathy after liver transplantation from controlled non-heart-beating donors-a
single-center experience. Transplantation proceedings, 39(9), 2793-5.
doi:10.1016/j.transproceed.2007.08.081
21. Perkins, J. D. (2009). Risk Factors for Developing Ischemic-Type Biliary Lesions After
Liver Transplantation Incidence of and risk factors for ischemic- type biliary lesions
following orthotopic liver Available at : Health-Related Quality of Life Scores Used to
Improve Patient. Liver Transplantation, 1882-1887. doi:10.1002/lt.
22. Adam, R., Cailliez, V., Majno, P., Karam, V., McMaster, P., Caine, R. Y., O’Grady, J., et
al. (2000). Normalised intrinsic mortality risk in liver transplantation: European Liver
Transplant Registry study. Lancet, 356(9230), 621-7. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/10968434
23. Merion, R. M. (2010). Current status and future of liver transplantation. Seminars in liver
disease, 30(4), 411-21. doi:10.1055/s-0030-1267541
24. Cardillo, M., Fazio, N. D., Pedotti, P., Feo, T. D., Fassati, L. R., Mazzaferro, V.,
Colledan, M., et al. (2006). Split and Whole Liver Transplantation Outcomes : A
Comparative Cohort Study. Organ, The, 402-410. doi:10.1002/lt.
25. Bonney, G. K., Aldouri, A., Attia, M., Lodge, P. a, Toogood, G. J., Pollard, S. G., &
Prasad, R. (2008). Outcomes in right liver lobe transplantation: a matched pair analysis.
Transplant international : official journal of the European Society for Organ
Transplantation, 21(11), 1045-51. doi:10.1111/j.1432-2277.2008.00722.x
26. Hong, J. C., Yersiz, H., & Busuttil, R. W. (2011). Where are we today in split liver
transplantation? Current opinion in organ transplantation, 16(3), 269-73.
doi:10.1097/MOT.0b013e328346572e
27. Hong, J. C., Yersiz, H., Farmer, D. G., Duffy, J. P., Ghobrial, R. M., Nonthasoot, B.,
Collins, T. E., et al. (2009). Longterm outcomes for whole and segmental liver grafts in
adult and pediatric liver transplant recipients: a 10-year comparative analysis of 2,988
cases. Journal of the American College of Surgeons, 208(5), 682-9; discusion 689-91.
American College of Surgeons. doi:10.1016/j.jamcollsurg.2009.01.023
28. Humar, A., Beissel, J., Crotteau, S., Kandaswamy, R., Lake, J., & Payne, W. (2008).
Whole liver versus split liver versus living donor in the adult recipient: an analysis of
outcomes by graft type. Transplantation, 85(10), 1420-4.
doi:10.1097/TP.0b013e31816de1a3
29. Wilms, C., Walter, J., Kaptein, M., Mueller, L., Lenk, C., Sterneck, M., Hillert, C., et al.
(2006). Long-term outcome of split liver transplantation using right extended grafts in
adulthood: A matched pair analysis. Annals of surgery, 244(6), 865-72; discussion 872-3.
doi:10.1097/01.sla.0000247254.76747.f3
30. Schaubel, D. E., Sima, C. S., Goodrich, N. P., Feng, S., & Merion, R. M. (2008). The
survival benefit of deceased donor liver transplantation as a function of
candidate disease severity and donor quality. American journal of transplantation :
The HALOS model
P a g e | 19
official journal of the American Society of Transplantation and the American Society of
Transplant Surgeons, 8(2), 419-25. doi:10.1111/j.1600-6143.2007.02086.x
31. Olthoff, K. M., Merion, R. M., Abecassis, M. M., Freise, C. E., Hulbert-shearon, T. E., &
Emond, J. C. (2005). Outcomes of 385 Adult-to-Adult Living Donor Liver Transplant
Recipients. Annals of Surgery, 242(3), 314-325. doi:10.1097/01.sla.0000179646.37145.ef
32. McKenna, G. J., & Klintmalm, G. B. (2011). The question of induction? Maybe not all
antibodies are equal …*. Transplant international : official journal of the European
Society for Organ Transplantation, 24(7), 637-9. doi:10.1111/j.1432-2277.2011.01262.x
33. Smith, J. O., Shiffman, M. L., Behnke, M., Stravitz, R. T., Luketic, V. A., Sanyal, A.
J., Heuman, D. M., et al. (2009). Incidence of Prolonged Length of Stay After
Orthotopic Liver Transplantation and Its Influence on Outcomes. Liver
Transplantation, 273-279. doi:10.1002/lt.
34. Taheri, P. a, Butz, D. a, & Greenfield, L. J. (2000). Length of stay has minimal
impact on the cost of hospital admission. Journal of the American College of
Surgeons, 191(2), 123-30. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/10945354
35. Hauck, K., & Zhao, X. (2011). How Dangerous is a Day in Hospital?: A Model of
Adverse Events and Length of Stay for Medical Inpatients. Med Care, E pub.
Table 1. Baseline recipient characteristics
Characteristic
Age, years
Male gender
BMI
Ethnicity
American Indian
Asian
Mean (SD) or Percentage
52.9 (10.2)
67.2%
28.0 (5.7)
0.5%
4.4%
The HALOS model
Black
Hispanic
Multiracial
Pacific Islander
White
Diagnosis
Acute hepatic necrosis
Autoimmune disease
Liver cancer
Cholestatic disease
Cryptogenic cirrhosis
Alcohol-related cirrhosis
Metabolic disease
NASH
Other
Viral cirrhosis
Blood type
A/AB/B/O
Medical condition at transplant
Home/Hospital/ICU
Primary payment
Medicaid or Medicare/Other or Unknown/Private/VA
Secondary payment
Medicaid or Medicare/Other or Unknown/Private/VA
Dialysis week prior to transplant
Encephalopathy
1-2/3-4/None
Ascites
Mild/Moderate/None
Any type of life support
On ventilator
Recipient diabetes
None/Other or unknown/Type I/Type II
Peripheral vascular disease
No/Unknown/Yes
Portal vein thrombosis
No/Unknown/Yes
Previous abdominal surgery
No/Unknown/Yes
TIPS presence at transplant
No/Unknown/Yes
Variceal bleeding prior to transplant
No/Unknown/Yes
Albumin – mg/dl
MELD score
Exception MELD points for HCC
Previous transplant
Number of previous liver transplants
0/1/≥2
Days waiting
Liver transplant with another transplant
Heart/Intestine/Kidney/Pancreas
Transplant procedure
Whole/Reduced or split
Liver type
Living left lobe
Living right lobe
Split/reduced left lobe
Split/reduced right lobe
Whole liver
P a g e | 20
9.3%
12.7%
0.4%
0.1%
72.6%
5.2%
2.4%
17.2%
9.4%
6.1%
17.2%
2.6%
4.4%
5.6%
29.9%
37.2/4.9/13.3/44.6%
71.7/16.9/11.4%
34.5/2.4/61.1/2.0%
4.9/89/5.7/0.4%
9.7%
54.7/11.2/34.1%
50.7/26.7/22.6%
6.0%
5.2%
75.0/9.5/2.5/13.0%
85.9/13.2/0.9%
90.7/3.8/5.5%
55.0/4.6/40.4%
88.3/3.8/7.9%
81.6/13.9/4.5%
2.9 ± 0.7
21.2 ± 9.9
19.9%
7.2%
92.8/6.4/0.8%
247 ± 473
0.1/0.5/6.2/0.5%
95.0/5.0%
0.3%
3.5%
0.2%
1.0%
95.0%
The HALOS model
P a g e | 21
Table2. Donor baseline characteristics
Characteristic
Age, years
Donor male gender
Donor blood type
A/AB/B/O
Cause of death or living
Anoxia/CNS tumor/CVA/Head trauma/Living/Other
Donor ethnicity
American Indian
Asian
Black
Hispanic
Multiracial
Pacific Islander
White
Donor after cardiac arrest
No/Unknown/Yes
Extended criteria donor (renal definition)
Donor weight – kg
Donor height – cm
Donor BMI
Serum creatinine, mg/dl
AST, mg/dl
ALT, mg/dl
Total bilirubin, mg/dl
Diabetes of donor
No
Insulin dependent
Non-insulin dependent
Diabetes present, type unknown
Unknown if diabetes present
Chronic hypertension of donor
No/Unknown/Yes
Donor HCV infection
No/Unknown/Yes
Donor HBV infection
No/Unknown/Yes
Clinical infection of donor
No/Unknown/Yes
Sharing region
Local/Regional/National
Distance Donor to Recipient, miles
Cold ischemia time, hours
Donor and recipient matching
ABO blood type match
Identical/Compatible/Incompatible
Incompatible
Mean (SD) or Percentage
40.9 (17)
60.0%
36.8/3.0/11.4/48.8%
15.5/0.6/40.5/37.5/3.8/2.1%
0.3%
2.3%
15.4%
12.8%
0.5%
0.3%
68.4%
88.9/5.5/5.6%
24.9%
79.1 (10.3)
171.7 (10.7)
26.7 (5.8)
1.5 (1.6)
81 (154)
67 (148)
1 (1.3)
86.6%
3.4%
1.6%
3.1%
5.3%
68.5/0.6/30.9%
97.0/0.2/2.8%
98.8/1.0/0.2%
56.6/7.8/35.6%
72.8/21.5/5.7%
140 (247)
7.2 (3.7)
92.7/6.6/0.7%
0.7%
Table 3. Hospital course
Acute rejection on transplant admission
No
Unknown
Percentage
79.3%
14.4%
The HALOS model
P a g e | 22
Yes, no treatment
Yes, treatment
1.4%
4.9%
Table 4. Recipient and payment factor univariable and multivariable analysis
Variable
Age, years
Male gender
BMI
Ethnicity
Asian
Black
Hispanic
Other
White
Diagnosis
Acute hepatic necrosis
Autoimmune disease
Liver cancer
Cholestatic disease
Cryptogenic disease
Alcohol-related disease
Metabolic disease
Other
Viral cirrhosis
Medical condition at
transplant
Home
Hospital-bound
ICU-bound
Dialysis week before tx
Encephalopathy
Grade 1-2
Grade 3-4
None
Ascites
Mild
Moderate
None
Any life support
Mechanical ventilation
Diabetes
Peripheral vascular disease
No
Unknown
Yes
Portal vein thrombosis
No
Unknown
Yes
Previous abdominal surgery
No
Unknown
Yes
TIPS presence at transplant
No
R
0.008
-2.93
-0.05
Univariable Analysis
95% CI
p-value
(-0.01 to 0.03)
0.5
(-3.39 to -2.47)
< 0.0001
(-0.09 to -0.01)
0.01
R
0.13
-1.42
Multivariable Analysis
95% CI
p-value
(0.11 to 0.15)
< 0.0001
(-1.93 to -0.92) < 0.0001
-1.26
0.25
0.66
0.70
Reference
(-2.32 to -0.19)
(-0.5 to 1)
(0.002 to 1.32)
(-2.81 to 1.41)
0.02
0.5
0.05
0.5
4.37
1.62
-3.74
-0.03
2.02
0.74
2.01
2.07
7.27
Reference
(3.35 to 5.39)
(0.19 to 3.05)
(-4.39 to -3.09)
(-0.83 to 0.77)
(1.06 to 2.98)
(0.09 to 1.39)
(0.63 to 3.39)
(0.97 to 3.16)
(6.29 to 8.26)
< 0.0001
0.03
< 0.0001
0.9
< 0.0001
0.02
0.004
0.0002
< 0.0001
-2.67
(-3.80 to -1.54)
< 0.0001
(7.45 to 8.59)
(15.93 to 17.26)
(11.14 to 12.59)
< 0.0001
< 0.0001
< 0.0001
4.49
7.25
3.62
(3.78 to 5.20)
(6.19 to 8.30)
(2.72 to 4.52)
< 0.0001
< 0.0001
< 0.0001
2.03
11.78
Reference
(1.57 to 2.50)
(11.05 to 12.52)
< 0.0001
< 0.0001
3.02
(2.18 to 3.87)
< 0.0001
0.74
5.62
Reference
20.36
20.93
1.37
(0.19 to 1.28)
(5.01 to 6.24)
0.008
< 0.0001
1.51
(0.94 to 2.08)
< 0.0001
(19.47 to 21.25)
(19.98 to 21.88)
(0.87 to 1.87)
< 0.0001
< 0.0001
< 0.0001
9.64
(8.35 to 10.93)
< 0.0001
0.91
(0.37 to 1.46)
0.001
Reference
1.24
4.22
(0.60 to 1.88)
(1.90 to 6.55)
0.0001
0.0004
Reference
1.24
4.11
(0.11 to 2.38)
(3.16 to 5.06)
0.03
< 0.0001
2.33
(1.31 to 3.37)
< 0.0001
Reference
2.26
2.79
(1.21 to 3.30)
(2.35 to 3.24)
< 0.0001
< 0.0001
0.96
(0.45 to 1.47)
0.0002
Reference
8.02
16.59
11.86
Reference
The HALOS model
Unknown
Yes
Variceal bleed prior to
transplant
No
Unknown
Yes
Albumin, mg/dl
MELD score
Exception MELD points for
HCC
Number of previous LT
0
1
≥2
Time on transplant list, days
Simultaneous
transplantation of another
organ
Heart
Intestine
Kidney
Pancreas
Living donor
Primary payment
Other
Medicare/Medicaid
VA
Private
Secondary payment
Medicare/Medicaid
Other
Private
P a g e | 23
1.03
2.08
(-0.10 to 2.15)
(1.27 to 2.88)
0.07
< 0.0001
1.18
(0.32 to 2.05)
0.007
Reference
1.02
1.5
-1.04
0.49
-7.18
(0.39 to 1.65)
(0.45 to 2.54)
(-1.33 to -0.74)
(0.47 to 0.51)
(-7.72 to -6.65)
0.001
0.005
< 0.0001
< 0.0001
< 0.0001
0.14
-1.89
(0.11 to 0.18)
(-2.56 to -1.21)
< 0.0001
< 0.0001
(9.24 to 10.99)
(11.47 to 16.47)
(-0.001 to 0.0005)
< 0.0001
< 0.0001
< 0.0001
5.40
10.34
0.0005
(4.39 to 6.42)
(7.60 to 13.10)
(0.00045 to
0.001)
< 0.0001
< 0.0001
0.03
16.95
35.67
5.70
33.24
-1.22
(11.35 to 22.54)
(32.62 to 38.71)
(4.80 to 6.60)
(30.23 to 36.25)
(-2.34 to -0.10)
< 0.0001
< 0.0001
< 0.0001
< 0.0001
0.03
18.70
34.58
(13.1 to 24.4)
(31.3 to 37.9)
< 0.0001
< 0.0001
2.50
(1.27 to 3.74)
< 0.0001
3.84
2.22
2.50
Reference
(2.40 to 5.27)
(1.76 to 2.68)
(0.96 to 4.04)
<0.0001
<0.0001
0.002
3.75
1.24
3.48
(2.22 to 5.28)
(0.84 to 1.85)
(1.84 to 5.13)
<0.0001
<0.0001
<0.0001
2.19
-0.33
Reference
(0.86 to 3.52)
(-1.26 to 0.60)
0.001
NS
1.46
(0.38 to 2.54)
0.008
Reference
10.11
13.97
-0.0009
Table 5. Donor and postoperativefactor univariable and multivariable analysis
Variable
Age, years
Male donor
Cause of death or living
Anoxia
CNS tumor
Stroke
Head trauma
Living
Other
Donor ethnicity
Asian
Black
Hispanic
Other
White
Non-heart beating donor
DCD donor
R
-0.003
-0.55
Univariable Analysis
95% CI
p-value
(-0.02 to 0.01)
0.6
(-0.99 to -0.11)
0.01
0.004
0.35
0.36
Reference
-1.1
0.48
(-0.65 to 0.65)
(-2.41 to 3.11)
(-0.13 to 0.85)
NS
NS
0.2
(-2.27 to 0.07)
(-1.05 to 2.01)
0.07
NS
0.45
0.35
2.07
0.83
Reference
2.29
(-1.01 to 1.91)
(-0.26 to 0.96)
(1.41 to 2.72)
(-1.23 to 2.88)
NS
NS
<0.0001
NS
(1.21 to 3.37)
<0.0001
R
0.04
Multivariable Analysis
95% CI
p-value
(0.02 to 0.05)
<0.0001
3.40
(2.23 to 4.60)
<0.0001
The HALOS model
No
Unknown
Yes
Extended criteria donor
(renal definition)
No
Unknown
Yes
Donor weight, kg
Donor height, cm
Donor BMI
Serum Cr, mg/dl
AST, mg/dl
ALT, mg/dl
Total bilirubin, mg/dl
Donor diabetes
No
Unknown
Yes
Chronic hypertension
No
Unknown
Yes
HCV-positive donor
Negative
Unknown
Positive
Clinical infection of donor
No
Unknown
Yes
Sharing region
Local
Regional
National
Donor distance, miles
Cold ischemia time, hours
ABO blood type match
Identical
Compatible
Incompatible
Transplanted lobes
Left
Right
Whole
Size mismatch
Donor 20% larger
Donor 20% smaller
No size mismatch
Transplant admission ACR
Yes, no treatment
Yes, treatment
Unknown
No
P a g e | 24
Reference
-0.35
-0.29
(-1.31 to 0.6)
(-0.97 to 0.91)
NS
NS
Reference
-1.31
-0.36
-0.02
-0.05
-0.03
-0.02
-0.001
-0.001
-0.03
(-2.43 to -0.18)
(-0.87 to 0.15)
(-0.03 to -0.006)
(-0.07 to -0.03)
(-0.06 to 0.01)
(-0.16 to 0.12)
(-0.003 to 0.0002)
(-0.002 to 0.0004)
(-0.20 to 0.15)
0.02
0.2
0.003
<0.0001
0.2
NS
0.1
0.2
NS
Reference
1.62
0.19
(-1.73 to 5.00)
(-0.56 to 0.94)
NS
NS
Reference
2.16
-0.15
(-0.64 to 4.96)
(-0.61 to 0.32)
0.1
NS
Reference
-0.69
-0.94
(-5.10 to 3.70)
(-2.24 to 0.36)
0.2
NS
Reference
-0.92
-0.02
(-1.74 to -0.09)
(-0.48 to 0.44)
0.03
NS
Reference
2.27
3.23
0.003
0.33
(1.74 to 2.80)
(2.29 to 4.17)
(0.002 to 0.004)
(0.27 to 0.39)
<0.0001
<0.0001
<0.0001
<0.0001
Reference
3.97
5.46
(3.10 to 4.80)
(2.73 to 8.18)
<0.0001
<0.0001
4.48
-1.50
Reference
(1.57 to 7.38)
(-2.56 to -0.46)
0.002
0.005
4.67
0.67
Reference
(2.07 to 7.27)
(-2.08 to 3.43)
0.0004
NS
9.51
8.54
0.68
Reference
(7.70 to 11.33)
(7.54 to 9.54)
(0.07 to 1.30)
<0.0001
<0.0001
0.03
0.24
(0.09 to 0.40)
0.002
1.44
(0.03 to 2.85)
0.05
1.39
(0.34 to 2.44)
0.01
0.36
(0.29 to 0.44)
<0.0001
6.04
2.56
(1.80 to 10.20)
(0.22 to 4.91)
0.005
0.03
8.20
(7.24 to 9.18)
<0.0001
The HALOS model
P a g e | 25
Figure 1. Patient survival by LOS group.
Kaplan-Meier survival with log-rank analysis was performed using the groups of patients at low,
moderate, and high risk for LOS. Those subjects at high risk for LOS had the worst predicted
survival. Based on the slope of these curves, this increase in graft failure and mortality risk is
most pronounced within the first year after transplant.