Peak urea level, leukocyte count and use of invasive ventilation as risk factors of mortality in acute pancreatitis: A retrospective study
Peak urea level, leukocyte count and use of invasive ventilation as risk factors of mortality in acute pancreatitis: A retrospective study
Chao-Nan Liu 0 2
Si Chen 0 2
Hao Chen 0 2
Li Yue 1 2
Li-Qin Ling 0 2
Chang-Wei Chen 2
Lei Du 2
Jing ZhouID 0 2
0 Department of Laboratory Medicine, West China Hospital, Sichuan University , Chengdu , China
1 Workplace Safety & Insurance Board , Toronto, Ontario , Canada , 3 Department of Anesthesiology and Translational Neuroscience Center, West China Hospital, Sichuan University , Chengdu , China
2 Editor: Zolta ?n Rakonczay, Jr., University of Szeged , HUNGARY
While mortality of AP patients can be predicted reasonably well based only on urea values
at admission, predictions are more reliable when they take into account in-hospital data on
peak urea level, leukocyte count and use of invasive ventilation.
Data Availability Statement: All relevant data are
within the manuscript and its Supporting
Funding: This work was supported by the National
Natural Science Foundation of China (81371878
and 81570374) awarded to Zhou Jing.
Competing interests: The authors have declared
that no competing interests exist.
Acute pancreatitis (AP) involves sudden abdominal inflammation, which leads to millions of
hospitalizations annually around the world [
]. Approximately 15?20% of patients die from
this disease in the intensive care unit [3,4]. Early, reliable prediction of which patients are at
higher risk of mortality may help improve treatment and management of AP. The Acute
Physiology and Chronic Health Evaluation (APACHE)  instrument has been used to predict
AP-related mortality based on weighted scoring of three sets of variables measured within a
few hours of hospital admission. Slightly simpler scoring systems have been developed [
but they have not been widely adopted, perhaps reflecting their complexity and relative
One of the simplest models to predict AP-related mortality [
] takes into account only
plasma urea level at admission, meaning that it can be applied to patients even when the time
from AP onset to admission is unknown. This model showed an ability to predict mortality in
Caucasian AP patients with an area under the receiver operating characteristic curve (AUC) of
0.79, comparable to the AUC of 0.83 obtained with the much more complex APACHE II
instrument. The power of this simple model was improved by incorporating the increase in
urea level during the first 24 h after admission (AUC 0.89) as well as during the first 48 h after
admission (AUC 0.90). These results suggest that reliable prediction of AP-related mortality
may require taking into account risk factors of AP-related mortality after admission [
Here we wanted to examine whether urea levels at admission can predict AP-related
mortality in a Chinese population of AP patients, and whether the predictive power can be
improved by incorporating in-hospital patient data into the model. Therefore we
retrospectively analyzed data for AP patients treated at our large hospital in southwest China over a
Materials and methods
This retrospective study included a consecutive series of patients admitted for AP at West
China Hospital of Sichuan University between January 1, 2011 and December 31, 2015. In
accordance with the revised Atlanta Classification (2012),[
] patients were diagnosed with
AP if they presented with two or more of the following: (1) abdominal pain consistent with
acute pancreatitis (acute onset of a persistent, severe, epigastric pain often radiating to the
back); (2) serum levels of amylase and/or lipase 3 times the upper limit of normal; and (3)
characteristic features on contrast-enhanced computed tomography (CECT) and, less
commonly, magnetic resonance imaging (MRI) or transabdominal ultrasonography. Patients were
excluded if they were younger than 18 years, or had been admitted to our hospital more than
48 h after AP onset. All data were fully anonymized before accessed. This study was approved
by the Ethics Committee of West China Hospital of Sichuan University.
Working with the hospital central database, two authors independently extracted data on
patient characteristics and clinical variables, including laboratory data, treatments and
outcomes. The two data sets were checked against each other, and discrepancies were resolved
through discussion and closer examination of the original data. Serum amylase and other
biochemical tests as well as blood counts were performed within 2 h of admission, as per standard
procedure at our hospital. Blood counts were performed every 1?2 days thereafter. Other
biochemical indicators were assayed at the discretion of the attending physician. Index-time
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curves were plotted to determine when these biochemical indices reached a maximum or
minimum. Data were also collected on patients who showed altered mental status [
] during AP
Data were collected on hospital procedures that might have influenced the prognosis of AP.
These included invasive ventilation, defined as involving endotracheal intubation or
tracheotomy; noninvasive ventilation; surgery; and dialysis. Invasive ventilation, which is much easier
to evaluate than respiratory function, served as an indicator of damaged pulmonary gas
exchange and/or respiratory muscle function.
The full dataset was divided into two subsets. The development dataset, used to construct risk
models of mortality, comprised patients treated from January 1, 2011 to December 31, 2012
and from January 1, 2014 to March 31, 2015. The resulting risk models were validated using
the remaining data (validation dataset) covering one-third of the study period, corresponding
to patients treated from January 1 to December 31, 2013 and from April 1, 2015 to December
Data in the development dataset were reported as mean ? standard deviation and analyzed
by unpaired or revised Student?s t test if they showed a normal distribution based on
ShapiroWilk tests, or as median (interquartile range) and analyzed using the rank sum test if they
showed a non-normal distribution. Data for categorical variables were expressed as
percentages, and inter-group differences were analyzed using chi-squared or Fisher?s exact tests.
All variables that differed significantly between groups were considered as potential risk or
confounding factors affecting mortality, and so were included in forward univariate logistic
regression to identify mortality risk factors at admission and during hospitalization. Variables
significant at the p < 0.05 level in univariate regression were then entered in multivariate
regression. Analysis was adjusted by other indices when one index was used to estimate risk.
The resulting odds ratio (OR) represents the predicted change in risk per unit increase in the
Mathematical models to predict AP-associated mortality were developed as follows. All
variables significant at the p < 0.05 level in multivariate logistic regression were entered into
binary logistic regression, and non-significant variables were eliminated from the model one
at a time. Each time that a variable was added, the stability of the model was checked by
examining the p value and ? value [? = ln (OR)]. We constructed a multimarker score H =
(?1?biomarker A) + (?2?biomarker B) + (?3?biomarker C), where the coefficients ?1, ?2 and ?3 were
estimated in the binary logistic regression. The correspondence between model predictions
and observed data was assessed using the Hosmer-Lemeshow chi-squared test.
Formulas based on data at admission were generated by taking into account demographic
characteristics, disease history and indices calculated within 2 h of admission. To test whether
incorporating post-admission data might improve the predictive power of the formulas, we
also generated formulas based on the maximum or minimum values of laboratory tests, altered
mental status and special procedures.
To assess the predictive power of scores derived from formulas or from urea level, receiver
operating characteristic (ROC) curves were generated against the development dataset.
Patients classified as low- or high-risk were defined based on the optimal ROC threshold
value, which was determined from Youden?s index based on specificity and sensitivity.
Highrisk patients were those with scores above the threshold value; others were low-risk. Then an
adjusted OR and 95% confidence interval (95%CI) referring to high-risk patients relative to
low-risk ones was obtained by logistic regression. Positive and negative predictive values
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(PPV, NPV) were assessed for scores and urea levels using the validation dataset. PPV assesses
how well the model predicts patients who actually experience the complication. The
numerator gives the number of those high-risk patients who actually experienced the mortality, while
the denominator gives the number of patients classified by the model as being at high risk
(scores > threshold value). NPV assesses how well the model predicts patients who don?t
experience the complication. The numerator gives the number of those low-risk patients who
actually don?t experienced the mortality, while the denominator gives the number of patients
classified by the model as being at low risk (scores < threshold value).
All statistical analyses were performed using SPSS 19.0 (IBM, Chicago, IL, USA) and
MedCalc 15.2.2 (Mariakerke, Belgium). In all analyses, p < 0.05 was considered significant.
Of the 2,927 potentially eligible patients treated at our hospital, 1,328 were excluded, and the
remaining 1,599 patients were included in the analysis (Fig 1). Of these, 1,062 were assigned to
the development dataset to construct risk models, while the remaining 537 were assigned to a
validation dataset to assess the quality of the models. Baseline characteristics of both groups of
patients are shown in Table 1. The cause of AP was not possible to determine definitively in
most of our sample because of the short disease course: the disease was related to food or was
not linked to any obvious cause in 977 patients (92%), it was linked to cholangitis in 21
patients (2%), and it was related to alcohol in the remaining 64 patients (6%).
Independent risk factors at admission. A total of 33 patients (3.11%) in the development
dataset and 13 (2.23%) in the validation dataset died during hospitalization. The validation
and development datasets showed similar distributions for variables of interest. In the
development dataset, patients who died were older (53 vs 47 years, p = 0.012) and less likely to smoke
Fig 1. Flow diagram of patients.
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a Continuous data were reported as mean ? SD (normal distribution), or median (interquartile range) (skewed distribution). Inter-group differences were assessed for
significance using the t test (normal distribution) or nonparametric rank sum test (skewed distribution). Data for categorical variables were reported as incidence (%),
and inter-group differences were assessed using the chi-squared or Fisher?s exact tests.
b A total of 1,062 patients were included in the development dataset (see Methods).
c Index-time curves were plotted for biochemical indices to identify peak and nadir values.
d Altered mental status included delirium, somnolence, lethargy and coma.
Abbreviations: ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; BMI, body mass index.
cigarettes (21.2 vs 39.7%, p = 0.033) than those who survived. Patients who died also had
higher levels at admission of blood glucose, lipase, amylase, urea, aspartate transaminase and
creatinine, as well as lower platelet counts. In multivariate regression, blood glucose, urea level
and platelet count at admission were independent risk factors for death, and they were
retained in forward-selection models to create risk models of mortality (Fig 2).
Independent risk factors during hospitalization. In the development dataset, patients
who died had higher peak levels of alanine transaminase, glucose, total bilirubin, conjugated
Fig 2. Univariate and multivariate logistic regression to identify risk factors of mortality due to acute pancreatitis. ?Ad, at admission. $ Score from the formula Y =
(0.069?Glu-ad) +(0.141?Urea-ad)?(0.008?PLT-ad) # Hos, during hospitalization. ? Score from the formula Y = (0.121?Urea-peak) + (0.060?WBC-Peak) +
(2.815?IMV). Abbreviations: 95%CI, 95% confidence interval; Peak, maximal value of a laboratory test.
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bilirubin, lipase, amylase, urea, aspartate aminotransferase, creatinine, and leukocyte counts
than patients who survived. Patients who died also had lower nadir platelet counts and higher
incidence of altered mental status and special procedures (Table 1). In multivariate logistic
regression, peak urea levels, leukocyte count, and use of invasive ventilation were independent
risk factors of death. These factors were incorporated into the formula to calculate hospital
scores (Fig 2).
Models developed based on the independent risk factors identified above were assessed for
predictive power using the data in the validation dataset. Patients with risk scores above the
threshold values were more likely to die: PPV was 8.4% and NPV was 99.3% based on scores at
admission, compared to 24.4% and 99.6% based on scores during hospitalization.
Models based only on risk factors at admission. Urea levels at admission were 4.83
mmol/L (95%CI, 1.61 to 11.62) among patients who survived and 9.82 mmol/L (95%CI, 2.88
to 22.40; p < 0.001) among non-survivors; the corresponding scores at admission (score-ad)
were 0.09 (95%CI, -1.38 to 1.70) and 1.28 (95%CI, -0.40 to 2.71; p < 0.001). Risk of mortality
increased in a stepwise manner with increasing urea levels at admission (OR 1.15, 95%CI 1.03
to 1.29, p = 0.015) and score-ad (OR 3.49, 95%CI 2.43 to 5.03, p < 0.001).
AUC for the model based solely on urea level at admission was 0.81 (95%CI 0.72 to 0.90).
AUC did not improve significantly (p = 0.472) after incorporating glucose levels and platelet
counts at admission (0.84, 95%CI 0.77 to 0.90; Fig 3). Based on Youden?s index, threshold
values from ROC curves were 6.43 mmol/L for urea level at admission and 0.567 for score-ad.
Sensitivity and specificity were 82.6% and 75.7% for urea level, similar to 80.4% and 75.9% for
score-ad (Table 2).
Models based on risk factors at admission and during hospitalization. In the
development dataset, peak urea levels during hospitalization were 6.00 mmol/L (95%CI 2.91 to 15.68)
among patients who survived and 16.48 mmol/L (95%CI 5.34 to 50.96) among those who died.
The corresponding scores during hospitalization (score-hos) were 1.62 (95%CI 0.87 to 5.21)
and 5.80 (95%CI 2.19 to 11.68). AUC for a model predicting mortality based only on peak urea
during hospitalization was 0.91 (95%CI 0.84 to 0.97), significantly higher than the AUC for a
model based only on urea level at admission (p < 0.001). AUC for a score-hos including peak
urea levels, leukocyte counts and use of invasive ventilation was 0.97 (95%CI 0.96 to 0.99),
significantly higher than that based only on score-ad (p = 0.025) or only on peak urea level during
hospitalization (p = 0.038; Fig 3).
Defining low and high mortality risk based on a threshold peak urea level during
hospitalization of 8.02 mmol/L led to high sensitivity of 90.3% but medium specificity of 79.8%. The
threshold score-hos was 2.916, which offered sensitivity of 93.5% and specificity of 92.9%.
These values were higher than those obtained based only on peak urea level (Table 2).
Various scoring systems for predicting risk of AP-related mortality have been developed, and
they show similar predictive power (AUC 0.79?0.84) in Caucasian populations [
]. In the
present study, we found that as in Caucasian AP patients, urea levels within 48 h after AP
onset can predict mortality in Chinese AP patients reasonably well (AUC 0.81). This predictive
power was not substantially improved by expanding the model to include glucose levels and
platelet counts at admission. In contrast, predictive power was significantly improved by
adding data on three in-hospital parameters: peak urea level, leukocyte count, and use of invasive
ventilation. Our results suggest that the most reliable prediction of AP-related mortality
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Fig 3. Receiver operating characteristic curve for urea level or scores based on data at admission or during
hospitalization. Curves were calculated based on the formulas in Table 2. Predictions were based on data at admission (Ad,
dotted lines) or on data during hospitalization (Hos, solid lines). Data based on urea levels are shown in purple; data based
on scores, in green.
requires taking into account these three in-hospital indicators that capture AP progression.
The simplified model that we present here may improve the management of AP patients from
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admission to discharge. It can be calculated in a straightforward way from routinely available
data so that it can easily be used even in primary care settings.
The AUC of 0.81 achieved here with Chinese patients using only urea level at admission is
similar to the 0.79 reported in Caucasian patients. [
] In fact, our predictive power is similar
to that reported for several more complex models: APACHE II, AUC 0.83;  BISAP, AUC
0.82;  BALI, AUC 0.82;[
] and POP, AUC 0.84 [
]. We were unable to improve the
predictive power of urea level by adding glucose level and platelet count at admission (AUC 0.84;
95%CI 0.77 to 0.90). These results suggest that in the absence of a better alternative, plasma
urea level at admission serves as a reasonably good early predictor of AP-related mortality.
We were able to substantially improve on the predictive power based on urea level at
admission simply by incorporating peak urea level during hospitalization (AUC 0.91, 95%CI 0.84 to
0.97, p = 0.025 vs urea level at admission). Adding this parameter improved sensitivity from
82.6% to 90.3%, although specificity improved only slightly from 75.7% to 79.8%. This
improvement in predictive power through inclusion of in-hospital data echoes results from
Wu et al , who achieved an AUC of 0.84 (95%CI 0.79 to 0.90) by incorporating data on
changes in urea levels during the first 24 h after admission.
We found that including even more in-hospital data substantially improved predictive
power. A model taking into account the three in-hospital parameters of peak urea level,
leukocyte count and use of invasive ventilation gave an AUC of 0.97, sensitivity of 93.5% and
specificity of 92.9%. These results suggest that including commonly measured data on a small
number of patient characteristics during hospitalization can reliably predict AP-related
This study involved a relatively small number of deaths in a patient population from a single
medical center, albeit one of the largest in China that draws patients from a large geographic
area. These factors increase the risk of bias in the study. Our model focused on factors
routinely analyzed in our hospital, which means that we neglected some factors that may influence
AP-related mortality and so may improve our minimal model here; these factors include levels
of some inflammatory factors, such as C-reactive protein and interleukin-6 [
], and levels of
arterial blood gases [
]. Our model may also be improved by taking into account aspects of AP
In conclusion, our results suggest that urea level at admission is a reasonably good predictor
of mortality in AP patients, but that much better prediction is obtained by incorporating
inhospital data on peak urea levels, leukocyte count and use of invasive ventilation. We suggest
this simple scoring system to improve management of AP patients. It may also be worth
investigating whether this straightforward scoring system can predict mortality in patients with
diseases other than AP.
S1 Table. The partial data of validation dataset.
We thank all patients and clinicians from the West China Hospital of Sichuan University, for
their cooperation and participation in the study.
Conceptualization: Lei Du, Jing Zhou.
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Formal analysis: Chao-Nan Liu, Si Chen.
Funding acquisition: Jing Zhou.
Investigation: Chao-Nan Liu, Si Chen, Hao Chen, Chang-Wei Chen.
Software: Chao-Nan Liu, Si Chen.
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