MELD score predicts mortality in critically ill cirrhotic patients M. Dustin Boone MD, Leo A. Celi MD, MPH, Ben G. Ho MS, Michael Pencina PhD, Michael P. Curry MD, Yotam Lior BSc, Daniel Talmor MD, MPH, Victor M. Novack MD, PhD PII: DOI: Reference:
S0883-9441(14)00214-7 doi: 10.1016/j.jcrc.2014.05.013 YJCRC 51527
To appear in:
Journal of Critical Care
Received date: Revised date: Accepted date:
27 November 2013 27 March 2014 22 May 2014
Please cite this article as: Boone M. Dustin, Celi Leo A., Ho Ben G., Pencina Michael, Curry Michael P., Lior Yotam, Talmor Daniel, Novack Victor M., MELD score predicts mortality in critically ill cirrhotic patients, Journal of Critical Care (2014), doi: 10.1016/j.jcrc.2014.05.013
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT TITLE PAGE Title: MELD score predicts mortality in critically ill cirrhotic patients
RI P
T
Authors: 1. M. Dustin Boone, MD. Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
SC
2. Leo A. Celi, MD, MPH. Department of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA and Harvard-Massachusetts Institute of Technology Division of Health Science Technology, Cambridge, MA
NU
3. Ben G. Ho, MS. Harvard-Massachusetts Institute of Technology Division of Health Science Technology, Cambridge, MA
MA
4. Michael Pencina, PhD. Department of Biostatistics, School of Public Health, Boston University, Boston, MA
ED
5. Michael P Curry, MD. Department of Medicine, Division of Hepatology, Beth Israel Deaconess Medical Center, and Harvard Medical School, Boston, MA
PT
6. Yotam Lior, BSc. Clinical Research Center, Soroka University Medical Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Israel.
CE
7. Daniel Talmor, MD, MPH. Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
AC
8. Victor M Novack, MD, PhD. Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA and Clinical Research Center, Soroka University Medical Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Israel. Work performed at Beth Israel Deaconess Medical Center, Boston, MA and Massachusetts Institute of Technology, Cambridge, MA Address for reprints: M. Dustin Boone, MD Beth Israel Deaconess Medical Center Department of Anesthesia, Critical Care and Pain Medicine Boston, MA 02215 [email protected] office telephone +1 617.754.2751 No financial support was used for this study
ACCEPTED MANUSCRIPT Abstract Purpose
T
Cirrhosis is a common condition that complicates the management of patients
RI P
who require critical care. There is interest in identifying scoring systems that may be used to predict outcome because of the poor odds for recovery despite high-intensity care. We
SC
sought to evaluate how MELD, an organ-specific scoring system, compares with other
NU
severity of illness scoring systems in predicting short and long-term mortality for
MA
critically ill cirrhotic patients.
Materials and Methods
ED
This was a retrospective cohort study involving seven intensive care units in a tertiary
PT
care, academic medical center. Adult patients with cirrhosis who were admitted to an intensive care unit between 2001 and 2008 were evaluated. Severity of illness scores
CE
(MELD, SOFA) were calculated on admission and at 24, 48 hours. The primary
Results
AC
endpoints were 28 day and one-year all-cause mortality.
848 out of 19,742 ICU hospitalizations had cirrhosis. Relevant data were available for 521 patients (73%). Out of these cases, 353 (69.5%) patients were admitted to medical ICU and the other 155 (30.5%) to surgical unit. Alcohol abuse and hepatitis C were the most common reasons for cirrhosis. Patients who died within 28 days were more likely to receive mechanical ventilation, pressors, and renal replacement therapy. Among 353 medical admissions, both MELD and SOFA were found to be significantly associated
ACCEPTED MANUSCRIPT with both 28-day and one-year mortality. Among the 155 surgical admissions, both scores were found to be not significant for 28-day mortality but were significant for one
RI P
T
year.
Conclusions
SC
Our results demonstrate that the prognostic ability of a variety of scoring systems
NU
strongly depends on the patient population. In the medical ICU population, each model (MELD + SOFA, MELD, SOFA) demonstrates excellent discrimination for 28-day and
MA
one-year mortality. However, these scoring systems did not predict 28 day mortality in the surgical ICU group, but were significant for one year mortality. This suggests that
ED
patients admitted to a surgical ICU will behave similarly to their medical ICU cohort if
CE
Keywords
PT
they survive the peripoerative period.
AC
cirrhosis; prognosis; epidemiology; outcome; critical care; organ dysfunction scores
ACCEPTED MANUSCRIPT Introduction Cirrhosis is a common co-morbid condition that complicates the management of
T
patients admitted to an ICU [1]. Cirrhosis is estimated to be present in over 1% of the
RI P
general population and remains the 12th leading cause of death in the United States [2]. When admitted to an ICU, mortality for patients with cirrhosis is high [3]; recent data
SC
suggests greater than 37% ICU mortality and 49% hospital mortality [4, 5]. Because of
NU
the poor odds for recovery despite aggressive interventions, several investigators have examined how scoring systems may be used to predict outcome and allocate resources in
MA
this cohort of ICU patients [6, 7]. Much of this effort has focused on determining
PT
predicting the risk of death.
ED
whether organ-specific scoring systems outperform traditional ICU scoring systems in
Patients admitted to an ICU with cirrhosis make an ideal population to study how
CE
organ-specific scoring systems compare with overall severity of illness scoring systems.
AC
Two liver-specific models, the Child-Pugh classification and the Model for End Stage Liver Disease (MELD) were originally created to predict the risk of death in patients with portal hypertension undergoing porto-systemic shunts [8, 9]. While intended as a risk model for a specific clinical diagnosis, these models have been used to predict the risk of death in patients with cirrhosis in a variety of settings [10-12]. In particular, recent interest in evaluating the prognostic ability of the MELD score to predict the risk of short-term death in cirrhotic patients admitted to an ICU has garnered interest [10, 13].
ACCEPTED MANUSCRIPT In this study we sought to evaluate how MELD, an organ-specific scoring system, compares with other severity of illness scoring systems in predicting short and long-term
T
mortality for cirrhotic patients admitted to both medical and surgical intensive care units
RI P
over a10-year span.
SC
Methods
NU
Patient data were extracted from the Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC) database (version 2.6). This is a publicly available intensive
MA
care unit (ICU) database that was developed at the Massachusetts Institute of Technology (MIT) and contains de-identified data from over 30,000 patients who were admitted to
ED
the ICUs at Beth Israel Deaconess Medical Center (BIDMC), a large, academic, tertiary
PT
medical center in Boston, Massachusetts between 2001 and 2008. The institutional
CE
review boards from MIT and BIDMC granted a waiver of informed consent.
AC
The MIMIC database includes physiologic information from bedside monitors. These data (heart rate, blood pressures, etc.) were validated by ICU nurses prior to entry into the database. MIMIC also contains records of all lab values, nursing progress notes, IV medications, fluid intake/output (I/O), and other clinical variables. Other clinical data subsequently added to the database include pharmacy provider order entry records, admission records, discharge summaries, ICD-9 codes, imaging and ECG reports and general demographic data (i.e. dates of admission and discharge from the hospital/ ICU, gender, weight, height, and ethnicity). Mortality data after hospital discharge was
ACCEPTED MANUSCRIPT obtained from the state death records. Further description of the database is available at
T
http://mimic.mit.edu.
SC
(Oracle Corporation, Redwood Shores, CA, USA).
RI P
Data extraction was conducted using Oracle SQL Developer version 3.0.02
NU
Patient population
All adult patients who were admitted to a medical ICU (MICU or CCU) or
MA
surgical ICU (SICU, Trauma or Cardiothoracic ICU) with a prior diagnosis of cirrhosis were included for analysis. The diagnosis of cirrhosis was confirmed by the manual
ED
review of the charts. For our study, patients were determined to have cirrhosis based on
PT
either a prior clinical diagnosis included in the past medical history or a histopathological diagnosis from review of pathology reports. Patients who underwent liver transplantation
CE
were excluded. Patients were stratified by the year of admission (three periods): 2001-
AC
2003, 2004-2006, 2007-2008.
Severity of illness scores (MELD, SAPS, SOFA) were calculated on admission to the ICU and at 24, 48 hours. In addition, the MELD score was calculated at discharge from the ICU. The Elixhauser comorbidity score was used as a comorbidity estimate [14]. The primary endpoints were 28 day and 1-year all-cause mortality.
Statistical analysis:
ACCEPTED MANUSCRIPT The method of analyses for continuous variables was parametric. Non-parametric procedures were used if parametric assumptions could not be satisfied, even after data
T
transformation attempts. Parametric model assumptions were assessed using Normal-plot
RI P
or Shapiro-Wilks statistic for verification of normality and Levene’s test for verification of homogeneity of variances. Categorical variables were tested using Pearson’s χ2 test
SC
for contingency tables or Fisher Exact test, as appropriate. Kaplan-Meier survival curve
NU
was built for the analysis of all-cause mortality at five years those who survived at least
MA
28 days (landmark analysis).
To evaluate the ability of different scoring systems to discriminate 28 day
ED
mortality, we assessed c-statistics by analyzing the area under the curve (AUC) for ROC
PT
of the predicted death probabilities. These were derived from multivariate logistic regression models based on MELD, SOFA and MELD+SOFA scores (all models were
CE
adjusted for age, gender, year of admission [three time periods] and 28 days Elixhauser
AC
score). To evaluate one-year mortality prediction, the same models were applied for 28day survivors (landmark analysis) adjusted for age, gender, year of admission [three time periods] and one-year Elixhauser score. The analysis was performed separately for medical and surgical admissions.
We used the Integrated Discrimination Improvement (IDI) approach to compare the discriminative ability of the different scoring systems and models [15] and relative IDI to assess the improvments’ significance. Logistic regression models based on SOFA on admission were used as the reference score for 28-day mortality prediction (adjusted
ACCEPTED MANUSCRIPT for age, gender, year of admission [three periods] and 28 days Elixhauser score) and for 1 year mortality prediction (among 28 days survivors, adjusted for age, gender, year of
RI P
T
admission [three periods] and 1 year Elixhauser).
The effect of introducing a new variable into the prediction model is usually
SC
assessed by demonstrating an improvement in c-statistics (AUC), which represents an
NU
improvement in the models' discriminatory abilities. However, for models that possess good discrimination and contain standard risk factors, the independent effect of the new
MA
variable has to be exceptionally high to improve the c-statistics. NRI (net reclassification improvement) and IDI (integrated discrimination improvement) methods are particularly
ED
useful in assessment of aforementioned prediction models incorporating new variables in
PT
situations where no meaningful improvement in the c-statistics was observed. These methods are based on the comparing the improvement in reclassification of subjects with
CE
and without event. Any increase in probability of the event as calculated based on the
AC
model with vs. without the new variable for event subjects implies improved classification, and any decrease indicates worsening. The interpretation is opposite for subjects without the event. IDI is a result of the integration of these changes, or statistically speaking the difference in discrimination slopes between two models-- one with, and the other without, the added variable. IDI above zero means improvement in the discrimination, while below zero signifies worsening.
ACCEPTED MANUSCRIPT Relative IDI is calculated as the ratio of IDI over the discrimination slope of the model without the new variable. A model is concluded to be improved if the relative IDI
RI P
T
was found to be greater than (1/the number of variables in the model). [16]
Cox proportional regression based on last measured MELD (adjusted for age, 1
SC
year Elixhouser, alcohol use, hepatitis B, hepatitis C, varices, and gender) was used for
NU
multivariate analysis of 2- year mortality among the 28- day survivors (landmark analysis) of both surgical and medical admissions. The regression was stratified by the
MA
patients' admission year (2001-2003, 2004-2006, 2007-2008).
ED
We calculated expected 3-month mortality based on admission MELD score using
PT
the report of Wiesner et al and compared it to the observed mortality in our cohort [17].
CE
This calculation was done separately for medical and surgical admission.
AC
All statistical tests and/or confidence intervals, as appropriate, were performed at α=0.05 (2-sided). All p-values reported were rounded to three decimal places. The data was analyzed using IBM SPSS Statistics software.
Results A total of 848 out of 19,742 ICU hospitalizations at Beth Israel Deaconess Medical Center between 2001-2008 had a diagnosis of liver cirrhosis. Of these 848 cases all study relevant data were available for 508 patients. For patients with more than one hospitalization (100 patients) we randomly selected one to be included in the analysis.
ACCEPTED MANUSCRIPT Out of these cases, 353 (69.5%) patients were admitted to medical ICU and the other 155
T
(30.5%) to surgical unit.
RI P
Patients were stratified into the three intervals (2001-2003, 2004-2006, 20072008) to account for the possible secular trend in clinical approach and patients case mix.
SC
The following mortality trends were observed: for 28 day, 42/136 (30.9%), 84/313
NU
(26.8%) and 16/59 (27.1%), respectively for the three intervals (p=0.67) and for one year, 75/136 (55.1%), 144/313 (46%) and 25/59 (42.4%), respectively for the three intervals
MA
(p= 0.13).
ED
Table 1 summarizes the baseline characteristics of this cohort divided to medical
PT
and surgical admissions. While the two groups had a similar mean day one MELD score, 28 days and 1 year Elixhouser score, they did differ significantly (p
S0883-9441(14)00214-7 doi: 10.1016/j.jcrc.2014.05.013 YJCRC 51527
To appear in:
Journal of Critical Care
Received date: Revised date: Accepted date:
27 November 2013 27 March 2014 22 May 2014
Please cite this article as: Boone M. Dustin, Celi Leo A., Ho Ben G., Pencina Michael, Curry Michael P., Lior Yotam, Talmor Daniel, Novack Victor M., MELD score predicts mortality in critically ill cirrhotic patients, Journal of Critical Care (2014), doi: 10.1016/j.jcrc.2014.05.013
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT TITLE PAGE Title: MELD score predicts mortality in critically ill cirrhotic patients
RI P
T
Authors: 1. M. Dustin Boone, MD. Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
SC
2. Leo A. Celi, MD, MPH. Department of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA and Harvard-Massachusetts Institute of Technology Division of Health Science Technology, Cambridge, MA
NU
3. Ben G. Ho, MS. Harvard-Massachusetts Institute of Technology Division of Health Science Technology, Cambridge, MA
MA
4. Michael Pencina, PhD. Department of Biostatistics, School of Public Health, Boston University, Boston, MA
ED
5. Michael P Curry, MD. Department of Medicine, Division of Hepatology, Beth Israel Deaconess Medical Center, and Harvard Medical School, Boston, MA
PT
6. Yotam Lior, BSc. Clinical Research Center, Soroka University Medical Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Israel.
CE
7. Daniel Talmor, MD, MPH. Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
AC
8. Victor M Novack, MD, PhD. Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA and Clinical Research Center, Soroka University Medical Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Israel. Work performed at Beth Israel Deaconess Medical Center, Boston, MA and Massachusetts Institute of Technology, Cambridge, MA Address for reprints: M. Dustin Boone, MD Beth Israel Deaconess Medical Center Department of Anesthesia, Critical Care and Pain Medicine Boston, MA 02215 [email protected] office telephone +1 617.754.2751 No financial support was used for this study
ACCEPTED MANUSCRIPT Abstract Purpose
T
Cirrhosis is a common condition that complicates the management of patients
RI P
who require critical care. There is interest in identifying scoring systems that may be used to predict outcome because of the poor odds for recovery despite high-intensity care. We
SC
sought to evaluate how MELD, an organ-specific scoring system, compares with other
NU
severity of illness scoring systems in predicting short and long-term mortality for
MA
critically ill cirrhotic patients.
Materials and Methods
ED
This was a retrospective cohort study involving seven intensive care units in a tertiary
PT
care, academic medical center. Adult patients with cirrhosis who were admitted to an intensive care unit between 2001 and 2008 were evaluated. Severity of illness scores
CE
(MELD, SOFA) were calculated on admission and at 24, 48 hours. The primary
Results
AC
endpoints were 28 day and one-year all-cause mortality.
848 out of 19,742 ICU hospitalizations had cirrhosis. Relevant data were available for 521 patients (73%). Out of these cases, 353 (69.5%) patients were admitted to medical ICU and the other 155 (30.5%) to surgical unit. Alcohol abuse and hepatitis C were the most common reasons for cirrhosis. Patients who died within 28 days were more likely to receive mechanical ventilation, pressors, and renal replacement therapy. Among 353 medical admissions, both MELD and SOFA were found to be significantly associated
ACCEPTED MANUSCRIPT with both 28-day and one-year mortality. Among the 155 surgical admissions, both scores were found to be not significant for 28-day mortality but were significant for one
RI P
T
year.
Conclusions
SC
Our results demonstrate that the prognostic ability of a variety of scoring systems
NU
strongly depends on the patient population. In the medical ICU population, each model (MELD + SOFA, MELD, SOFA) demonstrates excellent discrimination for 28-day and
MA
one-year mortality. However, these scoring systems did not predict 28 day mortality in the surgical ICU group, but were significant for one year mortality. This suggests that
ED
patients admitted to a surgical ICU will behave similarly to their medical ICU cohort if
CE
Keywords
PT
they survive the peripoerative period.
AC
cirrhosis; prognosis; epidemiology; outcome; critical care; organ dysfunction scores
ACCEPTED MANUSCRIPT Introduction Cirrhosis is a common co-morbid condition that complicates the management of
T
patients admitted to an ICU [1]. Cirrhosis is estimated to be present in over 1% of the
RI P
general population and remains the 12th leading cause of death in the United States [2]. When admitted to an ICU, mortality for patients with cirrhosis is high [3]; recent data
SC
suggests greater than 37% ICU mortality and 49% hospital mortality [4, 5]. Because of
NU
the poor odds for recovery despite aggressive interventions, several investigators have examined how scoring systems may be used to predict outcome and allocate resources in
MA
this cohort of ICU patients [6, 7]. Much of this effort has focused on determining
PT
predicting the risk of death.
ED
whether organ-specific scoring systems outperform traditional ICU scoring systems in
Patients admitted to an ICU with cirrhosis make an ideal population to study how
CE
organ-specific scoring systems compare with overall severity of illness scoring systems.
AC
Two liver-specific models, the Child-Pugh classification and the Model for End Stage Liver Disease (MELD) were originally created to predict the risk of death in patients with portal hypertension undergoing porto-systemic shunts [8, 9]. While intended as a risk model for a specific clinical diagnosis, these models have been used to predict the risk of death in patients with cirrhosis in a variety of settings [10-12]. In particular, recent interest in evaluating the prognostic ability of the MELD score to predict the risk of short-term death in cirrhotic patients admitted to an ICU has garnered interest [10, 13].
ACCEPTED MANUSCRIPT In this study we sought to evaluate how MELD, an organ-specific scoring system, compares with other severity of illness scoring systems in predicting short and long-term
T
mortality for cirrhotic patients admitted to both medical and surgical intensive care units
RI P
over a10-year span.
SC
Methods
NU
Patient data were extracted from the Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC) database (version 2.6). This is a publicly available intensive
MA
care unit (ICU) database that was developed at the Massachusetts Institute of Technology (MIT) and contains de-identified data from over 30,000 patients who were admitted to
ED
the ICUs at Beth Israel Deaconess Medical Center (BIDMC), a large, academic, tertiary
PT
medical center in Boston, Massachusetts between 2001 and 2008. The institutional
CE
review boards from MIT and BIDMC granted a waiver of informed consent.
AC
The MIMIC database includes physiologic information from bedside monitors. These data (heart rate, blood pressures, etc.) were validated by ICU nurses prior to entry into the database. MIMIC also contains records of all lab values, nursing progress notes, IV medications, fluid intake/output (I/O), and other clinical variables. Other clinical data subsequently added to the database include pharmacy provider order entry records, admission records, discharge summaries, ICD-9 codes, imaging and ECG reports and general demographic data (i.e. dates of admission and discharge from the hospital/ ICU, gender, weight, height, and ethnicity). Mortality data after hospital discharge was
ACCEPTED MANUSCRIPT obtained from the state death records. Further description of the database is available at
T
http://mimic.mit.edu.
SC
(Oracle Corporation, Redwood Shores, CA, USA).
RI P
Data extraction was conducted using Oracle SQL Developer version 3.0.02
NU
Patient population
All adult patients who were admitted to a medical ICU (MICU or CCU) or
MA
surgical ICU (SICU, Trauma or Cardiothoracic ICU) with a prior diagnosis of cirrhosis were included for analysis. The diagnosis of cirrhosis was confirmed by the manual
ED
review of the charts. For our study, patients were determined to have cirrhosis based on
PT
either a prior clinical diagnosis included in the past medical history or a histopathological diagnosis from review of pathology reports. Patients who underwent liver transplantation
CE
were excluded. Patients were stratified by the year of admission (three periods): 2001-
AC
2003, 2004-2006, 2007-2008.
Severity of illness scores (MELD, SAPS, SOFA) were calculated on admission to the ICU and at 24, 48 hours. In addition, the MELD score was calculated at discharge from the ICU. The Elixhauser comorbidity score was used as a comorbidity estimate [14]. The primary endpoints were 28 day and 1-year all-cause mortality.
Statistical analysis:
ACCEPTED MANUSCRIPT The method of analyses for continuous variables was parametric. Non-parametric procedures were used if parametric assumptions could not be satisfied, even after data
T
transformation attempts. Parametric model assumptions were assessed using Normal-plot
RI P
or Shapiro-Wilks statistic for verification of normality and Levene’s test for verification of homogeneity of variances. Categorical variables were tested using Pearson’s χ2 test
SC
for contingency tables or Fisher Exact test, as appropriate. Kaplan-Meier survival curve
NU
was built for the analysis of all-cause mortality at five years those who survived at least
MA
28 days (landmark analysis).
To evaluate the ability of different scoring systems to discriminate 28 day
ED
mortality, we assessed c-statistics by analyzing the area under the curve (AUC) for ROC
PT
of the predicted death probabilities. These were derived from multivariate logistic regression models based on MELD, SOFA and MELD+SOFA scores (all models were
CE
adjusted for age, gender, year of admission [three time periods] and 28 days Elixhauser
AC
score). To evaluate one-year mortality prediction, the same models were applied for 28day survivors (landmark analysis) adjusted for age, gender, year of admission [three time periods] and one-year Elixhauser score. The analysis was performed separately for medical and surgical admissions.
We used the Integrated Discrimination Improvement (IDI) approach to compare the discriminative ability of the different scoring systems and models [15] and relative IDI to assess the improvments’ significance. Logistic regression models based on SOFA on admission were used as the reference score for 28-day mortality prediction (adjusted
ACCEPTED MANUSCRIPT for age, gender, year of admission [three periods] and 28 days Elixhauser score) and for 1 year mortality prediction (among 28 days survivors, adjusted for age, gender, year of
RI P
T
admission [three periods] and 1 year Elixhauser).
The effect of introducing a new variable into the prediction model is usually
SC
assessed by demonstrating an improvement in c-statistics (AUC), which represents an
NU
improvement in the models' discriminatory abilities. However, for models that possess good discrimination and contain standard risk factors, the independent effect of the new
MA
variable has to be exceptionally high to improve the c-statistics. NRI (net reclassification improvement) and IDI (integrated discrimination improvement) methods are particularly
ED
useful in assessment of aforementioned prediction models incorporating new variables in
PT
situations where no meaningful improvement in the c-statistics was observed. These methods are based on the comparing the improvement in reclassification of subjects with
CE
and without event. Any increase in probability of the event as calculated based on the
AC
model with vs. without the new variable for event subjects implies improved classification, and any decrease indicates worsening. The interpretation is opposite for subjects without the event. IDI is a result of the integration of these changes, or statistically speaking the difference in discrimination slopes between two models-- one with, and the other without, the added variable. IDI above zero means improvement in the discrimination, while below zero signifies worsening.
ACCEPTED MANUSCRIPT Relative IDI is calculated as the ratio of IDI over the discrimination slope of the model without the new variable. A model is concluded to be improved if the relative IDI
RI P
T
was found to be greater than (1/the number of variables in the model). [16]
Cox proportional regression based on last measured MELD (adjusted for age, 1
SC
year Elixhouser, alcohol use, hepatitis B, hepatitis C, varices, and gender) was used for
NU
multivariate analysis of 2- year mortality among the 28- day survivors (landmark analysis) of both surgical and medical admissions. The regression was stratified by the
MA
patients' admission year (2001-2003, 2004-2006, 2007-2008).
ED
We calculated expected 3-month mortality based on admission MELD score using
PT
the report of Wiesner et al and compared it to the observed mortality in our cohort [17].
CE
This calculation was done separately for medical and surgical admission.
AC
All statistical tests and/or confidence intervals, as appropriate, were performed at α=0.05 (2-sided). All p-values reported were rounded to three decimal places. The data was analyzed using IBM SPSS Statistics software.
Results A total of 848 out of 19,742 ICU hospitalizations at Beth Israel Deaconess Medical Center between 2001-2008 had a diagnosis of liver cirrhosis. Of these 848 cases all study relevant data were available for 508 patients. For patients with more than one hospitalization (100 patients) we randomly selected one to be included in the analysis.
ACCEPTED MANUSCRIPT Out of these cases, 353 (69.5%) patients were admitted to medical ICU and the other 155
T
(30.5%) to surgical unit.
RI P
Patients were stratified into the three intervals (2001-2003, 2004-2006, 20072008) to account for the possible secular trend in clinical approach and patients case mix.
SC
The following mortality trends were observed: for 28 day, 42/136 (30.9%), 84/313
NU
(26.8%) and 16/59 (27.1%), respectively for the three intervals (p=0.67) and for one year, 75/136 (55.1%), 144/313 (46%) and 25/59 (42.4%), respectively for the three intervals
MA
(p= 0.13).
ED
Table 1 summarizes the baseline characteristics of this cohort divided to medical
PT
and surgical admissions. While the two groups had a similar mean day one MELD score, 28 days and 1 year Elixhouser score, they did differ significantly (p
