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. 2017 Sep 18;8(65):109772-109782.
doi: 10.18632/oncotarget.21012. eCollection 2017 Dec 12.

Prognostic nomogram for acute-on-chronic hepatitis B liver failure

Su Lin  1 , Juan Chen  2 , Mingfang Wang  1 , Lifen Han  3 , Haoyang Zhang  4 , Jing Dong  1 , Dawu Zeng  1 , Jiaji Jiang  1 , Yueyong Zhu  1
Affiliations

Prognostic nomogram for acute-on-chronic hepatitis B liver failure

Su Lin et al. Oncotarget. .

Abstract

Background & aims: To establish an effective prognostic nomogram for acute-on-chronic hepatitis B liver failure (ACHBLF).

Materials and methods: The nomogram was based on clinical data of 203 ACHBLF patients who admitted to the First Affiliated Hospital of Fujian Medical University from 2009 to 2014. The area under the receiver-operating characteristic curve (AUC) and calibration curve were carried out to verify the predictive accuracy ability of the nomogram. The result was validated in internal and external validation cohorts. Kaplan-Meier survival curve was used in survival analysis.

Results: We developed a new prognostic nomogram to predict 3-month mortality based on risk factors selected by multivariate analysis. This nomogram consisted three independent factors: age, liver to abdominal area ratio (LAAR) and model for end-stage liver disease (MELD) score. The AUC of this nomogram for survival prediction was 0.877 (95% CI 0.831-0.923), which was higher than that of MELD score, MELD-Na and Child-Turcotte-Pugh (CTP). Good agreement of calibration plot for the probability of survival at 3-month was shown between the prediction by nomogram and actual observation. These results were supported by internal and external validation studies.

Conclusions: The ACHBLF nomogram could predict the short-term survival for ACHBLF patients.

Keywords: age; liver to abdominal area ratio (LAAR); model for end-stage liver disease (MELD) score; prognosis; survival.

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Conflict of interest statement

CONFLICTS OF INTEREST None.

Figures

Figure 1
Figure 1
The difference between LAAR (A) and developed-LAAR (B) calculation schematic diagram. (A) The liver or abdominal area was calculated by drawing a ‘best-fit’ ellipsoid instead of maximum liver or abdominal area, the area was got by calculating the ellipsoid area. (B) The liver or abdominal area was measured by tracing the edge of the liver or the abdomen with a cursor, and then the area would be calculated by the computer automatically.
Figure 2
Figure 2. Flow chart of patient selection
Figure 3
Figure 3. Nomogram to predict overall survival in ACHBLF patients
Draw an upward vertical line from each variable axis to the points bar to get points of each variable. Based on the sum of each variable points, draw a downward vertical line from Total Points axis to calculate 3-month overall survival.
Figure 4
Figure 4. ROC curve of nomogram and other models to predict morbidity of patients with ACHBLF
(A) ROC curve in training cohort. (B) ROC curve in internal validation cohort. (C) ROC curve in external validation cohort.
Figure 5
Figure 5. The calibration curve for predicting patient survival
(A) Calibration curves for predicting 3-month overall survival rate in the training cohort. (B) Calibration curves for predicting 3-month overall survival rate in the internal validation cohort. (C) Calibration curves for predicting 3-month overall survival rate in the external validation cohort. X axis is the nomogram-predicted probability of overall survival; y axis is the actual overall survival in the calibration curves.
Figure 6
Figure 6. Kaplan-Meier survival curve
(A) training cohort. (B) internal validation cohort. (C) external validation cohort.
See this image and copyright information in PMC

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