Table 3

Predictive performance for our model versus the MELD, MELD-Na and CLIF-C AD

Our modelMELDMELD-NaCLIF-C AD
Globalperformance
 C-statistic0.863 (0.863–0.864)0.655 (0.655–0.655)0.675 (0.675–0.675)0.679 (0.679–0.679)
Mortalityprediction at90days
 AUC (95% CI)0.79 (0.79 to 0.79)0.65 (0.65 to 0.65)0.67 (0.67 to 0.67)0.68 (0.68 to 0.68)
 ECI (95% CI)2.46 (2.22 to 2.72)0.40 (0.36 to 0.45)0.37 (0.33 to 0.41)0.42 (0.38 to 0.47)
Classificationerror foridentifyinglow-riskpatients(predicted mortality<5%)
 Sensitivity (%)26.3n/an/a1.0
 Specificity (%)97.810010099.6
 PPV (%)97.3n/an/a88.5
 NPV (%)31.125.425.425.5
Classificationerror for identifying high-riskpatients(predicted mortality>40%)
 Sensitivity (%)28.53.84.94.4
 Specificity (%)29.986.882.885.6
 PPV (%)54.545.945.547.4
 NPV (%)12.523.522.923.4
  • Overall model performance is described by the C-statistic. Additionally, we analyse the discrimination and calibration for the specific use case of predicting mortality at 90 days as measured by the AUC and ECI. We defined low risk as discharged patients with <5% 90-day mortality and high risk as discharged patients with >40% 90-day mortality. We used these thresholds as they identified potentially clinically significant thresholds. For example, low-risk patients may be targeted for early discharge, whereas high-risk patients may benefit from early outpatient follow-up or even hospice referral. The MELD and MELD-NA models failed to generate risk scores <5% for any patients; that is, they could not identify any very low-risk patients, and therefore sensitivity and PPV could not be calculated. Therefore, their classification errors were incalculable.

  • AUC, area under the curve; CLIF-C AD, CLIF Consortium acute decompensation score; ECI, Estimated Calibration Index;MELD, model for end-stage liver disease; MELD-Na, model for end-stage liver disease with sodium; n/a, not applicable; NPV, negative predictive value; PPV, positive predictive value.