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A total of 620 patients were enrolled in the study. According to the 7:3 random allocation, the training and validation cohorts consisted of 434 and 186 patients, respectively. All baseline characteristics of the training and validation cohorts are shown in Table 1. The median age of patients was 54.72 years in the training cohort and 54.79 years in the validation cohort. Most patients in the training and validation cohorts were male (63.8% and 65.6%, respectively). The 90-day survival rate for the training cohort was 53.69%, and the 90-day survival rate for the validation cohort was 56.45%. Baseline information on survivors and deceased patients in the training and validation cohorts are shown in Tables 2 and 3, respectively. Table 2 shows the factors that showed significant differences between groups of survivors and deaths in the training cohort, including (p<0.05): age, MAP, mean respiratory rate, mean SpO2, mean temperature, cardiac arrhythmias, lactate, albumin, anion gap, total bilirubin, chloride, creatinine, magnesium, potassium, sodium, urea nitrogen, INR, PT, PTT, RDW, WBC, albumin use, furosemide use, PAD, SOFA, MELD, and urine output. Table 3 shows the factors that showed significant differences between groups of survivors and deaths in the validation cohort, including (p<0.05): MAP, mean SpO2, mean temperature, cardiac arrhythmias, congestive heart failure, ALT, albumin, AST, total bilirubin, creatinine, magnesium, potassium, sodium, urea nitrogen, INR, PT, PTT, RDW, WBC, albumin use, PAD, SOFA, MELD, and urine output.

Univariate Cox regression analysis was performed on all baseline data factors initially included in the training cohort, and the results showed 28 potential predictors for 90-day survival, just as age, mean heart rate, MAP, mean temperature, mean SpO2, mean respiratory rate, cardiac arrhythmias, SOFAMELD, lactate, urine output, albumin, total bilirubin, urea nitrogen, sodium, potassium, magnesium, chloride, INR, RDW, WBC, ALP, PT, PTT, albumin use, PPI, PAD and furosemide. These candidate factors were input into a multivariate Cox regression analysis, and eight risk factors were found, including age (hazard ratio [HR]=1.022, 95%Confidence interval [CI]=1.0061.037, P=0.006), mean heart rate (HR=1.013, 95%CI=1.0031.023, P=0.010), SOFA (HR=1.057, 95%CI=0.9981.119, P=0.059), RDW (HR=1.056, 95%CI=0.9941.122, P=0.078), albumin use (HR=1.428, 95%CI=1.0132.011, P=0.042), MAP (HR=0.982, 95%CI=0.9670.998, P=0.031), mean temperature (HR=0.731, 95%CI=0.5540.996, P=0.027) and PPI use (HR=0.702, 95%CI=0.5000.985, P=0.041). The results of the Cox regression analysis are shown in Table 4. The SOFA score and RDW were considered clinically significant for the prognosis of patients with cirrhosis and HE based on previous literature reports [22, 23] and clinical experience, so they were also included in the final prediction model.

Based on the multivariate Cox regression analysis results, a nomogram about the 90-day survival rate of patients with liver cirrhosis and HE was constructed, as shown in Fig.2. The nomogram indicated that age, higher SOFA score, higher RDW, higher mean heart rate, lower MAP, lower mean temperature, and the use of albumin were risk factors for the prognosis of patients, and the use of PPI was a protective factor.

Nomogram for predicting the 90-day probability of survival from liver cirrhosis with hepatic encephalopathy. MAP, Mean arterial pressure; SOFA, Sequential organ failure assessment; RDW, Red cell distribution width; PPI.use, Proton pump inhibitors use

The new nomogram was tested on the proportional hazard hypothesis, and the results showed that the P values of each factor and the overall P value were greater than 0.05, which conformed to the proportional hazard requirement. Then, C-index was used to evaluate the effect of the nomogram, which found that this was higher for the nomogram than for the single SOFA model in both the training cohort (0.704 versus 0.615) and the validation cohort (0.695 versus 0.638). In addition, the AUC value of the new nomogram was greater than that of the single SOFA model, both in the training cohort and the validation cohort. The ROC results are shown in Fig.3.

ROC curves for the nomogram and the SOFA mode. a: Result of the training cohort; b: Result of the validation cohort

The NRI value for the 90-day nomogram was 0.560(95%CI=0.4470.792) in the training cohort and 0.364 (95% CI=0.0540.756) in the validation cohort. In addition, the 90-day IDI value was 0.119 (P<0.001) for the training cohort and 0.083 (P<0.001)for the validation cohort, respectively. The NRI and IDI values obtained in this study were greater than zero, which indicated that the overall performance of the nomogram was better than that of the SOFA model alone.

Figure4 shows the calibration curves of the training and validation cohort for the nomogram. The standard curve of the 90-day forecast probability of the nomogram was very close to the standard 45-degree diagonal line, and the relevant four tangent points were evenly distributed. The result showed that the new nomogram had excellent calibration capabilities.

Calibration curves. Calibration curves for the 90-day probability of survival from liver cirrhosis with hepatic encephalopathy depict calibration of nomogram in terms of the agreement between the predicted probabilities and observed outcomes of the training cohort (a) and validation cohort (b)

The DCA curves of the nomogram and the single SOFA model are shown in Fig.5. The results demonstrated that the 90-day DCA curve of the nomogram produced a net benefit regardless of whether it was in the training cohort or the validation cohort, and the DCA curves of the nomogram were all enhanced, compared with the single SOFA model.

Decision curve for the new nomogram for 90-day prediction of survival probability in the training cohort (a) and validation cohort (b)

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Establishment and validation of a prognosis nomogram for MIMIC-III ... - BMC Gastroenterology