Using machine learning methods to predict 28-day mortality in patients with hepatic encephalopathy

Hepatic encephalopathy (HE), one of the most common complications of liver cirrhosis, is defined as a brain dysfunction [1]. Significant HE occurs in approximately 30–40% of patients with cirrhosis due to hepatocellular dysfunction and portosystemic shunt [2]. Typical clinical manifestations of HE include various types of neuropsychiatric disorders, such as progressive disorientation, sleep disorders, inappropriate behavior, somnolence, coma, asterixis, hypertonia, hyperreflexia, and extrapyramidal dysfunction [3, 4]. It is worth noting that HE is a serious complication of cirrhosis associated with significant mortality and heavy financial burdens [5]. To be specific, the costs associated with HE attain 11.6 billion$, and outweigh other decompensating events in liver cirrhosis [6]. Though HE patients have improved outcomes over the past decade, several studies have indicated that the prognosis and quality of life still remain poor [7]. Specifically, HE typically heralds hepatic decompensation, and its development is usually associated with high morbidity, implying the need for liver transplantation [8‐10]. Accordingly, it is critical to determine an easily accessible and simple model to estimate the risk of 28-day mortality in patients with HE.

Many existing scoring systems have been used to evaluate the prognosis of liver cirrhosis patients, but none of them are targeted at HE patients. Models for End-Stage Liver Disease (MELD) scores have been widely used as predictive tools for liver disease severity [11]. Originating from the MELD algorithm, Biggins et al. proposed a new score, the Model for End-Stage Liver Disease-Sodium (MELD-Na) model, which has a more accurate predictive ability than MELD [12].

Machine learning (ML), as part of artificial intelligence, was not limited by the state of data distribution and can handle complex relationships as well as high-dimensional data [13‐15]. Consequently, the study aimed to 1) identify the significant prognostic factors for HE patients from a large database, and then to construct and validate a model that predicts 28-day mortality, 2) to compare prognostic performance of this model with those of the MELD and MELD-Na scores.

Liver cirrhosis results from the development of various acute and chronic liver diseases, and HE is a common critical complication of decompensated cirrhosis. This retrospective study analyzed a relatively large population of MIMIC-IV, and found 8 variables were independent predictors of 28-day death. Notably, in this study, all indicators were obtained within the first 24 h of ICU admission, providing a short window for identifying severe patients with HE. Additionally, among the four ML models that were validated, NNET was the best model, with good discriminative (AUC = 0.837) and calibration ability simultaneously. Our model could potentially be useful to clinicians in their decision-making when it comes to the selection of therapeutic strategies. Features obtained in the final model included APSIII, SOFA, INR, TBIL, albumin, BUN, AKI and mechanical ventilation, which is consistent with the findings of other published studies.

In a recent research, researchers also identified SOFA and systemic inflammatory response syndrome (SIRS) as factors associated with 30-day mortality in patients with HE [18]. Patients with HE are functionally immuno-suppressed and susceptible to infection [19], which is a frequent precipitant for organ dysfunction. Likewise, the resultant organ failures as indicated by high SOFA and APSIII means significant mortality [20, 21]. Changes in TBIL and albumin often reflect liver function in patients with liver cirrhosis, which is closely associated with a poor prognosis [22]. Peng Y et al. in their study concluded that TBIL was independently correlated with in-hospital death in cirrhotic patients with HE [23]. Bai Z et al. reported that albumin level was an independent risk factor for HE-associated mortality during hospitalizations in cirrhosis (OR = 0.864, 95% CI = 0.771–0.967) [24]. Other studies have also validated this finding [25‐28]. By conducting a preliminary observation, Udayakumar N et al. found that high serum bilirubin values in chronic liver disease were simple parameters that would predict a poor outcome in patients with HE [9]. Moreover, serum bilirubin levels as prognostic biochemical markers have been reported elsewhere and are similar to our observations [29, 30]. One previous investigation identified elevated levels of TBIL, BUN, and decreased albumin as factors associated with poor prognosis in patients with acute HE [23]. In addition, Hung TH et al. in their study also identified that AKI increased the 3-year mortality of cirrhotic patients with HE [31]. Other study also found that AKI was the main independent predictor of ICU death and 1-year mortality [32]. Possible mechanisms may be that the increased ammonia from blood stream exacerbates cerebral edema in cirrhotic patients with HE [33‐35]. Additionally, according to Cui Y et al., hepatorenal syndrome (HRS) was independent factor associated with 3-month death [36]. Notably, decreased kidney function usually accompanies end-stage liver disease, including HRS. Renal dysfunction can increase BUN, and in turn, resultant diffusion of BUN into the intestine can cause enhanced ammonia uptake accompanied by worsening of HE [36]. Previous studies explored the prognostic factors correlated with 180 cirrhotic patients presenting with HE who were admitted to ICU. And researchers also found that the use of mechanical ventilation was a significant risk factor for mortality [37]. The study by Saffo S et al. has demonstrated that mechanical ventilation was the strongest predictor of in-hospital mortality in their primary analysis (OR, 3.00; 95% CI, 2.14–4.20; P < 0.001) and in all sensitivity analyses [38]. Studies by Benhaddouch Z et al. came to similar conclusion [8]. This may be due to the fact that patients with hepatic encephalopathy are at a greater risk for the complications of mechanical ventilation because of possible underlying circulatory, neurologic, and immunologic disturbance [39]. Mechanical ventilation itself may aggravate the existing condition, manifested as impairment in cardiovascular and cognitive function and immune defense, consequently, patients with hepatic encephalopathy may particularly be susceptible to developing such complications as shock, progressive delirium, and infection [40, 41]. Although MELD and MELD-Na are accurate, highly specific scores that are commonly used to reveal liver disease severity and quantify mortality risk [42, 43], the predicative performance for 28-day mortality of these scores in patients with HE remains unclear. Hence, we performed the ROC curves in this study, and found that the AUCs of the MELD and MELD-Na scores for 28-day mortality was lower than that of NNET. which suggested our model was superior to other models in terms of diagnostic discrimination. The reason for this may be, that only simple indicators (creatinine, bilirubin, and INR) cannot precisely assess the degree of cirrhosis [44, 45]. Moreover, a frequently reported drawback of the MELD score was that it has the disadvantage of lacking objective parameters reflecting the patient 's physical and nutritional status, including albumin [46].

There were several strengths in this study. First, multiple ML algorithms were used to build a predictive model. Third, 8 variables selected for the final model are readily available in clinical practice, enabling the model to be implemented easily in the real world.

This study had several limitations. First, because the model was based on MIMIC-IV, which is a single-center database, it still needs to be externally validated in other datasets. Secondly, all the variables were first obtained after an ICU admission, without considering that indicators were dynamically changing. Thirdly, originated from a retrospective cohort, the model needs further prospective validation before being considered for clinical application. Lastly, the majority of the patients included in this study were White, therefore, these findings may not be extrapolated to other populations, such as Asians.

In this study, we proposed an individualized predictive model based on ML for 28-day mortality in HE patients upon ICU admission. We demonstrated that APSIII, SOFA, mechanical ventilation, INR, TBIL, albumin and AKI are crucial for predicting 28-day mortality. The model in our study had superior performance to MELD score or MELD-Na score predicting 28-day mortality. In the future, real-time prediction of mortality risk among HE patients might be realized, which, in turn, will optimize treatment to improve clinical prognosis.