Abstract
Liver cirrhosis is a leading cause of death and effects millions of people in the United States. Early mortality prediction among patients with cirrhosis might give healthcare providers more opportunity to effectively treat the condition. We hypothesized that laboratory test results and other related diagnoses would be associated with mortality in this population. Our another assumption was that a deep learning model could outperform the current Model for End Stage Liver disease (MELD) score in predicting mortality. We utilized electronic health record data from 34,575 patients with a diagnosis of cirrhosis from a large medical center to study associations with mortality. Three time-windows of mortality (365 days, 180 days and 90 days) and two cases with different number of variables (all 41 available variables and 4 variables in MELD-NA) were studied. Missing values were imputed using multiple imputation for continuous variables and mode for categorical variables. Deep learning and machine learning algorithms, i.e., deep neural networks (DNN), random forest (RF) and logistic regression (LR) were employed to study the associations between baseline features such as laboratory measurements and diagnoses for each time window by 5-fold cross validation method. Metrics such as area under the receiver operating curve (AUC), overall accuracy, sensitivity, and specificity were used to evaluate models. Performance of models comprising all variables outperformed those with 4 MELD-NA variables for all prediction cases and the DNN model outperformed the LR and RF models. For example, the DNN model achieved an AUC of 0.88, 0.86, and 0.85 for 90, 180, and 365-day mortality respectively as compared to the MELD score, which resulted in corresponding AUCs of 0.81, 0.79, and 0.76 for the same instances. The DNN and LR models had a significantly better f1 score compared to MELD at all time points examined. Other variables such as alkaline phosphatase, alanine aminotransferase, and hemoglobin were also top informative features besides the 4 MELD-Na variables. Machine learning and deep learning models outperformed the current standard of risk prediction among patients with cirrhosis. Advanced informatics techniques showed promise for risk prediction in patients with cirrhosis.
Highlights
Background and significanceCirrhosis of the liver is a leading cause of morbidity and mortality in the United states, causing 40,000 deaths each year [1]
Performance of models comprising all variables outperformed those with 4 Model for End Stage Liver disease (MELD)-NA variables for all prediction cases and the deep neural networks (DNN) model outperformed the logistic regression (LR) and random forest (RF) models
In all 3 cases, all 3 models consistently indicated that performances with all 41 variables outperformed the cases of using only 4 variables used in the MELD-Na model
Summary
Background and significanceCirrhosis of the liver is a leading cause of morbidity and mortality in the United states, causing 40,000 deaths each year [1]. The vast majority of patients with cirrhosis have subclinical disease, once their disease progresses they often rapidly decompensate and are at high risk of morbidity, mortality, and poor quality of life [2, 3]. The current method for predicting mortality in sick patients relies on the Model for End Stage Sodium (MELD-Na) score, a modified logistic regression model developed in 2002 that accurately predicts 90 day mortality at high scores and can help triage treatment and monitoring [4, 5]. The vast majority of patients with cirrhosis have missing labs to calculate a MELD-Na score or have low MELD-Na scores, with 93% having a MELD-Na of less than 18 [9, 10]. An alternative method to predict mortality for the cohort of patients with cirrhosis at large is needed
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