Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning

OBJECTIVE: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: 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

Disparities persist for Black liver transplant recipients despite years of data collection: What is missing?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/170978/1/ajt16844_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/170978/2/ajt16844.pd

A Comprehensive Review of Outcome Predictors in Low MELD Patients.

Risk scoring for patients with cirrhosis has evolved greatly over the past several decades. However, patients with low Model for End-Stage Liver Disease-Sodium scores still suffer from liver-related morbidity and mortality. Unfortunately, it is not clear which of these low Model for End-Stage Liver Disease-Sodium score patients would benefit from earlier consideration of liver transplantation. This article reviews the literature of risk prediction in patients with cirrhosis, identifies which patients may benefit from earlier interventions, such as transplantation, and proposes directions for future research

Influencing medical student education via a voluntary shadowing program for trauma and acute care surgery

Decreasing application into trauma surgery may be attributed to decreased exposure during medical school. We instituted a voluntary trauma call program for students to provide exposure to the field. After 3 years, participants completed a survey to gauge their experience. Of 126 students who participated, 68 completed the survey (54%). Interest in trauma surgery among students not previously planning on a career in surgery (n = 48) improved after the experience (4.4-5.3 of 10 points; P \u3c .005). Operative experience, exposure to a higher number of trauma cases, and time with residents were associated with increased interest in trauma surgery. Witnessing patient death for the first time was associated with decreased interest in trauma surgery. A voluntary overnight shadowing program improves medical students\u27 perceptions of trauma surgery and increases their reported likelihood to apply into a surgical residenc