Prognostic value of preoperatively obtained clinical and laboratory data in predicting survival following orthotopic liver transplantation

Twenty‐seven clinical and laboratory data and the subsequent clinical course of 93 consecutive adult patients who underwent orthotopic liver transplantation for various chronic advanced liver diseases were analyzed retrospectively to assess the risk factors of early major bacterial infection and death after the procedure. Forty‐one patients (44%) had early major bacterial infection during hospitalization for orthotopic liver transplantation. The mortality rate was 70.7% in patients with early major bacterial infection and was 7.7% in patients without early major bacterial infection (p < 0.001). Total serum bilirubin, total white blood cell count and polymorphonuclear cell count, IgG (all p < 0.05) and plasma creatinine level (p < 0.001) were higher in patients that developed early major bacterial infection than in those who did not. By step‐wise discriminant analysis, the strongest risk factor for early major bacterial infection was the serum creatinine level, which achieved an accuracy of 69% for a creatinine level greater than 1.58 mg per dl. Seven variables (ascites, hepatic encephalopathy, elevated white blood and polymorphonuclear cell count, decreased helper to suppressor T cell ratio and elevated plasma creatinine and bilirubin levels) were associated with a significant increased risk for death. A step‐wise discriminant analysis of these seven factors resulted in the demonstration of serum creatinine as the greatest risk factor for mortality. A preoperative serum creatinine either less than or greater than 1.72 mg per dl accurately predicts survival or death, respectively, in 79% of cases. These data suggest that the baseline preoperative serum creatinine level provides the best indication of the short‐term prognosis after liver transplantation than does any other preoperatively obtained index of the patient's status. Copyright © 1986 American Association for the Study of Liver Disease

Preterm Birth Prediction: Deriving Stable and Interpretable Rules from High Dimensional Data

Preterm births occur at an alarming rate of 10-15%. Preemies have a higher risk of infant mortality, developmental retardation and long-term disabilities. Predicting preterm birth is difficult, even for the most experienced clinicians. The most well-designed clinical study thus far reaches a modest sensitivity of 18.2-24.2% at specificity of 28.6-33.3%. We take a different approach by exploiting databases of normal hospital operations. We aims are twofold: (i) to derive an easy-to-use, interpretable prediction rule with quantified uncertainties, and (ii) to construct accurate classifiers for preterm birth prediction. Our approach is to automatically generate and select from hundreds (if not thousands) of possible predictors using stability-aware techniques. Derived from a large database of 15,814 women, our simplified prediction rule with only 10 items has sensitivity of 62.3% at specificity of 81.5%.Comment: Presented at 2016 Machine Learning and Healthcare Conference (MLHC 2016), Los Angeles, C