Aula Asali1,2, Dorit Ravid3,4, Hila Shalev5, Liron David4, Eran Yogev6, Sabina Sapunar Yogev6, Ron Schonman3,4, Tal Biron-Shental3,4, Netanella Miller3,4. 1. Department of Obstetrics and Gynecology, Meir Medical Center, 59 Tchernichovsky St, Kfar Saba, Israel. aula_atamna@yahoo.com. 2. Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel. aula_atamna@yahoo.com. 3. Department of Obstetrics and Gynecology, Meir Medical Center, 59 Tchernichovsky St, Kfar Saba, Israel. 4. Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel. 5. Technion-Israel Institute of Technology, 3200003, Haifa, Israel. 6. Ben Gurion University of the Negev, Beersheba, Israel.
Abstract
PURPOSE: Applying machine-learning models to clinical and laboratory features of women with intrahepatic cholestasis of pregnancy (ICP) and creating algorithm to identify these patients without bile acid measurements. METHODS: This retrospective study included 336 pregnant women with a chief complaint of pruritis without rash during the second/third trimesters. Data extracted included: demographics, obstetric, clinical and laboratory features. The primary outcome was an elevated bile acid measurement ≥ 10 µmol/L, regardless of liver enzyme levels. We used different machine-learning models and statistical regression to predict elevated bile acid levels. RESULTS: Among 336 women who complained about pruritis, 167 had bile acids ≥ 10 µmol/L and 169 had normal levels. Women with elevated bile acids were older than those with normal levels (p = 0.001), higher parity (p = 0.001), and higher glutamic oxaloacetic transaminase ( GOT) (p = 0.001) and glutamic-pyruvic transaminase (GPT) levels (p = 0.001). Using machine-learning models, the XGB Classifier model was the most accurate (area under the curve (AUC), 0.9) followed by the K-neighbors model (AUC, 0.86); and then the Support Vector Classification (SVC) model (AUC, 0.82). The model with the lowest predicative ability was the logistic regression (AUC, 0.72). The maximum sensitivity of the XGB model was 86% and specificity 75%. The best predictive parameters of the XGB model were elevated GOT (Importance 0.17), elevated GPT (Importance 0.16), family history of bile disease (0.16) and previous pregnancy with ICP (0.13). CONCLUSION: Machine-learning models using clinical data may predict ICP more accurately than logistic regression does. Using detection algorithms derived from these techniques may improve identification of ICP, especially when bile acid testing is not available.
PURPOSE: Applying machine-learning models to clinical and laboratory features of women with intrahepatic cholestasis of pregnancy (ICP) and creating algorithm to identify these patients without bile acid measurements. METHODS: This retrospective study included 336 pregnant women with a chief complaint of pruritis without rash during the second/third trimesters. Data extracted included: demographics, obstetric, clinical and laboratory features. The primary outcome was an elevated bile acid measurement ≥ 10 µmol/L, regardless of liver enzyme levels. We used different machine-learning models and statistical regression to predict elevated bile acid levels. RESULTS: Among 336 women who complained about pruritis, 167 had bile acids ≥ 10 µmol/L and 169 had normal levels. Women with elevated bile acids were older than those with normal levels (p = 0.001), higher parity (p = 0.001), and higher glutamic oxaloacetic transaminase ( GOT) (p = 0.001) and glutamic-pyruvic transaminase (GPT) levels (p = 0.001). Using machine-learning models, the XGB Classifier model was the most accurate (area under the curve (AUC), 0.9) followed by the K-neighbors model (AUC, 0.86); and then the Support Vector Classification (SVC) model (AUC, 0.82). The model with the lowest predicative ability was the logistic regression (AUC, 0.72). The maximum sensitivity of the XGB model was 86% and specificity 75%. The best predictive parameters of the XGB model were elevated GOT (Importance 0.17), elevated GPT (Importance 0.16), family history of bile disease (0.16) and previous pregnancy with ICP (0.13). CONCLUSION: Machine-learning models using clinical data may predict ICP more accurately than logistic regression does. Using detection algorithms derived from these techniques may improve identification of ICP, especially when bile acid testing is not available.