Intrahepatic cholestasis of pregnancy: machine-learning algorithm to predict elevated bile acid based on clinical and laboratory data.

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.

Reduced Activity and Quality of Life in Women Soldiers with Heavy Menstrual Bleeding and Dysmenorrhea.

STUDY OBJECTIVE: The aim of the study was to examine the effect of heavy menstrual bleeding (HMB) and dysmenorrhea on daily activity and quality of life (QoL) in young women engaged in demanding activities. DESIGN: Cross-sectional study. PARTICIPANTS: A total of 422 Israeli women soldiers in combat and non-combat roles. INTERVENTIONS: Participants were asked to provide consent and to complete study questionnaires. MAIN OUTCOME MEASURES: A pictorial bleeding assessment chart (PBAC), visual analog scale (VAS), verbal multidimensional scoring system for assessment of dysmenorrhea, and approved Hebrew-translated age-appropriate Pediatric Quality of Life Inventory (PedsQL). RESULTS: HMB (PBAC >100) and severe HMB (PBAC >185) were demonstrated in 181 (50%) and 96 (26%) participants, respectively. A high PBAC score was recorded in 20% who answered "no" or "unknown" in the questionnaire on having HMB. Mild, moderate, and severe dysmenorrhea were demonstrated in 80 (21.5%), 115 (31%), and 142 (38%) participants, respectively. The prevalence of HMB and dysmenorrhea was similar in soldiers in combat and non-combat roles. Diagnosis of HMB was related to the lower fitness-for-service score, history of bleeding, and dysmenorrhea. Daily activity and QoL were both affected by the severity of HMB and dysmenorrhea. CONCLUSION: Underdiagnosis of HMB and dysmenorrhea results from a combination of unawareness from the women's side and inattention from the system. In an era of female empowerment, each woman should be at the optimal physiological and psychological level to start her career; thus, addressing the menstrual burden and providing effective treatment is needed in the military scenario and other settings with demanding activities.