Essential thrombocythemia (ET) may occur in women of childbearing age. To investigate the risk of pregnancy complications, we studied 103 pregnancies that occurred in 62 women with ET. The 2-tailed Fisher exact test showed that pregnancy outcome was independent from that of a previous pregnancy. The rate of live birth was 64%, and 51% of pregnancies were uneventful. Maternal complications occurred in 9%, while fetal complications occurred in 40% of pregnancies. The Mantel-Haenszel method showed that fetal loss in women with ET was 3.4-fold higher (95% confidence interval [CI]: 3-3.9; P < .001) than in the general population. Half of the women studied carried the JAK2 (617V>F) mutation, and a multivariate logistic regression model identified this mutation as an independent predictor of pregnancy complications (P = .01). Neither the platelet count nor the leukocyte count was a risk factor. JAK2 (617V>F)–positive patients had an odds ratio of 2.02 (95% CI: 1.1 - 3.8) of developing complications in comparison with JAK2 (617V>F)–negative patients. Aspirin did not prevent complication in JAK2 (617V>F)–positive patients and appeared to worsen outcome in JAK2 (617V>F)–negative patients. A relationship was found between JAK2 (617V>F) and fetal loss (P = .05). This study indicates that patients carrying the JAK2 (617V>F) mutation have higher risk of developing pregnancy complications.

The present work aims to identify the predictors of COVID-19 in-hospital mortality testing a set of Machine Learning Techniques (MLTs), comparing their ability to predict the outcome of interest. The model with the best performance will be used to identify in-hospital mortality predictors and to build an in-hospital mortality prediction tool. The study involved patients with COVID-19, proved by PCR test, admitted to the “Ospedali Riuniti Padova Sud” COVID-19 referral center in the Veneto region, Italy. The algorithms considered were the Recursive Partition Tree (RPART), the Support Vector Machine (SVM), the Gradient Boosting Machine (GBM), and Random Forest. The resampled performances were reported for each MLT, considering the sensitivity, specificity, and the Receiving Operative Characteristic (ROC) curve measures. The study enrolled 341 patients. The median age was 74 years, and the male gender was the most prevalent. The Random Forest algorithm outperformed the other MLTs in predicting in-hospital mortality, with a ROC of 0.84 (95% C.I. 0.78–0.9). Age, together with vital signs (oxygen saturation and the quick SOFA) and lab parameters (creatinine, AST, lymphocytes, platelets, and hemoglobin), were found to be the strongest predictors of in-hospital mortality. The present work provides insights for the prediction of in-hospital mortality of COVID-19 patients using a machine-learning algorithm.