OBJECTIVE: To employ machine learning (ML) and stacked ensemble learning (EL) methods in predicting short- and long-term mortality in Asian patients diagnosed with NSTEMI/UA and to identify the associated features, subsequently evaluating these findings against established risk scores.

METHODS: We analyzed data from the National Cardiovascular Disease Database for Malaysia (2006-2019), representing a diverse NSTEMI/UA Asian cohort. Algorithm development utilized in-hospital records of 9,518 patients, 30-day data from 7,133 patients, and 1-year data from 7,031 patients. This study utilized 39 features, including demographic, cardiovascular risk, medication, and clinical features. In the development of the stacked EL model, four base learner algorithms were employed: eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), with the Generalized Linear Model (GLM) serving as the meta learner. Significant features were chosen and ranked using ML feature importance with backward elimination. The predictive performance of the algorithms was assessed using the area under the curve (AUC) as a metric. Validation of the algorithms was conducted against the TIMI for NSTEMI/UA using a separate validation dataset, and the net reclassification index (NRI) was subsequently determined.

RESULTS: Using both complete and reduced features, the algorithm performance achieved an AUC ranging from 0.73 to 0.89. The top-performing ML algorithm consistently surpassed the TIMI risk score for in-hospital, 30-day, and 1-year predictions (with AUC values of 0.88, 0.88, and 0.81, respectively, all p < 0.001), while the TIMI scores registered significantly lower at 0.55, 0.54, and 0.61. This suggests the TIMI score tends to underestimate patient mortality risk. The net reclassification index (NRI) of the best ML algorithm for NSTEMI/UA patients across these periods yielded an NRI between 40-60% (p < 0.001) relative to the TIMI NSTEMI/UA risk score. Key features identified for both short- and long-term mortality included age, Killip class, heart rate, and Low-Molecular-Weight Heparin (LMWH) administration.

OBJECTIVES: To compare the effectiveness of anticoagulant therapies for the treatment of deep vein thrombosis in pregnancy. The anticoagulant drugs included are UFH, low molecular weight heparin (LMWH) and warfarin.

SEARCH STRATEGY: We searched the Cochrane Pregnancy and Childbirth Group's Trials Register (March 2010) and reference lists of retrieved studies.

SELECTION CRITERIA: Randomised controlled trials comparing any combination of warfarin, UFH, LMWH and placebo in pregnant women.

DATA COLLECTION AND ANALYSIS: We used methods described in the Cochrane Handbooks for Systemic Reviews of Interventions for assessing the eligibility of studies identified by the search strategy. A minimum of two review authors independently assessed each study.

MAIN RESULTS: We did not identify any eligible studies for inclusion in the review.We identified three potential studies; after assessing eligibility, we excluded all three as they did not meet the prespecified inclusion criteria. One study compared LMWH and UFH in pregnant women with previous thromboembolic events and, for most of these women, anticoagulants were used as thromboprophylaxis. There were only three women who had a thromboembolic event during the current pregnancy and it was unclear whether the anticoagulant was used as therapy or prophylaxis. We excluded one study because it included only women undergoing caesarean birth. The third study was not a randomised trial.