1-Year Mortality Prediction through Artificial Intelligence Using Hemodynamic Trace Analysis among Patients with ST Elevation Myocardial Infarction.

Background and Objectives: Patients presenting with ST Elevation Myocardial Infarction (STEMI) due to occlusive coronary arteries remain at a higher risk of excess morbidity and mortality despite being treated with primary percutaneous coronary intervention (PPCI). Identifying high-risk patients is prudent so that close monitoring and timely interventions can improve outcomes. Materials and Methods: A cohort of 605 STEMI patients [64.2 ± 13.2 years, 432 (71.41%) males] treated with PPCI were recruited. Their arterial pressure (AP) wave recorded throughout the PPCI procedure was analyzed to extract features to predict 1-year mortality. After denoising and extracting features, we developed two distinct feature selection strategies. The first strategy uses linear discriminant analysis (LDA), and the second employs principal component analysis (PCA), with each method selecting the top five features. Then, three machine learning algorithms were employed: LDA, K-nearest neighbor (KNN), and support vector machine (SVM). Results: The performance of these algorithms, measured by the area under the curve (AUC), ranged from 0.73 to 0.77, with accuracy, specificity, and sensitivity ranging between 68% and 73%. Moreover, we extended the analysis by incorporating demographics, risk factors, and catheterization information. This significantly improved the overall accuracy and specificity to more than 76% while maintaining the same level of sensitivity. This resulted in an AUC greater than 0.80 for most models. Conclusions: Machine learning algorithms analyzing hemodynamic traces in STEMI patients identify high-risk patients at risk of mortality.

Reduction of the Structure Parameter of Forward Osmosis Membranes by Using Sodium Bicarbonate as Pore-Forming Agent.

The forward osmosis (FO) process suffers from unfavorable internal concentration polarization (ICP) of the solute within the support layer of thin-film composite forward osmosis (TFC-FO) membranes. To lower the ICP effect, a support layer with low tortuosity, high porosity, and interconnected pores is necessary. In the present investigation, sodium bicarbonate has been presented as a simple pore-forming agent to decline the ICP within a poly(ethersulfone) substrate. In particular, the porous poly(ethersulfone) support layer was fabricated by embedding sodium bicarbonate into the casting solution to form CO2 gas bubbles in the substrate during phase inversion in an acidic nonsolvent. Experimental results revealed that the separation performance of the TFC-FO membranes significantly improved. The most water-permeable membrane was prepared in the acidic nonsolvent (TFC-SB.3) and it demonstrated a water flux of 26.6 LMH and a reverse salt flux of 3.6 gMH in the FO test. In addition, the TFC-SB.3 membrane showed an 85% increase in water permeability (2.13 LMH/bar) with negligible change in salt rejection (94.3%). Such observations were based on the increase of substrate porosity and the improved connectivity of the finger-like channels through in situ CO2 gas bubbling that alleviate the ICP phenomena. Therefore, the current study presents a simple, scalable method to design a high-performance TFC-FO membrane.