Predicting in-hospital mortality among patients admitted with a diagnosis of heart failure: a machine learning approach.

Existing risk prediction models for hospitalized heart failure patients are limited. We identified patients hospitalized with a diagnosis of heart failure between 7 May 2013 and 26 April 2022 from a large academic, quaternary care medical centre (training cohort). Demographics, medical comorbidities, vitals, and labs were collected and were used to construct random forest machine learning models to predict in-hospital mortality. Models were compared with logistic regression, and to commonly used heart failure risk scores. The models were subsequently validated in patients hospitalized with a diagnosis of heart failure from a second academic, community medical centre (validation cohort). The entire cohort comprised 21 802 patients, of which 14 539 were in the training cohort and 7263 were in the validation cohort. The median age (25th-75th percentile) was 70 (58-82) for the entire cohort, 43.2% were female, and 6.7% experienced inpatient mortality. In the overall cohort, 7621 (35.0%) patients had heart failure with reduced ejection fraction (EF ≤ 40%), 1271 (5.8%) had heart failure with mildly reduced EF (EF 41-49%), and 12 910 (59.2%) had heart failure with preserved EF (EF ≥ 50%). Random forest models in the validation cohort demonstrated a c-statistic (95% confidence interval) of 0.96 (0.95-0.97), sensitivity (SN) of 87.3%, and specificity (SP) of 90.6% for the prediction of in-hospital mortality. Models for those with HFrEF demonstrated a c-statistic of 0.96 (0.94-0.98), SN 88.2%, and SP 91.0%, and those for patients with HFpEF showed a c-statistic of 0.95 (0.93-0.97), SN 87.4%, and SP 89.5% for predicting in-hospital mortality. The random forest model significantly outperformed logistic regression (c-statistic 0.87, SN 75.9%, and SP 86.9%), and current existing risk scores including the Acute Decompensated Heart Failure National Registry risk score (c-statistic of 0.70, SN 69%, and SP 62%), and the Get With the Guidelines-Heart Failure risk score (c-statistic 0.69, SN 67%, and SP 63%); P < 0.001 for comparison. Machine learning models built from commonly recorded patient information can accurately predict in-hospital mortality among patients hospitalized with a diagnosis of heart failure.

Bio-Inspired Muco-Adhesive Polymers for Drug Delivery Applications.

Muco-adhesive drug delivery systems continue to be one of the most studied for controlled pharmacokinetics and pharmacodynamics. Briefly, muco-adhesive polymers, can be described as bio-polymers that adhere to the mucosal (mucus) surface layer, for an extended residency period of time at the site of application, by the help of interfacial forces resulting in improved drug delivery. When compared to traditional drug delivery systems, muco-adhesive carriers have the potential to enhance therapeutic performance and efficacy, locally and systematically, in oral, rectal, vaginal, amongst other routes. Yet, the achieving successful muco-adhesion in a novel polymeric drug delivery solution is a complex process involving key physico-chemico-mechanical parameters such as adsorption, wettability, polymer chain length, inter-penetration and cross-linking, to list a few. Hence, and in light of accruing progress, evidence and interest, during the last decade, this review aims to provide the reader with an overview of the theories, principles, properties, and underlying mechanisms of muco-adhesive polymers for pharmaceutics; from basics to design to characterization to optimization to evaluation to market. A special focus is devoted to recent advances incorporating bio-inspired polymers for designing controlled muco-adhesive drug delivery systems.

Supporting Equity and Inclusion of Deaf and Hard-of-Hearing Individuals in Professional Organizations.

Disability is an important and often overlooked component of diversity. Individuals with disabilities bring a rare perspective to science, technology, engineering, mathematics, and medicine (STEMM) because of their unique experiences approaching complex issues related to health and disability, navigating the healthcare system, creatively solving problems unfamiliar to many individuals without disabilities, managing time and resources that are limited by physical or mental constraints, and advocating for themselves and others in the disabled community. Yet, individuals with disabilities are underrepresented in STEMM. Professional organizations can address this underrepresentation by recruiting individuals with disabilities for leadership opportunities, easing financial burdens, providing equal access, fostering peer-mentor groups, and establishing a culture of equity and inclusion spanning all facets of diversity. We are a group of deaf and hard-of-hearing (D/HH) engineers, scientists, and clinicians, most of whom are active in clinical practice and/or auditory research. We have worked within our professional societies to improve access and inclusion for D/HH individuals and others with disabilities. We describe how different models of disability inform our understanding of disability as a form of diversity. We address heterogeneity within disabled communities, including intersectionality between disability and other forms of diversity. We highlight how the Association for Research in Otolaryngology has supported our efforts to reduce ableism and promote access and inclusion for D/HH individuals. We also discuss future directions and challenges. The tools and approaches discussed here can be applied by other professional organizations to include individuals with all forms of diversity in STEMM.

Optical Coherence Tomography to Measure Sound-Induced Motions Within the Mouse Organ of Corti In Vivo.

The measurement of mechanical vibrations within the living cochlea is critical to understanding the first nonlinear steps in auditory processing, hair cell stimulation, and cochlear amplification. However, it has proven to be a challenging endeavor. This chapter describes how optical coherence tomography (OCT) can be used to measure vibrations within the tissues of the organ of Corti. These experimental measurements can be performed within the unopened cochlea of living mice routinely and reliably.