Assessing the prognostic utility of clinical and radiomic features for COVID-19 patients admitted to ICU: challenges and lessons learned.

Severe cases of COVID-19 often necessitate escalation to the Intensive Care Unit (ICU), where patients may face grave outcomes, including mortality. Chest X-rays play a crucial role in the diagnostic process for evaluating COVID-19 patients. Our collaborative efforts with Michigan Medicine in monitoring patient outcomes within the ICU have motivated us to investigate the potential advantages of incorporating clinical information and chest X-ray images for predicting patient outcomes. We propose an analytical workflow to address challenges such as the absence of standardized approaches for image pre-processing and data utilization. We then propose an ensemble learning approach designed to maximize the information derived from multiple prediction algorithms. This entails optimizing the weights within the ensemble and considering the common variability present in individual risk scores. Our simulations demonstrate the superior performance of this weighted ensemble averaging approach across various scenarios. We apply this refined ensemble methodology to analyze post-ICU COVID-19 mortality, an occurrence observed in 21% of COVID-19 patients admitted to the ICU at Michigan Medicine. Our findings reveal substantial performance improvement when incorporating imaging data compared to models trained solely on clinical risk factors. Furthermore, the addition of radiomic features yields even larger enhancements, particularly among older and more medically compromised patients. These results may carry implications for enhancing patient outcomes in similar clinical contexts.

Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality.

As portable chest X-rays are an efficient means of triaging emergent cases, their use has raised the question as to whether imaging carries additional prognostic utility for survival among patients with COVID-19. This study assessed the importance of known risk factors on in-hospital mortality and investigated the predictive utility of radiomic texture features using various machine learning approaches. We detected incremental improvements in survival prognostication utilizing texture features derived from emergent chest X-rays, particularly among older patients or those with a higher comorbidity burden. Important features included age, oxygen saturation, blood pressure, and certain comorbid conditions, as well as image features related to the intensity and variability of pixel distribution. Thus, widely available chest X-rays, in conjunction with clinical information, may be predictive of survival outcomes of patients with COVID-19, especially older, sicker patients, and can aid in disease management by providing additional information.

Sexually Transmitted Infection Rates and Closure of Family Planning Clinics Because of Abortion Restrictions in Iowa.

This cohort study assesses rates of sexually transmitted infections in Iowa counties before and after closure of family planning health centers and compared with national rates.

Homoprejudiced violence among Chinese men who have sex with men: a cross-sectional analysis in Guangzhou, China.

BACKGROUND: Homoprejudiced violence, defined as physical, verbal, psychological and cyber aggression against others because of their actual or perceived sexual orientation, is an important public health issue. Most homoprejudiced violence research has been conducted in high-income countries. This study examined homoprejudiced violence among men who have sex with men (MSM) in Guangzhou, China. METHODS: MSM in a large Chinese city, Guangzhou, completed an online survey. Data about experiencing and initiating homoprejudiced violence was collected. Multivariable logistic regression analyses, controlling for age, residence, occupation, heterosexual marriage, education and income, were carried out to explore associated factors. RESULTS: A total of 777 responses were analyzed and most (64.9%) men were under the age of 30. Three-hundred-ninety-nine (51.4%) men experienced homoprejudiced violence and 205 (25.9%) men perpetrated homoprejudiced violence against others. Men who identified as heterosexual were less (AOR = 0.6, 95% CI: 0.4-0.9) likely to experience homoprejudiced violence compared to men who identified as gay. Men who experienced homoprejudiced violence were more likely to initiate homoprejudiced violence (AOR = 2.44, 95% CI: 1.6-3.5). Men who disclosed their sexual orientation to other people were more likely to experience homoprejudiced violence (AOR = 1.8, 95% CI:1.3-2.5). CONCLUSIONS: These findings suggest the importance of further research and the implementation of interventions focused on preventing and mitigating the effects of homoprejudiced violence among MSM in China.

Social innovation in diagnostics: three case studies.

BACKGROUND: Diagnostics are essential for identifying and controlling diseases. However, limited access to diagnostics hinders public health efforts in many settings. Social innovation may provide a framework for expanding access to diagnostics in the global south. Here social innovation is defined as implementing a known public health tool via a novel, community-driven technique. MAIN BODY: In this article, we discuss three diverse cases that show the potential for using social innovation in diagnostics. The cases chosen for inclusion here demonstrate the importance of social innovation in diagnostics across different geographic, cultural, and health system contexts. They include malaria testing via schools in Malawi, cervical human papillomavirus (HPV) sample self-collection in Peru, and crowdsourcing human immunodeficiency virus (HIV) testing in China. For each case, we present the public health problem and the impact of using social innovation to increase accessibility of diagnostics. We discuss implications of each diagnostic approach and the importance of social innovation in creating these potential solutions. We argue that social innovation is useful in improving the delivery of essential diagnostic tools in low- and middle-income countries. CONCLUSIONS: Interventions in Malawi, Peru, and China suggest social innovation increases uptake of diagnostics. The same tools and principles utilized in these cases can be adapted for use in other contexts. Such diagnostic innovations may help improve identification of and linkage to care for many diseases. The approach presents a unique opportunity to better address public health issues and increase accessibility in LMIC health systems.

Design and Sensitivity Analysis Simulation of a Novel 3D Force Sensor Based on a Parallel Mechanism.

Automated force measurement is one of the most important technologies in realizing intelligent automation systems. However, while many methods are available for micro-force sensing, measuring large three-dimensional (3D) forces and loads remains a significant challenge. Accordingly, the present study proposes a novel 3D force sensor based on a parallel mechanism. The transformation function and sensitivity index of the proposed sensor are analytically derived. The simulation results show that the sensor has a larger effective measuring capability than traditional force sensors. Moreover, the sensor has a greater measurement sensitivity for horizontal forces than for vertical forces over most of the measurable force region. In other words, compared to traditional force sensors, the proposed sensor is more sensitive to shear forces than normal forces.