Implementing organ-on-chip in a next-generation risk assessment of chemicals: a review.

Organ-on-chip (OoC) technology is full of engineering and biological challenges, but it has the potential to revolutionize the Next-Generation Risk Assessment of novel ingredients for consumer products and chemicals. A successful incorporation of OoC technology into the Next-Generation Risk Assessment toolbox depends on the robustness of the microfluidic devices and the organ tissue models used. Recent advances in standardized device manufacturing, organ tissue cultivation and growth protocols offer the ability to bridge the gaps towards the implementation of organ-on-chip technology. Next-Generation Risk Assessment is an exposure-led and hypothesis-driven tiered approach to risk assessment using detailed human exposure information and the application of appropriate new (non-animal) toxicological testing approaches. Organ-on-chip presents a promising in vitro approach by combining human cell culturing with dynamic microfluidics to improve physiological emulation. Here, we critically review commercial organ-on-chip devices, as well as recent tissue culture model studies of the skin, intestinal barrier and liver as the main metabolic organ to be used on-chip for Next-Generation Risk Assessment. Finally, microfluidically linked tissue combinations such as skin-liver and intestine-liver in organ-on-chip devices are reviewed as they form a relevant aspect for advancing toxicokinetic and toxicodynamic studies. We point to recent achievements and challenges to overcome, to advance non-animal, human-relevant safety studies.

Persons with Co-Existing Neurological Disorders: Risk Analysis, Considerations and Management in COVID-19 Pandemic.

Increasing reports of neurological symptoms in COVID-19 patient's warrant clinicians to adopt and define the standardized diagnostic and managing protocols in order to investigate the linkage of neurological symptoms in COVID-19. Encephalitis, anosmia, acute cerebrovascular disease and ageusia are some of the emerging neurological manifestations which are reported in several cohort studies on hospitalized patients with COVID-19. Although the COVID-19 pandemic is primarily associated with infection of the respiratory tract system, but measures like lockdown and restricted physical movements to control the spread of this infection will certainly have neurobehavioural implications. Additionally, some of the patients with pre-existing neurological manifestations like epilepsy, Parkinson's and Alzheimer's disease are more prone to infection and demand extra care as well as improvised treatment. In this review, we have focused on the neurovirological clinical manifestations associated with the COVID-19 pandemic. Although the prevalence of neurovirological manifestations is rare increasing reports cannot be ignored and needs to be discussed thoroughly with respect to risk analysis and considerations for developing a management strategy. This also helps in defining the burden of neurological disorders associated with COVID-19 patients.

Incorporating artificial intelligence in urology: Supervised machine learning algorithms demonstrate comparative advantage over nomograms in predicting biochemical recurrence after prostatectomy.

OBJECTIVE: After radical prostatectomy (RP), one-third of patients will experience biochemical recurrence (BCR), which is associated with subsequent metastasis and cancer-specific mortality. We employed machine learning (ML) algorithms to predict BCR after RP, and compare them with traditional regression models and nomograms. METHODS: Utilizing a prospective Uro-oncology registry, 18 clinicopathological parameters of 1130 consecutive patients who underwent RP (2009-2018) were recorded, yielding over 20,000 data points for analysis. The data set was split into a 70:30 ratio for training and validation. Three ML models: Naïve Bayes (NB), random forest (RF), and support vector machine (SVM) were studied, and compared with traditional regression models and nomograms (Kattan, CAPSURE, John Hopkins [JHH]) to predict BCR at 1, 3, and 5 years. RESULTS: Over a median follow-up of 70.0 months, 176 (15.6%) developed BCR, at a median time of 16.0 months (interquartile range [IQR]: 11.0-26.0). Multivariate analyses demonstrated strongest association of BCR with prostate-specific antigen (PSA) (p: 0.015), positive surgical margins (p < 0.001), extraprostatic extension (p: 0.002), seminal vesicle invasion (p: 0.004), and grade group (p < 0.001). The 3 ML models demonstrated good prediction of BCR at 1, 3, and 5 years, with the area under curves (AUC) of NB at 0.894, 0.876, and 0.894, RF at 0.846, 0.875, and 0.888, and SVM at 0.835, 0.850, and 0.855, respectively. All models demonstrated (1) robust accuracy (>0.82), (2) good calibration with minimal overfitting, (3) longitudinal consistency across the three time points, and (4) inter-model validity. The ML models were comparable to traditional regression analyses (AUC: 0.797, 0.848, and 0.862) and outperformed the three nomograms: Kattan (AUC: 0.815, 0.798, and 0.799), JHH (AUC: 0.820, 0.757, and 0.750) and CAPSURE nomograms (AUC: 0.706, 0.720, and 0.749) (p < 0.001). CONCLUSION: Supervised ML algorithms can deliver accurate performances and outperform nomograms in predicting BCR after RP. This may facilitate tailored care provisions by identifying high-risk patients who will benefit from multimodal therapy.

Current State and Future Direction of Postpartum Hemorrhage Risk Assessment.

In the United States, postpartum hemorrhage is a leading preventable cause of maternal mortality and morbidity. To reduce morbidity from postpartum hemorrhage, risk assessment is an important starting point for informing decisions about risk management and hemorrhage prevention. Current perinatal care guidelines from the Joint Commission recommend that all patients undergo postpartum hemorrhage risk assessment at admission and after delivery. Three maternal health organizations-the California Maternal Quality Care Collaborative, AWHONN, and the American College of Obstetricians and Gynecologists' Safe Motherhood Initiative-have developed postpartum hemorrhage risk-assessment tools for clinical use. Based on the presence of risk factors, each organization categorizes patients as low-, medium-, or high-risk, and ties pretransfusion testing recommendations to these categorizations. However, the accuracy of these tools' risk categorizations has come under increasing scrutiny. Given their low positive predictive value, the value proposition of pretransfusion testing in all patients classified as medium- and high-risk is low. Further, 40% of all postpartum hemorrhage events occur in low-risk patients, emphasizing the need for early vigilance and treatment regardless of categorization. We recommend that maternal health organizations consider alternatives to category-based risk tools for evaluating postpartum hemorrhage risk before delivery.

Adoption of New Risk Stratification Technologies Within US Hospital Referral Regions and Association With Prostate Cancer Management.

Importance: The clinical decisions that arise from prostate magnetic resonance imaging (MRI) and genomic testing in patients with prostate cancer are not well understood. Objective: To evaluate the association between regional uptake of prostate MRI and genomic testing and observation vs treatment for prostate cancer. Design, Setting, and Participants: This retrospective cohort study of commercial insurance claims for prostate MRI and genomic testing included 65 530 patients 40 to 89 years of age newly diagnosed with prostate cancer from July 1, 2012, through June 30, 2019. Exposures: Patient- and regional-level use of prostate MRI and genomic testing. Main Outcomes and Measures: Observation vs definitive treatment for prostate cancer. Patient-level analyses examined the association between receipt of testing or residing in a hospital referral region (HRR) that adopted testing and observation. In regional-level analyses, the dependent variable was the change in the proportion of patients observed for prostate cancer at the HRR level between 2 periods: July 1, 2012, to June 30, 2014, and July 1, 2017, to June 20, 2019. The independent study variables included HRR-level changes in the proportion of men undergoing prostate MRI and genomic testing between these periods, and the models were adjusted for contextual factors associated with prostate cancer care and socioeconomic status. Results: This study identified 65 530 patients, including 27 679 in the early period (mean [SD] age, 58.0 [5.9] years) and 37 851 in the late period (mean [SD] age, 59.0 [5.7] years). Use of prostate MRI increased significantly from 7.2% (95% CI, 6.9%-7.5%) to 16.7% (95% CI, 16.3%-17.1%) from the early to late period. Use of genomic testing increased significantly from 1.3% (95% CI, 1.1%-1.4%) to 12.7% (95% CI, 12.3%-13.0%) from the early to late period. Compared with the lowest, the highest HRR quartiles of prostate MRI and genomic testing uptake were associated with an adjusted 4.1% (SE, 1.1%; P < .001) and 2.5% (SE, 1.1%; P = .03) absolute increase in the proportion of patients receiving observation, respectively. Conclusions and Relevance: In this cohort study, uptake of prostate MRI and genomic testing was associated with increased use of initial observation vs treatment for prostate cancer. Marked geographic variation supports the need for further patient-level research to optimize the dissemination and outcome of testing.

A proposed risk assessment score for gastrointestinal stromal tumors based on evaluation of 19,030 cases from the National Cancer Database.

BACKGROUND: Standard risk assessment algorithms for gastrointestinal stromal tumor (GIST) are based on anatomic and histopathological variables with arbitrarily defined subcategories. Our goal was to improve risk assessment for GIST through retrospective analysis of patient data. METHODS: The National Cancer Database (NCDB) was queried for patients with GIST; the final cohort consisted of 19,030 cases. Main outcomes were metastasis at presentation and overall survival. A test dataset was used to reevaluate risk stratification parameters in multivariate regression models. A novel risk assessment system was applied to the validation dataset and compared to other currently used risk assessment schemes. RESULTS: Analysis of observed prevalence of metastases at presentation suggested 7 cm and mitotic rates > 10 per 5 mm2 as optimal threshold values. A proposed risk stratification score showed statistical superiority compared to the National Comprehensive Cancer Network, American Joint Committee on Cancer, and modified National Institute of Health classifications in predicting probability of presentation with metastasis at diagnosis and 4-year overall survival after accounting for important covariables including patient age and comorbidities, year of diagnosis, and surgical/systemic therapeutic regimen. CONCLUSIONS: Reexamination of prognostic factors for GIST demonstrated that current threshold values for tumor size and mitotic rate are suboptimal. A risk stratification score based on revised categorization of these factors outperformed currently used risk assessment algorithms.

Echocardiographic Assessments for Peripartum Cardiac Events in Pregnant Women with Low-Risk Congenital Heart Disease.

This retrospective cohort study aimed to explore the relationship between temporal changes in the cardiac function and peripartum cardiac events in pregnant women with low-risk congenital heart disease.We performed echocardiography at early and late pregnancy and postpartum in 76 pregnant women with low-risk congenital heart disease, and compared echocardiographic parameters between subjects with and without peripartum cardiac events. Median age at delivery was 27 (range, 24-31) years. The ZAHARA and CARPREG II scores suggested that most women were found to be at low-risk for pregnancy. Fifteen subjects had cardiac events that included heart failure in 10, arrhythmia in 4, and pulmonary hypertension in one subject. The left ventricular and atrial volumes significantly increased from early pregnancy toward late pregnancy, and the E/A ratio and global longitudinal strain significantly decreased from early pregnancy toward late pregnancy. The left atrial volume (67 [53-79] versus 45 [35-55] mL, P = 0.002) and plasma brain natriuretic peptide level (58 [36-123] versus 34 [18-48] pg/mL, P = 0.026) at late pregnancy were significantly higher in subjects with cardiac events than in those without cardiac events.An increase in the left atrial volume followed by mild left ventricular diastolic dysfunction is related to peripartum cardiac events in women with congenital heart disease who are at low risk for cardiac events during pregnancy.

Changes in Cardiovascular Risk Factors and Cardiovascular Events in the Elderly Population.

Background This study examines changes in the ideal cardiovascular health (CVH) status and whether these changes are associated with incident cardiovascular disease (CVD) and mortality in the elderly Asian population. Methods and Results In the Korea National Health Insurance Service-Senior cohort aged ≥60 years, 208 673 participants without prior CVD, including 109 431 who showed changes in CVH status, were assessed. The association of the changes in cardiovascular risk factors with incident CVD was assessed from 2004 to 2014 in the elderly (aged 60-74 years) and very elderly (≥75 years) groups. During the follow-up period (7.1 years for CVD and 7.2 years for mortality), 19 429 incident CVD events and 24 225 deaths occurred. In both the elderly and very elderly participants, higher CVH status resulted in a lower risk of CVD and mortality. In the very elderly participants, compared with consistently low CVH, consistently high CVH (subhazard ratio, 0.41; 95% CI, 0.23-0.73) was associated with a lower risk of CVD. This trend was consistently observed in the elderly population. In the very elderly participants, total cholesterol level was not informative enough for the prediction of CVD events. In both the elderly and very elderly groups, body mass index and total cholesterol were not informative enough for the prediction of all-cause mortality. Conclusions In both the elderly and very elderly Asian populations without CVD, a consistent relationship was observed between the improvement of a composite metric of CVH and the reduced risk of CVD. Body mass index and total cholesterol were not informative enough for the prediction of all-cause mortality in both the elderly and very elderly groups.

Application of Artificial Intelligence for Diagnosis and Risk Stratification in NAFLD and NASH: The State of the Art.

The diagnosis of nonalcoholic fatty liver disease and associated fibrosis is challenging given the lack of signs, symptoms and nonexistent diagnostic test. Furthermore, follow up and treatment decisions become complicated with a lack of a simple reproducible method to follow these patients longitudinally. Liver biopsy is the current standard to detect, risk stratify and monitor individuals with nonalcoholic fatty liver disease. However, this method is an unrealistic option in a population that affects about one in three to four individuals worldwide. There is an urgency to develop innovative methods to facilitate management at key points in an individual's journey with nonalcoholic fatty liver disease fibrosis. Artificial intelligence is an exciting field that has the potential to achieve this. In this review, we highlight applications of artificial intelligence by leveraging our current knowledge of nonalcoholic fatty liver disease to diagnose and risk stratify NASH phenotypes.

Association of the Estimated Coronary Artery Incidence Risk According to the Japan Atherosclerosis Society Guidelines 2017 with Cardio-Ankle Vascular Index.

AIMS: The categories in the comprehensive lipid and risk management guidelines were proposed by the Japan Atherosclerosis Society (JAS Guidelines 2017), which adopted the estimated 10 year absolute risk of coronary artery disease (CAD) incidence in the Suita score. We examined whether those categories were concordant with the degree of arterial stiffness. METHODS: In 2014, the cardio-ankle vascular index (CAVI), an arterial stiffness parameter, was measured in 1,972 Japanese participants aged 35-74 years in Tsuruoka City, Yamagata Prefecture, Japan. We examined the mean CAVI and the proportion and odds ratios (ORs) of CAVI ≥ 9.0 on the basis of the following three management classifications using the analysis of variance and logistic regression: "Category I (Low risk)," "Category II (Middle risk)," and "Category III (High risk)." RESULTS: The mean CAVI and proportion of CAVI ≥ 9.0 were 8.6 and 34.8% among males and 8.1 and 18.3% among females, respectively. The mean CAVI and proportion of CAVI ≥ 9.0 were associated with an estimated 10 year absolute risk for CAD among males and females, excluding High risk for females. These results were similar to the management classification by the guideline: the multivariable-adjusted ORs (95% confidence intervals) of CAVI ≥ 9.0 among Category II and Category III compared with those among Category I were 2.96 (1.61-5.43) and 7.33 (4.03-13.3) for males and 3.99 (2.55-6.24) and 3.34 (2.16-5.16) for females, respectively. CONCLUSIONS: The risk stratification, which was proposed in the JAS Guidelines 2017, is concordant with the arterial stiffness parameter.

Metabolic phenotyping and cardiovascular disease: an overview of evidence from epidemiological settings.

Metabolomics, the comprehensive measurement of low-molecular-weight molecules in biological fluids used for metabolic phenotyping, has emerged as a promising tool to better understand pathways underlying cardiovascular disease (CVD) and to improve cardiovascular risk stratification. Here, we present the main methodologies for metabolic phenotyping, the methodological steps to analyse these data in epidemiological settings and the associated challenges. We discuss evidence from epidemiological studies linking metabolites to coronary heart disease and stroke. These studies indicate the systemic nature of CVD and identify associated metabolic pathways such as gut microbial cometabolism, branched-chain amino acids, glycerophospholipid and cholesterol metabolism, as well as activation of inflammatory processes. Integration of metabolomic with genomic data can provide new evidence for involved biochemical pathways and potential for causality using Mendelian randomisation. The clinical utility of metabolic biomarkers for cardiovascular risk stratification in healthy individuals has not yet been established. As sample sizes with high-dimensional molecular data increase in epidemiological settings, integration of metabolomic data across studies and platforms with other molecular data will lead to new understanding of the metabolic processes underlying CVD and contribute to identification of potentially novel preventive and pharmacological targets. Metabolic phenotyping offers a powerful tool in the characterisation of the molecular signatures of CVD, paving the way to new mechanistic understanding and therapies, as well as improving risk prediction of CVD patients. However, there are still challenges to face in order to contribute to clinically important improvements in CVD.

Proactive Risk Assessment through FMEA of Home Parenteral Nutrition Care Processes: A Survey Analysis.

BACKGROUND: Home Parenteral Nutrition (HPN) is a lifesaving clinical care process. However, undetected hazards and vulnerabilities in care transitions from hospital to community care may pose risk to patient's safety. Avoidable complications and adverse events may hinder the benefits of treatment. OBJECTIVE: The analysis carried out aims at framing through Human Factors and Ergonomics (HF/E) the critical issues for patient safety related to clinical care practices for HPN in healthcare organization. METHODS: We present the results of a proactive risk assessment analysis based on the FMEA methodology (Failure Mode and Effects Analysis) carried out in three different areas of the regional health care system of Tuscany, Italy. The clinical risk management and patient safety unit assessed the risk perception of Healthcare Workers (HWs) in regard to patient safety and situational awareness throughout the HPN patient journey. RESULTS: The analysis revealed heterogeneity in the Risk Priority Index (RPI) expressed by HWs. A lower RPI is associated with a HPN process that deploys in continuity between hospital care and community care. A higher RPI is associated with a quality and safety improvement process that is still ongoing. We also observed HWs expressing low RPI in the areas of the region where HPN has a hospital- focused approach and has limited adherence to patient safety requirements. Low RPI for HPN process may relate both to extensively deployed continuity of care and to jeopardized awareness on HPN phases and coordination. The analysis carried out enabled the definition of a common HPN workflow used as reference schema allowing for the definition of a set of recommendations for improving the quality and safety of the care processes. Moreover, the outcome of the proactive risk assessment laid the groundwork for the advancement of the patient safety regional requirements. CONCLUSION: The analysis had the role of promoting the contextualization of the culture of quality and safety within the HPN process resulting in an improved awareness of the criticalities and the role of nutrition units throughout the care process.

Radiomics in neuro-oncology: Basics, workflow, and applications.

Over the last years, the amount, variety, and complexity of neuroimaging data acquired in patients with brain tumors for routine clinical purposes and the resulting number of imaging parameters have substantially increased. Consequently, a timely and cost-effective evaluation of imaging data is hardly feasible without the support of methods from the field of artificial intelligence (AI). AI can facilitate and shorten various time-consuming steps in the image processing workflow, e.g., tumor segmentation, thereby optimizing productivity. Besides, the automated and computer-based analysis of imaging data may help to increase data comparability as it is independent of the experience level of the evaluating clinician. Importantly, AI offers the potential to extract new features from the routinely acquired neuroimages of brain tumor patients. In combination with patient data such as survival, molecular markers, or genomics, mathematical models can be generated that allow, for example, the prediction of treatment response or prognosis, as well as the noninvasive assessment of molecular markers. The subdiscipline of AI dealing with the computation, identification, and extraction of image features, as well as the generation of prognostic or predictive mathematical models, is termed radiomics. This review article summarizes the basics, the current workflow, and methods used in radiomics with a focus on feature-based radiomics in neuro-oncology and provides selected examples of its clinical application.