Background: Adverse outcome pathway (AOP) networks are versatile tools in toxicology and risk assessment that capture and visualize mechanisms driving toxicity originating from various data sources. They share a common structure consisting of a set of molecular initiating events and key events, connected by key event relationships, leading to the actual adverse outcome. AOP networks are to be considered living documents that should be frequently updated by feeding in new data. Such iterative optimization exercises are typically done manually, which not only is a time-consuming effort, but also bears the risk of overlooking critical data. The present study introduces a novel approach for AOP network optimization of a previously published AOP network on chemical-induced cholestasis using artificial intelligence to facilitate automated data collection followed by subsequent quantitative confidence assessment of molecular initiating events, key events, and key event relationships. Methods: Artificial intelligence-assisted data collection was performed by means of the free web platform Sysrev. Confidence levels of the tailored Bradford-Hill criteria were quantified for the purpose of weight-of-evidence assessment of the optimized AOP network. Scores were calculated for biological plausibility, empirical evidence, and essentiality, and were integrated into a total key event relationship confidence value. The optimized AOP network was visualized using Cytoscape with the node size representing the incidence of the key event and the edge size indicating the total confidence in the key event relationship. Results: This resulted in the identification of 38 and 135 unique key events and key event relationships, respectively. Transporter changes was the key event with the highest incidence, and formed the most confident key event relationship with the adverse outcome, cholestasis. Other important key events present in the AOP network include: nuclear receptor changes, intracellular bile acid accumulation, bile acid synthesis changes, oxidative stress, inflammation and apoptosis. Conclusions: This process led to the creation of an extensively informative AOP network focused on chemical-induced cholestasis. This optimized AOP network may serve as a mechanistic compass for the development of a battery of in vitro assays to reliably predict chemical-induced cholestatic injury.
Objective: To describe the discrimination and calibration of clinical prediction models, identify characteristics that contribute to better predictions and investigate predictors that are associated with unplanned hospital readmissions.Design: Systematic review and meta-analysis.Data source: Medline, EMBASE, ICTPR (for study protocols) and Web of Science (for conference proceedings) were searched up to 25 August 2020.Eligibility criteria for selecting studies: Studies were eligible if they reported on (1) hospitalised adult patients with acute heart disease; (2) a clinical presentation of prediction models with c-statistic; (3) unplanned hospital readmission within 6 months. Primary and secondary outcome measures: Model discrimination for unplanned hospital readmission within 6 months measured using concordance (c) statistics and model calibration. Meta-regression and subgroup analyses were performed to investigate predefined sources of heterogeneity. Outcome measures from models reported in multiple independent cohorts and similarly defined risk predictors were pooled.Results: Sixty studies describing 81 models were included: 43 models were newly developed, and 38 were externally validated. Included populations were mainly patients with heart failure (HF) (n=29). The average age ranged between 56.5 and 84 years. The incidence of readmission ranged from 3% to 43%. Risk of bias (RoB) was high in almost all studies. The c-statistic was <0.7 in 72 models, between 0.7 and 0.8 in 16 models and >0.8 in 5 models. The study population, data source and number of predictors were significant moderators for the discrimination. Calibration was reported for 27 models. Only the GRACE (Global Registration of Acute Coronary Events) score had adequate discrimination in independent cohorts (0.78, 95% CI 0.63 to 0.86). Eighteen predictors were pooled. Conclusion: Some promising models require updating and validation before use in clinical practice. The lack of independent validation studies, high RoB and low consistency in measured predictors limit their applicability.PROSPERO registration number: CRD42020159839.
MULTIFILE
Background & aimsThe Scored Patient-Generated Subjective Global Assessment (PG-SGA©) is a validated nutritional screening, assessment, monitoring, and triage tool. When translated to other languages, the questions and answering items need to be conceptually, semantically, and operationally equivalent to the original tool. In this study, we aimed to assess linguistic and content validity of the PG-SGA translated and culturally adapted for the Norwegian setting, as perceived by Norwegian cancer patients and healthcare professionals (HCPs).MethodsWe have translated and culturally adapted the original PG-SGA for the Norwegian setting, in concordance with the International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Cancer patients and HCPs, including nurses, dietitians and physicians, were invited to participate. Comprehensibility and difficulty were assessed by patients for the patient component (PG-SGA Short Form), and by HCPs for the professional component. Content validity was assessed for the full PG-SGA by HCPs only. The data were collected by a questionnaire and evaluations were operationalized by a 4-point scale. Item and scale indices were calculated for comprehensibility (Item CI, Scale CI), difficulty (Item DI, Scale DI) and content validity (Item CVI, Scale CVI).ResultsFifty-one cancer patients and 92 HCPs participated in the study. The patients perceived comprehensibility and difficulty of the Norwegian PG-SGA Short Form as excellent (Scale CI = 0.99 and DI = 0.97). However, HCPs perceived comprehensibility and difficulty of the professional component as below acceptable (Scale CI = 0.78 and DI = 0.66), and the physical exam was being rated as the most difficult part (Item DI 0.26 to 0.65). Content validity for the full Norwegian PG-SGA was considered excellent (Scale CVI = 0.99) by the HCPs.ConclusionThe patient component of PG-SGA was considered clear and easy to complete, and the full Norwegian PG-SGA was considered as relevant by HCPs. In the final Norwegian PG-SGA, changes have been made to improve comprehensibility of the professional component. To improve perceived difficulty of completing the professional component, training of professionals is indicated.