Background: While clinical prediction models (CPMs) are used increasingly commonly to guide patient care, the performance and clinical utility of these CPMs in new patient cohorts is poorly... Show moreBackground: While clinical prediction models (CPMs) are used increasingly commonly to guide patient care, the performance and clinical utility of these CPMs in new patient cohorts is poorly understood. Methods: We performed 158 external validations of 104 unique CPMs across 3 domains of cardiovascular disease (primary prevention, acute coronary syndrome, and heart failure). Validations were performed in publicly available clinical trial cohorts and model performance was assessed using measures of discrimination, calibration, and net benefit. To explore potential reasons for poor model performance, CPM-clinical trial cohort pairs were stratified based on relatedness, a domain-specific set of characteristics to qualitatively grade the similarity of derivation and validation patient populations. We also examined the model-based C-statistic to assess whether changes in discrimination were because of differences in case-mix between the derivation and validation samples. The impact of model updating on model performance was also assessed. Results: Discrimination decreased significantly between model derivation (0.76 [interquartile range 0.73-0.78]) and validation (0.64 [interquartile range 0.60-0.67], P<0.001), but approximately half of this decrease was because of narrower case-mix in the validation samples. CPMs had better discrimination when tested in related compared with distantly related trial cohorts. Calibration slope was also significantly higher in related trial cohorts (0.77 [interquartile range, 0.59-0.90]) than distantly related cohorts (0.59 [interquartile range 0.43-0.73], P=0.001). When considering the full range of possible decision thresholds between half and twice the outcome incidence, 91% of models had a risk of harm (net benefit below default strategy) at some threshold; this risk could be reduced substantially via updating model intercept, calibration slope, or complete re-estimation. Conclusions: There are significant decreases in model performance when applying cardiovascular disease CPMs to new patient populations, resulting in substantial risk of harm. Model updating can mitigate these risks. Care should be taken when using CPMs to guide clinical decision-making. Show less
This review describes human and rodent-derived cell lines and xenografts developed over the last five decades that are suitable or potentially suitable models for paraganglioma-pheochromocytoma... Show moreThis review describes human and rodent-derived cell lines and xenografts developed over the last five decades that are suitable or potentially suitable models for paraganglioma-pheochromocytoma research. We outline the strengths and weaknesses of various models and emphasize the recurring theme that, despite the major challenges involved, more effort is required in the search for valid human and animal cell models of paraganglioma-pheochromocytoma, particularly those relevant to cancers carrying a mutation in one of the succinate dehydrogenase genes. Despite many setbacks, the recent development of a potentially important new model, the RSO cell line, gives reason for optimism regarding the future of models in the paraganglioma-pheochromocytoma field. We also note that classic approaches to cell line derivation such as SV40-mediated immortalization and newer approaches such as organoid culture or iPSCs have been insufficiently explored. As many existing cell lines have been poorly characterized, we provide recommendations for reporting of paraganglioma and pheochromocytoma cell lines, including the strong recommendation that cell lines are made widely available via the ATCC or a similar cell repository. Basic research in paraganglioma-pheochromocytoma is currently transition ing from the analysis of genetics to the analysis of disease mechanisms and the clinically exploitable vulnerabilities of tumors. A successful transition will require many more disease-relevant human and animal models to ensure continuing progress. Show less
Background To improve our understanding of the natural course of head and neck paragangliomas (HNPGL) and ultimately differentiate between cases that benefit from early treatment and those that are... Show moreBackground To improve our understanding of the natural course of head and neck paragangliomas (HNPGL) and ultimately differentiate between cases that benefit from early treatment and those that are best left untreated, we studied the growth dynamics of 77 HNPGL managed with primary observation.Methods Using digitally available magnetic resonance images, tumor volume was estimated at three time points. Subsequently, nonlinear least squares regression was used to fit seven mathematical models to the observed growth data. Goodness of fit was assessed with the coefficient of determination ( R-2 ) and root-mean-squared error. The models were compared with Kruskal-Wallis one-way analysis of variance and subsequent post-hoc tests. In addition, the credibility of predictions (age at onset of neoplastic growth and estimated volume at age 90) was evaluated.Results Equations generating sigmoidal-shaped growth curves (Gompertz, logistic, Spratt and Bertalanffy) provided a good fit (median R-2 : 0.996-1.00) and better described the observed data compared with the linear, exponential, and Mendelsohn equations ( p <0.001). Although there was no statistically significant difference between the sigmoidal-shaped growth curves regarding the goodness of fit, a realistic age at onset and estimated volume at age 90 were most often predicted by the Bertalanffy model.Conclusions Growth of HNPGL is best described by decelerating tumor growth laws, with a preference for the Bertalanffy model. To the best of our knowledge, this is the first time that this often-neglected model has been successfully fitted to clinically obtained growth data. Show less
This review establishes an inventory of existing toxicity data on nanoparticles (NPs) with thepurpose of developing (Quantitative) Structure–Activity Relationships for NPs (nano-[Q]SARs), and also... Show moreThis review establishes an inventory of existing toxicity data on nanoparticles (NPs) with thepurpose of developing (Quantitative) Structure–Activity Relationships for NPs (nano-[Q]SARs), and also ofmaximising the use of scientific sources for NP risk assessment. From a data search carried out on 27February 2014, a total of 910 publications were retrieved from the Web of Science™ Core Collection, anda database comprising 886 records of toxicity endpoints, based on these publications, was built. The testorganisms mainly comprised bacteria, algae, yeast, protozoa, nematoda, crustacea and fish. The NPs consistedmostly of metals, metal oxides, nanocomposites and quantum dots. The data were analysed further,in order to: a) categorise each toxicity endpoint and the biological effects triggered by the NPs; b) surveythe characterisation of the NPs used; and c) assess whether the data were suitable for nano-(Q)SAR development.Despite the efforts of numerous scientific programmes on nanomaterial safety and design, ourstudy concluded that lack of data consistency prevents the use of experimental data in developing and validatingnano-(Q)SARs. Finally, an outlook on the future of nano-(Q)SAR development is provided. Show less
