Using Evidence to Combat Overdiagnosis and Overtreatment:Evaluating Treatments, Tests, and Disease Definitions in the Time of Too Much

Ray Moynihan and colleagues outline suggestions for improving the way that medical evidence is produced, analysed, and interpreted to avoid problems of overdiagnosis and overtreatment. Please see later in the article for the Editors' Summar

Performance of binary prediction models in high-correlation low-dimensional settings:a comparison of methods

BACKGROUND: Clinical prediction models are developed widely across medical disciplines. When predictors in such models are highly collinear, unexpected or spurious predictor-outcome associations may occur, thereby potentially reducing face-validity of the prediction model. Collinearity can be dealt with by exclusion of collinear predictors, but when there is no a priori motivation (besides collinearity) to include or exclude specific predictors, such an approach is arbitrary and possibly inappropriate. METHODS: We compare different methods to address collinearity, including shrinkage, dimensionality reduction, and constrained optimization. The effectiveness of these methods is illustrated via simulations. RESULTS: In the conducted simulations, no effect of collinearity was observed on predictive outcomes (AUC, R(2), Intercept, Slope) across methods. However, a negative effect of collinearity on the stability of predictor selection was found, affecting all compared methods, but in particular methods that perform strong predictor selection (e.g., Lasso). Methods for which the included set of predictors remained most stable under increased collinearity were Ridge, PCLR, LAELR, and Dropout. CONCLUSIONS: Based on the results, we would recommend refraining from data-driven predictor selection approaches in the presence of high collinearity, because of the increased instability of predictor selection, even in relatively high events-per-variable settings. The selection of certain predictors over others may disproportionally give the impression that included predictors have a stronger association with the outcome than excluded predictors. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41512-021-00115-5

Validation of two age dependent D-dimer cut-off values for exclusion of deep vein thrombosis in suspected elderly patients in primary care: retrospective, cross sectional, diagnostic analysis

Objective To determine whether the use of age adapted D-dimer cut-off values can be translated to primary care patients who are suspected of deep vein thrombosis

Самоподобие массивов сетевых публикаций по компьютерной вирусологии

Описан подход к организации анализа потока тематических публикаций по компьютерной вирусологии, представленных в web-пространстве. Обоснована фрактальная природа информационных потоков, описаны основные алгоритмы, применяемые в процессе исследований, а также приведены прогнозные выводы на основе свойств персистентности временных рядов.Описано підхід до організації аналізу потоку тематичних публікацій з комп’ютерної вірусології, які наведені у web-просторі. Обґрунтовано фрактальну природу інформаційних потоків, описано основні алгоритми, що застосовуються в процесі досліджень, а також наведено прогнозні висновки на базі властивостей персистентності часових рядів.An approach to the organization of the analysis of a thematic publications stream on computer virology, submitted in web-space, is described. The fractal nature of information streams is proved, the basic algorithms used during researches are described and forecasts conclusions on the basis of persistent properties of time series are given

Prediction models for development of retinopathy in people with type 2 diabetes:systematic review and external validation in a Dutch primary care setting

Aims/hypothesis: The aims of this study were to identify all published prognostic models predicting retinopathy risk applicable to people with type 2 diabetes, to assess their quality and accuracy, and to validate their predictive accuracy in a head-to-head comparison using an independent type 2 diabetes cohort. Methods: A systematic search was performed in PubMed and Embase in December 2019. Studies that met the following criteria were included: (1) the model was applicable in type 2 diabetes; (2) the outcome was retinopathy; and (3) follow-up was more than 1 year. Screening, data extraction (using the checklist for critical appraisal and data extraction for systemic reviews of prediction modelling studies [CHARMS]) and risk of bias assessment (by prediction model risk of bias assessment tool [PROBAST]) were performed independently by two reviewers. Selected models were externally validated in the large Hoorn Diabetes Care System (DCS) cohort in the Netherlands. Retinopathy risk was calculated using baseline data and compared with retinopathy incidence over 5 years. Calibration after intercept adjustment and discrimination (Harrell’s C statistic) were assessed. Results: Twelve studies were included in the systematic review, reporting on 16 models. Outcomes ranged from referable retinopathy to blindness. Discrimination was reported in seven studies with C statistics ranging from 0.55 (95% CI 0.54, 0.56) to 0.84 (95% CI 0.78, 0.88). Five studies reported on calibration. Eight models could be compared head-to-head in the DCS cohort (N = 10,715). Most of the models underestimated retinopathy risk. Validating the models against different severities of retinopathy, C statistics ranged from 0.51 (95% CI 0.49, 0.53) to 0.89 (95% CI 0.88, 0.91). Conclusions/interpretation: Several prognostic models can accurately predict retinopathy risk in a population-based type 2 diabetes cohort. Most of the models include easy-to-measure predictors enhancing their applicability. Tailoring retinopathy screening frequency based on accurate risk predictions may increase the efficiency and cost-effectiveness of diabetic retinopathy care. Registration: PROSPERO registration ID CRD42018089122

Key challenges in normal tissue complication probability model development and validation:towards a comprehensive strategy

Normal Tissue Complication Probability (NTCP) models can be used for treatment plan optimisation and patient selection for emerging treatment techniques. We discuss and suggest methodological approaches to address key challenges in NTCP model development and validation, including: missing data, non-linear response relationships, multicollinearity between predictors, overfitting, generalisability and the prediction of multiple complication grades at multiple time points. The methodological approaches chosen are aimed to improve the accuracy, transparency and robustness of future NTCP-models. We demonstrate our methodological approaches using clinical data

Comprehensive toxicity risk profiling in radiation therapy for head and neck cancer:A new concept for individually optimised treatment

Background and purpose: A comprehensive individual toxicity risk profile is needed to improve radiation treatment optimisation, minimising toxicity burden, in head and neck cancer (HNC) patients. We aimed to develop and externally validate NTCP models for various toxicities at multiple time points. Materials and methods: Using logistic regression, we determined the relationship between normal tissue irradiation and the risk of 22 toxicities at ten time points during and after treatment in 750 HNC patients. The toxicities involved swallowing, salivary, mucosal, speech, pain and general complaints. Studied pre-dictors included patient, tumour and treatment characteristics and dose parameters of 28 organs. The resulting NTCP models were externally validated in 395 HNC patients. Results: The NTCP models involved 14 organs that were associated with at least one toxicity. The oral cavity was the predominant organ, associated with 12 toxicities. Other important organs included the parotid and submandibular glands, buccal mucosa and swallowing muscles. In addition, baseline toxicity, treatment modality, and tumour site were common predictors of toxicity. The median discrimination performance (AUC) of the models was 0.71 (interquartile range: 0.68-0.75) at internal validation and 0.67 (interquartile range: 0.62-0.71) at external validation. Conclusion: We established a comprehensive individual toxicity risk profile that provides essential insight into how radiation exposure of various organs translates into multiple acute and late toxicities. This comprehensive understanding of radiation-induced toxicities enables a new radiation treatment optimisation concept that balances multiple toxicity risks simultaneously and minimises the overall tox-icity burden for an individual HNC patient who needs to undergo radiation treatment. (C) 2021 The Author(s). Published by Elsevier B.V

Integrated management of atrial fibrillation in primary care:results of the ALL-IN cluster randomized trial

Aims To evaluate whether integrated care for atrial. fibrillation (AF) can be safely orchestrated in primary care. Methods and results The ALL-IN trial was a cluster randomized, open-label, pragmatic non-inferiority trial performed in primary care practices in the Netherlands. We randomized 26 practices: 15 to the integrated care intervention and 11 to usual care. The integrated care intervention consisted of (i) quarterly AF check-ups by trained nurses in primary care, also focusing on possibly interfering comorbidities, (ii) monitoring of anticoagulation therapy in primary care, and finally (iii) easy-access availability of consultations from cardiologists and anticoagulation clinics. The primary endpoint was all-cause mortality during 2 years of follow-up. In the intervention arm, 527 out of 941 eligible AF patients aged >65 years provided informed consent to undergo the intervention. These 527 patients were compared with 713 AF patients in the control arm receiving usual care. Median age was 77 (interquartile range 72-83) years. The all-cause mortality rate was 3.5 per 100 patient-years in the intervention arm vs. 6.7 per 100 patient-years in the control arm [adjusted hazard ratio (HR) 0.55; 95% confidence interval (CI) 0.37-0.82]. For non cardiovascular mortality, the adjusted HR was 0.47 (95% CI 0.27-0.82). For other adverse events, no statistically significant differences were observed. Conclusion In this cluster randomized trial, integrated care for elderly AF patients in primary care showed a 45% reduction in all-cause mortality when compared with usual care