[Targeted therapy:the benefit of new oncological tests].

Voor vele kankervormen komen doelgerichte behandelingen beschikbaar, waarvoor op grond van tumoreigenschappen ook rationele keuzes gemaakt kunnen worden.Er is grote behoefte aan adequate biomarkers die het effect van doelgerichte therapie bij individuele kankerpatiënten kunnen voorspellen, om daarmee de juiste oncologische behandeling voor de juiste patiënt te kunnen bepalen. Zo kunnen nutteloze behandelingen en onnodige bijwerkingen vermeden worden, en kosten worden gereduceerd.Bij borstkanker zijn de oestrogeenreceptor (ER) en de humane epidermale groeifactorreceptor 2 (HER2) voorbeelden van gestandaardiseerde, in gerandomiseerde onderzoeken gevalideerde, predictieve testen van behandeleffecten.Voor genexpressieprofielen die samenhangen met tumorgroei worden ook gerandomiseerde onderzoeksdata verwacht.Het kwantificeren van de predictieve waarde van testen op verwachte behandeleffecten in gerandomiseerde studies is kostbaar en tijdrovend. Gezien de toename van doelgerichte medicijnen en diagnostische en prognostische technieken, wordt in allerlei domeinen gezocht naar alternatieve onderzoeksopzetten die kunnen leiden tot snellere en efficiëntere bewijsvoering.An increasing number of targeted drug treatments are becoming available for many types of cancer. There is a great need for adequate biomarkers that can predict the effect of targeted therapy in individual cancer patients, in order to determine the correct oncological treatment per patient. This way, non-effective treatments can be spared, side-effects avoided, and costs reduced. Oestrogen receptor (ER) and the human epidermal growth factor receptor 2 (HER2) are examples of standardized tests for breast cancer that have been validated in randomised studies. Data from randomised studies is also expected for gene expression profiles that correlate with tumour growth. Quantifying the predictive value of tests for anticipated treatment effects is costly and time-consuming. Given the increasing availability of targeted agents and diagnostic and prognostic techniques, alternative clinical study designs that can lead to quicker and more efficient verification are being sought in many different domains.</p

Prognostic models for radiation-induced complications after radiotherapy in head and neck cancer patients

Objectives: This is a protocol for a Cochrane Review (prognosis). The objectives are as follows:. Primary objective The review question is “Which prognostic models are available to predict the risk of radiation-induced side effects after radiation exposure to patients with head and neck cancer, what is their quality, and what is their predictive performance?”. Investigation of sources of heterogeneity between studies We will assess sources of heterogeneity among the prognostic models developed in the eligible studies. The potential sources are study population (e.g. site/stage of cancer, the use of other treatment [surgery and chemotherapy]), predictors, definition and incidence of the predicted outcomes, and prediction horizons. If there are multiple validation studies for the same model, the same sources of between-study heterogeneity will be investigated

Propensity-based standardization to enhance the validation and interpretation of prediction model discrimination for a target population

External validation of the discriminative ability of prediction models is of key importance. However, the interpretation of such evaluations is challenging, as the ability to discriminate depends on both the sample characteristics (ie, case-mix) and the generalizability of predictor coefficients, but most discrimination indices do not provide any insight into their respective contributions. To disentangle differences in discriminative ability across external validation samples due to a lack of model generalizability from differences in sample characteristics, we propose propensity-weighted measures of discrimination. These weighted metrics, which are derived from propensity scores for sample membership, are standardized for case-mix differences between the model development and validation samples, allowing for a fair comparison of discriminative ability in terms of model characteristics in a target population of interest. We illustrate our methods with the validation of eight prediction models for deep vein thrombosis in 12 external validation data sets and assess our methods in a simulation study. In the illustrative example, propensity score standardization reduced between-study heterogeneity of discrimination, indicating that between-study variability was partially attributable to case-mix. The simulation study showed that only flexible propensity-score methods (allowing for non-linear effects) produced unbiased estimates of model discrimination in the target population, and only when the positivity assumption was met. Propensity score-based standardization may facilitate the interpretation of (heterogeneity in) discriminative ability of a prediction model as observed across multiple studies, and may guide model updating strategies for a particular target population. Careful propensity score modeling with attention for non-linear relations is recommended

What influences the outcome of active disinvestment processes in healthcare? A qualitative interview study on five recent cases of active disinvestment

Background: Recent attempts of active disinvestment (i.e. withdrawal of reimbursement by means of a policy decision) of reimbursed healthcare interventions in the Netherlands have differed in their outcome: some attempts were successful, with interventions actually being disinvested. Other attempts were terminated at some point, implying unsuccessful disinvestment. This study aimed to obtain insight into recent active disinvestment processes, and to explore what aspects affect their outcome. Methods: Semi-structured interviews were conducted from January to December 2018 with stakeholders (e.g. patients, policymakers, physicians) who were involved in the policy process of five cases for which the full or partial withdrawal of reimbursement was considered in the Netherlands between 2007 and 2017: benzodiazepines, medication for Fabry disease, quit smoking programme, psychoanalytic therapy and maternity care assistance. These cases covered both interventions that were eventually disinvested and interventions for which reimbursement was maintained after consideration. Interviews were transcribed verbatim, double coded and analyzed using thematic analysis. Results: The 37 interviews showed that support for disinvestment from stakeholders, especially from healthcare providers and policymakers, strongly affected the outcome of the disinvestment process. Furthermore, the institutional role of stakeholders as legitimized by the Dutch health insurance system, their financial interests in maintaining or discontinuing reimbursement, and the possibility to relieve the consequences of disinvestment for current patients affected the outcome of the disinvestment process as well. A poor organization of patient groups may make it difficult for patients to exert pressure, which may contribute to successful disinvestment. No evidence was found of a consistent role of the formal Dutch package criteria (i.e. effectiveness, cost-effectiveness, necessity and feasibility) in active disinvestment processes. Conclusions: Contextual factors as well as the possibility to relieve the consequences of disinvestment for current patients are important determinants of the outcome of active disinvestment processes. These results provide insight into active disinvestment processes and their determinants, and provide guidance to policymakers for a potentially more successful approach for future active disinvestment processes

Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models

Background and ObjectivesWe sought to summarize the study design, modelling strategies, and performance measures reported in studies on clinical prediction models developed using machine learning techniques.MethodsWe search PubMed for articles published between 01/01/2018 and 31/12/2019, describing the development or the development with external validation of a multivariable prediction model using any supervised machine learning technique. No restrictions were made based on study design, data source, or predicted patient-related health outcomes.ResultsWe included 152 studies, 58 (38.2% [95% CI 30.8–46.1]) were diagnostic and 94 (61.8% [95% CI 53.9–69.2]) prognostic studies. Most studies reported only the development of prediction models (n = 133, 87.5% [95% CI 81.3–91.8]), focused on binary outcomes (n = 131, 86.2% [95% CI 79.8–90.8), and did not report a sample size calculation (n = 125, 82.2% [95% CI 75.4–87.5]). The most common algorithms used were support vector machine (n = 86/522, 16.5% [95% CI 13.5–19.9]) and random forest (n = 73/522, 14% [95% CI 11.3–17.2]). Values for area under the Receiver Operating Characteristic curve ranged from 0.45 to 1.00. Calibration metrics were often missed (n = 494/522, 94.6% [95% CI 92.4–96.3]).ConclusionOur review revealed that focus is required on handling of missing values, methods for internal validation, and reporting of calibration to improve the methodological conduct of studies on machine learning–based prediction models

The use of imputation in clinical decision support systems: a cardiovascular risk management pilot vignette study among clinicians

Aims: A major challenge of the use of prediction models in clinical care is missing data. Real-time imputation may alleviate this. However, to what extent clinicians accept this solution remains unknown. We aimed to assess acceptance of real-time imputation for missing patient data in a clinical decision support system (CDSS) including 10-year cardiovascular absolute risk for the individual patient. Methods and results: We performed a vignette study extending an existing CDSS with the real-time imputation method joint modelling imputation (JMI). We included 17 clinicians to use the CDSS with three different vignettes, describing potential use cases (missing data, no risk estimate; imputed values, risk estimate based on imputed data; complete information). In each vignette, missing data were introduced to mimic a situation as could occur in clinical practice. Acceptance of end-users was assessed on three different axes: clinical realism, comfortableness, and added clinical value. Overall, the imputed predictor values were found to be clinically reasonable and according to the expectations. However, for binary variables, use of a probability scale to express uncertainty was deemed inconvenient. The perceived comfortableness with imputed risk prediction was low, and confidence intervals were deemed too wide for reliable decision-making. The clinicians acknowledged added value for using JMI in clinical practice when used for educational, research, or informative purposes. Conclusion: Handling missing data in CDSS via JMI is useful, but more accurate imputations are needed to generate comfort in clinicians for use in routine care. Only then can CDSS create clinical value by improving decision-making

Safety of off-label dose reduction of non-vitamin K antagonist oral anticoagulants in patients with atrial fibrillation

Aim: To investigate the effects of off-label non-vitamin K oral anticoagulant (NOAC) dose reduction compared with on-label standard dosing in atrial fibrillation (AF) patients in routine care. Methods: Population-based cohort study using data from the United Kingdom Clinical Practice Research Datalink, comparing adults with non-valvular AF receiving an off-label reduced NOAC dose to patients receiving an on-label standard dose. Outcomes were ischaemic stroke, major/non-major bleeding and mortality. Inverse probability of treatment weighting and inverse probability of censoring weighting on the propensity score were applied to adjust for confounding and informative censoring. Results: Off-label dose reduction occurred in 2466 patients (8.0%), compared with 18 108 (58.5%) on-label standard-dose users. Median age was 80 years (interquartile range [IQR] 73.0-86.0) versus 72 years (IQR 66-78), respectively. Incidence rates were higher in the off-label dose reduction group compared to the on-label standard dose group, for ischaemic stroke (0.94 vs 0.70 per 100 person years), major bleeding (1.48 vs 0.83), non-major bleeding (6.78 vs 6.16) and mortality (10.12 vs 3.72). Adjusted analyses resulted in a hazard ratio of 0.95 (95% confidence interval [CI] 0.57-1.60) for ischaemic stroke, 0.88 (95% CI 0.57-1.35) for major bleeding, 0.81 (95% CI 0.67-0.98) for non-major bleeding and 1.34 (95% CI 1.12-1.61) for mortality. Conclusion: In this large population-based study, the hazards for ischaemic stroke and major bleeding were low, and similar in AF patients receiving an off-label reduced NOAC dose compared with on-label standard dose users, while non-major bleeding risk appeared to be lower and mortality risk higher. Caution towards prescribing an off-label reduced NOAC dose is therefore required

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD):Explanation and Elaboration. Translation into Russian

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.</p

Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA)

Most clinical specialties have a plethora of studies that develop or validate one or more prediction models, for example, to inform diagnosis or prognosis. Having many prediction model studies in a particular clinical field motivates the need for systematic reviews and meta-analyses, to evaluate and summarise the overall evidence available from prediction model studies, in particular about the predictive performance of existing models. Such reviews are fast emerging, and should be reported completely, transparently, and accurately. To help ensure this type of reporting, this article describes a new reporting guideline for systematic reviews and meta-analyses of prediction model research