Original Title: Incorporación del análisis genético ampliado a la evaluación del riesgo de tromboembolismo venoso y abortos espontáneos de repetición

Authors' objectives: To assess the accuracy of the clinical-genetic tool named Thrombo inCode® to identify and grade persons in the high-risk category of venous thromboembolism and/or recurrent spontaneous miscarriages, its ability to modify the healthcare decisions adopted depending on the individual risk grading established by this tool and thus appraising its effectiveness in the National Health System.

Authors' results and conclusions: The standard prediction models to grade persons as being in high or low-risk groups of suffering a venous thromboembolism or recurrent spontaneous miscarriages episode are grounded on exclusively clinical predictor components, such as, for example, “Khorana score” and “Throly score” for persons diagnosed with cancer or clinically similar models or those which include the analysis of, at least, two polymorphisms of a sole nucleotide, for the population as a whole or women with recurrent spontaneous miscarriages. The new models display a notable risk of bias, due to the lack of calibration, the selection method of the predictor factors or the incorporation of predictor factors associated to the event under study. “Thrombo inCode score” or its variants offer a statistically significant improvement in clinical scenarios, as a means of aiding diagnostic grading of persons in risk of suffering a further venous thromboembolism or recurrent spontaneous miscarriages events Its implementation is effective and at a reduced cost that to that of the former clinical genetic model with 2 polymorphisms of a sole nucleotide. The new models for diagnostic prediction analysed improve the identification of genuine positives (persons in high-risk groups would benefit from the implantation of prophylactic treatment) at the expense of increasing the number of persons who would be graded as false positives (low-risk groups, those wrongly graded as being high-risk, liable to receive treatment types that will be of no benefit to them and put them at risk of suffering side effects). Introducing a more effective and reduced cost predictive model though with the downside of notably increasing the number of false positives, must mean that, as part of the decision making process, the analysis of the risk-benefit balance of prescribing a prophylactic treatment then acquires even greater relevance, taking into account if we are treating a patient with cancer, those with a history of unprovoked venous thromboembolism or recurrent spontaneous miscarriages and, probably on an individual scale, involving the person directly in the shared decision making process. The incorporation of new models for diagnostic prediction must not be used for routine procedures, moreover in individual clinical scenarios in which patient characteristics and family background lead us to suspect that there would be potential benefit from prescribing a prophylactic treatment. Against the backdrop of tailor-made healthcare, it is of the utmost importance that the health systems offer the instruments necessary to be able to incorporate in a streamlined manner diagnostic prediction models and likewise be able to exclude in an equally swift way any foreseeable or expeditious obsolescence.

Authors' methods: Systematic review of the scientific evidence on-hand for Thrombo inCode®’s assessment. Queries were made in Pubmed, Embase, The Cochrane Library, and the CRD Database, the web for the Spanish Network of Healthcare Technologies and Services of the National Health System (RedETS) as well as the database of the International Network of Healthcare Technologies Assessment Agencies (INAHTA). An active search was performed to appraise clinical studies registered on Thrombo inCode® in ClinicalTrials. gov, the International Standard Randomised Controlled Trial Number registry, International Clinical Trials Registry Platform and EU Clinical Trials Register. The discriminative capacity, reclassification and clinical utility of the models versus former models has been estimated through the area under the curve (AUC), the net reclassification index (NRI) and incremental clinical utility (∆UC), respectively. To evaluate the methodological capability of the studies included and the risk of bias, the tool PROBAST has been used. For the economic assessment of the tool, the critical reading (FLC 3.0) given in OSTEBA has been taken

Project Status: Completed

Year Published: 2023

URL for published report: https://doi.org/10.46994/ets_24

English language abstract: An English language summary is available

Publication Type: Full HTA

Country: Spain

DOI: https://doi.org/10.46994/ets_24

Organisation Name: Health Sciences Institute in Aragon (IACS)

Contact Address: Avda, San Juan Bosco, 13, planta 2

Contact Name: María Pilar Calvo Pérez

Contact Email: direccion.iacs@aragon.es