Derivation and Validation of a Prediction Model for Venous Thromboembolism in Primary Care

Abstract

Background Most episodes of venous thromboembolism (VTE) occurred in primary care. To date, no score potentially able to identify those patients who may deserve an antithrombotic prophylaxis has been developed. Aim The objective of this study is to develop and validate a prediction model for VTE in primary care. Methods Using the Health Search Database, we identified a cohort of 1,359,880 adult patients between 2002 and 2013. The date of the first General Practitioner's (GP) visit was the cohort entry date. All VTE cases (index date) observed up to December 2014 were identified. The cohort was randomly divided in a development and a validation cohort. According to nested case-cohort analysis, up to five controls were matched to their respective cases on month and year of cohort entry and duration of follow-up.The score was evaluated according to explained variance (pseudo R2) as a performance measure, ratio of predicted to observed cases as model calibration and area under the curve (AUC) as discrimination measure. Results The score was able to explain 27.9% of the variation for VTE occurrence. The calibration measure revealed a margin of error lower than 10% in 70% of the population. In terms of discrimination, AUC was 0.82 (95% confidence interval: 0.82–0.83). Results of sensitivity analyses substantially confirmed these findings. Conclusion The present score demonstrated a very good accuracy in predicting the risk of VTE in primary care. This score may be therefore implemented in clinical practice so aiding GPs in making decision on patients potentially at risk of VTE.

A Cohort Study on Influenza Vaccine and All-Cause Mortality in Older Adults: Methodological Concerns and Public Health Implications

“…it/). For these reasons, the HSD has been used as a data source in many studies and publications [15][16][17].…”

Anti-COVID-19 vaccines and rare cases of cerebral venous sinus thrombosis with thrombocytopenia: what about the pragmatic benefit/risk evaluation for still unvaccinated young women

“…Thus, to fuel the discussion on the individual benefit/risk evaluation for anti-COVID-19 vaccines and to support the public health and ethical role of vaccination against COVID-19, we used the Health Search Database (HSD) [ 19 ], a general practice research database that covers the electronic healthcare records of about 1 million patients, to quantify the individual risk of VTE among those subjects who developed CVST before vaccine availability, according to age and a validated score employed to predict such a risk [ 20 ]. Including 21 clinical risk factors, our VTE score showed good accuracy with an AUC of 0.82 (95% CI: 0.82–0.83), explaining 27.9% of the variation for VTE occurrence, and a margin of error between the predicted and observed risk of less than 10% (under or overestimation) in 70% of the validation cohort [ 20 ].…”

Predicting Venous Thromboembolism in Primary Care

“…In this issue of Thrombosis and Haemostasis, Dentali et al make a new attempt to identify predictors of VTE in primary care. 8 Their risk assessment model was derived using data from a large Italian database of more than one million adults followed by 1,100 general practitioners. After derivation and internal validation, they performed external validation in an independent cohort used by local authorities for health care assessment.…”

“…In a world where new clinical scores are constantly developed, published, and then frequently discarded as clinically irrelevant, the authors must be commended for scrutinising their risk assessment model by determining its discrimination, calibration and potential clinical benefit if it were to be used for thromboprophylaxis. 8 For the readers who are not familiar with these parameters, discrimination corresponds to the probability of correctly classifying patients into those who will and those who will not have the outcome, in this case VTE. Discrimination alone, however, has no clinical utility and is a poor method for comparing risk assessment models.…”

“…In an additional decision curve analysis, 10 the authors provided initial proof that using this model in decision-making concerning thromboprophylaxis might provide a benefit, in terms of both VTE and bleeding risk, compared with treating all patients or treating none. 8 So, how should these results be interpreted in the context of the available strategies for primary and secondary VTE prevention? Concerning primary prevention, the first important comment on the present study is that the strongest predictors of VTE in primary care were related to recent hospitalisation or to other conditions that would have received thromboprophylaxis anyway based on current standards.…”