To cite this article: Hendriksen JMT, Geersing GJ, Moons KGM, de Groot JAH. Diagnostic and prognostic prediction models. J Thromb Haemost 2013; 11 (Suppl. 1): 129-41.Summary. Risk prediction models can be used to estimate the probability of either having (diagnostic model) or developing a particular disease or outcome (prognostic model). In clinical practice, these models are used to inform patients and guide therapeutic management. Examples from the field of venous thrombo-embolism (VTE) include the Wells rule for patients suspected of deep venous thrombosis and pulmonary embolism, and more recently prediction rules to estimate the risk of recurrence after a first episode of unprovoked VTE. In this paper, the three phases that are recommended before a prediction model may be used in daily practice are described: development, validation, and impact assessment. In the development phase, the focus is on model development commonly using a multivariable logistic (diagnostic) or survival (prognostic) regression analysis. The performance of the developed model is expressed by discrimination, calibration and (re-) classification. In the validation phase, the developed model is tested in a new set of patients using these same performance measures. This is important, as model performance is commonly poorer in a new set of patients, e.g. due to case-mix or domain differences. Finally, in the impact phase the ability of a prediction model to actually guide patient management is evaluated. Whereas in the development and validation phase single cohort designs are preferred, this last phase asks for comparative designs, ideally randomized designs; therapeutic management and outcomes after using the prediction model is compared to a control group not using the model (e.g. usual care).

ObjeCtiveTo validate all diagnostic prediction models for ruling out pulmonary embolism that are easily applicable in primary care. DesignSystematic review followed by independent external validation study to assess transportability of retrieved models to primary care medicine. setting 300 general practices in the Netherlands. PartiCiPantsIndividual patient dataset of 598 patients with suspected acute pulmonary embolism in primary care. Main OutCOMe MeasuresDiscriminative ability of all models retrieved by systematic literature search, assessed by calculation and comparison of C statistics. After stratification into groups with high and low probability of pulmonary embolism according to pre-specified model cut-offs combined with qualitative D-dimer test, sensitivity, specificity, efficiency (overall proportion of patients with low probability of pulmonary embolism), and failure rate (proportion of pulmonary embolism cases in group of patients with low probability) were calculated for all models. results Ten published prediction models for the diagnosis of pulmonary embolism were found. Five of these models could be validated in the primary care dataset: the original Wells, modified Wells, simplified Wells, revised Geneva, and simplified revised Geneva models. Discriminative ability was comparable for all models (range of C statistic 0.75-0.80). Sensitivity ranged from 88% (simplified revised Geneva) to 96% (simplified Wells) and specificity from 48% (revised Geneva) to 53% (simplified revised Geneva). Efficiency of all models was between 43% and 48%. Differences were observed between failure rates, especially between the simplified Wells and the simplified revised Geneva models (failure rates 1.2% (95% confidence interval 0.2% to 3.3%) and 3.1% (1.4% to 5.9%), respectively; absolute difference −1.98% (−3.33% to −0.74%)). Irrespective of the diagnostic prediction model used, three patients were incorrectly classified as having low probability of pulmonary embolism; pulmonary embolism was diagnosed only after referral to secondary care. COnClusiOnsFive diagnostic pulmonary embolism prediction models that are easily applicable in primary care were validated in this setting. Whereas efficiency was comparable for all rules, the Wells rules gave the best performance in terms of lower failure rates.

ObjectivesTo evaluate the extent of delay in the diagnosis of pulmonary embolism (PE) in primary care, and to identify determinants that are associated with such diagnostic delay.DesignRetrospective observational study.Setting6 primary care practices across the Netherlands.ParticipantsData from patients with an objectively confirmed diagnosis of PE (International Classification of Primary Care (ICPC) code K93) up to June 2015 were extracted from the electronic medical records. For all these PE events, we reviewed all consultations with their general practitioner (GP) and scored any signs and symptoms that could be attributed to PE in the 3 months prior to the event. Also, we documented actual comorbidity and the diagnosis considered initially.Primary and secondary outcome measuresDelay was defined as a time gap of >7 days between the first potentially PE-related contact with the GP and the final PE diagnosis. Multivariable logistic regression analysis was performed to identify independent determinants for delay.ResultsIn total, 180 incident PE cases were identified, of whom 128 patients had 1 or more potential PE-related contact with their GP within the 3 months prior to the diagnosis. Based on our definition, in 33 of these patients (26%), diagnostic delay was observed. Older age (age >75 years; OR 5.1 (95% CI 1.8 to 14.1)) and the absence of chest symptoms (ie, chest pain or pain on inspiration; OR 5.4 (95% CI 1.9 to 15.2)) were independent determinants for diagnostic delay. A respiratory tract infection prior to the PE diagnosis was reported in 13% of cases without delay, and in 33% of patients with delay (p=0.008).ConclusionsDiagnostic delay of more than 7 days in the diagnosis of PE is common in primary care, especially in the elderly, and if chest symptoms, like pain on inspiration, are absent.

Background Claims of influenza vaccination increasing COVID‐19 risk are circulating. Within the I‐MOVE‐COVID‐19 primary care multicentre study, we measured the association between 2019‐20 influenza vaccination and COVID‐19. Methods We conducted a multicentre test‐negative case‐control study at primary care level, in study sites in five European countries, from March to August 2020. Patients presenting with acute respiratory infection were swabbed, with demographic, 2019‐20 influenza vaccination and clinical information documented. Using logistic regression, we measured the adjusted odds ratio (aOR), adjusting for study site and age, sex, calendar time, presence of chronic conditions. The main analysis included patients swabbed ≤7 days after onset from the three countries with <15% of missing influenza vaccination. In secondary analyses, we included five countries, using multiple imputation with chained equations to account for missing data. Results We included 257 COVID‐19 cases and 1631 controls in the main analysis (three countries). The overall aOR between influenza vaccination and COVID‐19 was 0.93 (95% CI: 0.66‐1.32). The aOR was 0.92 (95% CI: 0.58‐1.46) and 0.92 (95% CI: 0.51‐1.67) among those aged 20‐59 and ≥60 years, respectively. In secondary analyses, we included 6457 cases and 69 272 controls. The imputed aOR was 0.87 (95% CI: 0.79‐0.95) among all ages and any delay between swab and symptom onset. Conclusions There was no evidence that COVID‐19 cases were more likely to be vaccinated against influenza than controls. Influenza vaccination should be encouraged among target groups for vaccination. I‐MOVE‐COVID‐19 will continue documenting influenza vaccination status in 2020‐21, in order to learn about effects of recent influenza vaccination.

PURPOSE Diagnostic prediction models such as the Wells rule can be used for safely ruling out pulmonary embolism (PE) when it is suspected. A physician's own probability estimate ("gestalt"), however, is commonly used instead. We evaluated the diagnostic performance of both approaches in primary care. METHODSFamily physicians estimated the probability of PE on a scale of 0% to 100% (gestalt) and calculated the Wells rule score in 598 patients with suspected PE who were thereafter referred to secondary care for definitive testing. We compared the discriminative ability (c statistic) of both approaches. Next, we stratified patients into PE risk categories. For gestalt, a probability of less than 20% plus a negative point-of-care d-dimer test indicated low risk; for the Wells rule, we used a score of 4 or lower plus a negative d-dimer test. We compared sensitivity, specificity, efficiency (percentage of low-risk patients in total cohort), and failure rate (percentage of patients having PE within the low-risk category).RESULTS With 3 months of follow-up, 73 patients (12%) were confirmed to have venous thromboembolism (a surrogate for PE at baseline). The c statistic was 0.77 (95% CI, 0.70-0.83) for gestalt and 0.80 (95% CI, 0.75-0.86) for the Wells rule. Gestalt missed 2 out of 152 low-risk patients (failure rate = 1.3%; 95% CI, 0.2%-4.7%) with an efficiency of 25% (95% CI, 22%-29%); the Wells rule missed 4 out of 272 low-risk patients (failure rate = 1.5%; 95% CI, 0.4%-3.7%) with an efficiency of 45% (95% CI, 41%-50%).CONCLUSIONS Combined with d-dimer testing, both gestalt using a cutoff of less than 20% and the Wells rule using a score of 4 or lower are safe for ruling out PE in primary care. The Wells rule is more efficient, however, and PE can be ruled out in a larger proportion of suspected cases.