Introduction: 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: 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 was 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.

Results: 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 CDSS can create clinical value by improving decision making.

Background: The Framingham risk models and pooled cohort equations (PCE) are widely used and advocated in guidelines for predicting 10-year risk of developing coronary heart disease (CHD) and cardiovascular disease (CVD) in the general population. Over the past few decades, these models have been extensively validated within different populations, which provided mounting evidence that local tailoring is often necessary to obtain accurate predictions. The objective is to systematically review and summarize the predictive performance of three widely advocated cardiovascular risk prediction models (Framingham Wilson 1998, Framingham ATP III 2002 and PCE 2013) in men and women separately, to assess the generalizability of performance across different subgroups and geographical regions, and to determine sources of heterogeneity in the findings across studies.

Methods: A search was performed in October 2017 to identify studies investigating the predictive performance of the aforementioned models. Studies were included if they externally validated one or more of the original models in the general population for the same outcome as the original model. We assessed risk of bias for each validation and extracted data on population characteristics and model performance. Performance estimates (observed versus expected (OE) ratio and c-statistic) were summarized using a random effects models and sources of heterogeneity were explored with meta-regression.

Results: The search identified 1585 studies, of which 38 were included, describing a total of 112 external validations. Results indicate that, on average, all models overestimate the 10-year risk of CHD and CVD (pooled OE ratio ranged from 0.58 (95% CI 0.43-0.73; Wilson men) to 0.79 (95% CI 0.60-0.97; ATP III women)). Overestimation was most pronounced for high-risk individuals and European populations. Further, discriminative performance was better in women for all models. There was considerable heterogeneity in the c-statistic between studies, likely due to differences in population characteristics.

Conclusions: The Framingham Wilson, ATP III and PCE discriminate comparably well but all overestimate the risk of developing CVD, especially in higher risk populations. Because the extent of miscalibration substantially varied across settings, we highly recommend that researchers further explore reasons for overprediction and that the models be updated for specific populations.

Objectives: A radiological risk score that determines 5-year cardiovascular disease (CVD) risk using routine care CT and patient information readily available to radiologists was previously developed. External validation in a Scottish population was performed to assess the applicability and validity of the risk score in other populations.

Methods: 2915 subjects aged ≥40 years who underwent routine clinical chest CT scanning for non-cardiovascular diagnostic indications were followed up until first diagnosis of, or death from, CVD. Using a case-cohort approach, all cases and a random sample of 20% of the participant's CT examinations were visually graded for cardiovascular calcifications and cardiac diameter was measured. The radiological risk score was determined using imaging findings, age, gender, and CT indication.

Results: Performance on 5-year CVD risk prediction was assessed. 384 events occurred in 2124 subjects during a mean follow-up of 4.25 years (0-6.4 years). The risk score demonstrated reasonable performance in the studied population. Calibration showed good agreement between actual and 5-year predicted risk of CVD. The c-statistic was 0.71 (95%CI:0.67-0.75).

Conclusions: The radiological CVD risk score performed adequately in the Scottish population offering a potential novel strategy for identifying patients at high risk for developing cardiovascular disease using routine care CT data.