Citations

Background: Most strokes and cardiovascular diseases (CVDs) are potentially preventable if their risk factors are identified and well controlled. Digital platforms, such as the PreventS-MD web app (PreventS-MD) may aid health care professionals (HCPs) in assessing and managing risk factors and promoting lifestyle changes for their patients. Methods: This is a mixed-methods cross-sectional two-phase survey using a largely positivist (quantitative and qualitative) framework. During Phase 1, a prototype of PreventS-MD was tested internationally by 59 of 69 consenting HCPs of different backgrounds, age, sex, working experience, and specialties using hypothetical data. Collected comments/suggestions from the study HCPs in Phase 1 were reviewed and implemented. In Phase 2, a near-final version of PreventS-MD was developed and tested by 58 of 72 consenting HCPs using both hypothetical and real patient (n = 10) data. Qualitative semi-structured interviews with real patients (n = 10) were conducted, and 1 month adherence to the preventive recommendations was assessed by self-reporting. The four System Usability Scale (SUS) groups of scores (0–50 unacceptable; 51–68 poor; 68–80.3 good; >80.3 excellent) were used to determine usability of PreventS-MD. Findings: Ninety-nine HCPs from 27 countries (45% from low- to middle-income countries) participated in the study, and out of them, 10 HCPs were involved in the development of PreventS before the study, and therefore were not involved in the survey. Of the remaining 89 HCPs, 69 consented to the first phase of the survey, and 59 of them completed the first phase of the survey (response rate 86%), and 58 completed the second phase of the survey (response rate 84%). The SUS scores supported good usability of the prototype (mean score = 80.2; 95% CI [77.0–84.0]) and excellent usability of the final version of PreventS-MD (mean score = 81.7; 95% CI [79.1–84.3]) in the field. Scores were not affected by the age, sex, working experience, or specialty of the HCPs. One-month follow-up of the patients confirmed the high level of satisfaction/acceptability of PreventS-MD and (100%) adherence to the recommendations. Interpretation: The PreventS-MD web app has a high level of usability, feasibility, and satisfaction by HCPs and individuals at risk of stroke/CVD. Individuals at risk of stroke/CVD demonstrated a high level of confidence and motivation in following and adhering to preventive recommendations generated by PreventS-MD.

The intima–media thickness (IMT) of carotid arteries is a reliable indicator of cardiovascular risk. IMT is usually manually measured on longitudinal B-mode ultrasound images. Many computer-based techniques for IMT measurement have been proposed to overcome the limits of manual segmentation. Most of these, however, require a certain degree of user interaction. We recently developed two automated techniques for segmentation of carotid arteries in ultrasound images and its intima–media thickness (IMT) measurement. The first technique was determined on the basis of local statistics and signal analysis (we named it CULEXsa, ie, Completely User-independent Layer EXtraction based on signal analysis). The second technique was determined on the basis of an integrated approach consisting of feature extraction, line fitting, and classification (or CALEXia, Completely Automated Layers EXtraction based on integrated approach). Both the techniques automatically traced the lumen—intima (LI) and the media—adventitia (MA) boundaries of the carotid wall. IMT was measured as the distance between LI and MA. We observed that CULEXsa offers better performance in the LI segmentation and CALEXia offers better performance in MA segmentation. The goal of this research was to estimate the IMT measurement by using a greedy approach by fusing CULEXsa and CALEXia segmentations, given the ground truth (manually traced by experts). Starting from the technique with the lower system error (CULEXsa for LI and CALEXia for MA), we iteratively swapped the vertices of the profiles until we minimized its overall distance with respect to manual boundary. The fusion boundary, consisting of points of CULEXsa and points of CALEXia, was the Greedy boundary. We used the polyline distance as a metric for both performance evaluation and error minimization. We ran the segmentation protocol over the database of 200 carotid longitudinal B-mode ultrasound images and compared the performance of all the three techniques. The mean error of the greedy technique yielded 0.42 ± 0.89 pixel (26.3 ± 55.6 μm) for the LI boundary (a 12.5 ± 5.6% improvement over CULEXsa) and 0.26 ± 0.56 pixel (16.2 ± 31.3 μm) for MA boundary (a 16.1 ± 6.7% improvement over CALEXia). IMT measurement error is 1.33 ± 0.99 pixel (83.1 ± 61.8 μm), a 3.6 ± 1.4% improvement over CULEXsa.