No abstract

Background: Cardiac myxoma (CM) is the most frequent, cardiac benign tumor and is associated with enhanced risk for cerebrovascular events (CVE). Although surgical CM excision is the only curative treatment to prevent CVE recurrence, in recent reports conservative treatment with antiplatelet or anticoagulant agents in high-risk patients with CM-related CVE has been discussed.Methods: Case records at the University Hospital of Tübingen between 2005 and 2017 were screened to identify patients with CM-related CVE. Clinical features, brain and cardiac imaging findings, histological reports, applied treatments and long-term neurological outcomes were assessed.Results: 52 patients with CM were identified and among them, 13 patients with transient ischemic attack, ischemic stroke or retinal ischemia were included to the (to our knowledge) largest reported retrospective study of CM-related CVE. In all identified patients, CVE was the first manifestation of CM; 61% suffered ischemic stroke, 23% transient ischemic attack and 15% retinal ischemia. In 46% of the patients, CVE occurred under antiplatelet or anticoagulation treatment, while 23% of the patients developed recurrent CVE under bridging-antithrombotic-therapy prior to CM surgical excision. Prolonged time interval between CVE and CM-surgery was significantly associated with CVE recurrence (p = 0.021). One patient underwent i.v. thrombolysis, followed by thrombectomy, with good post-interventional outcome and no signs of hemorrhagic transformation.Discussion: Our results suggest that antiplatelet or anticoagulation treatment is no alternative to cardiac surgery in patients presenting with CM-related CVE. We found significantly prolonged time-intervals between CVE and CM surgery in patients with recurrent CVE. Therefore, we suggest that the waiting- or bridging-interval with antithrombotic therapy until curative CM excision should be kept as short as possible. Based on our data and review of the literature, we suggest that in patients with CM-related CVE, i.v. thrombolysis and/or endovascular interventions may present safe and efficacious acute treatments.

ObjectiveTo develop an NIH Stroke Scale (NIHSS)-compatible, all-in-one scale for rapid and comprehensive prehospital stroke assessment including stroke recognition, severity grading and progression monitoring as well as prediction of large vessel occlusion (LVO).MethodsEmergency medical services (EMS) personnel and stroke physicians (n=326) rated each item of the NIHSS regarding suitability for prehospital use; best rated items were included. Stroke recognition was evaluated retrospectively in 689 consecutive patients with acute stroke or stroke mimics, prediction of LVO in 741 consecutive patients with ischaemic stroke with acute vessel imaging independent of admission NIHSS score.ResultsNine of the NIHSS items were rated as ‘suitable for prehospital use.’ After excluding two items in order to increase specificity, the final scale (termed shortened NIHSS for EMS, sNIHSS-EMS) consists of ‘level of consciousness’, ‘facial palsy’, ‘motor arm/leg’, ‘sensory’, ‘language’ and ‘dysarthria’. Sensitivity for stroke recognition of the sNIHSS-EMS is 91% (95% CI 86 to 94), specificity 52% (95% CI 47 to 56). Receiver operating curve analysis revealed an optimal cut-off point for LVO prediction of ≥6 (sensitivity 70% (95% CI 65 to 76), specificity 81% (95% CI 76 to 84), positive predictive value 70 (95% CI 65 to 75), area under the curve 0.81 (95% CI 0.78 to 0.84)). Test characteristics were non-inferior to non-comprehensive scales.ConclusionsThe sNIHSS-EMS may overcome the sequential use of multiple emergency stroke scales by permitting parallel stroke recognition, severity grading and LVO prediction. Full NIHSS-item compatibility allows for evaluation of stroke progression starting at the prehospital phase.

Background Online activity‐based epidemiological surveillance and forecasting is getting more and more attention. To date, Google search volumes have not been assessed for forecasting of tick‐borne diseases. Thus, we performed an analysis of forecasting of the Lyme disease incidence based on the traditional data extended with Google Trends. Methods Data on the weekly incidence of Lyme disease in Germany from 16 June 2013 to 27 May 2018 were obtained from the database of the Robert Koch Institute. Data of Internet searches were obtained from Google Trends searching “Borreliose” in Germany for the “last 5 years” as a timespan category. Data were split into the training (from 16 June 2013 to 11 June 2017) and validation (from 12 June 2017, to 27 May 2018) data sets. A seasonal autoregressive moving average model, SARIMA (0,1,1) (0,1,1) [52] model was selected to describe the time series of the weekly Lyme incidence. After this, we added the Google Trends data as an external regressor and identified the SARIMA (0,1,1) (0,1,1) [52] model as optimal. We made predictions for the validation interval using these two models and compared predictions with the values of the validation data set. Results Forecasting for the validation timespan resulted in similar values for the models. Comparing the forecasted values with the reported ones resulted in an residual mean squared error (RMSE) of 0.3763; the mean absolute percentage error (MAPE) was 8.233 for the model without Google searches with an RMSE of 0.3732; and the MAPE was 8.17495 for the Google Trends values‐expanded model. The difference between the predictive performances was insignificant (Diebold‐Mariano Test, p‐value = 0.4152). Conclusion Google Trends data are a good correlate of the reported incidence of Lyme disease in Germany, but it failed to significantly improve the forecasting accuracy in models based on traditional data.