Prediction model for future OHCAs based on geospatial and demographic data: An observational study.

This study used demographic data in a novel prediction model to identify areas with high risk of out-of-hospital cardiac arrest (OHCA) in order to target prehospital preparedness. We combined data from the nationwide Danish Cardiac Arrest Registry with geographical- and demographic data on a hectare level. Hectares were classified in a hierarchy according to characteristics and pooled to square kilometers (km2). Historical OHCA incidence of each hectare group was supplemented with a predicted annual risk of at least 1 OHCA to ensure future applicability. We recorded 19,090 valid OHCAs during 2016 to 2019. The mean annual OHCA rate was highest in residential areas with no point of public interest and 100 to 1000 residents per hectare (9.7/year/km2) followed by pedestrian streets with multiple shops (5.8/year/km2), areas with no point of public interest and 50 to 100 residents (5.5/year/km2), and malls with a mean annual incidence per km2 of 4.6. Other high incidence areas were public transport stations, schools and areas without a point of public interest and 10 to 50 residents. These areas combined constitute 1496 km2 annually corresponding to 3.4% of the total area of Denmark and account for 65% of the OHCA incidence. Our prediction model confirms these areas to be of high risk and outperforms simple previous incidence in identifying future risk-sites. Two thirds of out-of-hospital cardiac arrests were identified in only 3.4% of the area of Denmark. This area was easily identified as having multiple residents or having airports, malls, pedestrian shopping streets or schools. This result has important implications for targeted intervention such as automatic defibrillators available to the public. Further, demographic information should be considered when implementing such interventions.

The variability of 2D and 3D transthoracic echocardiography applied in a general population : Intermodality, inter- and intraobserver variability.

Assessment of the left ventricular (LV) function by three-dimensional echocardiography (3DE) is potentially superior to 2D echo echocardiography (2DE) for LV performance assessment. However, intra- and interobserver variation needs further investigation. We examined the intra- and interobserver variability between 2 and 3DE in a general population. In total, 150 participants from the Copenhagen City Heart Study were randomly chosen. Two observers assessed left ventricular ejection fraction (LVEF), end-diastolic (EDV) and end-systolic volumes (ESV) by 2DE and 3DE. Inter-, intraobserver and intermodality variabilities are presented as means of difference (MD), limits of agreement (LoA), coefficient of correlation (r), intraclass correlation coefficients (ICC). The lowest MD and LoA and highest r- and ICC-values was generally seen among the 3D acquisitions, with the 3D EDV interobserver as the best performing estimate (r = 0.95, ICC = 0.94). The largest MD, LoA and lowest r- and ICC-values was found in the interobserver 2D LVEF (r = 0.76, ICC = 0.63. For the intraobserver analysis, there were statistically significant differences between observations for all but 3DE EDV (p = 0.06). For interobserver analysis, there were statistically significant differences between observers for all estimates but 2DE EDV (p = 0.11), 3D ejection fraction (p = 0.9), 3DE EDV (p = 0.11) and 3D ESV (p = 0.15). Three-dimensional echocardiography is more robust and reproducible than 2DE and should be preferred for assessment of LV function.