Stroke recognition in medical emergency calls: A novel sensitivity definition as a basis for developing artificial intelligence decision support

Background The sensitivity of emergency medical communication centers (EMCC) for stroke detection varies widely. However, few studies offer detailed insights into the entirety of prehospital pathways in patients with stroke. Therefore, this study aimed to lay the foundation for artificial intelligence (AI) decision support tools in EMCCs by exploring their ability to detect strokes in medical emergency calls, describe a novel method for stroke sensitivity calculation in the EMCC, and identify factors associated with stroke recognition during a call. Methods In total, 1,164 patients with stroke in the catchment area of Bergen EMCC in 2018 and 2019 were included, and a dataset from the EMCC was established manually and linked with data from the Norwegian Stroke Registry (NSR) for analysis. Descriptive statistics, Chi-square test for categorical variables, Mann–Whitney U test for continuous variables, and multivariate logistic regression (LR) were performed on data obtained from patients primarily assessed by EMCC (n=838). Results Using a novel method, we found a stroke detection sensitivity of 76.8% in our study, compared to the 63.4% when using the traditional sensitivity detection method. LR analysis showed a positive association between stroke suspicion and ischemic strokes (odds ratio [OR]=0.317 [0.209–0.481]; p<0,001, with ischemic stroke as the reference) and wake-up strokes (OR=1.716 [1.110–2.653]; p=0.015). Among the NSR symptoms, only aphasia/dysarthria was positively associated with stroke suspicion (OR=1.600 [1.087–2.353]; p=0.017), while leg paresis (OR=0.609 [0.390–0.953]; p=0.009) and vertigo (OR=0.376 [0.204–0.694]; p=0.002) were negatively associated. Conclusions This study introduced a novel and more accurate method for calculating EMCC stroke sensitivity, which is relevant for developing decision support tools, such as AI. Moreover, we identified factors of particular interest for future EMCC research that are relevant to developing AI decision-support tools. Clinical trials https://clinicaltrials.gov/study/NCT04648449