AIM: Fast recognition of out-of-hospital cardiac arrest (OHCA) by dispatchers might increase survival. The aim of this observational study of emergency calls was to (1) examine whether a machine learning framework (ML) can increase the proportion of calls recognizing OHCA within the first minute compared with dispatchers, (2) present the performance of ML with different false positive rate (FPR) settings, (3) examine call characteristics influencing OHCA recognition. METHODS: ML can be configured with different FPR settings, i.e., more or less inclined to suspect an OHCA depending on the predefined setting. ML OHCA recognition within the first minute is evaluated with a 1.5 FPR as the primary endpoint, and other FPR settings as secondary endpoints. ML was exposed to a random sample of emergency calls from 2018. Voice logs were manually audited to evaluate dispatchers time to recognition. RESULTS: Of 851 OHCA calls, the ML recognized 36% (n = 305) within 1 min compared with 25% (n = 213) by dispatchers. The recognition rate at any time during the call was 86% for ML and 84% for dispatchers, with a median time to recognition of 72 versus 94 s. OHCA recognized by both ML and dispatcher showed a 28 s mean difference in favour of ML (P < 0.001). ML with higher FPR settings reduced recognition times. CONCLUSION: ML recognized a higher proportion of OHCA within the first minute compared with dispatchers and has the potential to be a supportive tool during emergency calls. The optimal FPR settings need to be evaluated in a prospective study.
AIM: Fast recognition of out-of-hospital cardiac arrest (OHCA) by dispatchers might increase survival. The aim of this observational study of emergency calls was to (1) examine whether a machine learning framework (ML) can increase the proportion of calls recognizing OHCA within the first minute compared with dispatchers, (2) present the performance of ML with different false positive rate (FPR) settings, (3) examine call characteristics influencing OHCA recognition. METHODS: ML can be configured with different FPR settings, i.e., more or less inclined to suspect an OHCA depending on the predefined setting. ML OHCA recognition within the first minute is evaluated with a 1.5 FPR as the primary endpoint, and other FPR settings as secondary endpoints. ML was exposed to a random sample of emergency calls from 2018. Voice logs were manually audited to evaluate dispatchers time to recognition. RESULTS: Of 851 OHCA calls, the ML recognized 36% (n = 305) within 1 min compared with 25% (n = 213) by dispatchers. The recognition rate at any time during the call was 86% for ML and 84% for dispatchers, with a median time to recognition of 72 versus 94 s. OHCA recognized by both ML and dispatcher showed a 28 s mean difference in favour of ML (P < 0.001). ML with higher FPR settings reduced recognition times. CONCLUSION: ML recognized a higher proportion of OHCA within the first minute compared with dispatchers and has the potential to be a supportive tool during emergency calls. The optimal FPR settings need to be evaluated in a prospective study.
Authors: Kuan-Chen Chin; Yu-Chia Cheng; Wen-Chu Chiang; Albert Y Chen; Jen-Tang Sun; Chih-Yen Ou; Chun-Hua Hu; Ming-Chi Tsai; Matthew Huei-Ming Ma Journal: J Med Internet Res Date: 2022-06-10 Impact factor: 7.076
Authors: Fredrik Byrsell; Andreas Claesson; Martin Jonsson; Mattias Ringh; Leif Svensson; Per Nordberg; Sune Forsberg; Jacob Hollenberg; Anette Nord Journal: Resusc Plus Date: 2021-12-24