Joonghee Kim1, Kyuseok Kim2, Clifton W Callaway3, Kibbeum Doh4, Jungho Choi2, Jongdae Park2, You Hwan Jo2, Jae Hyuk Lee2. 1. Department of Emergency Medicine, Seoul National University Bundang Hospital, 166 Gumi-ro, Bundang-gu, Seongnam-si, Gyeonggi-do 463-707, Republic of Korea. Electronic address: dremkks@snubh.org. 2. Department of Emergency Medicine, Seoul National University Bundang Hospital, 166 Gumi-ro, Bundang-gu, Seongnam-si, Gyeonggi-do 463-707, Republic of Korea. 3. Department of Emergency Medicine, University of Pittsburgh, Iroquois Building, Suite 400 A, 3600 Forbes Avenue, Pittsburgh, PA 15261, United States. 4. Medical Device Research and Development Center, Seoul National University Bundang Hospital, Bundang-gu, Seongnam-si, Gyeonggi-do 463-707, Republic of Korea.
Abstract
PURPOSE: The probability of the return of spontaneous circulation (ROSC) and subsequent favourable outcomes changes dynamically during advanced cardiac life support (ACLS). We sought to model these changes using time-to-event analysis in out-of-hospital cardiac arrest (OHCA) patients. METHODS: Adult (≥18 years old), non-traumatic OHCA patients without prehospital ROSC were included. Utstein variables and initial arterial blood gas measurements were used as predictors. The incidence rate of ROSC during the first 30min of ACLS in the emergency department (ED) was modelled using spline-based parametric survival analysis. Conditional probabilities of subsequent outcomes after ROSC (1-week and 1-month survival and 6-month neurologic recovery) were modelled using multivariable logistic regression. The ROSC and conditional probability models were then combined to estimate the likelihood of achieving ROSC and subsequent outcomes by providing k additional minutes of effort. RESULTS: A total of 727 patients were analyzed. The incidence rate of ROSC increased rapidly until the 10th minute of ED ACLS, and it subsequently decreased. The conditional probabilities of subsequent outcomes after ROSC were also dependent on the duration of resuscitation with odds ratios for 1-week and 1-month survival and neurologic recovery of 0.93 (95% CI: 0.90-0.96, p<0.001), 0.93 (0.88-0.97, p=0.001) and 0.93 (0.87-0.99, p=0.031) per 1-min increase, respectively. Calibration testing of the combined models showed good correlation between mean predicted probability and actual prevalence. CONCLUSIONS: The probability of ROSC and favourable subsequent outcomes changed according to a multiphasic pattern over the first 30min of ACLS, and modelling of the dynamic changes was feasible.
PURPOSE: The probability of the return of spontaneous circulation (ROSC) and subsequent favourable outcomes changes dynamically during advanced cardiac life support (ACLS). We sought to model these changes using time-to-event analysis in out-of-hospital cardiac arrest (OHCA) patients. METHODS: Adult (≥18 years old), non-traumatic OHCApatients without prehospital ROSC were included. Utstein variables and initial arterial blood gas measurements were used as predictors. The incidence rate of ROSC during the first 30min of ACLS in the emergency department (ED) was modelled using spline-based parametric survival analysis. Conditional probabilities of subsequent outcomes after ROSC (1-week and 1-month survival and 6-month neurologic recovery) were modelled using multivariable logistic regression. The ROSC and conditional probability models were then combined to estimate the likelihood of achieving ROSC and subsequent outcomes by providing k additional minutes of effort. RESULTS: A total of 727 patients were analyzed. The incidence rate of ROSC increased rapidly until the 10th minute of ED ACLS, and it subsequently decreased. The conditional probabilities of subsequent outcomes after ROSC were also dependent on the duration of resuscitation with odds ratios for 1-week and 1-month survival and neurologic recovery of 0.93 (95% CI: 0.90-0.96, p<0.001), 0.93 (0.88-0.97, p=0.001) and 0.93 (0.87-0.99, p=0.031) per 1-min increase, respectively. Calibration testing of the combined models showed good correlation between mean predicted probability and actual prevalence. CONCLUSIONS: The probability of ROSC and favourable subsequent outcomes changed according to a multiphasic pattern over the first 30min of ACLS, and modelling of the dynamic changes was feasible.
Authors: Dong Keon Lee; Eugi Jung; You Hwan Jo; Joonghee Kim; Jae Hyuk Lee; Seung Min Park; Yu Jin Kim Journal: Emerg Med Int Date: 2020-06-29 Impact factor: 1.112