Pieter F Fouche1, Jestin N Carlson2, Arindam Ghosh2, Kristina M Zverinova3, Suhail A Doi4, Jon C Rittenberger5. 1. Monash University, Department of Community Emergency Health and Paramedic Practice, Australia. Electronic address: pfouche@ambulance.nsw.gov.au. 2. Allegheny Health Network, Department of Emergency Medicine, United States. 3. Paramedics Australasia, Australia. 4. Research School of Population Health, Australian National University, Canberra, Australia; College of Medicine, Qatar University, Doha, Qatar. 5. University of Pittsburgh, Department of Emergency Medicine, United States.
Abstract
BACKGROUND: Previous research demonstrates that results from observational research correlate well with results from clinical trials, and if the former are well designed these can guide clinical practice. Observational studies in cardiac arrest research are beset by confounding due to illness severity and comorbidity. We aimed to count the number of studies that utilize comorbidity and illness severity scores and indices, and to measure the change in results across analyses that adjust for scores and indices. METHODS: A systematic search of databases for cardiac arrest studies that report survival outcomes for 2015 and that utilize illness severity and comorbidity indices and scores was conducted. We quantified the proportion of studies and the change in magnitude of estimates when adjustment for indices and scores were used. RESULTS: Sixty (28%) of 213 cardiac arrest studies that report survival outcomes utilize illness severity or comorbidity indices and scores, of which 39 studies (65%) used risk scores and indices to account for the confounding effect of comorbidity or illness severity. A 14% change towards the null in the magnitude of effect sizes was apparent when models included illness severity or comorbidity adjustment (interquartile range -37.7 to 4.4). CONCLUSIONS: A small proportion of cardiac arrest studies account for illness severity and comorbidity with scores and indices, and such adjustment tend to drive estimates towards the null (no difference in groups being compared). Confounding by illness severity and comorbidity is a significant source of bias in non-randomized cardiac arrest studies.
BACKGROUND: Previous research demonstrates that results from observational research correlate well with results from clinical trials, and if the former are well designed these can guide clinical practice. Observational studies in cardiac arrest research are beset by confounding due to illness severity and comorbidity. We aimed to count the number of studies that utilize comorbidity and illness severity scores and indices, and to measure the change in results across analyses that adjust for scores and indices. METHODS: A systematic search of databases for cardiac arrest studies that report survival outcomes for 2015 and that utilize illness severity and comorbidity indices and scores was conducted. We quantified the proportion of studies and the change in magnitude of estimates when adjustment for indices and scores were used. RESULTS: Sixty (28%) of 213 cardiac arrest studies that report survival outcomes utilize illness severity or comorbidity indices and scores, of which 39 studies (65%) used risk scores and indices to account for the confounding effect of comorbidity or illness severity. A 14% change towards the null in the magnitude of effect sizes was apparent when models included illness severity or comorbidity adjustment (interquartile range -37.7 to 4.4). CONCLUSIONS: A small proportion of cardiac arrest studies account for illness severity and comorbidity with scores and indices, and such adjustment tend to drive estimates towards the null (no difference in groups being compared). Confounding by illness severity and comorbidity is a significant source of bias in non-randomized cardiac arrest studies.
Authors: Araz Rawshani; Fredrik Hessulf; Sebastian Völz; Christian Dworeck; Jacob Odenstedt; Truls Råmunddal; Geir Hirlekar; Petur Petursson; Oskar Angerås; Dan Ioanes; Anna Myredal Journal: Resusc Plus Date: 2022-08-24
Authors: Geir Hirlekar; Martin Jonsson; Thomas Karlsson; Maria Bäck; Araz Rawshani; Jacob Hollenberg; Per Albertsson; Johan Herlitz Journal: Heart Date: 2020-01-23 Impact factor: 5.994