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In the field of hospital epidemiology, there is a need for higher-quality trials that demonstrate more conclusive results, which in turn require larger sample sizes, according to a study published in JAMA Network Open.
Hospital or healthcare epidemiology focuses on the understanding, prevention, and control of healthcare-associated infections (HAIs). Recent data have shown that approximately 1 in 25 hospitalized patients had at least 1 HAI. Because of the emergence of multidrug-resistant organisms, a critical domain of global research has focused on evaluating the effectiveness of infection control and antibiotic stewardship interventions. Cluster-randomized trial (CRT) designs are frequently used for this purpose, in which intact social units or clusters of individuals are randomly assigned to intervention groups, rather than independent individuals.
However, there is a shortage of CRTs that have published their intraclass correlation coefficient or coefficient of variation, which has made calculating prospective sample sizes difficult. Further, the lack of methodologic rigor when conducting and/or reporting power calculations in hospital epidemiology CRTs has led to a number of potentially underpowered studies. Therefore, researchers conducted a longitudinal cohort study to estimate the number of hospitals needed to adequately power parallel CRTs of interventions aimed at reducing HAIs and demonstrated how various study parameters are associated with sample size estimates in practice.
This study estimated sample size calculation parameters using national rates developed by the Centers for Disease Control and Prevention for methicillin-resistant Staphylococcus aureus (MRSA) bacteremia, central line-associated bloodstream infection (CLABSI), catheter-associated urinary tract infection (CAUTI), and Clostridioides difficile infection (CDI) from 2016. Using data from 2012 from the Benefits of Universal Glove and Gown study, MRSA and vancomycin-resistant enterococci acquisition outcomes were estimated. Data were collected from 2017 to 2018 and analyzed from 2018 to 2019. The main outcome was the calculated number of clusters needed to detect an intervention effect with adequate power.
Results suggested that to appropriately power parallel CRTs targeting infection prevention outcomes, sample sizes need to be large. For example, it was calculated that to study an intervention with a 30% decrease in daily rates and a 0.55 coefficient of variation, 73 total clusters would be needed to observe a significant effect on cases of MRSA bacteremia, and 60 for CLABSI. With a coefficient of variation of 0.70, a total of 82 clusters would be needed to effect a 30% decrease in cases of CAUTI, whereas with a coefficient of variation of 0.44, a total of 31 clusters would be needed for a reduction in cases of CDI. To observe a 30% decrease for MRSA or vancomycin-resistant enterococci acquisition, 50 or 40 total clusters were needed, respectively.
Of note, to study an intervention in which a 10% decrease in rates was expected, the following number of clusters were needed: 768 for MRSA, 875 for CAUTI, 631 for CLABSI, and 329 for CDI. To observe a 10% decrease for MRSA or vancomycin-resistant enterococci acquisition, 540 or 426 total clusters were required, respectively.
Overall, the study authors concluded that, “We hope that the findings presented herein lead to more carefully designed, definitive, controlled CRTs that are properly powered and that more studies report the parameters used to generate their sample size estimates.”
Reference
Blanco N, Harris AD, Magder L, et al. Sample size estimates for cluster-randomized trials in hospital infection control and antimicrobial stewardship. JAMA Network Open. 2019;2(10):e1912644.
