hospital variability of antibiotic use in hct 801


table 2. Spearman Correlation Coefficient Between Hospital Antibiotic Use, Days of Significant Illness, and Mortality Variable


Days of significant illness 30-day mortality 0.167 −0.396


.410 .040


0.467


carbapenems (meropenem, imipenem-cilastin, ertapenem, and doripenem). bVancomycin, linezolid, and daptomycin.


inclusion of these factors, statistically significant between- hospital variation remained for all ME models (Table 3). The full details of the ME models are provided in Supple-


mental Table 3. Hospital volume was negatively associated with antipseudomonal antibiotic use (incident rate ratio, 0.973 per 1,000 patients; P = .04). No other statistically significant associations were identified.


discussion


The study results demonstrate that HCT patients at free- standing children’s hospitals are frequently exposed to broad- spectrum antibiotics during the immediate posttransplant period. However, significant between-hospital variability in rates of broad-spectrum antibiotic prescribing exists, even after adjusting for patient characteristics. These analyses did not identify any specific patient-level or hospital-level drivers of variability. Overall, 90% of patients received at least 1 antipseudomonal


antibiotic, and>65% received a gram-positive antibiotic. Similar rates of antibiotic utilization have been reported among adult transplant patients.6 There was substantial variability in DOTs across hospitals. A 2.2-fold increase existed between the hospitals with the lowest and highest utilization for antipseudomonal antibiotics, and there was a 5.7-fold increase for gram-positive antibiotic use. Although this range of antibiotic utilization across institutions is consistent with reports for patients with other diseases,2,5–8,13 this degree of variability in a patient population where infection risk should be consistent from one hospital to the next is striking. While these analyses cannot determine appropriate antibiotic prescribing, the range suggests that some hospitals are either undertreating patients or, conversely, are exposing patients to antibiotics unnecessarily. We have shown here (1) a low mortality rate and (2) no association between antibiotic utili- zation and mortality. Both findings argue against under- treatment. Our analyses did not identify any statistically significant predictors antibiotic utilization, including patient age or ICU-level care, which are predictors of use in other studies.2,8 The persistence in statistically significant utilization varia-


bility even after adjusting for patient and hospital covariates suggests that patient need does not explain this variance. Instead, we hypothesize that individual physician preference or practice standards developed by local transplant groups drive this differential antibiotic utilization. Physician or hospital


practices may reflect local antibiograms, institutional stew- ardship initiatives, hospital formulary choices, or the reliance of transplant groups on historical practice outside the influ- ence of a stewardship program.26 Data regarding physician- or hospital-specific practice patterns were not available in this dataset so this hypothesis could not be evaluated. However, it should be considered in future investigations. The inverse correlation between antibiotic class and DSIwas


notable. It is possible that starting antipseudomonal agents early and continuing them through the duration of febrile neutropenia aborts downstream days of severe illness, leading to the negative correlation between antipseudomonal anti- biotics and DSI. Conversely, vancomycin use was positively associated with DSI and likely indicates that some hospitals reserve vancomycin for critically ill patients. However, carba- penems are often employed for critical illness, so we might have expected a similar association between carbapenems and DSI, yet no correlation was identified. These relationships deserve further exploration. Ultimately, these findings demonstrate that HCT recipients


have a differential exposure to antibiotics that is not explained by patient- or hospital-level characteristics. This variability presents an opportunity for standardization and education on appropriate use via antimicrobial stewardship interventions. No specific recommendations exist for stewardship in trans- plant units.26 Options for intervention recommended by the 2016 IDSA guidelines include prospective audit of antibiotic prescriptions with feedback to individual prescribers and formulary restriction.27 Both strategies have been successfully implemented in an adult hematology/oncology and transplant units.28–30 An alternative intervention would provide iterative feedback to institutions based on hospital-level data like ours, enabling stewardship programs to benchmark how their quantity and composition of antibiotic utilization compare to peer institutions for similar patients. The absence of correla- tion between antibiotic utilization and outcomes, such as mortality, suggests that initial targets for utilization should be at or below the median across institutions. Publication of guidelines is another mechanism that may


.640 .014


Antipseudomonal Antibioticsa P Value Gram-Positive Antibioticsb P Value Carbapenems P Value −0.095


0.149 −0.097


.460 .630


aAnti-pseudomonal antibiotics: cephalosporins (cefepime, ceftazidime), penicillins (piperacillin-tazobactam, ticarcillin-clauvulanate), and


help ameliorate variation in antibiotic utilization for a specific patient population. The initial IDSA guidelines for antibiotic utilization during neutropenia were available prior to the start of our cohort,31 but disappointingly, they did not seem effec- tive in harmonizing antibiotic prescribing practices. Notably, these guidelines were updated in 2011.17 Most of our cohort predates this update; thus, it is possible that the revised


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72  |  Page 73  |  Page 74  |  Page 75  |  Page 76  |  Page 77  |  Page 78  |  Page 79  |  Page 80  |  Page 81  |  Page 82  |  Page 83  |  Page 84  |  Page 85  |  Page 86  |  Page 87  |  Page 88  |  Page 89  |  Page 90  |  Page 91  |  Page 92  |  Page 93  |  Page 94  |  Page 95  |  Page 96  |  Page 97  |  Page 98  |  Page 99  |  Page 100  |  Page 101  |  Page 102  |  Page 103  |  Page 104  |  Page 105  |  Page 106  |  Page 107  |  Page 108  |  Page 109  |  Page 110  |  Page 111  |  Page 112  |  Page 113  |  Page 114  |  Page 115  |  Page 116  |  Page 117  |  Page 118  |  Page 119  |  Page 120  |  Page 121  |  Page 122  |  Page 123  |  Page 124  |  Page 125  |  Page 126  |  Page 127  |  Page 128  |  Page 129  |  Page 130  |  Page 131  |  Page 132  |  Page 133  |  Page 134  |  Page 135  |  Page 136  |  Page 137  |  Page 138  |  Page 139  |  Page 140  |  Page 141  |  Page 142  |  Page 143  |  Page 144