hospital variability of antibiotic use in hct 799
transplant (first clinical remission (CR1), ≥CR2, induction failure), donor (matched related, matched unrelated, or mismatched unrelated), graft source (bone marrow, cord blood, or peripheral stem cells), conditioning regimen (chemotherapy or total body irradiation), and recipient CMV status were summarized by hospital. Age was included as a continuous variable and all others were categorical variables. Hospital-level variables. Hospital-level variables were
captured from the PHIS database. We hypothesized that hospital-level contributors to variation in antibiotic use would span departments. Therefore, hospital-level variables were based on total hospital admissions in 2011. Volume was defined as total inpatients, 1–19 years old; female gender, non- white race, and public insurance were reported as respective proportions of all inpatients at each hospital. All hospital-level variables were utilized as continuous variables. Days of significant illness. We assumed a priori that
patients requiring intensive care unit (ICU)–level care would receive more broad-spectrum antibiotics5,21 and, thus, that an increased prevalence of significant illness days at an institution would confound the comparison of antibiotic utilization between hospitals. Therefore, like DOTs, days of significant illness (DSIs) were indexed to total neutropenic days at the hospital level (for correlation testing) and at the patient level (for multivariable modeling). A DSI was defined using PHIS healthcare utilization data as follows: (1) administration of a vasopressor or cardiac support medication; (2) resource code indicating respiratory support; (3) procedure code denoting advanced cardiovascular monitoring or resuscitation; (4) procedure code for extracorporeal membrane oxygenation; or (5) resource or procedure code indicating dialysis. This metric has been employed previously to identify ICU–level care.10,22–25
Statistical Analysis
Patient demographic and transplant characteristics were summarized for the entire cohort and within each institution. We estimated 30-mortality with 95% confidence intervals. The Spearman correlation coefficient was used to measure the correlation of hospitals’ unadjusted DOTs with mortality and hospital-level DSIs. To compare hospital-level antibiotic utilization we
employed a multivariable negative binomial regression analy- sis to establish rates of DOTs per 1,000 neutropenic days, adjusting for patient-level demographic and transplant char- acteristics and DSIs. In calculating hospital-adjusted utiliza- tion rates from the fitted negative binomial models, average values of demographics, transplant characteristics, and DSIs of the study cohort were used. To account for secular changes in antibiotic use or stewardship, we performed a sensitivity ana- lysis considering utilization rates in only the latter years of the cohort (2008–2011). Additionally, we assessed the impact of ICU-level care on antibiotic utilization by repeating the aforementioned models agnostic to DSIs.
To better quantify the magnitude of between-hospital variation in utilization rates and to explore factors that might account for variation, we constructed mixed-effects (ME) negative binomial models. The base ME model included only a hospital-level random effect without fixed effects. The estimated variance of the random effect reflects the magnitude of antibiotic variation across hospitals, and a test of variance greater than zero suggests that the between- hospital variation is statistically significant. We then included patient-level and additional hospital-level factors as defined above to the base model as fixed effects to explore whether the variation remained significant after adjusting for these factors. Excel version 14.7 software (Microsoft, Seattle, WA) and
Stata version 14.0 software (StataCorp, College Station, TX) were used for all analyses.
Human Subjects Oversight
The merger of PHIS and CIBMTR data occurred under the guidance of the CIBMTR via the National Marrow Donor Program institutional review board. Analysis was performed on a limited dataset; therefore informed consent was waived.
results
We identified 793 patients common to both the PHIS and CIBMTR databases (Figure 1). Among them, 5 hospitals
(23 patients) were excluded for low patient numbers. The final cohort included 770 patients representing 27 hospitals, ranging from 13 to 65 patients per hospital. Table 1 shows the demographic characteristics for the cohort by institution. Additional transplant characteristics and hospital-level char- acteristics are provided in Supplemental Tables 1 and 2. Overall, 38 (4.9%) patients failed to engraft. Of those, 32
patients (84.2%) died before engraftment at a median of 42 days posttransplant. Among those who did engraft, the median time to engraftment was 20 days (range, 8–84 days). The remaining patients received additional therapy but were censored at 30 days for DOT and DSI assessments.
figure 1. Flow diagram depicting cohort creation.
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
