Quantifying Antibiotic Prescribing in Children with Tracheostomies

Quantifying Antibiotic Prescribing in Children with Tracheostomies | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Pediatric Pulmonology This is a preprint and has not been peer reviewed. Data may be preliminary. 7 March 2025 V1 Latest version Share on Quantifying Antibiotic Prescribing in Children with Tracheostomies Authors : Rebecca Steuart 0000-0002-5973-475X [email protected] , Austin Slone , Joshua Courter , Dan Benscoter , Amy Pan , Samir S. Shah , and Joanna Thomson Authors Info & Affiliations https://doi.org/10.22541/au.174137405.51160217/v1 Published Pediatric Pulmonology Version of record Peer review timeline 266 views 201 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Objectives: To quantify and characterize systemic antibiotic prescribing among children with tracheostomies, and to identify predictors of higher prescribing. Study Design: This single-center retrospective cohort study included children with tracheostomies 2 months-18 years old cared for at a quaternary care pediatric institution between 2010-2018. Antibiotics prescribed for >1 day were recorded, classified by spectrum of activity, and stratified by setting and year. Presumptively prophylactic antibiotics were excluded. Child-level antibiotic prescribing was quantified using number of courses and days of therapy (DOT) per child per year. Group-level prescribing was summarized using total courses and DOT per 1000 person-days and analyzed by year over study course. Predictors of child-level prescribing were identified using backward elimination multivariable analyses within a generalized linear model. Results: The 548 children with tracheostomies were prescribed a median 4 antibiotic courses per child per year (IQR: 1.9-10.0) for a total median exposure of 33.8 DOT per child per year (IQR: 16.1-71.6). The group was prescribed 100.7 DOT per 1000 person-days. Most courses (72.7%) were broad-spectrum, with vancomycin, piperacillin-tazobactam, and ampicillin-sulbactam/ampicillin-clavulanate being the most frequently prescribed courses. Annual antibiotic prescribing decreased 30-33% between 2010 and 2018. Child ventilator use at baseline was associated with fewer DOT per child per year, while higher complexity and more ICU hospital days were risk factors for higher DOT. Conclusion: Children with tracheostomies have high systemic antibiotic use and are predominantly prescribed broad-spectrum antibiotics. Chronic ventilator use was associated with lower prescribing. This data may inform antimicrobial stewardship work. Quantifying Antibiotic Prescribing in Children with Tracheostomies Rebecca Steuart, MD, MS 1 , Austin Slone, MD, 2 Joshua Courter, PharmD 5 , Dan Benscoter, DO, MS 3,4 , Amy Y. Pan, PhD, 6 Samir S. Shah, MD, MSCE 4,7,8 , Joanna Thomson, MD, MPH 4,7 Affiliations : 1 Section of Complex Care, Department of Pediatrics, Medical College of Wisconsin 2 University of Cincinnati College of Medicine, Cincinnati, Ohio 3 Division of Pulmonary Medicine, Cincinnati Children’s Hospital Medical Center 4 Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio 5 Division of Pharmacy, Cincinnati Children’s Hospital Medical Center 6 Division of Bioinformatics and Quantitative Child Health, Medical College of Wisconsin 7 Division of Hospital Medicine, Cincinnati Children’s Hospital Medical Center 8 Division of Infectious Diseases, Cincinnati Children’s Hospital Medical Center Corresponding Author : Rebecca Steuart, MD, MS E-mail: [email protected] . Phone: 414-266-2899. Fax: 414-266-2926. ORCID: https://orcid.org/0000-0002-5973-475X Abbreviations : 1GCP, first generation cephalosporin; 2GCP, second generation cephalosporin; 3GCP, third generation cephalosporin; 4GCP, fourth generation cephalosporin; 5GCP, fifth generation cephalosporin; CCC, complex chronic condition; CCHMC, Cincinnati Children’s Hospital Medical Center; DOT, Antibiotic day of therapy; TMP-SMX, trimethoprim-sulfamethoxazole Financial Disclosure : The authors have no financial relationships relevant to this article to disclose. Funding Sources : Dr. Steuart was supported by The Gerber Foundation under a Novice Researcher Award and the National Center for Advancing Translational Sciences, National Institutes of Health, Award Number UL1 TR001436. Mr. Slone was supported by the National Institutes of Health under award number 1T35HL113229-02. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Dr. Thomson was supported by the Agency for Healthcare Research and Quality under award number K08HS025138. Potential Conflicts of Interest : The authors have no conflicts of interest relevant to this article to disclose. Key Words : Antibiotic utilization, Antibiotic prescribing, Tracheostomy, Antimicrobial stewardship, Days of Therapy Abstract: Objectives: To quantify and characterize systemic antibiotic prescribing among children with tracheostomies, and to identify predictors of higher prescribing. Study Design: This single-center retrospective cohort study included children with tracheostomies 2 months-18 years old cared for at a quaternary care pediatric institution between 2010-2018. Antibiotics prescribed for >1 day were recorded, classified by spectrum of activity, and stratified by setting and year. Presumptively prophylactic antibiotics were excluded. Child-level antibiotic prescribing was quantified using number of courses and days of therapy (DOT) per child per year. Group-level prescribing was summarized using total courses and DOT per 1000 person-days and analyzed by year over study course. Predictors of child-level prescribing were identified using backward elimination multivariable analyses within a generalized linear model. Results: The 548 children with tracheostomies were prescribed a median 4 antibiotic courses per child per year (IQR: 1.9-10.0) for a total median exposure of 33.8 DOT per child per year (IQR: 16.1-71.6). The group was prescribed 100.7 DOT per 1000 person-days. Most courses (72.7%) were broad-spectrum, with vancomycin, piperacillin-tazobactam, and ampicillin-sulbactam/ampicillin-clavulanate being the most frequently prescribed courses. Annual antibiotic prescribing decreased 30-33% between 2010 and 2018. Child ventilator use at baseline was associated with fewer DOT per child per year, while higher complexity and more ICU hospital days were risk factors for higher DOT. Conclusion: Children with tracheostomies have high systemic antibiotic use and are predominantly prescribed broad-spectrum antibiotics. Chronic ventilator use was associated with lower prescribing. This data may inform antimicrobial stewardship work. Introduction: Frequent, prolonged, and broad-spectrum antibiotic prescribing are widespread public health problems, and contribute to the emergence of drug-resistant pathogens and poor patient outcomes. 1-4 Antibiotic-resistant infections are associated with increases in patient mortality, morbidity, longer hospitalizations, and higher cost of care. 3,5,6 Children have among the highest rates of antibiotic use. 1 To date, pediatric antibiotic prescribing has been primarily summarized among inpatient or outpatient populations in the aggregate, rather than targeting specific groups of children. Children with tracheostomies are at risk for frequent and broad-spectrum antibiotic exposures because they are frequently hospitalized and medically fragile due to their tracheostomy status and associated co-morbidities. 7-9 These children are commonly prescribed antibiotics to treat or prevent various types of infections; they are at particular risk for respiratory infections due to altered airway anatomy as well as underlying lung disease, but also at higher risk for other common infections (e.g., urinary tract, skin infections) related to co-morbidities. 10,11 Children with tracheostomies have among the highest rates of drug-resistant bacterial identification, particularly in tracheal aspirate mucus specimens. 12 However, antibiotic prescribing among children with tracheostomies has not been well-described, and no prescribing guidelines exist specific for this population. Antimicrobial stewardship initiatives are difficult to tailor to this population without a better understanding of prescribing patterns. In this study, we sought to quantify all antibiotics prescribed to the group of children with tracheostomies for benchmarking purposes, to characterize antibiotic prescribing by type, spectrum of activity, setting, and year, and to identify predictors of antibiotic prescribing. We hypothesized that children with tracheostomies are prescribed high quantities of antibiotics in both inpatient and outpatient settings, and that medical complexity, chronic ventilator use, and frequent inpatient utilization would predict higher prescribing. Study Design and Patient Population: This single-center, retrospective cohort study included children with tracheostomies ages 2 months-18 years old cared for at Cincinnati Children’s Hospital Medical Center (CCHMC) between January 2010 and December 2018. Children were identified using an existing internal tracheostomy patient registry. Due to expected differences in management, children with cystic fibrosis were excluded. Data Source: For all children in the registry, demographics, clinical characteristics [age at each antibiotic use, dates of tracheostomy placement and removal (if applicable), diagnoses throughout enrollment], and antibiotics prescribed (name, start and end dates, route) were obtained from the electronic medical record (EMR). Antibiotics were included only for the time the child had a tracheostomy in place. Inhaled antibiotics were included. Single dose antibiotics, topical antibiotics, and antibiotics administered by bladder distillation were excluded as these were felt to have a lower likelihood of achieving steady state effect or leading to adverse outcomes. Courses of azithromycin administered for >5 days and erythromycin were excluded, as these were assumed to be prescribed for anti-inflammatory or gastrointestinal motility effects respectively. Courses of trimethoprim-sulfamethoxazole (TMP/SMX) prescribed for >30 days were excluded as these were presumed to be prophylactic in nature. This study was reviewed by the CCHMC and Children’s Wisconsin Institutional Review Boards and determined to be exempt research (CCHMC IRB ID 2019-0938, CW IRBNet ID 1805584), without the requirement for informed consent. This determination was based on the use of existing medical records without additional patient interventions, and that all data were de-identified to ensure patient privacy. Time enrolled: The duration of time each child was enrolled was defined as the difference between date of enrollment and date of disenrollment. Children were enrolled in the study on the date of tracheostomy placement or, if this was not available, on the date of first antibiotic prescribed or respiratory culture obtained. Children were disenrolled on the date of tracheostomy removal, date of death, 6 months after the last available encounter date (presumed lost to follow up), or the end of the study period, whichever came first. Antibiotic categorization: Antibiotic prescribing was quantified using two measures: number of courses prescribed and days of therapy (DOT). An antibiotic course was defined as >1 consecutive day a child was prescribed the same antibiotic, and course duration defined as the difference in days from antibiotic start and end dates. Courses of the same antibiotic prescribed to the same child with overlapping start and end dates or prescribed sequentially, (i.e., start date within 2 days of the end date of the same antibiotic) were combined into a single course. DOT were determined using previously described methods. 13-15 Antibiotics were categorized as broad- or narrow-spectrum according to Center for Disease Control’s (CDC) National Healthcare Safety Network antimicrobial groupings framework ( Supplemental Table 1 ). 16 Due to similar indications, ampicillin and amoxicillin were grouped together for analysis; ampicillin-sulbactam and amoxicillin-clavulanate acid were also grouped. Cephalosporins were grouped by generation. Intravenous and oral vancomycin were considered separate medications for analysis, due to differing prescribing indications, as were intravenous and inhaled tobramycin. Measures of antibiotic quantification: To summarize child-level prescribing, the number of all prescribed antibiotic courses was determined individually per child per year of his/her enrollment. Antibiotic DOT were summarized per child per year of enrollment. Child-level DOT were also summarized for each child’s first year following tracheostomy placement, because in the first year a child typically has higher healthcare utilization. 17,18 For group-level prescribing, the number of all prescribed antibiotic courses was determined per 1000 person-days of enrollment. DOT per 1000 person-days were calculated for all antibiotics in aggregate, and for each antibiotic type separately. Group-level antibiotic prescribing was additionally stratified by setting (inpatient, outpatient, or emergency department) and year prescribed. Covariates: Child demographics and clinical characteristics that might influence antibiotic prescribing were collected to be included as covariates, including race, ethnicity, and insurance type at enrollment, as well as whether the child’s zip code was within or outside of the CCHMC primary service area. Sex, race, and ethnicity were collected from the EMR and included as descriptive covariates and were not treated as biologic constructs. Race was categorized as White, Black or African American, or Other; the latter category included 2+ racial categories, Unknown, and Refused. Measures of clinical complexity were number of diagnosed complex chronic conditions (CCCs), 19 diagnosis of bronchopulmonary dysplasia (BPD) or chronic lung disease of infancy (CLDI), diagnosis of high-intensity neurologic impairment (HINI), 20 and chronic mechanical ventilator use; additional variables included each child’s total hospitalization days and intensive care unit (ICU) days per year of enrollment. In order to assess for an effect of frequency of respiratory culture testing on antibiotic prescribing, the number of respiratory cultures (tracheostomy aspirate or bronchoalveolar lavage) obtained per child per year of enrollment were determined and analyzed as a clinical variable. Because group-level prescribing decreased during the study period ( Figure 2 ), each child’s year or years during which they were prescribed antibiotics was categorized as “early prescribing” (2010-2014), “late prescribing” (2015-2018), or prescribing occurring during both periods. Statistical analysis: Child-level demographic and clinical variables were summarized descriptively. Continuous variables were described using medians and interquartile ranges (IQR). Categorical variables were described using counts and percentages. To examine the univariate effects of child-level demographic and clinical covariates on prescribed DOT per child per year, generalized linear models with negative binomial distribution and log link function were used. Incidence rate ratios (IRR) and 95% confidence intervals (CI) were generated. Each child’s observed enrollment time was incorporated into the model to ensure the appropriate estimation of number of days of antibiotic therapy per year. For multivariate modeling, backwards elimination was performed manually using predictors with p<0.2 from the univariate analysis to identify covariate predictors of prescribed DOT per child per year. The variable with the highest p-value was removed at each step and all predictors with p<0.05 were left in the final model. Race and ethnicity were not included in the multivariable model. Year of antibiotic prescribing was significantly associated with all predictors, therefore it was not included in the multivariate analysis. Analyses were performed with R v4.1.1 (Vienna, Austria) 21 and with SAS V9.4 (SAS Institute Inc., Cary, NC, USA). P -values To assess for non-captured antibiotic prescribing, i.e., prescribing that occurred outside of our hospital system and not available in our EMR, a sub-analysis was performed comparing antibiotic utilization for children with zip codes in the CCHMC primary service area to the utilization of children outside this area. Results: Patient population: During the 9-year study period, a total of 548 children with tracheostomies were enrolled in the dataset for a median of 2.11 years each (IQR 0.71-4.44). Complete descriptive data was available for all children. The median age at tracheostomy placement was 0.47 years (IQR 0.28-2.81), and most children were male (56.9%), White (69.0%), non-Hispanic (96.5%), and publicly-insured (54.0%; Table 1 ). Nearly half (47.6%) of children used a ventilator at baseline after initial hospital discharge with tracheostomy, and 33.4% had 6 or more CCCs. Hospital utilization was high, with a median 22.2 hospitalization days per child per year enrolled (IQR 7.0-55.1). Child-level antibiotic prescribing: Nearly all children (98.9%) received ≥1 antibiotic course ( Table 1 ). Children were prescribed a median of 4 antibiotic courses per child per year (IQR: 1.9-10) for a total median exposure of 33.8 DOT per child per year (IQR 16.1-71.6, full range 1.2-1355.7). Children were a median age of 2.53 years (IQR 0.93-5.87) at antibiotic prescription and two-thirds (69.2%) of antibiotic courses were for children with baseline ventilator use. Child-level antibiotic prescribing was similar for children in the first 12 months following tracheostomy placement as compared with the time enrolled thereafter, with median 4 antibiotic courses (IQR 1-8) per child and 27 DOT per child (IQR 7-61) in the first year ( Table 1 ). Group-level antibiotic prescribing: The full cohort was prescribed a total of 6,912 antibiotic courses during the 9-year study period ( Figure 1 ), representing 11.6 antibiotic courses per 1000 person-days. A total of 59,984 antibiotic DOT were prescribed, representing 100.7 DOT per 1000 person-days. The specific antibiotics and durations are listed in Table 2 . Group-level antibiotic prescribing decreased over the period 2010-2018, most prominently for broad-spectrum antibiotics ( Figure 2 ). Antibiotic duration and spectrum: Median antibiotic course duration was 7 days (IQR 3-11, full range 2-313, Table 2 ). Among the drugs with shortest median course durations were intravenous vancomycin (median 3 days, IQR 3-5), piperacillin-tazobactam (4 days, IQR 3-8), and fourth generation cephalosporins (4 days, IQR 3-8). The majority of both antibiotic courses (5,024 courses, 72.7%) and DOT (41,949 DOT, 69.9%) prescribed were with broad-spectrum antibiotics. Nearly all (95.4%) children ever received a broad-spectrum antibiotic, with a median of 2.8 broad-spectrum courses per child per year (IQR 1.1-7.0) and 21.6 broad-spectrum DOT per child per year (IQR 8.7-51.9, Table 1 ). The three most frequently prescribed broad-spectrum antibiotic courses were third generation cephalosporins, ampicillin-sulbactam/amoxicillin-clavulanate, and intravenous vancomycin ( Table 2, Figure 1 ); half of children were ever prescribed ampicillin-sulbactam or ampicillin-clavulanate (270, 49%), and half were ever prescribed intravenous vancomycin (281, 51.3%). Predictor variables for child-level antibiotic prescribing: In univariate analysis, more CCCs, HINI diagnosis, more ICU hospital days, Other race, and non-public insurance were associated with higher DOT per child ( Table 3 ); BPD diagnosis, baseline ventilator use, and Black race were associated with fewer DOT per child. Compared with late prescribing, children with early prescribing (i.e., only prescribed antibiotics between 2010-2014) had higher DOT. In multivariate analysis using backward elimination, the final model included 4 significant variables: baseline chronic ventilator use and, to a lesser degree, having more respiratory cultures collected, were associated with fewer antibiotic DOT per child; number of CCCs and more ICU hospital days as were associated with higher antibiotic DOT per child. Table 3 shows the final model. Sub-analysis by zip code: Children who were residents of our hospital’s primary service area zip codes had slightly higher antibiotic prescribing than children outside the primary service area, both at the group-level (101.9 vs. 98.6 DOT per 1000 person-days) and at the child-level (median 35.8 DOT per child per year [IQR 18.3-71.0] vs. 30.8 DOT per child per year [IQR 13.7-71.8], p<0.001; median 4.0 courses per child per year [IQR 2.1-9.9] vs. 3.9 courses per child per year [IQR 1.8-10.0], p<0.001, Supplemental Table 2 ). This represents a 3.3% higher DOT prescribing rate. Excluded antibiotics: One in three (196, 35.8%) children received an antibiotic that was excluded due to presumptive prophylactic or non anti-infective indications, most commonly azithromycin >5 days (111 children, 20.3% of full cohort, Supplemental Table 3 ). These antibiotics had relatively few courses but very long course durations (up to 1,299 days=3.6 years). Collectively, these 4 antibiotics contributed an additional 49,556 DOT (83.7 DOT per 1000 PDs, 54% being from azithromycin alone), and 557 courses for the population ( Table 4 ). Azithromycin prescribing was similarly distributed between children with vs. without ventilator use at baseline (76 vs. 64 children, representing 12,529 DOT vs. 13,198 DOT). Discussion : In this single-center retrospective study, children with tracheostomies were frequently prescribed systemic antibiotics, as measured both in terms of antibiotic courses and DOT. Most antibiotic courses prescribed in this population were broad-spectrum. Increasing medical complexity and higher ICU utilization were identified as risk factors for higher antibiotic prescribing while ventilator use at baseline was an important protective factor. Our findings provide benchmarking data in this understudied population and suggest that different strategies of prescribing exist by antibiotic type and by child-level clinical factors. Our study identified a high frequency of antibiotic prescribing among children with tracheostomies. The median 33.8 DOT per child per year may be interpreted as a typical child receiving antibiotics in one out of every 11 days. This is remarkable prescribing for a predominantly outpatient population. At the group-level, this cohort’s inpatient prescribing rate of 593.6 DOT per 1000 person-days is somewhat lower than that described for overall inpatient pediatric care, which is disproportionally infectious disease-related (645-893 DOT per 1000 person-days). 1,13,22 We hypothesize that this is an effect of the known prolonged hospitalizations and low-resource hospital days experienced by this population, in which children are hospitalized but receiving transitional care rather than acute illness care, thus increasing the denominator of inpatient person-days. 23-27 However, the tracheostomy group’s outpatient prescribing rate of 39.0 DOT per 1000 person-days is higher than the general pediatric population (28 DOT per 1000 person-days), 28 reflecting children with tracheostomies’ frequent infections, medical fragility, vulnerability, and complex comorbidities which caregivers regularly manage outpatient. Further study is needed to identify opportunities to limit unnecessary antibiotic exposures. The group-level prescribing by antibiotic type in our study suggests patterned strategies of antibiotic use. Vancomycin and piperacillin-tazobactam course initiation was very high, consistent with studies of overall inpatient pediatrics. 1 However, the low total DOT per 1000 person-days and short median duration for these antibiotics is evidence of early empiric prescribing followed by swift de-escalation, consistent with antimicrobial stewardship recommendations. 30-33 Similar patterns of swift discontinuation of antibiotics have been documented in the NICU. 34 Our results indicating the majority of broad-spectrum courses are very short suggest that more narrow empiric prescribing may be possible for many patients. Further study is needed to understand the implications of repeated, short-duration, broad-spectrum antibiotic courses on the development of antibiotic resistance. Although excluded from the primary quantification and analysis, which focuses on treatment antibiotics, the quantity of antibiotic prescribing for presumptively prophylactic or non anti-infective indications was substantial. Use of these antibiotics represented an additional 37% of antibiotic exposure for the population (additional 83.2 DOT per 1000 person-days and 557 courses). This additional antibiotic load could have important implications for microbiome dysbiosis and other adverse effect profiles. Further study is needed to understand the implications of prophylactic and non anti-infective prescribing on the outcomes of this population. This study identified a 30-33% reduction in group-level prescribing over the 9-year study period. This decrease was driven primarily by a decrease in broad-spectrum antibiotic prescribing and antibiotic course initiation, and was consistent with decreases in inpatient prescribing identified in the wider pediatric population since 2010. 13,29 Decreased prescribing likely reflects the positive effect of antimicrobial stewardship efforts. It may also reflect the changing practice to limit aggressive treatment of respiratory tract bacterial colonization, identified using respiratory cultures, in light of emerging findings that respiratory colonization may not lead to respiratory dysfunction among similar populations. 35 Our data shows a pattern of a stable number of antibiotic courses but increasing DOT per child per year after the first year post-tracheostomy, which is in contrast to the previously-documented decreasing healthcare utilization and costs after the first year. 17,18 Most children with tracheostomies remain cannulated for 2-4 years, with some requiring a tracheostomy indefinitely. 35-38 The pattern of increasing antibiotic DOT in the years following tracheostomy placement may reflect the vulnerability to infectious respiratory illnesses (e.g., bacterial tracheitis, pneumonia, stomatitis) that are known risks of tracheostomy use, 11,18,39 or treatment of non-respiratory illnesses in this medically complex population. 18,40 At the child-level, findings from our multivariate model of increased risk of antibiotic prescribing for children with higher medical complexity and higher ICU days complement findings in other complex populations. 22,41,42 In our model, each additional CCC predicted a 10% increase in DOT per child per year. Surprisingly however, children in our study using chronic ventilation had only 65% of the antibiotic DOT seen in their non-ventilated counterparts. Ventilator dependency has previously been associated with decreased risk of hospitalization for bacterial respiratory infection in this population, which is an important source of antibiotic prescribing. 41 Chronic ventilation may have a protective effect against respiratory infection, whether by supporting airway clearance via positive pressure or due to decreased exposure to aerosolized microbes with a closed-circuit system. Antimicrobial stewardship efforts targeting children with chronic ventilation, if present, may also contribute to this protective effect. In contrast to recent population-level findings from multisite pediatric studies that have shown an association between increased respiratory culture acquisition and increased hospital-level antibiotic use, 43,44 more frequent respiratory culture acquisition was not associated with higher prescribing in our child-level analysis. It may be that hospital-level practice norms are larger drivers of an institution’s prescribing patterns than child-specific factors. This study has several limitations. Due to the retrospective design, only antibiotics prescribed and included in the CCHMC EMR could be quantified; quantity of antibiotics actually administered may differ from our findings, particularly in the outpatient setting. Sub-analysis by our institution’s primary service area suggests that some prescribing occurred outside of the CCHMC EMR; our calculations suggest true prescribing is an estimated 3.3% higher for children outside the primary service. Exclusion of single antibiotic doses biases results towards the null and underestimates true prescribing. Although we excluded antibiotics that our clinical experience suggested were for prophylactic indications, it is possible that additional antibiotic courses were also for prophylactic or non-infective indications; it is also possible that some excluded antibiotic courses or DOT had a treatment indication and should have been included in analysis. Using our methods, we were unable to group non-identical medication courses (i.e., during de-escalation), which may lead to overestimation for the number of antibiotic courses prescribed. Use of the CDC antimicrobial groupings framework to categorize spectrum of activity over-generalizes broad spectrum antibiotics, which have varying degrees of broad-spectrum antimicrobial activity. The retrospective design of this study also creates potential for residual confounding, in which other clinical or demographic factors influencing prescribing are not captured by our dataset. Furthermore, our center’s results may not be generalizable to other institutions. Conclusion Children with tracheostomies were prescribed systemic antibiotics frequently, most commonly broad-spectrum antibiotics. Ventilator use at baseline was an important protective factor against the highest antibiotic use. Our findings highlight the need to develop data-driven antibiotic prescribing guidelines for the most common prescribing indications within this population. The decrease in antibiotic prescribing over time and the frequency of short-course, broad-spectrum prescribing suggest that some empiric prescribing may be unnecessary or unnecessarily broad spectrum. Further study is needed to identify groups and situations in which children may be safely observed while off antibiotics, as well as the role respiratory pathogen testing can play in limiting unnecessary prescribing. Table 1. Child-Level Demographics, Clinical Characteristics, and Antibiotics Prescribed Demographics n (%) Male sex 312 (56.9%) Publicly insured 296 (54.0%) Race White Black Other a 378 106 64 (69.0%) (19.3%) (11.7%) Hispanic ethnicity 19 (3.5%) Resident of primary service area zip code 271 (49.5%) Resident of a long term care facility 6 (1.1%) Clinical Characteristics Age at tracheostomy placement in years (median, [IQR]) 0.47 [0.28, 2.81] Number of complex chronic condition categories (CCC) b, 45 0-2 3-5 6-8 9-12 80 285 145 38 (14.6%) (52.0%) (26.5%) (6.9%) BPD/CLDI diagnosis 179 (32.7%) HINI diagnosis 20 323 (58.9%) Baseline ventilator use c 261 (47.6%) Time enrolled in dataset in years (median, [IQR]) 2.11 [0.71, 4.44] Hospitalization days per year enrolled (median, [IQR]) 23.6 [7.3, 62.7] ICU days per year enrolled (median, [IQR]) 0 [0, 0.8] Respiratory cultures obtained per year (median, [IQR]) 2 [1.1, 3.5] ARI respiratory cultures obtained per year (median, [IQR]) 0.4 [0, 1] Died during study 118 (21.5%) Child-Level Antibiotics Prescribed All antibiotics Children with an antibiotic ever prescribed during enrollment Days of Therapy per child per year (DOT, median, [IQR]) Courses per child per year (median, [IQR]) 542 33.8 3.9 (98.9%) [16.1, 71.6] [1.9, 10.0] Broad-spectrum antibiotics Children with a broad-spectrum antibiotic ever prescribed Broad-spectrum DOT per child per year (median, [IQR]) Broad-spectrum courses per child per year (median, [IQR]) 517 21.6 2.8 (95.4%) [8.7, 51.9] [1.1, 7.0] First year post-tracheostomy Children with an antibiotic ever prescribed in first year DOT in first year (median, [IQR]) Courses in first year (median, [IQR]) 452 27 4 (82.5%) [7, 61] [1, 8] Abbreviations: ARI: Acute respiratory infection; BPD: Bronchopulmonary dysplasia; CCC: Complex chronic condition; CLDI: Chronic lung disease of infancy; DOT: Antibiotic days of therapy; HINI: High intensity neurologic impairment; IQR: Interquartile range; ICU: Intensive care unit. a Category of “Other” race includes children with EMR race of Unknown (3.0%), more than 1 race category (2.9%), Asian (1.5%), Pacific Islander (0.7%), American Indian and Alaska Native (0.4%), Middle Eastern (0.2%), Other (2.7%), and Refused (0.2%). b Represents each child’s maximum number of complex chronic condition categories over all encounters within the 3-year study period. Condition categories include: respiratory, cardiovascular, gastrointestinal, neuromuscular, renal, hematologic, metabolic, genetic, malignancy, neonatal, and technology dependence. c Home ventilator use was defined at the time of first Pulmonary clinic visit after initial hospital discharge as the requirement, when well, for mechanical ventilation for any portion of the day or night and lasting for any duration of time after initial discharge with tracheostomy. Figure 1. Group-level Antibiotic Prescribing by Location Left: Number of antibiotic courses prescribed for each antibiotic. Right : Antibiotic days of therapy (DOT) per 1000 person-days for each antibiotic. Colors : Location of antibiotic course initiation. A total of 6,912 antibiotic courses representing 59,984 antibiotic DOT were prescribed to 548 children over the 9 year study period (2010-2018). The most frequently-prescribed antibiotic courses were with 3 rd generation cephalosporins (3GCP), ampicillin-sulbactam/amoxicillin-clavulanic acid, vancomycin, and piperacillin-tazobactam. The highest antibiotic DOT per 1000 person-days were for ampicillin-sulbactam/amoxicillin-clavulanic acid, 3GCP, ampicillin/amoxicillin, and TMP/SMX. Abbreviations : 1GCP: First generation cephalosporins; 2GCP: Second generation cephalosporins; 3GCP: Third generation cephalosporins; 4GCP: Fourth generation cephalosporins; 5GCP: Fifth generation cephalosporins; DOT: Antibiotic days of therapy; ED: Emergency department without associated hospitalization; IV: intravenous route; PO: oral or enteral route; TMP/SMX: Trimethoprim-sulfamethoxazole. Figure 2. Group-level Antibiotic Prescribing by Year Abbreviations: DOT: Antibiotic days of therapy. Time category of antibiotic prescribing: 129 children had “early prescribing” (2010-2014), 237 children had “late prescribing” (2015-2018), and 182 children had prescribing during both periods. See Supplemental Table 1 for antibiotic groupings by spectrum of activity. Table 2. Group-level Antibiotics Prescribed Antibiotic Name n (%) Median (IQR) (full range) n (%) 3GCP 858 (12.4%) 5 (3-11) (2-44) 10.96 (10.9%) Ampicillin-sulbactam/ Amoxicillin-clavulanate 778 (11.3%) 11 (8-14) (2-34) 15.29 (15.2%) Vancomycin IV 698 (10.1%) 3 (3-5) (2-13) 4.93 (4.9%) Piperacillin-tazobactam 657 (9.5%) 4 (3-8) (2-16) 5.99 (5.9%) TMP/SMX >30 days 519 (7.5%) 10 (5-14) (2-30) 9.50 (9.4%) Ampicillin/Amoxicillin 492 (7.1%) 11 (8-15) (2-45) 10.33 (10.3%) 1GCP 444 (6.4%) 3 (2-8) (2-56) 4.72 (4.7%) Clindamycin 442 (6.4%) 7.5 (3-11) (2-31) 6.01 (6.0%) Gentamicin IV/PO 298 (4.3%) 3 (3-7) (2-17) 2.65 (2.6%) Tobramycin nebulized 266 (3.6%) 10 (6-15) (2-100) 6.80 (6.8%) Ciprofloxacin 245 (3.5%) 9 (4-13) (2-36) 4.03 (4.0%) Meropenem 219 (3.2%) 6 (3-12) (2-60) 3.25 (3.2%) Metronidazole 182 (2.6%) 11 (7-15) (2-39) 3.50 (3.5%) 4GCP 176 (2.5%) 4 (3-8) (2-21) 1.87 (1.9%) Penicillin/Nafcillin 143 (2.1%) 4 (3-8) (2-120) 1.80 (1.8%) 2GCP* 135 (2.0%) 2 (2-3) (2-7) 0.96 (1.0%) Tobramycin IV 77 (1.1%) 7 (3-11) (2-31) 1.03 (1.0%) Azithromycin >5 days 62 (0.9%) 4 (3-5) (2-5) 0.38 (0.4%) Levofloxacin 34 (0.5%) 7.5 (3-15) (2-18) 0.50 (0.5%) Amikacin 29 (0.4%) 11 (3-13) (2-33) 0.52 (0.5%) Linezolid 29 (0.4%) 5 (3-12) (2-16) 0.34 (0.3%) Rifaximin 22 (0.3%) 11 (8-15) (2-49) 0.53 (0.5%) Nitazoxanide 17 (0.2%) 14 (4-38) (2-233) 1.18 (1.2%) Doxycycline 16 (0.2%) 8 (5-93) (3-313) 1.73 (1.7%) Daptomycin 15 (0.2%) 5 (3-12) (2-30) 0.21 (0.1%) Rifampin 15 (0.2%) 13 (3.5-37) (2-118) 0.66 (0.4%) Colistimethate 10 (0.1%) 12.5 (8-15) (2-17) 0.19 (0.1%) Moxifloxacin 8 (0.1%) 15.5 (15-19) (10-32) 0.24 (0.2%) Ticarcillin-clavulanate 8 (0.1%) 6 (4-8) (3-11) 0.08 (0.08%) Minocycline 6 (0.1%) 2.5 (2-3) (2-6) 0.03 (0.03%) 5GCP 3 (0.04%) 16 (11.5-18) (7-20) 0.07 (0.07%) Tigecycline 3 (0.04%) 4 (3-11.5) (2-17) 0.04 (0.04%) Aztreonam 1 (0.01%) 3 - - 0.01 (0.01%) Clarithromycin 1 (0.01%) 11 - - 0.02 (0.02%) Vancomycin PO 1 (0.01%) 11 - - 0.02 (0.02%) Total 6,912 100% 7 (3-11) (2-313) 100.7 100% Abbreviations: DOT: Antibiotic days of therapy; IQR: Interquartile range; 1GCP: First generation cephalosporins; 2GCP: Second generation cephalosporins; 3GCP: Third generation cephalosporins; 4GCP: Fourth generation cephalosporins; 5GCP: Fifth generation cephalosporins; IV: intravenous route; PO: oral or enteral route; TMP/SMX: Trimethoprim-sulfamethoxazole. *Among 2GCP, 108 (80%) courses consisted of cefoxitin, a narrow-spectrum antibiotic. Table 3. Child-Level Predictors of Antibiotic DOT Per Child Per Year Enrolled: Univariate and Multivariate Analyses Variable IRR 95% CI Race White Black or African American Other b (reference) 0.69 1.85 (reference) 0.54 – 0.88 1.34 – 2.55 Hispanic ethnicity 1.00 0.58 – 1.70 Insurance Public Private Uninsured, Other insurance (reference) 1.33 2.30 (reference) 1.07 – 1.64 1.65 – 3.22 Number of CCCs 1.13 1.08 – 1.18 BPD/CLDI diagnosis 0.74 0.59 – 0.91 HINI diagnosis 1.34 1.09 – 1.64 Ventilator use at baseline c 0.51 0.42 – 0.62 Hospitalization days per year 1.0002 0.9995 –1.001 ICU Hospitalization days per year 1.006 1.003 – 1.009 Number of respiratory cultures obtained per year 0.97 0.96 – 0.98 Number of respiratory cultures obtained during ARI per year 0.97 0.93 – 0.997 Time of antibiotic prescribing Late prescribing only (2015-2018) Early prescribing only (2010-2014) Both early and late time period prescribing (reference) 1.53 0.41 (reference) 1.20 – 1.94 0.33 – 0.51 Multivariate Analysis d : Final Predictor Model Variable IRR 95% CI Number of CCCs 1.17 1.12 – 1.22 Ventilator use at baseline c 0.57 0.46 – 0.70 ICU Hospitalization days per year 1.003 1.0006 – 1.005 Number of respiratory cultures obtained per year 0.97 0.96 – 0.99 Abbreviations: ARI: Acute respiratory infection; BPD: Bronchopulmonary dysplasia; CCC: Complex chronic condition; CLDI: Chronic lung disease of infancy; CI: Confidence interval; DOT: Antibiotic days of therapy; HINI: High intensity neurologic impairment; IRR: Incidence rate ratio; ICU: Intensive care unit. a For univariate analysis, generalized linear models with negative binomial distribution and log link function were used. b Category of “Other” race includes children with EMR race of Unknown (3.0%), more than 1 race category (2.9%), Asian (1.5%), Pacific Islander (0.7%), American Indian and Alaska Native (0.4%), Middle Eastern (0.2%), Other (2.7%), and Refused (0.2%). c Home ventilator use was defined at the time of first Pulmonary clinic visit after initial hospital discharge as the requirement, when well, for mechanical ventilation for any portion of the day or night and lasting for any duration of time after initial discharge with tracheostomy until 3 years after placement. d For multivariate analysis, backwards elimination was performed manually to identify predictors of prescribed DOT per patient per year. Variable with the highest p-value was removed at each step and all predictors with p<0.05 were left in the final model. Each child’s enrollment time was incorporated into the model to ensure appropriate estimation for time-based variables. Time of antibiotic prescribing category was associated with all predictors; therefore, it was not included in multivariate analysis. Race and ethnicity were not included in multivariate analysis. Number of respiratory cultures overall and number during ARI were highly correlated, therefore, only number of respiratory cultures was included in the multivariate analysis. Table 4. Group-level prescribing of presumptively prophylactic or non anti-infective antibiotics Azithromycin >5 days 26,762 44.9 158 210 (62-432) (6-1,299) Gentamicin bladder irrigation 12,621 21.2 173 1,138 (606-1,940) (11-2,724) TMP/SMX >30 days 6,160 10.3 109 113 (53-220) (31-802) Erythromycin 4,013 6.7 117 115 (36-454) (2-508) Total 49,556 83.2 557 Abbreviations: DOT: Antibiotic days of therapy; IQR: Interquartile range; TMP/SMX: Trimethoprim-sulfamethoxazole. These antibiotics were excluded from the main results quantification and analysis. Supplemental Table 1. Antibiotic Groupings and Spectrum Amikacin Broad Ampicillin-sulbactam/ Amoxicillin-clavulanate Ampicillin-sulbactam Amoxicillin-clavulanate Broad Ampicillin/Amoxicillin Ampicillin Amoxicillin Narrow Azithromycin Broad Aztreonam Broad Cephalosporins, first generation Cefazolin Cephalexin Narrow Cephalosporins, second generation Cefoxitin Narrow Cefprozil Cefuroxime Broad Cephalosporins, third generation Cefdinir Cefixime Ceftazidime Cefotaxime Ceftriaxone Broad Cephalosporins, fourth generation Cefepime Ceftaroline Broad Cephalosporins, fifth generation Ceftolozane-tazobactam Broad Ciprofloxacin Broad Clarithromycin Broad Clindamycin Broad Colistimethate Broad Daptomycin Broad Doxycycline Broad Gentamicin IV and enteral Gentamicin Broad Levofloxacin Broad Linezolid Broad Meropenem Broad Metronidazole Narrow Minocycline Broad Moxifloxacin Broad Nitazoxanide Broad Penicillin/Nafcillin Dicloxacillin Nafcillin Penicillin G potassium Penicillin V potassium Narrow Piperacillin-tazobactam Broad Rifampin Broad Rifaximin Broad Ticarcillin-clavulanate Broad Tigecycline Broad Tobramycin, IV Broad Tobramycin, nebulized Broad TMP/SMX Narrow Vancomycin IV Broad Vancomycin PO Broad Supplemental Table 2. Subanalysis by residence in primary service area zip code n (%) n (%) n (%) Difference Children enrolled 548 (100%) 271 (49.5%) 277 (50.5%) Courses, total 6,912 (100%) 3,865 (55.9%) 3,047 (44.1%) DOT for cohort 59,984 (100%) 32,637 (54.4%) 27,347 (45.6%) DOT per 1000 person-days 100.7 - 101.9 - 98.6 - ∆ 3.3/100.7 = 3.3% median [IQR] median [IQR] median [IQR] p-value Time enrolled, years 2.1 [0.7-4.4] 2.4 [0.9-4.9] 1.9 [0.6-3.7] <0.001 Courses per child per year 3.9 [1.9-10.0] 4.0 [2.1-9.9] 3.9 [1.8-10.0] <0.001 DOT per child per year 33.8 [16.1-71.6] 35.8 [18.3-71.0] 30.8 [13.7-71.8] <0.001 Supplemental Table 3. Child-level prescribing of presumptively prophylactic or non anti-infective antibiotics n (%) Children with 1+ non anti-infective antibiotic use 196 (35.8%) Children with 2+ non anti-infective antibiotic use 55 (10.0%) By medication name Azithromycin >5 days TMP/SMX >30 days Erythromycin Gentamicin bladder irrigations 111 64 51 20 (20.3%) (11.7%) (9.3%) (3.6%) Abbreviations: TMP/SMX: Trimethoprim-sulfamethoxazole. These antibiotics were excluded from the main quantification and analysis. References : https://doi.org/10.1002/ppul.26746 1. Levy ER, Swami S, Dubois SG, Wendt R, Banerjee R. Rates and appropriateness of antimicrobial prescribing at an academic children’s hospital, 2007-2010. Infect Control Hosp Epidemiol . Apr 2012;33(4):346-53. doi:10.1086/6647612. Tamma PD, Turnbull AE, Milstone AM, Lehmann CU, Sydnor ER, Cosgrove SE. Ventilator-associated tracheitis in children: does antibiotic duration matter? Clin Infect Dis . Jun 2011;52(11):1324-31. doi:10.1093/cid/cir2033. Cosgrove SE. The Relationship between Antimicrobial Resistance and Patient Outcomes: Mortality, Length of Hospital Stay, and Health Care Costs. Clin Infect Dis . 2006;42(Supplement_2):S82-S89. doi:10.1086/4994064. Paschke AA, Zaoutis T, Conway PH, Xie D, Keren R. Previous antimicrobial exposure is associated with drug-resistant urinary tract infections in children. Pediatrics . Apr 2010;125(4):664-72. doi:10.1542/peds.2009-15275. Antibiotic resistance threats in the United States (2013).6. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet . Feb 12 2022;399(10325):629-655. doi:10.1016/s0140-6736(21)02724-07. Graf JM, Montagnino BA, Hueckel R, McPherson ML. Pediatric tracheostomies: a recent experience from one academic center. Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies . Jan 2008;9(1):96-100. doi:10.1097/01.pcc.0000298641.84257.538. Gold JM, Hall M, Shah SS, et al. Long length of hospital stay in children with medical complexity. Journal of hospital medicine : an official publication of the Society of Hospital Medicine . Jul 5 2016;doi:10.1002/jhm.26339. Liu C, Heffernan C, Saluja S, et al. Indications, Hospital Course, and Complexity of Patients Undergoing Tracheostomy at a Tertiary Care Pediatric Hospital. Otolaryngol Head Neck Surg . Aug 2014;151(2):232-9. doi:10.1177/019459981453173110. Berry JG, Graham DA, Graham RJ, et al. Predictors of clinical outcomes and hospital resource use of children after tracheotomy. Pediatrics . Aug 2009;124(2):563-72. doi:10.1542/peds.2008-349111. Zhu H, Das P, Roberson DW, et al. Hospitalizations in children with preexisting tracheostomy: a national perspective. Laryngoscope . Feb 2015;125(2):462-8. doi:10.1002/lary.2479712. Chen G, Xu K, Sun F, Sun Y, Kong Z, Fang B. Risk Factors of Multidrug-Resistant Bacteria in Lower Respiratory Tract Infections: A Systematic Review and Meta-Analysis. The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale / AMMI Canada . 2020;2020:7268519. doi:10.1155/2020/726851913. Dalton BR, MacTavish SJ, Bresee LC, et al. Antimicrobial use over a four-year period using days of therapy measurement at a Canadian pediatric acute care hospital. The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale . Sep-Oct 2015;26(5):253-258. doi:10.1155/2015/83520914. Polk RE, Fox C, Mahoney A, Letcavage J, MacDougall C. Measurement of adult antibacterial drug use in 130 US hospitals: comparison of defined daily dose and days of therapy. Clin Infect Dis . Mar 1 2007;44(5):664-70. doi:10.1086/51164015. Pakyz AL, Gurgle HE, Ibrahim OM, Oinonen MJ, Polk RE. Trends in antibacterial use in hospitalized pediatric patients in United States academic health centers. Infect Control Hosp Epidemiol . Jun 2009;30(6):600-3. doi:10.1086/59754516. National Healthcare Safety Network CDC. Antimicrobial groupings for SAAR & Rate Table calculations. Antimicrobial Use and Resistance Module . 2019;17. Song IG, Kim YS, Kim MS, et al. Healthcare service use and medical outcomes of tracheostomy-dependent children: a nationwide study. BMJ paediatrics open . Mar 19 2024;8(1)doi:10.1136/bmjpo-2023-00237718. Watters K, O’Neill M, Zhu H, Graham RJ, Hall M, Berry J. Two-year mortality, complications, and healthcare use in children with medicaid following tracheostomy. Laryngoscope . Nov 2016;126(11):2611-2617. doi:10.1002/lary.2597219. Cohen E, Kuo DZ, Agrawal R, et al. Children with medical complexity: an emerging population for clinical and research initiatives. Review. Pediatrics . Mar 2011;127(3):529-38. doi:10.1542/peds.2010-091020. Thomson JE, Feinstein JA, Hall M, Gay JC, Butts B, Berry JG. Identification of Children With High-Intensity Neurological Impairment. JAMA pediatrics . Aug 19 2019;doi:10.1001/jamapediatrics.2019.267221. Team RC. R: A language and environment for statistical computing. MSOR connections . 2014;122. Brogan TV, Thurm C, Hersh AL, et al. Variability in Antibiotic Use Across PICUs. Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies . Jun 2018;19(6):519-527. doi:10.1097/pcc.000000000000153523. Cecil CA, Dziorny AC, Hall M, et al. Low-Resource Hospital Days for Children Following New Tracheostomy. Pediatrics . Aug 8 2024;doi:10.1542/peds.2023-06492024. Enzer KG, Dawson JA, Langevin JA, Brinton JT, Baker CD. Medical and social factors associated with prolonged length of stay for chronically ventilated children. Pediatr Pulmonol . Oct 10 2024;doi:10.1002/ppul.2732025. Edwards EA, O’Toole M, Wallis C. Sending children home on tracheostomy dependent ventilation: Pitfalls and outcomes. Review. Arch Dis Child . 2004;89(3):251-255. doi:10.1136/adc.2003.02831626. Sobotka SA, Foster C, Lynch E, Hird-McCorry L, Goodman DM. Attributable Delay of Discharge for Children with Long-Term Mechanical Ventilation. J Pediatr . Sep 2019;212:166-171. doi:10.1016/j.jpeds.2019.04.03427. Benscoter D, Voos K, Schuler CL, et al. Transitioning medically complex infants home: lessons learned from quality improvement efforts. J Perinatol . Dec 14 2024;doi:10.1038/s41372-024-02189-728. Kinoshita N, Morisaki N, Uda K, Kasai M, Horikoshi Y, Miyairi I. Nationwide study of outpatient oral antimicrobial utilization patterns for children in Japan (2013-2016). Journal of infection and chemotherapy : official journal of the Japan Society of Chemotherapy . Jan 2019;25(1):22-27. doi:10.1016/j.jiac.2018.10.00229. Quaak CH, Cové E, Driessen GJ, Tramper-Stranders GA. Trends in paediatric inpatient antibiotic therapy in a secondary care setting. Eur J Pediatr . 2018;177(8):1271-1278. doi:10.1007/s00431-018-3185-z30. Tersigni C, Venturini E, Montagnani C, Chiappini E, de Martino M, Galli L. Antimicrobial stewardship in children: more shadows than lights? Expert review of anti-infective therapy . Nov 2019;17(11):871-876. doi:10.1080/14787210.2019.168635531. Araujo da Silva AR, Albernaz de Almeida Dias DC, Marques AF, et al. Role of antimicrobial stewardship programmes in children: a systematic review. J Hosp Infect . Jun 2018;99(2):117-123. doi:10.1016/j.jhin.2017.08.00332. Schuts EC, Hulscher M, Mouton JW, et al. Current evidence on hospital antimicrobial stewardship objectives: a systematic review and meta-analysis. The Lancet Infectious diseases . Jul 2016;16(7):847-856. doi:10.1016/s1473-3099(16)00065-733. Beltran Ale G, Benscoter D, Hossain MM, Zhang Y, Courter J, Thomson J. Impact of respiratory viral polymerase chain reaction testing on de-escalation of antibiotic therapy in children who require chronic positive pressure ventilation. Pediatr Pulmonol . Jun 3 2020;doi:10.1002/ppul.2488434. Cantey JB, Wozniak PS, Sanchez PJ. Prospective surveillance of antibiotic use in the neonatal intensive care unit: results from the SCOUT study. Pediatr Infect Dis J . Mar 2015;34(3):267-72. doi:10.1097/inf.000000000000054235. Steuart R, Pan AY, Woolums A, et al. Respiratory culture growth and 3-years lung health outcomes in children with bronchopulmonary dysplasia and tracheostomies. Pediatr Pulmonol . 2023/11/08 2023;n/a(n/a)doi:36. Smith MM, Hart CK, Benscoter DT, et al. The Impact of Socioeconomic Status on Time to Decannulation Among Children With Tracheostomies. Otolaryngol Head Neck Surg . Dec 2021;165(6):876-880. doi:10.1177/019459982098850137. Cristea AI, Carroll AE, Davis SD, Swigonski NL, Ackerman VL. Outcomes of children with severe bronchopulmonary dysplasia who were ventilator dependent at home. Pediatrics . Sep 2013;132(3):e727-34. doi:10.1542/peds.2012-299038. Henningfeld JK, Maletta K, Ren B, Richards KL, Wegner C, D’Andrea LA. Liberation from home mechanical ventilation and decannulation in children. Pediatr Pulmonol . Aug 2016;51(8):838-49. doi:10.1002/ppul.2339639. Beams DR, Chorney SR, Kou YF, Teplitzky TB, Wynings EM, Johnson RF. Emergency Department Visits and Hospitalizations After Pediatric Tracheostomy. Laryngoscope . Sep 30 2022;doi:10.1002/lary.3041640. Kukora SK, Van Horn A, Thatcher A, Pace RA, Schumacher RE, Attar MA. Risk of death at home or on hospital readmission after discharge with pediatric tracheostomy. J Perinatol . Aug 2023;43(8):1020-1028. doi:10.1038/s41372-023-01721-541. Russell CJ, Thurm C, Hall M, Simon TD, Neely MN, Berry JG. Risk factors for hospitalizations due to bacterial respiratory tract infections after tracheotomy. Pediatr Pulmonol . Mar 2018;53(3):349-357. doi:10.1002/ppul.2393842. Hicks LA, Bartoces MG, Roberts RM, et al. US outpatient antibiotic prescribing variation according to geography, patient population, and provider specialty in 2011. Clin Infect Dis . May 1 2015;60(9):1308-16. doi:10.1093/cid/civ07643. Prinzi A, Parker SK, Thurm C, Birkholz M, Sick-Samuels A. Association of Endotracheal Aspirate Culture Variability and Antibiotic Use in Mechanically Ventilated Pediatric Patients. JAMA Netw Open . Dec 1 2021;4(12):e2140378. doi:10.1001/jamanetworkopen.2021.4037844. Willson DF, Hoot M, Khemani R, et al. Pediatric Ventilator-Associated Infections: The Ventilator-Associated INfection Study. Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies . Jan 2017;18(1):e24-e34. doi:10.1097/pcc.000000000000100145. Feudtner C, Feinstein JA, Zhong W, Hall M, Dai D. Pediatric complex chronic conditions classification system version 2: updated for ICD-10 and complex medical technology dependence and transplantation. BMC pediatrics . 2014;14:199. doi:10.1186/1471-2431-14-199 CRediT Author Statement Rebecca Steuart : Conceptualization, Methodology, Investigation, Visualization, Data curation, Formal analysis, Writing- Original draft preparation Austin Slone : Investigation, Formal analysis, Visualization, Writing- Review and editing Joshua Courter : Methodology, Resources, Writing- Review and editing Dan Benscoter : Conceptualization, Methodology, Resources, Writing- Review and editing Amy Pan : Methodology, Formal analysis, Writing- Review and editing Samir Shah : Conceptualization, Writing- Review and editing Joanna Thomson : Conceptualization, Methodology, Writing- Review and editing, Supervision All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work. Information & Authors Information Version history V1 Version 1 07 March 2025 Peer review timeline Published Pediatric Pulmonology Version of Record 8 Aug 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Pediatric Pulmonology Keywords antibiotic prescribing antibiotic utilization antimicrobial stewardship days of therapy tracheostomy Authors Affiliations Rebecca Steuart 0000-0002-5973-475X [email protected] Medical College of Wisconsin Department of Pediatrics View all articles by this author Austin Slone University of Cincinnati College of Medicine View all articles by this author Joshua Courter Cincinnati Children's Hospital Medical Center View all articles by this author Dan Benscoter Cincinnati Children's Hospital Medical Center View all articles by this author Amy Pan Medical College of Wisconsin View all articles by this author Samir S. Shah Cincinnati Children's Hospital Medical Center View all articles by this author Joanna Thomson Cincinnati Children's Hospital Medical Center View all articles by this author Metrics & Citations Metrics Article Usage 266 views 201 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Rebecca Steuart, Austin Slone, Joshua Courter, et al. Quantifying Antibiotic Prescribing in Children with Tracheostomies. Authorea . 07 March 2025. DOI: https://doi.org/10.22541/au.174137405.51160217/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.174137405.51160217/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a00fe06efd4f1b23',t:'MTc3OTY2MzIwOA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();