OBJECTIVES:

To estimate the association between fluoroquinolone use and tendon injury in adolescents.

METHODS:

We conducted an active-comparator, new-user cohort study using population-based claims data from 2000 to 2018. We included adolescents (aged 12–18 years) with an outpatient prescription fill for an oral fluoroquinolone or comparator broad-spectrum antibiotic. The primary outcome was Achilles, quadricep, patellar, or tibial tendon rupture identified by diagnosis and procedure codes. Tendinitis was a secondary outcome. We used weighting to adjust for measured confounding and a negative control outcome to assess residual confounding.

RESULTS:

The cohort included 4.4 million adolescents with 7.6 million fills for fluoroquinolone (275 767 fills) or comparator (7 365 684) antibiotics. In the 90 days after the index antibiotic prescription, there were 842 tendon ruptures and 16 750 tendinitis diagnoses (crude rates 0.47 and 9.34 per 1000 person-years, respectively). The weighted 90-day tendon rupture risks were 13.6 per 100 000 fluoroquinolone-treated adolescents and 11.6 per 100 000 comparator-treated adolescents (fluoroquinolone-associated excess risk: 1.9 per 100 000 adolescents; 95% confidence interval −2.6 to 6.4); the corresponding number needed to treat to harm was 52 632. For tendinitis, the weighted 90-day risks were 200.8 per 100 000 fluoroquinolone-treated adolescents and 178.1 per 100 000 comparator-treated adolescents (excess risk: 22.7 per 100 000; 95% confidence interval 4.1 to 41.3); the number needed to treat to harm was 4405.

CONCLUSIONS:

The excess risk of tendon rupture associated with fluoroquinolone treatment was extremely small, and these events were rare. The excess risk of tendinitis associated with fluoroquinolone treatment was also small. Other more common potential adverse drug effects may be more important to consider for treatment decision-making, particularly in adolescents without other risk factors for tendon injury.

What’s Known on This Subject:

A 2008 US Food and Drug Administration warning notes an elevated risk of tendon rupture and tendinitis after fluoroquinolone antibiotics. The authors of a 2002 epidemiological analysis of this potential adverse effect in children did not observe an elevated risk.

What This Study Adds:

In this analysis of 7.6 million antibiotic prescription fills for US adolescents, tendon rupture and tendinitis after exposure were rare. Fluoroquinolone was associated with small excess risk for tendon rupture and tendinitis, which may not be clinically meaningful.

Antibiotics are the most common medication class used in children and are associated with a significant burden of adverse events.1  Children, when compared with adults, are particularly vulnerable to adverse drug reactions because of limited dosing and pharmacokinetic data, common off-label prescribing,2,3  and scarce postmarketing surveillance studies, which are needed to identify rare harmful effects.

Fluoroquinolone antibiotics are commonly prescribed to adults because of convenient dosing, broad-spectrum coverage, high bioavailability, and favorable tolerability.47  Additionally, oral fluoroquinolones may be important alternatives to parenteral antibiotics when equivalent oral antibiotics are unavailable.7,8  Pediatric use of fluoroquinolones, however, has been more limited4,9  because of safety concerns extrapolated from animal studies revealing cartilage damage in weight-bearing joints.10,11 

In multiple studies in adults, fluoroquinolone use has been associated with increased risk of tendon injury,1221  which causes disability, pain, and need for surgery.22  Evidence suggests that fluoroquinolones may impact tendons and cartilage in the load-bearing joints of the lower limbs through collagen degradation, necrosis, and disruption of the extracellular matrix.2325  Fluoroquinolones have an affinity for connective tissue,25  and upregulation of tenocytes caused by fluoroquinolone use has been shown to negatively affect collagen fibrils in tendons, leading to an increased risk of tendon rupture and tendinitis.24  In some studies, it was found that age modified the association with the highest increased risk among older adults.13,14,19,25  In 2008, the US Food and Drug Administration (FDA) issued a boxed warning regarding fluoroquinolone-associated elevated risk of tendon injury.26,27 

One study has been conducted in children, published in 2002,28  in which fluoroquinolone use was examined, and tendon and joint injury was a composite outcome. Although no association was observed, there was a limited sample size and potential confounding bias. Although restrictions of fluoroquinolone use for children have recently been relaxed,8  low fluoroquinolone prescribing rates in children compared with adults suggests ongoing safety concerns.5,9  To improve on this evidence base, we examined the association between fluoroquinolone use and tendon rupture and tendinitis among millions of adolescents over 2 decades.

We implemented an active-comparator, new-user29,30  cohort study using IBM Watson Health MarketScan Commercial Claims and Encounters data (IBM Watson Health; Armonk, NY).31  MarketScan includes beneficiaries enrolled in employer-sponsored private insurance across the United States and with adjudicated claims data on outpatient and inpatient health services and outpatient pharmacy dispensing. The University of North Carolina Institutional Review Board reviewed and exempted this study (19-2483).

We included adolescents (aged 12–18 years) with an outpatient pharmacy claim for a dispensed prescription (henceforth, “index fill”) for oral formulations of fluoroquinolone or comparator antibiotics between 2000 and 2018. The cohort was restricted to this age group to improve comparability across treatment groups because fluoroquinolone dispensing is rare for children aged <12 years (1 fill per 1000 person-years in this database) as compared with comparator antibiotic dispensing (319 fills per 1000 person-years).32 

In Fig 1 and Supplemental Fig 5,33  we illustrate cohort inclusion and exclusion criteria. To restrict to new users of these antibiotics, we required ≥100 days of continuous prescription drug coverage before their index fill. We excluded adolescents with a systemic fluoroquinolone or comparator fill in that 100-day washout period on the basis of our hypothesis that prophylactic antibiotic prescriptions would not exceed a 90-day supply. To assess baseline covariates, we required continuous fee-for-service insurance coverage for ≥180 days before the index fill. We also excluded adolescents who experienced an outcome before 2 days post index fill (explained below).

FIGURE 1

Flowchart of cohort creation.

FIGURE 1

Flowchart of cohort creation.

Close modal

Treatment was defined by using outpatient pharmacy dispensing data for oral antibiotics (fluoroquinolones and comparators). Fluoroquinolones included ciprofloxacin, levofloxacin, moxifloxacin, and gatifloxacin; comparator antibiotics included amoxicillin-clavulanate, azithromycin, cefalexin, cefixime, cefdinir, nitrofurantoin, and sulfamethoxazole-trimethoprim. Comparators were chosen because they are broad-spectrum antibiotics used for similar indications, such as urinary tract infections (UTIs), respiratory tract infections, and gastrointestinal infections. To classify medication type, we identified National Drug Codes using generic names in the National Drug Data File Plus (First Databank, South San Francisco, CA; www.firstdatabank.com) and Redbook (IBM Watson Health).

Our primary outcome was tendon rupture of the Achilles, quadricep, patellar, or a tibial tendon. Tendinitis was a secondary outcome. Additionally, we leveraged clavicle fracture as a negative control outcome to assess potential uncontrolled confounding; in the absence of confounding, the analysis of this outcome should produce a null result.34 

To define outcomes, we used International Classification of Diseases, Ninth Revision, Clinical Modification and International Classification of Diseases, 10th Revision, Clinical Modification diagnosis codes and, for surgical repair of rupture or clavicle fracture, Current Procedural Terminology (CPT) codes (Supplemental Table 2). Outcome codes were assessed starting 2 days after the index fill (1) because we hypothesized that antibiotic treatment could not plausibly affect risk of the outcome in less than a day and (2) to reduce the potential for reverse causality, in which the antibiotic is received prophylactically for a procedure to treat the injury.

We used a causal diagram to inform our selection of confounders.35  Demographic factors included age, sex, geographic region of residence, and calendar time of index fill. Indication for the index fill was defined by using diagnosis codes from claims in the 3 days before and on the index fill date. Codes were summarized by using categories developed by Fleming-Dutra et al.36 

Other baseline covariates included comorbid conditions (complex chronic conditions37,38 ), preindex inpatient and outpatient visit frequency (ie, health care use), preindex fills for other systemic antibiotics (Anatomical Therapeutic Chemical group J0139 ) or systemic corticosteroids (budesonide, dexamethasone, hydrocortisone, methylprednisolone, prednisone, prednisolone, and triamcinolone hexacetonide), and systemic corticosteroid fills that occurred on the index fill date (Supplemental Figs 5 and 6, Supplemental Table 3)

We estimated the average treatment effect in the treated, which answers the question, “How much would risk of the outcome change among adolescents who received a fluoroquinolone if these adolescents had instead received a comparator antibiotic?” To estimate the average treatment effect in the treated and control for measured confounding,40  we used standardized mortality ratio (SMR) weighting.41  In this approach, adolescents treated with a fluoroquinolone have a weight of 1 because they are the target population; adolescents treated with a comparator have a weight equal to their propensity score divided by its complement. The propensity score model parameterization is shown in Supplemental Table 3. We examined balance of measured covariates between treatment groups using standardized mean differences before and after weighting.42,43 

We used Kaplan-Meier–based methods to estimate the cumulative incidence (ie, risk) of the outcome in each treatment group during follow-up.44  We implemented 2 primary analyses: (1) an intention-to-treat (ITT) analysis and (2) a per-protocol analysis.45  In the ITT analysis, patients were managed until they experienced the outcome, until disenrollment from insurance coverage, until 180 days after the index fill, or until the end of the study period (December 31, 2018), whichever came first. In the per-protocol analysis, the same criteria for follow-up were used, but follow-up was also stopped when the other treatment was dispensed, if that occurred.

We estimated the risk difference for every day of follow-up with 95% confidence intervals (CIs) using nonparametric bootstrap methods. We also calculated the inverse of the risk difference to estimate the number needed to treat to harm (NNTH),46  which is the number of adolescents who would need to be treated with fluoroquinolones instead of a comparator antibiotic for 1 additional adolescent to experience the outcome, in expectation. Analyses were performed by using SAS version 9.3 (SAS Institute, Inc, Cary, NC).

To assess the robustness of our results to assumptions, we conducted a number of sensitivity analyses: (1) To reduce confounding by indication, we restricted to adolescents with codes for UTI. (2) To minimize the influence of adolescents with frequent antibiotic use, we restricted to their first eligible follow-up. (3) To mitigate channeling bias47  and outcome measurement error,15  we restricted to the time period before the 2008 FDA boxed warning. (4) To reduce the potential obscuring of an association by short-course antibiotic treatment, we restricted to adolescents with index fills with at least a 3-day supply (98.8% of fluoroquinolone group and 98.7% of comparator group). (5) To increase comparability between fluoroquinolone and comparator groups,48,49  before constructing SMR weights, we implemented asymmetric trimming of the propensity score50  at the first and 99th percentiles. (6) To reduce potential bias due to missing data on indication (20.5% of the cohort), we used inverse probability-of-missingness weights51  in combination with refit SMR weights estimated in adolescents with complete data. (7) To examine associations with specific fluoroquinolones, we restricted the fluoroquinolone-treated group to ciprofloxacin (rupture events were too rare to examine other fluoroquinolones alone). Finally, we also conducted a bias analysis to assess the impact of possible differential outcome misclassification (Supplemental Information).

There were 15.9 million outpatient fills for an oral fluoroquinolone or comparator antibiotic among adolescents (aged 12–18 years) between 2000 and 2018. In the final study population (Fig 1), 4.4 million unique adolescents experienced a total of 7.6 million eligible antibiotic treatments, including 275 767 (3.6%) for fluoroquinolones. Ciprofloxacin was the most common fluoroquinolone (73%), and azithromycin (45%) and amoxicillin-clavulanate (19%) were the most common comparators; day supply was similar between fluoroquinolones and comparators (Supplemental Table 4).

Table 1 includes the cohort characteristics stratified by treatment. Before weighting (crude), adolescents treated with fluoroquinolones were more likely to be female (69%) and older (median age 17) compared with adolescents treated with the comparator antibiotics (female 54%, median age 15). A greater proportion of fluoroquinolone treatments (32%) than comparator treatments (8%) included a diagnosis that almost always requires an antibiotic (tier 1). As shown in Supplemental Table 5, UTI was the most common indication for fluoroquinolone treatment (29%), followed by gastrointestinal infections (8%) and sinusitis (7%); for comparators, the most common indications were sinusitis (18%), pharyngitis (17%), and skin, cutaneous, and mucosal infections (8%).

TABLE 1

Demographic and Clinical Characteristics by Treatment Group, Crude and Weighted

CrudeWeighteda
Fluoroquinolones (n = 275 767)Comparatorsb (n = 7 365 684)Standardized Mean DifferenceComparatorsb (n = 280 168)Standardized Mean Difference
Region, n (%)c   0.038  0.004 
 Northeast 37 331 (13.5) 977 952 (13.3) — 38 019 (13.6) — 
 North central 67 143 (24.3) 1 890 835 (25.7) — 68 071 (24.3) — 
 South 129 936 (47.1) 3 464 911 (47.0) — 132 236 (47.2) — 
 West 38 306 (13.9) 959 147 (13.0) — 38 683 (13.8) — 
 Unknown 3051 (1.1) 72 839 (1.0) — 3159 (1.1) — 
Sex, n (%)   −0.311  0.005 
 Male 84 950 (30.8) 3 367 759 (45.7) — 85 689 (30.6) — 
 Female 190 817 (69.2) 3 997 925(54.3) — 194 479 (69.4) — 
Age, y   0.872  0.003 
 Median (IQR) 17 (16 to 18) 15 (14 to 17) — 17 (16 to 18) — 
 12–13, n (%) 13 256 (4.8) 1 824 333 (24.8) — 13 329 (4.8) — 
 14–15, n (%) 37 423 (13.6) 1 995 660 (27.1) — 37 859 (13.5) — 
 16–17, n (%) 109 504 (39.7) 2 374 101 (32.2) — 111 416 (39.8) — 
 18, n (%) 115 584 (41.9) 1 171 590 (15.9) — 117 564 (42.0) — 
Indication, n (%)      
 Diagnosis identifiedd      
  Tier 1 87 876 (31.9) 590 646 (8.0) 0.625 93 322 (33.3) −0.031 
  Tier 2 62 273 (22.6) 3 501 615 (47.5) −0.542 61 500 (22.) 0.015 
  Tier 3 47 613 (17.3) 1 781 506 (24.2) −0.171 46 886 (16.7) 0.014 
 Diagnosis not identifiede 78 005 (28.3) 1 491 917 (20.3) 0.145 78 459 (28.0) 0.005 
Recent antibiotic, n (%)f      
 Previous 14 d 12 396 (4.5) 236 531 (3.2) 0.067 13 027 (4.6) −0.007 
 Previous 30 d 21 814 (7.9) 447 580 (6.1) 0.072 22 747 (8.1) −0.008 
 Previous 100 d 46 671 (16.9) 1 049 909 (14.3) 0.074 48 109 (17.2) −0.007 
Recent corticosteroids, n (%)g      
 Same day 9334 (3.4) 423 015 (5.7) −0.113 9389 (3.4) 0.002 
 Previous 100 d 10 847 (3.9) 236 543 (3.2) 0.039 11 327 (4.0) −0.006 
Health care useh      
 Count of outpatient encounters, median (IQR) 3 (2 to 6) 3 (2 to 5) 0.088 3 (2 to 6) −0.021 
 Any inpatient admission, n (%) 2930 (1.1) 40 867 (0.6) 0.057 3137 (1.1) −0.006 
Comorbid conditions, n (%)      
 Any complex chronic conditioni 20 534 (7.4) 400 659 (5.4) 0.082 22 026 (7.9) −0.016 
  Neurologic or neuromuscular 2152 (0.8) 40 682 (0.6) 0.028 2410 (0.9) −0.009 
  Cardiovascular 2949 (1.1) 64 971 (0.9) 0.019 3051 (1.1) −0.002 
  Respiratory 1530 (0.6) 7445 (0.1) 0.079 1867 (0.7) −0.014 
  Renal or urologic 1372 (0.50 12 162 (0.2) 0.058 1605 (0.6) −0.010 
  Gastrointestinal 3629 (1.3) 24 688 (0.3) 0.109 4395 (1.6) −0.021 
  Hematologic or immunologic 1279 (0.5) 21 887 (0.3) 0.027 1405 (0.5) −0.005 
  Metabolic 3584 (1.3) 71 998 (1.0) 0.030 3761 (1.3) −0.004 
  Other congenital or genetic defect 3410 (1.2) 105 587 (1.4) −0.017 3603 (1.3) −0.004 
  Malignancy 4020 (1.5) 93 256 (1.3) 0.017 4087 (1.5) <0.001 
  Premature or neonatal 24 (<0.1) 611 (<0.1) <0.001 26 (<0.1) −0.001 
  Transplantation 213 (0.1) 3704 (0.1) 0.011 250 (0.1) −0.004 
  Device 1105 (0.4) 14 668 (0.2) 0.037 1335 (0.5) −0.011 
CrudeWeighteda
Fluoroquinolones (n = 275 767)Comparatorsb (n = 7 365 684)Standardized Mean DifferenceComparatorsb (n = 280 168)Standardized Mean Difference
Region, n (%)c   0.038  0.004 
 Northeast 37 331 (13.5) 977 952 (13.3) — 38 019 (13.6) — 
 North central 67 143 (24.3) 1 890 835 (25.7) — 68 071 (24.3) — 
 South 129 936 (47.1) 3 464 911 (47.0) — 132 236 (47.2) — 
 West 38 306 (13.9) 959 147 (13.0) — 38 683 (13.8) — 
 Unknown 3051 (1.1) 72 839 (1.0) — 3159 (1.1) — 
Sex, n (%)   −0.311  0.005 
 Male 84 950 (30.8) 3 367 759 (45.7) — 85 689 (30.6) — 
 Female 190 817 (69.2) 3 997 925(54.3) — 194 479 (69.4) — 
Age, y   0.872  0.003 
 Median (IQR) 17 (16 to 18) 15 (14 to 17) — 17 (16 to 18) — 
 12–13, n (%) 13 256 (4.8) 1 824 333 (24.8) — 13 329 (4.8) — 
 14–15, n (%) 37 423 (13.6) 1 995 660 (27.1) — 37 859 (13.5) — 
 16–17, n (%) 109 504 (39.7) 2 374 101 (32.2) — 111 416 (39.8) — 
 18, n (%) 115 584 (41.9) 1 171 590 (15.9) — 117 564 (42.0) — 
Indication, n (%)      
 Diagnosis identifiedd      
  Tier 1 87 876 (31.9) 590 646 (8.0) 0.625 93 322 (33.3) −0.031 
  Tier 2 62 273 (22.6) 3 501 615 (47.5) −0.542 61 500 (22.) 0.015 
  Tier 3 47 613 (17.3) 1 781 506 (24.2) −0.171 46 886 (16.7) 0.014 
 Diagnosis not identifiede 78 005 (28.3) 1 491 917 (20.3) 0.145 78 459 (28.0) 0.005 
Recent antibiotic, n (%)f      
 Previous 14 d 12 396 (4.5) 236 531 (3.2) 0.067 13 027 (4.6) −0.007 
 Previous 30 d 21 814 (7.9) 447 580 (6.1) 0.072 22 747 (8.1) −0.008 
 Previous 100 d 46 671 (16.9) 1 049 909 (14.3) 0.074 48 109 (17.2) −0.007 
Recent corticosteroids, n (%)g      
 Same day 9334 (3.4) 423 015 (5.7) −0.113 9389 (3.4) 0.002 
 Previous 100 d 10 847 (3.9) 236 543 (3.2) 0.039 11 327 (4.0) −0.006 
Health care useh      
 Count of outpatient encounters, median (IQR) 3 (2 to 6) 3 (2 to 5) 0.088 3 (2 to 6) −0.021 
 Any inpatient admission, n (%) 2930 (1.1) 40 867 (0.6) 0.057 3137 (1.1) −0.006 
Comorbid conditions, n (%)      
 Any complex chronic conditioni 20 534 (7.4) 400 659 (5.4) 0.082 22 026 (7.9) −0.016 
  Neurologic or neuromuscular 2152 (0.8) 40 682 (0.6) 0.028 2410 (0.9) −0.009 
  Cardiovascular 2949 (1.1) 64 971 (0.9) 0.019 3051 (1.1) −0.002 
  Respiratory 1530 (0.6) 7445 (0.1) 0.079 1867 (0.7) −0.014 
  Renal or urologic 1372 (0.50 12 162 (0.2) 0.058 1605 (0.6) −0.010 
  Gastrointestinal 3629 (1.3) 24 688 (0.3) 0.109 4395 (1.6) −0.021 
  Hematologic or immunologic 1279 (0.5) 21 887 (0.3) 0.027 1405 (0.5) −0.005 
  Metabolic 3584 (1.3) 71 998 (1.0) 0.030 3761 (1.3) −0.004 
  Other congenital or genetic defect 3410 (1.2) 105 587 (1.4) −0.017 3603 (1.3) −0.004 
  Malignancy 4020 (1.5) 93 256 (1.3) 0.017 4087 (1.5) <0.001 
  Premature or neonatal 24 (<0.1) 611 (<0.1) <0.001 26 (<0.1) −0.001 
  Transplantation 213 (0.1) 3704 (0.1) 0.011 250 (0.1) −0.004 
  Device 1105 (0.4) 14 668 (0.2) 0.037 1335 (0.5) −0.011 

IQR, interquartile range; —, not applicable.

a

SMR weighting: comparator-treated group weighted to covariate distribution of the fluoroquinolone-treated group.

b

Comparators: amoxicillin-clavulanate, azithromycin, cefalexin, cefdinir, cefixime, nitrofurantoin, and sulfamethoxazole-trimethoprim.

c

Northeast: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont; Midwest: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin; South: Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia; West: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.

d

Codes were obtained from any encounter up to the day of the index fill or 3 d before. Codes were classified by using diagnostic categories developed by Fleming-Dutra et al.36  Beneficiaries were only counted in the priority category (priority order: tier 1, tier 2, tier 3).

e

Either no recent codes were available (ie, the child did not have any health care encounters on the day of the antibiotic fill or in the 3 d before) or the diagnoses codes captured during that period were for an unrelated antibiotic or were nonspecific (E-codes and codes for symptoms, signs, or ill-defined health conditions). This category includes all other diagnosis codes that were not included in tier 3.

f

Any outpatient pharmacy prescription fill for a systemic antibiotic (Anatomical Therapeutic Chemical group J01).

g

Outpatient pharmacy prescription fill for systemic budesonide, triamcinolone hexacetonide, prednisone, prednisolone, methylprednisolone, dexamethasone, or hydrocortisone.

h

Captured during the 180 d before the index fill. Count of outpatient encounters is the count of unique service and discharge dates. The presence of any inpatient admission during 180 to 101 d before the index fill (exclusion criteria prohibited inpatient admission during the 100 d before).

i

Feudtner et al.37,38 

We identified 1478 tendon ruptures in the 180 days of follow-up for a crude rate of 0.43 per 1000 person-years (Supplemental Table 6). There were 32 335 tendinitis diagnoses (rate of 9.45 per 1000 person-years). For the negative control outcome, there were 7366 clavicle fractures for a crude rate of 2.15 per 1000 person-years.

SMR weights effectively balanced the treatment groups on measured covariates (Table 1, Supplemental Fig 6, Supplemental Table 5). The weighted risk difference per 100 000 adolescents for clavicle fracture and the negative control outcomes from the ITT analysis and the per-protocol analysis are depicted in Supplemental Figure 7. The difference was close to zero (null) before 90 days of follow-up. After 90 days, the difference became negative. Given that it is not biologically plausible that fluoroquinolones are protective against clavicle fracture >90 days after treatment initiation, we believe there is residual bias in this period and restrict presentation of the remaining results to 90 days of follow-up.

Figures 2 and 3 present the crude and weighted risk curves by treatment group and the weighted risk difference for tendon rupture and tendinitis, respectively, from the ITT analysis. The numeric results at 15, 30, and 90 days are presented in Supplemental Table 7. The weighted 90-day risk of tendon rupture was higher among the fluoroquinolone-treated adolescents (13.6 per 100 000 adolescents; 95% CI 9.4 to 17.8) compared with those treated with the comparator antibiotics (11.6 per 100 000 adolescents; 95% CI 9.8 to 13.5) for a difference of 1.9 per 100 000 adolescents (95% CI −2.6 to 6.4). The NNTH indicated that 52 632 adolescents would need to be treated with fluoroquinolones instead of a comparator antibiotic for 1 additional adolescent to experience tendon rupture within 90 days. For tendinitis, the 90-day ITT risk difference was 22.7 per 100 000 adolescents (95% CI 4.1 to 41.3) for an NNTH of 4405. The per-protocol analysis results were similar (Supplemental Table 8, Supplemental Fig 8).

FIGURE 2

ITT analysis results for tendon rupture. A, Crude risk per 100 000 adolescents. B, Weighted risk per 100 000 adolescents. C, Weighted risk difference per 100 000 adolescents. Shaded areas are 95% CIs.

FIGURE 2

ITT analysis results for tendon rupture. A, Crude risk per 100 000 adolescents. B, Weighted risk per 100 000 adolescents. C, Weighted risk difference per 100 000 adolescents. Shaded areas are 95% CIs.

Close modal
FIGURE 3

ITT analysis results for tendinitis. A, Crude risk per 100 000 adolescents. B, Weighted risk per 100 000 adolescents. C, Weighted risk difference per 100 000 adolescents. Shaded areas are 95% CIs.

FIGURE 3

ITT analysis results for tendinitis. A, Crude risk per 100 000 adolescents. B, Weighted risk per 100 000 adolescents. C, Weighted risk difference per 100 000 adolescents. Shaded areas are 95% CIs.

Close modal

The 30- and 90-day weighted risk differences per 100 000 adolescents for tendon rupture and tendinitis from each sensitivity analysis and the ITT analysis are presented in Figure 4. The tendon rupture sensitivity analysis estimates for 90-day risk difference per 100 000 adolescents ranged from −1.8 (95% CI −8.6 to 5.0) from the analysis restricted to the first eligible new-user period to 4.2 (95% CI −0.8 to 9.1) from the analysis in which missing data weights were used. The tendinitis sensitivity analysis 90-day estimates ranged from 8.3 (95% CI −14.9 to 31.4) from the first new-user period analysis to 38.7 (95% CI 7.5 to 70.0) from the analysis restricted to UTI indications. The bias analysis indicated that if differential misclassification of the outcome were present, our primary analysis results would have overestimated the fluoroquinolone-associated excess risk of tendon rupture and tendinitis (Supplemental Table 9).

FIGURE 4

Weighted 30- and 90-day risk differences of tendon injury per 100 000 adolescents from sensitivity analyses. Error bars are 95% CIs. Primary analysis n = 7 641 541; UTI only n = 401 652; first eligible period n = 4 364 270; before FDA warning n = 1 858 304; >2-day supply n = 7 545 272; asymmetric trimming n = 6 602 672; missing data weights n = 7 641 541; ciprofloxacin only n = 7 565 647.

FIGURE 4

Weighted 30- and 90-day risk differences of tendon injury per 100 000 adolescents from sensitivity analyses. Error bars are 95% CIs. Primary analysis n = 7 641 541; UTI only n = 401 652; first eligible period n = 4 364 270; before FDA warning n = 1 858 304; >2-day supply n = 7 545 272; asymmetric trimming n = 6 602 672; missing data weights n = 7 641 541; ciprofloxacin only n = 7 565 647.

Close modal

In a cohort of 4.4 million 12 to 18-year-olds with 7.6 million antibiotic fills, we observed an elevated risk of tendon rupture associated with fluoroquinolone antibiotics compared with other broad-spectrum antibiotics. However, tendon rupture was rare, the risk difference was extremely small (even absent in some sensitivity analyses), and our primary results would overestimate the fluoroquinolone-associated excess risk in the presence of differential outcome misclassification. According to these data, on average, >50 000 adolescents would need to be treated with a fluoroquinolone for 1 additional tendon rupture to occur. The fluoroquinolone-associated excess risk was larger for tendinitis but was still small (1 additional event for every 4400 adolescents exposed) and was potentially inflated by differential outcome misclassification.

Few epidemiological studies have been focused on tendon-related adverse effects of fluoroquinolones in children. The first and only large comparative epidemiological study in children was conducted in 2002, and the 60-day risk of a composite outcome of tendon or joint injury was compared between fluoroquinolone and azithromycin treatment in nearly 21 500 children ≤18 years old.28  The age- and sex-adjusted relative risk was 1.08 (95% CI 0.77 to 1.52), and the authors concluded that fluoroquinolone treatment was not associated with an increased risk. The authors validated outcomes via review of linked medical records, reducing potential bias from outcome misclassification52 ; however, the sample size was small (given the rarity of the outcome), the analysis did not robustly address confounding, and azithromycin alone may not be an appropriate comparator because it is not used for UTI, a common fluoroquinolone indication. In addition, by using a composite outcome, tendon-related adverse effects alone were not examined.

Investigators of multiple large epidemiological studies1221  have observed an association between fluoroquinolones and tendon injury or rupture in adults. Because the authors of these studies typically reported odds ratios or risk ratios without presenting adjusted risks, comparison with our results is challenging. Although investigators of some adult studies have observed large associations (eg, relative risk of 6.2918 ), it can be difficult to translate this association into potential harms for treated patients. Of the 3 adult studies in which rate differences were estimated, the excess rate difference for tendon rupture in 60 days of follow-up was 29 cases per 100 000 person-years,21  and rate differences for tendon injury were 320 cases per 100 000 person-years in one study12  and 1461 cases per 100 000 person-years in another.13  Although direct comparison of rates and risks requires assumptions (eg, constant rates over follow-up, valid published estimates), it appears that previous published estimates were higher in adults than what we observed in adolescents in this study.

In one adult study, the authors did not observe an association between fluoroquinolones and Achilles tendon rupture and hypothesized that observed associations in other studies may be the result of bias from differential misclassification of the outcome.15  The authors hypothesized that increased awareness of potential tendon-related adverse effects made providers more likely to include a tendon-related code for patients with recent fluoroquinolone exposure. To address this potential source of bias, we conducted a sensitivity analysis restricted to the time period before the 2008 FDA warning and a bias analysis. Although awareness of tendon-related adverse effects was likely present before the FDA warning, we hypothesize that it would have been lower before the warning than after the warning, which thus provides information on the likely direction of bias. These analyses suggest that our primary analysis, assuming no misclassification, is likely an overestimate of the excess risk due to fluoroquinolones.

Our analysis has a number of strengths. Our cohort includes >7.6 million antibiotic fills for >4 million privately-insured adolescents across the United States over 2 decades. We implemented both an ITT analysis and a per-protocol analysis, which accounts for treatment switching and can elucidate potential bias toward the null inherent to ITT designs.45  We conducted a number of sensitivity analyses and a bias analysis to understand how our study design and analytic decisions might impact results. Importantly, the results of these analyses did not change our interpretations of the findings. Use of comparator broad-spectrum antibiotics that cover the range of potential indications of fluoroquinolones was also a strength, as was our implementation of a weighted analysis to control for measured confounding and estimate marginal risk differences. Lastly, we used a negative control outcome to assess residual confounding.

This study has limitations. Despite the size and scope afforded by MarketScan, including antibiotic prescriptions and injury care across the full spectrum of health care settings, our results may not be generalizable to adolescents outside this database, particularly those on Medicaid or without insurance. Our outcome definition relied on diagnosis and CPT codes, which are susceptible to misclassification. In particular, tendinitis identification may have a low positive predictive value (ie, many false-positives) because of “rule-out” diagnoses and subjectivity of this diagnosis. Results from our bias analysis indicate that differential outcome misclassification likely resulted in upward bias, which would overestimate the excess harm associated with fluoroquinolones. Adolescents who filled a prescription but did not actually take the medication would have been inappropriately included in the analysis, although this would likely be nondifferential with respect to the outcome. In our analysis, we assumed that censoring due to disenrollment or treatment switching was random, and if incorrect, our results could be biased.53  Finally, although our negative control outcome analysis suggested good control of confounding, there could be residual bias related to differences in the confounding structure of tendon injury and clavicle fracture.

Fluoroquinolone treatment was associated with excess risk of tendon rupture and tendinitis compared with alternate antibiotic therapy. However, these differences were extremely small, and tendon-related adverse events were rare. This analysis provides important context for understanding the risk/benefit ratio of fluoroquinolones for adolescents. Given the rarity of tendon injury and the small excess risks associated with fluoroquinolones, more common adverse drug effects may be more important to consider for treatment decision-making, particularly in adolescents without other risk factors for tendon injury.

Ms Ross conceptualized and designed the study, conducted the analysis, interpreted the data, drafted the initial manuscript, and reviewed and revised the manuscript; Dr Kinlaw conceptualized and designed the study, supervised the analysis, interpreted the data, and critically reviewed and revised the manuscript; Drs Herzog and Jonsson Funk contributed to the design of the study, interpreted the data, and critically reviewed and revised the manuscript; Dr Gerber conceptualized and designed the study, interpreted the data, and critically reviewed and revised the manuscript; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

FUNDING: Ms Ross was supported by the National Institute on Aging at the National Institutes of Health (R01 AG056479), by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (T32 HD52468), and by the Antimicrobial Stewardship Fellowship Award from the Pediatric Infectious Diseases Society. Dr Jonsson Funk was supported by investigator-initiated research funding as a principal investigator from the US Food and Drug Administration (75F40119C10115) and the National Heart, Lung, and Blood Institute (R01 HL118255) and as a coinvestigator from the US Food and Drug Administration (Comparative Effectiveness Research‐2017C3‐9230), Centers for Disease Control and Prevention (1U01DP006369‐01), National Center for Advancing Translational Sciences (1 U54 TR002255), National Institute on Aging (1R01AG056479), and Health Resources and Services Administration (R40MC29455‐01‐00). Dr Jonsson Funk also received salary support from the Center for Pharmacoepidemiology, Department of Epidemiology, University of North Carolina at Chapel Hill (UNC) (members: GlaxoSmithKline, UCB Biosciences, Merck [past member], Takeda, Abbvie, and Boehringer Ingelheim), and consulting fees via UNC from GlaxoSmithKline. The database infrastructure used for this project was funded by the Pharmacoepidemiology Gillings Innovation Laboratory for the Population-Based Evaluation of Drug Benefits and Harms in Older US Adults (GIL200811.0010); by the Center for Pharmacoepidemiology, Department of Epidemiology, UNC Gillings School of Global Public Health; by the Comparative Effectiveness Research Strategic Initiative of UNC’s Clinical and Translational Science Award (UL1TR 002489); by The Cecil G. Sheps Center for Health Services Research, UNC; and by the UNC School of Medicine. Funders had no role in the design and conduction of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Funded by the National Institutes of Health (NIH).

COMPANION PAPER: A companion to this article can be found online at www.pediatrics.org/cgi/doi/10.1542/peds.2021-050321.

     
  • CI

    confidence interval

  •  
  • CPT

    Current Procedural Terminology

  •  
  • FDA

    US Food and Drug Administration

  •  
  • ITT

    intention-to-treat

  •  
  • NNTH

    number needed to treat to harm

  •  
  • SMR

    standardized mortality ratio

  •  
  • UTI

    urinary tract infection

1
Lovegrove
MC
,
Geller
AI
,
Fleming-Dutra
KE
,
Shehab
N
,
Sapiano
MRP
,
Budnitz
DS
.
US emergency department visits for adverse drug events from antibiotics in children, 2011–2015
.
J Pediatric Infect Dis Soc
.
2019
;
8
(
5
):
384
391
2
Elzagallaai
AA
,
Greff
M
,
Rieder
MJ
.
Adverse drug reactions in children: the double-edged sword of therapeutics
.
Clin Pharmacol Ther
.
2017
;
101
(
6
):
725
735
3
US Food and Drug Administration
.
Drug research and children
.
2016
.
4
Hersh
AL
,
Gerber
JS
,
Hicks
LA
,
Pavia
AT
.
Lessons learned in antibiotic stewardship: fluoroquinolone use in pediatrics
.
J Pediatric Infect Dis Soc
.
2015
;
4
(
1
):
57
59
5
Patel
K
,
Goldman
JL
.
Safety concerns surrounding quinolone use in children
.
J Clin Pharmacol
.
2016
;
56
(
9
):
1060
1075
6
Committee on Infectious Diseases
.
The use of systemic fluoroquinolones
.
Pediatrics
.
2006
;
118
(
3
):
1287
1292
7
Bradley
JS
,
Jackson
MA
;
Committee on Infectious Diseases
;
American Academy of Pediatrics
.
The use of systemic and topical fluoroquinolones
.
Pediatrics
.
2011
;
128
(
4
).
8
Jackson
MA
,
Schutze
GE
;
Committee on Infectious Diseases
.
The use of systemic and topical fluoroquinolones
.
Pediatrics
.
2016
;
138
(
5
):
e20162706
9
King
LM
,
Bartoces
M
,
Fleming-Dutra
KE
,
Roberts
RM
,
Hicks
LA
.
Changes in US outpatient antibiotic prescriptions from 2011–2016
.
Clin Infect Dis
.
2020
;
70
(
3
):
370
377
10
Ingham
B
,
Brentnall
DW
,
Dale
EA
,
McFadzean
JA
.
Arthropathy induced by antibacterial fused N-alkyl-4-pyridone-3-carboxylic acids
.
Toxicol Lett
.
1977
;
1
(
1
):
21
26
11
Adefurin
A
,
Sammons
H
,
Jacqz-Aigrain
E
,
Choonara
I
.
Ciprofloxacin safety in paediatrics: a systematic review
.
Arch Dis Child
.
2011
;
96
(
9
):
874
880
12
van der Linden
PD
,
van de Lei
J
,
Nab
HW
,
Knol
A
,
Stricker
BH
.
Achilles tendinitis associated with fluoroquinolones
.
Br J Clin Pharmacol
.
1999
;
48
(
3
):
433
437
13
van der Linden
PD
,
Sturkenboom
MC
,
Herings
RM
,
Leufkens
HG
,
Stricker
BH
.
Fluoroquinolones and risk of Achilles tendon disorders: case-control study
.
BMJ
.
2002
;
324
(
7349
):
1306
1307
14
van der Linden
PD
,
Sturkenboom
MC
,
Herings
RM
,
Leufkens
HM
,
Rowlands
S
,
Stricker
BH
.
Increased risk of Achilles tendon rupture with quinolone antibacterial use, especially in elderly patients taking oral corticosteroids
.
Arch Intern Med
.
2003
;
163
(
15
):
1801
1807
15
Seeger
JD
,
West
WA
,
Fife
D
,
Noel
GJ
,
Johnson
LN
,
Walker
AM
.
Achilles tendon rupture and its association with fluoroquinolone antibiotics and other potential risk factors in a managed care population
.
Pharmacoepidemiol Drug Saf
.
2006
;
15
(
11
):
784
792
16
Corrao
G
,
Zambon
A
,
Bertù
L
, et al
.
Evidence of tendinitis provoked by fluoroquinolone treatment: a case-control study
.
Drug Saf
.
2006
;
29
(
10
):
889
896
17
Sode
J
,
Obel
N
,
Hallas
J
,
Lassen
A
.
Use of fluroquinolone and risk of Achilles tendon rupture: a population-based cohort study
.
Eur J Clin Pharmacol
.
2007
;
63
(
5
):
499
503
18
Hori
K
,
Yamakawa
K
,
Yoshida
N
,
Ohnishi
K
,
Kawakami
J
.
Detection of fluoroquinolone-induced tendon disorders using a hospital database in Japan
.
Pharmacoepidemiol Drug Saf
.
2012
;
21
(
8
):
886
889
19
Wise
BL
,
Peloquin
C
,
Choi
H
,
Lane
NE
,
Zhang
Y
.
Impact of age, sex, obesity, and steroid use on quinolone-associated tendon disorders
.
Am J Med
.
2012
;
125
(
12
):
1228.e23
1228.e28
20
Jupiter
DC
,
Fang
X
,
Ashmore
Z
,
Shibuya
N
,
Mehta
HB
.
The relative risk of Achilles tendon injury in patients taking quinolones
.
Pharmacotherapy
.
2018
;
38
(
9
):
878
887
21
Morales
DR
,
Slattery
J
,
Pacurariu
A
,
Pinheiro
L
,
McGettigan
P
,
Kurz
X
.
Relative and absolute risk of tendon rupture with fluoroquinolone and concomitant fluoroquinolone/corticosteroid therapy: population-based nested case-control study
.
Clin Drug Investig
.
2019
;
39
(
2
):
205
213
22
Thomopoulos
S
,
Parks
WC
,
Rifkin
DB
,
Derwin
KA
.
Mechanisms of tendon injury and repair
.
J Orthop Res
.
2015
;
33
(
6
):
832
839
23
Bidell
MR
,
Lodise
TP
.
Fluoroquinolone-associated tendinopathy: does levofloxacin pose the greatest risk?
Pharmacotherapy
.
2016
;
36
(
6
):
679
693
24
Tsai
WC
,
Hsu
CC
,
Chen
CP
, et al
.
Ciprofloxacin up-regulates tendon cells to express matrix metalloproteinase-2 with degradation of type I collagen
.
J Orthop Res
.
2011
;
29
(
1
):
67
73
25
Lewis
T
,
Cook
J
.
Fluoroquinolones and tendinopathy: a guide for athletes and sports clinicians and a systematic review of the literature
.
J Athl Train
.
2014
;
49
(
3
):
422
427
26
Tanne
JH
.
FDA adds “black box” warning label to fluoroquinolone antibiotics
.
BMJ
.
2008
;
337
:
a816
27
US Food and Drug Administration
.
Information for healthcare professionals: fluoroquinolone antimicrobial drugs [ciprofloxacin (marketed as Cipro and generic ciprofloxacin), ciprofloxacin extended-release (marketed as Cipro XR and Proquin XR), gemifloxacin (marketed as Factive), levofloxacin (marketed as Levaquin), moxifloxacin (marketed as Avelox), norfloxacin (marketed as Noroxin), and ofloxacin (marketed as Floxin)]
.
2008
.
28
Yee
CL
,
Duffy
C
,
Gerbino
PG
,
Stryker
S
,
Noel
GJ
.
Tendon or joint disorders in children after treatment with fluoroquinolones or azithromycin
.
Pediatr Infect Dis J
.
2002
;
21
(
6
):
525
529
29
Lund
JL
,
Richardson
DB
,
Stürmer
T
.
The active comparator, new user study design in pharmacoepidemiology: historical foundations and contemporary application
.
Curr Epidemiol Rep
.
2015
;
2
(
4
):
221
228
30
Ray
WA
.
Evaluating medication effects outside of clinical trials: new-user designs
.
Am J Epidemiol
.
2003
;
158
(
9
):
915
920
31
IBM Watson Health
.
IBM MarketScan research databases for life sciences researchers
.
2016
.
Available at: https://www.ibm.com/downloads/cas/OWZWJ0QO. Accessed August 6, 2020
32
Ross
RK
,
Gerber
JS
,
Willis
ZI
,
Hersh
AL
,
Kinlaw
AC
.
Outpatient fluoroquinolone use in children, 2000–2018 [published online ahead of print December 30, 2020]
.
J Pediatric Infec Dis Soc
.
2020
33
Schneeweiss
S
,
Rassen
JA
,
Brown
JS
, et al
.
Graphical depiction of longitudinal study designs in health care databases
.
Ann Intern Med
.
2019
;
170
(
6
):
398
406
34
Lipsitch
M
,
Tchetgen Tchetgen
E
,
Cohen
T
.
Negative controls: a tool for detecting confounding and bias in observational studies
.
Epidemiology
.
2010
;
21
(
3
):
383
388
35
Greenland
S
,
Pearl
J
,
Robins
JM
.
Causal diagrams for epidemiologic research
.
Epidemiology
.
1999
;
10
(
1
):
37
48
36
Fleming-Dutra
KE
,
Hersh
AL
,
Shapiro
DJ
, et al
.
Prevalence of inappropriate antibiotic prescriptions among US ambulatory care visits, 2010-2011
.
JAMA
.
2016
;
315
(
17
):
1864
1873
37
Feudtner
C
,
Christakis
DA
,
Connell
FA
.
Pediatric deaths attributable to complex chronic conditions: a population-based study of Washington State, 1980-1997
.
Pediatrics
.
2000
;
106
(
1 pt 2
):
205
209
38
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 Pediatr
.
2014
;
14
:
199
39
WHO Collaborating Centre for Drug Statistics Methodology
.
ATC/DDD index
.
Available at: https://www.whocc.no/atc_ddd_index/?code=J01. Accessed October 11, 2019
40
Hernán
MA
,
Robins
JM
.
Causal Inference: What If
.
Boca Raton, FL
:
Chapman & Hall/CRC
;
2020
41
Sato
T
,
Matsuyama
Y
.
Marginal structural models as a tool for standardization
.
Epidemiology
.
2003
;
14
(
6
):
680
686
42
Austin
PC
.
An introduction to propensity score methods for reducing the effects of confounding in observational studies
.
Multivariate Behav Res
.
2011
;
46
(
3
):
399
424
43
Yang
D
,
Dalton
JE
.
A unified approach to measuring the effect size between two groups using SAS
. In:
Proceedings from SAS Global Forum 2012
;
April 22–25, 2012
;
Orlando, FL
44
Cole
SR
,
Hernán
MA
.
Adjusted survival curves with inverse probability weights
.
Comput Methods Programs Biomed
.
2004
;
75
(
1
):
45
49
45
Hernán
MA
,
Hernández-Díaz
S
.
Beyond the intention-to-treat in comparative effectiveness research
.
Clin Trials
.
2012
;
9
(
1
):
48
55
46
Altman
DG
.
Confidence intervals for the number needed to treat
.
BMJ
.
1998
;
317
(
7168
):
1309
1312
47
Petri
H
,
Urquhart
J
.
Channeling bias in the interpretation of drug effects
.
Stat Med
.
1991
;
10
(
4
):
577
581
48
Petersen
ML
,
Porter
KE
,
Gruber
S
,
Wang
Y
,
van der Laan
MJ
.
Diagnosing and responding to violations in the positivity assumption
.
Stat Methods Med Res
.
2012
;
21
(
1
):
31
54
49
Westreich
D
,
Cole
SR
.
Invited commentary: positivity in practice
.
Am J Epidemiol
.
2010
;
171
(
6
):
674
677
,
NaN–681
50
Stürmer
T
,
Rothman
KJ
,
Avorn
J
,
Glynn
RJ
.
Treatment effects in the presence of unmeasured confounding: dealing with observations in the tails of the propensity score distribution–a simulation study
.
Am J Epidemiol
.
2010
;
172
(
7
):
843
854
51
Seaman
SR
,
White
IR
.
Review of inverse probability weighting for dealing with missing data
.
Stat Methods Med Res
.
2013
;
22
(
3
):
278
295
52
West
SL
,
Ritchey
ME
,
Poole
C
.
Validity of Pharmacoepidemiologic Drug and Diagnosis Data
. In:
Strom
BL
,
Kimmel
SE
,
Hennessy
S
, eds.
Pharmacoepidemiology
, 5th ed.
Chichester, United Kingdom
:
John Wiley & Sons, Ltd
;
2012
53
Robins
JM
,
Finkelstein
DM
.
Correcting for noncompliance and dependent censoring in an AIDS clinical trial with inverse probability of censoring weighted (IPCW) log-rank tests
.
Biometrics
.
2000
;
56
(
3
):
779
788

Competing Interests

POTENTIAL CONFLICT OF INTEREST: Dr Herzog is an employee of IQVIA, a human data science company. Dr Jonsson Funk directs the Center for Pharmacoepidemiology and receives salary support through the Center for Pharmacoepidemiology from member companies, including GlaxoSmithKline, Merck (past member), Takeda, AbbVie, Boehringer Ingelheim, and UCB Bioscience, which have collaborative agreements with the Center for Pharmacoepidemiology, housed in the Department of Epidemiology. Dr Jonsson Funk is a member of the scientific steering committee for a postapproval safety study of an unrelated drug class funded by GlaxoSmithKline. All compensation for services provided on the scientific steering committee is invoiced by and paid to the University of North Carolina at Chapel Hill; and Ms Ross and Drs Kinlaw and Gerber have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

Supplementary data