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BACKGROUND:

Little is known about the prescribing of medications with potential drug-drug interactions (DDIs) in the pediatric population. The objective of this study was to determine the prevalence and variation of prescribing medications with clinically significant DDIs across children’s hospitals in the United States.

METHODS:

We performed a retrospective cohort study of patients <26 years of age who were discharged from 1 of 52 US children’s hospitals between January 2016 and December 2018. Fifty-three drug pairings with clinically significant DDIs in children were evaluated. We identified patient-level risk factors associated with DDI using multivariable logistic regression. Adjusted hospital-level rates of DDI exposure were derived by using a generalized linear mixed-effects model, and DDI exposure variations were examined across individual hospitals.

RESULTS:

Across 52 children’s hospitals, 47 414 (2.0%) hospitalizations included exposure to a DDI pairing (34.9 per 1000 patient-days) during the study period. One-quarter of pairings were considered contraindicated (risk grade X). After adjusting for hospital and clinical factors, there was wide variation in the percentage of DDI prescribing across hospitals, ranging from 1.05% to 4.92%. There was also substantial hospital-level variation of exposures to individual drug pairings. Increasing age, number of complex chronic conditions, length of stay, and surgical encounters were independently associated with an increased odds of DDI exposure.

CONCLUSIONS:

Patients hospitalized at US children’s hospitals are frequently exposed to medications with clinically significant DDIs. Exposure risk varied substantially across hospitals. Further study is needed to determine the rate of adverse events due to DDI exposures and factors amenable for interventions promoting safer medication use.

What’s Known on This Subject:

Prescribing of medications with drug-drug interactions (DDIs) is common in US children’s hospitals. In previous studies, researchers used adult-based software to identify DDI exposures. The prescribing patterns of clinically significant DDIs in the pediatric population are unknown.

What This Study Adds:

DDIs that are high risk for adverse drug events in the hospitalized pediatric population are frequently prescribed. There are wide variations in the rates of DDI prescribing across hospitals. These results suggest potential targets to improve safe medication use in children.

The Institute of Medicine has identified medication safety and prevention of adverse drug events (ADEs), in particular, as a priority in caring for children.1  The rate of medication errors is similar among hospitalized children and adults; however, the potential rate of ADEs in hospitalized children may be up to 3 times higher than in hospitalized adults.2  The inpatient setting is an important area of focus as >50% of hospitalized children are exposed to ≥5 medications during their hospitalization. However, the clinical impact and safety of concurrent medication use has been difficult to assess in children. There is limited information on both the frequency of exposure to medications with a drug-drug interaction (DDI) and the rate of associated adverse events. In recent evidence, researchers suggest that up to 49% of hospitalized children are exposed to drug combinations associated with a DDI.3  However, the clinical relevance of many of these exposures is unclear.36  DDIs in children are likely different from those in adults because of differences in physiology and the prevalence of comorbidities, medications used, dosing of medication, medication clearance, and likelihood that a DDI will manifest when exposed.4  Furthermore, DDI software programs are known to differ substantially in their assessment of DDIs in children.4  Clinician assessment of the severity and significance of a DDI often differs from that found in adult’s drug classification systems.7 

In 2014, to better address DDIs in children, the Children’s Hospital Association clinical decision support expert working group identified 53 DDI pairings with “clinically significant” safety implications in the pediatric and adolescent population.4  These DDI pairings were identified on the basis of the frequency of medication use, severity of interaction, evidence of interaction adverse effects, and expert opinion on the importance of the interaction. The purpose of identifying these DDIs was to prioritize clinical care and research of DDIs in hospitalized children. A similar prioritization of DDIs in adults by national patient advocacy organizations has been used to develop safety protocols for health care systems.4,5,8  This prioritized tiering of DDIs can be used to increase provider compliance rates with DDI alerts.9 

However, the prevalence of exposure and prescribing patterns of these clinically significant DDIs in hospitalized children is currently unknown. The objectives of this study were to (1) determine the prevalence and risk factors of clinically significant pediatric DDI exposures in hospitalized children and (2) determine the variation of clinically significant DDI prescribing across children’s hospitals in the United States.

We conducted a multicenter retrospective cohort study of individuals admitted (inpatient and observation status) to children’s hospitals that contribute data to the Pediatric Health Information System (PHIS) database. The PHIS administrative database includes billing and use data from 52 tertiary care children’s hospitals across the United States that are affiliated with the Children’s Hospital Association (Lenexa, KS). Data quality and reliability are ensured through a joint effort between the Children’s Hospital Association and participating hospitals. Hospitals submit discharge data including demographics, diagnoses, procedures using the International Classification of Diseases, 10th Revision, Clinical Modification, as well as detailed daily pharmacy and claims information.

We included all hospitalized patients <26 years of age who were discharged from a PHIS hospital between January 1, 2016 and December 31, 2018. An age cutoff of 26 years was chosen because young adults admitted children’s hospitals represent those with chronic comorbidities stemming from birth or childhood. These individuals are likely a high-risk group for DDIs, and the risk of ADEs are likely more reflective of those experienced by children and adolescents in comparison with healthy young adults or older adults with comorbidities. We excluded normal newborns and those receiving neonatal intensive care and peri- or postpartum obstetrical care.

Medication pairings with clinically significant DDIs were identified by using a published expert consensus definition that includes 53 medication pairings with significant safety implications in pediatric populations.4  These DDI pairings were chosen on the basis of their clinical relevance, including the prevalence of prescribing, severity of interaction, and likelihood of resulting adverse event, in individuals treated at US children’s hospitals. Individuals were considered exposed to a DDI pair if there was ≥1 hospital day in which both medications were administered on the same hospital day. Medications captured include those from any hospital setting while inpatient, including operating rooms. Medications with topical, ophthalmic, and otic routes of delivery were excluded because these are unlikely to achieve the serum concentration levels necessary to be involved in a DDI. If the drug pairing involved a drug class or pharmacologic mechanism effect, exposure was determined for all drugs in the class or mechanism associated with the DDI and denoted with a “+” sign. Drugs in the class not associated with the DDI were excluded from the analysis. For example, for the “citalopram+, linezolid+” pair, selective serotonin reuptake inhibitors associated with the DDI include citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline, vilazodone, and vortioxetine but not dapoxetine. Therefore, all the selective serotonin reuptake inhibitors except dapoxetine were included in the analyses.

DDIs and associated risk grades were further classified by using the Lexicomp-Interactions System (Lexicomp, Inc, Hudson, OH). The Lexicomp software classifies the drug interactions into levels of DDI risk. The risk grades also provide a recommended action on the basis of the relevance of the interaction, level of evidence, and iatrogenic risk. Actions associated with risk grades are as follows: “A” (no interaction), “B” (no action is necessary), “C” (monitor therapy), “D” (modify regimen), and “X” (avoid combination). The DDI effects associated with each pairing were categorized in the following 5 groups on the basis of the Lexicomp description of the DDI: increased serum concentration, decreased serum drug concentration, increased risk of ADEs, altered drug metabolism, and multiple effects (constituting ≥2 effects).

Demographic characteristics were summarized by using frequencies and percentages for categorical variables and median and interquartile range (IQR) for continuous variables. Bivariate comparisons were made between patients with and without DDI exposures.

To define variation in DDI prescribing across hospitals, we first calculated the unadjusted proportions of drug pairings in aggregate and by individual drug pairings for each hospital. Proportions were calculated by dividing the number of hospitalizations with ≥1 DDI exposure by the total number of hospitalizations during the study period. The DDI exposure rate was calculated by dividing the number of hospital days with DDI exposures by the total number of hospital days and expressed per 1000 hospital days. Proportions and rates were stratified by risk grade and summarized by All Patients Refined Diagnosis Related Groups (3M Company, St Paul, Minnesota) service line assignment.

We calculated the adjusted hospital-level proportions of DDI exposure using a generalized linear mixed-effects model, with hospital included as a random effect. Covariates in the model included age, sex, race and ethnicity, payer, number of complex chronic conditions10  (CCCs), length of stay (LOS), service lines, geographic region, ICU status, and case mix index for each hospitalization. Factors independently associated with clinically significant DDI were evaluated by using multivariable logistic regression incorporating the covariates listed above. All analyses were performed by using SAS version 9.4 (SAS Institute, Inc, Cary, NC), and P < .05 was considered statistically significant. The Institutional Review Board at Vanderbilt University Medical Center determined that this study of deidentified data did not constitute human subjects research (Institutional Review Board 160244).

A total of 2 331 305 hospitalizations were included in the study. During the study period, 2.0% (47 414) of hospitalizations had an exposure to a clinically significant DDI drug pair. Of the total study population, 42 735 (1.83%), 3661 (0.16%), and 1018 (0.04%) were exposed to 1, 2, and ≥3 clinically significant DDI pairs, respectively. Children who were exposed to DDI pairs were older, compared with those not exposed to a DDI (median age of 10 vs 6 years; IQR 2–15; P < .001) and had a longer median LOS (5 vs 2 days; IQR 2–18; P < .001). Of those exposed to a DDI, 82.1% (38 911) had a CCC, 34.9% (16 556) had an ICU stay, and 51.4% (24 350) were non-Hispanic white (Table 1).

TABLE 1

Prevalence and Classification of DDIs Across US Children’s Hospitals

No. Encounters With DDIPercentage of Hospitalizations With DDI ExposureTotal Patient-Days With DDI ExposureDDI Exposure Rate per 1000 d (All Hospitalized Patients)DDI Exposure Rate per 1000 d (DDI Patients Only)
Any DDI 47 414 2.03 328 585 34.91 370.72 
Risk grade      
 X: avoid combination 11 734 0.50 28 644 3.04 221.27 
 D: consider treatment modification 12 841 0.55 103 820 11.03 117.13 
 C: monitor therapy 26 651 1.14 196 121 20.84 32.32 
No. Encounters With DDIPercentage of Hospitalizations With DDI ExposureTotal Patient-Days With DDI ExposureDDI Exposure Rate per 1000 d (All Hospitalized Patients)DDI Exposure Rate per 1000 d (DDI Patients Only)
Any DDI 47 414 2.03 328 585 34.91 370.72 
Risk grade      
 X: avoid combination 11 734 0.50 28 644 3.04 221.27 
 D: consider treatment modification 12 841 0.55 103 820 11.03 117.13 
 C: monitor therapy 26 651 1.14 196 121 20.84 32.32 

Number of encounters = 2 331 305; number of hospital days = 9 412 578; number of hospital days for DDI exposed cases = 886 354.

In multivariable analysis, increasing age, number of CCC, and longer LOS were associated with increasing odds of clinically significant DDI pair exposure (Table 2). Surgical service line, compared to medical service line, was associated with a 42% increase in the odds of DDI exposure. Factors associated with a decreased odds of clinically significant DDI pair exposure included female sex (adjusted odds ratio [aOR]: 0.91; 95% confidence interval [CI] 0.90–0.93), non-Hispanic Black race (aOR: 0.81; 95% CI 0.79–0.83), and private payer insurance (aOR: 0.89; 95% CI 0.87–0.91).

TABLE 2

Patient Demographics by DDI Exposures

DDI−, No. (%)DDI+, No. (%)aOR (95% CI)
N 2 283 891 (98.0) 47 414 (2.0) — 
Age group, y    
 <1 444 995 (19.5) 6167 (13) Reference 
 1–4 598 947 (26.2) 9291 (19.6) 1.40 (1.35–1.45) 
 5–11 575 257 (25.2) 11 828 (24.9) 1.91 (1.84–1.97) 
 12–17 568 256 (24.9) 14 750 (31.1) 2.38 (2.30–2.46) 
 18–25 96 436 (4.2) 5378 (11.3) 3.39 (3.25–3.53) 
Sex    
 Male 1 223 624 (53.6) 26 265 (55.4) Reference 
 Female 1 059 241 (46.4) 21 143 (44.6) 0.91 (0.90–0.93) 
Race    
 Non-Hispanic white 1 135 016 (49.7) 24 350 (51.4) Reference 
 Non-Hispanic Black 427 649 (18.7) 7555 (15.9) 0.81 (0.79–0.83) 
 Hispanic 452 954 (19.8) 9912 (20.9) 1.00a (0.97–1.03) 
 Asian American 64 155 (2.8) 1603 (3.4) 1.00a (0.94–1.05) 
 Other 204 117 (8.9) 3994 (8.4) 0.85 (0.82–0.88) 
Payer    
 Government 1 272 948 (55.7) 26 385 (55.6) Reference 
 Private 926 364 (40.6) 18 107 (38.2) 0.89 (0.87–0.91) 
 Other 84 579 (3.7) 2922 (6.2) 1.12 (1.07–1.17) 
Any CCC    
 No 1 373 029 (60.1) 8503 (17.9) — 
 Yes 910 862 (39.9) 38 911 (82.1) — 
No. CCCs    
 0 1 375 883 (60.2) 8629 (18.2) Reference 
 1 551 793 (24.2) 14 400 (30.4) 2.95 (2.87–3.03) 
 2–3 312 349 (13.7) 18 980 (40) 4.49 (4.37–4.63) 
 >3 43 866 (1.9) 5405 (11.4) 5.17 (4.96–5.39) 
ICU    
 No 1 980 285 (86.7) 30 858 (65.1) Reference 
 Yes 303 606 (13.3) 16 556 (34.9) 0.98a (0.95–1.00) 
LOS, d    
 1 961 435 (42.1) 8386 (17.7) Reference 
 2–3 724 539 (31.7) 9889 (20.9) 1.32 (1.27–1.36) 
 4–7 379 069 (16.6) 8791 (18.5) 1.61 (1.55–1.66) 
 8–14 135 926 (6) 6594 (13.9) 2.52 (2.43–2.62) 
 15–30 58 831 (2.6) 5729 (12.1) 3.70 (3.55–3.85) 
 >30 24 091 (1.1) 8025 (16.9) 7.63 (7.30–7.99) 
Service line    
 Medical 1 737 091 (76.1) 26 121 (55.1) Reference 
 Surgical 546 800 (23.9) 21 293 (44.9) 1.42 (1.39–1.45) 
Geographic regions    
 South 890 307 (39) 17 758 (37.5) Reference 
 Midwest 596 512 (26.1) 11 796 (24.9) 0.92a (0.74–1.14) 
 Northeast 326 095 (14.3) 7389 (15.6) 0.93a (0.71–1.21) 
 West 470 977 (20.6) 10 471 (22.1) 0.86a (0.69–1.08) 
DDI−, No. (%)DDI+, No. (%)aOR (95% CI)
N 2 283 891 (98.0) 47 414 (2.0) — 
Age group, y    
 <1 444 995 (19.5) 6167 (13) Reference 
 1–4 598 947 (26.2) 9291 (19.6) 1.40 (1.35–1.45) 
 5–11 575 257 (25.2) 11 828 (24.9) 1.91 (1.84–1.97) 
 12–17 568 256 (24.9) 14 750 (31.1) 2.38 (2.30–2.46) 
 18–25 96 436 (4.2) 5378 (11.3) 3.39 (3.25–3.53) 
Sex    
 Male 1 223 624 (53.6) 26 265 (55.4) Reference 
 Female 1 059 241 (46.4) 21 143 (44.6) 0.91 (0.90–0.93) 
Race    
 Non-Hispanic white 1 135 016 (49.7) 24 350 (51.4) Reference 
 Non-Hispanic Black 427 649 (18.7) 7555 (15.9) 0.81 (0.79–0.83) 
 Hispanic 452 954 (19.8) 9912 (20.9) 1.00a (0.97–1.03) 
 Asian American 64 155 (2.8) 1603 (3.4) 1.00a (0.94–1.05) 
 Other 204 117 (8.9) 3994 (8.4) 0.85 (0.82–0.88) 
Payer    
 Government 1 272 948 (55.7) 26 385 (55.6) Reference 
 Private 926 364 (40.6) 18 107 (38.2) 0.89 (0.87–0.91) 
 Other 84 579 (3.7) 2922 (6.2) 1.12 (1.07–1.17) 
Any CCC    
 No 1 373 029 (60.1) 8503 (17.9) — 
 Yes 910 862 (39.9) 38 911 (82.1) — 
No. CCCs    
 0 1 375 883 (60.2) 8629 (18.2) Reference 
 1 551 793 (24.2) 14 400 (30.4) 2.95 (2.87–3.03) 
 2–3 312 349 (13.7) 18 980 (40) 4.49 (4.37–4.63) 
 >3 43 866 (1.9) 5405 (11.4) 5.17 (4.96–5.39) 
ICU    
 No 1 980 285 (86.7) 30 858 (65.1) Reference 
 Yes 303 606 (13.3) 16 556 (34.9) 0.98a (0.95–1.00) 
LOS, d    
 1 961 435 (42.1) 8386 (17.7) Reference 
 2–3 724 539 (31.7) 9889 (20.9) 1.32 (1.27–1.36) 
 4–7 379 069 (16.6) 8791 (18.5) 1.61 (1.55–1.66) 
 8–14 135 926 (6) 6594 (13.9) 2.52 (2.43–2.62) 
 15–30 58 831 (2.6) 5729 (12.1) 3.70 (3.55–3.85) 
 >30 24 091 (1.1) 8025 (16.9) 7.63 (7.30–7.99) 
Service line    
 Medical 1 737 091 (76.1) 26 121 (55.1) Reference 
 Surgical 546 800 (23.9) 21 293 (44.9) 1.42 (1.39–1.45) 
Geographic regions    
 South 890 307 (39) 17 758 (37.5) Reference 
 Midwest 596 512 (26.1) 11 796 (24.9) 0.92a (0.74–1.14) 
 Northeast 326 095 (14.3) 7389 (15.6) 0.93a (0.71–1.21) 
 West 470 977 (20.6) 10 471 (22.1) 0.86a (0.69–1.08) 

All DDI−/DDI+ comparisons significant at P < .001. All aOR values statistically significant (P < .05) except where noted with footnote “a.” —, not applicable.

a

Not statistically significant.

Across the 2-year study period encompassing 328 585 total hospital days, there was a DDI exposure rate of 34.9 per 1000 hospital days. The most common DDI pairings were nicardipine and tacrolimus (14 893; 27.8%), fentanyl and linezolid (10 596; 19.7%), lamotrigine and valproic acid (2918; 5.5%), and haloperidol and quetiapine (2706; 5.1%). Individual drug pairing exposure rates and associated DDIs are listed in Table 3.

TABLE 3

Variation in Clinically Significant DDI Exposure Stratified by Risk Grade

Risk Grade and DDI No.Drug 1Drug 2Total Patient-Days of ExposureDDI Exposure Rate per 1000 DaysDDI Description
Δ [Drug]Δ Drug Metabolism↑ADE
       
 33 Fentanyl Linezolid+ 14 306 1.52 — — Xa 
 41 Methadone+ Voriconazole 8053 0.86 ↓ — — 
 18 Sirolimus Voriconazole 3133 0.33 ↑ — Xa 
 7 Citalopram+ Linezolid+ 1349 0.14 — — — 
 17 Rifampin+ Voriconazole 666 0.07 ↑ — — 
 15 Phenobarbital+ Voriconazole 438 0.05 ↓ — Xb 
 52 Voriconazole+ Ziprasidone+ 258 0.03 — — Xb 
 31 Erythromycin Sotalol+ 245 0.03 — — — 
 45 Nifedipine Phenytoin 101 0.01 ↑ and ↓ — — 
 12 Linezolid+ Sumatriptan+ 16 <0.01 — — 
 39 Levofloxacin+ Sotalol+ 79 0.01 — — Xb 
 10 Fosamprenavir Rifampin+ 0.00 ↓ — — 
       
 11 Lamotrigine+ Valproic acid 13 769 1.46 — — Xc 
 51 Tacrolimus Voriconazole 26 068 2.77 ↑ — — 
 43 Methotrexate Sulfamethoxazole/trimethoprim 2324 0.25 — — Xd 
 29 Cyclosporine Voriconazole+ 22 268 2.37 — — 
 20 Amlodipine+ Erythromycin+ 8772 0.93 — — 
 1 Amiodarone Digoxin 4669 0.50 ↑ — — 
 38 Ibuprofen+ Methotrexate 625 0.07 ↑ — — 
 8 Clopidogrel+ Omeprazole+ 3182 0.34 ↓ — — 
 13 Meropenem+ Valproic acid 2217 0.24 ↓ — — 
 2 Amiodarone Methadone 5145 0.55 — — Xb 
 3 Amiodarone Warfarin 4248 0.45 ↑ — — 
 37 Ibuprofen+ Lithium 1416 0.15 ↑ — — 
 30 Doxycycline Phenobarbital+ 999 0.11 ↓ —  
 50 Tacrolimus Rifampin+ 1422 0.15 ↓ —  
 47 Quetiapine Voriconazole+ 1335 0.14 — — Xb 
 21 Aspirin+ Methotrexate 181 0.02 ↑ — — 
 25 Carbamazepine Erythromycin 825 0.09 ↑ — — 
 14 Metronidazole Warfarin 452 0.05 ↑ — — 
 49 Sotalol+ Voriconazole+ 745 0.08 — — Xb 
 48 Rifampin+ Sirolimus 1247 0.13 ↓ — — 
 6 Ciprofloxacin+ Theophylline+ 304 0.03 — — 
 9 Clopidogrel Fluconazole 708 0.08 ↓ — — 
 34 Fluconazole+ Sotalol+ 316 0.03 — — Xb 
 28 Cyclosporine+ Rifampin+ 381 0.04 — — 
 27 Caspofungin Rifampin 122 0.01 ↓ — — 
 46 Phenytoin Warfarin+ 45 <0.01 ↑ — — 
 24 Carbamazepine Cyclosporine 35 <0.01 ↓ — — 
 16 Rifampin+ Tacrolimus 0.00 ↓ — — 
       
 44 Nicardipine+ Tacrolimus 111 427 11.84 ↑ — — 
 36 Haloperidol Quetiapine+ 6951 0.74 — — Xb 
 35 Furosemide+ Risperidone 19 882 2.11 — — Xe 
 42 Methotrexate Penicillin+ 2749 0.29 ↑ — — 
 4 Amitriptyline+ Sertraline+ 7806 0.83 ↑ — Xa 
 19 Amiodarone+ Ciprofloxacin+ 12 638 1.34 — — Xb 
 40 Methadone Phenobarbital 23 044 2.45 ↓ — — 
 53 Warfarin+ Rifampin+ 5883 0.63 ↓ — — 
 32 Erythromycin Tacrolimus 2995 0.32 ↑ — — 
 5 Amitriptyline+ Trazodone+ 1682 0.18 — — 
 23 Atorvastatin Fluconazole 496 0.05 ↑ — — 
 26 Carbamazepine Fluoxetine 341 0.04 ↑ — — 
 22 Atorvastatin Erythromycin 227 0.02 ↑ — — 
Risk Grade and DDI No.Drug 1Drug 2Total Patient-Days of ExposureDDI Exposure Rate per 1000 DaysDDI Description
Δ [Drug]Δ Drug Metabolism↑ADE
       
 33 Fentanyl Linezolid+ 14 306 1.52 — — Xa 
 41 Methadone+ Voriconazole 8053 0.86 ↓ — — 
 18 Sirolimus Voriconazole 3133 0.33 ↑ — Xa 
 7 Citalopram+ Linezolid+ 1349 0.14 — — — 
 17 Rifampin+ Voriconazole 666 0.07 ↑ — — 
 15 Phenobarbital+ Voriconazole 438 0.05 ↓ — Xb 
 52 Voriconazole+ Ziprasidone+ 258 0.03 — — Xb 
 31 Erythromycin Sotalol+ 245 0.03 — — — 
 45 Nifedipine Phenytoin 101 0.01 ↑ and ↓ — — 
 12 Linezolid+ Sumatriptan+ 16 <0.01 — — 
 39 Levofloxacin+ Sotalol+ 79 0.01 — — Xb 
 10 Fosamprenavir Rifampin+ 0.00 ↓ — — 
       
 11 Lamotrigine+ Valproic acid 13 769 1.46 — — Xc 
 51 Tacrolimus Voriconazole 26 068 2.77 ↑ — — 
 43 Methotrexate Sulfamethoxazole/trimethoprim 2324 0.25 — — Xd 
 29 Cyclosporine Voriconazole+ 22 268 2.37 — — 
 20 Amlodipine+ Erythromycin+ 8772 0.93 — — 
 1 Amiodarone Digoxin 4669 0.50 ↑ — — 
 38 Ibuprofen+ Methotrexate 625 0.07 ↑ — — 
 8 Clopidogrel+ Omeprazole+ 3182 0.34 ↓ — — 
 13 Meropenem+ Valproic acid 2217 0.24 ↓ — — 
 2 Amiodarone Methadone 5145 0.55 — — Xb 
 3 Amiodarone Warfarin 4248 0.45 ↑ — — 
 37 Ibuprofen+ Lithium 1416 0.15 ↑ — — 
 30 Doxycycline Phenobarbital+ 999 0.11 ↓ —  
 50 Tacrolimus Rifampin+ 1422 0.15 ↓ —  
 47 Quetiapine Voriconazole+ 1335 0.14 — — Xb 
 21 Aspirin+ Methotrexate 181 0.02 ↑ — — 
 25 Carbamazepine Erythromycin 825 0.09 ↑ — — 
 14 Metronidazole Warfarin 452 0.05 ↑ — — 
 49 Sotalol+ Voriconazole+ 745 0.08 — — Xb 
 48 Rifampin+ Sirolimus 1247 0.13 ↓ — — 
 6 Ciprofloxacin+ Theophylline+ 304 0.03 — — 
 9 Clopidogrel Fluconazole 708 0.08 ↓ — — 
 34 Fluconazole+ Sotalol+ 316 0.03 — — Xb 
 28 Cyclosporine+ Rifampin+ 381 0.04 — — 
 27 Caspofungin Rifampin 122 0.01 ↓ — — 
 46 Phenytoin Warfarin+ 45 <0.01 ↑ — — 
 24 Carbamazepine Cyclosporine 35 <0.01 ↓ — — 
 16 Rifampin+ Tacrolimus 0.00 ↓ — — 
       
 44 Nicardipine+ Tacrolimus 111 427 11.84 ↑ — — 
 36 Haloperidol Quetiapine+ 6951 0.74 — — Xb 
 35 Furosemide+ Risperidone 19 882 2.11 — — Xe 
 42 Methotrexate Penicillin+ 2749 0.29 ↑ — — 
 4 Amitriptyline+ Sertraline+ 7806 0.83 ↑ — Xa 
 19 Amiodarone+ Ciprofloxacin+ 12 638 1.34 — — Xb 
 40 Methadone Phenobarbital 23 044 2.45 ↓ — — 
 53 Warfarin+ Rifampin+ 5883 0.63 ↓ — — 
 32 Erythromycin Tacrolimus 2995 0.32 ↑ — — 
 5 Amitriptyline+ Trazodone+ 1682 0.18 — — 
 23 Atorvastatin Fluconazole 496 0.05 ↑ — — 
 26 Carbamazepine Fluoxetine 341 0.04 ↑ — — 
 22 Atorvastatin Erythromycin 227 0.02 ↑ — — 

[Drug], serum drug concentration; Δ, altered; —, not applicable.

a

Serotonin syndrome.

b

Prolonged QT syndrome.

c

Lamotrigine-specific ADE.

d

Methotrexate-specific ADEs.

e

Risperidone-specific ADEs.

The most common risk grade for DDIs was “C: monitor therapy,” with a rate of 20.84 per 1000 hospital days. For pairings with a risk grade of “D: consider treatment modification,” there was an exposure rate of 11.03 per 1000 hospital days. For drug pairings with the highest risk grade of “X: avoid combination,” there were 11 734 exposures with a rate of 3.04 rate per 1000 hospital days. Conversely, the rate of clinically significant DDI exposure per 1000 DDI patient hospital days increased with increasing risk grade. The DDI exposure rates per 1000 DDI patient hospital days was 32.3, 117.1, and 221.27 for C, D, and X risk grades, respectively, suggesting that clinically significant DDI pairings with higher risk grades were prescribed for a longer duration than lower risk DDI pairings. The DDI effects associated with each pairing were categorized into 5 groups, with increased serum concentration being the most common (32.1%) followed by an increased risk of ADE (30.2%), decreased serum drug concentration (26.4%), altered drug metabolism (9.43%), and multiple effects (1.89%) (Table 3).

Among patients with a clinically significant DDI exposure, 55.1% (26 121) had care with a diagnosis in the medical service line. The medical services lines with the highest percentage of DDI exposures were solid organ transplant, bone marrow transplant, and nephrology and urology. The most common DDI pairing across service lines was nicardipine and tacrolimus (Table 4). The surgical services with the highest proportion of DDI exposures were craniofacial and plastic, nephrology and urology, and other trauma. Fentanyl and linezolid was the most common DDI pairing for these surgical service lines.

TABLE 4

DDI Exposures by Service Line Diagnosis

% Discharges With DDIMost Common DDI
Medical service line
 Solid organ transplant 62.11 Nicardipine and tacrolimus 
 Bone marrow transplant 17.98 Nicardipine and tacrolimus 
 Nephrology and urology 4.86 Nicardipine and tacrolimus 
 Rehabilitation 4.56 Nicardipine and tacrolimus 
 Hepatobiliary 3.40 Nicardipine and tacrolimus 
 Neonates with ECMO or major prognosis 2.24 Fentanyl and linezolid 
 Mental health 1.79 Haloperidol and quetiapine 
 Substance abuse 1.61 Haloperidol and quetiapine 
 Oncology 1.29 Methotrexate and sulfamethoxazole/trimethoprim 
 Cardiovascular 1.18 Nicardipine and tacrolimus 
Surgical service line   
 Craniofacial and/or plastic 15.23 Fentanyl and linezolid 
 Nephrology and/or urology 4.31 Fentanyl and linezolid 
 Other trauma 3.77 Fentanyl and linezolid 
 Infectious disease 3.12 Fentanyl and linezolid 
 Oncology 2.20 Methotrexate and sulfamethoxazole/trimethoprim 
 Interventional and EP 1.76 Nicardipine and tacrolimus 
 Cardiothoracic 1.48 Furosemide and risperidone 
 Otolaryngology l 1.47 Fentanyl and linezolid 
 Other surgical 1.34 Fentanyl and linezolid 
 Mental health 1.21 Amitriptyline and sertraline 
 Respiratory 1.14 Fentanyl and linezolid 
% Discharges With DDIMost Common DDI
Medical service line
 Solid organ transplant 62.11 Nicardipine and tacrolimus 
 Bone marrow transplant 17.98 Nicardipine and tacrolimus 
 Nephrology and urology 4.86 Nicardipine and tacrolimus 
 Rehabilitation 4.56 Nicardipine and tacrolimus 
 Hepatobiliary 3.40 Nicardipine and tacrolimus 
 Neonates with ECMO or major prognosis 2.24 Fentanyl and linezolid 
 Mental health 1.79 Haloperidol and quetiapine 
 Substance abuse 1.61 Haloperidol and quetiapine 
 Oncology 1.29 Methotrexate and sulfamethoxazole/trimethoprim 
 Cardiovascular 1.18 Nicardipine and tacrolimus 
Surgical service line   
 Craniofacial and/or plastic 15.23 Fentanyl and linezolid 
 Nephrology and/or urology 4.31 Fentanyl and linezolid 
 Other trauma 3.77 Fentanyl and linezolid 
 Infectious disease 3.12 Fentanyl and linezolid 
 Oncology 2.20 Methotrexate and sulfamethoxazole/trimethoprim 
 Interventional and EP 1.76 Nicardipine and tacrolimus 
 Cardiothoracic 1.48 Furosemide and risperidone 
 Otolaryngology l 1.47 Fentanyl and linezolid 
 Other surgical 1.34 Fentanyl and linezolid 
 Mental health 1.21 Amitriptyline and sertraline 
 Respiratory 1.14 Fentanyl and linezolid 

Only service lines with percent of discharges with a DDI >1.0 are reported. ECMO, extracorporeal membrane oxygenation; EP, electrophysiology.

The proportion of exposure to DDIs varied widely across US children’s hospitals, ranging from 0.65% to 7.33%; adjusted proportions ranged from 1.05% to 4.92% (Fig 1). When compared by individual clinically significant DDIs, there was significant variation across hospitals (Fig 2). For example, compared to other hospitals, hospital 1 prescribed DDIs more frequently (as indicated by the red squares across DDIs on the heatmap), whereas hospital 52 was a frequent prescriber of DDI pair number 22 (atorvastatin and erythromycin) but not other DDIs. There was also significant variation among the prescribing of individual drug pairings. For example, DDI pair number 44 (nicardipine and tacrolimus) had a large variation, with every hospital with at least some DDI prescribing (ranging from 0.01% to 3.6%). In contrast, DDI pair number 24 (carbamazepine and cyclosporine) has little variation. Out of 52 hospitals, only 1 hospital had any exposure to the combination.

FIGURE 1

Variation in DDI exposures across US children’s hospitals: hospital-level variation in total DDI exposure across 52 children’s hospitals. Adjusted proportions were derived by using multivariable logistic regression incorporating the following covariates: age, sex, race and/or ethnicity, payer, number of CCCs, LOS, service lines, geographic region, ICU status, and case mix index for each hospitalization.

FIGURE 1

Variation in DDI exposures across US children’s hospitals: hospital-level variation in total DDI exposure across 52 children’s hospitals. Adjusted proportions were derived by using multivariable logistic regression incorporating the following covariates: age, sex, race and/or ethnicity, payer, number of CCCs, LOS, service lines, geographic region, ICU status, and case mix index for each hospitalization.

Close modal
FIGURE 2

Variation of individual DDI pairings in US children’s hospitals. Hospital-level variation in DDI prescribing as a heat map: proportion of individual hospital rates of individual DDI exposure are ordered from the highest exposure rates (top) to lowest (bottom). DDI pairings are represented as columns. Color values correspond to proportions of exposure to individual DDI pairings (highest to lowest exposure rate color coding: red, yellow, orange, and green) using a linear gradation conditional model. For example, DDI pair number 44 (nicardipine and tacrolimus) has a large variation, with all hospitals having exposure (ranging from 0.01% to 3.6%). In contrast, DDI pair number 24 (carbamazepine and cyclosporine) has little variation, in which, out of 52 hospitals, only 1 had any exposure. The DDI numbers in figure correspond to the DDI numbers in Table 3.

FIGURE 2

Variation of individual DDI pairings in US children’s hospitals. Hospital-level variation in DDI prescribing as a heat map: proportion of individual hospital rates of individual DDI exposure are ordered from the highest exposure rates (top) to lowest (bottom). DDI pairings are represented as columns. Color values correspond to proportions of exposure to individual DDI pairings (highest to lowest exposure rate color coding: red, yellow, orange, and green) using a linear gradation conditional model. For example, DDI pair number 44 (nicardipine and tacrolimus) has a large variation, with all hospitals having exposure (ranging from 0.01% to 3.6%). In contrast, DDI pair number 24 (carbamazepine and cyclosporine) has little variation, in which, out of 52 hospitals, only 1 had any exposure. The DDI numbers in figure correspond to the DDI numbers in Table 3.

Close modal

The 5 most frequent drug combinations had a wide distribution in prescribing rates across children’s hospitals: specifically, fentanyl and linezolid (median rate per 1000 patient-days: 0.36; IQR 0.25–0.59), lamotrigine and valproic acid (0.11; IQR 0.08–0.17), nicardipine and tacrolimus (0.54; IQR 0.28–0.76), haloperidol and quetiapine (0.06; IQR 0.03–0.14), and tacrolimus and voriconazole (0.05; IQR 0.01–0.11; Fig 3).

FIGURE 3

Comparison of the 5 most frequent DDI exposures across US children’s hospitals: violin plot of the distribution of the rate of DDI exposure per 1000 patient-days across children’s hospitals. The width of each plot represents the distribution of hospitals with a particular rate of DDI exposure. Inside the violin plot, the central dotted line represents the median rate of DDI exposure across all 52 children’s hospitals, and thin lines represent the 25th and 75th percentiles.

FIGURE 3

Comparison of the 5 most frequent DDI exposures across US children’s hospitals: violin plot of the distribution of the rate of DDI exposure per 1000 patient-days across children’s hospitals. The width of each plot represents the distribution of hospitals with a particular rate of DDI exposure. Inside the violin plot, the central dotted line represents the median rate of DDI exposure across all 52 children’s hospitals, and thin lines represent the 25th and 75th percentiles.

Close modal

In this large multicenter study in which we evaluate the exposure of individuals hospitalized at US children’s hospitals to medications with clinically significant DDIs, there are 3 main findings. First, the proportion individuals exposed to clinically significant DDIs is substantial, encompassing 2% of all those hospitalized at US children’s hospitals. We found that older age, CCC, LOS, and surgical service line diagnoses were associated with a greater odds of DDI exposure. Second, one-quarter of DDI exposures were considered contraindicated (risk grade: “X”) and were prescribed across a broad range of medical and surgical service lines. These drugs were prescribed for longer durations than lower risk grade DDIs. Third, there was wide variation in prescribing patterns of DDIs across children’s hospitals, suggesting exposure to some of these DDIs may be unwarranted and preventable.

In previous studies, researchers reported that 49% of all hospitalized children,3  41.7% of hospitalized children with epilepsy,11  and 75% of PICU patients6  were exposed to drugs with potential DDIs. However, in these studies, researchers used adult-based drug classification software to identify potential DDIs, and these could vary in their clinical relevance. In this study, we evaluated 53 medication pairings with known DDIs of clinical importance in those admitted to children’s hospitals. We found that clinically significant DDIs had a lower prevalence compared to all potential DDIs.3,6,11  Nonetheless, these DDIs were prescribed in 2% of hospital encounters, exposing >47 000 children to potential ADEs during our 2-year study period.

Medications with DDIs are often categorized by the severity of a potential DDI, rather than risk grade, by using drug classification DDI software.3,6  Severity classification (typically categorized as minor, moderate, major, and contraindicated) are similar but distinct from risk grade. Severity classifications indicate the potential severity of the interaction if it occurs but not the likelihood that it would occur. There is also significant variation in the interrater reliability of severity classifications between drug software programs as well as compared to clinicians’ assessment of DDI severity.4,7  Feinstein and colleagues assessed all potential DDI exposures in children’s hospitals and found the following severity classifications: 5% contraindicated, 41% major, 28% moderate, and 11% minor. The risk grade provides a recommended action based on the relevance of the interaction, level of evidence, and iatrogenic risk. Risk grade, therefore, is likely a better indicator of the potential DDI risk in children than severity stratifications. Of the DDIs included in this study, the recommended actions were contraindicated (risk grade X: 25%), consider therapy modification (risk grade D: 27%), and monitor therapy (risk grade C: 56.2%).

Finally, after adjusting for hospital and clinical factors, we found significant variation in the rates of DDI prescribing across US children’s hospitals, with a greater than fourfold difference between hospitals. The reasons for this differential prescribing pattern are unclear. Some hospitals may have measures in place to identify and prevent unnecessary DDI exposures, such as embedded ICU pharmacists or electronic medical record prescription clinical decision support tools. It is possible that there is unmeasured confounding that was not accounted for in the statistical model, such specialization of cases (eg, dedicated transplant centers) not accounted for in case mix index classification. Almost certainly, a portion of DDI prescribing, such as immune suppression in transplant patients, is unavoidable. However, it is likely that a number of DDI exposures are preventable, indicating an opportunity for improvement in care. For example, in DDIs in which there are multiple medication options (eg, quetiapine and haloperidol or citalopram and linezolid, among others) choosing a safer alternative medication combination may be feasible.

Our results align with others that reveal exposure to potential DDIs is common in US children’s hospitals.6,1115  Although DDI safety alerts are available in several systems, not all DDIs identified may be clinically relevant. There are some drug pairings that, because of the frequency and severity of their DDI, should not be used together (such as those with a risk grade of X).8  There is a larger set of other potential DDIs that might be unavoidable or have clinical reasons for concomitant use (those with risk grades of C and D) but are likely to cause harm due their frequency of use. In this study, we found that drugs with risk grades of C and D were prescribed more often and for longer durations than those with a risk grade of X. Finally, there are other drug combinations not included in this study that are rated as having DDIs in safety databases but are likely not clinically relevant to those cared for in children’s hospitals. Most institutions use a limited set of existing medication safety databases to inform their safety alerts, and, to our knowledge, none of these databases are pediatric specific. When these vendor-based alerts are perceived to be of limited value, physicians may discount all alerts, including appropriate ones.9  In US children’s hospitals, >95% of DDI alerts are overridden.15,16 

Therefore, it is important to identify pediatric-specific clinically significant drug combinations to prioritize in drug alert systems. Factors that may lead to increased rates of preventable DDI prescribing include DDI-alert fatigue, insufficient communication across care providers, decreased accessibility of prescribing information across electronic medical records, and limited dissemination of information relaying the appropriate risk of medication combinations. By using the fentanyl and linezolid combination as an example, there are multiple providers who could potentially be involved in prescribing 1 drug but not the other, including members of the surgery, infectious disease, ICU, and anesthesiology teams, among others. Efforts to improve the efficacy of DDI alerts and better communication of the medication management plan across prescribing providers and locations of care (emergency department, inpatient floor, ICU, operating room, etc) may aid in preventing unnecessary DDI exposures.

Given that severe ADEs can occur as a result of DDIs, prioritizing DDIs on the basis of frequency of exposure, probability of occurrence, and magnitude of harm is essential. Identifying children at high risk for DDI exposure provides a potential target for care improvement. We found that older age, CCC, LOS, and surgical service line diagnoses were associated with greater odds of DDI exposure. Rather than focusing efforts on all hospitalized children, targeting clinical decision support interventions and DDI alerts to patients and providers involved in frequent DDI exposures is of high value. Implementing DDI alerts incorporating patient-specific information, (including complexity, laboratory values, illness severity, etc) and customizing alerts may better relay DDI risk to practitioners.17  In evidence, it is also suggested that embedding a pharmacist within the physician care team caring for high-risk patients improves the identification of DDIs before patient exposure.18 

Limitations of our findings include the retrospective nature of the study as well as incomplete clinical patient-level data in the PHIS data set. The PHIS data set only includes children’s hospitals, which may limit the generalizability of our results to community hospitals. We also excluded newborns, NICU patients, and those admitted peri- or postpartum obstetrical care, and our findings may not be generalizable to these populations. However, children’s hospitals generally care for those at higher risk for DDIs, suggesting this hospital population is of particular importance. Furthermore, we only evaluated 53 clinically significant DDI medication pairings. Although we selected these interactions given their known clinical relevance, there are likely other clinically significant DDI combinations not included in our analysis, and, thus, we may have underestimated the frequency of clinically significant DDI exposure in children. In addition, in our study, we did not measure the rates and severity of ADEs resulting from DDI exposures, and pediatric-specific data are lacking. Additional studies specifically designed to better understand the association between DDIs and ADEs in the pediatric population would aid practitioners in understanding the risk to benefit ratios for these drug pairings. Despite these limitations, our findings represent an important step in understanding clinically significant DDIs in hospitalized children, and, with those findings, we provide several potential targets in which to prioritize efforts to improve safe medication prescribing in hospitalized children.

Approximately 2% of those treated at US children’s hospitals are prescribed clinically significant DDI combinations, with a greater odds of exposure in older individuals, those with increased number of CCCs, and those with surgical service line diagnoses. We found a wide variation in the rates of DDI prescribing across children’s hospitals, suggesting an opportunity to improve safe medication use in children. Further study is needed to determine the rate of adverse events due to DDI exposures and factors amenable for interventions done to promote safer medication use.

Dr Antoon led the overall conceptualization and design of the study, analyzed and interpreted the data, drafted the initial manuscript, and reviewed and revised the manuscript; Dr Hall led the acquisition and analysis of the data and contributed to the conceptualization and design of the study and drafting and critical review of the manuscript; Drs Herndon, Carroll, Ngo, Freundlich, Stassun, Frost, Johnson, Chokshi, Brown, Browning, Feinstein, Grijalva, and Williams contributed to the overall conceptualization and design of the study, analysis and interpretation of data, and critical review of the manuscript; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

FUNDING: Dr Antoon was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K12 HL137943. Dr Feinstein was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health under award number K23HD091295. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Dr Grijalva has received research support from Sanofi Pasteur, Campbell Alliance, the Centers for Disease Control and Prevention, the National Institutes of Health, the Food and Drug Administration, and the Agency for Healthcare Research and Quality. Funded by the National Institutes of Health (NIH).

     
  • ADE

    adverse drug event

  •  
  • aOR

    adjusted odds ratio

  •  
  • CCC

    complex chronic condition

  •  
  • CI

    confidence interval

  •  
  • DDI

    drug-drug interaction

  •  
  • IQR

    interquartile range

  •  
  • LOS

    length of stay

  •  
  • PHIS

    Pediatric Health Information System

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Competing Interests

POTENTIAL CONFLICT OF INTEREST: Dr Grijalva has received consulting fees from Pfizer, Sanofi, and Merck; the other authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: Dr Grijalva has received consulting fees from Pfizer, Sanofi, and Merck; the other authors have indicated they have no financial relationships relevant to this article to disclose.