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Drug Safety

, Volume 43, Issue 1, pp 7–16 | Cite as

Exploratory Study of Signals for Asthma Drugs in Children, Using the EudraVigilance Database of Spontaneous Reports

  • Esmé J. BaanEmail author
  • Veronique A. de Smet
  • Christina E. Hoeve
  • Alexandra C. Pacurariu
  • Miriam C. J. M. Sturkenboom
  • Johan C. de Jongste
  • Hettie M. Janssens
  • Katia M. C. Verhamme
Open Access
Original Research Article

Abstract

Introduction

As asthma medications are frequently prescribed for children, knowledge of the safety of these drugs in the paediatric population is important. Although spontaneous reports cannot be used to prove causality of adverse events, they are important in the detection of safety signals.

Objective

Our objective was to provide an overview of adverse drug events associated with asthma medications in children from a spontaneous reports database and to identify new signals.

Methods

Spontaneous reports concerning asthma drugs were obtained from EudraVigilance, the European Medicine Agency’s database for suspected adverse drug reactions. For each drug–event combination, we calculated the proportional reporting ratio (PRR) in the study period 2011–2017. Signals in children (aged 0–17 years) were compared with signals in the whole population. Analyses were repeated for different age categories, by sex and by therapeutic area.

Results

In total, 372,345 reports in children resulted in 385 different signals concerning asthma therapy. The largest group consisted of psychiatric events (65 signals). Only 30 signals were new, with seven, including herpes viral infections, associated with omalizumab. Stratification by age, sex and therapeutic area provided additional new signals, such as hypertrichoses with budesonide and encephalopathies with theophylline. Of all signals in children, 60 (16%) did not appear in the whole population.

Conclusions

The majority of signals regarding asthma therapy in children were already known, but we also identified new signals. We showed that signals can be masked if age stratification is not conducted. Further exploration is needed to investigate the risk and causality of the newly found signals.

Key Points

Most safety signals regarding asthma therapy in children are known, but we identified several new signals that need to be explored further.

Lack of stratification by age and sex may mask safety signals in children.

1 Introduction

Asthma drugs are one of the most frequently used drugs in children, with approximately 21% of children aged 2–11 years using asthma medication [1, 2]. Since asthma is a chronic disease, this medication may be used for many years [3]. Like any drug, asthma medication may cause adverse reactions. Randomized controlled trials (RCTs) are the gold standard to investigate the efficacy of drugs but are limited in size and follow-up and therefore can only detect events that occur frequently and may miss rare or long-term side effects. Children remain underrepresented in RCTs, even though attempts have been made to increase these numbers, including the Paediatric Regulation that came into force in the EU in 2007 [4, 5, 6, 7]. Given the lack of data, medication—including asthma drugs—is often not authorised for use in children, leading to substantial off-label use [8]. This is concerning because the efficacy and safety profile of drugs might differ between children and adults [9]. The risk assessment of drugs does not stop after market authorisation but should be monitored throughout the lifecycle of the drug [10]. Clearly, more child-specific safety and efficacy information on asthma drugs is needed, something the European Medicines Agency (EMA) emphasises [11].

Spontaneous report databases are an important source of safety information, and in fact remain the most frequent source from which regulatory action is initiated [12]. They cover large source populations, including special populations such as children, and may have more power to detect rare signals than mining of electronic healthcare records [13, 14, 15]. The EMA defines an adverse drug reaction as “a response to a medicinal product which is noxious and unintended” [16]. Spontaneous reports only contain information on the event and the drug of interest, not on the non-exposed events or exposure without events, so cannot be used to measure incidence rates. However, signal detection based on disproportionality analyses is still possible [15, 17, 18]. Since the risk of adverse events can be age specific, stratification of these analyses is recommended [19]. A previous study using EudraVigilance, the European database for suspected adverse reactions, characterised spontaneous reports of asthma drugs in children over the study period 2007–2011 [20]. The authors could not identify new signals in relation to asthma drugs, potentially because of low numbers. In this study, we aimed to investigate the safety of asthma drugs in children in EudraVigilance in the period 2011–2017. The objective of this study was to provide an overview of safety signals associated with asthma drugs in children and to identify new signals in the EudraVigilance database.

2 Methods

2.1 Database

We used the EudraVigilance database, which is developed and maintained by the EMA to collect reports of suspected adverse reactions. It contains adverse events reported in relation to any drug authorised or being studied in the European economic area. EudraVigilance contains spontaneous reports from healthcare professionals, patients and marketing authorisation holders and clinical trial safety data dating back to 1995. The number of new reports in this database increases each year, with 1,238,178 reports of adverse events collected in 2016 [21]. Events are coded using the Medical Dictionary for Regulatory Activities (MedDRA). For this study, information on the frequency of MedDRA (version 20.1) ‘high-level terms’ in spontaneous reports regarding asthma drugs was obtained. These terms are grouped by ‘system organ classes’ (SOCs), for example, by aetiology (‘infections and infestations’) or manifestation site (‘respiratory, thoracic and mediastinal disorders’) [22]. Drugs are coded by anatomical therapeutic chemical (ATC) classification.

2.2 Data Selection

All spontaneous reports submitted to EudraVigilance between 1 January 2011 and 1 January 2017 and concerning 24 asthma drugs were analysed. Reports concerning suspect or interacting single drugs or fixed-dose combinations from the following drug classes were included: β2-adrenergic receptor agonists, inhaled corticosteroids (ICS), muscarinic receptor antagonist, chromones, xanthines, leukotriene receptor antagonists (LTRAs) and anti-immunoglobulin E (IgE) drugs (Table 1). Counts of spontaneous reports were provided for the whole paediatric group, by sex, by age category (0–2, 3–11 and 12–17 years) and for adults. Reports that did not include the patient’s age were excluded. To calculate the measure of disproportionality, the background frequencies of adverse events for all drugs were also included in the data files. The free text narrative from the reports was not available because of data protection regulations. The retrieved data were at the group level only and included non-identifiable patient data, so no ethics review board approval was required.
Table 1

Number of reports per drug in children

ATC code

Drug name

Administration

Drug class

Reports (N)

R03DC03

Montelukast

Oral

LTRA

2210

R03AC02

Salbutamol

Inhaled

SABA

947

R03DX05

Omalizumab

Subcutaneous

Monoclonal antibody

690

R03CC02

Salbutamol, systemic

Oral, intravenous

SABA

676

R03BA02

Budesonide

Inhaled

ICS

584

R03AK06

Salmeterol + fluticasone

Inhaled

LABA + ICS

565

R03BA05

Fluticasone

Inhaled

ICS

391

R03BA01

Beclomethasone

Inhaled

ICS

169

R03AK07

Formoterol + budesonide

Inhaled

LABA + ICS

161

R03DA04

Theophylline

Oral, intravenous

Xanthine

98

R03CC03

Terbutaline, systemic

Oral, intravenous

SABA

87

R03BB01

Ipratropium bromide

Inhaled

SAMA

85

R03AC13

Formoterol

Inhaled

LABA

80

R03AC03

Terbutaline

Inhaled

SABA

73

R03BA08

Ciclesonide

Inhaled

ICS

36

R03BC01

Cromoglicic acid

Inhaled

Chromones

34

R03AC12

Salmeterol

Inhaled

LABA

32

R03BA07

Mometasone

Inhaled

ICS

31

R03DC01

Zafirlukast

Oral

LTRA

29

R03AL01

Fenoterol and ipratropium bromide

Inhaled

LABA + SAMA

24

R03AL02

Salbutamol and ipratropium bromide

Inhaled

SABA + SAMA

21

R03AK10

Vilanterol and fluticasone furoate

Inhaled

LABA + ICS

16

R03BB04

Tiotropium bromide

Inhaled

LAMA

9

R03AK11

Formoterol and fluticasone

Inhaled

LABA + ICS

3

ATC anatomical therapeutic chemical, ICS inhaled corticosteroids, LABA long-acting β-agonist, LAMA long-acting muscarinic antagonist, LTRA leukotriene receptor antagonist, SABA short-acting β-agonist, SAMA short-acting muscarinic antagonist

2.3 Signal Detection

As one report may contain multiple drugs and/or multiple adverse events, we defined the combination of one drug and one event as drug–event combinations (DECs). To identify signals, we calculated the disproportionality in reporting using the proportional reporting ratio (PRR), defined as the proportion of the event of interest for a specific drug, with the comparator being the proportion of the event of interest for all other drugs from the database (other asthma and non-asthma drugs) [23]. The PRR was calculated only if a DEC was reported at least five times. We also calculated 95% confidence intervals (CIs), and the criterion for a signal was a lower bound higher than one (PRR025 > 1) [19, 24, 25, 26]. We first calculated the PRR in reports concerning children only, then in the whole dataset including children and adults to examine masking of safety signals if stratification for children had not been done. We also repeated the analyses of paediatric reports while stratifying for age and sex [17]. Finally, we calculated another measurement of disproportionality—the PRR by therapeutic area (PRR-TA)—for paediatric reports. For this calculation, only reports related to asthma drugs for other events were used as the comparator. The PRR-TA has been proposed in the literature as a method to eliminate false signals due to confounding by indication [27].

2.4 Definition of New Signals

To identify already known signals, we searched the safety information as documented in the summary of product characteristics (SmPC). If the event was not listed in the SmPC, we also searched UpToDate and Martindale [28]. UpToDate is a widely used point-of-care clinical tool to support decision making that includes summaries of the current available evidence on medication, including possible adverse events [29, 30, 31]. Martindale is an online drug database published by the Pharmaceutical Press and is updated every 3 months [32]. If an adverse event was not mentioned by any of these sources, we considered the signal on this adverse event to be a new signal.

3 Results

From January 2011 to January 2017, a total of 372,345 spontaneous reports on adverse events in children and 3,182,083 in adults were reported to EudraVigilance. From these reports, we identified 21,264 asthma medication-related DECs in children and 172,035 in adults for the same drugs. The asthma drug most often included in reports in children was montelukast (2210 reports), followed by inhaled salbutamol (947) and anti-IgE (omalizumab, 690) (Table 1). We identified 3697 unique DECs, 385 of which met the criteria of a safety signal in children. All safety signals and corresponding SOCs are listed in the Electronic Supplementary Material (ESM) 1. Signals were mainly related to the SOCs ‘psychiatric disorders’ (n = 65), ‘respiratory, thoracic and mediastinal disorders’ (n = 56) and ‘nervous system disorders’ (n = 29) (Fig. 1). Signals also often related to ‘Injury, poisoning and procedural complications’ (n = 49), which included for example ‘off-label uses’ and ‘accidental exposure to product’. We identified 31 signals of psychiatric adverse events in combination with montelukast, which also included the five most frequently reported DECs (Table 2). The highest PRR in children was for the association between montelukast use and ‘vasculitides’ (PRR 90.7; 95% CI 46.3–177.9; 13 reports), followed by fluticasone and ‘adrenal cortical hypofunctions’ (PRR 59.5; 95% CI 39.1–90.4; 22 reports) (Table 3). All ten signals with the highest PRR were already known.
Fig. 1

Signals in children categorized as to whether it remains a signal in the whole dataset (children and adults)

Table 2

Drug–event combinations of montelukast and psychiatric events

Reaction HLT

Reports (N)

PRR (95% CI)

Behaviour and socialisation disturbances

328

16.0 (14.4–17.8)

Parasomnias

252

49.5 (43.3–56.6)

Anxiety symptoms

249

6.4 (5.7–7.2)

Emotional and mood disturbances NEC

247

6.9 (6.1–7.7)

Suicidal and self-injurious behaviour

219

7.1 (6.3–8.1)

Abnormal behaviour NEC

211

9.7 (8.5–11.1)

Disturbances in initiating and maintaining sleep

163

10.4 (8.9–12.1)

Depressive disorders

142

10.2 (8.6–12.0)

Fear symptoms and phobic disorders (including social phobia)

72

27.5 (21.5–35.1)

Mood alterations with depressive symptoms

67

8.0 (6.3–10.2)

Sleep disorders NEC

66

7.0 (5.5–9.0)

Increased physical activity levels

63

3.4 (2.7–4.3)

Speech articulation and rhythm disturbances

61

6.3 (4.9–8.1)

Fluctuating mood symptoms

59

20.6 (15.8–26.9)

Attention deficit and disruptive behaviour disorders

33

14.8 (10.4–21.0)

Tic disorders

32

8.1 (5.7–11.5)

Mood disorders NEC

32

2.5 (1.7–3.5)

Obsessive-compulsive disorders and symptoms

32

14.8 (10.4–21.3)

Panic attacks and disorders

30

10.7 (7.4–15.5)

Thinking disturbances

28

15.4 (10.5–22.7)

Confusion and disorientation

25

2.0 (1.3–2.9)

Sleep disturbances NEC

22

4.6 (3.0–7.0)

Affect alterations NEC

21

9.3 (6.0–14.4)

Personality disorders NEC

20

23.3 (14.6–37.1)

Psychotic disorder NEC

18

3.2 (2.0–5.1)

Educational issues

18

26.0 (15.8–42.6)

Psychiatric elimination disorders

17

12.7 (7.8–20.8)

Criminal activity

12

12.4 (6.9–22.3)

Anxiety disorders NEC

10

14.8 (7.8–28.3)

Stereotypies and automatisms

9

4.4 (2.3–8.5)

Impulse control disorders

7

6.1 (2.9–12.9)

CI confidence interval, HLT high-level term, NEC not elsewhere classified, PRR proportional reporting ratio

Table 3

Top ten strongest signals

Name

Reaction HLT

Reports (N)

PRR (95% CI)

Montelukast

Parasomnias

252

49.5 (43.3–56.6)

Salmeterol + fluticasone

Adrenal cortical hypofunctions

30

57.4 (39.9–82.6)

Fluticasone

Adrenal cortical hypofunctions

22

59.5 (39.1–90.4)

Budesonide

Adrenal cortical hypofunctions

22

39.8 (26.1–60.7)

Budesonide

Cataract conditions

17

58.5 (35.8–95.5)

Montelukast

Vasculitides

13

90.7 (46.3–177.9)

Salbutamol, inhaled

Thermal burns

13

35.4 (20.1–62.2)

Salmeterol + fluticasone

Glaucomas (excluding congenital)

9

34.0 (17.5–66.2)

Fluticasone

Cataract conditions

7

34.1(16.2–72.1)

Fluticasone

Dental and periodontal infections and inflammations

5

46.2 (18.9–112.7)

CI confidence interval, HLT high-level term

3.1 New Signals

Most of the 385 signals in children were already known and thus described in the SmPC, UpToDate and/or Martindale; only 30 were classified as new. New signals most often concerned omalizumab, with seven new signals, followed by the fixed combination of salmeterol and fluticasone (six new signals) and montelukast (five new signals) (Table 4). See the ESM for a table presenting all signals not described in the SmPC but mentioned in UpToDate and/or Martindale.
Table 4

All new signals by drug

Reaction HLT

Reports

PRR (95% CI)

PRR-TA (95% CI)

SOC

Budesonide

 Leukopenias NEC

8

3.5 (1.7–6.9)

14.8 (5.1–42.4)

Immune system disorders

 Hypertrichoses

5

17.8 (7.3–43.1)

11.1 (3.2–38.1)

Skin and subcutaneous disorders

Fluticasone

    

 Dyskinesias and movement disorders NEC

13

2.3 (1.3–3.9)

1.3 (0.7–2.2)

Nervous system disorders

 Mental impairment (excluding dementia and memory loss)

5

2.8 (1.2–6.6)

1.3 (0.5–3.2)

Nervous system disorders

 Hypoglycaemic conditions NEC

5

2.9 (1.2–7.1)

2.7 (1.0–6.8)

Metabolism and nutrition disorders

Ipratropium bromide

 Tremor (excluding congenital)

6

6.6 (3.1–14.3)

2.2 (1.0–4.7)

Nervous system disorders

Montelukast

 Paraesthesias and dysaesthesias

43

2.0 (1.5–2.7)

1.5 (1.0–2.2)

Nervous system disorders

 Skin injuries NEC

15

2.2 (1.3–3.7)

1.8 (0.9–3.6)

Skin and subcutaneous disorders

 Eyelid movement disorders

10

3.9 (2.1–7.2)

5.5 (1.7–17.4)

Eye disorders

 Lacrimation disorders

6

2.4 (1.1–5.3)

1.1 (0.4–2.9)

Eye disorders

 Panniculitides

5

4.8 (2.0–11.8)

a

Skin and subcutaneous disorders

Omalizumab

 Neurological signs and symptoms NEC

38

1.6 (1.2–2.2)

2.7 (1.9–3.8)

Nervous system disorders

 Feelings and sensations NEC

27

2.5 (1.7–3.6)

2.5 (1.6–3.8)

Nervous system disorders

 Visual disorders NEC

11

2.0 (1.1–3.6)

2.0 (1.1–3.9)

Eye disorders

 Speech and language abnormalities

9

2.2 (1.1–4.1)

2.1 (1.0–4.3)

Nervous system disorders

 Exfoliative conditions

6

3.1 (1.4–7.0)

3.5 (1.4–8.8)

Skin and subcutaneous disorders

 Sensory abnormalities NEC

5

2.7 (1.1–6.5)

2.4 (0.9–6.5)

Nervous system disorders

 Oral soft tissue pain and paraesthesia

5

6.7 (2.8–16.3)

4.6 (1.6–13.5)

Gastrointestinal disorders

Salbutamol, inhaled

 Non-site specific vascular disorders NEC

9

3.9 (2.0–7.5)

4.8 (2.0–11.4)

Vascular disorders

 Psychotic disorder NEC

8

3.3 (1.6–6.6)

1.2 (0.6–2.5)

Psychiatric disorders

Salbutamol, systemic

 Psychotic disorder NEC

8

4.6 (2.3–9.2)

1.7 (0.8–3.6)

Psychiatric disorders

 Non-site specific vascular disorders NEC

6

3.6 (1.6–8.1)

3.8 (1.5–9.7)

Vascular disorders

Salmeterol + fluticasone

 Visual disorders NEC

10

2.2 (1.2–4.2)

2.3 (1.2–4.4)

Eye disorders

 Increased intracranial pressure disorders

9

4.3 (2.2–8.2)

4.1 (1.9–8.8)

Nervous system disorders

 Confusion and disorientation

7

2.2 (1.0–4.5)

1.7 (0.8–3.8)

Psychiatric disorders

 Hypoglycaemic conditions NEC

7

2.9(1.4–6.0)

2.7 (1.2–6.1)

Metabolism and nutrition disorders

 Tic disorders

6

5.7 (2.6–12.7)

2.1 (0.9–5.0)

Psychiatric disorders

 Memory loss (excluding dementia)

5

3.1 (1.3–7.4)

2.1 (0.8–5.5)

Nervous system disorders

Theophylline

 Encephalopathies NEC

6

9.8 (4.5–21.4)

212.9 (43.5–1041.5)

Nervous system disorders

 Abnormal behaviour NEC

5

4.9 (2.1–11.6)

1.2 (0.5–2.9)

Psychiatric disorders

CI confidence interval, HLT high-level term, NEC not elsewhere classified, PRR-TA proportional reporting ratio by therapeutic area, SOC system organ class

aPRR-TA could not be calculated as the event was not reported in combination with other asthma drugs

3.2 Proportional Reporting Ratio by Therapeutic Area

The results of the PRR-TA differed from those of the overall PRR. Of all the signals, 126 disappeared when calculating the PRR-TA. This difference was most pronounced for ‘respiratory, thoracic and mediastinal’ disorders, as half of these signals disappeared when calculating the PRR-TA. On the other hand, the overall PRR of 38 DECs did not meet the definition of a safety signal, whereas the PRR-TA did show a signal. Three of these safety signals were new: omalizumab with ‘bacterial infections NEC [not elsewhere classified]’ and both omalizumab and budesonide with ‘herpes viral infections’ (Table 5).
Table 5

New signals unmasked by PRR-TA

Drug

Reaction HLT

Reports (N)

PRR (95% CI)

PRR-TA (95% CI)

Budesonide

Herpes viral infections

9

1.0 (0.5–1.9)

7.7 (3.3–17.9)

Omalizumab

Herpes viral infections

8

0.8 (0.4–1.5)

5.3 (2.2–12.5)

Omalizumab

Bacterial infections NEC

6

0.8 (0.4–1.8)

13.8 (3.9–48.9)

CI confidence interval, HLT high-level term, NEC not elsewhere classified, PRR-TA proportional reporting ratio by therapeutic area

3.3 Children Versus All Ages

Of the 385 signals in children, 60 (16%) DECs no longer met the criteria of a signal when calculating the PRR in the whole dataset including all ages (both children and adults). All signals in children concerning the SOC ‘respiratory, thoracic and mediastinal disorders’ remained in the dataset that included children and adults together. In contrast, only 35 of the 50 signals in children concerning ‘psychiatric disorders’ remained in the whole dataset (Fig. 1).

3.4 Stratification by Age

Of all asthma medication-related DECs in children, 4429 (21%) pertained to children aged 0–2 years, 11,319 (53%) to children aged 3–11 years and 5516 (26%) to children aged 12–17 years. Upon stratification by age, 42 additional safety signals appeared: 16 of these were in the age category 0–2 years, 14 in the category 3–11 years and 12 in the category 12–17 years. These safety signals concerned ‘pregnancy, puerperium and perinatal disorders’ for events referring to in utero exposure and ‘general disorders and administration site conditions’, ‘gastrointestinal disorders’, ‘psychiatric disorders’ and ‘nervous system disorders’. Six (14%) of the signals that only appeared upon age stratification were classified as new (Table 6).
Table 6

New signals unmasked by stratification for age

Drug

Reaction HLT

Overall PRR (95% CI)

Age group, years

Reports in age group

PRR in age group (95% CI)

Salbutamol, inhaled

Perception disturbances

1.5 (0.9–2.6)

0–2

6

14.8 (6.6–33.0)

Salbutamol, inhaled

Cardiac septal defects congenital

2.0 (0.8–4.7)

0–2

5

2.8 (1.2–6.6)

Budesonide

Neutropenias

1.8 (1.0–3.3)

0–2

5

3.6 (1.5–8.5)

Theophylline

Febrile disorders

1.1 (0.7–1.7)

3–11

13

2.3 (1.4–3.6)

Montelukast

Diabetes mellitus (including subtypes)

1.6 (0.9–3.0)

3–11

11

2.3 (1.3–4.2)

Omalizumab

Cardiac signs and symptoms NEC

1.2 (0.7–2.1)

3–11

5

2.8 (1.2–6.6)

Montelukast

Gastrointestinal atonic and hypomotility disorders NEC

1.2 (0.7–2.1)

12–17

5

2.8 (1.2–6.6)

CI confidence interval, HLT high-level term, NEC not elsewhere classified, PRR proportional reporting ratio

3.5 Stratification by Sex

The sex of the patient was known in 20,726 DECs (98%), with 11,926 (56%) DECs concerning boys and 8800 (41%) concerning girls. Upon sex stratification, 289 (77%) signals remained. On the other hand, 30 DECs additionally met the definition of a safety signal, four of which (two each in males/females) were classified as new (Table 7). More signals appeared in boys only (97 signals) than in girls only (35 signals). Signals more often appeared in boys than in girls for ‘psychiatric disorders’, ‘musculoskeletal and connective tissue disorders’ and ‘eye disorders’, whereas they appeared more often in girls for ‘cardiac disorders’ (Fig. 2).
Table 7

New signals unmasked by stratification for sex

Drug

Reaction HLT

Overall PRR (95% CI)

Sex

Reports

PRR-sex (95% CI)

Fluticasone

Appetite disorders

1.5 (0.9–2.8)

Female

7

2.5 (1.2–5.1)

Montelukast

Alopecias

1.5 (0.8–2.8)

Male

6

2.9 (1.3–6.5)

Montelukast

Flatulence, bloating and distension

1.3 (0.7–2.5)

Female

6

2.5 (1.1–5.5)

Omalizumab

Lid, lash and lacrimal infections, irritations and inflammations

1.9 (0.8–4.5)

Male

5

3.5 (1.5–8.5)

CI confidence interval, HLT high-level term, NEC not elsewhere classified, PRR proportional reporting ratio

Fig. 2

Signals after stratification by sex categorized as a signal in boys only, girls only or both sexes

4 Discussion

In this study, we investigated spontaneous reports of adverse events related to asthma drugs in children. We were able to include fivefold more reports than a previous analysis of EudraVigilance data from 2007 to 2011 [20]. We observed that spontaneous reports for montelukast were the most frequent, followed by reports for salbutamol, omalizumab and budesonide. Although 92% of the statistical signals were already known, we observed 30 new signals, especially for omalizumab. Calculation of the PRR by therapeutic area, age and sex revealed additional new signals, pointing to masking due to confounding by indication or effect modification.

Safety signals of asthma drugs were mainly related to psychiatric disorders, especially in combination with the use of montelukast. This is in line with a recent review that concluded that LTRAs are frequently associated with neuropsychiatric adverse events [6]. Awareness of the potential for psychiatric adverse events when initiating treatment with asthma drugs is important so that stopping treatment may be considered when the first symptoms of psychiatric events are observed. The second largest group of safety signals, ‘respiratory, thoracic and mediastinal disorders’, is of course prone to confounding by indication. This is further supported by our observation that half of the signals from this group disappeared when we calculated the PRR-TA.

Some observed new signals were already reported in the literature but not included in the SmPC, Martindale and UpToDate. For example, a study of spontaneous reports from Lareb, The Netherlands Pharmacovigilance Centre [33] associated hypertrichosis, both statistically and through causality assessment, with ICS, and cases of acute encephalopathies have been described with theophylline use. Although seizures are already listed as adverse reactions in the SmPC of theophylline, multiple cases of post-ictal coma lasting > 24 h have been described, regarding it as acute encephalopathy instead of a mere seizure [34]. We also found ‘herpes viral infections’ as a new signal associated with omalizumab. An increase in oral herpes in patients using omalizumab, without reaching statistical significance, has been mentioned in the literature [35]. One case of herpes labialis has been described in the literature, with the timing of herpes relapses directly related to omalizumab treatment [36]. Further studies should be conducted to confirm these new safety signals and investigate possible mechanisms.

Omalizumab, a monoclonal anti-IgE antibody, has been authorized in Europe since 2005. It is prescribed by specialist physicians to patients with asthma and IgE-mediated allergy that is difficult to control. Our results indicated new safety signals for omalizumab, including herpes viral infections, bacterial infections and visual disorders. The indication of use—namely, severe allergic asthma—implies that these patients were treated not only with omalizumab but also with other asthma drugs, especially high-dose ICS and systemic corticosteroids, both of which have immunosuppressive effects. Omalizumab is a systemic drug administered via intramuscular injection instead of inhaler therapy, which could also explain the relatively high number of reports.

We showed that many safety signals in children did not meet the criteria of a signal when calculating the PRR in the whole dataset that included adults. This means that such signals would have been missed had we not examined spontaneous reports in children separately. We also observed the importance of stratification for age within the paediatric group. Studies have demonstrated that stratification for age may unmask signals but adjustment for age does not improve the analyses [37, 38]. This emphasises the importance of actively monitoring the safety of drugs in children. When stratifying by sex, most signals remained. The prevalence of asthma, and thus the use of asthma drugs, is higher in boys than in girls. This may explain why more signals were reported for boys than for girls, as at least five reports are required for a signal [39].

The strength of our study lies in our use of one of the largest databases of spontaneous reports and data from many different countries, covering a heterogeneous paediatric population. This provided us with the opportunity to study the safety of a drug in real life and to report on signals of a relatively new drug, omalizumab. It also allowed us to stratify for age and sex. Spontaneous reports remain the most important source of detecting (new) safety signals, but the use and interpretation of these data has some limitations [14]. First, underreporting is likely in spontaneous report databases, and the adverse events that are reported are only the tip of the iceberg [40]. Another challenge of this type of study is that the MedDRA terms do not always match exactly the adverse reactions as mentioned in the SmPC and literature. This makes it difficult to interpret whether a signal is an already known adverse reaction. Reports might consist of a combination of multiple events and multiple drugs, and we were unable to deduct which was the culprit drug from these data, even though we only included those that were reported as suspected or interacting. Additional information from the report, such as the complete narrative, might have given more insight; unfortunately, data protection regulations meant these were unavailable. Also, some events were not reported frequently. This resulted in wide CIs, which require careful interpretation of the PRR. Finally, although we did have different combinations of medication and drugs, we did not have the number of reports containing a certain event combined with at least one type of asthma medication. This may have led to an underestimation of the true PRR-TA as the denominator was the number of combinations between the event and all asthma drugs.

For pharmacovigilance, it is paramount that any new suspected adverse drug reactions are reported immediately and include important details such as age and sex. This reporting is facilitated via online submission through the EudraVigilance website [41]. EudraVigilance also provides healthcare professionals with a graphical overview of the type of reported adverse drug reactions (by age, sex, type of reporter and outcome) of all centrally authorised drugs.

5 Conclusion

We characterized spontaneous reports of asthma drugs in children. We again showed the importance of investigating adverse events in children separately from adults and in different age and sex strata. We identified new signals that need to be further followed-up to investigate the true associations and causality.

Notes

Compliance with Ethical Standards

Funding

No sources of funding were used to assist in the preparation of this study.

Conflict of interest

Esmé J. Baan, Veronique A. de Smet, Christina E. Hoeve, Alexandra C. Pacurariu and Johan C. de Jongste have no conflicts of interest that are directly relevant to the content of this article. Hettie Janssens is involved in a study funded by Vectura with an unconditional research grant and has received a travel grant from Vertex, neither of which were related to the content of this work. Katia Verhamme works for a research group that has received unconditional research grants from Yamanouchi, Pfizer, Boehringer Ingelheim, Novartis and GSK, none of which was related to the content of this work. Miriam Sturkenboom led a research group that received research grants from GSK, Servier, Novartis; the GSK and Novartis studies were respiratory related. This study was independent. The views expressed in this article are the personal views of the authors and may not be understood or quoted as being made on behalf of or reflecting the position of the regulatory agency/agencies or organisations with which the authors are employed and/or affiliated.

Data sharing

The data that support the findings of this study are available from the EMA, but restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. A request for the data can be submitted to the EMA.

Ethical approval

Ethical approval was not required for this study.

Supplementary material

40264_2019_870_MOESM1_ESM.pdf (438 kb)
Supplementary material 1 (PDF 438 kb)
40264_2019_870_MOESM2_ESM.docx (77 kb)
Supplementary material 2 (DOCX 77 kb)

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Copyright information

© The Author(s) 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Esmé J. Baan
    • 1
    Email author
  • Veronique A. de Smet
    • 2
  • Christina E. Hoeve
    • 1
  • Alexandra C. Pacurariu
    • 1
  • Miriam C. J. M. Sturkenboom
    • 4
  • Johan C. de Jongste
    • 3
  • Hettie M. Janssens
    • 3
  • Katia M. C. Verhamme
    • 1
    • 2
    • 5
  1. 1.Department of Medical Informatics, Erasmus Medical CentreErasmus UniversityRotterdamThe Netherlands
  2. 2.Department of PharmacyGhent University HospitalGhentBelgium
  3. 3.Department of Pediatrics/Respiratory MedicineErasmus University/Sophia Children’s HospitalRotterdamThe Netherlands
  4. 4.Julius Global HealthUniversity Medical Centre UtrechtUtrechtThe Netherlands
  5. 5.Department of Infection Control and EpidemiologyOLV HospitalAalstBelgium

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