Skip to main content
The World Allergy Organization Journal logoLink to The World Allergy Organization Journal
. 2023 Jan 12;16(1):100738. doi: 10.1016/j.waojou.2022.100738

Clinical aspects of severe cutaneous adverse reactions caused by beta-lactam antibiotics: A study from the Korea SCAR registry

Min-Hye Kim a, Dong Yoon Kang q, Young-Hee Nam c, Da Woon Sim d, Sujeong Kim e, Jun Kyu Lee f, Jung-Won Park g, Hye-Kyung Park h, Jae-Woo Jung i, Cheol-Woo Kim j, Min-Suk Yang k, Joo-Hee Kim l, Young-Min Ye m, Young-Il Koh d, Hye-Ryun Kang b, Seoung Ju Park n,1,∗∗, Sae-Hoon Kim o,p,1,
PMCID: PMC9852789  PMID: 36694620

Abstract

Background

Although beta-lactams are 1 of the major causative agents of severe cutaneous adverse reactions (SCAR), their epidemiology and clinical aspects have been poorly studied. This study aimed to investigate the characteristics of SCAR caused by beta-lactams in the Korean SCAR registry.

Methods

We retrospectively analyzed beta-lactam-induced SCAR cases collected from 28 tertiary university hospitals in Korea between 2010 and 2015. The SCAR phenotypes included Stevens-Johnson syndrome (SJS), toxic epidermal necrolysis (TEN), SJS-TEN overlap, and drug reaction with eosinophilia and systemic symptoms (DRESS). Beta-lactams were classified according to their chemical structures: penicillins, cephalosporins, and carbapenems. The causative beta-lactams, clinical and laboratory features, treatments, and outcomes were evaluated.

Results

Among the 275 antibiotic-induced SCAR cases, 170 patients developed SCAR induced by beta-lactams. Beta-lactam antibiotic-induced SCAR showed more frequent SJS/TEN compared to SCAR induced by non-beta-lactam antibiotics (SJS/TEN/SJS-TEN overlap/DRESS: 36.5/11.2/5.9/46.5% vs. 23.8/10.5/2.9/62.9%, P = 0.049). Cephalosporin was the most common culprit drug. Particularly, 91 and 79 patients presented with SJS/TEN and DRESS, respectively. The odds ratio (OR) for poor prognosis, such as sequelae and death, was significantly increased in subjects with SJS-TEN overlap and TEN and carbapenem as culprit drug in the multivariate analysis (OR, 35.61; P = 0.016, OR, 28.07; P = 0.006, OR 30.46; P = 0.027).

Conclusion

Among antibiotic-induced SCAR, clinical features were different depending on whether the culprit drug was a beta-lactam antibiotic or SCAR type. The poor prognosis was related to SJS-TEN overlap, TEN type, and carbapenem as the culprit drug.

Keywords: Antibacterial agents, Beta-lactams, Drug hypersensitivity syndrome, Stevens-Johnson syndrome, Toxic epidermal necrolysis

Introduction

Drug hypersensitivity reactions are common obstacles that interfere with patient care. These reactions are caused by immunological mechanisms that occur in approximately 2.3–3.6 cases per 1000 patients.1 Although most drug eruptions are mild maculopapular exanthema, they also include severe phenotypes, such as Stevens-Johnson syndrome (SJS), toxic epidermal necrolysis (TEN), and drug-induced hypersensitivity syndrome (DIHS)/drug reaction with eosinophilia and systemic symptoms (DRESS), termed severe cutaneous adverse drug reactions (SCAR).2 SCAR are known to develop in 2% of hospitalized patients, 2–7 cases/million per year for SJS/TEN and 1/1000–1/10,000 cases for DIHS.2 Overall, this is an extremely rare but serious problem encountered during treatment.2

The clinical features of SCAR are diverse. They may have various types of skin involvement with characteristic mucocutaneous involvement. In contrast, DIHS/DRESS has almost no skin detachment and is mainly accompanied by internal organ involvement and hematological abnormalities, such as fever, hepatitis, and eosinophilia. However, there can be overlap in some of the clinical features of SJS/TEN and DRESS, and there is no clear boundary.2 There have been several reports of complications caused by SCAR.2 In the case of SJS/TEN, there may be skin scarring and ophthalmic, genitourinary, and respiratory complications, and in the case of DIHS, end organ failure, and autoimmune disease may develop.2 It has been shown that certain human leukocyte antigen types are associated with an increased risk of SCAR with certain drugs.3, 4, 5 However, there are insufficient studies on other factors related to SCAR occurrence and prognosis.

Beta-lactam antibiotics, including penicillins and cephalosporins, are the most commonly used antibiotics for infectious diseases in clinical practice and the most common cause of drug allergy.6 Nevertheless, few studies have been conducted on beta-lactam antibiotic-induced SCAR; therefore, we aimed to study the clinical characteristics and factors related to poor prognosis of SCAR caused by beta-lactam antibiotics.

Methods

Study subjects and resources

This study was based on data collected from the Korean SCAR registry, these data were described in previous studies.7,8 The Korean SCAR registry is web-based and originated from the Regional Pharmacovigilance Center (RPVC) of the Korean Food and Drug Administration (KFDA). Currently, 36 tertiary hospitals are participating, and at least 1 allergist reviews cases at each institution participating in this registry.9 Briefly, the Korean SCAR registry retrospectively collected drug allergy data from 36 tertiary hospitals nationwide from 2010 to 2015. Each case was registered after reviewing medical records by 2 physicians, including at least 1 allergist, and the definition of SCAR was based on the same criteria as in previous studies. The causal relationship between the suspected drug and SCAR was evaluated using the World Health Organization–Uppsala Monitoring Center (WHO-UMC) criteria.10 The WHO-UMC criteria is based on 5 items: plausible time relationship between drug intake and events, cannot be explained by disease or other drugs, response to withdrawal plausible drug, event definitive pharmacologically or phenomenologically, and rechallenge satisfactory. Causalities were then classified into 4 stages: certain, probable, possible, and unlikely (Supplementary Table 1).11 Cases with possible or higher levels of causality were included.

If more than 2 possible causal drugs were used simultaneously, all suspected drugs assigned as culprit drugs based on expert judgment. Demographic information, hospitalization status, vital signs, laboratory tests, medical history, drug history, and clinical course were collected from a review of medical records.

Institutional Review Board (IRB) approval was obtained from each research participating institution (IRB approval number of the representative institution, Seoul National University Bundang Hospital is B-1802-450-401.).

Evaluation of clinical data on beta-lactam SCAR

The cases of SCAR caused by antibiotics were divided into cases of beta-lactam antibiotic-induced SCAR and cases of non-beta-lactam antibiotic-induced SCAR. Within beta-lactam antibiotic-induced SCAR cases, penicillins, cephalosporins, and carbapenems-were classified according to the culprit drug classifications. We evaluated whether there was a difference in the clinical characteristics and prognosis according to the beta-lactam or non-beta-lactam antibiotics and SCAR types.

Statistical analysis

Statistical analysis was performed using SPSS software (version 22.0; SPSS, IBM Inc., Chicago, IL, USA). Continuous variables were presented as means ± standard deviation or median interquartile range and analyzed using the t-test or Mann-Whitney U test. Categorical variables were presented as numbers or percentages and were analyzed using Pearson's χ2 test or Fisher's exact test. A P-value <0.05 indicated statistical significance. Binary logistic regression analysis was performed to find factors related to the prognosis of SCAR, and statistically significant factors in univariate analysis and factors judged to be clinically important, such as age and sex, were adjusted.

Results

Demographic and clinical characteristics

Data of 275 patients with SCAR due to antibiotics were extracted, of which 170 were classified as SCAR due to beta-lactam antibiotics and 105 as SCAR due to non-beta-lactam antibiotics (Table 1, Fig. 1A). Patients with beta-lactam antibiotic-induced SCAR had a mean age of 50.8 years, and 55.9% were male. Hypertension was the most common comorbidity, followed by diabetes mellitus, cancer, chronic kidney disease, and chronic liver disease (Table 1). On average, 72.4% of the body surface area (BSA) was affected. Mucosal involvement was detected in 61.6% of patients, fever in 62.3%, and lymphadenitis in 14.9%. The median disease duration was 18.0 (Interquartile range, IQR 13.0–27.0) days, the administration duration of culprit drugs was 8.0 (IQR 3.0–17.0) days, latent period which is the period form the antibiotics intake to the onset of reaction was 9.0 (IQR 1.0–22.0) days, and admission duration was 17.0 (IQR 10.0–35.5) days. In 14.6% of the patients, there was a history of exposure to the suspected drug. As treatment, 78.3% received systemic corticosteroids, 19.0% received intravenous immunoglobulin (IVIG), and 4.7% received intensive care. The mean SCORTEN score was 1.0 on day 1 and 1.7 on day 7. However, 85.7% of patients recovered without sequelae, sequelae remained in 7.7%, and 6.5% of patients died.

Table 1.

Demographic and clinical characteristics of antibiotics-induced SCAR patients.

Total Non-beta-lactam Beta-lactam P-value
Number of cases 275 105 (38.2%) 170 (61.8%)
Age 53.3 ± 20.6 54.7 ± 21.5 50.8 ± 22.1 0.158
Male % 146 (53.1%) 51 (48.6%) 95 (55.9%) 0.264
BMI 22.6 ± 3.7 22.8 ± 4.0 21.6 ± 3.3 0.022
Smoking history (Non-/Ex-/Current smoker) 177/20/23 (80.5/9.1/10.5%) 77/6/7 (85.6/6.7/7.8%) 100/14/16 (76.9/10.8/12.3%) 0.298
33/238 (13.9%) 18/88 (20.5%) 15/150 (10.0%) 0.032
History of drug allergy 18/191 (9.4%) 11/76 (14.5%) 7/108 (6.1%) 0.075
Comorbidities
 Diabetes mellitus 44/246 (17.9%) 15/93 (16.1%) 29/153 (19.0%) 0.611
 Hypertension 81/245 (33.1%) 31/93 (33.3%) 50/152 (32.9%) 1.000
 Chronic liver disease 12/236 (5.1%) 4/92 (4.3%) 8/144 (5.6%) 0.770
 Chronic kidney disease 16/229 (7.0%) 6/89 (6.7%) 10/140 (7.1%) 1.000
 Cancer 24/229 (10.5%) 6/87 (6.9%) 18/142 (12.7%) 0.189
Admission route
 SCAR onset during hospitalization (%) 71/273 (26.0%) 25 (23.8%) 46 (27.4%) 0.572
 Via OPD (%) 76/273 (27.8%) 34 (32.4%) 42 (25.0%) 0.212
 Via ER (%) 126/273 (46.2%) 46 (43.8%) 80 (47.6%) 0.618
SCAR type (SJS/TEN/SJS-TEN overlap/DRESS or DHS) 87/30/13/145 (31.6/10.9/4.7/52.7%) 25/11/3/66 (23.8/10.5/2.9/62.9%) 62/19/10/79 (36.5/11.2/5.9/46.5%) 0.049
Presenting symptoms
 Skin involvement, BSA (%) 74.2 ± 30.2 76.9 ± 29.7 72.4 ± 30.5 0.246
 Mucosal involvement 115/204 (56.4%) 38/79 (48.1%) 77/125 (61.6%) 0.062
 Fever 173/261 (66.3%) 74/102 (72.5%) 99/159 (62.3%) 0.107
 Lymphadenitis 21/119 (17.6%) 10/45 (22.2%) 11/74 (14.9%) 0.330
 Highest body temperature 39.0 ± 0.7 39.1 ± 0.7 39.0 ± 0.8 0.432
 Duration of fever 6.2 ± 7.2 7.3 ± 8.8 5.3 ± 5.5 0.093
 Highest WBC count 18653.0 ± 27901.0 24258.2 ± 42531.7 14847.0 ± 7506.7 0.110
 Highest eosinophil count 1493.9 ± 2083.8 1612.3 ± 2023.0 1408.7 ± 2131.2 0.501
 Highest creatinine level 2.0 ± 2.2 2.3 ± 2.6 1.7 ± 1.8 0.130
 Highest ALT level 211.4 ± 286.1 241.7 ± 300.2 190.5 ± 275.1 0.205
Administration of culprit (days) 11.0 (IQR 4.0–21.0) 17.0 (IQR 7.0–27.0) 8.0 (IQR 3.0–17.0) 0.140
Duration of drug administration after symptom onset (days) 3.0 (IQR 2.0–5.0) 4.0 (IQR 2.0–9.0) 2.0 (IQR 1.0–4.0) 0.006
Latent period 11.0 (IQR 2.0–23.0) 13.0 (IQR 6.0–26.0) 9.0 (IQR 1.0–22.0) 0.314
Disease duration (days) 20.0 (IQR 14.0–30.0) 22.0 (IQR 14.0–34.0) 18.0 (IQR 130–27.0) 0.640
Admission duration (days) 18.0 (IQR 11.0–33.0) 18.0 (IQR 11.0–32.5) 17.0 (IQR 10.0–35.5) 0.673
 SJS 13.0 (IQR 7.75–26.25) 14.0 (IQR 6.5–28.0) 13.0 (IQR 8.0–20.0) 0.904
 SJS/TEN Overlap 26.0 (IQR 18.0–39.0) 27.0 (IQR 26.0–27.0) 25.0 (IQR 17.25–39.0) 0.346
 TEN 32.0 (IQR 14.0–51.5) 26.0 (IQR 16.0–40.0) 34.5 (IQR 13.0–56.0) 0.715
 DRESS or DHS 18.0 (IQR 11.0–37.0) 18.0 (IQR 11.75–32.25) 19.0 (IQR 11.0–39.0) 0.912
Past exposure to culprit drug 16/144 (11.1%) 4/62 (6.5%) 12/82 (14.6%) 0.180
Treatment
 Systemic steroid 216/268 (80.6%) 86/102 (84.3%) 130/166 (78.3%) 0.267
 IVIG 46/250 (18.4%) 16/92 (17.4%) 30/158 (19.0%) 0.866
 Other immunosuppressant 5/248 (2.0%) 0/91 (0.0%) 5/157 (3.2%) 0.161
 ICU care 13/275 (4.7%) 5/105 (4.8%) 8/170 (4.7%) 1.000
 SCORTEN Day 1 (total 7) 1.0 ± 0.9 0.9 ± 1.0 1.0 ± 0.8 0.681
 SCORTEN Day 7 (total 7) 1.8 ± 0.9 1.7 ± 1.0 0.843
Prognosis (%) 0.588
 Improved 228/272 (83.8%) 84/104 (80.8%) 144/168 (85.7%)
 With sequelae 24/272 (8.8%) 11/104 (10.6%) 13/168 (7.7%)
 Death (%) 20/272 (7.4%) 9/104 (8.75) 11/168 (6.5%)

SCAR, severe cutaneous adverse reaction; BMI, body mass index; OPD, out-patient department; ER, emergency room; SJS, Stevens-Johnson syndrome; overlap, combined Stevens-Johnson syndrome and toxic epidermal necrolysis; TEN, toxic epidermal necrolysis; DRESS, drug reaction with eosinophilia and systemic symptoms; DHS, drug hypersensitivity syndrome; BSA, body surface area; WBC, white blood cells; Cr, creatinine; ALT, alkaline phosphatase; IQR, Interquartile range; IVIG, intravenous immunoglobulin; ICU, intensive care unit; SCORTEN, SCORe of Toxic Epidermal Necrosis

Compared with patients with non-beta-lactam antibiotic-induced SCAR, patients with beta-lactam antibiotic-induced SCAR had a lower body mass index (BMI) (21.6 ± 3.3 vs. 22.8 ± 4.0, P = 0.022), and fewer allergic diseases (10.0% vs. 20.5%, P = 0.032); SJS/TEN was more common, and DRESS was less in SCAR type (SJS/TEN/SJS-TEN overlap/DRESS 36.5/11.2/5.9/46.5% vs. 23.8/10.5/2.9/62.9%, P = 0.049). The duration of drug administration after symptom onset was shorter in beta-lactam antibiotic-induced SCAR (3.0 vs. 7.0 days, P = 0.006).

Among the 170 patients with beta-lactam antibiotic-induced SCAR, the most common suspected drug class was cephalosporins, especially third-generation cephalosporins, and the second most common suspected class was penicillins, especially aminopenicillins (Fig. 1B). SJS/TEN was more frequent in penicillins and DRESS was more frequent in carbapenems, but the differences were not statistically significant (Fig. 1C).

Fig. 1.

Fig. 1

The flowchart of case classification (A), distribution of subjects (B) and the proportions of SCAR types (C) according to culprit beta-lactam antibiotic class

Comparison according to SCAR phenotype

In the analysis of SCAR types, patients with DRESS had the highest BMI (SJS/SJS-TEN overlap/TEN/DRESS 21.0/21.6/20.2/22.6, P = 0.021), and other allergic diseases were most frequently accompanied by TEN (26.7%, P = 0.042) (Table 2). Mucosal involvement was lowest in patients with DRESS (SJS/SJS-TEN overlap/TEN/DRESS 86.8, 88.0, 92.5, 21.7, P < 0.001), and BSA was highest in patients with TEN (48.3%, P < 0.001). As expected, subjects with DRESS more frequently showed fever, lymphadenitis and higher white blood cell (WBC) count, eosinophil count, and alanine aminotransferase (ALT) levels than those with other SCAR phenotypes. Conversely, the duration of fever was longest in patients with TEN, and serum creatinine (Cr) level was highest in patients with SJS-TEN overlap. The duration of drug administration even after symptom onset and latent period were longest in patients with DRESS as 14.6 days (P < 0.041) and 24.7 days (P < 0.001), respectively. The disease duration and hospitalization days were longer in patients with SJS-TEN overlap and TEN, at 40.4 days (P < 0.011) and 33.2 days (P < 0.001), respectively. Systemic corticosteroids were used more frequently in patients with SJS/TEN than in those with DRESS (SJS/SJS-TEN overlap/TEN/DRESS 93.2/100.0/94.9/77.1, P < 0.001), and IVIG was administered to 51.9% of patients with TEN (P < 0.001). Although the SCORTEN score on day 1 was highest in patients with SJS-TEN overlap (1.6 ± 0.7 [P = 0.010]), the SCORTEN score on day 7 was highest in patients with TEN (2.2 ± 1.2 [P = 0.017]), and the proportion of patients with a score of ≥2 on day 7 was also highest in patients with TEN (SJS/SJS-TEN overlap/TEN/DRESS 51.3, 68.6, 76.8, 62.0, P < 0.001). The most favorable prognosis was in patients with DRESS, with 92.4% of recovered patients and the worst prognosis was in patients with TEN, with 26.3% of the complications or death (P < 0.001). SCAR types and prognosis according to WHO-UMC causality evaluation was provided in Supplementary Table 2.

Table 2.

Demographics and clinical presentations of beta-lactam antibiotics-induced SCARs according to phenotypes.

SJS (n = 62) Overlap (n = 10) TEN (n = 19) DRESS (n = 79) P-value
Age (median, year) 46.1 ± 25.4 53.3 ± 24.7 52.1 ± 25.2 53.9 ± 17.7 0.479
Sex (% of males) 59.7 50.0 57.9 53.2 0.860
BMI (kg/m2) 21.0 ± 3.3 21.6 ± 2.6 20.2 ± 4.9 22.6 ± 2.7 0.021
Other allergic disease (%) 3.8 0.0 26.7 12.5 0.042
Other drug hypersensitivity (%) 4.8 0.0 9.1 7.5 0.856
Comorbidities
 Chronic kidney disease 6.7 11.5 14.3 10.0 0.190
 Chronic liver disease 5.9 0.0 2.7 6.3 0.476
 Diabetes mellitus 14.1 20.7 15.4 19.3 0.370
 Hypertension 35.1 46.9 38.7 36.6 0.613
 Cancer 7.3 3.3 13.9 11.4 0.164
Presenting symptoms
 Mucosal involvement (%) 86.8 88.0 92.5 21.7 < 0.001
 Involved BSA (%) 11.0 ± 28.7 19.9 ± 12.5 48.3 ± 28.7 14.1 ± 32.6 < 0.001
 Fever (%) 48.0 56.3 63.6 67.7 < 0.001
 Lymphadenitis (%) 3.1 6.7 9.3 28.3 < 0.001
 Highest body temperature 38.9 ± 0.7 38.9 ± 0.6 39.1 ± 0.8 39.0 ± 0.7 0.231
 Duration of fever (days) 3.9 ± 4.1 5.1 ± 4.3 9.2 ± 10.6 5.4 ± 6.2 < 0.001
 Highest WBC count 13494.2 ± 7079.5 12098.8 ± 6570.4 14645.7 ± 7224.6 20165.7 ± 24518.5 < 0.001
 Highest eosinophil count 868.4 ± 1462.1 797.7 ± 1175.4 894.7 ± 1906.9 2376.7 ± 3513.1 < 0.001
 Highest serum Cr (mg/mL) 1.4 ± 1.2 2.4 ± 2.0 2.2 ± 2.0 2.2 ± 2.3 0.001
 Highest serum ALT (IU/L) 172.7 ± 272.6 110.7 ± 116.3 197.1 ± 324.5 320.1 ± 506.8 < 0.001
Culprit drugs 0.600
 Penicillins 27 (42.9%) 4 (6.3%) 8 (12.7%) 24 (38.1%)
 Cephalosporins 31 (33.3%) 6 (5.9%) 10 (9.8%) 52 (51.0%)
 Carbapenems 1 (20.0%) 0 (0.0%) 1 (20.0%) 3 (60.0%)
Administration of culprit (days) 19.0 ± 30.1 4.9 ± 3.6 4.0 ± 4.2 18.6 ± 14.3 0.268
Duration of drug administration after symptom 6.7 ± 9.1 3.3 ± 2.4 8.0 ± 12.3 14.6 ± 80.0 0.041
Latent period (days) 17.8 ± 25.8 18.7 ± 28.7 18.8 ± 28.2 24.7 ± 23.8 < 0.001
Disease duration (days) 18.9 ± 11.0 40.4 ± 38.7 27.9 ± 12.9 27.6 ± 28.9 0.011
Admission duration (days) 15.7 ± 12.6 25.5 ± 17.5 33.2 ± 25.4 24.5 ± 26.7 < 0.001
Previous exposure to culprit (%) 9.9 31.3 13.0 9.5 0.075
Treatment
Systemic steroid (%) 93.2 100.0 94.9 77.1 < 0.001
IVIG (%) 15.7 38.2 51.9 8.0 < 0.001
Other immunosuppressant 5.7 2.9 6.8 2.1 0.054
ICU care 4.6 17.6 26.3 6.3 < 0.001
SCORTEN Day 1 (total 7) 0.9 ± 0.8 1.6 ± 0.7 1.4 ± 0.9 0.9 ± 0.8 0.010
SCORTEN Day 7 (total 7) 1.6 ± 1.0 1.2 ± 0.6 2.2 ± 1.2 1.8 ± 0.9 0.017
SCORTEN Day 1 (% of ≥ 2) 23.2 28.6 36.6 24.1 0.092
SCORTEN Day 7 (% of ≥ 2) 51.3 68.6 76.8 62.0 < 0.001
Prognosis (%) < 0.001
 Improved 90.2 77.8 47.4 92.4
 With sequelae 6.6 22.2 26.3 2.5
 Death 3.3 0.0 26.3 5.1

SCAR, severe cutaneous adverse reaction; BMI, body mass index; OPD, out-patient department; ER, emergency room; SJS, Stevens-Johnson syndrome; overlap, combined Stevens-Johnson syndrome and toxic epidermal necrolysis; TEN, toxic epidermal necrolysis; DRESS, drug reaction with eosinophilia and systemic symptoms; DHS, drug hypersensitivity syndrome; BSA, body surface area; WBC, white blood cells; Cr, creatinine; ALT, alkaline phosphatase; IVIG, intravenous immunoglobulin; ICU, intensive care unit; SCORTEN, SCORe of Toxic Epidermal Necrosis

Factors related with prognosis

Regarding the factors related to prognosis, 28.7% of fully recovered patients developed SCAR during hospitalization, whereas no patient with sequelae developed SCAR during hospitalization (P = 0.021). DRESS was more frequent in patients with full recovery; TEN and SJS-TEN overlap were more frequent in patients with sequelae (SJS/TEN/SJS-TEN overlap/DRESS 38.2,6.3,4.9,50.7%vs.30.8,38.5,15.4,15.4%, P = 0.001) (Table 3). Moreover, the latent period was shorter (16.4 ± 23.6vs.4.9 ± 7.3, P = 0.016), and the rates of IVIG use and ICU care were higher in patients with sequelae (12.7%vs.41.7%, P = 0.019, 1.5%vs.33.3%, P < 0.001).

Table 3.

Prognosis factors of full recovery, sequelae, and mortality.


Survival



Mortality
P-value
Full recovery Sequelae P-value
Number of cases 144 (91.7%) 13 (8.3%) 157 (93.5%) 11 (6.5%)
Age 50.6 ± 22.3 45.7 ± 19.0 0.447 50.2 ± 22.0 59.6 ± 24.3 0.173
Male % 80/144 (55.6%) 6/13 (46.2%) 0.570 86/157 (54.8%) 8/11 (72.7%) 0.350
21.9 ± 3.3 20.5 ± 2.8 0.177 21.8 ± 3.3 19.7 ± 3.6 0.053
Smoking history (Non-/Ex-/Current smoker) 15/12/83 (13.6/10.9/75.5%) 1/0/10 (9.1/0.0/90.9%) 0.740 16/12/93 (13.2/9.9/76.9%) 0/2/6 (0.0/25.0/75.0%) 0.195
Smoking history (pack-year) 4.0 ± 10.2 1.8 ± 6.0 0.483 3.8 ± 9.9 0.0 ± 0.0 <0.001
Allergic disease 12/126 (9.5%) 0/12 (0.0%) 0.600 12/138 (8.7%) 3/10 (30.0%) 0.066
History of drug allergy 6/97 (6.2%) 0/9 (0.0%) 1.000 6/106 (5.7%) 1/7 (14.3%) 0.369
Comorbidities
 Diabetes mellitus 24/130 (18.5%) 1/12 (8.3%) 0.692 25/142 (17.6%) 4/10 (40.0%) 0.098
 Hypertension 42/129 (32.6%) 3/12 (25.0%) 0.752 45/141 (31.9%) 5/10 (50.0%) 0.300
 Chronic liver disease 8/121 (6.6%) 0/12 (0.0%) 1.000 8/133 (6.0%) 0/10 (0.0%) 0.650
 Chronic kidney disease 6/117 (5.1%) 1/12 (8.3%) 0.504 7/129 (5.4%) 2/9 (22.2%) 0.107
 Cancer 14/119 (11.8%) 1/12 (8.3%) 1.000 15/131 (11.5%) 3/10 (30.0%) 0.118
Admission route
 SCAR onset during hospitalization (%) 41/143 (28.7%) 0/13 (0.0%) 0.021 41/156 (26.3%) 5/11 (45.5%) 0.177
 OPD (%) 34/143 (23.8%) 5/13 (38.5%) 0.313 39/156 (25.0%) 3/11 (27.3%) 1.000
 ER (%) 68/143 (47.6%) 8/13 (61.5%) 0.394 76/156 (48.7%) 3/11 (27.3%) 0.219
SCAR type (SJS/TEN/SJS-TEN overlap/DRESS or DHS) 55/9/7/73 (38.2/6.3/4.9/50.7%) 4/5/2/2 (30.8/38.5/15.4/15.4%) 0.001 59/14/9/75 (37.6/8.9/5.7/47.8%) 2/5/0/4 (18.2/45.5/0.0/36.4%) 0.015
Presenting symptoms
 Skin involvement 70.4 ± 31.3 81.8 ± 26.7 0.206 71.4 ± 31.0 87.5 ± 19.9 0.036
 Mucosal involvement 62/105 (59.0%) 10/12 (83.3%) 0.126 72/117 (61.5%) 4/7 (57.1%) 1.000
 Fever 81/135 (60.0%) 7/12 (58.3%) 1.000 88/147 (59.9%) 10/11 (90.9%) 0.053
 Lymphadenitis 9/61 (14.8%) 1/8 (12.5%) 1.000 10/69 (14.5%) 1/4 (25.0%) 0.487
 SCORTEN 1.0 ± 0.8 1.2 ± 0.7 0.396 1.0 ± 0.8 1.4 ± 0.9 0.127
 Highest body temperature 38.9 ± 0.7 39.5 ± 0.8 0.061 39.0 ± 0.7 39.0 ± 1.1 0.958
 Duration of fever 5.0 ± 5.0 4.0 ± 2.2 0.651 5.0 ± 4.9 9.0 ± 10.6 0.355
 Highest WBC count 14426.2 ± 7051.0 10865.0 ± 3997.8 0.322 18872.4 ± 27816.1 21957.1 ± 23487.1 0.775
 Highest eosinophil count 1456.6 ± 2208.1 709.5 ± 711.7 0.503 1428.9 ± 2174.3 1044.0 ± 1144.9 0.669
 Highest creatinine level 1.6 ± 1.5 2.3 ± 3.0 0.573 1.6 ± 1.6 2.9 ± 2.9 0.244
 Highest ALT level 190.7 ± 283.0 157.6 ± 199.5 0.761 188.7 ± 278.2 219.6 ± 235.0 0.775
Administration of culprit (days) 18.9 ± 21.9 7.0 ± 7.5 0.133 17.8 ± 21.2 5.9 ± 5.0 0.042
Duration of drug administration after symptom onset (days) 3.17 ± 2.6 2.6 ± 2.1 0.641 3.1 ± 2.3 2.3 ± 1.4 0.407
Latent period (days) 16.4 ± 23.6 4.9 ± 7.3 0.016 15.4 ± 22.9 14.3 ± 19.8 0.873
Disease duration (days) 24.4 ± 22.1 30.6 ± 28.2 0.348 25.0 ± 22.6 40.2 ± 49.5 0.334
Admission duration (days) 26.8 ± 30.3 27.2 ± 29.1 0.961 26.8 ± 30.1 48.3 ± 50.6 0.193
Past exposure to culprit drug 8/68 (11.8%) 1/7 (14.3%) 1.000 9/75 (12.0%) 3/7 (42.9%) 0.061
Treatment
 Systemic steroid 107/142 (75.4%) 12/12 (100%) 0.069 119/154 (77.3%) 9/10 (90.0%) 0.693
 Duration of steroid use (days)
 Total dose of steroid (mg)
 IVIG 17/134 (12.7%) 5/12 (41.7%) 0.019 22/146 (15.1%) 8/10 (80.0%) <0.001
 Other immunosuppressant 4/134 (3.0%) 1/12 (8.3%) 0.353 5/146 (3.4%) 0/9 (0.0%) 1.000
 ICU care 2/134 (1.5%) 4/12 (33.3%) <0.001 6/146 (4.1%) 5/11 (45.5%) <0.001
SCORTEN Day 1 (total 7) 0.9 ± 0.8 1.2 ± 0.7 0.396 1.0 ± 0.8 1.4 ± 0.9 0.127
SCORTEN Day 7 (total 7) 1.7 ± 0.8 1.5 ± 1.5 0.771 1.7 ± 0.9 2.9 ± 1.2 <0.001
SCORTEN Day 1 (% of ≥2) 32 (22.2%) 4 (30.8%) 0.497 36 (22.9%) 5 (45.5%) 0.139
SCORTEN Day 7 (% of ≥2) 86 (59.7%) 5 (38.5%) 0.153 91 (58.0%) 9 (81.8%) 0.202

SCAR, severe cutaneous adverse reaction; BMI, body mass index; OPD, out-patient department; ER, emergency room; SJS, Stevens-Johnson syndrome; overlap, combined Stevens-Johnson syndrome and toxic epidermal necrolysis; TEN, toxic epidermal necrolysis; DRESS, drug reaction with eosinophilia and systemic symptoms; DHS, drug hypersensitivity syndrome; BSA, body surface area; WBC, white blood cells; Cr, creatinine; ALT, alkaline phosphatase; IVIG, intravenous immunoglobulin; ICU, intensive care unit; SCORTEN, SCORe of Toxic Epidermal Necrosis

In patients with mortality, the amount of cigarette consumption was lower (3.8 ± 9.9% vs. 0.0 ± 0.0, P < 0.001), TEN was more frequent SCAR types (SJS/TEN/SJS-TEN overlap/DRESS 37.6/8.9/5.7/47.8% vs. 18.2/45.5/0.0/36.4%, P = 0.015), and the involved skin area was broader than in patients who survived (71.4 ± 31.0 vs. 87.5 ± 19.9, P = 0.036). Moreover, administration days of culprit drug were shorter (17.8 ± 21.2 vs. 5.9 ± 5.0, P = 0.042), and the rates of IVIG use and ICU care were higher (15.1% vs. 80.0%, P < 0.001, 4.1% vs. 45.5%, P < 0.001). Finally, a higher SCORTEN score on day 7 was associated with increased mortality (1.7 ± 0.9 vs. 2.9 ± 1.2, P < 0.001) (Table 3).

When the risk factors for poor prognosis leading to sequelae or death were analyzed, a higher SJS-TEN overlap and TEN, serum Cr level, and SCORTEN score on day 7 and, if carbapenems were the causative agents, a higher odds ratio for poor prognosis was observed in the univariate analysis (Table 4). However, in the multivariate analysis, the statistical significance of other factors disappeared, and the odds ratio of poor prognosis remained significant with SJS-TEN overlap/and TEN and was also significant when carbapenems were the causative agents.

Table 4.

Factors associated with poor prognosis (sequelae or death) of beta-lactam-induced SCARs using binary logistic regression (univariate and multivariable analysis).

Variable
Univariate analysis

Multivariate analysisa

OR (95% C·I.) P-value OR (95% C·I.) P-value
Age
 <60 years 1
 ≥60 years 0.78 (0.32–1.91) 0.592
Sex
 Female 1
 Male 1.12 (0.47–2.69) 0.800
SCAR type
 SJS 1 1
 Overlap 2.62 (0.44–15.58) 0.290 35.61 (1.92–660.25) 0.016
 TEN 10.19 (2.97–34.96) <0.001 28.07 (2.56–307.19) 0.006
 DRESS 0.75 (0.23–2.46) 0.639 2.13 (0.21–22.24) 0.527
Mucosal involvement
 No 1
 Yes 1.94 (0.65–5.79) 0.234
Fever
 No 1
 Yes 1.89 (0.70–5.10) 0.209
Highest serum Cr (mg/mL) 1.30 (1.01–1.67) 0.046 1.00 (0.64–1.56) 0.992
Highest serum ALT (IU/L) 1.00 (0.99–1.00) 0.978
Administration of culprit (day) 0.93 (0.88–1.01) 0.106
Previous exposure to culprit
 No 1
 Yes 3.00 (0.76–11.86) 0.117
Time to symptom onset (day) 0.98 (0.94–1.01) 0.149
Antibiotic class
 Penicillin 1 1
 Cephalosporin 0.66 (0.26–1.64) 0.368 0.94 (0.19–4.65) 0.938
 Carbapenem 7.95 (1.17–53.82) 0.034 30.46 (1.47–632.84) 0.027
SCORTEN Day 1 (total 7) 1.52 (0.92–2.52) 0.106
SCORTEN Day 7 (total 7) 1.71 (1.09–2.69) 0.020 2.05 (0.81–5.15) 0.129
SCORTEN Day 1 (% of ≥ 2) 2.10 (0.84–5.24) 0.112
SCORTEN Day 7 (% of ≥ 2) 0.94 (0.39–2.27) 0.898
Hospitalization (day) 1.01 (0.99–1.02) 0.169
Comorbidities
 Other allergic disease 1.50 (0.39–5.82) 0.558
 Other drug hypersensitivity 1.03 (0.41–2.61) 0.946
 Chronic kidney disease 3.08 (0.70–13.44) 0.134
 Diabetes mellitus 1.29 (0.44–3.87) 0.638
 Hypertension 1.18 (0.46–3.04) 0.726
 Cancer 1.67 (0.49–5.64) 0.411

SCAR, severe cutaneous adverse reaction; SJS, Stevens-Johnson syndrome; overlap, combined Stevens-Johnson syndrome and toxic epidermal necrolysis; TEN, toxic epidermal necrolysis; DRESS, drug reaction with eosinophilia and systemic symptoms; Cr, creatinine; ALT, alkaline phosphatase; SCORTEN, SCORe of Toxic Epidermal Necrosis.

a

Adjusted for age, sex, SCAR type, highest serum creatinine level, antibiotics class, and SCORTEN Day 7

Discussion

In this study, there was a clinical difference between beta-lactam antibiotic-induced SCAR and non-beta-lactam antibiotic-induced SCAR. SJS/TEN was more common than DRESS in patients with beta-lactam antibiotic-induced SCAR, and the number of culprit drug administration days was shorter. Moreover, among patients with beta-lactam antibiotic-induced SCAR, different clinical features were observed according to the SCAR type. Patients with DRESS showed a higher BMI, higher frequency of fever and lymphadenitis as presenting symptoms, more elevated WBC count, eosinophil count, and ALT level, and longer duration of drug administration after symptoms and latent periods. Patients with TEN were more commonly accompanied by other allergic diseases and showed larger involved BSA, longer duration of fever and admission duration, higher proportion of IVIG use, higher SCORTEN score on day 7, and a higher proportion of patients with poor prognosis (with sequelae or death). The factors associated with poor prognosis were SJS-TEN overlap, TEN as the SCAR phenotype. and carbapenems as the causative drugs.

To the best of our knowledge, this is the first large-scale study that has been conducted on beta-lactam antibiotic-induced SCAR. In 2014, a retrospective study on SCAR related to systemic antibiotics in 74 cases was published.12 In this study, penicillins and cephalosporins were the most common causative agents of SJS, TEN, and acute generalized exanthematous pustulosis, and glycopeptides were the most common causative agents of DRESS. Although the ranking of penicillins and cephalosporins was reversed compared to that in our study, the most common causative antibiotic class, beta-lactam antibiotics, was identical. In that study, the mortality rate was the highest in the SJS and TEN groups, and it was associated with old age and underlying sepsis. The average latent period was 6.52 ± 4.59, 5 ± 3.52, and 11.3 ± 8.20 days for SJS, TEN, and DRESS, respectively, while it was 17.8 ± 25.8, 18.8 ± 28.2, and 24.7 ± 23.8, respectively, in our study. The latent period was longer in our study, but the longest latent period was observed in the DRESS subtype in both studies. The proportion of corticosteroid use was similar at 70.3% and 80.6%, respectively, but IVIG was used more frequently in our study (1.3% and 18.4%, respectively). The overall mortality was 20%, which was higher than 7.4% in our study, and mortality by SCAR type was TEN (66.7%), SJS or SJS-TEN overlap (20%), and DRESS (12%), which was also lower in our study (26.3% for TEN, 3.3% for SJS, 0% for SJS-TEN overlap, and 5.1% for DRESS. The average SCORTEN score was 1.25 in survivors and 2.77 in the dead; similarly, the SCORTEN score on day 7 was 1.7 and 2.9, respectively.12

The SCAR study was published in 2019, but it was also a retrospective review of electronic medical records of only 35 cases.13 SCARs caused by all drugs were targeted, and antibiotics were the most common agents (88.1%). Among them, cephalosporins (23.7%) and penicillin (16.9%) were the most common culprit drugs, as shown in our study. In that study, the latent period was 6.2 days for SJS/TEN and 14.0 days for DRESS, which were shorter than those in our study. Systemic corticosteroids were used in 71.4% of the patients, and IVIG was not administered. There was female preponderance in SJS and TEN, and male preponderance in DRESS, while there were no sex differences in our study.13

In another retrospective observational comparative study between antibiotic- and non-antibiotic-associated delayed cutaneous adverse drug reactions, 48% of the 84 patients were antibiotic-associated cases.14 When compared with antibiotic-related SCAR in our study, age and sex were similar, while the latency period was longer in our study (11.0 vs. 6.0 days). As in our study, beta-lactam antibiotics (61.8% vs. 45.0%), especially cephalosporins, were the most common implicated drug. Mortality were lower in our study (7.4% vs. 10–20%).

Although studies on risk factors for drug allergy are still lacking, previous studies have suggested that female sex, age, systemic lupus erythematosus, and human immunodeficiency virus infection are risk factors.15 Conversely, atopy is not considered a major risk factor for most drug allergies.15 Risk factors for the development of SCAR or the poor prognosis of SCAR have been rarely studied, and risk factors for the occurrence of beta-lactam antibiotic-induced SCAR could not be analyzed in our study. However, analysis of risk factors related to poor prognosis showed that there was no statistical significance with sex, age, comorbidities, or other allergic diseases; only TEN subtype and carbapenem use were significant risk factors in our study.

Although antibiotics and anticonvulsants are known to be the most common causative drugs of SCAR,15 few studies have been conducted on antibiotic-induced SCAR. The clinical features of antibiotic-induced SCAR have not been studied; thus, we have no clinical information regarding this. Moreover, although cotrimoxazole, allopurinol, carbamazepine, phenytoin, phenobarbital, and oxicam-NSAIDs are known as “high risk” for the development of SCAR,16 the commonly listed culprit drugs are penicillins and cephalosporins in antibiotic-induced SCAR, not cotrimoxazole.12,13 For the first time, carbapenem-induced SCAR was found to be associated with poor prognosis in our study. However, carbapenem is expected to be used in clinically severe and antibiotic-resistant patients. Therefore, it is likely that the prognosis was poor because patients who used carbapenems had severe baseline medical conditions, rather than because of a difference in the type of antibiotics. Although we considered the effect of comorbidities as a contributing factor in the univariate analysis, detailed patient conditions were not evaluated. Moreover, the number of patients with carbapenem-induced SCAR was small and larger-scale studies are required to confirm our result.

There are some limitations to our study. First, we only collected clinical data, and we did not study possible mechanisms. Therefore, this aspect needs to be further investigated. Secondly, the causative drugs were not certain. Since not all cases were confirmed by drug skin test or in vitro test, the culprit drug had to be presumed. In particular, it was more difficult to select a suspected drug when multiple drugs were administered simultaneously. In these cases, all suspected drugs used simultaneously were assumed to be causative agents based on expert judgment. Thirdly, we did not assess the effects of type or severity of infection for the prognosis of SCAR as unified and valid evaluation of infection severity is complicating. The association between specific beta-lactam antibiotics and outcome of SCAR needs to be clarified controlling these factors in the further study. Fourth, SCAR may be related to viral infection, but testing for this has not been performed. In the case of SJS/TEN, they may be related or mimic to herpes simplex virus (HSV) or Mycoplasma pneumonia infection. In addition, reactivation of human herpesvirus (HHV) is often seen in DRESS patients. Since the relationship between viral infection and SCAR cannot be ruled out, further investigation is needed in the next study.17 Finally, the retrospective study design had limitations in predicting causal relationships.

In conclusion, we analyzed the clinical characteristics, common causative drugs, and risk factors of poor prognosis of beta-lactam antibiotic-induced SCARs, using large scale, nationwide data. SJS/TEN is the most common type of beta-lactam antibiotic-induced SCAR. Among the beta-lactam antibiotic-induced SCAR, prognosis was the most favorable in patients with DRESS and the worst in patients with TEN. The risk factors for poor prognosis leading to sequelae or death were SJS-TEN overlap and TEN, and carbapenems as the causative agents.

SCAR is a serious and lethal disease, and beta-lactam antibiotic-related SCAR accounts for a large proportion of SCAR cases. In particular, SJS/TEN which is very severe SCARs with poor prognosis were common type in beta-lactam antibiotic-related SCARs. Therefore, more attention should be paid to monitoring skin reactions while using beta-lactam antibiotics. In addition, it was observed in this study that the prognosis may be worse when SCAR caused by carbapenem occurs. Careful observation is also necessary in patients taking carbapenem, and early discontinuation and treatment would be needed when symptoms develop. Moreover, in-depth research is required to understand the mechanism in order to prevent SCAR from happening in the future.

Abbreviations

BMI, body mass index; BSA, body surface area; DIHS, drug-induced hypersensitivity syndrome; DRESS, drug reaction with eosinophilia and systemic symptoms; IVIG, intravenous immune globulin; OR, odds ratio; SCAR, severe cutaneous adverse reactions; SJS, Stevens-Johnson syndrome; TEN, toxic epidermal necrolysis; WBC, white blood cell.

Funding

There is no funding source of this study.

Availability of data and materials

The data used in this paper is not public data and therefore cannot be disclosed.

Ethics approval

IRB approval number of the representative institution, Seoul National University Bundang Hospital is B-1802-450-401.

Authors’ consent for publication

All authors consented to the publication of this paper.

Declaration of competing interest

Authors have no conflicts to disclose.

Footnotes

Full list of author information is available at the end of the article

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.waojou.2022.100738.

Contributor Information

Seoung Ju Park, Email: sjp@jbnu.ac.kr.

Sae-Hoon Kim, Email: shkrins@snu.ac.kr.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Multimedia component 1
mmc1.docx (14.5KB, docx)

References

  • 1.McNulty C.M.G., Park M.A. Delayed cutaneous hypersensitivity reactions to antibiotics: management with desensitization. Immunol Allergy Clin. 2017;37:751–760. doi: 10.1016/j.iac.2017.07.002. [DOI] [PubMed] [Google Scholar]
  • 2.Chung W.H., Wang C.W., Dao R.L. Severe cutaneous adverse drug reactions. J Dermatol. 2016;43:758–766. doi: 10.1111/1346-8138.13430. [DOI] [PubMed] [Google Scholar]
  • 3.Kim B.K., Jung J.W., Kim T.B., et al. HLA-A∗31:01 and lamotrigine-induced severe cutaneous adverse drug reactions in a Korean population. Ann Allergy Asthma Immunol. 2017;118:629–630. doi: 10.1016/j.anai.2017.02.011. [DOI] [PubMed] [Google Scholar]
  • 4.Kim S.H., Lee K.W., Song W.J., et al. Carbamazepine-induced severe cutaneous adverse reactions and HLA genotypes in Koreans. Epilepsy Res. 2011;97:190–197. doi: 10.1016/j.eplepsyres.2011.08.010. [DOI] [PubMed] [Google Scholar]
  • 5.Park H.W., Kim D.K., Kim S.H., et al. Efficacy of the HLA-B(∗)58:01 screening test in preventing allopurinol-induced severe cutaneous adverse reactions in patients with chronic renal insufficiency-A prospective study. J Allergy Clin Immunol Pract. 2019;7:1271–1276. doi: 10.1016/j.jaip.2018.12.012. [DOI] [PubMed] [Google Scholar]
  • 6.Har D., Solensky R. Penicillin and beta-lactam hypersensitivity. Immunol Allergy Clin. 2017;37:643–662. doi: 10.1016/j.iac.2017.07.001. [DOI] [PubMed] [Google Scholar]
  • 7.Oh H.L., Kang D.Y., Kang H.R., et al. Severe cutaneous adverse reactions in Korean pediatric patients: a study from the Korea SCAR registry. Allergy Asthma Immunol Res. 2019;11:241–253. doi: 10.4168/aair.2019.11.2.241. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Lee S.Y., Nam Y.H., Koh Y.I., et al. Phenotypes of severe cutaneous adverse reactions caused by nonsteroidal anti-inflammatory drugs. Allergy Asthma Immunol Res. 2019;11:212–221. doi: 10.4168/aair.2019.11.2.212. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Park C.S., Kang D.Y., Kang M.G., et al. Severe cutaneous adverse reactions to antiepileptic drugs: a nationwide registry-based study in Korea. Allergy Asthma Immunol Res. 2019;11:709–722. doi: 10.4168/aair.2019.11.5.709. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Meyboom R.H., Hekster Y.A., Egberts A.C., Gribnau F.W., Edwards I.R. Causal or casual? The role of causality assessment in pharmacovigilance. Drug Saf. 1997;17:374–389. doi: 10.2165/00002018-199717060-00004. [DOI] [PubMed] [Google Scholar]
  • 11.Shukla A.K., Jhaj R., Misra S., Ahmed S.N., Nanda M., Chaudhary D. Agreement between WHO-UMC causality scale and the Naranjo algorithm for causality assessment of adverse drug reactions. J Fam Med Prim Care. 2021;10:3303–3308. doi: 10.4103/jfmpc.jfmpc_831_21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Lin Y.F., Yang C.H., Sindy H., et al. Severe cutaneous adverse reactions related to systemic antibiotics. Clin Infect Dis. 2014;58:1377–1385. doi: 10.1093/cid/ciu126. [DOI] [PubMed] [Google Scholar]
  • 13.Zhang C., Van D.N., Hieu C., Craig T. Drug-induced severe cutaneous adverse reactions: determine the cause and prevention. Ann Allergy Asthma Immunol. 2019;123:483–487. doi: 10.1016/j.anai.2019.08.004. [DOI] [PubMed] [Google Scholar]
  • 14.Trubiano J.A., Aung A.K., Nguyen M., et al. A comparative analysis between antibiotic- and nonantibiotic-associated delayed cutaneous adverse drug reactions. J Allergy Clin Immunol Pract. 2016;4:1187–1193. doi: 10.1016/j.jaip.2016.04.026. [DOI] [PubMed] [Google Scholar]
  • 15.Thong B.Y., Tan T.C. Epidemiology and risk factors for drug allergy. Br J Clin Pharmacol. 2011;71:684–700. doi: 10.1111/j.1365-2125.2010.03774.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Mockenhaupt M., Viboud C., Dunant A., et al. Stevens-Johnson syndrome and toxic epidermal necrolysis: assessment of medication risks with emphasis on recently marketed drugs. The EuroSCAR-study. J Invest Dermatol. 2008;128:35–44. doi: 10.1038/sj.jid.5701033. [DOI] [PubMed] [Google Scholar]
  • 17.Pavlos R., White K.D., Wanjalla C., Mallal S.A., Phillips E.J. Severe delayed drug reactions: role of genetics and viral infections. Immunol Allergy Clin. 2017;37:785–815. doi: 10.1016/j.iac.2017.07.007. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Multimedia component 1
mmc1.docx (14.5KB, docx)

Data Availability Statement

The data used in this paper is not public data and therefore cannot be disclosed.


Articles from The World Allergy Organization Journal are provided here courtesy of World Allergy Organization

RESOURCES