Abstract
There exists a disagreement in the current literature on whether febrile seizures (FS) are associated with adverse neurodevelopmental outcomes such as attention-deficit hyperactivity disorder (ADHD). This systematic review and meta-analysis was thus commenced to determine the association between childhood FS and ADHD and quantitatively measure its magnitude. This review was reported according to the PRISMA and MOOSE guideline. PubMed, Embase, Web of Science, Google Scholar, CINAHL, Scopus, and PsycINFO were systematically searched for observational studies reporting the occurrence of ADHD in patients with childhood FS. Then, unadjusted and adjusted odds ratio (OR) and 95% confidence interval (CI) were extracted from studies and pooled. The protocol for this review was registered in PROSPERO (CRD42024541299). Our meta-analysis included 12 studies with a total of 958,082 participants. FS was associated with 1.91 times greater odds of ADHD in the unadjusted analysis (95% CI = 1.32 to 2.76) and 2.68 times greater odds in the adjusted analysis (95% CI = 1.21 to 5.93). Subgroup and meta-regression analyses identified mean baseline age, follow-up duration, study aim, ADHD diagnostic criteria, study design, and region as moderators of the overall effect.
Conclusion: In the present meta-analysis, a significant association between childhood FS and later ADHD was observed. To our knowledge, this is the first systematic review and meta-analysis investigating the association between the two disorders. Our findings contradict the common belief that FS are benign, thus suggesting that children with FS may benefit from closer developmental monitoring given the observed association with ADHD.
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What is Known: • Febrile seizure (FS) is the most common type of seizure in childhood, affecting 2% to 5% in all infants and children between ages 6 through 60 months. • Several studies and guidelines have determined FS as a universally benign disorder, but in recent years, an increasing amount of literature has challenged this idea. |
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What is New: • Our findings suggest a positive association between FS and ADHD, challenging the widely accepted belief that childhood FS is benign. • Subgroup analyses and meta-regression have identified study aim, ADHD diagnostic criteria, study design, region, mean age, and follow-up duration as factors that modify the overall effect. |
Supplementary Information
The online version contains supplementary material available at 10.1007/s00431-025-06285-4.
Keywords: Febrile seizure, Convulsion, Adverse outcomes, Long-term, ADHD, Meta-analysis
Introduction
Febrile seizure (FS) is a type of childhood seizure accompanied by a fever and is not caused by infection in the central nervous system or other triggers of acute seizures [1]. It is the most common form of childhood seizure with an occurrence of 2% to 5% in all infants and children between ages 6 through 60 months [2]. Several studies have reported that most FS have benign prognosis and that very low morality risk is estimated in the FS population [3]. To date, several existing guidelines on evaluation and management of FS have determined childhood FS, especially brief, simple FS, as an universally benign disorder [2, 4, 5]. However, in recent years, an increasing amount of literature has challenged this idea. In particular, increased prevalence of neurodevelopmental disorders, such as attention-deficit hyperactivity disorder (ADHD), were observed in children with childhood FS [6].
According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), ADHD is a neurodevelopmental disorder defined by impaired levels of inattention, disorganization, and hyperactivity–impulsivity, and it is the most common neurodevelopmental disorder in children and adolescents [7, 8]. In addition to inattention and hyperactivity, ADHD may lead to complications such as poor academic performance, antisocial behavior, obesity, or impaired social functions [9, 10]. Therefore, because FS is the most common childhood seizure, there may be value in re-examining the potential association between FS and ADHD.
Despite its importance, the correlation between FS and ADHD remains unclear. In contrast to the generally accepted notion that FS has a positive long-term prognosis, an expanding body of evidence suggests a positive association between FS and ADHD [11–13]. Additionally, there is a notable paucity of evidence on factors that moderate this effect. To the best of our knowledge, no systematic synthesis of literature has assessed the nature and magnitude of the association between FS and ADHD. A recent meta-analysis has reported a higher propensity of behavioral outcomes in children with FS; however, this study synthesized different neurodevelopmental and behavioral outcomes, obscuring the true magnitude of the association between FS and ADHD [14]. In this systematic review and meta-analysis, we attempt a comprehensive and systematic synthesis of all available evidence to evaluate whether FS in childhood is associated with the later occurrence of ADHD and explore factors that influence the association.
Materials and methods
This study was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and the Meta-analyses Of Observational Studies in Epidemiology (MOOSE) checklist (Online Resource 1, Table S1−3) [15, 16]. The protocol for this systematic review and meta-analysis was preregistered in PROSPERO (CRD42024541299).
Search strategy
PubMed, Embase, Web of Science, CINAHL, Scopus, Google Scholar, and PsycINFO were systematically searched for academic articles and grey literature published from 1902 to June 8, 2025 [17]. Searches were limited to this time frame because the first description of ADHD was made in 1902 [18]. The search strategy was developed by combining keywords related to FS and ADHD. The final keyword was used to search for records in seven databases (Online Resource 1, Table S4). Citations were retrieved directly from each database except for Google Scholar. Publish or Perish (version 8.17.4863.9118) was used to retrieve article information from Google Scholar [19]. References of all included studies and related review articles were manually screened.
Study selection
We included observational studies published in peer-reviewed journals that evaluated the occurrence of ADHD in individuals with childhood FS compared to a control group. In our meta-analysis, FS is defined as a seizure in children aged 6 months to 6 years during a febrile illness, without infection or prior afebrile seizures [1]. Studies were included if FS was diagnosed as a part of the study by a definition similar to ours, or if previous records of FS diagnosis based on diagnostic criteria such as International Classification of Diseases (ICD) were used. There were no limitations on age, ethnicity, or sex. Studies that are not relevant to our meta-analysis, case reports, letters, editorials, doctoral thesis, and in vitro studies were excluded. Studies with single-arm design and those involving patients diagnosed with or presenting symptoms of ADHD before the onset of first FS were excluded. When several studies were conducted in the same population, the most recent study that could best answer our research question was selected. Review articles were excluded, but their references were manually searched. No language restrictions were imposed.
After an initial search of seven electronic databases, duplicates were removed. Titles and abstracts were then reviewed to identify relevant studies, followed by thorough full-text screening based on the abovementioned eligibility criteria. Five authors (H.W., S.L., D.Y., S.J., and H.C.) independently screened the records for studies to include, and disagreements were resolved through discussion.
Data extraction
The following characteristics were extracted from the studies included in the final analysis: author, publication year, country, region, study design, study aim, mean baseline age, mean follow-up duration, number of participants, male ratio, sample source, matching, ADHD assessment, ADHD diagnostic criteria, and ratio of complex FS. “Study aim” indicates whether studies investigated FS and ADHD as primary findings or secondary findings. “Male ratio” was only calculated in FS populations. “Sample source” refers to the setting of the study. “Matching” indicates if a matched design was employed. “ADHD assessment” shows how ADHD was diagnosed in each study, and “ADHD diagnostic criteria” specifies the tool used to diagnose ADHD. “Ratio of complex FS” indicates the ratio of complex FS within the FS group of each study.
The quality of the studies included was assessed using the Newcastle–Ottawa Scale (NOS) [20]. The NOS assesses observational studies based on three criteria: selection, comparability, and outcome for cohort studies; and selection, comparability, and exposure for case–control studies. Five authors (H.W., S.L., D.Y., S.J., and H.C.) independently completed data extraction and evaluation of study quality. Discrepancies were resolved through discussion.
Statistical analysis
In our study, two separate meta-analyses—one consisting of unadjusted OR and corresponding 95% CI and other consisting of adjusted OR and corresponding 95% CI—were conducted. Unadjusted OR and 95% CI were extracted from studies or calculated by constructing two-by-two tables of ADHD outcome by FS history. Adjusted OR and corresponding 95% CI were either extracted from studies that have reported them or calculated from the data provided. Unadjusted estimates and adjusted estimates were then each pooled separately using the generic inverse variance method [21]. Methodological heterogeneity was expected among studies, hence random-effects model was used in both analyses. Heterogeneity between studies was measured using the Higgins I2 statistic [22].
Subgroup meta-analysis and meta-regression were performed to identify sources of heterogeneity. Subgroup meta-analyses were conducted using the following categorical variables: study aim, sample source, diagnostic criteria, ADHD assessment, matching, study design, region, and ADHD subtype. Meta-regression analyses were performed for the following continuous variables: male sex, mean age at baseline, study quality, follow-up duration, and ratio of complex FS in FS group. Studies that did not report a certain variable were excluded from that analysis.
Publication bias was examined using the funnel plot and Egger’s regression test. The funnel plot suggests the presence of publication bias if it is asymmetric, and the Egger’s regression test indicates publication bias if the P-value is less than 0.05 [23]. If publication bias was detected, the trim and fill method was applied post hoc to adjust for the bias. The trim and fill method returns inaccurate results under substantial between-study heterogeneity [24]. Therefore, if I2 was greater than 50%, a leave-one-out sensitivity analysis was conducted to identify outlier studies. The trim and fill corrected OR was calculated after removing the outliers. All statistical analyses were performed on R software version 4.3.2 [25].
Results
A systematic search of seven databases retrieved 1448 studies. Only 998 of the most relevant records could be retrieved from Google Scholar, but this is more than enough for a comprehensive coverage of the literature [26]. After removing duplicates, the records were screened by reviewing only titles and abstracts, leaving 70 articles for full-text review. Based on the predetermined inclusion and exclusion criteria, 58 studies were excluded. Twelve studies were included in the final analysis (Fig. 1) [6, 11–13, 27–34].
Fig. 1.
PRISMA flow diagram showing the process of identifying studies eligible for inclusion in the meta-analysis
Study characteristics
A total of 12 studies with 958,082 participants were included in this systematic review [6, 11–13, 27–34] (Table 1). Included studies were conducted across 11 countries, with most of the studies conducted in Europe [6, 11, 12, 27] and Asia [28, 29, 32, 33]. The remaining studies were conducted in North America [30], Oceania [31], South America [34], and the Middle East [13]. Our analysis comprised seven cohort studies [6, 11, 12, 27, 29–31] and five case–control studies [13, 28, 32–34]. Out of the twelve studies, six studies explored the association between FS and ADHD as their primary outcome of interest [6, 11–13, 28, 29]. The remaining six studies examined the relationship between FS and ADHD as their secondary outcome of interest [27, 30–34]. At baseline, the mean age of participants enrolled in each study ranged from 2.45 to 11.2 years. The participants were followed in most studies, with the mean duration ranging from 1.4 to 22.0 years. Of the twelve studies, seven studies were conducted in a clinical setting, such as in a hospital [6, 11, 13, 27, 31, 33, 34]. Four studies matched their control group to the FS group in order to reduce the effects of confounding [12, 28–30, 32]. In the included studies, ADHD was assessed by specialists (n = 6) [6, 13, 27, 30, 33, 34], through questionnaires (n = 3) [28, 31, 32], or simply determined from records (n = 3) [11, 12, 29]. Most studies used DSM as their criteria for diagnosis of ADHD [12, 13, 29–34]. All of the studies included for final analysis had high study quality, with NOS scores ranging from 5 to 9 (Online Resource 1, Table S5-6). Out of 12 studies, six studies showed significant association between FS and ADHD [6, 27, 28, 30, 31, 33]. Of the twelve studies included, four studies reported adjusted odds ratio [12, 27, 32, 34]. More information on the adjusted variables is shown in Table 1.
Table 1.
General characteristics of 12 studies included in the meta-analysis
| Source | Country (region) | Study design | Study aim | Mean age, y | Mean follow-up duration, y | No. of participants (% of male) | ||
| Bertelsen et al., 2016 [11] | Denmark (Europe) | Cohort | Primary | NR | 22.0 | 891,962 (55.5) | ||
| Cavirani et al., 2024 [27] | Italy (Europe) | Cohort | Secondary | 0.0 | 14.8 | 168 (34.6) | ||
| Chang et al., 2000 [28] | Taiwan (Asia) | Case–control | Primary | 3.1 | 4.1 | 174 (59.8) | ||
| Davis et al., 2010 [30] | USA (North America) | Cohort | Secondary | 0.0 | 19.2 | 1086 (75.0) | ||
| Deng et al., 2020 [31] | Australia (Oceania) | Cohort | Secondary | 1.1 | 1.4 | 138 (44.3) | ||
| Gao et al., 2022 [32] | China (Asia) | Case–control | Secondary | 11.2 | NR | 5409 (NR) | ||
| Gillberg et al., 2017 [12] | Sweden (Europe) | Cohort | Primary | 9.7 | NR | 27,092 (55.3) | ||
| Kim et al., 2014 [33] | Korea (Asia) | Case–control | Secondary | 7.4 | 3.0 | 74 (NR) | ||
| Ku et al., 2014 [29] | Taiwan (Asia) | Cohort | Primary | 2.5 | 11.0 | 5405 (58.1) | ||
| Nilsson et al., 2022 [6] | Sweden (Europe) | Cohort | Primary | 9 | 5.0 | 25,882 (57.4) | ||
| Pineda et al., 2007 [34] | Colombia (South America) | Case–control | Secondary | 8.1 | NR | 486 (NR) | ||
| Salehi et al., 2016 [13] | Iran (Middle East) | Case–control | Primary | 5.1 | NR | 206 (62.1) | ||
| Source | Sample source | Matching | ADHD assessment | ADHD diagnostic criteria | Ratio of Complex FS, % | Adjusted variables | ||
| Bertelsen et al., 2016 [11] | Clinical | No | Record | ICD | NR | None | ||
| Cavirani et al., 2024 [27] | Clinical | No | Specialist | NR | NR | Sex, family history of febrile seizure, age at first febrile seizure, developmental delay or ID | ||
| Chang et al., 2000 [28] | Non-clinical | Yes | Questionnaire | Conners Scale | 19.5 | None | ||
| Davis et al., 2010 [30] | Non-clinical | Yes | Specialist | DSM | NR | None | ||
| Deng et al., 2020 [31] | Clinical | No | Questionnaire | DSM | 20 | None | ||
| Gao et al., 2022 [32] | Non-clinical | No | Questionnaire | DSM | NR | Sex, history of febrile seizure, history of epilepsy, history of head trauma, type of delivery, father’s education, mother’s education, exposure to cigarettes, maternal smoking for more than 1-year, maternal drinking, number of siblings, quiet home environment, violent education | ||
| Gillberg et al., 2017 [12] | Non-clinical | No | Record | DSM | NR | Epilepsy | ||
| Kim et al., 2014 [33] | Clinical | No | Specialist | DSM | NR | None | ||
| Ku et al., 2014 [29] | Non-clinical | Yes | Record | DSM | NR | None | ||
| Nilsson et al., 2022 [6] | Clinical | No | Specialist | A-TAC | NR | None | ||
| Pineda et al., 2007 [34] | Clinical | No | Specialist | DSM | NR | Sex, age, school grades | ||
| Salehi et al., 2016 [13] | Yes | Specialist | DSM | 0 | None | |||
NR not reported, ICD International Classification of Diseases and Related Health, DSM Diagnostic and Statistical Manual of Mental Disorders
FS and ADHD
Dichotomous outcomes were extracted from all 12 included studies, and unadjusted OR with corresponding 95% CI were calculated. Of the 12 studies included, four studies reported adjusted OR. Three reported adjusted OR and corresponding 95% CI [12, 32, 34] while one study provided raw data that were used to calculate the adjusted OR and 95% CI [27].
The pooled ORs for the association between FS and ADHD were 1.91 (95% CI = 1.32 to 2.76, P < 0.001) in the unadjusted meta-analysis and 2.68 (1.21 to 5.93, P < 0.05) in the adjusted meta-analysis (Fig. 2, Table 2). Significant heterogeneity was measured in both analyses, with I2 = 84.5% in the unadjusted meta-analysis and I2 = 80.3% in the adjusted meta-analysis.
Fig. 2.
Forest plots showing association between FS and ADHD. A Forest plot shows adjusted association between FS and ADHD. B Forest plot shows unadjusted association between FS and ADHD. The studies are presented in order from the smallest to the largest effect size. Weights were calculated using the random-effects model. Squares represent the odds ratio of ADHD in patients with and without febrile seizure history. The size of each square is proportional to the number of participants in each study. The diamond represents the overall effect size. OR, odds ratio; CI, confidence interval
Table 2.
Results of unadjusted analysis, adjusted analysis, and trim and fill analysis
| No. of studies | OR (95% CI) | I2, % | P value | |
|---|---|---|---|---|
| Unadjusted analysisa | 4 [6, 11–13, 27–34] | 1.91 (1.32 to 2.76) | 84.5 | < 0.001 |
| Adjusted analysisb | 12 [6, 11–13, 27–34] | 2.68 (1.21 to 5.93) | 80.3 | < 0.05 |
| Trim and fill analysisc | 11 [6, 12, 13, 27–31, 33, 34] | 1.79 (1.48 to 2.16) | 0.0 | < 0.0001 |
aMeta-analysis of unadjusted effect sizes
bMeta-analysis of adjusted effect sizes
cTrim and fill analysis was applied to the unadjusted analysis. Outlier studies (“Bertelson et al.,” “Gao et al.”) were removed prior to trim and fill. One study was added to the left side
Subgroup meta-analyses and meta-regression
Subgroup analysis and meta-regression were performed to explore the sources of heterogeneity between studies in both meta-analyses. The subgroup effect is considered statistically significant if the P-value for the test is less than 0.1 [35]. In subgroup meta-analyses for unadjusted OR, the subgroup effect was statistically significant for study aim, ADHD diagnostic criteria, study design, and region (Table 3). In all of these subgroups, the heterogeneity decreased, indicating that these variables are capable of explaining high heterogeneity. Specifically, a subgroup meta-analysis by study aim revealed decreased OR in studies that investigated FS and ADHD as primary outcome of interest. In terms of ADHD diagnostic criteria, the association between FS and ADHD was greatest in studies that used DSM (OR = 2.37, 95% CI = 1.62 to 3.47). The association was lower in studies that used ICD (OR = 1.12, 95% CI = 1.05 to 1.20) and not significant in studies that used other diagnostic criteria. Furthermore, subgroup meta-analysis by study design presented greater odds of ADHD in case–control studies than in cohort studies. Lastly, by region, the magnitude of association between FS and ADHD was greatest in South America (OR = 3.13, 95% = 1.69 to 5.79), then in Asia (OR = 2.63, 95% = 1.24 to 5.58), and smallest in the Middle East (OR = 2.03, 95% = 1.16 to 3.53). This association was not significant in other regions.
Table 3.
Subgroup meta-analysis and meta-regression analysis for unadjusted meta-analysis
| Factors | No. of studiesa | β | OR | 95% CI | I2, % | P valueb |
|---|---|---|---|---|---|---|
| Male ratio | 9 [6, 11–13, 27–31] | − 0.002 | − 0.046 to 0.041 | 55.8 | 0.91 | |
| Mean age | 11 [6, 12, 13, 27–34] | 0.079 | 0.009 to 0.150 | 35.1 | 0.03 | |
| NOS | 12 [6, 11–13, 27–34] | − 0.159 | − 0.486 to 0.168 | 80.0 | 0.34 | |
| Follow-up duration | 8 [6, 11, 27–31, 33] | − 0.030 | − 0.051 to − 0.010 | 0.0 | 0.004 | |
| Ratio of complex FS | 3 [13, 28, 31] | − 1.121 | − 6.614 to 4.372 | 0.0 | 0.69 | |
| Study aim | 0.05 | |||||
| Primary | 6 [6, 11–13, 28, 29] | 1.49 | 1.12 to 2.00 | 70.6 | ||
| Secondary | 6 [27, 30–34] | 2.89 | 1.58 to 5.31 | 54.1 | ||
| Sample Source | 0.69 | |||||
| Clinical | 7 [6, 11, 13, 27, 31, 33, 34] | 1.77 | 1.08 to 2.89 | 67.2 | ||
| Non-clinical | 5 [12, 28–30, 32] | 2.04 | 1.24 to 3.37 | 79.5 | ||
| ADHD diagnostic criteria | 0.006 | |||||
| ICD | 1 [6] | 1.12 | 1.05 to 1.20 | NA | ||
| Conners rating | 1 [28] | 1.43 | 0.55 to 3.75 | NA | ||
| DSM | 8 [12, 13, 29–34] | 2.37 | 1.62 to 3.47 | 67.6 | ||
| A-TAC | 1 [11] | 1.07 | 0.46 to 2.50 | NA | ||
| ADHD assessment | 0.34 | |||||
| Record | 3 [28, 31, 32] | 1.48 | 1.02 to 2.14 | 84.6 | ||
| Specialist | 6 [6, 13, 27, 30, 33, 34] | 1.87 | 1.21 to 2.89 | 31.7 | ||
| Questionnaire | 3[11, 12, 29] | 3.11 | 1.16 to 8.33 | 61.8 | ||
| Matching | 0.44 | |||||
| No | 8 [6, 11, 12, 27, 31–34] | 2.19 | 1.19 to 4.04 | 88.5 | ||
| Yes | 4 [13, 28–30] | 1.69 | 1.34 to 2.13 | 0.0 | ||
| Study design | 0.01 | |||||
| Cohort | 7 [6, 11, 12, 27, 29–31] | 1.40 | 1.06 to 1.84 | 57.3 | ||
| Case–control | 5 [13, 28, 32–34] | 2.93 | 1.80 to 4.77 | 57.4 | ||
| Region | 0.08 | |||||
| Europe | 4 [6, 11, 12, 27] | 1.28 | 0.94 to 1.76 | 39.6 | ||
| Asia | 4 [28, 29, 32, 33] | 2.63 | 1.24 to 5.58 | 83.1 | ||
| North America | 1 [30] | 1.15 | 0.50 to 2.62 | NA | ||
| Oceania | 1 [31] | 5.79 | 0.27 to 122.80 | NA | ||
| South America | 1 [34] | 3.13 | 1.69 to 5.79 | NA | ||
| Middle East | 1 [13] | 2.03 | 1.16 to 3.53 | NA | ||
| ADHD subtype | 0.13 | |||||
| Combined | 1 [13] | 1.78 | 0.94 to 3.44 | NA | ||
| Hyperactivity/impulsive | 1 [13] | 1.90 | 0.99 to 3.65 | NA | ||
| Inattentive and distractable | 1 [13] | 0.09 | 0.00 to 1.59 | NA | ||
OR odds ratio, CI confidence interval, NA not applicable
aStudies that have not reported subgroup data were not included in the analysis
bP-value for the subgroup effect. P-value less than 0.1 indicates statistically significant subgroup difference
In subgroup meta-analyses for adjusted OR, the subgroup effect was statistically significant for study aim, ADHD assessment, study design, and region (Table 4). Studies reporting the adjusted estimate between FS and ADHD as their secondary outcome of interest exhibited a much higher OR (OR = 3.77, 95% CI = 1.84 to 7.74), whereas the association was not significant in a study reporting the association as its primary outcome of interest. Studies that have assessed ADHD using any form of questionnaire showed a high OR (OR = 5.96, 95% CI = 3.56 to 9.98) compared to studies that had a specialist to do the assessment (OR = 2.66, 95% CI = 1.42 to 4.98). When separated by study design, the subgroup with case–control design presented a much higher OR (OR = 4.25, 95% CI = 2.07 to 8.70) whereas the subgroup with cohort design showed no significant association (OR = 1.48, 95% CI = 0.91 to 2.39). Lastly, when stratified by region, the association between FS and ADHD was significant in Asia and South America (OR = 5.96, 95% CI = 3.56 to 9.98; OR = 2.86, 95% CI = 1.49 to 5.48).
Table 4.
Subgroup meta-analysis and meta-regression analysis for adjusted meta-analysis
| Factors | No. of studiesa | β | OR | 95% CI | I2, % | P valuea |
|---|---|---|---|---|---|---|
| Mean age | 4 [12, 27, 32, 34] | 0.131 | − 0.135 to 0.396 | 81.87 | 0.33 | |
| NOS | 4 [12, 27, 32, 34] | 1.069 | − 1.787 to 3.924 | 86.17 | 0.46 | |
| Study aim | 0.04 | |||||
| Secondary | 3 [27, 32, 34] | 3.77 | 1.84 to 7.74 | 55.53 | ||
| Primary | 1 [12] | 1.50 | 0.92 to 2.45 | NA | ||
| Sample source | 0.88 | |||||
| Non-clinical | 2 [27, 34] | 2.98 | 0.77 to 11.53 | 93.08 | ||
| Clinical | 2 [12, 32] | 2.66 | 1.42 to 4.98 | 0.00 | ||
| ADHD assessment | 0.0007 | |||||
| Questionnaire | 1 [32] | 5.96 | 3.56 to 9.98 | NA | ||
| Record | 1 [12] | 1.50 | 0.92 to 2.45 | NA | ||
| Specialist | 2 [27, 34] | 2.66 | 1.42 to 4.98 | 0.00 | ||
| Study design | 0.02 | |||||
| Case–control | 2 [32, 34] | 4.25 | 2.07 to 8.70 | 66.72 | ||
| Cohort | 2 [12, 27] | 1.48 | 0.91 to 2.39 | 0.00 | ||
| Region | 0.0005 | |||||
| Asia | 1 [32] | 5.96 | 3.56 to 9.98 | NA | ||
| Europe | 2 [12, 27] | 1.48 | 0.91 to 2.39 | 0.00 | ||
| South America | 1 [34] | 2.86 | 1.49 to 5.48 | NA | ||
OR odds ratio, CI confidence interval, NA not applicable
aP-value for the subgroup effect. P-value less than 0.1 indicates statistically significant subgroup difference
Meta-regression consisting of unadjusted OR was significant for analysis of mean age and follow-up duration. Eleven studies were included in the meta-regression analysis of mean age. Higher mean age among patients with a history of FS was associated with greater odds of ADHD (β = 0.079, 95% CI = 0.009 to 0.150, I2 = 35.1%). Eight studies were included in the meta-regression analysis of mean follow-up duration [6, 11, 27–31, 33]. Longer mean follow-up duration was associated with decreased odds of ADHD (β = − 0.030, 95% CI = − 0.051 to − 0.010, I2 = 0.0%). Meta-regression analyses for male ratio, mean baseline age, study quality, and ratio of complex FS were not statistically significant (Table 3). Meta-regression analyses were not significant in adjusted meta-analysis (Table 4).
Publication bias
Contour-enhanced funnel plot and Egger’s regression test were utilized to assess publication bias in both meta-analyses with unadjusted OR and adjusted OR. Because high between-study heterogeneity is a potential source of funnel plot asymmetry, contour-enhanced funnel plot was examined instead of traditional funnel plot in order to distinguish small size effect from other sources of funnel plot asymmetry [36]. In meta-analysis with unadjusted OR, contour-enhanced funnel plot suggested missing studies in the lower right region of the plot, indicating the absence of negative studies (Online Resource 1, Fig. S1). In addition, Egger’s test was statistically significant (P = 0.03). Neither the funnel plot nor Egger’s test suggested publication bias in meta-analysis with adjusted OR (Online Resource 1, Fig. S2, P = 0.83). To address the publication bias in unadjusted meta-analysis, post hoc trim and fill analysis was adopted. Because trim-and-fill method produces unreliable results when between-study heterogeneity is large (I2 = 84.5%), a leave-one-out sensitivity analysis was first conducted and identified two outlier studies (Online Resource 1, Fig. S3) [11, 32]. The results of trim and fill analysis excluding these two studies then indicated that one study was missing from the left side (Online Resource 1, Fig. S4). Even after correcting for publication bias, the association between FS and ADHD remained significant, with only a slight decrease in the magnitude of effect (OR = 1.79, 95% CI = 1.48 to 2.16, I2 = 0.0%, P < 0.001) (Table 2).
Discussions
In this systematic review and meta-analysis, we conducted a methodical literature review to collect comprehensive evidence exploring the potential association between FS and ADHD. The final analysis included 12 studies from 11 countries with 958,082 patients. In the random-effects meta-analysis of unadjusted OR from 12 studies, we observed that one or more FS during childhood is associated with greater odds of later ADHD (OR = 1.91, 95% CI = 1.32 to 2.76, I2 = 84.5%, P < 0.001). In the meta-analysis of adjusted OR from 4 studies, the magnitude of correlation increased, estimating an approximately 2.68 times greater odds of ADHD in patients with childhood FS (95% CI = 1.21 to 5.93, I2 = 80.3%, P < 0.05). After adjusting for publication bias, the positive association between FS and ADHD remained statistically significant and the magnitude declined only marginally (OR = 1.79, 95% CI = 1.48 to 2.16, I2 = 0.0%, P < 0.0001). Our results contrast with prior studies suggesting normal outcomes in children with FS [2, 3, 37]. Moreover, there was a divergence among the included studies, with eight out of 12 studies showing no association between FS and ADHD [6, 12, 13, 27, 28, 30, 31, 33]. Interestingly, the overall effect after pooling all studies showed a positive association.
Factors affecting the association between FS and ADHD
In subgroup analyses, our findings were moderated by study aim, study design, and region in both unadjusted and adjusted meta-analyses. ADHD diagnostic criteria was a significant moderator for unadjusted meta-analysis while ADHD assessment significantly influenced the adjusted meta-analysis. First, it was observed that the magnitude of association between FS and ADHD was smaller in studies where the relationship was examined as the primary outcome of interest. This may be due to the bias that forms when examining FS and ADHD as secondary study aim, yielding an increased association. Second, our results have shown a decreased association in the subgroup pooling only cohort studies. In general, prospective cohort studies eliminate selection bias and recall bias present in case–control studies, hence, yielding a much lower OR compared to case–control studies. Next, the region where the studies were conducted modified the pooled OR. This is in accordance with the cumulative incidence of FSs across different countries, where greater incidence was observed in Asian countries [38]. The association was not significant in other regions, but this could be attributed to small sample size. In the unadjusted analysis, the magnitude of association between FS and ADHD was different in studies using DSM and ICD. The discrepancy between these subgroups could have resulted from the definitions of ADHD: compared to the ICD, the DSM can identify broader cases of ADHD [39]. In the adjusted analysis, ADHD assessment significantly influenced the correlation between FS and ADHD. This may be accredited to the reliability of each assessment method. Overall, diagnostic record and specialists’ assessment would be the most reliable, while assessing ADHD through questionnaires would be of questionable reliability.
In meta-regressions for unadjusted meta-analysis, mean age and mean follow-up duration influenced the pooled effect size. The mean age parameter refers to the average age of the FS group in each study at baseline. In other words, participants who had FS onset at an older age were associated with greater odds of ADHD occurrence. Studies with longer mean follow-up durations reported a decrease in OR. A likely explanation is that the symptoms of ADHD in some patients subsided by the time they were followed up. Further work is required to ascertain whether other factors modify the association between FS and ADHD.
Based on our analysis, our results may have slightly overestimated the true effect size between FS and ADHD. The OR derived from the trim and fill method was lower than our initial estimate (OR = 1.79, 95% CI = 1.48 to 2.16), with a very low between-study heterogeneity (I2 = 0.0%). In terms of statistical quality, the trim and fill OR is much more reliable than our initial estimate. Moreover, subgroup analyses have revealed higher OR in studies reporting FS and ADHD as secondary outcomes, which are likely to be biased. Decreased OR in cohort studies compared to case–control studies also corroborate this. In addition, there may exist common risk factors that simultaneously affect FS and ADHD, leading to bias towards increased odds. Overall, these findings imply that the true association between FS and ADHD may be lower than we initially suggested. Nevertheless, our data suggests a considerable association between FS and ADHD.
Potential mechanisms underlying the association between FS and ADHD
Our findings are plausible, considering the pathophysiological changes underlying FS and ADHD. Several studies have suggested that FS may be associated with structural brain changes that could contribute to later ADHD. A study observed an increase in hippocampal volume following prolonged FS [40]. Similar morphological changes have been observed in patients with ADHD [41]. Another possible link between FS and ADHD involves neurotransmitter systems. A study has shown retrograde inhibition of Gamma-Aminobutyric Acid (GABA) release in rat FS models, which may be related to reduced GABA concentrations in patients with ADHD [42, 43]. In a series of in vitro studies, rat models exhibited hyperactivity after exposure to hyperthermic convulsions, likely due to synaptic reorganization and changes in GABAergic interneurons following FS [44, 45]. Inflammation may also be involved in the etiology of post-FS ADHD. Studies on FS have reported increased levels of High Mobility Group Box 1 (HMGB1) and pro-inflammatory cytokines such as interleukin (IL)−1β, IL-6, and tumor necrosis factor-α (TNF-α), and these factors, particularly IL-1β, may affect neuronal excitability [46–48]. Common risk factors of FS and ADHD may also predispose patients with FS to ADHD. The elevated cytokine levels serve as risk factors for both FS and ADHD. Studies have shown that dysregulation of glutamatergic and GABAergic signaling caused by cytokines can cause seizures [49, 50]. The pathophysiological pathways hypothesized to facilitate the development of ADHD must be further researched for more evidence.
Strengths and limitations
The main strengths of our study are its originality and comprehensive design. To the best of our knowledge, our study is the first systematic review and meta-analysis to investigate the nature and magnitude of association between childhood FS and later outcome of ADHD. Moreover, to include all available evidence, we have pre-defined systematic protocols. In addition, we did not impose language restrictions and focused on incorporating the most recent findings. Lastly, we included grey literature and references to ensure no potentially relevant studies were overlooked. Finally, the qualities of the included studies were generally high, with no scores below 5 on the NOS.
This study has some limitations. One limitation is the lack of research conducted on this topic. This may potentially lead to small study effects, where the negative effects in studies that were not published are not captured. However, we have thoroughly addressed the publication bias present in our study and the correct estimate of effect continued to show significant association between FS and ADHD. Moreover, the analysis of factors that may potentially confound the association between FS and ADHD—such as recurrence of FS, FS subtype, ADHD subtype—was not sufficiently analyzed in each study [51]. For this reason, cautious interpretation of our results is required. Nevertheless, we ran subgroup and meta-regression analyses based on potential confounders and other factors that may potentially modify the overall effect. Another limitation is that our study only includes observational studies. Observational studies cannot imply causal relationships and tend to be more variable in terms of methodology. The methodological variability across the studies included contributed to the high statistical heterogeneity in our analysis. Nonetheless, our subgroup and meta-regression analyses have successfully identified the sources of these heterogeneity, and through trim-and-fill analysis, we have addressed the publication bias as well as removed this statistical heterogeneity. Though we have discovered a general association between the two disorders in this study, future studies on different subtypes of FS and ADHD are warranted.
Conclusion
Overall, our systematic review and meta-analysis has shown a significant positive association between childhood FS and later occurrence of ADHD. Our findings add to the growing body of evidence questioning the notion that childhood FS are universally benign. In addition, the results highlight the need for longitudinal studies to better understand the association between FS and ADHD. Moreover, further research is warranted to verify whether specific pathophysiological pathways contribute to long-term neurodevelopmental outcomes in patients with FS.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We thank Jeong-Eun Lee, Ewha Womans University, Tae-Yang Lee, Korea Advanced Institute of Science and Technology (KAIST), and Gahyun Ryu, Seoul National University for their assistance with literature search.
Abbreviations
- FS
Febrile seizure
- ADHD
Attention-deficit hyperactivity disorder
- PRISMA
Preferred reporting items for systematic reviews and meta-analysis
- MOOSE
Meta-analyses of observational studies in epidemiology
- OR
Odds ratio
- CI
Confidence interval
- DSM
Diagnostic and statistical manual of mental disorders
- ICD
International classification of diseases
- GABA
Gamma-aminobutyric acid
Authors contributions
Hyunjun Woo: Conceptualization, Methodology, Software, Validation, Formal Analysis, Investigation, Resources, Data Curation, Writing—Original Draft, Writing—Review & Editing, Visualization. Sang-Ung Lee: Conceptualization, Methodology, Validation, Investigation, Resources, Data Curation, Writing—Original Draft, Writing—Review & Editing, Visualization. Dahyeon Yun: Conceptualization, Methodology, Validation, Investigation, Resources, Data Curation, Writing—Original Draft, Writing—Review & Editing. Suha Jeong: Conceptualization, Methodology, Validation, Investigation, Resources, Data Curation, Writing—Original Draft, Writing—Review & Editing. Hyewon Cho: Conceptualization, Methodology, Validation, Investigation, Resources, Data Curation, Writing—Original Draft, Writing—Review & Editing. Yong-Sik Kim: Conceptualization, Validation, Investigation, Resources, Data Curation, Writing—Original Draft, Writing—Review & Editing, Visualization, Supervision, Project administration, Funding acquisition.
Funding
No funds, grants, or other support was received.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval
The current study does not require institutional review board approval. All authors have reviewed and approved the manuscript.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Hyunjun Woo, Sang-Ung Lee, Dahyeon Yun, Suha Jeong, and Hyewon Cho contributed equally to this work.
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Data Availability Statement
No datasets were generated or analysed during the current study.