Prenatal Exposure to Antiseizure Medication and Risk of Neurodevelopmental Outcomes: evidence from the Nordics

Prenatal exposure to antiseizure medication

What is known?

SCAN-AED
- Nordics: Denmark, Finland, Iceland, Norway, and Sweden:
  - 4 494 926 participants
  - Prenatal exposure to antiseizure medication: topiramate, valproate, lamotrigine, carbamazepine, valproate, pregabalin, gabapentin, mono- and duotherapies
  - Comparators:
    - Children born from antiseizure therapy-unexposed pregnancies of women with epilepsy
    - Children born antiseizure therapy-unexposed of women in the total population
  - Outcomes:
    - Autism spectrum disorder
    - Intellectual disability
    - Any neurodevelopmental disorder

Previous results from the Nordics

No increased risks were identified for levetiracetam, gabapentin, pregabalin, or phenobarbital
Topiramate and valproate in monotherapy were associated with a 2- to 4-fold increased risk of autism spectrum disorders and intellectual disability¹

Further questions

Use of active comparators for most used antiepileptic medication: pregabalin
Exposure unrestricted to mono-therapy (any prenatal exposure to pregabalin)

vs

Pregabalin indications

Approved indications for pregabalin (in the European Union):

Epilepsy
Neuropathic pain
Generalized anxiety disorder (GAD)

Previous evidence on prenatal pregabalin exposure and adverse outcomes

European Network of Teratology Information Services (ENTIS):
- 3-fold increased risk of any major non-chromosomal congenital malformation
Medicaid beneficiaries + MarketScan in the US:
- Pooled RR=1.33 (95% CI: 0.83-2.15); interpreted as no association
Confounding by indication is a major concern since most of the earlier studies compared exposure to pregabalin with no exposure to AED
Postnatal outcomes are rarely investigated
- Particular postnatal outcomes are of interest, eg, ADHD

Country-level data on pregabalin use in women

Pregabalin is an antiepileptic drug (AED), prescribed to approximately 0.5 per thousand pregnant women in Europe, with increasing use after 2010.

Code
Graph

library(tidyverse)
library(magrittr)

link_list_dk <- list(
  "1996_atc_code_data.txt" = "https://medstat.dk/da/download/file/MTk5Nl9hdGNfY29kZV9kYXRhLnR4dA==",
  "1997_atc_code_data.txt"  = "https://medstat.dk/da/download/file/MTk5N19hdGNfY29kZV9kYXRhLnR4dA==",
  "1998_atc_code_data.txt" = "https://medstat.dk/da/download/file/MTk5OF9hdGNfY29kZV9kYXRhLnR4dA==",
  "1999_atc_code_data.txt" = "https://medstat.dk/da/download/file/MTk5OV9hdGNfY29kZV9kYXRhLnR4dA==",
  "2000_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAwMF9hdGNfY29kZV9kYXRhLnR4dA==",
  "2001_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAwMV9hdGNfY29kZV9kYXRhLnR4dA==",
  "2002_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAwMl9hdGNfY29kZV9kYXRhLnR4dA==",
  "2003_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAwM19hdGNfY29kZV9kYXRhLnR4dA==",
  "2004_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAwNF9hdGNfY29kZV9kYXRhLnR4dA==",
  "2006_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAwNl9hdGNfY29kZV9kYXRhLnR4dA==",
  "2007_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAwN19hdGNfY29kZV9kYXRhLnR4dA==",
  "2008_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAwOF9hdGNfY29kZV9kYXRhLnR4dA==",
  "2009_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAwOV9hdGNfY29kZV9kYXRhLnR4dA==",
  "2010_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAxMF9hdGNfY29kZV9kYXRhLnR4dA==",
  "2011_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAxMV9hdGNfY29kZV9kYXRhLnR4dA==",
  "2012_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAxMl9hdGNfY29kZV9kYXRhLnR4dA==",
  "2013_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAxM19hdGNfY29kZV9kYXRhLnR4dA==",
  "2014_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAxNF9hdGNfY29kZV9kYXRhLnR4dA==",
  "2015_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAxNV9hdGNfY29kZV9kYXRhLnR4dA==",
  "2016_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAxNl9hdGNfY29kZV9kYXRhLnR4dA==",
  "2017_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAxN19hdGNfY29kZV9kYXRhLnR4dA==",
  "2018_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAxOF9hdGNfY29kZV9kYXRhLnR4dA==",
  "2019_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAxOV9hdGNfY29kZV9kYXRhLnR4dA==",
  "2020_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAyMF9hdGNfY29kZV9kYXRhLnR4dA==",
  "2021_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAyMV9hdGNfY29kZV9kYXRhLnR4dA==",
  "2022_atc_code_data.txt" = "https://medstat.dk/da/download/file/MjAyMl9hdGNfY29kZV9kYXRhLnR4dA==",
  "atc_code_text.txt" = "https://medstat.dk/da/download/file/YXRjX2NvZGVfdGV4dC50eHQ=",
  "atc_groups.txt" = "https://medstat.dk/da/download/file/YXRjX2dyb3Vwcy50eHQ=",
  "population_data.txt" = "https://medstat.dk/da/download/file/cG9wdWxhdGlvbl9kYXRhLnR4dA=="
)

# ATC codes are stored in files
seq_atc <- c(1:26)
atc_code_data_list <- link_list_dk[seq_atc]

# drugs names
eng_drug <- read_delim(link_list_dk[["atc_groups.txt"]], delim = ";", col_names = c(paste0("V", c(1:6))), col_types = cols(V1 = col_character(), V2 = col_character(), V3 = col_character(), V4 = col_character(), V5 = col_character(), V6 = col_character())) %>% 
  # keep drug classes labels in English
  filter(V5 == "1") %>% 
  select(ATC = V1,
         drug = V2,
         unit_dk = V4)

# drugs data
atc_data <- map(atc_code_data_list, ~read_delim(file = .x, delim = ";", trim_ws = T, col_names = c(paste0("V", c(1:14))), col_types = cols(V1 = col_character(), V2 = col_character(), V3 = col_character(), V4 = col_character(), V5 = col_character(),V6 = col_character(), V7 = col_character(), V8 = col_character(), V9 = col_character(), V10 = col_character(), V11 = col_character(), V12 = col_character(), V13 = col_character(), V14 = col_character()))) %>% 
  # bind atc_data from all years
  bind_rows()

# population data
pop_data <- read_delim(link_list_dk[["population_data.txt"]], delim = ";", col_names = c(paste0("V", c(1:7))), col_types = cols(V1 = col_character(), V2 = col_character(), V3 = col_character(), V4 = col_character(), V5 = col_character(), V6 = col_character(), V7 = col_character())) %>%
  # rename and keep columns
  select(year = V1,
         region_text = V2,
         region = V3,
         gender = V4,
         age = V5,
         denominator_per_year = V6) %>% 
  # human-reading friendly label on sex categories
  mutate(
      gender_text = case_when(
      gender == "1" ~ "men",
      gender == "2" ~ "women",
      T ~ as.character(gender)
    )
  ) %>% 
  # make numeric variables
  mutate_at(vars(year, age, denominator_per_year), as.numeric) %>% 
  arrange(year, age, region, gender)

atc_data %<>% 
  # rename and keep columns
  rename(
    ATC = V1,
    year = V2,
    sector = V3,
    region = V4,
    gender = V5, # 0 - both; 1 - male; 2 - female; A - age in categories
    age = V6,
    number_of_people = V7,
    patients_per_1000_inhabitants = V8,
    turnover = V9,
    regional_grant_paid = V10,
    quantity_sold_1000_units = V11,
    quantity_sold_units_per_unit_1000_inhabitants_per_day = V12,
    percentage_of_sales_in_the_primary_sector = V13
  ) 

atc_data %<>% 
  # clean columns names and set-up labels
  mutate(
    year = as.numeric(year),
    region_text = case_when(
      region == "0" ~ "DK",
      region == "1" ~ "Region Hovedstaden",
      region == "2" ~ "Region Midtjylland",
      region == "3" ~ "Region Nordjylland",
      region == "4" ~ "Region Sjælland",
      region == "5" ~ "Region Syddanmark",
      T ~ NA_character_
    ),
    gender_text = case_when(
      gender == "0" ~ "both sexes",
      gender == "1" ~ "men",
      gender == "2" ~ "women",
      T ~ as.character(gender)
    )
  ) %>% 
  mutate_at(vars(turnover, regional_grant_paid, quantity_sold_1000_units, quantity_sold_units_per_unit_1000_inhabitants_per_day, number_of_people, patients_per_1000_inhabitants), as.numeric) %>% 
  select(-V14) %>%
  filter(
  region == "0",
  sector == "0",
  gender == "2"
)

atc_data %<>% 
  # deal with non-numeric age in groups
  filter(
    str_detect(age, "[0-9][0-9][0-9]")
  ) %>% 
  mutate(
    age = parse_number(age)
  ) %>% 
  select(year, ATC, gender, age, number_of_people, patients_per_1000_inhabitants, region_text, gender_text)

regex_pregabalin <- "^N02BF02$"

atc_data %<>% filter(str_detect(ATC, regex_pregabalin))
atc_data %<>% left_join(eng_drug)
atc_data %<>% left_join(pop_data)

# group to the same categories as in Swedish data
atc_data_se <- readxl::read_xlsx(path = "C:/Users/au595736/OneDrive - Aarhus Universitet/ISPOR 2023/Statistikdatabasen_2023-03-15 10_54_00.xlsx", sheet = 1) %>% 
  pivot_longer(cols = c("2006":"2022")) %>% 
  mutate(
    name = as.numeric(name),
    country = "Sweden"
  )

atc_data %<>% mutate(
  age_cat = cut(age, c(-Inf, 14, 19, 24, 29, 34, 39, 44, 49, Inf))
) %>% 
  group_by(age_cat, year) %>% 
  mutate(
    number_of_people_age_cat = sum(number_of_people),
    denominator_per_year_age_cat = sum(denominator_per_year),
    patients_per_1000_inhabitants_age_cat = (number_of_people_age_cat/denominator_per_year_age_cat)*1000
  ) %>% 
  ungroup() %>% 
  filter(
    age_cat != "(-Inf,14]",
    age_cat != "(49, Inf]"
  ) %>% 
  mutate(
    age_cat = case_when(
      age_cat == "(14,19]" ~ "15-19",
      age_cat == "(19,24]" ~ "20-24",
      age_cat == "(24,29]" ~ "25-29",
      age_cat == "(29,34]" ~ "30-34",
      age_cat == "(34,39]" ~ "35-39",
      age_cat == "(39,44]" ~ "40-44",
      age_cat == "(44,49]" ~ "45-49"
    ),
    country = "Denmark"
  ) %>% 
  distinct(age_cat, year, number_of_people_age_cat, denominator_per_year_age_cat, patients_per_1000_inhabitants_age_cat)

atc <- full_join(atc_data, atc_data_se, by = c("year" = "name", "age_cat" = "Ålder", "patients_per_1000_inhabitants_age_cat" = "value", "country" = "country")) %>% 
  select(year, age_cat, patients_per_1000_inhabitants_age_cat, country)

ggplot <- atc %>% 
  # filter(!is.na(patients_per_1000_inhabitants_age_cat)) %>%
  mutate(label = if_else(year == 2022, as.character(age_cat), NA_character_),
         value = case_when(
             age_cat == "30-34" ~"#3E9286",
             age_cat == "35-39" ~ "#E8B88A",
             T ~ "gray"
         )) %>%
  ggplot(aes(x = year, y = patients_per_1000_inhabitants_age_cat, group = age_cat, color = value)) +
  geom_line() +
  facet_grid(cols = vars(country), scales = "free", drop = F) +
  theme_light(base_size = 14, base_family = "Ubuntu") +
  scale_x_continuous(breaks = c(seq(2004, 2022, 5))) +
  scale_color_identity() +
  theme(plot.caption = element_text(hjust = 0, size = 10),
        legend.position = "none",
        panel.spacing = unit(0.8, "cm")) +
  labs(y = "Female patients of reproductive age\nper 1,000 women in the population", title = "Pregabalin utilization in women", subtitle = "aged between 15 and 49 years in DK and SE\n2004-2022", caption = "Elena Dudukina | @evpatora") +
    ggrepel::geom_label_repel(aes(label = label), na.rm = TRUE, nudge_x = 3, hjust = 0.5, direction = "y", segment.size = 0.1, segment.colour = "black", show.legend = F, family = "Ubuntu")

ggsave(filename = paste0(Sys.Date(), "_pregabalin use in DK and SE.pdf"), plot = ggplot, path = "C:/Users/au595736/OneDrive - Aarhus Universitet/ISPOR 2023/", dpi = 900, height = 6, width = 8, device = cairo_pdf)

Pregabalin and neurodevelopment study in the Nordics

Setting

Non-interventional study was a post-authorisation safety (PAS) study conducted as a commitment to the European Medicines Agency (EUPAS27339)
Participants:
- Denmark
- Finland
- Sweden
- Norway
Time period: 2005-2016
Population-based
All births identified in the respective medical birth registers from 01 January 2005 through 31 December 2015

Pregabalin and neurodevelopment study in the Nordics

Exposure

Any-trimester pregabalin exposure as at least one dispensing of pregabalin from LMP-90 days to the day before date of birth
Pregabalin monotherapy in sensitivity analysis

Comparators

No exposure to pregagbalin or other antiepileptics or active comparators
Active comparators
- Lamotrigine
- Duloxetine
- Lamotrigine and/or duloxetine and/or

Pregabalin and neurodevelopment study in the Nordics

Design

Pregabalin and neurodevelopment study in the Nordics

Covariables and confounding control

Pregabalin and neurodevelopment study in the Nordics

Covariables

All covariables were available for at least 12 months before the end of the earliest identified pregnancy
Maternal pre-pregnancy comorbidities using diagnostic codes from the national patient registries
Maternal pre-pregnancy medication use from the national prescription registers

Pregabalin and neurodevelopment study in the Nordics

Descriptive variables:

Caesarean delivery
Children’s sex distributions
Distributions of potential indications for pregabalin use inferred by recorded diagnoses before pregnancy (epilepsy, pain, GAD)

Pregabalin and neurodevelopment study in the Nordics

Postnatal outcomes

Attention deficit/hyperactivity disorder (ADHD)
Autism spectrum disorder (ASD)
Intellectual disability

Pregabalin and neurodevelopment study in the Nordics

Statistical analyses

Crude and adjusted hazard ratios (HRs)
Robust cluster-adjusted 95% CIs
Cox proportional-hazards regression adjusted using propensity score (PS) fine stratification
- Distinct PS model for each country and contrast
- Trimming of the non-overlapping areas of PS distribution based on the PS distribution among exposed
Pooling of country-specific relative risk estimates in a meta-analysis with fixed effects for different countries
Post-hoc analysis using Mantel-Haenszel (MH) pooling (retaining zero-exposed)

Pregabalin and neurodevelopment study in the Nordics

Main results

Pregabalin and neurodevelopment study in the Nordics

Additional results

Pregabalin and neurodevelopment study in the Nordics

Monotherapy analyses

Results summary

There was a 1.2 to 1.3-fold increased risk of SGA and ADHD among offspring with prenatal exposure to pregabalin vs no exposure to AED
Associations attenuated in analyses using active comparators
At least partly can be explained by residual confounding
No evidence of an association between prenatal exposure to pregabalin and increased risk of ASD or intellectual disability

Pregabalin and neurodevelopment study in the Nordics

Residual confounding

Unbalance for the contrast of offspring with exposure to pregabalin vs no exposure
- analgesics
- antipsychotics
- antidepressants
- history of depression and other neurological disorders
Residual confounding by indication cannot be ruled out

Pregabalin and neurodevelopment study in the Nordics

Comparisons with previous studies

Valproate is associated with 1.5-fold increased risk of ADHD and 2.4-fold increased risk of any postnatal neurodevelopmental disorder¹

Pregabalin and neurodevelopment study in the Nordics

Outcome misclassification

Few false-positives are expected
- Positive predictive values for ADHD coding 87% (DK)
- Positive predictive values for autism coding 94% (DK)
Age at ADHD onset:
- 8 years in Denmark
- 7.5 years in Finland
- 10.5 years in Norway
- 12 years in Sweden
Children with subdiagnostic symptoms

Pregabalin and neurodevelopment study in the Nordics

Exposure misclassification

Possible: true intake of pregabalin and comparators is unknown
Redeemed prescriptions > issued prescriptions

Conclusion

Increased risks in excess of 1.8 were unlikely for ADHD
There was no evidence of an association with other examined neurodevelopmental postnatal outcomes
Overall more reassuring finding than not