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Acute and longitudinal magnetic resonance imaging abnormalities in antibody-mediated encephalitis.

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The authors' code

R · 885 lines · 41 KB · no license

  1. # MRI Ab-mediated encephalitis data code
  3. library(tidyverse)
  4. library(lubridate)
  5. library(dplyr)
  7. MRI1 <- MRI1 |>
  8. mutate(date_first_immunotherapy = pmin(
  9. pulsesteroids1,
  10. ivig1,
  11. plasma_exchange1,
  12. na.rm = TRUE
  13. ))
  15. MRI1$first_line_received <- ifelse(!is.na(MRI1$date_first_immunotherapy), 1, 0)
  17. # time to first
  18. MRI1$time_to_firstline <- difftime(MRI1$date_first_immunotherapy, MRI1$date_symptoms, "days")
  19. # limit time to first to maximum of 365 days from admission_date
  20. MRI1 <- MRI1 %>%
  21. mutate(time_to_firstline = pmin(time_to_firstline, as.numeric(first_admission_date + 365 - date_symptoms)))
  23. # replace "0" with NA in incomplete MRI's - ask Paul for a way to do this along columns in one go #
  25. MRI1$T1 <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$T1)
  26. MRI1$T2 <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$T2)
  27. MRI1$CEL <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$CEL)
  28. MRI1$DWI <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$DWI)
  29. MRI1$T2_left_frontal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$T2_left_frontal)
  30. MRI1$T2_left_parietal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$T2_left_parietal)
  31. MRI1$T2_left_occipital <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$T2_left_occipital)
  32. MRI1$T2_left_brainstem <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$T2_left_brainstem)
  33. MRI1$T2_left_cerebellum <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$T2_left_cerebellum)
  34. MRI1$T2_left_temporal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$T2_left_temporal)
  35. MRI1$T2_left_mesial_temporal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$T2_left_mesial_temporal)
  36. MRI1$T2_left_insular <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$T2_left_insular)
  38. MRI1$T2_right_frontal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$T2_right_frontal)
  39. MRI1$T2_right_parietal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$T2_right_parietal)
  40. MRI1$T2_right_occipital <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$T2_right_occipital)
  41. MRI1$T2_right_brainstem <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$T2_right_brainstem)
  42. MRI1$T2_right_cerebellum <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$T2_right_cerebellum)
  43. MRI1$T2_right_temporal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$T2_right_temporal)
  44. MRI1$T2_right_mesial_temporal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$T2_right_mesial_temporal)
  45. MRI1$T2_right_insular <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$T2_right_insular)
  47. MRI1$CEL_left_frontal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$CEL_left_frontal)
  48. MRI1$CEL_left_parietal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$CEL_left_parietal)
  49. MRI1$CEL_left_occipital <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$CEL_left_occipital)
  50. MRI1$CEL_left_brainstem <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$CEL_left_brainstem)
  51. MRI1$CEL_left_cerebellum <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$CEL_left_cerebellum)
  52. MRI1$CEL_left_temporal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$CEL_left_temporal)
  53. MRI1$CEL_left_mesial_temporal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$CEL_left_mesial_temporal)
  54. MRI1$CEL_left_insular <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$CEL_left_insular)
  56. MRI1$CEL_right_frontal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$CEL_right_frontal)
  57. MRI1$CEL_right_parietal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$CEL_right_parietal)
  58. MRI1$CEL_right_occipital <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$CEL_right_occipital)
  59. MRI1$CEL_right_brainstem <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$CEL_right_brainstem)
  60. MRI1$CEL_right_cerebellum <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$CEL_right_cerebellum)
  61. MRI1$CEL_right_temporal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$CEL_right_temporal)
  62. MRI1$CEL_right_mesial_temporal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$CEL_right_mesial_temporal)
  63. MRI1$CEL_right_insular <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$CEL_right_insular)
  65. MRI1$DWI_left_frontal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$DWI_left_frontal)
  66. MRI1$DWI_left_parietal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$DWI_left_parietal)
  67. MRI1$DWI_left_occipital <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$DWI_left_occipital)
  68. MRI1$DWI_left_brainstem <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$DWI_left_brainstem)
  69. MRI1$DWI_left_cerebellum <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$DWI_left_cerebellum)
  70. MRI1$DWI_left_temporal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$DWI_left_temporal)
  71. MRI1$DWI_left_mesial_temporal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$DWI_left_mesial_temporal)
  72. MRI1$DWI_left_insular <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$DWI_left_insular)
  74. MRI1$DWI_right_frontal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$DWI_right_frontal)
  75. MRI1$DWI_right_parietal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$DWI_right_parietal)
  76. MRI1$DWI_right_occipital <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$DWI_right_occipital)
  77. MRI1$DWI_right_brainstem <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$DWI_right_brainstem)
  78. MRI1$DWI_right_cerebellum <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$DWI_right_cerebellum)
  79. MRI1$DWI_right_temporal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$DWI_right_temporal)
  80. MRI1$DWI_right_mesial_temporal <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$DWI_right_mesial_temporal)
  81. MRI1$DWI_right_insular <- ifelse(is.na(MRI1$mri_abnormal), MRI1$mri_abnormal, MRI1$DWI_right_insular)
  83. # create regions #
  85. MRI1$T2_mesial_temporal <- MRI1$T2_left_mesial_temporal + MRI1$T2_right_mesial_temporal
  86. MRI1$T2_mesial_temporal_bilateral <- car::recode(MRI1$T2_mesial_temporal, "2=1; 0=0; 1=0")
  87. MRI1$T2_mesial_temporal <- car::recode(MRI1$T2_mesial_temporal, "2=1; 1=1; 0=0")
  88. MRI1$T2_temporal <- MRI1$T2_left_temporal + MRI1$T2_right_temporal
  89. MRI1$T2_temporal <- car::recode(MRI1$T2_temporal, "2=1; 1=1; 0=0")
  90. MRI1$T2_extratemporal <- MRI1$T2_left_basal_ganglia + MRI1$T2_left_brainstem + MRI1$T2_left_cerebellum + MRI1$T2_left_frontal + MRI1$T2_left_insular + MRI1$T2_left_occipital + MRI1$T2_right_occipital + MRI1$T2_right_basal_ganglia + MRI1$T2_right_brainstem + MRI1$T2_right_cerebellum + MRI1$T2_right_frontal+MRI1$T2_right_parietal+MRI1$T2_right_insular+MRI1$T2_right_occipital
  91. MRI1$T2_extratemporal <- car::recode(MRI1$T2_extratemporal, "1:7=1; 0=0")
  93. MRI1$DWI_coded <- MRI1$DWI_left_brainstem + MRI1$DWI_left_cerebellum + MRI1$DWI_left_frontal + MRI1$DWI_left_parietal + MRI1$DWI_left_insular + MRI1$DWI_left_temporal + MRI1$DWI_left_basal_ganglia + MRI1$DWI_left_mesial_temporal + MRI1$DWI_right_brainstem + MRI1$DWI_right_cerebellum + MRI1$DWI_right_frontal + MRI1$DWI_right_parietal + MRI1$DWI_right_insular + MRI1$DWI_right_basal_ganglia + MRI1$DWI_right_mesial_temporal + MRI1$DWI_right_occipital + MRI1$DWI_right_parietal + MRI1$DWI_right_temporal
  94. MRI1$DWI_coded <- car::recode(MRI1$DWI_coded, "1:2=1; 0=0")
  96. MRI1$CEL_coded <- MRI1$CEL_left_brainstem + MRI1$CEL_left_cerebellum + MRI1$CEL_left_frontal + MRI1$CEL_left_insular + MRI1$CEL_left_basal_ganglia + MRI1$CEL_left_mesial_temporal + MRI1$CEL_left_mesial_temporal + MRI1$CEL_left_occipital + MRI1$CEL_left_parietal + MRI1$CEL_left_temporal + MRI1$CEL_right_brainstem + MRI1$CEL_right_cerebellum + MRI1$CEL_right_frontal + MRI1$CEL_right_insular + MRI1$CEL_right_basal_ganglia + MRI1$CEL_right_mesial_temporal + MRI1$CEL_right_occipital + MRI1$CEL_right_parietal+ MRI1$CEL_right_temporal
  97. MRI1$CEL_coded <- car::recode(MRI1$CEL_coded, "1:3=1; 0=0")
  99. MRI1$hippocampi_swelling_any <- ifelse((MRI1$size_hippo_l == 2 | MRI1$size_hippo_r == 2), 1, 0)
  100. MRI1$hippocampi_swelling_bilat <- ifelse((MRI1$size_hippo_l == 2 & MRI1$size_hippo_r == 2), 1, 0)
  102. MRI1$T2_extra_mesial_temporal <- MRI1$T2_temporal + MRI1$T2_extratemporal
  103. MRI1$T2_extra_mesial_temporal <- car::recode(MRI1$T2_extra_mesial_temporal, "1:2=1; 0=0")
  105. MRI1$focal_atrophy <- ifelse(MRI1$atrophy == 1 | MRI1$atrophy == 3, 1, 0)
  106. MRI1$time_to_MRI <- difftime(MRI1$date_of_mri, MRI1$date_symptoms, "days")
  108. dat2 <- MRI1 |>
  109. filter(final_diag == 5)
  110. dat2$m_mrs_good <- ifelse(dat2$m_mrs < 3, 1, 0)
  111. dat2$CASE_12 <- factor(dat2$CASE_12, ordered = TRUE)
  112. dat2$time_to_firstline <- ifelse(dat2$participant_id == "9057-81", difftime(dat2$date_visit_3, dat2$date_symptoms, units = "days"), dat2$time_to_firstline)
  113. # adjust time to first line to be a maximum of time to visit_3
  114. dat2 <- dat2 %>%
  115. mutate(
  116. time_to_firstline = as.numeric(time_to_firstline),
  117. time_to_firstline = if_else(
  118. !is.na(date_visit_3) & as.numeric(date_visit_3 - date_symptoms) < time_to_firstline,
  119. as.numeric(date_visit_3 - date_symptoms),
  120. time_to_firstline
  121. )
  122. )
  123. dat2$time_to_firstline <- ifelse(dat2$participant_id == "9057-81", dat2$date_visit_3 - dat2$date_symptoms, dat2$time_to_firstline)
  125. dat2$twelve_good <- ifelse(dat2$m_mrs_good == 1 & dat2$seizures_12 < 2 & dat2$memory_12 < 2, 1, 0)
  127. model1 <- glm(m_mrs_good ~ hippocampi_swelling_any, family = "binomial", data = dat2)
  128. exp(cbind(OR=coef(model1), confint.default(model1)))
  129. summary(model1)
  131. model2 <- glm(m_mrs_good ~ time_to_firstline, family = "binomial", data = dat2)
  132. exp(cbind(OR=coef(model2), confint.default(model2)))
  133. summary(model2)
  135. model3 <- glm(m_mrs_good ~ hippocampi_swelling_any + time_to_firstline + nadir_mrs+age_symptom_onset, family = "binomial", data = dat2)
  136. exp(cbind(OR=coef(model3), confint.default(model3)))
  137. summary(model3)
  139. model4 <- glm(m_mrs_good ~ T2 + time_to_firstline + nadir_mrs+age_symptom_onset, family = "binomial", data = dat2)
  140. exp(cbind(OR=coef(model4), confint.default(model4)))
  141. summary(model4)
  143. model5 <- glm(twelve_good ~ hippocampi_swelling_any + time_to_firstline, family = "binomial", data = dat2)
  144. exp(cbind(OR=coef(model5), confint.default(model5)))
  145. summary(model5)
  148. # NMDAR
  150. dat4 <- MRI1 |>
  151. filter(final_diag == 4)
  152. dat4$m_mrs_good <- ifelse(dat4$m_mrs < 3, 1, 0)
  154. dat4$CASE_12 <- factor(dat4$CASE_12, ordered = TRUE)
  156. dat4$time_to_firstline <- as.numeric(dat4$time_to_firstline)
  157. dat4 <- dat4 %>%
  158. mutate(
  159. time_to_firstline = as.numeric(time_to_firstline),
  160. time_to_firstline = if_else(
  161. !is.na(date_visit_3) & as.numeric(date_visit_3 - date_symptoms) < time_to_firstline,
  162. as.numeric(date_visit_3 - date_symptoms),
  163. time_to_firstline
  164. )
  165. )
  168. model6 <- glm(m_mrs_good ~ T2, family = "binomial", data = dat4)
  169. exp(cbind(OR=coef(model6), confint.default(model6)))
  170. summary(model6)
  171. model7 <- glm(m_mrs_good ~ age_symptom_onset, family = "binomial", data = dat4)
  172. exp(cbind(OR=coef(model7), confint.default(model7)))
  173. summary(model7)
  174. model8 <- glm(m_mrs_good ~ sex, family = "binomial", data = dat4)
  175. exp(cbind(OR=coef(model8), confint.default(model8)))
  176. summary(model8)
  177. model9 <- glm(m_mrs_good ~ nadir_mrs, family = "binomial", data = dat4)
  178. exp(cbind(OR=coef(model9), confint.default(model9)))
  179. summary(model9)
  180. model10 <- glm(m_mrs_good ~ time_to_firstline, family = "binomial", data = dat4)
  181. exp(cbind(OR=coef(model10), confint.default(model10)))
  182. summary(model10)
  184. model11 <- glm(m_mrs_good ~ T2 + time_to_firstline + nadir_mrs+age_symptom_onset+sex, family = "binomial", data = dat4)
  185. exp(cbind(OR=coef(model11), confint.default(model11)))
  186. summary(model11)
  188. # sensitivity analyses
  189. # Time to MRI, VIF, NMDAR
  190. library(car)
  191. model12 <- glm(
  192. m_mrs_good ~ age_symptom_onset +
  193. nadir_mrs +
  194. time_to_firstline +
  195. time_to_MRI +
  196. T2,
  197. family = binomial,
  198. data = dat2
  199. )
  200. vif(model12)
  202. exp(cbind(OR=coef(model12), confint.default(model12)))
  203. summary(model12)
  205. model13 <- glm(
  206. m_mrs_good ~ age_symptom_onset +
  207. nadir_mrs +
  208. time_to_firstline +
  209. time_to_MRI +
  210. T2,
  211. family = binomial,
  212. data = dat4
  213. )
  214. vif(model13)
  216. model14 <- glm(
  217. m_mrs_good ~ age_symptom_onset +
  218. nadir_mrs +
  219. time_to_firstline +
  220. time_to_MRI +
  221. T2,
  222. family = binomial,
  223. data = dat4a
  224. )
  225. vif(model14)
  226. # VIF here breached including both times
  227. model15 <- glm(m_mrs_good ~ T2 + time_to_MRI + nadir_mrs+age_symptom_onset+sex, family = "binomial", data = dat4)
  228. exp(cbind(OR=coef(model15), confint.default(model15)))
  229. summary(model15)
  231. # Time to MRI, VIF, LGI1
  232. # VIF
  233. model16 <- glm(
  234. m_mrs_good ~ hippocampi_swelling_any +
  235. age_symptom_onset +
  236. nadir_mrs +
  237. time_to_firstline +
  238. time_to_MRI,
  239. family = binomial,
  240. data = dat2
  241. )
  242. vif(model16)
  244. exp(cbind(OR=coef(model16), confint.default(model16)))
  245. summary(model16)
  247. # Table 1
  249. library(gtsummary)
  251. MRI1 <- MRI1 |>
  252. filter(!is.na(date_of_mri))
  253. MRI1$MRI_before_first <- ifelse(MRI1$date_of_mri < MRI1$date_first_immunotherapy | MRI1$date_of_mri == MRI1$date_first_immunotherapy, 1, 0)
  255. MRI1 <- MRI1 |>
  256. filter(final_diag == 4 | final_diag == 5 | final_diag == 6 | final_diag == 7 | final_diag == 8)
  258. MRI1 <- MRI1 |>
  259. ungroup()
  261. MRI1 <- MRI1 |>
  262. filter(!is.na(mri_abnormal))
  265. MRI1$final_diag_new <- factor(MRI1$final_diag,
  266. levels = c("4","5","6","7","8"),
  267. labels = c(
  268. "NMDAR",
  269. "LGI1",
  270. "CASPR2",
  271. "GABAB",
  272. "AMPAR"))
  274. tbl3 <- MRI1 |>
  275. select(final_diag_new, time_to_MRI, MRI_before_first, T2, T2_mesial_temporal, T2_mesial_temporal_bilateral, T2_temporal, T2_extratemporal, DWI_coded, CEL_coded, hippocampi_swelling_any, hippocampi_swelling_bilat, focal_atrophy, hippo_sclerosis) |>
  276. tbl_summary(
  277. by = final_diag_new,
  278. statistic = list(
  279. all_continuous() ~ "{median} ({p25},{p75})", # Median and IQR for continuous
  280. all_categorical() ~ "{n} ({p}%)" # Count and percentage for categorical
  281. ),
  282. digits = list(
  283. all_continuous() ~ 0, # No decimal places for continuous variables
  284. all_categorical() ~ c(0, 0) # No decimal places for categorical percentages
  285. ),
  286. missing_text = "(Missing)",
  287. label = c(
  288. time_to_MRI ~ "Time to MRI",
  289. MRI_before_first ~ "MRI before 1st line immunotherapy",
  290. T2 ~ "T2/FLAIR hyperintensity",
  291. T2_mesial_temporal ~ "T2/FLAIR mesial temporal",
  292. T2_mesial_temporal_bilateral ~ "Bilateral T2/FLAIR mesial temporal",
  293. T2_temporal ~ "T2/FLAIR temporal",
  294. T2_extratemporal ~ "T2/FLAIR extratemporal",
  295. DWI_coded ~ "DWI",
  296. CEL_coded ~ "CEL",
  297. hippocampi_swelling_any ~ "any hippocampi swelling",
  298. hippocampi_swelling_bilat ~ "bilateral hippocampi swelling",
  299. focal_atrophy ~ "focal atrophy",
  300. hippo_sclerosis ~ "Hippocampal sclerosis"
  301. )
  302. ) |>
  303. add_overall() # Add overall summary column
  306. # follow up evaluations
  308. # remove additional dates
  309. dat1 <- dat |>
  310. filter(final_diag == 4 | final_diag == 5 | final_diag == 6 | final_diag == 7 | final_diag == 8)
  311. # populate treatment variables further
  314. dat1 <- dat1 |>
  315. group_by(participant_id) |>
  316. fill(pulsesteroids1, .direction = "down")
  317. dat1 <- dat1 |>
  318. group_by(participant_id) |>
  319. fill(ivig1, .direction = "down")
  320. dat1 <- dat1 |>
  321. group_by(participant_id) |>
  322. fill(plasma_exchange1, .direction = "down")
  323. dat1 <- dat1 |>
  324. group_by(participant_id) |>
  325. fill(rituximab1, .direction = "down")
  327. dat1$hippocampi_swelling_any <- ifelse((dat1$size_hippo_l == 2 | dat1$size_hippo_r == 2), 1, 0)
  328. dat1$hippocampi_swelling_bilat <- ifelse((dat1$size_hippo_l == 2 & dat1$size_hippo_r == 2), 1, 0)
  330. dat1 <- dat1 |>
  331. group_by(participant_id) |>
  332. fill(nadir_mrs, .direction = "down")
  334. dat1 <- dat1 |>
  335. filter(!is.na(date_of_mri))
  339. dat1$date_visit_12m <- as.Date(ifelse(dat1$row_num == 1, dat1$date_visit_3, NA))
  340. dat1 <- dat1 |>
  341. group_by(participant_id) |>
  342. fill(date_visit_12m, .direction = "down")
  344. # remove duplicates of first row/date:
  346. dat1_cleaned <- dat1 %>%
  347. group_by(participant_id) %>%
  348. mutate(first_mri_date = first(date_of_mri)) %>%
  349. filter(!(date_of_mri == first_mri_date & row_number() < max(row_number()[date_of_mri == first_mri_date]))) %>%
  350. ungroup()
  353. # Create a new dat1_cleaned with only patients who have more than one MRI date
  354. dat1_follow_up <- dat1_cleaned %>%
  355. group_by(participant_id) %>% # Group by patient ID
  356. filter(n() > 1) %>% # Filter patients with more than one MRI date
  357. ungroup() # Ungroup the data
  359. # create variable depicting date of first MRI:
  360. dat1_follow_up <- dat1_follow_up %>%
  361. group_by(participant_id) %>%
  362. mutate(date_of_first_MRI = min(date_of_mri, na.rm = TRUE)) %>%
  363. ungroup()
  366. # create atrophy
  367. # create mesial temporal atrophy
  368. dat1_follow_up$mesial_temporal_atrophy <- ifelse(dat1_follow_up$atrophy_left_mesial_temporal == 1 | dat1_follow_up$atrophy_right_mesial_temporal == 1, 1, 0)
  369. # frontal atrophy
  370. dat1_follow_up$frontal_atrophy <- ifelse(dat1_follow_up$atrophy_left_frontal == 1 | dat1_follow_up$atrophy_right_frontal == 1, 1, 0)
  371. # parietal atrophy
  372. dat1_follow_up$parietal_atrophy <- ifelse(dat1_follow_up$atrophy_left_parietal == 1 | dat1_follow_up$atrophy_right_parietal == 1, 1, 0)
  373. # temporal atrophy
  374. dat1_follow_up$temporal_atrophy <- ifelse(dat1_follow_up$atrophy_left_temporal == 1 | dat1_follow_up$atrophy_right_temporal == 1, 1, 0)
  375. # occipital atrophy
  376. dat1_follow_up$occipital_atrophy <- ifelse(dat1_follow_up$atrophy_left_occipital == 1 | dat1_follow_up$atrophy_right_occipital == 1, 1, 0)
  377. # cerebellar atrophy
  378. dat1_follow_up$cerebellar_atrophy <- ifelse(dat1_follow_up$atrophy_left_cerebellum == 1 | dat1_follow_up$atrophy_right_cerebellum == 1, 1, 0)
  380. # 'dat1_follow_up' will have patients with more than one MRI date
  382. dat1_follow_up2 <- dat1_follow_up |>
  383. select(participant_id, final_diag, age_symptom_onset, sex, nadir_mrs, first_admission_date, date_symptoms, date_of_mri, date_of_first_MRI, mri_abnormal, T2, atrophy, atrophy_left_mesial_temporal, atrophy_right_mesial_temporal, hippo_sclerosis, T2_right_mesial_temporal, T2_left_mesial_temporal, pulsesteroids1, ivig1, plasma_exchange1, rituximab1, hippocampi_swelling_any, hippocampi_swelling_bilat, final_visit_date, final_visit_mrs, final_visit_CASE, date_final_CASE, final_visit_memory, final_visit_seizures, dre, mesial_temporal_atrophy, frontal_atrophy, parietal_atrophy, temporal_atrophy, occipital_atrophy, cerebellar_atrophy, date_symptoms, date_visit_3, m_mrs, CASE_12, memory_12, date_visit_3, date_visit_12m)
  384. dat1_follow_up2$focal_atrophy <- ifelse(dat1_follow_up2$atrophy == 1 | dat1_follow_up2$atrophy == 3, 1, 0)
  385. dat1_follow_up2 <- dat1_follow_up2 |>
  386. group_by(participant_id) |>
  387. fill(date_symptoms, .direction = "down")
  389. # (below for T2 persistence)
  391. dat1_follow_up2$time_symptoms_to_MRI <- difftime(dat1_follow_up2$date_of_mri, dat1_follow_up2$date_symptoms, units = "days")
  392. dat1_follow_up2 <- dat1_follow_up2 |>
  393. group_by(participant_id) |>
  394. mutate(row_num = row_number()) |> # sequence row within each person
  395. mutate(max_num = n()) # total number rows per person
  397. dat1_follow_up2$time_MRI_to_MRI <- difftime(dat1_follow_up2$date_of_mri, dat1_follow_up2$date_of_first_MRI, units = "days")
  398. dat1_follow_up2 <- dat1_follow_up2 |>
  399. group_by(participant_id) |>
  400. mutate(row_num = row_number()) |> # sequence row within each person
  401. mutate(max_num = n()) # total number rows per person
  403. dat1_follow_up2 <- dat1_follow_up2 %>%
  404. group_by(participant_id) %>%
  405. mutate(hippocampi_swelling_initial = hippocampi_swelling_any[row_num == 1]) %>%
  406. ungroup()
  408. # flag last MRI, or if there is focal atrophy, the first MRI with focal atrophy
  409. dat1_follow_up2$flag_last_MR <- ifelse(dat1_follow_up2$row_num == dat1_follow_up2$max_num & dat1_follow_up2$focal_atrophy == 0, 1, 0)
  410. dat1_follow_up2$flag_last_MR <- ifelse((!is.na(dat1_follow_up2$focal_atrophy) & dat1_follow_up2$focal_atrophy == 1), 1, dat1_follow_up2$flag_last_MR)
  412. dat1_follow_up2$T2_initial <- ifelse((dat1_follow_up2$row_num == 1 & dat1_follow_up2$T2 == 1), 1, 0)
  413. dat1_follow_up2$T2_initial <- ifelse(dat1_follow_up2$T2_initial == 0, NA, dat1_follow_up2$T2_initial)
  414. dat1_follow_up2 <- dat1_follow_up2 |>
  415. group_by(participant_id) |>
  416. fill(T2_initial, .direction = "down")
  417. dat1_follow_up2$T2_initial <- ifelse(is.na(dat1_follow_up2$T2_initial), 0, dat1_follow_up2$T2_initial)
  419. dat1_follow_up2$T2_right_mesial_temporal_initial <- ifelse((dat1_follow_up2$row_num == 1 & dat1_follow_up2$T2_right_mesial_temporal == 1), 1, 0)
  420. dat1_follow_up2$T2_right_mesial_temporal_initial <- ifelse(dat1_follow_up2$T2_right_mesial_temporal_initial == 0, NA, dat1_follow_up2$T2_right_mesial_temporal_initial)
  421. dat1_follow_up2 <- dat1_follow_up2 |>
  422. group_by(participant_id) |>
  423. fill(T2_right_mesial_temporal_initial, .direction = "down")
  424. dat1_follow_up2$T2_right_mesial_temporal_initial <- ifelse(is.na(dat1_follow_up2$T2_right_mesial_temporal_initial), 0, dat1_follow_up2$T2_right_mesial_temporal_initial)
  426. dat1_follow_up2$T2_left_mesial_temporal_initial <- ifelse((dat1_follow_up2$row_num == 1 & dat1_follow_up2$T2_left_mesial_temporal == 1), 1, 0)
  427. dat1_follow_up2$T2_left_mesial_temporal_initial <- ifelse(dat1_follow_up2$T2_left_mesial_temporal_initial == 0, NA, dat1_follow_up2$T2_left_mesial_temporal_initial)
  428. dat1_follow_up2 <- dat1_follow_up2 |>
  429. group_by(participant_id) |>
  430. fill(T2_left_mesial_temporal_initial, .direction = "down")
  431. dat1_follow_up2$T2_left_mesial_temporal_initial <- ifelse(is.na(dat1_follow_up2$T2_left_mesial_temporal_initial), 0, dat1_follow_up2$T2_left_mesial_temporal_initial)
  433. dat1_follow_up2$T2_mesial_temporal_initial <- ifelse(dat1_follow_up2$T2_left_mesial_temporal_initial == 1 | dat1_follow_up2$T2_right_mesial_temporal_initial == 1, 1, 0)
  436. dat1_filtered <- dat1_follow_up2 %>%
  437. group_by(participant_id) %>% # Group by participant
  438. arrange(participant_id, date_of_mri) %>% # Arrange by participant and MRI date
  439. filter(row_number() == 1 | date_of_mri >= first(date_of_mri) + months(3)) %>% # Keep the first MRI and those >= 3 months later
  440. ungroup() # Ungroup the data
  442. dat1_filtered <- dat1_filtered |>
  443. group_by(participant_id) |>
  444. mutate(row_num = row_number()) |> # sequence row within each person
  445. mutate(max_num = n()) # total number rows per person
  447. dat1_filtered2 <- dat1_filtered |>
  448. filter(!(max_num ==1))
  450. # flag last MR if no atrophy, first MR that shows atrophy
  451. dat1_filtered2$flag_last_MR <- NA
  452. dat1_filtered2$flag_last_MR <- ifelse(dat1_filtered2$row_num == dat1_filtered2$max_num, 1, 0)
  453. dat1_filtered2$flag_last_MR <- ifelse(!is.na(dat1_filtered2$focal_atrophy) & dat1_filtered2$focal_atrophy == 1, 1, dat1_filtered2$flag_last_MR)
  455. dat1_flagged <- dat1_filtered2 %>%
  456. group_by(participant_id) %>% # Group by participant
  457. mutate(first_flag_last_MR = ifelse(row_number() == which.max(flag_last_MR == 1), 1, 0)) %>% # Flag the first occurrence of "1"
  458. ungroup() # Ungroup the data
  460. # 'dat1_flagged' will contain the flagged data
  462. dat1_flagged <- dat1_flagged |>
  463. filter(first_flag_last_MR == 1)
  465. # create first immunotherapy
  466. dat1_flagged <- dat1_flagged |>
  467. mutate(date_first_immunotherapy = pmin(
  468. pulsesteroids1,
  469. ivig1,
  470. plasma_exchange1,
  471. na.rm = TRUE
  472. ))
  474. # create Cox time variable
  475. dat1_flagged$time <- difftime(dat1_flagged$date_of_mri, dat1_flagged$first_admission_date, "days")
  477. # time to first
  478. dat1_flagged$time_to_firstline <- difftime(dat1_flagged$date_first_immunotherapy, dat1_flagged$date_symptoms, "days")
  479. # limit time to first to maximum date of MRI
  481. dat1_flagged <- dat1_flagged %>%
  482. mutate(
  483. time_to_firstline = as.numeric(time_to_firstline), # Ensure numeric
  484. time_to_firstline = if_else(
  485. !is.na(date_of_mri) & (as.numeric(date_of_mri - date_symptoms) < time_to_firstline),
  486. as.numeric(date_of_mri - date_symptoms), # Replace with the new value if condition is met
  487. time_to_firstline # Otherwise, retain the old value
  488. )
  489. )
  492. dat1_flagged$time_to_firstline <- ifelse(dat1_flagged$participant_id == "9057-81", (dat1_flagged$date_of_mri - dat1_flagged$date_symptoms), dat1_flagged$time_to_firstline)
  494. # dat1_flagged <- dat1_flagged %>%
  495. # mutate(time_to_firstline = pmin(time_to_firstline, as.numeric(first_admission_date + 365 - date_symptoms)))
  497. dat1_flagged$IT30 <- ifelse(dat1_flagged$time_to_firstline < 30, 1, 0) # only a few LGI1
  498. dat1_flagged$LGI1 <- ifelse(dat1_flagged$final_diag == 5, 1, 0)
  500. dat5 <- dat1_flagged |>
  501. filter(final_diag == 5)
  503. dat5$memory_12bad <- ifelse(dat5$memory_12 > 1, 1, 0)
  505. dat5$m_mrs_good <- ifelse(dat5$m_mrs < 3, 1, 0)
  506. dat5$sex <- ifelse(dat5$participant_id == "9061-16", 1, dat5$sex)
  508. model17 <- glm(focal_atrophy ~ hippocampi_swelling_initial, family = "binomial", data = dat5)
  509. exp(cbind(OR=coef(model17), confint.default(model17)))
  510. summary(model17)
  511. model18 <- glm(focal_atrophy ~ age_symptom_onset, family = "binomial", data = dat5)
  512. exp(cbind(OR=coef(model18), confint.default(model18)))
  513. summary(model18)
  514. model19 <- glm(focal_atrophy ~ sex, family = "binomial", data = dat5)
  515. exp(cbind(OR=coef(model19), confint.default(model19)))
  516. summary(model19)
  517. model20 <- glm(focal_atrophy ~ time_to_firstline, family = "binomial", data = dat5)
  518. exp(cbind(OR=coef(model20), confint.default(model20)))
  519. summary(model20)
  520. model21 <- glm(focal_atrophy ~ nadir_mrs, family = "binomial", data = dat5)
  521. exp(cbind(OR=coef(model21), confint.default(model21)))
  522. summary(model21)
  523. model22 <- glm(focal_atrophy ~ T2_initial, family = "binomial", data = dat5)
  524. exp(cbind(OR=coef(model22), confint.default(model22)))
  525. summary(model22)
  526. model23 <- glm(focal_atrophy ~ T2_mesial_temporal_initial, family = "binomial", data = dat5)
  527. exp(cbind(OR=coef(model23), confint.default(model23)))
  528. summary(model23)
  529. model24 <- glm(focal_atrophy ~ hippocampi_swelling_initial + time_to_firstline, family = "binomial", data = dat5)
  530. exp(cbind(OR=coef(model24), confint.default(model24)))
  531. summary(model24)
  532. model25 <- glm(focal_atrophy ~ T2_initial + time_to_firstline, family = "binomial", data = dat5)
  533. exp(cbind(OR=coef(model25), confint.default(model25)))
  534. summary(model25)
  535. model26 <- glm(focal_atrophy ~ T2_mesial_temporal_initial + time_to_firstline, family = "binomial", data = dat5)
  536. exp(cbind(OR=coef(model26), confint.default(model26)))
  537. summary(model26)
  538. model27 <- glm(memory_12bad ~ focal_atrophy, family = "binomial", data = dat5)
  539. exp(cbind(OR=coef(model27), confint.default(model27)))
  540. summary(model27)
  541. model28 <- glm(memory_12bad ~ mesial_temporal_atrophy + age_symptom_onset, family = "binomial", data = dat5)
  542. exp(cbind(OR=coef(model28), confint.default(model28)))
  543. summary(model28)
  545. # HS
  547. dat1_filtered3 <- dat1_filtered |>
  548. filter(!(max_num ==1))
  550. # flag last MR if no atrophy, first MR that shows atrophy
  551. dat1_filtered3$flag_last_MR <- NA
  552. dat1_filtered3$flag_last_MR <- ifelse(dat1_filtered3$row_num == dat1_filtered3$max_num, 1, 0)
  553. dat1_filtered3$flag_last_MR <- ifelse(!is.na(dat1_filtered3$hippo_sclerosis) & dat1_filtered3$hippo_sclerosis == 1, 1, dat1_filtered3$flag_last_MR)
  555. dat1_flagged2 <- dat1_filtered3 %>%
  556. group_by(participant_id) %>% # Group by participant
  557. mutate(first_flag_last_MR = ifelse(row_number() == which.max(flag_last_MR == 1), 1, 0)) %>% # Flag the first occurrence of "1"
  558. ungroup() # Ungroup the data
  560. # 'dat1_flagged2' will contain the flagged data
  562. dat1_flagged2 <- dat1_flagged2 |>
  563. filter(first_flag_last_MR == 1)
  565. # create first immunotherapy
  566. dat1_flagged2 <- dat1_flagged2 |>
  567. mutate(date_first_immunotherapy = pmin(
  568. pulsesteroids1,
  569. ivig1,
  570. plasma_exchange1,
  571. na.rm = TRUE
  572. ))
  575. # create Cox time variable
  576. dat1_flagged2$time <- difftime(dat1_flagged2$date_of_mri, dat1_flagged2$first_admission_date, "days")
  578. # time to first
  579. dat1_flagged2$time_to_firstline <- difftime(dat1_flagged2$date_first_immunotherapy, dat1_flagged2$date_symptoms, "days")
  583. dat1_flagged2 <- dat1_flagged2 %>%
  584. mutate(
  585. time_to_firstline = as.numeric(time_to_firstline), # Ensure numeric
  586. time_to_firstline = if_else(
  587. !is.na(date_of_mri) & (as.numeric(date_of_mri - date_symptoms) < time_to_firstline),
  588. as.numeric(date_of_mri - date_symptoms), # Replace with the new value if condition is met
  589. time_to_firstline # Otherwise, retain the old value
  590. )
  591. )
  594. dat1_flagged2$time_to_firstline <- ifelse(dat1_flagged2$participant_id == "9057-81", dat1_flagged2$date_visit_3 - dat1_flagged2$date_symptoms, dat1_flagged2$time_to_firstline)
  598. dat5a <- dat1_flagged2 |>
  599. filter(final_diag == 5)
  601. dat5a$memory_12bad <- ifelse(dat5a$memory_12 > 1, 1, 0)
  603. dat5a$sex <- ifelse(dat5a$participant_id == "9061-16", 1, dat5a$sex)
  605. model29 <- glm(hippo_sclerosis ~ hippocampi_swelling_initial, family = "binomial", data = dat5a)
  606. exp(cbind(OR=coef(model29), confint.default(model29)))
  607. summary(model29)
  608. model30 <- glm(hippo_sclerosis ~ age_symptom_onset, family = "binomial", data = dat5a)
  609. exp(cbind(OR=coef(model30), confint.default(model30)))
  610. summary(model30)
  611. model31 <- glm(hippo_sclerosis ~ sex, family = "binomial", data = dat5a)
  612. exp(cbind(OR=coef(model31), confint.default(model31)))
  613. summary(model31)
  614. model32 <- glm(hippo_sclerosis ~ nadir_mrs, family = "binomial", data = dat5a)
  615. exp(cbind(OR=coef(model32), confint.default(model32)))
  616. summary(model32)
  617. model33 <- glm(hippo_sclerosis ~ time_to_firstline, family = "binomial", data = dat5a)
  618. exp(cbind(OR=coef(model33), confint.default(model33)))
  619. summary(model33)
  620. model34 <- glm(hippo_sclerosis ~ T2_initial, family = "binomial", data = dat5a)
  621. exp(cbind(OR=coef(model34), confint.default(model34)))
  622. summary(model34)
  623. model35 <- glm(hippo_sclerosis ~ T2_mesial_temporal_initial, family = "binomial", data = dat5a)
  624. exp(cbind(OR=coef(model35), confint.default(model35)))
  625. summary(model35)
  626. model36 <- glm(hippo_sclerosis ~ hippocampi_swelling_initial+time_to_firstline, family = "binomial", data = dat5a)
  627. exp(cbind(OR=coef(model36), confint.default(model36)))
  628. summary(model36)
  629. model37 <- glm(hippo_sclerosis ~ T2_mesial_temporal_initial+time_to_firstline, family = "binomial", data = dat5a)
  630. exp(cbind(OR=coef(model37), confint.default(model37)))
  631. summary(model37)
  632. model38 <- glm(hippo_sclerosis ~ T2_initial+time_to_firstline, family = "binomial", data = dat5a)
  633. exp(cbind(OR=coef(model38), confint.default(model38)))
  634. summary(model38)
  635. model39 <- glm(memory_12bad ~ hippo_sclerosis+age_symptom_onset, family = "binomial", data = dat5a)
  636. exp(cbind(OR=coef(model39), confint.default(model39)))
  637. summary(model39)
  639. # sensitivity analyses 1 & 2 for atrophy/HS analyses
  640. # 1 time symptoms to initial MRI (in relation to hippo swelling)
  641. dat5$time_to_MRI <- difftime(dat5$date_of_first_MRI, dat5$date_symptoms, "days")
  642. dat5$time_to_MRI <- as.numeric(dat5$time_to_MRI)
  644. model40 <- glm(focal_atrophy ~ hippocampi_swelling_initial + time_to_firstline + time_to_MRI, family = "binomial", data = dat5)
  646. library(car)
  647. vif(model40)
  648. exp(cbind(OR=coef(model40), confint.default(model40)))
  649. summary(model40)
  651. model41 <- glm(focal_atrophy ~ T2_initial + time_to_firstline + time_to_MRI, family = "binomial", data = dat5)
  652. vif(model41)
  653. exp(cbind(OR=coef(model41), confint.default(model41)))
  654. summary(model41)
  656. model42 <- glm(focal_atrophy ~ T2_mesial_temporal_initial + time_to_firstline + time_to_MRI, family = "binomial", data = dat5)
  657. vif(model42)
  658. exp(cbind(OR=coef(model42), confint.default(model42)))
  659. summary(model42)
  661. # 2 time MRI to MRI
  662. # given for this analysis time from one MR to the other was extremely skewed - e.g. over 2000 days for some - log used
  663. dat5$time_MRI_to_MRI <- as.numeric(dat5$time_MRI_to_MRI)
  665. model43 <- glm(focal_atrophy ~ hippocampi_swelling_initial + time_to_firstline + log(time_MRI_to_MRI), family = "binomial", data = dat5)
  666. vif(model43)
  667. exp(cbind(OR=coef(model43), confint.default(model43)))
  668. summary(model43)
  670. model44 <- glm(focal_atrophy ~ T2_initial + time_to_firstline + log(time_MRI_to_MRI), family = "binomial", data = dat5)
  671. vif(model44)
  672. exp(cbind(OR=coef(model44), confint.default(model44)))
  673. summary(model44)
  675. model45 <- glm(focal_atrophy ~ T2_mesial_temporal_initial + time_to_firstline + log(time_MRI_to_MRI), family = "binomial", data = dat5)
  676. vif(model45)
  677. exp(cbind(OR=coef(model45), confint.default(model45)))
  678. summary(model45)
  680. # HS sensitivity analysis
  681. dat5a$time_to_MRI <- difftime(dat5a$date_of_first_MRI, dat5a$date_symptoms, "days")
  682. dat5a$time_to_MRI <- as.numeric(dat5a$time_to_MRI)
  683. dat5a$time_MRI_to_MRI <- as.numeric(dat5a$time_MRI_to_MRI)
  685. # 1
  686. model46 <- glm(hippo_sclerosis ~ hippocampi_swelling_initial+time_to_firstline + time_to_MRI, family = "binomial", data = dat5a)
  687. vif(model46)
  688. exp(cbind(OR=coef(model46), confint.default(model46)))
  689. summary(model46)
  690. model47 <- glm(hippo_sclerosis ~ T2_mesial_temporal_initial+time_to_firstline + time_to_MRI, family = "binomial", data = dat5a)
  691. vif(model47)
  692. exp(cbind(OR=coef(model47), confint.default(model47)))
  693. summary(model47)
  694. model48 <- glm(hippo_sclerosis ~ T2_initial+time_to_firstline+ time_to_MRI, family = "binomial", data = dat5a)
  695. vif(model48)
  696. exp(cbind(OR=coef(model48), confint.default(model48)))
  697. summary(model48)
  699. # 2
  700. model49 <- glm(hippo_sclerosis ~ hippocampi_swelling_initial+time_to_firstline + log(time_MRI_to_MRI), family = "binomial", data = dat5a)
  701. vif(model49)
  702. exp(cbind(OR=coef(model49), confint.default(model49)))
  703. summary(model49)
  704. model50 <- glm(hippo_sclerosis ~ T2_mesial_temporal_initial+time_to_firstline + log(time_MRI_to_MRI), family = "binomial", data = dat5a)
  705. vif(model50)
  706. exp(cbind(OR=coef(model50), confint.default(model50)))
  707. summary(model50)
  708. model51 <- glm(hippo_sclerosis ~ T2_initial+time_to_firstline+ log(time_MRI_to_MRI), family = "binomial", data = dat5a)
  709. vif(model51)
  710. exp(cbind(OR=coef(model51), confint.default(model51)))
  711. summary(model51)
  713. # Atrophy table
  714. dat1_flagged <- dat1_flagged |>
  715. filter(final_diag == 4 | final_diag == 5 | final_diag == 6 | final_diag == 7 | final_diag == 8)
  717. # If dat1_flagged was not grouped before, you can remove the ungroup() line
  718. dat1_flagged <- dat1_flagged |>
  719. ungroup()
  721. dat1_flagged$final_diag_new <- factor(dat1_flagged$final_diag,
  722. levels = c(4, 5, 6, 7, 8),
  723. labels = c(
  724. "NMDAR",
  725. "LGI1",
  726. "CASPR2",
  727. "GABAB",
  728. "AMPAR"))
  730. tbl4 <- dat1_flagged |>
  731. select(final_diag_new, time_symptoms_to_MRI, time_MRI_to_MRI, focal_atrophy, mesial_temporal_atrophy, temporal_atrophy, frontal_atrophy, parietal_atrophy, occipital_atrophy, cerebellar_atrophy) |>
  732. tbl_summary(
  733. by = final_diag_new,
  734. statistic = list(
  735. all_continuous() ~ "{median} ({p25},{p75})", # Display median and IQR
  736. all_categorical() ~ "{n} ({p}%)" # Display counts and percentages
  737. ),
  738. digits = all_continuous() ~ 0, # No decimal points for continuous variables
  739. missing_text = "(Missing)",
  740. label = c(
  741. time_symptoms_to_MRI ~ "Time symptoms to MRI",
  742. time_MRI_to_MRI ~ "Time MRI from initial MRI",
  743. focal_atrophy ~ "Focal atrophy",
  744. mesial_temporal_atrophy ~ "Mesial temporal atrophy",
  745. temporal_atrophy ~ "Temporal atrophy",
  746. frontal_atrophy ~ "Frontal atrophy",
  747. parietal_atrophy ~ "Parietal atrophy",
  748. occipital_atrophy ~ "Occipital atrophy",
  749. cerebellar_atrophy ~ "Cerebellar atrophy"
  750. )
  751. ) |>
  752. add_overall() # Add overall summary column
  754. # HS table
  755. # Atrophy table
  756. dat1_flagged2 <- dat1_flagged2 |>
  757. filter(final_diag == 4 | final_diag == 5 | final_diag == 6 | final_diag == 7 | final_diag == 8)
  759. # If dat1_flagged was not grouped before, you can remove the ungroup() line
  760. dat1_flagged2 <- dat1_flagged2 |>
  761. ungroup()
  763. dat1_flagged2$final_diag_new <- factor(dat1_flagged2$final_diag,
  764. levels = c(4, 5, 6, 7, 8),
  765. labels = c(
  766. "NMDAR",
  767. "LGI1",
  768. "CASPR2",
  769. "GABAB",
  770. "AMPAR"))
  772. tbl5 <- dat1_flagged2 |>
  773. select(final_diag_new, time_symptoms_to_MRI, time_MRI_to_MRI, hippo_sclerosis) |>
  774. tbl_summary(
  775. by = final_diag_new,
  776. statistic = list(all_continuous() ~ "{median} ({p25},{p75})",
  777. all_categorical() ~ "{n} ({p}%)"),
  778. digits = all_continuous() ~ 0,
  779. missing_text = "(Missing)",
  780. label = c(time_symptoms_to_MRI ~ "Time symptoms to MRI",
  781. time_MRI_to_MRI ~ "Time MRI from initial MRI",
  782. hippo_sclerosis ~ "Hippocampal sclerosis")) |>
  783. add_overall()
  785. # T2 persistence
  787. dat1_filtered3 <- dat1_follow_up2 %>%
  788. group_by(participant_id) %>% # Group by participant
  789. arrange(participant_id, date_of_mri) %>% # Arrange by participant and MRI date
  790. filter(row_number() == 1 | date_of_mri >= first(date_of_mri) + months(1)) %>% # Keep the first MRI and those >= 1 months later
  791. ungroup() # Ungroup the data
  793. dat1_filtered3 <- dat1_filtered3 |>
  794. group_by(participant_id) |>
  795. mutate(row_num = row_number()) |> # sequence row within each person
  796. mutate(max_num = n()) # total number rows per person
  798. dat1_filtered3 <- dat1_filtered3 |>
  799. filter(!(max_num ==1))
  802. dat1 <- dat1_filtered3 |>
  803. select(participant_id, final_diag, date_of_mri, time_symptoms_to_MRI, T2, hippo_sclerosis, focal_atrophy, T2_mesial_temporal_initial, T2_left_mesial_temporal, T2_right_mesial_temporal, atrophy_left_mesial_temporal, atrophy_right_mesial_temporal)
  804. ## dat3 <- dat1 |>
  805. ## filter(final_diag == 5)
  806. dat3 <- dat1
  807. dat3 <- dat3 |>
  808. group_by(participant_id) |>
  809. mutate(row_num = row_number()) |>
  810. mutate(max_num = n())
  812. # create T2 initial MRI variable
  813. dat3$T2_initial <- ifelse(dat3$row_num == 1, dat3$T2, NA)
  814. dat3 <- dat3 |>
  815. group_by(participant_id) |>
  816. fill(T2_initial, .direction = "down")
  819. # Ensure that dat3 is sorted by participant_id and date_of_MRI
  820. dat3 <- dat3 |>
  821. arrange(participant_id, date_of_mri)
  823. # Calculate the time between MRIs relative to the first MRI for each participant
  824. dat3 <- dat3 |>
  825. group_by(participant_id) |>
  826. mutate(time_between_MRIs = time_symptoms_to_MRI - first(time_symptoms_to_MRI)) |>
  827. ungroup()
  828. dat3b <- dat3 |>
  829. group_by(participant_id) |>
  830. mutate(row_num = row_number()) |>
  831. mutate(max_num = n())
  832. dat3b <- dat3b |>
  833. filter(max_num > 1)
  834. # remove initial MRI row
  835. dat3bb <- dat3b |>
  836. filter(!(time_between_MRIs == 0))
  838. dat3bb <- dat3bb |>
  839. group_by(participant_id) |>
  840. mutate(row_num = row_number()) |>
  841. mutate(max_num = n())
  842. # filter first f/u MRI
  843. dat3bc <- dat3bb |>
  844. filter(row_num == 1)
  846. dat3bc$T2_no_left_mesial_atrophy <- ifelse(dat3bc$T2_left_mesial_temporal == 1 & dat3bc$atrophy_left_mesial_temporal == 0, 1, 0)
  847. dat3bc$T2_no_right_mesial_atrophy <- ifelse(dat3bc$T2_right_mesial_temporal == 1 & dat3bc$atrophy_right_mesial_temporal == 0, 1, 0)
  848. dat3bc$T2_no_mesial_atrophy <- ifelse(dat3bc$T2_no_left_mesial_atrophy == 1 | dat3bc$T2_no_right_mesial_atrophy == 1, 1, 0)
  849. dat3bc$T2_mesial_temporal <- ifelse(dat3bc$T2_left_mesial_temporal == 1 | dat3bc$T2_right_mesial_temporal == 1, 1, 0)
  851. dat3bc$time_symptoms_to_MRI <- as.numeric(dat3bc$time_symptoms_to_MRI)
  852. dat3bc$time_between_MRIs <- as.numeric(dat3bc$time_between_MRIs)
  854. dat3bc <- dat3bc |>
  855. ungroup()
  857. dat3bc$final_diag_new <- factor(dat3bc$final_diag,
  858. levels = c(4, 5, 6, 7, 8),
  859. labels = c(
  860. "NMDAR",
  861. "LGI1",
  862. "CASPR2",
  863. "GABAB",
  864. "AMPAR"))
  866. tbl5 <- dat3bc |>
  867. select(final_diag_new, time_symptoms_to_MRI, time_between_MRIs, T2_initial, T2, T2_mesial_temporal, T2_no_mesial_atrophy) |>
  868. tbl_summary(
  869. by = final_diag_new,
  870. statistic = list(
  871. all_continuous() ~ "{median} ({p25},{p75})", # Display median and IQR
  872. all_categorical() ~ "{n} ({p}%)" # Display counts and percentages
  873. ),
  874. digits = all_continuous() ~ 0, # No decimal points for continuous variables
  875. missing_text = "(Missing)",
  876. label = c(
  877. time_symptoms_to_MRI ~ "Time symptoms to MRI",
  878. time_between_MRIs ~ "Time MRI from initial MRI",
  879. T2_initial ~ "T2/FLAIR hyperintensity initial MRI",
  880. T2 ~ "T2/FLAIR hyperintensity follow-up MRI",
  881. T2_mesial_temporal ~ "Mesial temporal T2/FLAIR hyperintensity follow-up MRI",
  882. T2_no_mesial_atrophy ~ "Mesial temporal T2/FLAIR hyperintensity follow-up MRI, no HS"
  883. )
  884. ) |>
  885. add_overall() # Add overall summary column

Data_code_R.R at commit 7baaa12, no license · at the source

Overview

Authors: Nabil Seery1,2, Paul Beech3,4, Robb Wesselingh1,2, Mark Schoenwaelder3, James Broadley1,2, Laurie McLaughlin5,6, Tiffany Rushen1,2, Liora ter Horst5,6, Andrew Duncan7, Tracie Tan1,2, Christina Kazzi1,2, Genevieve Skinner5,6, Cassie Nesbitt2,8, Katherine Buzzard9, Wendyl J D’Souza7, Yang Tran7,10, Anneke van der Walt1,2, Amy Halliday7, Mirasol Forcadela11, Bruce Taylor12
and 17 other authorsAndrew Swayne5,6, Amy Brodtmann1,9,13, David Gillis14, Ernest G Butler15, Tomas Kalincik13,16,17, Udaya Seneviratne1,11,18, Richard A Macdonell11, Sudarshini Ramanathan19,20, Stefan Blum5,6, Charles B Malpas1,21,22, Stephen W Reddel19,23, Todd A Hardy19,23, Terence J O’Brien1,2, Paul Sanfilippo1, Helmut Butzkueven1,2, Mastura Monif1,2, Australian Autoimmune Encephalitis Consortium
ORCID iDs: Robb Wesselingh, Anneke van der Walt, Bruce Taylor, Tomas Kalincik, Sudarshini Ramanathan
23 affiliations
  1. Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Victoria 3004, Australia
  2. Department of Neurology, Alfred Health, Melbourne, Victoria 3004, Australia
  3. Department of Radiology, Alfred Health, Melbourne, Victoria 3004, Australia
  4. Department of Radiology, Monash Health, Clayton, Victoria 3168, Australia
  5. Department of Neurology, Princess Alexandra Hospital, Woolloongabba, Queensland 4102, Australia
  6. School of Medicine, The University of Queensland, UQ, Herston, Queensland 4006, Australia
  7. Department of Medicine, St Vincent’s Hospital, University of Melbourne, Fitzroy, Victoria 3065, Australia
  8. Department of Neuroscience, Barwon Health, Geelong, Victoria 3220, Australia
  9. Department of Neurosciences, Eastern Health, Box Hill, Victoria 3128, Australia
  10. Department of Pathology, St Vincent’s Hospital, Fitzroy, Victoria 3065, Australia
  11. Department of Neurology, Austin Health, Heidelberg, Victoria 3084, Australia
  12. Department of Neurology, Royal Hobart Hospital, Hobart, Tasmania 7000, Australia
  13. Department of Neurology, Royal Melbourne Hospital, Parkville, Victoria 3052, Australia
  14. Division of Immunology, Pathology Queensland Central Laboratory, Herston, Queensland 4006, Australia
  15. Department of Neurology, Peninsula Health, Frankston, Victoria 3199, Australia
  16. Department of Medicine, CORE, The University of Melbourne, Parkville, Victoria 3010, Australia
  17. Department of Neurology, Neuroimmunology Centre, Royal Melbourne Hospital, Melbourne, Victoria 3000, Australia
  18. Department of Neuroscience, Monash Health, Clayton, Victoria 3168, Australia
  19. Department of Neurology and Concord Clinical School, Concord Hospital, Concord, New South Wales 2139, Australia
  20. Translational Neuroimmunology Group, Kids Neuroscience Centre and Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Westmead, New South Wales 2145, Australia
  21. Department of Medicine (Royal Melbourne Hospital), University of Melbourne, Parkville, Victoria 3052, Australia
  22. Melbourne School of Psychological Sciences, University of Melbourne, Parkville, Victoria 3052, Australia
  23. Brain and Mind Centre, University of Sydney, Camperdown, New South Wales 2050, Australia
Institutions: The Alfred Hospital (Australia); Alfred Health (Australia); Monash University (Australia); Monash Medical Centre (Australia); Monash Health (Australia); The University of Queensland (Australia); Princess Alexandra Hospital (Australia); The University of Melbourne (Australia); St Vincent's Hospital Melbourne (Australia); Barwon Health (Australia); Eastern Health (Australia); Austin Hospital (Australia); Austin Health (Australia); Royal Hobart Hospital (Australia); The Royal Melbourne Hospital (Australia); Peninsula Health (Australia); The University of Sydney (Australia); Concord Hospital (United States); Concord Repatriation General Hospital (Australia)
Journal: Brain communications, volume 8, issue 3, article fcag193
Dates: received 1 October 2025; accepted 31 May 2026; published online 17 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag193 · PMID 42318510 · PMCID PMC13273915 · OpenAlex W4415496120
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), other condition (population), clinical / translational (subfield)
Methods: Machine learning
Keywords: MRI, LGI1, NMDAR, hippocampal swelling
Topic: Autoimmune Neurological Disorders and Treatments (Neurology, Medicine), according to OpenAlex
Funding: National Health and Medical Research Council (APP1201062); Western Australian Future Health and Innovation Fund, Government of Western Australia
Citations: not cited yet (Europe PMC); 40 references in the paper

Abstract

Brain magnetic resonance imaging (MRI) abnormalities are an important finding in the evaluation of patients with suspected autoimmune encephalitis (AE). There have been few studies evaluating the frequency and prognostic significance of MRI abnormalities, especially hippocampal swelling, in anti-N-methyl-D-aspartate receptor (NMDAR) and anti-leucine-rich glioma-inactivated 1 (LGI1) Ab-mediated encephalitides. We conducted a multi-centre, retrospective study involving adult patients with confirmed antibody-mediated encephalitis and at least one MRI scan from 10 Australian hospitals (n = 139). MRI scans were evaluated by a neuroradiologist blinded to the specific autoimmune encephalitis diagnosis. We evaluated associations between acute MRI abnormalities (e.g. hippocampal swelling) with 12-month function (modified Rankin scale, mRS ≥2 = worse outcome) and radiological findings. In patients with anti-LGI1 Ab-mediated encephalitis, we identified hippocampal swelling on initial MRI to be associated with worse function at 12 months (OR 0.03; 95% CI 0.003, 0.34; P = 0.005). We found initial AE-associated T2/fluid attenuated inversion recovery (FLAIR) hyperintensities were not associated with 12-month mRS in either the anti-NMDAR (OR 0.39; 95% CI 0.04, 3.97; P = 0.42) or anti-LGI1 Ab-mediated encephalitis groups (OR 0.34; 95% CI 0.07, 1.57; P = 0.17). In anti-LGI1 Ab-mediated encephalitis, both hippocampal swelling (OR 5.76; 95% CI 1.14, 29.02; P = 0.03) and T2/FLAIR hyperintensity (OR 6.81; 95% CI 1.28, 36.22; P = 0.03) were related to the development of mesial temporal atrophy and hippocampal sclerosis. Acute hippocampal swelling is associated with worse outcomes in anti-LGI1 Ab-mediated encephalitis and, alongside initial T2/FLAIR hyperintensity, is associated with the development of both mesial temporal atrophy and hippocampal sclerosis.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

nabilseery/Antibody-mediated-encephalitis-MRI-analysis-code

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 7baaa126afb2a5ca3c0d2c5cbed3b0624eb77ae9, 31 January 2026
Languages: R (1)
Size: 3 files, 1 script
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: car (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2 files
At the source: github.com/nabilseery/Antibody-mediated-encephalitis-MRI-analysis-code

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

Anonymized data is available to any qualified investigator by the corresponding author upon reasonable request and after approval from the relevant HREC. The R code used for statistical analysis and table generation is available at: https://github.com/nabilseery/Antibody-mediated-encephalitis-MRI-analysis-code.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Funding: added National Health and Medical Research Council: APP1201062; Western Australian Future Health and Innovation Fund, Government of Western Australia

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 37 authors, 4 keywords, 40 references.

Cite

This paper

Seery, N., Beech, P., Wesselingh, R., Schoenwaelder, M., Broadley, J., McLaughlin, L., Rushen, T., ter Horst, L., Duncan, A., Tan, T., Kazzi, C., Skinner, G., Nesbitt, C., Buzzard, K., D’Souza, W. J., Tran, Y., van der Walt, A., Halliday, A., Forcadela, M., . . . Australian Autoimmune Encephalitis Consortium. (2026). Acute and longitudinal magnetic resonance imaging abnormalities in antibody-mediated encephalitis. Brain communications, 8(3), fcag193. https://doi.org/10.1093/braincomms/fcag193

BibTeX

@article{seery2026acute,
author = {Seery, Nabil and Beech, Paul and Wesselingh, Robb and Schoenwaelder, Mark and Broadley, James and McLaughlin, Laurie and Rushen, Tiffany and ter Horst, Liora and Duncan, Andrew and Tan, Tracie and Kazzi, Christina and Skinner, Genevieve and Nesbitt, Cassie and Buzzard, Katherine and D’Souza, Wendyl J and Tran, Yang and van der Walt, Anneke and Halliday, Amy and Forcadela, Mirasol and Taylor, Bruce and Swayne, Andrew and Brodtmann, Amy and Gillis, David and Butler, Ernest G and Kalincik, Tomas and Seneviratne, Udaya and Macdonell, Richard A and Ramanathan, Sudarshini and Blum, Stefan and Malpas, Charles B and Reddel, Stephen W and Hardy, Todd A and O’Brien, Terence J and Sanfilippo, Paul and Butzkueven, Helmut and Monif, Mastura and {Australian Autoimmune Encephalitis Consortium}},
title = {{Acute and longitudinal magnetic resonance imaging abnormalities in antibody-mediated encephalitis}},
journal = {Brain communications},
year = {2026},
month = jun,
volume = {8},
number = {3},
pages = {fcag193},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag193},
url = {https://doi.org/10.1093/braincomms/fcag193},
pmid = {42318510},
pmcid = {PMC13273915}
}

RIS

TY - JOUR
AU - Seery, Nabil
AU - Beech, Paul
AU - Wesselingh, Robb
AU - Schoenwaelder, Mark
AU - Broadley, James
AU - McLaughlin, Laurie
AU - Rushen, Tiffany
AU - ter Horst, Liora
AU - Duncan, Andrew
AU - Tan, Tracie
AU - Kazzi, Christina
AU - Skinner, Genevieve
AU - Nesbitt, Cassie
AU - Buzzard, Katherine
AU - D’Souza, Wendyl J
AU - Tran, Yang
AU - van der Walt, Anneke
AU - Halliday, Amy
AU - Forcadela, Mirasol
AU - Taylor, Bruce
AU - Swayne, Andrew
AU - Brodtmann, Amy
AU - Gillis, David
AU - Butler, Ernest G
AU - Kalincik, Tomas
AU - Seneviratne, Udaya
AU - Macdonell, Richard A
AU - Ramanathan, Sudarshini
AU - Blum, Stefan
AU - Malpas, Charles B
AU - Reddel, Stephen W
AU - Hardy, Todd A
AU - O’Brien, Terence J
AU - Sanfilippo, Paul
AU - Butzkueven, Helmut
AU - Monif, Mastura
AU - Australian Autoimmune Encephalitis Consortium
TI - Acute and longitudinal magnetic resonance imaging abnormalities in antibody-mediated encephalitis
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/06/17
VL - 8
IS - 3
SP - fcag193
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag193
UR - https://doi.org/10.1093/braincomms/fcag193
LA - en
ER -

CSL-JSON

{
"id": "10.1093/braincomms/fcag193",
"type": "article-journal",
"title": "Acute and longitudinal magnetic resonance imaging abnormalities in antibody-mediated encephalitis",
"container-title": "Brain communications",
"author": [
{
"family": "Seery",
"given": "Nabil"
},
{
"family": "Beech",
"given": "Paul"
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{
"family": "Wesselingh",
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{
"family": "Schoenwaelder",
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},
{
"family": "Broadley",
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{
"family": "McLaughlin",
"given": "Laurie"
},
{
"family": "Rushen",
"given": "Tiffany"
},
{
"family": "ter Horst",
"given": "Liora"
},
{
"family": "Duncan",
"given": "Andrew"
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{
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{
"family": "van der Walt",
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{
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{
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{
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{
"family": "Ramanathan",
"given": "Sudarshini"
},
{
"family": "Blum",
"given": "Stefan"
},
{
"family": "Malpas",
"given": "Charles B"
},
{
"family": "Reddel",
"given": "Stephen W"
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{
"family": "Hardy",
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{
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"container-title-short": "Brain Commun",
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"issued": {
"date-parts": [
[
2026,
6,
17
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}

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