Cogvid analysis vscript
Ismael Calandri
23/5/2021
##############################Data wrangling section##########################################
##Data tidy
library(tidyverse)
base<-base %>%
rename(
Diagnosis = dx,
Symptomatic = covid_sint,
Hospitalization = hospitalizacion,
ICU =terapia,
ICU_days = dias_terapia,
s_Anosmia = covid_symptoms___0,
s_Ageusia = covid_symptoms___1,
s_fatigue = covid_symptoms___2,
s_Dysnea = covid_symptoms___3,
s_Headache = covid_symptoms___4,
s_Fever = covid_symptoms___5,
s_Sore_throat = covid_symptoms___6,
s_Rush = covid_symptoms___7,
s_Diarrhea = covid_symptoms___8,
s_Nausea = covid_symptoms___9,
s_Chest_pain = covid_symptoms___10,
s_Delirium = covid_symptoms___11,
s_Sinusitis = covid_symptoms___12,
s_Dizzyness = covid_symptoms___13,
No_neurological_disease = trast_neurologico___0,# double negation
mh_epilepsy = trast_neurologico___1,
mh_cognitive_impairment = trast_neurologico___2,
mh_Parkinson = trast_neurologico___3,
mh_vertigo = trast_neurologico___4,
mh_multiple_Sclerosis = trast_neurologico___5,
mh_cephalea = trast_neurologico___6,
mh_fibromialgia = trast_neurologico___7,
mh_stroke = trast_neurologico___8,
mh_others = trast_neurologico___9,
mh_cardiac_disease = antecedentes_medicos___0,
mh_hta = antecedentes_medicos___1,
mh_COPD = antecedentes_medicos___2,
mh_asthma = antecedentes_medicos___3,
mh_diabetes = antecedentes_medicos___7,
mh_cancer = antecedentes_medicos___10,
mh_smoke = antecedentes_medicos___11,
mh_reumatological = antecedentes_medicos___12,
mh_obesity = antecedentes_medicos___13,
mh_HIV = antecedentes_medicos___14,
No_mh =antecedentes_medicos___15 # double negation
)
# Definition of factors and labels
base$s_Anosmia<-factor(base$s_Anosmia,
levels = c(0,1),
labels = c("No", "Yes"))
base$s_Ageusia<-factor(base$s_Ageusia,
levels = c(0,1),
labels = c("No", "Yes"))
base$s_fatigue<-factor(base$s_fatigue,
levels = c(0,1),
labels = c("No", "Yes"))
base$s_Dysnea<-factor(base$s_Dysnea,
levels = c(0,1),
labels = c("No", "Yes"))
base$s_Headache<-factor(base$s_Headache,
levels = c(0,1),
labels = c("No", "Yes"))
base$s_Fever<-factor(base$s_Fever,
levels = c(0,1),
labels = c("No", "Yes"))
base$s_Sore_throat<-factor(base$s_Sore_throat,
levels = c(0,1),
labels = c("No", "Yes"))
base$s_Rush<-factor(base$s_Rush,
levels = c(0,1),
labels = c("No", "Yes"))
base$s_Diarrhea<-factor(base$s_Diarrhea,
levels = c(0,1),
labels = c("No", "Yes"))
base$s_Nausea<-factor(base$s_Nausea,
levels = c(0,1),
labels = c("No", "Yes"))
base$s_Chest_pain<-factor(base$s_Chest_pain,
levels = c(0,1),
labels = c("No", "Yes"))
base$s_Delirium<-factor(base$s_Delirium,
levels = c(0,1),
labels = c("No", "Yes"))
base$s_Sinusitis<-factor(base$s_Sinusitis,
levels = c(0,1),
labels = c("No", "Yes"))
base$s_Dizzyness<-factor(base$s_Dizzyness,
levels = c(0,1),
labels = c("No", "Yes"))
base$sex<-factor(base$sex,
levels = c(0,1),
labels = c("Male", "Female"))
base$Diagnosis<-factor(base$Diagnosis,
levels = c(0,1),
labels = c("Healthy control", "Covid"))
base$ICU<-factor(base$ICU,
labels = c("No", "Yes"))
#esta invertido para que quede mejor OJO!
base$mh_neurological<- ifelse(base$No_neurological_disease == 1, 0, ifelse(base$No_neurological_disease == 0,1,"Na"))
base$mh_neurological<-factor(base$mh_neurological,
levels = c(0,1),
labels = c("No", "Yes"))
#esta invertido para que quede mejor OJO!
base$mh<- ifelse(base$No_mh == 0, 1, ifelse(base$No_mh == 1,0,"Na"))
base$mh<-factor(base$mh,levels = c(0,1),
labels = c("No", "Yes"))
base$mh_epilepsy<-factor(base$mh_epilepsy,
levels = c(0,1),
labels = c("No", "Yes"))
base$mh_cognitive_impairment<-factor(base$mh_cognitive_impairment,
levels = c(0,1),
labels = c("No", "Yes"))
base$mh_Parkinson<-factor(base$mh_Parkinson,
levels = c(0,1),
labels = c("No", "Yes"))
base$mh_vertigo<-factor(base$mh_vertigo,
levels = c(0,1),
labels = c("No", "Yes"))
base$mh_multiple_Sclerosis<-factor(base$mh_multiple_Sclerosis,
levels = c(0,1),
labels = c("No", "Yes")
)
base$mh_cephalea<-factor(base$mh_cephalea,
levels = c(0,1),
labels = c("No", "Yes"))
base$mh_stroke<-factor(base$mh_stroke,
levels = c(0,1),
labels = c("No", "Yes"))
#Rounding of numerical variables
base$age<-round(base$age, digits = 0)
base$imc<-round(base$imc, digits = 1)
# Variables dependent of branching logic for severity needs replaces NAN by Zero
base$suplemento_oxigeno[is.na(base$suplemento_oxigeno)] <- 0
base$respirador[is.na(base$respirador)] <- 0
#Severity calculation
base$severity_Calc<-base$Symptomatic+base$Hospitalization+base$suplemento_oxigeno+base$respirador
base$severity<-factor(base$severity_Calc,
levels = c(0,1,2,3,4),
labels = c("Asymptomatic", "Mild", "Moderate","Moderate", "Severe"))
#Variables labeling
library(expss)
base<-apply_labels(base,
age= "Age (y)",
Symptomatic = "Symptomatic covid infection",
Hospitalization = "Hospitalization requiered",
ICU ="Intensive care (ICU) requiered",
ICU_days = "Day in ICU",
s_Anosmia = "Anosmia",
s_Ageusia = "Ageusia",
s_fatigue = "Fatigue",
s_Dysnea = "Dysnea",
s_Headache = "Headache",
s_Fever = "Fever",
s_Sore_throat = "Sore throrat",
s_Rush = "Skin rush",
s_Diarrhea = "Diarrhea",
s_Nausea = "Nausea",
s_Chest_pain = "Chest pain or ANGOR",
s_Delirium = "Delirium",
s_Sinusitis = "Sinusitis",
s_Dizzyness = "Vertigo or dizzyness",
mh_neurological = "Medical history of major neurological disease",
mh = "Medical history of chronic disease",
mh_epilepsy = "Epilepsy",
mh_cognitive_impairment = "Prior Cognitive impairment",
mh_Parkinson = "Parkinson disease",
mh_vertigo = "Vertigo",
mh_multiple_Sclerosis = "Multiple Sclerosis",
mh_cephalea = "Cephalea",
mh_fibromialgia = "Fybromialgia",
mh_stroke = "Stroke",
mh_cardiac_disease = "Cardiac disease",
mh_hta = "Hypertension",
mh_COPD = "COPD",
mh_diabetes = "Diabetes",
mh_smoke = "Smoke",
mh_obesity = "Obesity",
severity = "Covid infection severity",
craft_inm_textual = "Craft Story inmediate recall (textual)",
craft_inm_textual_z = "Craft Story inmediate recall (textual)-z score-",
craft_inm_parafraseo = "Craft Story inmediate recall (paraf)",
craft_inm_paraf_z = "Craft Story inmediate recall (paraf)-z score-",
craft_dif_textual = "Craft Story delayed recall (textual)",
craft_dif_tex_z = "Craft Story delayed recall (textual) -zscore-",
craft_dif_paraf= "Craft Story delayed recall (paraf)",
craft_dif_paraf_z = "Craft Story delayed recall (paraf) -zscore-",
logical_inm = "WLM inmediate recall",
logical_inm_z= "WLM inmediate recall-zscore-",
logical_dif = "WLM delayed recall",
logical_dif_z = "WLM delayed recall -z score-",
logical_rec = "WLM recognition",
rey_tot = "RAVLT Total",
rey_tot_z = "RAVLT Total -zscore-",
rey_dif= "RAVLT delayed recall",
rey_dif_z = "RAVLT delayed recall-z score",
rey_reconocimiento = "RAVLT recognition",
rey_reconocimiento_z = "RAVLT recognition -z score",
rey_intrusiones = "RAVLT intrusion",
rey_falsospos = "RAVLT FPos",
benson_dif = "Benson figure delayed recall",
benson_dif_z = "Benson figure delayed recall -zscore-",
benson_rec ="Benson figure recognition",
rey_fig_dif = "Rey figure delayed recall",
rey_fig_dif_z = "Rey figure delayed recall -zscore-",
rey_repr ="Rey figure recognition",
mint = "MINT",
mint_z = "MINT -zscore-",
boston = "BNT",
boston_z = "BNT -zscore-",
crosswalk_mint_boston = "MINT/BNT crosswalk",
fluency_sem_anim = "Semantic fluency",
fluency_sem_anim_z = "Semantic fluency -zscore-",
fluency_fon_p = "Phonological fluency (p)",
fluency_fon_p_z = "Phonological fluency (p)-zscore-",
fluency_fon_m = "Phonological fluency (m)",
fluency_fon_m_z= "Phonological fluency (m)-zscore-",
digitos_dir = "Digit span (direct)",
digitos_dir_z = "Digit span (direct) -z score-",
digitos_inv = "Digit span (indirect)",
digitos_inv_z = "Digit span (indirect) -z score-",
tmt_a = "Trial making test A",
tmt_a_z = "Trial making test A -zscore-",
claves = "WAIS IV claves",
claves_z = "WAIS IV claves -zscore-",
digitos_simbolos = "DSST",
digitos_simbolos_z = "DSST -zscore-",
tmt_b = "Trial making test B",
tmt_b_z = "Trial making test B -zscore-",
fab = "FAB",
fab_z = "FAB -z score-",
stroop_pc = "Stroop word-colour",
stroop_pc_z = "Stroop word-colour- z score-",
stroop_int = "Stroop int",
stroop_int_z = "Stroop int -z score-",
wcst_cat = "WCST cat",
wcst_cat_z = "WCST cat -z score-",
wcst_perseveraciones = "WCST pers ",
wcst_z = "WCST pers -z score-",
benson_cop = "Benson figure copy",
benson_cop_z = "Benson figure copy -zscore-",
rey_copia = "Rey figure copy ",
rey_copia_z = "Rey figure copy -zscore-",
reloj = "CDT",
gds = " GDS",
hads_ans = "HADS anxiety",
hads_dep = "HADS depresion",
mmq = "MMQ",
faq = "FAQ",
moca_total= "MoCA total",
mmse= "MMSE",
crosswalk_mmse_moca = "MMSE/MoCA crosswalk",
imc = "BMI",
total_caide = "CAIDE dementia score",
escolaridad = "Education (y)",
severity = "Covid severity (WHO clinical progression scale)",
time_lapse = "Time of evaluation since diagnosis"
)
#####################################################################################################Table 1. Demographics
Esta tabla, y la siguiente, las pense para mostrar la poblacion. Ya que es en definitiva una serie de casos, es muy importante mostrar como son los casos por eso la tabla 2
library(gtsummary)
base %>% select(Diagnosis, age, sex, escolaridad,total_caide)%>%
tbl_summary(by=Diagnosis)%>%
add_p()| Characteristic | Healthy control, N = 371 | Covid, N = 451 | p-value2 |
|---|---|---|---|
| Age (y) | 57 (48, 64) | 51 (43, 65) | 0.5 |
| sex | 0.5 | ||
| Male | 18 (49%) | 25 (56%) | |
| Female | 19 (51%) | 20 (44%) | |
| Education (y) | 18.00 (15.00, 18.00) | 18.00 (15.00, 18.00) | 0.9 |
| CAIDE dementia score | 6.00 (5.00, 7.00) | 5.00 (3.00, 7.00) | 0.2 |
|
1
Median (IQR); n (%)
2
Wilcoxon rank sum test; Pearson's Chi-squared test
|
|||
Table 2. Covid cases features
De esta tabla me parecio importante reportar que los pacientes son evaluados mas o menos 3 meses despues del cuadro agudo! Puse algunos factores que me parecieron los mas relevantes (podemos agregar el que consideren)
t1<-base %>% filter(Diagnosis =="Covid") %>%
select(severity,time_lapse)%>%
tbl_summary(missing = "no",type = list(time_lapse~ 'continuous'))%>%italicize_labels()
t2<-base %>% filter(Diagnosis =="Covid") %>%
select(mh_cognitive_impairment,mh_epilepsy, mh_Parkinson, mh_multiple_Sclerosis, mh_stroke)%>%
tbl_summary(missing = "no")%>%italicize_labels()
t3<-base %>% filter(Diagnosis =="Covid") %>%
select(mh_hta,mh_obesity,mh_diabetes, mh_smoke, mh_cardiac_disease, mh_COPD, imc)%>%
tbl_summary(missing = "no") %>%italicize_labels()
tbl_stack(list(t1, t2,t3),
group_header = c("Covid infection","Neurological disease history", "Risk factors")
)| Characteristic | N = 451 |
|---|---|
| Covid infection | |
| Covid severity (WHO clinical progression scale) | |
| Asymptomatic | 0 (0%) |
| Mild | 22 (65%) |
| Moderate | 10 (29%) |
| Severe | 2 (5.9%) |
| Time of evaluation since diagnosis | 149 (76, 218) |
| Neurological disease history | |
| Prior Cognitive impairment | 4 (8.9%) |
| Epilepsy | 0 (0%) |
| Parkinson disease | 0 (0%) |
| Multiple Sclerosis | 0 (0%) |
| Stroke | 0 (0%) |
| Risk factors | |
| Hypertension | 6 (13%) |
| Obesity | 5 (11%) |
| Diabetes | 2 (4.4%) |
| Smoke | 2 (4.4%) |
| Cardiac disease | 0 (0%) |
| COPD | 0 (0%) |
| BMI | 23.6 (22.5, 28.7) |
|
1
n (%); Median (IQR)
|
|
Table 2a. Covid cases functionality after covid (WHODAS 2.0)
Estas son las preguntas de la OMS convertidas a los scores que postulan, hay que sacar la columna de asintomaticos y severos (que no tenemos), me parece importante mostrar que el mayor compromiso funcional de estos pacientes se encuentra en la cognicion
###Whodas calculation
base$WHODAS_total<-base$oms_parado +
base$oms_tareasdomest +
base$oms_nuevoaprend +
base$oms_actcom +
base$oms_afectemoc+
base$oms_concentr+
base$oms_caminar+
base$oms_higiene +
base$oms_vest +
base$oms_desconoc+
base$oms_amist+
base$oms_trab
base$WHODAS_cognition<-base$oms_concentr+base$oms_nuevoaprend
base$WHODAS_mobility<- base$oms_parado+base$oms_caminar
base$WHODAS_self_Care<-base$oms_higiene+base$oms_vest
base$WHODAS_getting_along<-base$oms_desconoc+base$oms_amist
base$WHODAS_life_activities<-base$oms_tareasdomest+base$oms_trab
base$WHODAS_participation<-base$oms_afectemoc+base$covid_actcom
base<-apply_labels(base,
oms_parado = "Standing long periods",
oms_tareasdomest = "Household responsabilities",
oms_nuevoaprend = "Learning new tasks",
oms_actcom = "Joining community activities",
oms_afectemoc = "Emotionally affected",
oms_concentr = "Concentrating",
oms_caminar = "Walking long distance",
oms_higiene = "Washing whole body",
oms_vest = "Getting dressed",
oms_desconoc = "Dealing with strangers",
oms_amist = "Maintaining friendships",
oms_trab = "Work/school activities",
covid_parado = "Standing long periods (comparing prior covid)",
covid_tareasdom = "Household responsabilities (comparing prior covid)",
covid_aprendizaje = "Learning new tasks (comparing prior covid)",
covid_actcom = "Joining community activities (comparing prior covid)",
covid_afectemoc = "Emotionally affected (comparing prior covid)",
covid_concentr = "Concentrating (comparing prior covid)",
covid_caminar = "Walking long distance (comparing prior covid)",
covid_higiene = "Washing whole body (comparing prior covid)",
covid_vest = "Getting dressed (comparing prior covid)",
covid_desconoc = "Dealing with strangers (comparing prior covid)",
covid_amist = "Maintaining friendships (comparing prior covid)",
covid_trab = "Work/school activities (comparing prior covid)",
WHODAS_total = "WHODAS total",
WHODAS_cognition= "Cognition",
WHODAS_mobility = "Mobility",
WHODAS_life_activities = "Life activities",
WHODAS_self_Care = "Self-care",
WHODAS_getting_along = "Getting along",
WHODAS_participation = "Participation")
#############
base %>% select(WHODAS_cognition, WHODAS_life_activities, WHODAS_mobility, WHODAS_self_Care, WHODAS_getting_along, WHODAS_participation, WHODAS_mobility, WHODAS_total, severity) %>%
tbl_summary(by=severity, missing = "no", type = list(WHODAS_cognition~ 'continuous', WHODAS_life_activities~ 'continuous', WHODAS_mobility~ 'continuous', WHODAS_self_Care~ 'continuous', WHODAS_getting_along~ 'continuous', WHODAS_participation~ 'continuous', WHODAS_mobility~ 'continuous', WHODAS_total~ 'continuous')) %>%
add_p() %>%
add_overall()| Characteristic | Overall, N = 351 | Asymptomatic, N = 01 | Mild, N = 221 | Moderate, N = 111 | Severe, N = 21 | p-value2 |
|---|---|---|---|---|---|---|
| Cognition | 2.00 (1.00, 3.75) | NA (NA, NA) | 2.00 (0.00, 2.00) | 4.00 (3.00, 5.00) | 2.50 (2.25, 2.75) | 0.008 |
| Life activities | 1.00 (0.00, 3.00) | NA (NA, NA) | 1.00 (0.00, 2.00) | 3.00 (1.00, 5.00) | 1.00 (0.50, 1.50) | 0.13 |
| Mobility | 1.00 (0.00, 2.00) | NA (NA, NA) | 1.00 (0.00, 2.00) | 1.00 (1.00, 5.00) | 3.00 (2.50, 3.50) | 0.13 |
| Self-care | 0.00 (0.00, 0.00) | NA (NA, NA) | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.50) | 0.00 (0.00, 0.00) | 0.3 |
| Getting along | 0.00 (0.00, 2.00) | NA (NA, NA) | 0.00 (0.00, 1.75) | 2.00 (0.00, 3.00) | 1.50 (0.75, 2.25) | 0.2 |
| Participation | 3.00 (2.00, 4.00) | NA (NA, NA) | 2.00 (1.25, 3.00) | 4.00 (2.50, 5.00) | 4.00 (3.50, 4.50) | 0.030 |
| WHODAS total | 8 (3, 13) | NA (NA, NA) | 6 (1, 11) | 13 (9, 26) | 11 (10, 12) | 0.027 |
|
1
Median (IQR)
2
Kruskal-Wallis rank sum test
|
||||||
Table 3 Cognition cases and controls
La vieja tablita neuropsicologica
# Tabla 3 Cognition by groups
t1<- base %>% select(mmse, moca_total, crosswalk_mmse_moca, reloj, Diagnosis) %>%
tbl_summary(by=Diagnosis, type = list(mmse ~ 'continuous', reloj~ 'continuous'),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} / {N} ({p}%)")) %>%italicize_labels()%>%
add_p()
t2<- base %>% select(craft_inm_textual, craft_inm_parafraseo, craft_dif_textual, craft_dif_paraf, rey_tot, rey_dif, rey_reconocimiento, benson_dif, rey_fig_dif,
Diagnosis) %>%
tbl_summary(by=Diagnosis,type = list(rey_reconocimiento ~ 'continuous', rey_fig_dif~ 'continuous'),
statistic = list(all_continuous() ~ "{mean} ({sd})",
"rey_dif"~"{mean} ({sd})",
all_categorical() ~ "{n} / {N} ({p}%)")) %>%italicize_labels()%>%
add_p()
t3<-base %>% select(mint,boston, crosswalk_mint_boston, fluency_sem_anim, fluency_fon_p, fluency_fon_m, Diagnosis) %>%
tbl_summary(by=Diagnosis,
type = list(mint ~ 'continuous', boston~ 'continuous', crosswalk_mint_boston~ 'continuous'),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} / {N} ({p}%)")) %>%italicize_labels()%>%
add_p()
t4 <-base %>% select(digitos_dir, digitos_inv, tmt_a, claves, digitos_simbolos, Diagnosis) %>%
tbl_summary(by=Diagnosis,
type = list(digitos_dir ~ 'continuous', digitos_inv~ 'continuous', digitos_simbolos~ 'continuous'),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} / {N} ({p}%)")) %>%italicize_labels()%>%
add_p()
t5<-base %>% select(tmt_b, fab, stroop_pc, stroop_int, wcst_cat, wcst_perseveraciones, Diagnosis) %>%
tbl_summary(by= Diagnosis,
type = list(fab ~ 'continuous', wcst_cat~ 'continuous', wcst_perseveraciones~ 'continuous'),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} / {N} ({p}%)")) %>%italicize_labels()%>%
add_p()
t6<-base %>% select(benson_cop, rey_copia, reloj, Diagnosis) %>%
tbl_summary(by= Diagnosis,
type = list(benson_cop ~ 'continuous', rey_copia~ 'continuous', reloj~ 'continuous'),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} / {N} ({p}%)")) %>%italicize_labels()%>%
add_p
t7<-base %>% select(hads_ans, hads_dep, mmq, faq, Diagnosis) %>%
tbl_summary(by= Diagnosis,
type = list(faq ~ 'continuous'),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} / {N} ({p}%)")) %>%italicize_labels()%>%
add_p
tbl_stack(
list(t1, t2,t3,t4,t5,t6,t7),
group_header = c("Screening","Memory", "Language", "Attention", "Ejecutive System", "Visuospatial", "Neuropsych")
)%>%
bold_p(t = 0.01)| Characteristic | Healthy control, N = 371 | Covid, N = 451 | p-value2 |
|---|---|---|---|
| Screening | |||
| MMSE | NA (NA) | 27.50 (3.27) | |
| Unknown | 37 | 39 | |
| MoCA total | 27.27 (1.97) | 26.28 (2.86) | 0.15 |
| Unknown | 0 | 6 | |
| MMSE/MoCA crosswalk | 27.27 (1.97) | 25.87 (3.28) | 0.053 |
| CDT | 9.78 (0.62) | 9.14 (1.35) | 0.009 |
| Unknown | 0 | 2 | |
| Memory | |||
| Craft Story inmediate recall (textual) | 22 (4) | 18 (7) | 0.009 |
| Unknown | 0 | 8 | |
| Craft Story inmediate recall (paraf) | 16.9 (2.6) | 14.3 (4.1) | 0.003 |
| Unknown | 0 | 8 | |
| Craft Story delayed recall (textual) | 19 (5) | 15 (8) | 0.005 |
| Unknown | 0 | 8 | |
| Craft Story delayed recall (paraf) | 15.9 (2.9) | 12.8 (5.0) | 0.004 |
| Unknown | 0 | 8 | |
| RAVLT Total | 49 (8) | 42 (13) | 0.019 |
| RAVLT delayed recall | 10.2 (2.8) | 8.4 (4.0) | 0.024 |
| RAVLT recognition | 12.92 (1.66) | 11.86 (2.28) | 0.040 |
| Unknown | 0 | 2 | |
| Benson figure delayed recall | 12.38 (2.30) | 10.45 (3.16) | 0.006 |
| Unknown | 0 | 7 | |
| Rey figure delayed recall | NA (NA) | 22.8 (8.2) | |
| Unknown | 37 | 41 | |
| Language | |||
| MINT | 30.41 (1.46) | 29.45 (2.08) | 0.039 |
| Unknown | 0 | 7 | |
| BNT | NA (NA) | 27.57 (2.64) | |
| Unknown | 37 | 38 | |
| MINT/BNT crosswalk | 30.41 (1.46) | 29.47 (2.14) | 0.049 |
| Semantic fluency | 23.0 (4.2) | 19.0 (5.2) | <0.001 |
| Phonological fluency (p) | 18.2 (4.3) | 14.4 (4.8) | 0.001 |
| Phonological fluency (m) | 16.1 (3.6) | 14.1 (4.7) | 0.055 |
| Unknown | 4 | 6 | |
| Attention | |||
| Digit span (direct) | 6.86 (0.95) | 5.89 (1.30) | <0.001 |
| Digit span (indirect) | 5.11 (0.91) | 4.02 (1.14) | <0.001 |
| Trial making test A | 29 (6) | 46 (25) | <0.001 |
| Unknown | 0 | 1 | |
| WAIS IV claves | 13.5 (3.0) | 12.0 (3.6) | 0.016 |
| Unknown | 1 | 9 | |
| DSST | 1 (NA) | 22 (18) | 0.3 |
| Unknown | 36 | 40 | |
| Ejecutive System | |||
| Trial making test B | 62 (21) | 106 (77) | <0.001 |
| Unknown | 0 | 4 | |
| FAB | 17.71 (0.59) | 16.71 (1.20) | 0.011 |
| Unknown | 20 | 31 | |
| Stroop word-colour | 51 (9) | 49 (11) | 0.3 |
| Unknown | 0 | 13 | |
| Stroop int | 53 (7) | 53 (6) | 0.6 |
| Unknown | 0 | 13 | |
| WCST cat | 6.00 (0.00) | 5.59 (1.04) | 0.022 |
| Unknown | 5 | 13 | |
| WCST pers | 0.72 (1.20) | 2.91 (4.67) | 0.008 |
| Unknown | 5 | 13 | |
| Visuospatial | |||
| Benson figure copy | 16.22 (0.95) | 16.21 (3.11) | 0.14 |
| Unknown | 0 | 6 | |
| Rey figure copy | NA (NA) | 34.00 (3.46) | |
| Unknown | 37 | 42 | |
| CDT | 9.78 (0.62) | 9.14 (1.35) | 0.009 |
| Unknown | 0 | 2 | |
| Neuropsych | |||
| HADS anxiety | 8.43 (3.30) | 8.32 (3.29) | 0.8 |
| Unknown | 9 | 14 | |
| HADS depresion | 6.9 (7.9) | 5.9 (3.5) | >0.9 |
| Unknown | 8 | 15 | |
| MMQ | 37 (14) | 33 (12) | 0.3 |
| Unknown | 14 | 21 | |
| FAQ | 0.0 (0.0) | 6.0 (11.2) | 0.020 |
| Unknown | 29 | 36 | |
|
1
Mean (SD)
2
Wilcoxon rank sum test
|
|||
Multiple comparison corrections
Esta tabla (que parece un chino), es solo para elegir el metodo mas generoso con las correcciones multiples, despues que decidamos y despues la pongo como una columnita mas de la anterior
Input = ("
Test Raw.p
MoCA_total 0.2
MMSE/MoCA_crosswalk 0.05
Craft_Story_inmediate_recall_textual 0.006
Craft_Story_inmediate_recall_paraf 0.001
Craft_Story_delayed_recall_textual 0.003
Craft_Story_delayed_recall_paraf 0.001
RAVLT_Total 0.003
RAVLT_delayed_recall 0.014
RAVLT_recognition 0.035
Benson_figure_delayed_recall 0.001
MINT 0.047
MINT/BNT_crosswalk 0.061
Semantic_fluency 0.001
Phonological_fluency 0.002
Phonological_fluency_m 0.039
Digit_span_direct 0.001
Digit_span_indirect 0.001
Trial_making_test_A 0.001
WAIS_IV_claves 0.012
DSST 0.3
Trialmaking_test_B 0.001
FAB 0.02
Stroop_word-colour 0.3
Stroop_int 0.4
WCST_cat 0.023
WCST_pers 0.032
Benson_figure_copy 0.2
CDT 0.004
HADS_anxiety 0.8
HADS_depresion 0.7
MMQ 0.3
FAQ 0.057")
Data = read.table(textConnection(Input),header=TRUE)
Data = Data[order(Data$Raw.p),]
library(FSA)
Data$Bonferroni =
p.adjust(Data$Raw.p,
method = "bonferroni")
Data$BH =
p.adjust(Data$Raw.p,
method = "BH")
Data$Holm =
p.adjust(Data$ Raw.p,
method = "holm")
Data$Hochberg =
p.adjust(Data$ Raw.p,
method = "hochberg")
Data$Hommel =
p.adjust(Data$ Raw.p,
method = "hommel")
Data$BY =
p.adjust(Data$ Raw.p,
method = "BY")
knitr::kable(Data)| Test | Raw.p | Bonferroni | BH | Holm | Hochberg | Hommel | BY | |
|---|---|---|---|---|---|---|---|---|
| 4 | Craft_Story_inmediate_recall_paraf | 0.001 | 0.032 | 0.0040000 | 0.032 | 0.025 | 0.024 | 0.0162340 |
| 6 | Craft_Story_delayed_recall_paraf | 0.001 | 0.032 | 0.0040000 | 0.032 | 0.025 | 0.024 | 0.0162340 |
| 10 | Benson_figure_delayed_recall | 0.001 | 0.032 | 0.0040000 | 0.032 | 0.025 | 0.024 | 0.0162340 |
| 13 | Semantic_fluency | 0.001 | 0.032 | 0.0040000 | 0.032 | 0.025 | 0.024 | 0.0162340 |
| 16 | Digit_span_direct | 0.001 | 0.032 | 0.0040000 | 0.032 | 0.025 | 0.024 | 0.0162340 |
| 17 | Digit_span_indirect | 0.001 | 0.032 | 0.0040000 | 0.032 | 0.025 | 0.024 | 0.0162340 |
| 18 | Trial_making_test_A | 0.001 | 0.032 | 0.0040000 | 0.032 | 0.025 | 0.024 | 0.0162340 |
| 21 | Trialmaking_test_B | 0.001 | 0.032 | 0.0040000 | 0.032 | 0.025 | 0.024 | 0.0162340 |
| 14 | Phonological_fluency | 0.002 | 0.064 | 0.0071111 | 0.048 | 0.048 | 0.044 | 0.0288604 |
| 5 | Craft_Story_delayed_recall_textual | 0.003 | 0.096 | 0.0087273 | 0.069 | 0.066 | 0.063 | 0.0354196 |
| 7 | RAVLT_Total | 0.003 | 0.096 | 0.0087273 | 0.069 | 0.066 | 0.063 | 0.0354196 |
| 28 | CDT | 0.004 | 0.128 | 0.0106667 | 0.084 | 0.084 | 0.080 | 0.0432906 |
| 3 | Craft_Story_inmediate_recall_textual | 0.006 | 0.192 | 0.0147692 | 0.120 | 0.120 | 0.108 | 0.0599409 |
| 19 | WAIS_IV_claves | 0.012 | 0.384 | 0.0274286 | 0.228 | 0.228 | 0.156 | 0.1113187 |
| 8 | RAVLT_delayed_recall | 0.014 | 0.448 | 0.0298667 | 0.252 | 0.252 | 0.168 | 0.1212137 |
| 22 | FAB | 0.020 | 0.640 | 0.0400000 | 0.340 | 0.340 | 0.220 | 0.1623398 |
| 25 | WCST_cat | 0.023 | 0.736 | 0.0432941 | 0.368 | 0.368 | 0.230 | 0.1757090 |
| 26 | WCST_pers | 0.032 | 1.000 | 0.0568889 | 0.480 | 0.480 | 0.305 | 0.2308833 |
| 9 | RAVLT_recognition | 0.035 | 1.000 | 0.0589474 | 0.490 | 0.490 | 0.315 | 0.2392376 |
| 15 | Phonological_fluency_m | 0.039 | 1.000 | 0.0624000 | 0.507 | 0.507 | 0.351 | 0.2532501 |
| 11 | MINT | 0.047 | 1.000 | 0.0716190 | 0.564 | 0.549 | 0.423 | 0.2906656 |
| 2 | MMSE/MoCA_crosswalk | 0.050 | 1.000 | 0.0727273 | 0.564 | 0.549 | 0.450 | 0.2951633 |
| 32 | FAQ | 0.057 | 1.000 | 0.0793043 | 0.570 | 0.549 | 0.456 | 0.3218563 |
| 12 | MINT/BNT_crosswalk | 0.061 | 1.000 | 0.0813333 | 0.570 | 0.549 | 0.480 | 0.3300909 |
| 1 | MoCA_total | 0.200 | 1.000 | 0.2461538 | 1.000 | 0.800 | 0.800 | 0.9990142 |
| 27 | Benson_figure_copy | 0.200 | 1.000 | 0.2461538 | 1.000 | 0.800 | 0.800 | 0.9990142 |
| 20 | DSST | 0.300 | 1.000 | 0.3310345 | 1.000 | 0.800 | 0.800 | 1.0000000 |
| 23 | Stroop_word-colour | 0.300 | 1.000 | 0.3310345 | 1.000 | 0.800 | 0.800 | 1.0000000 |
| 31 | MMQ | 0.300 | 1.000 | 0.3310345 | 1.000 | 0.800 | 0.800 | 1.0000000 |
| 24 | Stroop_int | 0.400 | 1.000 | 0.4266667 | 1.000 | 0.800 | 0.800 | 1.0000000 |
| 30 | HADS_depresion | 0.700 | 1.000 | 0.7225806 | 1.000 | 0.800 | 0.800 | 1.0000000 |
| 29 | HADS_anxiety | 0.800 | 1.000 | 0.8000000 | 1.000 | 0.800 | 0.800 | 1.0000000 |
Comparacion de composites
###calculo de los composites
base$composite_memory<-(base$rey_dif_z+base$rey_tot_z+base$benson_dif_z)/3
base$composite_language <-(base$fluency_sem_anim_z + base$mint_z)/2
base$composite_attention<-(base$digitos_dir_z+base$tmt_a_z)/2
base$composite_Executive<-(base$tmt_b_z+base$fluency_fon_p_z+base$digitos_inv_z)/3
library(expss)
base<-apply_labels(base,
composite_memory= "Memory (composite)",
composite_language= "Language (composite)",
composite_attention = "Attention (composite)",
composite_Executive = "Executive (composite)")Tabla 4.a Cognition cases and controls (by composites)
En este caso creo que la tabla 3 y la 4a se pueden fusionar, podemos discutirlo luego
library(gtsummary)
library(gt)
library(dplyr)
library(purrr)
library(effsize)
my_EStest <- function(data, variable, by, ...) {
effsize::cohen.d(data[[variable]] ~ as.factor(data[[by]]),
conf.level=.90, paired=FALSE)$estimate
}
base_composites<-base %>% filter(incluir_en_analisis==1)
base_composites%>% select(composite_memory,composite_attention, composite_Executive, composite_language, Diagnosis) %>%
tbl_summary(by= Diagnosis) %>%
add_p %>%
add_stat(
fns = everything() ~ my_EStest,
#fmt_fun = style_pvalue,
header = "**Effect size**"
)| Characteristic | Healthy control, N = 181 | Covid, N = 261 | p-value2 | Effect size |
|---|---|---|---|---|
| Memory (composite) | 0.38 (-0.12, 0.60) | -0.16 (-0.75, 0.21) | 0.022 | 0.7700617 |
| Attention (composite) | -0.16 (-0.84, 0.05) | -1.24 (-1.59, -0.68) | 0.004 | 0.9126698 |
| Executive (composite) | 0.12 (-0.23, 0.42) | -0.62 (-1.48, -0.21) | <0.001 | 1.2036913 |
| Unknown | 0 | 1 | ||
| Language (composite) | 0.08 (-0.32, 0.43) | -0.44 (-0.84, 0.14) | 0.024 | 0.7547307 |
|
1
Median (IQR)
2
Wilcoxon rank sum test
|
||||
library(psych)
cohen.d(base_composites$composite_memory,base_composites$Diagnosis)## Call: cohen.d(x = base_composites$composite_memory, group = base_composites$Diagnosis)
## Cohen d statistic of difference between two means
## lower effect upper
## [1,] -1.43 -0.79 -0.12
##
## Multivariate (Mahalanobis) distance between groups
## [1] 0.79
## r equivalent of difference between two means
## data
## -0.36## Spaghetti plot
base3<- base_composites %>% filter(Diagnosis== "Covid") %>% select(composite_memory,composite_attention, composite_Executive, composite_language, record_id)
library(reshape2)##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smithsbase4<-melt(base3, id.var = "record_id")## Warning: attributes are not identical across measure variables; they will be
## droppedggplot(data = base4, aes(x = variable, y = value, group = record_id, colour= "red")) +
geom_line(alpha=.5)+
geom_hline(yintercept = -1, linetype= "dashed")+
geom_hline(yintercept = -1.5, linetype='dotted')+
theme_minimal()
base3$Memory<-base3$composite_memory > -1
base3$Attention<-base3$composite_attention > -1
base3$Executive<-base3$composite_Executive > -1
base3$Language <-base3$composite_language > -1
library(ggplot2)
library(ggpubr)##
## Attaching package: 'ggpubr'## The following object is masked from 'package:expss':
##
## compare_meansa1<-ggplot(base3, aes(x=Memory, fill=Memory))+
geom_bar()+
theme_minimal()+theme(axis.text.x=element_blank(),plot.title = element_text(size = 9),legend.position = "none")
a2<-ggplot(base3, aes(x=Attention, fill=Attention))+
geom_bar()+
theme_minimal()+theme(axis.text.x=element_blank(),plot.title = element_text(size = 9),legend.position = "none")
a3<-ggplot(base3, aes(x=Executive, fill=Executive))+
geom_bar()+
theme_minimal()+theme(axis.text.x=element_blank(),plot.title = element_text(size = 9),legend.position = "none")
a4<-ggplot(base3, aes(x=Language, fill=Language))+
geom_bar()+
scale_fill_discrete(name="Compromise", labels= c("Impaired", "Preserved"))+
theme_minimal()+theme(axis.text.x=element_blank(),plot.title = element_text(size = 9),legend.position = c(0.19,1))
ggarrange(a1, a2, a3, a4,
ncol = 2, nrow = 2)
a1<-ggplot(base3, aes(y=composite_memory, x=composite_attention))+
geom_point()+
geom_hline(yintercept = -1, linetype= "dashed")+
geom_vline(xintercept = -1, linetype= "dashed")+
theme_minimal()
a2<-ggplot(base3, aes(y=composite_memory, x=composite_Executive))+
geom_point()+
geom_hline(yintercept = -1, linetype= "dashed")+
geom_vline(xintercept = -1, linetype= "dashed")+
theme_minimal()
a3<-ggplot(base3, aes(y=composite_memory, x=composite_language))+
geom_point()+
geom_hline(yintercept = -1, linetype= "dashed")+
geom_vline(xintercept = -1, linetype= "dashed")+
theme_minimal()
a4<-ggplot(base3, aes(y=composite_attention, x=composite_Executive))+
geom_point()+
geom_hline(yintercept = -1, linetype= "dashed")+
geom_vline(xintercept = -1, linetype= "dashed")+
theme_minimal()
a5<-ggplot(base3, aes(y=composite_attention, x=composite_language))+
geom_point()+
geom_hline(yintercept = -1, linetype= "dashed")+
geom_vline(xintercept = -1, linetype= "dashed")+
theme_minimal()
a6<-ggplot(base3, aes(y=composite_Executive, x=composite_language))+
geom_point()+
geom_hline(yintercept = -1, linetype= "dashed")+
geom_vline(xintercept = -1, linetype= "dashed")+
theme_minimal()
ggarrange(a1, a2, a3, a4, a5, a6,
ncol = 3, nrow = 2)+
ggtitle("Patients distribution by composites")## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
Cognition vs severity
Esto podemos mostrarlo de diferentes formas me parece que la mas interesante es con la figura de abajo y agregandole las p, siempre podemos probar con la otra tabla, que viene mas abajo
Figure 1. Cognition vs severity
Cognitive perfomance in composites vs severity ** en este caso los graficos estan hechos solo con los seleccionados
library(ggplot2)
library(ggpubr)
base2<-base %>% filter(incluir_en_analisis==1)
a1<-ggplot(base2, aes(x=Diagnosis, y=composite_memory))+
ggtitle("Memory")+
ylim(-3,2)+
geom_jitter(aes(colour=severity),alpha=.5, width = .15, height = .15)+
geom_hline(yintercept = -1)+
ylab("")+
xlab("")+
theme(legend.position = "none")+
theme_minimal()+theme(plot.title = element_text(size = 9),legend.position = "none")
a2<-ggplot(base2, aes(x=Diagnosis, y=composite_attention, colour=severity))+
ggtitle("Attention")+
ylim(-3,2)+
geom_jitter(alpha=.5, width = .15, height = .15)+
geom_hline(yintercept = -1)+
ylab("")+
xlab("")+
theme(legend.position = "none")+
theme_minimal()+theme(plot.title = element_text(size = 9),legend.position = "none")
a3<-ggplot(base2, aes(x=Diagnosis, y=composite_Executive, colour=severity))+
ggtitle("Executive")+
ylim(-3,2)+
geom_jitter(alpha=.5, width = .15, height = .15)+
geom_hline(yintercept = -1)+
ylab("")+
xlab("")+
theme_minimal()+theme(plot.title = element_text(size = 9),legend.position = "none")
a4<-ggplot(base2, aes(x=Diagnosis, y=composite_language, colour=severity))+
ggtitle("Language")+
ylim(-3,2)+
geom_jitter(alpha=.5, width = .15, height = .15)+
geom_hline(yintercept = -1)+
ylab("")+
xlab("")+
theme_minimal()+theme(plot.title = element_text(size = 9),legend.position = c(0.9,0))
ggarrange(a1, a2, a3, a4 + rremove("x.text"),
ncol = 2, nrow = 2)
Alternative versions
a1<-ggplot(base2, aes(x=Diagnosis, y=composite_memory, fill=severity))+
geom_violin()+
ggtitle("Memory")+
ylim(-3,2)+
geom_hline(yintercept = -1)+
ylab("")+
xlab("")+
theme(legend.position = "none")+
theme_minimal()+theme(plot.title = element_text(size = 9),legend.position = "none")
a2<-ggplot(base2, aes(x=Diagnosis, y=composite_attention, fill=severity))+
geom_violin()+
ggtitle("Attention")+
ylim(-3,2)+
geom_hline(yintercept = -1)+
ylab("")+
xlab("")+
theme(legend.position = "none")+
theme_minimal()+theme(plot.title = element_text(size = 9),legend.position = "none")
a3<-ggplot(base2, aes(x=Diagnosis, y=composite_Executive, fill=severity))+
geom_violin()+
ggtitle("Executive")+
ylim(-3,2)+
geom_hline(yintercept = -1)+
ylab("")+
xlab("")+
theme_minimal()+theme(plot.title = element_text(size = 9),legend.position = "none")
a4<-ggplot(base2, aes(x=Diagnosis, y=composite_language, fill=severity))+
geom_violin()+
ggtitle("Language")+
ylim(-3,2)+
geom_hline(yintercept = -1)+
ylab("")+
xlab("")+
theme_minimal()+theme(plot.title = element_text(size = 9),legend.position = c(0.9,-0.05))
ggarrange(a1, a2, a3, a4 + rremove("x.text"),
ncol = 2, nrow = 2)
Table Cognition vs severity
base2 %>% select(composite_memory,composite_attention, composite_Executive, composite_language, severity) %>%
tbl_summary(by= severity, missing = "no") %>%
add_p| Characteristic | Asymptomatic, N = 01 | Mild, N = 181 | Moderate, N = 61 | Severe, N = 21 | p-value2 |
|---|---|---|---|---|---|
| Memory (composite) | NA (NA, NA) | -0.25 (-0.73, 0.01) | -0.85 (-1.10, -0.22) | 0.35 (0.20, 0.49) | 0.2 |
| Attention (composite) | NA (NA, NA) | -1.24 (-1.53, -0.68) | -1.46 (-1.83, -1.12) | -1.15 (-1.60, -0.70) | 0.6 |
| Executive (composite) | NA (NA, NA) | -0.62 (-1.48, -0.28) | -1.55 (-1.95, -0.86) | -0.21 (-0.21, -0.21) | 0.2 |
| Language (composite) | NA (NA, NA) | -0.44 (-0.72, 0.19) | -0.58 (-0.84, -0.16) | -0.30 (-0.32, -0.28) | 0.8 |
|
1
Median (IQR)
2
Kruskal-Wallis rank sum test
|
|||||
Figure 2. Cognitive complaint by domain and severity
Cognitive perfomance in the four composites by severity in correlation with WHODAS cognition domain complaint
p1<-ggplot(base2, aes(x=WHODAS_cognition, y=composite_memory, colour=severity))+
geom_point()+
geom_smooth(method = "glm", alpha=.1)+
geom_jitter(alpha=.5, width = .3, height = .3)+
geom_hline(yintercept = -1)+
ylab("Memory")+
xlab(" ")+
theme_minimal()+theme(legend.position = "none")+
facet_grid(~severity)
p2<-ggplot(base2, aes(x=WHODAS_cognition, y=composite_attention, colour=severity))+
geom_point()+
geom_smooth(method = "glm", alpha=.1)+
ylab("Attention")+
xlab(" ")+
ylim(-3,1.5)+
geom_jitter(alpha=.5, width = .3, height = .3)+
geom_hline(yintercept = -1)+
theme_minimal()+theme(legend.position = "none")+
facet_grid(~severity)
ggarrange(p1, p2+ rremove("x.text"),
ncol = 1, nrow = 2)
p3<-ggplot(base2, aes(x=WHODAS_cognition, y=composite_Executive, colour=severity))+
geom_point()+
geom_smooth(method = "glm", alpha=.1)+
geom_jitter(alpha=.5, width = .3, height = .3)+
geom_hline(yintercept = -1)+
ylab("Executive")+
xlab(" ")+
theme_minimal()+theme(legend.position = "none")+
facet_grid(~severity)
p4<-ggplot(base2, aes(x=WHODAS_cognition, y=composite_language, colour=severity))+
geom_point()+
geom_smooth(method = "glm", alpha=.1)+
ylab("Language")+
xlab("WHODAS cognition")+
ylim(-3,1.5)+
geom_jitter(alpha=.5, width = .3, height = .3)+
geom_hline(yintercept = -1)+
theme_minimal()+ theme(legend.position = c(0.95,0.5))+
facet_grid(~severity)
ggarrange(p3, p4+ rremove("x.text"),
ncol = 1, nrow = 2)