##chargement des packages----
library(questionr)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.1 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(tableone)
library(labelled)
library(gtsummary)
library(GGally)
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
library(readxl)
library(effects)
## Le chargement a nécessité le package : carData
## lattice theme set by effectsTheme()
## See ?effectsTheme for details.
library(survival)
library(survminer)
## Le chargement a nécessité le package : ggpubr
##
## Attachement du package : 'survminer'
##
## L'objet suivant est masqué depuis 'package:survival':
##
## myeloma
library(ggplot2)
library(scales)
##
## Attachement du package : 'scales'
##
## L'objet suivant est masqué depuis 'package:purrr':
##
## discard
##
## L'objet suivant est masqué depuis 'package:readr':
##
## col_factor
library(labelled)
library(ggpattern)
##chargement des données
data_thomas <- read_excel("Y:/DMs/Internes/Thomas 2023-2024/Picc Line/Mémoire PICC Line/analyses stats/20240531/data_thomas.xlsx")
## Warning: Expecting numeric in B12 / R12C2: got a date
## Warning: Expecting numeric in B22 / R22C2: got a date
## Warning: Expecting numeric in B24 / R24C2: got a date
## Warning: Expecting numeric in B37 / R37C2: got a date
## Warning: Expecting numeric in B41 / R41C2: got a date
## Warning: Expecting numeric in B46 / R46C2: got a date
## Warning: Expecting numeric in B52 / R52C2: got a date
## Warning: Expecting numeric in B65 / R65C2: got a date
## Warning: Expecting numeric in B67 / R67C2: got a date
## Warning: Expecting numeric in B75 / R75C2: got a date
## Warning: Expecting numeric in B76 / R76C2: got a date
## Warning: Expecting numeric in B79 / R79C2: got a date
## Warning: Expecting numeric in B82 / R82C2: got a date
## Warning: Expecting numeric in B89 / R89C2: got a date
## Warning: Expecting numeric in B105 / R105C2: got a date
##recodage bases de données le cas échéant----
picc<-filter(data_thomas, c(elligible=="oui" ))
##recodage des variables----
picc$age.cat<-ifelse(picc$age>65, 1, 0)
picc$duree_bloc.cat<-ifelse(picc$duree_bloc>50, 1, 0)
picc$sexe.cat<-ifelse(picc$sexe=="F", 1, 0)
picc$indic_atk<-ifelse(picc$indic_picc=="traitement anticancereux", 1, 0)
picc$indic_horsatk<-ifelse(picc$indic_picc=="hors anticancereux", 1, 0)
picc$indic_soinpal<-ifelse(picc$indic_picc=="soins paliatifs", 1, 0)
picc$indic_accesveineux<-ifelse(picc$indic_picc=="accès veineux", 1, 0)
picc$presta.cat<-ifelse(picc$circuit_presta=="oui", 1, 0)
picc$orl.cat<-ifelse(picc$spe_medicale=="orl", 1, 0)
picc$hemato.cat<-ifelse(picc$spe_medicale=="hemato", 1, 0)
picc$neuro.cat<-ifelse(picc$spe_medicale=="neuro", 1, 0)
picc$seno.cat<-ifelse(picc$spe_medicale=="senologie", 1, 0)
picc$sarcome.cat<-ifelse(picc$spe_medicale=="sarcome", 1, 0)
picc$gyneco.cat<-ifelse(picc$spe_medicale=="gyneco", 1, 0)
picc$uroo.cat<-ifelse(picc$spe_medicale=="urologie", 1, 0)
##renommer des variables pour présentation dans les tableaux de résultats
##renommer les variables pour un tableau population tous patients y compris ceux avec blases >5%
var_label(picc$age.cat) <- "Patients de + de 65 ans"
var_label(picc$sexe.cat) <- "Femmes"
var_label(picc$duree_bloc.cat) <- "Durée de bloc >50 min"
var_label(picc$circuit_presta) <- "Patient avec prestataire de soin à domicile"
var_label(picc$presta.cat) <- "Patient avec prestataire de soin à domicile"
var_label(picc$entretien_cat) <- "Patient ayant bénéficié d'un entretien picc_line"
var_label(picc$indic_picc) <- "Indication du picc-line"
var_label(picc$cancer_hemato) <- "Patient avec hémopathie maligne"
var_label(picc$duree_bloc) <- "Durée du bloc pour pose de picc-line"
var_label(picc$spe_medicale) <- "spécialité médicale"
var_label(picc$indic_atk) <- "Picc-line pour administration atk"
var_label(picc$indic_horsatk) <- "Picc-line pour administration medt autre que atk"
var_label(picc$indic_soinpal) <- "Picc-line pour administration soins paliatifs"
var_label(picc$indic_accesveineux) <- "Picc-line pour accès veineux"
var_label(picc$orl.cat) <- "Patient ORL"
var_label(picc$neuro.cat) <- "Patient neuro-oncologie"
var_label(picc$seno.cat) <- "Patient senologie"
var_label(picc$sarcome.cat) <- "Patient sarcome"
var_label(picc$gyneco.cat) <- "Patient gynecologie"
var_label(picc$uroo.cat) <- "Patient urologie"
var_label(picc$hemato.cat) <- "Patient hematologie"
##tableau descriptif population globales ----
tbl_summary(
picc, include = c("age", "sexe", "spe_medicale", "orl.cat", "neuro.cat", "seno.cat", "sarcome.cat", "gyneco.cat", "uroo.cat", "hemato.cat", "duree_bloc","presta.cat", "indic_picc", "indic_atk", "indic_horsatk", "indic_soinpal", "indic_accesveineux", "cancer_hemato", ),
statistic = all_continuous() ~ "{median} [{min} - {max}]",
by="entretien_cat",
digits=all_categorical()~ c(0,1)
)%>%
add_p
## There was an error in 'add_p()/add_difference()' for variable 'spe_medicale', p-value omitted:
## Error in stats::fisher.test(structure(c("orl", "orl", "hemato", "orl", : FEXACT erreur 7(location). LDSTP=18630 est trop petit pour ce problème,
## (pastp=30.6162, ipn_0:=ipoin[itp=397]=4607, stp[ipn_0]=30.2062).
## Augmentez la taille de l’environnement de travail ou considérez l’utilisation de ‘simulate.p.value=TRUE’.
| Characteristic |
0, N = 155 |
1, N = 22 |
p-value |
| age |
63 [19 - 88] |
67 [31 - 77] |
0.4 |
| sexe |
|
|
0.3 |
| F |
83 (53.5%) |
9 (40.9%) |
|
| M |
72 (46.5%) |
13 (59.1%) |
|
| spécialité médicale |
|
|
|
| dermato |
9 (5.8%) |
0 (0.0%) |
|
| digestif |
3 (1.9%) |
1 (4.5%) |
|
| gyneco |
21 (13.5%) |
0 (0.0%) |
|
| hemato |
40 (25.8%) |
4 (18.2%) |
|
| hors_onco |
1 (0.6%) |
0 (0.0%) |
|
| neuro |
13 (8.4%) |
0 (0.0%) |
|
| orl |
28 (18.1%) |
5 (22.7%) |
|
| sarcome |
18 (11.6%) |
8 (36.4%) |
|
| senologie |
9 (5.8%) |
2 (9.1%) |
|
| thyroide |
2 (1.3%) |
0 (0.0%) |
|
| urologie |
11 (7.1%) |
2 (9.1%) |
|
| Patient ORL |
28 (18.1%) |
5 (22.7%) |
0.6 |
| Patient neuro-oncologie |
13 (8.4%) |
0 (0.0%) |
0.4 |
| Patient senologie |
9 (5.8%) |
2 (9.1%) |
0.6 |
| Patient sarcome |
18 (11.6%) |
8 (36.4%) |
0.006 |
| Patient gynecologie |
21 (13.5%) |
0 (0.0%) |
0.080 |
| Patient urologie |
11 (7.1%) |
2 (9.1%) |
0.7 |
| Patient hematologie |
40 (25.8%) |
4 (18.2%) |
0.4 |
| Durée du bloc pour pose de picc-line |
52 [26 - 120] |
43 [34 - 69] |
0.047 |
| Patient avec prestataire de soin à domicile |
22 (14.2%) |
14 (63.6%) |
<0.001 |
| Indication du picc-line |
|
|
0.026 |
| absence donnee |
1 (0.6%) |
1 (4.5%) |
|
| accès veineux |
14 (9.0%) |
0 (0.0%) |
|
| hors anticancereux |
40 (25.8%) |
2 (9.1%) |
|
| soins paliatifs |
20 (12.9%) |
1 (4.5%) |
|
| traitement anticancereux |
80 (51.6%) |
18 (81.8%) |
|
| Picc-line pour administration atk |
80 (51.6%) |
18 (81.8%) |
0.008 |
| Picc-line pour administration medt autre que atk |
40 (25.8%) |
2 (9.1%) |
0.085 |
| Picc-line pour administration soins paliatifs |
20 (12.9%) |
1 (4.5%) |
0.5 |
| Picc-line pour accès veineux |
14 (9.0%) |
0 (0.0%) |
0.2 |
| Patient avec hémopathie maligne |
40 (25.8%) |
4 (18.2%) |
0.4 |
###tableau descriptif devenir des patients
tbl_summary(
picc, include = c("retrait", "infection", "thrombose", "complic_meca","echec", "deces" ),
statistic = all_continuous() ~ "{median} [{min} - {max}]",
by="entretien_cat",
digits=all_categorical()~ c(0,1)
)%>%
add_p
| Characteristic |
0, N = 155 |
1, N = 22 |
p-value |
| retrait |
|
|
0.018 |
| Non |
77 (49.7%) |
5 (22.7%) |
|
| Oui |
78 (50.3%) |
17 (77.3%) |
|
| infection |
|
|
0.8 |
| Non |
126 (81.3%) |
17 (77.3%) |
|
| Oui |
29 (18.7%) |
5 (22.7%) |
|
| thrombose |
|
|
>0.9 |
| Non |
149 (96.1%) |
22 (100.0%) |
|
| Oui |
6 (3.9%) |
0 (0.0%) |
|
| complic_meca |
|
|
0.4 |
| Non |
127 (81.9%) |
16 (72.7%) |
|
| Oui |
28 (18.1%) |
6 (27.3%) |
|
| echec |
|
|
>0.9 |
| Non |
154 (99.4%) |
22 (100.0%) |
|
| Oui |
1 (0.6%) |
0 (0.0%) |
|
| deces |
|
|
0.2 |
| Non |
115 (74.2%) |
19 (86.4%) |
|
| Oui |
40 (25.8%) |
3 (13.6%) |
|
#Courbe de Kaplan Meier population globale durée sans complication----
km_complic<-survfit(Surv(picc$jdc, picc$complic)~1)
km_complic
## Call: survfit(formula = Surv(picc$jdc, picc$complic) ~ 1)
##
## n events median 0.95LCL 0.95UCL
## [1,] 177 73 71 61 117
ggsurvplot(km_complic, data = picc,
risk.table=TRUE,
surv.scale="percent",
break.time.by=30,
surv.median.line = "hv"
)
#Courbe de Kaplan Meier selon entretien pharma----
km_complic_entretien<-survfit(Surv(jdc, complic)~entretien_cat, data=picc)
km_complic_entretien
## Call: survfit(formula = Surv(jdc, complic) ~ entretien_cat, data = picc)
##
## n events median 0.95LCL 0.95UCL
## entretien_cat=0 155 62 71 61 122
## entretien_cat=1 22 11 97 37 NA
survdiff(Surv(jdc, complic)~entretien_cat, data=picc)
## Call:
## survdiff(formula = Surv(jdc, complic) ~ entretien_cat, data = picc)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## entretien_cat=0 155 62 60.9 0.0182 0.112
## entretien_cat=1 22 11 12.1 0.0918 0.112
##
## Chisq= 0.1 on 1 degrees of freedom, p= 0.7
ggsurvplot(km_complic_entretien, data = picc,
risk.table=TRUE,
surv.scale="percent",
break.time.by=30,
surv.median.line = "hv"
)
##modeles de cox univariés--
modsurv<-coxph(Surv(jdc, complic)~entretien_cat, data=picc)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| Patient ayant bénéficié d'un entretien picc_line |
0.90 |
0.47, 1.71 |
0.7 |
modsurv<-coxph(Surv(jdc, complic)~age, data=picc)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| age |
1.01 |
0.99, 1.02 |
0.5 |
modsurv<-coxph(Surv(jdc, complic)~age.cat, data=picc)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| Patients de + de 65 ans |
1.29 |
0.81, 2.04 |
0.3 |
modsurv<-coxph(Surv(jdc, complic)~duree_bloc, data=picc)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| Durée du bloc pour pose de picc-line |
1.00 |
0.98, 1.01 |
0.7 |
modsurv<-coxph(Surv(jdc, complic)~duree_bloc.cat, data=picc)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| Durée de bloc >50 min |
1.22 |
0.77, 1.93 |
0.4 |
modsurv<-coxph(Surv(jdc, complic)~sexe.cat, data=picc)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| Femmes |
1.12 |
0.70, 1.79 |
0.6 |
modsurv<-coxph(Surv(jdc, complic)~presta.cat, data=picc)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| Patient avec prestataire de soin à domicile |
0.54 |
0.30, 0.99 |
0.048 |
modsurv<-coxph(Surv(jdc, complic)~indic_atk, data=picc)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| Picc-line pour administration atk |
0.77 |
0.48, 1.23 |
0.3 |
modsurv<-coxph(Surv(jdc, complic)~indic_horsatk, data=picc)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| Picc-line pour administration medt autre que atk |
1.35 |
0.81, 2.26 |
0.3 |
modsurv<-coxph(Surv(jdc, complic)~indic_accesveineux, data=picc)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| Picc-line pour accès veineux |
1.09 |
0.44, 2.71 |
0.9 |
modsurv<-coxph(Surv(jdc, complic)~hemato.cat, data=picc)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| Patient hematologie |
1.42 |
0.86, 2.33 |
0.2 |
modsurv<-coxph(Surv(jdc, complic)~orl.cat, data=picc)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| Patient ORL |
0.73 |
0.37, 1.43 |
0.4 |
modsurv<-coxph(Surv(jdc, complic)~sarcome.cat, data=picc)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| Patient sarcome |
1.19 |
0.64, 2.24 |
0.6 |
modsurv<-coxph(Surv(jdc, complic)~gyneco.cat, data=picc)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| Patient gynecologie |
0.99 |
0.47, 2.06 |
>0.9 |
modsurv<-coxph(Surv(jdc, complic)~seno.cat, data=picc)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| Patient senologie |
0.39 |
0.12, 1.25 |
0.11 |
modsurv<-coxph(Surv(jdc, complic)~neuro.cat, data=picc)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| Patient neuro-oncologie |
0.92 |
0.42, 2.00 |
0.8 |
##modeles multivarié de cox--
modsurv<-coxph(Surv(jdc, complic)~presta.cat+entretien_cat+indic_atk+indic_horsatk+hemato.cat+seno.cat, data=picc)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| Patient avec prestataire de soin à domicile |
0.55 |
0.27, 1.12 |
0.10 |
| Patient ayant bénéficié d'un entretien picc_line |
1.31 |
0.64, 2.70 |
0.5 |
| Picc-line pour administration atk |
0.98 |
0.47, 2.06 |
>0.9 |
| Picc-line pour administration medt autre que atk |
1.03 |
0.46, 2.31 |
>0.9 |
| Patient hematologie |
1.17 |
0.67, 2.06 |
0.6 |
| Patient senologie |
0.47 |
0.14, 1.51 |
0.2 |
ggforest(modsurv)
## Warning in .get_data(model, data = data): The `data` argument is not provided.
## Data will be extracted from model fit.
modsurv<-coxph(Surv(jdc, complic)~presta.cat+entretien_cat+hemato.cat+seno.cat, data=picc)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| Patient avec prestataire de soin à domicile |
0.54 |
0.27, 1.08 |
0.082 |
| Patient ayant bénéficié d'un entretien picc_line |
1.32 |
0.64, 2.69 |
0.5 |
| Patient hematologie |
1.19 |
0.71, 1.99 |
0.5 |
| Patient senologie |
0.47 |
0.14, 1.51 |
0.2 |
ggforest(modsurv)
## Warning in .get_data(model, data = data): The `data` argument is not provided.
## Data will be extracted from model fit.
modsurv<-coxph(Surv(jdc, complic)~presta.cat+entretien_cat+seno.cat, data=picc)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| Patient avec prestataire de soin à domicile |
0.52 |
0.26, 1.03 |
0.059 |
| Patient ayant bénéficié d'un entretien picc_line |
1.33 |
0.65, 2.74 |
0.4 |
| Patient senologie |
0.45 |
0.14, 1.43 |
0.2 |
ggforest(modsurv)
## Warning in .get_data(model, data = data): The `data` argument is not provided.
## Data will be extracted from model fit.
modsurv<-coxph(Surv(jdc, complic)~presta.cat+entretien_cat, data=picc)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| Patient avec prestataire de soin à domicile |
0.48 |
0.24, 0.96 |
0.037 |
| Patient ayant bénéficié d'un entretien picc_line |
1.33 |
0.64, 2.76 |
0.4 |
ggforest(modsurv)
## Warning in .get_data(model, data = data): The `data` argument is not provided.
## Data will be extracted from model fit.
