Oxy_COV_V3
MALLAH Siham
24/04/2023
Descriptif population OxyCov par médiane et l’intervalle interquartile (IQR)
library(gtsummary)## Warning: le package 'gtsummary' a été compilé avec la version R 4.2.2library(flextable)## Warning: le package 'flextable' a été compilé avec la version R 4.2.2##
## Attachement du package : 'flextable'## Les objets suivants sont masqués depuis 'package:gtsummary':
##
## as_flextable, continuous_summary## L'objet suivant est masqué depuis 'package:purrr':
##
## composetbl_summary(Oxy_Cov)| Characteristic | N = 1621 |
|---|---|
| Age | 62 (52, 70) |
| sexe | |
| F | 53 (33%) |
| M | 109 (67%) |
| Poids | 84 (74, 94) |
| Unknown | 7 |
| IMC | |
| 5 (3.1%) | |
| IMC<30 | 97 (60%) |
| IMC>30 | 60 (37%) |
| Vaccination | |
| Non | 152 (94%) |
| Oui | 10 (6.2%) |
| ATCD_Diabète | |
| Non | 124 (77%) |
| Oui | 38 (23%) |
| ATCD_HTA | |
| Non | 89 (55%) |
| Oui | 73 (45%) |
| ATCD_ID | |
| Non | 153 (94%) |
| Oui | 9 (5.6%) |
| ATCD_Path_respi | |
| Non | 134 (83%) |
| Oui | 28 (17%) |
| ATCD_Cardiopathie | |
| Non | 137 (85%) |
| Oui | 25 (15%) |
| ATCD_IR_Dialyse | |
| Non | 158 (98%) |
| Oui | 4 (2.5%) |
| J_symptome_hospit | 7 (6, 10) |
| J_symptome_USIR | 9 (7, 11) |
| TDM_severité | |
| majeur | 8 (4.9%) |
| minime | 22 (14%) |
| modérée | 80 (49%) |
| sévère | 52 (32%) |
| TDM_EP | |
| Non | 154 (95%) |
| Oui | 8 (4.9%) |
| TA_Tocilizumab | |
| Non | 120 (74%) |
| Oui | 42 (26%) |
| TA_plasma_conv | |
| Non | 158 (98%) |
| Oui | 4 (2.5%) |
| ATC | |
| ATC_C | 12 (7.4%) |
| ATC_P | 150 (93%) |
| Rox_H1 | 38 (19, 53) |
| Rox_H24 | 38 (14, 58) |
| Rox_H48 | 20 (1, 43) |
| Rox_H72 | 4 (1, 31) |
| var_RoxH72_H1 | 1 (1, 27) |
| amelioration | |
| 90 (56%) | |
| Non | 50 (31%) |
| Oui | 22 (14%) |
| var_RoxH48_H1 | 16 (1, 43) |
| var_RoxH24_H1 | 30 (7, 56) |
| Sat_H1 | 95.00 (93.00, 96.00) |
| Sat_H24 | 95.00 (93.00, 96.00) |
| Unknown | 4 |
| Sat_H48 | 95.00 (92.00, 96.00) |
| Unknown | 26 |
| Sat_H72 | 95.00 (93.00, 97.00) |
| Unknown | 52 |
| Freq_respi_H1 | 22.0 (19.0, 25.8) |
| Freq_respi_H24 | 22.0 (19.0, 27.0) |
| Unknown | 4 |
| Freq_respi_H48 | 22 (19, 26) |
| Unknown | 26 |
| Freq_respi_H72 | 23.0 (19.0, 26.0) |
| Unknown | 52 |
| OHD_FIO2_H1 | 50 (45, 60) |
| Unknown | 9 |
| OHD_FIO2_H24 | 50 (45, 60) |
| Unknown | 20 |
| OHD_FIO2_H48 | 50 (45, 60) |
| Unknown | 48 |
| OHD_FIO2_H72 | 50 (45, 60) |
| Unknown | 78 |
| OHD_debit_H1 | |
| 35 | 1 (0.6%) |
| 40 | 59 (38%) |
| 45 | 8 (5.2%) |
| 50 | 82 (53%) |
| 55 | 2 (1.3%) |
| 60 | 2 (1.3%) |
| Unknown | 8 |
| OHD_debit_H24 | |
| 35 | 2 (1.4%) |
| 40 | 44 (31%) |
| 45 | 8 (5.6%) |
| 50 | 81 (57%) |
| 55 | 5 (3.5%) |
| 60 | 3 (2.1%) |
| Unknown | 19 |
| OHD_debit_H48 | |
| 30 | 1 (0.9%) |
| 35 | 3 (2.7%) |
| 40 | 35 (31%) |
| 45 | 15 (13%) |
| 50 | 49 (43%) |
| 55 | 4 (3.5%) |
| 60 | 6 (5.3%) |
| Unknown | 49 |
| OHD_debit_H72 | |
| 35 | 3 (3.6%) |
| 40 | 30 (36%) |
| 45 | 6 (7.2%) |
| 50 | 40 (48%) |
| 55 | 2 (2.4%) |
| 60 | 2 (2.4%) |
| Unknown | 79 |
| OHD_duree | 65 (35, 110) |
| OHD | 162 (100%) |
| OHD.1 | |
| Oui | 162 (100%) |
| CPAP_PEP_H1 | |
| 6 | 4 (3.0%) |
| 7 | 85 (63%) |
| 8 | 41 (30%) |
| 9 | 3 (2.2%) |
| 10 | 1 (0.7%) |
| 12 | 1 (0.7%) |
| Unknown | 27 |
| CPAP_PEP_H24 | |
| 6 | 4 (3.0%) |
| 7 | 77 (58%) |
| 8 | 41 (31%) |
| 9 | 9 (6.8%) |
| 10 | 1 (0.8%) |
| 12 | 1 (0.8%) |
| Unknown | 29 |
| CPAP_PEP_H48 | |
| 4 | 1 (0.9%) |
| 5 | 1 (0.9%) |
| 6 | 2 (1.8%) |
| 7 | 57 (50%) |
| 8 | 41 (36%) |
| 9 | 10 (8.8%) |
| 12 | 1 (0.9%) |
| Unknown | 49 |
| CPAP_PEP_H72 | |
| 4 | 1 (1.1%) |
| 5 | 2 (2.2%) |
| 6 | 2 (2.2%) |
| 7 | 45 (49%) |
| 8 | 32 (35%) |
| 9 | 8 (8.8%) |
| 12 | 1 (1.1%) |
| Unknown | 71 |
| CPAP_FIO2_H1 | 50 (45, 50) |
| Unknown | 27 |
| CPAP_FIO2_H24 | 50 (40, 60) |
| Unknown | 30 |
| CPAP_FIO2_H48 | 50 (40, 60) |
| Unknown | 50 |
| CPAP_FIO2_H72 | 50 (40, 50) |
| Unknown | 70 |
| CPAP_duree | 20 (10, 35) |
| Unknown | 24 |
| CPAP | 144 (89%) |
| CPAP.1 | |
| Non | 18 (11%) |
| Oui | 144 (89%) |
| OHD_CPAP | 144 (89%) |
| OHD_CPAP.1 | |
| OHD seule | 18 (11%) |
| OHD_CPAP | 144 (89%) |
| OHD_DV | |
| OHD seule | 50 (31%) |
| OHD_DV | 112 (69%) |
| X.DV_DL | |
| Fait | 112 (69%) |
| Non fait | 50 (31%) |
| Duree_.DV_DL | 9 (4, 19) |
| Unknown | 50 |
| Mortalite_J28 | 13 (8.0%) |
| Mortalite_J90 | 15 (9.3%) |
| IOT_J28 | 29 (18%) |
| IOT_J90 | 29 (18%) |
| IOT_mortalite_J28 | |
| Non | 127 (78%) |
| Oui | 35 (22%) |
| IOT_mortalite_J90 | |
| Non | 126 (78%) |
| Oui | 36 (22%) |
| Duree_Hospit | 12 (9, 19) |
| Duree_SI | 6 (4, 9) |
| Unknown | 1 |
| Duree_Rea | 18 (12, 34) |
| Unknown | 132 |
| Duree_SI_Rea | 7 (4, 12) |
| Survie_J_x | 21 (16, 27) |
| Survie_J | 21 (16, 27) |
| DCD_IOT | |
| Non | 126 (78%) |
| Oui | 36 (22%) |
| DC_IOT | 36 (22%) |
| 1 Median (IQR); n (%) | |
Par défaut, les variables quantitatives, sont décrites par la médiane et l’intervalle interquartile (IQR).
rectification données tableau:
des erreurs de sortie du script se sont glissé dans le tableau descriptif , Rectification faite manuellement ci-dessous concernant OHD_debit, CPAP_PEP: H1,H24, H48 et H72, duree_Rea
summary(Oxy_Cov$OHD_debit_H1)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 35.00 40.00 50.00 46.01 50.00 60.00 8summary(Oxy_Cov$OHD_debit_H24)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 35.00 40.00 50.00 46.82 50.00 60.00 19summary(Oxy_Cov$OHD_debit_H48)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 30.00 40.00 50.00 46.37 50.00 60.00 49summary(Oxy_Cov$OHD_debit_H72)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 35.00 40.00 50.00 45.84 50.00 60.00 79summary(Oxy_Cov$CPAP_PEP_H1)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 6.000 7.000 7.000 7.378 8.000 12.000 27summary(Oxy_Cov$CPAP_PEP_H24)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 6.000 7.000 7.000 7.474 8.000 12.000 29summary(Oxy_Cov$CPAP_PEP_H48)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.000 7.000 7.000 7.522 8.000 12.000 49summary(Oxy_Cov$CPAP_PEP_H72)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.000 7.000 7.000 7.484 8.000 12.000 71summary(Oxy_Cov$Duree_Rea)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.00 11.50 18.00 28.87 33.75 128.00 132description générale:
summary(Oxy_Cov)## Age sexe Poids IMC Vaccination ATCD_Diabète
## Min. :24.00 F: 53 Min. : 50.00 : 5 Non:152 Non:124
## 1st Qu.:52.00 M:109 1st Qu.: 74.00 IMC<30:97 Oui: 10 Oui: 38
## Median :62.00 Median : 84.00 IMC>30:60
## Mean :60.30 Mean : 85.74
## 3rd Qu.:69.75 3rd Qu.: 94.50
## Max. :89.00 Max. :154.00
## NA's :7
## ATCD_HTA ATCD_ID ATCD_Path_respi ATCD_Cardiopathie ATCD_IR_Dialyse
## Non:89 Non:153 Non:134 Non:137 Non:158
## Oui:73 Oui: 9 Oui: 28 Oui: 25 Oui: 4
##
##
##
##
##
## J_symptome_hospit J_symptome_USIR TDM_severité TDM_EP TA_Tocilizumab
## Min. : 1.000 Min. : 2.00 majeur : 8 Non:154 Non:120
## 1st Qu.: 6.000 1st Qu.: 7.00 minime :22 Oui: 8 Oui: 42
## Median : 7.000 Median : 9.00 modérée:80
## Mean : 8.019 Mean :10.01 sévère :52
## 3rd Qu.: 9.750 3rd Qu.:11.00
## Max. :51.000 Max. :51.00
##
## TA_plasma_conv ATC Rox_H1 Rox_H24 Rox_H48
## Non:158 ATC_C: 12 Min. : 1.00 Min. : 1.00 Min. : 1.00
## Oui: 4 ATC_P:150 1st Qu.:19.00 1st Qu.:14.25 1st Qu.: 1.00
## Median :37.50 Median :38.50 Median :20.00
## Mean :36.16 Mean :36.20 Mean :23.82
## 3rd Qu.:53.00 3rd Qu.:57.75 3rd Qu.:43.00
## Max. :70.00 Max. :76.00 Max. :65.00
##
## Rox_H72 var_RoxH72_H1 amelioration var_RoxH48_H1 var_RoxH24_H1
## Min. : 1.00 Min. : 1.00 :90 Min. : 1.00 Min. : 1.00
## 1st Qu.: 1.00 1st Qu.: 1.00 Non:50 1st Qu.: 1.00 1st Qu.: 7.25
## Median : 3.50 Median : 1.00 Oui:22 Median :16.50 Median :29.50
## Mean :15.54 Mean :14.73 Mean :23.91 Mean :32.28
## 3rd Qu.:30.75 3rd Qu.:26.75 3rd Qu.:42.75 3rd Qu.:55.75
## Max. :53.00 Max. :59.00 Max. :75.00 Max. :78.00
##
## Sat_H1 Sat_H24 Sat_H48 Sat_H72
## Min. : 90.00 Min. : 88.0 Min. : 89.00 Min. : 23.00
## 1st Qu.: 93.00 1st Qu.: 93.0 1st Qu.: 92.00 1st Qu.: 93.00
## Median : 95.00 Median : 95.0 Median : 95.00 Median : 95.00
## Mean : 94.65 Mean : 94.7 Mean : 94.45 Mean : 94.17
## 3rd Qu.: 96.00 3rd Qu.: 96.0 3rd Qu.: 96.00 3rd Qu.: 97.00
## Max. :100.00 Max. :100.0 Max. :100.00 Max. :100.00
## NA's :4 NA's :26 NA's :52
## Freq_respi_H1 Freq_respi_H24 Freq_respi_H48 Freq_respi_H72
## Min. :10.00 Min. :12.00 Min. :11.00 Min. :12.00
## 1st Qu.:19.00 1st Qu.:19.00 1st Qu.:19.00 1st Qu.:19.00
## Median :22.00 Median :22.00 Median :22.00 Median :23.00
## Mean :22.54 Mean :23.02 Mean :22.68 Mean :23.05
## 3rd Qu.:25.75 3rd Qu.:27.00 3rd Qu.:26.00 3rd Qu.:26.00
## Max. :40.00 Max. :40.00 Max. :44.00 Max. :60.00
## NA's :4 NA's :26 NA's :52
## OHD_FIO2_H1 OHD_FIO2_H24 OHD_FIO2_H48 OHD_FIO2_H72
## Min. :30.00 Min. : 30.00 Min. : 30.00 Min. :35.00
## 1st Qu.:45.00 1st Qu.: 45.00 1st Qu.: 45.00 1st Qu.:45.00
## Median :50.00 Median : 50.00 Median : 50.00 Median :50.00
## Mean :51.27 Mean : 54.37 Mean : 54.56 Mean :54.05
## 3rd Qu.:60.00 3rd Qu.: 60.00 3rd Qu.: 60.00 3rd Qu.:60.00
## Max. :80.00 Max. :100.00 Max. :100.00 Max. :95.00
## NA's :9 NA's :20 NA's :48 NA's :78
## OHD_debit_H1 OHD_debit_H24 OHD_debit_H48 OHD_debit_H72
## Min. :35.00 Min. :35.00 Min. :30.00 Min. :35.00
## 1st Qu.:40.00 1st Qu.:40.00 1st Qu.:40.00 1st Qu.:40.00
## Median :50.00 Median :50.00 Median :50.00 Median :50.00
## Mean :46.01 Mean :46.82 Mean :46.37 Mean :45.84
## 3rd Qu.:50.00 3rd Qu.:50.00 3rd Qu.:50.00 3rd Qu.:50.00
## Max. :60.00 Max. :60.00 Max. :60.00 Max. :60.00
## NA's :8 NA's :19 NA's :49 NA's :79
## OHD_duree OHD OHD.1 CPAP_PEP_H1 CPAP_PEP_H24
## Min. : 1.00 Min. :1 Oui:162 Min. : 6.000 Min. : 6.000
## 1st Qu.: 35.00 1st Qu.:1 1st Qu.: 7.000 1st Qu.: 7.000
## Median : 65.00 Median :1 Median : 7.000 Median : 7.000
## Mean : 82.58 Mean :1 Mean : 7.378 Mean : 7.474
## 3rd Qu.:109.50 3rd Qu.:1 3rd Qu.: 8.000 3rd Qu.: 8.000
## Max. :332.00 Max. :1 Max. :12.000 Max. :12.000
## NA's :27 NA's :29
## CPAP_PEP_H48 CPAP_PEP_H72 CPAP_FIO2_H1 CPAP_FIO2_H24
## Min. : 4.000 Min. : 4.000 Min. :30.0 Min. :30.00
## 1st Qu.: 7.000 1st Qu.: 7.000 1st Qu.:45.0 1st Qu.:40.00
## Median : 7.000 Median : 7.000 Median :50.0 Median :50.00
## Mean : 7.522 Mean : 7.484 Mean :49.7 Mean :49.77
## 3rd Qu.: 8.000 3rd Qu.: 8.000 3rd Qu.:50.0 3rd Qu.:60.00
## Max. :12.000 Max. :12.000 Max. :80.0 Max. :90.00
## NA's :49 NA's :71 NA's :27 NA's :30
## CPAP_FIO2_H48 CPAP_FIO2_H72 CPAP_duree CPAP CPAP.1
## Min. :30.00 Min. : 0.00 Min. : 3.00 Min. :0.0000 Non: 18
## 1st Qu.:40.00 1st Qu.:40.00 1st Qu.: 10.00 1st Qu.:1.0000 Oui:144
## Median :50.00 Median :50.00 Median : 19.50 Median :1.0000
## Mean :48.66 Mean :45.95 Mean : 25.82 Mean :0.8889
## 3rd Qu.:60.00 3rd Qu.:50.00 3rd Qu.: 34.75 3rd Qu.:1.0000
## Max. :90.00 Max. :95.00 Max. :125.00 Max. :1.0000
## NA's :50 NA's :70 NA's :24
## OHD_CPAP OHD_CPAP.1 OHD_DV X.DV_DL
## Min. :0.0000 OHD seule: 18 OHD seule: 50 Fait :112
## 1st Qu.:1.0000 OHD_CPAP :144 OHD_DV :112 Non fait: 50
## Median :1.0000
## Mean :0.8889
## 3rd Qu.:1.0000
## Max. :1.0000
##
## Duree_.DV_DL Mortalite_J28 Mortalite_J90 IOT_J28
## Min. : 1.00 Min. :0.00000 Min. :0.00000 Min. :0.000
## 1st Qu.: 4.00 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.000
## Median : 9.00 Median :0.00000 Median :0.00000 Median :0.000
## Mean :13.43 Mean :0.08025 Mean :0.09259 Mean :0.179
## 3rd Qu.:19.25 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.000
## Max. :67.00 Max. :1.00000 Max. :1.00000 Max. :1.000
## NA's :50
## IOT_J90 IOT_mortalite_J28 IOT_mortalite_J90 Duree_Hospit
## Min. :0.000 Non:127 Non:126 Min. : 4.00
## 1st Qu.:0.000 Oui: 35 Oui: 36 1st Qu.: 9.00
## Median :0.000 Median : 12.00
## Mean :0.179 Mean : 17.88
## 3rd Qu.:0.000 3rd Qu.: 19.00
## Max. :1.000 Max. :143.00
##
## Duree_SI Duree_Rea Duree_SI_Rea Survie_J_x
## Min. : 1.000 Min. : 4.00 Min. : 0.00 Min. : 9.0
## 1st Qu.: 4.000 1st Qu.: 11.50 1st Qu.: 4.00 1st Qu.: 16.0
## Median : 6.000 Median : 18.00 Median : 7.00 Median : 21.0
## Mean : 6.882 Mean : 28.87 Mean : 12.19 Mean : 25.9
## 3rd Qu.: 9.000 3rd Qu.: 33.75 3rd Qu.: 11.75 3rd Qu.: 27.0
## Max. :39.000 Max. :128.00 Max. :131.00 Max. :148.0
## NA's :1 NA's :132
## Survie_J DCD_IOT DC_IOT
## Min. : 9.00 Non:126 Min. :0.0000
## 1st Qu.:16.00 Oui: 36 1st Qu.:0.0000
## Median :21.00 Median :0.0000
## Mean :25.17 Mean :0.2222
## 3rd Qu.:27.00 3rd Qu.:0.0000
## Max. :90.00 Max. :1.0000
## IOT/mortalité J28
library(tidyverse)
library(finalfit)## Warning: le package 'finalfit' a été compilé avec la version R 4.2.2dependent_1="IOT_mortalite_J28"
explanatory_A=c("CPAP.1","OHD_CPAP.1")
explanatory_B=c("Rox_H1","Rox_H24","Rox_H48","Rox_H72")
explanatory_C=c("Duree_Hospit","Duree_SI_Rea")
explanatory_E=c("var_RoxH24_H1", "var_RoxH48_H1","var_RoxH72_H1")Relation IOT/mortalité J28 avec CPAP/OHD
res_glm_uni_A <- Oxy_Cov%>%
glmuni(dependent_1, explanatory_A) %>%
fit2df(estimate_suffix=" (univarié)")## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...kable(res_glm_uni_A,row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))| explanatory | OR (univarié) |
|---|---|
| CPAP.1Oui | 1.43 (0.44-6.44, p=0.591) |
| OHD_CPAP.1OHD_CPAP | 1.43 (0.44-6.44, p=0.591) |
Relation IOT/mortalité J28 avec ROX
res_glm_uni_B<- Oxy_Cov%>%
glmuni(dependent_1, explanatory_B) %>%
fit2df(estimate_suffix=" (univarié)")## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...kable(res_glm_uni_B,row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))| explanatory | OR (univarié) |
|---|---|
| Rox_H1 | 1.01 (1.00-1.03, p=0.141) |
| Rox_H24 | 1.00 (0.99-1.02, p=0.841) |
| Rox_H48 | 0.99 (0.98-1.01, p=0.458) |
| Rox_H72 | 0.97 (0.94-0.99, p=0.014) |
Relation IOT/mortalité J28 avec durée d’hospitalisation et de séjour
res_glm_uni_C <- Oxy_Cov%>%
glmuni(dependent_1, explanatory_C) %>%
fit2df(estimate_suffix=" (univarié)")## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...## Warning: glm.fit: des probabilités ont été ajustées numériquement à 0 ou 1kable(res_glm_uni_C,row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))| explanatory | OR (univarié) |
|---|---|
| Duree_Hospit | 1.10 (1.06-1.16, p<0.001) |
| Duree_SI_Rea | 1.19 (1.12-1.28, p<0.001) |
Relation IOT/mortalité J28 avec la variation de l’evolution Rox dans le temps
res_glm_uni_E <- Oxy_Cov%>%
glmuni(dependent_1, explanatory_E) %>%
fit2df(estimate_suffix=" (univarié)")## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...kable(res_glm_uni_E,row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))| explanatory | OR (univarié) |
|---|---|
| var_RoxH24_H1 | 0.99 (0.97-1.00, p=0.098) |
| var_RoxH48_H1 | 0.98 (0.96-1.00, p=0.034) |
| var_RoxH72_H1 | 0.96 (0.93-0.99, p=0.010) |
IOT mortalité J90
library(tidyverse)
library(finalfit)
dependent_2="IOT_mortalite_J90"
explanatory_A=c("CPAP.1","OHD_CPAP.1")
explanatory_B=c("Rox_H1","Rox_H24","Rox_H48","Rox_H72")
explanatory_C=c("Duree_Hospit","Duree_SI_Rea")
explanatory_E=c("var_RoxH24_H1", "var_RoxH48_H1","var_RoxH72_H1")Relation IOT/mortalité J90 avec CPAP/OHD
res_glm_uni_A1 <- Oxy_Cov%>%
glmuni(dependent_2, explanatory_A) %>%
fit2df(estimate_suffix=" (univarié)")## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...kable(res_glm_uni_A1,row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))| explanatory | OR (univarié) |
|---|---|
| CPAP.1Oui | 1.49 (0.46-6.70, p=0.550) |
| OHD_CPAP.1OHD_CPAP | 1.49 (0.46-6.70, p=0.550) |
Relation IOT/mortalité J28 avec ROX
res_glm_uni_B1<- Oxy_Cov%>%
glmuni(dependent_2, explanatory_B) %>%
fit2df(estimate_suffix=" (univarié)")## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...kable(res_glm_uni_B1,row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))| explanatory | OR (univarié) |
|---|---|
| Rox_H1 | 1.02 (1.00-1.04, p=0.087) |
| Rox_H24 | 1.00 (0.98-1.02, p=0.993) |
| Rox_H48 | 0.99 (0.98-1.01, p=0.569) |
| Rox_H72 | 0.97 (0.95-1.00, p=0.031) |
Relation IOT/mortalité J28 avec durée d’hospitalisation et de séjour
res_glm_uni_C1 <- Oxy_Cov%>%
glmuni(dependent_2, explanatory_C) %>%
fit2df(estimate_suffix=" (univarié)")## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...## Warning: glm.fit: des probabilités ont été ajustées numériquement à 0 ou 1kable(res_glm_uni_C,row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))| explanatory | OR (univarié) |
|---|---|
| Duree_Hospit | 1.10 (1.06-1.16, p<0.001) |
| Duree_SI_Rea | 1.19 (1.12-1.28, p<0.001) |
Relation IOT/mortalité J90 avec variation Rox dans le temps
res_glm_uni_E1 <- Oxy_Cov%>%
glmuni(dependent_2, explanatory_E) %>%
fit2df(estimate_suffix=" (univarié)")## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...kable(res_glm_uni_E1,row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))| explanatory | OR (univarié) |
|---|---|
| var_RoxH24_H1 | 0.99 (0.97-1.00, p=0.152) |
| var_RoxH48_H1 | 0.98 (0.96-1.00, p=0.034) |
| var_RoxH72_H1 | 0.96 (0.93-0.99, p=0.008) |
impact des criteres secondaire sur l’IOT/mortalité à J28
dependent_1="IOT_mortalite_J28"
explanatory_AT=c("Age","sexe","Poids","IMC","Vaccination","ATCD_Diabète","ATCD_HTA","ATCD_ID","ATCD_Path_respi","ATCD_Cardiopathie","ATCD_IR_Dialyse","J_symptome_hospit","TDM_severité","TA_Tocilizumab","TA_plasma_conv","var_RoxH72_H1","var_RoxH48_H1","var_RoxH24_H1")Analyse univariées
res_glm_uni_AT <- Oxy_Cov%>%
glmuni(dependent_1, explanatory_AT) %>%
fit2df(estimate_suffix=" (univarié)")## Attente de la réalisation du profilage...
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## Attente de la réalisation du profilage...
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## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
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## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...kable(res_glm_uni_AT,row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))| explanatory | OR (univarié) |
|---|---|
| Age | 1.02 (1.00-1.06, p=0.115) |
| sexeM | 0.78 (0.36-1.73, p=0.529) |
| Poids | 1.00 (0.98-1.02, p=0.841) |
| IMCIMC<30 | 0.79 (0.11-16.00, p=0.838) |
| IMCIMC>30 | 1.71 (0.23-34.79, p=0.640) |
| VaccinationOui | 0.90 (0.13-3.81, p=0.899) |
| ATCD_DiabèteOui | 1.41 (0.59-3.23, p=0.421) |
| ATCD_HTAOui | 1.61 (0.76-3.44, p=0.218) |
| ATCD_IDOui | 3.15 (0.74-12.59, p=0.101) |
| ATCD_Path_respiOui | 2.42 (0.97-5.82, p=0.051) |
| ATCD_CardiopathieOui | 1.18 (0.40-3.07, p=0.752) |
| ATCD_IR_DialyseOui | 1.22 (0.06-9.84, p=0.868) |
| J_symptome_hospit | 0.96 (0.85-1.04, p=0.423) |
| TDM_severitéminime | 0.13 (0.02-0.75, p=0.028) |
| TDM_severitémodérée | 0.13 (0.02-0.58, p=0.009) |
| TDM_severitésévère | 0.18 (0.03-0.84, p=0.032) |
| TA_TocilizumabOui | 1.19 (0.50-2.68, p=0.687) |
| TA_plasma_convOui | 1.22 (0.06-9.84, p=0.868) |
| var_RoxH72_H1 | 0.96 (0.93-0.99, p=0.010) |
| var_RoxH48_H1 | 0.98 (0.96-1.00, p=0.034) |
| var_RoxH24_H1 | 0.99 (0.97-1.00, p=0.098) |
Analyse multivariées
res_glm_uni_multi_AT <- Oxy_Cov %>%
finalfit(dependent_1, explanatory_AT)## Attente de la réalisation du profilage...
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## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
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## Attente de la réalisation du profilage...
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## Attente de la réalisation du profilage...
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## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...kable(res_glm_uni_multi_AT,row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))| Dependent: IOT_mortalite_J28 | Non | Oui | OR (univariable) | OR (multivariable) | |
|---|---|---|---|---|---|
| Age | Mean (SD) | 59.4 (13.7) | 63.5 (11.9) | 1.02 (1.00-1.06, p=0.115) | 1.03 (0.98-1.08, p=0.209) |
| sexe | F | 40 (75.5) | 13 (24.5) | - | - |
| M | 87 (79.8) | 22 (20.2) | 0.78 (0.36-1.73, p=0.529) | 1.12 (0.34-3.88, p=0.860) | |
| Poids | Mean (SD) | 85.6 (19.1) | 86.3 (19.3) | 1.00 (0.98-1.02, p=0.841) | 0.99 (0.96-1.03, p=0.662) |
| IMC | 4 (80.0) | 1 (20.0) | - | - | |
| IMC<30 | 81 (83.5) | 16 (16.5) | 0.79 (0.11-16.00, p=0.838) | - | |
| IMC>30 | 42 (70.0) | 18 (30.0) | 1.71 (0.23-34.79, p=0.640) | 4.70 (1.22-20.34, p=0.030) | |
| Vaccination | Non | 119 (78.3) | 33 (21.7) | - | - |
| Oui | 8 (80.0) | 2 (20.0) | 0.90 (0.13-3.81, p=0.899) | 0.47 (0.02-4.41, p=0.566) | |
| ATCD_Diabète | Non | 99 (79.8) | 25 (20.2) | - | - |
| Oui | 28 (73.7) | 10 (26.3) | 1.41 (0.59-3.23, p=0.421) | 1.08 (0.32-3.42, p=0.893) | |
| ATCD_HTA | Non | 73 (82.0) | 16 (18.0) | - | - |
| Oui | 54 (74.0) | 19 (26.0) | 1.61 (0.76-3.44, p=0.218) | 0.76 (0.24-2.39, p=0.634) | |
| ATCD_ID | Non | 122 (79.7) | 31 (20.3) | - | - |
| Oui | 5 (55.6) | 4 (44.4) | 3.15 (0.74-12.59, p=0.101) | 7.21 (1.01-57.06, p=0.049) | |
| ATCD_Path_respi | Non | 109 (81.3) | 25 (18.7) | - | - |
| Oui | 18 (64.3) | 10 (35.7) | 2.42 (0.97-5.82, p=0.051) | 3.17 (0.97-10.52, p=0.055) | |
| ATCD_Cardiopathie | Non | 108 (78.8) | 29 (21.2) | - | - |
| Oui | 19 (76.0) | 6 (24.0) | 1.18 (0.40-3.07, p=0.752) | 0.76 (0.16-3.15, p=0.707) | |
| ATCD_IR_Dialyse | Non | 124 (78.5) | 34 (21.5) | - | - |
| Oui | 3 (75.0) | 1 (25.0) | 1.22 (0.06-9.84, p=0.868) | 0.91 (0.02-15.66, p=0.955) | |
| J_symptome_hospit | Mean (SD) | 8.2 (5.2) | 7.4 (3.7) | 0.96 (0.85-1.04, p=0.423) | 0.93 (0.79-1.06, p=0.343) |
| TDM_severité | majeur | 3 (37.5) | 5 (62.5) | - | - |
| minime | 18 (81.8) | 4 (18.2) | 0.13 (0.02-0.75, p=0.028) | 0.17 (0.02-1.46, p=0.120) | |
| modérée | 66 (82.5) | 14 (17.5) | 0.13 (0.02-0.58, p=0.009) | 0.11 (0.01-0.68, p=0.021) | |
| sévère | 40 (76.9) | 12 (23.1) | 0.18 (0.03-0.84, p=0.032) | 0.14 (0.02-0.85, p=0.036) | |
| TA_Tocilizumab | Non | 95 (79.2) | 25 (20.8) | - | - |
| Oui | 32 (76.2) | 10 (23.8) | 1.19 (0.50-2.68, p=0.687) | 1.08 (0.31-3.55, p=0.896) | |
| TA_plasma_conv | Non | 124 (78.5) | 34 (21.5) | - | - |
| Oui | 3 (75.0) | 1 (25.0) | 1.22 (0.06-9.84, p=0.868) | 0.59 (0.00-56.07, p=0.834) | |
| var_RoxH72_H1 | Mean (SD) | 16.8 (19.5) | 7.3 (11.5) | 0.96 (0.93-0.99, p=0.010) | 0.96 (0.92-0.99, p=0.022) |
| var_RoxH48_H1 | Mean (SD) | 26.0 (24.3) | 16.3 (18.0) | 0.98 (0.96-1.00, p=0.034) | 1.00 (0.98-1.03, p=0.785) |
| var_RoxH24_H1 | Mean (SD) | 34.0 (25.9) | 26.1 (18.9) | 0.99 (0.97-1.00, p=0.098) | 0.98 (0.96-1.00, p=0.071) |
odds ratio de tous les modèles
Il s’agit ici d’obtenir et de présenter les odds ratio :
des modèles de régression univariés, du modèle de régression multivarié complet, d’un modèle de régression multicarié restreint (obtenu par sélection des variables)
dependent_1="IOT_mortalite_J28"
explanatory_full=c("Age","sexe","Poids","IMC","Vaccination","ATCD_Diabète","ATCD_HTA","ATCD_ID","ATCD_Path_respi","ATCD_Cardiopathie","ATCD_IR_Dialyse","J_symptome_hospit","TDM_severité","TA_Tocilizumab","TA_plasma_conv","var_RoxH72_H1","var_RoxH48_H1","var_RoxH24_H1")
explanatory_final = c("IMC","TDM_severité","ATCD_Path_respi","var_RoxH72_H1","var_RoxH48_H1")res_summary <- Oxy_Cov %>%
summary_factorlist(dependent_1, explanatory_full, fit_id=TRUE)
res_uni <- Oxy_Cov%>%
glmuni(dependent_1, explanatory_full) %>%
fit2df(estimate_suffix="(univarié)") ## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...res_multi_full <- Oxy_Cov%>%
glmmulti(dependent_1, explanatory_full) %>%
fit2df(estimate_suffix="(ajustés - modèle complet)") ## Attente de la réalisation du profilage...res_multi_final <- Oxy_Cov%>%
glmmulti(dependent_1, explanatory_final) %>%
fit2df(estimate_suffix="(ajustés - modèle final)") ## Attente de la réalisation du profilage...tab_res <- res_summary %>%
finalfit_merge(res_uni) %>%
finalfit_merge(res_multi_full) %>%
finalfit_merge(res_multi_final) %>%
dplyr::select(-levels, fit_id, -index)
tab_res## fit_id label Non Oui
## 1 Age Age 59.4 (13.7) 63.5 (11.9)
## 19 sexeF sexe 40 (31.5) 13 (37.1)
## 20 sexeM 87 (68.5) 22 (62.9)
## 18 Poids Poids 85.6 (19.1) 86.3 (19.3)
## 14 IMC IMC 4 (3.1) 1 (2.9)
## 15 IMCIMC<30 81 (63.8) 16 (45.7)
## 16 IMCIMC>30 42 (33.1) 18 (51.4)
## 29 VaccinationNon Vaccination 119 (93.7) 33 (94.3)
## 30 VaccinationOui 8 (6.3) 2 (5.7)
## 4 ATCD_DiabèteNon ATCD_Diabète 99 (78.0) 25 (71.4)
## 5 ATCD_DiabèteOui 28 (22.0) 10 (28.6)
## 6 ATCD_HTANon ATCD_HTA 73 (57.5) 16 (45.7)
## 7 ATCD_HTAOui 54 (42.5) 19 (54.3)
## 8 ATCD_IDNon ATCD_ID 122 (96.1) 31 (88.6)
## 9 ATCD_IDOui 5 (3.9) 4 (11.4)
## 12 ATCD_Path_respiNon ATCD_Path_respi 109 (85.8) 25 (71.4)
## 13 ATCD_Path_respiOui 18 (14.2) 10 (28.6)
## 2 ATCD_CardiopathieNon ATCD_Cardiopathie 108 (85.0) 29 (82.9)
## 3 ATCD_CardiopathieOui 19 (15.0) 6 (17.1)
## 10 ATCD_IR_DialyseNon ATCD_IR_Dialyse 124 (97.6) 34 (97.1)
## 11 ATCD_IR_DialyseOui 3 (2.4) 1 (2.9)
## 17 J_symptome_hospit J_symptome_hospit 8.2 (5.2) 7.4 (3.7)
## 25 TDM_severitémajeur TDM_severité 3 (2.4) 5 (14.3)
## 26 TDM_severitéminime 18 (14.2) 4 (11.4)
## 27 TDM_severitémodérée 66 (52.0) 14 (40.0)
## 28 TDM_severitésévère 40 (31.5) 12 (34.3)
## 23 TA_TocilizumabNon TA_Tocilizumab 95 (74.8) 25 (71.4)
## 24 TA_TocilizumabOui 32 (25.2) 10 (28.6)
## 21 TA_plasma_convNon TA_plasma_conv 124 (97.6) 34 (97.1)
## 22 TA_plasma_convOui 3 (2.4) 1 (2.9)
## 33 var_RoxH72_H1 var_RoxH72_H1 16.8 (19.5) 7.3 (11.5)
## 32 var_RoxH48_H1 var_RoxH48_H1 26.0 (24.3) 16.3 (18.0)
## 31 var_RoxH24_H1 var_RoxH24_H1 34.0 (25.9) 26.1 (18.9)
## OR(univarié) OR(ajustés - modèle complet)
## 1 1.02 (1.00-1.06, p=0.115) 1.03 (0.98-1.08, p=0.209)
## 19 - -
## 20 0.78 (0.36-1.73, p=0.529) 1.12 (0.34-3.88, p=0.860)
## 18 1.00 (0.98-1.02, p=0.841) 0.99 (0.96-1.03, p=0.662)
## 14 - -
## 15 0.79 (0.11-16.00, p=0.838) -
## 16 1.71 (0.23-34.79, p=0.640) 4.70 (1.22-20.34, p=0.030)
## 29 - -
## 30 0.90 (0.13-3.81, p=0.899) 0.47 (0.02-4.41, p=0.566)
## 4 - -
## 5 1.41 (0.59-3.23, p=0.421) 1.08 (0.32-3.42, p=0.893)
## 6 - -
## 7 1.61 (0.76-3.44, p=0.218) 0.76 (0.24-2.39, p=0.634)
## 8 - -
## 9 3.15 (0.74-12.59, p=0.101) 7.21 (1.01-57.06, p=0.049)
## 12 - -
## 13 2.42 (0.97-5.82, p=0.051) 3.17 (0.97-10.52, p=0.055)
## 2 - -
## 3 1.18 (0.40-3.07, p=0.752) 0.76 (0.16-3.15, p=0.707)
## 10 - -
## 11 1.22 (0.06-9.84, p=0.868) 0.91 (0.02-15.66, p=0.955)
## 17 0.96 (0.85-1.04, p=0.423) 0.93 (0.79-1.06, p=0.343)
## 25 - -
## 26 0.13 (0.02-0.75, p=0.028) 0.17 (0.02-1.46, p=0.120)
## 27 0.13 (0.02-0.58, p=0.009) 0.11 (0.01-0.68, p=0.021)
## 28 0.18 (0.03-0.84, p=0.032) 0.14 (0.02-0.85, p=0.036)
## 23 - -
## 24 1.19 (0.50-2.68, p=0.687) 1.08 (0.31-3.55, p=0.896)
## 21 - -
## 22 1.22 (0.06-9.84, p=0.868) 0.59 (0.00-56.07, p=0.834)
## 33 0.96 (0.93-0.99, p=0.010) 0.96 (0.92-0.99, p=0.022)
## 32 0.98 (0.96-1.00, p=0.034) 1.00 (0.98-1.03, p=0.785)
## 31 0.99 (0.97-1.00, p=0.098) 0.98 (0.96-1.00, p=0.071)
## OR(ajustés - modèle final)
## 1 -
## 19 -
## 20 -
## 18 -
## 14 -
## 15 0.69 (0.08-14.65, p=0.754)
## 16 1.75 (0.21-37.55, p=0.644)
## 29 -
## 30 -
## 4 -
## 5 -
## 6 -
## 7 -
## 8 -
## 9 -
## 12 -
## 13 2.48 (0.93-6.43, p=0.064)
## 2 -
## 3 -
## 10 -
## 11 -
## 17 -
## 25 -
## 26 0.15 (0.02-1.00, p=0.058)
## 27 0.15 (0.03-0.79, p=0.027)
## 28 0.19 (0.03-1.00, p=0.054)
## 23 -
## 24 -
## 21 -
## 22 -
## 33 0.97 (0.94-1.00, p=0.060)
## 32 0.99 (0.97-1.01, p=0.234)
## 31 -tab_res## fit_id label Non Oui
## 1 Age Age 59.4 (13.7) 63.5 (11.9)
## 19 sexeF sexe 40 (31.5) 13 (37.1)
## 20 sexeM 87 (68.5) 22 (62.9)
## 18 Poids Poids 85.6 (19.1) 86.3 (19.3)
## 14 IMC IMC 4 (3.1) 1 (2.9)
## 15 IMCIMC<30 81 (63.8) 16 (45.7)
## 16 IMCIMC>30 42 (33.1) 18 (51.4)
## 29 VaccinationNon Vaccination 119 (93.7) 33 (94.3)
## 30 VaccinationOui 8 (6.3) 2 (5.7)
## 4 ATCD_DiabèteNon ATCD_Diabète 99 (78.0) 25 (71.4)
## 5 ATCD_DiabèteOui 28 (22.0) 10 (28.6)
## 6 ATCD_HTANon ATCD_HTA 73 (57.5) 16 (45.7)
## 7 ATCD_HTAOui 54 (42.5) 19 (54.3)
## 8 ATCD_IDNon ATCD_ID 122 (96.1) 31 (88.6)
## 9 ATCD_IDOui 5 (3.9) 4 (11.4)
## 12 ATCD_Path_respiNon ATCD_Path_respi 109 (85.8) 25 (71.4)
## 13 ATCD_Path_respiOui 18 (14.2) 10 (28.6)
## 2 ATCD_CardiopathieNon ATCD_Cardiopathie 108 (85.0) 29 (82.9)
## 3 ATCD_CardiopathieOui 19 (15.0) 6 (17.1)
## 10 ATCD_IR_DialyseNon ATCD_IR_Dialyse 124 (97.6) 34 (97.1)
## 11 ATCD_IR_DialyseOui 3 (2.4) 1 (2.9)
## 17 J_symptome_hospit J_symptome_hospit 8.2 (5.2) 7.4 (3.7)
## 25 TDM_severitémajeur TDM_severité 3 (2.4) 5 (14.3)
## 26 TDM_severitéminime 18 (14.2) 4 (11.4)
## 27 TDM_severitémodérée 66 (52.0) 14 (40.0)
## 28 TDM_severitésévère 40 (31.5) 12 (34.3)
## 23 TA_TocilizumabNon TA_Tocilizumab 95 (74.8) 25 (71.4)
## 24 TA_TocilizumabOui 32 (25.2) 10 (28.6)
## 21 TA_plasma_convNon TA_plasma_conv 124 (97.6) 34 (97.1)
## 22 TA_plasma_convOui 3 (2.4) 1 (2.9)
## 33 var_RoxH72_H1 var_RoxH72_H1 16.8 (19.5) 7.3 (11.5)
## 32 var_RoxH48_H1 var_RoxH48_H1 26.0 (24.3) 16.3 (18.0)
## 31 var_RoxH24_H1 var_RoxH24_H1 34.0 (25.9) 26.1 (18.9)
## OR(univarié) OR(ajustés - modèle complet)
## 1 1.02 (1.00-1.06, p=0.115) 1.03 (0.98-1.08, p=0.209)
## 19 - -
## 20 0.78 (0.36-1.73, p=0.529) 1.12 (0.34-3.88, p=0.860)
## 18 1.00 (0.98-1.02, p=0.841) 0.99 (0.96-1.03, p=0.662)
## 14 - -
## 15 0.79 (0.11-16.00, p=0.838) -
## 16 1.71 (0.23-34.79, p=0.640) 4.70 (1.22-20.34, p=0.030)
## 29 - -
## 30 0.90 (0.13-3.81, p=0.899) 0.47 (0.02-4.41, p=0.566)
## 4 - -
## 5 1.41 (0.59-3.23, p=0.421) 1.08 (0.32-3.42, p=0.893)
## 6 - -
## 7 1.61 (0.76-3.44, p=0.218) 0.76 (0.24-2.39, p=0.634)
## 8 - -
## 9 3.15 (0.74-12.59, p=0.101) 7.21 (1.01-57.06, p=0.049)
## 12 - -
## 13 2.42 (0.97-5.82, p=0.051) 3.17 (0.97-10.52, p=0.055)
## 2 - -
## 3 1.18 (0.40-3.07, p=0.752) 0.76 (0.16-3.15, p=0.707)
## 10 - -
## 11 1.22 (0.06-9.84, p=0.868) 0.91 (0.02-15.66, p=0.955)
## 17 0.96 (0.85-1.04, p=0.423) 0.93 (0.79-1.06, p=0.343)
## 25 - -
## 26 0.13 (0.02-0.75, p=0.028) 0.17 (0.02-1.46, p=0.120)
## 27 0.13 (0.02-0.58, p=0.009) 0.11 (0.01-0.68, p=0.021)
## 28 0.18 (0.03-0.84, p=0.032) 0.14 (0.02-0.85, p=0.036)
## 23 - -
## 24 1.19 (0.50-2.68, p=0.687) 1.08 (0.31-3.55, p=0.896)
## 21 - -
## 22 1.22 (0.06-9.84, p=0.868) 0.59 (0.00-56.07, p=0.834)
## 33 0.96 (0.93-0.99, p=0.010) 0.96 (0.92-0.99, p=0.022)
## 32 0.98 (0.96-1.00, p=0.034) 1.00 (0.98-1.03, p=0.785)
## 31 0.99 (0.97-1.00, p=0.098) 0.98 (0.96-1.00, p=0.071)
## OR(ajustés - modèle final)
## 1 -
## 19 -
## 20 -
## 18 -
## 14 -
## 15 0.69 (0.08-14.65, p=0.754)
## 16 1.75 (0.21-37.55, p=0.644)
## 29 -
## 30 -
## 4 -
## 5 -
## 6 -
## 7 -
## 8 -
## 9 -
## 12 -
## 13 2.48 (0.93-6.43, p=0.064)
## 2 -
## 3 -
## 10 -
## 11 -
## 17 -
## 25 -
## 26 0.15 (0.02-1.00, p=0.058)
## 27 0.15 (0.03-0.79, p=0.027)
## 28 0.19 (0.03-1.00, p=0.054)
## 23 -
## 24 -
## 21 -
## 22 -
## 33 0.97 (0.94-1.00, p=0.060)
## 32 0.99 (0.97-1.01, p=0.234)
## 31 -knitr::kable(tab_res, row.names=FALSE, align=c("l", "l", "r", "r", "r", "r", "r")) | fit_id | label | Non | Oui | OR(univarié) | OR(ajustés - modèle complet) | OR(ajustés - modèle final) |
|---|---|---|---|---|---|---|
| Age | Age | 59.4 (13.7) | 63.5 (11.9) | 1.02 (1.00-1.06, p=0.115) | 1.03 (0.98-1.08, p=0.209) | - |
| sexeF | sexe | 40 (31.5) | 13 (37.1) | - | - | - |
| sexeM | 87 (68.5) | 22 (62.9) | 0.78 (0.36-1.73, p=0.529) | 1.12 (0.34-3.88, p=0.860) | - | |
| Poids | Poids | 85.6 (19.1) | 86.3 (19.3) | 1.00 (0.98-1.02, p=0.841) | 0.99 (0.96-1.03, p=0.662) | - |
| IMC | IMC | 4 (3.1) | 1 (2.9) | - | - | - |
| IMCIMC<30 | 81 (63.8) | 16 (45.7) | 0.79 (0.11-16.00, p=0.838) | - | 0.69 (0.08-14.65, p=0.754) | |
| IMCIMC>30 | 42 (33.1) | 18 (51.4) | 1.71 (0.23-34.79, p=0.640) | 4.70 (1.22-20.34, p=0.030) | 1.75 (0.21-37.55, p=0.644) | |
| VaccinationNon | Vaccination | 119 (93.7) | 33 (94.3) | - | - | - |
| VaccinationOui | 8 (6.3) | 2 (5.7) | 0.90 (0.13-3.81, p=0.899) | 0.47 (0.02-4.41, p=0.566) | - | |
| ATCD_DiabèteNon | ATCD_Diabète | 99 (78.0) | 25 (71.4) | - | - | - |
| ATCD_DiabèteOui | 28 (22.0) | 10 (28.6) | 1.41 (0.59-3.23, p=0.421) | 1.08 (0.32-3.42, p=0.893) | - | |
| ATCD_HTANon | ATCD_HTA | 73 (57.5) | 16 (45.7) | - | - | - |
| ATCD_HTAOui | 54 (42.5) | 19 (54.3) | 1.61 (0.76-3.44, p=0.218) | 0.76 (0.24-2.39, p=0.634) | - | |
| ATCD_IDNon | ATCD_ID | 122 (96.1) | 31 (88.6) | - | - | - |
| ATCD_IDOui | 5 (3.9) | 4 (11.4) | 3.15 (0.74-12.59, p=0.101) | 7.21 (1.01-57.06, p=0.049) | - | |
| ATCD_Path_respiNon | ATCD_Path_respi | 109 (85.8) | 25 (71.4) | - | - | - |
| ATCD_Path_respiOui | 18 (14.2) | 10 (28.6) | 2.42 (0.97-5.82, p=0.051) | 3.17 (0.97-10.52, p=0.055) | 2.48 (0.93-6.43, p=0.064) | |
| ATCD_CardiopathieNon | ATCD_Cardiopathie | 108 (85.0) | 29 (82.9) | - | - | - |
| ATCD_CardiopathieOui | 19 (15.0) | 6 (17.1) | 1.18 (0.40-3.07, p=0.752) | 0.76 (0.16-3.15, p=0.707) | - | |
| ATCD_IR_DialyseNon | ATCD_IR_Dialyse | 124 (97.6) | 34 (97.1) | - | - | - |
| ATCD_IR_DialyseOui | 3 (2.4) | 1 (2.9) | 1.22 (0.06-9.84, p=0.868) | 0.91 (0.02-15.66, p=0.955) | - | |
| J_symptome_hospit | J_symptome_hospit | 8.2 (5.2) | 7.4 (3.7) | 0.96 (0.85-1.04, p=0.423) | 0.93 (0.79-1.06, p=0.343) | - |
| TDM_severitémajeur | TDM_severité | 3 (2.4) | 5 (14.3) | - | - | - |
| TDM_severitéminime | 18 (14.2) | 4 (11.4) | 0.13 (0.02-0.75, p=0.028) | 0.17 (0.02-1.46, p=0.120) | 0.15 (0.02-1.00, p=0.058) | |
| TDM_severitémodérée | 66 (52.0) | 14 (40.0) | 0.13 (0.02-0.58, p=0.009) | 0.11 (0.01-0.68, p=0.021) | 0.15 (0.03-0.79, p=0.027) | |
| TDM_severitésévère | 40 (31.5) | 12 (34.3) | 0.18 (0.03-0.84, p=0.032) | 0.14 (0.02-0.85, p=0.036) | 0.19 (0.03-1.00, p=0.054) | |
| TA_TocilizumabNon | TA_Tocilizumab | 95 (74.8) | 25 (71.4) | - | - | - |
| TA_TocilizumabOui | 32 (25.2) | 10 (28.6) | 1.19 (0.50-2.68, p=0.687) | 1.08 (0.31-3.55, p=0.896) | - | |
| TA_plasma_convNon | TA_plasma_conv | 124 (97.6) | 34 (97.1) | - | - | - |
| TA_plasma_convOui | 3 (2.4) | 1 (2.9) | 1.22 (0.06-9.84, p=0.868) | 0.59 (0.00-56.07, p=0.834) | - | |
| var_RoxH72_H1 | var_RoxH72_H1 | 16.8 (19.5) | 7.3 (11.5) | 0.96 (0.93-0.99, p=0.010) | 0.96 (0.92-0.99, p=0.022) | 0.97 (0.94-1.00, p=0.060) |
| var_RoxH48_H1 | var_RoxH48_H1 | 26.0 (24.3) | 16.3 (18.0) | 0.98 (0.96-1.00, p=0.034) | 1.00 (0.98-1.03, p=0.785) | 0.99 (0.97-1.01, p=0.234) |
| var_RoxH24_H1 | var_RoxH24_H1 | 34.0 (25.9) | 26.1 (18.9) | 0.99 (0.97-1.00, p=0.098) | 0.98 (0.96-1.00, p=0.071) | - |
Oxy_Cov %>%
or_plot(dependent_1, explanatory_final)## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...
## Attente de la réalisation du profilage...## Warning: Removed 3 rows containing missing values (`geom_errorbarh()`).
Courbe de survie ( DC/IOT à J28) selon durée de maladie
La durée de la maladie a été calculée en additionnant le nombre de jour de sympatome à l’arrivée + le nombre de jour d’hospitalisation Les données sont comptabilisées en jours sont noté oui les données de décès et ou IOT à J28
library(rmarkdown)
library(markdown)
library(tidyverse)
library(finalfit)
library(survival)
library(survminer)## Le chargement a nécessité le package : ggpubr## Warning: le package 'ggpubr' a été compilé avec la version R 4.2.2##
## Attachement du package : 'ggpubr'## Les objets suivants sont masqués depuis 'package:flextable':
##
## border, font, rotate##
## Attachement du package : 'survminer'## L'objet suivant est masqué depuis 'package:survival':
##
## myelomaSurv_J28<-survfit(Surv(Survie_J,DC_IOT )~1,data=Oxy_Cov)
Surv_J28## Call: survfit(formula = Surv(Survie_J, DC_IOT) ~ 1, data = Oxy_Cov)
##
## n events median 0.95LCL 0.95UCL
## [1,] 162 36 50 39 68ggsurvplot(Surv_J28,xlab="Time(Days)")
Courbe de survie OHD CPAP
S_OHD_CPAP<-survfit(Surv(Survie_J,DC_IOT)~OHD_CPAP.1, data=Oxy_Cov)
S_OHD_CPAP## Call: survfit(formula = Surv(Survie_J, DC_IOT) ~ OHD_CPAP.1, data = Oxy_Cov)
##
## n events median 0.95LCL 0.95UCL
## OHD_CPAP.1=OHD seule 18 3 90 38 NA
## OHD_CPAP.1=OHD_CPAP 144 33 45 39 68le test du logrank est utilisé ici afin de comparer des courbes de survie. La mortalité ches les patients diffère-t-elle significativement selon OHT /CPAP ?
survdiff(Surv(Survie_J,DC_IOT)~OHD_CPAP.1, data=Oxy_Cov)## Call:
## survdiff(formula = Surv(Survie_J, DC_IOT) ~ OHD_CPAP.1, data = Oxy_Cov)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## OHD_CPAP.1=OHD seule 18 3 6.13 1.597 2.32
## OHD_CPAP.1=OHD_CPAP 144 33 29.87 0.328 2.32
##
## Chisq= 2.3 on 1 degrees of freedom, p= 0.1ggsurvplot(S_OHD_CPAP, conf.int = TRUE, risk.table = TRUE, pval = TRUE, data = Oxy_Cov)
Courbe de survie OHD DV
S_OHD_DV<-survfit(Surv(Survie_J,DC_IOT)~OHD_DV, data=Oxy_Cov)
S_OHD_DV## Call: survfit(formula = Surv(Survie_J, DC_IOT) ~ OHD_DV, data = Oxy_Cov)
##
## n events median 0.95LCL 0.95UCL
## OHD_DV=OHD seule 50 14 64 29 NA
## OHD_DV=OHD_DV 112 22 45 39 86le test du logrank est utilisé ici afin de comparer des courbes de survie. La mortalité ches les patients diffère-t-elle significativement selon OHT /CPAP ?
survdiff(Surv(Survie_J,DC_IOT)~OHD_DV, data=Oxy_Cov)## Call:
## survdiff(formula = Surv(Survie_J, DC_IOT) ~ OHD_DV, data = Oxy_Cov)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## OHD_DV=OHD seule 50 14 12.4 0.217 0.368
## OHD_DV=OHD_DV 112 22 23.6 0.113 0.368
##
## Chisq= 0.4 on 1 degrees of freedom, p= 0.5ggsurvplot(S_OHD_DV, conf.int = TRUE, risk.table = TRUE, pval = TRUE, data = Oxy_Cov)
Courbe de survie DV
S_DV_DL<-survfit(Surv(Survie_J,DC_IOT)~X.DV_DL, data=Oxy_Cov)
S_DV_DL## Call: survfit(formula = Surv(Survie_J, DC_IOT) ~ X.DV_DL, data = Oxy_Cov)
##
## n events median 0.95LCL 0.95UCL
## X.DV_DL=Fait 112 22 45 39 86
## X.DV_DL=Non fait 50 14 64 29 NAle test du logrank est utilisé ici afin de comparer des courbes de survie. La mortalité ches les patients diffère-t-elle significativement selon OHT /CPAP ?
survdiff(Surv(Survie_J,DC_IOT)~X.DV_DL, data=Oxy_Cov)## Call:
## survdiff(formula = Surv(Survie_J, DC_IOT) ~ X.DV_DL, data = Oxy_Cov)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## X.DV_DL=Fait 112 22 23.6 0.113 0.368
## X.DV_DL=Non fait 50 14 12.4 0.217 0.368
##
## Chisq= 0.4 on 1 degrees of freedom, p= 0.5ggsurvplot(S_DV_DL, conf.int = TRUE, risk.table = TRUE, pval = TRUE, data = Oxy_Cov)
Rcherche significativité Rox
Rox H72_H1
Rox_H72 <- read.csv2("C:/Users/mallah.s/Desktop/Stats et Theses/OxyCov/Rox_H72.csv", stringsAsFactors=TRUE)qqPlot(Rox_H72$var_RoxH72_H1)
## [1] 16 69la normalité est satisfaisante
shapiro.test(Rox_H72$var_RoxH72_H1)##
## Shapiro-Wilk normality test
##
## data: Rox_H72$var_RoxH72_H1
## W = 0.97979, p-value = 0.2437tLe te de Shapiro-Wilk ne rejette pas l’hypothèse de normalité. Au final, nous acceptons cette hypothèse. Nous allons donc pouvoir comparer les moyennes de Rox H1et H72
t.test(Oxy_Cov$Rox_H1,Oxy_Cov$Rox_H72, paired=TRUE)##
## Paired t-test
##
## data: Oxy_Cov$Rox_H1 and Oxy_Cov$Rox_H72
## t = 9.938, df = 161, p-value < 2.2e-16
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## 16.52038 24.71419
## sample estimates:
## mean difference
## 20.61728la diffrence moyenne de Rox entre H72 et H1est 0.67, la p-value du t-test est <0.05, ainsi les résultats nous indiquent que Les Rox à H72 est significativement different de H1 dans le sens decroissant ## Rox H48_H1
Rox_H48 <- read.csv2("C:/Users/mallah.s/Desktop/Stats et Theses/OxyCov/Rox_H48.csv", stringsAsFactors=TRUE)qqPlot(Rox_H48$var_RoxH48_H1)
## [1] 90 67la normalité n’est pas parfaite mais satisfaisante
shapiro.test(Rox_H48$var_RoxH48_H1)##
## Shapiro-Wilk normality test
##
## data: Rox_H48$var_RoxH48_H1
## W = 0.97691, p-value = 0.05701tLe te de Shapiro-Wilk ne rejette pas l’hypothèse de normalité. Au final, nous acceptons cette hypothèse. Nous allons donc pouvoir comparer les moyennes de Rox H1et H72
t.test(Rox_H48$Rox_H1,Rox_H48$Rox_H48, paired=TRUE)##
## Paired t-test
##
## data: Rox_H48$Rox_H1 and Rox_H48$Rox_H48
## t = 0.9546, df = 107, p-value = 0.3419
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -0.3479240 0.9942203
## sample estimates:
## mean difference
## 0.3231481la diffrence moyenne de Rox entre H48 et h1 est de 0.323, la p-value du t-test est >0.05, ainsi les résultats nous indiquent que Les Rox à H48 n’est pas significativement different de H1
Rox H24_H1
Rox_H24 <- read.csv2("C:/Users/mallah.s/Desktop/Stats et Theses/OxyCov/Rox_H24.csv", stringsAsFactors=TRUE)qqPlot(Rox_H24$var_RoxH24_H1)
## [1] 95 87la normalité est satisfaisante
shapiro.test(Rox_H24$var_RoxH24_H1)##
## Shapiro-Wilk normality test
##
## data: Rox_H24$var_RoxH24_H1
## W = 0.99072, p-value = 0.5064tLe te de Shapiro-Wilk ne rejette pas l’hypothèse de normalité. Au final, nous acceptons cette hypothèse. Nous allons donc pouvoir comparer les moyennes de Rox H1et H24
t.test(Rox_H24$Rox_H1,Rox_H24$Rox_H24, paired=TRUE)##
## Paired t-test
##
## data: Rox_H24$Rox_H1 and Rox_H24$Rox_H24
## t = 1.822, df = 135, p-value = 0.07067
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -0.04179095 1.01973212
## sample estimates:
## mean difference
## 0.4889706difference non significative entre RoxH1 et Rox H24