EVENTS

TYPE OF EVENTS ¹

Total number of events is 1996, distributed in 10classes:

table(rdb$DGN_PAL)

   G45   G46   G81   H53   I60   I61   I62   I63   I64   R47 
   360    16    23     3    47   245   122   989   101    90

CIM-10	Diagnostic
G45	accidents ischémiques cérébraux transitories et syndromes apparentés
G46	syndromes vasculaires cérébraux au cours de maladies cérébrovasculaires
I60	hémorragie sous-arachnoïdienne
I61	hémorragie intracérébrale
I62	autres hémorragies intracrâniennes non traumatiques
I63	infarctus cérébral
I64	accident vasculaire cérébral non précisé (hémorragique ou infarctus)
R47	dysarthrie et anarthrie
G81	hémiplégie
H53	troubles de la vision

TRANSPLANT VAR

Taking into account the transplantation, the total number of events is 1903.
93 events occur before the kidney transplantation.

	no event (`DGN_PAL(-)`)	event (`DGN_PAL(+)`)
before transplantation	0	1903
after transplantation	42581	93

MODELS

REIN POPULATION (`n=45026`)

UNIVARIATE

             beta HR (95% CI for HR) wald.test  p.value
cardiovasc   0.33      1.4 (1.3-1.5)        46  1.4e-11
tabac2     -0.062      0.94 (0.85-1)       1.5     0.22
dial        0.022       1 (0.88-1.2)      0.09     0.77
apkd01      -0.59   0.55 (0.43-0.71)        21  5.2e-06
AVCAITn       1.4        4 (3.7-4.4)       790 2.4e-174
sex          0.14        1.1 (1-1.3)       8.4   0.0038
age         0.024            1 (1-1)       180  6.1e-42
tabac2.1   -0.062      0.94 (0.85-1)       1.5     0.22
diabetes     0.31      1.4 (1.2-1.5)        45  1.7e-11
bmi        -0.013      0.99 (0.98-1)       8.4   0.0037

MODEL 1

Call:
coxph(formula = Surv(time, status) ~ cardiovasc + tabac2 + dial + 
    apkd01 + sex + age, data = rdb)

                 coef exp(coef)  se(coef)      z Pr(>|z|)    
cardiovasc1  0.178794  1.195775  0.055954  3.195  0.00140 ** 
tabac2       0.030223  1.030684  0.057404  0.526  0.59854    
dial2        0.006842  1.006866  0.085411  0.080  0.93615    
apkd011     -0.341017  0.711047  0.150038 -2.273  0.02303 *  
sex          0.164627  1.178953  0.057833  2.847  0.00442 ** 
age          0.019391  1.019580  0.002122  9.139  < 2e-16 ***
---

            exp(coef) exp(-coef) lower .95 upper .95
cardiovasc1     1.196     0.8363    1.0716    1.3344
tabac2          1.031     0.9702    0.9210    1.1534
dial2           1.007     0.9932    0.8517    1.1903
apkd011         0.711     1.4064    0.5299    0.9541
sex             1.179     0.8482    1.0526    1.3205
age             1.020     0.9808    1.0153    1.0238

Concordance= 0.592  (se = 0.007 )
Likelihood ratio test= 157.1  on 6 df,   p=<2e-16
Wald test            = 143.2  on 6 df,   p=<2e-16
Score (logrank) test = 145.3  on 6 df,   p=<2e-16

MODEL 2: VARIABLE `DIABETES`

Call:
coxph(formula = Surv(time, status) ~ cardiovasc + tabac2 + dial + 
    apkd01 + diabetes + sex + age, data = rdb)

                 coef exp(coef)  se(coef)      z Pr(>|z|)    
cardiovasc1  0.147901  1.159399  0.056243  2.630  0.00855 ** 
tabac2       0.029278  1.029711  0.057577  0.509  0.61110    
dial2        0.023845  1.024132  0.085499  0.279  0.78032    
apkd011     -0.275127  0.759476  0.153024 -1.798  0.07219 .  
diabetes1    0.238940  1.269903  0.053319  4.481 7.42e-06 ***
sex          0.156082  1.168922  0.058066  2.688  0.00719 ** 
age          0.019491  1.019682  0.002156  9.042  < 2e-16 ***
---

            exp(coef) exp(-coef) lower .95 upper .95
cardiovasc1    1.1594     0.8625    1.0384     1.295
tabac2         1.0297     0.9711    0.9198     1.153
dial2          1.0241     0.9764    0.8661     1.211
apkd011        0.7595     1.3167    0.5627     1.025
diabetes1      1.2699     0.7875    1.1439     1.410
sex            1.1689     0.8555    1.0432     1.310
age            1.0197     0.9807    1.0154     1.024

Concordance= 0.596  (se = 0.007 )
Likelihood ratio test= 179.4  on 7 df,   p=<2e-16
Wald test            = 161.5  on 7 df,   p=<2e-16
Score (logrank) test = 164.3  on 7 df,   p=<2e-16

Defintion of the variable diabetes:

rdb$diabetes = if_else(rdb$TYPDIABn > 0,  1 ,  0 )
table(rdb$diabetes)
rdb$diabetesMISS <- rdb$diabetes
rdb$diabetesMISS[is.na(rdb$diabetes)] <-  miss 
prop.table(table(rdb$diabetesMISS))*100

#                0                 1          missing 
# 22943 (50.955004) 20156 (44.765247)  1927 (4.279749)

ADPKD POPULATION (`n=2560`)

UNIVARIATE

              beta HR (95% CI for HR) wald.test p.value
cardiovasc    0.55          1.7 (1-3)       3.8    0.05
tabac2     -0.0079    0.99 (0.57-1.7)         0    0.98
dial         0.015       1 (0.48-2.1)         0    0.97
AVCAITn        1.5      4.3 (2.4-7.5)        26 4.1e-07
sex          -0.42    0.66 (0.39-1.1)       2.5    0.11
age          0.042          1 (1-1.1)        19 1.4e-05
tabac2.1   -0.0079    0.99 (0.57-1.7)         0    0.98
diabetes      0.34       1.4 (0.67-3)      0.81    0.37
bmi         -0.036      0.96 (0.91-1)       1.4    0.24

MODEL 1

Call:
coxph(formula = Surv(time, status) ~ cardiovasc + tabac2 + dial + 
    sex + age, data = apdb)

                coef exp(coef) se(coef)      z Pr(>|z|)   
cardiovasc1  0.21598   1.24108  0.33956  0.636  0.52474   
tabac2       0.19321   1.21314  0.31788  0.608  0.54331   
dial2        0.47303   1.60485  0.39020  1.212  0.22540   
sex         -0.12280   0.88444  0.31274 -0.393  0.69457   
age          0.03786   1.03859  0.01206  3.141  0.00168 ** 
---

            exp(coef) exp(-coef) lower .95 upper .95
cardiovasc1    1.2411     0.8058    0.6379     2.415
tabac2         1.2131     0.8243    0.6506     2.262
dial2          1.6049     0.6231    0.7470     3.448
sex            0.8844     1.1307    0.4791     1.633
age            1.0386     0.9628    1.0143     1.063

Concordance= 0.651  (se = 0.044 )
Likelihood ratio test= 14.62  on 5 df,   p=0.01
Wald test            = 14.93  on 5 df,   p=0.01
Score (logrank) test = 15.25  on 5 df,   p=0.009

MODEL 2: VARIABLE `DIABETES`

Call:
coxph(formula = Surv(time, status) ~ cardiovasc + tabac2 + dial + 
    sex + age + diabetes, data = apdb)

                coef exp(coef) se(coef)      z Pr(>|z|)   
cardiovasc1  0.16003   1.17354  0.34647  0.462   0.6442   
tabac2       0.15446   1.16703  0.32268  0.479   0.6322   
dial2        0.49600   1.64214  0.39120  1.268   0.2048   
sex         -0.11117   0.89479  0.31532 -0.353   0.7244   
age          0.03835   1.03910  0.01222  3.139   0.0017 ** 
diabetes1   -0.06794   0.93431  0.47738 -0.142   0.8868   
---

            exp(coef) exp(-coef) lower .95 upper .95
cardiovasc1    1.1735     0.8521    0.5951     2.314
tabac2         1.1670     0.8569    0.6200     2.197
dial2          1.6421     0.6090    0.7628     3.535
sex            0.8948     1.1176    0.4823     1.660
age            1.0391     0.9624    1.0145     1.064
diabetes1      0.9343     1.0703    0.3666     2.381

Concordance= 0.646  (se = 0.044 )
Likelihood ratio test= 13.82  on 6 df,   p=0.03
Wald test            = 14.1  on 6 df,   p=0.03
Score (logrank) test = 14.38  on 6 df,   p=0.03

Variable `AGE`

summary(apdb$age)
Min.  1st Qu.  Median    Mean  3rd Qu.   Max. 
18.64   49.43   57.36   58.95   67.83   98.62 
  
sd(apdb$age)
12.79057

EXCLUSION OF `DIABETES(+)` PATIENTS

WHOLE POPULATION (`n=22943`)

Call:
coxph(formula = Surv(time, status) ~ cardiovasc + tabac2 + dial + 
    apkd01 + sex + age, data = didb)

                 coef exp(coef)  se(coef)      z Pr(>|z|)    
cardiovasc1  0.202314  1.224233  0.083604  2.420   0.0155 *  
tabac2       0.058089  1.059809  0.083845  0.693   0.4884    
dial2       -0.171095  0.842742  0.128771 -1.329   0.1840    
apkd011     -0.168263  0.845131  0.164204 -1.025   0.3055    
sex          0.138032  1.148012  0.084972  1.624   0.1043    
age          0.025470  1.025798  0.002939  8.666   <2e-16 ***
---

            exp(coef) exp(-coef) lower .95 upper .95
cardiovasc1    1.2242     0.8168    1.0392     1.442
tabac2         1.0598     0.9436    0.8992     1.249
dial2          0.8427     1.1866    0.6548     1.085
apkd011        0.8451     1.1832    0.6126     1.166
sex            1.1480     0.8711    0.9719     1.356
age            1.0258     0.9749    1.0199     1.032

Concordance= 0.63  (se = 0.01 )
Likelihood ratio test= 135.4  on 6 df,   p=<2e-16
Wald test            = 120  on 6 df,   p=<2e-16
Score (logrank) test = 124.2  on 6 df,   p=<2e-16

ADPKD POPULATION (`n=2006`)

Call:
coxph(formula = Surv(time, status) ~ cardiovasc + tabac2 + dial + 
    sex + age, data = apdbdiab)

                coef exp(coef) se(coef)      z Pr(>|z|)   
cardiovasc1  0.10450   1.11016  0.37717  0.277  0.78173   
tabac2       0.17251   1.18829  0.33990  0.508  0.61178   
dial2        0.42113   1.52368  0.41760  1.008  0.31324   
sex         -0.03857   0.96216  0.33073 -0.117  0.90715   
age          0.03459   1.03519  0.01276  2.710  0.00672 **
---

            exp(coef) exp(-coef) lower .95 upper .95
cardiovasc1    1.1102     0.9008    0.5301     2.325
tabac2         1.1883     0.8415    0.6104     2.313
dial2          1.5237     0.6563    0.6721     3.454
sex            0.9622     1.0393    0.5032     1.840
age            1.0352     0.9660    1.0096     1.061

Concordance= 0.626  (se = 0.046 )
Likelihood ratio test= 9.68  on 5 df,   p=0.08
Wald test            = 9.91  on 5 df,   p=0.08
Score (logrank) test = 10.06  on 5 df,   p=0.07

NUMBER OF EVENTS (STROKE)

ALL THE EVENTS (BEFORE THE INCLUSION AND REPEATED) ²

table(hosp$DGN_PAL01h)

    0     1 
13165  5378

THE EVENTS IN THE REGISTRY

table(rdb$AVCAITn)

    0     1 
38803  5026

HOW MANY EVENTS ARE A RELAPSE

table(dgn_oneclassifier$relapse)

  event   multiple events 
   7888              4629

DESCRIPTIVE

REIN POPULATION

                level   Overall      
  n                       45026        
  age (mean (SD))         68.25 (15.20)
  sex (%)         1       28965 (64.3) 
                  2       16061 (35.7) 
  URGn (%)        0       29375 (71.3) 
                  1       11822 (28.7) 
  KTTINIn (%)     0       17840 (44.8) 
                  1       22021 (55.2) 
  EPOINIn (%)     0       19397 (52.9) 
                  1       17244 (47.1) 
  nephgp (%)      APKD     2560 ( 5.7) 
                  autre    6345 (14.1) 
                  diabète 10172 (22.6) 
                  gnc      5016 (11.1) 
                  HTA     11225 (24.9) 
                  Inconnu  7326 (16.3) 
                  pyelo    2012 ( 4.5) 
                  vasc      370 ( 0.8) 
  METHOn (%)      1       38725 (86.0) 
                  2        4516 (10.0) 
                  3        1785 ( 4.0) 
  techn (%)       1       30816 (68.4) 
                  2          79 ( 0.2) 
                  3        7778 (17.3) 
                  4         980 ( 2.2) 
                  5        3532 ( 7.8) 
                  6          33 ( 0.1) 
                  7           4 ( 0.0) 
                  8        1785 ( 4.0) 
                  9          19 ( 0.0) 
  MODALn (%)      1       35709 (82.6) 
                  2        1256 ( 2.9) 
                  3        3003 ( 6.9) 
                  4          27 ( 0.1) 
                  5         361 ( 0.8) 
                  6        2885 ( 6.7) 
  VAVn (%)        1       17189 (44.6) 
                  2       18224 (47.3) 
                  3         495 ( 1.3) 
                  4        2593 ( 6.7) 
  traitement (%)  1       35447 (78.7) 
                  2        1256 ( 2.8) 
                  3         388 ( 0.9) 
                  4          14 ( 0.0) 
                  5        1620 ( 3.6) 
                  6        2442 ( 5.4) 
                  7         691 ( 1.5) 
                  8        1383 ( 3.1) 
                  9        1785 ( 4.0) 
  IRCn (%)        0       37767 (86.6) 
                  1        5865 (13.4) 
  O2n (%)         0       40761 (93.4) 
                  1        2882 ( 6.6) 
  ICn (%)         0       32457 (74.2) 
                  1       11297 (25.8) 
  ICOROn (%)      0       32909 (75.4) 
                  1       10735 (24.6) 
  IDMn (%)        0       39033 (89.4) 
                  1        4632 (10.6) 
  RYTHMn (%)      0       33539 (76.5) 
                  1       10301 (23.5) 
  ANEVn (%)       0       39464 (96.1) 
                  1        1602 ( 3.9) 
  AMIn (%)        0       34511 (80.2) 
                  1        8524 (19.8) 
  AVCAITn (%)     0       38803 (88.5) 
                  1        5026 (11.5) 
  KCn (%)         0       37298 (88.5) 
                  1        4826 (11.5) 
  VHBn (%)        0       41679 (98.1) 
                  1         795 ( 1.9) 
  VHCn (%)        0       41095 (98.9) 
                  1         461 ( 1.1) 
  CIRHn (%)       0       42650 (97.2) 
                  1        1238 ( 2.8) 
  VIHn (%)        0       41590 (99.2) 
                  1         316 ( 0.8) 
  SIDAn (%)       0       41604 (99.7) 
                  1         107 ( 0.3) 
  HANDn (%)       0       34509 (84.7) 
                  1        6255 (15.3) 
  AMPn (%)        0       38797 (97.8) 
                  1         857 ( 2.2) 
  PLEGn (%)       0       38968 (98.3) 
                  1         670 ( 1.7) 
  CECITEn (%)     0       38230 (96.3) 
                  1        1456 ( 3.7) 
  COMPORTn (%)    0       38482 (96.8) 
                  1        1273 ( 3.2) 
  TABACn (%)      0       20288 (55.4) 
                  1        4850 (13.2) 
                  2       11476 (31.3) 
  bmi (mean (SD))         26.71 (5.85) 
  tabac2 (%)      0       20288 (55.4) 
                  1       16326 (44.6) 
  iresp (%)       0       36276 (83.3) 
                  1        7249 (16.7) 
  sero (%)        0       41269 (99.2) 
                  1         327 ( 0.8) 
  coro (%)        0       32334 (74.2) 
                  1       11244 (25.8) 
  foie (%)        0       39519 (94.4) 
                  1        2328 ( 5.6) 
  apkd01 (%)      0       42466 (94.3) 
                  1        2560 ( 5.7) 
  TRANSP (%)      0       37730 (83.8) 
                  1        7296 (16.2) 
  diabetes (%)    0       22943 (53.2) 
                  1       20156 (46.8)

APKD POPULATION

                level Overall       
  n                      2560         
  age (mean (SD))       58.95 (12.79) 
  sex (%)         1      1351 ( 52.8) 
                  2      1209 ( 47.2) 
  URGn (%)        0      1895 ( 88.3) 
                  1       250 ( 11.7) 
  KTTINIn (%)     0      1455 ( 71.6) 
                  1       577 ( 28.4) 
  EPOINIn (%)     0      1029 ( 54.1) 
                  1       874 ( 45.9) 
  nephgp (%)      APKD   2560 (100.0) 
  METHOn (%)      1      1931 ( 75.4) 
                  2       290 ( 11.3) 
                  3       339 ( 13.2) 
  techn (%)       1      1561 ( 61.0) 
                  3       365 ( 14.3) 
                  4       120 (  4.7) 
                  5       170 (  6.6) 
                  6         1 (  0.0) 
                  8       339 ( 13.2) 
                  9         4 (  0.2) 
  MODALn (%)      1      1569 ( 70.6) 
                  2       117 (  5.3) 
                  3       288 ( 13.0) 
                  4         3 (  0.1) 
                  5        51 (  2.3) 
                  6       193 (  8.7) 
  VAVn (%)        1      1360 ( 70.7) 
                  2       464 ( 24.1) 
                  3        39 (  2.0) 
                  4        60 (  3.1) 
  traitement (%)  1      1549 ( 60.5) 
                  2       117 (  4.6) 
                  3        54 (  2.1) 
                  5       211 (  8.2) 
                  6       129 (  5.0) 
                  7        84 (  3.3) 
                  8        77 (  3.0) 
                  9       339 ( 13.2) 
  IRCn (%)        0      2389 ( 95.2) 
                  1       120 (  4.8) 
  O2n (%)         0      2460 ( 98.0) 
                  1        49 (  2.0) 
  ICn (%)         0      2313 ( 92.2) 
                  1       196 (  7.8) 
  ICOROn (%)      0      2293 ( 91.2) 
                  1       220 (  8.8) 
  IDMn (%)        0      2410 ( 95.9) 
                  1       102 (  4.1) 
  RYTHMn (%)      0      2295 ( 91.3) 
                  1       219 (  8.7) 
  ANEVn (%)       0      2101 ( 97.9) 
                  1        44 (  2.1) 
  AMIn (%)        0      2363 ( 94.9) 
                  1       127 (  5.1) 
  AVCAITn (%)     0      2296 ( 91.2) 
                  1       221 (  8.8) 
  KCn (%)         0      2085 ( 95.8) 
                  1        92 (  4.2) 
  VHBn (%)        0      2158 ( 94.9) 
                  1       117 (  5.1) 
  VHCn (%)        0      2133 ( 99.3) 
                  1        15 (  0.7) 
  CIRHn (%)       0      2511 ( 99.6) 
                  1        10 (  0.4) 
  VIHn (%)        0      2164 ( 99.5) 
                  1        10 (  0.5) 
  SIDAn (%)       0      2160 ( 99.8) 
                  1         4 (  0.2) 
  HANDn (%)       0      1984 ( 93.6) 
                  1       136 (  6.4) 
  AMPn (%)        0      2082 ( 99.9) 
                  1         3 (  0.1) 
  PLEGn (%)       0      2071 ( 98.9) 
                  1        22 (  1.1) 
  CECITEn (%)     0      2073 ( 99.3) 
                  1        15 (  0.7) 
  COMPORTn (%)    0      2058 ( 98.2) 
                  1        37 (  1.8) 
  TABACn (%)      0      1193 ( 57.9) 
                  1       326 ( 15.8) 
                  2       542 ( 26.3) 
  bmi (mean (SD))       25.80 (4.97)  
  tabac2 (%)      0      1193 ( 57.9) 
                  1       868 ( 42.1) 
  iresp (%)       0      2357 ( 94.1) 
                  1       149 (  5.9) 
  sero (%)        0      2149 ( 99.5) 
                  1        10 (  0.5) 
  coro (%)        0      2272 ( 90.6) 
                  1       235 (  9.4) 
  foie (%)        0      2101 ( 93.8) 
                  1       139 (  6.2) 
  apkd01 (%)      1      2560 (100.0) 
  TRANSP (%)      0      1317 ( 51.4) 
                  1      1243 ( 48.6) 
  diabetes (%)    0      2006 ( 90.2) 
                  1       217 (  9.8)

G81* (n=23), H53* (n=3) and R47* (n=90) are never associated with a second relevant event.↩︎
G45* + G46* + I60* + I61* + I62* + I63* + I64*↩︎

REIN - MARCH 22

Damiano

3/9/2022

EVENTS

TYPE OF EVENTS ¹

TRANSPLANT VAR

MODELS

REIN POPULATION (`n=45026`)

UNIVARIATE

MODEL 1

MODEL 2: VARIABLE `DIABETES`

ADPKD POPULATION (`n=2560`)

UNIVARIATE

MODEL 1

MODEL 2: VARIABLE `DIABETES`

Variable `AGE`

EXCLUSION OF `DIABETES(+)` PATIENTS

WHOLE POPULATION (`n=22943`)

ADPKD POPULATION (`n=2006`)

NUMBER OF EVENTS (STROKE)

ALL THE EVENTS (BEFORE THE INCLUSION AND REPEATED) ²

THE EVENTS IN THE REGISTRY

HOW MANY EVENTS ARE A RELAPSE

DESCRIPTIVE

REIN POPULATION

APKD POPULATION

REIN - MARCH 22

Damiano

3/9/2022

EVENTS

TYPE OF EVENTS 1

TRANSPLANT VAR

MODELS

REIN POPULATION (n=45026)

UNIVARIATE

MODEL 1

MODEL 2: VARIABLE DIABETES

ADPKD POPULATION (n=2560)

UNIVARIATE

MODEL 1

MODEL 2: VARIABLE DIABETES

Variable AGE

EXCLUSION OF DIABETES(+) PATIENTS

WHOLE POPULATION (n=22943)

ADPKD POPULATION (n=2006)

NUMBER OF EVENTS (STROKE)

ALL THE EVENTS (BEFORE THE INCLUSION AND REPEATED) 2

THE EVENTS IN THE REGISTRY

HOW MANY EVENTS ARE A RELAPSE

DESCRIPTIVE

REIN POPULATION

APKD POPULATION

TYPE OF EVENTS ¹

REIN POPULATION (`n=45026`)

MODEL 2: VARIABLE `DIABETES`

ADPKD POPULATION (`n=2560`)

MODEL 2: VARIABLE `DIABETES`

Variable `AGE`

EXCLUSION OF `DIABETES(+)` PATIENTS

WHOLE POPULATION (`n=22943`)

ADPKD POPULATION (`n=2006`)

ALL THE EVENTS (BEFORE THE INCLUSION AND REPEATED) ²