Stratified by centre
Overall a
n 562 141
PAPS_11nov (median [IQR]) 36.0 [30.0, 42.0] 36.0 [30.0, 41.0]
gender_1_men = 1 (%) 233 (41.5) 67 (47.5)
ageatprocedure (median [IQR]) 83.0 [80.0, 87.0] 83.0 [80.0, 86.0]
NHYA_baseline (%)
1 14 ( 2.5) 13 ( 9.2)
2 265 (47.2) 57 (40.4)
3 270 (48.0) 63 (44.7)
4 13 ( 2.3) 8 ( 5.7)
Test_cognitivo (%)
0 145 (25.8) 39 (27.7)
1 112 (19.9) 48 (34.0)
2 131 (23.3) 40 (28.4)
3 62 (11.0) 5 ( 3.5)
4 40 ( 7.1) 6 ( 4.3)
5 39 ( 6.9) 2 ( 1.4)
6 31 ( 5.5) 0 ( 0.0)
7 2 ( 0.4) 1 ( 0.7)
BADL (%)
1 36 ( 6.4) 4 ( 2.8)
2 5 ( 0.9) 3 ( 2.1)
3 24 ( 4.3) 2 ( 1.4)
4 68 (12.1) 6 ( 4.3)
5 144 (25.7) 33 (23.4)
6 283 (50.5) 93 (66.0)
IADL (%)
0 2 ( 0.4) 1 ( 0.7)
1 16 ( 3.0) 5 ( 3.5)
2 20 ( 3.8) 7 ( 5.0)
3 23 ( 4.4) 9 ( 6.4)
4 50 ( 9.5) 7 ( 5.0)
5 87 (16.6) 11 ( 7.8)
6 83 (15.8) 16 (11.3)
7 82 (15.6) 17 (12.1)
8 162 (30.9) 68 (48.2)
MNA_sh (%)
4 2 ( 0.4) 0 ( 0.0)
5 4 ( 0.7) 1 ( 0.7)
6 13 ( 2.3) 1 ( 0.7)
7 20 ( 3.6) 2 ( 1.4)
8 36 ( 6.4) 8 ( 5.7)
9 54 ( 9.6) 15 (10.6)
10 85 (15.1) 36 (25.5)
11 81 (14.4) 13 ( 9.2)
12 134 (23.8) 46 (32.6)
13 49 ( 8.7) 10 ( 7.1)
14 84 (14.9) 9 ( 6.4)
LVEF (median [IQR]) 56.0 [48.0, 62.0] 59.0 [54.0, 64.0]
Grad_picco (median [IQR]) 73.0 [58.7, 88.0] 76.0 [67.0, 87.0]
Grad_medio (median [IQR]) 45.0 [33.0, 59.0] 47.0 [39.0, 55.0]
AVAplan (median [IQR]) 0.5 [0.4, 0.6] 0.4 [0.4, 0.5]
Crea_pre_op_feb2024 (median [IQR]) 0.9 [0.7, 1.2] 0.8 [0.7, 1.0]
CKDEPI_Syn (median [IQR]) 65.5 [47.8, 79.8] 75.6 [60.6, 83.8]
RHYTHM_1FA2PM3RS (%)
1 74 (13.2) 34 (24.1)
2 31 ( 5.5) 12 ( 8.5)
3 457 (81.3) 95 (67.4)
RHYTHM_FA = 1 (%) 105 (18.7) 46 (32.6)
BAV_1 (%)
0 56 (10.0) 0 ( 0.0)
1 477 (84.9) 126 (89.4)
2 29 ( 5.2) 15 (10.6)
LBBB_10 (%)
0 60 (10.7) 0 ( 0.0)
1 488 (86.8) 127 (90.1)
2 14 ( 2.5) 14 ( 9.9)
Composite_rechek = 1 (%) 78 (13.9) 10 ( 7.1)
Decesso_10 = 1 (%) 75 (13.3) 10 ( 7.1)
Stratified by centre
b c
n 37 384
PAPS_11nov (median [IQR]) 36.0 [30.0, 41.0] 36.0 [30.0, 42.0]
gender_1_men = 1 (%) 14 (37.8) 152 (39.6)
ageatprocedure (median [IQR]) 84.0 [82.0, 87.0] 83.0 [80.0, 87.0]
NHYA_baseline (%)
1 0 ( 0.0) 1 ( 0.3)
2 21 (56.8) 187 (48.7)
3 14 (37.8) 193 (50.3)
4 2 ( 5.4) 3 ( 0.8)
Test_cognitivo (%)
0 31 (83.8) 75 (19.5)
1 6 (16.2) 58 (15.1)
2 0 ( 0.0) 91 (23.7)
3 0 ( 0.0) 57 (14.8)
4 0 ( 0.0) 34 ( 8.9)
5 0 ( 0.0) 37 ( 9.6)
6 0 ( 0.0) 31 ( 8.1)
7 0 ( 0.0) 1 ( 0.3)
BADL (%)
1 1 ( 2.9) 31 ( 8.1)
2 0 ( 0.0) 2 ( 0.5)
3 1 ( 2.9) 21 ( 5.5)
4 2 ( 5.7) 60 (15.6)
5 15 (42.9) 96 (25.0)
6 16 (45.7) 174 (45.3)
IADL (%)
0 0 ( NaN) 1 ( 0.3)
1 0 ( NaN) 11 ( 2.9)
2 0 ( NaN) 13 ( 3.4)
3 0 ( NaN) 14 ( 3.6)
4 0 ( NaN) 43 (11.2)
5 0 ( NaN) 76 (19.8)
6 0 ( NaN) 67 (17.4)
7 0 ( NaN) 65 (16.9)
8 0 ( NaN) 94 (24.5)
MNA_sh (%)
4 0 ( 0.0) 2 ( 0.5)
5 0 ( 0.0) 3 ( 0.8)
6 1 ( 2.7) 11 ( 2.9)
7 1 ( 2.7) 17 ( 4.4)
8 3 ( 8.1) 25 ( 6.5)
9 2 ( 5.4) 37 ( 9.6)
10 2 ( 5.4) 47 (12.2)
11 9 (24.3) 59 (15.4)
12 12 (32.4) 76 (19.8)
13 3 ( 8.1) 36 ( 9.4)
14 4 (10.8) 71 (18.5)
LVEF (median [IQR]) 60.0 [55.0, 62.0] 55.0 [46.0, 60.0]
Grad_picco (median [IQR]) 83.5 [70.0, 95.5] 71.0 [56.0, 88.0]
Grad_medio (median [IQR]) 52.0 [45.0, 62.0] 44.0 [29.0, 61.0]
AVAplan (median [IQR]) NA [NA, NA] 0.5 [0.4, 0.6]
Crea_pre_op_feb2024 (median [IQR]) NA [NA, NA] 0.9 [0.8, 1.3]
CKDEPI_Syn (median [IQR]) 75.0 [55.0, 88.0] 61.0 [43.2, 77.6]
RHYTHM_1FA2PM3RS (%)
1 4 (10.8) 36 ( 9.4)
2 1 ( 2.7) 18 ( 4.7)
3 32 (86.5) 330 (85.9)
RHYTHM_FA = 1 (%) 5 (13.5) 54 (14.1)
BAV_1 (%)
0 30 (81.1) 26 ( 6.8)
1 7 (18.9) 344 (89.6)
2 0 ( 0.0) 14 ( 3.6)
LBBB_10 (%)
0 33 (89.2) 27 ( 7.0)
1 4 (10.8) 357 (93.0)
2 0 ( 0.0) 0 ( 0.0)
Composite_rechek = 1 (%) 6 (16.2) 62 (16.1)
Decesso_10 = 1 (%) 6 (16.2) 59 (15.4) EDA TAVI
Caratteristiche del campione, divise per setting. Ho diviso basandomi sul ID (non so se ha senso): A = PD, B = FIN, C = niente
Faccio factor analysis sul setting C
Decido di utilizzare 4 fattori (elbow method sul grafico sotto). Identifico i pesi delle variabili in 4 fattori (tabella) e mi concentro sugli ultimi due (ML3 e ML4). I due fattori sono ortogonali (lo scatter mostra come non ci sia relazione tra score derivato da ML3 e da ML4)
Loadings:
ML1 ML2 ML3 ML4
PAPS_11nov 0.416
gender_1_men
ageatprocedure
NHYA_baseline
Test_cognitivo -0.357
BADL -0.653
MNA_sh 0.472
LVEF
Grad_picco 0.981
Grad_medio 0.996
CKDEPI_Syn
RHYTHM_FA
BAV_1 0.831
LBBB_10 0.886
ML1 ML2 ML3 ML4
SS loadings 2.009 1.652 0.790 0.699
Proportion Var 0.144 0.118 0.056 0.050
Cumulative Var 0.144 0.262 0.318 0.368
Valuto la capacità discriminativa - nel training - dei due score, dopo averli creati utilizzando i pesi di prima. Gli intervalli di confidenza sono calcolati in bootstrapping.
Call:
roc.formula(formula = Composite_rechek ~ score1, data = train.df, quiet = TRUE, ci = TRUE, ci.method = "boot")
Data: score1 in 322 controls (Composite_rechek 0) < 62 cases (Composite_rechek 1).
Area under the curve: 0.8427
95% CI: 0.7742-0.9005 (2000 stratified bootstrap replicates)
Call:
roc.formula(formula = Composite_rechek ~ score2, data = train.df, quiet = TRUE, ci = TRUE, ci.method = "boot")
Data: score2 in 322 controls (Composite_rechek 0) > 62 cases (Composite_rechek 1).
Area under the curve: 0.75
95% CI: 0.6864-0.8104 (2000 stratified bootstrap replicates)
Scelgo ML3. Valuto le caratteristiche del campione di training in base ai terzili di score:
Stratified by score1.tertile
Overall 1
n 384 129
PAPS_11nov (median [IQR]) 36.0 [30.0, 42.0] 28.0 [25.0, 30.0]
gender_1_men = 1 (%) 152 (39.6) 50 (38.8)
ageatprocedure (median [IQR]) 83.0 [80.0, 87.0] 82.0 [80.0, 85.0]
NHYA_baseline (%)
1 1 ( 0.3) 1 ( 0.8)
2 187 (48.7) 68 (52.7)
3 193 (50.3) 59 (45.7)
4 3 ( 0.8) 1 ( 0.8)
Test_cognitivo (%)
0 75 (19.5) 31 (24.0)
1 58 (15.1) 31 (24.0)
2 91 (23.7) 29 (22.5)
3 57 (14.8) 19 (14.7)
4 34 ( 8.9) 5 ( 3.9)
5 37 ( 9.6) 8 ( 6.2)
6 31 ( 8.1) 5 ( 3.9)
7 1 ( 0.3) 1 ( 0.8)
BADL (%)
1 31 ( 8.1) 0 ( 0.0)
2 2 ( 0.5) 0 ( 0.0)
3 21 ( 5.5) 6 ( 4.7)
4 60 (15.6) 9 ( 7.0)
5 96 (25.0) 35 (27.1)
6 174 (45.3) 79 (61.2)
IADL (%)
0 1 ( 0.3) 0 ( 0.0)
1 11 ( 2.9) 3 ( 2.3)
2 13 ( 3.4) 3 ( 2.3)
3 14 ( 3.6) 1 ( 0.8)
4 43 (11.2) 8 ( 6.2)
5 76 (19.8) 18 (14.0)
6 67 (17.4) 29 (22.5)
7 65 (16.9) 26 (20.2)
8 94 (24.5) 41 (31.8)
MNA_sh (%)
4 2 ( 0.5) 1 ( 0.8)
5 3 ( 0.8) 3 ( 2.3)
6 11 ( 2.9) 2 ( 1.6)
7 17 ( 4.4) 5 ( 3.9)
8 25 ( 6.5) 5 ( 3.9)
9 37 ( 9.6) 5 ( 3.9)
10 47 (12.2) 19 (14.7)
11 59 (15.4) 18 (14.0)
12 76 (19.8) 30 (23.3)
13 36 ( 9.4) 14 (10.9)
14 71 (18.5) 27 (20.9)
LVEF (median [IQR]) 55.0 [46.0, 60.0] 55.0 [45.0, 61.0]
Grad_picco (median [IQR]) 71.0 [56.0, 88.0] 71.0 [56.0, 88.0]
Grad_medio (median [IQR]) 44.0 [29.0, 61.0] 44.0 [28.0, 59.0]
AVAplan (median [IQR]) 0.5 [0.4, 0.6] 0.5 [0.4, 0.6]
Crea_pre_op_feb2024 (median [IQR]) 0.9 [0.8, 1.3] 0.9 [0.8, 1.1]
CKDEPI_Syn (median [IQR]) 61.0 [43.2, 77.6] 68.8 [49.3, 79.5]
RHYTHM_1FA2PM3RS (%)
1 36 ( 9.4) 10 ( 7.8)
2 18 ( 4.7) 9 ( 7.0)
3 330 (85.9) 110 (85.3)
RHYTHM_FA = 1 (%) 54 (14.1) 19 (14.7)
BAV_1 (%)
0 26 ( 6.8) 15 (11.6)
1 344 (89.6) 108 (83.7)
2 14 ( 3.6) 6 ( 4.7)
LBBB_10 = 1 (%) 357 (93.0) 115 (89.1)
Composite_rechek = 1 (%) 62 (16.1) 4 ( 3.1)
Decesso_10 = 1 (%) 59 (15.4) 2 ( 1.6)
Stratified by score1.tertile
2 3
n 130 125
PAPS_11nov (median [IQR]) 36.0 [34.0, 39.0] 44.0 [42.0, 50.0]
gender_1_men = 1 (%) 47 (36.2) 55 (44.0)
ageatprocedure (median [IQR]) 84.0 [80.0, 87.0] 83.0 [80.0, 86.0]
NHYA_baseline (%)
1 0 ( 0.0) 0 ( 0.0)
2 54 (41.5) 65 (52.0)
3 75 (57.7) 59 (47.2)
4 1 ( 0.8) 1 ( 0.8)
Test_cognitivo (%)
0 24 (18.5) 20 (16.0)
1 15 (11.5) 12 ( 9.6)
2 36 (27.7) 26 (20.8)
3 20 (15.4) 18 (14.4)
4 9 ( 6.9) 20 (16.0)
5 16 (12.3) 13 (10.4)
6 10 ( 7.7) 16 (12.8)
7 0 ( 0.0) 0 ( 0.0)
BADL (%)
1 5 ( 3.8) 26 (20.8)
2 0 ( 0.0) 2 ( 1.6)
3 8 ( 6.2) 7 ( 5.6)
4 22 (16.9) 29 (23.2)
5 34 (26.2) 27 (21.6)
6 61 (46.9) 34 (27.2)
IADL (%)
0 0 ( 0.0) 1 ( 0.8)
1 1 ( 0.8) 7 ( 5.6)
2 5 ( 3.8) 5 ( 4.0)
3 8 ( 6.2) 5 ( 4.0)
4 11 ( 8.5) 24 (19.2)
5 26 (20.0) 32 (25.6)
6 23 (17.7) 15 (12.0)
7 21 (16.2) 18 (14.4)
8 35 (26.9) 18 (14.4)
MNA_sh (%)
4 0 ( 0.0) 1 ( 0.8)
5 0 ( 0.0) 0 ( 0.0)
6 3 ( 2.3) 6 ( 4.8)
7 4 ( 3.1) 8 ( 6.4)
8 13 (10.0) 7 ( 5.6)
9 12 ( 9.2) 20 (16.0)
10 14 (10.8) 14 (11.2)
11 20 (15.4) 21 (16.8)
12 32 (24.6) 14 (11.2)
13 12 ( 9.2) 10 ( 8.0)
14 20 (15.4) 24 (19.2)
LVEF (median [IQR]) 55.0 [48.0, 60.0] 54.0 [45.0, 60.0]
Grad_picco (median [IQR]) 67.5 [54.0, 85.0] 73.0 [59.0, 93.0]
Grad_medio (median [IQR]) 42.5 [28.2, 56.8] 47.0 [34.0, 65.0]
AVAplan (median [IQR]) 0.5 [0.4, 0.6] 0.5 [0.4, 0.6]
Crea_pre_op_feb2024 (median [IQR]) 0.9 [0.8, 1.2] 1.1 [0.8, 1.4]
CKDEPI_Syn (median [IQR]) 58.9 [42.8, 78.9] 56.9 [41.4, 69.0]
RHYTHM_1FA2PM3RS (%)
1 10 ( 7.7) 16 (12.8)
2 5 ( 3.8) 4 ( 3.2)
3 115 (88.5) 105 (84.0)
RHYTHM_FA = 1 (%) 15 (11.5) 20 (16.0)
BAV_1 (%)
0 2 ( 1.5) 9 ( 7.2)
1 123 (94.6) 113 (90.4)
2 5 ( 3.8) 3 ( 2.4)
LBBB_10 = 1 (%) 126 (96.9) 116 (92.8)
Composite_rechek = 1 (%) 11 ( 8.5) 47 (37.6)
Decesso_10 = 1 (%) 11 ( 8.5) 46 (36.8) Applico lo score al dataset di test (A+B) e ne valuto la capacità discriminativa
Call:
roc.formula(formula = Composite_rechek ~ score1, data = df[df$centre != "c", ], quiet = TRUE, ci = TRUE, ci.method = "boot")
Data: score1 in 162 controls (Composite_rechek 0) < 14 cases (Composite_rechek 1).
Area under the curve: 0.8058
95% CI: 0.6838-0.9085 (2000 stratified bootstrap replicates)
Valuto le caratteristiche in base ai terzili di score (ottenuti nel train) nel test
Stratified by score1.tertile
Overall 1
n 178 41
PAPS_11nov (median [IQR]) 36.0 [30.0, 41.0] 27.0 [25.0, 30.0]
gender_1_men = 1 (%) 81 (45.5) 24 (58.5)
ageatprocedure (median [IQR]) 83.0 [80.0, 86.0] 82.0 [79.0, 86.0]
NHYA_baseline (%)
1 13 ( 7.3) 3 ( 7.3)
2 78 (43.8) 14 (34.1)
3 77 (43.3) 23 (56.1)
4 10 ( 5.6) 1 ( 2.4)
Test_cognitivo (%)
0 70 (39.3) 16 (39.0)
1 54 (30.3) 8 (19.5)
2 40 (22.5) 15 (36.6)
3 5 ( 2.8) 0 ( 0.0)
4 6 ( 3.4) 2 ( 4.9)
5 2 ( 1.1) 0 ( 0.0)
7 1 ( 0.6) 0 ( 0.0)
BADL (%)
1 5 ( 2.8) 0 ( 0.0)
2 3 ( 1.7) 1 ( 2.4)
3 3 ( 1.7) 0 ( 0.0)
4 8 ( 4.5) 2 ( 4.9)
5 48 (27.3) 10 (24.4)
6 109 (61.9) 28 (68.3)
IADL (%)
0 1 ( 0.7) 0 ( 0.0)
1 5 ( 3.5) 1 ( 3.1)
2 7 ( 5.0) 2 ( 6.2)
3 9 ( 6.4) 3 ( 9.4)
4 7 ( 5.0) 3 ( 9.4)
5 11 ( 7.8) 1 ( 3.1)
6 16 (11.3) 2 ( 6.2)
7 17 (12.1) 5 (15.6)
8 68 (48.2) 15 (46.9)
MNA_sh (%)
5 1 ( 0.6) 0 ( 0.0)
6 2 ( 1.1) 0 ( 0.0)
7 3 ( 1.7) 0 ( 0.0)
8 11 ( 6.2) 3 ( 7.3)
9 17 ( 9.6) 0 ( 0.0)
10 38 (21.3) 11 (26.8)
11 22 (12.4) 4 ( 9.8)
12 58 (32.6) 13 (31.7)
13 13 ( 7.3) 7 (17.1)
14 13 ( 7.3) 3 ( 7.3)
LVEF (median [IQR]) 60.0 [55.0, 64.0] 60.0 [55.0, 64.0]
Grad_picco (median [IQR]) 77.0 [67.0, 88.0] 77.0 [66.0, 85.2]
Grad_medio (median [IQR]) 48.0 [40.0, 56.8] 48.0 [40.0, 55.0]
AVAplan (median [IQR]) 0.4 [0.4, 0.5] 0.4 [0.4, 0.5]
Crea_pre_op_feb2024 (median [IQR]) 0.8 [0.7, 1.0] 0.8 [0.7, 1.0]
CKDEPI_Syn (median [IQR]) 75.0 [60.0, 84.7] 79.3 [64.7, 87.0]
RHYTHM_1FA2PM3RS (%)
1 38 (21.3) 4 ( 9.8)
2 13 ( 7.3) 5 (12.2)
3 127 (71.3) 32 (78.0)
RHYTHM_FA = 1 (%) 51 (28.7) 9 (22.0)
BAV_1 (%)
0 30 (16.9) 6 (14.6)
1 133 (74.7) 28 (68.3)
2 15 ( 8.4) 7 (17.1)
LBBB_10 (%)
0 33 (18.5) 8 (19.5)
1 131 (73.6) 29 (70.7)
2 14 ( 7.9) 4 ( 9.8)
Composite_rechek = 1 (%) 16 ( 9.0) 0 ( 0.0)
Decesso_10 = 1 (%) 16 ( 9.0) 0 ( 0.0)
Stratified by score1.tertile
2 3
n 76 42
PAPS_11nov (median [IQR]) 36.0 [35.0, 38.0] 48.0 [44.0, 55.0]
gender_1_men = 1 (%) 33 (43.4) 18 (42.9)
ageatprocedure (median [IQR]) 82.0 [79.8, 86.0] 86.0 [83.2, 87.0]
NHYA_baseline (%)
1 7 ( 9.2) 2 ( 4.8)
2 37 (48.7) 17 (40.5)
3 27 (35.5) 20 (47.6)
4 5 ( 6.6) 3 ( 7.1)
Test_cognitivo (%)
0 32 (42.1) 12 (28.6)
1 26 (34.2) 16 (38.1)
2 11 (14.5) 11 (26.2)
3 4 ( 5.3) 1 ( 2.4)
4 3 ( 3.9) 0 ( 0.0)
5 0 ( 0.0) 1 ( 2.4)
7 0 ( 0.0) 1 ( 2.4)
BADL (%)
1 0 ( 0.0) 5 (11.9)
2 0 ( 0.0) 2 ( 4.8)
3 0 ( 0.0) 2 ( 4.8)
4 2 ( 2.6) 4 ( 9.5)
5 23 (30.3) 10 (23.8)
6 51 (67.1) 19 (45.2)
IADL (%)
0 0 ( 0.0) 1 ( 2.9)
1 1 ( 1.7) 3 ( 8.6)
2 1 ( 1.7) 4 (11.4)
3 2 ( 3.4) 2 ( 5.7)
4 3 ( 5.1) 1 ( 2.9)
5 9 (15.3) 1 ( 2.9)
6 7 (11.9) 5 (14.3)
7 4 ( 6.8) 5 (14.3)
8 32 (54.2) 13 (37.1)
MNA_sh (%)
5 1 ( 1.3) 0 ( 0.0)
6 0 ( 0.0) 1 ( 2.4)
7 2 ( 2.6) 1 ( 2.4)
8 4 ( 5.3) 4 ( 9.5)
9 7 ( 9.2) 9 (21.4)
10 14 (18.4) 8 (19.0)
11 7 ( 9.2) 9 (21.4)
12 28 (36.8) 8 (19.0)
13 4 ( 5.3) 1 ( 2.4)
14 9 (11.8) 1 ( 2.4)
LVEF (median [IQR]) 59.0 [53.8, 63.2] 60.0 [52.8, 63.8]
Grad_picco (median [IQR]) 76.5 [68.2, 88.0] 77.0 [68.0, 95.0]
Grad_medio (median [IQR]) 47.5 [40.8, 56.8] 48.5 [41.0, 57.8]
AVAplan (median [IQR]) 0.4 [0.3, 0.5] 0.4 [0.3, 0.5]
Crea_pre_op_feb2024 (median [IQR]) 0.8 [0.7, 1.0] 0.8 [0.6, 1.0]
CKDEPI_Syn (median [IQR]) 71.8 [62.0, 83.9] 73.9 [54.1, 80.4]
RHYTHM_1FA2PM3RS (%)
1 20 (26.3) 11 (26.2)
2 3 ( 3.9) 5 (11.9)
3 53 (69.7) 26 (61.9)
RHYTHM_FA = 1 (%) 23 (30.3) 16 (38.1)
BAV_1 (%)
0 16 (21.1) 5 (11.9)
1 57 (75.0) 34 (81.0)
2 3 ( 3.9) 3 ( 7.1)
LBBB_10 (%)
0 16 (21.1) 5 (11.9)
1 55 (72.4) 33 (78.6)
2 5 ( 6.6) 4 ( 9.5)
Composite_rechek = 1 (%) 4 ( 5.3) 8 (19.0)
Decesso_10 = 1 (%) 4 ( 5.3) 8 (19.0) Valuto l’associazione tra outcome e score nel test, considerando diversi confounder
Call:
glm(formula = Composite_rechek ~ score1 + ageatprocedure + gender_1_men +
Test_cognitivo + NHYA_baseline + RHYTHM_FA, family = binomial("logit"),
data = df[df$centre != "c", ])
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.517e+01 1.696e+03 -0.009 0.992862
score1 1.961e-01 5.541e-02 3.538 0.000403 ***
ageatprocedure -6.361e-02 6.141e-02 -1.036 0.300281
gender_1_men -9.552e-01 7.209e-01 -1.325 0.185205
Test_cognitivo -5.365e-03 2.663e-01 -0.020 0.983928
NHYA_baseline2 1.494e+01 1.696e+03 0.009 0.992969
NHYA_baseline3 1.589e+01 1.696e+03 0.009 0.992522
NHYA_baseline4 1.559e+01 1.696e+03 0.009 0.992664
RHYTHM_FA 2.884e-01 6.730e-01 0.429 0.668244
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 97.736 on 175 degrees of freedom
Residual deviance: 72.284 on 167 degrees of freedom
(2 observations deleted due to missingness)
AIC: 90.284
Number of Fisher Scoring iterations: 17