Analisis exploratorio de los datos

library(readxl)
library(summarytools)
library(dplyr)

BASE <- read_excel("Base 2025.xlsx")
BASE <- BASE %>% 
  mutate(across(
    matches("fecha", ignore.case = TRUE), 
    ~ as.Date(., format = "%d/%m/%Y")
  ))

print(dfSummary(BASE[ , -c(1,2,3)]), method = "render")

Data Frame Summary

BASE

Dimensions: 43 x 145
Duplicates: 0
No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
1 EPS [character]
1. ALIANSALUD
2. CAFAM
3. COLMEDICA
4. COMPENSAR
5. NUEVA EPS
10(23.3%)
2(4.7%)
1(2.3%)
26(60.5%)
4(9.3%)
 43 (100.0%) 0 (0.0%)
2 ESTRATO [numeric]
Mean (sd) : 2.7 (1)
min ≤ med ≤ max:
1 ≤ 3 ≤ 5
IQR (CV) : 1 (0.4)
1:6(16.7%)
2:5(13.9%)
3:20(55.6%)
4:3(8.3%)
5:2(5.6%)
 36 (83.7%) 7 (16.3%)
3 SEXO [numeric]
Min : 0
Mean : 0.2
Max : 1
0:35(81.4%)
1:8(18.6%)
 43 (100.0%) 0 (0.0%)
4 FECHA NACIMIENTO...7 [Date]
min : 1939-11-11
med : 1953-01-02
max : 1991-05-14
range : 51y 6m 3d
 43 distinct values 43 (100.0%) 0 (0.0%)
5 TALLA (M) [numeric]
Mean (sd) : 1.6 (0.1)
min ≤ med ≤ max:
1.4 ≤ 1.6 ≤ 1.8
IQR (CV) : 0.1 (0.1)
 26 distinct values 43 (100.0%) 0 (0.0%)
6 PESO (KG) [numeric]
Mean (sd) : 59.8 (11.5)
min ≤ med ≤ max:
37 ≤ 62 ≤ 86
IQR (CV) : 13.5 (0.2)
 27 distinct values 43 (100.0%) 0 (0.0%)
7 IMC [numeric]
Mean (sd) : 24.2 (4.4)
min ≤ med ≤ max:
15.8 ≤ 23.9 ≤ 38.2
IQR (CV) : 5.1 (0.2)
 43 distinct values 43 (100.0%) 0 (0.0%)
8 GLAUCOMA [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
9 HPB [numeric]
Min : 0
Mean : 0
Max : 1
0:41(95.3%)
1:2(4.7%)
 43 (100.0%) 0 (0.0%)
10 TUBERCULOSIS [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
11 LES [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
12 DESPRENDIMIENTO RETI. [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
13 ESTEATOSIS [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
14 VALVULOPATIA [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
15 MENINGIOMA [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
16 VASCULITIS [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
17 ACV [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
18 ADENOMA HIPOFISIARIO [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
19 MIOMATOSIS UTERINA [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
20 ENFERMEDAD DIVERTICULAR [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
21 COLON IRRITABLE [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
22 CARDIOPATÍA HIPERTENSIVA [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
23 DEPRESIÓN [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
24 CEFALEA PRIMARIA [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
25 EPILEPSIA [numeric]
Min : 0
Mean : 0
Max : 1
0:41(95.3%)
1:2(4.7%)
 43 (100.0%) 0 (0.0%)
26 HIPOTIROIDISMO [numeric]
Min : 0
Mean : 0.2
Max : 1
0:33(76.7%)
1:10(23.3%)
 43 (100.0%) 0 (0.0%)
27 HT PULMONAR [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
28 SD GILBERT [numeric]
Min : 0
Mean : 0.1
Max : 1
0:40(93.0%)
1:3(7.0%)
 43 (100.0%) 0 (0.0%)
29 SAHOS [numeric]
Min : 0
Mean : 0.1
Max : 1
0:39(90.7%)
1:4(9.3%)
 43 (100.0%) 0 (0.0%)
30 EPOC [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
31 CARDIOPATÍA ISQUÉMICA [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
32 ARRITMIAS [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
33 ETEV [numeric]
Min : 0
Mean : 0.1
Max : 1
0:39(90.7%)
1:4(9.3%)
 43 (100.0%) 0 (0.0%)
34 VÉRTIGO [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
35 CONSUMO PSICOACTIVOS [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
36 TRASTORNO ADAPTATIVO [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
37 CAVERNOMATOSIS MÚLTIPLE [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
38 HIPERTENSIÓN [numeric]
Min : 0
Mean : 0.4
Max : 1
0:25(58.1%)
1:18(41.9%)
 43 (100.0%) 0 (0.0%)
39 DISLIPIDEMIA [numeric]
Min : 0
Mean : 0.1
Max : 1
0:39(90.7%)
1:4(9.3%)
 43 (100.0%) 0 (0.0%)
40 PREDIABETES [numeric]
Min : 0
Mean : 0
Max : 1
0:41(95.3%)
1:2(4.7%)
 43 (100.0%) 0 (0.0%)
41 DIABETES [numeric]
Min : 0
Mean : 0.1
Max : 1
0:37(86.0%)
1:6(14.0%)
 43 (100.0%) 0 (0.0%)
42 SOBREPESO [numeric]
Min : 0
Mean : 0
Max : 1
0:41(95.3%)
1:2(4.7%)
 43 (100.0%) 0 (0.0%)
43 OBESIDAD [numeric]
Min : 0
Mean : 0
Max : 1
0:41(95.3%)
1:2(4.7%)
 43 (100.0%) 0 (0.0%)
44 ARTROSIS [numeric]
Min : 0
Mean : 0.1
Max : 1
0:40(93.0%)
1:3(7.0%)
 43 (100.0%) 0 (0.0%)
45 OSTEOPOROSIS [numeric]
Min : 0
Mean : 0.1
Max : 1
0:37(86.0%)
1:6(14.0%)
 43 (100.0%) 0 (0.0%)
46 HIPERPLASIA ENDOMETRIAL [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
47 Charlson [numeric]
Mean (sd) : 8.9 (1.8)
min ≤ med ≤ max:
5 ≤ 9 ≤ 15
IQR (CV) : 1.5 (0.2)
5:1(2.3%)
6:2(4.7%)
7:4(9.3%)
8:9(20.9%)
9:16(37.2%)
10:7(16.3%)
11:2(4.7%)
14:1(2.3%)
15:1(2.3%)
 43 (100.0%) 0 (0.0%)
48 Otra neoplasia concomitante [numeric]
Min : 0
Mean : 0.2
Max : 1
0:36(83.7%)
1:7(16.3%)
 43 (100.0%) 0 (0.0%)
49 SOLO SI NEOPLASIA [character]
1. Ca ca.l a.l - Ca endometr
2. Ca Cromófobo de riñón 201
3. Ca de mama ductal
4. Ca papilar tiroides, CBC
5. Ca seroso de endometrio
6. Leucemia linfocítica crón
7. Linfoma cutáneo
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
 7 (16.3%) 36 (83.7%)
50 ANTECEDENTE TABAQUISMO [numeric]
Min : 0
Mean : 0.3
Max : 1
0:31(72.1%)
1:12(27.9%)
 43 (100.0%) 0 (0.0%)
51 ANTECEDENTE FAMILIAR DE CANCER [numeric]
Min : 0
Mean : 0.3
Max : 1
0:28(65.1%)
1:15(34.9%)
 43 (100.0%) 0 (0.0%)
52 ECOG [numeric]
Mean (sd) : 1.1 (0.8)
min ≤ med ≤ max:
0 ≤ 1 ≤ 3
IQR (CV) : 0 (0.7)
0:9(20.9%)
1:25(58.1%)
2:6(14.0%)
3:3(7.0%)
 43 (100.0%) 0 (0.0%)
53 KARNOSFKY [numeric]
Mean (sd) : 84.7 (15.8)
min ≤ med ≤ max:
40 ≤ 90 ≤ 100
IQR (CV) : 0 (0.2)
40:1(2.3%)
50:3(7.0%)
60:3(7.0%)
70:2(4.7%)
80:1(2.3%)
90:25(58.1%)
100:8(18.6%)
 43 (100.0%) 0 (0.0%)
54 VALORACION GERIATRICA INTEGRAL [numeric]
Min : 0
Mean : 0.1
Max : 1
0:38(88.4%)
1:5(11.6%)
 43 (100.0%) 0 (0.0%)
55 DIAGNOSTICO [numeric]
Mean (sd) : 1.2 (0.6)
min ≤ med ≤ max:
1 ≤ 1 ≤ 3
IQR (CV) : 0 (0.5)
1:37(86.0%)
2:3(7.0%)
3:3(7.0%)
 43 (100.0%) 0 (0.0%)
56 ESTADIO [character]
1. IA3
2. IB
3. IIA
4. IIIA
5. IIIB
6. IIIC
7. IV
1(2.3%)
3(7.0%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
35(81.4%)
 43 (100.0%) 0 (0.0%)
57 T [character]
1. 1
2. 1c
3. 1C
4. 2
5. 2A
6. 2B
7. 3
8. 3A
9. 4
10. x
11. X
1(2.3%)
1(2.3%)
1(2.3%)
7(16.3%)
2(4.7%)
3(7.0%)
6(14.0%)
1(2.3%)
11(25.6%)
1(2.3%)
9(20.9%)
 43 (100.0%) 0 (0.0%)
58 N [character]
1. 0
2. 1
3. 2
4. 2A
5. 3
6. X
7(16.3%)
4(9.3%)
6(14.0%)
1(2.3%)
9(20.9%)
16(37.2%)
 43 (100.0%) 0 (0.0%)
59 M [numeric]
Min : 0
Mean : 0.8
Max : 1
0:8(18.6%)
1:35(81.4%)
 43 (100.0%) 0 (0.0%)
60 VISCERALNONCNS METS [numeric]
Min : 0
Mean : 0.6
Max : 1
0:19(44.2%)
1:24(55.8%)
 43 (100.0%) 0 (0.0%)
61 PLEURA [numeric]
Min : 0
Mean : 0.2
Max : 1
0:33(76.7%)
1:10(23.3%)
 43 (100.0%) 0 (0.0%)
62 MET HEPATICA [numeric]
Min : 0
Mean : 0.1
Max : 1
0:38(88.4%)
1:5(11.6%)
 43 (100.0%) 0 (0.0%)
63 MET GANGLIONAR [numeric]
Min : 0
Mean : 0.1
Max : 1
0:38(88.4%)
1:5(11.6%)
 43 (100.0%) 0 (0.0%)
64 MET SNC [numeric]
Min : 0
Mean : 0.2
Max : 1
0:33(76.7%)
1:10(23.3%)
 43 (100.0%) 0 (0.0%)
65 METS HUESO [numeric]
Min : 0
Mean : 0.3
Max : 1
0:28(65.1%)
1:15(34.9%)
 43 (100.0%) 0 (0.0%)
66 MET PULMON CONTRALATERAL [numeric]
Min : 0
Mean : 0.4
Max : 1
0:26(60.5%)
1:17(39.5%)
 43 (100.0%) 0 (0.0%)
67 TRATAMIENTO DE METASTASIS 0=Cirugía, 1=Radioterapia, 2= Ninguno [character]
1. .
2. 0
3. 1
4. 2
6(14.0%)
2(4.7%)
14(32.6%)
21(48.8%)
 43 (100.0%) 0 (0.0%)
68 DIAGNÓSTICO HISTOLÓGICO [numeric] 1 distinct value
2:43(100.0%)
 43 (100.0%) 0 (0.0%)
69 GRADO DE DIFERENCIACIÓN [numeric]
Mean (sd) : 2.3 (0.6)
min ≤ med ≤ max:
1 ≤ 2 ≤ 3
IQR (CV) : 1 (0.3)
1:2(6.9%)
2:16(55.2%)
3:11(37.9%)
 29 (67.4%) 14 (32.6%)
70 PERFIL MUTACIONAL [character]
1. Deleción exon 19
2. L858R - exon 21
30(69.8%)
13(30.2%)
 43 (100.0%) 0 (0.0%)
71 PDL-1 [character]
1. < 1
2. <1
3. >10
4. 0
5. 1
6. 10
7. 5
8. 95
12(35.3%)
4(11.8%)
1(2.9%)
10(29.4%)
2(5.9%)
2(5.9%)
2(5.9%)
1(2.9%)
 34 (79.1%) 9 (20.9%)
72 TMB (mut/megaPb) [numeric]
Min : 0.6
Mean : 14.4
Max : 28.2
 2 distinct values 2 (4.7%) 41 (95.3%)
73 MEDIASTINOSCOPIA DX PREVIA A CIRUGIA: 1.SI 0.NO [numeric]
Mean (sd) : 0.3 (0.6)
min ≤ med ≤ max:
0 ≤ 0 ≤ 2
IQR (CV) : 0 (2)
0:20(76.9%)
1:4(15.4%)
2:2(7.7%)
 26 (60.5%) 17 (39.5%)
74 TIPO DE BIOPSIA [character]
1. BIOPSIA DE PULMON
2. BIOPSIA PLEURAL
3. BX PERCUTANEA
4. EBUS
5. EXCISIO.L
6. LOBECTOMIA
7. PLEURA PARIETAL
8. PULMO.R
9. PULMÓN PERCUTÁNEA
10. PULMÓN TRANSBRONQUIAL
1(3.2%)
6(19.4%)
1(3.2%)
1(3.2%)
5(16.1%)
1(3.2%)
1(3.2%)
1(3.2%)
12(38.7%)
2(6.5%)
 31 (72.1%) 12 (27.9%)
75 BIOPSIA POR TAC 1: SI 0: NO [numeric]
Min : 0
Mean : 0.6
Max : 1
0:11(40.7%)
1:16(59.3%)
 27 (62.8%) 16 (37.2%)
76 Broncoscopia 1, SI 0, NO [numeric]
Min : 0
Mean : 0.2
Max : 1
0:22(75.9%)
1:7(24.1%)
 29 (67.4%) 14 (32.6%)
77 EBUS-EUS DIAGNÓSTICO ENFERMEDAD MEDIASTI.L PREVIO A CIRUGÍA: 1:SI 0:NO [numeric]
Min : 0
Mean : 0.2
Max : 1
0:29(82.9%)
1:6(17.1%)
 35 (81.4%) 8 (18.6%)
78 FECHA DE DIAGNOSTICO (FECHA DE BIOPSIA) [Date]
min : 2018-12-28
med : 2023-01-03
max : 2024-11-29
range : 5y 11m 1d
 41 distinct values 43 (100.0%) 0 (0.0%)
79 FECHA NACIMIENTO...82 [Date]
min : 1939-11-11
med : 1953-01-02
max : 1991-05-14
range : 51y 6m 3d
 43 distinct values 43 (100.0%) 0 (0.0%)
80 EDAD [numeric]
Mean (sd) : 67.7 (10.7)
min ≤ med ≤ max:
33 ≤ 70 ≤ 84
IQR (CV) : 14 (0.2)
 24 distinct values 43 (100.0%) 0 (0.0%)
81 INGRESA A LA UFC DE PULMÓN [numeric]
Min : 0
Mean : 1
Max : 1
0:1(2.3%)
1:42(97.7%)
 43 (100.0%) 0 (0.0%)
82 FECHA DE INGRESO [Date]
min : 2019-03-11
med : 2023-07-21
max : 2025-02-10
range : 5y 10m 30d
 42 distinct values 42 (97.7%) 1 (2.3%)
83 FECHA DE 1ª VALORACION HUSI [Date]
min : 2019-02-15
med : 2023-03-28
max : 2024-12-23
range : 5y 10m 8d
 42 distinct values 43 (100.0%) 0 (0.0%)
84 ESPECIALIDAD DE INGRESO [character]
1. CIRUGIA DE TORAX
2. CUIDADOS PALIATIVOS
3. NEUMOLOGIA
4. ONCOLOGIA CLINICA
5. ONCOLOGIA CLINICA
5(11.6%)
1(2.3%)
3(7.0%)
1(2.3%)
33(76.7%)
 43 (100.0%) 0 (0.0%)
85 VIA DE INGRESO 1: AMBULATORIO 2: HOSPITALIZADO [numeric]
Min : 1
Mean : 1
Max : 2
1:41(95.3%)
2:2(4.7%)
 43 (100.0%) 0 (0.0%)
86 FECHA VALORACIÓN ONCOLOGÍA [Date] 1. 2023-11-08
1(100.0%)
 1 (2.3%) 42 (97.7%)
87 FECHA DE ORDEN JUNTA MEDICA [Date] 1. 2023-08-25
1(100.0%)
 1 (2.3%) 42 (97.7%)
88 FECHA PACIENTE COMPLETA ESTUDIOS POR EPS / IPS [Date]
min : 2019-03-07
med : 2023-06-10
max : 2025-01-16
range : 5y 10m 9d
 40 distinct values 41 (95.3%) 2 (4.7%)
89 FECHA DE JUNTA MEDICA [Date]
min : 2019-03-11
med : 2023-05-22
max : 2025-02-10
range : 5y 10m 30d
 40 distinct values 42 (97.7%) 1 (2.3%)
90 VALORACIÓN POR GRUPO DE SOPORTE CUIDADO PALIATIVO [POSIXct, POSIXt]
min : 2019-03-05
med : 2023-08-02
max : 2025-02-25
range : 5y 11m 20d
 39 distinct values 39 (90.7%) 4 (9.3%)
91 INDICACIÓN QUIRÚRGICA INICIAL [character]
1. CURATIVA
2. DIAGNÓSTICA
3. MEDIASTINOSCOPIA
4. NO APLICA
5. PALIATIVA
6. PELURECTOMIA PARIETAL
7. TERAPÉUTICA
1(5.6%)
6(33.3%)
1(5.6%)
6(33.3%)
1(5.6%)
1(5.6%)
2(11.1%)
 18 (41.9%) 25 (58.1%)
92 PROCEDIMIENTO QUIRÚRGICO [character]
1. 1. NINGU.
2. 11.RESECCIÓN TUMOR DEL ME
3. CUÑA
4. LOBECTOMIA
5. LOBECTOMÍA + VG
6. MEDIASTINOSCOPIA + VG
7. PLEURECTOMIA PARITETAL
8. PLEURECTOMIA POR TORACOSC
7(36.8%)
1(5.3%)
1(5.3%)
4(21.1%)
1(5.3%)
3(15.8%)
1(5.3%)
1(5.3%)
 19 (44.2%) 24 (55.8%)
93 BORDES COMPROMETIDOS - SOLO LLE.R SI CIRUGIA DE PRIMARIO [character]
1. LIBRES
2. NO
3. SI
4(57.1%)
2(28.6%)
1(14.3%)
 7 (16.3%) 36 (83.7%)
94 GANGLIOS RESECADOS - SOLO LLE.R SI CIRUGIA DE PRIMARIO [character]
1. 10
2. 13
3. 16
4. 7
5. 8
6. VACIAMIENTO N1 Y N2
1(12.5%)
1(12.5%)
2(25.0%)
1(12.5%)
2(25.0%)
1(12.5%)
 8 (18.6%) 35 (81.4%)
95 GANGLIOS COMPROMETIDOS - SOLO LLE.R SI CIRUGIA DE PRIMARIO [numeric]
Min : 0
Mean : 1.2
Max : 5
0:6(75.0%)
5:2(25.0%)
 8 (18.6%) 35 (81.4%)
96 FECHA DE ORDEN DE CIRUGÍA [Date] 1. 2023-06-06
1(100.0%)
 1 (2.3%) 42 (97.7%)
97 FECHA VALORACIÓN DE ANESTESIA [Date]
1. 1970-01-01
2. 2092-03-02
3. 2092-05-21
4. 2092-09-23
5. 2093-04-28
6. 2093-05-03
7. 2093-09-15
8. 2093-12-28
15(68.2%)
1(4.5%)
1(4.5%)
1(4.5%)
1(4.5%)
1(4.5%)
1(4.5%)
1(4.5%)
 22 (51.2%) 21 (48.8%)
98 FECHA DE CIRUGIA [Date]
min : 1970-01-01
med : 1970-01-01
max : 2094-01-19
range : 124y 0m 18d
 12 distinct values 26 (60.5%) 17 (39.5%)
99 MODALIDAD DE TRATAMIENTO ORDE.DO [character]
1. TERAPIA DIRIGIDA
2. iTK
3. QUIMIOTERAPIA
4. TERAPIA DIRIGIDA - QUIMIO
5. QUMIOTERAPIA
6. CIRUGIA
7. ITK
8. PALIATIVO
9. QUIMIOTERAPIA
ITK
10. QUIMITERAPIA
[ 4 others ]
14(32.6%)
6(14.0%)
5(11.6%)
5(11.6%)
3(7.0%)
2(4.7%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
4(9.3%)
 43 (100.0%) 0 (0.0%)
100 ESQUEMA DE QUIMIOTERAPIA [character]
1. CARBOPLATINO PEMETREXED
2. CISPLATINO PEMETREXED
3. NO APLICA
4. PENDIENTE
15(34.9%)
3(7.0%)
24(55.8%)
1(2.3%)
 43 (100.0%) 0 (0.0%)
101 FECHA DE ORDEN DE QUIMIOTERAPIA [Date]
All NA's
 0 (0.0%) 43 (100.0%)
102 FECHA INICIO QUIMIOTERAPIA [Date]
min : 1970-01-01
med : 2091-03-04
max : 2095-02-01
range : 125y 1m 0d
 20 distinct values 33 (76.7%) 10 (23.3%)
103 iTK ORDENADO [character]
1. ERLOTINIB
2. OSIMERTINIB
1(2.3%)
42(97.7%)
 43 (100.0%) 0 (0.0%)
104 FECHA DE ORDEN DE iTK [Date] 1. 2023-10-03
1(100.0%)
 1 (2.3%) 42 (97.7%)
105 FECHA INICIO iTK [Date]
min : 2019-03-26
med : 2023-07-23
max : 2025-01-29
range : 5y 10m 3d
 40 distinct values 43 (100.0%) 0 (0.0%)
106 RADIOTERAPIA [character]
1. NO
2. SI
3(15.0%)
17(85.0%)
 20 (46.5%) 23 (53.5%)
107 DOSIS RT ADMINISTRADA [character]
1. 20
2. 2 Gy
3. 20 gy
4. 20 Gy
5. 2000/400 cGy
6. 25 Gy
7. 25 LUEGO 30 GY
8. 30
9. 3000
10. 3000 GY
[ 7 others ]
2(11.1%)
1(5.6%)
1(5.6%)
1(5.6%)
1(5.6%)
1(5.6%)
1(5.6%)
1(5.6%)
1(5.6%)
1(5.6%)
7(38.9%)
 18 (41.9%) 25 (58.1%)
108 TIPO DE RADIOTERAPIA [character]
1. 3DCRT
2. IMRT
3. IMRT/HOLOCRANEA.
4. NO APLICA
5. Radiocirugía - GammaKnife
6. RADIOCIRUGIA FOTONES
7. SBRT
1(5.6%)
6(33.3%)
1(5.6%)
2(11.1%)
1(5.6%)
1(5.6%)
6(33.3%)
 18 (41.9%) 25 (58.1%)
109 RADIOTERAPIA HUESO [numeric]
Min : 0
Mean : 0.4
Max : 1
0:14(56.0%)
1:11(44.0%)
 25 (58.1%) 18 (41.9%)
110 RADIOTERAPIA SNC [numeric]
Min : 0
Mean : 0.2
Max : 1
0:19(76.0%)
1:6(24.0%)
 25 (58.1%) 18 (41.9%)
111 RADIOTERAPIA PULMON [numeric]
Min : 0
Mean : 0.2
Max : 1
0:21(84.0%)
1:4(16.0%)
 25 (58.1%) 18 (41.9%)
112 FECHA DE VALORACIÓN DE RADIOTERAPIA [Date]
min : 1970-01-01
med : 2092-06-08
max : 2094-10-12
range : 124y 9m 11d
 16 distinct values 23 (53.5%) 20 (46.5%)
113 FECHA DE INICIO DE RADIOTERAPIA [Date]
All NA's
 0 (0.0%) 43 (100.0%)
114 INTENCIÓN INICIAL DE TRATAMIENTO [character]
1. CURATIVO
2. PALIATIVO
3(7.0%)
40(93.0%)
 43 (100.0%) 0 (0.0%)
115 TRATAMIENTO INICIAL ORDENADO [character]
1. iTK
2. QUIMIOTERAPIA
3. ITK
4. CIRUGIA
5. CIRUGÍA
6. RADIOTERAPIA MET + iTK
7. CIRUGÍA DE RESECCIÓN
8. PALIATIVO
9. QUIMIOTERAPIA + iTK
10. QUIMIOTERAPIA
iTK
[ 3 others ]
19(44.2%)
7(16.3%)
4(9.3%)
2(4.7%)
2(4.7%)
2(4.7%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
3(7.0%)
 43 (100.0%) 0 (0.0%)
116 TRATAMIENTO ACTUAL [character]
1. CARBO/PEMETREXED
2. FLAURA-2
3. OSIMERTINIB
4. OSIMERTINIB - PEMETREXED
5. OSIMERTINIB
+ PEMETRE
6. OSIMERTINIB
CIRUGIA
Q
1(2.3%)
2(4.7%)
37(86.0%)
1(2.3%)
1(2.3%)
1(2.3%)
 43 (100.0%) 0 (0.0%)
117 FECHA DE INICIO DE TRATAMIENTO CX-QXT-RXT-INMUNO [Date]
min : 2019-03-26
med : 2023-07-23
max : 2025-01-30
range : 5y 10m 4d
 43 distinct values 43 (100.0%) 0 (0.0%)
118 OPORTUNIDAD DE JUNTA MEDICA [numeric]
Mean (sd) : 31 (29.8)
min ≤ med ≤ max:
0 ≤ 21.5 ≤ 132
IQR (CV) : 39 (1)
 33 distinct values 42 (97.7%) 1 (2.3%)
119 OPORTUNIDAD DE VALORACIÒN PREANESTESIA [numeric]
Mean (sd) : 1.8 (5.1)
min ≤ med ≤ max:
0 ≤ 0 ≤ 23
IQR (CV) : 0 (2.9)
0:35(85.4%)
1:1(2.4%)
9:2(4.9%)
15:1(2.4%)
16:1(2.4%)
23:1(2.4%)
 41 (95.3%) 2 (4.7%)
120 OPORTUNIDAD DE CIRUGÍA [numeric]
Mean (sd) : 4308.6 (13242.3)
min ≤ med ≤ max:
0 ≤ 0 ≤ 44552
IQR (CV) : 0 (3.1)
 11 distinct values 41 (95.3%) 2 (4.7%)
121 OPORTUNIDAD DE INICIO QXT [numeric]
Mean (sd) : 6.4 (8.1)
min ≤ med ≤ max:
0 ≤ 0 ≤ 29
IQR (CV) : 13 (1.3)
 13 distinct values 41 (95.3%) 2 (4.7%)
122 OPORTUNIDAD DE INICIO TRATAMIENTO DIRIGIDO [numeric]
Mean (sd) : 8.2 (40.2)
min ≤ med ≤ max:
-239 ≤ 13 ≤ 47
IQR (CV) : 12.5 (4.9)
 27 distinct values 42 (97.7%) 1 (2.3%)
123 OPORTUNIDAD DE INICIO RXT [numeric]
Mean (sd) : 1096.3 (6941.6)
min ≤ med ≤ max:
0 ≤ 0 ≤ 44460
IQR (CV) : 17 (6.3)
 16 distinct values 41 (95.3%) 2 (4.7%)
124 OPORTUNIDAD DE INICIO DE TRATAMIENTO DESDE EL DIAGNÓSTICO POR PATOLOGÍA [numeric]
Mean (sd) : 83.9 (95.3)
min ≤ med ≤ max:
-13 ≤ 70 ≤ 565
IQR (CV) : 52.5 (1.1)
 39 distinct values 43 (100.0%) 0 (0.0%)
125 OPORTUNIDAD DE INICIO DESDE LA PRIMERA ATENCIÓN DEL HUSI [numeric]
Mean (sd) : 58.8 (111.6)
min ≤ med ≤ max:
-351 ≤ 41.5 ≤ 542
IQR (CV) : 68.2 (1.9)
 38 distinct values 42 (97.7%) 1 (2.3%)
126 FECHA DE MUERTE [Date]
1. 2024-01-26
2. 2024-02-19
3. 2024-05-07
4. 2024-05-10
5. 2024-07-08
6. 2024-07-15
7. 2024-12-09
8. 2025-01-31
1(11.1%)
1(11.1%)
1(11.1%)
1(11.1%)
2(22.2%)
1(11.1%)
1(11.1%)
1(11.1%)
 9 (20.9%) 34 (79.1%)
127 CAUSAS DE EGRESO [character] 1. MUERTE
9(100.0%)
 9 (20.9%) 34 (79.1%)
128 # DE DÍAS PCTE VIVO DESDE LA PATOLOGÍA [numeric]
Mean (sd) : 811.2 (502.4)
min ≤ med ≤ max:
129 ≤ 662 ≤ 2256
IQR (CV) : 614 (0.6)
 43 distinct values 43 (100.0%) 0 (0.0%)
129 # DE DÍAS PACTE VIVO DESDE INICIO DE TRATAMIENTO [numeric]
Mean (sd) : 727.3 (507.4)
min ≤ med ≤ max:
102 ≤ 588 ≤ 2168
IQR (CV) : 520 (0.7)
 41 distinct values 43 (100.0%) 0 (0.0%)
130 EDAD EN AÑOS (FALLECE) [numeric]
Mean (sd) : 70.2 (10.1)
min ≤ med ≤ max:
49 ≤ 70 ≤ 85
IQR (CV) : 10.8 (0.1)
 11 distinct values 12 (27.9%) 31 (72.1%)
131 FECHA DE DIAGNÓSTICO POR PATOLOGÍA [Date]
min : 2018-12-28
med : 2023-02-14
max : 2024-11-29
range : 5y 11m 1d
 42 distinct values 43 (100.0%) 0 (0.0%)
132 TIPO DE PROGRESIÓN 0=OLIGOPROGRESIÓN, 1=PROGRESIÓN [numeric]
Min : 0
Mean : 0.7
Max : 1
0:3(30.0%)
1:7(70.0%)
 10 (23.3%) 33 (76.7%)
133 TRATAMIENTO POSTERIOR A PROGRESIÓN [character]
1. Carboplatino/Etoposido/Du
2. FLAURA-2
3. OSIMERTINIB
4. QT
5. QT+INMUNO
6. QUIMIOTERAPIA
7. Radiocirugía SNC
8. RADIOTERAPIA . SE CONTINU
9. Reinicio ITK (suspendió)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
2(20.0%)
1(10.0%)
 10 (23.3%) 33 (76.7%)
134 FECHAS IMAGENES REVALORACION [Date]
min : 2020-01-10
med : 2023-11-22
max : 2025-05-08
range : 5y 3m 28d
 40 distinct values 42 (97.7%) 1 (2.3%)
135 FECHA DE RECAIDA O PROGRESION [Date]
1. 2020-01-10
2. 2022-03-01
3. 2022-09-22
4. 2023-04-28
5. 2023-08-10
6. 2023-09-22
7. 2023-11-21
8. 2023-12-07
9. 2024-04-24
10. 2025-04-28
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
 10 (23.3%) 33 (76.7%)
136 PROGRESIÓN O RECAIDA 1- MENOR AL AÑO 2- MAYOR AL AÑO [numeric]
Min : 1
Mean : 1.7
Max : 2
1:3(30.0%)
2:7(70.0%)
 10 (23.3%) 33 (76.7%)
137 MUERTE 1- MENOR AL AÑO 2- MAYOR AL AÑO [numeric]
Min : 1
Mean : 1.9
Max : 2
1:1(11.1%)
2:8(88.9%)
 9 (20.9%) 34 (79.1%)
138 ESTADO VITAL 1: VIVO 2: MUERTO [numeric]
Min : 1
Mean : 1.2
Max : 2
1:35(81.4%)
2:8(18.6%)
 43 (100.0%) 0 (0.0%)
139 ESTADO DE TRATAMIENTO [character]
1. ACTIVO
2. NO INICIÓ
3. SUSPENDIDO
32(74.4%)
1(2.3%)
10(23.3%)
 43 (100.0%) 0 (0.0%)
140 ÚLTIMO CONTROL [POSIXct, POSIXt]
min : 2024-01-05
med : 2025-04-10
max : 2025-05-17
range : 1y 4m 12d
 36 distinct values 43 (100.0%) 0 (0.0%)
141 fecha biopsia [Date]
min : 2018-12-28
med : 2023-03-07
max : 2024-11-29
range : 5y 11m 1d
 41 distinct values 42 (97.7%) 1 (2.3%)
142 fecha inicio itK [Date]
min : 2019-03-26
med : 2023-08-07
max : 2025-01-09
range : 5y 9m 14d
 39 distinct values 42 (97.7%) 1 (2.3%)
143 Dias a inicio de iTK desde dx histopatologico [numeric]
Mean (sd) : 97.1 (73.8)
min ≤ med ≤ max:
6 ≤ 80.5 ≤ 322
IQR (CV) : 89 (0.8)
 40 distinct values 42 (97.7%) 1 (2.3%)
144 Concordante con guia 0=no 1=si [numeric]
Min : 0
Mean : 1
Max : 1
0:2(4.7%)
1:41(95.3%)
 43 (100.0%) 0 (0.0%)
145 Adherencia 0=no 1=si (asumir que si a menos que en HC diga que problemas para entrega) [numeric]
Min : 0
Mean : 0.9
Max : 1
0:3(7.0%)
1:40(93.0%)
 43 (100.0%) 0 (0.0%)

Generated by summarytools 1.1.4 (R version 4.4.2)
2025-10-28

Supervivencia a muerte

library(dplyr)
library(lubridate)
library(knitr)
library(kableExtra)

# 1. Convertir las fechas a clase Date
BASE <- BASE %>%
  mutate(
    INICIO = as.Date(BASE$`FECHA DE DIAGNOSTICO (FECHA DE BIOPSIA)`, format = "%d/%m/%Y"),
    MUERTE = as.Date(BASE$`FECHA DE MUERTE`, format = "%d/%m/%Y"),
    PROGRE_RECA = as.Date(BASE$`FECHA DE RECAIDA O PROGRESION`, format = "%d/%m/%Y"),
    SEGUIM = as.Date(BASE$`ÚLTIMO CONTROL`, format = "%d/%m/%Y")
  )


BASE <- BASE %>%
  mutate(
    fecha_evento1 = coalesce(MUERTE, SEGUIM),   # toma la primera no-NA
    Tiempo_meses = interval(INICIO, fecha_evento1) %/% months(1),
    evento1 = ifelse(!is.na(MUERTE), 1, 0),
    evento1_1 = ifelse(!is.na(PROGRE_RECA), 1, 0),# 1 = evento, 0 = censura
    ID = row_number()
  )

# 3. Seleccionar las variables clave
SUP1 <- BASE %>% select(ID, Tiempo_meses, evento1)

# 4. Visualizar en tabla bonita
kable(SUP1, format = "html", table.attr = "style='width:60%;'") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
ID Tiempo_meses evento1
1 75 0
2 58 0
3 58 0
4 50 0
5 46 0
6 38 0
7 36 0
8 30 0
9 30 0
10 32 0
11 14 1
12 28 0
13 29 0
14 28 0
15 14 1
16 21 0
17 15 0
18 18 0
19 18 0
20 16 0
21 10 0
22 6 0
23 5 0
24 8 0
25 25 1
26 5 1
27 55 1
28 30 1
29 35 0
30 21 0
31 22 0
32 17 0
33 15 0
34 17 0
35 22 1
36 14 0
37 39 0
38 14 0
39 5 0
40 15 0
41 57 1
42 36 0
43 48 1

Identificando las censuras

library(survival)
Surv(SUP1$Tiempo_meses, SUP1$evento1)
##  [1] 75+ 58+ 58+ 50+ 46+ 38+ 36+ 30+ 30+ 32+ 14  28+ 29+ 28+ 14  21+ 15+ 18+ 18+
## [20] 16+ 10+  6+  5+  8+ 25   5  55  30  35+ 21+ 22+ 17+ 15+ 17+ 22  14+ 39+ 14+
## [39]  5+ 15+ 57  36+ 48

Graficando el seguimiento

library(ggplot2)
library(plotly)
SUP1$evento2 <- factor(SUP1$evento1, levels = c(0,1), labels = c("Censura", "Muerte"))
m <- SUP1 %>%
  mutate(t_inicio = 0,
         t_fin = Tiempo_meses) %>%
  ggplot(aes(x = as.factor(ID), y = t_fin)) +
  geom_linerange(aes(ymin = t_inicio, ymax = t_fin)) +
  geom_point(aes(shape = factor(evento2), color = factor(evento2)), stroke = 1, size = 2) +
  scale_shape_manual(values = c(1, 4)) +  # Diferentes formas para censura y eventos
  labs(y = "Tiempo de Supervivencia (meses)", x = "ID") + 
  coord_flip() + 
  theme_classic() +
  theme(legend.title = element_blank(), legend.position = "bottom")
ggplotly(m)

Limpieza de la base de datos


library(dplyr)

#ESTRATO
BASE <- BASE %>% 
  mutate(
    Estrato_cat = cut(
      ESTRATO, 
      breaks = c(-Inf, 2, 3, Inf), 
      right = TRUE,
      labels = c("1-2", "3", "4-5")
    )
  )


#EDAD
BASE <- BASE %>% 
  mutate(
    EDAD_CAT = cut(
      EDAD, 
      breaks = c(-Inf, 70, Inf), 
      right = TRUE,
      labels = c("<=70", ">70")
    )
  )


#ESTADIO
BASE$metastasis <- factor(BASE$M, levels = c(0,1), labels = c("no metastasico", "metastasico"))

colnames(BASE)[colnames(BASE) == "VISCERALNONCNS METS"] <- "VISCERALNONCNS_METS"
colnames(BASE)[colnames(BASE) == "MET SNC"] <- "MET_SNC"

BASE$VISCERALNONCNS_METS <- factor(BASE$VISCERALNONCNS_METS, levels = c(0,1), labels = c("No", "Si"))
BASE$MET_SNC <- factor(BASE$MET_SNC, levels = c(0,1), labels = c("No", "Si"))

colnames(BASE)[93] <- "Cuidado_paliativo"

BASE$Cuidado_paliativocat <- ifelse(!is.na(BASE$Cuidado_paliativo), "Si", "No")

print(dfSummary(BASE), method = "render")

Data Frame Summary

BASE

Dimensions: 43 x 160
Duplicates: 0
No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
1 ID [integer]
Mean (sd) : 22 (12.6)
min ≤ med ≤ max:
1 ≤ 22 ≤ 43
IQR (CV) : 21 (0.6)
 43 distinct values (Integer sequence) 43 (100.0%) 0 (0.0%)
2 NOMBRE DE PACIENTE [character]
1. A. ISABEL SANCHEZ MANZANE
2. ALEYDA YALILE BEJARANO JA
3. ALVARO SILVA ROZO
4. ANA GILMA TENJO AVIRAMA
5. BEATRIZ LOPEZ DE BONILLA
6. BLANCA CECILIA HERNANDEZ
7. BLANCA INES RAMOS SOSA
8. BLANCA INES SANCHEZ ALVAR
9. CARMEN ESTHER REDONDO ORT
10. CONSUELO CASTILLO RESTREP
[ 33 others ]
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
33(76.7%)
 43 (100.0%) 0 (0.0%)
3 DOCUMENTO [numeric]
Mean (sd) : 58263094 (159032073)
min ≤ med ≤ max:
2515356 ≤ 39565808 ≤ 1072660632
IQR (CV) : 20835579 (2.7)
 43 distinct values 43 (100.0%) 0 (0.0%)
4 EPS [character]
1. ALIANSALUD
2. CAFAM
3. COLMEDICA
4. COMPENSAR
5. NUEVA EPS
10(23.3%)
2(4.7%)
1(2.3%)
26(60.5%)
4(9.3%)
 43 (100.0%) 0 (0.0%)
5 ESTRATO [numeric]
Mean (sd) : 2.7 (1)
min ≤ med ≤ max:
1 ≤ 3 ≤ 5
IQR (CV) : 1 (0.4)
1:6(16.7%)
2:5(13.9%)
3:20(55.6%)
4:3(8.3%)
5:2(5.6%)
 36 (83.7%) 7 (16.3%)
6 SEXO [numeric]
Min : 0
Mean : 0.2
Max : 1
0:35(81.4%)
1:8(18.6%)
 43 (100.0%) 0 (0.0%)
7 FECHA NACIMIENTO...7 [Date]
min : 1939-11-11
med : 1953-01-02
max : 1991-05-14
range : 51y 6m 3d
 43 distinct values 43 (100.0%) 0 (0.0%)
8 TALLA (M) [numeric]
Mean (sd) : 1.6 (0.1)
min ≤ med ≤ max:
1.4 ≤ 1.6 ≤ 1.8
IQR (CV) : 0.1 (0.1)
 26 distinct values 43 (100.0%) 0 (0.0%)
9 PESO (KG) [numeric]
Mean (sd) : 59.8 (11.5)
min ≤ med ≤ max:
37 ≤ 62 ≤ 86
IQR (CV) : 13.5 (0.2)
 27 distinct values 43 (100.0%) 0 (0.0%)
10 IMC [numeric]
Mean (sd) : 24.2 (4.4)
min ≤ med ≤ max:
15.8 ≤ 23.9 ≤ 38.2
IQR (CV) : 5.1 (0.2)
 43 distinct values 43 (100.0%) 0 (0.0%)
11 GLAUCOMA [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
12 HPB [numeric]
Min : 0
Mean : 0
Max : 1
0:41(95.3%)
1:2(4.7%)
 43 (100.0%) 0 (0.0%)
13 TUBERCULOSIS [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
14 LES [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
15 DESPRENDIMIENTO RETI. [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
16 ESTEATOSIS [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
17 VALVULOPATIA [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
18 MENINGIOMA [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
19 VASCULITIS [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
20 ACV [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
21 ADENOMA HIPOFISIARIO [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
22 MIOMATOSIS UTERINA [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
23 ENFERMEDAD DIVERTICULAR [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
24 COLON IRRITABLE [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
25 CARDIOPATÍA HIPERTENSIVA [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
26 DEPRESIÓN [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
27 CEFALEA PRIMARIA [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
28 EPILEPSIA [numeric]
Min : 0
Mean : 0
Max : 1
0:41(95.3%)
1:2(4.7%)
 43 (100.0%) 0 (0.0%)
29 HIPOTIROIDISMO [numeric]
Min : 0
Mean : 0.2
Max : 1
0:33(76.7%)
1:10(23.3%)
 43 (100.0%) 0 (0.0%)
30 HT PULMONAR [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
31 SD GILBERT [numeric]
Min : 0
Mean : 0.1
Max : 1
0:40(93.0%)
1:3(7.0%)
 43 (100.0%) 0 (0.0%)
32 SAHOS [numeric]
Min : 0
Mean : 0.1
Max : 1
0:39(90.7%)
1:4(9.3%)
 43 (100.0%) 0 (0.0%)
33 EPOC [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
34 CARDIOPATÍA ISQUÉMICA [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
35 ARRITMIAS [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
36 ETEV [numeric]
Min : 0
Mean : 0.1
Max : 1
0:39(90.7%)
1:4(9.3%)
 43 (100.0%) 0 (0.0%)
37 VÉRTIGO [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
38 CONSUMO PSICOACTIVOS [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
39 TRASTORNO ADAPTATIVO [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
40 CAVERNOMATOSIS MÚLTIPLE [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
41 HIPERTENSIÓN [numeric]
Min : 0
Mean : 0.4
Max : 1
0:25(58.1%)
1:18(41.9%)
 43 (100.0%) 0 (0.0%)
42 DISLIPIDEMIA [numeric]
Min : 0
Mean : 0.1
Max : 1
0:39(90.7%)
1:4(9.3%)
 43 (100.0%) 0 (0.0%)
43 PREDIABETES [numeric]
Min : 0
Mean : 0
Max : 1
0:41(95.3%)
1:2(4.7%)
 43 (100.0%) 0 (0.0%)
44 DIABETES [numeric]
Min : 0
Mean : 0.1
Max : 1
0:37(86.0%)
1:6(14.0%)
 43 (100.0%) 0 (0.0%)
45 SOBREPESO [numeric]
Min : 0
Mean : 0
Max : 1
0:41(95.3%)
1:2(4.7%)
 43 (100.0%) 0 (0.0%)
46 OBESIDAD [numeric]
Min : 0
Mean : 0
Max : 1
0:41(95.3%)
1:2(4.7%)
 43 (100.0%) 0 (0.0%)
47 ARTROSIS [numeric]
Min : 0
Mean : 0.1
Max : 1
0:40(93.0%)
1:3(7.0%)
 43 (100.0%) 0 (0.0%)
48 OSTEOPOROSIS [numeric]
Min : 0
Mean : 0.1
Max : 1
0:37(86.0%)
1:6(14.0%)
 43 (100.0%) 0 (0.0%)
49 HIPERPLASIA ENDOMETRIAL [numeric]
Min : 0
Mean : 0
Max : 1
0:42(97.7%)
1:1(2.3%)
 43 (100.0%) 0 (0.0%)
50 Charlson [numeric]
Mean (sd) : 8.9 (1.8)
min ≤ med ≤ max:
5 ≤ 9 ≤ 15
IQR (CV) : 1.5 (0.2)
5:1(2.3%)
6:2(4.7%)
7:4(9.3%)
8:9(20.9%)
9:16(37.2%)
10:7(16.3%)
11:2(4.7%)
14:1(2.3%)
15:1(2.3%)
 43 (100.0%) 0 (0.0%)
51 Otra neoplasia concomitante [numeric]
Min : 0
Mean : 0.2
Max : 1
0:36(83.7%)
1:7(16.3%)
 43 (100.0%) 0 (0.0%)
52 SOLO SI NEOPLASIA [character]
1. Ca ca.l a.l - Ca endometr
2. Ca Cromófobo de riñón 201
3. Ca de mama ductal
4. Ca papilar tiroides, CBC
5. Ca seroso de endometrio
6. Leucemia linfocítica crón
7. Linfoma cutáneo
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
1(14.3%)
 7 (16.3%) 36 (83.7%)
53 ANTECEDENTE TABAQUISMO [numeric]
Min : 0
Mean : 0.3
Max : 1
0:31(72.1%)
1:12(27.9%)
 43 (100.0%) 0 (0.0%)
54 ANTECEDENTE FAMILIAR DE CANCER [numeric]
Min : 0
Mean : 0.3
Max : 1
0:28(65.1%)
1:15(34.9%)
 43 (100.0%) 0 (0.0%)
55 ECOG [numeric]
Mean (sd) : 1.1 (0.8)
min ≤ med ≤ max:
0 ≤ 1 ≤ 3
IQR (CV) : 0 (0.7)
0:9(20.9%)
1:25(58.1%)
2:6(14.0%)
3:3(7.0%)
 43 (100.0%) 0 (0.0%)
56 KARNOSFKY [numeric]
Mean (sd) : 84.7 (15.8)
min ≤ med ≤ max:
40 ≤ 90 ≤ 100
IQR (CV) : 0 (0.2)
40:1(2.3%)
50:3(7.0%)
60:3(7.0%)
70:2(4.7%)
80:1(2.3%)
90:25(58.1%)
100:8(18.6%)
 43 (100.0%) 0 (0.0%)
57 VALORACION GERIATRICA INTEGRAL [numeric]
Min : 0
Mean : 0.1
Max : 1
0:38(88.4%)
1:5(11.6%)
 43 (100.0%) 0 (0.0%)
58 DIAGNOSTICO [numeric]
Mean (sd) : 1.2 (0.6)
min ≤ med ≤ max:
1 ≤ 1 ≤ 3
IQR (CV) : 0 (0.5)
1:37(86.0%)
2:3(7.0%)
3:3(7.0%)
 43 (100.0%) 0 (0.0%)
59 ESTADIO [character]
1. IA3
2. IB
3. IIA
4. IIIA
5. IIIB
6. IIIC
7. IV
1(2.3%)
3(7.0%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
35(81.4%)
 43 (100.0%) 0 (0.0%)
60 T [character]
1. 1
2. 1c
3. 1C
4. 2
5. 2A
6. 2B
7. 3
8. 3A
9. 4
10. x
11. X
1(2.3%)
1(2.3%)
1(2.3%)
7(16.3%)
2(4.7%)
3(7.0%)
6(14.0%)
1(2.3%)
11(25.6%)
1(2.3%)
9(20.9%)
 43 (100.0%) 0 (0.0%)
61 N [character]
1. 0
2. 1
3. 2
4. 2A
5. 3
6. X
7(16.3%)
4(9.3%)
6(14.0%)
1(2.3%)
9(20.9%)
16(37.2%)
 43 (100.0%) 0 (0.0%)
62 M [numeric]
Min : 0
Mean : 0.8
Max : 1
0:8(18.6%)
1:35(81.4%)
 43 (100.0%) 0 (0.0%)
63 VISCERALNONCNS_METS [factor]
1. No
2. Si
19(44.2%)
24(55.8%)
 43 (100.0%) 0 (0.0%)
64 PLEURA [numeric]
Min : 0
Mean : 0.2
Max : 1
0:33(76.7%)
1:10(23.3%)
 43 (100.0%) 0 (0.0%)
65 MET HEPATICA [numeric]
Min : 0
Mean : 0.1
Max : 1
0:38(88.4%)
1:5(11.6%)
 43 (100.0%) 0 (0.0%)
66 MET GANGLIONAR [numeric]
Min : 0
Mean : 0.1
Max : 1
0:38(88.4%)
1:5(11.6%)
 43 (100.0%) 0 (0.0%)
67 MET_SNC [factor]
1. No
2. Si
33(76.7%)
10(23.3%)
 43 (100.0%) 0 (0.0%)
68 METS HUESO [numeric]
Min : 0
Mean : 0.3
Max : 1
0:28(65.1%)
1:15(34.9%)
 43 (100.0%) 0 (0.0%)
69 MET PULMON CONTRALATERAL [numeric]
Min : 0
Mean : 0.4
Max : 1
0:26(60.5%)
1:17(39.5%)
 43 (100.0%) 0 (0.0%)
70 TRATAMIENTO DE METASTASIS 0=Cirugía, 1=Radioterapia, 2= Ninguno [character]
1. .
2. 0
3. 1
4. 2
6(14.0%)
2(4.7%)
14(32.6%)
21(48.8%)
 43 (100.0%) 0 (0.0%)
71 DIAGNÓSTICO HISTOLÓGICO [numeric] 1 distinct value
2:43(100.0%)
 43 (100.0%) 0 (0.0%)
72 GRADO DE DIFERENCIACIÓN [numeric]
Mean (sd) : 2.3 (0.6)
min ≤ med ≤ max:
1 ≤ 2 ≤ 3
IQR (CV) : 1 (0.3)
1:2(6.9%)
2:16(55.2%)
3:11(37.9%)
 29 (67.4%) 14 (32.6%)
73 PERFIL MUTACIONAL [character]
1. Deleción exon 19
2. L858R - exon 21
30(69.8%)
13(30.2%)
 43 (100.0%) 0 (0.0%)
74 PDL-1 [character]
1. < 1
2. <1
3. >10
4. 0
5. 1
6. 10
7. 5
8. 95
12(35.3%)
4(11.8%)
1(2.9%)
10(29.4%)
2(5.9%)
2(5.9%)
2(5.9%)
1(2.9%)
 34 (79.1%) 9 (20.9%)
75 TMB (mut/megaPb) [numeric]
Min : 0.6
Mean : 14.4
Max : 28.2
 2 distinct values 2 (4.7%) 41 (95.3%)
76 MEDIASTINOSCOPIA DX PREVIA A CIRUGIA: 1.SI 0.NO [numeric]
Mean (sd) : 0.3 (0.6)
min ≤ med ≤ max:
0 ≤ 0 ≤ 2
IQR (CV) : 0 (2)
0:20(76.9%)
1:4(15.4%)
2:2(7.7%)
 26 (60.5%) 17 (39.5%)
77 TIPO DE BIOPSIA [character]
1. BIOPSIA DE PULMON
2. BIOPSIA PLEURAL
3. BX PERCUTANEA
4. EBUS
5. EXCISIO.L
6. LOBECTOMIA
7. PLEURA PARIETAL
8. PULMO.R
9. PULMÓN PERCUTÁNEA
10. PULMÓN TRANSBRONQUIAL
1(3.2%)
6(19.4%)
1(3.2%)
1(3.2%)
5(16.1%)
1(3.2%)
1(3.2%)
1(3.2%)
12(38.7%)
2(6.5%)
 31 (72.1%) 12 (27.9%)
78 BIOPSIA POR TAC 1: SI 0: NO [numeric]
Min : 0
Mean : 0.6
Max : 1
0:11(40.7%)
1:16(59.3%)
 27 (62.8%) 16 (37.2%)
79 Broncoscopia 1, SI 0, NO [numeric]
Min : 0
Mean : 0.2
Max : 1
0:22(75.9%)
1:7(24.1%)
 29 (67.4%) 14 (32.6%)
80 EBUS-EUS DIAGNÓSTICO ENFERMEDAD MEDIASTI.L PREVIO A CIRUGÍA: 1:SI 0:NO [numeric]
Min : 0
Mean : 0.2
Max : 1
0:29(82.9%)
1:6(17.1%)
 35 (81.4%) 8 (18.6%)
81 FECHA DE DIAGNOSTICO (FECHA DE BIOPSIA) [Date]
min : 2018-12-28
med : 2023-01-03
max : 2024-11-29
range : 5y 11m 1d
 41 distinct values 43 (100.0%) 0 (0.0%)
82 FECHA NACIMIENTO...82 [Date]
min : 1939-11-11
med : 1953-01-02
max : 1991-05-14
range : 51y 6m 3d
 43 distinct values 43 (100.0%) 0 (0.0%)
83 EDAD [numeric]
Mean (sd) : 67.7 (10.7)
min ≤ med ≤ max:
33 ≤ 70 ≤ 84
IQR (CV) : 14 (0.2)
 24 distinct values 43 (100.0%) 0 (0.0%)
84 INGRESA A LA UFC DE PULMÓN [numeric]
Min : 0
Mean : 1
Max : 1
0:1(2.3%)
1:42(97.7%)
 43 (100.0%) 0 (0.0%)
85 FECHA DE INGRESO [Date]
min : 2019-03-11
med : 2023-07-21
max : 2025-02-10
range : 5y 10m 30d
 42 distinct values 42 (97.7%) 1 (2.3%)
86 FECHA DE 1ª VALORACION HUSI [Date]
min : 2019-02-15
med : 2023-03-28
max : 2024-12-23
range : 5y 10m 8d
 42 distinct values 43 (100.0%) 0 (0.0%)
87 ESPECIALIDAD DE INGRESO [character]
1. CIRUGIA DE TORAX
2. CUIDADOS PALIATIVOS
3. NEUMOLOGIA
4. ONCOLOGIA CLINICA
5. ONCOLOGIA CLINICA
5(11.6%)
1(2.3%)
3(7.0%)
1(2.3%)
33(76.7%)
 43 (100.0%) 0 (0.0%)
88 VIA DE INGRESO 1: AMBULATORIO 2: HOSPITALIZADO [numeric]
Min : 1
Mean : 1
Max : 2
1:41(95.3%)
2:2(4.7%)
 43 (100.0%) 0 (0.0%)
89 FECHA VALORACIÓN ONCOLOGÍA [Date] 1. 2023-11-08
1(100.0%)
 1 (2.3%) 42 (97.7%)
90 FECHA DE ORDEN JUNTA MEDICA [Date] 1. 2023-08-25
1(100.0%)
 1 (2.3%) 42 (97.7%)
91 FECHA PACIENTE COMPLETA ESTUDIOS POR EPS / IPS [Date]
min : 2019-03-07
med : 2023-06-10
max : 2025-01-16
range : 5y 10m 9d
 40 distinct values 41 (95.3%) 2 (4.7%)
92 FECHA DE JUNTA MEDICA [Date]
min : 2019-03-11
med : 2023-05-22
max : 2025-02-10
range : 5y 10m 30d
 40 distinct values 42 (97.7%) 1 (2.3%)
93 Cuidado_paliativo [POSIXct, POSIXt]
min : 2019-03-05
med : 2023-08-02
max : 2025-02-25
range : 5y 11m 20d
 39 distinct values 39 (90.7%) 4 (9.3%)
94 INDICACIÓN QUIRÚRGICA INICIAL [character]
1. CURATIVA
2. DIAGNÓSTICA
3. MEDIASTINOSCOPIA
4. NO APLICA
5. PALIATIVA
6. PELURECTOMIA PARIETAL
7. TERAPÉUTICA
1(5.6%)
6(33.3%)
1(5.6%)
6(33.3%)
1(5.6%)
1(5.6%)
2(11.1%)
 18 (41.9%) 25 (58.1%)
95 PROCEDIMIENTO QUIRÚRGICO [character]
1. 1. NINGU.
2. 11.RESECCIÓN TUMOR DEL ME
3. CUÑA
4. LOBECTOMIA
5. LOBECTOMÍA + VG
6. MEDIASTINOSCOPIA + VG
7. PLEURECTOMIA PARITETAL
8. PLEURECTOMIA POR TORACOSC
7(36.8%)
1(5.3%)
1(5.3%)
4(21.1%)
1(5.3%)
3(15.8%)
1(5.3%)
1(5.3%)
 19 (44.2%) 24 (55.8%)
96 BORDES COMPROMETIDOS - SOLO LLE.R SI CIRUGIA DE PRIMARIO [character]
1. LIBRES
2. NO
3. SI
4(57.1%)
2(28.6%)
1(14.3%)
 7 (16.3%) 36 (83.7%)
97 GANGLIOS RESECADOS - SOLO LLE.R SI CIRUGIA DE PRIMARIO [character]
1. 10
2. 13
3. 16
4. 7
5. 8
6. VACIAMIENTO N1 Y N2
1(12.5%)
1(12.5%)
2(25.0%)
1(12.5%)
2(25.0%)
1(12.5%)
 8 (18.6%) 35 (81.4%)
98 GANGLIOS COMPROMETIDOS - SOLO LLE.R SI CIRUGIA DE PRIMARIO [numeric]
Min : 0
Mean : 1.2
Max : 5
0:6(75.0%)
5:2(25.0%)
 8 (18.6%) 35 (81.4%)
99 FECHA DE ORDEN DE CIRUGÍA [Date] 1. 2023-06-06
1(100.0%)
 1 (2.3%) 42 (97.7%)
100 FECHA VALORACIÓN DE ANESTESIA [Date]
1. 1970-01-01
2. 2092-03-02
3. 2092-05-21
4. 2092-09-23
5. 2093-04-28
6. 2093-05-03
7. 2093-09-15
8. 2093-12-28
15(68.2%)
1(4.5%)
1(4.5%)
1(4.5%)
1(4.5%)
1(4.5%)
1(4.5%)
1(4.5%)
 22 (51.2%) 21 (48.8%)
101 FECHA DE CIRUGIA [Date]
min : 1970-01-01
med : 1970-01-01
max : 2094-01-19
range : 124y 0m 18d
 12 distinct values 26 (60.5%) 17 (39.5%)
102 MODALIDAD DE TRATAMIENTO ORDE.DO [character]
1. TERAPIA DIRIGIDA
2. iTK
3. QUIMIOTERAPIA
4. TERAPIA DIRIGIDA - QUIMIO
5. QUMIOTERAPIA
6. CIRUGIA
7. ITK
8. PALIATIVO
9. QUIMIOTERAPIA
ITK
10. QUIMITERAPIA
[ 4 others ]
14(32.6%)
6(14.0%)
5(11.6%)
5(11.6%)
3(7.0%)
2(4.7%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
4(9.3%)
 43 (100.0%) 0 (0.0%)
103 ESQUEMA DE QUIMIOTERAPIA [character]
1. CARBOPLATINO PEMETREXED
2. CISPLATINO PEMETREXED
3. NO APLICA
4. PENDIENTE
15(34.9%)
3(7.0%)
24(55.8%)
1(2.3%)
 43 (100.0%) 0 (0.0%)
104 FECHA DE ORDEN DE QUIMIOTERAPIA [Date]
All NA's
 0 (0.0%) 43 (100.0%)
105 FECHA INICIO QUIMIOTERAPIA [Date]
min : 1970-01-01
med : 2091-03-04
max : 2095-02-01
range : 125y 1m 0d
 20 distinct values 33 (76.7%) 10 (23.3%)
106 iTK ORDENADO [character]
1. ERLOTINIB
2. OSIMERTINIB
1(2.3%)
42(97.7%)
 43 (100.0%) 0 (0.0%)
107 FECHA DE ORDEN DE iTK [Date] 1. 2023-10-03
1(100.0%)
 1 (2.3%) 42 (97.7%)
108 FECHA INICIO iTK [Date]
min : 2019-03-26
med : 2023-07-23
max : 2025-01-29
range : 5y 10m 3d
 40 distinct values 43 (100.0%) 0 (0.0%)
109 RADIOTERAPIA [character]
1. NO
2. SI
3(15.0%)
17(85.0%)
 20 (46.5%) 23 (53.5%)
110 DOSIS RT ADMINISTRADA [character]
1. 20
2. 2 Gy
3. 20 gy
4. 20 Gy
5. 2000/400 cGy
6. 25 Gy
7. 25 LUEGO 30 GY
8. 30
9. 3000
10. 3000 GY
[ 7 others ]
2(11.1%)
1(5.6%)
1(5.6%)
1(5.6%)
1(5.6%)
1(5.6%)
1(5.6%)
1(5.6%)
1(5.6%)
1(5.6%)
7(38.9%)
 18 (41.9%) 25 (58.1%)
111 TIPO DE RADIOTERAPIA [character]
1. 3DCRT
2. IMRT
3. IMRT/HOLOCRANEA.
4. NO APLICA
5. Radiocirugía - GammaKnife
6. RADIOCIRUGIA FOTONES
7. SBRT
1(5.6%)
6(33.3%)
1(5.6%)
2(11.1%)
1(5.6%)
1(5.6%)
6(33.3%)
 18 (41.9%) 25 (58.1%)
112 RADIOTERAPIA HUESO [numeric]
Min : 0
Mean : 0.4
Max : 1
0:14(56.0%)
1:11(44.0%)
 25 (58.1%) 18 (41.9%)
113 RADIOTERAPIA SNC [numeric]
Min : 0
Mean : 0.2
Max : 1
0:19(76.0%)
1:6(24.0%)
 25 (58.1%) 18 (41.9%)
114 RADIOTERAPIA PULMON [numeric]
Min : 0
Mean : 0.2
Max : 1
0:21(84.0%)
1:4(16.0%)
 25 (58.1%) 18 (41.9%)
115 FECHA DE VALORACIÓN DE RADIOTERAPIA [Date]
min : 1970-01-01
med : 2092-06-08
max : 2094-10-12
range : 124y 9m 11d
 16 distinct values 23 (53.5%) 20 (46.5%)
116 FECHA DE INICIO DE RADIOTERAPIA [Date]
All NA's
 0 (0.0%) 43 (100.0%)
117 INTENCIÓN INICIAL DE TRATAMIENTO [character]
1. CURATIVO
2. PALIATIVO
3(7.0%)
40(93.0%)
 43 (100.0%) 0 (0.0%)
118 TRATAMIENTO INICIAL ORDENADO [character]
1. iTK
2. QUIMIOTERAPIA
3. ITK
4. CIRUGIA
5. CIRUGÍA
6. RADIOTERAPIA MET + iTK
7. CIRUGÍA DE RESECCIÓN
8. PALIATIVO
9. QUIMIOTERAPIA + iTK
10. QUIMIOTERAPIA
iTK
[ 3 others ]
19(44.2%)
7(16.3%)
4(9.3%)
2(4.7%)
2(4.7%)
2(4.7%)
1(2.3%)
1(2.3%)
1(2.3%)
1(2.3%)
3(7.0%)
 43 (100.0%) 0 (0.0%)
119 TRATAMIENTO ACTUAL [character]
1. CARBO/PEMETREXED
2. FLAURA-2
3. OSIMERTINIB
4. OSIMERTINIB - PEMETREXED
5. OSIMERTINIB
+ PEMETRE
6. OSIMERTINIB
CIRUGIA
Q
1(2.3%)
2(4.7%)
37(86.0%)
1(2.3%)
1(2.3%)
1(2.3%)
 43 (100.0%) 0 (0.0%)
120 FECHA DE INICIO DE TRATAMIENTO CX-QXT-RXT-INMUNO [Date]
min : 2019-03-26
med : 2023-07-23
max : 2025-01-30
range : 5y 10m 4d
 43 distinct values 43 (100.0%) 0 (0.0%)
121 OPORTUNIDAD DE JUNTA MEDICA [numeric]
Mean (sd) : 31 (29.8)
min ≤ med ≤ max:
0 ≤ 21.5 ≤ 132
IQR (CV) : 39 (1)
 33 distinct values 42 (97.7%) 1 (2.3%)
122 OPORTUNIDAD DE VALORACIÒN PREANESTESIA [numeric]
Mean (sd) : 1.8 (5.1)
min ≤ med ≤ max:
0 ≤ 0 ≤ 23
IQR (CV) : 0 (2.9)
0:35(85.4%)
1:1(2.4%)
9:2(4.9%)
15:1(2.4%)
16:1(2.4%)
23:1(2.4%)
 41 (95.3%) 2 (4.7%)
123 OPORTUNIDAD DE CIRUGÍA [numeric]
Mean (sd) : 4308.6 (13242.3)
min ≤ med ≤ max:
0 ≤ 0 ≤ 44552
IQR (CV) : 0 (3.1)
 11 distinct values 41 (95.3%) 2 (4.7%)
124 OPORTUNIDAD DE INICIO QXT [numeric]
Mean (sd) : 6.4 (8.1)
min ≤ med ≤ max:
0 ≤ 0 ≤ 29
IQR (CV) : 13 (1.3)
 13 distinct values 41 (95.3%) 2 (4.7%)
125 OPORTUNIDAD DE INICIO TRATAMIENTO DIRIGIDO [numeric]
Mean (sd) : 8.2 (40.2)
min ≤ med ≤ max:
-239 ≤ 13 ≤ 47
IQR (CV) : 12.5 (4.9)
 27 distinct values 42 (97.7%) 1 (2.3%)
126 OPORTUNIDAD DE INICIO RXT [numeric]
Mean (sd) : 1096.3 (6941.6)
min ≤ med ≤ max:
0 ≤ 0 ≤ 44460
IQR (CV) : 17 (6.3)
 16 distinct values 41 (95.3%) 2 (4.7%)
127 OPORTUNIDAD DE INICIO DE TRATAMIENTO DESDE EL DIAGNÓSTICO POR PATOLOGÍA [numeric]
Mean (sd) : 83.9 (95.3)
min ≤ med ≤ max:
-13 ≤ 70 ≤ 565
IQR (CV) : 52.5 (1.1)
 39 distinct values 43 (100.0%) 0 (0.0%)
128 OPORTUNIDAD DE INICIO DESDE LA PRIMERA ATENCIÓN DEL HUSI [numeric]
Mean (sd) : 58.8 (111.6)
min ≤ med ≤ max:
-351 ≤ 41.5 ≤ 542
IQR (CV) : 68.2 (1.9)
 38 distinct values 42 (97.7%) 1 (2.3%)
129 FECHA DE MUERTE [Date]
1. 2024-01-26
2. 2024-02-19
3. 2024-05-07
4. 2024-05-10
5. 2024-07-08
6. 2024-07-15
7. 2024-12-09
8. 2025-01-31
1(11.1%)
1(11.1%)
1(11.1%)
1(11.1%)
2(22.2%)
1(11.1%)
1(11.1%)
1(11.1%)
 9 (20.9%) 34 (79.1%)
130 CAUSAS DE EGRESO [character] 1. MUERTE
9(100.0%)
 9 (20.9%) 34 (79.1%)
131 # DE DÍAS PCTE VIVO DESDE LA PATOLOGÍA [numeric]
Mean (sd) : 811.2 (502.4)
min ≤ med ≤ max:
129 ≤ 662 ≤ 2256
IQR (CV) : 614 (0.6)
 43 distinct values 43 (100.0%) 0 (0.0%)
132 # DE DÍAS PACTE VIVO DESDE INICIO DE TRATAMIENTO [numeric]
Mean (sd) : 727.3 (507.4)
min ≤ med ≤ max:
102 ≤ 588 ≤ 2168
IQR (CV) : 520 (0.7)
 41 distinct values 43 (100.0%) 0 (0.0%)
133 EDAD EN AÑOS (FALLECE) [numeric]
Mean (sd) : 70.2 (10.1)
min ≤ med ≤ max:
49 ≤ 70 ≤ 85
IQR (CV) : 10.8 (0.1)
 11 distinct values 12 (27.9%) 31 (72.1%)
134 FECHA DE DIAGNÓSTICO POR PATOLOGÍA [Date]
min : 2018-12-28
med : 2023-02-14
max : 2024-11-29
range : 5y 11m 1d
 42 distinct values 43 (100.0%) 0 (0.0%)
135 TIPO DE PROGRESIÓN 0=OLIGOPROGRESIÓN, 1=PROGRESIÓN [numeric]
Min : 0
Mean : 0.7
Max : 1
0:3(30.0%)
1:7(70.0%)
 10 (23.3%) 33 (76.7%)
136 TRATAMIENTO POSTERIOR A PROGRESIÓN [character]
1. Carboplatino/Etoposido/Du
2. FLAURA-2
3. OSIMERTINIB
4. QT
5. QT+INMUNO
6. QUIMIOTERAPIA
7. Radiocirugía SNC
8. RADIOTERAPIA . SE CONTINU
9. Reinicio ITK (suspendió)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
2(20.0%)
1(10.0%)
 10 (23.3%) 33 (76.7%)
137 FECHAS IMAGENES REVALORACION [Date]
min : 2020-01-10
med : 2023-11-22
max : 2025-05-08
range : 5y 3m 28d
 40 distinct values 42 (97.7%) 1 (2.3%)
138 FECHA DE RECAIDA O PROGRESION [Date]
1. 2020-01-10
2. 2022-03-01
3. 2022-09-22
4. 2023-04-28
5. 2023-08-10
6. 2023-09-22
7. 2023-11-21
8. 2023-12-07
9. 2024-04-24
10. 2025-04-28
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
 10 (23.3%) 33 (76.7%)
139 PROGRESIÓN O RECAIDA 1- MENOR AL AÑO 2- MAYOR AL AÑO [numeric]
Min : 1
Mean : 1.7
Max : 2
1:3(30.0%)
2:7(70.0%)
 10 (23.3%) 33 (76.7%)
140 MUERTE 1- MENOR AL AÑO 2- MAYOR AL AÑO [numeric]
Min : 1
Mean : 1.9
Max : 2
1:1(11.1%)
2:8(88.9%)
 9 (20.9%) 34 (79.1%)
141 ESTADO VITAL 1: VIVO 2: MUERTO [numeric]
Min : 1
Mean : 1.2
Max : 2
1:35(81.4%)
2:8(18.6%)
 43 (100.0%) 0 (0.0%)
142 ESTADO DE TRATAMIENTO [character]
1. ACTIVO
2. NO INICIÓ
3. SUSPENDIDO
32(74.4%)
1(2.3%)
10(23.3%)
 43 (100.0%) 0 (0.0%)
143 ÚLTIMO CONTROL [POSIXct, POSIXt]
min : 2024-01-05
med : 2025-04-10
max : 2025-05-17
range : 1y 4m 12d
 36 distinct values 43 (100.0%) 0 (0.0%)
144 fecha biopsia [Date]
min : 2018-12-28
med : 2023-03-07
max : 2024-11-29
range : 5y 11m 1d
 41 distinct values 42 (97.7%) 1 (2.3%)
145 fecha inicio itK [Date]
min : 2019-03-26
med : 2023-08-07
max : 2025-01-09
range : 5y 9m 14d
 39 distinct values 42 (97.7%) 1 (2.3%)
146 Dias a inicio de iTK desde dx histopatologico [numeric]
Mean (sd) : 97.1 (73.8)
min ≤ med ≤ max:
6 ≤ 80.5 ≤ 322
IQR (CV) : 89 (0.8)
 40 distinct values 42 (97.7%) 1 (2.3%)
147 Concordante con guia 0=no 1=si [numeric]
Min : 0
Mean : 1
Max : 1
0:2(4.7%)
1:41(95.3%)
 43 (100.0%) 0 (0.0%)
148 Adherencia 0=no 1=si (asumir que si a menos que en HC diga que problemas para entrega) [numeric]
Min : 0
Mean : 0.9
Max : 1
0:3(7.0%)
1:40(93.0%)
 43 (100.0%) 0 (0.0%)
149 INICIO [Date]
min : 2018-12-28
med : 2023-01-03
max : 2024-11-29
range : 5y 11m 1d
 41 distinct values 43 (100.0%) 0 (0.0%)
150 MUERTE [Date]
1. 2024-01-26
2. 2024-02-19
3. 2024-05-07
4. 2024-05-10
5. 2024-07-08
6. 2024-07-15
7. 2024-12-09
8. 2025-01-31
1(11.1%)
1(11.1%)
1(11.1%)
1(11.1%)
2(22.2%)
1(11.1%)
1(11.1%)
1(11.1%)
 9 (20.9%) 34 (79.1%)
151 PROGRE_RECA [Date]
1. 2020-01-10
2. 2022-03-01
3. 2022-09-22
4. 2023-04-28
5. 2023-08-10
6. 2023-09-22
7. 2023-11-21
8. 2023-12-07
9. 2024-04-24
10. 2025-04-28
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
1(10.0%)
 10 (23.3%) 33 (76.7%)
152 SEGUIM [Date]
min : 2024-01-05
med : 2025-04-10
max : 2025-05-17
range : 1y 4m 12d
 36 distinct values 43 (100.0%) 0 (0.0%)
153 fecha_evento1 [Date]
min : 2024-01-26
med : 2025-04-10
max : 2025-05-17
range : 1y 3m 21d
 35 distinct values 43 (100.0%) 0 (0.0%)
154 Tiempo_meses [numeric]
Mean (sd) : 27.3 (16.9)
min ≤ med ≤ max:
5 ≤ 22 ≤ 75
IQR (CV) : 21 (0.6)
 27 distinct values 43 (100.0%) 0 (0.0%)
155 evento1 [numeric]
Min : 0
Mean : 0.2
Max : 1
0:34(79.1%)
1:9(20.9%)
 43 (100.0%) 0 (0.0%)
156 evento1_1 [numeric]
Min : 0
Mean : 0.2
Max : 1
0:33(76.7%)
1:10(23.3%)
 43 (100.0%) 0 (0.0%)
157 Estrato_cat [factor]
1. 1-2
2. 3
3. 4-5
11(30.6%)
20(55.6%)
5(13.9%)
 36 (83.7%) 7 (16.3%)
158 EDAD_CAT [factor]
1. <=70
2. >70
22(51.2%)
21(48.8%)
 43 (100.0%) 0 (0.0%)
159 metastasis [factor]
1. no metastasico
2. metastasico
8(18.6%)
35(81.4%)
 43 (100.0%) 0 (0.0%)
160 Cuidado_paliativocat [character]
1. No
2. Si
4(9.3%)
39(90.7%)
 43 (100.0%) 0 (0.0%)

Generated by summarytools 1.1.4 (R version 4.4.2)
2025-10-28

Analisis de supervivencia - muerte


library(survival)
library(survminer)

SUP1$evento1 <- as.numeric(SUP1$evento1)
SUP1$evento1[SUP1$evento1 != 1] <- 0   # asegura que solo hay 0/1

surv_object <- Surv(time = SUP1$Tiempo_meses, event = SUP1$evento1)
fit <- survfit(surv_object ~ 1, data = SUP1)
summary_fit <- summary(fit)
df_summary <- data.frame(
  time     = summary_fit$time,
  n.risk   = summary_fit$n.risk,
  n.event  = summary_fit$n.event,
  survival = summary_fit$surv,
  std.err  = summary_fit$std.err,
  lower    = summary_fit$lower,
  upper    = summary_fit$upper
)


kable(df_summary, format = "html", table.attr = "style='width:80%;'") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
time n.risk n.event survival std.err lower upper
5 43 1 0.9767442 0.0229838 0.9327198 1.0000000
14 37 2 0.9239472 0.0423215 0.8446130 1.0000000
22 23 1 0.8837756 0.0564123 0.7798462 1.0000000
25 21 1 0.8416910 0.0676258 0.7190561 0.9852414
30 17 1 0.7921798 0.0797384 0.6503459 0.9649462
48 7 1 0.6790113 0.1250954 0.4732151 0.9743059
55 5 1 0.5432090 0.1573819 0.3078587 0.9584787
57 4 1 0.4074068 0.1666262 0.1827651 0.9081615

Curva de superviviencia

ggsurvplot(
  fit, 
  data = SUP1,
  conf.int = TRUE,                 # mostrar intervalo de confianza
  risk.table = TRUE,               # mostrar tabla de número en riesgo
  pval = TRUE,                     # valor p si hay grupos
  surv.median.line = "hv",         # línea en mediana de supervivencia
  xlab = "Tiempo (meses)", 
  ylab = "Probabilidad de supervivencia",
  palette = "Dark2",               # paleta de colores
  ggtheme = theme_minimal(base_size = 14)
)

surv_median(fit)
##   strata median lower upper
## 1    All     57    48    NA

La mediana fue de 57 años, el limite superior no fue determinado

*Curvas por edad


SUP1.1 <- BASE %>% select(ID, Tiempo_meses, evento1, EDAD_CAT,metastasis, `PERFIL MUTACIONAL`, VISCERALNONCNS_METS, SEXO )

fit1 <- survfit(Surv(Tiempo_meses, evento1) ~ EDAD_CAT, data = SUP1.1, type = "kaplan-meier")
ggsurvplot(
  fit1, 
  data = SUP1.1,
  xlab = "Tiempo en meses", 
  ylab = "Probabilidad de Supervivencia",
  title = "Curvas Kaplan-Meier según edad",
  conf.int = FALSE,        # intervalos de confianza
  pval = TRUE,             # valor p del log-rank
  risk.table = TRUE,       # tabla de riesgo
  risk.table.fontsize = 3, # tamaño fuente tabla
  ggtheme = theme_minimal(),
  tables.theme = theme(
    text = element_text(size = 5),
    axis.text = element_text(size = 5),
    strip.text = element_text(size = 14)
  )
)


fit1.2 <- survfit(Surv(Tiempo_meses, evento1) ~ metastasis, data = SUP1.1, type = "kaplan-meier")
ggsurvplot(
  fit1.2, 
  data = SUP1.1,
  xlab = "Tiempo en meses", 
  ylab = "Probabilidad de Supervivencia",
  title = "Curvas Kaplan-Meier según metastasis",
  conf.int = FALSE,        # intervalos de confianza
  pval = TRUE,             # valor p del log-rank
  risk.table = TRUE,       # tabla de riesgo
  risk.table.fontsize = 3, # tamaño fuente tabla
  ggtheme = theme_minimal(),
  tables.theme = theme(
    text = element_text(size = 5),
    axis.text = element_text(size = 5),
    strip.text = element_text(size = 14)
  )
)

summary(fit1.2)$table
##                           records n.max n.start events    rmean se(rmean)
## metastasis=no metastasico       8     8       8      1 64.83333  9.280854
## metastasis=metastasico         35    35      35      8 55.51867  5.066177
##                           median 0.95LCL 0.95UCL
## metastasis=no metastasico     NA      NA      NA
## metastasis=metastasico        57      48      NA
colnames(SUP1.1)[6] <- "PERFIL"
fit1.3 <- survfit(Surv(Tiempo_meses, evento1) ~ PERFIL, data = SUP1.1, type = "kaplan-meier")
ggsurvplot(
  fit1.3, 
  data = SUP1.1,
  xlab = "Tiempo en meses", 
  ylab = "Probabilidad de Supervivencia",
  title = "Curvas Kaplan-Meier según perfil mutacional",
  conf.int = FALSE,        # intervalos de confianza
  pval = TRUE,             # valor p del log-rank
  risk.table = TRUE,       # tabla de riesgo
  risk.table.fontsize = 3, # tamaño fuente tabla
  ggtheme = theme_minimal(),
  tables.theme = theme(
    text = element_text(size = 5),
    axis.text = element_text(size = 5),
    strip.text = element_text(size = 14)
  )
)

fit1.4 <- survfit(Surv(Tiempo_meses, evento1) ~ VISCERALNONCNS_METS, data = SUP1.1, type = "kaplan-meier")
ggsurvplot(
  fit1.4, 
  data = SUP1.1,
  xlab = "Tiempo en meses", 
  ylab = "Probabilidad de Supervivencia",
  title = "Curvas Kaplan-Meier según METASTASIS VISCERALNON",
  conf.int = FALSE,        # intervalos de confianza
  pval = TRUE,             # valor p del log-rank
  risk.table = TRUE,       # tabla de riesgo
  risk.table.fontsize = 3, # tamaño fuente tabla
  ggtheme = theme_minimal(),
  tables.theme = theme(
    text = element_text(size = 5),
    axis.text = element_text(size = 5),
    strip.text = element_text(size = 14)
  )
)

Superviviencia recaida progresion


# 3. Seleccionar las variables clave
SUP1_1 <- BASE %>% select(ID, Tiempo_meses, evento1_1)

# 4. Visualizar en tabla bonita
kable(SUP1_1, format = "html", table.attr = "style='width:60%;'") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
ID Tiempo_meses evento1_1
1 75 0
2 58 1
3 58 0
4 50 1
5 46 0
6 38 0
7 36 0
8 30 1
9 30 0
10 32 0
11 14 0
12 28 0
13 29 0
14 28 0
15 14 1
16 21 0
17 15 0
18 18 0
19 18 0
20 16 0
21 10 0
22 6 0
23 5 0
24 8 0
25 25 1
26 5 0
27 55 1
28 30 1
29 35 0
30 21 0
31 22 1
32 17 0
33 15 0
34 17 0
35 22 0
36 14 0
37 39 1
38 14 0
39 5 0
40 15 1
41 57 0
42 36 0
43 48 0 

library(ggplot2)
library(plotly)
SUP1_1$evento2 <- factor(SUP1_1$evento1_1, levels = c(0,1), labels = c("Censura", "Progresion o recaida"))
m <- SUP1_1 %>%
  mutate(t_inicio = 0,
         t_fin = Tiempo_meses) %>%
  ggplot(aes(x = as.factor(ID), y = t_fin)) +
  geom_linerange(aes(ymin = t_inicio, ymax = t_fin)) +
  geom_point(aes(shape = factor(evento2), color = factor(evento2)), stroke = 1, size = 2) +
  scale_shape_manual(values = c(1, 4)) +  # Diferentes formas para censura y eventos
  labs(y = "Tiempo de Supervivencia (meses)", x = "ID") + 
  coord_flip() + 
  theme_classic() +
  theme(legend.title = element_blank(), legend.position = "bottom")
ggplotly(m)

library(survival)
library(survminer)

SUP1_1$evento1_1 <- as.numeric(SUP1_1$evento1_1)
SUP1_1$evento1_1[SUP1_1$evento1_1 != 1] <- 0   # asegura que solo hay 0/1

surv_object1 <- Surv(time = SUP1_1$Tiempo_meses, event = SUP1_1$evento1_1)
fit1 <- survfit(surv_object1 ~ 1, data = SUP1_1)
summary_fit1 <- summary(fit1)
df_summary1 <- data.frame(
  time     = summary_fit1$time,
  n.risk   = summary_fit1$n.risk,
  n.event  = summary_fit1$n.event,
  survival = summary_fit1$surv,
  std.err  = summary_fit1$std.err,
  lower    = summary_fit1$lower,
  upper    = summary_fit1$upper
)


kable(df_summary1, format = "html", table.attr = "style='width:80%;'") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
time n.risk n.event survival std.err lower upper
14 37 1 0.9729730 0.0266593 0.9220999 1.0000000
15 33 1 0.9434889 0.0388750 0.8702908 1.0000000
22 23 1 0.9024677 0.0547018 0.8013778 1.0000000
25 21 1 0.8594930 0.0668803 0.7379166 1.0000000
30 17 2 0.7583762 0.0894051 0.6019167 0.9555051
39 9 1 0.6741122 0.1123706 0.4862300 0.9345932
50 6 1 0.5617602 0.1388813 0.3460289 0.9119886
55 5 1 0.4494081 0.1498089 0.2338274 0.8637469
58 3 1 0.2996054 0.1579085 0.1066399 0.8417434 

ggsurvplot(
  fit1, 
  data = SUP1_1,
  conf.int = TRUE,                 # mostrar intervalo de confianza
  risk.table = TRUE,               # mostrar tabla de número en riesgo
  pval = TRUE,                     # valor p si hay grupos
  surv.median.line = "hv",         # línea en mediana de supervivencia
  xlab = "Tiempo (meses)", 
  ylab = "Probabilidad de supervivencia",
  palette = "Dark2",               # paleta de colores
  ggtheme = theme_minimal(base_size = 14)
)

SUP1_1.1 <- BASE %>% select(ID, Tiempo_meses, evento1_1, EDAD_CAT,metastasis, `PERFIL MUTACIONAL`, VISCERALNONCNS_METS )


fit1_1 <- survfit(Surv(Tiempo_meses, evento1_1) ~ EDAD_CAT, data = SUP1_1.1, type = "kaplan-meier")
ggsurvplot(
  fit1_1, 
  data = SUP1_1.1,
  xlab = "Tiempo en meses", 
  ylab = "Probabilidad de Supervivencia",
  title = "Curvas Kaplan-Meier según edad",
  conf.int = FALSE,        # intervalos de confianza
  pval = TRUE,             # valor p del log-rank
  risk.table = TRUE,       # tabla de riesgo
  risk.table.fontsize = 3, # tamaño fuente tabla
  ggtheme = theme_minimal(),
  tables.theme = theme(
    text = element_text(size = 5),
    axis.text = element_text(size = 5),
    strip.text = element_text(size = 14)
  )
)


fit1_2<- survfit(Surv(Tiempo_meses, evento1_1) ~ metastasis, data = SUP1_1.1, type = "kaplan-meier")
ggsurvplot(
  fit1_2, 
  data = SUP1_1.1,
  xlab = "Tiempo en meses", 
  ylab = "Probabilidad de Supervivencia",
  title = "Curvas Kaplan-Meier según metastasis",
  conf.int = FALSE,        # intervalos de confianza
  pval = TRUE,             # valor p del log-rank
  risk.table = TRUE,       # tabla de riesgo
  risk.table.fontsize = 3, # tamaño fuente tabla
  ggtheme = theme_minimal(),
  tables.theme = theme(
    text = element_text(size = 5),
    axis.text = element_text(size = 5),
    strip.text = element_text(size = 14)
  )
)


colnames(SUP1_1.1)[6] <- "PERFIL"
fit1_3<- survfit(Surv(Tiempo_meses, evento1_1) ~ PERFIL, data = SUP1_1.1, type = "kaplan-meier")
ggsurvplot(
  fit1_3, 
  data = SUP1_1.1,
  xlab = "Tiempo en meses", 
  ylab = "Probabilidad de Supervivencia",
  title = "Curvas Kaplan-Meier según metastasis",
  conf.int = FALSE,        # intervalos de confianza
  pval = TRUE,             # valor p del log-rank
  risk.table = TRUE,       # tabla de riesgo
  risk.table.fontsize = 3, # tamaño fuente tabla
  ggtheme = theme_minimal(),
  tables.theme = theme(
    text = element_text(size = 5),
    axis.text = element_text(size = 5),
    strip.text = element_text(size = 14)
  )
)

fit1_4<- survfit(Surv(Tiempo_meses, evento1_1) ~ VISCERALNONCNS_METS, data = SUP1_1.1, type = "kaplan-meier")
ggsurvplot(
  fit1_4, 
  data = SUP1_1.1,
  xlab = "Tiempo en meses", 
  ylab = "Probabilidad de Supervivencia",
  title = "Curvas Kaplan-Meier según metastasis",
  conf.int = FALSE,        # intervalos de confianza
  pval = TRUE,             # valor p del log-rank
  risk.table = TRUE,       # tabla de riesgo
  risk.table.fontsize = 3, # tamaño fuente tabla
  ggtheme = theme_minimal(),
  tables.theme = theme(
    text = element_text(size = 5),
    axis.text = element_text(size = 5),
    strip.text = element_text(size = 14)
  )
)

Grafico Sankey


#se codificia las nuevas variables

#1 Completitud del tratamiento Quimio + Radio + Braqui
BASE2 <- BASE
BASE2$evento1 <- factor(BASE2$evento1,
                       levels = c(1, 0),
                       labels = c("Muerte", "Censura"))

BASE2$ITK <- factor(ifelse(!is.na(BASE2$`FECHA INICIO iTK`), "+ITK", "-ITK"))
BASE2$quimio <- factor(ifelse(!is.na(BASE2$`FECHA INICIO QUIMIOTERAPIA`), "+quim", "-quim"))
BASE2$EGFR_cat <- ifelse(grepl("19", BASE2$`PERFIL MUTACIONAL`, ignore.case = TRUE),"Exon19","Exon21")

BASE_AL <- BASE2 %>% select(EGFR_cat,ITK, quimio, evento1)
BASE_transformado <- BASE_AL %>% group_by(EGFR_cat,ITK, quimio, evento1) %>% 
  summarise(Freq = n()) %>% ungroup() %>% rename(EGFR = EGFR_cat, ITK = ITK, quimio = quimio, evento = evento1)


library(easyalluvial) 

alluvial_wide( data = BASE_AL, 
               max_variables = 4, fill_by = 'first_variable',
               col_vector_flow = c("skyblue", "salmon"))


kable(BASE_transformado)
EGFR ITK quimio evento Freq
Exon19 +ITK -quim Muerte 2
Exon19 +ITK -quim Censura 5
Exon19 +ITK +quim Muerte 4
Exon19 +ITK +quim Censura 19
Exon21 +ITK -quim Censura 3
Exon21 +ITK +quim Muerte 3
Exon21 +ITK +quim Censura 7 
library(survival)

# Modelo de Cox
modelo_cox <- coxph(Surv(Tiempo_meses, evento1) ~ EDAD_CAT + metastasis + PERFIL +  SEXO + VISCERALNONCNS_METS,data = SUP1.1)

# Resultado
summary(modelo_cox)
## Call:
## coxph(formula = Surv(Tiempo_meses, evento1) ~ EDAD_CAT + metastasis + 
##     PERFIL + SEXO + VISCERALNONCNS_METS, data = SUP1.1)
## 
##   n= 43, number of events= 9 
## 
##                             coef  exp(coef)   se(coef)      z Pr(>|z|)
## EDAD_CAT>70            5.275e-01  1.695e+00  9.427e-01  0.560    0.576
## metastasismetastasico -1.939e+01  3.791e-09  9.133e+03 -0.002    0.998
## PERFILL858R - exon 21  2.424e-01  1.274e+00  9.424e-01  0.257    0.797
## SEXO                   8.768e-01  2.403e+00  9.071e-01  0.967    0.334
## VISCERALNONCNS_METSSi  1.957e+01  3.149e+08  9.133e+03  0.002    0.998
## 
##                       exp(coef) exp(-coef) lower .95 upper .95
## EDAD_CAT>70           1.695e+00  5.901e-01    0.2671     10.75
## metastasismetastasico 3.791e-09  2.638e+08    0.0000       Inf
## PERFILL858R - exon 21 1.274e+00  7.848e-01    0.2010      8.08
## SEXO                  2.403e+00  4.161e-01    0.4061     14.22
## VISCERALNONCNS_METSSi 3.149e+08  3.175e-09    0.0000       Inf
## 
## Concordance= 0.724  (se = 0.059 )
## Likelihood ratio test= 7.25  on 5 df,   p=0.2
## Wald test            = 1.24  on 5 df,   p=0.9
## Score (logrank) test = 4.58  on 5 df,   p=0.5
exp(cbind(HR = coef(modelo_cox), confint(modelo_cox)))
##                                 HR     2.5 %    97.5 %
## EDAD_CAT>70           1.694689e+00 0.2671073 10.752125
## metastasismetastasico 3.791070e-09 0.0000000       Inf
## PERFILL858R - exon 21 1.274276e+00 0.2009694  8.079735
## SEXO                  2.403189e+00 0.4060944 14.221609
## VISCERALNONCNS_METSSi 3.149401e+08 0.0000000       Inf