Kiểm định phân phối chuẩn và tạo biến mới (BMI phân độ) và (MAFLD phân nhóm)

library(readxl)
mafld_mace <- read_excel("~/Desktop/Nghiên cứu của Duy/Nghiên cứu MAFLD - biến cố tim mạch/mafld_mace.xlsx")
View(mafld_mace)
head(mafld_mace)

## # A tibble: 6 × 58
##     stt   sex  year   age   dtd sagbt  sagm giatrinhm donhm  doxh giatrixh    cc
##   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>     <dbl> <dbl> <dbl>    <dbl> <dbl>
## 1     1     0  1958    66     1     0     1       336     4     1      7.1  1.58
## 2     2     1  1974    50     1     1     0       189     1     2      7.9  1.62
## 3     3     1  1955    69     0     1     0       238     1     1      4.8  1.6 
## 4     4     0  1936    88     1     0     1       302     4     3     12.1  1.48
## 5     5     0  1975    49     0     0     1       271     3     1      5.3  1.46
## 6     6     0  1953    71     0     1     0       224     1     2      9.2  1.5 
## # ℹ 46 more variables: cn <dbl>, bmi <dbl>, eo <dbl>, mafld <dbl>, hbsag <dbl>,
## #   antihcv <dbl>, cholestp <dbl>, trig <dbl>, hld <dbl>, ldl <dbl>,
## #   nonhdl <dbl>, fli <dbl>, ldlhdl <dbl>, hdlnonhdl <dbl>, fib4 <dbl>,
## #   glucose <dbl>, hba1c <dbl>, alt <dbl>, ast <dbl>, ggt <dbl>, ure <dbl>,
## #   cre <dbl>, plt <dbl>, agaston <dbl>, multisteno <dbl>, ntbnp <dbl>,
## #   auric <dbl>, canthiepct <dbl>, nmctc <dbl>, cdtnkod <dbl>, dqn <dbl>,
## #   stc <dbl>, maceg <dbl>, toa_ucmc <chr>, toa_bb <chr>, toa_kkttc <chr>, …

# Chọn các biến số (numeric variables)
num_vars <- sapply(mafld_mace, is.numeric)
mafld_num <- mafld_mace[, num_vars]

# 1. Kiểm định Shapiro-Wilk cho từng biến
shapiro_results <- lapply(mafld_num, function(x) {
  x <- x[!is.na(x)]  # bỏ NA
  if(length(x) >= 3 & length(x) <= 5000){  # Shapiro khuyến nghị
    shapiro.test(x)
  } else {
    NA
  }
})
# Xem kết quả Shapiro-Wilk
shapiro_results

## $stt
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.95469, p-value = 1.498e-09
## 
## 
## $sex
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.60548, p-value < 2.2e-16
## 
## 
## $year
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.98104, p-value = 5.642e-05
## 
## 
## $age
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.98104, p-value = 5.642e-05
## 
## 
## $dtd
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.58734, p-value < 2.2e-16
## 
## 
## $sagbt
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.6359, p-value < 2.2e-16
## 
## 
## $sagm
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.63597, p-value < 2.2e-16
## 
## 
## $giatrinhm
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.97337, p-value = 1.513e-06
## 
## 
## $donhm
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.77873, p-value < 2.2e-16
## 
## 
## $doxh
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.59055, p-value < 2.2e-16
## 
## 
## $giatrixh
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.89516, p-value = 1.141e-15
## 
## 
## $cc
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.98542, p-value = 0.0006018
## 
## 
## $cn
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.9925, p-value = 0.04856
## 
## 
## $bmi
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.97816, p-value = 1.351e-05
## 
## 
## $eo
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.98332, p-value = 0.0001874
## 
## 
## $mafld
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.63651, p-value < 2.2e-16
## 
## 
## $hbsag
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.27863, p-value < 2.2e-16
## 
## 
## $antihcv
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.16137, p-value < 2.2e-16
## 
## 
## $cholestp
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.98859, p-value = 0.003978
## 
## 
## $trig
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.80795, p-value < 2.2e-16
## 
## 
## $hld
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.8901, p-value = 4.607e-16
## 
## 
## $ldl
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.9833, p-value = 0.0001851
## 
## 
## $nonhdl
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.98771, p-value = 0.002323
## 
## 
## $fli
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.98824, p-value = 0.03366
## 
## 
## $ldlhdl
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.98551, p-value = 0.000636
## 
## 
## $hdlnonhdl
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.87985, p-value < 2.2e-16
## 
## 
## $fib4
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.83017, p-value < 2.2e-16
## 
## 
## $glucose
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.71002, p-value < 2.2e-16
## 
## 
## $hba1c
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.80987, p-value < 2.2e-16
## 
## 
## $alt
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.81245, p-value < 2.2e-16
## 
## 
## $ast
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.83676, p-value < 2.2e-16
## 
## 
## $ggt
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.63383, p-value < 2.2e-16
## 
## 
## $ure
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.88828, p-value = 3.346e-16
## 
## 
## $cre
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.96603, p-value = 7.724e-08
## 
## 
## $plt
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.94906, p-value = 2.655e-10
## 
## 
## $agaston
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.73707, p-value < 2.2e-16
## 
## 
## $multisteno
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.63573, p-value < 2.2e-16
## 
## 
## $ntbnp
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.35099, p-value < 2.2e-16
## 
## 
## $auric
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.9919, p-value = 0.09193
## 
## 
## $canthiepct
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.60661, p-value < 2.2e-16
## 
## 
## $nmctc
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.087319, p-value < 2.2e-16
## 
## 
## $cdtnkod
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.17169, p-value < 2.2e-16
## 
## 
## $dqn
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.059862, p-value < 2.2e-16
## 
## 
## $stc
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.044226, p-value < 2.2e-16
## 
## 
## $maceg
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.59826, p-value < 2.2e-16
## 
## 
## $toa_trime
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.5813, p-value < 2.2e-16
## 
## 
## $toa_loitieu
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.41782, p-value < 2.2e-16
## 
## 
## $toa_metfor
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.44, p-value < 2.2e-16
## 
## 
## $toa_su
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.22088, p-value < 2.2e-16
## 
## 
## $toa_sglt2
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.27579, p-value < 2.2e-16
## 
## 
## $toa_dpp4
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.18905, p-value < 2.2e-16
## 
## 
## $toa_insulin
## 
##  Shapiro-Wilk normality test
## 
## data:  x
## W = 0.07411, p-value < 2.2e-16

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

# Tạo biến mới theo phân độ BMI châu Á
mafld_mace <- mafld_mace %>%
  mutate(
    bmi_phando = case_when(
      bmi < 18.5 ~ "Thiếu cân",
      bmi >= 18.5 & bmi < 23 ~ "Bình thường",
      bmi >= 23 & bmi < 25 ~ "Thừa cân",
      bmi >= 25 & bmi < 30 ~ "Béo phì độ I",
      bmi >= 30 ~ "Béo phì độ II",
      TRUE ~ NA_character_
    )
  )
# Kiểm tra kết quả
table(mafld_mace$bmi_phando, useNA = "ifany")

## 
##  Béo phì độ I Béo phì độ II   Bình thường     Thiếu cân      Thừa cân 
##           104            14           120            13           137

mafld_mace <- mafld_mace %>%
  mutate(
    mafld_sub = case_when(
      mafld == 1 & dtd == 1 ~ 1,
      mafld == 1 & dtd != 1 & bmi >= 23 ~ 2,
      mafld == 1 & dtd != 1 & bmi < 23 ~ 3,
      TRUE ~ NA_real_
    )
  )
head(mafld_mace)

## # A tibble: 6 × 60
##     stt   sex  year   age   dtd sagbt  sagm giatrinhm donhm  doxh giatrixh    cc
##   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>     <dbl> <dbl> <dbl>    <dbl> <dbl>
## 1     1     0  1958    66     1     0     1       336     4     1      7.1  1.58
## 2     2     1  1974    50     1     1     0       189     1     2      7.9  1.62
## 3     3     1  1955    69     0     1     0       238     1     1      4.8  1.6 
## 4     4     0  1936    88     1     0     1       302     4     3     12.1  1.48
## 5     5     0  1975    49     0     0     1       271     3     1      5.3  1.46
## 6     6     0  1953    71     0     1     0       224     1     2      9.2  1.5 
## # ℹ 48 more variables: cn <dbl>, bmi <dbl>, eo <dbl>, mafld <dbl>, hbsag <dbl>,
## #   antihcv <dbl>, cholestp <dbl>, trig <dbl>, hld <dbl>, ldl <dbl>,
## #   nonhdl <dbl>, fli <dbl>, ldlhdl <dbl>, hdlnonhdl <dbl>, fib4 <dbl>,
## #   glucose <dbl>, hba1c <dbl>, alt <dbl>, ast <dbl>, ggt <dbl>, ure <dbl>,
## #   cre <dbl>, plt <dbl>, agaston <dbl>, multisteno <dbl>, ntbnp <dbl>,
## #   auric <dbl>, canthiepct <dbl>, nmctc <dbl>, cdtnkod <dbl>, dqn <dbl>,
## #   stc <dbl>, maceg <dbl>, toa_ucmc <chr>, toa_bb <chr>, toa_kkttc <chr>, …

View(mafld_mace)

# Kiểm tra tần suất các nhóm
table(mafld_mace$mafld_sub, useNA = "ifany")

## 
##    1    2    3 <NA> 
##   77   99   17  195

mafld_mace <- mafld_mace %>%
  mutate(
    mace = if_else(
      nmctc == 1 | cdtnkod == 1 | dqn == 1 | stc == 1,
      1, 0
    )
  )

# Kiểm tra tần suất
table(mafld_mace$mace, useNA = "ifany")

## 
##   0   1 
## 367  21

Đặc điểm dân số nghiên cứu chung

library(dplyr)
library(gtsummary)
tbl1 <- mafld_mace %>%
  select(
    sex, age, dtd, toa_ucmc, toa_bb, toa_kkttc, toa_statin, toa_nitrate, 
    toa_trime, toa_ccb, toa_loitieu, toa_metfor, toa_su, toa_sglt2, 
    toa_dpp4, toa_insulin, sagbt, sagm, giatrinhm, donhm, doxh, 
    giatrixh, cc, cn, bmi, bmi_phando, eo, mafld, hbsag, antihcv, cholestp,
    trig, hld, ldl, nonhdl, fli, ldlhdl, hdlnonhdl, fib4, glucose,
    hba1c, alt, ast, ggt, ure, cre, plt, agaston, multisteno,
    ntbnp, auric, canthiepct, nmctc, cdtnkod, dqn, stc, maceg, mace
  ) %>%
  tbl_summary(
    type = all_continuous() ~ "continuous2",
    statistic = list(
      all_continuous() ~ c("{mean} ({sd})", "{median} ({p25}, {p75})", "{min}, {max}"),
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = all_continuous() ~ 2,
    label = list(
      sex ~ "Giới tính",
      age ~ "Tuổi",
      dtd ~ "Đái tháo đường",
      toa_ucmc ~ "Thuốc ức chế men chuyển",
      toa_bb ~ "Thuốc chẹn beta",
      toa_kkttc ~ "Thuốc kháng kết tập tiểu cầu",
      toa_statin ~ "Statin",
      toa_nitrate ~ "Nitrate",
      toa_trime ~ "Trimetazidine",
      toa_ccb ~ "Thuốc chẹn kênh canxi",
      toa_loitieu ~ "Thuốc lợi tiểu",
      toa_metfor ~ "Metformin",
      toa_su ~ "Sulfonylurea",
      toa_sglt2 ~ "SGLT2i",
      toa_dpp4 ~ "DPP4i",
      toa_insulin ~ "Insulin",
      sagbt ~ "SAGBT",
      sagm ~ "SAGM",
      giatrinhm ~ "Giá trị nhiễm mỡ",
      donhm ~ "Độ nhiễm mỡ",
      doxh ~ "Độ xơ hóa",
      giatrixh ~ "Giá trị xơ hóa",
      cc ~ "Chiều cao",
      cn ~ "Cân nặng",
      bmi ~ "BMI",
      bmi_phando ~ "Phân độ BMI",
      eo ~ "Vòng eo",
      mafld ~ "MAFLD",
      hbsag ~ "HBsAg (+)",
      antihcv ~ "Anti-HCV (+)",
      cholestp ~ "Cholesterol toàn phần",
      trig ~ "Triglyceride",
      hld ~ "HDL-C",
      ldl ~ "LDL-C",
      nonhdl ~ "Non-HDL-C",
      fli ~ "FLI",
      ldlhdl ~ "Tỷ số LDL/HDL",
      hdlnonhdl ~ "Tỷ số HDL/Non-HDL",
      fib4 ~ "Chỉ số FIB-4",
      glucose ~ "Glucose máu",
      hba1c ~ "HbA1c",
      alt ~ "ALT",
      ast ~ "AST",
      ggt ~ "GGT",
      ure ~ "Ure",
      cre ~ "Creatinin",
      plt ~ "Tiểu cầu",
      agaston ~ "Điểm vôi hóa (Agatston)",
      multisteno ~ "Hẹp nhiều nhánh",
      ntbnp ~ "NT-proBNP",
      auric ~ "Acid uric máu",
      canthiepct ~ "Can thiệp ĐMV",
      nmctc ~ "Nhồi máu cơ tim cấp",
      cdtnkod ~ "Đau thắt ngực không ổn định",
      dqn ~ "Đột quỵ não",
      stc ~ "Suy tim cấp",
      maceg ~ "MACE mở rộng",
      mace ~ "MACE"
    ),
    missing_text = "(Missing)"
  ) %>%
  modify_caption("**Đặc điểm dân số nghiên cứu**") %>%
  bold_labels()

tbl1

**Đặc điểm dân số nghiên cứu**
Characteristic	N = 388¹
Giới tính	250 / 388 (64%)
Tuổi
Mean (SD)	69.02 (9.23)
Median (IQR)	70.00 (63.00, 75.00)
Range	37.00, 88.00
Đái tháo đường	124 / 388 (32%)
Thuốc ức chế men chuyển
0	147 / 388 (38%)
1	241 / 388 (62%)
Thuốc chẹn beta
0	155 / 388 (40%)
1	233 / 388 (60%)
Thuốc kháng kết tập tiểu cầu
0	102 / 388 (26%)
1	286 / 388 (74%)
Statin
0	82 / 388 (21%)
1	306 / 388 (79%)
Nitrate
0	295 / 388 (76%)
1	93 / 388 (24%)
Trimetazidine	120 / 388 (31%)
Thuốc chẹn kênh canxi
0	284 / 388 (73%)
1	104 / 388 (27%)
Thuốc lợi tiểu	56 / 388 (14%)
Metformin	62 / 388 (16%)
Sulfonylurea	19 / 388 (4.9%)
SGLT2i	27 / 388 (7.0%)
DPP4i	15 / 388 (3.9%)
Insulin	4 / 388 (1.0%)
SAGBT	186 / 388 (48%)
SAGM	186 / 387 (48%)
(Missing)	1
Giá trị nhiễm mỡ
Mean (SD)	250.93 (38.27)
Median (IQR)	245.50 (222.00, 279.25)
Range	179.00, 354.00
Độ nhiễm mỡ
1	188 / 388 (48%)
2	77 / 388 (20%)
3	63 / 388 (16%)
4	60 / 388 (15%)
Độ xơ hóa
1	282 / 388 (73%)
2	76 / 388 (20%)
3	22 / 388 (5.7%)
4	8 / 388 (2.1%)
Giá trị xơ hóa
Mean (SD)	6.55 (1.99)
Median (IQR)	6.10 (5.20, 7.40)
Range	2.50, 16.90
Chiều cao
Mean (SD)	1.61 (0.07)
Median (IQR)	1.60 (1.55, 1.66)
Range	1.45, 1.80
Cân nặng
Mean (SD)	62.08 (9.37)
Median (IQR)	62.00 (56.00, 68.00)
Range	37.00, 95.00
BMI
Mean (SD)	23.99 (3.05)
Median (IQR)	23.88 (22.22, 25.63)
Range	15.06, 35.16
Phân độ BMI
Béo phì độ I	104 / 388 (27%)
Béo phì độ II	14 / 388 (3.6%)
Bình thường	120 / 388 (31%)
Thiếu cân	13 / 388 (3.4%)
Thừa cân	137 / 388 (35%)
Vòng eo
Mean (SD)	89.85 (7.89)
Median (IQR)	90.00 (85.00, 94.00)
Range	63.00, 119.00
MAFLD	193 / 388 (50%)
HBsAg (+)	14 / 198 (7.1%)
(Missing)	190
Anti-HCV (+)	6 / 197 (3.0%)
(Missing)	191
Cholesterol toàn phần
Mean (SD)	5.53 (1.35)
Median (IQR)	5.60 (4.60, 6.40)
Range	2.10, 11.80
Triglyceride
Mean (SD)	2.21 (1.31)
Median (IQR)	1.90 (1.30, 2.70)
Range	0.50, 13.00
HDL-C
Mean (SD)	1.23 (0.28)
Median (IQR)	1.20 (1.00, 1.40)
Range	0.70, 3.60
LDL-C
Mean (SD)	3.36 (1.19)
Median (IQR)	3.31 (2.48, 4.18)
Range	0.77, 9.45
Non-HDL-C
Mean (SD)	4.31 (1.27)
Median (IQR)	4.30 (3.50, 5.10)
Range	1.30, 10.40
FLI
Mean (SD)	47.54 (21.54)
Median (IQR)	46.87 (31.38, 62.86)
Range	3.72, 98.08
(Missing)	130
Tỷ số LDL/HDL
Mean (SD)	2.80 (1.00)
Median (IQR)	2.76 (2.07, 3.50)
Range	0.75, 6.75
Tỷ số HDL/Non-HDL
Mean (SD)	0.31 (0.12)
Median (IQR)	0.28 (0.23, 0.36)
Range	0.13, 0.86
Chỉ số FIB-4
Mean (SD)	1.80 (0.87)
Median (IQR)	1.61 (1.27, 2.14)
Range	0.07, 7.90
(Missing)	1
Glucose máu
Mean (SD)	6.50 (2.25)
Median (IQR)	5.90 (5.20, 6.80)
Range	3.90, 20.50
HbA1c
Mean (SD)	6.48 (1.27)
Median (IQR)	6.05 (5.70, 6.90)
Range	4.10, 13.40
(Missing)	94
ALT
Mean (SD)	28.61 (16.94)
Median (IQR)	23.00 (18.00, 34.00)
Range	6.00, 113.00
(Missing)	1
AST
Mean (SD)	28.16 (10.37)
Median (IQR)	26.00 (22.00, 31.00)
Range	2.50, 87.20
(Missing)	1
GGT
Mean (SD)	49.54 (43.38)
Median (IQR)	38.50 (25.00, 57.00)
Range	6.30, 421.00
(Missing)	130
Ure
Mean (SD)	5.60 (1.77)
Median (IQR)	5.30 (4.40, 6.40)
Range	2.30, 19.10
Creatinin
Mean (SD)	89.03 (19.59)
Median (IQR)	88.00 (75.93, 100.00)
Range	46.30, 182.00
Tiểu cầu
Mean (SD)	229.68 (60.66)
Median (IQR)	221.00 (189.00, 263.25)
Range	96.00, 561.00
Điểm vôi hóa (Agatston)
Mean (SD)	364.51 (461.86)
Median (IQR)	188.91 (63.17, 485.14)
Range	0.00, 2,893.28
(Missing)	64
Hẹp nhiều nhánh	203 / 388 (52%)
NT-proBNP
Mean (SD)	499.27 (1,421.86)
Median (IQR)	98.25 (42.56, 232.38)
Range	5.00, 10,005.00
(Missing)	218
Acid uric máu
Mean (SD)	408.20 (96.73)
Median (IQR)	410.00 (340.50, 469.50)
Range	161.00, 742.60
(Missing)	81
Can thiệp ĐMV	139 / 388 (36%)
Nhồi máu cơ tim cấp	5 / 388 (1.3%)
Đau thắt ngực không ổn định	13 / 388 (3.4%)
Đột quỵ não	3 / 388 (0.8%)
Suy tim cấp	2 / 388 (0.5%)
MACE mở rộng	132 / 388 (34%)
MACE	21 / 388 (5.4%)
¹ n / N (%)

Đặc điểm dân số nghiên cứu ở hai nhóm MACE - Không tính can thiệp chương trình

tbl2 <- mafld_mace %>%
  select(
    sex, age, dtd, toa_ucmc, toa_bb, toa_kkttc, toa_statin, toa_nitrate, 
    toa_trime, toa_ccb, toa_loitieu, toa_metfor, toa_su, toa_sglt2, 
    toa_dpp4, toa_insulin, sagbt, sagm, giatrinhm, donhm, doxh, 
    giatrixh, cc, cn, bmi, bmi_phando, eo, mafld, mafld_sub, hbsag, antihcv, cholestp,
    trig, hld, ldl, nonhdl, fli, ldlhdl, hdlnonhdl, fib4, glucose,
    hba1c, alt, ast, ggt, ure, cre, plt, agaston, multisteno,
    ntbnp, auric, mace
  ) %>%
  tbl_summary(
    by = mace,
    type = all_continuous() ~ "continuous2",
    statistic = list(
      all_continuous() ~ c("{mean} ({sd})", "{median} ({p25}, {p75})", "{min}, {max}"),
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = all_continuous() ~ 2,
    label = list(
      sex ~ "Giới tính",
      age ~ "Tuổi",
      dtd ~ "Đái tháo đường",
      toa_ucmc ~ "Thuốc ức chế men chuyển",
      toa_bb ~ "Thuốc chẹn beta",
      toa_kkttc ~ "Thuốc kháng kết tập tiểu cầu",
      toa_statin ~ "Statin",
      toa_nitrate ~ "Nitrate",
      toa_trime ~ "Trimetazidine",
      toa_ccb ~ "Thuốc chẹn kênh canxi",
      toa_loitieu ~ "Thuốc lợi tiểu",
      toa_metfor ~ "Metformin",
      toa_su ~ "Sulfonylurea",
      toa_sglt2 ~ "SGLT2i",
      toa_dpp4 ~ "DPP4i",
      toa_insulin ~ "Insulin",
      sagbt ~ "SAGBT",
      sagm ~ "SAGM",
      giatrinhm ~ "Giá trị HM",
      donhm ~ "Đơn HM",
      doxh ~ "Độ xơ hóa",
      giatrixh ~ "Giá trị xơ hóa",
      cc ~ "Chiều cao",
      cn ~ "Cân nặng",
      bmi ~ "BMI",
      bmi_phando ~ "Phân độ BMI",
      eo ~ "Vòng eo",
      mafld ~ "MAFLD",
      mafld_sub ~ "Phân nhóm MAFLD",
      hbsag ~ "HBsAg (+)",
      antihcv ~ "Anti-HCV (+)",
      cholestp ~ "Cholesterol toàn phần",
      trig ~ "Triglyceride",
      hld ~ "HDL-C",
      ldl ~ "LDL-C",
      nonhdl ~ "Non-HDL-C",
      fli ~ "FLI",
      ldlhdl ~ "Tỷ số LDL/HDL",
      hdlnonhdl ~ "Tỷ số HDL/Non-HDL",
      fib4 ~ "Chỉ số FIB-4",
      glucose ~ "Glucose máu",
      hba1c ~ "HbA1c",
      alt ~ "ALT",
      ast ~ "AST",
      ggt ~ "GGT",
      ure ~ "Ure",
      cre ~ "Creatinin",
      plt ~ "Tiểu cầu",
      agaston ~ "Điểm vôi hóa (Agatston)",
      multisteno ~ "Hẹp nhiều nhánh",
      ntbnp ~ "NT-proBNP",
      auric ~ "Acid uric máu",
    missing_text = "(Missing)"
  )) %>%
  add_p(pvalue_fun = ~ style_pvalue(.x, digits = 3)) %>%
  add_overall() %>%
  add_n() %>%
  modify_caption("**Đặc điểm dân số nghiên cứu**") %>%
  bold_labels()

tbl2

**Đặc điểm dân số nghiên cứu**
Characteristic	N	Overall, N = 388¹	0, N = 367¹	1, N = 21¹	p-value²
Giới tính	388	250 / 388 (64%)	233 / 367 (63%)	17 / 21 (81%)	0.104
Tuổi	388				0.653
Mean (SD)		69.02 (9.23)	69.11 (9.08)	67.57 (11.78)
Median (IQR)		70.00 (63.00, 75.00)	70.00 (63.00, 75.00)	70.00 (62.00, 75.00)
Range		37.00, 88.00	37.00, 88.00	43.00, 85.00
Đái tháo đường	388	124 / 388 (32%)	116 / 367 (32%)	8 / 21 (38%)	0.535
Thuốc ức chế men chuyển	388				0.344
0		147 / 388 (38%)	137 / 367 (37%)	10 / 21 (48%)
1		241 / 388 (62%)	230 / 367 (63%)	11 / 21 (52%)
Thuốc chẹn beta	388				0.035
0		155 / 388 (40%)	142 / 367 (39%)	13 / 21 (62%)
1		233 / 388 (60%)	225 / 367 (61%)	8 / 21 (38%)
Thuốc kháng kết tập tiểu cầu	388				0.076
0		102 / 388 (26%)	93 / 367 (25%)	9 / 21 (43%)
1		286 / 388 (74%)	274 / 367 (75%)	12 / 21 (57%)
Statin	388				0.001
0		82 / 388 (21%)	71 / 367 (19%)	11 / 21 (52%)
1		306 / 388 (79%)	296 / 367 (81%)	10 / 21 (48%)
Nitrate	388				0.285
0		295 / 388 (76%)	277 / 367 (75%)	18 / 21 (86%)
1		93 / 388 (24%)	90 / 367 (25%)	3 / 21 (14%)
Trimetazidine	388	120 / 388 (31%)	118 / 367 (32%)	2 / 21 (9.5%)	0.029
Thuốc chẹn kênh canxi	388				0.409
0		284 / 388 (73%)	267 / 367 (73%)	17 / 21 (81%)
1		104 / 388 (27%)	100 / 367 (27%)	4 / 21 (19%)
Thuốc lợi tiểu	388	56 / 388 (14%)	53 / 367 (14%)	3 / 21 (14%)	>0.999
Metformin	388	62 / 388 (16%)	57 / 367 (16%)	5 / 21 (24%)	0.354
Sulfonylurea	388	19 / 388 (4.9%)	18 / 367 (4.9%)	1 / 21 (4.8%)	>0.999
SGLT2i	388	27 / 388 (7.0%)	25 / 367 (6.8%)	2 / 21 (9.5%)	0.649
DPP4i	388	15 / 388 (3.9%)	14 / 367 (3.8%)	1 / 21 (4.8%)	0.573
Insulin	388	4 / 388 (1.0%)	4 / 367 (1.1%)	0 / 21 (0%)	>0.999
SAGBT	388	186 / 388 (48%)	177 / 367 (48%)	9 / 21 (43%)	0.632
SAGM	387	186 / 387 (48%)	175 / 366 (48%)	11 / 21 (52%)	0.684
Unknown		1	1	0
Giá trị HM	388				0.944
Mean (SD)		250.93 (38.27)	251.04 (38.41)	249.14 (36.48)
Median (IQR)		245.50 (222.00, 279.25)	245.00 (222.00, 279.50)	248.00 (222.00, 274.00)
Range		179.00, 354.00	179.00, 354.00	185.00, 313.00
Đơn HM	388				0.337
1		188 / 388 (48%)	180 / 367 (49%)	8 / 21 (38%)
2		77 / 388 (20%)	70 / 367 (19%)	7 / 21 (33%)
3		63 / 388 (16%)	61 / 367 (17%)	2 / 21 (9.5%)
4		60 / 388 (15%)	56 / 367 (15%)	4 / 21 (19%)
Độ xơ hóa	388				0.717
1		282 / 388 (73%)	264 / 367 (72%)	18 / 21 (86%)
2		76 / 388 (20%)	73 / 367 (20%)	3 / 21 (14%)
3		22 / 388 (5.7%)	22 / 367 (6.0%)	0 / 21 (0%)
4		8 / 388 (2.1%)	8 / 367 (2.2%)	0 / 21 (0%)
Giá trị xơ hóa	388				0.138
Mean (SD)		6.55 (1.99)	6.59 (2.02)	5.85 (1.16)
Median (IQR)		6.10 (5.20, 7.40)	6.10 (5.30, 7.40)	5.70 (5.10, 6.50)
Range		2.50, 16.90	2.50, 16.90	3.90, 8.30
Chiều cao	388				0.176
Mean (SD)		1.61 (0.07)	1.61 (0.07)	1.62 (0.06)
Median (IQR)		1.60 (1.55, 1.66)	1.60 (1.55, 1.66)	1.63 (1.60, 1.67)
Range		1.45, 1.80	1.45, 1.80	1.50, 1.70
Cân nặng	388				0.822
Mean (SD)		62.08 (9.37)	62.02 (9.40)	63.05 (9.00)
Median (IQR)		62.00 (56.00, 68.00)	62.00 (56.00, 68.00)	62.00 (59.00, 67.00)
Range		37.00, 95.00	37.00, 95.00	50.00, 85.00
BMI	388				0.414
Mean (SD)		23.99 (3.05)	24.00 (3.04)	23.89 (3.29)
Median (IQR)		23.88 (22.22, 25.63)	23.88 (22.27, 25.74)	23.88 (22.15, 24.44)
Range		15.06, 35.16	15.06, 35.16	18.82, 31.99
Phân độ BMI	388				0.048
Béo phì độ I		104 / 388 (27%)	103 / 367 (28%)	1 / 21 (4.8%)
Béo phì độ II		14 / 388 (3.6%)	12 / 367 (3.3%)	2 / 21 (9.5%)
Bình thường		120 / 388 (31%)	111 / 367 (30%)	9 / 21 (43%)
Thiếu cân		13 / 388 (3.4%)	13 / 367 (3.5%)	0 / 21 (0%)
Thừa cân		137 / 388 (35%)	128 / 367 (35%)	9 / 21 (43%)
Vòng eo	388				0.713
Mean (SD)		89.85 (7.89)	89.79 (7.79)	90.90 (9.61)
Median (IQR)		90.00 (85.00, 94.00)	90.00 (85.00, 94.00)	88.00 (86.00, 92.00)
Range		63.00, 119.00	63.00, 112.00	79.00, 119.00
MAFLD	388	193 / 388 (50%)	180 / 367 (49%)	13 / 21 (62%)	0.252
Phân nhóm MAFLD	193				0.909
1		77 / 193 (40%)	71 / 180 (39%)	6 / 13 (46%)
2		99 / 193 (51%)	93 / 180 (52%)	6 / 13 (46%)
3		17 / 193 (8.8%)	16 / 180 (8.9%)	1 / 13 (7.7%)
Unknown		195	187	8
HBsAg (+)	198	14 / 198 (7.1%)	13 / 186 (7.0%)	1 / 12 (8.3%)	0.596
Unknown		190	181	9
Anti-HCV (+)	197	6 / 197 (3.0%)	6 / 186 (3.2%)	0 / 11 (0%)	>0.999
Unknown		191	181	10
Cholesterol toàn phần	388				0.285
Mean (SD)		5.53 (1.35)	5.51 (1.35)	5.78 (1.32)
Median (IQR)		5.60 (4.60, 6.40)	5.60 (4.60, 6.30)	5.90 (5.20, 6.70)
Range		2.10, 11.80	2.10, 11.80	3.10, 8.60
Triglyceride	388				0.307
Mean (SD)		2.21 (1.31)	2.20 (1.33)	2.34 (1.07)
Median (IQR)		1.90 (1.30, 2.70)	1.90 (1.30, 2.70)	2.15 (1.60, 3.30)
Range		0.50, 13.00	0.50, 13.00	0.90, 4.60
HDL-C	388				0.569
Mean (SD)		1.23 (0.28)	1.23 (0.28)	1.20 (0.29)
Median (IQR)		1.20 (1.00, 1.40)	1.20 (1.00, 1.40)	1.20 (1.00, 1.30)
Range		0.70, 3.60	0.70, 3.60	0.72, 2.00
LDL-C	388				0.514
Mean (SD)		3.36 (1.19)	3.36 (1.20)	3.46 (1.10)
Median (IQR)		3.31 (2.48, 4.18)	3.30 (2.48, 4.14)	3.70 (2.70, 4.28)
Range		0.77, 9.45	0.77, 9.45	1.33, 5.51
Non-HDL-C	388				0.220
Mean (SD)		4.31 (1.27)	4.29 (1.28)	4.58 (1.16)
Median (IQR)		4.30 (3.50, 5.10)	4.30 (3.44, 5.10)	4.70 (4.11, 5.10)
Range		1.30, 10.40	1.30, 10.40	2.10, 7.01
FLI	258				0.721
Mean (SD)		47.54 (21.54)	47.63 (21.70)	45.30 (17.86)
Median (IQR)		46.87 (31.38, 62.86)	47.02 (31.56, 62.86)	39.88 (31.53, 61.67)
Range		3.72, 98.08	3.72, 98.08	24.27, 70.32
Unknown		130	119	11
Tỷ số LDL/HDL	388				0.524
Mean (SD)		2.80 (1.00)	2.80 (1.01)	2.89 (0.77)
Median (IQR)		2.76 (2.07, 3.50)	2.75 (2.05, 3.50)	3.02 (2.43, 3.47)
Range		0.75, 6.75	0.75, 6.75	1.33, 4.11
Tỷ số HDL/Non-HDL	388				0.228
Mean (SD)		0.31 (0.12)	0.31 (0.12)	0.27 (0.08)
Median (IQR)		0.28 (0.23, 0.36)	0.29 (0.23, 0.37)	0.27 (0.22, 0.33)
Range		0.13, 0.86	0.13, 0.86	0.17, 0.48
Chỉ số FIB-4	387				0.088
Mean (SD)		1.80 (0.87)	1.82 (0.89)	1.52 (0.60)
Median (IQR)		1.61 (1.27, 2.14)	1.61 (1.28, 2.15)	1.42 (1.09, 1.74)
Range		0.07, 7.90	0.07, 7.90	0.77, 3.24
Unknown		1	1	0
Glucose máu	388				0.734
Mean (SD)		6.50 (2.25)	6.50 (2.28)	6.41 (1.75)
Median (IQR)		5.90 (5.20, 6.80)	5.90 (5.20, 6.75)	5.80 (5.40, 7.30)
Range		3.90, 20.50	3.90, 20.50	4.00, 11.70
HbA1c	294				0.018
Mean (SD)		6.48 (1.27)	6.45 (1.27)	7.11 (1.06)
Median (IQR)		6.05 (5.70, 6.90)	6.00 (5.70, 6.85)	7.20 (6.15, 7.60)
Range		4.10, 13.40	4.10, 13.40	5.60, 9.00
Unknown		94	84	10
ALT	387				0.127
Mean (SD)		28.61 (16.94)	28.78 (16.77)	25.66 (19.84)
Median (IQR)		23.00 (18.00, 34.00)	23.40 (18.00, 35.00)	22.00 (14.00, 31.00)
Range		6.00, 113.00	6.00, 113.00	10.00, 99.00
Unknown		1	1	0
AST	387				0.135
Mean (SD)		28.16 (10.37)	28.36 (10.53)	24.78 (6.24)
Median (IQR)		26.00 (22.00, 31.00)	26.00 (22.00, 31.00)	24.40 (21.00, 28.00)
Range		2.50, 87.20	2.50, 87.20	15.00, 41.00
Unknown		1	1	0
GGT	258				0.318
Mean (SD)		49.54 (43.38)	50.08 (44.03)	36.05 (17.78)
Median (IQR)		38.50 (25.00, 57.00)	39.00 (25.75, 57.85)	30.00 (20.50, 51.00)
Range		6.30, 421.00	6.30, 421.00	18.00, 68.00
Unknown		130	119	11
Ure	388				0.692
Mean (SD)		5.60 (1.77)	5.59 (1.73)	5.61 (2.43)
Median (IQR)		5.30 (4.40, 6.40)	5.30 (4.50, 6.40)	4.90 (3.90, 7.30)
Range		2.30, 19.10	2.30, 19.10	2.50, 11.30
Creatinin	388				0.436
Mean (SD)		89.03 (19.59)	88.77 (19.08)	93.48 (27.31)
Median (IQR)		88.00 (75.93, 100.00)	88.00 (75.35, 100.00)	90.00 (85.00, 104.00)
Range		46.30, 182.00	46.30, 176.00	51.00, 182.00
Tiểu cầu	388				0.121
Mean (SD)		229.68 (60.66)	228.60 (60.67)	248.43 (58.68)
Median (IQR)		221.00 (189.00, 263.25)	220.00 (188.00, 260.50)	255.00 (202.00, 268.00)
Range		96.00, 561.00	96.00, 561.00	174.00, 415.00
Điểm vôi hóa (Agatston)	324				0.156
Mean (SD)		364.51 (461.86)	354.50 (448.99)	604.07 (684.82)
Median (IQR)		188.91 (63.17, 485.14)	182.45 (61.80, 472.69)	277.40 (174.18, 896.23)
Range		0.00, 2,893.28	0.00, 2,893.28	4.41, 2,183.94
Unknown		64	56	8
Hẹp nhiều nhánh	388	203 / 388 (52%)	189 / 367 (51%)	14 / 21 (67%)	0.176
NT-proBNP	170				0.858
Mean (SD)		499.27 (1,421.86)	456.59 (1,353.34)	1,061.18 (2,132.75)
Median (IQR)		98.25 (42.56, 232.38)	98.25 (42.56, 230.05)	78.43 (46.85, 448.95)
Range		5.00, 10,005.00	5.00, 10,005.00	17.50, 5,808.00
Unknown		218	209	9
Acid uric máu	307				0.240
Mean (SD)		408.20 (96.73)	406.98 (96.55)	433.69 (100.64)
Median (IQR)		410.00 (340.50, 469.50)	409.00 (340.00, 468.20)	461.50 (375.25, 497.00)
Range		161.00, 742.60	161.00, 742.60	259.00, 601.30
Unknown		81	74	7
¹ n / N (%)
² Pearson’s Chi-squared test; Wilcoxon rank sum test; Fisher’s exact test

Đặc điểm dân số nghiên cứu giữa hai nhóm MACE mở rộng - Bao gồm mace và can thiệp mạch vành chương trình

tbl3 <- mafld_mace %>%
  select(
    sex, age, dtd, toa_ucmc, toa_bb, toa_kkttc, toa_statin, toa_nitrate, 
    toa_trime, toa_ccb, toa_loitieu, toa_metfor, toa_su, toa_sglt2, 
    toa_dpp4, toa_insulin, sagbt, sagm, giatrinhm, donhm, doxh, 
    giatrixh, cc, cn, bmi, bmi_phando, eo, mafld, mafld_sub, hbsag, antihcv, cholestp,
    trig, hld, ldl, nonhdl, fli, ldlhdl, hdlnonhdl, fib4, glucose,
    hba1c, alt, ast, ggt, ure, cre, plt, agaston, multisteno,
    ntbnp, auric, maceg
  ) %>%
  tbl_summary(
    by = maceg,
    type = all_continuous() ~ "continuous2",
    statistic = list(
      all_continuous() ~ c("{mean} ({sd})", "{median} ({p25}, {p75})", "{min}, {max}"),
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = all_continuous() ~ 2,
    label = list(
      sex ~ "Giới tính",
      age ~ "Tuổi",
      dtd ~ "Đái tháo đường",
      toa_ucmc ~ "Thuốc ức chế men chuyển",
      toa_bb ~ "Thuốc chẹn beta",
      toa_kkttc ~ "Thuốc kháng kết tập tiểu cầu",
      toa_statin ~ "Statin",
      toa_nitrate ~ "Nitrate",
      toa_trime ~ "Trimetazidine",
      toa_ccb ~ "Thuốc chẹn kênh canxi",
      toa_loitieu ~ "Thuốc lợi tiểu",
      toa_metfor ~ "Metformin",
      toa_su ~ "Sulfonylurea",
      toa_sglt2 ~ "SGLT2i",
      toa_dpp4 ~ "DPP4i",
      toa_insulin ~ "Insulin",
      sagbt ~ "Siêu âm gan bình thường",
      sagm ~ "Siêu âm gan nhiễm mỡ",
      giatrinhm ~ "Giá trị nhiễm mỡ",
      donhm ~ "Độ nhiễm mỡ",
      doxh ~ "Độ xơ hóa",
      giatrixh ~ "Giá trị xơ hóa",
      cc ~ "Chiều cao",
      cn ~ "Cân nặng",
      bmi ~ "BMI",
      bmi_phando ~ "Phân độ BMI",
      eo ~ "Vòng eo",
      mafld ~ "MAFLD",
      mafld_sub ~ "Phân nhóm MAFLD",
      hbsag ~ "HBsAg (+)",
      antihcv ~ "Anti-HCV (+)",
      cholestp ~ "Cholesterol toàn phần",
      trig ~ "Triglyceride",
      hld ~ "HDL-C",
      ldl ~ "LDL-C",
      nonhdl ~ "Non-HDL-C",
      fli ~ "FLI",
      ldlhdl ~ "Tỷ số LDL/HDL",
      hdlnonhdl ~ "Tỷ số HDL/Non-HDL",
      fib4 ~ "Chỉ số FIB-4",
      glucose ~ "Glucose máu",
      hba1c ~ "HbA1c",
      alt ~ "ALT",
      ast ~ "AST",
      ggt ~ "GGT",
      ure ~ "Ure",
      cre ~ "Creatinin",
      plt ~ "Tiểu cầu",
      agaston ~ "Điểm vôi hóa (Agatston)",
      multisteno ~ "Hẹp nhiều nhánh",
      ntbnp ~ "NT-proBNP",
      auric ~ "Acid uric máu",
    missing_text = "(Missing)"
  )) %>%
  add_p(pvalue_fun = ~ style_pvalue(.x, digits = 3)) %>%
  add_overall() %>%
  add_n() %>%
  modify_caption("**Đặc điểm dân số nghiên cứu**") %>%
  bold_labels()

tbl3

**Đặc điểm dân số nghiên cứu**
Characteristic	N	Overall, N = 388¹	0, N = 256¹	1, N = 132¹	p-value²
Giới tính	388	250 / 388 (64%)	159 / 256 (62%)	91 / 132 (69%)	0.183
Tuổi	388				0.295
Mean (SD)		69.02 (9.23)	69.29 (9.56)	68.51 (8.58)
Median (IQR)		70.00 (63.00, 75.00)	70.00 (63.75, 75.00)	69.50 (62.00, 74.00)
Range		37.00, 88.00	37.00, 88.00	43.00, 86.00
Đái tháo đường	388	124 / 388 (32%)	84 / 256 (33%)	40 / 132 (30%)	0.616
Thuốc ức chế men chuyển	388				0.378
0		147 / 388 (38%)	93 / 256 (36%)	54 / 132 (41%)
1		241 / 388 (62%)	163 / 256 (64%)	78 / 132 (59%)
Thuốc chẹn beta	388				0.014
0		155 / 388 (40%)	91 / 256 (36%)	64 / 132 (48%)
1		233 / 388 (60%)	165 / 256 (64%)	68 / 132 (52%)
Thuốc kháng kết tập tiểu cầu	388				0.006
0		102 / 388 (26%)	56 / 256 (22%)	46 / 132 (35%)
1		286 / 388 (74%)	200 / 256 (78%)	86 / 132 (65%)
Statin	388				<0.001
0		82 / 388 (21%)	39 / 256 (15%)	43 / 132 (33%)
1		306 / 388 (79%)	217 / 256 (85%)	89 / 132 (67%)
Nitrate	388				0.873
0		295 / 388 (76%)	194 / 256 (76%)	101 / 132 (77%)
1		93 / 388 (24%)	62 / 256 (24%)	31 / 132 (23%)
Trimetazidine	388	120 / 388 (31%)	83 / 256 (32%)	37 / 132 (28%)	0.375
Thuốc chẹn kênh canxi	388				0.413
0		284 / 388 (73%)	184 / 256 (72%)	100 / 132 (76%)
1		104 / 388 (27%)	72 / 256 (28%)	32 / 132 (24%)
Thuốc lợi tiểu	388	56 / 388 (14%)	33 / 256 (13%)	23 / 132 (17%)	0.229
Metformin	388	62 / 388 (16%)	42 / 256 (16%)	20 / 132 (15%)	0.749
Sulfonylurea	388	19 / 388 (4.9%)	15 / 256 (5.9%)	4 / 132 (3.0%)	0.221
SGLT2i	388	27 / 388 (7.0%)	18 / 256 (7.0%)	9 / 132 (6.8%)	0.938
DPP4i	388	15 / 388 (3.9%)	13 / 256 (5.1%)	2 / 132 (1.5%)	0.085
Insulin	388	4 / 388 (1.0%)	1 / 256 (0.4%)	3 / 132 (2.3%)	0.116
Siêu âm gan bình thường	388	186 / 388 (48%)	135 / 256 (53%)	51 / 132 (39%)	0.008
Siêu âm gan nhiễm mỡ	387	186 / 387 (48%)	112 / 255 (44%)	74 / 132 (56%)	0.023
Unknown		1	1	0
Giá trị nhiễm mỡ	388				0.325
Mean (SD)		250.93 (38.27)	250.21 (39.48)	252.34 (35.91)
Median (IQR)		245.50 (222.00, 279.25)	239.00 (220.75, 278.25)	250.50 (223.50, 280.25)
Range		179.00, 354.00	179.00, 354.00	182.00, 331.00
Độ nhiễm mỡ	388				0.002
1		188 / 388 (48%)	139 / 256 (54%)	49 / 132 (37%)
2		77 / 388 (20%)	38 / 256 (15%)	39 / 132 (30%)
3		63 / 388 (16%)	39 / 256 (15%)	24 / 132 (18%)
4		60 / 388 (15%)	40 / 256 (16%)	20 / 132 (15%)
Độ xơ hóa	388				0.291
1		282 / 388 (73%)	193 / 256 (75%)	89 / 132 (67%)
2		76 / 388 (20%)	44 / 256 (17%)	32 / 132 (24%)
3		22 / 388 (5.7%)	13 / 256 (5.1%)	9 / 132 (6.8%)
4		8 / 388 (2.1%)	6 / 256 (2.3%)	2 / 132 (1.5%)
Giá trị xơ hóa	388				0.370
Mean (SD)		6.55 (1.99)	6.51 (2.02)	6.62 (1.93)
Median (IQR)		6.10 (5.20, 7.40)	6.10 (5.30, 7.20)	6.20 (5.10, 7.60)
Range		2.50, 16.90	3.60, 16.90	2.50, 13.80
Chiều cao	388				0.046
Mean (SD)		1.61 (0.07)	1.60 (0.07)	1.62 (0.07)
Median (IQR)		1.60 (1.55, 1.66)	1.60 (1.55, 1.65)	1.62 (1.58, 1.66)
Range		1.45, 1.80	1.45, 1.80	1.45, 1.76
Cân nặng	388				0.020
Mean (SD)		62.08 (9.37)	61.40 (9.87)	63.40 (8.19)
Median (IQR)		62.00 (56.00, 68.00)	61.00 (55.00, 67.00)	63.00 (59.00, 69.25)
Range		37.00, 95.00	37.00, 95.00	43.00, 85.00
BMI	388				0.357
Mean (SD)		23.99 (3.05)	23.87 (3.15)	24.24 (2.84)
Median (IQR)		23.88 (22.22, 25.63)	23.88 (22.22, 25.53)	24.06 (22.26, 25.82)
Range		15.06, 35.16	15.06, 35.16	17.58, 33.78
Phân độ BMI	388				0.354
Béo phì độ I		104 / 388 (27%)	67 / 256 (26%)	37 / 132 (28%)
Béo phì độ II		14 / 388 (3.6%)	9 / 256 (3.5%)	5 / 132 (3.8%)
Bình thường		120 / 388 (31%)	78 / 256 (30%)	42 / 132 (32%)
Thiếu cân		13 / 388 (3.4%)	12 / 256 (4.7%)	1 / 132 (0.8%)
Thừa cân		137 / 388 (35%)	90 / 256 (35%)	47 / 132 (36%)
Vòng eo	388				0.251
Mean (SD)		89.85 (7.89)	89.40 (8.32)	90.72 (6.90)
Median (IQR)		90.00 (85.00, 94.00)	90.00 (84.00, 94.00)	90.00 (87.00, 94.00)
Range		63.00, 119.00	63.00, 112.00	76.00, 119.00
MAFLD	388	193 / 388 (50%)	112 / 256 (44%)	81 / 132 (61%)	0.001
Phân nhóm MAFLD	193				0.567
1		77 / 193 (40%)	47 / 112 (42%)	30 / 81 (37%)
2		99 / 193 (51%)	57 / 112 (51%)	42 / 81 (52%)
3		17 / 193 (8.8%)	8 / 112 (7.1%)	9 / 81 (11%)
Unknown		195	144	51
HBsAg (+)	198	14 / 198 (7.1%)	7 / 126 (5.6%)	7 / 72 (9.7%)	0.271
Unknown		190	130	60
Anti-HCV (+)	197	6 / 197 (3.0%)	4 / 127 (3.1%)	2 / 70 (2.9%)	>0.999
Unknown		191	129	62
Cholesterol toàn phần	388				0.359
Mean (SD)		5.53 (1.35)	5.47 (1.39)	5.64 (1.26)
Median (IQR)		5.60 (4.60, 6.40)	5.55 (4.50, 6.33)	5.65 (4.78, 6.40)
Range		2.10, 11.80	2.10, 11.80	2.90, 9.00
Triglyceride	388				0.096
Mean (SD)		2.21 (1.31)	2.16 (1.36)	2.29 (1.21)
Median (IQR)		1.90 (1.30, 2.70)	1.80 (1.30, 2.70)	2.10 (1.46, 2.70)
Range		0.50, 13.00	0.50, 13.00	0.60, 6.60
HDL-C	388				0.119
Mean (SD)		1.23 (0.28)	1.23 (0.25)	1.21 (0.34)
Median (IQR)		1.20 (1.00, 1.40)	1.20 (1.00, 1.40)	1.20 (1.00, 1.30)
Range		0.70, 3.60	0.70, 2.20	0.70, 3.60
LDL-C	388				0.331
Mean (SD)		3.36 (1.19)	3.32 (1.23)	3.44 (1.12)
Median (IQR)		3.31 (2.48, 4.18)	3.31 (2.44, 4.10)	3.36 (2.66, 4.22)
Range		0.77, 9.45	0.77, 9.45	0.82, 6.30
Non-HDL-C	388				0.155
Mean (SD)		4.31 (1.27)	4.24 (1.30)	4.45 (1.22)
Median (IQR)		4.30 (3.50, 5.10)	4.20 (3.30, 5.03)	4.50 (3.60, 5.10)
Range		1.30, 10.40	1.30, 10.40	1.70, 7.60
FLI	258				0.122
Mean (SD)		47.54 (21.54)	46.16 (22.25)	50.16 (19.98)
Median (IQR)		46.87 (31.38, 62.86)	44.84 (30.13, 59.81)	49.50 (37.00, 65.33)
Range		3.72, 98.08	3.72, 98.08	7.97, 93.39
Unknown		130	87	43
Tỷ số LDL/HDL	388				0.029
Mean (SD)		2.80 (1.00)	2.73 (0.97)	2.95 (1.03)
Median (IQR)		2.76 (2.07, 3.50)	2.66 (2.03, 3.36)	3.02 (2.18, 3.65)
Range		0.75, 6.75	0.82, 6.75	0.75, 6.30
Tỷ số HDL/Non-HDL	388				0.006
Mean (SD)		0.31 (0.12)	0.32 (0.12)	0.30 (0.12)
Median (IQR)		0.28 (0.23, 0.36)	0.29 (0.24, 0.37)	0.25 (0.22, 0.34)
Range		0.13, 0.86	0.13, 0.77	0.13, 0.86
Chỉ số FIB-4	387				0.590
Mean (SD)		1.80 (0.87)	1.81 (0.88)	1.78 (0.86)
Median (IQR)		1.61 (1.27, 2.14)	1.61 (1.28, 2.16)	1.61 (1.23, 2.11)
Range		0.07, 7.90	0.07, 7.90	0.58, 6.72
Unknown		1	1	0
Glucose máu	388				0.575
Mean (SD)		6.50 (2.25)	6.44 (2.17)	6.61 (2.39)
Median (IQR)		5.90 (5.20, 6.80)	5.85 (5.20, 6.70)	5.90 (5.30, 7.10)
Range		3.90, 20.50	3.90, 20.50	4.00, 19.70
HbA1c	294				0.878
Mean (SD)		6.48 (1.27)	6.48 (1.28)	6.47 (1.24)
Median (IQR)		6.05 (5.70, 6.90)	6.00 (5.70, 6.90)	6.10 (5.70, 7.10)
Range		4.10, 13.40	4.10, 13.40	4.20, 12.00
Unknown		94	68	26
ALT	387				0.886
Mean (SD)		28.61 (16.94)	29.16 (18.25)	27.55 (14.07)
Median (IQR)		23.00 (18.00, 34.00)	23.00 (18.00, 34.25)	24.00 (18.00, 33.25)
Range		6.00, 113.00	6.00, 113.00	7.00, 99.00
Unknown		1	1	0
AST	387				0.897
Mean (SD)		28.16 (10.37)	28.53 (11.26)	27.45 (8.38)
Median (IQR)		26.00 (22.00, 31.00)	25.70 (22.00, 31.00)	26.00 (22.00, 31.00)
Range		2.50, 87.20	2.50, 87.20	11.00, 61.00
Unknown		1	1	0
GGT	258				0.422
Mean (SD)		49.54 (43.38)	51.98 (49.10)	44.90 (29.32)
Median (IQR)		38.50 (25.00, 57.00)	39.00 (26.00, 57.00)	34.00 (25.00, 56.00)
Range		6.30, 421.00	8.60, 421.00	6.30, 162.00
Unknown		130	87	43
Ure	388				0.039
Mean (SD)		5.60 (1.77)	5.74 (1.87)	5.31 (1.53)
Median (IQR)		5.30 (4.40, 6.40)	5.49 (4.50, 6.60)	5.15 (4.30, 6.22)
Range		2.30, 19.10	2.40, 19.10	2.30, 11.30
Creatinin	388				0.882
Mean (SD)		89.03 (19.59)	89.24 (19.17)	88.62 (20.45)
Median (IQR)		88.00 (75.93, 100.00)	87.50 (75.53, 101.00)	89.00 (76.00, 98.00)
Range		46.30, 182.00	53.00, 176.00	46.30, 182.00
Tiểu cầu	388				0.915
Mean (SD)		229.68 (60.66)	229.92 (62.10)	229.21 (58.00)
Median (IQR)		221.00 (189.00, 263.25)	220.00 (190.50, 260.50)	224.50 (188.50, 265.25)
Range		96.00, 561.00	97.00, 561.00	96.00, 415.00
Điểm vôi hóa (Agatston)	324				0.001
Mean (SD)		364.51 (461.86)	327.49 (456.00)	446.24 (466.44)
Median (IQR)		188.91 (63.17, 485.14)	142.13 (55.02, 423.30)	277.40 (119.28, 609.94)
Range		0.00, 2,893.28	0.00, 2,893.28	0.00, 2,183.94
Unknown		64	33	31
Hẹp nhiều nhánh	388	203 / 388 (52%)	114 / 256 (45%)	89 / 132 (67%)	<0.001
NT-proBNP	170				0.581
Mean (SD)		499.27 (1,421.86)	421.38 (1,266.60)	613.28 (1,625.74)
Median (IQR)		98.25 (42.56, 232.38)	99.22 (41.40, 219.40)	83.50 (48.09, 290.00)
Range		5.00, 10,005.00	5.00, 9,306.00	8.43, 10,005.00
Unknown		218	155	63
Acid uric máu	307				0.892
Mean (SD)		408.20 (96.73)	409.84 (97.83)	404.99 (94.95)
Median (IQR)		410.00 (340.50, 469.50)	410.00 (340.50, 468.75)	405.50 (339.25, 476.25)
Range		161.00, 742.60	161.00, 742.60	216.00, 601.30
Unknown		81	53	28
¹ n / N (%)
² Pearson’s Chi-squared test; Wilcoxon rank sum test; Fisher’s exact test

Đặc điểm dân số nghiên cứu ở phân nhóm MAFLD

tbl4 <- mafld_mace %>%
  select(
    sex, age, dtd, toa_ucmc, toa_bb, toa_kkttc, toa_statin, toa_nitrate, 
    toa_trime, toa_ccb, toa_loitieu, toa_metfor, toa_su, toa_sglt2, 
    toa_dpp4, toa_insulin, sagbt, sagm, giatrinhm, donhm, doxh, 
    giatrixh, cc, cn, bmi, bmi_phando, eo, hbsag, antihcv, cholestp,
    trig, hld, ldl, nonhdl, fli, ldlhdl, hdlnonhdl, fib4, glucose,
    hba1c, alt, ast, ggt, ure, cre, plt, agaston, multisteno,
    ntbnp, auric, canthiepct, nmctc, cdtnkod, dqn, stc, maceg, mace, mafld_sub
  ) %>%
  tbl_summary(
    by = mafld_sub,
    type = all_continuous() ~ "continuous2",
    statistic = list(
      all_continuous() ~ c("{mean} ({sd})", "{median} ({p25}, {p75})", "{min}, {max}"),
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = all_continuous() ~ 2,
    label = list(
      sex ~ "Giới tính",
      age ~ "Tuổi",
      dtd ~ "Đái tháo đường",
      toa_ucmc ~ "Thuốc ức chế men chuyển",
      toa_bb ~ "Thuốc chẹn beta",
      toa_kkttc ~ "Thuốc kháng kết tập tiểu cầu",
      toa_statin ~ "Statin",
      toa_nitrate ~ "Nitrate",
      toa_trime ~ "Trimetazidine",
      toa_ccb ~ "Thuốc chẹn kênh canxi",
      toa_loitieu ~ "Thuốc lợi tiểu",
      toa_metfor ~ "Metformin",
      toa_su ~ "Sulfonylurea",
      toa_sglt2 ~ "SGLT2i",
      toa_dpp4 ~ "DPP4i",
      toa_insulin ~ "Insulin",
      sagbt ~ "Siêu âm gan bình thường",
      sagm ~ "Siêu âm gan nhiễm mỡ",
      giatrinhm ~ "Giá trị nhiễm mỡ",
      donhm ~ "Độ nhiễm mỡ",
      doxh ~ "Độ xơ hóa",
      giatrixh ~ "Giá trị xơ hóa",
      cc ~ "Chiều cao",
      cn ~ "Cân nặng",
      bmi ~ "BMI",
      bmi_phando ~ "Phân độ BMI",
      eo ~ "Vòng eo",
      hbsag ~ "HBsAg (+)",
      antihcv ~ "Anti-HCV (+)",
      cholestp ~ "Cholesterol toàn phần",
      trig ~ "Triglyceride",
      hld ~ "HDL-C",
      ldl ~ "LDL-C",
      nonhdl ~ "Non-HDL-C",
      fli ~ "FLI",
      ldlhdl ~ "Tỷ số LDL/HDL",
      hdlnonhdl ~ "Tỷ số HDL/Non-HDL",
      fib4 ~ "Chỉ số FIB-4",
      glucose ~ "Glucose máu",
      hba1c ~ "HbA1c",
      alt ~ "ALT",
      ast ~ "AST",
      ggt ~ "GGT",
      ure ~ "Ure",
      cre ~ "Creatinin",
      plt ~ "Tiểu cầu",
      agaston ~ "Điểm vôi hóa (Agatston)",
      multisteno ~ "Hẹp nhiều nhánh",
      ntbnp ~ "NT-proBNP",
      auric ~ "Acid uric máu",
      canthiepct ~ "Can thiệp ĐMV",
      nmctc ~ "Nhồi máu cơ tim cấp",
      cdtnkod ~ "Đau thắt ngực không ổn định",
      dqn ~ "Đột quỵ não",
      stc ~ "Suy tim cấp",
      maceg ~ "MACE mở rộng",
      mace ~ "MACE",
      missing_text = "(Missing)"
    )) %>%
  add_p(pvalue_fun = ~ style_pvalue(.x, digits = 3)) %>%
  add_overall() %>%
  add_n() %>%
  modify_caption("**Đặc điểm dân số nghiên cứu**") %>%
  bold_labels()

## 195 observations missing `mafld_sub` have been removed. To include these observations, use `forcats::fct_na_value_to_level()` on `mafld_sub` column before passing to `tbl_summary()`.

## There was an error in 'add_p()/add_difference()' for variable 'bmi_phando', p-value omitted:
## Error in stats::fisher.test(c("Béo phì độ I", "Thiếu cân", "Béo phì độ I", : FEXACT error 7(location). LDSTP=18630 is too small for this problem,
##   (pastp=24.5859, ipn_0:=ipoin[itp=27]=1162, stp[ipn_0]=35.2311).
## Increase workspace or consider using 'simulate.p.value=TRUE'

## There was an error in 'add_p()/add_difference()' for variable 'stc', p-value omitted:
## Error in stats::chisq.test(x = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, : 'x' and 'y' must have at least 2 levels

tbl4

**Đặc điểm dân số nghiên cứu**
Characteristic	N	Overall, N = 193¹	1, N = 77¹	2, N = 99¹	3, N = 17¹	p-value²
Giới tính	193	116 / 193 (60%)	43 / 77 (56%)	64 / 99 (65%)	9 / 17 (53%)	0.407
Tuổi	193					0.862
Mean (SD)		69.48 (9.28)	69.45 (10.37)	69.26 (8.86)	70.82 (6.30)
Median (IQR)		70.00 (63.00, 75.00)	70.00 (63.00, 77.00)	71.00 (62.00, 75.00)	69.00 (68.00, 75.00)
Range		44.00, 88.00	44.00, 88.00	44.00, 88.00	60.00, 84.00
Đái tháo đường	193	77 / 193 (40%)	77 / 77 (100%)	0 / 99 (0%)	0 / 17 (0%)	<0.001
Thuốc ức chế men chuyển	193					0.969
0		62 / 193 (32%)	25 / 77 (32%)	32 / 99 (32%)	5 / 17 (29%)
1		131 / 193 (68%)	52 / 77 (68%)	67 / 99 (68%)	12 / 17 (71%)
Thuốc chẹn beta	193					0.578
0		74 / 193 (38%)	28 / 77 (36%)	41 / 99 (41%)	5 / 17 (29%)
1		119 / 193 (62%)	49 / 77 (64%)	58 / 99 (59%)	12 / 17 (71%)
Thuốc kháng kết tập tiểu cầu	193					0.903
0		44 / 193 (23%)	17 / 77 (22%)	24 / 99 (24%)	3 / 17 (18%)
1		149 / 193 (77%)	60 / 77 (78%)	75 / 99 (76%)	14 / 17 (82%)
Statin	193					0.465
0		38 / 193 (20%)	13 / 77 (17%)	23 / 99 (23%)	2 / 17 (12%)
1		155 / 193 (80%)	64 / 77 (83%)	76 / 99 (77%)	15 / 17 (88%)
Nitrate	193					0.358
0		144 / 193 (75%)	61 / 77 (79%)	72 / 99 (73%)	11 / 17 (65%)
1		49 / 193 (25%)	16 / 77 (21%)	27 / 99 (27%)	6 / 17 (35%)
Trimetazidine	193	53 / 193 (27%)	14 / 77 (18%)	30 / 99 (30%)	9 / 17 (53%)	0.011
Thuốc chẹn kênh canxi	193					0.701
0		132 / 193 (68%)	55 / 77 (71%)	65 / 99 (66%)	12 / 17 (71%)
1		61 / 193 (32%)	22 / 77 (29%)	34 / 99 (34%)	5 / 17 (29%)
Thuốc lợi tiểu	193	32 / 193 (17%)	14 / 77 (18%)	15 / 99 (15%)	3 / 17 (18%)	0.804
Metformin	193	43 / 193 (22%)	43 / 77 (56%)	0 / 99 (0%)	0 / 17 (0%)	<0.001
Sulfonylurea	193	12 / 193 (6.2%)	12 / 77 (16%)	0 / 99 (0%)	0 / 17 (0%)	<0.001
SGLT2i	193	15 / 193 (7.8%)	12 / 77 (16%)	3 / 99 (3.0%)	0 / 17 (0%)	0.006
DPP4i	193	9 / 193 (4.7%)	9 / 77 (12%)	0 / 99 (0%)	0 / 17 (0%)	<0.001
Insulin	193	3 / 193 (1.6%)	3 / 77 (3.9%)	0 / 99 (0%)	0 / 17 (0%)	0.125
Siêu âm gan bình thường	193	12 / 193 (6.2%)	2 / 77 (2.6%)	9 / 99 (9.1%)	1 / 17 (5.9%)	0.204
Siêu âm gan nhiễm mỡ	193	178 / 193 (92%)	74 / 77 (96%)	88 / 99 (89%)	16 / 17 (94%)	0.167
Giá trị nhiễm mỡ	193					0.119
Mean (SD)		281.07 (26.97)	285.34 (26.38)	277.46 (27.21)	282.71 (26.75)
Median (IQR)		278.00 (256.00, 302.00)	286.00 (263.00, 306.00)	270.00 (254.00, 297.50)	282.00 (257.00, 297.00)
Range		244.00, 354.00	245.00, 346.00	244.00, 354.00	252.00, 328.00
Độ nhiễm mỡ	193					0.276
2		76 / 193 (39%)	23 / 77 (30%)	46 / 99 (46%)	7 / 17 (41%)
3		58 / 193 (30%)	26 / 77 (34%)	27 / 99 (27%)	5 / 17 (29%)
4		59 / 193 (31%)	28 / 77 (36%)	26 / 99 (26%)	5 / 17 (29%)
Độ xơ hóa	193					0.601
1		123 / 193 (64%)	45 / 77 (58%)	66 / 99 (67%)	12 / 17 (71%)
2		48 / 193 (25%)	20 / 77 (26%)	24 / 99 (24%)	4 / 17 (24%)
3		16 / 193 (8.3%)	9 / 77 (12%)	7 / 99 (7.1%)	0 / 17 (0%)
4		6 / 193 (3.1%)	3 / 77 (3.9%)	2 / 99 (2.0%)	1 / 17 (5.9%)
Giá trị xơ hóa	193					0.129
Mean (SD)		6.99 (2.18)	7.29 (2.12)	6.77 (2.17)	6.84 (2.49)
Median (IQR)		6.50 (5.50, 7.80)	6.90 (5.80, 8.50)	6.30 (5.35, 7.60)	6.00 (5.60, 7.30)
Range		3.80, 16.90	3.90, 13.80	3.80, 16.90	4.40, 14.90
Chiều cao	193					0.647
Mean (SD)		1.60 (0.07)	1.60 (0.07)	1.60 (0.07)	1.61 (0.08)
Median (IQR)		1.60 (1.55, 1.65)	1.60 (1.53, 1.65)	1.60 (1.55, 1.65)	1.63 (1.55, 1.68)
Range		1.45, 1.76	1.45, 1.75	1.45, 1.76	1.45, 1.72
Cân nặng	193					<0.001
Mean (SD)		64.85 (8.80)	64.05 (8.77)	66.88 (8.17)	56.65 (7.36)
Median (IQR)		65.00 (60.00, 71.00)	64.00 (59.00, 70.00)	67.00 (60.00, 72.00)	59.00 (50.00, 63.00)
Range		40.00, 92.00	40.00, 90.00	52.00, 92.00	45.00, 68.00
BMI	193					<0.001
Mean (SD)		25.29 (2.84)	25.10 (3.02)	26.05 (2.40)	21.73 (1.21)
Median (IQR)		24.80 (23.53, 26.67)	24.62 (23.42, 26.67)	25.48 (24.19, 27.12)	22.21 (21.88, 22.32)
Range		18.26, 35.16	18.26, 35.16	23.11, 33.98	18.49, 22.99
Phân độ BMI	193
Béo phì độ I		76 / 193 (39%)	25 / 77 (32%)	51 / 99 (52%)	0 / 17 (0%)
Béo phì độ II		12 / 193 (6.2%)	5 / 77 (6.5%)	7 / 99 (7.1%)	0 / 17 (0%)
Bình thường		29 / 193 (15%)	13 / 77 (17%)	0 / 99 (0%)	16 / 17 (94%)
Thiếu cân		2 / 193 (1.0%)	1 / 77 (1.3%)	0 / 99 (0%)	1 / 17 (5.9%)
Thừa cân		74 / 193 (38%)	33 / 77 (43%)	41 / 99 (41%)	0 / 17 (0%)
Vòng eo	193					<0.001
Mean (SD)		93.07 (7.00)	93.08 (7.35)	94.15 (6.50)	86.76 (4.83)
Median (IQR)		92.00 (89.00, 96.00)	92.00 (88.00, 95.00)	93.00 (90.00, 96.00)	89.00 (85.00, 90.00)
Range		75.00, 119.00	75.00, 112.00	80.00, 119.00	78.00, 95.00
HBsAg (+)	103	5 / 103 (4.9%)	3 / 43 (7.0%)	2 / 50 (4.0%)	0 / 10 (0%)	0.798
Unknown		90	34	49	7
Anti-HCV (+)	101	4 / 101 (4.0%)	0 / 43 (0%)	2 / 49 (4.1%)	2 / 9 (22%)	0.020
Unknown		92	34	50	8
Cholesterol toàn phần	193					0.682
Mean (SD)		5.70 (1.26)	5.76 (1.42)	5.63 (1.14)	5.75 (1.18)
Median (IQR)		5.70 (4.80, 6.50)	5.80 (4.60, 6.80)	5.60 (4.80, 6.30)	6.00 (4.90, 6.30)
Range		2.30, 9.00	2.30, 9.00	3.00, 8.90	3.40, 8.30
Triglyceride	193					0.042
Mean (SD)		2.37 (1.45)	2.69 (1.76)	2.18 (1.20)	2.06 (0.81)
Median (IQR)		2.10 (1.40, 2.90)	2.30 (1.70, 3.10)	2.00 (1.30, 2.55)	2.10 (1.50, 2.60)
Range		0.80, 13.00	0.80, 13.00	0.90, 6.10	0.80, 3.70
HDL-C	193					0.660
Mean (SD)		1.24 (0.25)	1.21 (0.23)	1.25 (0.27)	1.26 (0.27)
Median (IQR)		1.20 (1.00, 1.40)	1.20 (1.00, 1.40)	1.20 (1.06, 1.40)	1.30 (1.00, 1.50)
Range		0.70, 2.00	0.77, 1.90	0.70, 2.00	0.80, 1.70
LDL-C	193					0.951
Mean (SD)		3.46 (1.11)	3.44 (1.20)	3.45 (1.02)	3.55 (1.18)
Median (IQR)		3.30 (2.65, 4.24)	3.20 (2.54, 4.30)	3.31 (2.72, 4.18)	3.55 (2.76, 4.22)
Range		0.85, 6.53	0.85, 6.53	1.20, 6.01	1.74, 6.30
Non-HDL-C	193					0.678
Mean (SD)		4.47 (1.21)	4.55 (1.36)	4.41 (1.08)	4.49 (1.32)
Median (IQR)		4.40 (3.60, 5.30)	4.60 (3.50, 5.50)	4.30 (3.75, 5.00)	4.90 (3.40, 5.20)
Range		1.30, 7.60	1.30, 7.60	1.70, 7.56	2.10, 7.30
FLI	131					<0.001
Mean (SD)		57.54 (19.21)	60.19 (19.37)	58.76 (18.01)	34.57 (9.34)
Median (IQR)		57.07 (42.46, 71.94)	60.62 (49.28, 74.89)	58.09 (44.80, 72.55)	34.27 (27.94, 42.97)
Range		21.21, 98.08	21.72, 98.08	24.27, 96.05	21.21, 46.87
Unknown		62	20	35	7
Tỷ số LDL/HDL	193					0.904
Mean (SD)		2.87 (0.97)	2.88 (0.98)	2.83 (0.88)	3.04 (1.40)
Median (IQR)		2.80 (2.10, 3.58)	2.76 (2.12, 3.58)	2.80 (2.14, 3.54)	3.32 (1.84, 4.09)
Range		0.82, 6.30	0.82, 5.42	0.92, 5.00	1.17, 6.30
Tỷ số HDL/Non-HDL	193					0.468
Mean (SD)		0.30 (0.11)	0.29 (0.12)	0.30 (0.10)	0.32 (0.16)
Median (IQR)		0.28 (0.22, 0.34)	0.27 (0.22, 0.32)	0.28 (0.23, 0.34)	0.25 (0.19, 0.43)
Range		0.13, 0.77	0.15, 0.77	0.13, 0.76	0.14, 0.62
Chỉ số FIB-4	193					0.995
Mean (SD)		1.81 (0.87)	1.86 (1.03)	1.77 (0.73)	1.78 (0.80)
Median (IQR)		1.61 (1.26, 2.17)	1.61 (1.26, 2.17)	1.61 (1.26, 2.20)	1.72 (1.40, 1.83)
Range		0.07, 6.72	0.07, 6.72	0.54, 4.12	0.89, 4.54
Glucose máu	193					<0.001
Mean (SD)		6.75 (2.23)	8.33 (2.67)	5.63 (0.70)	6.10 (1.62)
Median (IQR)		6.10 (5.40, 7.20)	7.30 (6.60, 9.10)	5.60 (5.16, 6.00)	5.90 (5.30, 6.50)
Range		4.20, 19.70	5.10, 19.70	4.20, 7.80	4.20, 11.70
HbA1c	160					<0.001
Mean (SD)		6.59 (1.20)	7.34 (1.24)	5.93 (0.59)	5.99 (1.11)
Median (IQR)		6.20 (5.80, 7.23)	7.20 (6.45, 7.80)	5.80 (5.60, 6.10)	5.80 (5.55, 5.90)
Range		4.20, 12.00	5.20, 12.00	4.20, 8.07	5.10, 9.00
Unknown		33	2	24	7
ALT	193					0.156
Mean (SD)		32.46 (18.24)	35.53 (20.25)	30.33 (16.67)	30.94 (16.30)
Median (IQR)		28.00 (20.00, 39.00)	30.00 (21.00, 41.00)	27.00 (19.00, 35.85)	25.00 (21.00, 35.00)
Range		7.00, 113.00	9.00, 113.00	7.00, 99.00	14.00, 69.00
AST	193					0.579
Mean (SD)		29.98 (10.91)	31.02 (11.93)	29.33 (10.36)	29.01 (9.24)
Median (IQR)		27.00 (23.00, 34.00)	27.20 (24.00, 37.00)	27.00 (23.00, 33.70)	25.70 (24.40, 30.00)
Range		2.50, 73.00	2.50, 73.00	15.00, 66.00	20.00, 57.00
GGT	131					0.535
Mean (SD)		56.95 (51.73)	65.40 (70.25)	51.20 (29.61)	45.56 (31.25)
Median (IQR)		46.00 (29.00, 65.50)	49.00 (29.00, 67.00)	44.85 (30.75, 65.75)	36.50 (29.00, 53.25)
Range		6.30, 421.00	15.00, 421.00	6.30, 148.00	16.00, 125.60
Unknown		62	20	35	7
Ure	193					0.167
Mean (SD)		5.43 (1.53)	5.43 (1.50)	5.53 (1.58)	4.87 (1.29)
Median (IQR)		5.20 (4.30, 6.27)	5.10 (4.50, 6.40)	5.40 (4.20, 6.20)	4.50 (4.00, 5.30)
Range		2.30, 11.30	2.30, 11.30	2.80, 10.20	3.17, 8.10
Creatinin	193					0.772
Mean (SD)		87.51 (18.62)	87.21 (17.35)	88.45 (20.02)	83.44 (15.83)
Median (IQR)		88.00 (75.00, 98.00)	85.00 (77.00, 98.10)	89.00 (73.85, 97.30)	88.00 (70.00, 95.00)
Range		51.00, 148.00	51.00, 148.00	52.00, 145.00	56.70, 107.00
Tiểu cầu	193					0.584
Mean (SD)		229.72 (56.35)	224.26 (54.84)	234.11 (59.16)	228.88 (46.08)
Median (IQR)		222.00 (192.00, 265.00)	225.00 (183.00, 257.00)	222.00 (194.50, 276.00)	218.00 (207.00, 257.00)
Range		96.00, 415.00	96.00, 415.00	108.00, 371.00	127.00, 318.00
Điểm vôi hóa (Agatston)	170					0.431
Mean (SD)		350.90 (438.74)	341.08 (365.08)	381.80 (507.09)	174.77 (150.89)
Median (IQR)		207.31 (61.49, 427.60)	220.79 (73.44, 443.05)	192.34 (61.10, 456.17)	180.69 (51.25, 233.36)
Range		0.00, 2,407.55	0.00, 1,691.05	0.00, 2,407.55	8.39, 489.40
Unknown		23	9	9	5
Hẹp nhiều nhánh	193	97 / 193 (50%)	38 / 77 (49%)	52 / 99 (53%)	7 / 17 (41%)	0.674
NT-proBNP	89					0.943
Mean (SD)		277.36 (647.51)	293.48 (647.24)	278.80 (699.99)	180.87 (204.71)
Median (IQR)		99.22 (41.40, 239.00)	87.51 (40.69, 216.48)	113.80 (40.36, 239.25)	88.40 (59.35, 203.30)
Range		5.00, 4,633.00	5.00, 3,563.00	7.32, 4,633.00	41.40, 611.00
Unknown		104	39	55	10
Acid uric máu	162					0.024
Mean (SD)		417.07 (91.12)	409.15 (96.87)	429.99 (86.70)	369.97 (75.82)
Median (IQR)		412.50 (368.00, 475.75)	391.00 (344.00, 464.00)	429.00 (380.03, 481.50)	363.00 (324.00, 422.00)
Range		216.00, 661.40	226.70, 661.40	222.00, 632.00	216.00, 505.00
Unknown		31	14	13	4
Can thiệp ĐMV	193	87 / 193 (45%)	32 / 77 (42%)	45 / 99 (45%)	10 / 17 (59%)	0.430
Nhồi máu cơ tim cấp	193	1 / 193 (0.5%)	0 / 77 (0%)	0 / 99 (0%)	1 / 17 (5.9%)	0.088
Đau thắt ngực không ổn định	193	10 / 193 (5.2%)	5 / 77 (6.5%)	5 / 99 (5.1%)	0 / 17 (0%)	0.797
Đột quỵ não	193	3 / 193 (1.6%)	1 / 77 (1.3%)	2 / 99 (2.0%)	0 / 17 (0%)	>0.999
Suy tim cấp	193	0 / 193 (0%)	0 / 77 (0%)	0 / 99 (0%)	0 / 17 (0%)
MACE mở rộng	193	81 / 193 (42%)	30 / 77 (39%)	42 / 99 (42%)	9 / 17 (53%)	0.567
MACE	193	13 / 193 (6.7%)	6 / 77 (7.8%)	6 / 99 (6.1%)	1 / 17 (5.9%)	0.909
¹ n / N (%)
² Pearson’s Chi-squared test; Kruskal-Wallis rank sum test; Fisher’s exact test

Hồi quy đơn biến - Kết cục MACE mở rộng

library(purrr)
library(broom)
mafld_mace <- mafld_mace %>%
  mutate(mafld_sub = case_when(
    mafld == 1 & dtd == 1 ~ 1,          # MAFLD + ĐTĐ
    mafld == 1 & dtd == 0 & bmi >= 23 ~ 2,  # MAFLD + BMI ≥ 23
    mafld == 1 & dtd == 0 & bmi < 23  ~ 3,  # MAFLD + BMI < 23
    TRUE ~ NA_real_
  ))
# Lấy danh sách tất cả biến ngoại trừ maceg
vars <- setdiff(names(mafld_mace), "maceg")
# Hàm chạy logistic đơn biến
uni_logistic <- function(var) {
  form <- as.formula(paste("maceg ~", var))
  glm(form, data = mafld_mace, family = binomial) %>%
    tidy(exponentiate = TRUE, conf.int = TRUE) %>%
    filter(term != "(Intercept)") %>%
    mutate(variable = var)
}
# Chạy hồi quy đơn biến cho tất cả biến
results <- map_dfr(vars, uni_logistic) %>%
  select(variable, term, estimate, conf.low, conf.high, p.value) %>%
  arrange(p.value)

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

# Toàn bộ kết quả
results

## # A tibble: 63 × 6
##    variable   term        estimate conf.low conf.high  p.value
##    <chr>      <chr>          <dbl>    <dbl>     <dbl>    <dbl>
##  1 canthiepct canthiepct  1058.     331.     4855.    1.67e-25
##  2 multisteno multisteno     2.58     1.67      4.03  2.42e- 5
##  3 toa_statin toa_statin1    0.372    0.225     0.612 1.02e- 4
##  4 mafld      mafld          2.04     1.33      3.15  1.10e- 3
##  5 toa_kkttc  toa_kkttc1     0.523    0.329     0.834 6.34e- 3
##  6 sagbt      sagbt          0.564    0.367     0.863 8.75e- 3
##  7 toa_bb     toa_bb1        0.586    0.382     0.898 1.41e- 2
##  8 ure        ure            0.855    0.743     0.974 2.32e- 2
##  9 sagm       sagm           1.63     1.07      2.49  2.39e- 2
## 10 agaston    agaston        1.00     1.00      1.00  3.55e- 2
## # ℹ 53 more rows

# Lọc các biến có p < 0.05
results_sig <- results %>%
  filter(p.value < 0.05)
results_sig

## # A tibble: 13 × 6
##    variable   term        estimate conf.low conf.high  p.value
##    <chr>      <chr>          <dbl>    <dbl>     <dbl>    <dbl>
##  1 canthiepct canthiepct  1058.     331.     4855.    1.67e-25
##  2 multisteno multisteno     2.58     1.67      4.03  2.42e- 5
##  3 toa_statin toa_statin1    0.372    0.225     0.612 1.02e- 4
##  4 mafld      mafld          2.04     1.33      3.15  1.10e- 3
##  5 toa_kkttc  toa_kkttc1     0.523    0.329     0.834 6.34e- 3
##  6 sagbt      sagbt          0.564    0.367     0.863 8.75e- 3
##  7 toa_bb     toa_bb1        0.586    0.382     0.898 1.41e- 2
##  8 ure        ure            0.855    0.743     0.974 2.32e- 2
##  9 sagm       sagm           1.63     1.07      2.49  2.39e- 2
## 10 agaston    agaston        1.00     1.00      1.00  3.55e- 2
## 11 ldlhdl     ldlhdl         1.25     1.01      1.55  3.81e- 2
## 12 cn         cn             1.02     1.00      1.05  4.70e- 2
## 13 stt        stt            0.998    0.996     1.00  4.92e- 2

Kết quả cho thấy: Cân nặng, ldlhdl, ure, agaston, MAFLD, multisteno, toa_statin, toa_kkttc, toa_bb, sagbt, sagm

Loại đa cộng tuyến - Hồi quy ELastic-Net. Sau đó chạy hồi quy Logistic đa biến

library(glmnet)

## Loading required package: Matrix

## Loaded glmnet 4.1-8

# Tạo công thức
form <- maceg ~ cn + ldlhdl + ure + agaston + mafld + multisteno + 
  toa_statin + toa_kkttc + toa_bb + sagbt + sagm
# Loại bỏ hàng có NA ở bất kỳ biến nào
dat_model <- mafld_mace %>%
  select(all.vars(form)) %>%
  na.omit()
# X và y đồng bộ
xvars <- model.matrix(form, data = dat_model)[, -1]   # bỏ intercept
yvar  <- dat_model$maceg   # giữ nguyên nhị phân 0/1, không cần scale
# Chạy Elastic Net
set.seed(123)
cvfit <- cv.glmnet(xvars, yvar, family = "binomial", alpha = 0.5)
# Vẽ biểu đồ CV
plot(cvfit)

# Hệ số tại lambda tối ưu
coef(cvfit, s = "lambda.min")

## 12 x 1 sparse Matrix of class "dgCMatrix"
##                        s1
## (Intercept) -0.8537967704
## cn           .           
## ldlhdl       0.2149197686
## ure         -0.1439849611
## agaston      0.0002965299
## mafld        0.6905698467
## multisteno   0.7735296052
## toa_statin1 -0.7590100138
## toa_kkttc1   .           
## toa_bb1     -0.1135805025
## sagbt        .           
## sagm         .

Chạy mô hình Đa biến với các biến được chọn. Sau đó chạy Dominance để đánh giá độ mạnh trong tiên lượng MACEG. Tiếp theo dùng mô hình BMA để tạo ra mô hình tốt nhất.

library(dplyr)
library(broom)
# Mô hình logistic đa biến với các biến được chọn
model_multi <- glm(
  maceg ~ ldlhdl + ure + agaston + mafld + multisteno + toa_statin + toa_bb,
  data = mafld_mace,
  family = binomial
)

# Tóm tắt mô hình
summary(model_multi)

## 
## Call:
## glm(formula = maceg ~ ldlhdl + ure + agaston + mafld + multisteno + 
##     toa_statin + toa_bb, family = binomial, data = mafld_mace)
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)   
## (Intercept) -0.8326853  0.6991554  -1.191  0.23366   
## ldlhdl       0.2821955  0.1322354   2.134  0.03284 * 
## ure         -0.1945685  0.0844208  -2.305  0.02118 * 
## agaston      0.0003976  0.0002862   1.390  0.16468   
## mafld        0.8321174  0.2648677   3.142  0.00168 **
## multisteno   0.9084059  0.2777045   3.271  0.00107 **
## toa_statin1 -0.8974041  0.3655036  -2.455  0.01408 * 
## toa_bb1     -0.1750188  0.3033837  -0.577  0.56401   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 402.07  on 323  degrees of freedom
## Residual deviance: 357.98  on 316  degrees of freedom
##   (64 observations deleted due to missingness)
## AIC: 373.98
## 
## Number of Fisher Scoring iterations: 4

# Bảng OR, CI 95%, p-value
results_multi <- broom::tidy(model_multi, exponentiate = TRUE, conf.int = TRUE)
results_multi

## # A tibble: 8 × 7
##   term        estimate std.error statistic p.value conf.low conf.high
##   <chr>          <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl>
## 1 (Intercept)    0.435  0.699       -1.19  0.234      0.110     1.72 
## 2 ldlhdl         1.33   0.132        2.13  0.0328     1.02      1.72 
## 3 ure            0.823  0.0844      -2.30  0.0212     0.693     0.967
## 4 agaston        1.00   0.000286     1.39  0.165      1.00      1.00 
## 5 mafld          2.30   0.265        3.14  0.00168    1.38      3.90 
## 6 multisteno     2.48   0.278        3.27  0.00107    1.45      4.31 
## 7 toa_statin1    0.408  0.366       -2.46  0.0141     0.197     0.830
## 8 toa_bb1        0.839  0.303       -0.577 0.564      0.465     1.53

library(dominanceanalysis)
temp1 <- glm(maceg ~ ldlhdl + ure + agaston + mafld + multisteno + toa_statin + toa_bb
             , family="binomial", data=mafld_mace)
dom <- dominanceAnalysis(temp1)
plot(dom, which.graph ="general",fit.function ="r2.m")

df <- data.frame(mafld_mace$ldlhdl, mafld_mace$ure, mafld_mace$agaston, mafld_mace$mafld, 
                 mafld_mace$multisteno, mafld_mace$toa_statin, mafld_mace$toa_bb
                 , mafld_mace$maceg)
head(df)

##   mafld_mace.ldlhdl mafld_mace.ure mafld_mace.agaston mafld_mace.mafld
## 1          2.935714            7.3             128.30                1
## 2          1.328571            3.5             243.70                0
## 3          1.972727            7.2              39.60                0
## 4          2.100000            5.3             338.94                1
## 5          1.920000            3.1                 NA                1
## 6          3.594444            7.8              34.40                0
##   mafld_mace.multisteno mafld_mace.toa_statin mafld_mace.toa_bb
## 1                     0                     1                 1
## 2                     1                     1                 1
## 3                     0                     1                 1
## 4                     0                     1                 1
## 5                     0                     1                 1
## 6                     1                     1                 1
##   mafld_mace.maceg
## 1                0
## 2                0
## 3                0
## 4                0
## 5                0
## 6                0

attach(df)
names(df)

## [1] "mafld_mace.ldlhdl"     "mafld_mace.ure"        "mafld_mace.agaston"   
## [4] "mafld_mace.mafld"      "mafld_mace.multisteno" "mafld_mace.toa_statin"
## [7] "mafld_mace.toa_bb"     "mafld_mace.maceg"

xvars <- df[,1:7]
y <- mafld_mace$maceg
library(survival)

## Warning: package 'survival' was built under R version 4.4.1

library(leaps)
library(robustbase)

## 
## Attaching package: 'robustbase'

## The following object is masked from 'package:survival':
## 
##     heart

library(inline)
library(rrcov)

## Warning: package 'rrcov' was built under R version 4.4.1

## Scalable Robust Estimators with High Breakdown Point (version 1.7-6)

library(BMA)
bma.search1 <- bic.glm(xvars, y, strict=F, OR=20, glm.family="binomial")

## Warning in bic.glm.data.frame(xvars, y, strict = F, OR = 20, glm.family =
## "binomial"): There were 64 records deleted due to NA's

summary(bma.search1)

## 
## Call:
## bic.glm.data.frame(x = xvars, y = y, glm.family = "binomial",     strict = F, OR = 20)
## 
## 
##   13  models were selected
##  Best  5  models (cumulative posterior probability =  0.814 ): 
## 
##                        p!=0    EV         SD         model 1     model 2   
## Intercept              100    -0.9580728  8.019e-01  -1.064e+00  -3.987e-02
## mafld_mace.ldlhdl       22.1   0.0547791  1.194e-01       .           .    
## mafld_mace.ure          33.5  -0.0579335  9.466e-02       .      -1.714e-01
## mafld_mace.agaston       4.9   0.0000131  8.469e-05       .           .    
## mafld_mace.mafld        96.0   0.8109491  3.040e-01   8.622e-01   8.298e-01
## mafld_mace.multisteno  100.0   0.9755691  2.613e-01   9.702e-01   1.008e+00
## mafld_mace.toa_statin   85.4  -0.8199539  4.485e-01  -9.310e-01  -1.027e+00
## mafld_mace.toa_bb        5.3  -0.0191572  1.095e-01       .           .    
##                                                                            
## nVar                                                    3           4      
## BIC                                                  -1.481e+03  -1.480e+03
## post prob                                             0.349       0.199    
##                        model 3     model 4     model 5   
## Intercept              -1.768e+00  -7.424e-01  -1.768e+00
## mafld_mace.ldlhdl       2.434e-01   2.549e-01       .    
## mafld_mace.ure              .      -1.794e-01       .    
## mafld_mace.agaston          .           .           .    
## mafld_mace.mafld        8.517e-01   8.213e-01   8.224e-01
## mafld_mace.multisteno   9.775e-01   1.022e+00   9.424e-01
## mafld_mace.toa_statin  -9.286e-01  -1.025e+00       .    
## mafld_mace.toa_bb           .           .           .    
##                                                          
## nVar                      4           5           2      
## BIC                    -1.479e+03  -1.478e+03  -1.478e+03
## post prob               0.119       0.076       0.071

imageplot.bma(bma.search1)

library(dplyr)
library(broom)
library(gtsummary)

# --- Mô hình 1: 3 biến mạnh nhất ---
model1 <- glm(
  maceg ~ multisteno + mafld + toa_statin,
  data = mafld_mace,
  family = binomial
)

tbl_model1 <- model1 %>%
  tbl_regression(
    exponentiate = TRUE,     # xuất OR thay vì log(OR)
    label = list(
      multisteno ~ "Hẹp nhiều nhánh",
      mafld ~ "MAFLD",
      toa_statin ~ "Statin"
    )
  ) %>%
  modify_caption("**Hồi quy logistic đa biến (mô hình 1: 3 biến mạnh nhất)**")

# --- Mô hình 2: mở rộng với ure + ldlhdl ---
model2 <- glm(
  maceg ~ multisteno + mafld + toa_statin + ure + ldlhdl,
  data = mafld_mace,
  family = binomial
)

tbl_model2 <- model2 %>%
  tbl_regression(
    exponentiate = TRUE,
    label = list(
      multisteno ~ "Hẹp nhiều nhánh",
      mafld ~ "MAFLD",
      toa_statin ~ "Statin",
      ure ~ "Ure",
      ldlhdl ~ "Tỷ số LDL/HDL"
    )
  ) %>%
  modify_caption("**Hồi quy logistic đa biến (mô hình 2: mở rộng)**")

# Hiển thị kết quả
tbl_model1

**Hồi quy logistic đa biến (mô hình 1: 3 biến mạnh nhất)**
Characteristic	OR¹	95% CI¹	p-value
Hẹp nhiều nhánh	2.77	1.76, 4.42	<0.001
MAFLD	2.37	1.51, 3.76	<0.001
Statin
0	—	—
1	0.34	0.20, 0.58	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

tbl_model2

**Hồi quy logistic đa biến (mô hình 2: mở rộng)**
Characteristic	OR¹	95% CI¹	p-value
Hẹp nhiều nhánh	2.85	1.80, 4.58	<0.001
MAFLD	2.23	1.41, 3.56	<0.001
Statin
0	—	—
1	0.33	0.19, 0.56	<0.001
Ure	0.84	0.72, 0.96	0.016
Tỷ số LDL/HDL	1.24	0.99, 1.56	0.058
¹ OR = Odds Ratio, CI = Confidence Interval

Tạo thang điểm tiên lượng từ mô hình gồm 4 biến (MAFLD; toa thuốc Statin LDL/HDL; bệnh mạch vành đa nhánh)

library(pROC)

## Type 'citation("pROC")' for a citation.

## 
## Attaching package: 'pROC'

## The following objects are masked from 'package:stats':
## 
##     cov, smooth, var

roc_ldlhdl <- roc(mafld_mace$maceg, mafld_mace$ldlhdl, ci=TRUE)

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

plot(roc_ldlhdl)

coords(roc_ldlhdl, "best", ret=c("threshold", "sensitivity", "specificity"))

##   threshold sensitivity specificity
## 1       3.2   0.4621212   0.7070312

mafld_mace <- mafld_mace %>%
  mutate(ldlhdl_cat = if_else(ldlhdl >= 2.5, 1, 0))
library(dplyr)
# Chạy lại mô hình logistic
model2 <- glm(
  maceg ~ multisteno + mafld + toa_statin + ldlhdl_cat,
  data = mafld_mace,
  family = binomial
)
# Lấy hệ số
coefs <- coef(model2)
# Bỏ intercept
coefs <- coefs[-1]
# Quy đổi hệ số thành điểm
# chọn hệ số nhỏ nhất làm chuẩn
base <- min(abs(coefs[coefs != 0]))
points <- round(coefs / base)
score_table <- data.frame(
  Variable = names(points),
  Beta = coefs,
  Point = points
)
score_table

##                Variable       Beta Point
## multisteno   multisteno  1.0321914     2
## mafld             mafld  0.8533359     2
## toa_statin1 toa_statin1 -1.1024571    -2
## ldlhdl_cat   ldlhdl_cat  0.4559020     1

mafld_mace <- mafld_mace %>%
  mutate(
    multisteno_num = as.numeric(as.character(multisteno)), 
    mafld_num      = as.numeric(as.character(mafld)),
    toa_statin_num = as.numeric(as.character(toa_statin)),
    ldlhdl_cat_num = as.numeric(as.character(ldlhdl_cat)),
    
    D_MAFLD = 2*multisteno_num + 
              2*mafld_num + 
              (-2)*toa_statin_num + 
              1*ldlhdl_cat_num
  )

table(mafld_mace$D_MAFLD)

## 
##  -2  -1   0   1   2   3   4   5 
##  28  41  70 112  47  68   8  14

Thang điểm: D_MAFLD = 2(Có bệnh đa nhánh mạch vành) + 2(Có MAFLD)) + (-2)(Đang sử dụng statin) + 1(Tỷ số LDL/HDL>=2.5)

Đánh giá độ chính xác của mô hình D_MAFLD theo test Hosmer–Lemeshow và vẽ Calibration

library(ResourceSelection)

## ResourceSelection 0.3-6   2023-06-27

library(ggplot2)
library(dplyr)

# Chạy lại logistic với thang điểm D_MAFLD
model_dmafld <- glm(maceg ~ D_MAFLD, data = mafld_mace, family = binomial)

# --- Hosmer–Lemeshow test ---
hl_test <- hoslem.test(model_dmafld$y, fitted(model_dmafld), g = 5)

## Warning in hoslem.test(model_dmafld$y, fitted(model_dmafld), g = 5): The data
## did not allow for the requested number of bins.

hl_test

## 
##  Hosmer and Lemeshow goodness of fit (GOF) test
## 
## data:  model_dmafld$y, fitted(model_dmafld)
## X-squared = 0.68585, df = 2, p-value = 0.7097

# --- Calibration plot ---
# Tính xác suất dự đoán
mafld_mace <- mafld_mace %>%
  mutate(pred_prob = predict(model_dmafld, type = "response"))

# Chia thành 10 nhóm theo decile của xác suất dự đoán
mafld_mace <- mafld_mace %>%
  mutate(decile = ntile(pred_prob, 10))

calib_data <- mafld_mace %>%
  group_by(decile) %>%
  summarise(
    mean_pred = mean(pred_prob),
    obs_rate  = mean(maceg)
  )

# Vẽ Calibration plot
ggplot(calib_data, aes(x = mean_pred, y = obs_rate)) +
  geom_point(size=3, color="red") +
  geom_abline(intercept=0, slope=1, linetype="dashed", color="blue") +
  labs(
    x = "Xác suất dự đoán trung bình",
    y = "Tỷ lệ quan sát thực tế",
    title = "Calibration plot cho mô hình D_MAFLD"
  ) +
  theme_minimal()

Kiểm định mô hình bằng phương pháp thống kê khác như Hồi quy Rừng ngẫu nhiên - Và tính độ chính xác của mô hình trên lâm sàng

# Hồi quy rừng ngẫu nhiên

library(dplyr)
library(missForest)
library(randomForest)

## Warning: package 'randomForest' was built under R version 4.4.1

## randomForest 4.7-1.2

## Type rfNews() to see new features/changes/bug fixes.

## 
## Attaching package: 'randomForest'

## The following object is masked from 'package:ggplot2':
## 
##     margin

## The following object is masked from 'package:dplyr':
## 
##     combine

# 1. Chọn predictors
Xvars <- mafld_mace %>%
  select(cn, ldlhdl, ure, agaston, mafld, multisteno,
         toa_statin, toa_kkttc, toa_bb, sagbt, sagm)

# 2. Kiểm tra class từng biến
sapply(Xvars, class)

##          cn      ldlhdl         ure     agaston       mafld  multisteno 
##   "numeric"   "numeric"   "numeric"   "numeric"   "numeric"   "numeric" 
##  toa_statin   toa_kkttc      toa_bb       sagbt        sagm 
## "character" "character" "character"   "numeric"   "numeric"

# 3. Chuẩn hóa kiểu dữ liệu:
# - Biến nhị phân: factor
# - Biến liên tục: numeric
Xvars <- Xvars %>%
  mutate(
    mafld       = as.factor(as.numeric(mafld)),
    multisteno  = as.factor(as.numeric(multisteno)),
    toa_statin  = as.factor(as.numeric(toa_statin)),
    toa_kkttc   = as.factor(as.numeric(toa_kkttc)),
    toa_bb      = as.factor(as.numeric(toa_bb)),
    sagbt       = as.factor(as.numeric(sagbt)),
    sagm        = as.factor(as.numeric(sagm)),
    cn          = as.numeric(cn),
    ldlhdl      = as.numeric(ldlhdl),
    ure         = as.numeric(ure),
    agaston     = as.numeric(agaston),
    ldlhdl_cat = as.numeric(agaston)
  )

# 4. Impute NA bằng missForest
set.seed(123)
imputed <- missForest(as.data.frame(Xvars))
X_imp <- imputed$ximp

# 5. Ghép lại với biến outcome
dat_rf_imp <- cbind(maceg = mafld_mace$maceg, X_imp)

# 6. Huấn luyện Random Forest
set.seed(123)
rf_model <- randomForest(
  as.factor(maceg) ~ multisteno + mafld + toa_statin + ldlhdl_cat,
  data = dat_rf_imp,
  ntree = 500,
  mtry = 3,
  importance = TRUE
)

# 7. Xem độ quan trọng biến
importance(rf_model)

##                    0        1 MeanDecreaseAccuracy MeanDecreaseGini
## multisteno  8.514372 8.401177            11.906458         9.904173
## mafld      -1.542926 4.143104             1.182035         8.803516
## toa_statin 16.200768 7.994709            17.173032        10.083594
## ldlhdl_cat  4.380304 6.084525             6.880204        98.437307

varImpPlot(rf_model)

library(pROC)

# --- ROC cho RF ---
rf_prob <- predict(rf_model, type = "prob")[,2]  # xác suất dự đoán lớp = 1
roc_rf <- roc(dat_rf_imp$maceg, rf_prob)

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

# --- ROC cho D_MAFLD ---
roc_dmafld <- roc(mafld_mace$maceg, predict(model_dmafld, type = "response"))

## Setting levels: control = 0, case = 1
## Setting direction: controls < cases

# --- Vẽ ROC cùng lúc ---
plot(roc_rf, col = "blue", lwd = 2, main = "So sánh AUC")
plot(roc_dmafld, col = "red", lwd = 2, add = TRUE)
legend("bottomright", legend = c("Random Forest", "D_MAFLD"),
       col = c("blue", "red"), lwd = 2)

# --- In AUC ---
auc(roc_rf)

## Area under the curve: 0.6062

auc(roc_dmafld)

## Area under the curve: 0.7058

# OOB Error rate
print(rf_model)

## 
## Call:
##  randomForest(formula = as.factor(maceg) ~ multisteno + mafld +      toa_statin + ldlhdl_cat, data = dat_rf_imp, ntree = 500,      mtry = 3, importance = TRUE) 
##                Type of random forest: classification
##                      Number of trees: 500
## No. of variables tried at each split: 3
## 
##         OOB estimate of  error rate: 30.93%
## Confusion matrix:
##     0  1 class.error
## 0 217 39   0.1523438
## 1  81 51   0.6136364

# Độ chính xác OOB
oob_error <- rf_model$err.rate[rf_model$ntree, "OOB"]
oob_accuracy <- 1 - oob_error
oob_accuracy

##       OOB 
## 0.6907216

Vẽ Nomogram cho mô hình tiên lượng

library(rms)

## Loading required package: Hmisc

## 
## Attaching package: 'Hmisc'

## The following objects are masked from 'package:dplyr':
## 
##     src, summarize

## The following objects are masked from 'package:base':
## 
##     format.pval, units

library(dplyr)
library(pROC)
library(ggplot2)

set.seed(12345)
# Chuyển 0/1 thành factor có nhãn để in đẹp trên nomogram
mafld_mace <- mafld_mace %>%
  mutate(
    multisteno  = factor(multisteno,  levels = c(0,1), labels = c("Không","Có")),
    mafld       = factor(mafld,       levels = c(0,1), labels = c("Không","Có")),
    toa_statin  = factor(toa_statin,  levels = c(0,1), labels = c("Không","Có")),
    ldlhdl_cat  = factor(ldlhdl_cat,  levels = c(0,1), labels = c("<2.5","≥2.5")),
    maceg       = as.integer(maceg)   # outcome nhị phân 0/1 để fit logistic
  )

# Đặt nhãn biến cho rms (hiển thị trên nomogram)
label(mafld_mace$multisteno)  <- "Hẹp nhiều nhánh"
label(mafld_mace$mafld)       <- "MAFLD"
label(mafld_mace$toa_statin)  <- "Đang dùng statin"
label(mafld_mace$ldlhdl_cat)  <- "Tỷ số LDL/HDL (cắt 2.5)"
label(mafld_mace$maceg)       <- "Biến cố MACE"
# Khai báo phân bố dữ liệu cho rms
dd <- datadist(mafld_mace)
options(datadist = "dd")
# Mô hình đúng như bạn đã dùng:
fit2 <- lrm(
  maceg ~ multisteno + mafld + toa_statin + ldlhdl_cat,
  data = mafld_mace,
  x = TRUE, y = TRUE
)
print(fit2)

## Logistic Regression Model
## 
## lrm(formula = maceg ~ multisteno + mafld + toa_statin + ldlhdl_cat, 
##     data = mafld_mace, x = TRUE, y = TRUE)
## 
##                        Model Likelihood      Discrimination    Rank Discrim.    
##                              Ratio Test             Indexes          Indexes    
## Obs           388    LR chi2      51.00      R2       0.170    C       0.706    
##  0            256    d.f.             4      R2(4,388)0.114    Dxy     0.413    
##  1            132    Pr(> chi2) <0.0001    R2(4,261.3)0.165    gamma   0.447    
## max |deriv| 3e-09                            Brier    0.195    tau-a   0.186    
## 
##                 Coef    S.E.   Wald Z Pr(>|Z|)
## Intercept       -1.1259 0.3262 -3.45  0.0006  
## multisteno=Có    1.0322 0.2360  4.37  <0.0001 
## mafld=Có         0.8533 0.2340  3.65  0.0003  
## toa_statin=Có   -1.1025 0.2720 -4.05  <0.0001 
## ldlhdl_cat=≥2.5  0.4559 0.2394  1.90  0.0569

# nomogram sẽ nhận linear predictor và chuyển bằng plogis -> xác suất
nom2 <- nomogram(
  fit2,
  fun = list("Xác suất MACE" = plogis),
  fun.at = seq(0.05, 0.9, by = 0.05),  # các mốc xác suất cần hiển thị
  lp = TRUE                            # hiển thị thêm thang LP (tuỳ chọn)
)
# Hiển thị trên R plotting device
plot(nom2, xfrac = 0.5)  # xfrac điều chỉnh khoảng trống nhãn

# Lưu ra PDF (tuỳ chọn)
pdf("nomogram_model2.pdf", width = 10, height = 7)
plot(nom2, xfrac = 0.5)

## Warning in text.default(xl, y, paste(strgraphwrap(ns[[i]], abs(xl), cex =
## cex.var), : conversion failure on 'Hẹp nhiều nhánh' in 'mbcsToSbcs': for ẹ
## (U+1EB9)

## Warning in text.default(xl, y, paste(strgraphwrap(ns[[i]], abs(xl), cex =
## cex.var), : conversion failure on 'Hẹp nhiều nhánh' in 'mbcsToSbcs': for ề
## (U+1EC1)

## Warning in text.default(xl, y, paste(strgraphwrap(ns[[i]], abs(xl), cex =
## cex.var), : conversion failure on 'Đang dùng statin' in 'mbcsToSbcs': for Đ
## (U+0110)

## Warning in text.default(xl, y, paste(strgraphwrap(ns[[i]], abs(xl), cex =
## cex.var), : conversion failure on 'Tỷ số LDL/HDL (cắt 2.5)' in 'mbcsToSbcs':
## for ỷ (U+1EF7)

## Warning in text.default(xl, y, paste(strgraphwrap(ns[[i]], abs(xl), cex =
## cex.var), : conversion failure on 'Tỷ số LDL/HDL (cắt 2.5)' in 'mbcsToSbcs':
## for ố (U+1ED1)

## Warning in text.default(xl, y, paste(strgraphwrap(ns[[i]], abs(xl), cex =
## cex.var), : conversion failure on 'Tỷ số LDL/HDL (cắt 2.5)' in 'mbcsToSbcs':
## for ắ (U+1EAF)

## Warning in axis(..., cex.axis = cex): for '≥2.5' in 'mbcsToSbcs': >=
## substituted for ≥ (U+2265)

## Warning in text.default(xl, y, funlabel[i], adj = 0, cex = cex.var): conversion
## failure on 'Xác suất MACE' in 'mbcsToSbcs': for ấ (U+1EA5)

dev.off()

## quartz_off_screen 
##                 2

MAFLD_MACE

Vũ Anh Duy

2025-08-16

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