Bicuspid

library(readxl)
library(ggplot2)
library(CGPfunctions)
library(ggplot2)
library(plotly)
library(lmtest)

#Definir directorio
setwd("G:/TRABAJO/CONSULTORIAS/TRABAJOS VARIOS/JORGE CHAVARRIA/analisis2")

data1 = read_excel("Bicuspid.xlsx")
head(data1,5)

ANALISIS RELATIVE STENT EXPANSION

# Función para agregar coeficientes de correlación
panel.cor <- function(x, y, digits = 2, prefix = "", cex.cor, ...) {
    usr <- par("usr")
    on.exit(par(usr))
    par(usr = c(0, 1, 0, 1))
    Cor <- abs(cor(x, y)) # Elimina la función abs si lo prefieres
    txt <- paste0(prefix, format(c(Cor, 0.123456789), digits = digits)[1])
    if(missing(cex.cor)) {
        cex.cor <- 0.4 / strwidth(txt)
    }
    text(0.5, 0.5, txt,
         cex = 1 + cex.cor * Cor) # Escala el texto al nivel de correlación
}

#Matriz de Correlación Respecto a  Relative Stent Expansion
pairs(~ RelativeStentExpansion + MaxSinAnnDcalc + MinSinusAnnDcalc + CCV, 
        upper.panel = panel.cor,
        lower.panel = panel.smooth,data = data1)

Warning in par(usr) : argument 1 does not name a graphical parameter
Warning in par(usr) : argument 1 does not name a graphical parameter
Warning in par(usr) : argument 1 does not name a graphical parameter
Warning in par(usr) : argument 1 does not name a graphical parameter
Warning in par(usr) : argument 1 does not name a graphical parameter
Warning in par(usr) : argument 1 does not name a graphical parameter

#Relacion entre Maximum Sinus Diameter Indexed vs Relative Stent Expansion %
g1=ggplot(data=data1,mapping=
            aes(x=MaxSinAnnDcalc,y=RelativeStentExpansion,))+geom_point()+theme_bw()+
            geom_smooth(method = "lm") 

g1 +  labs(title = "Relative Stent Expansion % vs Maximum Sinus Diameter Indexed",
           x = "Maximum Sinus Diameter Indexed",
           y= "Relative Stent Expansion %") 

`geom_smooth()` using formula 'y ~ x'

#Relacion entre Minimun Sinus Diameter Indexed vs Relative Stent Expansion %
g2=ggplot(data=data1,mapping=
            aes(x=MinSinusAnnDcalc,y=RelativeStentExpansion,))+geom_point()+theme_bw()+
            geom_smooth(method = "lm") 

g2 +  labs(title = "Relative Stent Expansion % vs Minimun Sinus Diameter Indexed",
           x = "Minimun Sinus Diameter Indexed",
           y= "Relative Stent Expansion %") 

`geom_smooth()` using formula 'y ~ x'

#Relacion entre CONTRAST CALCIUM VOLUME vs Relative Stent Expansion %
g3=ggplot(data=data1,mapping=
            aes(x=CCV,y=RelativeStentExpansion,))+geom_point()+theme_bw()+
            geom_smooth(method = "lm") 

g3 +  labs(title = "Relative Stent Expansion % vs Contrast Calcium Volume",
           x = "Contrast Calcium Volume",
           y= "Relative Stent Expansion %") 

`geom_smooth()` using formula 'y ~ x'

#Relacion entre Postdilatation vs Relative Stent Expansion %
boxplot(data1$RelativeStentExpansion~data1$Postdilation,
        xlab = 'Postdilation',
        ylab = 'Relative Stent Expansion %',
        title=('Postdilatation vs Relative Stent Expansion %'),
          col= 'bisque')

ggplot(data = data1) + geom_density(aes(x=RelativeStentExpansion,fill=factor(Postdilation)),
                                    bins=10, position = "identity",alpha = 0.5)

Warning: Ignoring unknown parameters: bins

#Relacion entre Raphe Calcification vs Relative Stent Expansion %
boxplot(data1$RelativeStentExpansion~data1$Raphaecalcification,
        xlab = 'Raphae Calcification',
        ylab = 'Relative Stent Expansion %',
        title=('Raphae Calcification vs Relative Stent Expansion %'),
          col= 'ivory')

ggplot(data = data1) + geom_density(aes(x=RelativeStentExpansion,fill=factor(Raphaecalcification)),
                                    bins=10, position = "identity",alpha = 0.5)

Warning: Ignoring unknown parameters: bins

#Relacion entre AVAi vs Relative Stent Expansion %
g4=ggplot(data=data1,mapping=
            aes(x=AVAi,y=RelativeStentExpansion,))+geom_point()+theme_bw()+
            geom_smooth(method = "lm") 

g4 +  labs(title = "Relative Stent Expansion % vs AVAi",
           x = "AVAi",
           y= "Relative Stent Expansion %") 

`geom_smooth()` using formula 'y ~ x'
Warning: Removed 26 rows containing non-finite values (stat_smooth).
Warning: Removed 26 rows containing missing values (geom_point).

#Relacion entre Mean Gradient (mmHg) vs Relative Stent Expansion %
g5=ggplot(data=data1,mapping=
            aes(x=MeanGradientmmHg,y=RelativeStentExpansion,))+geom_point()+theme_bw()+
            geom_smooth(method = "lm") 

g5 +  labs(title = "Relative Stent Expansion % vs Mean Gradient (mmHg)",
           x = "Mean Gradient (mmHg)",
           y= "Relative Stent Expansion %") 

`geom_smooth()` using formula 'y ~ x'

#Regresion inicial
mod1=lm(RelativeStentExpansion ~ MaxSinAnnDcalc + MinSinusAnnDcalc + Postdilation +CCV + Raphaecalcification,
        data = data1)
summary(mod1)

Call:
lm(formula = RelativeStentExpansion ~ MaxSinAnnDcalc + MinSinusAnnDcalc + 
    Postdilation + CCV + Raphaecalcification, data = data1)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.096436 -0.011235  0.003484  0.019744  0.066353 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)          8.499e-01  4.013e-02  21.178  < 2e-16 ***
MaxSinAnnDcalc      -5.700e-03  3.044e-02  -0.187 0.851864    
MinSinusAnnDcalc     4.679e-02  3.740e-02   1.251 0.213979    
Postdilation         2.651e-02  7.364e-03   3.600 0.000508 ***
CCV                  3.406e-06  5.278e-06   0.645 0.520271    
Raphaecalcification -4.582e-03  6.782e-03  -0.676 0.500935    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.03043 on 95 degrees of freedom
Multiple R-squared:  0.1468,    Adjusted R-squared:  0.1019 
F-statistic:  3.27 on 5 and 95 DF,  p-value: 0.009087

#Modelo binario
mod2 <- glm(RSEcalc ~ MaxSinAnnDcalc + MinSinusAnnDcalc + 
    Postdilation + CCV + Raphaecalcification,
                data = data1, family = "binomial")
summary(mod2)

Call:
glm(formula = RSEcalc ~ MaxSinAnnDcalc + MinSinusAnnDcalc + Postdilation + 
    CCV + Raphaecalcification, family = "binomial", data = data1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9256  -1.2399   0.5795   1.0343   1.2307  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)  
(Intercept)         -2.5695037  3.0095653  -0.854    0.393  
MaxSinAnnDcalc       0.6706370  2.1665003   0.310    0.757  
MinSinusAnnDcalc     1.5877584  2.6703586   0.595    0.552  
Postdilation         1.5656236  0.6674808   2.346    0.019 *
CCV                 -0.0001900  0.0003786  -0.502    0.616  
Raphaecalcification  0.2157918  0.4809451   0.449    0.654  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 132.71  on 100  degrees of freedom
Residual deviance: 123.99  on  95  degrees of freedom
AIC: 135.99

Number of Fisher Scoring iterations: 4

ANALISIS Ellipticity

#Matriz de Correlación Respecto a  Relative Stent Expansion
pairs(~ Ellipticity + MaxSinAnnDcalc + MinSinusAnnDcalc + CCV, 
        upper.panel = panel.cor,
        lower.panel = panel.smooth,data = data1)

Warning in par(usr) : argument 1 does not name a graphical parameter
Warning in par(usr) : argument 1 does not name a graphical parameter
Warning in par(usr) : argument 1 does not name a graphical parameter
Warning in par(usr) : argument 1 does not name a graphical parameter
Warning in par(usr) : argument 1 does not name a graphical parameter
Warning in par(usr) : argument 1 does not name a graphical parameter

#Relacion entre Maximum Sinus Diameter Indexed vs Ellipticity
g6=ggplot(data=data1,mapping=
            aes(x=MaxSinAnnDcalc,y=Ellipticity,))+geom_point()+theme_bw()+
            geom_smooth(method="lm") 

g6 +  labs(title = "Ellipticity vs Maximum Sinus Diameter Indexed",
           x = "Maximum Sinus Diameter Indexed",
           y= "Ellipticity") 

`geom_smooth()` using formula 'y ~ x'

#Relacion entre Minimun Sinus Diameter Indexed vs Ellipticity
g7=ggplot(data=data1,mapping=
            aes(x=MinSinusAnnDcalc,y=Ellipticity,))+geom_point()+theme_bw()+
            geom_smooth(method="lm") 

g7 +  labs(title = "Ellipticity vs Minimun Sinus Diameter Indexed",
           x = "Minimun Sinus Diameter Indexed",
           y= "Ellipticity") 

`geom_smooth()` using formula 'y ~ x'

#Relacion entre CONTRAST CALCIUM VOLUME vs Ellipticity
g8=ggplot(data=data1,mapping=
            aes(x=CCV,y=Ellipticity,))+geom_point()+theme_bw()+
            geom_smooth(method="lm") 

g8 +  labs(title = "Ellipticity vs Contrast Calcium Volume",
           x = "",
           y= "Ellipticity") 

`geom_smooth()` using formula 'y ~ x'

#Relacion entre AVAi vs Ellipticity
g9=ggplot(data=data1,mapping=
            aes(x=AVAi,y=Ellipticity,))+geom_point()+theme_bw()+
            geom_smooth(method="lm") 

g9 +  labs(title = "Ellipticity vs AVAi",
           x = "AVAi",
           y= "Ellipticity") 

`geom_smooth()` using formula 'y ~ x'
Warning: Removed 26 rows containing non-finite values (stat_smooth).
Warning: Removed 26 rows containing missing values (geom_point).

#Relacion entre Mean Gradient (mmHg) vs Ellipticity
g10=ggplot(data=data1,mapping=
            aes(x=MeanGradientmmHg,y=Ellipticity,))+geom_point()+theme_bw()+
            geom_smooth(method="lm") 

g10 +  labs(title = "Ellipticity vs Mean Gradient (mmHg)",
           x = "Mean Gradient (mmHg)",
           y= "Ellipticity") 

`geom_smooth()` using formula 'y ~ x'

#Relacion entre Postdilatation vs Ellipticity
boxplot(data1$Ellipticity~data1$Postdilation,
        xlab = 'Postdilation',
        ylab = 'Ellipticity',
        title=('Postdilatation vs Ellipticity'),
          col= 'bisque')

ggplot(data = data1) + geom_density(aes(x=Ellipticity,fill=factor(Postdilation)),
                                    bins=10, position = "identity",alpha = 0.5)

Warning: Ignoring unknown parameters: bins

#Relacion entre Raphe Calcification vs Ellipticity
boxplot(data1$Ellipticity~data1$Raphaecalcification,
        xlab = 'Raphae Calcification',
        ylab = 'Ellipticity',
        title=('Raphae Calcification vs Ellipticity'),
          col= 'ivory')

ggplot(data = data1) + geom_density(aes(x=Ellipticity,fill=factor(Raphaecalcification)),
                                    bins=10, position = "identity",alpha = 0.5)

Warning: Ignoring unknown parameters: bins

#Regresion inicial
mod3=lm(Ellipticity ~ MaxSinAnnDcalc + MinSinusAnnDcalc + Postdilation +CCV + Raphaecalcification,
        data = data1)
summary(mod3)

Call:
lm(formula = Ellipticity ~ MaxSinAnnDcalc + MinSinusAnnDcalc + 
    Postdilation + CCV + Raphaecalcification, data = data1)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.074869 -0.032489 -0.001692  0.022949  0.107501 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)          1.069e+00  5.200e-02  20.563   <2e-16 ***
MaxSinAnnDcalc      -1.618e-02  3.944e-02  -0.410   0.6827    
MinSinusAnnDcalc     3.080e-02  4.846e-02   0.636   0.5266    
Postdilation        -1.026e-02  9.541e-03  -1.075   0.2851    
CCV                  3.804e-06  6.839e-06   0.556   0.5793    
Raphaecalcification  1.522e-02  8.788e-03   1.732   0.0865 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.03943 on 95 degrees of freedom
Multiple R-squared:  0.06378,   Adjusted R-squared:  0.01451 
F-statistic: 1.294 on 5 and 95 DF,  p-value: 0.2729

#Modelo binario
mod4 <- glm(Ellipticitycalc ~ MaxSinAnnDcalc + MinSinusAnnDcalc + 
    Postdilation + CCV + Raphaecalcification,
                data = data1, family = "binomial")
summary(mod4)

Call:
glm(formula = Ellipticitycalc ~ MaxSinAnnDcalc + MinSinusAnnDcalc + 
    Postdilation + CCV + Raphaecalcification, family = "binomial", 
    data = data1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4406  -1.0762  -0.8559   1.1592   1.5535  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)
(Intercept)          2.795e+00  2.739e+00   1.021    0.307
MaxSinAnnDcalc      -4.545e-01  2.061e+00  -0.221    0.825
MinSinusAnnDcalc    -2.122e+00  2.534e+00  -0.838    0.402
Postdilation        -4.690e-01  5.048e-01  -0.929    0.353
CCV                  6.213e-05  3.594e-04   0.173    0.863
Raphaecalcification  5.670e-01  4.601e-01   1.232    0.218

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 139.53  on 100  degrees of freedom
Residual deviance: 134.98  on  95  degrees of freedom
AIC: 146.98

Number of Fisher Scoring iterations: 4

Mean gradient analysis

#Relacion entre Mean gradient analysis vs RSE calc
boxplot(data1$MeanGradientmmHg~data1$RSEcalc,
        xlab = 'Relative Stent Expansion >90%',
        ylab = 'Mean Gradient (mmHg)',
        title=('Mean Gradient (mmHg) vs Relative Stent Expansion >90%'),
          col= '#87CEFA')

ggplot(data = data1) + geom_density(aes(x=MeanGradientmmHg,fill=factor(RSEcalc)),
                                    bins=10, position = "identity",alpha = 0.5)

Warning: Ignoring unknown parameters: bins

#Relacion entre Mean Gradient (mmHg) vs Relative Stent Expansion %
g11=ggplot(data=data1,mapping=
            aes(x=MeanGradientmmHg,y=RelativeStentExpansion,))+
            geom_point(aes(colour = factor(Postdilation))) +theme_bw()+ 
            geom_smooth(method = "lm", color="#838B8B", linetype="dashed", fill="#EEEEE0") +
            scale_color_manual(values=c('#0000FF','#FF3030'))

g11 +  labs(title = "Relative Stent Expansion % vs Mean Gradient (mmHg)",
           x = "Mean Gradient (mmHg)",
           y= "Relative Stent Expansion %") 

`geom_smooth()` using formula 'y ~ x'

#Relacion entre Mean gradient analysis vs Ellipticity calc
boxplot(data1$MeanGradientmmHg~data1$Ellipticitycalc,
        xlab = 'Ellipticity >1.1',
        ylab = 'Mean Gradient (mmHg)',
        title=('Mean Gradient (mmHg) vs Ellipticity >1.1'),
          col= '#836FFF')

ggplot(data = data1) + geom_density(aes(x=MeanGradientmmHg,fill=factor(Ellipticitycalc)),
                                    bins=10, position = "identity",alpha = 0.5)

Warning: Ignoring unknown parameters: bins

#Relacion entre Mean Gradient (mmHg) vs Ellipticity
g12=ggplot(data=data1,mapping=
            aes(x=MeanGradientmmHg,y=Ellipticity,))+
            geom_point(aes(colour = factor(Postdilation))) +theme_bw()+ 
            geom_smooth(method = "lm", color="#838B8B", linetype="dashed", fill="#EEEEE0") +
            scale_color_manual(values=c('#0000FF','#FF3030'))

g12 +  labs(title = "Ellipticity vs Mean Gradient (mmHg)",
           x = "Mean Gradient (mmHg)",
           y= "Ellipticity") 

`geom_smooth()` using formula 'y ~ x'